Current System-on-Chips (SoCs) execute applications with task dependency that compete for shared resources such as buses, memories, and accelerators. In such a structure, the arbitration policy becomes a critical part of the system to guarantee access and bandwidth suitable for the competing applications. Some strategies proposed in the literature to cope with these issues are Round-Robin, Weighted Round-Robin, Lottery, Time Division Access Multiplexing (TDMA), and combinations. However, a fine-grained bandwidth control arbitration policy is missing from the literature. We propose an innovative arbitration policy based on opportunistic access and a supervised utilization of the bus in terms of transmitted flits (transmission units) that settle the access and fine-grained control. In our proposal, every competing element has a budget. Opportunistic access grants the bus to request even if the component has spent all its flits. Supervised debt accounts a record for every transmitted flit when it has no flits to spend. Our proposal applies to interconnection systems such as buses, switches, and routers. The presented approach achieves deadlock-free behavior even with task dependency applications in the scenarios analyzed through cycle-accurate simulation models. The synergy between opportunistic and supervised debt techniques outperforms Lottery, TDMA, and Weighted Round-Robin in terms of bandwidth control in the experimental studies performed. Embedded applications on Systems on Chip (SoC) present task dependencies and share resources such as memories, peripherals, and accelerators. Real application tasks have data needs that translate into dependencies  . The sysThe central performance bottleneck of current Systems on Chip resides in the on-chip interconnection rather than in the processing of its elements ,3,4,5,6.An SoC design must fulfill requirements and constraints imposed by the applications running in the system  . It is tThe options for interconnections in the SoC are diverse and depend on the application. Interconnection systems impact directly on the performance of the SoC. The literature then proposed numerous interconnection architectures that increase performance . However, there is no single solution to the SoC interconnection problem ,10.The different interconnection systems are suited to specific applications and scenarios. The bus-based systems are the alternative for SoCs that integrate a small number of processing elements. Benini and Michele in  pointed Regardless of the interconnection system used, all the proposed alternatives require an arbitration system that controls shared resources access. In a bus-based interconnection system, the processing elements compete to obtain access to the bus. In crossbars, two or more masters contend for a slave port. In the case of NoC, two or more flows fight for access to a link. The arbitration system and its policy become a fundamental part of the interconnection system within the SoC.Arbitration systems traditionally focused on achieving fair access, but to regulate the bandwidth allocated is essential too. In collaboration with other components, they can control the utilization of the processing elements to fulfill QoS requirements of the system. A desirable characteristic of an arbitration system is controlling the bandwidth assigned to each of the processing elements . DiffereDespite the extensive literature about buses and arbitration, we believe that bandwidth allocation in arbitration systems is not entirely analyzed. Different studies propose solutions to meet QoS requirements for applications running in SoCs such as ,22,23,24A cycle-accurate framework to model arbitration policies in a bus-based SoC using SystemC.Performance analyses of different arbitration policies regarding their possibility of granting and regulating the allocated bandwidth of a bus-based interconnection.Derived from the analyses above mentioned; we identify conditions where Weighted Round-Robin policy causes a deadlock in applications with task dependencies.A novel deadlock-free arbitration policy with fine-grained bandwidth control based on budgets for every communication transaction.Analysis of the architecture of the budget policy implemented in FPGA.This paper makes the following contributions:We present the rest of this work as follows: to grant fast high-priority access to the shared resource while avoiding starvation for low priority transactions.to enable a delicate control of the communication bandwidth assigned to every processing element.to overcome the sensitivity of system performance to various communication patterns generated by the SoC applications.This section classifies the related work in two parts: the first one, literature regarding QoS metrics, and the second one, regarding arbitration policies. An effective arbitration policy must have the following features :to grantAccess.Starvation is a result of unfair access arbitration (an entity requesting necessary resources never gets access). The fair access policies overcome the starvation problem guaranteeing an allocation technique where resources are accessible to every request.Latency.Latency is a measure for QoS for real-time applications. Arbitration policies must guarantee application response latency within a specified time constraint.Utilization.This metric focuses on maximizing the utilization of the interconnection within an SoC and increasing the performance of the system to comply with the restrictions established to the applications running within the SoC.EoS.Equality of Service comprises several QoS metrics; it refers to the overall experienced service for every processing element attached to the interconnection system. This metric has a more significant impact on NoCs due to the diverse spatial traffic patterns that affect different parts of the NoC.Bandwidth.As we mentioned above, SoCs need fine-grained bandwidth control. This bandwidth control will permit a bandwidth to every processing element and accurately predict latencies and throughput to applications running in the SoC.Most studied QoS metrics are access, latency, utilization, Equality of Service (EoS), and bandwidth. Arbiters assign resources with fair, prioritized, or regulated policies, which impact on the metrics above. Every policy or allocation technique has pros and cons affecting applications running on the SoC. Here, we use buses to explain arbitration policies, but switches and routers use them too. The second classification indicates the arbitration policy used by the different proposals to reach the QoS metric. Many works studied well-known arbitration policies: Round-Robin, probabilistic access such as Lottery or derivatives, Time Division Access Multiplexing (TDMA), static or dynamic priority access. According to the established scenario, other approaches base their proposal on reconfiguring traditional arbitration policies . Finally, many papers integrate additional monitoring, scheduling, control, etc., to conventional arbitration policies to help meet QoS requirements. In Poletti et al.  present Xu et al.  present To provide a proportional bandwidth allocation, Li et al.  present In this approach, we perceive that there is no regulatory element in the system. The more bandwidth a master demands, the higher weight it gets. Besides, the system suffers from inertia because it works with the values calculated in the previous period. The system cannot know a priori the demand for bandwidth that a master can make in a given period. While the paper indicates that a proper time selection can satisfy practical applications, it is not well suited to applications transmitting data bursts.Akesson et al. ,23 preseIn , the autYang et al.  describeSlijepcevic et al.  report aAlthough this paper shows a scheme to distribute the bandwidth, we observe that it lacked indications of how the bandwidth spreads among the masters or how this affects the total performance of the system.Pagani et al.  introducWhile all of the above approaches allow for compliance with the bandwidth requirements established for them, they do not specify the contribution of the arbitration policy to bandwidth distribution. There is no evidence of how the arbitration policy influences bandwidth allocation. Here we present an arbitration policy that resolves the fine-grained bandwidth control effectively.With the arrival of new paradigms for on-Chip interconnection systems such as Networks-on-Chip, the literature opened several research lines in this area. However, most researchers have devoted their efforts to study and propose solutions to the new challenges that Networks-on-Chip present . Typical implementations of channel arbitration for Networks-on-Chip routers commonly use Round-Robin since it is simple, fast, and uses fewer resources. Researchers set their mind to Round-Robin, which provides fair access, avoids starvation, and left the arbitration issues to a second term. Our contribution is to return the attention of the researchers to arbitration issues since there are still gaps in the literature to be filled in this sense. We aim to develop a new arbitration policy that provides fine-grained control of a resource bandwidth’s share that increases SoCs’ performance predictability.Bus Controller (BC) is the central element of the interconnection system. Three elements constitute it: the first one is the arbiter, who according to the implemented policy, is in charge of granting access to one of the masters who request the media; the second one is the Bus Statistics Generator, which is in charge of keeping the accounting of the bus utilization at a general level and for each master. Also, it registers the execution time for both applications and masters; the third element is the Data Bus Controller in charge of establishing the route so that a master can carry out a transaction.Network Interface (NI) is in charge of the communication with the other elements of the bus; it is the one that requests the bus to the arbiter and transmits the information when having the bus control. It is in charge of releasing the bus when it is no longer needed.Masters are responsible for running the SoC applications; they transmit the results of the tasks performed to the interconnection system through the NI they have connected. Each of the masters can execute a set of tasks from a specific application. The configuration unit tells the master which subset to use, and it takes the corresponding trace file from a trace database per master.Config and Report Controller (CRC), this element has two functions: first, to set configuration parameters with which the Bus Controller will operate; secondly, to collect the operation statistics of the Bus Controller. Trace File Adapter Tool as a result of the mapping process established in [ished in , each scTo evaluate the behavior of arbitration policies in terms of their ability to allow for a differentiated use of a shared resource, we developed in SystemC a system that simulates an SoC with a bus-based interconnection system. The system works cycle-accurate. In n is the number of applications running in the SoC.Master throughput, is the throughput in bits/cycle of the Master bus utilization percentage, is the percentage of bus utilization that the Total running time, is the total time it takes the set of applications running in a scenario to complete all the tasks. And is given by:Application throughput, is the throughput in bits/cycle of the Application bus utilization percentage, is the percentage of bus utilization when running the Overall throughput, is the total system throughput in bits/cycle when all applications running. It is given by:Overall bus utilization percentage, is the total percentage of bus utilization when all applications running. It is given byTo evaluate the performance of the arbitration policies, we established the following assumptions and metrics:A set of interdependent tasks correspond to the applications that run on our system. A Task Communication Graph (TCG) expresses the tasks and their relationships for every application. A TCG consists of a set of linked nodes (the tasks) and communication links through which the data flow between the tasks. An application can exploit the computing power within an SoC by using a set of processing elements to execute tasks that constitute it. To do this is necessary to use a technique that allows mapping the task graph of an application within the processing elements attached to the interconnection system of the SoC. The distribution made by each technique can significantly affect the traffic generated in the interconnection system. Our study does not delve into the mapping techniques for a bus-based interconnection system. We use the trace files generated by the suite presented in ; in thism files according to the number of processing elements involved in the mapping. Each of our files contains the information of the tasks to execute: initial tasks, final tasks, time of execution of the tasks, size of the messages generated by each task, dependencies of each task, and children nodes of each task. When starting the simulation process, each master obtains from the CRC an In , the autAs stated above, one of the objectives of this study is to understand the degree to which an arbitration policy can regulate the bandwidth allocations in the interconnection of an SoC. This section exposes some drawbacks in the arbitration policies used in the literature to implement a fine-grained bandwidth control.When in an SoC, there are one or several applications with dependent tasks running, and also, it is necessary to make a differentiated use of resources, traditional policies have limitations. Below are some facts that cause these limitations.Round-Robin (RR), is a policy that does not have a control mechanism to allow differentiated bus utilization or bandwidth allocation. Round-Robin focuses its efforts on allowing fair access to the bus.Lottery (LTY)  is a polTime Division Multiple Access (TDMA), this policy allows the differentiated use of the resources but generally at the cost of decreasing overall performance with coarse-grained bandwidth control.Weighted Round-Robin (WRR) , in scenWeighted Round-Robin Modified (WRRM) , this poGiven the gap described above, we present Supervised Debt Opportunistic (SuDO), a new arbitration policy that allows contenders for a shared resource to make a differentiated use of it. Our proposal achieves fine-grained bandwidth control avoiding reduction in the overall performance of the system.Our policy achieves fine-grain control of the bandwidth—or utilization—assigned to every master in the system. It utilizes counters to record every clock cycle used for every master in the system. The policy manages an accounting balance of the bandwidth assigned. It controls the bandwidth manipulating the value assigned to the counter at the beginning of the process. We describe the arbitration method here.We start setting a—different or equal—budget for every master in terms of the number of flits allowed to transmit as its share of bandwidth. In a contention round, the masters with the largest number of flits enter to compete in a Round-Robin policy. If only one master has the highest budget, it gets the bus. For every flit transmitted or clock cycle that the master uses, we decrement its value by one unit until it finishes its transaction or runs out of flits. A debt record starts and increments for every flit used until it completes its transaction. If a master runs out of flits or it has a debt, and if no other master requests the bus, it acquires it. However, the arbiter policy records the flits used in this event as a debt. Once every master runs out of flits, they get the budget again. When the master obtains more flits, we subtract the debt, and the result remains as flits to spend. With this mechanism, we ensure that the mean bandwidth assigned stays the same over an observation window.Our policy has two main features, debt supervision and an opportunistic bus allocation. Debt supervision refers to the accounting of the debt every time we reassigned the budget. Opportunistic behavior refers to the resource allocation to a master that requests the bus when no other masters do, even when the master has no flits to spend.We use the number of transmitted flits for each master as the metric for utilization or bandwidth. We translate clock cycles to flits defining a flit size as the bus data width. This assumption may change depending on the architecture because a flit transmission may take one or more clock cycles. In Networks on Chip, the one flit equals one clock cycle assumption is commonly valid. We can then transform the flits per clock cycle to bits per clock cycle to have a metric associated with bandwidth allocation.For the SuDO filter to operate, it is necessary to provide the budget each master has. The source of these values can come from different sources: a simple set of registers or a processor that allows the dynamic control of these weights under a Software-Defined Network-on-Chip philosophy as in the proposal made by Sandoval-Arechiga et al. in . The numDown-Counter Unit accounts for the balance of the budgets and debts for every master in the system. We use two branches to find the highest and lowest values in the system. The superior tree, Greater-Budget, finds the highest amount for flits to spend among the budget of the masters. The inferior tree, Lower-Debt, selects the master with the lowest accumulated debt. The Equal-Comparator Units choose the requests from the master that corresponds to the input value to compare. The multiplexor selects the requests from one of the branches. If we have at least one master with zero debt, the OR gate sets the upper input to the multiplexor.Down-Counter Unit consists of m elements of the Down-Counter type one for each master in the system. Down-Counter. This number will decrease by one unit each clock cycle that the enable signal is active. The enable signal is activated by the grant signal from the RR, indicating that the master associated with this signal has control of the bus. Every Down-Counter accounts for the budget and debt for a master, and it has three outputs:1.budgetout, which indicates the number of flits that a master has to spend at a given time.2.budget0, this output means when the internal counter of the Down-Counter is equal to zero; this indicates that the master has consumed all its flits.3.debt signal specifies the number of debt flits.The budget0 signal from all Down-Counters is active, the load signal is activated, and all Down-Counters recharge their flits again. An external configuration unit manages this budget value. Moreover, at each recharge, the CRC Unit could modify the number of flits. When the internal counter of a Down-Counter reaches zero but still requires the bus because the associated master is transmitting a packet, the budget0 signal is activated. The budget counter value stays at zero. A debt record starts, and it allows the master to conclude with the current transaction.When the Down-Counter cell has two registers to record the budget left (superior block) and accumulated debt (inferior block), respectively, as TC blocks are the Two’s complement functions to implement subtraction. The left subtraction block accounts for the budget initialization process to balance the accumulated debt. Every clock cycle that enable/grant signal is high; the subtraction block at the right of budget0 signal is high.A Greater-Budget to get the maximum budget value, and Lower-Debt to obtain the minimum debt value. They differ in the comparison operators, but internally they have the same structure. Here we explain the Greater-Budget tree’s architecture but can be easily translated to the Lower-Debt changing the > operator for < and budget(m) for debt(m) signals. The Greater-Budget has its first level with Greater-Request, which present the greater of the two entries at the output. In this comparator, we add the request inputs to indicate whether the next stage of comparators use this value or not. If the signal is high, the data goes into the comparator; otherwise, the value zero. This action makes sense because comparators should only consider the budget of the masters applying for access. We present a cell of this type of element in SuDO filter uses two comparator trees: Equal-Comparator perform the comparison. Equal-Comparator generates the output equal=1 if the two values are equal. Finally, the module applies an The value of the highest budget obtained is compared individually with the budget of each master. A set of comparators named All masters are assigned a starting budget in terms of flits.In an arbitration round, the winning master is the one with the most flits.If two or more masters have the same amount of flits to spend that corresponds to the highest number of flits detected in the system, the Round-Robin policy resolves the tie.A master who has obtained access to the bus decreases one unit for each transmitted flit.A master who runs out of flits will be allowed to complete the current transaction, but a debt record starts.A master gets the bus if it requires it even if it has used up his budget, as long as there is no other master with flits to spend is requesting it.If two or more masters who have used up their budget apply simultaneously for the bus, the one with less debt gets it.If the accumulated debt is the same for all, Round-Robin breaks the tie.If a master who has no budget takes the bus, this master accumulates debts in terms of the number of flits used by the bus.The arbitration policy reloads the original budget when all masters’ accounts reached zero.The policy subtracts the assigned budget and the accumulated debt; the remaining are the flits to spend.For its operation, the SuDO policy takes into account the following considerations:The characteristics of the applications running on the SoC condition the interconnection performance. To establish scenarios that allow us to make an objective evaluation, we identified features that enable us to classify the applications.1.Application with independent tasks: in this type of application, each of the processing elements executes tasks whose operation does not depend on the execution of another task. Once the processing elements transmit a packet with the task result, they can continue executing pending tasks. There is no need for a response. In this way, they have the possibility of requesting access to the bus again when they complete the execution of their new task.2.Application with dependent tasks: the applications have tasks that communicate with each other to execute an algorithm. The tasks are distributed in different processing elements to take advantage of their ability to work in parallel and obtain better performance in the processing of the application. The tasks communicate with each other through the interconnection system. A task only can run if it has received all the data from the anterior tasks on which it depends.According to how the applications running in the processing elements of an SoC, we identify two types of applications:1.Homogeneous traffic: All the masters generate transactions with a similar size payload.2.Heterogeneous traffic: The size of the packets transmitted by each of the masters varies significantly between them.According to the type of traffic generated by the applications running on the processing elements of an SoC. We classify the traffic as:1.Small-sized packets: with a payload flits in the order of the units. As an example, the mapped FFT-1024 complex algorithm generates packets between five and seven flits payload.2.Medium-sized packets: which have a payload in the order of tens of flits. Such as those generated by the mapped FPPPP algorithm.3.Large-sized packets – We observe this size in the mapping of the H264 video encoding algorithm. The generated packets have a payload of between two and three hundred flits.The size of the packets transmitted through the interconnection system depends on the application running. Taking as a reference to the traffic patterns presented in , we haveOur system implemented the RR, LTY, TDMA, WRR, WRRM, and SuDO arbitration policies in the arbiter unit. In a simulation, only runs the police that CRC indicates. Depending on the arbitration policy selected, the CRC associates to each master a weight. We show the policy origin and the purpose of the associated weights in Today, it is common for SoCs to run different task-dependent applications while meeting QoS requirements simultaneously. Wich implies that the arbitration system must provide two capabilities: one, to operate, the ability to avoid starvation and deadlock; two, to achieve QoS, the ability to regulate the utilization of the bus, allowing each processing element a differentiated use of it.As far as we know, the study of the behavior of arbitration policies in scenarios involving task-dependent applications has been little studied . From ouIn the tests, we use two traffic patterns obtained from the suite presented in . The firWe selected these patterns because the FFT-1024_complex application generates intense communication with small packets, while the FPPPP application generates moderate communication with medium-sized packets. Also, these patterns were selected over the other patterns in the suite because, in the tests we performed, they are the ones that generate the highest volume of traffic. A mapping process was carried out on eight processing elements for both patterns because, in our tests with this mapping, we have the best processor/performance ratio. When we run the standalone mode applications, FFT-1024_complex mapping on eight processors achieved a 29.13 bits/cycle performance, and FPPPP mapping on eight processors achieved a performance of 15.95 bits/cycle. To perform the evaluation, we use the previously established metrics: Next, we propose a couple of scenarios running applications with dependent tasks. It is of our interest to know to what extent an arbitration policy allows a differentiated resource utilization. We are also interested in observing how this differentiated utilization impacts the overall performance of the system and application execution time.In the first test, the proposed scenario runs three applications with dependent tasks, one application is FPPPP, and the other two applications are FFT-1024_complex. The applications run using the RR, WRRM, and SuDO arbitration policies. For WRRM, the weight ratio is 1/1/1; for SuDO, the budget has a weight ratio of 1/1/1. This ratio assumes that the processors of three applications have the same possibility of access to the bus.In the next test, only run the WRRM and SuDO arbitration policies. In this test, we want to see if an application can make a differentiated utilization of the bus. We set the weighting ratio at 1/1/3, which would mean a theoretically expected bus utilization of 20% for the FPPPP application, 20% for one of the FFT-1024_complex applications, and 60% for the other FFT-1024_complex application. It can be seen in The third test performed in this scenario sets the weight ratio to 1/2/2. We expect a bus utilization percentage of 20% for the FPPPP application and 40% for each FFT-1024 complex application. The results show that the best distribution of bus utilization The set of tests that we performed shows that the policy that makes the best control of the bus utilization and the bandwidth is SuDO.Another common scenario in SoCs is having different instances of the same application running simultaneously. In this case, we use three instances of the FFT-1024_complex traffic pattern for eight processors. The selection of this application is because the tests we have carried out show that the performance of the system is more compromised when small packets travel in the interconnection system. We run the tests for the same arbitration policies outlined in the previous scenario.For this scenario, the first test takes as arbitration policies Round Robin, WRRM, and SuDO. The established weight/budget ratio is 1/1/1.The The second test in this scenario runs with the WRRM and SuDO arbitration policies. We set the weighting ratio at 1/1/3. The last test establishes a 1/2/2 weight ratio, which means that one of the FFT-1024 complex applications has 20% of the bus utilization percentage while the other two FFT-1024_complex applications have 40% each. The tests conducted in this scenario show that the policy that makes the best control of the utilization of the bus is SuDO.The tests we have performed show that more than the number of processors in the system, the arbitration policy’s behavior is more influenced by the packet injection rate of each application. For example, in scenarios with task-dependent applications, when we mapped an application in a few processors, the packet exchange is more intensive. In this case, the results are similar to the scenarios tested in this work. When there is a scenario whose applications are not bus-intensive, there is less contention, so the behavior of any arbiter cannot be easily observed no matter how many processors the applications are mapped (few or many).We test different applications with different transaction sizes. But we observed that FPPPP and FFT-1024_complex are the ones that stress out the system higher. Other applications utilize less the bus; then, we fail to perceive the bandwidth control. Our policy assigns bandwidth to a master, but the arbiter transferred the rest to other masters that needed it if it uses less. In other words, our arbitration policy limits the maximum bandwidth assigned to a master, but the master can use less in a given period. Therefore, to observe more precise bandwidth control, the paper’s scenarios comprise various applications with different transaction sizes that increase bus utilization. Although we used only three applications, they employed eight masters each. Therefore, we use a total of 24 masters in the test. We consider that these scenarios are enough to get reliable conclusions as the bus utilization is adequate to express the possible issues associated with bandwidth control.We implement the hardware architecture of the arbitration policies in the Verilog Hardware Description Language. For the synthesis process, Xilinx’s SoC, Zynq 7020, was used as a reference. The number of masters that are supported by the arbitrators is eight. The implementation of the proposed policy obtains an excellent compromise between complexity and performance. Tests conducted on scenarios with task-dependent applications show that SuDO is the policy that allows better control of the bus. When we set the support values to 1/1/1, the behavior of the three tested arbitration policies is very similar in the two scenarios.When there is a ratio with different weights, WRRM can not maintain this ratio. This behavior is because WRRM allows a processing element to use the bus even though it has already consumed the quota, making this element more resource-intensive. Suppose a processing element frequently requests the bus. In that case, it gets it, in the interest of better overall system performance, but the policy has no control over how much extra bus utilization occurred. This behavior prevents having a fine-grained control of bus utilization. In both scenarios, we can see that the gain in bus utilization that has the application with a higher weight relationship does so at the expense of only one of the other applications. This issue is a consequence of giving the bus to any processing element that requests it even if it has consumed its budget to obtain a higher bus utilization. As shown in scenario 2, the 1/1/3 WRRM ratio has an idle bus percentage of 15%.In the case of SuDO, the results show that although it does not achieve the desired weight ratio, there is a tendency to achieve it. The main reason why this happens is that applications do not request the bus at a fixed weight ratio because the mapping process tried that the most significant task dependency is between tasks within the same processing element. Given the above, a processing element during specific periods does not request access to the bus. We can use the packet injection rate as a measure to adjust the budget values.Regarding the overall performance, we can see that taking control of the bus utilization does not significantly impact the arbitration policies evaluated in the scenarios proposed. SuDO, in most of the tests with differentiated weights, presents a slightly higher performance. The price to be paid for achieving a better distribution of bus utilization is the total running time of the applications. In this sense, SuDO in all tests presents a higher execution time of the task that takes longer to execute, approximately 10%.We can implement our SuDO policy in other interconnection systems such as crossbar switches and routers. Here we explain such extension in routers, but switches benefit from SuDO policy in the same manner. In a scenario with multiple applications with dependent tasks where it is necessary to make a fine-grained control of the bandwidth allocation in a bus, it is better to use the SuDO policy since it is the one that has presented better results in the experiments carried out. While our arbitration policy guarantees bandwidth for each master if they do not need it—unlike the bandwidth waste of TDMA–, and opportunistically assigns the bus to the arbiter who needs it. To maintain fine-grained control of bandwidth allocation, SuDO has a debt monitor for each particular master. So both features: supervised debt and opportunistic sharing, give us a proper synergy to have fine-grained control of the bandwidth without wasting the bus utilization. However, the packet injection rate of the application causes that applications cannot reach the established weight ratio. It is necessary to adjust the bandwidth through the budget values to the packet injection rate of every master.We have observed that one arbitration policy is more suitable than another depending on the type of scenario presented. For example, in a scenario with task-dependent applications where we do not desire to regulate the bus utilization, the policy that best fits is the Round-Robin policy because it is lightweight and avoids starvation and deadlock.There are several aspects to follow as future work.The ratio of support weights to the bus utilization percentage that a processing element tries to achieve is not straightforward. One of the reasons already mentioned is a limited packet injection rate of the application. This behavior suggests the possibility of coupling a regulator that monitors and dynamically adjusts in runtime the support weights about the request ratio of the processing elements.On the other hand, there is a lack of studies where applications themselves generate heterogeneous packets. We believe that it is necessary to study and characterize this traffic type to fine-tune the bus utilization.This work focuses on the behavior of the SuDO policy. The optimization of the hardware implementation in terms of maximum operating frequency, resource utilization, and energy consumption is desirable. The policy presented here can directly apply the effective bandwidth regulation of output ports in switches and routers. Furthermore, since the SuDO policy guarantees the differentiated allocation of bus bandwidth, it can simplify the mathematical analysis to measure SoCs, buses, switches, and Networks-on-Chip performance.


Strobilurin fungicides have been widely used in agricultural fields for decades. These pesticides are designed to manage fungal pathogens, although their broad-spectrum mode of action also produces non-target impacts. Therefore, the removal of strobilurins from ecosystems has received much attention. Different remediation technologies have been developed to eliminate pesticide residues from soil/water environments, such as photodecomposition, ozonation, adsorption, incineration, and biodegradation. Compared with conventional methods, bioremediation is considered a cost-effective and ecofriendly approach for the removal of pesticide residues. Several strobilurin-degrading microbes and microbial communities have been reported to effectively utilize pesticide residues as a carbon and nitrogen source. The degradation pathways of strobilurins and the fate of several metabolites have been reported. Further in-depth studies based on molecular biology and genetics are needed to elaborate their role in the evolution of novel catabolic pathways and the microbial degradation of strobilurins. The present review summarizes recent progress in strobilurin degradation and comprehensively discusses the potential of strobilurin-degrading microorganisms in the bioremediation of contaminated environments. Basidiomycetes) are natural sources of strobilurins, and the first natural strobilurin compound, Strobilurin-A, was originally isolated from the mushroom Strobilurus tenacellus by oI fungicides because of their unique mechanism of action. They specifically bind to the quinol oxidation (Qo) site of cytochrome b to inhibit mitochondrial respiration. This binding blocks electron transfer between cytochrome b and cytochrome c1 and inhibits the synthesis of nicotinamide adenine dinucleotide (NADH) oxidation and the mitochondrial membrane protein adenosine triphosphate (ATP)  . The funStrobilurins present broad-spectrum, rapid and highly efficient germicidal activities, are cost effective and rapidly degrade during plant metabolism, and these benefits have contributed to the large-scale use of these fungicides. However, environmental contamination and non-target toxicity due to the long-term use of strobilurins has raised serious public health concerns. For instance, according to the European Food Safety Authority (EFSA), azoxystrobin is frequently found in foodstuffs . EnvironBacillus, Pseudomonas, Klebsiella, Stenotrophomonas, Arthrobacter, Rhodanobacter, Cupriavidus, and Aphanoascus. The metabolic pathways of strobilurins and the fate of several metabolites have been reported. However, there is a limited number of studies on strobilurin-degrading enzymes and the corresponding genes in microorganisms. In addition, few reviews have focused on the mechanisms and degradation pathways of strobilurins. In this review, we aim to summarize strobilurin degradation mechanisms and analyze the bioremediation potential of strobilurin-degrading microorganisms in contaminated soil/water environments.Strobilurins can be degraded either through biotic or abiotic approaches, such as incineration, photodecomposition, adsorption, and biodegradation. Compared with physicochemical methods, the microbial degradation of pesticide residues is emerging as an efficient “green” strategy . Severalt1/2) of less than 14 days under field conditions while the half-life of azoxystrobin was approximately 8–12 weeks under dark aerobic conditions. The study also revealed that hydrolysis of the ester moiety is the major metabolic pathway of azoxystrobin. E→Z), acrylate double bond cleavage, photohydrolysis of nitrile group and methyl ester, and phenol and oxidative cleavage of acrylate double bonds after the cleavage of aromatic rings. The results also showed that azoxystrobin absorbs light at higher wavelengths (290 nm) in aqueous environments, which facilitate its photodegradation. Strobilurin residues remain in the air, soil, or water after field applications, and the physicochemical properties of the associated environment affect the behavior and distribution of these residues . Pestici2 levels and light in aqueous conditions and revealed that it readily forms acid metabolites. The study emphasized that abiotic factors have significant effects on the dissipation of kresoxim-methyl under aqueous conditions. Similarly, photolysis has also been reported as the main degradation pathway of trifloxystrobin under field conditions, and the number of sunshine hours is the key influencing factor for the photolysis process (Azoxystrobin metabolism is similar to the degradation of methoxyiminoacetate  and the  process . Triflox process .t1/2 9.2 days). Contrary to –1 at 20°C) at pH values between 4 and 9, which demonstrates its potential risk to water quality , and they concluded that after 24 h of activity, all response parameters decreased except the focal spine at an azoxystrobin concentration of 500 μg⋅L–1. DNA damage was observed in earthworms (Eisenia fetida) after azoxystrobin treatment . Their results indicated that both azoxystrobin and picoxystrobin caused dose- and time-dependent effects on embryonic development. Evaluations of the effects of azoxystrobin on biological activity in soil revealed that it changes microbial biodiversity by inhibiting the growth of organotrophic bacteria, actinomycetes, and fungi  as a photocatalyst and UV light irradiation. The photodegradation pathway included the rupture of the phenyl bond and pyrazol, scission of oxygen, substitution of a chloride atom by the hydroxyl group, and hydroxylation of aromatic rings  followed by the loss of the N-methoxy group  isolated from soil could use trifloxystrobin as a carbon source; however, they failed to isolate azoxystrobin-degrading bacteria. Cupriavidus sp. CCH2 and Rhodanobacter sp. CCH1) that use azoxystrobin as a sole carbon and nitrogen source. In the presence of an additional nitrogen source, both isolates also partially degraded other strobilurins, such as trifloxystrobin, pyraclostrobin and kresoxim-methyl. Four species of Bacillus spp. and two species of Aphanoascus spp. isolated from contaminated soil also survived on a high dose of azoxystrobin (22.50 mg⋅kg–1)  . In addiAmong strobilurin-degrading microbes, including bacteria and fungi, bacteria play the most critical role. The long-term application of strobilurins affects microbial counts and microbial biodiversity in ecosystems. Due to the unique mechanism of action, strobilurins may directly affect fungal biomass by inhibiting mitochondrial respiration, which can induce a shift from fungal to bacterial dominance in soil activities . For insGenerally, degrading enzymes, especially esterase, occupy an important place in the process of ester containing pesticide biodegradation , 2020. CThe accumulation of toxic and carcinogenic environmental pollutants, such as pesticides, plastics, dyes and other hydrocarbon-containing substances, is hazardous to ecosystems . Thus, tArthrobacter, Mycobacterium, and Rhodococcus reported and correlated with increase and decrease of soil microbial community in combination of antibiotics. Metagenomics study suggested that some of the soil microbial genera significantly decreased (Proteobacteria and Firmicutes) whereas abundance of other like Actinobacteria increased in presence of strobilurins (Soil microbes exhibited bioremediation potential of strobilurin-contaminated environments. According to bilurins . After sbilurins . Additioin situ make this process unsuitable for strobilurin residues. The microbial removal of strobilurin contaminations from the environment is an efficient and cost-effective alternative approach. Therefore, the potential of strobilurin-degrading microorganisms and their enzymes should be studied. However, published literature about the use of microorganisms to bioremediate a strobilurin-contaminated environment is still insufficient. In addition, the efficient strobilurin-degrading enzymes and correlated genes in microorganisms, have not yet been investigated. Studies on cooperative degradation activities by microbial communities can help prevent the accumulation of toxic metabolites during degradation. Therefore, detailed foundation work should be accomplished before the field application of strobilurin-degrading microorganisms is undertaken. The application of recently developed high throughput technologies for detecting strobilurin-degrading microbes in agricultural soil/water can generate better information about strobilurin degradation.The intensive/large-scale application of strobilurins in agricultural fields has increased contamination of the surrounding soil/water environments. Strobilurin toxicity may result in ecosystem imbalance and food-web disruption. Strobilurin residues have increased in trophic level due to biomagnification; thus, the development of degradation technologies for these pesticides is necessary. Strobilurin phototransforms under sunlight via various mechanisms, and photochemical degradation is one of the most prevalent processes underlying strobilurin degradation in water. However, uncontrollable reaction conditions and inefficiency in cleaning the environment SC conceived of the presented idea. YF contributed to the writing and prepared the figures and tables. YH, HZ, PB, and SC participated in revising the manuscript. All authors approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Understanding video files is a challenging task. While the current video understanding techniques rely on deep learning, the obtained results suffer from a lack of real trustful meaning. Deep learning recognizes patterns from big data, leading to deep feature abstraction, not deep understanding. Deep learning tries to understand multimedia production by analyzing its content. We cannot understand the semantics of a multimedia file by analyzing its content only. Events occurring in a scene earn their meanings from the context containing them. A screaming kid could be scared of a threat or surprised by a lovely gift or just playing in the backyard. Artificial intelligence is a heterogeneous process that goes beyond learning. In this article, we discuss the heterogeneity of AI as a process that includes innate knowledge, approximations, and context awareness. We present a context-aware video understanding technique that makes the machine intelligent enough to understand the message behind the video stream. The main purpose is to understand the video stream by extracting real meaningful concepts, emotions, temporal data, and spatial data from the video context. The diffusion of heterogeneous data patterns from the video context leads to accurate decision-making about the video message and outperforms systems that rely on deep learning. Objective and subjective comparisons prove the accuracy of the concepts extracted by the proposed context-aware technique in comparison with the current deep learning video understanding techniques. Both systems are compared in terms of retrieval time, computing time, data size consumption, and complexity analysis. Comparisons show a significant efficient resource usage of the proposed context-aware system, which makes it a suitable solution for real-time scenarios. Moreover, we discuss the pros and cons of deep learning architectures. Current smartphones come with great hardware and software capabilities. These devices gave their owners the ability to become active online publishers. Smartphone owners are media producers through their YouTube channels, authors, journalists on their personal Facebook profiles, and news reporters via their tweets on Twitter. All these productions come in the form of multimedia content. Almost every YouTube video comes as an audio and visual signal synchronized with a subtitle, released through a YouTube channel, and may be within a playlist as well. The audience may watch YouTube and express their interpretation of the story told via comments, likes, and dislikes. The same scenario occurs in other social media platforms such as tweets on Twitter, posts on Facebook, and photos on Instagram.Generally, multimedia production and consumption are instantaneous. YouTube daily users count to more than two billion, with one billion hours watched daily . This heEvery media producer is a human being with a story to tell. While that story is composed, the author uses every aspect of his human intelligence. The story consumer is also a human who will use all his intelligence toolbox to understand the concept behind the story told. Human intelligence is a multidimensional toolbox. It includes common sense, innate knowledge, approximations, learning capability, context-awareness, and reasoning. The problem now is how to make the machine intelligent enough to understand the video stream.Recently, researchers tend to use deep learning to solve such problems. However, it is neither fair nor possible to expect a human-like understanding from machines based only on deep learning architectures. So, rich artificial cognitive models are needed for a deep understanding of the concepts behind media productions, charging them with every possible aspect of human intelligence. That is the path toward real artificial general intelligence which could exist in our daily life and bring real value.To make this possible, we need to mind the semantic gap , shown iIn the context of video understanding, we define an artificially intelligent machine as a machine capable of deciding the true meaning of the video file. We describe artificial general intelligence (AGI) as a heterogeneous process that includes not only learning but also innate knowledge, approximation, and context-awareness.Spot the light on the limitations of deep learning, not only its capabilities.Design and implement a context-aware system for video understanding. The proposed system diffuses the video context into weighted concepts, emotions, categories, and temporal information about the video.Design a benchmark of real videos . RealVids represents real-life videos we watch daily on social media platforms. RealVids is not a fine-tuned dataset that acts properly only with well-trained algorithms.The proposed context-aware system and classical deep learning technique are evaluated against the RealVids benchmark. Performance analysis is carried out via objective and subjective comparison of recognized concepts. The context-aware system is compared with the deep learning baseline in terms of data retrieval time, time consumed to generate concepts, data size consumed to generate such concepts, and complexity analysis of both algorithms.This article presents a transition phase between understanding of video content era and our proposed framework on video context-aware understanding. The proposed context-aware cognition system utilizes metadata, emotions, comments, title, description, temporal data, spatial data, and likes/dislikes playing a vital role in the interpretation of concepts within the video. The proposed system consumes less time, computing power, and data compared to the classical learning approach. The main contributions of this paper can be summarized as follows:The remainder of the paper is organized as follows. In The objective of this section is to discuss the core problems of video understanding, accomplishments, and limitations of deep learning architectures toward these problems.Deep learning models are systems of multiple neural layers that build by improving the level of abstraction starting from the first layer and then onto the next layer, as shown in In 2011 , videos Understanding human activities in visual information is based on progress in other research areas such as object recognition, semantic segmentation , and domThe convolutional design adequately uses the picture structure in diminishing the search space of the network by pooling and weight-sharing. Pooling and weight-sharing also help bring robustness to the scale and spatial dimensions. Breaking down channels learned by CNN designs tells that the first layers learn low-level features (such as SIFT features), while top layers learn high-level semantics , making In visual perception, the ventral stream of the human visual cortex processes objects properties, for example, appearance, shading, and personality. The movement of an object and its location are taken care of independently through the dorsal stream . A classDifferent research challenges, such as the ActivityNet Large-Scale Activity Recognition Challenge, are prepared by researchers to let algorithms recognize actions in videos , 22. TheCurrently, the largest artificial neural networks, built on supercomputers, have the size of a frog brain (about 16 million neurons). On the other hand, the human brain contains 100 billion neurons, passing signals to each other via as many as 1,000 trillion synaptic connections .Deep learning architectures learn from videos and images captured by cameras with 45 megapixels, at their best, while the human eye has a resolution of 576 megapixels .Deep learning is data-hungry and works best when there are millions and billions of training examples , while hDeep learning suffers from a learning rate that stops at a certain limit of data size .Deep learning cannot represent hierarchical structures where large structures are constructed from smaller ones , such asith node in layer j) in network module L. The importance of deep learning transparency depends on whether the deep learning models are self-contained standalone systems [Deep learning represents a black box for researchers and system designers. The millions of parameters within the neural network are not known in an interpretable human way; all we know is their geography within the network , and context-awareness.It is interesting to see where the previously mentioned deep learning techniques originally come from. Rosenblatt introduced the concept of the one-layer architecture of the perceptron learning algorithm  and SuttArtificial intelligence has got different definitions from different perspectives . We chosHuman intelligence is multidimensional. It includes emotional intelligence, linguistic intelligence, social intelligence, common sense, context-awareness intelligence, perception, and approximation. The AGI needs a general cognitive model, not a statistical approximation model, because it is not possible to understand the world within a probability distribution model. Thus, methods different from deep learning are needed, methods that not only need fewer data to learn but also are able to represent abstract knowledge.Innate knowledge is essential knowledge that typical individuals are anticipated to have regardless of whether they do not have the foggiest idea of what precisely it is. Innate knowledge is the mystery of how to settle on choices about novel cases for which there are not many or no examples to learn from. There is sufficient proof, both social and mental, that biological creatures start from rich starting points, even before learning starts [g starts . Starting starts . ResearcApproximations had been of interest to AI from the early days. For instance, Edward Feigenbaum wrote the following [ollowing : “A usefContext representation: it defines how to represent the elements of context and how to define them.Context determination: it defines how to determine the context elements in an uncertain environment.Context analytics: it defines how to summarize and predict the context in an agile way.The word “context” has Latin roots, where the word “con” means to join together and “texere” means to make or to weave, implying weaving together the circumstances that form the setting of a scenario. The context of concern could be user, computation, time, or cognitive. Context-awareness is the ability of a system to give user-relevant information by utilizing the contextual information of the concerned event. Context is a multidimensional feature space that evolves with time. Considering the context of a situation into consideration brings insights and intuitions that could help make better decisions and understanding. The context supports decision-making by filling the gaps in uncertain environments. Context means different things to different people; one well-cited and accepted definition is the following: “Context: any information that can be used to characterize the situation of entities  that are considered relevant to the interaction between a user and an application, including the user and the application themselves. Context is typically the location, identity, and state of people, groups, and computational and physical objects” . ContextIn this section, we present an implementation of a context-aware understanding of the video file. We compare context-aware video understanding with deep learning video recognition. The aim is to understand the concepts in a typical social media video file that we see daily. We choose a set of videos that represent a topic from YouTube, in this case “screaming kid.” YouTube is an ideal representation of social media platforms. It allows publishers to publish videos with titles, descriptions, and metatags. YouTube audience can comment on, like, and dislike the video, thus developing a context around the video. The same environment exists for Facebook posts, Twitter tweets, and Instagram photos. The main feature that distinguishes YouTube is its publicity as all videos are public and available to everyone, which is not the case for content on other social media platforms.Retrieve video context metadataProcess video context metadataSegregate words and emojisGenerate concepts from contextThe start is with retrieving video content and metadata from an external source, YouTube in this case, and the end is with extracting concepts from the video's context.Video context is built by harvesting and inspecting the metadata available for YouTube videos through the YouTube Data API . The algThe system starts by fetching video information from an external source, YouTube in this case. Comments are ordered in a descending order. The video title, top 100 comments, and description are filtered by removing stop words. After filtering, the system uses NLTK   functionSegregating words and emojis from the list enables detecting the global social-emotional expression within the video context. After creating lists of words and emojis that summarize the context, the weight for each word and emoji is calculated. The weight of each concept is estimated based on the ratio of the word occurrence to the total occurrences of all the words in the context. In this way, the weights are valued relative to the total counts of every extracted word and emoji.C is the set of all extracted comments.C| is the cardinality of the finite set C. We construct a set of words wi belonging to each comment i.We define a functionW isSo, the set of all words C is the set of comments.Word frequency within the context is calculated according to equation , where CI isEquations  and 8)  8) and  emphasizLearning from millions of videos is not a feasible solution to build AI systems that exist in daily life. Real-life situations that meet us every day are not frequently available in terms of millions or even hundreds of examples. To build real daily life AI machines, systems should need fewer examples.Currently, existing video datasets require huge computing power  which maRecent research claimed that research results from already prepared datasets are not reproducible . RecommeThe common practice of the research community is to evaluate algorithms and proposed theories against already prepared datasets such as YouTube-8M Large-Scale Video Understanding Challenge  and the In response to the above critics, we designed a new test environment called RealVids, described in The proposed system is developed and tested on a Linux machine (Ubuntu 18.4). The hardware specifications are Intel Core I7-6500U @2.5 GHz, 8 GB RAM.The “screaming kid” collection is trajected against the action recognition deep learning model of the Moments in time dataset . AccordiAll videos are retrieved in response to the “screaming kid” query, but the content of the video message is different from one video to another. The context-aware system interpreted the video implied message from the context. For example, videos 2, 3, 4, and 6 represent a violent scene, but only 2, 3, and, 6 are actual violence. Video 4 represents an online video game. Concepts extracted from the context of 2, 3, and 6 represented such violence. These concepts included psycho, ruin, murder, scream, flight, annoy, and brat. Emotions of video 2 “joy and laughing” reflected the comedy behind the scene. Emotion of video 3 “Insane” reflected the real violence of the court scene. Meanwhile video 4 was represented by much fewer violent concepts such as gmod and rage. Emotions of video 4 “joy and laughing” reflected the video as just an online game.Video 1 concepts extracted from the context came in accordance with the video message, such as a parent and learn. Extracted emotions such as thumbs up and proud express user satisfaction. Video 10 is a funny homemade video. Concepts extracted from the context such as funny, reaction, laughing, and joy expressed the real message behind the screaming kid in the video.Videos 5 and 9 were modified advertisements with air rape and a song, accordingly. This mix-up caused disjoint discussions through the video comments. The context-aware system was unable to extract any meaningful concepts or emotions. Videos 7 and 8 did not contain any contextual information, so no concepts or emotions were extracted.Categories of videos 2 and 4 integrated with extracted concepts giving a complete understanding of the video message. Categories and concepts of videos 1, 3, 6, and 10 came matching each other.During the experiments, we realized that the context-aware system performs perfectly for videos that have rich metadata and objective discussions and comments that fulfill our defined criteria for the targeted media. On the other side, the context-aware system fails in extracting concepts from the context of poor metadata like a small number of comments or low levels of interaction, and this is visible for videos 7 and 8 in The context-aware system achieved these results efficiently in terms of retrieval time, processing time, consumed data size, and algorithmic complexity. The next section will discuss these metrics and their implications.The objective and subjective comparisons of the deep learning algorithm and the proposed technique are presented in The objective comparison shows how the proposed technique outperforms the classical learning one. Concepts extracted from the context were affirmative and more confirmed than those recognized by deep learning. This proves that the context-aware video understanding is more precise than the video understanding depending on the video content only.The subjective point of view reveals a cutting-edge comparison between the generality of the classical learning technique and the definite clear and determined results of the proposed technique. The context-aware system recognized not only concepts but also emotions around the video, temporal, and categorical information.Unfortunately, context-awareness was not able to predict any spatial information, though. Social media producers and users did not leave enough data about their geographical location, probably for privacy concerns.The context-aware system processes textual data to extract concepts, while the deep learning approach processes visual signal. The textual data describing a video is smaller than the visual signal for the same video. This explains the improved retrieval and processing time by the context-aware approach in comparison with the deep learning approach.Comparing the results, the power of context-awareness analysis for real-time videos is noticeable. The context-aware system developed an understanding of the video message without trained models and without dataset dependencies. All results achieved by the context-aware system did not depend on trained models or big size datasets in contrast to the deep learning. Also, there was no training time for the context-awareness system.For a machine-independent comparison, we carried out a complexity analysis between our proposed context-aware algorithm and the ResNet-50 architecture.O(n2) to extract words and emojis. O(n2 + n). The overall complexity of the proposed system is described by equation (O(1) + O(n) + O(n2) + O(n2 + n).The overall complexity could be introduced as the complexity measure for all the four building blocks of applied algorithms. equation  as resulO(n2).Hence, in this comparison, the total complexity of the proposed system is l as the convolutional layer index, d is the depth , nl is the number of filters  in the l-th layer, nl-1 is the number of input channels of the l-th layer, sl is the spatial size (length) of the filter, ml is the spatial size of the output feature map, and this time complexity applies for both training and testing time.Equation  introducAs a result, the complexity comparison had evolved to favor the context-aware system from an execution perspective due to the benefits of reducing the size of data to be processed, unnecessary training models, and the elimination of training time. Also, the context-aware system cares about human feedback and emotions around the video, which will lead to a better understanding of the video in a valid real context.First, we observe that some videos may have no context metadata so extracting concepts become not possible.Second, it becomes difficult to extract concepts from sparse context metadata. An example of a sparse context is when the social media audience may fill the video context with their emotions more than their thoughts. In this case, context-aware video understanding technique could be able only to extract emotional states rather than concepts.The presented research work introduces high-performance and precision context-aware video understanding technique. This context-aware video understanding depends on the diffusion of heterogeneous multimedia data, where an artificially intelligent algorithm is employed to recognize the concepts from the video message. This results in overall precise concepts, emotions, and temporal and categorical understanding of the video. The generated concepts of the context-aware video understanding technique are precise, determined, and clear compared to the general concepts obtained from the deep learning technique. These improvements are accomplished by utilizing less time, computing power, and data. This makes our approach fit better in real-time scenarios, where a fast decision needs to be made. Besides this subjective comparison, an objective comparison is presented to show clearly how our proposed technique outperforms the learning-based algorithm.Future work shall consider merging relevance feedback in the video understanding process. Relevance feedback is a practical and applicable way to represent human preferences because the relevancy of a video file is a user's opinion. In that case, similarity measures could be used to assess the relevancy of the video file content. A heterogeneous artificial intelligent system composed of context-awareness, deep learning, relevance feedback, and similarity measures would enable a human-like intelligent performance.


In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census. In many modern hospitals, resources such as beds, theatre time, medical equipment and staff are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partially known to the hospital some time in advance (elective patients). Caring for these two types of patients poses a logistical challenge in the sense that some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. Hospitals have guidelines for the number of emergency patients they might expect to see in each planning period, although the exact number is unknown. If too many elective patients are admitted, the hospital’s ability to treat emergency patients will be reduced, potentially resulting in negative patient outcomes, such as having to turn patients away, and “outliers” - a term which refers to patients whose ward might not be ideally suited to their condition. On the other hand, if too few elective patients are admitted, patients can be left on waiting lists unnecessarily in the case of public health services, or hospital income can be lost in the case of private health care.The potential benefits of using discrete event simulation (DES) models in health care are well established, and they are often preferred to other modelling approaches because of their ability to emulate the randomness seen in physical systems at a level of detail which is necessary for models to be convincing. Numerous literature surveys have tracked the progress of this modelling approach, including –5. Howevsymbiotic simulation decision support system. In this type of symbiotic simulation, the simulation reads data from the physical system regularly . The simulation outputs are then used for what-if analysis, and an external decision maker can choose to change the behaviour of the physical system. In other words, the simulation system indirectly controls the physical system via the external decision maker, instead of an automatic actuator. As operational and real-time data becomes more readily available in health care, the use of symbiotic simulation in health care is becoming more feasible and some early work, for example )Care and Stay databases. The Care database contains information about the type of treatment a patient is receiving; such as their specialty at any given time, and the identification code of the doctor responsible for their care. The Stay database contains information about the physical location of the patient; including the identification code of the bed they occupy and the ward on which they are staying. A new row in the database is created when any of these features change, and each row is populated with start and end dates/times. The data are sufficiently detailed for us to develop a simulation model.When the AGH approached us, they expressed interest in “… a core piece of work built around a predictive bed modelling tool for operational purposes …” After further email correspondence, we were supplied with an anonymised extract of the patient administrative (PA) database, for all patient episodes from the 1st of January 2010 to the 30th of June 2012. The PA data supplied by AGH is split into two databases, known as the Since AGH is interested in a predictive bed modelling tool, the objective of the model is to estimate the number of occupied beds. The PA data allows us to obtain bed occupancy at any time of day. In this work we use a frequently-used metric called the midnight bed census, and its breakdown between emergency and elective patients, as shown in Fig. Other). This means that the population of interest is captured entirely, while modelling effort is reserved for wards which are individually significant.Since this project is concerned with estimates of inpatient bed occupancy at the ward level, the minimum level of structural detail includes a network of wards see Fig. . PatientFigure After being admitted to the hospital, patients occupy a bed for some period before being discharged or transferred to a bed on another ward. This period is known as the patient’s ward length-of-stay (WLOS). In the case of elective admissions, it may well be estimated by clinicians and planners responsible for scheduling procedures (which is the basis of one of the example applications presented in 6.2). However, there will still be random variation in length-of-stay from patient to patient. For emergency patients arriving at the hospital, length-of-stay may be even less predictable due to the unscheduled nature of their admission. For this reason, WLOS are modelled as random variables. Patients can stay in more than one ward while being treated in the hospital. Hence, the sum of their WLOS form their total length-of-stay (LOS) in the hospital. For readers who are familiar with the UK NHS, the terminologies are ward stay and spell, respectively.Once a patient’s ward stay is over, they may be discharged from hospital or they can be transferred to another ward. If a transfer to another ward is necessary, the choice of ward is not only dependent on the patient’s clinical requirements, but also the availability of the resources needed to treat the patient, such as beds, nurses and monitoring equipment. The potential for uncertainty in the sequence of visited wards, along with the unknown types of emergency arrivals occurring in each planning horizon, justify the use of stochastic transfers between wards in the simulation. The probabilities which govern the transfers from each ward are estimated from the PA data by calculating the proportion of total departures moving to each subsequent ward or to discharge. Modelling transitions in this way is a simplification of the real transfer/discharge process, since the probabilities depend on the current ward and do not consider previously visited wards (memorylessness). However, this approach has been shown to work well in other models, see for example , and maiIn his review of the development of queueing theory and applications,  commentsInfinite server models deliberately exclude real-world resource constraints, and hence produce simpler models. The use of simple models that are fit for purpose is a good practice for simulation modelling. In our case, infinite server models are well suited to estimate the probability of demand exceeding a certain level in the short term, which is a key piece of information for hospital managers trying to assess the risk of not being able to cope, given the number of beds at their disposal. Similar statistics could be derived using a fixed capacity approach, however doing so adds considerable complexity in terms of modelling the extent to which patients are turned-away.proof of concept symbiotic simulation model that can be used to investigate the issues of model development, model validation and model application. It is not the purpose of this paper to argue that the infinite-server model adopted here is the only, or ‘best’, way of modelling inpatient bed occupancies. There will certainly be occasions where it would be useful to increase the complexity of the model to reflect management decisions taken when wards are full, or for example to model the sorts of self-regulatory behaviours described in [As noted in section ribed in .The conceptual model described in the previous sections is implemented in the Micro Saint Sharp simulation package. We applied white-box validation as defined in  whilst wThis section describes the development of a symbiotic simulation model from the DES model described in Section This functionality ensures that the simulation can be initialised to the state of the physical system being modelled to investigate how the physical system might evolve given its current state. In general, having a communication line with the physical system means that the physical system state can be queried by the simulation model at any time. However, in a hospital setting this may not be possible, since data entry into the patient administration system may not occur automatically. This may change in the future as more data are being collected automatically in hospitals via sensors, RFID, mobile electronic devices and other technologies. Likewise, future commercial simulation software is likely to include the functionality to interface with these real-time data sources. Currently, most commercial simulation tools (including Micro Saint Sharp) have the ability to read a file that can be used to initialise a simulation. Hence, a symbiotic simulation can be implemented using existing software packages that share this feature.The number of emergency admissions resident on each ward.The number of elective admissions resident on each ward.The day of the week on which each patient was admitted.The amount of time already spent on the current ward for each patient at the time the state data is collected.The system state that is needed to make the simulation model in Section The first and second pieces of information are the most obvious requirements when attempting to describe the state of the model. The third piece of information relates to the way in which ward length-of-stay is modelled. More specifically, a statistically significant relationship was found between the day of the week on which a patient was admitted, and the length of time they subsequently spent in hospital. Thus, day of admission information is required for each patient’s ward length of stay to be drawn from the appropriate empirical distribution. The use of empirical distributions aligns closely with the data-driven nature of symbiotic simulation. However, this does not prevent the use of theoretical distribution when appropriate. The fourth piece of information ensures that the patients who are resident on a ward when the state data is captured are loaded as simulation entities who have spent the same amount of time on the ward.remaining length-of-stay of a patient already in a ward when the simulation is initialised is stochastic, and cannot be known at run-time. However, this remaining WLOS is likely to be dependent on the time already spent on the ward and hence necessitates the use of conditional distributions  is the service time of interest and is treated as a random variable conditional on admission type, weekday of admission and hospital ward. Therefore odels of –47).T represents the total number of nights a given patient will spend on the ward, and that when the simulation is initialised the patient has already been on the ward for s midnights. The random variable R = T − s therefore represents the number of midnights the patient remains on the ward after the simulation is initialised. If the CDF of total length of stay, FT(t), is estimated using the empirical data, the conditional CDF FT = ℙ{T ≤ t| T ≥ s} can be obtained simply using the formula:The conditional WLOS distribution for each resident patient is straightforward to derive, given their time already spent on the ward, and the marginal distribution of length of stay  applicable to the patient. Suppose the random variable R is the difference between T and s, the sampling distribution for R is then readily given by:Since s, realisations of R can then be drawn from FR using the inverse transform sampling method, and these realisations represent remaining length of stay on the ward, given length of stay already spent on the ward at the time the simulation is initialised. Most commercial simulation software will allow the user to specify any function for the sampling distribution of activity durations, and hence this is simple to implement in such software.For a given It is worth noting that conditional WLOS does not need to be considered for models whose service times are best described using exponential distributions (when service times are continuous) or geometric distributions (when service times are discrete), due to the memoryless property. However, memorylessness does not apply to the empirical distributions from which WLOS is drawn in this model.One of the characteristics of stochastic symbiotic simulation (e.g. built from Discrete-Event Simulation or Agent-Based Simulation) is that the simulation needs to be re-initialised with the data from the physical system it is meant to control. Hence the distribution of a metric estimated by the stochastic symbiotic simulation (e.g. midnight occupancy in this paper) changes as a function of the elapsed time from initialisation. Figure Figure h (e.g. 1 to 6 days ahead). With 560 days of observed data, for each ward we have 560-h observations of:Mt represents the measure of metric M, t days from the start of the observation period.The validation test is based on comparing the distributions of simulated and observed changes in ward occupancies over the range of prediction periods of interest t0, then h ). If Fsimulation(δh ) and Fphysical system(δh ) denote these empirical cumulative distribution functions over the support of ∆h, denoted by δh, then the coordinates (Fsimulation(δh ), Fphysical system(δh)) form a probability-probability plot or P-P plot, see for example [y = x), providing a visual indication of the similarity of the distributions of Δh at each possible elapsed time h from initialisation, or equivalently, on each of the h days in the planning horizon.If the symbiotic simulation is initialised at time  example  for a deh-occupancy on ward w is:t days from the start of the observation period on ward w.To illustrate how Δ-Method works, we apply it to our symbiotic simulation. In this case we are interested in the midnight occupancy of each ward. Hence, our Δh-occupancy observed in the historic data, with equivalent distributions generated by the output of 100 replications of the symbiotic simulation model, for the ED ward. The results in Fig. h-occupancy across the six-day planning horizon. This indicates that the simulation model, including the infinite server assumption, seems to be representing the performance of these wards quite well. However the result in Fig. h, this pattern is indicative of lower variance in the distributions plotted on the vertical axis, compared to the distributions plotted on the horizontal axis.Figures positively-skewed, is quite unlikely to be near, or at its maximum capacity, and hence is relatively well represented by infinite-server assumption. On the other hand, as Fig. Figures h random variable, the observed metric can be pooled in such a way that comparisons can be made with the simulated metric, whose distribution evolves with time-from-initialisation. Since Δh is analysed via a comparison of the entire empirical distribution function, differences in trend, variability, or cycling behaviour which may occur over time, can all be detected.In summary, this section has focused on the development of a new validation technique suitable for stochastic symbiotic simulation in which the time-dependence of the simulation outputs is accounted for, and which are well suited to bed management applications. By defining the ΔWe present the following two examples to demonstrate how the symbiotic simulation could be used in practice.Motivated in part by the needs of AGH, the first example application demonstrates how the symbiotic simulation model could be used as an early-warning system to anticipate days in the planning horizon when the demand for beds is at risk of exceeding the maximum capacity of the wards. A particularly busy week is chosen from the PA database, and the symbiotic simulation is used to assess the likelihood of demand exceeding capacity for the observed elective schedule. We initialise the symbiotic simulation with the PA data on Monday of the chosen busy week and run the symbiotic simulation for 400 replications. Based on our testing, 400 replications resulted in sufficiently stable midnight occupancy distributions when only the random seed was changed. For brevity, only Wards 5B and 5D are shown in Fig. Figure In addition to indicating the days when midnight occupancy is likely to be above the 90% threshold, Fig. Suppose we now want to evaluate two possible revisions to the elective admissions schedule for the busy week in question. Many of the admissions take place on the Other ward which is an aggregate of smaller wards, meaning the actual ward of admission has low average midnight occupancy. The actual admissions schedule for the busy week is shown in Table Clearly, the likelihood of demand exceeding capacity should be assessed for every ward, in a holistic way. Hence, to compare the three elective schedules , we chart the probability of bed demand exceeding the maximum capacity on every simulated ward as shown in Figs. The charts clearly show that Ward 5B is the most likely ward to encounter capacity issues for most days of the week if the observed schedule is not revised. The Postponement Schedule is able to reduce the peaks in probability on both Wednesday and Friday for Ward 5B, and as expected, increases the probabilities on Thursday and Saturday. However, the level of risk is now more even across the week, and hospital managers may consider these two days to be in a better position to accommodate additional patients than Wednesday and Friday. The estimated BMOC also sees a decrease of 0.49 bed-midnights, also indicating a net improvement across all wards using this schedule. The Cancellation Schedule further reduces the probability of Ward 5B encountering capacity issues by approximately 5%, and BMOC by 0.29. Whilst operation cancellations are clearly undesirable, hospital managers may nevertheless sometimes need to trade-off this sort of an improvement against the consequences of cancelling the patient.This example illustrates how our symbiotic simulation might be used to best reduce the likelihood of excessive bed demand, thereby balancing emergency and elective workloads.One of the benefits of symbiotic simulation is the ability to use newly available data that are made available after the simulation model is developed; including at simulation run-time. This example investigates the potential for improving the accuracy of results generated by the symbiotic simulation (e.g. estimates of bed demand) by making use of additional  patient information that might be available at simulation run-time. This could include information about the location of subsequent ward stays, or the likelihood of requiring an Intensive Care bed. In this example, the additional information being considered concerns the length-of-stay of patients on the elective admissions schedule, and the remaining length-of-stay of any patient (emergency or elective) who occupies a bed when the symbiotic simulation is initialised. This information is used as a proxy for the Estimated Date of Discharge (EDD) which , 51 emphEDD  aligns with the symbiotic simulation method particularly well. New system state data is already read into the model at regular intervals, and this can easily be augmented with information about a patient’s condition (i.e. how long they are expected to stay) as it develops. For the incoming elective patients, clinicians will have an approximate EDD in mind to help manage hospital resources, and to inform patients of the time they can expect to spend in hospital. For the emergency patients already in the hospital, clinicians should have more information than when they were first admitted, which can contribute to an EDD. In fact,  recommenHowever, clinician’s assignment of an EDD is by no means a guarantee that the corresponding patient will be discharged on their estimated date. Factors such as variation in individual recovery times, and complications associated with treatment, can contribute to differences between the EDD and the actual date of discharge. Therefore, as part of assessing the value of using discharge date estimates in a symbiotic simulation, it is also important to consider how accurate they might be.d, which controls the proportion of correct estimates of current ward LOS. Hence, in the simulation, clinicians make correct estimates of LOS with the probability of d, and inaccurate but still good estimates  with the probability of 1-d. The symbiotic simulation is run for 400 replications using the same settings used in Section Although estimates of LOS/EDD were not explicitly provided by the AGH participating in this study, the actual ward lengths-of-stay can be loaded from the PA data, retrospectively. The use of actual observations would represent a scenario in which clinicians were able to perfectly predict LOS. To model the uncertainty associated with clinicians’ LOS estimates, we considered information about LOS on each patient’s current ward, and introduce a parameter d) on the standard deviation of the midnight occupancy on Ward 5B, where d = 0 means no additional information is used .Figure d increases the standard deviation of midnight occupancy on Ward 5B decreases, resulting in increased accuracy of the midnight occupancy estimates. However, Fig. As one would expect, as The main contributions of this paper are concerned with model development, model validation and model application for symbiotic simulation models in the context of operational management of inpatient beds. In particular we have shown how a symbiotic simulation can be developed from an existing and validated (non-symbiotic) simulation model. This should reduce the cost of implementing and validating the symbiotic simulation model and promote model reuse.The second contribution is the development of the Δ-Method; whilst based on the simple principle of compared simulated and observed outputs this new validation technique is suitable for validating a stochastic symbiotic simulation models and is well-suited to bed management problems. While other methods for validating symbiotic simulations are known to exist (such as ), the Δ-The third contribution is in the form of example applications which show how our symbiotic simulation can be used in practice and something of their potential value.Whilst these are the main contributions of the work, we also note that the data requirements of the symbiotic model were quite manageable, and were met by extracts from AGH’s patient administrative (PA) database. Clearly, in practice, the outcome of the model validation might have been to request a more complex model, in which case the data required for calibration could become considerably more demanding.Reflecting on Aydt’s definition of symbiotic simulation  as “a clIn the future, we plan to extend our work to include two further components into our symbiotic simulation model. The first component provides an optimisation model to find a good elective patient schedule given the availability of the new information when the simulation is re-initialised. The second component provides a learning algorithm so that the symbiotic simulation can learn by adjusting its parameters to improve the accuracy of the outputs. In a separate stream, we plan to apply the proposed development approach and validation method to different problems.


In the evolving situation of COVID-19, an outbreak of the contagious disease in a nursing home near Seattle prompted urgent calls for precautionary tactics in senior-living facilities. Worldwide, the USA and China are the countries with the largest and second largest populations of people aged 80+. More than two million older adults live in senior-living facilities in the USA and the number of beds in senior-living facilities in China has increased to 6.5 million in recent five years. The risk of infections including COVID-19 is tripled in senior-living residents because of age, close living conditions, and underlying health conditions. Infectious diseases account for one third of all deaths in people age 65+ in the USA. COVID-19 has a case fatality rate of 2.3% overall and as high as 15% in patients age 80+ in China. Together with health and services interventions, environmental interventions should be considered to prevent the possible spread of infections in senior-living settings. Based on a literature review and using empirical data of 12 senior-living facility designs collected in both countries, this research analyzed the advantages and disadvantages of existing facility environments in terms of infection control. Environmental interventions to promote infection control are suggested in the context of individual facilities. Proposed interventions are analyzed at two levels: spatial design and design details. Five factors are included: visitors screening, ventilation, isolation rooms, hand washing, and daily temperature checks. From the perspective of infection control, the cross-country similarities and differences of senior-living facility designs are discussed.


Malnutrition continues to be a major cause of mortality and morbidity among children in resource limited settings. Children with severe acute malnutrition (SAM) experience severe thymus atrophy, possibly reflecting poor immune function. This immune dysfunction is responsible for the severe infections they experience which lead to mortality. Since their immune dysfunction is not fully understood and there has been a lapse in research in this field, more research is needed. Knowing the correlates of thymus size may help clinicians identify those with more severe atrophy who might have more severe immune impairment. We aimed to describe thymus size and its correlates at admission among children hospitalized with SAM.This cross-sectional study involved children 6-59 months admitted with complicated SAM in Mulago National Referral Hospital. Well-nourished children from same communities were used as a community reference group for thymus size. At admission, thymus size was measured by ultrasound scan. Demographic, clinical and laboratory variables were identified at admission. A linear regression model was used to determine correlates of thymus size among children with SAM.2 lower than that of the 27 healthy community reference children  when controlled for age. Thymus size positively correlated with current breastfeeding , anthropometric measurements at admission  and suspected tuberculosis . Thymus size negatively correlated with > 2 weeks duration of sickness .Among 388 children with SAM, the mean age was 17±8.5 months and 58% were boys. The mean thymus size was 3.14  cmThe thymus is indeed a barometer for nutrition since all anthropometric measurements and breastfeeding were associated with bigger thymus. The immune benefits of breastfeeding among children with SAM is underscored. Children with longer duration of illness had a smaller thymus gland indicating that infections have a role in the cause or consequence of thymus atrophy. Malnutrition continues to be a major cause of morbidity and mortality among children, with 45% of all child deaths being estimated to be linked to malnutrition . The higThe thymus gland is a lymphoid organ where maturation of the bone marrow derived T lymphocyte takes place. Children with SAM have been found to have severe atrophy of the thymus gland –6. This The thymus gland has been found to be sensitive to environmental factors, and its size seems to reflect the influence of these different factors , 15. Sinwww.isrctn.com (ISRCTN16454889) and whose findings were published  and all the healthy controls were found to have visible thymus. The mean thymus gland size among 388 children with SAM was 1.06 (± 0.41) cmCorrelates of thymus size are summarized in Table 2 compared to 4.20 (± 0.93) cm2 among healthy controls. The factors which were positively associated with thymus size were current breastfeeding, suspected tuberculosis at admission and all anthropometric measurements at admission while sickness for 2 weeks or more appeared to be negatively correlated with thymus size The children with SAM had severe thymus atrophy as has been reported by several studies involving this population [2 in SAM, and 3.5 cm2 in healthy children [Our study found the mean thymus gland size among 388 children with SAM was 1.06 (± 0.41) cmpulation –8. Our mchildren .The mechanism of thymus atrophy in SAM is not fully understood. Animal studies suggest that hormonal factors play a role. Thymus growth and function is regulated by several hormones including Leptin, Prolactin and Growth hormone, mediated by insulin-like growth factor 1, all of which are low in malnutrition . In addiChildren who were breastfed at the time of study had a larger thymus when controlled for age. Some other studies involving children, who are not malnourished also report that breastfeeding children have a bigger thymus , 22, 23.The main strength of our study is that we had large numbers of well-characterized children with SAM and the thymus scans were almost exclusively performed by one trained investigator (NNB). One limitation is that we used clinical diagnosis of suspected tuberculosis and septicemia, which were subjective methods and yet the former was a positive correlate. Furthermore, since the study was observational, we are unable to establish causality. It remains unclear whether thymus atrophy is a cause or effect of malnutrition. This calls for a prospective study looking at children who are at risk of malnutrition. Considering the large sample size, these results are generalizable to children with SAM in similar settings. Future research should look at how thymus size predicts function using T cell receptor excision cycles.In conclusion, our study found that children with SAM had severe thymic atrophy. Children with higher anthropometric measurements and those who were breastfed had a bigger thymus. Breastfeeding should continue to be promoted since it may have current immune-modulating effects even among children with SAM.