The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics of WiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities. The Internet of Things (IoT) is a dynamic global information network consisting of internet-connected devices . Due to HAR techniques can be categorized into three groups: vision-based, sensor-based, and WiFi-based . ExistinThe purpose of WiFi-based activity recognition is to distinguish the executed actions by analyzing the specific effects of each activity on the surrounding WiFi signals. In other words, the individual’s movement affects the propagated signal from WiFi access points and can be used to recognize activities. WiFi signals can be described by two characteristics: Received Signal Strength (RSS) and Channel State Information (CSI) . RSS is Although business applications of HAR are in the beginning stages, many studies in this field introduce the issues that must be addressed before any practical action. One of the main issues is the specific hardware/software combination that is required to extract CSI data. After choosing the proper hardware, the collected CSI data can be further used as inputs of the Deep Learning (DL) algorithms for HAR task. The effects of each activity in characteristics of the collected CSI can be used in different DL algorithms to distinguish activities and finally classify them .Since CSI is a time-series data with temporal dependency, Recurrent Neural Network (RNN) and its subsets have been exerted more than other DL algorithms in the HAR task. Long Short-Term Memory (LSTM) and RNN apply sequential processing to long-term information, meaning that these data pass through all cells in the network before reaching the present cell. RNNs structure cannot perform efficiently when we need to analyze long sequences, resulting in vanishing gradients. The vanishing gradient problem persists even when the switch gates and long memory in the LSTM network are maintained . FurtherWe exploit Raspberry Pi for CSI data collection and offer a public CSI dataset for seven different activities including sit down, stand up, lie down, run, walk, fall and bend in an indoor environment using the Nexmon CSI tool . Due to We propose a new concept in improving the precision of HAR by converting CSI data into images using pseudocolor plots and feeding them into 2D-CNN. This method overcomes the mentioned limitations of LSTM and also the training time and computational complexity are less than those of other existing methods. We also exert a BLSTM network with an attention layer to address LSTM problems with future information. The results demonstrate that the conversion idea with 2D-CNN outperforms BLSTM in accuracy and consumed time.We also perform a deep evaluation by implementing two other algorithms, including 1D-CNN and LSTM, and compare our results with four different models used for HAR, including Random Forest (RF) , Hidden In real-time activity monitoring, especially for elderly people, each activity’s period and further information are essential. Therefore, we consider two approaches: 2D-CNN and Attention-based Bi-directional LSTM. Unlike RNNs and LSTMs, where long-term data is analyzed sequentially, convolutions analyze the data in parallel. Furthermore, the training time of LSTMs is slightly longer than the CNNs, and as a result, they require a greater memory bandwidth for processing. Less consumed time in training and lower computational complexity, along with mentioned problems, encouraged us to use 2D-CNN. Since 2D-CNN has high potential in image processing, we convert CSI data into RGB images. In order to generate RGB images, we made a pseudocolor plot from CSI matrices. Each element of the matrices is linearly mapped to the RGB colormap. Furthermore, we applied BLSTM on raw CSI data. HAR’s performance can be improved by using attention-based BLSTM, which concentrates on regions of greater relevance and assigns them higher weights to improve performance. The main contributions of this research are as follows:The rest of this paper is organized as follows: HAR techniques can be divided into three groups: vision-based, sensor-based, and WiFi-based. Several image-based methods for HAR have been published in recent years, using datasets such as RGB , depth In sensor-based methods, wearable sensors capture activities, causing inconvenience and long-time monitoring unavailability . In the Due to the growing interest in sensor-less activity detection, the research and industry communities have joined on CSI analytics with the help of neural networks. Common CSI-based applications include a wide range of activity detection scenarios such as WiTraffic  to delicFor multi-class classification based on extracted features such as HAR, a variety of machine learning algorithms such as RF, SVM, and HMM and also DL algorithms such as CNN, RNN, and LSTM can be applied. In , they apSince the static objects in an environment can also affect wireless signals and, respectively, HAR model, authors in  propose tation θ . For eacOne of the main issues in Wifi-based HAR is the specific hardware/software combination for CSI data collection. In other words, Linux 802.11n CSI Tool is limited to older linux kernels versions and the required hardware cannot be found easily in market. Following the release of Nexmon CSI Tool , it is nTransmitting a signal from the transmitter to the receiver, it is deflected, reflected, and scattered when it comes into contact with obstacles and objects. This results in multipath overlaid signals at the receiver when the signal encounters obstacles and objects . Fine-grt transmit and r receiving antennas, a given packet transmission n:Several subcarriers can be present in the physical link between each pair of transmitter and receiver antennas. As each subcarrier might serve many data streams, the CSI obtained from each subcarrier will be unique . CSI cant,rH represents a vector that includes complex pairs for each subcarrier. Depending on the hardware we use and channel bandwidth, the number of available subcarriers is different [t,rH can be expressed as below for m data subcarrier in which mh is a complex number, containing both amplitude and phase of the CSI:ifferent . RaspberTo the best of our knowledge, the specialized hardware/software combinations that is required to extract CSI data, are intel 5300 WiFi Network Interface Card (NIC) (Linux 802.11n CSI Tool) , AtherosThe release of Nexmon CSI Tool  has enabOnce an activity is performed between transmitter and receiver, it will affect CSI characteristics. When a person performs a particular activity, the received CSI signals generates a unique pattern . RecentlCNN is a feed-forward neural network that excavates features from data with convolution operations. It contains several layers, including Convolution, Pooling, Dense and Flatten. This classification network requires less pre-processing rather than other classification techniques. Additionally, CNN can learn required filters or characteristics without the assistance of the user. CNNs use filters  to extract features which are performed using the convolution function . The iniAfter each two 2D-ConLayer, we use the LeakyReLU activation function, an upgraded variant of ReLU (Rectified Linear Unit). According to the gradient in the negative direction, every value of inputs less than zero causes the gradient to be zero. Therefore, the neurons located in that region are deactivated and may suffer from the dying ReLU problem. In order to address this problem, instead of claiming that negative inputs values should be considered zero, a small linear component of S is defined. LeakyReLU can be formulated as f(S) = max , meaning that if the input is positive, the function returns S and if the input is negative, it returns 0.01 × S. This minor alteration causes a non-zero gradient for negative values; thus, we would not find any dead neurons in that location. Since the feature map output of ConvLayer specifies the specific position of features in the input, a slight movement in the location of the feature in the input data will create a significant difference in the feature map. To address this problem, we use the down-sampling strategy. A better and more widespread strategy is to utilize a pooling layer. After feature detection in ConvLayer, the Max pooling layer is applied to down-sampled feature maps and helps in extracting low-level features. After the first ConvLayer with Leaky ReLU activation function and max pooling, Batch Normalization (B.N.) is applied to stabilize the network during training and speed up training. B.N. makes the variable mean and standard deviation estimations more stable across mini-batches and, respectively, closer to 0 and 1. Dropout layers are applied between convolutional layers, decreasing overfitting while improving the network’s generalization capability. The pooled features (the max pooling’s output) should be flattened. Flattening involves concatenating the feature map matrix to create a single-column matrix. This matrix is passed through a dense layer where we get our predicted classes. The proposed 2D-CNN structure is depicted in In addition to 2D-CNN exerted on converted RGB images, we also apply 1D-CNN to CSI data as depicted in RNN has been successfully applied to sequential modeling applications, such as language understanding  and HAR Since CSI signals are time-series and the LSTM can learn complicated and temporal dynamics, this network has obtained a remarkable performance for CSI-based HAR. In the HAR task, LSTM has two advantages. First, it can extract the features automatically without pre-processing. On top of that, it can hold temporal state information of the activity, resulting in better performance for similar activities such as lie down and sit down comparing to 1D-CNN, RF, and HMM. In this paper, we apply a simple LSTM with one hidden layer and 128 hidden units in which the feature vector is a 52-dimensional vector of CSI amplitudes. The proposed LSTM structure is depicted in The traditional LSTM network only analyze the CSI data in one direction, meaning that the present hidden state only considers the past CSI information. Furthermore, future CSI information is also important for HAR. In this paper, an attention-based BLSTM is utilized to analyze past and future information and overcome long-term dependency. It contains a forward and backward layer for extracting information from the two directions. In other words, it’s a two-layer LSTM sequence processing paradigm: one in which the input moves forward and the other in which the input moves backward. As the name suggests, attention is a technique that can allow input sequences of arbitrary length to pay attention to specified timesteps . The conThe comparison between these four networks and five other networks in HAR researches, i.e., RF , HMM 1414, DenseThe amount of data we need for the HAR task depends on the complexity of the task and the chosen algorithm, hence there is no specific rule about the number of samples, needed to train a neural network and it is just a process of trial and error. For vision-based HAR task,  used 320https://github.com/parisafm/CSI-HAR-Dataset (accessed on 27 October 2021).Despite the numerous advantages that accessibility to CSI would provide to users, chip manufacturers continue to treat CSI as a private feature. Only a few devices that are still using the 802.11g and 802.11n technologies are capable of dumping CSI, and they do so with a number of restrictions. Additionally, the Linux 802.11n CSI Tool is only compatible with older Linux kernel versions, which can cause significant inconvenience. In IoT, wireless connectivity is critical for monitoring and control purposes such as HAR. When it comes to experimentation, the Raspberry Pi might be considered a cheap and available WiFi-enabled platform. We employ Nexmon Tool  and collNo data pre-processing is applied on the CSI amplitude since any additional filtering can result in losing important information and affect the system’s performance. If the simulation results or generated images are disappointing, we can use a low pass filter for high-frequency reduction, as mentioned in . In ordeThese images and CSI data are then fed into neural networks. As CSI signals are typical time-series with temporal dependency, the future information in each step is crucial for HAR, and also LSTMs cannot effectively analyze more than 100 s term, we consider two methods. First, we convert CSI signals to RGB images using pseudocolor plot and feed them into 2D-CNN. By converting CSI to RGB images, the signal pattern for each activity can be seen in one look. Meaning that the pattern changes due to the human movements are depicted in image.Therefore, in contrast to LSTM that does not have any information about future steps, CNN can analyze the whole signals’ alteration. Additionally, CNN process information parallelly, resulting in faster training than LSTMs with better accuracy. Another method to address LSTMs mentioned problems is to apply BLSTM on CSI data. BLSTM contains a forward and backward layer and can analyze both past and future information by extracting information from the two directions. Since the sequential features learned by the BLSTM network have high dimensions and feature contributions and timesteps may vary for each activity, we exploit the attention layer to learn the relative importance of features. Although BLSTM have high potential to recognize human activities, it needs a greater memory bandwidth for processing and thus it has more training time than the proposed 2D-CNN. Lower consumed time in training and less computational complexity, along with the ability to observe the whole pattern alteration in one look, make the novel image conversion idea and 2D-CNN implementation the best choice over other mentioned methods.Buster lite 4.19.97 raspian and the main branch of nexmon-csi  were insThe proposed deep learning architectures can discover more complex patterns in time series data, compared to hand-crafted features techniques such as RF  and HMM CNN can detect simple patterns in data, which are subsequently utilized to create more complex patterns within higher layers. 1D-CNN is highly effective when features are derived from fixed-length parts of the dataset and the feature’s position in the section is not crucial, including the analysis of time sequences data (such as gyroscope or accelerometer data or CSI). Since the LSTM network analyzes temporal dependencies in sequential data, it outperforms the 1D-CNN technique. As mentioned in Different activities have different CSI values, resulting in different recognition accuracy . We use Consumed time is another critical performance evaluation indicator representing how much time the model spends training and testing. Due to the ubiquity of WiFi devices, HAR based on wireless signals, including CSI, has witnessed more interest in smart house health monitoring systems. A few CSI datasets for the HAR task collected with 5300 NIC or Atheros PCI chips, are currently available. This paper presented a CSI dataset for indoor HAR using a Raspberry Pi, which is one of the most accessible embedded boards. In this work, we have designed four neural networks to conduct WiFi-based HAR with more than 87% accuracy for our dataset. We used a BLSTM network with an attention layer to address LSTM problems with future information. We also convert CSI data to images using pseudocolor plots and feeding them into 2D-CNN to overcome the mentioned limitations of LSTM. We showed that the idea of CSI conversion to images can obtain high accuracy of 95%, close to BLSTM, which is one of the most successful DL algorithms in time-sequential analysis. Additionally, as CNN processes different features parallelly, it is faster than other methods and less complex in computations. The strong performance of the proposed methods indicates that the data collected by Raspberry Pi can effectively be employed in smart house HAR. The proposed methods can boost elderly health monitoring systems since it meets the requirements for acceptable recognition accuracy and recognition speed for the most commonly performed actions in this task.Nevertheless, we presented the first version of our public dataset and plan to improve it by investigating different environments and scenarios. In the future, we will study human-to-human interactions and the CSI changes in multiple user-multiple environments scenarios. Since different ages may perform activities differently, according to their physical ability, we collected CSI data from three different ages, including an adult, a middle-aged person, and an elderly person and try to study other ages, including child and teen. Additionally, we will investigate activities with different initial movements, such as standing + walking and running + walking.


Treatment of asymptomatic bacteriuria (ASB) outside of pregnancy and urological procedures increases the risk of antibiotic resistance without improving outcomes. At Olive View-UCLA Medical Center , the CDC U.S. Antibiotic Awareness Week (AAW) was utilized as a platform to promote antimicrobial stewardship (AS) for ASB. We evaluated the incidence of antibiotic treatment of ASB pre-AAW vs post-AAW, and the impact of AS education on future prescribing practices for ASB.In this single-center retrospective observational study, AS education defining ASB vs urinary tract infection (UTI) was provided via visual aids distributed throughout the hospital during AAW from 11/18/2020 to 11/24/2020 . All positive urine cultures (Ucx) for adult inpatients were reviewed prior to AAW from 9/2020 to 11/2020 and after AAW from 12/2020 to 1/2021. Patients were excluded if they were unable to report UTI symptoms, pregnant, or undergoing urological procedure. The incidence of ASB treatment pre- and post-AAW was compared. A survey was sent to providers to compare the impact on antibiotic prescribing behavior for ASB pre- and post-AAW. Fisher’s exact and Chi-squared tests were used for statistical analysis.Figure 1. Antimicrobial Stewardship Education and Poster DistributionA total of 260 cases met study eligibility. In the pre-AAW group, 56 of 131 cases presented with ASB, of which 16 were treated with antibiotics (28.6%). In the post-AAW group, 55 of 129 cases presented with ASB, and 5 were treated with antibiotics (9.1%). Antibiotics were prescribed more often for patients with ASB in the pre-AAW group compared to those in the post-AAW group (p=0.014). Forty providers completed the survey, of which 97.5% had seen the visual aids, 70% had found the education "very” or “extremely" useful, and 43.6% reported they “always or sometimes” treated ASB pre-AAW vs 15% post-AAW (p< 0.01).AS posters and education defining ASB significantly decreased the treatment of ASB. AAW education on ASB antimicrobial stewardship demonstrated a high value and shifted prescribing behavior to avoid antibiotic treatment of ASB. A similar approach to deliver provider education could serve as a valuable model to change provider AS practices for ASB.All Authors: No reported disclosures


The annual worldwide production rate of waste glass is a million tons; the waste glass is non-biodegradable, resulting in environmental pollution. However, the chemical composition of waste glass (WG) is promoted to be used as a partial substitution of binding or filler (aggregate) material in concrete production. Although significant research has been conducted in this area, the results of these studies are scattered, and it is difficult to judge the suitability of waste glass in concrete. This review looks at the effects of waste glass on concrete’s fresh, mechanical, and durability properties. It concludes that waste glass decreased the flowability of concrete. Furthermore, waste glass can be used as pozzolanic material, creating secondary cementitious compound (CSH) gel. CSH gel increased the cement paste’s binding properties, leading to increased mechanical performance. Moreover, this study reveals that the optimum dose of waste glass is important to minimize the possibility of an alkali–silica reactions. Based on this review, most researchers conclude that 20% substitution of waste glass as binding material is the optimum dose. The wide range of discussion provides the necessary guideline for the best research practice in the future. Each year, the world presently manufactures about 3.6 billion metric tons of the material [2, it is important to incorporate waste materials in concrete instead of cement.Sustainable construction usage means responsible management for producing a beneficial environment while overseeing ecology and the development of resources . Cement material . In 2030material ,13. Evenmaterial . To redu2 emissions [2 emissions can be decreased by substituting OPC with cementation materials [Presently, cement manufacturing causes more than 5% of global COmissions . CO2 emiaterials ,17,18. Baterials . Severalaterials ,23,24,25aterials . The praaterials .In contrast, the amount of waste generated by the industry has increased worldwide due to increased demand and product utilization. Only a small amount has been utilized, and the remainder has been trashed indiscriminately, causing environmental problems. The tremendous increase in the amount of garbage that must be discarded, the scarcity of discarding locations, and the quick rise in transportation and discarding expenses all have an adverse effect on the environment, placing a halt to sustainable growth. The hindrance of waste removal is becoming intense . The totCurrently, the uses of waste glass in concrete production are being extended, involving precast concrete blocks, road paving blocks, marine structures, precast concrete slabs, and blocks . AccordiWaste glass can be used as a cement replacement or as filler material (aggregate) in cement concrete production. However, the results depend on the particle size of waste glass, substitution rate, and chemical composition, which must be considered during the mix design. 2 (70 to 75%), CaO (8 to 10%), Al2O3 (1 to 3%), MgO (1.5 to 3%), NaO2 (0 to 15%), and Fe2O3 (0.5 to 1%) [2 present in waste glass plays a vital role in the performance of concrete, starting from hydration up to the final development of strength [2, CaO, Al2O3, MgO, NaO2, and Fe2O3) up to more than 70% can be used as pozzolanic material waste glass containing more than 70%. 2) was observed at 27-degree angles. Some minor peaks were also observed at different angles, which shows the amorphous nature of waste glass. A study concluded that the peak heat flow during the hydration process and Ca(OH)2 decreased with the substitution of waste glass as a binding material, which undoubtfully improved mechanical performance [Mechanical and durability performance of concrete with waste glass also depends on the percentage of different chemicals present in waste glass. A study reported that the pozzolanic activity of waste glass mainly depends on chemical composition and particle size . Table 15 to 1%) ,51,52. Tstrength . Accordistrength , the accformance . Howeverformance . It has formance . TherefoConcrete workability is a broad and subjective term that describes how easily freshly concrete can be mixed, placed, consolidated, and finished while maintaining homogeneity. A summary of slump flow with different percentages of waste glass (0 to 30% in increment of 10%) according to the paste studies is shown in The workability of concrete increased with the substitution of waste glass as binding or as fine aggregates . This isConcrete compressive strength primary effects on the flowability of concrete. Low flowability increased the compaction, resulting in increased pores in concrete and leading to less dense concrete, adversely affecting the compressive strength. That is why density is one of the most critical factors that must be considered during concrete mix designs. Concrete density with glass is shown in Compressive strength is one of the most significant characteristics of concrete, which provides the idea about the quality of concrete. This particular test determines whether concrete work has been performed properly or not. A study showed that, at 10% replacements, the compressive strength increased by about 31%, 16%, and 9% at 7, 14, and 28 days of curing for the reference concrete, respectively . SimilarThe stress–strain curve of different replacement ratios of WG is shown in Split tensile strength is an important test of concrete to find the tensile capacity of concrete. Lower split tensile strength of concrete will cause tensile cracks to decrease shear capacity and concrete failure in tension, resulting in brittle failure without any warning (deformation). The split tensile strength is about 10 to 15% of compressive strength. The split tensile strength of concrete with partial substitution of waste glass as per past studies is presented in According to Ghayoo et al. , the splWater absorption tests can be used to determine the durability of concrete. Water includes a toxic chemical that, when combined with other chemicals contained in cement concrete, can cause concrete disintegration. Furthermore, water absorption generates freeze and thaw actions, which add stress to the surrounding concrete, resulting in less durable concrete. According to one study, water absorption is one of the reasons for alkali–silica reactions . Figure Drying shrinkage is referred to as the contraction of a hardened concrete mixture as a result of a loss of capillary water. As concrete shrinks, it experiences an increase in tensile stress, which may cause cracking, internal warping and external deflection before any loading occurs. Concrete made with Portland cement undergoes drying shrinkage or a change in hydraulic volume with time. In the design of a structure, an engineer should pay attention to hydraulic volume changes in concrete. Drying shrinkage can occur in a range of areas including slabs, beams, columns, bearing walls, prestressed members, tanks, and foundations.Several factors contribute to drying shrinkage. These factors include properties of the components, proportions of the components, mixing method, amount of moisture during curing, dry environment, and size of the member. The main reason for drying shrinkage is due to the reduction in capillary water by evaporation and the presence of water in cement paste during the drying process. The greater the amount of water present in fresh concrete, the greater the shrinkage phenomenon during drying. Several aspects influence the shrinkage potential of concrete, including the amount of mixing, the elapsed time after the addition of water, temperature fluctuations, slumping, placement, and curing. It is also important to consider the composition of the concrete. The shrinkage of concrete varies depending on the type of aggregate and cement used, and each contributes to shrinkage differently from the others. During mixing, the amount of water and additives used in concrete has a direct and indirect effect on the drying shrinkage of concrete. It is believed that concrete shrinkage is largely caused by the evaporation of the capillary water that mixes the concrete. A dry shrinkage test may also be used to determine the durability of concrete. Water and other potentially dangerous compounds can quickly enter the concrete through microscopic surface cracks created by dry shrinkage. The availability of internal water, aggregate types, and voids in concrete all influence dry shrinkage. Generally, the dry shrinkage of waste glass is less than conventional concrete. A study reported that concrete with 20% waste glass as a cement replacement shows 50% less dry shrinkage . SimilarNevertheless, the WG powder, due to its micro filler property, decreases the size of the pore, and the internal water in concrete remains internally. The shrinkage due to the loss of water minimizes and the shrinkage in volume decreases . Moreove2/g, and it is produced by glass-based cement, which reduces 83% of CO2 emission when using concrete consisting of OPC [The environment is protected from dumping and the landfills of WG . PC and g of OPC . A similg of OPC  in theirg of OPC . WHO repGlass is a material that takes the maximum time for biodegradability when used in landfills. This property of glass makes it a huge problem in the waste management sector throughout the world . MoreoveOne of the major disadvantages of using waste glass in concrete is the alkali–silica reaction (ASR). The availability of significant silica in waste glass is a key difficulty because alkali-concentrated cement produces ASR, leading to expansive gel formations . A reseaThe ASR gel creates expanding strains along the reaction zone that may exceed the concrete’s tensile strength restriction, allowing cracks to arise. As a result, an extra opportunity for penetration and absorption of the external solution is produced, lowering the durability. Serpa et al.  used Por2/kg to 540 m2/kg were also shown to minimize the expansions of mortars subjected to ASR, according to the researchers [As seen from the preceding discussion in this section, ASR growth increased with increasing glass sand content. The increase in expansion is due to the 100% glass sand having many amorphous ASR products, as shown in earchers . Du and earchers  used sevearchers . Howeverearchers . The preearchers  found thFurthermore, researchers found that glass sand with a grain size of less than 4.5 mm and no surface defects did not generate expanding ASR gels at a sand replacement level of up to 40% . The preThis review highlights a reduction in glass waste problems in nature, and current research progress is discussed. The properties such as the strength (compression and tension) of concrete using glass waste are a major concern to know in this research area. The detailed conclusion is given below.The decreased in flowability of concrete with waste glass is mainly due to the sharper and larger surface area of waste glass. However, it can be controlled by using a superplasticizer.The chemical composition of waste glass is similar to clay, which indicates that it can be used as a raw material for cement production without any negative effects on cement’s physical and chemical properties. However, there is a gap in knowledge in this regard, and further research is recommended before practical applications.Mechanical performance and durability aspects of waste glass mainly depend on particle size and optimum dose. Fine particles of waste glass improve mechanical performance and durability, while coarse particles decrease concrete’s mechanical and durability performance. The particle of waste glassless 4.75 mm can be used without any negative effects on concrete’s mechanical and durability performance. However, for high strength, it is suggested to use glass particles that are less than 100 µm.The optimum dose of waste glass also plays an important on the mechanical and durability performance of concrete. The optimum dose depends on various parameters such as particle size, mix design, types of glass, chemical, and physical aspects.It can also be observed that the careful selection of waste glass particle size and optimum dose improved concrete’s mechanical and durability performance considerably.The overall study demonstrates that waste can be used successfully in concrete as binding materials or as a fine aggregate. The utilization of waste glass in concrete provides multiple benefits, including problems of natural resources presentation, ecofriendly environment, low cost, air pollution problems, and waste management problems without any negative effects on the mechanical and durability performance of concrete provided that optimum doses and particles size are correctly used. However, less information is available on the durability aspects of concrete with waste glass. Furthermore, although waste glass improved the mechanical performance of concrete, it still has lower tensile capacities, leading to brittle failure. Therefore, further research was recommended to enhance the tensile capacity of concrete with the addition of fibers.


Service-learning (SL) helps engineering students to be involved in community activities and to be motivated by their studies. Although several reviews and research studies have been published about SL, it is not widespread in sciences and engineering at the university level. The purpose of this research is to analyze the different community services or projects where SL is implemented by engineering students and faculty and to identify the procedures that were usually implemented to assess SL-based courses and activities. Assessment could be considered as the evaluation of a specific module and the engineering competencies, the evaluation of the effectiveness of the SL program, the assessment of the participation of the student in those programs, and the assessment of whether students have achieved certain outcomes or gained specific skills. We conducted a systematic review with a search in three scientific databases: Scopus, Science Direct, and ERIC educational database to analyze the assessment methods and what that assessment covers. From 14,107 publications related to SL, 120 documents were analyzed to inform the conclusions of this study. We found that SL is widely used in several universities as experiential education, and it is considered an academic activity. The most widely used assessment technique is a survey to evaluate the engagement and attitudes of students and, to a lesser extent, teamwork presentations. Undergraduate students in engineering usually do not have the opportunity to develop their personal and social competencies and skills during their studies at the university level, except when they undertake practices in companies. Students will need a solid technical background as well as educational experiences that could help them to develop a sense of responsibility, self-efficacy, professional skills , and outside-of-the-classroom skills among fellow students and among the community . The widespread use of SL increased in the 90s. This pedagogy was defined by Jacoby . As a result of the different definitions and interpretations that have emerged and the different contexts and objectives, the National Service Learning Clearing House  was Simpson  program began in 1995 at the Purdue University in the USA  What is the general character of the corpus in terms of categories of published articles about SL in engineering education?(2) According to the bibliography analyzed, what are the main courses where SL is implemented in engineering degrees? As SL is sometimes part of the curricula, this could shape how these SL courses are evaluated.(3) Which assessment tools are used to assess SL activities and outcomes?The population considered in this systematic review is engineering courses; the intervention is SL in engineering; comparisons are methods and tools for assessing SL in engineering; the outcome is SL assessment tools in engineering; and finally, the context is higher education.This review is structured as follows: “Methods” section details the search method and the procedures that were performed to conduct the systematic review and to get the results; “Results” section summarizes the results of the review, including answers to the research questions. Finally, “Discussion and conclusion” section includes a discussion of this study.We searched three different databases: Scopus, Science Direct, and Education Resources Information Center (ERIC). The ERIC database is the only one (from the three chosen) devoted entirely to education. We decided to add Scopus and Science Direct databases because there is currently a great number of faculty and researchers working in educational research and publishing articles indexed in these databases. The results showed that this election was appropriate.The search was conducted for published journal articles, book chapters, conference articles, or dissertations until 2019 in the context of higher education and specifically related to engineering students.With this initial search, we found that it is not common to develop SL projects in scientific, technological, and engineering areas; and it is even less common to use this pedagogy in engineering courses at universities. Thus, from 5,993 results related to SL in the Scopus database, most of the activities were done in social science areas (46.46%) and the remaining articles are distributed in the rest of the areas with a smaller percentage: engineering (9.25%), computer science (6.32), environmental science (1.15%), mathematics (0.85%), chemistry (0.59%), physics, and astronomy (0.17%).The search flow conducted for this study is detailed in (1) The study considered SL articles in higher education and engineering studies.(2) Non-English results were discarded because English is the most widespread in scientific publications.(3) The study included results related to SL assessment.After these inclusion criteria, the final number of articles is 156, where:(1) English results about SL. Total number = 5,998 (Scopus) + 2,187 (Science direct) + 5,922 (ERIC) = 14,107.(2) Results in higher education and engineering studies. Total number = 315 (Scopus) + 18 (Science direct) + 106 (ERIC) = 439. To develop the search, as each database has its own queries format, filters, and technical specifications, we restricted the subject areas to engineering, proceeding as follows:⋆ In the case of Scopus, the search included the limitation to the exact keyword “Engineering Education” and subarea “Engineering.”⋆ In the Science Direct database, the advance search included “engineering education” as title, abstract, or keywords.⋆ For the ERIC database (through EBSCOhost), the search was done by adding “engineering education” as “DE Descriptors [exact].”(3) We made an exhaustive search for assessment or evaluation in the title, keywords, and abstract. Finally, the total number of articles that were considered for the systematic analysis, without 35 duplicates, was Total number = 123 (Scopus) + 33 (ERIC) = 156.From those 156 results, we conducted a new filtration considering the title and abstract contents. By doing this, 36 articles were discarded because they were based on one of the following:Conference proceedings book: Proceedings were not included because specific articles from them are already included in the results. The number of excluded results: 3.Disciplines that were not related to engineering: English pedagogy, second language learning, “Fill-in Worksheets” tool, microbiology, and future secondary school teachers. The number of excluded results: 5.Proposals were not directly linked to university students: SL activities targeted at K-12 educators and students, related to STEM disciplines. The number of excluded results: 7.Activities, courses, and studies that did not add value to the research questions, such as, internationalization activities at the university (not related to SL), faculty workshops on LTS, EWB (note related to engineering students or faculty), game-based learning, a reform of chemical engineering undergraduate curriculum, and general assessment of Accreditation Board of Engineering and Technology (ABET) professional skills (without the specific characteristics of SL). The number of excluded results: 18.Monographs and reviews general SL activities and experiential learning. These search results were related to general aspects such as SL pedagogy in engineering, SL reflection, and the institutionalization of SL, with no relation with assessment. The number of excluded results: 3.After screening for inclusion, the final number of publications was 120. These publications are indicated with an asterisk in the References Section.There is a great difference between the number of articles published about SL in different countries. Of 120 articles, 93% were published in the USA. This is the country where SL started and probably where this methodology is most prevalent. Moreover, 75.20% of the results were published in conference proceedings of the American Society of Engineering Education Annual (ASEE) Conference & Exposition. This conference is organized every year (since 1893) mainly in the USA, but sometimes in Canada.We have also analyzed the authors of those articles, and we found that Bielefeldt participated in 14 publications, Swan in 13, Oakes in 10, Paterson in 9, Duffy in 7, Barrington and McCormick in 5 publications each, Karmer, Pierrakos, Thomson, and Zlotkowski in 4, Canney, Dewoolkar, Matson, and Tsang in 3, 35 authors participated in 2 publications and 233 authors only appear once.The big difference in country allocation could lead to the conclusion that SL has been implemented and is being carried out as part of the university studies in some countries. It has become an institutional pedagogy. However, SL projects are developed in many other places. Thus, for example, the European Observatory of Service-Learning in Higher Education, in the 2019 Annual Report, analyzed active SL projects in different European countries. In this report, 11 SL projects developed from Erasmus+ or H2020 proposals were mentioned  to include SL in engineering curricula in USA colleges, faculties, and universities. ABET EC 2000 set the following requirements for engineering universities . Some articles that will be detailed later in this review defined different tools to measure if these (a)–(k) criteria are achieved by students during SL activities.In this systematic review, results were split into two different categories:(1) General/SL results: General aspects of SL, including more theoretical aspects and reviews, deepening of SL pedagogy, and some results from analyzing experiences developed in different academic years (where the name of the project is not included in the article).(2) Projects: This category included SL projects and the work that is proposed or developed for a specific project. This refers to literature that included specific studies about one or more projects. These articles included, in some cases, information about assessment methods.Although most of the articles are related to specific SL projects, the main difference between both categories was that the first one does not include the name or title of the project.General/SL results included, among others, the following topics: general views of Engineering for Developing Communities (EDC), which integrated social needs into the engineering courses and proposed new courses where SL was implemented . Any of these published projects could give an idea to a reader about a new proposal for addressing SL activities.This list of projects is quite long as suggested by Dennis and Hall , who claA table with detailed information about all the search articles is included in The most common curricular model for SL in the reviewed literature was implemented in first-year courses as part of engineering introductory courses. However, SL is also commonly used in senior courses and, to a lesser extent, in the second and junior courses (nine results showed the use of SL in courses of graduate students). From the search result, 114 articles contained information about the level of students and 21.93% were related to all undergraduate years, 28.5% to first-year students, 10.53% to sophomore, and the same percentage to junior students, 17.54% corresponded to senior students, and the remaining 10.53% are projects developed by graduate students.The way in which SL is implemented is through a curricular approach, cocurricular activities, SL-based courses, or also through extracurricular activities or SL programs, such as SLICE, or Engineers for a Sustainable World (ESW) project. When SL is integrated into existing courses or new courses are defined to implement this pedagogy, we consider a curricular approach. Cocurricular activities may be outside of class time, including the development of a capstone project or a research proposed by a teacher. Finally, extracurricular projects mean activities that may or may not be linked to the studies. Although SL is an activity integrated into credit-bearing courses, in many cases, projects can pass between curricular and extracurricular activities Oakes, .Several introductory courses implemented SL as innovative pedagogy. These included Introduction to Engineering  by its practitioners   criteria with additional open-ended questions   method of behavioral interviewing  scale  freshman survey, and the “Your First College Year” (YFCY) survey from the Higher Education Research Institute (HERI), and the BarOn Emotional Quotient inventory, which are implemented to assess leadership    and at the end of the SL project   to assess SL activities. So, this is the most widely used method for this purpose.Some other methods that have been used to assess SL were:To write a short or a long final project report made by students summarizing their experience. This was used to improve and reinforce the written communication skills in engineering education  .On the other hand, students participating in an SL program , no matter the type of activity, were evaluated via standard grading  , a significant number of articles (around 44%) were related to the planning, design, and development of SL specific projects in different countries and communities. The use of SL in higher education and in engineering courses is increasing. There is a great difference between the number of articles published about SL in different countries. The USA is, so far, the country where SL is most widespread. This is related, presumably, to the interest in the publication of the research carried out regarding the SL projects. Some universities have established SL as a curricular methodology in several courses with specific programs, such as the EPICS program . Surveys allowed assessing students (75% of 98 articles), academics (8% of 98), and community partners (15% of 98) outcomes, although these last two actors were rarely taken into consideration in assessment processes. In the case of students, the assessment impact analyzed student knowledge, technical and non-technical skills, attitudes, recruiting/retention/diversity, and post-educational professional performance.From these results, we can conclude that most of the assessment systems did not include technical (engineering) aspects of the courses. Only 29.9% of the assessment tools reported were not surveyed. Although SL is considered a curricular activity, technical aspects of the SL projects were not reported because technical aspects are assessed out of SL activities. No article in the literature reported using an exam to assess knowledge and skills acquired in the SL program, because these search results showed the outcomes of SL and not their relationship with the rest of engineering topics and courses.As future research, we plan to study university websites and analyze whether they incorporate information about SL and what type of information they include. It is common to use websites and social networks while implementing SL activities and before publishing results. Some higher education institutions participate every year in local, national, or international projects where SL projects are being developed. This is promoting the use of SL.Red Española de Aprendizaje-Servicio” in SpainCentro Latinoamericano de Aprendizaje y Servicio Solidario”Moreover, several countries have different associations for SL, such as the “European Observatory of Service-Learning in Higher Education” in IrelandFinally, the authors want to express that educating engineering students with the SL pedagogy is investing in a better future for society and humanity. This was the idea of the first community service programs developed by the universities.To this date, there are no studies like the one presented in this study, which is a relevant topic for engineering education, because project development is a key part of the engineering curriculum. A deepening in the experiences of already completed and ongoing SL projects and the assessment mechanisms of students will allow the implementation of this pedagogy in more universities. The limited use of active methodologies in university contexts is not very widespread. This is usually due to the lack of time of the faculty, the absence of knowledge of other pedagogies, and their benefits for the integral training of the student. In this sense, the SL approach could complement the training of the engineering student in the aspects and skills mentioned above.The original contributions presented in the study are included in the article/MQ-D, PA, and AQ-D have contributed to the initial part of the documents search, the analysis of data, and results. MS and MÁQ-D contributed to the methodology and discussion. All the authors participated in the document elaboration and revision of the article.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Concrete mix design is one of the most critical issues in concrete technology. This process aims to create a concrete mix which helps deliver concrete with desired features and quality. Contemporary requirements for concrete concern not only its structural properties, but also increasingly its production process and environmental friendliness, forcing concrete producers to use both chemically and technologically complex concrete mixtures. The concrete mix design methods currently used in engineering practice are joint analytical and laboratory procedures derived from the Three Equation Method and do not perform well enough for the needs of modern concrete technology. This often causes difficulties in predicting the final properties of the designed mix and leads to precautionary oversizing of concrete properties for fear of not providing the required parameters. A new approach that would make it possible to predict the newly designed concrete mix properties is highly desirable. The answer to this challenge can be methods based on machine learning, which have been intensively developed in recent years, especially in predicting concrete compressive strength. Machine learning-based methods have been more or less successful in predicting concrete compressive strength, but they do not reflect well the variability that characterises the currently used concrete mixes. A new adaptive solution that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete is proposed herein. In presented study, a machine learning model was built with a deep neural network architecture, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were calculated according to contemporary design methods (Bolomey and Fuller method), and a comparative analysis was conducted. It was found out that the new algorithm performs significantly better than that without adaptive features trained on the same dataset. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process. Concrete mix is a mixture of cement, water, and fine-grained and coarse-grained aggregate, as well as additives and admixtures. Additives and admixtures are designed to improve the chemical characteristics and performance parameters of concrete, especially compressive strength, durability, and workability. One can distinguish many such supplements, including accelerating admixtures, admixtures for improved fresh concrete properties, durability-enhancing admixtures, fibers for concrete strengthening, set-retarding admixtures, and water-reducing admixtures. The concrete mix’s appropriate design is one of the most critical issues in the construction process, which is considered on many levels. The concrete mix must be designed economically. This means that it must allow obtaining the desired properties at the lowest possible cost of raw materials. A concrete mixture is dedicated to a specific technological process, where properties such as workability or cement setting speed are vital. Subsequently, there are environmental conditions related to precipitation, temperature during concreting, the distance from the construction site, and the traffic volume. The composition of the concrete mix significantly depends on the assumed construction specification resulting from the construction design, in which it is assumed, for example, the compressive strength of concrete, or environmental aggression, such as chloride ingression. The last important factor determining the composition of the concrete mix is ecological considerations, which have recently gained particular importance. There are currently many solutions to reduce the carbonation of concrete, such as admixtures of graphene nanoparticles .In conclusion, it can be said that designing a concrete mix comes down to the appropriate selection of the proportions of the primary and secondary components in order to obtain concrete with the desired properties. During the technological process of concrete production, the concrete mix is transported to the construction site and placed in the concrete formwork; then, the process of concrete hardening and gaining strength occurs. The hydration of the cement initiates the concrete hardening process. Cement hydration is an exothermic chemical reaction that occurs when cement comes in contact with water. After starting the hydration process, the cement forms tobermorite gel, hydroxide, and other ingredients, which enhance adhesion between the fine-grained and coarse-grained aggregate. Throughout this process, hydration products continuously deposit on the cement grains and fill the area occupied by the water. The final stage of the hydration process is when all water molecules are bound, or there is no more unreacted cement. Hardened concrete obtains partial compressive strength a few days after the hydration process starts, and most of compressive strength after around 28 days (some types of concrete reach their full strength later) ,4. The aThe presented field needs new technological solutions that will face current challenges, and the answer might lay in machine learning-based methods that are more or less successful in predicting concrete compressive strength; however, they do not reflect well the variability of new chemically complex concrete mixes. This paper presents a novel adaptive approach that allows estimating concrete compressive strength on the basis of the concrete mix main ingredient composition by including two observations for a given batch of concrete. The proposed solution is a deep neural network with 46 hidden neurons, clustered in seven layers. The machine learning model was built, trained on an extensive database of concrete recipes, and translated into a mathematical formula. Testing on four concrete mix recipes was performed, which were also calculated according to the Bolomey and Fuller method, and a comparative analysis was conducted. The presented algorithm can be used as a concrete strength checking tool for the concrete mix design process. Machine learning is part of a rapidly developing field of knowledge, within the broader domain of artificial intelligence. It enables systems to automatically learn and improve from experience without direct programming and specific tasks. There are many different approaches to machine learning, among which the most popular are artificial neural networks (ANNs), with many branches such as deep neural networks (DNNs), recurrent neural networks (RNNs), and convolutional neural networks (CNNs). There are also other approaches, such as gene expression programming (GEP) and multi expression programming (MEP) ,13,14. TConcrete mix design is a complex issue, often requiring extensive knowledge of concrete technology and vast experience. The main task in the design process is to select appropriate material compositions to obtain a concrete mix with desired properties, both in the form of a fresh mix during transport and concreting and in the form of hardened concrete. Specific properties can be expected at each stage of the concrete structure manufacturing process. Several features characterize concrete performance, such as plasticity, durability, compressive strength, and modulus of elasticity. The properties mentioned above have different priorities at different times; for example, adequate compressive strength is essential from the point of view of designed ultimate limit state, while adequate durability is essential in an aggressive environment ,20,21,223 of concrete, expressed in units of volume , 3 of concrete, expressed in kg, 3 that should be added to 1 kg of a given class of cement, expressed in L/kg, 3 of concrete, expressed in kg, and 3 that should be added to 1 kg of dry aggregate of a certain fraction to obtain the desired consistency, expressed in L/kg. The cement–water demand index and aggregate–water demand index depend on the grain size, shape, surface roughness, proportion in a given composition, and required consistency of the concrete mix. The water demand for concrete additives and admixtures is considered by adding it to aggregate or cement depending on the grain size. The cement–water and aggregate–water demand indices were developed by Stern and Bolomey [3 of concrete, expressed in kg, 3 of concrete, expressed in L; 3 of concrete, expressed in dm3, and 3 of concrete, expressed in L,3 of concrete, expressed in kg,3, 3 of concrete, expressed in kg, and 3. The quantitative composition of the concrete mix, considered as the amount of cement, water, and aggregate in 1 m3 of mixture, can be calculated using the equations described above. The Three Equations Method has certain boundary conditions. The concrete mix porosity should not exceed 0.002 of the mix volume without air-entraining admixtures or 0.008 of the mix volume using air-entraining admixtures.Corporate engineering practice varies across the world, while also sharing significant similarities. In the European Union, the norm governing concrete technology issues is “EN 206 Concrete: Specification, performance, production, and conformity”, while the design of concrete structures is described in the standard “EN 1992-1-1: Eurocode 2: Design of concrete structures”. There are equivalents and national appendices for each standard, e.g., in Great Britain, the BS EN 206: 2013 + A1: 2016 standard is used, while, in Poland, the PN-EN 206 + A1: 2016-12 standard is used. Depending on the member state of the European Union, various methods of designing concrete mix are popular. In Poland, the methods of Bukowski, Eyman, Klaus, Kopycinski and Paszkowski are most often used, along with the so-called double coating method . On the  Bolomey ,28. The 3 of concrete mix. It is also vital to consider the recipe changes that may result from the dampness of the aggregate and adapt it to individual conditions, such as the capacity of a transport vehicle [The entire process of concrete mix design consists of the following stages: determining the initial assumptions, determining the required properties of hardened concrete and fresh concrete mix, selecting and evaluating the components of the concrete mix, designing the mix composition, checking the technical characteristics of the fresh concrete mix and hardened concrete in a laboratory, and preparing a working recipe. During the formulation of initial assumptions, several factors need to be considered, such as the concrete mix’s intended use, which depends on properties of the newly designed structure, including the location, amount of reinforcement, and geometric characteristics of the cross-section. The primary technical characteristics of fresh concrete mix are the bulk density, consistency, and air content, whereas those for hardened concrete are the frost resistance, fire resistance, and class of concrete compressive strength. It is necessary to analyze the technological process, as well as assess the conditions of concrete maturation and the method of compacting fresh concrete mix. The concrete exposure class, which corresponds to the degree and type of environmental aggression and additional properties, such as concrete tightness, should also be specified. It is necessary to determine maximum aggregate diameter and mix workability. The concrete mix components should be selected and evaluated, including the proper type of cement, appropriate water, and aggregate quality, characterized by the relevant standards. After designing the concrete mix composition and laboratory tests, the last part of the process is preparing a working recipe for 1 m vehicle ,30.Machine learning is used in many science areas, from forecasting real-estate prices to identifying conditions on the basis of computed tomography images. One of these areas is civil and structural engineering. In civil and structural engineering, machine learning is used in structural health monitoring, crack detection, life-cycle cost analysis, prediction of soil compression coefficient, and many more. In forecasting concrete properties, research focuses on predicting a concrete’s compressive strength, which is one of the most critical parameters of concrete and defines its class.The challenge in predicting concrete strength by machine learning was first described by Yeh et al.  in 1998.In 2003, the topic was developed further by Seung-Chang Lee . He utilIn 2005, Hola J. and Schabowicz K. ,34 preseIn 2006, Gupta et al.  proposedIn 2018, Fangming Deng et al.  introducIn 2019, Ziolkowski P. et al.  presenteIn 2020, Adil M. et al.  presenteIn 2020, Nunez I. et al.  presenteIn 2020, Marani A. et al.  presenteMost of the solutions described in the literature that could support the concrete mix design process consist of predicting the concrete compressive strength as a function of the concrete mix composition or basic technical parameters. The previously mentioned approaches could be improved by introducing an algorithm calibrated for a specific batch of concrete. This is essential for several reasons, the most important of which is the chemical complexity caused by various concrete admixtures, which is currently a standard. There are many different concrete admixtures, such as accelerating admixtures, admixtures for improved fresh concrete properties, durability-enhancing admixtures, fibers for concrete strengthening, set-retarding admixtures, water-reducing admixtures, and novel additions of nanomaterials such as graphene. These admixtures can significantly affect the obtained concrete parameters, especially compressive strength. It is also difficult to predict how a mixture with many different admixtures or admixtures produced by different manufacturers will behave in service. The second reason concerns the variability caused by the use of raw materials from different manufacturers, from different mining sources, and characterized by different properties. The variability can manifest itself on many levels, from the shape of the coarse aggregate to the presence of clay in the fine-grained aggregate. The algorithm’s primary task is to estimate concrete compressive strength as a function of the concrete mix composition calibrated by two observations. Observation encompasses the complete concrete recipe, along with compressive strength for a given type of concrete. The algorithm uses two recipes for a given concrete and tries to estimate the third recipe’s concrete compressive strength.In current considerations, the database from a previous study  was usedThe parameters presented in It is recommended to operate only within the limits set by range values for each input variable. Since the DNN is trained on a specific dataset, going beyond the range values can lead to the wrong results. The study did not directly analyze the effects of using con-crete additives and admixtures. Their influence is accounted for indirectly by including two observations if they affect the target variable. Many other processes influence hard-ened concrete properties, such as the curing process, but their influence was not consid-ered in this study. It is assumed that the quality control of the production of concrete mix and concrete was sufficient. As a result of this research, a trained DNN was obtained, translated into the source code, and interpreted as an equation, defining the 28 day com-pressive strength of concrete as a function of the 17 parameters. Of these, 12 parameters relate to the two recipes . The remaining five parameters describe the recipe for calculating the desired target value. A hyperbolic tangent was used for hid-den layers as the activation function and a linear tangent was used for the output layer. The practical application of the presented solution in the concrete mix design process is presented in Later in this paper, the designed algorithm is referred to as MAFM21 (Ma-chine-learning Adaptive Forecasting Model 2021). In the comparative analysis presented below, we used the machine learning algorithm developed in the previous research . This alThe dataset was divided into three subsets to perform DNN training, namely, training, selection, and testing datasets. These three datasets have distinct functions. The training dataset is used to feed the DNN, the selection dataset helps to adjust the hyperparameters of DNN, and the testing dataset serves as a tool to assess the DNN’s effectiveness. The whole database had 741 records, whereby the training dataset had 440 records (59%), the selection dataset had 146 records (20%), and the testing dataset had 146 records (20%). There were nine records (1%) excluded from the analysis as univariate outliers, with a maximum distance from the center of the dataset defined as three times the median value for each variable. This is a less conservative approach than n used in the previous study, where about 11% of the records were excluded. In The initial DNN architecture comprised 17 input variables that refer to the 17 principal components and introduce one target output. The initial architecture contained three hidden layers, representing the complexity of the model. A DNN includes principal components, perceptron neurons, scaling neurons, and unscaling neurons. In the analysis, the feature scaling was used, hence the scaling and unscaling neurons. Two recipes were treated as observations by introducing into the equation a set of input variables representing cement, water, fine-grained and coarse-grained aggregate, water–cement ratio, and concrete compressive strength. These two recipes generated 12 input variables. The remaining five input variables corresponded to the composition of the third recipe for which it was tried to predict the concrete compressive strength. The input variables  presented in However, it should be noted that the strength of concrete is influenced by several other factors, primarily related to the environmental conditions and technological process of concrete production. The first important aspect is proper concrete curation after the built-in process. Improper concrete curation can significantly deteriorate the properties of concrete, especially its durability. Another vital issue is concreting during unfavorable weather conditions, mainly concrete freezing in the early stage of the hydration process and too high shrinkage due to drying during high temperatures. Considering environmental factors, one must refer to adverse environmental aggression, which harms the quality of concrete, for example, a high risk of carbonation  and chloThe dataset had many input variables. Principal component analysis (PCA) was used to keep critical information while reducing input variables in a smaller feature space. Thanks to this method, among other things, it was possible to reduce the data dimensionality by feature extraction. The PCA uses auxiliary variables to explain most of the variability in the dataset ,60,61,62The final architecture of the adopted DNN is presented in The developed DNN had one target variable, the concrete compressive strength, with the 17 input variables representing two observation recipes and one recipe for the targeted value. Input variables expressed several concrete mix characteristics, such as cement, water, fine-grained and coarse-grained aggregate content, and water–cement ratio. The DNN was translated into a mathematical formula, presented as Equation (5), along with auxiliary Equations (A6)–(A49). Equation (5) refers to the 28 day strength of concrete, which can be considered as the full strength of concrete. The equation was optimized and simplified. The principal components were drawn into the equation.3 or l/m3 for variables C, W, FA, and CA. Variable WC is a numerical ratio that is a dimensionless value. There were 36 auxiliary formulas, with Equations (A6) through (A39) containing variables with the index The variables 3 of concrete designed as a concrete slab, with a plastic slump , direct pouring with little environmental aggression, no special desired finishing, no special ambient conditions when casting, air content at 2.5%, and 20 mm maximum grain diameter. The following materials were adopted for the design: network water, ordinary Portland cement, natural sand, limestone gravel 4/10 mm, and limestone gravel 10/20 mm. Combinations of admixtures commonly used in engineering practice were selected. The exact composition of the designed mixtures is presented in The comparative analysis was prepared, which included a comparison of the MAFM21 algorithm with the MLM19 algorithm from 2019 presented in , which wGradings and fitting curves for each designed concrete recipe are presented in The comparative analysis of errors is presented in It can be seen in One of the most critical issues in the concrete structure manufacturing technological process is to obtain predictable properties of both fresh concrete mix and hardened concrete. Manufacturers of concrete mix are obliged to guarantee the appropriate properties of concrete delivered to the construction site. However, obtaining the appropriate concrete properties at all stages of the production process is difficult, especially when chemically complex additives and admixtures for concrete are present. Therefore, the properties of concrete mixes are often oversized, which can lead to very unfavorable phenomena. For example, increasing concrete strength more than the designed value may cause changes in the structure’s stiffness and lead to so-called concrete superstrength . PredictThe primary goal of this research was to introduce machine learning to the concrete mix design process and pave the way for new quality in this area of science. The MAFM21 algorithm can improve the ability to predict concrete technical parameters by including two observations. This adaptive approach allows us to predict the concrete mix behavior depending on its unique characteristics. Concrete mixes used in engineering practice have a very diverse composition. Designing a separate model for each specific composition of the concrete mix may turn out to be impractical and, in the long run, could be an obstacle to its widespread implementation. The core of the presented study was to develop an optimal deep neural network architecture and train it using an extensive database of concrete mix recipes with corresponding laboratory destructive tests. The developed algorithm estimates the concrete mix compressive strength according to its composition. The database used for training had 741 records, whereby the training dataset had 440 records (59%), the selection dataset had 146 records (20%), and the testing dataset had 146 records (20%). Nine records (1%) were excluded from the analysis as univariate outliners, with a maximum distance from the center of the dataset defined as three times the median value for each variable. The initially adopted DNN model had 17 input variables, 17 principal components, four hidden neurons, and one target output. The final DNN architecture had 17 input variables, 17 principal components, 46 hidden neurons, clustered into seven layers, and one target output. The applicable training rate and the step for the quasi-Newton training direction were calculated using the Broyden–Fletcher–Goldfarb–Shanno algorithm and the Brent method, respectively. The feature correlation analysis revealed that the most significant impact on concrete strength was the water–cement ratio and cement content. The dimensionality reduction was performed using principal component analysis. An incremental order algorithm was used to find the optimal number of neurons in DNN and minimize loss. The DNN source code was translated into a mathematical equation, which was then optimized and simplified. The final equation had 44 auxiliary equations. This method was tested on four concrete mix recipes, calculated according to contemporary design methods (Bolomey and Fuller method) and a comparative analysis of the new MAFM21 algorithm with the algorithm developed in 2019 MLM19 was conducted by analyzing the root-mean-squared error (RMSE), mean absolute error (MAE), and mean squared error (MSE). The conducted tests allowed us to see how MAFA21 complied with the methods used in engineering practice, and whether the adaptive approach performed better than the previous MLM19 algorithm trained on the same dataset.The comparative analysis brought us a number of findings. The MAFM21 algorithm was more volatile, but had significantly lower RMSE, MAE, and MSE than MLM19 for mixtures designed according to the Bolomey method. MAFM21 gave slightly lower RMSE, MAE, and MSE for mixtures designed according to the Fuller method depending on the tested mixture. MAFM21 had the best fit for Bolomey mixes, characterized by particularly good convergence for higher strength levels above 30 MPa. MAFM21 performed well in lower ranges (below 20 MPa) for mixtures designed according to Fuller. MLM19 had low resilience for high-strength recipes, whereas MAFM21 performed better in that spectrum. The presented approach has boundary conditions and does not fully reflect all the relationships between the components of concrete mix and concrete properties. This issue requires further research. However, the results presented in this paper give hope for broader use of this method in engineering practice. Future research should extend the method’s use in the concrete mix design process by predicting other properties of fresh concrete mixtures and hardened concrete, such as durability, consistency, air content, and service life estimation. It would also be vital to create a more comprehensive approach for concrete mix optimization.


IntroductionAntibiotic dispensing regulations have been implemented in most countries worldwide. Saudi Arabia’s Ministry of Health (MOH) recently implemented a policy for dispensing antibiotics, in which pharmacies in Saudi Arabia are strictly prohibited from dispensing antibiotics without a medical prescription. Failure to adhere to the regulations will result in a fine and may lead to the revoking of the pharmacy's license. The aim of this study was to investigate public perception among the Saudi population toward the recently implemented policy, to examine the effect of antibiotic dispensing regulations on consumers’ request for non-prescribed antibiotics at retail pharmacies, and to elucidate the implications of the recent policies on patient visitation to family physicians.MethodA cross-sectional study was conducted using three questionnaires targeting the general public in Saudi Arabia in addition to pharmacists and family physicians working in Saudi Arabia. ResultsA total of 380 participants completed the questionnaire for the general public, 299 for the pharmacist questionnaire, and 94 for the family physician questionnaire. Most participants in the general public questionnaire obtained their antibiotics using a prescription after consulting with a family physician (72.4%). Most of these individuals also agreed with the strict regulations for antibiotic dispensing (82.1%). Most pharmacist participants either always or frequently received requests to dispense antibiotics without a prescription from customers (71.5%). In addition, most physicians (59.6%) acknowledged that patient demand increased after the implementation of the new policies.ConclusionOur assessment of public perception towards the recent regulation for dispensing antibiotics indicates that most participants who participated in the General Public questionnaire supported the MOH’s strict regulations for antibiotic dispensing. However, customer requests for antibiotics without a prescription from pharmacists were a bit high, which could be attributed to customers' lack of awareness of the regulations. The results also indicate an increase in the number of family physician visits after the regulations were implemented. In the past few decades, dispensing of non-prescribed antibiotics has been a growing concern for the World Health Organization (WHO), especially with the dramatic increase in trajectory seen in developing countries . The WHOHu and Wang surveyed Chinese immigrants residing in Australia on the question of whether they had used antibiotics in the past 12 months, and if the antibiotic was prescribed by a General Practitioner (GP) . Out of In a study that sought to evaluate antibiotic use among adults (n = 323) in Greek urban areas, researchers found that 163 of the surveyed participants used non-prescribed antibiotics . Thirty-In the United States, for instance, strict regulatory policies have been implemented for dispensing antibiotics in most states, but the use of antibiotics without a prescription has increased, especially among Latinos . In a stIn Saudi Arabia, several studies have been conducted over the past few years to assess public perception and awareness toward indications for antibiotic use and their associated complications -8. In a The aim of this study is to investigate public perception among the Saudi population towards the recently implemented policies for dispensing antibiotics, to examine the effect of antibiotic dispensing regulations on consumers’ request for non-prescribed antibiotics at retail pharmacies, and to elucidate the implications of the recent policies on patient visitation to family physicians.Study designA cross-sectional study was done in Saudi Arabia between November 2019 and May 2020 using three self-administered questionnaires. The questionnaires used in this study were obtained from previously published articles with their permission ,10.QuestionnairesGeneral PublicThe questionnaires were self-administered by members of the general public. Questionnaires were distributed randomly in the waiting area of the National Guard Hospital in Riyadh, as well as public places in the city . All Saudi citizens above 18 years old were eligible to participate, and all non-Saudi participants or individuals younger than 18 were excluded. Participants were randomly selected regardless of their age, gender, or level of education.PharmacistGoogle Forms was used to create the pharmacist questionnaire, which were randomly distributed electronically to all pharmacists working in two well-known pharmacy chains in Saudi Arabia. We included all Saudi and non-Saudi pharmacists working in private pharmacies in Saudi Arabia.Family PhysicianGoogle Forms was used to create the family physician questionnaire. We contacted the Saudi Commission for Health Specialties (SCFHS) to send it by email to all Saudi and non-Saudi physicians who are registered and working in the primary health clinics in Saudi Arabia as Family Medicine Physicians.Data analysisData were checked for completeness and correctness. Categorical variables were presented as frequencies and percentages. Continuous variables were presented as mean ± standard deviation (SD). The Chi-square test was used to test the association between categorical variables, while the Mann-Whitney U test was used to examine the relationship between categorical and continuous variables. The analysis was performed with a 95% confidence interval using the Statistical Package for the Social Sciences (SPSS) version 23.0 .Ethical considerationsThe study was reviewed and approved by the Institutional Review Board (IRB) at King Abdullah International Medical Research Center (KAIMRC), the Ministry of National Guard Health Affairs (MNGHA), Riyadh, Saudi Arabia. The study’s protocol number is #RC19/079/R. Participation was voluntary and informed consent was obtained from all participants prior to administering the questionnaire. All collected data were obtained confidentially, kept confidential, and stored in a secure place.General publicOf the 400 questionnaires distributed to the general public, 380 were completed and returned, which led to a response rate of 95%. For the remaining 20 questionnaires, either the participants refused to participate, or the data were incomplete. The mean age of the participants was 34.01 ± 12.84 years, and 58% were female. More than half (56.1%) held a bachelor’s degree (Table Most participants obtained antibiotics with a prescription after consulting with a family physician 72.4%). Only a few participants obtained antibiotics after consulting with a pharmacist with a prior valid prescription (6.8%), and the remainder obtained their antibiotics without a prescription (4.7%). In addition, a handful of participants suggested that they would stop their antibiotic regimen as soon as they felt better (28.7%), while most were committed to finishing their course (71.3%). Surprisingly, almost one-quarter of the participants were willing to share their antibiotics with others (23.7%) , while MOH advertisement for illegal antibiotic dispensing without a prescription was the second most common source of participants awareness (21.3%). Most participants agreed with the strict regulations (82.1%) and penalties for dispensing non-prescribed antibiotics (67.9%). However, almost one quarter stated that they would prefer to purchase antibiotics at a higher price without a physician's prescription instead of going through the trouble of getting a prescription (22.4%). Regarding the participants’ adherence, the overwhelming majority declared they would conform to the new regulations for antibiotic dispensing implemented by the MOH (81.1%)  knew that antibiotics are prescribed to treat bacterial infections Figure . However% knew thPharmacistsPharmacists from all major areas of Saudi Arabia were included in this study. Approximately 81% of pharmacists stored their antibiotics. Most pharmacists either always or frequently received requests to dispense antibiotics without a prescription from customers (71.5%). Three-point four percent of pharmacists responded that customers could purchase antibiotics without a prescription.Most customers requested antibiotics either because they suspected infection or because they had used it before and were unwilling to visit a hospital for medical consultation 56.8%). The overwhelming majority of pharmacists (80.3%) acknowledged that antibiotic misuse has a substantial community impact, and 59.2% believed that public and professional education was required on the rational use of antibiotics to prevent misuse. Interestingly, very few pharmacists warned customers of the potential side effects associated with using specific antibiotics (2.3%), and only slightly more educated patients on correct dosing and potential side effects (7.7%). Further questions related to pharmacists’ antibiotic dispensing behaviors are presented in , Associated/Assistant Consultants (19.1%), Residents (31.9%), and General Practitioners (11.7%). Most participants were Saudi nationals (68.1%). All participants had previously prescribed antibiotics to all age groups for various reasons.After the MOH’s implementation of regulatory policies for antibiotic dispensing, most physicians (58%) acknowledged that between 11% and 20% of consultations resulted in antibiotics being prescribed to patients. About 27.7% of physicians acknowledged that more than 20% of visits resulted in an antibiotic prescription. Furthermore, 66% of physicians stated that they occasionally prescribe antibiotics for upper respiratory tract infections (URTIs), cold, or flu, while 30% had never prescribed antibiotics for the same reason. More than half of respondents (54.3%) did not prescribe antibiotics without a clear indication for their need, while one-third (33%) admitted to dispensing antibiotics even when not clinically indicated. More than one-third of physicians attributed their prescription of antibiotics to patients to diagnostic uncertainty. Out of the signs that might influence a physician to prescribe antibiotics, the most common sign was the presence of tonsillar exudate, where the overwhelming majority (96.8%) were very likely or likely to prescribe antibiotics (Table Most physicians (98.2%) almost always or often mentioned to their patients the possibility of aggravating AMR if they improperly used their antibiotic regimen. Furthermore, most respondents (97.9%) advised their patients to adopt lifestyle modification and better self-management . A majority of physicians discussed with patients the ineffectiveness of antibiotics against viral illnesses. Regarding physician perceptions of AMR severity in Saudi Arabia, most (90.4%) rated severity as 3 or more on a 5-point scale. More than half (54.3%) did not receive supplemental information from the MOH regarding the safe use of antibiotics. Finally, in terms of patient demand for antibiotics after the implementation of the MOH’s regulatory policies, most physicians 59.6%) acknowledged that demand increased . In a study conducted in Saudi Arabia before the implementation of the regulatory policies by the MOH, 48% of the participants admitted to obtaining antibiotics without a prescription [2.4%. In Approximately one-third of the participants indicated that they were likely to stop their antibiotic course midway through if they felt better (28.7%). This is one of the factors that has contributed to the global rise of AMR. An alarming observation was that almost one-quarter 23.7%) of the surveyed participants were willing to share their antibiotics if asked. These alarming behaviors require further investigation to identify possible factors that contribute to them. Compared to other communities, such as Chinese migrants in Australia, 3.4% indicated that they had used non-prescribed antibiotics obtained from friends .7% of th. This tyThe medical community in Saudi Arabia is advised to place greater emphasis on educating patients and raising awareness of the potential hazards of using antibiotics without physician supervision. Most members of the general public were likely to adhere to regulatory policies for dispensing antibiotics. Noteworthily, almost one-quarter of the participants (22.4%) were more willing to purchase antibiotics without a prescription than to book an appointment with a family physician. Similar behaviors have been identified in other communities, for instance, members of the Asian community in Australia have highlighted barriers to GP visits as the main reason for their seeking antibiotics without a prescription . More thIn this study, public perception towards indications for antibiotic use was alarming. A handful of respondents suggested that antibiotics would help them treat the common flu and other viral illness. Some also went further and suggested the use of antibiotics as analgesics. Although the majority knew that antibiotics are primarily used to treat bacterial infections, some suggested that they can also be used to treat viral and fungal infections, or that they can serve as analgesics. Therefore, our results indicate a misconception regarding the simple indication for antibiotic usage. We recommend that futures campaigns for antibiotic awareness targeting audiences nationwide should incorporate a simplified format in order to convey the proper and effective usage of antibiotics and the potential danger of AMR.Our results showed that the level of education was not associated with the degree of knowledge of antibiotic harmfulness. Furthermore, there was a significant relationship between gender and prior knowledge of the new regulatory policies. Gender differences in the proper/improper use of antibiotics have been observed in other communities. For instance, in Greece, it was found that males tend to adhere less to the correct dosing compared to females . AdditioComparing our results with previous studies in Saudi Arabia regarding public attitudes toward obtaining non-prescribed antibiotics, we can clearly see a change in public perception towards antibiotics. In a 2016 study by Bin Nafisah et al., 48% of participants obtained antibiotics without a prescription . In our PharmacistsApproximately 71.5% of pharmacists reported that either always or frequently been asked by customers to dispense antibiotics, and only 3.3% of them dispensed antibiotics without prescription. This can be attributed to customers' lack of awareness of the recent regulations for dispensing antibiotics enforced by MOH. In a study conducted prior to the enforcement of regulatory policies for dispensing antibiotics in May 2018, involving a simulated patient presenting to a pharmacy, 82% of pharmacists prescribed antibiotics . Two stuA recent study in Saudi Arabia compared the impact of the new regulations on community pharmacists, and it was conducted in two phases, pre-implementation and post-implementation of the new regulations . In bothIn a study that was conducted among community pharmacists working in Makkah province, they found that the main reasons for patients requesting antibiotics without prescription were patients' unwillingness to consult a physician for non-serious infection, inability to afford consultation, and pharmacist's good knowledge about antibiotic use . The latFamily physiciansMore than half of the participating physicians noticed an increase in demand for antibiotics after the introduction of the MOH’s regulatory policies (59.6%). We could not find any local studies that assessed patient demand for antibiotics prior to the implementation of the MOH’s policies. However, in comparison, in a study done by the Department of Health in Hong Kong, only 16% of patients asked for antibiotics more often from their primary care physician after launching the community promotion campaign in March 2011 .In our study, 66% of physicians prescribed antibiotics for patients presenting with signs and symptoms of cold, flu, or URTI without a clear indication of bacterial infection. This is clearly an issue to be addressed and should be further investigated. Several studies have indicated that dispensing antibiotics for non-bacterial infections is implicated in the rise of AMR . FortunaMoreover, we recommend that more support should be provided by the MOH to primary care providers regarding the safe use of antibiotics. This is based on the finding that 54.3% of physicians did not receive any support materials from the MOH on safe antibiotic use. In comparison, in the study carried by the Department of Health in Hong Kong, 82% of physicians received materials related to the safe use of antibiotics . In PalmLimitationsThe general public questionnaire was limited to a single geographical area, Riyadh city. The pharmacist questionnaire was only distributed to pharmacists working in two well-known pharmacy chains in Saudi Arabia. This study’s assessment of public perception towards the recent regulations implemented by the MOH in the Kingdom of Saudi Arabia for dispensing antibiotics indicates that most participants of the general public, as well as pharmacists, support those measures. In addition, compared to the results of previous studies in Saudi Arabia prior to the implementation of the dispensing regulations for antibiotics, the prevalence of non-prescribed antibiotic dispensing has fallen sharply after the enforcement of the regulation. Most pharmacists reported that they either always or frequently receive requests from customers to dispense antibiotics without a prescription, which is likely attributed to customers' lack of awareness. Most participants were found to obtain their antibiotics through prescription, but the results also show an increase in the incidence of family physician visits after the enforcement of the regulation. A handful of participants were willing to share their antibiotics if asked, which is an alarming behavior that should be addressed in future research. In this study, we cannot assess whether public support for MOH regulation is due to public awareness of the consequence of improper use of antibiotics or simply due to the fines associated with obtaining non-prescribed antibiotics. Further research should examine the factors influencing public attitudes toward antibiotic usage and dispensing as well as effective awareness campaigns addressing the proper usage of antibiotics.