Nowadays, broadband applications that use the licensed spectrum of the cellular network are growing fast. For this reason, Long-Term Evolution-Unlicensed (LTE-U) technology is expected to offload its traffic to the unlicensed spectrum. However, LTE-U transmissions have to coexist with the existing WiFi networks. Most existing coexistence schemes consider coordinated LTE-U and WiFi networks where there is a central coordinator that communicates traffic demand of the co-located networks. However, such a method of WiFi traffic estimation raises the complexity, traffic overhead, and reaction time of the coexistence schemes. In this article, we propose Experience Replay (ER) and Reward selective Experience Replay (RER) based Q-learning techniques as a solution for the coexistence of uncoordinated LTE-U and WiFi networks. In the proposed schemes, the LTE-U deploys a WiFi saturation sensing model to estimate the traffic demand of co-located WiFi networks. We also made a performance comparison between the proposed schemes and other rule-based and Q-learning based coexistence schemes implemented in non-coordinated LTE-U and WiFi networks. The simulation results show that the RER Q-learning scheme converges faster than the ER Q-learning scheme. The RER Q-learning scheme also gives 19.1% and 5.2% enhancement in aggregated throughput and 16.4% and 10.9% enhancement in fairness performance as compared to the rule-based and Q-learning coexistence schemes, respectively. Recently, the modern industry has expanded the deployment of wireless networks in search of effective networking solutions that can improve network performance. The rapid increase of wireless network deployments in the industry, along with the quick penetration of wireless networks consumer devices such as smartphones and tablets, has resulted in an exponential increase in wireless traffic demand. Furthermore, the Internet-of-Things (IoT), which connect an unprecedented number of intelligent objects to next-generation mobile networks, consume a major chunk of the wireless spectrum .As the wireless network industry keeps expanding, the licensed spectrum has become a scarce resource and the wireless network industry has shifted its focus towards exploiting unlicensed spectrum as an efficient approach of addressing spectrum shortages and customers’ fast-expanding need for data traffic . In thisWhen LTE is introduced into a common unlicensed spectrum, it will certainly cause issues while competing and coexisting with other unlicensed communications technologies that use the same spectrum. In traditional communication technologies that use unlicensed spectrum for data transmission, such as WiFi, channel access can only be obtained competitively to achieve fair spectrum sharing. On the other hand, LTE technology, which is initially developed in the licensed spectrum, needs complete spectrum control during its data transmission .LTE eNodeB (eNB) performs centralized scheduling of wireless resources to maximize spectral efficiency. Even in the absence of data traffic, LTE uses continuous signal transmission with minimum time gaps within the allocated resources. On the other hand, WiFi uses the carrier sense multiple access with collision avoidance (CSMA/CA) protocol to coexist with other unlicensed band wireless technologies. If the unlicensed spectrum is used as new LTE frequency bands, the transmission of LTE-U will cause significant interference to WiFi. As a result, it is important to design a reasonable and equitable coexistence scheme to ensure fair and efficient coexistence between the two technologies .Carrier Sense Adaptive Transmission (CSAT) is one of the LTE/WiFi coexistence mechanisms which have been proposed by Qualcomm . In CSATInvestigation on non-coordinated coexistence of LTE and WiFi networks.Propose coexistence scheme that use ER based Q-learning and RER based Q-learning solutions for uncoordinated LTE-U and WiFi networks.Performance analysis and comparison of the proposed coexistence schemes with each other and with a rule-based and Q-learning based coexistence schemes in terms of model complexity, convergence, the accuracy of best action selection, fairness, and throughput. We are able to observe that the RER Q-learning scheme converges faster than the ER Q-learning scheme and gives 19.1% and 5.2% better aggregated throughput performance than the rule-based and Q-learning schemes, respectively. The RER Q-learning scheme also achieves 16.4% and 10.9% better fairness performance as compared to the rule-based and Q-learning schemes, respectively.In our previous work , a ConvoIn general, this paper presents a coexistence scheme that does not require a collaboration protocol for the traffic status exchange between the co-located networks. In the proposed coexistence scheme, the LTE-U eNB uses a WiFi saturation sensing model to estimate the WiFi traffic load and selects an optimal configuration according to the WiFi saturation status. Therefore, the proposed scheme requires modifications on the LTE-U eNB side only and this makes it compatible with commercial off-the-shelf WiFi devices. This also enhances the deployment of real-time coexistence decisions as there is no delay introduced due to traffic load status exchange between the technologies. Furthermore, the proposed coexistence strategy utilizes an experience replay technique to train a model that determines the optimal configuration, making it an excellent coexistence solution for co-located networks with dynamic traffic loads.The rest of this paper is structured as follows. Many researchers have done an extensive study on the coexistence of WiFi and other networks that operate concurrently in unlicensed spectrum bands. In the next sections, we will discuss existing coexistence schemes, particularly on the solutions proposed for the fair coexistence of coordinated and uncoordinated LTE and WiFi networks.In , AlmeidaIn ,13,14, QIn our previous work , Q-learnTo ensure fair coexistence of LTE and WiFi, authors in  proposesThe studies are described in this section assume that co-located LTE eNB and WiFi Access Point (AP) can exchange the exact traffic requirements between each other. In practice, the two systems do not have a dedicated common control channel that may be utilized to share traffic status. As a result, the suggested coexistence schemes necessitate a system architectural change on the two technologies to create a new channel for traffic status reporting. In other words, modifications are required in both legacy LTE eNB and WiFi AP sides and these additional system requirements make implementing coexistence schemes difficult.In this section, we discuss coexistence mechanisms that consider uncoordinated LTE and WiFi networks. In uncoordinated LTE and WiFi networks, there is no cooperative channel used to exchange the traffic status between the technologies. Hence, additional features that enable to sense the WiFi traffic have to be included in the co-located technologies.In ,24, numbIn our previous work , the CNNThe authors in  propose In , reinforWe reviewed different coexistence schemes that are proposed to achieve fair and efficient coexistence of LTE and WiFi networks. However, most of the proposed solutions consider coordinated LTE and WiFi networks in which the traffic status of the technologies is exchangeable via a collaboration protocol. There are also some papers that propose WiFi traffic demand estimation techniques used in uncoordinated coexistence schemes. However, WiFi traffic load estimation is mostly done based on wrong assumptions such as the number of active APs, or COT. Therefore, this paper proposes an efficient coexistence scheme for uncoordinated LTE and WiFi which requires modifications only on the LTE eNB side. To estimate the traffic demand of WiFi, the coexistence scheme uses the saturation sensing model proposed in our previous work . MoreoveIn this work, we aim to propose a fair and efficient coexistence scheme for uncoordinated LTE-U and WiFi networks. In our previous work , we haveThe main goal of the algorithm is to select an optimal LTE-U ON and OFF duration ratio based on LTE-U and WiFi network traffic loads. The LTE-U estimates the traffic demand of the WiFi network based on the saturation sensing model and classifies the WiFi networks as either saturated or unsaturated based on their offered traffic load. A WiFi network is considered as unsaturated if its aggregated throughput has not reached the maximum system throughput limit and saturated otherwise . SimilarThe goal of the coexistence scheme is to select an optimal LTE-U ON and OFF duration ratio so that the WiFi network remains unsaturated while the Technology recognition model, the WiFi saturation sensing model, and the Coexistence decision model. The Technology recognition model is used to capture and identify concurrent transmissions by different co-located technologies. In practical applications, the LTE and WiFi transmissions are classified based on technology recognition solution that requires capturing and processing the IQ samples of LTE and WiFi traffic [WiFi saturation sensing model, where the saturation state of WiFi is determined based on the CNN model proposed in our previous work [WiFi saturation sensing model is used to distinguish saturated and unsaturated WiFi traffic in real-time by analyzing the Inter-Frame Spacing (IFS) distribution. The IFS distribution is derived by processing the statistics obtained from the Technology recognition model. Finally, the Coexistence decision model is used to decide the optimal action that leads to efficient coexistence. This decision is made based on the saturation status of the WiFi network and the status of the LTE-U network. The proposed coexistence decision algorithms are discussed in the next sections.In this section, we propose and discuss the architecture of the coexistence scheme for uncoordinated LTE and WiFi networks that use WiFi saturation status to estimate the traffic demand of the WiFi network.  traffic . Once thous work . The WiFservices . Once thservices .In the proposed coexistence solutions, we consider coordinated eNBs which exchange coexistence decision made by a central coordinator eNB. In terms of simulation, the parameter parsing is done by functions and receiving the updated configurations from the central node takes negligible latency and overhead. However, for real-world deployments, this communication between nodes can be accomplished by combining the X2 interface and the X2AP services . The X2 Technology recognition model based on technology recognition solution as proposed in our work in [In practical applications, the LTE and WiFi frames are classified in  work in . In the To minimize the computational complexity, a coexistence scheme has to report the optimal ON-OFF duty-cycle ratio every prediction duration T seconds, where T is an optimally selected network traffic prediction period based on the traffic dynamics of the co-located networks. In practice, the traffic dynamics of the co-located networks have to be analyzed and the value of prediction duration T has to be set low enough to match the traffic dynamics of the WiFi network . HoweverThe ultimate goal of this article is to propose a WiFi saturation based fair and efficient coexistence scheme for uncoordinated LTE and WiFi networks. In this section, we formulate and describe coexistence schemes for non-coordinated coexistence solutions. For comparison and validation purposes, we formulated simple rule based and classical Q-learning coexistence schemes. These coexistence schemes are formulated in such a way that they can be utilized for uncoordinated LTE-U and Wi-Fi networks. For this reason, the first two subsections of this section describe a simple rule based adaptive coexistence scheme and Q-learning based coexistence scheme for uncoordinated LTE-U and WiFi networks. The proposed ER and RER based Q-learning coexistence schemes are described in the last two subsections of this section.LTEState and WiFiState. The WiFiState represents WiFi state which can be either saturated (WS) or unsaturated (WU). In this subsection, we describe a rule-based coexistence scheme that is used to determine the number of LTE blank subframes (10) and the WiFi network is unsaturated. We also consider the action as 100% Nb is adjusted based on LTE and WiFi states. If the WiFi network is saturated and the LTE network achieves sufficient throughput (L10), the number of blank LTE subframes is increased so that WiFi gets more spectrum thereby leading to better fairness. Contrarily, if the WiFi network is unsaturated and the LTE network does not achieve sufficient throughput, i.e., Algorithm 1 shows the steps of the proposed rule-based coexistence scheme. Initially, 50% of the LTE subframes are set as blank ensuring equal spectrum share for both technologies. Then, the fairness of this action is evaluated. We consider the action as 100% s on the environment. The agent then selects the corresponding action a according to the policy R from the environment. After the action is taken on the environment the state changes to Algorithm 1: Rule based coexistence schemeReinforcement Learning (RL) is an area of machine learning where an agent learns from its interaction with the environment. Q-learning is a type of reinforcement learning algorithm that implements dynamic programming and Monte Carlo concepts to solve sequential decision-making problems in the form of Markov Decision Processes (MDP) . In MDP,rd value .Algorita after observing the current state s of the environment. In the exploration case, the agent selects an action randomly, expecting that the random action will lead to a higher cumulative reward in the next iterations. On the contrary, in the exploitation process, an action is selected based on the latest expected optimal policy. Generally, during exploitation, the agent uses the experience from already investigated actions and selects the optimal action based on them, while during exploration, the agent investigates and experiences new actions. In the Q-learning process, the agent balances between exploration and exploitation while it tries to find an optimal strategy in the selection of action Nb), aiming at maximizing fairness and the aggregated throughput of LTE and WiFi networks. The procedures show how the agent selects an optimal value of Nb based on LTE throughput status and WiFi saturation status. The algorithm also shows how exploration and exploitation are balanced by an adjustable value of Algorithm 2 shows how Q-learning can be used for optimally selecting the number of blank subframes  or unsaturated (WU). LTEState represents the state of LTE which is determined based on LTE target throughput and LTE obtained throughput. The state of the WiFi network is represented by its saturation status which is determined based on our previous work [LTEobtained and LTETarget throughput. The possible states of LTE are shown in Nb is the number of blank LTE subframes in a single frame. The state-space S includes all combination of WiFiState, LTEState, and Nb values.ous work . On the Action: The action a at time t represents the selection of Reward: LTE network is required to attain the highest possible throughput by accessing the spectrum while maintaining fairness with WiFi. However, the performance of the WiFi can not be directly accessed by the agent as our goal is to propose a coexistence scheme that does not require signaling exchanges between the WiFi AP and LTE eNB. For this reason, we consider the CNN-based WiFi network saturation sensing model to determine the status of the co-located WiFi. The reward value for action a taken at a state s is computed based on the newly achieved state Nb is the number of blank LTE subframes and WU and 0.5 if WiFi state is WS. The value of LTEObtained gets close to LTETarget. Similarly, the value of L10) and WiFi is unsaturated (WU). Similarly, the highest reward can be achieved if both technologies are spectrum hungry while both technologies are given equal spectrum share, i.e., if LTE is not at state L10 and WiFi is at state WS while Nb = 5. Otherwise, the reward function depends on the values of Nb. As Nb increases, LTE gets a lower spectrum share and hence the value of Nb increases, there is a higher probability that the WiFi gets sufficient spectrum to attain unsaturated state leading to follows:rQ=100ifsThe key elements of the Q-learning process for optimal Algorithm 2: Q-learning based algorithm for dynamic selection of NbThe traffic load of wireless networks is mostly dynamic. In such a dynamic environment state–action pairs are not consistent over time. This makes predicting future rewards more complex. For this reason, the Q-learning algorithm is mostly applied in stationary environments where state-action pairs are consistent. However, this problem can be solved by using Experience Replay (ER). ER is an approach used to minimize action–state pair oscillation. This is achieved by storing a large number of past experiences. A single stored experience is represented by a combination of the current state, action, reward, and next state =ρ∗βWiFistate and LTEstate. The value of In case of experience replay based Q-learning, all possible actions are observed for each given traffic load. All actions are recorded with their corresponding The reward function presented in Equation  leads toa. These values are then stored to Algorithm 3 shows how the ER based Q-learning can be used to predict an optimal policy We have seen that the ER-based Q-learning solution stores experience Equation , and theAlgorithm 3: ER Q-learning based algorithm for dynamic selection of NbIn the RER based Q-learning, the main procedures used to update the Q-table are similar to the procedures used in ER based Q-learning scheme depicted in Algorithm 3. The difference between ER based Q-learning scheme and RER based Q-learning scheme is, in ER based Q-learning scheme every recorded experience is used to update the Q-table while RER based Q-learning scheme updates the Q-table based on experiences with best reward value. This reward based experience record update is adopted from .AlgoritBefore going to the performance analysis of the proposed coexistence schemes we start by defining and describing the evaluation scenario. In , 3GPP prn UE/stations, where n is randomly selected between 1 and 5. The UE/stations are randomly located within the room. The distance between base stations from different operators is 5 m while the distance between base stations of the same operator is 25 m.We implement and analyze the proposed coexistence schemes using the ns-3 simulator as it is one of the prominent simulators to model wireless networks. The simulator is also convenient to model coexistence scheme as it can be used to generate dynamic traffic loads of LTE-U and WiFi networks. Hence, we use ns-3 version specifically released with LTE/WiFi coexistence implementations . This nsThe simulation parameters used to model the co-located technologies are shown in ted from .ConstantSpeedPropagationDelayModel and a propagation loss model named ITU InH [The considered network uses a constant speed propagation delay model named as  ITU InH ,41. In tA simulation scenario modeled in ns-3 can be integrated with the RL models using the framework described in . In our In this section, we will discuss the convergence and complexity analysis of the coexistence schemes described in The first coexistence scheme described in It is not convenient for co-located networks that have very dynamic traffic as determining the best action requires an average time of 5.832 s.Each trial is a new configuration and it affects the performance of the technologies as far as it is not the best one. i.e., even though the traffic remains constant for a long time, the best action is found after 5.832 s and the performance remains poor as wrong configurations are used in the meantime until the best action is not finally taken.Rule-based solutions are not easily scalable if the number of possible actions is increased and this will further increase the time required to find the best action.Using the rule-based approach, the total time required to report an LTE frame configuration in each trial is 1 s  + 1.032 s (processing time of technology recognition classification + saturation sensing) + (rule-based decision − negligible processing time) = 2.032 s. As the best action requires 2.87 trials on average, the best action requires a period of 2.87 × 2.042 s = 5.832 s. Basic challenges in using the rule-based approach include:The second coexistence scheme adopted for uncoordinated LTE-U and WiFi networks is the Q-learning based solution described in K) was set to 4. These values were also chosen by inspection because they lead to the best convergence, while the remaining hyper-parameters were left unchanged from the Q-learning based scheme. s, Q-learning based selection of optimal action a requires an average time of One prevalent issue with machine learning-based solutions is that they require a learning phase before providing an optimal solution. The learning phase is a computational cost of the Q-learning process, as is the case with other learning methods. During this phase, an agent investigates several possible actions in each potential condition to learn about the environment. In the case of using experience relay, the training part requires registering and reading experiences. However, after the environment has been learned, the best action in every given condition can be taken, resulting in the optimal solution. For a given state solution  to captusolution .However, we employ a discrete event simulator (ns-3) in our simulation, and parameter parsing is done via functions, thus the collection of WiFi frame statistics occurs with negligible processing time. On the other hand, the proposed coexistence schemes do not necessitate information exchange between the LTE and WiFi networks and this makes the solutions less complex as there is no need to make any modifications to the WiFi network elements.In this section, we present the performance of the schemes presented in The aggregated throughput in each fixed load duration is the sum of WiFi obtained throughput and LTE obtained throughput whereas the corresponding fairness value is given by:X, where X = (target throughput − obtained throughput)/(reference throughput) for each technology.Equation  is used For the fairness performance evaluation, each LTE UE or WiFi station generates random data rates, which are randomly picked between {50 kbps, 500 kbps, 1 Mbps, 2 Mbps, 4 Mbps}. Likewise the throughput performance evaluation, each randomly generated data rate remains constant for The table shows that the traditional optimal policy of the Q-learning based solution can only get the best action at a probability of 66.7%. This low performance occurs due to the dynamic nature of the environment. The ER Q-learning and RER Q-learning based solutions on the other hand have better performance leading to the best action at a probability of 86.3% and 89.4%, respectively. Even though there is a significant number of decisions where the second-best actions are executed by the optimal policy, the performance of the coexistence schemes is not highly degraded (as the second-best action can still lead to sufficient fairness and aggregated throughput).In cellular networks, broadband applications that employ licensed spectra are expanding and the demand for high-throughput services is expected to rise. As a result, licensed spectra are becoming increasingly scarce. LTE is expected to use the unlicensed band for some of its transmissions to alleviate the spectrum scarcity problem. To effectively use the unlicensed spectrum, however, a number of issues must be addressed. The most significant issue is establishing a harmonious coexistence with the WiFi networks that already exist on the unlicensed spectrum.Many coexistence strategies have been proposed to ensure that WiFi and LTE networks coexist together. Convolutional Neural Network (CNN) and Q-learning-based machine learning algorithms are mostly used in current coexistence methods to improve performance. Most existing coexistence systems take into account coordinated LTE and WiFi networks, in which a co-located LTE network picks its transmission time based on the quantity of WiFi traffic generated in its collision domain. The WiFi traffic demand is determined by a central coordinator which can communicate with co-located networks using a collaboration protocol to exchange status and requirements.Collaboration protocol-based information exchange to identify traffic status requires changes to the infrastructures of the co-located WiFi and LTE networks. Moreover, adopting this inter-technology collaboration protocol increases the coexistence schemes’ complexity, traffic overhead, and reaction time. As a result, we present a coexistence scheme that can operate in uncoordinated LTE and WiFi networks without the necessity for a collaboration protocol to exchange traffic status between the technologies. In the proposed scheme, the LTE-U deploys a WiFi saturation sensing model to estimate the traffic demand of co-located WiFi networks. In particular, RER and ER Q-learning based non-cooperative coexistence schemes are proposed and evaluated. We also compare the performance of the proposed schemes with each other and with other non-coordinated schemes which use rule-based and Q-learning based approaches. The comparison is carried out in terms of model complexity, convergence, the accuracy of best action selection, fairness, and throughput. Our results show that the proposed RER Q-learning scheme converges faster than the ER Q-learning scheme and gives better aggregated throughput and fairness performance as compared to the rule-based and Q-learning schemes.T, where T is optimally selected considering computational complexity and the traffic dynamics of the co-located networks estimated by a network traffic prediction model.In the future, a system that integrates the traffic prediction model and the proposed coexistence scheme can be developed to enhance the overall system performance. By integrating the traffic prediction model with the proposed coexistence scheme, the optimal execution frequency of the coexistence decision can be determined. In other words, the coexistence scheme is executed every period of time


In nutrition science, malnutrition is a state of imbalance between intake and the needs of the organism, leading to metabolic changes, impaired physiological functions, and weight loss. Regardless of the countless efforts being taken and researched for years, the burden of malnutrition is still alarming and considered a significant agent of mortality across the globe. Around 45% of 12 million children deaths (0–5 years old) annually are due to malnutrition, mostly from developing countries. Malnutrition develops associations with other infections and leads to substantial clinical outcomes, such as mortality, more visits to hospitals, poor quality of life and physical frailty, and socioeconomic issues. Here, in this review, we intend to provide an overview of the current burden, underlying risk factors, and co-existence of malnutrition and other infections, such as cancer. Following the rising concern of the vicious interplay of malnutrition and other medical illnesses, we believed that this narrative review would highlight the need to re-make and re-define the future strategies by giving comprehensive and sustainable programs to alleviate poverty and combat the rampant infectious diseases and those nutrition-related health problems. Furthermore, the study also raises the concern for hospitalized malnourished cancer patients as it is crucially important to knowledge the caregiver healthcare staff for early interventions of providing nutritional support to delay or prevent the onset of malnutrition. For in nutrition sciences, malnutrition is the state of the disproportion of food intake and energy requirement of an organism, which leads to impaired metabolism, body dysfunction, and diminishing of body mass , 2. MalnPresently, malnutrition is considered as a significant agent of mortality across the globe, and it is urgently needed to design the strategies to cope with the challenges about the prevention of malnutrition: common nomenclature, screening methods, accurately reported data, and defined distinct silos to address the malnutrition by bridging the existing gaps –21. The Relevant articles were selected against the specific keywords as per outlines of study by using the different search databases, PubMed, Google, Google Scholar, and Research Gate. In addition to the relevancy of the title and abstracts, for strong conceptual and logical understanding, more recent studies were selected for inclusion based on the year of publication, which was between 2016 and 2021. Though, little older publications and some news agencies, government data reports were also cited to strengthen the background of the subject. All selected articles have been cited accordingly.Nowadays, malnutrition is considered as a complex problem to solve and has been recognized as a world health crisis: a major risk factor for various medical illnesses. Thanks to the vision “Sustainable Development Goals (SDG) 2030, since 1990, there has been a 50% reduction in under-5 years” children deaths by malnutrition . To addrAny disease, whether chronic or acute, is a significant risk factor of malnutrition in third world countries and could cause or exaggerate malnutrition in some respects. As aforementioned, malnutrition is considered as a complex problem to solve due to the contribution of several driving factors in its development, so there is no way to list out the factors that may contribute to malnutrition in the population . With thIn elderly persons, malnutrition is a serious threat that leads to disability, institutionalization, and more exposure to illnesses. In these patients, the underlying cause of being malnourished is anorexia of aging: an individual's food intake decreases with age . ClinicaProtein energy malnutrition is a range of pathological conditions arising out of coincident of lack of protein and energy in varying proportions, most frequently seen in infants, and young children and usually associated with infections . It is uAlthough, deficiencies in non-nutrients, such as vitamins, amino acids, iron, and even micronutrients, can affect the normal functioning of the body, however, PEM is considered to be associated with delayed recovery from disease, poorer quality of life, and increased risk of morbidity and mortality . It makeA vicious relationship between malnutrition and infectious diseases has been long established. Malnutrition causes an exponential decrease in the chances of survival per exposure to any fatal infectious disease while non-fatal and subclinical infections may impair the process of growth . PublishIt is a need of time to add important nuance to establish facts and understandings about the complex associations of chronic diseases, such as diarrhea, respiratory infection, HIV, malaria, measles, and the most hateful disease cancer, with malnutrition for the specific therapeutic approaches to break these associations and treat the diseases.The vicious cycle between malnutrition and diarrhea is being explored for decades of years. According to a statistic report, around 1.5 million children die every year of diarrhea . It has American Journal of Clinical Nutrition, with the objective to find whether undernutrition is an underlying cause of children deaths associated with diarrhea, pneumonia, malaria, and measles, reveals that overall 52.5% of all deaths in young children were attributable to undernutrition varying from 44.8% for death by measles and 60.7% for death because of diarrhea  and the Henan Provincial Science and Technology Research Project (No. 212102310147).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.


Ablation of the globus pallidus internus  is an effective surgical intervention for dystonia. However, the current literature on the efficacy and safety of pallidotomy for dystonia is derived only from single‐case reports and small cohort studies.We retrospectively analyzed patients with primary dystonia who underwent pallidotomy at our institution between 2014 and 2019. Neurological conditions were evaluated using the Burke‐Fahn‐Marsden Dystonia Rating Scale . We evaluated the total BFMDRS score and each subitem score (nine body regions) in the patients who underwent unilateral and bilateral pallidotomy before surgery and at last available follow‐up. Moreover, postoperative complications were analyzed.p < 0.001). Furthermore, there was a significant and independent improvement in all midline BFMDRS subitems, including eyes, mouth, speech/swallow, and neck, after unilateral pallidotomy. Bilateral pallidotomy significantly improved the total BFMDRS score from 14.6 ± 10.2 preoperatively to 3.8 ± 8.2 at last available follow‐up . However, bilateral pallidotomy induced medically refractory parkinsonism  in five patients, dysarthria in three patients, and dysphagia in one patient.We found that 69 and 20 patients underwent unilateral and bilateral pallidotomy respectively. The mean age at dystonia onset was 40.4 ± 15.2 years. The mean clinical follow‐up period was 17.2 ± 11.6 months. Unilateral pallidotomy significantly improved the total BFMDRS score from 11.2 ± 14.7 preoperatively to 5.4 ± 7.6 at last available follow‐up (51.8% improvement, Unilateral radiofrequency pallidotomy remains a viable treatment option for patients with some forms of dystonia. Bilateral pallidotomy cannot be recommended due to unacceptably high complication rates. Dystonia is a common movement disorder that is characterized by sustained and repetitive movements or abnormal postures.Deep brain stimulation (DBS) of the globus pallidus internus (GPi) is the most applied surgical treatment for dystonia and has been validated by numerous clinical studies.GPi ablation  is an alternative surgical option for dystonia that is rarely used in most developed countries where DBS is widely available. The small cohort studies report the efficacies of pallidotomy and GPi‐DBS for dystonia are comparable.In this study, we aimed to perform a detailed assessment of the efficacy and safety of pallidotomy, including bilateral procedures. To the best of our knowledge, this is the largest study to assess the efficacy and safety of pallidotomy for dystonia.This retrospective study was approved by the ethics committee of our institution, which waived the requirement for informed patient consent given the observational nature of this study.Between 2014 and 2019, 95 consecutive patients with primary dystonia underwent unilateral or bilateral pallidotomy at our institution. The inclusion criteria were a minimum of 1‐year post‐symptom onset and a previous diagnosis of primary dystonia by a neurologist or neurosurgeon specialized in movement disorders. The exclusion criteria were secondary dystonia  and having undergone previous neurosurgical treatments, including other intracranial surgeries such as DBS and selective peripheral denervation. We excluded six patients  and included 89 patients in the final analysis.Under local anesthesia, a Leksell stereotactic frame  was fixed onto the patient’s skull. T1‐weighted axial and T2‐weighted coronal magnetic resonance imaging (MRI) and computed tomography scans were used to determine the stereotactic GPi target. The tentative target was a location 2–4 mm below, 18.5–22 mm lateral, and 2 mm anterior to the midpoint of the anterior and posterior commissures. A radiofrequency probe (1.0 mm diameter with a 4.0 mm uninsulated tip) and Leksell neurogenerator were used for stimulation and coagulation. Surgery was performed under local anesthesia without microelectrode recording. After ensuring the absence of capsular response and phosphene through macrostimulation via the electrode , coagulation was performed at 70°C for 30–40 sec. Immediate post‐surgery MRI was used to examine for coagulated lesions and hemorrhagic complications. Detailed information regarding the pallidotomy procedure has been described in previous articles.All patients except for two patients who underwent simultaneous bilateral pallidotomy, underwent initial unilateral pallidotomy. The surgery site for unilateral pallidotomy was decided based on the dystonia distribution. Generally, the contralateral hemisphere to the most affected side by dystonia was chosen as the surgical site. For torticollis, the contralateral hemisphere to the direction of head deviation was chosen as the surgical side, for example, right‐side rotation–left pallidotomy or left‐side rotation–right pallidotomy. For laterocollis, the contralateral side to the direction of neck tilting was chosen as the surgical side, for example, right laterocollis–left pallidotomy. Based on the patients’ discretion, they underwent contralateral side surgery or continued follow‐up at 6 months after unilateral pallidotomy. Patients seeking further dystonia improvement were scheduled to undergo contralateral side pallidotomy. There was an interval of ≥6 months between the first and second surgery.We used the Burke‐Fahn‐Marsden Dystonia Rating Scale (BFMDRS) to evaluate the patients’ dystonic conditions preoperatively and at last available follow‐up. The BFMDRS, which has a score that ranges between 0 (minimum/best) and 120 (maximum/worst), is used to evaluate dystonia severity in 9 body regions, including the eyes (0–8), mouth (0–8), speech/swallow (0–16), neck (0–8), trunk (0–16), right arm (0–16), left arm (0–16), right leg (0–16), and left leg (0–16). We evaluated the total BFMDRS score and each subitem score in patients who underwent unilateral and bilateral pallidotomy. Regarding unilateral pallidotomy evaluation, the subitems for the arms and legs were divided into the contralateral or ipsilateral surgery side. Moreover, we analyzed postoperative complications.p‐value <0.05.We performed a Shapiro–Wilk normality test to confirm data normality. Since the data were non‐normally distributed, the Wilcoxon signed‐rank test was used to compare the preoperative total and subitem BFMDRS scores with those at last available follow‐up. All statistical analyses were performed using the SPSS 25.0 . All statistical tests were two‐tailed and statistical significance was set at Table p < 0.001). Arm and leg dystonia in the contralateral side was significantly improved compared with those in the ipsilateral side. There was a significant improvement in all midline BFMDRS subitems, including eyes, mouth, speech/swallow, and neck, after unilateral pallidotomy at the last available follow‐up. Table There were 29 and 40 patients who underwent right‐side and left‐side pallidotomy, respectively Fig. . The meap < 0.001). Regarding midline dystonia distribution, all subitems other than “trunk,” were significantly improved after bilateral pallidotomy. Table There were 2 and 18 patients who underwent simultaneous and staged bilateral pallidotomy, respectively Fig. . The meaTable Parkinsonism was observed in five (25%) and 2 (2.9%) patients who underwent bilateral and unilateral pallidotomy. Those five patients who underwent bilateral pallidotomy presented with postural instability and gait disturbance; among them, two cases were severe with assistance from another person being required. During the follow‐up period, postural instability and gait disturbance did not improve in all patients. Micrographia was observed in two and three patients who underwent unilateral and bilateral pallidotomy respectively. Oral levodopa intake was attempted but was not effective for all patients. There was a postoperative spontaneous improvement of micrographia for 3–6 months. However, postural instability and gait disturbance persisted in all patients at the last available follow‐up evaluation. Persistent dysarthria and dysphagia were observed in four and one patient respectively. Among four patients presenting permanent dysarthria, one patient had concomitant parkinsonism .This study has three major findings. First, unilateral pallidotomy achieved 51.8% improvement of the total BFMDRS score, which was consistent with the findings of open‐label studies on bilateral GPi‐DBS for dystonia (45%–51% BFMDRS score improvement).Pallidotomy for dystonia was commonly used until the 1990s.Previous studies on bilateral pallidotomy for dystonia have reported excellent results with a BFMDRS score improvement of 50%–90%, which is consistent with our findings (74.0% improvement of BFMDRS).There have been few reports of unilateral pallidotomy for dystonia.This is the first study to report complications of parkinsonism induced by pallidotomy for dystonia. Recent studies have reported GPi‐DBS‐induced parkinsonism, including bradykinesia, postural instability, and gait disturbance.Previous studies have indicated that bilateral pallidotomy has a much higher risk than unilateral pallidotomy. In our study, unilateral pallidotomy did not induce dysarthria and dysphagia. One patient who underwent bilateral pallidotomy presented with severe dysarthria and dysphagia without parkinsonism. Except for parkinsonism, the remaining adverse events were very mild, which did not interfere with daily activities. The adverse events associated with unilateral pallidotomy were considered acceptable. However, there was an unacceptably high incidence of postural instability and gait disturbance, which was refractory to levodopa and unpredictable, after bilateral pallidotomy. Regarding avoiding parkinsonism associated with bilateral pallidal interventions, we recently reported that the pallidothalamic tract could be a possible treatment target for dystonia.Cerebral infarction in the posterior limb of internal capsule (PLIC) adjacent to the pallidotomy lesion developed after a few postoperative weeks in four (4.5%) patients, which caused temporary hemifacial palsy or hemiparesis. There have been few reports of delayed infarction associated with pallidotomy, including 3 (6%) cases reported by Lim et al after radiofrequency pallidotomy.This study has several limitations. First, open‐label and retrospective studies have observer and patient bias. Subtle neurological deficits may be missed given the lack of preoperative unified evaluation procedures. Second, given the limited number of patients who underwent bilateral pallidotomy, we could not determine the effect of bilateral pallidotomy on focal affected regions other than the neck. Third, recommended dystonia rating scales for each focal dystonia  was not performed in this study.In conclusion, we found that unilateral and bilateral pallidotomy provided 51.8% and 74.0% improvement, respectively, in the total BFMDRS score. Bilateral pallidotomy often induced medically refractory parkinsonism, which are newly reported adverse effects of bilateral pallidotomy. The advent of MRI‐guided focused ultrasound procedures, which do not require skin incision, has prompted the reappraisal of ablative treatments. Unilateral radiofrequency pallidotomy remains a viable treatment option for patients with some forms of dystonia. Bilateral pallidotomy cannot be recommended due to unacceptably high complication rates.SH and TT conceived the study. SH, AF, NT and TT collected data. SH and TT designed the study. AF performed the statistical analysis. SH analyzed the data. SH and TT interpreted the data. SH performed literature search and made figures.The authors declare no conflicts of interest.The deidentified participant data will be available for a qualified investigator. Please email the corresponding author for more information.


Video-delivered care is a rapidly emerging area with potential to transform assessment and treatment strategies. The coronavirus (COVID-19) pandemic has accelerated these changes. Limited evidence exists for experiences of video care in secondary mental health services. We aimed to assess the acceptability of video care in mental health clinical practice during COVID-19.Structured questionnaires were developed with the help of patients and clinicians. The patient experience questionnaire was built into video sessions and completed online, using the Attend Anywhere (AA) platform from July 2020 to March 2021. A Trust-wide clinician views and experiences survey was conducted from July 2020 to October 2020. Descriptive analysis was performed using SPSS (version 27.0).Of 1,296 patients who completed the online feedback, the majority provided positive feedback for all aspects of video care. Most patients felt their needs were met (90%) and were supported (93%) during the video call. Positive experiences were informed by clinicians’ communication skills. For future appointments, just over half (51.7%) of patients preferred using video calls, followed by face-to-face (33%). Future video preference was informed by reasons reducing social anxiety and practical aspects such as child/carer needs, physical disability and travel.Of 252 clinicians completing the survey, 161 (64.7%) had used video for remote care delivery. Clinicians also provided positive feedback, with Microsoft-teams as the preferred platform. Most clinicians felt the therapeutic relationship (76.4%) and privacy (78.7%) were maintained using video. While 73% felt there were no safeguarding issues that impacted adversely, 30% felt that care quality was affected, and (69.9%) reported limited visual cues for video calls. Most clinicians (73%) felt confident about clinical decision-making remotely, though there were areas where clinicians felt less confident, such as assessing patients’ appearance and behaviour. Additionally, compared with face-to-face, video consultations seemed to be effective for social anxiety, but less so for Autism spectrum disorders, and with no perceived difference for depression or self harm. For future, more clinicians preferred face-to-face (40.1%) than video care (36.1%).Mental health care delivered remotely via video is experienced positively by patients and clinicians alike. However, clinicians felt that quality of care is impacted, and additional remote clinical skills training may be beneficial. Going forward, there is acceptability for the use of video care in routine mental health practice for certain mental health presentations.


Background. Evolutionary theory-driven alcohol prevention programs for adolescents are lacking. This study introduced a binge drinking impression formation paradigm to test whether emphasizing sexual dysfunction induced by alcohol abuse lowers positive attitudes and expectancies related to binge drinking when compared with cognitive or long-term health consequences. Method. In a between-subjects experiment, 269 French high school students  watched professional-quality videos emphasizing sexual impotence (n = 60), cognitive impairment (n = 72), or long-term effects  induced by alcohol and then had to evaluate a drinking scene. We predicted that the video on impotence would be the most impactful when compared with the other videos. Results. Results showed that women evaluated the target as less attractive after viewing the cognitive video compared with the video on impotence. Men were more willing to play sports against the target after viewing the cognitive video, compared with the video on impotence. Conclusions. These results showed that evolutionary meaning might shape impressions formed by participants depending on the context. This study calls for further replications using the same design and materials. Alcohol is a harmful substance per se and has a direct impact on many risky related behaviors  and the World Health Organization considers alcohol production and consumption as highly relevant for the 2030 Agenda for Sustainable Development . Individd = 0.14 to 0.68 with massive heterogeneity, see [Past decades have seen the emergence of numerous social-psychological theories of binge drinking. Oei and Morawska , for insity, see ).In the present study, we used an evolutionary framework to differentiate the risks at play in binge drinking videos. Costly signaling theory ,13 propoThe short- and long-term risks involved in binge drinking made our hypothesis compete with another one from a behavioral economic perspective. Tymula and colleagues  used an In the present study, we developed professional videos that were shown to participants during different classes at a French high school in Grenoble (France). We predicted that the video on impotence would be the most impactful when compared with the other videos. Moreover, we expected that men would be less willing to be a teammate of the male target presented in the video after viewing the video on impotence, and would prefer to play against him in a sports competition when compared with the other video . The present project aims at developing specific binge drinking videos differing only on the type of risks to precisely manipulate the signal sent. The objective is to test whether cues of sexual impotence deteriorate more binge drinking impression formations when compared to cues on cognitive deficits or long-term diseases.This is a randomized single-blind order study design in 13 different classes (eight 10th grade and five 11th grade classes) from a high school in Grenoble, France. A total of 269 participants were recruited during class sessions in Spring 2017. Groups of 60, 72, and 68 participants were made by watching the videos on impotence, cognitive impairment, and long-term risks, respectively. Additionally, a control group of 69 students did not view any of the videos (see f = 0.23).Because of study constraints, a sensitivity power analysis was used to determine the minimal detectable effect (MDE). Giving an α level at 5%, 90% of statistical power, 269 participants and 4 groups (the three video and the control group), the MDE for a one-way ANOVA was of medium size (Cohen’s https://osf.io/dhk7j/ accessed on 26 May 2021) with the help of French high school students and a professional video company (B Production), focusing on a young man binge drinking during a social gathering. The main structure of each video was the same, with the primary difference being the consequence of binge drinking experienced by the young man. In the video on cognitive impairment, the adolescent woke up the day after a party and experienced memory loss/lack of attention, and therefore could not properly study for his exam, which was followed by a short message on the cognitive risks involved in binge drinking. In the video on impotence, the young man was not able to have sex with his girlfriend, which was followed by a short message on the connection between sexual impotence and binge drinking. These two videos constituted short-term risks as they generally occur within minutes or hours after a binge drinking episode whereas the third condition is a problem that occurs later in life. Finally, the video on long-term risks depicted him years later, in a hospital bed, after having developed a terminal disease, followed by a short message on the effects of alcohol toxicity on the heart and other vital organs.During Spring 2016, we made three different videos  by his side, and men were shown a highly intoxicated man (Nicolas) playing a drinking game. Both genders were asked to picture themselves in the situation (“You would not like to be in the same situation as Nicolas”). Women were asked to describe Nicolas’s features , whereas men evaluated Nicolas’s competitiveness used as two different DVs .Participants were also asked standard sociodemographic questions , drinking habits (frequency and intensity), and understanding of the experiments  potentially induced by excessive alcohol consumption. During a class session, participants were told that the university was running a program aimed at preventing alcohol abuse among youth. The study was simple blind as participants were not aware of which group was the experimental one, but researchers present were aware of the different hypotheses and conditions. To test our hypothesis that exposing students to sexual risks will have more impact on the impression formation of a binge drinker, we used contrast analysis to compare different conditions while controlling for the other one . This inWe also conducted a multilevel analysis with the student as level-1 and the class as level-2 analysis. Given the nature of our experimental design (exposure to messages was not randomized by students but by class), it was particularly appropriate to use a multilevel linear regression . The modp < 0.001). However, there were no significant differences on gender distribution (p = 0.17) or drinking intensity and frequency between the different experimental conditions (p’s > 0.10). Participants, on average, reported understanding very well the different spots , and no differences were observed between the understanding of the different spots (p = 0.49).Participants were first asked standard sociodemographic questions. We observed that participants in the long-term video group were slightly younger (15.36 years old) compared to the other groups  compared with the other videos  = 1.82, p = 0.07, ηa2 = 0.02; the effect did not reach significance when excluding women who never consumed alcohol, p = 0.16). When comparing the videos on clear, short-term risks with the video on long-term risks, women were less willing to be in Nicolas’s situation after viewing the clear risks videos  than the long-term ambiguous-risks video  = 2.07, p = 0.04, ηa2 = 0.02; the effect was no longer significant when excluding women who never used alcohol, p = 0.32). In a multilevel model, the effect was no longer significant, t(164) = 1.03, p = 0.30  than women who watched the other videos , t(168) = 2.43, p = 0.016, ηa2 = 0.02 , as was the case in the second contrast analysis, Nicolas was judged as even less attractive, t(164) = 2.39, p = 0.02, η2 = 0.04.). The effect stills hold in multilevel analysis, t(168) = 2.14, p = 0.03 . No differences were observed when women evaluated the reproductive success of the target (p = 0.78). On the other hand, neither the reproductive success nor the attractiveness measures were shown to be significant when comparing the short- and long-term risks videos . Howevep = 0.37). However, they did indicate they would be more willing to play against Nicolas after viewing the video on impotence  versus viewing videos on cognitive impairment  or long-term diseases , or being in the control group  = 2.47, p = 0.015, ηa2 = 0.04). However, our second comparison also came out significant, indicating participants would rather confront Nicolas in competition after viewing the video on cognitive impairment than the video on impotence, t(98) = 2.61, p = 0.01, ηa2 = 0.04 . Multilevel analysis indicates the effect hold when taking into account the level 2 unit “classrooms” (t(98) = 2.52, p = 0.01 and t(98) = 2.66, p = 0.009, respectively). Neither the teammate nor the competition measures were shown to be significant when comparing the short- and long-term risks videos . Women who viewed the video on impotence evaluated the target as less attractive than women who watched the other videos. This demonstrates that alcohol-led sexual powerlessness affects women’s impression formation of men’s attractiveness. However, the effect was too small to reach significance on reproductive success measures. A possible explanation was also the virtual character of the experiment , and more ecological design  may display an effect on reproductive success evaluation because of cues-dependent context .Teammates and competition (men only). Finally, when asked if they would want Nicolas as a teammate, male participants did not respond differently after viewing the video on impotence, compared with the other videos. However, they did report they would rather confront Nicolas in the competition after watching the video on cognitive impairment than the video on sexual impotence. This may indicate that in a school context, cognitive cues are more reliable and better signal one’s genetic quality than sexual capabilities. A field study comparing these measures would be a good way to explore how much binge drinking impression formation is context-dependent.As with any innovative program, our study had several limitations. First, we did not directly evaluate the participants’ evaluation of the main actor in the video for ethical and methodological reasons. We used an indirect impression formation task after participants watched a video, with our primary interest being to observe how risk signaling could impact the way individuals judge an intoxicated peer during a social gathering. Showing that specific signals impact impressions more than others could be of great help to the health and education community and contribute to a deeper understanding of binge drinking as a multi-faceted phenomenon. Further research should include impression tasks where the actors tolerate alcohol to further test Zahavian’s prediction.One possible limitation to our videos was that a man who was not tolerant to alcohol sent de facto signals of low genetic quality to participants. To that extent, possible floor-effects could have emerged, making the target (Nicolas) unfit, whatever the situation following the video may be. Receiving short- or long-term signals, sexual or not sexual, would in that case make no difference regarding the target’s capabilities. Conversely, measuring the transfer of those signals to a man with a high alcohol sensitivity may show higher variations between videos, leading to more subtle measurements.Another constraint is that our study did not directly test the impact of the different videos on future drinking intentions. It would be particularly useful to realize longer videos and evaluate whether exposure to such messages could help to decrease binge drinking frequency and intensity compared to other effective brief interventions such as theTertiary Health Research Intervention Via Email (THRIVE), the AlcoholEDU programs , and mesIn addition, the number of participants who never drank alcohol was relatively high compared to the general population, and some findings were no longer significant when excluding them. This calls for a better understanding of the difference between drinkers’ and nondrinkers’ impression formation. Meta-analysis on brief interventions indicates modest but positive effects on the reduction of both alcohol and other drug use . Further studies should explore the cognitive mechanism in the impression formation of binge drinking and other drugs to optimize the use of brief alcohol intervention.The main objective of this study was to create evolutionary-based binge drinking videos to test whether different signals linked to alcohol excessive consumption might influence impression formation of a binge drinker. While some data confirmed our expectations, others did not. Women did evaluate as less attractive the binge drinker victim of sexual impotence compared to other groups, but this did not influence their evaluation of his reproduction success. On the other hand, male participants tend to be more confrontational against the actor with cognitive deficit than the sexually powerless one. These data suggest that binge drinking impression formation may also be context-dependent and that health information presented in a classroom does not probably encompass all social cues that are present during a drinking social event . Collect


This study examined the effect of the handball warm-up program (HWP) on dynamic balance among elite adolescent handball players. In this case, 24 handball players were randomly assigned into experimental warm-up and control groups. The HWP was performed over 8 weeks (3 times per week). Dynamic balance before and after the intervention training programs were measured by the Biodex Balance System (BBS) and Y Balance test for the dominant (DL) and non-dominant legs (non-DL). After HWP training, BBS scores significantly improved in the overall (OSI) (30.4% and 31.1%), anterior-posterior (APSI) (44.6% and 35.2%), and medial-lateral stability indexes (MLSI) (38.8% and 43%) for both DL and non-DL. Post-training, the Y Balance test exhibited significant improvements in OSI (13.2% and 10.6%), anterior (17.2% and 12.6%), posteriolateral (12.8% and 11.3%), and posteriomedial stability indexes (9.2% and 7.9%) with DL and non-DL, respectively. In conclusion, dynamic balance improved overall after eight weeks of the HWP, with this improvement significantly greater with the trained versus the control group. Optimal balance during handball is an important factor to ensure coordinated and efficient movements and it is speculated that improved balance could positively impact injury prevention. Hence, the HWP program can be used as an alternative to a conventional warm-up program. Handball commenced in 1946 and is presently composed of almost 170 members of the International Handball Federation including 795,000 handball teams and an estimated 25 million players worldwide . The injOliano and their colleagues  measuredThe role of balance is believed to be a critical component of neuromuscular control and as a modifiable risk factor contributes to limit mediolateral knee displacement and loading during dynamic activities. Balance is fundamental for people to cope with activities of daily living and to successfully engage in sport training such as; running and jumping . BalanceThe Handball warm-up program (HWP) is a multifaceted program and is a modified version of the FIFA 11+ warm-up injury prevention program based on the specific skills and demands of handball. HWP replaces the regular warm-up before technical and tactical drills. HWP consists of three parts included three levels of increasing difficulty including strength, balance, agility and jumping exercises. HWP also consists of a novel set of advanced running exercises that make it better suited as a comprehensive warm-up program for handball training and matches. In addition, the HWP is a feasible and easy program that can be incorporated into a small space. To date, little research exists quantifying balance measures as potential risk factors for handball players after multifaceted warm-up program. It is unclear whether a structured warm-up intervention program can enhance the dynamic balance among elite handball players. Hence, the aim of this study was to investigate the effect of the HWP on dynamic balance among elite, adolescent handball players. The first hypothesis was that implementing the HWP would improve dynamic balance scores in both DL and NDL of elite handball players. The second hypothesis was that implementing comprehensive HWP can enhance dynamic balance in both sagittal (anterior-posterior) and frontal  planes.This experimental intervention study involved participants recruited among handball players who participated in the Iranian national league (with at least 5 years experiences). The 24 male handball players were randomly and equally divided into experimental (HWP: n = 12) and control (n = 12) groups . WrittenThe HWP is a modified version of the FIFA 11+ warm-up injury prevention program specific to handball. HWP replaces the regular warm-up before technical and tactical drills. HWP consisted of three parts. The initial component is running exercises combined with dynamic stretching, lunges, walking, crawling side-to-side run, and smooth swaying carioca exercises with a partner. This component is performed in the width of a handball pitch with 5 cones, 4 m apart over 6 min (part I). The second part consisted of 6 exercises having three levels of increasing difficulty included strength, balance, agility, and jumping exercises (part II). The final component included advanced running exercises such as cutting and bounding with sudden changes in direction (part III) . We encoWe asked the control group to continue with their regular warm-up training during the season, which typically included running across the pitch and static stretching. In addition, the control group was promised that if the HWP was effective, they would receive the same program in eight weeks.The Biodex Balance SD System (BBS)  was used to measure dynamic single-leg balance. The three measured postural stability indices were overall stability index (OSI), medial-lateral stability index (MLSI) and anterior-posterior stability index (APSI). These indices indicated the standard deviation that assesses the sway displacement from the origin point of the platform. The foot displacement for the medial-lateral axes along the horizontal line as an x-direction, and the anterior-posterior axes along the vertical line as y-direction were evaluated as MLSI and APSI, respectively. With the integration of tilt degree in the anterior-posterior and medial-lateral direction, the OSI was calculated. All stability indices were recorded in degrees. Cachupe et al. (2001) showed the reliability measure across 3 test evaluations as 0.83 (OSI), 0.81 (MLSI) and 0.73 (APSI) .Participants were asked to stand stationary on the BBS platform with their arms crossed over their chest. They were required to maintain their visual focus on the liquid crystal display. Participants were instructed to stand with the dominant leg  as the supporting leg and the contralateral, non-dominant, leg was flexed at 90° . The fooThe highly reliable (ICC ≤ 0.96)  Y Balancp > 0.05) and variance among groups was homogenous as assessed with the Shapiro-Wilk test and Levene’s test, respectively. The Bonferroni post-hoc test was conducted to detect the significant differences between groups for each test. The effect sizes were tested using partial eta (pη2) squared  [A 3-way (2 groups vs. 2 times vs. 2 legs) mixed model repeated measures ANOVA  was employed to compare the variables between groups , times , and legs. Data distribution was normally distributed ( effect) . A signiParticipant characteristics and demographic factors of participants were presented in 1,22 =20.88, p < 0.0001, pη2 = 0.49), APSI  and MLSI . The Bonferroni post-hoc test showed significant pre- to post-test improvements with the HWP group for both the DL and non-DL in OSI (30.4% and 31.1%) APSI (44.6% and 35.2%), and MLSI (38.8% and 43%) , APSI  and MLSI . A significant main time effect (data combined for HWP and control) demonstrated increases from pre- to post-test for OSI , APSI  and MLSI .The results showed significant interactions between group and time with OSI (Fand 43%) . Howeverand 43%) . There w1,22 = 208.86, p < 0.0001, pη2 = 0.90), anterior , PLSI , and PMSI . The Bonferroni post-hoc test showed significant pre- to post-test increases in the HWP group in OSI, anterior, PLSI, and PMSI , anterior , PLSI , but no significant difference in PMSI . The results did reveal significant improvements  between pre- and post-tests with the Y balance test for OSI  by 13.2% and 10.6%, anterior  by 17.2% and 12.6%, PLSI  by 12.8% and 11.3%, and PMSI  by 9.2% and 7.9% in DL and non-DL, respectively.The results showed significant interactions between group and time with the Y-test in OSI , APSI , but no significant differences in MLSI . Post-hoc tests showed significant increases with the DL in HWP group for APSI (p = 0.007) and with the control group with the Non-DL for OSI (p < 0.0001) as well as with the control DL for APSI (p < 0.0001). No significant difference in HWP group in OSI (p = 0.950). For Y balance test, results showed no significant main effect between legs in OSI , anterior , PLSI  and PMSI .For the BBS dynamic single leg balance test, there were significant main effect between legs in OSI  can enhance balance in adolescent handball players. Balance during sport activities is an important factor for sport injury prevention. A decline in balance is seen following ankle and knee injuries , so it iHWP is a multifaceted exercise program, which includes dynamic stretching, strength, postural stability, plyometric, and balance components. Warm-up programs fundamentally include mild or moderate exercise types that are geared toward enhancing skill performance ,24,25 byOlmsted et al. (2003) found that the supporting leg requires ankle dorsiflexion, knee, and hip flexion with an adequate range of motion, strength, proprioception, and neuromuscular control to perform the Y dynamic balance test . Earl anThe result showed dynamic balance improvements in both anterior-posterior and medial- lateral directions. The HWP is multifactorial program, which included sagittal plane exercises as well as exercises such as single leg jump and cutting exercises, which may have contributed to balance improvements in the medial-lateral direction. Based on the concept of training specificity , traininThe results showed no significant pre-test to post-test differences between legs with the dynamic balance tests after an 8-week HWP. The results mainly suggests that a functional symmetry exists between the dominant and non-dominant legs in dynamic balance tests among adolescent handball players. These findings are in agreement with some of the research that examined single-leg stance performance in healthy subjects ,39. The Finally, there are a few limitations need to be considered. First, the sample size was small and future studies need to consider a larger sample size. Second, the researchers could not conduct follow-up tests over an extended duration . More research is required to determine the persistence of the intervention protocols over an extended period.It can be concluded that HWP improved dynamic balance among elite, male adolescent, handball players with no significant difference between legs. The results of the present study support that handball training programs and competitions (control group) did not negatively affect the dynamic balance of adolescent players. It is suggested that the HWP could be implemented and incorporated into regular handball practice before starting technical and tactical drills. Future studies are also needed to investigate the impact of HWP on other physical performance skills and modifiable risk factors of handball players. The findings of this research can be helpful for coaches and trainers who can strategize training programs to improve balance of elite, adolescent handball players. Although not monitored in this research, higher levels of dynamic balance are associated with lower incidence of injuries ,12,13,21A handball-specific multifaceted warm-up program (HWP) which combines strength, neuromuscular control, balance and activity specific motion patterns without using special equipment can be incorporated instead of conventional warm-up program to improve dynamic balance among adolescent handball players. Playing handball did not negatively affect the dynamic balance of adolescent players.


The Yale New Haven Hospital Adler Geriatric Assessment Center is an outpatient consultative service that provides comprehensive assessment of older adults. As elsewhere, at Adler the COVID crisis necessitated a rapid shift in mode of care following a total cessation of in-person visits from late March 2020 to the end of May 2020. While our patients initially preferred telephone visits, video visits as a proportion of total scheduled increased from an average of 6% in the last full week of March to 24% in the last week in May possibly indicating increasing familiarity and comfort with the technology during that time. In addition, while video appointments as a proportion of total scheduled dropped rapidly in June 2020 as face-to-face appointments were reintroduced, we found a steady increase in the proportion of video visits from 3% in the first week of July 2020 to 7% in the second week of February 2021. To test for significance, we ran logistic regression models modelling the dichotomous video-appointment variable as the outcome and week and day of week as continuous variables. We found there was a significant increase in the proportion of appointments delivered over video both during the time when no face-to-face video appointments were allowed  and later in the pandemic . Durbin-Watson statistics were run to ensure that autocorrelation could be ignored. Sensitivity analyses limiting the sample to those with non-cancelled appointments gave similar results. Future analyses will examine patient clinical and demographic characteristics that might influence these trends.


Human action recognition in videos has become a popular research area in artificial intelligence (AI) technology. In the past few years, this research has accelerated in areas such as sports, daily activities, kitchen activities, etc., due to developments in the benchmarks proposed for human action recognition datasets in these areas. However, there is little research in the benchmarking datasets for human activity recognition in educational environments. Therefore, we developed a dataset of teacher and student activities to expand the research in the education domain. This paper proposes a new dataset, called EduNet, for a novel approach towards developing human action recognition datasets in classroom environments. EduNet has 20 action classes, containing around 7851 manually annotated clips extracted from YouTube videos, and recorded in an actual classroom environment. Each action category has a minimum of 200 clips, and the total duration is approximately 12 h. To the best of our knowledge, EduNet is the first dataset specially prepared for classroom monitoring for both teacher and student activities. It is also a challenging dataset of actions as it has many clips (and due to the unconstrained nature of the clips). We compared the performance of the EduNet dataset with benchmark video datasets UCF101 and HMDB51 on a standard I3D-ResNet-50 model, which resulted in 72.3% accuracy. The development of a new benchmark dataset for the education domain will benefit future research concerning classroom monitoring systems. The EduNet dataset is a collection of classroom activities from 1 to 12 standard schools. AI topics include everything from video monitoring and surveillance to human activity recognition and behavioral analysis. There is increasing demand for the development of domain-specific datasets that perform deep research in specific fields. ImageNet, for example, is a largescale database that allows collecting and annotating a large number of image datasets , as it hAn emerging trend in AI research is detection and recognition in online videos or real-time camera feed. Nowadays, in schools, classrooms are equipped with CCTV cameras, indicating the possibility of automatically analyzing classroom activity. Therefore, the development of a new benchmark dataset for the education domain is needed; it will benefit related research concerning classroom monitoring systems. The lack of a dataset designed explicitly for student–teacher classroom activities is a significant obstacle in this research area. Video data generated from CCTV cameras installed in classrooms can utilize AI technology to automatically recognize human activity in live classroom environments.https://www.statista.com/statistics/259477/hours-of-video-uploaded-to-youtube-every-minute/, accessed on 19 August 2021), YouTube reported that over 500 h of video were uploaded to their servers every minute. It was observed that, despite the rapid growth in video data generation, computer vision research is lagging in regard to automatically recognizing human activities. Due to the lack of domain-specific video datasets, the research in this area has not advanced much. The majority of existing action recognition datasets consist of everyday activities of humans. Some specific human action recognition datasets, such as Sports-1M [One focus of the proposed dataset concerns the granularity levels of human activities. Every day, large amounts of video data are uploaded to online media platforms, and data captured from mobile and surveillance cameras accelerate the rate of video data generation. In a recent survey  ,13,14, sThere is a high demand for live camera feeds to recognize human actions, e.g., for surveillance purposes. However, there is a niche for action recognition in the classroom environment. The lack of such a dataset is problematic. Therefore, we propose the EduNet dataset, which consists of 7851 well-annotated video clips of student–teacher actions in classroom environments.Education is a fundamental right, and the quality education is equally important. In the era of AI, research into the education system can boost the quality of education by utilizing action recognition from video data. For example, a student dataset was prepared  to recogMoreover, the authors of  proposedEduNet, a classroom activity monitoring dataset, contains 20 unique action classes. Each class has a minimum of 200 clips extracted from YouTube videos and recorded from a Nikon DSLR D5600 camera  in a real classroom environment of 1 to 12 standard schools. The total number of video clips was 7851, approximately 12 h long. EduNet is the largest  dataset in the classroom environment to date, to the best of our knowledge, containing both student and teacher actions. Each clip is manually annotated and validated to ensure consistency. We conducted an experiment on the popular human action recognition model—Inflated 3D Convolutional Neural Network (I3D) . We compThe rest of the paper is organized as follows. The impact of AI in the education field is already gaining popularity, with the creation of several advanced applications. For example, one application could involve intelligent classroom monitoring, by analyzing the activities of students and teachers in the classroom environment, which is different from regular or daily routine activities. The majority of (popular) human action recognition datasets are available for individuals to research/analyze everyday activities. A specially designed dataset perfects the application. Therefore, we developed the EduNet dataset, used by the computer-vision algorithm, which, in the future, could efficiently use classroom activity monitoring (similar to applications).The primary objective of human activity recognition is to accurately describe human actions and their interactions from a previously unseen data sequence. It is often challenging to accurately recognize human activities from video data due to several problems, e.g., dynamic background and low-quality videos from cameras installed at a long-distance range, lighting, multi-subject interaction, and occlusion. Many applications, such as video surveillance, human–computer interaction, human behavior characterization, robotics, etc., require multiple activity recognition methods. Here actions may or may not be performed throughout the entire duration of the video. Technically, an action can be defined as a spatiotemporal sequence of human body movements, and videos are a collection of a set of images arranged a specific order. These image sequences are referred to as frames. Thus, the fundamental goal of action recognition is to process the input video clips to recognize the subsequent human action frames. Ideally, video data are a combination of spatial and temporal aspects. A spatial part are the “frames” extracted from the video clips and “temporal” involves the sequence of these frames at the time series. Real-time HAR application requires differentiating ambiguous actions, such as standing vs. bending, running vs. jogging, etc. These are complicated tasks and might need more than one frame’s information to identify them accurately.In simple words, benchmarking is one way to discover the best performance being achieved. In regard to benchmark video datasets for action recognition—these datasets are adequately prepared, annotated, validated, and proven to be accurate, when compared to contemporary datasets, to identify human actions. Many action recognition algorithms were top-ranked and achieved higher accuracy on these well-prepared datasets. That is why these datasets are termed benchmark datasets. A minimum of 26 human action video datasets  are avaiIn the past few years, deep learning techniques ,5,6,7 haIn our experiment, we chose the I3D model  to evaluWith rapid advancement in AI application development, building datasets are also considered immediate needs. AI algorithms heavily depend on standard and “up-to-the-mark” datasets; a successful model relies on benchmark datasets. Unfortunately, existing action recognition video datasets are cumbersome, with limitations such as lack of domain-specific dataset, fewer video clips under each class, and limited action categories.This section presents some standard human action recognition video datasets, which are popular in the computer vision research community and similar to the EduNet dataset. For example, the Hollywood dataset  is a colAnother popular sports video dataset publicly available is the sports-1M dataset , with abCharades  is a vidTo understand the actions and events in videos, the Moments in Time (MIT) dataset  was releThe Kinetics-400 dataset  focuses EduNet has a unique objective in recognizing teacher–student activities, to improve the quality of education, from the primary level to senior secondary, targeting rural and remote areas where quality education is just a term, and not at the ground level. EduNet provides the collection of classroom activities to analyze the active participation of both teachers and students in the actual classroom environment. Student–teacher actions in the classroom are considered essential events that translate into quality education.The development of any visual recognition dataset is challenging. It takes a long time to extract clips, annotate, validate, and set a benchmark dataset for computer vision algorithms. Any custom dataset  preparatWe prepared a textual list of 20 action categories with the primary objective to search the videos of listed actions on YouTube in the first phase. Additionally, we used WordNet  to find In the second phase, we recorded videos against each action category in the actual classroom environment. Since this research work is non-funded, we visited multiple primary and secondary schools in rural India (north) to record the classroom videos with consent from school officials. We extracted approximately 3000 video clips from the recorded videos. The camera used was Nikon DSRL 5600. All videos were sampled to a fixed HD resolution of 1280 × 720 pixels with a frame rate of 30 fps. Dataset classes are organized into two groups: teacher centric, student centric.We verified all videos retrieved from both sources and removed those not related to the activity of our interest. With the certainty that every video must have contained at least one label, all videos were played and clipped at fixed temporal length. For the action recognition algorithm, it needed to have data with exact name to the labels. However, it is not easy to find YouTube videos only containing scenes related to our activity class. For example, when searching for YouTube videos related to the query “hand raise,” results also included videos that contained students writing on textbooks. Therefore, we decided to manually cut the video clips from entire videos within those temporal boundaries where the activity was actually performed. To ensure quality, each clip was again validated by another skilled individual. Thus, we confirmed that each clip was associated with the ground truth activity label. Teacher centric labels:Explaining_the_subject, Hitting, Holding_Book, Holding_Mobile_Phone, Holding_Sticks, Sitting_on_Chair, Slapping, Walking_in_Classroom, Writing_On_Board.Student centric labels:Arguing, Clapping, Eating_in_Cassroom, Gossip, HandRaise, Reading_Book, Sitting_on_Desk, Sleeping, Standing, Talking, Writting_on_Textbook.The EduNet dataset contained 20 folders, each containing the clips of one action class. The name of each clip had the following format: X_Y.mp4.Where X is the folder name and Y is the clip number. All of the clips were in .mp4 format. For instance, HandRaise_005.mp4 corresponded to clip 5 of the action class hand raise. X is in the same order of capital and small letter in which the name of the action class is. The purpose behind this specific and easy naming convention is for the easiness of computer-vision researchers to fetch the class name by splitting the clip name. Thus, ‘_’ will split into two strings. The first string will be the action class name, and the second is the clip number.The train–test split procedure is used to estimate machine-learning algorithm performances to make predictions on data not used to train the model. Additionally, a validation dataset is a sample of datum held back from training the model to estimate the model’s skills, while tuning the model’s hyperparameters and evaluate the skills of the final-tuned model, when comparing or selecting between final models. Following the current principles of dataset division, we divided the samples of each class randomly into a training set, validation set, and test set, at a ratio of 80%, 10%, 10%, as shown in To design the EduNet as a standard dataset, we compared its primary characteristics with several action recognition video datasets in the specific domain. From Videos were downloaded by YouTube and recorded with a DSLR camera in the live classroom. All clips were cut using Adobe Premiere Pro into varying lengths, from a minimum of 3.5 s to a maximum of 12.7 s. Most of the clips are close to 10 s long and annotated manually. All clips have a fixed frame rate and resolution of 30 FPS and 1280 × 780, respectively. The videos were saved as .mp4 files. The audio was preserved for all 20 actions at 44.100 kHz. All clips are in color format.Recognition of scene-based activities of teachers and students: for example, “Explaining_the_subject” must have a background containing a blackboard. Then this activity must be detected as a teacher activity.Identify activity of teachers and students distinctly: Writing_on_notebook activity is related to student activity, whereas writing_on_board is associated with the teacher activity.Differentiating one activity from others: HandRaise activity performed by students must be distinguished from the Clapping activity, as the movement recognizes both by hands.Categorizing positive and negative actions of the classroom: Example -Holding_Mobile_Phone shows the negative action performed by the teacher, whereas Holding_Book shows the positive action. Multiple scopes of research on these datasets are listed below:To avoid vast computational complexity, it is always good to utilize transfer learning. Reference  used traMany researchers have successfully utilized the ImageNet pre-trained model for the image classification task. A fine-tuning experiment was conducted for this paper, for classroom action classification to follow the same principle. Fine-tuning also seems to be a good practice when you do not have the computing resources to train a model from scratch for your dataset. Since there is no similar dataset currently available, we used fine-tuning to validate our dataset. We prepared Annotation.txt to pass EduNet labels and data (video clips) for the pre-trained model.We provided baseline results on the EduNet dataset on NVIDIA GTX 1650 Ti single GPU for training and model building. For inferencing, we used NVIDIA GTX GeForce GPU and i7 CPU, 16 GB RAM machine. We initialized the models with I3D weights pre-trained on the Kinetics-400 dataset and trained our models with a stochastic gradient descent optimizer for 40 epochs and 100 iterations per epoch. We employed a batch size of eight on a single GPU.We experimented using a popular I3D  state-ofhttps://cv.gluon.ai/, accessed on 13 July 2021) was used for the experiment in this paper. We only excluded the top layer and replaced it with 20 classes instead of 400 output classes. Then, the network was trained as per the dataset split distribution  to differentiate these actions accurately. The development of a novel algorithm also focuses on identifying student and teacher scenes, to recognize actions, such as “Holding_Book” and “Reading_Book” efficiently.Because EduNet comprises of unconstrained videos downloaded from YouTube and recorded in an actual classroom environment, obstacles include poor lighting, cluttered backgrounds, and severe camera motion. Moreover, due to the change in teacher-centric and student-centric scenes, differentiating the actions of teachers and students, this task creates complexity because of the duality of work (in regard to action recognition) under the scene recognition. For computer vision researchers, the EduNet dataset will provide an opportunity to develop newer algorithms in the future, for intelligent action recognition in the classroom environment. It is assumed that this dataset will contribute, as a baseline, in the development of computer, vision-based education domain applications.