A workplace-based assessment (WBA) is used to assess learners’ competencies in their workplaces. Many workplace assessment tools are available and validated to assess various constructs. The implementation of workplace-based assessment requires proper training of the staff.This study aimed to explore the impact of staff training on WBA practices and evaluate the inter-rater reliability of these practices while using entrustment scales, performance descriptors, and personal judgment.A quasi-experimental study, in which the staff members of the orthopedic department were invited to participate in a training program on the use of entrustment scales and assessment descriptors within the WBA tools. As a response to the training, subjective judgment was replaced by entrustment scales and performance descriptors in a trauma course offered by the orthopedic department. The inter-rater reliability of the WBA was evaluated using various rating scales.The entrustment scales had higher inter-rater reliability of the assessment tools than performance descriptors and the personal judgment.The inter-rater reliability was highest when using entrustment scales for WBAs, which could indicate that the entrustment scales achieve good psychometric properties as regards consistency among different raters. Thus, they decrease the confounding effect of differences in assessors. They may also give a clearer image of the actual academic level of the learners. Workplace-based assessment (WBA) is an authentic assessment approach. It is used to assess the actual performance of a physician in healthcare settings in his/ her everyday work. It has been used for postgraduate assessment since 2005. Recently, it was included in the undergraduate assessment as well. Unlike the traditional clinical assessment methods, WBA is not a snapshot of a learner’s performance and competence in an artificial assessment setting. WBA tools are to be used multiple times during the training to monitor the development and advancement of learners in their actual daily working environment. Based on the first Miller’s pyramid, the WBA is concerned with assessments at the highest level of the pyramid (Does level). At the ‘Does’ level, the everyday performance of the learner as a physician is assessed. Various WBA tools are validated for use in assessment purposes in medicine and many specialties. For example, the mini clinical evaluation exercise (mini-CEX)  is used The first generation of WBA tools used performance descriptors that help the assessors to evaluate the performance of learners based on detailed performance criteria (rubrics). Recently, with the concept of entrusted professional activities, there was a move toward entrustment scales in the WBA tools. An entrustable professional activity is a key task in a training program that a learner can be trusted to perform in each healthcare context when the learner achieves competence. In addition to validity, reliability is an important aspect of the psychometric properties of any assessment tool. Reliability is concerned with consistency in assessment among raters and over time.  The reliEvaluations of the training activities help judge their value.  There arThe scoping review by Chan et al.  recommenAfter reviewing the gaps in the literature on the implementation of WBA tools, we aimed to answer the following questions:What is the impact of staff training on participants? Do the entrustment scales influence the inter-rater reliability of WBA tools?In the first phase, all staff members and teaching assistants in the orthopedic surgery department were invited to participate in an online training program on ‘WBA practices.’In the second phase, grades given by the assessors of 33 learners enrolled in the hybrid trauma b course in 2020 and 2021 and those of 32 learners enrolled in the traditional face-to-face design in 2018 and 2019 were studied.We carried out a quasi-experimental study in two phases. The first phase was staff training, during which we used Zoom, and there were 16 attendees. We adopted the experiential Kolb’s cycle  in our dA trainee in the surgical theatre is practicing fixation of femur fracture using intramedullary nailing. He is aware of the sterile techniques and how the results will assist in further management. However, he requires direction on patient positioning, land-marking, and the appropriate implant. You would have expected that at his level, he would know and be able to complete these technical portions of the procedure. assessment of learning’ to ‘assessment for learning.’ Examples of descriptive and judgmental expressions were explained. For example, if the student attended the clinic late, then the description in the feedback could be ‘he was late’ not ‘he was careless.’Participants were asked to rate the trainee in the scenario using the entrustment scales  11] and and11] aThe second phase was implementing what was learned in the workshop. The attendees (staff or orthopedics) were invited to use WBA tools with the entrustment scales to assess their learners in postgraduate courses offered by the Orthopedic and Traumatology Department to provide fairer assessments.All the attendees were invited to use the WBA tools with the entrustment scales and the performance descriptors within their program of assessment.The first opportunity to evaluate the level of impact of the training on changing the assessment practices in the orthopedic department was through the trauma B course. It is a 15-week course in the orthopedic master’s degree program. It exposes learners to common concepts in the management of trauma patients. It includes the following topics: Biomechanics of Fractures, Pathological and Stress Fractures, Peri-prosthetic Fractures, Osteoporotic Fractures, Injuries in Children, Compartmental Syndrome, Implant Failure, Poly-trauma Patients, Dislocations, Injuries in the Elderly, and Sports Injuries.The trauma B course was delivered in a traditional lecture-based, face-to-face clinical training format. The assessment was in the form of an end-of-course multiple choice question (MCQ) exam and a CBD by two assessors.In 2020, the course was changed to a hybrid format to provide more flexible distant lectures while maintaining the clinical training in a face-to-face format. A program of assessment was created for the hybrid course. It included continuous assessments in the form of weekly low-stake online assignments such as gaming, problem- and script-based activities, quizzes, and script-concordance tests. After the staff training, WBAs were used eight times all through the course. The high-stake assessment was in the form of MCQs, modified essay questions, and CBD by the end of the course. The written exam was built based on the test blueprint created for the course; however, this is out of the scope of evaluation in this manuscript.The used WBA tools were the mini clinical evaluation exercise (mini-CEX), DOPS, procedure-based assessment (PBA), mini-PAT, and CBD. Each of the previously mentioned tools was implemented twice during the course, by a single assessor each time, except for the CBD. The CBD was implemented only once during the course, by two assessors. Entrustment scales  were useThe study aims to evaluate the inter-rater reliability for the different practices in the WBA. The staff training was also evaluated reflecting on the Kirkpatrick evaluation model. In phase 1, group discussions with the attendees of the staff training activity were used to evaluate the training activity. Discussions that lasted for 15–20 minutes were held online by the end of the training. The attendees were invited to participate, and their comments were recorded.In phase 2, learners’ grades reported by different assessors while using WBA tools were used to calculate the inter-rater reliability.Assessing the impact of the staff training on the participants.Evaluation of the inter-rater reliability of the WBA tools using entrustment scales, performance descriptors (after the training), and experts’ judgment (without training).For qualitative data, the descriptive and interpretive approach for data analysis was adopted. Note-based analysis and memory-based analysis were used for transcription.Quantitative data were coded, checked for errors, and entered into Statistical Package for the Social Sciences Version 22.0 software for statistical analysis. Data were summarized and presented using the appropriate statistics. Descriptive statistics: Frequencies, percentages, means, standard deviations and medians were used to present the data. Quantitative variables were tested for normality using the Shapiro-Wilk test.In phase 1, a thematic analysis was carried out to analyze qualitative data. The following themes emerged from the participants’ discussion on their assessment practices and the impact of the staff training.By the end of the staff training, attendees were asked to describe their past practices in assessment. The emerged subthemes were personal diligence and accuracy. Many of them described the assessment practices as personal diligence. They declared that the assessment of learners was based on the personal judgment of the assessors without using any rubrics, and without undergoing training in the assessment practices.Some of them agreed that a single assessment point can’t give accurate significant data on the competency level of the learner.The entrustment scales were simple to use and uniformly interpreted by the assessors.During the staff training, there was a consensus on the grades given by the trainees while using the entrustment scales as compared to those given while using the performance descriptors and personal judgment. Then, during discussions, the subthemes of feasibility and training time were raised. Most staff members confirmed that the entrustment scales were used with no need for creating a frame of reference for the criteria of performance in each clinical skill. For example, one of the trainees mentioned that:Many of them considered that they required little training time to master the assessment using the entrustment scales.The documentation of feedback and the continuous assessment are demanding. We are overwhelmed with our duties in patient care.Time constraints and record storage were the main subthemes. Some of the staff members expressed their worries about the time constrains for them as clinicians to record written feedback, which they considered a demanding task. Most of them considered keeping these records of learners’ performances challenging. For example, one of the staff said:After exploring the satisfaction and perception of participants by the end of the staff training, its impact at level 4 of the Kirkpatric model was evaluated. At this level, the focus of the evaluation was on the results (did the change in the assessment practices of the staff after the training have a positive impact?). The previously asked question will be answered in phase 2 when evaluating the inter-rater reliability in different WBA assessment practicesIn phase 2, Moreover, On correlating the grades of students in the two points of assessment of PBA (PBA 1 and PBA 2), there was a strong significant association . Regarding DOPS 1 and 2, there was a strong significant association . Concerning mini-CEX 1 and 2, the correlation was strongly positive as well . On correlating the grades of the CBD rated by two different assessors for each student using the performance descriptors, there was a significant strong association . Regarding the CBD, rated by two assessors holistically and based on personal judgment, the association was moderate and positive .The reliability of each assessment was calculated using the Cronbach alpha. The Cronbach alpha of the two assessment points of mini-CEX was 0.81 while that of DOPS was 0.74. Additionally, the Cronbach alpha of the two assessment points of the PBA was 0.68. However, the Cronbach alpha of the ratings of the two assessors in the CBD while using the performance descriptors was 0.54. All the previously mentioned assessment points were implemented after undergoing the staff training. The Cronbach alpha of the ratings of the two assessors in the CBD exam while using personal judgment (before the staff training) was 0.27.Our findings revealed that staff training contributed to changing the behavior of the staff as regards using the entrustment scales WBA tools, which in turn improved the inter-rater reliability of assessment as compared to those using performance descriptors and those depending on subjective judgment. Additionally, the grades of the two different assessors in the WBA tools showed that using the entrustment scales had better associations between the grades given by the two assessors. This can be explained by the consistency of the entrustment scales that is more than that of the performance descriptors and personal judgment. During the staff training, it was clear that there was a consensus in the grades given by the assessors (trainees) using the entrustment scales. However, there was a discrepancy in the grades given by some of the staff while using the performance descriptors. The reason for this could be the different interpretations of the performance descriptors. On the contrary, the personal judgment without using a guiding rubric revealed more discrepancies between the assessors. This could be explained by the difference in the level of expertise among the assessors.Trainees mentioned that they did not need lengthy training to master the use of the entrustment scales. They might have perceived this as the performance descriptors needing a stage of discussion and a consensus on the standards of performance for each clinical competency.On exploring similar studies, the one by Holzhausen et al.  exploredThere is a paucity of available studies on the evaluation of the inter-rater reliability of WBA tools using different rating strategies. According to the findings of this study, the inter-rater reliability of the WBA using entrustment scales was higher than that of those using performance descriptors and those depending on subjective judgment.This study had some limitations. The study design we chose may not have been the best for this kind of study. A randomized controlled experimental design would have stronger evidence than the quasi-experimental design we used. Additionally, the number of participants in the staff training activity was limited because the study was carried out at a single clinical department as a first step. Future studies with larger samples involving multiple clinical departments could provide stronger evidence.By the end of the staff training, most of the participants showed positive reactions toward the use of entrustment scales within their WBA tools. Many agreed that they needed little time to master the use of the entrustment scales. They expressed their worries about the time constraints in providing written feedback. The majority of them considered the storage of WBA data challenging. In the second phase, the evaluation of the impact of the staff- training on the assessment practices using the Kirkpatrick evaluation model, the staff members were encouraged to implement what they had learned in the training about the use of entrustment scales in WBA tools. Finally, the WBA using the entrustment scales showed better inter-rater reliability than those using performance descriptors and those depending on subjective judgment.


In today’s data networks, the main protocol used to ensure reliable communications is the transmission control protocol (TCP). The TCP performance is largely determined by the used congestion control (CC) algorithm. TCP CC algorithms have evolved over the past three decades and a large number of CC algorithm variations have been developed to accommodate various network environments. The fifth-generation (5G) mobile network presents a new challenge for the implementation of the TCP CC mechanism, since networks will operate in environments with huge user device density and vast traffic flows. In contrast to the pre-5G networks that operate in the sub-6 GHz bands, the implementation of TCP CC algorithms in 5G mmWave communications will be further compromised with high variations in channel quality and susceptibility to blockages due to high penetration losses and atmospheric absorptions. These challenges will be particularly present in environments such as sensor networks and Internet of Things (IoT) applications. To alleviate these challenges, this paper provides an overview of the most popular single-flow and multy-flow TCP CC algorithms used in pre-5G networks. The related work on the previous examinations of TCP CC algorithm performance in 5G networks is further presented. A possible implementation of TCP CC algorithms is thoroughly analysed with respect to the specificities of 5G networks, such as the usage of high frequencies in the mmWave spectrum, the frequent horizontal and vertical handovers, the implementation of the 5G core network, the usage of beamforming and data buffering, the exploitation of edge computing, and the constantly transmitted always-on signals. Moreover, the capabilities of machine learning technique implementations for the improvement of TCPs CC performance have been presented last, with a discussion on future research opportunities that can contribute to the improvement of TCP CC implementation in 5G networks. This survey paper can serve as the basis for the development of novel solutions that will ensure the reliable implementation of TCP CC in different usage scenarios of 5G networks. For every new generation of mobile network, the demand for new services and use cases increases. Today’s trends of ubiquitous computing tend to incorporate technology into every device, changing the way that we perform our daily activities and do business. Accommodating these trends imposes implementation and operation challenges for mobile network operators. To satisfy future needs, the International Telecommunications Union–Radiocommunications Sector (ITU-R) has defined three main technology advancements that fifth-generation (5G) mobile networks should support  1]: ult: ult1]: The usage scenarios characterised as eMBB aim to provide high data rates, high traffic capacity, and high seamless mobility for both hotspots and wide-area coverage . As 5G mThe URLLC use case presents applications that require ultra-low latencies, reliable communication and high availability . High reMachine-to-machine communication (M2M) represents machine-centric communication between different devices and sensors without human interaction . With thPrevious generations of cellular networks e.g., 3rd (3G) and 4th (4G), have used frequency bands under 6 GHz. As these frequencies are becoming increasingly saturated and as there are limitations that mean that they cannot fulfil the new increasing demands, the need for higher frequency bands above 6 GHz has arisen. Accordingly, 5G mobile networks are designed to operate on a variety of frequency bands, including the sub-6 GHz band used by the previous generations of mobile networks and the above 6 GHz frequency bands as well. This is why 5G technology is also known as 5G New Radio (NR). For the first time in the history of mobile network generations, information exchange uses a frequency spectrum known as millimetre-wave (mmWave) characterized with signal transmission at carrier frequencies larger than 24 GHz. The main reason for using these frequencies is to accommodate the emerging high requirements for the larger data rates . In contAlthough mmWave frequency bands classified by the 3rd generation partnership project (3GPP) as FR2 are expected to bring major throughput improvements in mobile communications, sub-6 GHz frequencies classified by 3GPP as FR1 frequency band will still be predominantly used in the initial launches of 5G networks. To accommodate specific purposes, the 5G frequency spectrum is divided into three broad categories : low-banThe main advantage of deploying mmWave frequencies is the ability to deliver high data rates using the large bandwidth. Due to the short wavelengths (below 10 mm) of the high-frequency waves, transmitting and receiving antennas can be produced in compact sizes and easily implemented within base stations (BSs) and user equipment (UE). Ultra-dense antenna arrays deployed within a BS can produce narrow directional beams, and therefore enable frequency reuse among concurrent transmissions within a small geographic area . However, the propagation attributes of mmWave frequencies have some drawbacks that 5G mobile networks need to overcome. The main drawbacks that are facing mmWave communication systems are increased path loss, susceptibility to blockages and atmospheric absorption . The useSome approaches such as massive multiple-input and multiple-output (mMIMO) transmissions with beam-forming and small cell densification have been developed to address these shortcomings. However, a problem that arises using directional beamforming is beam misalignment. This problem is characterised with transmitter’s beam not aligned with the receiver’s beam, causing signal degradation or even a loss of connection.Besides affecting the physical and media access control (MAC) layers, ensuring the high data rates in environments characterised with fluctuations in channel quality will also deteriorate the performance of the transport layer of the network . EnsurinOne of the main purposes of the CC mechanism is to probe the network for available capacity by exponentially increasing the window size. This approach enables avoiding the congestion of the network that can be caused by a large burst of data at the beginning of transmission . BlockagThe rest of the paper is organised as follows. The TCP CC mechanism is explained and a brief review of the popular single and multipath CC algorithms is presented in The TCP is a connection-oriented, reliable end-to-end protocol that guarantees the ordered delivery of byte streams in a full-duplex inter-process communication ,17. InitAs defined in Ref. , the proNetwork congestion is the result of a network node being overwhelmed with more data than it can process. Buffers are added to the communication nodes to prevent packet loss caused by the burst of packets. This consequently increases the delay of each packet passing through the buffer, which results in the degradation of the network performance . On the The TCP CC is one of the main parts of the TCP protocol. Over the years, it has experienced numerous improvements through the application of different algorithms. The first TCP specification considered only flow control as a mechanism to prevent the buffer overflowing on the receiver end, while neglecting the congestion of the network itself . The gencwnd) size and the transmission RTT of the TCP segment for the TCP version based on a slow start with congestion avoidance has been presented in cwnd variable that controls the amount of data that can be sent before receiving an ACK (cwnd size is a sender-specific variable that is maintained for each TCP session. The bandwidth-delay product (BDP), which determines the maximum amount of incoming data over a network link is considered to be the ideal value of the cwnd size. The network congestion is evaluated based on the received ACK packets of transmitted data. The slow start phase is the initial phase in the beginning of transmission (cwnd is increased and the sender can send more data (iw) is determined and can be large as 10 maximum segment size (MSS) . Af. Af29]. TCP selective acknowledgments (SACKs) have been introduced to overcome the limitations of the algorithms that use cumulative ACKs . This apcwnd control mechanism  TCP algorithm has been developed to address unfairness issues and to provide bandwidth scalability in high-speed networks with large delays  34]. Th. Th34]. cwnd size, the TCP CUBIC uses a cubic function that is characterised by the aggressive growth of the window size following the event when a packet loss is detected. When the cwnd size approaches the point of the last congestion event . By encompassing the delay-based component, Compound TCP provides faster network utilisation and a reduction of the sending rate according to the bottleneck queue. This ensures better TCP fairness and the reduction of the sending rate in a packet loss event.The Compound TCP algorithm is representative of a hybrid-based CC algorithm developed for high-speed and long-distance networks. It was the default TCP algorithm in Microsoft Windows operating systems  42]. Co. Co42]. cwnd is incremented aggressively as in the Scalable TCP algorithm  has been estimated, the BBR deliberately reduces the pacing gain to drain the queues in order to estimate the round trip propagation time (RTprop). Periodical measurements of BDP calculated as the multiplication of the BtlBw and RTprop parameters ensure performance within the Kleinrock optimal operating point [The TCP BBR algorithm was developed by Google to overcome the problems faced by loss-based CC algorithms such as throughput issues in a small buffer scenario and the bufferbloat problem in a network with large bottleneck buffers  44]. It. It44]. ng point , where dng point . Howeverng point . For thang point . Google ng point  showed tIn a situation of multiple concurrent TCP flows competing for the same bandwidth on the network link, some TCP CC algorithms may receive more of a bandwidth share than other TCP flows. The challenge of satisfying bandwidth allocation fairness is a serious problem in TCP CC. Hence, one of the main goals that CC algorithms need to accomplish is to ensure fairness among flows that are competing for the same bottleneck bandwidth . Due to cwnd until packet loss occurs. On the other hand, delay-based algorithms  use RTT estimation to predict network congestion before an actual loss has occurred, reducing the sending rate accordingly. In a network with many competing flows that are loss-based, delay-based flows cannot get their fair throughput share as shown in Ref. . S. S107]. The impact on TCP performance using remote servers and edge server deployment has been analysed in Ref.  using UTThe authors have shown that, in the dense urban scenario, only the TCP BBR in the edge server deployment can satisfy the typical 5G requirements (goodput larger than 100 Mbps and a latency lower than 10 ms) operating under stable channel conditions. As presented, the usage of edge computing technology can greatly improve the data rates and decrease latency, especially for loss-based algorithms. The reason for this is due to the shorter control loop resulting from the shorter distance between the UEs and the server, which leads to a faster reaction to congestion. However, further investigations are needed in order to deduce how TCP CC in different 5G use cases can benefit from the implementation of edge computing technologies.To prevent packet drops caused by high transmission link occupancy, the RLC layer performs a temporal buffering of the packets . The sizThe described issues related to the selection of the optimal buffer size in the RLC of mmWave 5G networks motivates investigations which analyse the impact of buffer size on the TCP performance. More specifically, the authors in Ref.  analysedRef.  analysedAdditionally, the authors analysed the usage of large buffers in combination with the CoDel AQM technique. In comparison to the scenario lacking AQM technique, the obtained results show a decrease in goodput in relation to the loss-based CC algorithms. However, the decreased goodput was still larger than that obtained by the small buffer size. It is further shown that applying AQM for loss-based CC algorithms contributes to the significant latency reduction, which is comparable to the levels of latency reduction in the case of small buffer deployment. The hybrid TCP BBR algorithm in a remote server scenario using AQM showed a large decrease in latency and the same goodput as when no AQM was applied. The authors showed that the best goodput was the hybrid TCP BBR algorithm in combination with a large buffer size. However, the obtained goodput was still under the maximum achievable rate. A downside was that the hybrid TCP BBR with a large buffer size had the highest latency compared to the other CC algorithms. According to the presented analyses, it can be seen that an inappropriate RLC buffer size can cause buffer overflow or the bufferbloat problem. This can lead to an increased packet latency or packet drops respectively. Determining the optimal size of the RLC buffer and using different AQM techniques, such as CoDel and Flow Queue CoDel , can ensBesides the user traffic signals, in mobile networks, BSs transmit signals at regular intervals used for different purposes such as BS detection, synchronisation, system information broadcasts, channel estimations etc.  Table 3Table 3. The main advancement in 5G mobile networks dedicated to the mitigation of negative effects of constantly transmitted signals is the implementation of an ultra-lean design in the network . The aimToday, 5G cellular networks are emerging with a large number of new use cases and applications. Conventional TCP CC algorithms that have been implemented in the last few decades are rule-based and their performance in next generation networks is often suboptimal. Conventional CC algorithms base their decisions on pre-defined criteria such as packet loss or delay and they lack the ability to learn and adapt their behaviour in complex and changing environments such as contemporary 5G cellular networks. Ref.  detectedAs opposed to conventional CC algorithms that use predefined parameters as a measure of performance effectiveness, the ML approach exploits learning techniques in order to adapt to network dynamics . The ML Furthermore, the online learning approach does not use fixed mappings and pre-defined actions. Instead, this approach uses the ML techniques in a live environment that adapt to network conditions on the fly. This approach provides a better level of performance when changing the network environment where the network parameters cannot be accurately predicted. If the network model is accurately predicted and corresponds to the actual network environment in which the algorithm is implemented, then the offline models outperform the online models . The percwnd size or increasing the sending rate can be viewed as the actions of the DRL agent in the decision-making process. The strategies of taking actions under the actual network environment are based on the policy that maximises the expected cumulative reward. The reward metric can be observed as either the throughput or network delay that is optimised for a particular system state by the actions of the DRL agent using trained DNN [The DRL is an ML technique that uses reinforcement learning (RL) and deep learning methods . It is ained DNN . Some ofined DNN , Eagle [ined DNN  and Orcained DNN   model can use the information obtained from the different layers to adjust the Latency vs. throughput trade-off challenge: There are various solutions to achieve low latencies. However, many of them are at the expense of bandwidth. It will be a challenge to find a solution that will achieve the best compromise for the chosen scenario.Queuing delay and bufferbloat problem: The problem of excess packet buffering, which creates very large queuing delays known as bufferbloat, was detected in many research studies. Nevertheless, it is a research area that still needs to be further explored in the mmWave band.Fairness problem: This occurs in non-isolated environments such as the public Internet where multiple competing TCP flows use different CC algorithms. Fairness emerges as one of the main problems and it is an area where further research is needed to find the optimal solution.Regardless of the method used for congestion detection, from the analysis of the presented works, we have addressed the three main challenges to TCP CC when operating in the mmWave mobile network:Although CUBIC is the dominant CC algorithm for the broad internet traffic today, the BBR algorithm increased its share in terms of the practical implementation and it can be expected to become the dominant algorithm in the future. As the BBR algorithm is slowly replacing the CUBIC algorithm, further research regarding their mutual interaction is needed to ensure the stability of the Internet. Despite the fact that BBR achieves good performance in terms of maximising the throughput and minimising latency, in highly variable mmWave environments with a massive number of connected devices, achieving an optimal network performance will be challenging. It can be concluded that in the near future, the use of BBR will be sufficient for eMBB usage scenarios, especially if it becomes the dominant algorithm. However, further research is needed to implement TCP CC in the upcoming Internet of Everything (IoE) concept characterised by a large number of installed sensors. This will use 5G mobile networks as an infrastructural backbone. Academic and industry researchers are constantly making efforts to improve traditional rule-based algorithms that use predefined heuristics to address new requirements. On the other hand, ML TCP CC is in its early stages and it remains to be seen whether the future will be consistent with the traditional CC paradigm or if the future of TCP CC lies in intelligent ML algorithms. The deployment of the ML in TCP CC is still in its infancy and it is too early to expect any significant application at a higher level. However, with the advent of 6G networks, ML will need to be considered as the dominant direction in the field of network CC. There are possible directions in the implementation of the ML for CC. The first one is based on the development of entirely new ML-based algorithms that will completely replace the existing ones. The second is based on the integration of some of the ML properties into the existing algorithms and maintaining its backward compatibility with rule-based algorithms. The future will show which of these directions will dominate. The 5G mobile networks must accommodate high demands and support various use cases. Besides sub-6 GHz frequency bands, the new mmWave spectrum is considered in the 5G mobile communications due to the high spectral efficiency, high spatial reuse, low latencies, and multi-gigabit data rates. However, the implementation of the 5G network in sub-6 GHz and mmWave frequency bands brings new challenges in the realisation of TCP CC.In this paper, a comprehensive survey of TCP algorithms used for CC is presented. An overview of surveyed algorithms includes single-flow, multi-flow TCP CC algorithms, and alterative algorithms that have been envisioned as the future replacement for TCP. TCP and the concept of CC have been presented through an overview of the relevant related works that are primarily focused on the current cognitions concerning the challenges in the improvement of CC functionality in the 5G networks. The CC implementation challenges in the 5G networks can be caused by blockage problems, beam misalignment, frequent handovers, inadequate buffer sizes, interference due to the constant transmission of non-data signals, and changes in the data flow due to the usage of edge computing. These challenges can degrade the TCP CC performance and, in this work, each challenge has been explained. The current research attempts for alleviating the issues related to every challenge have been elaborated on. Due to the challenges and limitations that conventional CC algorithms involve, it is realistic to expect further advancements of the TCP CC algorithms in the years to come. These advancements are also expected in the implementation of the ML techniques, which been overviewed in terms of improving CC in the 5G networks. Therefore, the most relevant smart ML-based CC algorithms with the ability to learn and adapt to future complex networks are overviewed. Finally, a discussion related to the main challenges that must be addressed for the efficient implementation of CC algorithms in the upcoming 5G mobile networks has been presented. The discussion emphasises the potential directions in the development of ML-based algorithms as the most promising candidates for the implementation of CC in complex 5G and future 6G networks.


The sensing system is composed of a custom UHF RFID antenna, a low-loss piezoelectric MEMS resonator with two modes within the frequency range of interest, and a base transceiver unit. The difference in temperature and moisture content of inhaled and exhaled air modulates the resonance frequency of the MEMS resonator which in turn is used to monitor respiration. To detect changes in the resonance frequency of the MEMS devices, the sensor is excited by a pulsed sinusoidal signal received through an external antenna directly coupled to the device. The signal reflected from the device through the antenna is then analyzed via Fast Fourier Transform (FFT) to extract and monitor the resonance frequency of the resonator. By tracking the resonance frequency over time, the respiration profile of a patient is tracked. A compensation method for the removal of motion-induced artifacts and drift is proposed and implemented using the difference in the resonance frequency of two resonance modes of the same resonator.In this work, we present a battery-less wireless Micro-Electro-Mechanical (MEMS)-based respiration sensor capable of measuring the respiration profile of a human subject from up to 2 m distance from the transceiver unit for a mean excitation power of 80 µW and a measured SNR of 124.8 dB at 0.5 m measurement distance. The sensor with a footprint of ~10 cm It is estimated that from the hundreds of millions burdened with chronic respiratory conditions globally, annually four million die prematurely . This imRespiration rate is an excellent predictor for the need to admit patients at risk of cardiac arrest into intensive care units . StudiesIn addition to diagnostic applications, studies have shown the benefit of long-term continuous respiration monitoring for patient’s recovery . As suchDespite the stated benefits of monitoring the respiratory rate, research shows that, even in cases where the patient’s main complication is a respiratory condition, proper measurement is routinely neglected . This coMonitoring systems designed to fill this gap in healthcare can be categorized according to the physiological signatures that are monitored in proxy of respiration . The maiTo address the aforementioned needs in our previous work, a resonant wireless respiration monitoring sensor capable of measuring respiration rate without the need for onboard batteries or circuits was demonstrated for the first time . With a 2), negligible thickness, and low unit cost, the relatively limited measurement range (~25 cm) was less than desired. Here, we build on this work by significantly improving the measurement range from a maximum of 25 to 2 m in this paper, enhancing resilience to motion-induced artifacts, and improving SNR and thus sensor accuracy without sacrificing the sensor’s small footprint and unit cost. To achieve this, we utilize a dual-mode low-loss, high Q, MEMS resonator with a custom-designed RFID antenna. We illustrated that our sensor is capable of wirelessly measuring human respiratory profiles by proxy of moisture condensation and heat exchange.Although this sensor offered a compact form-factor  on the sensor.Here, we utilize thin-film piezoelectric-on-substrate (TPoS) MEMS resonators operating near the ISM band (902–928 MHz). TPoS MEMS resonators were chosen based on their low insertion loss and high quality-factor . The MEMTo model the sensor, an equivalent circuit for the MEMS resonator is developed. Here, the well-established modified Butterworth-Van Dyke (mBVD) model is used , which mThe complete model of the sensor including the antenna and the calculated mBVD model values for the two MEMS resonators used in this paper is shown in The impedance responses of the two resonators intended to be used for sensing are shown in The impedances of the two resonators, as modeled by the mBVD model shown in , were plThe response of the sensor to respiratory flow can be thought of as a reaction to the modulation of the MEMS resonators’ temperature and surface vapor condensation due to the periodic expulsion of air from the lungs . ThroughHeat transferred from hot exhalatory flow to the resonator has the principal effect of lowering the effective stiffness of the resonator which in turn decreases the resonance frequency . The temHere, sonators  used herThe frequency shift as a function of temperature was simulated using COMSOL for all three modes measured in As exhaled air with high moisture concentration passes over the MEMS resonator, a thin layer of water vapor condenses on the surface of the device. This thin layer increases the effective mass of the resonator proportional to its coverage and thickness. Modeling an MEMS resonator as a mass-spring-damper system, the resonance frequency can be calculated as:Here, In addition to mass loading, adsorption of moisture results in higher acoustic energy loss stemming from both acoustic mismatch between condensation and the micromechanical structure and also high acoustic loss in the condensation layer ,24.Both changes in temperature and vapor mass loading are periodic time-varying parameters that are coupled through heat transfer occurring during vapor condensation and evaporation. Therefore, the total sensor frequency shift corresponding to a single resonance mode resulting from exposure to respiratory flow can be written as:The same sensing pathways that facilitate respiration sensing can also act as a source of measurement error considering environmental variability. As described in Equations (3) and (4) . Given that the two MEMS devices are electrically connected in parallel, Keysight Advanced Design System (ADS) was used to calculate the equivalent impedance.The major challenge in designing this antenna is achieving acceptable impedance matching between the antenna and MEMS sensor despite the difference in impedance at their respective resonance frequencies. Rather than matching the antenna perfectly at only one of the MEMS resonance modes, our solution was to match the regression line of the equivalent MEMS sensor’s impedance at the resonance modes. Whilst the power lost to impedance mismatch is not eliminated at either of the MEMS sensor’s resonance modes, this design achieves the overall minimum impedance mismatch. It should be noted that this antenna was originally designed with only 879.9 and 880 MHz modes in mind.To realize this design, first, the antenna dimensions are estimated such that the effective length of the antenna is equivalent to a half wavelength at the antenna’s center frequency. In the next step, ANSYS High-Frequency Structure Simulator (HFSS) is utilized to fine-tune the dimensions to achieve matching to the equivalent impedance of the two MEMS. This fine-tuning process is illustrated in the HFSS simulation results shown in The impedance profile of the finalized design with dimensions of The antenna was fabricated using the print-screen method  on a flePower delivery is a fundamental limitation of passive sensing. In our respiratory sensor, the distance between the patient and transceiver unit, and hence the quality of the mobility offered to the patient, is directly limited by the strength of the signal delivered to and received from the sensor. As such, understanding the relationship between sensing distance and wireless signal strength is crucial. For this reason, we have developed a framework that connects the transmitted power to antenna characteristics, sensor-transceiver distance, sensor characteristics, signal acquisition settings, and ultimately the strength of the sensor’s signal, as measured by the receiving unit.For a given excitation signal, the power delivered to the passive wireless sensor is determined by the antenna gain, sensor distance, and sensor loss and can be calculated using the Friis Equation .(6)PSPTHere, esonator . ModifyiHere, The measurement setup and the acquisition flow chart used here are shown in The high quality-factor of the MEMS resonance modes combined with the frequency-modulated sensing mechanism necessitates a dynamic frequency tracking system whereby the frequency of the excitation signal locks onto that of the resonance modes throughout the measurement. For this purpose, the frequency of the excitation signal targeting a given mode was set as the moving average of the preceding measured resonance frequencies of that mode.In sensing applications, typically the size of the moving average window is determined as a compromise between responsiveness and SNR; however, in this case, given that a slow response to shifts in resonance frequency results in lower SNR, a small window is preferred. A moving average with a larger window is applied in the post-processing step. The window size for the tracking system and post-processing step was 2 and 8, respectively.The time resolution (sampling rate) of the respiratory profile determines how faithfully the patient’s respiration is reconstructed and is a critical parameter as it sets the maximum detectable feature frequency through the Nyquist–Shannon sampling theorem. Given that the typical respiratory cycle of an adult human lasts ~4 s, our measurement system’s time resolution of ~130 ms fulfills the Nyquist frequency condition and can theoretically resolve features up to ~3.85 Hz. It should be noted that, in our measurement system, the transfer of signals acquired by the oscilloscope to the PC and their processing by LabVIEW is the determining factor for time resolution.The goal of incorporating more than one MEMS resonator into the sensor was to distinguish between the effects of respiratory flow and environmental variability on the sensor. The two major sources of environmental variability focused in this work are (1) discerning changes stemming from respiratory flow from environmental temperature variation and (2) negating motional artifacts stemming from small changes in the performance of the antenna as a function of spatial and temporal variability in electromagnetic environmental loading.Given that a MEMS resonator’s resonance frequency is a function of temperature, any sudden change in environmental temperature can be miscategorized as a respiratory event. As such, by incorporating two MEMS devices with similar TCF , for sensor, transmitter, and receiver antenna gains of 1.4 dBi, 8 dBi, and 8 dBi, respectively, to predict signal strength as a function of sensor distance. The simulated and measured signal power is shown in By fitting Equation (5) to the measured power at the receiver, the total effective loss of the sensor for the 874.7 and 879.9 MHz modes was found to be ~15 and ~13.4 dB, respectively. The difference between these values and the simulation results in The precision of the sensing system was evaluated by calculating the measurement variance for a relatively long duration (~45 min) at a distance of 0.5 m from the receiver and excitation antenna in an anechoic chamber. Similar to the previous experiment, the peak and mean transmitted power were 80 mW and 80 µW, respectively. Following measurement, frequency drift stemming from environmental temperature variation was removed in post-processing. As shown in 2, the sensor was shown to be capable of measuring a respiratory profile from up to a distance of 2 m from the transceiver unit for mean transmitted power of 80 µW and peak transmitted power of 80 mW. The SNR was measured to be 124.8 dB at a 50 cm sensor distance with the same transmitted power.A passive wireless respiratory sensing system based on the frequency modulation of a TPoS MEMS resonator was developed. With a size of only ~10 cmThe respiratory sensor is composed of an antenna wire bonded to two low-loss piezoelectric MEMS resonators and is excited via a transmitter antenna and its response is recorded by a second receiver antenna. We showed that an increase in temperature and mass loading from surface vapor condensation, both stemming from exposure to exhaled airflow, results in a substantial and reversible drop in the MEMS resonance frequencies. FFT analysis is then used to extract the shift in MEMS devices’ resonance frequencies and is plotted over time to construct the patient’s respiratory profile.To minimize energy lost to impedance mismatch between the MEMS resonators and sensor antenna, a custom RFID antenna was designed to achieve optimal impedance matching with two MEMS resonators. This was achieved despite their significant difference in impedance at their respective resonance modes.We showed that the Friis equation, widely used to calculate the maximum operating distance for wireless passive devices, is inadequate for ringdown-type sensors. A modified version of the equation, taking into account the FFT window and sensor quality factor, was presented and used to measure the effective loss of the sensor at different operational modes.The sensor was shown to be capable of clearly distinguishing shallow and irregular breathing from normal respiration and was also utilized to detect brief cessation of breathing events, important for studies of sleep apnea.The sensing system utilizes a novel method for canceling the effect of motion-induced artifacts and drift on the response of the sensor. It was shown that by incorporating an MEMS resonator with two modes near the frequency of interest and monitoring the difference between their resonance frequencies, frequency shifts stemming from variation in the antenna’s position can be discerned from respiration.


Motion classification can be performed using biometric signals recorded by electroencephalography (EEG) or electromyography (EMG) with noninvasive surface electrodes for the control of prosthetic arms. However, current single-modal EEG and EMG based motion classification techniques are limited owing to the complexity and noise of EEG signals, and the electrode placement bias, and low-resolution of EMG signals. We herein propose a novel system of two-dimensional (2D) input image feature multimodal fusion based on an EEG/EMG-signal transfer learning (TL) paradigm for detection of hand movements in transforearm amputees. A feature extraction method in the frequency domain of the EEG and EMG signals was adopted to establish a 2D image. The input images were used for training on a model based on the convolutional neural network algorithm and TL, which requires 2D images as input data. For the purpose of data acquisition, five transforearm amputees and nine healthy controls were recruited. Compared with the conventional single-modal EEG signal trained models, the proposed multimodal fusion method significantly improved classification accuracy in both the control and patient groups. When the two signals were combined and used in the pretrained model for EEG TL, the classification accuracy increased by 4.18–4.35% in the control group, and by 2.51–3.00% in the patient group. The functional prosthesis is an important rehabilitation aid for upper limb amputees, as it allows them to successfully perform social activities by restoring lost functions. About 1.6 million people suffer from limb loss in the United States, 35% of whom are upper limb amputees . ProstheThe first myoelectric prosthesis was commercialized in the 1980s by Otto Bock . Since tNoninvasive EEG signals might be more appropriate for upper limb amputees’ motion classification than EMG signals, because, according to the extent of amputation, the forearm or hand muscles required for performance of daily living activities are not fully intact or are even absent. In addition, owing to the fact that body movements are fundamentally controlled by the brain, many previous studies have reported using surface EEG or EMG signals for motion classification, the impetus of which has driven the primary motion classification research trend of using noninvasive EEG signals ,19. A prIn the present study, we adopted the transfer learning (TL) concept of motion classification. With it, input data can be enhanced by learning the features of other input data. Therefore, we tested the hypothesis that a motion classification algorithm of EEG signals, which is independent of EMG signals, can be improved by learning EMG data features. First, single-modal classification of EEG and EMG signals was performed. After that, each EEG-to-EMG and EMG-to-EEG TL was performed, and features extracted from each signal were trained when the two signals were combined. Preparatorily, we had recruited six transforearm amputees along with nine nonamputees for the control group, and then we verified, from experimental data, the effectiveness of the proposed multimodal classification algorithm for detection of motion intention. We believe that the proposed multimodal motion classification technique benefits from the advantages of both single model EEG- and EMG-based techniques. We measured the EEG and EMG signals corresponding to the wrist and hand movements of transforearm amputees and combined them to improve motion classification using deep learning techniques. We used the event-related desynchronization/synchronization (ERDS) map  as an inFor the experiments, we recruited six transforearm amputees  who had no form of neurological disease and had preserved forearm muscles and nervous tissues needed to acquire the data necessary for algorithm development and verification. The research protocol and procedures were approved by the Institutional Review Board (IRB) of Dankook University Hospital (IRB No. 2020-05-009). For the control experiment, we recruited nine healthy controls without amputation . The research protocol was approved by the Pohang University of Science and Technology and is in compliance with their IRB procedure (IRB No. PIRB-2020-E016). Sixty-four (64) EEG signal channels were acquired from ANTneuro’s EEGO mylab  at a sampling rate of 500 Hz as shown in As shown in Based on the principle of the ERDS map, which is one of the feature extraction methods for the EEG signals, a two-dimensional (2D) image visualized along the frequency axis between 5 and 35 Hz was created using Biosig toolbox  and averaging the randomly picked 5 trials of the EEG signal. The principle of the ERDS map was applied to the EMG signal to extract the features in the frequency domain between 30 and 247 Hz. For ERDS computation, we utilized the bootstrap resampling technique from the Biosig toolbox . In geneConsequently, a 46 × 279 matrix was generated using the EEG data from the nine channels, a 46 × 124 matrix was generated using the EMG data from the four channels, and a 46 × 403 matrix was generated in order for a pretrained model to be obtained from the both EEG and EMG data. The CNN algorithm does not extract information from data to learn, but rather extracts the features of data and identifies the patterns of those features . The CNNThe TL concept was implemented to create a pretrained model using both EEG and EMG signals. Transfer learning (TL) is an emerging method in computer vision because it can achieve high accuracy in a relatively short time . By learIn the pretrained model, the image size in the network input layer was 46 × 403 × 1 because it consisted of gray-scale images. The five convolutional layers of the CNN architecture were defined. Thereafter, a fully connected layer and Softmax activation function layer were used to normalize the output value of the fully connected layer. Finally, in the classification layer, using the probability returned by the Softmax activation function for each input value, the input value was assigned to one of the mutually exclusive classes and the loss was calculated. The maximum number of epochs was set to 10. The validation data were not used to update the neural network weights.An important characteristic of deep learning models is their ability to learn instrumental features. The later the layers are in the model structure, the more advanced learning is achieved, specifically by extracting more specific features. At this point in the process, the layers at the front can be reused when learning images from other datasets, although the layers in the back will need to be learned anew whenever they encounter a new problem .Retraining the entire pretrained model involves using only the structure of the prelearning model and performing all retraining based on the data set. Therefore, such operations require a high level of computation and a large data set. If the data set is small, there is a risk of overfitting if the entire set is newly trained ; hence, −4 so as to slow the learning of the nonfixed transferred layer.The convolutional layer of the neural network extracts image features that are used by the last learnable layer and the last classification layer to classify the input image. For training the pretrained neural network to classify new images, the last layer with learnable weights are trained as a new layer. The first-half layers fix weights to accelerate neural network training and prevent overfitting to new data sets. The learning speed is set to a relatively small value of 3 × 10p-values less than 0.05 were considered to indicate statistical significance.Statistical analysis was performed using IBM SPSS Statistics 26 . A Kolmogorov–Smirnov test was used to confirm the normal distributions of obtained data, and according to the results, a nonparametric test was chosen. Mann–Whitney U tests were also performed to detect any differences in the classification accuracies between the control and patient groups, left and right sides in the control group, and the intact and amputated sides in the patient group. Wilcoxon signed-rank test was used to compare the classification accuracies between single-modal EEG and transfer-learned EEG in the control and patient groups. After the classification, the performance of each fusion method was evaluated. For this study, performance analysis based on two types of metrics was adopted . The firWe created the single-modal classification model using EEG/EMG signals as input data for both the amputated and nonamputated subjects based on the CNN classification algorithm. Furthermore, we formulated a new multimodal CNN classification algorithm using both the EEG and EMG signals as input data for the training model, as obtained from both the control and patient groups. Then, the lower four layers of the multimodal classification model were reused as a pretrained model for TL. In this TL model, only the EEG or EMG signal was used as the training input data; then, the features of the pretrained model were trained. We tested the classification accuracy of the three sets of wrist and hand movements: wrist dorsiflexion and volar flexion, wrist radial deviation and ulnar deviation, and hand grasping and opening. The convergence curve of the accuracy (upward convergence curve) and loss rate (downward convergence curve) according to cyclic iteration training is shown in The prediction of motion classification based on EEG or EMG signals is very important to the development of prosthetic arms for amputees. However, technical challenges to motion classification studies based on single-modal EEG or EMG signals remain. To solve these problems, in this study, we developed a motion classification algorithm for intention-based prosthesis control using EEG and EMG signals with a CNN-based TL algorithm. We improved the input feature extraction method based on 2D images of an EEG signal in the frequency domain by applying the same process to the EMG signal. The proposed classification technique can be used in the following way: in the rehabilitation of upper limb amputees, the multimodal fusion model is pretrained using both the EEG and EMG signals with the TL technique, and then, in real-life usage, the pretrained model detects patients’ intention using only the single-modal EEG or EMG signal in order to greatly enhance both the usability and accuracy of the motion detection algorithm.Other means of improving a muscle signal’s low spatial resolution exist. If we acquire electroneurography signals by invasive insertion of electrodes, we can obtain more accurate muscle signals with a higher spatial resolution of muscles. However, in the present study, a noninvasive method was adopted because an invasive electrode entails surgical steps and cannot be used for a long period. For feature extraction with high spatial resolution, it will be possible to develop an algorithm for ultimate high-fidelity prosthetic control by extracting additional high-quality features.For amputees, it is difficult to control, in real life, a prosthetic arm using both sensor types in multimodal sensor fusion. However, patients can usually wear both sensors during the rehabilitation period. Therefore, the scenario becomes more realistic where TL with both EEG and EMG signals are performed during the rehabilitation session, after which the actual prosthetic arm is controlled by a single EEG signal in daily life outside the hospital. In addition, the approach to movement intention detection with only a single EEG signal is needed for the goal of avoiding eventual EMG-signal dependence owing to various muscle problems, such as the conditions of the amputation site, the signal strength, and/or the need for surgery ,13,40. IIn this study, when the EEG and EMG signals were fused, the fusion accuracy of the EEG signal increased while that of the EMG signal tended to decrease. It seems that the signal was saturated due to the fact that the classification accuracy of the single-modal EMG sensor was high. The current classification accuracy result seems biased to the surface EMG signal. In addition, since the accuracy of the EMG signal was somewhat low in the patient group experiment, the rate of accuracy increase was also somewhat low in the transfer-learned EEG. We believe that this was due to the study protocol having included only relatively simple movements. The purpose of this study was to show that the EEG signal can be trained using the EMG signal and that the multimodal fusion algorithm enhances the performance of motion classification. When more complex motions such as finger movements are performed, the high-resolution characteristics of the EEG signal might be required, and furthermore, its resolution can be further increased by incorporation of a larger number of electrodes. In recent studies, high-density EMG also has been highly accurate in predicting finger movements ,42,43. IIn this study, the classification accuracy of EEG signals was increased by 2.51–4.35% in the control and patient groups after training . PreviouWe found that the accuracy of multimodal EEG and EMG was lower in the patient group than in the control group  than in the control group (4.35 and 4.18% on the left and right sides). Although this difference was not obvious statistically, if it is possible to modify EEG signal processing according to the reorganization of brain mapping, a further improvement of EEG accuracy also will be possible.In this study, the image generated by the features of the EEG or EMG signal in the frequency domain was provided as input to the CNN and transfer learning classification models. When the single-signal classification accuracy of the EEG data was high, the EEG and EMG data showed high-quality feature learning and improved classification accuracy when TL was performed. Additionally, the classification accuracy of EMG TL tended to decrease when the classification accuracy of EEG data was relatively low, owing to the difference in accuracy between the EEG and EMG data. Conversely, the classification accuracy of EEG TL tended to increase when the classification accuracy of EMG data was relatively high. These findings serve to highlight the potentiality of high-quality feature learning of EEG data.The data-level fusion of motion classification can be further divided into data-level, feature-level, and decision-level fusions after the data processing step . VariousThe key challenges associated with the use of deep learning in BCI research are related to the amount of data involved. The training data set used for the classification network model proposed in this study was relatively small, because data were obtained from a limited number of subjects in a relatively uncomplicated experimental environment. Therefore, the learning model was also performed in a shallow-depth structure to avoid the overfitting problem. If the amount of available data increases, it will be possible to establish a more complex-structured deep learning network and TL model that will afford enhanced classification accuracy. In this study, we had intended to increase the accuracy of EEG signals for the control of wrist and hand movement. However, the classification accuracy of EEG was not superior to that of EMG, even after transfer learning, which is the main limitation of our study. Since EMG signals vary greatly depending on the extent of remnant forearm muscle(s), and cannot be obtained from nonexistent intrinsic hand muscles, they are difficult to standardize. Therefore, to increase the usefulness of EEG signal employment, we plan to further apply our deep learning algorithms to complicated hand motions that involve additional hand muscles. In general, the classification accuracy of EMG data is higher than that of EEG data. Therefore, using EMG signals together will inevitably increase the accuracy rather than using EEG signals alone, and it is general that the accuracy is the highest when EMG signals is used alone. However, current prosthetic prostheses based on EMG data experience many inconveniences for users. Therefore, it is necessary to develop direction of prosthetic development for motion classification using EEG signals rather than EMG signals. As a first step, our thesis focused on transfer learning by fusion of EEG and EMG signals.There is a limit in real-life rather than EMG signals measurement due to the use of EEG cap. However, convenience is also developing in measuring equipment such as dry electrode caps. Although we did not fully train with EMG, we proposed a fusion of EEG and EMG because the purpose of the study itself was to try transfer learning. In this case, since the classification accuracy of EMG data is high, the classification accuracy of EEG data can also be improved. Recently, a primary contributing factor for poor myoelectrical control is variability in muscle contraction intensity. Our study tried to control this in the experimental design. However, the muscle contraction intensity may not have been completely controlled in our study, and a study in which the muscle contraction intensity was controlled is needed in the future.In this study, we revealed that the proposed multimodal fusion method using EEG and EMG signals significantly improved classification accuracy of wrist and hand movements in transforearm amputees and healthy controls. We concluded that the proposed multimodal fusion method might contribute to develop a more practical prosthesis for patients with upper extremity amputation.


Risk-based monitoring (RBM) is a slow uptake in some trial sponsors. There are three main reasons for this. First, there is the fear of making large investments into advanced RBM technology solutions. Second, it is considered that RBM is most suitable for large, complex trials. Third, there is the fear of errors in both critical and non-critical data, appearing as reduced on-site monitoring is being conducted.Our RBM team identified, evaluated, and mitigated trial risks, as well as devised a monitoring strategy. The clinical research associate (CRA) assessed the site risks, and the RBM team conducted central monitoring. We compared all data errors and on-site monitoring time between the partial switching sites [sites that had switched to partial source data verification (SDV) and source data review (SDR)] and the 100% SDV and SDR sites (sites that had implemented 100% SDV and SDR).Partial switching sites did not require any critical data correction and had a smaller number of data corrections through on-site monitoring than the 100% SDV and SDR sites. The RBM strategy reduced the on-site monitoring time by 30%.The results suggest that RBM can be successfully implemented through the use of site risk assessment and central monitoring with practically no additional investment in technology and still produced similar results in terms of subject safety and data quality, as well as the cost savings that have been reported in global complex studies. Risk-based monitoring (RBM) was first introduced into studies in 2011–2012. By the time ICH E6(R2) was issued, RBM was already a well-known concept. The FDA, EMA, and PMDA had included it in their regulations for clinical trials, making it mandatory in their jurisdictions. The upcoming ICH E6(R3) in 2021 is expected to strengthen it even further. However, there has been rather slow uptake by some trial sponsors all over the world. There are three main reasons for this. First, there is a fear of making large investments in advanced RBM technology solutions. Second, it is thought that RBM is only suitable for large complex trials. Third, there is a fear of erroneous data, in both critical and non-critical data when the on-site monitoring is reduced.According to ICH E6(R2), sponsors should develop systems to manage quality throughout all stages of the trial process, and quality management systems should use a risk-based approach (RBA). An RBA process is commonly known as RBM ; this ap. showed that a median of 1.1% of the total eCRF datasets in 1168 phase I–IV biopharmaceutical studies performed with 53 sponsors was corrected by SDV [. reported that in three randomized, double-blind, placebo-controlled, multicenter phase III trials, 100% SDV yielded an error rate of 0.27%, and the error rate for partial SDV was 0.53% [Sheetz et ald by SDV . This anas 0.53% . In the However, no RBM studies have been performed in relation to all errors and deviations of eCRF data and source data as detected by central monitoring, off-site monitoring, and on-site monitoring, including SDV and SDR. No observations have yet been conducted on the combination of central monitoring and site risk assessment as seen in previous studies.Whether a simple RBM without advanced technology will produce satisfactory results in terms of managing data and safety risks.Whether an RBM provides benefits in a low-risk trial with minimal complexity.Whether an RBM can ensure quality regarding all errors in eCRF and source data, minimize protocol deviations, and optimize the monitoring resource utilization.We implemented RBM in our trial with the three-fold objective of investigating the following:We included non-critical data errors to simulate traditional monitoring strategies with 100% SDV and SDR. We compared errors and on-site monitoring time between sites that switched to partial SDV and SDR and sites with 100% SDV and SDR.Our study was an open-label, multicenter trial performed to evaluate 52-week long-term safety, tolerability, and efficacy of tofogliflozin in conjunction with glucagon-like peptide-1 (GLP-1) analogue treatment in type 2 diabetes mellitus. The 67 participants at 11 sites in Japan were randomized. The primary endpoint for this trial was change from baseline in HbA1c. Trial data were collected using electronic data capture (EDC). Data freezing was conducted at each site.The trial sponsors were Kowa Company, Ltd. and Sanofi K.K. The trial was managed and conducted by A2 Healthcare Corporation, a Japanese contract research organization. The members of Kowa Company, Ltd. and A2 Healthcare Corporation discussed and determined the RBM strategy. This trial was registered at ClinicalTrials.gov with number NCT02537834.We identified and assessed trial risk with the TransCelerate Risk Assessment and Categorization Tool (RACT). We formulated a risk mitigation plan for the identified risks. This was performed by the monitoring leader and the RBM team members, including central monitors, data managers (DMs), and statisticians. The risk mitigation plan was adapted to accommodate manuals and/or training for site staff, patient diaries, and discussion of site processes with site staff. The discussions were conducted at all sites, and the results of the discussion were recorded in the process-building sheet, which included an optimized clinical process designed to prevent protocol deviations.We located sites that switched to partial SDV and SDR. These were referred to as partial switching sites, using information related to the previous involvement of the trial sponsor  and the results of the site risk assessments performed by the CRA. The assessed sites were those where the CRA had implemented monitoring in other trials. The CRA determined whether issues had arisen that could affect subject safety or the quality of the trial data, including non-reporting or late reporting of SAEs, late EDC entry, or inadequately trained site staff. Using information on the previous involvement of the sponsor and the site risk assessments, all sites for which information was already present were termed partial switching site.At the beginning of the trial, the CRA discussed the site processes with the site staff at all sites and completed the process-building sheet. At the partial switching sites, where the process-building sheets were completed, the CRA assessed the site risks and switched to partial SDV and SDR at the low-risk sites, using a Monitor-driven Risk Assessment Categorization Tool (MRACT). Until that point, the CRA had performed 100% SDV and SDR at these sites. MRACT included the items necessary to identify the issues and risks that would be difficult to detect with data-driven risk indicators, such as the quality of the source documentation. At the 100% SDV and SDR sites, which referred to all sites that were not partial switching sites and for which there was no information before starting the trial, the CRA also carried out an assessment using MRACT to evaluate the site risks without switching. The implementation details for on-site monitoring, off-site monitoring, and central monitoring activities for this trial are presented in Table After the last patient last visit, the CRA conducted SDV and SDR on the data that had not received SDV and SDR at the partial switching sites during the trial implementation phase Fig. .Figure 1MRACT consists of 17 checklist items, categorized into three groups: critical processes (four items), data quality (three items), and staffing and facilities (ten items). Each checklist item had a risk score from 1 to 3  in accordance with the risk assessment criteria. The CRA began the assessment of the site using MRACT items following the first on-site monitoring. After the first assessment, the CRA carried out monthly assessments at each site, using MRACT. All of the results of the assessments were reviewed and approved by the CRA leader. The RBM team members reviewed the results and queried the CRA leader where necessary. The implementation of the review and the approval minimized the bias and subjectivity of the site risk assessment. The MRACT was not updated during the trial.The CRAs collected all queries to the doctor or clinical research coordinator (CRC) in Microsoft Excel software on a data point basis after on-site monitoring and off-site monitoring. The data collected by the CRAs included corrections of the eCRF and the source data. Where data were corrected, the CRA presented the data and the reason for the error in the spreadsheet file. The reasons for the corrections included lack of understanding of the trial protocol, errors in source documents, transcription errors, lack of data entry, and others. To minimize the human error in data entry by the CRAs, the spreadsheet file for the data collection was set to select and input from the drop-down list, and the CRA leader confirmed the data entered by CRA to allow it to be detected when a human error occurred. A team member of central monitoring collected all errors detected by central monitoring in an Excel spreadsheet. The DM extracted the created query data and the data corrected through the DM queries from the EDC audit trail data at the end of the trial. The dates and times of the beginning and the end of the on-site monitoring were collected by the CRAs. The collection of the data continued from the first patient first visit to the last patient last visit for partial switching sites and the 100% SDV and SDR sites. After the last patient last visit, the CRA conducted SDV and SDR on the data that had not received SDV and SDR at the partial switching sites to collect the error data.The number of corrected eCRF and source data per subject was compared between the 100% SDV and SDR sites and the partial switching sites. The number of corrected eCRF and source data per subject was also compared between monitoring activities. This involved on-site monitoring by the CRA, off-site monitoring by the CRA, off-site monitoring by the DM, central monitoring, and correction by site. The time required for on-site monitoring per completed subject was compared between the 100% SDV and SDR sites and the partial switching sites. The number of corrected eCRF and source data per subject was also compared between the data risk categories, which were determined using the impact of data errors, classified as high, medium, or low. The high-risk category included critical data related to the protection of the human subjects, the reliability of the trial results, and GCP compliance. The high-risk category data were adverse event, primary endpoint, and informed consent. The medium-risk category included secondary endpoint data. The low-risk category included all other data not covered by the high- or medium-risk categories.To summarize the metrics assessed in this investigation, the following definitions were used:The number of eCRF and source data per subject corrected for the 100% SDV and SDR sites and that for the partial switching sites was nearly equal, at 24.3 and 21.8, respectively , the focus on quality management shifted from ensuring the integrity of the trial data to ensuring the reliability of the trial results. That is, it is important to ascertain what approach might be more effective and efficient. This study investigates whether RBM can ensure the protection of human subjects and the reliability of trial results while optimizing monitoring resources.The partial switching sites showed no data corrections in high-risk category items. This means that no critical errors occurred at those sites. Our definition of partial switching site was based on our own information, but we did detect one problem site from the MRACT results and central monitoring during the trial implementation phase. After the detection, we switched it from partial SDV and SDR to 100% SDV and SDR. After the switching, we determined the cause of the critical error and implemented corrective action. These results suggest that the strategy of switching the site monitoring based on the MRACT results and central monitoring is an option for RBM strategy that could ensure the reliability of the trial results.The number of eCRF and source data per subject corrected by off-site monitoring at partial switching sites was higher than that found at 100% SDV and SDR sites. All CRAs were in charge of monitoring the partial switching sites and the 100% SDV and SDR sites. Off-site monitoring activities depended on the CRAs, and all four CRAs stated that the time required and the motivation for off-site monitoring were same for both the partial switching sites and the 100% SDV and SDR sites. These observations suggest that the result cannot be attributed to the differences in the implementation of monitoring among the CRAs. Because the CRAs were not prohibited from conducting on-site monitoring before the weekly off-site monitoring, their on-site monitoring probably detected the errors that the CRAs could detect with weekly off-site monitoring at the 100% SDV and SDR sites.The number of eCRF and source data corrections per subject with on-site monitoring at the partial switching sites was lower than that at the 100% SDV and SDR sites, and the partial switching sites had only low-risk category correction with on-site monitoring. These results suggest that the strategy of switching site monitoring based on the MRACT results and central monitoring effectively detects data error.The partial SDV and SDR reduced the on-site monitoring time by 30%. This suggests that the reduction in time spent on on-site monitoring at the partial switching sites could be attributed to the reduction in the frequency of on-site monitoring from once a month to once every three months.Sheetz et al. showed retrospectively that a median of 1.1% of the total eCRF datasets was corrected by SDV in their data , while tIn the ADAMON study, the monitoring intensities were adjusted, but the issues that affected critical data and processes were identified after completion of the trial . On the It should be noted as a limitation of the study that the trial was conducted with a small sample size. Therefore, its results cannot be generalized. It is unclear whether the same results would be obtained if a more complex trial was conducted, as this trial required few complex or uncommon procedures that go beyond the usual standard of care.We obtained the partial switching sites using prior information. Although this introduces bias, the following two points probably ensured the comparability among the 100% SDV and SDR sites and the partial switching sites.First, we did not assess or select sites for which no information was available. Accordingly, the 100% SDV and SDR sites probably included low-risk sites, which we could probably defined as partial switching sites.Second, the information that we used to assess and select of the partial switching sites was probably insufficient. This is because the partial switching sites included a site that was switched from the partial switching sites to the 100% SDV and SDR sites, following the MRACT results and central monitoring. Furthermore, the CRAs held discussions with the site staff at all sites for risk mitigation, assessed all sites using MRACT, and verified that all sites were low risk at the beginning of the trial.In our study, we suggested that the implementation of RBM can efficiently ensure the quality of trials. Switching strategies are commonly practiced in the industry. However, to our knowledge, this is the first prospective study to demonstrate the effects of RBM, evaluated using all data corrections, adopting a strategy wherein site monitoring can be switched based on the results of site risk assessment and central monitoring.Our study shows that RBM can be successfully implemented with site risk assessment and central monitoring with practically no investment in technology and can still produce similar results in terms of subject safety and data quality, along with showing the savings that have been reported in global complex studies. The time saved by the CRAs can be used to perform additional studies or engage in other activities. We believe that this study will provide a convincing support for the wider spread of RBM studies.


Viruses are extraordinary biological entities that can only thrive as obligate intracellular parasites, exploiting other living cellular components in order to reproduce. Despite the diversity in the virosphere, a central component of the viral replication machinery is the nucleic acid polymerases that are responsible for copying the viral genome and for generating genetic diversity. In the RNA virus world, with the exception of retroviruses, the RNA-dependent RNA polymerases (RdRPs) catalyze RNA synthesis, functioning either as single polypeptides, or in a complex with other viral or host components to transcribe and replicate viral RNA genomes. In this Special Issue, we seek to highlight recent progress in our understanding of the structure and function of different viral replication complexes. Four original studies and seven reviews contributed to the issue to increase our general knowledge of RNA virus replication.From the original articles, Ferrero and colleagues describe a remarkable series of X-ray structures of the non-canonical RdRP of the (+) ssRNA Thosea asigna virus in a complex with RNA, nucleotide substrates and the ions involved in its catalytic activity . The binAlbentosa-González and colleagues explain the effects of phosphorylation on the modulation of RdRP activity in the Usutu, Zika and West Nile viruses  . The flaSong and colleagues provide new insights into therapeutic intervention against AIDS, using viral antigen-specific iPSCs to suppress HIV replication in a murine model .Finally, we dive into the conformational dynamics of the poliovirus multifunctional 3CD polyprotein, guided by the work of Winston and Boehr . The picThe reviews in this Special Issue leave no doubt that exciting new discoveries in the regulation of genome replication in (+) and (−) ssRNA viruses are far from over. The seven reviews in this section cover a broad range of topics. Domingo and colleagues consider the error rates displayed by the RdRPs during template copying as an important parameter to understand the genetic and antigenic variability of RNA viruses. However, there is a limit to the number of mutations that can be accepted to preserve de-encoded genetic information. Authors also review how, in some viruses , mutation rates may be modulated by proofreading repair activity . This SpFor the (−) ssRNA virus world, the review by Cao and colleagues updates the molecular insights into the replicative complex of the respiratory syncytial virus (RSV), reviewing the fundamentals of RNA synthesis and proposing a model of multiprotein coordination during different RNA synthesis processes . In addiFinally, we would like to acknowledge all the authors for their contributions to this Special Issue. It has been a pleasure to read their work and learn from it. We will encourage our colleagues to continue with their exceptional research, even during the difficult times of the SARS-CoV-2 pandemic. Because of this pandemic, we are more conscious of the transcendence of understanding viral replication and the multiple mechanisms that viruses use to regulate this essential activity during infection.


Nanoparticle (NP)–cell interaction mediated by receptor–ligand bonds is a crucial phenomenon in pathology, cellular immunity, and drug delivery systems, and relies strongly on the shape of NPs and the stiffness of the cell. Given this significance, a fundamental question is raised on how the ligand distribution may affect the membrane wrapping of non-spherical NPs under the influence of cytoskeleton deformation. To address this issue, in this work we use a coupled elasticity–diffusion model to systematically investigate the role of ligand distribution in the cytoskeleton-associated endocytosis of ellipsoidal NPs for different NP shapes, sizes, cytoskeleton stiffness, and the initial receptor densities. In this model, we have taken into account the effects of receptor diffusion, receptor–ligand binding, cytoskeleton and membrane deformations, and changes in the configuration entropy of receptors. By solving this model, we find that the uptake process can be significantly influenced by the ligand distribution. Additionally, there exists an optimal state of such a distribution, which corresponds to the fastest uptake efficiency and depends on the NP aspect ratio and cytoskeleton stiffness. We also find that the optimal distribution usually needs local ligand density to be sufficiently high at the large curvature region. Furthermore, the optimal state of NP entry into cells can tolerate slight changes to the corresponding optimal distribution of the ligands. The tolerance to such a change is enhanced as the average receptor density and NP size increase. These results may provide guidelines to control NP–cell interactions and improve the efficiency of target drug delivery systems. A thorough understanding of the interaction between nanoparticles (NPs) and cells is of significant interest. For instance, the effective design of NPs with penetration capability in target drug delivery systems is expected, and the safety and toxicity in extensive applications of industrial NPs ,2,3 shouRecently, Schubertová et al.  focused Endocytosis includes a number of biophysical processes by which cells internalize NPs. It has been recognized that the cytoskeleton plays an essential role in several of these processes . The molAlthough tremendous progress in understanding cell–NP interactions has been achieved, the role of ligand distribution in the cytoskeleton-associated endocytosis of non-spherical NPs remains unclear. In this work, we propose a coupled elasticity–diffusion statistical dynamic model to investigate the influence of ligand distribution on the cellular uptake of ellipsoidal NPs, where we assume that the uptake process is driven by the binding energy between diffusive receptors on the cell membrane and ligands on the NP surface to overcome resistances from membrane deformation, cytoskeleton deformation, and changes in the configuration entropy of receptors.t. During the cytoskeleton-associated endocytosis of NPs, engulfment is driven by the formation of ligand-receptor complexes to overcome the resistance from the membrane and cytoskeleton deformations, and the changes in the configuration entropy of receptors. The energies that determine the internalization process include: (1) The binding energy during the wrapping process can be obtained as,The fluid membrane theory of Canham–Helfrich  states tT is the absolute temperature in the order of 300 K.To estimate the entropic contribution to the energy associated with the receptor distribution, we adopted the model based on the theory of ideal gas ,52. The In the cytoskeleton-associated endocytosis, the entry of NPs into the cells inevitably deforms the cytoskeleton. Obataya et al.  used an The differentiation of free energy in Equation (1) with respect to time yields,Receptors on the membrane are driven by the density gradient to the adhesion region, and this driving force is,D.Further, the diffusion flux can be given as,Finally, owing to membrane fluidity, the free receptors outside the contact region are mobile within the membrane, and the receptor density can be governed by the diffusion equation as follows ,52,(12)The conservation condition of receptors on the membrane can be given as ,52,(13)Substitution of the continuity equation ∞ yields ,52,(14)By incorporating the conservation relation and integrating by parts, the integral term in Equation (8) takes the form ,52,(15)The first term on the right side represents the energy transport across the front at Similar to the Griffith crack growth criterion, the decreasing rate of free energy should equal to the energy dissipated during receptor diffusion for a power-balanced process . Hence, Given the ligand distribution on the NP’s surface, the diffusion equation (12) can be numerically calculated by adopting the finite difference method subjected to the boundary conditions c represents the degree of non-uniformity of ligand distribution. For example, the case of c and is denoted by c, determines the ligand distribution on an NP with different shapes.To simulate the complex situation of non-uniform ligand distribution, we denote a distribution pattern of the ligand density with an approximate harmonic distribution,Then, we consider the typical values of the binding energy of a single bond L=15 kBT , bendingκ=20 kBT , surface kBT/nm2 , diffusi04 nm2/s , and ξL0c. When the degree of non-uniformity of ligand distribution, We exclude the effects of the cytoskeletal deformation and set the initial receptor density as i et al.  and Schui et al. . HoweverWe consider the internalization of spherical NPs under the influence of cytoskeleton deformation and determine how the cytoskeleton stiffness may affect the optimal ligand distribution associated with the smallest wrapping time., for spherical NPs with a radius of 50 nm for entry into cells. It can be seen that the cytoskeleton deformation can significantly affect the uptake. In the membrane-mediated endocytosis, the optimal distribution of the ligands coated on the spherical NP surface tends to be uniform. With an increase of Young’s modulus of the cytoskeleton, the optimal ligand distribution becomes dense at the NP ends with a large curvature, in which the large deformation energy of the cytoskeleton plays a key role in resistance to the cellular uptake. When NPs are uptaken by stiff cells, since a large amount of energy for elastic deformation needs to be overcome, long wrapping times will be needed. Similar results for the oblate ellipsoid and elongated NPs can be seen in c, corresponding to an almost identical wrapping time. The ranges of coefficient c are almost independent of the initial density of the receptors. However, the corresponding wrapping time decreases with the increase of the initial density of the receptors.The relation between the wrapping time of NPs and the degree of non-uniformity of ligand distribution, Size-dependent NP endocytosis has been demonstrated in previous studies ,5,6. Howc corresponding to a wrapping time almost identical to the optimal one. Under this situation, resistance to the cellular uptake from membrane deformation becomes negligible when compared to the energy associated with the receptor distribution, and a long wrapping time mainly results from the large area of NPs that should be wrapped.For the wrapping of large NPs, there exists a very large range of It has been revealed that the optimal ligand distributions of NPs depend on the NP’s shape and cytoskeleton stiffness in cytoskeleton-associated endocytosis. Such optimal ligand distributions become non-uniform when cytoskeleton deformation is taken into account, which is different from that of membrane-mediated endocytosis on the spherical or cylindrical NPs . The nonIn the cellular uptake of spherical or cylindrical NPs via membrane-mediated endocytosis, the uniform distribution of ligands becomes the optimal one corresponding to the high uptake efficiency. By contrast, during the cellular uptake of ellipsoidal NPs via cytoskeleton-associated endocytosis, the mean curvature of the membrane at each wrapping edge is no longer a constant, but is determined by NP shape. In the power balance in Equation (18), the fourth term at the left hand is the energy contribution of cytoskeleton deformation, which is dependent on the engulfment depth. Therefore, the optimal ligand distribution turns to non-uniform and becomes strongly dependent on NP shape and cytoskeleton stiffness.Bio-inspired methods from viruses are suitable for designing drug delivery systems. Thus, a biophysical understanding of NP–cell interactions is urgently needed. For spherical NPs, fast wrapping occurs in a large range of different ligand distribution patterns around a uniform distribution; this finding provides a physical insight into robust virus infection rather than the point of view of gene expression ,58. The We also examined the critical state as follows:By solving Equation (16),When the receptor density in front of the contact edge If the ligand density at the wrapping edge is larger than the critical value, then the wrapping process cannot occur due to the large unfavorable entropic contribution of the energy from the receptor configuration change.Our study presents certain limitations. Although we provide a detailed description on the dependence of the optimal ligand distribution on the NP shape and cytoskeleton stiffness, we have treated the NPs as a rigid body. We did not consider in detail whether NP deformation significantly influences the optimal ligand distribution. We also disregard membrane tension , the kinWe show the systematically distinct effects of ligand distribution on the dynamics of cytoskeleton-associated endocytosis of ellipsoidal NPs with different sizes under different initial receptor densities. NPs with the same number of ligands but different distributions can have a wide range of different dynamic processes of cellular uptake. We find that there exist optimal ligand distributions corresponding to the minimum wrapping times for NPs with different shapes. Unlike the findings in previous studies on the cell entry of spherical and cylindrical NPs with different ligand distributions via membrane-mediated endocytosis, such optimal ligand distributions become non-uniform and dependent on the NP shape and cytoskeleton stiffness. For example, the optimal distribution favors that the ligands are densely distributed in the region with large local curvature or large cytoskeleton deformation. These findings supply an insight into the physical understanding of NP–cell interactions and may provide guidelines for targeted drug delivery.