Changes In Workload Research Articles

Virtual Monitoring Technician Performance in High-Fidelity Simulations of Remote Patient Monitoring: An Exploratory Study.

Virtual Monitor Technicians (VMTs) are crucial in remotely monitoring inpatient telemetry. However, little is known about VMT workload and intratask performance changes, and their potential impact on patient safety. This exploratory study used a high-fidelity simulation aimed to evaluate VMTs' workload and performance changes over time in telemetry monitoring and identify future research directions for performance improvement. The research team created a simulation of the current remote telemetry stations with 36 patient waveforms across 3 screens alongside a documentation screen, replicating VMTs' work. Twelve VMTs participated in a 1-hour session, and time-to-escalate and detection accuracy to auditory/visual alerts were recorded. Workload was measured using the NASA-Task Load Index. The post-task NASA-Task Load Index score showed an increased workload score of 64 of 100 from a prescore of 38 of 100, with mental and temporal demands being the largest contributors. The performance of VMTs did not change significantly over time, with a 52% correct response rate. Participants' ability to detect signals was slightly better than chance (d' = 0.477), and they tended to be cautious in their responses, β (M = 1.989, SD = 1.635). Urgent, Warning, and Medium audiovisual alerts were recognized in 9, 35, and 39 seconds, respectively, whereas advisory alerts (visual only) were recognized in 13 minutes. This study sets a foundation for future work on VMT workload expectations. Although our work is exploratory, the results indicate a significant increase in VMT workload with no decline in performance; VMTs responded most quickly and accurately to urgent alerts, whereas overall response accuracy to nonurgent alerts was marginally better than chance. Future research needs to explore techniques to improve response accuracy rate beyond the 52% measured in this study.

Исследование способов диспетчеризации видео на множестве конечных устройств в контексте языка программирования Rust

Abstract. The modern world is actively moving towards digitalization, which requires efficient distribution of video content on a variety of end devices. The problem is that different usage scenarios impose different requirements on security, quality, and latency when transmitting video. The aim of the study is to identify the most suitable protocols for video dispatching in educational and real-world applications using the Rust programming language, known for its security and performance. Protocols such as HTTPS (TCP), BitTorrent, HLS, WebRTC, SRT and DASH have been investigated and analyzed. The results show that HLS and DASH have advantages in adaptive streaming in a changing network environment, while SRT and WebRTC provide low latency and high reliability for applications requiring real-time operation. The practical significance of these findings is confirmed by successful integration into educational systems, which ensures stable work even with a change in workload. Discussion: in the future, it is recommended to explore the combined use of several protocols, such as HLS and SRT, to improve the overall efficiency and reliability of video data transmission, as well as integration with CDN to improve quality and reduce load.

Investigating the interplay between cognitive workload and situation awareness during full driving automation

This study investigates concurrent changes in cognitive workload and situation awareness during the use of full driving automation. Research shows that automation use has a negative effect on situation awareness. Driving studies posit a decline in cognitive workload when driving automation is engaged. Yet little is known about how changes in cognitive workload affects situation awareness, or vice versa, when full driving automation is used. Participants were instructed to operate a virtual reality fixed-base driving simulator in full driving automation mode. No driving input was required by participants making the driving demand minimal. Situation awareness was measured by means of the situation awareness global assessment scale. Cognitive workload was measured by means of behavioral, ocular, and neurophysiological metrics. Lower awareness was observed over time. Cognitive workload increased over time as evidenced by an increase in pupil size and oxygenated hemoglobin in the right dorsolateral prefrontal cortex. Results indicate an inverse relationship between cognitive workload and situation awareness, with increments in the former leading to reductions in the latter. Although this pattern runs counter to our hypothesis, this is consistent with prior work observing a decline in situation awareness under increasingly higher workload.

An automatic IoT application placement using an improved equilibrium optimizer algorithm in fog computing

PurposeThe rapid proliferation of Internet of Things (IoT) devices across various domains has created a demand for real-time computing resources that traditional cloud computing models struggle to meet. Fog computing, which brings computation resources closer to IoT devices, has emerged as a promising solution. An automatic service placement framework is needed to use fog computing resources efficiently.Design/methodology/approachIn this study, first a three-layer independent service framework is introduced to define relationships between IoT devices and fog layers, facilitating automatic application deployment. Next, an enhanced version of the equilibrium optimizer (EO) algorithm, inspired by physics, is designed for service placement in fog computing environments.FindingsSimulations reveal that the proposed approach surpasses existing methods, achieving a 99% success rate compared to the closest alternative’s 93%. The algorithm also significantly reduces waiting and planning times for service placement, proving its efficiency and effectiveness in optimizing IoT service deployment in fog computing.Research limitations/implicationsOne of the primary limitations is the computational complexity involved in dynamically adjusting to real-time changes in network conditions and IoT workloads. Although improved EO offers improvements in placement efficiency, it may not be fully optimized for highly fluctuating environments. Another important limitation is the uncertainty in node resources. Fog computing environments often face unpredictable changes in the availability and capacity of resources across nodes. This uncertainty can affect the algorithm’s ability to consistently make optimal decisions for IoT service placement.Practical implicationsFrom a practical perspective, the implementation of the proposed framework and the improved EO algorithm can drastically enhance the efficiency of IoT service deployment in fog computing systems. Organizations that rely on IoT networks, particularly those with critical real-time requirements, can benefit from reduced service placement times and lower failure rates. This can lead to better resource utilization, reduced operational costs and improved overall performance of IoT systems. The commercial impact is evident in industries such as smart cities, healthcare, where fast data processing is crucial.Social implicationsOur proposed framework has important implications for real-world IoT applications, particularly in areas requiring low latency processing, such as healthcare, smart cities. By reducing service delays and optimizing resource allocation, the framework can significantly improve the quality and reliability of services. Additionally, improved resource management leads to cost savings and better system efficiency, making the technology accessible to a wider range of applications.Originality/valueExisting resource placement strategies have shown inadequate performance, highlighting the need for more advanced algorithms. This study introduces a three-layer automatic framework for enhancing the application deployment of a fog system beside a novel improved EO algorithm to offer a robust solution for assigning IoT applications to fog nodes.

Veterinary medicine under COVID-19: a mixed-methods analysis of student and practitioner experiences in Austria.

This study aimed to provide a detailed analysis of the pandemic's impact on the veterinary profession and education in Austria. Two online surveys were conducted from November 2022 to January 2023, inviting all veterinarians and veterinary students in Austria to share their experiences on the impact of the pandemic on their veterinarian work and education in a free text question. A total of n = 289 veterinarians and n = 272 veterinary students provided an answer. In summary, 39.8% of veterinarians reported no impact by the pandemic, while others experienced changes in workload (19.7%), changes in client behavior (14.9%), implementation of safety measures (13.1%), and organizational changes (13.1%). Additional impacts included effects on mental health, private life, finances, as well as physical health. Changes were more frequently mentioned in veterinarians working with pets compared to those working with livestock. In the student panel, only a minority reported no significant impact (8.1%) but 44.9% reporting changes due to online and hybrid learning. Key issues included impaired learning conditions (34.9%), and social distancing (34.6%) leading to social isolation. Some students appreciated increased flexibility and reduced commuting (10.7%), while others experienced mental health challenges (10.7%). Further impacts were related to the implementation of safety measures (5.5%), organizational changes (4.8%) and impaired physical health due to infection (1.1%). Negative changes related to distance learning were mainly reported by students in the final study phase, whereas those in the first study phase reported more positive aspects related to online/hybrid learning. Overall, this study highlights the significant impact of the COVID-19 pandemic on veterinary education and professional practice in Austria, revealing diverse challenges for students and relatively lower but still notable effects on practicing veterinarians. Future research should monitor these impacts longitudinally and explore the integration of beneficial practices into standard veterinary education and care, such as effective digital learning platforms and appointment-based systems.

A (contra)reforma do ensino médio e o capitalismo histórico

The Brazilian high school, the last stage of basic education, is at an emblematic moment in its history. The law 13.415/2017 puts structural changes in this stage of education, such as changes in workload and inclusion of new curricular components, in line with a new national curriculum, the Common National Curricular Base. We seek to contribute to the understanding of the situation of the curricular component of Sociology (representing the Social Sciences) in this significant movement, specifying the case of the State of São Paulo, given that the component has a history of instability in school education, minimized with its inclusion as compulsory throughout the country in 2008. This contribution focuses on the relationships between the historical moment of changes in secondary education and the ruptures and continuities of the capitalist productive system, using the Systemic Cycles of Accumulation as a tool for analysis. The configuration of Sociology will be approached from the category of powerful knowledge, and how this knowledge is compromised in the current changes.

Psychological Impact of Covid-19 Pandemic on Health Care Workers of a Field Hospital in UN Peacekeeping Mission

Background: The Covid-19 pandemic has significantly affected the healthcare system worldwide as well as the psychological state of all. The objective of the current study was to assess the psychological conditions of the healthcare workers in Enhanced Level 1+ Hospital, Bouar, Central African Republic (CAR). This assessment can help in the guideline development and implication psychological interventions which in turn can improve the quality of life, working environment and decision-making capabilities of health care workers during widespread pandemic. Methods: This descriptive cross-sectional study was conducted on 40 medical personnel working in Enhanced Level 1+ Hospital, Bouar, Central African Republic. The study was performed from June 2022 to August 2022. Assessment of psychological status of military healthcare providers was done by analyzing the results derived from the Mental health survey, Perceived Stress Scale (PSS-10), Generalized Anxiety Disorder scale (GAD-7) and Patient Health Questionnaire (PHQ-9). Hospital records were analyzed to assess the changes in workload. Results: The working hour increased significantly from Pre Covid to Post Covid period (1920±34.1 hours/week to 2120±67.3 hours/week) although the number of weekly patients in OPD and Emergency decreased slightly from 51.9 to 40.6. Elevated mental stress was observed during outbreak (average PSS-10 score in June 22 was 20.4 which reduced to 9.7 in August). The same pattern was observed in case of GAD-7 score (15.6 to 10.8) and PHQ-9 score (12.4 to 6.1). The percentage of concerns regarding Covid19 was high during the outbreak, 37/40 (92.5%), which decreased after wards to 18/40 (45%). Conclusion: The UN field hospitals are inadequate to support mass outbreak like COVID 19 in the context of both staff and medical supplies. During Covid 19 outbreak, sharp rise of workload and difficulties in restocking of essential medical items played a vital role in the mental state of the medical professionals. The field hospitals in UN peacekeeping missions require intensive effort especially on disaster and epidemic management during pre-deployment training. Moreover, reconsidering distribution of human resources in field hospitals is also required. Bangladesh Armed Forces Med J Vol 57 No (1) June 2024, pp 35-42

Implementation of Computerized Prescriber Order Entry Systems: A Review of Impacts, Barriers, and Facilitators.

This review evaluated the impact of a digitized computerized prescriber order entry (CPOE) system and described barriers and facilitators for introducing a digitized system. A systematic literature search was conducted in PubMed, Medline, and CINAHL using keywords. Articles in English during the last 10 years were included. Study selection was presented using a PRISMA flow diagram. Forty-eight studies were included. Data from the articles were presented to address each of the three objectives. CPOE systems improved the quality of care provided but also introduced new types of errors. Facilitating factors for implementation included leadership, stakeholder engagement, training, and user-centered design. Inadequate training, software design, changes in workload, and workflow disruptions were identified as barriers. Recommendations for implementation included dedicated training of users, user-centered design, a backup for system downtimes, and stakeholder engagement. Application of knowledge of the facilitators and barriers for the introduction of a CPOE system supports this change-management process within the specific context and augurs for more successful implementation.

Dynamic Performance and Power Optimization with Heterogeneous Processing-in-Memory for AI Applications on Edge Devices

The rapid advancement of artificial intelligence (AI) technology, combined with the widespread proliferation of Internet of Things (IoT) devices, has significantly expanded the scope of AI applications, from data centers to edge devices. Running AI applications on edge devices requires a careful balance between data processing performance and energy efficiency. This challenge becomes even more critical when the computational load of applications dynamically changes over time, making it difficult to maintain optimal performance and energy efficiency simultaneously. To address these challenges, we propose a novel processing-in-memory (PIM) technology that dynamically optimizes performance and power consumption in response to real-time workload variations in AI applications. Our proposed solution consists of a new PIM architecture and an operational algorithm designed to maximize its effectiveness. The PIM architecture follows a well-established structure known for effectively handling data-centric tasks in AI applications. However, unlike conventional designs, it features a heterogeneous configuration of high-performance PIM (HP-PIM) modules and low-power PIM (LP-PIM) modules. This enables the system to dynamically adjust data processing based on varying computational load, optimizing energy efficiency according to the application’s workload demands. In addition, we present a data placement optimization algorithm to fully leverage the potential of the heterogeneous PIM architecture. This algorithm predicts changes in application workloads and optimally allocates data to the HP-PIM and LP-PIM modules, improving energy efficiency. To validate and evaluate the proposed technology, we implemented the PIM architecture and developed an embedded processor that integrates this architecture. We performed FPGA prototyping of the processor, and functional verification was successfully completed. Experimental results from running applications with varying workload demands on the prototype PIM processor demonstrate that the proposed technology achieves up to 29.54% energy savings.

Drastic hourly changes in hand hygiene workload and performance rates: A multicenter time series analysis

High hand hygiene (HH) workload is a commonly cited barrier to optimal HH performance. The objective of this study was to assess trends of HH workload as defined by HH opportunities (HHO) and performance rates over different timescales using automated HH monitoring system data. This multiyear retrospective observational study was conducted in 58 inpatient units located in 10 North American hospitals. HHO and HH rates were analyzed by time series mixedeffects general additive model. Median HH rates peaked at 50.0 between 6 and 7 AM with a trough of 38.2 at 5 PM. HHO over hours in a day were thehighest at 184 per hospital unit per hour at 10 AM with a trough of 49.0 between 2 and 3 AM. Median rates for day and night shifts were 40.8 and 45.5, respectively (P=.078). Weekend day shift had the lowest median rate (39.4) compared with any other 12-hour shift (P<.1018). The median rates and HHO varied little across days in a week and months. HH workload and performance rates were negatively correlated and changed drastically over hours in a day. Hospitals should consider HH workload in the development and timely delivery of improvement interventions.

K-medoid clustering containerized allocation algorithm for cloud computing environment

Load balancing is critical for container-based cloud computing environments for several reasons. A lack of appropriate load balancing techniques could result in a decrease in performance and possible service interruptions as some nodes get overloaded, while others are left underutilized. Cloud service providers can reduce latency and boost system performance by strategically placing containers using clustering algorithms. These techniques aid in efficiently using resources and load balancing by clustering related containers together according to their shared attributes. Clustering strategies are effective in allocating and controlling resources to meet the demands of a changing workload. Algorithms for clustering combine related workloads or containers into clusters, improving performance isolation and maximizing resource usage. One popular methodology for data clustering is the K-Medoid Clustering Algorithm. It is especially helpful when working with categorical data or when the dataset contains outliers. K-medoids is an unsupervised clustering approach where the core of the cluster is made up of data points known as “medoids.” A medoid is a location in the cluster whose total distance to every object in the cluster—also known as its dissimilarity—is as small as possible. Any appropriate distance function may be used, such as the Manhattan distance, the Euclidean distance, or another one. Thus, by choosing K medoids from our data sample, the K-medoids method splits the data into K clusters. This work presents the K-Medoid clustering technique for containers, which can enhance load balancing, decrease resource execution times, and increase resource utilization rates all at the same time. The results of the experiment show that the proposed method outperforms MACO and FCFS in terms of throughput by about 70% when number of cloudlets increased. The relative improvement of execution time of the proposed K-medoid algorithm w.r.t FCFS is about 50%.

A particle swarm optimization inspired global and local stability driven predictive load balancing strategy

In distributed systems and parallel computing, optimal load balancing is difficult. These abstract addresses load balancing in distributed situations, highlighting current solutions' flaws and emphasizing the need for new ones. Load balancing research includes centralized and distributed algorithms, heuristics, and predictive models. Despite various successful methods, workload adaptability, overhead reduction, and scaling to large systems remain unresolved. This study proposes a particle swarm optimization (PSO) load balancing method that considers global and local stability considerations. The proposed method uses PSO principles to balance exploration and exploitation and allocate resources among distributed nodes. Predictive components improve preventative load management by predicting workload changes. Global and local load balancing stability criteria distinguish this study. The recommended method considers global system-wide performance indicators, local node-level characteristics, and micro-level stability to maximize system efficiency. A dual-focus technique distinguishes the proposed load balancing strategy from others, solving dynamic distributed system challenges. The study examines load balancing system advances and suggests improvements and further research. More accurate prediction modeling, stability measures, and application-specific enhancements may be studied in the future. Experimental validation and real-world implementation of the recommended approach are necessary to determine its practicality and ability to handle modern distributed computing systems.

Economic Evaluation of Remote Monitoring for Implantable Cardiac Devices: Evidence from a Remote-Care Study.

The adoption of remote monitoring (RM) is especially relevant for patients with implantable cardiac devices due to their high risk of hospitalization and the need for frequent outpatient visits. Though RM can help with early detection of cardiac episodes, it may also increase the number of tasks healthcare providers engage in to monitor patients' health. The adoption of RM may increase healthcare providers' workloads, potentially impacting the quality of care and increasing the risk of clinician-provider burnout. Little is known about the link between RM adoption and changes in healthcare providers' workloads. Using data from a non-randomized clinical trial conducted in 2021-2022 at a University Hospital in Korea, we examined the relationship between RM adoption and changes in patient time savings and healthcare providers' workloads. The clinical trial included patients with a cardiac implantable electronic device compatible with the Biotronik Home Monitoring System. For patients, RM was associated with a 41-minute decrease in total visit duration, attributed to reductions in both wait time (37 minutes; P<0.001) and total examination time (3.7 minutes; P=0.137). For healthcare providers, RM was linked to an increase in overall workload by 107.9 minutes per patient. The increase was primarily due to managing RM alerts (91.8 minutes) and preparing monthly patient reports (19.9 minutes). Our findings suggest that RM was associated with a decrease of 1540 KRW (44%) in average cost of care per minute. RM is associated with time-saving patient benefits and increased healthcare providers' workloads. Even though this was a single-center study with a small number of patients, our research highlights the importance of carefully examining changes in healthcare staff workloads linked to the adoption of RM within the national health insurance system.

Elastic circuit de-constructor: a pattern to enhance resiliency in microservices

Cloud-based workloads have proliferated with the deep penetration of the internet. Microservices based handling of high volume transactions and data have become extremely popular owing to their scalability and elasticity. The major challenge that cloud-based microservice patterns face is predicting dynamic load and failure patterns, which affect resiliency and uptime. Existing Circuit breaker patterns are biased toward denying incoming requests to maintain acceptable latency values, at the cost of availability. This paper proposes the Elastic Circuit De-Constructor (ECD) pattern to address these gaps. The proposed ECD pattern addresses this challenge by dynamically adapting to changing workloads and adjusting circuit-breaking thresholds based on real-time performance metrics. The proposed ECD pattern introduces a novel ‘De-constructed’ state, that allows the ECD to identify alternate paths pre-defined by the application, ensuring user requests continue to be routed to the microservice. By leveraging ‘Availability’, ‘Latency’ and ‘Error rate’ as performance metrics, the ECD pattern is able to balance the fault tolerance and resiliency imperatives in the cloud-based microservices environment. The performance of the proposed ECD pattern has been verified against both no Circuit Breaker and a default Circuit Breaker setting.

Optimizing Cloud Load Balancing: A Nature-Inspired Approach for Efficient Task Scheduling and Resource Optimization in Scalable Cloud Computing Environments

Cloud load balancing is a key part of making sure that tasks are scheduled and resources are used in the best way possible in scalable cloud computing settings. This study suggests a way to improve load balancing that is based on nature and uses methods as Particle Swarm Optimization (PSO) and NIVM PSO Optimized (NIVM-PSO). By acting like natural processes, these programs can adapt to changing workloads and make sure that tasks are spread out evenly across computers. The goal of the suggested model is to cut down on reaction time, make the best use of resources, and boost system performance as a whole. The results of the experiments show that when compared to standard load balancing methods, they are much better at spreading out the load, lowering delay, and increasing speed. This nature-inspired method is a strong way to handle the complexity and demands of modern cloud systems. It creates a framework that can grow and work well for future cloud computing apps.

AI Services-Oriented Dynamic Computing Resource Scheduling Algorithm Based on Distributed Data Parallelism in Edge Computing Network of Smart Grid

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

Exploring Cognitive Workload Changes in Teleoperators: Collaboration with On-Site Workers and Robots in Assembly Tasks

Teleoperation is commonly employed to perform industrial tasks in remote or inaccessible areas. However, there is a noticeable gap in evaluating cognitive workload of teleoperators in collaboration environments. This study compared the variation of cognitive workload for teleoperators guiding an on-site participant to complete a wire assembly task in two scenarios: one aided by a robot arm (tRH) and another without any robot assistance (HH). Additionally, the task demands for on-site participants were manipulated to measure its impact on teleoperator’s workload. NASA-TLX and EEG activity were utilized to assess workload. The results indicated that EEG theta activity was significantly higher for the HH group than tRH group, potentially showing lower workload for teleoperators in the scenario with robot assistance. Task difficulty did not affect any of the workload measures. The study highlights the importance of cognitive workload assessment in human-robot collaborations to optimize human cognitive demands in complex settings.

A Pilot Phase 2 Randomized Trial to Evaluate the Safety and Potential Efficacy of Etravirine in Friedreich Ataxia Patients.

A drug repositioning effort supported the possible use of the anti-HIV drug etravirine as a disease-modifying drug for Friedreich ataxia (FRDA). Etravirine increases frataxin protein and corrects the biochemical defects in cells derived from FRDA patients. Because of these findings, and since etravirine displays a favorable safety profile, we conducted a pilot open-label phase 2 clinical trial assessing the safety and potential efficacy of etravirine in FRDA patients. Thirty-five patients were stratified into three severity groups and randomized to etravirine 200 mg/day or 400 mg/day. They were treated for 4 months. Safety endpoints were the number and type of adverse events and number of dropouts. Efficacy endpoints were represented by changes in peak oxygen uptake and workload as measured by incremental exercise test, SARA score, cardiac measures, measures of QoL and disability. Data were collected 4 months before the start of the treatment (T - 4), at the start (T0), at the end (T4) and 4 months after the termination of the treatment (T + 4). Etravirine was reasonably tolerated, and adverse events were generally mild. Four months of etravirine treatment did not significantly increase the peak oxygen uptake but was associated with a change in the progression of the SARA score (p value < 0.001), compared to the 4 months pre- and post-treatment. It also significantly increased peak workload (p value = 0.021). No changes in the cardiac measures were observed. Health and QoL measures showed a worsening at the suspension of the drug. In this open trial etravirine treatment was safe, reasonably well tolerated and appreciably improved neurological function and exercise performance. Even though a placebo effect cannot be ruled out, these results suggest that etravirine may represent a potential therapeutic agent in FRDA deserving testing in a randomized placebo-controlled clinical trial.

Perspectives of Undergraduate Students on the Academic Workload and Suicidal thoughts at Nigerian University

This study examined the impact of academic activities on suicidal thoughts among undergraduate students of Nnamdi Azikiwe University, Awka. The study adopted a descriptive survey research design. The population comprised of 100 students in Nnamdi Azikiwe University, Awka. The study selected respondents using a simple random sampling technique. Instrument for data collection was a 20 item structured questionnaire titled Perceived Effects of Academic Activities on Suicidal Thoughts Questionnaire (PEAASTQ). The instrument was validated by three experts from the Faculty of Education in Nnamdi Azikiwe University, Awka. The instrument's reliability was established using the Cronbach Alpha coefficient that yielded coefficient values of 0.86 from section A, 0.96 from section B, 0.80 from section C, and 0.96 from section D, indicating that the instrument was valid. The data collected for the study were analyzed using mean and standard deviation. Findings from the study showed that the students agreed that excess academic workload can lead to depression, limit someone's social life, and contribute to suicidal thoughts among undergraduate students and that unplanned, d changes in academic workload negatively impact on someone's mental well-being. Based on these findings, the study recommends among others that there should be an extension of academic sessions to allow for a more balanced and manageable workload throughout the semester. These can help prevent burnout and reduce the cumulative stress of a continuous academic schedule.

Genetic Cache: A Machine Learning Approach to Designing DRAM Cache Controllers in HBM Systems

DRAM memory controller plays a critical role in maximizing the performance of High Bandwidth Memory by efficiently managing data transfers between the CPU and the memory modules. Thus, they are suitable for low-power data-intensive applications. However, the complexity of DRAM command scheduling combined with tag management overheads in the cache makes the design of in-package cache controllers significantly challenging. Traditional memory controllers often face challenges with static, inflexible access scheduling designed for general applications, leading to suboptimal performance in dynamic environments. On the other hand, while some advanced controllers employing reinforcement learning offer adaptability to workload fluctuations, they tend to introduce hardware complexity and incur longer training latencies. Our approach aims to design low-power and efficient DRAM cache controllers using a machine learning model that dynamically adjusts to workload changes with optimized hardware efficiency and reduced training time. Therefore, we propose a machine learning approach to design low-power and efficient DRAM cache controllers that will leverage the trained model to produce optimal cache command schedules at runtime. The model considers several conditions for each request queue, and chooses the best response among the options. The simulation results show the superiority of our proposed design over the previous algorithms in a set of twelve data-intensive applications; our model is able to improve the performance up to 40% in some cases, and an average of 15% in performance and 10% in power consumption.