Flexible Scheduling Research Articles

Charting the dual career journey: insights from adolescent athletes in lower secondary sport schools

ABSTRACT A dual career (DC) involves the integration of a sport career with education or employment, including various stages and transitions. Recently, there has been a notable trend of implementing DC programs within specialized sport schools for early adolescence athletes (ages 13–15). As this a crucial but understudied phase of the DC, the aim of this study was to identify current and projected resources and barriers associated with adolescent athletes’ DC in lower secondary school. As part of a larger three-year initiative, the Finnish Lower Secondary Sports Schools Pilot Project, sixteen (9 females and 7 males) adolescent DC athletes were interviewed twice in the eighth grade (M age = 14.4 years) and one year later in the ninth grade. Using the holistic athletic career model and push, pull, anti-push, anti-pull framework and a directed content analysis, the results revealed a range of immediate personal and environmental resources and barriers across athletic, academic, psychological, psychosocial and financial dimensions. They also suggested that interpersonal and institutional support were regarded as crucial assets for managing current challenges, while personal DC competencies, in particular, were emphasized as essential projected resources contributing to the sustainability of a DC in the future. Based on the findings, sports schools should increase the amount of hours dedicated to teaching DC competencies (e.g. emotional awareness, adaptability and social intelligence) to help athletes effectively cope with barriers and transitions. Additionally, flexible scheduling of teaching and training sessions could facilitate a better balance during demanding training periods.

Unlocking the Potential of Remanufacturing Through Machine Learning and Data-Driven Models—A Survey

As a key strategy for achieving a circular economy, remanufacturing involves bringing end-of-use (EoU) products or cores back to a ‘like new’ condition, providing more affordable and sustainable alternatives to new products. Despite the potential for substantial resources and energy savings, the industry faces operational challenges. These challenges arise from uncertainties surrounding core quality and functionality, return times, process variation required to meet product specifications, and the end-of-use (EoU) product values, as well as their new life expectancy after extended use as a ‘market product’. While remanufacturing holds immense promise, its full potential can only be realized through concerted efforts towards resolving the inherent complexities and obstacles that impede its operations. Machine learning (ML) and data-driven models emerge as transformative tools to mitigate numerous challenges encountered by manufacturing industry. Recently, the integration of cutting-edge technologies, such as sensor-based product data acquisition and storage, data analytics, machine health management, artificial intelligence (AI)-driven scheduling, and human–robot collaboration (HRC), in remanufacturing procedures has received significant attention from remanufacturers and the circular economy community. These advanced computational technologies help remanufacturers to implement flexible operation scheduling, enhance quality control, and streamline workflows for EoU products. This study embarks on a comprehensive review and in-depth analysis of state-of-the-art algorithms across various facets of remanufacturing processes and operations. Additionally, it identifies key challenges to advancing remanufacturing practices through data-driven and ML methods and uncovers research opportunities in synergy with smart manufacturing techniques. The study aims to offer guidelines for stakeholders and to reinforce the industry’s pivotal role in circular economy initiatives.

MIVNDN: Ultra-Short-Term Wind Power Prediction Method with MSDBO-ICEEMDAN-VMD-Nons-DCTransformer Net

Wind speed, wind direction, humidity, temperature, altitude, and other factors affect wind power generation, and the uncertainty and instability of the above factors bring challenges to the regulation and control of wind power generation, which requires flexible management and scheduling strategies. Therefore, it is crucial to improve the accuracy of ultra-short-term wind power prediction. To solve this problem, this paper proposes an ultra-short-term wind power prediction method with MIVNDN. Firstly, the Spearman’s and Kendall’s correlation coefficients are integrated to select the appropriate features. Secondly, the multi-strategy dung beetle optimization algorithm (MSDBO) is used to optimize the parameter combinations in the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, and the optimized decomposition method is used to decompose the historical wind power sequence to obtain a series of intrinsic modal function (IMF) components with different frequency ranges. Then, the high-frequency band IMF components and low-frequency band IMF components are reconstructed using the t-mean test and sample entropy, and the reconstructed high-frequency IMF component is decomposed quadratically using the variational modal decomposition (VMD) to obtain a new set of IMF components. Finally, the Nons-Transformer model is improved by adding dilated causal convolution to its encoder, and the new set of IMF components, as well as the unreconstructed mid-frequency band IMF components and the reconstructed low-frequency IMF, component are used as inputs to the model to obtain the prediction results and perform error analysis. The experimental results show that our proposed model outperforms other single and combined models.

A Comprehensive Review on Novel Potassium Competitive Acid Blocker Vonoprazan in the Treatment of Gastrointestinal Disorders

Vonoprazan, a first-in-class potassium-competitive acid blocker (P-CAB), represents a significant advancement in the management of acid-related disorders. This novel agent demonstrates rapid, potent, and sustained acid suppression through its unique mechanism of reversibly binding to the gastric H+/K+-ATPase proton pump. Vonoprazan maintains stable inhibition of acid secretion regardless of CYP2C19 polymorphisms, offering advantages over traditional proton pump inhibitors (PPIs). Clinical studies have established its superior efficacy in treating various acid-related conditions, including gastroesophageal reflux disease (GERD), peptic ulcer disease, and Helicobacter pylori infection. The drug exhibits remarkable healing rates in erosive esophagitis and maintains long-term remission effectively. In H. pylori eradication therapy, vonoprazan-based regimens have shown higher success rates compared to conventional PPI-based treatments, particularly in areas with high antibiotic resistance. The pharmacokinetic profile of vonoprazan allows for flexible dosing schedules and rapid onset of action, enhancing patient compliance. Safety analyses from long-term studies indicate a favorable tolerability profile, with most adverse events being mild to moderate in severity. The emergence of vonoprazan has expanded therapeutic options for acid-related disorders, particularly benefiting patients who show inadequate response to conventional PPI therapy. Recent data also suggest potential applications in prevention of low-dose aspirin-induced ulcers and management of non-erosive reflux disease (NERD).

Adherence to diabetic retinopathy screening among children and young adults in Bangladesh

BackgroundEffective diabetic retinopathy screening (DRS) programmes are important in preventing vision impairment and blindness caused by diabetes. This study focuses on identifying the factors affecting attendance or non-adherence to DRS among children and young adults with diabetes mellitus (DM) in Bangladesh.MethodsA mixed-methods approach was used, which included patients diagnosed with DM aged 12–26 years from Bangladesh who were registered at BIRDEM Women and Children hospital in Dhaka. Data collection occurred between July 2019 and July 2020, mainly through telephone and email due to restrictions imposed by the COVID-19 pandemic. Statistical analyses, including chi-squared tests, t-tests, and logistic regression, were used to assess the demographic and clinical factors influencing attendance at DRS.ResultsThe study reported a high 88% attendance rate for DRS among children and young adults in Bangladesh. However, some barriers to attendance were identified. Children under 15 years of age showed a higher tendency to attend their last DRS appointment when compared to older age participants (16–26 years), P < 0.05. Male participants demonstrated a lower likelihood of attending their DRS appointments than females (OR 0.29, CI: 0.17 to 0.50), P < 0.001. Additionally, participants with higher HbA1c levels (mean 9.1%, IQR 2.5) attended their last DRS appointment compared to those with lower levels (mean 8.0%) (p < 0.05). The primary barriers leading to missed DRS appointments were distance to the hospital (15, 31.9%), financial limitations (19, 40.4%), and busy schedules (14, 29.8%).ConclusionsCompliance with DRS was high in this setting especially among younger patients, females, and those with higher HbA1c levels, highlighting the effectiveness of current DRS initiatives in Bangladesh. Addressing barriers such as cost, service accessibility and transportation could improve attendance rates further, and strategies such as flexible scheduling, transport subsidies, telemedicine, and use of artificial intelligence may help overcome these challenges.

A multi-stage balancing scheduling method for flexible loads of large-scale electric vehicles

To address the degradation of grid quality and charging efficiency associated with the large-scale integration of electric vehicles (EVs), a multi-stage balanced flexible load scheduling method is proposed. This approach is designed to facilitate peak shaving and valley filling, balance intermittent energy fluctuations, and provide auxiliary services, thereby significantly altering system load characteristics, smoothing energy fluctuations, reducing operational costs, and enhancing the regulatory capabilities of power grid dispatching operations. A multi-objective optimization mathematical model is developed, focusing on key indicators that impact the scheduling process, including network loss, operational cost, and user satisfaction. A multi-stage flexible load scheduling framework is introduced within the competitive swarm optimization (CSO) algorithm, resulting in the design of an advanced CSO algorithm. This algorithm is distinguished from traditional methods by the implementation of advanced learning based on grouping after a random competitive learning phase, which enhances the efficiency of particle swarm learning while ensuring stable population convergence throughout the optimization process. Furthermore, the CSO framework is maintained to ensure effective population diversity, greatly improving the optimization performance. Simulation results indicate that the voltage fluctuation index of the proposed algorithm is 1.8% lower than that of the standard CSO algorithm, while network loss and operational costs are reduced by 2.83 and 5.81%, respectively, thereby validating the effectiveness and efficiency of the proposed approach.

Commentary: Transforming Mental Health Care in Pakistan through Telemedicine.

This commentary explores the transformative potential of telemedicine in addressing mental health care challenges in Pakistan, particularly in the context of the COVID-19 pandemic. With approximately 34% of the population suffering from various mental disorders and significant barriers to treatment—such as social stigma, poverty, and limited access to resources—telemedicine offers a critical solution for delivering care, especially in underserved rural areas where traditional services are lacking. The article discusses the benefits of remote consultations, including increased accessibility, flexible scheduling, and reduced stigma, particularly for vulnerable populations like women who face additional societal barriers. However, it also highlights key challenges, such as the digital divide, with approximately 45% of the population lacking reliable internet access, and the urgent need for training among mental health professionals to effectively utilize telehealth platforms. The commentary advocates for a comprehensive approach that includes improving technological infrastructure, enhancing digital literacy, and community education to fully harness telemedicine's potential. Future research is essential to evaluate its long-term efficacy and inform policy decisions. Ultimately, telemedicine could play a vital role in reshaping mental health care delivery in Pakistan, fostering greater access and engagement in a post-pandemic world.

Talent retention in Asian emerging markets: evidence from China and Malaysia

Purpose The purpose of this paper is to study talent retention, which has long been an important area of inquiry across many industries. Consistently, both academicians and practitioners have recommended several “posthire” solutions to retain employees, such as allowing employees to work from home, providing them with flexible work schedules, promoting a work–life balance and rewarding good performance. In this study, the authors focus on how the “prehire” career-related characteristics of call center agents and their personality traits relate to their turnover intentions. This is important because selecting the right person at the employee selection stage can prevent firms from trying to retain someone who is likely to quit his/her job. Design/methodology/approach The authors surveyed 442 employees working in the call centers of a major multinational logistics company across China and Malaysia and used covariance-based structural equation modeling (CB-SEM) to test the model. Findings The results showed that call center agents who reapplied for a job in the current company had lower turnover intentions than first-time applicants. Similarly, call center agents who provided supplementary information during their application had lower turnover intentions than the ones who did not provide any such information. As for the personality traits, the results indicated that while the call center agents’ agreeableness and openness to experience were positively associated with their turnover intentions, their consciousness and emotional stability were negatively associated with them. Originality/value This study shows the uniqueness of Asian emerging markets and the call center industry by presenting several interesting patterns that are different from those that have been found in other industries.

3D gamma analysis between treatment plans for nominally beam‐matched medical linear accelerators using PyMedPhys

AbstractBeam‐matched linear accelerators (linacs) enable flexible patient scheduling and efficient treatment delivery in the event of unexpected machine downtime. The purpose of this study was to test the feasibility of 3D gamma index as an additional metric beyond standard measurement‐based comparisons for more efficient evaluation of treatment plans between linacs with nominally matched beam models to ensure safe patient transfer. Seventeen 3D conformal radiotherapy (3DCRT) plans and thirty‐six volumetric‐modulated radiation therapy (VMAT) plans for different disease sites were selected from the original linac. An in‐house script was used to automatically create new plans for the target linac and calculate dose using parameters of the original plans. 3D gamma analysis was performed to compare plan dose distributions between the target and original linacs using PyMedPhys. The 2%/2 mm gamma pass (γ≤1) rate was &gt;99.99% for all 3DCRT plans. The median 1%/1 mm pass rate was 99.86% but two cases failed (&lt; 90%). For VMAT plans, the median and minimum 2%/2 mm gamma pass rates were 99.43% and 93.81%. For 1%/1 mm, the median pass rate was 92.02% but ten cases failed. The results indicated using 3D gamma index can enhance the confidence and add an extra layer for safe patient transfer.

Case Analysis of a 70-Hour Work Week and Its Associated Cost-Benefit Outcomes

The phenomenon of a 70-hour work week has become increasingly prevalent in certain industries, raising concerns regarding work-life balance and employee well-being. While specific sectors, such as finance, technology, and healthcare, often necessitate extended hours due to high-pressure environments and stringent deadlines, this practice is not universally accepted or sustainable. Generational differences in perceptions of work ethic contribute to this issue, with older generations occasionally equating prolonged hours with dedication. However, there is growing awareness of the potential adverse effects on mental and physical health. Recent reports, including those from NVIDIA employees, highlight the challenges associated with such demanding schedules. To address this issue, organizations should prioritize employee wellness and implement policies that promote work-life balance. This may include flexible scheduling, mandatory time-off, and productivity measures that focus on output rather than hours worked. It is important to note that work pressure is not exclusive to any single generation, including Gen Z, but rather a systemic issue that affects employees across age groups. Addressing the 70-hour workweek phenomenon requires a multifaceted approach involving corporate cultural changes, policy reforms, and a shift in societal attitudes towards work and success. Keywords: 70-hour work, work-life balance, sustainable, work ethic, Gen Z.

Part-Time Employment System for UTM Student

In today's higher-education system, students frequently face financial difficulties due to high tuition fees and living expenses. Additionally, they risk falling victim to scams while searching for employment opportunities, further worsening their struggles. The lack of a centralized platform for job-seeking increases the problems, making it difficult for students to find suitable part-time employment that aligns with their interests and pay well. Employers also struggle to find qualified candidates with the necessary skills for specific roles. This thesis proposes a part-time employment system designed to allow students to find decent, relevant part-time job opportunities. By connecting students with legitimate businesses through this platform, the system reduces the risk of fraud and simplifies the job search process. Additionally, the system makes it easier for employers to hire students, who are ideal for part-time roles requiring flexible schedules. Utilizing advanced technology and a user-friendly interface, the proposed system enhances the efficiency and reliability of the part-time job market, creating a mutually beneficial interaction between students and businesses.

Factors Affecting the Quality of Work Life for MBA Faculty In Kerala

Objectives: The primary objective of this study is to identify and examine the factors influencing the quality of work life (QWL) for MBA faculty members in Kerala, India. The study aims to assess elements such as workload, physical workspace, compensation, opportunities for advancement, flexibility in scheduling, recognition, decision-making participation, student interactions, organizational culture, technology access, ethnic compliance, and adherence to legal requirements. By identifying these influencing factors, the study seeks to offer insights that can help improve job satisfaction and work-life balance for MBA faculty, thereby supporting their professional well-being and academic effectiveness. Methods: To achieve these objectives, the study utilizes a factor analysis methodology, a statistical technique designed to identify underlying relationships among various variables. This method helps determine which specific factors significantly impact the quality of work life for MBA faculty members. Data for the analysis was collected through surveys administered to faculty members across several educational institutions in Kerala. Factor analysis was then applied to the collected data to categorize the factors and understand their impact on work-life quality for faculty. Results: The results of the factor analysis reveal several critical factors that substantially impact the quality of work life for MBA faculty members in Kerala. Key findings include the importance of manageable workloads, supportive organizational culture, adequate physical workspace, and a fair compensation structure. Additionally, factors such as flexible scheduling options, opportunities for professional growth, and recognition for achievements were shown to contribute positively to faculty satisfaction. The findings suggest that these elements collectively influence faculty job satisfaction and their overall quality of work life. Conclusion: The study concludes that enhancing the quality of work life for MBA faculty in Kerala requires a comprehensive approach addressing multiple work environment factors. Educational institutions should focus on optimizing workloads, improving workplace facilities, offering competitive compensation, and fostering an inclusive and supportive organizational culture. Implementing such improvements is anticipated to result in higher levels of job satisfaction, which could positively impact faculty performance and academic success in Kerala’s educational institutions. The insights gained from this study are expected to be valuable for policymakers and administrators aiming to create a supportive and productive work environment for MBA faculty.

Toward real-time operations of modular-vehicle transit services: From rolling horizon control to learning-based approach

Recent technological advancements have opened doors for real-time adjustments and controls during public transport operations. In particular, the introduction of modular vehicles has the potential to significantly enhance public transit service quality. This innovative public transit service with modular vehicles, characterized by its flexible schedules and vehicle formations, allows for the dynamic management of transit capacity to meet the fluctuating passenger demands. This paper proposes to schedule the flexible modular-vehicle transit service in real time considering the varying demands. To jointly optimize the service schedule and vehicle formations, we propose the rolling horizon control approach to decompose the complex problem into subproblems that can be solved efficiently during the process. On top of this, we introduce a learning-based optimization proxy to streamline the optimization process within the rolling horizon framework, enabling near-optimal decisions to be made with minimal execution time without directly solving the optimization problem. Through numerical studies, we demonstrate the effectiveness and efficiency of the proposed methods in terms of solution quality and efficiency. Furthermore, our case studies show that modular vehicles can adapt to the changing demand and effectively reduce the total costs in the transit system.

Flexible low carbon optimal dispatch of distribution networks considering the demand response of heat storage industrial loads

To cope with the uncertainty brought by the large-scale integration of renewable energy under the goal of carbon neutrality, it is necessary to tap and utilize flexible and adjustable resources from both the source and the load side at the same time. Hence, a flexible low-carbon optimal scheduling method for distribution networks is proposed in this paper, which takes into account the participation of heat storage industrial loads in demand response. Firstly, the model of the gas turbine equipped with a flexible carbon capture device is established, and the non-convex constraint introduced by the adjustable flue gas diversion ratio is convexified. Then the model of the fused magnesium load, a representative of heat storage industrial loads, is established for its participation in demand response. The segment linearization and convexification methods are performed on the conditional productivity constraints of the fused magnesium load. On this basis, a mixed-integer linear programming model for flexible and low-carbon optimal dispatch of the distribution network is developed by using the stochastic optimization theory and solved by commercial solvers. The proposed method is verified to be able to ensure the economic operation of the distribution network while reducing carbon emissions and promoting renewable energy consumption.

GPU Coroutines for Flexible Splitting and Scheduling of Rendering Tasks

We introduce coroutines into GPU kernel programming, providing an automated solution for flexible splitting and scheduling of rendering tasks. This approach addresses a prevalent challenge in harnessing the power of modern GPUs for complex, imbalanced graphics workloads like path tracing. Usually, to accommodate the SIMT execution model and latency-hiding architecture, developers have to decompose a monolithic mega-kernel into smaller sub-tasks for improved thread coherence and reduced register pressure. However, involving the handling of intricate nested control flows and numerous interdependent program states, this process can be exceedingly tedious and error-prone when performed manually. Coroutines, a building block for asynchronous programming in many high-level CPU languages, exhibit untapped potential for restructuring GPU kernels due to their versatility in control representation. By extending Luisa [Zheng et al. 2022], we implement an asymmetric, stackless coroutine model with programming language support and multiple built-in schedulers for modern GPUs. To showcase the effectiveness of our model and implementation, we examine them in different application scenarios, including path tracing, SDF rendering, and incorporation with custom passes.

Day-Ahead Optimization of Proton Exchange Membrane Electrolyzer Operations Considering System Efficiency and Green Hydrogen Production Constraints Imposed by the European Regulatory Framework

Clean hydrogen (H2) use (i.e., produced using either renewable or low-carbon energy sources) can help decarbonize energy-intensive industries, the transport sector, and the power sector. The European regulatory framework establishes that the production of green H2 must be supported either by the electricity grid through a power purchase agreement (PPA) or by intermittent renewable energy source (RES) plants owned by the hydrogen producer. Although the issue of the optimization of hydrogen production costs has already been approached, constraints related to the current regulatory framework and the modeling of nonlinear electrolyzer efficiency still represent open problems. In this paper, a mixed-integer linear programming (MILP) problem, assuming as the objective function the overall cost minimization of the allowed energy mix for green H2 production, is formulated. Two approaches are compared: in the first one, electrolyzers can only operate at 100% load, whereas the second one allows for more flexible electrolyzer scheduling, by enabling partial-load working operations. The simulation results of several scenarios considering different H2 production targets, forecasted RES production, and cost for PPAs demonstrate the effectiveness of the proposed methodology.

A hybrid machine learning-based cyber-threat mitigation in energy and flexibility scheduling of interconnected local energy networks considering a negawatt demand response portfolio

The interconnection of local energy networks (LENs) enables efficient exchange of energy and flexibility among them, fostering the integration of distributed energy resources and demand-side management strategies. Thus, the interconnected local energy systems (ILEN) structure is a viable approach to electrical distribution systems’ operation and management. However, implementing distributed energy management structures such as ILEN entails a great amount of information transactions. Therefore, these structures are more vulnerable to cyber threats. Thus, the newly developed efficient and secure power systems’ operation methods should take digitalization-related security risks into account. As a result, this paper is focused on the development of a secure operation method, equipped with a hybrid algorithm to mitigate cyber threats in the context of ILEN. In this regard, this research proposes a novel hybrid XGBoost-based cyber threat mitigation (HXGBTM) method to cope with the vulnerabilities of the physical and information layers of the cyber-infrastructure. The proposed cyber threat mitigation method is built upon the classification and regression capabilities of the XGBoost ensemble of decision trees to identify and mitigate anomalies in the electrical consumption data. Therefore, in the first step, the ILEN’s multi-objective energy and flexibility scheduling problem considering demand response portfolio i.e., MOEFSDRPILENis developed that encompasses a bi-level optimization problem, in which the operator of the ILEN optimizes energy and flexibility trading in the upper level. While in the lower level, each LEN operator minimizes scheduling costs along with maximizing the local flexibility as well as providing a demand response portfolio as a negawatt resource. Here, the flexibility index, which is later maximized using the second objective function, is considered as the proportion between "the available ramping capacity" and "required ramping capacity". In this paper, direct load control, and interruptible/curtailable demand response comprehensive models are implemented as candidate programs for the suggested portfolio. Furthermore, a hybrid cyber threat is modeled considering the communication line intrinsic vulnerability, as a result of natural causes, wear, and aging of the infrastructure etc., as well as false data injection (FDI) attacks that target each LEN’s electrical consumption database. Finally, the proposed HXGBTM is employed to mitigate the above-mentioned cyber-vulnerabilities and achieve near real-world conditions.

Literacy Deep Reinforcement Learning-Based Federated Digital Twin Scheduling for the Software-Defined Factory

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting its ability to meet a wide range of needs. To address this issue, a new manufacturing concept called the software-defined factory has emerged. It is an autonomous manufacturing system that provides reconfigurable manufacturing services to produce tailored products. Reinforcement learning has been suggested for flexible scheduling to satisfy user requirements. However, fixed rule-based methods struggle to accommodate conflicting needs. This study proposes a novel federated digital twin scheduling that combines large language models and deep reinforcement learning algorithms to meet diverse user requirements in the software-defined factory. The large language model-based literacy module analyzes requirements in natural language and assigns weights to digital twin attributes to achieve highly relevant KPIs, which are used to guide scheduling decisions. The deep reinforcement learning-based scheduling module optimizes scheduling by selecting the job and machine with the maximum reward. Different types of user requirements, such as reducing manufacturing costs and improving productivity, are input and evaluated by comparing the flow-shop scheduling with job-shop scheduling based on reinforcement learning. Experimental results indicate that in requirement case 1 (the manufacturing cost), the proposed method outperforms flow-shop scheduling by up to 14.9% and job-shop scheduling by 5.6%. For requirement case 2 (productivity), it exceeds the flow-shop method by up to 13.4% and the job-shop baseline by 7.2%. The results confirm that the literacy DRL scheduling proposed in this paper can handle the individual characteristics of requirements.

Heuristic Optimal Scheduling for Road Traffic Incident Detection Under Computational Constraints.

The intelligent monitoring of road surveillance videos is a crucial tool for detecting and predicting traffic anomalies, swiftly identifying road safety risks, rapidly addressing potential hazards, and preventing accidents or secondary incidents. With the vast number of surveillance cameras in operation, conducting traditional real-time video analysis across all cameras at once requires substantial computational resources. Alternatively, methods that employ periodic camera patrol analysis frequently overlook a significant number of anomalous traffic events, thereby hindering the effectiveness of traffic event detection. To overcome these challenges, this paper introduces a heuristic optimal scheduling approach designed to enhance traffic event detection efficiency while operating within limited computational resources. This method leverages historical data and prior knowledge to compute a weighted event feature value for each camera, providing a quantitative measure of its detection efficiency. To optimize resource allocation, a cyclic elimination mechanism is implemented to exclude low-performing cameras, enabling the dynamic reallocation of resources to higher-performing cameras, thereby enhancing overall detection performance. Finally, the effectiveness of the proposed method is validated through a case study conducted in a representative region of a major metropolitan city in China. The results revealed a substantial improvement in traffic event detection efficiency, with increases of 40%, 28%, 17%, and 28% across different time periods when compared to the pre-optimized state. Furthermore, the proposed method outperformed existing resource scheduling algorithms in terms of average load degree, load balance degree, and higher computational resource utilization. By avoiding the common issues of resource wastage and insufficiency often found in static allocation models, this approach offers greater flexibility and adaptability in computational resource scheduling, thereby effectively addressing the practical demands of traffic anomaly detection and early warning systems.

Leadership in Crisis: Navigating the Challenges of COVID-19

Abstract—The COVID-19 pandemic presented unprecedented challenges to global industries, with the Consumer Packaged Goods (CPG) sector being significantly impacted due to lock- downs, supply chain disruptions, and workforce safety concerns. This paper examines how a leading CPG manufacturer navigated the crisis, highlighting the leadership strategies and operational adjustments employed to sustain production, ensure employee safety, and maintain market presence. Key areas of focus in- clude the implementation of automation, establishment of robust quarantine protocols, adoption of flexible work schedules, and incentivization of front-line employees. Additionally, the paper explores the complexities of managing supply chains and vendor relationships during the pandemic and illustrates how, while many competitors were forced to shut down or faced shortages due to inadequate preparedness and inability to adapt safety protocols, the manufacturer remained operational by swiftly reacting to the evolving situation, resulting in increased sales. Drawing on this case study, the paper provides actionable steps for organizations to prepare for future similar scenarios, emphasizing the importance of resilience and proactive leadership in crisis management. Index Terms—Leadership, Crisis Management, COVID-19, CPG Industry, Automation, Supply Chain Resilience, Employee Incentives, Flexible Work Schedules, Food Manufacturing, Or- ganizational Resilience