Training Schedule Research Articles

Unified View Empirical Study for Large Pretrained Model on Cross-Domain Few-Shot Learning

The challenge of cross-domain few-shot learning (CD-FSL) stems from the substantial distribution disparities between target and source domain images, necessitating a model with robust generalization capabilities. In this work, we posit that large-scale pretrained models are pivotal in addressing the CD-FSL task owing to their exceptional representational and generalization prowess. To our knowledge, no existing research comprehensively investigates the utility of large-scale pretrained models in the CD-FSL context. Addressing this gap, our study presents an exhaustive empirical assessment of the Contrastive Language–Image Pre-Training model within the CD-FSL task. We undertake a comparison spanning six dimensions: base model, transfer module, classifier, loss, data augmentation, and training schedule. Furthermore, we establish a straightforward baseline model, E-base, based on our empirical analysis, underscoring the importance of our investigation. Experimental results substantiate the efficacy of our model, yielding a mean gain of 1.2% in 5-way 5-shot evaluations on the BSCD dataset.

Exploring Journey Experiences of Disabled Passengers in Using Public Transport

This study aims to enhance the understanding of disabled passengers' experiences with public transport by addressing the challenges in measuring their satisfaction and service quality. The central problem is the difficulty in accurately assessing and improving the service quality for disabled passengers, given the diverse needs and barriers they face. Utilizing the SERVQUAL model, which focuses on Tangibility, Reliability, Responsiveness, Assurance, and Empathy, the study identifies and evaluates these key dimensions in the context of public transport services. Findings reveal both unique needs and commonalities among different disability groups: hearing-impaired passengers require visual signals and nonverbal communication, visually impaired passengers need clear auditory announcements and staff assistance, and physically disabled passengers require accessible infrastructure and supportive services. Commonalities include the appreciation for reliable train schedules, cleanliness, and the need for empathetic and responsive staff. Based on these insights, recommendations are provided to enhance service quality by improving physical accessibility features, enhancing communication methods, and providing targeted staff training. Implementing these recommendations will address specific needs and improve the overall satisfaction of disabled passengers, contributing to a more inclusive and effective public transport system. Future work should focus on integrating additional models and exploring other key variables to uncover further insights into service quality and passenger satisfaction. This approach will offer a more comprehensive understanding of how various factors interact and contribute to a more inclusive and effective public transport system.

Two-step Passenger-to-Train and Route Assignment Model for Urban Rail Transit

Accurate passenger flow assignment results are crucial for the operational organization of urban rail transit. However, because of barrier-free transfer, the transfer information of passengers is not recorded. The paper proposes the two-step passenger-to-train and route assignment (TPTRA) model to address this issue. Each passenger is assigned to the trains and one route considering the complex transfer situation using TPTRA compared with the existing passenger-to-train model. Constraints on passengers, trains, and routes are constructed based on a large amount of real automatic fare collection data, train schedules, and metro topology networks to improve accuracy and transferability over traditional survey-based methods. The generalized logistic is first used to evaluate the egress time, access time, and transfer time distributions. Finally, TPTRA is developed based on the constraints and distributions. The model estimates the probability of passengers choosing the feasible routes and boarding the first train. It also calculates transfer passenger flow values and the probability of passengers being left behind. The analysis demonstrates a notable enhancement in the accuracy of the egress time distribution fitting using the generalized logistic distribution, with an improvement of 84.41% compared with the lognormal distribution and 15.78% compared with the normal distribution. Furthermore, the model’s effectiveness is validated through sectional passenger flow comparisons, yielding R-squared values between 0.847 and 0.902, indicating the effectiveness and reliability of the TPTRA model.

The Multiple Advantages of Self-Leadership in Higher Education: The Role of Health-Promoting Self-Leadership among Executive MBA Students

The purpose of this conceptual and explorative study is to document the advantages of health-promoting self-leadership practice. Professional certification programs are criticized due to a lack of practice among participants, involvement, and short duration. This study is based on self-leadership practices over several weeks of two executive MBA student classes from spring 2020 and 2021. A preliminary theoretical framework is developed suggesting that health-promoting self-leadership, which includes three orientations, has an impact on self-efficacy, mental fitness, and performance. Two different self-leadership classes with managers from the public and private sectors went through a self-development process as a semester project. The content analyses of 112 student reports unfolded students’ self-development processes and results identified in diaries, notes, tables, training schedules, and reflections. The findings, based on two different samples and aggregated data, support the theoretical framework. Moreover, the processes of self-development and self-leadership seem to be advantageous for almost all managers. The health-promoting self-leadership practice improved managers’ mental and physical fitness, energy balance, self-efficacy beliefs, and performance. These outcomes were achieved through physical activity, self-talk, meditative exercises, reward systems, visualizing, diet programs, and routines. The findings underscore the importance of self-development processes amongst managers, and that health-promoting self-leadership courses should last over several weeks.

Railway condition information modelling in a BIM framework for maintenance applications

ABSTRACT Railway maintenance management involves various interventions that, considering safety and use needs, require an integrated approach for supporting decisions. Defining an infrastructure digital twin can represent an efficient basis to implement management processes. BIM, offering a simplified and realistic digitalized model, may ensure immediate benefits for asset administrators in maintenance management and planning. It would permit to properly maintain full control of infrastructure degradation, to optimize investments and ensure adequate quality levels. In this context, a preliminary procedural framework for modelling railway condition information in an I-BIM environment is proposed, using specific survey data acquired by scheduled diagnostic trains. The authors developed customized routines and scripts to elaborate rail track geometry attributes (with real-time checks of the dynamic effects on the track) and simplify asset quality evaluation for the administrators. This may suggest a novel vision and framework to support maintenance management procedures in future developments of the overall approach.

Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning

Real-Time Optimization of Urban Rail Transit Train Scheduling via Advantage Actor–Critic Deep Reinforcement Learning

The Impact of Competitive Sports on the Mental Health of Young People

Introduction Mental health is a crucial component of overall well-being for everyone, and it becomes particularly important in the context of young student-athletes who must simultaneously meet educational and athletic challenges. Athletes, like their peers, struggle with various mental health issues such as anxiety, depression, suicidal thoughts, eating disorders, sleep deficits, burnout, and overtraining. The additional activity of engaging in competitive sports, compared to those who do not, can exacerbate these symptoms. The aim of study Identify and analyze specific sources of stress and mental health issues among young student-athletes and young individuals. Compare different research groups. Conclusion Student-athletes face the unique challenge of balancing intense training and sports competitions with educational demands. The constant pursuit of peak performance and coping with high expectations can lead to significant psychological stress. Factors such as injuries, overtraining, social identity issues, and sleep problems further impact their mental health, often leading to the development or worsening of existing mental health issues. It is important to note that professional psychological care is essential in managing the training schedule of a young athlete. For their development to be balanced across various dimensions such as proper physical growth, well-being, and mental wellness, this support is crucial.

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.

Routing and scheduling of trains and engines in a railway marshalling station yard

In a busy railway marshalling station, train and engine traffic management in the receiving/departure yard plays a crucial role in efficient and stable operations. Traditionally, a track assignment problem (TAP) is solved to assign tracks to trains for berthing at a receiving/departure yard. However, the TAP does not encompass shunting operations in the yard (e.g., engine replacement and train disassembly), which can result in additional scheduling challenges for dispatchers and route conflicts between operations. This paper investigates a train and engine routing and scheduling problem (TERSP) in a receiving/departure yard of railway marshalling stations, which involves simultaneously assigning routes and scheduling route-setting start times for both train and shunting operations to be conducted in the yard. By introducing the concepts of task, activity, and pattern, we transform the original problem into assigning pre-generated patterns incorporating both route and route-setting start time alternatives to activities. The transformed problem is formulated into a compact binary integer linear programming model with a linear number of constraints and the objective of minimizing the total time deviation of all involved tasks. An improved technique that relies on listing all maximal (bi)cliques in a constructed graph is designed to effectively model the time coherence and track section occupation constraints. A heuristic that gradually expands the patterns for the identified key activities by adding more start time alternatives is applied to remedy an infeasible model caused by potential route conflicts. In addition, a rolling horizon algorithm that decomposes the original problem into consecutive smaller stages using either a time-rolling or a train-rolling rule is developed to efficiently solve instances. Finally, numerical experiments based on the physical layouts and real timetables of a receiving yard and a departure yard of a large marshalling station in China are conducted to assess the performance and applicability of our proposed approaches. The results demonstrate that our approaches typically find (near-)optimal solutions within several minutes for the investigated instances by simultaneously addressing different classes of yard operations and resources.

Freight train scheduling for industrial lines with multiple railway undertakings

With the liberalisation of the rail freight market, the number of railway undertakings in operation is increasing. Trains run by several railway undertakings (RUs) converge at industrial lines leading up to terminals. Here, uncoordinated interaction between mainline RUs and limited infrastructure capacity leads to bottlenecks, which reduces resource utilisation of railway undertakings. Outsourcing last-mile operations to an independent local railway undertaking can improve capacity utilisation and decrease the time engines of mainline engines spend within the considered network. In this paper, we propose the Industrial Line Scheduling Problem with multiple RU, a resource scheduling model for freight trains at industrial lines. The aim is to minimise unproductive time of mainline engines and the number of deployed local engines. The results show that the potential savings per employed local engine are highly dependent the timetables of inbound and outbound trains within the network, the dwell time of railcars and on the degree of local railway undertaking involvement.

Flexible yard crane scheduling for mixed railway and road container operations in sea-rail intermodal ports with the sharing storage yard

The sea-rail intermodal port, serving as the “cornerstone” supporting the seamless connection of landside and quayside transportation, plays a crucial role in determining the service level of the container multimodal transport through port’s operational organization efficiency. With on-dock rails, the physical integration between railways and water transport is smoothly achieved. However, within the port, the diversity in container flows adds complexity to operational organization and decision-making. In this paper, we formulate a flexible crane scheduling model to reduce the mutual interference during the mixed operations of railway containers and road containers in the sharing yard. The model addresses the time window constraints of railway containers required by train schedule, and the uncertain scenarios of external truck arrivals. To solve this problem, we design a two-stage approach that encompasses a task reassignment phase and a sequential decision-making phase. Furthermore, we test the model and algorithm performance through various experiments of different scales. Our results show that the designed algorithm designed solves instances of 20 tasks in 18.7 s, compared to 3600 s for the model solver. It efficiently handles real-world instances with over 200 tasks and 100 scenarios. Finally, sensitivity analysis is carried out to capture the influence of railway container ratio, truck arrival patterns, and train time windows on yard operations. Under intensive operation, we find that road containers, following a uniform arrival pattern, are least affected by railway container operations when the proportion of railway containers in the sharing block ranges from 70% to 80%. We also observe that the flexibility of crane service increases as the time window for railway container operations becomes more lenient.

THE EFFECT OF TRAINING EFFECTIVENESS ON INDIVIDUAL WORK PERFORMANCE IN BUSINESS ORGANIZATIONS IN CAMBODIA

On-the-job training or off-the-job training are considered training effectiveness which is very crucial for all employees to have earned more skills and knowledge to improve work performance for effective outcomes. Since some employers are not much concerned about training programs for their employees in business organizations which result in poor work performance. Therefore, this study aims to measure the training effectiveness which impacts on individual work performance in business organizations in Cambodia. Quantitative research has been employed in this study with descriptive and inferential data were analyzed. Simple and multiple regressions are used in order to test the training effectiveness variables on individual work performance. The study has constructed the valid questionnaires to survey 200 participants with different business organizations in Cambodia. A regression analysis reveals that training significantly influences individual work performance. All training variables, including style, content, environment, schedule, and materials, showed a positive relationship with performance. Training materials and training content have had positive effects on individual work performance. The study emphasizes the importance of investing in well-rounded training programs that consider various aspects of delivery and content. The study's value lies in its demonstration of how to apply successful aspects of training programs to improve individual work performance in business firms. Eventually, the study has suggested that the leaders of business organizations should consider the efficient techniques of organizing training programs for their employees.

Stress-induced increase in heart-rate during sleep as an indicator of PTSD risk among combat soldiers.

Discerning the differential contribution of sleep behavior and sleep physiology to the subsequent development of posttraumatic-stress-disorder (PTSD) symptoms following military operational service among combat soldiers. Longitudinal design with three measurement time points: during basic training week (T1), during intensive stressed training week (T2), and following military operational service (T3). Participating soldiers were all from the same unit, ensuring equivalent training schedules and stress exposures. During measurement weeks soldiers completed the Depression Anxiety and Stress Scale (DASS) and the PTSD Checklist for DSM-5 (PCL-5). Sleep physiology (sleep heart-rate) and sleep behavior (duration, efficiency) were monitored continuously in natural settings during T1 and T2 weeks using wearable sensors. Repeated measures ANOVA revealed a progressive increase in PCL-5 scores from T1 and T2 to T3, suggesting an escalation in PTSD symptom severity following operational service. Hierarchical linear regression analysis uncovered a significant relation between the change in DASS stress scores from T1 to T2 and subsequent PCL-5 scores at T3. Incorporating participants' sleep heart-rate markedly enhanced the predictive accuracy of the model, with increased sleep heart-rate from T1 to T2 emerging as a significant predictor of elevated PTSD symptoms at T3, above and beyond the contribution of DASS stress scores. Sleep behavior did not add to the accuracy of the model. Findings underscore the critical role of sleep physiology, specifically elevated sleep heart-rate following stressful military training, in indicating subsequent PTSD risk following operational service among combat soldiers. These findings may contribute to PTSD prediction and prevention efforts.

Maintenance cost optimisation in critical single-component systems: A technician’s training approach based on a joint learning–forgetting and fuzzy maintenance quality model

Effective maintenance in critical systems is crucial to ensure their operability and safety. Maintenance technicians play an important role in achieving the safety and reliability objectives of critical system components. However, the role of the technical team in maintenance quality is often overlooked. Our study addresses this knowledge gap by developing a methodology to optimise the training scheduling of maintenance technicians in single-component systems, focusing on a learn-and-forget approach. Our main objective is to improve maintenance efficiency by mitigating the negative impact of technician expertise level. To this end, we implemented a simulation-based optimisation, modelling the evolution of technician experience and its influence on maintenance costs. As a case study, we used an urban aerial passenger ropeway system, demonstrating that proper training of technical staff can significantly reduce both corrective and preventive maintenance costs. Our methodology identified three strategic areas for training technical staff throughout the system lifecycle. The results highlight the importance of investing in the training scheduling of technical equipment in critical systems and suggest that an appropriate training strategy can improve operational efficiency and reduce maintenance costs. In conclusion, our research underlines the need to actively consider the human factor in the maintenance of critical systems to ensure their optimal performance and long-term safety.

Public Transit for Special Events: Ridership Prediction and Train Scheduling

Public Transit for Special Events: Ridership Prediction and Train Scheduling

Rhythmanalysis in coaching: grasping the everyday rhythm(s) of coaching

ABSTRACT This article uses Lefebvre’s rhythmanalysis to critically explore and make-sense-of the (co-)productive schemes of ‘coaching rhythm(s)’ at Alvour Football Club (a pseudonym). The first author spent seven months at a semi-professional football club by living with the group’s weekly schedule of training, meetings, and matches. The precise research methods included (written) field-notes, individual semi-structured interviews, and visual data. The findings introduce and explicate how the group’s rhythmic flow of the week was subject to the perpetual manipulation and imposition of the coaching staff, while also being politically negotiated, and simultaneously resisted by athletes. Specifically, the analysis uses the interplay between ‘eurhythmia’, ‘polyrhythmia’, and ‘arrythmia’ to understand the subtle and nuanced routines of coaching, and how individuals become sensitive to their production. In addition to introducing the value of understanding rhythm(s) in coaching, it is hoped the rhythmanalysis offered in this paper contributes an original interpretation of the taken-for-granted everydayness of coaching, which can challenge many of the assumed mechanical overtones that currently exist. Doing so, the paper offers a new reading of coaching as manipulating rhythm(s).

Penerapan Model Kirkpatrick dalam Evaluasi Program Pelatihan Kepemimpinan Perempuan di Pusat Pengembangan Sumber Daya Wanita Pasoendan Digdaya

Purpose – The purpose of this study was to evaluate how participants responded to the women's Leadership Training Program held by the Pasoendan Digdaya women's Resource Development Center (PPSW) in Lebak regency, Banten. To measure participants ' satisfaction with the training program, this evaluation used the Kirkpatrick reaction Level Model.Method – This study was designed to use descriptive qualitative methods. Data were collected through interviews with 10 trainees. The questionnaire addresses various things, such as the relevance of the material, instructor performance, and training facilities. To determine the level of satisfaction of participants, descriptive was used to analyze the data.Findings – The results indicated that participants were generally satisfied with the training program. The training and consumption schedules have been adjusted to meet the needs of the trainees, and the material presented is considered relevant and applicable. However, the instructor is considered to have strong presentation skills. Additionally, the media and facilities used received positive responses, although there are some suggestions for improvements. Research Implications – Providing women with leadership opportunities significantly advances gender equality by ensuring equal access for both women and men. The findings indicate that while the overall training program effectively meets participants' needs, there is room for improvement in the facilitators' skills and the customization of training materials. It is recommended that PPSW Pasoendan Digdaya enhance facilitator competencies and tailor training content more closely to the specific needs of the participants. This study also highlights the potential for further research into effective teaching methods to bolster women's leadership capabilities.

The Design and Impact of Interactive Online Modules for Dental Faculty Calibration

The diverse backgrounds of health professions faculty often result in inconsistent teaching, clinical techniques, and feedback for students. Faculty calibration is essential for uniform, high-quality instruction. However, scheduling training sessions is challenging due to faculty availability. This study introduces a self-paced, interactive online approach to dental faculty calibration. Four self-paced online modules were developed using an interactive cloud-based platform. A variety of learning activities were interspersed throughout the module to promote active learning. A survey captured faculty’s perception of the online format. ANOVA analyses examined differences in perceived effectiveness of the online format between full-time, part-time, and adjunct faculty. The platform analytics offered insights into the faculty’s module performance. Anecdotal feedback from faculty provided evidence of behavioral changes. 94% of the faculty expressed high satisfaction with the online format. The majority of faculty agreed or strongly agreed that the online format was effective (89%), engaging (88%), and easy to navigate (84%). They highlighted the modules’ user-friendliness, flexibility, and engaging content. ANOVA analyses revealed no significant differences in perceived effectiveness of the online format between full-time, part-time, and adjunct faculty. Anecdotal feedback demonstrated that faculty were incorporating the knowledge gained from the modules into their teaching practices. This positive online experience also motivated several faculty to integrate similar online approaches into their own courses. This online approach provides a more flexible, sustainable, and interactive approach to faculty development that could be beneficial to other institutions.

Improving train services to increase student mobility and safety on the Purwakarta-Karawang route

Transportation is essential for the mobility of workers and students, yet many prefer motorcycles over public transportation like trains due to inadequate schedules and limited train availability. This choice contributes to Indonesia's high motorcycle usage and associated traffic accident risks. This study focuses on the Purwakarta-Karawang train route and its impact on student mobility at Universitas Singaperbangsa Karawang. Employing a qualitative method with descriptive data collection and SWOT analysis, this research gathers student feedback on current train schedules and their impact on daily commutes. Findings indicate that increasing the number of train schedules could significantly improve student mobility and reduce the reliance on motorcycles, thereby decreasing traffic accidents. Moreover, better train schedules could enhance access to educational and employment opportunities. The study suggests that the local government should consider expanding and optimizing train services to address these issues. This research contributes to the broader field of transportation studies by highlighting the importance of efficient public transport schedules in reducing traffic accidents and supporting student mobility. It emphasizes the need for policy adjustments to improve Indonesia's public transportation infrastructure and services by proposing practical solutions.

Bereavement scheduling policy for emergency medicine residents: A descriptive pilot study.

The Accreditation Council for Graduate Medical Education has tasked residency programs to prioritize resident wellness, reduce trainee stress, and prevent burnout. Grief and bereavement can significantly impact residents' wellness during difficult clinical training schedules. There are no best practices on how to support residents during this time. In a split academic county emergency medicine (EM) residency, this pilot study documents a resident-driven change to scheduling practices for bereavement leave. An advisory group of residents, chief residents, and program directors informally polled peer institutions to develop bereavement leave guidelines. Considerations were made to balance resident wellness, education, and patient care in developing a bereavement scheduling policy. The bereavement policy was adopted in January 2023, aiming to "support the resident during a difficult time and reduce concerns around shift coverage" following the death of a family member without impacting sick call. The number of covered days depended on the relationship of the resident to the deceased. Residents covering bereavement days for their peers were financially compensated. During the first 7 months following implementation, five residents utilized the policy. These residents noted this to be the most positive impact on the residency during the past year. Based on resident feedback, the scope was expanded to include grave medical illness of a family member as an implementation criterion. This article outlines the creation, implementation, and benefits of a bereavement scheduling policy within an EM residency. Describing this approach will provide guidance for other residencies to adopt similar wellness-focused strategies.