Training Schedule Research Articles

Effect of a 14-Day Period of Heat Acclimation on Horses Using Heated Indoor Arenas in Preparation for Tokyo Olympic Games.

To optimise the performance and welfare of horses during equestrian competitions in hot climates, it is advised to acclimate them to the heat. The effects of training in a heated indoor arena were studied. Four Olympic horses (13.3 ± 2.2 years; three eventers, one para-dressage horse) were trained for 14 consecutive days in a heated indoor arena (32 ± 1 °C; 50-60% humidity) following their normal training schedule in preparation for the Tokyo Olympic games. Standardised exercise tests (SETs) were performed on Day 1 and Day 14, measuring heart rate (HR; bpm), plasma lactate concentration (LA; mmol/L), deep rectal temperature (Trec; °C), sweat loss (SL; L), and sweat composition (K+, Cl- and Na+ concentration). The data were analysed using linear mixed models. The Trec and HR were significantly decreased after acclimation (estimate: -0.106, 95% CI -0.134, -0.078; estimate: -4.067, 95% CI -7.535, -0.598, respectively). Furthermore, for all the horses, the time taken to reach their peak Trec and heat storage increased, while their LA concentrations decreased. The SL, Cl-, and Na+ concentrations decreased in three out of the four horses. Conclusions: Fourteen days of normal training in a heated indoor arena resulted in a reduction in cardiovascular and thermal strain. This is advantageous because it shows that elite sport horses can be acclimated while training as usual for a championship.

Effects of plyometric jump training on measures of physical fitness and lower-limb asymmetries in prepubertal male soccer players: a randomized controlled trial

BackgroundHigh level of physical fitness is a paramount soccer performance factor. As such, developing key components of physical fitness such as sprinting, jumping, and change of direction (CoD) at an early age empowers both short- and long-term performance success. Although previous research in prepubertal male soccer players has reported physical fitness performance enhancements following plyometric jump training (PJT), the effects on inter-limb asymmetries remain unclear.ObjectiveTo assess the effects of PJT on measures of physical fitness and inter-limb asymmetries in prepubertal male soccer players.MethodsA total of 27 participants were recruited, and randomly assigned to either a PJT group (n = 13; age = 12.7 ± 0.2 years; maturity offset = -1.6 ± 0.7) or an active control group (CG) (n = 14; age = 11.8 ± 0.4 years; maturity offset = -2.51 ± 0.61). The training intervention lasted eight-week and was conducted during the in-season period, with twice-weekly sessions. Physical fitness tests were conducted before and after the intervention, including the 505 change-of-direction (CoD; [505 CoD test]), countermovement-jump (CMJ) height, standing-long-jump (SLJ) distance, and single-leg hop test for distance with dominant (SHTD-D) and non-dominant legs (SHTD-ND). A jump-based asymmetry score was calculated as the difference between HTD and HTND.ResultsANCOVA analysis revealed significant between-group differences in all physical fitness measures at post-test. Specifically, the PJT group showed significant large improvements in CMJ height, SLJ distance, HTD and HTND, and CoD speed (d = 0.84 to 2.00; ∆1.05% to 16.85%). Moreover, the PJT group showed a significant, small reduction in the inter-limb asymmetry score (d = 0.43; ∆-45.21%). In contrast, no significant changes were reported in the CG between pre-and post-tests (d = 0.07 to 0.24; ∆0.21% to 0.98%).ConclusionsThe incorporation of PJT into the training schedules of prepubertal male soccer players resulted in positive effects on various measures of physical fitness. Furthermore, our findings suggest that PJT can reduce lower-limb asymmetry, which could potentially decrease the risk of lower limb injuries.Trial registrationThis study does not report results related to healthcare interventions using human participants and therefore it was not prospectively registered.

Subway network optimization and passenger travel experience

This conference paper examines the significance of subway network optimization in relation to passenger travel experience. It begins with an introduction that highlights the importance of subway networks in urban transportation and establishes the objectives of network optimization. A comprehensive literature review explores previous research on subway network optimization and passenger travel experience, identifying strengths, limitations, and research gaps. The paper then explores various methods of subway network optimization, including network structure optimization, train scheduling optimization, and station layout optimization, providing explanations of their principles and application scenarios. The impact of these methods on passenger travel experience is discussed, considering evaluation metrics such as crowding level, punctuality, transfer efficiency, and comfort. A case study of the China Metro system is then presented to illustrate the implementation process of network optimization and an analysis of its impact on passenger travel experience using evaluation metrics. The findings emphasize the importance of subway network optimization and its potential to enhance passenger satisfaction, connectivity, and sustainable urban development. The conclusion connects with the paper thesis and main findings that underscore the significance of subway network optimization, proposing future research directions and exploring the relationship between optimization efforts and passenger travel experience. Therefore, the paper will help understand why subway network optimization and gives insights for enhancing passenger travel experience in urban subway systems.

Enhanced Scheduling to Improve Resident Continuity in a Family Medicine Teaching Clinic.

Continuity of care is a core concept at the heart of primary care practices. Increased patient-provider continuity of care is associated with better satisfaction scores, better clinical outcomes, decreased hospitalizations and emergency department utilization, improved completion of preventive health services, adherence to medical treatment plans, and improved show rates. Compared to traditional outpatient practices, resident teaching clinics traditionally have lower rates of continuity and face unique challenges in improving continuity given the curricular demands, complex scheduling, and high turnover of providers. The objective of our study was to assess the impact of front office training and new electronic medical record (EMR) scheduling protocols on resident continuity in a family medicine teaching clinic. From July 2021 through May 2022, optimized scheduling through a provider search function in the EMR was implemented in a family medicine teaching clinic. We compared the monthly continuity rates between corresponding months in the prior year and the intervention year. Over an 11-month implementation process, continuity for resident physicians increased from 36.4% to 64.6% (χ2=675.41, P<.001) using EMR tools and scheduling search functions to improve and sustain continuity over the study period. This intervention to enhance continuity in a family medicine residency clinic led to rapid and sustained improvement in provider continuity. This result demonstrates that optimization of EMR scheduling with tools and protocols can improve overall continuity. This scheduling process can likely be applied to clinical sites for residency programs across disciplines.

How Community Health Workers can Address Tobacco Dependence of High-Risk patients: A Qualitative Analysis of the Unique Training Needs of Community Health Workers

Introduction Communities with low socioeconomic status have disproportionately high rates of tobacco use, and community health workers (CHWs) have an increasing role in delivering tobacco cessation interventions. However, existing tobacco cessation trainings are not appropriate for the CHW model of care. The aim of this study was to identify training needs of CHWs to develop a tailored tobacco cessation curriculum to help them effectively serve their high-risk patients. Incorporating results of a previously conducted needs assessment survey, we developed a preliminary outline of a tobacco cessation training curriculum that was specific to the CHW experience. Methods Participants (N = 14) discussed their impressions of (a) the training content, (b) the unique needs of patients seen by CHWs, and (c) tailoring to the CHW care model. We conducted virtual qualitative interviews and focus groups with stakeholders (i.e., managers/directors of CHW programs) and CHWs, respectively, to obtain feedback on training, as well as a sample 2-hour training schedule and curriculum. Two independent coders analyzed the data using the Framework method. Results Two overarching themes emerged: the need for strategies to (a) personalize treatment to each high-risk patient and (b) increase and maintain patient motivation. CHWs also reported the need for specific language to engage patients who were not ready to quit. Conclusion A tailored training might focus on motivational interviewing, including how to personalize treatment to the patient’s experiences, and role-playing scenarios with scenes on how to flexibly support patients who are overburdened and unmotivated.

Analysis of Game Specific Skill Practices on Hand-eye Coordination and Balance of Cricket Players

Game-specific skill practices are simply fitness and performance training designed specifically for athletic performance enhancement. If the training is done in the correct way it can be very beneficial, if not, it can be detrimental. The main objective of this study was to investigate and assess the analysis of Game Specific Skill Practices on hand-eye coordination and balance of cricket players. Thirty club cricket players (18-25 years of age) were randomly selected and divided into experimental and control groups of fifteen. Only the experimental group was treated with the Game Specific Skill Practices intervention for eight weeks, and the control group was kept without any specific training except regular cricket practices. Both group’s pre and post-test data were collected before and after the training schedule. The pertaining data of hand-eye coordination and balance were collected using a standardized test namely, ball transfer test and strok stand test. There were found significant differences among the pre and post-test means comparison of hand-eye coordination and balance between experimental and control groups (P<0.05). Appropriate Game Specific Skill Practices can be treated to develop hand-eye coordination and balance and may impact the improvement of the performance of cricket players and other similar games. Game Specific Skill Practices can be introduced to other parameters of psycho-motor also in order to improve the performances of cricket players at different levels.

Robust optimization modelling of passenger evacuation control in urban rail transit for uncertain and sudden passenger surge

ABSTRACT Effectively addressing the surge in passenger flow caused by emergencies represents a critical challenge in the emergency management of urban rail transit operations. This article introduces a comprehensive control strategy for passenger evacuation, employing robust optimization methods to tackle the uncertainties associated with sudden increases in passenger numbers. Initially, a comprehensive mathematical optimization model for train scheduling and passenger flow coordination is established to ensure efficient passenger transport while maximizing the reduction of operational costs for the operating company’s emergency response. Subsequently, the impact of uncertainty factors on the evacuation model is considered. The fluctuation in passenger flow is represented using a range of intervals, and a robust corresponding model is formulated by introducing an uncertainty budget coefficient. Furthermore, small-scale numerical examples are utilized to discuss passenger evacuation plans, conduct robustness analysis, and assess demand sensitivity. The practical case of the Beijing subway demonstrates that, in contrast to the most optimistic scenario, the robust passenger plan, accounting for a 1% fluctuation in passenger flow, exhibits a reduced cost increase from 10.96% to 9.13%, as compared to the most conservative situation.These findings contribute to enhancing the emergency response capabilities of operational management departments.

Passive exposure to task-relevant stimuli enhances categorization learning.

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.

Passive exposure to task-relevant stimuli enhances categorization learning

Learning to perform a perceptual decision task is generally achieved through sessions of effortful practice with feedback. Here, we investigated how passive exposure to task-relevant stimuli, which is relatively effortless and does not require feedback, influences active learning. First, we trained mice in a sound-categorization task with various schedules combining passive exposure and active training. Mice that received passive exposure exhibited faster learning, regardless of whether this exposure occurred entirely before active training or was interleaved between active sessions. We next trained neural-network models with different architectures and learning rules to perform the task. Networks that use the statistical properties of stimuli to enhance separability of the data via unsupervised learning during passive exposure provided the best account of the behavioral observations. We further found that, during interleaved schedules, there is an increased alignment between weight updates from passive exposure and active training, such that a few interleaved sessions can be as effective as schedules with long periods of passive exposure before active training, consistent with our behavioral observations. These results provide key insights for the design of efficient training schedules that combine active learning and passive exposure in both natural and artificial systems.

Early Implementation of Robotic Training in Surgical and Surgical Subspecialty Residency.

Robotic surgery has emerged as an operative tool for many elective and urgent surgical procedures. The purpose of this study was to evaluate early surgical trainees' experiences and opinions of robotic surgery. An introductory robotic training course consisting of online da Vinci Xi/X training and in-person, hands on training was implemented for residents and medical students across surgical subspecialties at a single institution. A voluntary survey evaluating perceptions of and interest in robotic surgery and prior robotic surgery experience, as well as a basics of robotics quiz, was distributed to participants prior to the start of the in-person session. Descriptive statistics were used to evaluate the cohort. 85 trainees participated in the course between 2020 and 2023, including 58 first- and second-year surgical residents (general surgery, urology, OB/GYN, and thoracic surgery) and 27 fourth-year medical students. 9.4% of participants reported any formal robotic surgery training prior to the session, with only 19% of participants reporting robotic operative experience. 52% of the participants knew of and/or had completed the da Vinci online course modules prior to the scheduled training session. Participants unanimously (100%) agreed that robotic surgery should be implemented into surgical training. There is rising enthusiasm for robotic surgery, yet early exposure and training remain infrequent and inconsistent amongst medical students and new surgical residents. A standardized introduction of multi-disciplinary robotic surgery training should be incorporated into medical school and/or early residency education to ensure surgical residents receive appropriate exposure and training to achieve competency.

Effect of Task Oriented Exercise with Wrist Flexibility Training among Badminton Players to Improve Wrist Mobility and to Prevent Wrist Injury: Comparative Study

Background: This study was conducted explicitly on the efficacy of task-oriented exercises paired with wristflexibility training on enhancing wrist mobility and reducing wrist injuries in badminton players.Purpose: The purpose of this study was to see how a structured intervention integrating task- oriented exercisesand wrist flexibility training benefited wrist mobility and injury prevention in badminton players.Materials and Methods: A randomized controlled trial design was used, with a sample of 70 intermediate- levelbadminton players participating. The participants were split into two groups: conventional and intervention. Theintervention group participated in a four-week programme that included task-oriented workouts with badmintonmovements and targeted wrist flexibility training. The typical group continued with their regular training schedule,with no wrist mobility modification. Validated metrics - ROM and Quick DASH score - were used to assess wristmobility and function.(Study period : September 2022-May 2023)Result: Preliminary research found that the intervention group had significantly better wrist mobility than thecontrol group. They also demonstrated enhanced ROM, flexibility, and functional performance of the wrist joint,as well as a significant decrease in wrist injury occurrence.Conclusion: These data indicate that combining task-oriented exercises with wrist flexibility training in badmintonplayers can successfully improve wrist mobility and lower the incidence of wrist injuries.

Heuristic for Railway Crew Scheduling With Connectivity of Schedules

This paper addresses the crew scheduling for long-distance passenger train services. A heuristic with bin packing features is developed to generate repeatable crew schedules that satisfy the operational and crew allocation rules. By ensuring the connectivity of crew duties that can be repeated over periodic train schedules, a better estimate of the crew requirement in a region is also obtained. Further, the heuristic ensures a fair division of the total workload and creates long duty cycles, which also makes the process of cyclic rostering easier. The paper also presents an exact approach for crew scheduling using a combination of constraint programming and set covering formulations. The exact approach is not computationally viable for practical scale problem instances, but the heuristic generates good quality solutions (often very close to optimal) even on large data sets. We illustrate the approach on data from the Mumbai Division in Indian Railways and the computational results show that there is potential to reduce the total number of crew duties in the region by around 12%. The heuristic approach provides an efficient way to generate improved crew schedules every time there is a change in the train timetable.

Synchronizing train, aircraft, shuttle, and passenger flows in intermodal timetabling: A time–space network-based formulation and a decomposition algorithm using Alternating Direction method of multipliers

The design of synchronized services is of great significance to the development of intermodal transportation. In the planning of Air and High-Speed Railway (HSR) integration services (AHIS), the operators aim to synchronize flight and train schedules and determine the possible transferring options for intermodal passengers. This paper attempts to design an intermodal timetable for train and flight services considering the time-dependent passenger demand. Different transferring modes are further considered in this paper including both shuttle and rapid walking services. The synchronization problem of “train unit flows”, “aircraft flows”, “shuttle flows” and “passenger flows” is formulated using a multi-commodity flow model in a time–space network. Based on the Lagrangian relaxation method, we first decompose the original problem into four types of routing subproblems associated with train units, aircrafts, shuttle buses and passengers, respectively. Then the coordination of the four different types of flows is achieved through both the pricing mechanism and Alternating Direction Method of Multipliers (ADMM) method. The proposed model and solution approach are validated using both hypothetical and real-world cases and the results show that the synchronized timetables are able to serve more intermodal passengers and shorten their travelling times.

Training stress, neuromuscular fatigue and well-being in volleyball: a systematic review

BackgroundVolleyball, with its unique calendar structure, presents distinct challenges in training and competition scheduling. Like many team sports, volleyball features an unconventional schedule with brief off-season and pre-season phases, juxtaposed against an extensive in-season phase characterized by a high density of matches and training. This compact calendar necessitates careful management of training loads and recovery periods. The effectiveness of this management is a critical factor, influencing the overall performance and success of volleyball teams. In this review, we explore the associations between training stress measures, fatigue, and well-being assessments within this context, to better inform future research and practice.MethodsA systematic literature search was conducted in databases including PsycINFO, MEDLINE/PubMed, SPORTDiscus, Web of Science, and Scopus. Inclusion criteria were original research papers published in peer-reviewed journals involving volleyball athletes.ResultsOf the 2535 studies identified, 31 were thoroughly analysed. From these 31 articles, 22 included professional athletes, seven included collegiate-level volleyball athletes, and two included young athletes. Nine studies had female volleyball players, while the remaining 22 had male volleyball athletes.ConclusionsInternal training load should be collected daily after training sessions and matches with the session rating of perceived exertion method. External training load should also be measured daily according to the methods based on jump height, jump count, and kinetic energy. If force platforms are available, neuromuscular fatigue can be assessed weekly using the FT:CT ratio of a countermovement jump or, in cases where force platforms are not available, the average jump height can also be used. Finally, the Hooper Index has been shown to be a measure of overall wellness, fatigue, stress, muscle soreness, mood, and sleep quality in volleyball when used daily.

One-Bit Supervision for Image Classification: Problem, Solution, and Beyond

This article presents one-bit supervision, a novel setting of learning with fewer labels, for image classification. Instead of the training model using the accurate label of each sample, our setting requires the model to interact with the system by predicting the class label of each sample and learn from the answer whether the guess is correct, which provides one bit (yes or no) of information. An intriguing property of the setting is that the burden of annotation largely is alleviated in comparison to offering the accurate label. There are two keys to one-bit supervision: (i) improving the guess accuracy and (ii) making good use of the incorrect guesses. To achieve these goals, we propose a multi-stage training paradigm and incorporate negative label suppression into an off-the-shelf semi-supervised learning algorithm. Theoretical analysis shows that one-bit annotation is more efficient than full-bit annotation in most cases and gives the conditions of combining our approach with active learning. Inspired by this, we further integrate the one-bit supervision framework into the self-supervised learning algorithm, which yields an even more efficient training schedule. Different from training from scratch, when self-supervised learning is used for initialization, both hard example mining and class balance are verified to be effective in boosting the learning performance. However, these two frameworks still need full-bit labels in the initial stage. To cast off this burden, we utilize unsupervised domain adaptation to train the initial model and conduct pure one-bit annotations on the target dataset. In multiple benchmarks, the learning efficiency of the proposed approach surpasses that using full-bit, semi-supervised supervision.

Patient Training and Patient Safety in Home Hemodialysis.

The success of a home hemodialysis program depends largely on a patient safety framework and the risk tolerance of a home dialysis program. Dialysis treatments require operators to perform dozens of steps repeatedly and reliably in a complex procedure. For home hemodialysis, those operators are patients themselves or their care partners, so attention to safety and risk mitigation is front of mind. While newer, smaller, and more user-friendly dialysis machines designed explicitly for home use are slowly entering the marketplace, teaching patients to perform their own treatments in an unsupervised setting hundreds of times remains a foundational programmatic obligation regardless of machine. Just how safe is home hemodialysis? How does patient training affect this safety? There is a surprising lack of literature surrounding these questions. No consensus exists among home hemodialysis programs regarding optimized training schedules or methods, with each program adopting its own approach on the basis of local experience. Furthermore, there are little available data on the safety of home hemodialysis as compared with conventional in-center hemodialysis. This review will outline considerations for training patients on home hemodialysis, discuss the safety of home hemodialysis with an emphasis on the risk of serious and life-threatening adverse effects, and address the methods by which adverse events are monitored and prevented.

Project Orchestrator

The ever-increasing growth and complexity of software-intensive systems over the past 20 years and the ensuing rise in geographically distributed projects are trends that are here to stay. Solving the distributed project management problem by automating it is a top priority, one that relies not simply on the intelligent application of technology, but also on coordinated, complementary efforts focused on people and processes. These systems must evolve to support increased decision velocity and cohesiveness in today’s increasingly distributed world. The unique features of a software product line organization challenge the conventional definition of project management. Among those features are different project types, each of which adds value in a different way. A Project Management Office System (PMOS) is designed to help project office personnel manage project and resource information. The system, built using Oracle Application Express (APEX) and hosted on Oracle Cloud, shall allow input of new project and new resource details, and manipulation of project/resource information. It will facilitate the allocation of resources to projects and the transfer of resources across projects. The system will also manage training requirements, training schedules, and training closure functions.

Assessment of the effect of train traction by locomotives of different types on the quality indicators of the train schedule

The precinct and technical speed of freight trains is one of the main parameters of railway transport characterizing qualitative indicators. The higher the local and technical speed, the better the quality indicators of railway transport, the higher the speed of cargo delivery to the addresses. These speeds directly depend on the distance between stations and the time of their passage. The types of electric locomotives are one of the main factors determining the travel time and energy consumption of trains moving along the site. The differentiated accounting of inter-station runs in the train schedule is based on traction calculations. This article analyzes the regulatory timetable of the trains of the “Tukimachi – Akhangaran” section of Uzbekistan Railways JSC and develops a methodology for determining the travel time of trains moving on the section for the 3VL80r electric locomotive based on traction calculations. Based on the developed methodology, the influence of the time of inter-station runs under traction conditions by locomotives of various brands on the site and technical speed of trains on the railway section was estimated.

Deeply Supervised Block-Wise Neural Architecture Search.

Neural architecture search (NAS) has shown great promise in automatically designing neural network models. Recently, block-wise NAS has been proposed to alleviate deep coupling problem between architectures and weights existed in the well-known weight-sharing NAS, by training the huge weight-sharing supernet block-wisely. However, the existing block-wise NAS methods, which resort to either supervised distillation or self-supervised contrastive learning scheme to enable block-wise optimization, take massive computational cost. To be specific, the former introduces an external high-capacity teacher model, while the latter involves supernet-scale momentum model and requires a long training schedule. Considering this, in this work, we propose a resource-friendly deeply supervised block-wise NAS (DBNAS) method. In the proposed DBNAS, we construct a lightweight deeply-supervised module after each block to enable a simple supervised learning scheme and leverage ground-truth labels to indirectly supervise optimization of each block progressively. Besides, the deeply-supervised module is specifically designed as structural and functional condensation of the supernet, which establishes global awareness for progressive block-wise optimization and helps search for promising architectures. Experimental results show that the DBNAS method only takes less than 1 GPU day to search out promising architectures on the ImageNet dataset with less GPU memory footprint than the other block-wise NAS works. The best-performing model among the searched DBNAS family achieves 75.6% Top-1 accuracy on ImageNet, which is competitive with the state-of-the-art NAS models. Moreover, our DBNAS family models also achieve good transfer performance on CIFAR-10/100, as well as two downstream tasks: object detection and semantic segmentation.

YOLOH: You Only Look One Hourglass for Real-Time Object Detection.

Multi-scale detection based on Feature Pyramid Networks (FPN) has been a popular approach in object detection to improve accuracy. However, using multi-layer features in the decoder of FPN methods entails performing many convolution operations on high-resolution feature maps, which consumes significant computational resources. In this paper, we propose a novel perspective for FPN in which we directly use fused single-layer features for regression and classification. Our proposed model, You Only Look One Hourglass (YOLOH), fuses multiple feature maps into one feature map in the encoder. We then use dense connections and dilated residual blocks to expand the receptive field of the fused feature map. This output not only contains information from all the feature maps, but also has a multi-scale receptive field for detection. The experimental results on the COCO dataset demonstrate that YOLOH achieves higher accuracy and better run-time performance than established detector baselines, for instance, it achieves an average precision (AP) of 50.2 on a standard 3× training schedule and achieves 40.3 AP at a speed of 32 FPS on the ResNet-50 model. We anticipate that YOLOH can serve as a reference for researchers to design real-time detection in future studies. Our code is available at https://github.com/wsb853529465/YOLOH-main.