Training Capacity Research Articles

An assessment of the current epidemiological and laboratory capacities for influenza-like illnesses and severe acute respiratory infection surveillance, Yemen 2022.

We aim to re-activate influenza sentinel surveillance system in Yemen after disruption related to repurposing for COVID-19 pandemic. WHO Country Office (CO) in collaboration with Yemen's Ministry of Public Health and Population (MOPH&P) jointly conducted an assessment mission to assess the current situation of the influenza sentinel surveillance system and assess its capacity to detect influenza epidemics and monitor trends in circulating influenza and other respiratory viruses of epidemic and pandemic potential. This study presents the results of the assessment for three sentinel sites located in Aden, Taiz, and Hadramout/Mukalla. A mixed methods approach was used to guide the assessment process and to help achieve the objectives. Data were collected as follows: desk review of the sentinel sites records and data; interviews with stakeholders, including key informants and partners; and direct observation through field visits to the sentinel sites, MOPH&P and the Central Public Health Laboratory (CPHL). Two assessment checklists were used: assessment of sentinel sites for SARI surveillance, and checklist for assessment of availability of SARI sentinel surveillance. COVID-19 has affected health systems and services, and this was demonstrated in this assessment. The influenza sentinel surveillance system in Yemen is not effectively functional; however, there is plenty of room for improvement if investment in the system's restructuring, training, building technical and laboratory capacities, and conducting continuous and regular supervision visits.

Parallel and Collaborative Passenger Flow Control of Urban Rail Transit Under Comprehensive Emergency Situation

The artificial cooperative control methods are used to fully utilize the train capacity and reduce the passenger delays by coordinating the number of entering and boarding passengers at each station, but it ignores the potential risk associated with the aggregation attributes of passengers in the station under emergency and the unfairness caused by the imbalance of travel opportunities for different origin–destination (OD) passengers. A new parallel and cooperative control model under comprehensive emergency situation is constructed from the perspective of guaranteeing travel safety, improving travel fairness, and increasing travel efficiency to generate passenger flow control strategies. The model aims to maximize passenger turnovers, minimize passenger delays and reduce the difference in the ratio of stranded passengers at stations. The complex multi-objective integer programming problem involving nonlinear equation constraints is difficult to solve numerically. A dynamic programming model taking train sequence as stage variable is then constructed as well as an NSGA-II algorithm with multiple improved strategies (MIS-NSGA-II) is proposed to solve the model. Numerical experiments on a line of Beijing Rail Transit show that the MIS-NSGA-II outperforms the traditional NSGA-II, and that increasing the overload coefficient of the train and the tolerance coefficient of each area in the station can further alleviate passenger congestion and promote the operation efficiency. Compared with other control strategies mentioned in this study, the proposed control model from a comprehensively perspective, which can ensure the travel safety and promote the travel efficiency while forcefully decongesting the bottleneck areas and improving the fairness of passenger travel.

A Learning Based Intelligent Train Regulation Method With Dynamic Prediction for the Metro Passenger Flow

With the acceleration of urbanization, the dynamic passenger flow has an ever-growing impact on the actual train operation. In this paper, we propose a learning based intelligent train regulation method with dynamic passenger flow prediction. To capture the characteristics of the dynamic metro passenger flow, a convolutional neural network is established to predict the real-time passenger flow from two dimensions including space and time. As the prediction accuracy is restricted by the insufficiency of the practical passenger flow data, a deep convolutional generative adversarial network is constructed to generate data that have the same distribution as the original passenger flow dataset. Then, by considering the effects of the dynamic passenger flow on the train operation and the train capacity constraints, the dynamic train regulation is formulated as a multi-stage optimal control problem with the objective function of minimizing the train traction energy consumption and the total traveling time of passengers. To efficiently obtain the optimal regulation strategy at each decision step, a deep Q-network algorithm is proposed to solve the formulated problem such the dimensionality curse caused by the excessive state space is avoided. The numerical experiments demonstrate the high efficiency and effectiveness of our proposed algorithm and model.

Các nhân tố tác động đến năng lực đào tạo của cơ sở đào tạo đại học- Nghiên cứu thực nghiệm tại tỉnh Bắc Ninh

This study was conducted to determine the factors affecting the training capacity of higher education institutions. The direct survey was implemented with a sample of 363 observations in November 2022, including staffs, lecturers, students working and studying at universities and staffs working at commercial banks in BacNinh province. The authors used multivariate regression to analyze and showed that there are 6 factors that directly affect the training capacity of a university. From that, the factor with the strongest impact is Digital competence with coefficient Beta = 0.467; Scientific research of students has the second strongest impact with Beta = 0.209, followed by Facilities, Training objectives, Training program, Teaching staff. All factors in the research model may explain 74.4% of the change in training capacity of higher education institutions. The research results add more empirical evidence and serve as a reference to improve the training capacity of higher education institutions.

Establishing a sound scientific data ecosystem through the implementation of a data element policy

It has been widely recognized that data have become a basic and strategic resource at home and abroad, ranking as a production factor with land, labor, capital and technology. Scientific data are an important part of data elements. High-quality scientific data are a necessary strategic resource to promote the development of science and technology. Focusing on the characteristics of scientific data (e.g. openness, multi-level integration and evolution, and scientific data life cycle management), this paper analyzes the development and problems of scientific data in China, and puts forward comprehensive suggestions on establishing a sound scientific data ecosystem. By establishing and improving the data basic policy system, building a multi-agent governance framework, and strengthening the capacity of scientific data infrastructure and talent training, the paper aims to gradually create a virtuous circle of scientific data production, utilization, sharing and reuse.

South Africa’s Psychiatric training capacity in 2008 and in 2018. Has training capacity improved?

Background: There is a deficit of psychiatrists in South Africa, and to our knowledge, there is no situational analysis of training posts for psychiatrists in the country.Aim: To compare the number of specialists and subspecialists in training and training posts available in 2008 and 2018.Setting: South African medical schools with departments of psychiatry.Methods: A situational analysis involving data collection through a survey completed by eight heads of academic psychiatric departments followed by a comparative analysis of the two aforementioned years.Results: Data shows an 11% increase in funded and unfunded posts combined and a 9.3% increase in funded posts. The occupancy of funded posts decreased (92% in 2008 to 82% in 2018). When considering both funded and unfunded posts, only three more psychiatrists were being trained in 2018. Supernumeraries appointed in unfunded posts can be expected to return to their countries of origin. As such, a decrease in filled funded posts likely reflects a decrease in training psychiatrists destined to work in South Africa. While child and adolescent psychiatry was the only sub-speciality with accredited training posts in 2008, all sub-specialities included on the questionnaire had accredited training posts in 2018, and the number of accredited training posts in child and adolescent psychiatry doubled. That said, many of the posts were unfunded and vacant.Conclusion: While there was an increase in posts from 2008 to 2018, many posts remained unfilled. As such, not only are additional funded training posts required but also strategies to increase post-occupancy and successful completion of training.Contribution: This study is the first situational analysis of specialist and subspecialist training posts in Psychiatry in South Africa, at two time points over a 10 year period, that draws on academic heads of departments of psychiatry as respondents. The study highlights the nominal increase in funded training posts over this period, especially subspecialist training posts. The majority of Health Professions Council of South Africa (HPCSA) accredited subspecialities in Psychiatry have no funded training posts which is particularly concerning.

The Dynamic Programming Model for Calculating the Maximum Number of Gathering Passengers in High-Speed Railway Station Based on Gaussian distribution

in this paper, based on the actual train operation plan and evacuation plan of the high-speed railway station, taking the train capacity and occupancy rate as the basic data, the Gaussian function is used to describe the passenger arrival law and the uniform distribution is used to describe the passenger evacuation law. Also, the calculation model of the number of gathering passengers at the high-speed railway station is established by MATLAB. Taking Guangzhou South Railway Station as an actual case, the maximum number of gathering passengers in the whole day and the change curve of the number of gathering passengers with time are obtained. The calculation results are consistent with the actual situation, which can provide a reference for the passenger flow organization and management of the high-speed railway station and the new construction and reconstruction of the high-speed railway station.

Spatiotemporal synchronous coupling algorithm for urban rail transit timetables design under dynamic passenger demand

This paper focuses on the problem of designing the schedule of urban rail transit systems under dynamic passenger demand considering limited train capacity and congestion. From the point of view of operators, the goal of timetable optimization consists of using as few as possible service trains to transport the arriving passengers to their destinations securely and quickly. On the other hand, passengers want to spend the minimum time travelling to their destinations, including the time they wait on platforms until they board the first and successive trains in case of transferring between lines. Due to the variability of passenger flows and the difficulty in exactly solving full-day timetabling problems in short computation times, it is difficult in practice to use the approaches proposed in the literature. Moreover, when the capacity of trains is considered and oversaturation conditions emerge, the problem becomes even more complex, converting into a challenge the design of schedules from an operational point of view. By considering fixed train dwell times at stations and train running times between stations, we first propose a non-linear programming formulation to model the problem of determining the most convenient interdeparture times of services at the first station of a line, aiming at minimizing the waiting time of passengers. Since the dynamic behaviour of passenger demand and the maximum sectional passenger flow are approximately synchronous, an innovative method, the spatiotemporal synchronization coupling algorithm, based on working with the time-varying maximum sectional passenger flow instead of the demand patterns is proposed to quickly solve in an approximate way the demand-driven timetabling problem. The proposed approach is applied to a real case of Chengdu Metro Line 1 in China. The results show the effectiveness of the algorithm, both in terms of computational effort and in terms of reducing the waiting time currently experienced by passengers.

Multi proxy anchor family loss for several types of gradients

The deep metric learning (DML) objective is to learn a neural network that maps into an embedding space where similar data are near and dissimilar data are far. However, conventional proxy-based losses for DML have two problems: gradient problem and application of the real-world dataset with multiple local centers. Additionally, the performance metrics of DML also have some issues with stability and flexibility. This paper proposes three multi-proxies anchor (MPA) family losses and a normalized discounted cumulative gain (nDCG@k) metric. This paper makes three contributions. (1) MPA-family losses can learn using a real-world dataset with multi-local centers. (2) MPA-family losses improve the training capacity of a neural network owing to solving the gradient problem. (3) MPA-family losses have data-wise or class-wise characteristics with respect to gradient generation. Finally, we demonstrate the effectiveness of MPA-family losses, and MPA-family losses achieves higher accuracy on two datasets for fine-grained images.

Proactive in-house part-feeding for mixed-model assembly systems with dynamics

The rich data collected from smart shop floors necessitate proactive decision-making to relieve system nervousness in a dynamic environment. This proactivity takes real-time actions based on disruption-driven prediction but not on disruption-created negative effects. This paper focuses on investigating a proactive in-house part-feeding (PIP) approach by integrating real-time data collected from shop floors with the physical properties of a mixed-model assembly system. An event-driven proactive prediction for replenishments is first implemented after coupling such data as disruptions, job rescheduling, part re-releasing, and real-time lineside inventories with a widely used reorder-point-based replenishment policy. This prediction can provide sufficient prior information about replenishments and, in turn, trigger tow-train rerouting to simultaneously minimize the costs of distribution and lineside inventories. In terms of limited tow train capacity and the just-in-time part-feeding goal, this rerouting problem should address various constraints on multiple trips, time windows, and the real-time working status of tow trains. An adaptive large neighbourhood search (ALNS) is developed to obtain the best solution for rerouting by designing customized removal and insertion operators, as well as local search heuristics. A case study of a real-life car seat assembly system is presented to verify the efficiency of the PIP. Several rerouting benchmark instances are obtained from the case study to evaluate the performance of ALNS, and the computational results indicate that the proposed ALNS has a satisfactory performance in solving the rerouting instances compared with some of the formerly proposed and published methods. The results of the practical case study illustrate that the PIP may increase the lineside inventory cost slightly but can significantly reduce the distribution cost compared with prevailing reactive approaches

International comparative analysis to understand the capacity for arthroscopy training and practice in developing countries

PurposeArthroscopy is an efficacious and popular treatment modality in developed nations for a variety of musculoskeletal conditions. However, arthroscopy requires specialized training, complex infrastructure, and expensive equipment, occasionally causing barriers to use in developing countries. Consequently, the utilization of resources to perform and teach arthroscopy in low- and middle-income countries (LMICs) is controversial. Through this investigation, we assessed the current capacity and barriers to arthroscopy use and training in these settings.MethodsFocused interviews were conducted with surgeons from Haiti (low-income) and Romania (middle-income) regarding their experience with arthroscopy. Based on responses, a multiple-choice survey was developed and administered to orthopaedic trainees and practicing orthopaedic surgeons during national orthopaedics conferences in each country.ResultsFifty-eight orthopaedists in Haiti, and 29 in Romania completed the survey. Most (91% from Haiti; 79% from Romania) reported that learning arthroscopy is essential or important for orthopaedic training in their country. Yet only 17% from Haiti compared to 69% from Romania indicated their primary hospital has the equipment necessary for arthroscopy. In Haiti, equipment was the main barrier to use of arthroscopy, followed by training, while in Romania, the main barrier was training, followed by equipment. Simulations and telemedicine were ranked as top choices of effective methods for learning arthroscopy.ConclusionsRegardless of their country’s resource limitations, most participants place high value on the practice of arthroscopy and arthroscopic training. The results from this study highlight a hierarchy of needs in developing nations. Furthermore, local providers report a strong belief in the need for arthroscopic treatment to benefit their patients, and a clear desire for further training and development of these techniques. By identifying similarities and differences by location, we may better tailor global orthopaedic training initiatives and partnerships in LMICs.

A decision model for high-speed railway express systems

With the rapid development of high-speed railway (HSR) networks, HSR express systems are becoming one of the most promising logistics systems. Such systems rely on road transportation to connect customers and railway stations in the HSR networks. HSR express systems also provide four main freight services such as arriving today, arriving the next morning, arriving the next day, arriving the day after tomorrow. This paper investigates a network planning and freight allocation problem for HSR express systems that considers decisions on intermodal station selection, vehicle assignment in road transportation, and freight flow organization. We also take into account factors such as the varying demand for different types of freight services and the uncertain capacity of high-speed trains in various scenarios. A two-stage stochastic programming model is proposed to minimize the total operational cost, which consists of fixed costs and variable costs, in random scenarios. To solve the problem, a metaheuristic algorithm is proposed that is validated by numerical experiments. Some sensitivity analysis on numerical experiments and a case study on the high-speed railway express systems of the China Railway Nanchang Group are conducted, the results of which yield useful managerial insights for practitioners.

Lung Transplant Rehabilitation—A Review

Both lung transplant recipients and candidates are characterised by reduced training capacity and low average quality of life (QoL). This review investigates the impact of training on exercise ability and QoL in patients before and after lung transplant. Searches were conducted from the beginning to 7 March 2022 using the terms "exercise," "rehabilitation," "lung transplant," "exercise ability," "survival," "quality of life" and "telerehabilitation" in six databases, including Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, CINAHL, Nursing and Allied Health, and Scopus. The inclusion criteria were studies evaluating the effects of an exercise training programme concurrent with lung transplantation as well as patients and candidates (>18 years old) through any lung diseases. The term "lung transplant rehabilitation" was used to refer to all carefully thought-out physical activities with the ultimate or intermediate objective of improving or maintaining physical health. Out of 1422 articles, 10 clinical- and 3 telerehabilitation studies, candidates (n = 420) and recipients (n = 116) were related to the criteria and included in this review. The main outcome significantly improved in all studies. The 6-min walk distance, maximum exercise capacity, peak oxygen uptake, or endurance for constant load rate cycling improved measuring physical activity [aerobic exercises, breathing training, and aerobic and inspiratory muscle training sessions (IMT)]. Overall scores for dyspnoea improved after exercise training. Furthermore, health-related quality of life (HRQOL) also improved after aerobic exercise training, which was performed unsupervised or accompanied by breathing sessions. Aerobic training alone rather than combined with inspiratory muscle- (IMT) or breathing training enhanced exercise capacity. In conclusion, rehabilitation programmes seem to be beneficial to patients both preceding and following lung transplantation. More studies are required to determine the best training settings in terms of time scale, frequency, and work intensity in terms of improving exercise ability, dyspnoea, and HRQOL.

Iowa Funds Expansion of Rural Psychiatry Training, Services for Underserved Populations

Iowa Funds Expansion of Rural Psychiatry Training, Services for Underserved Populations

Key factors influencing public health students and curricula in India: Recommendations from a mixed methods analysis.

Building on a distinguished history of community medicine training, public health programs have been expanding in India in recent years. The COVID-19 pandemic has brought additional attention to the importance of public health programs and the need for a strong workforce. This paper aims to assess the current capacity for public health education and training in India and provide recommendations for improved approaches to meet current and future public health needs. We conducted a desk review of public health training programs via extensive internet searches, literature reviews, and expert faculty consultations. Among those programs, we purposively selected faculty members to participate in in-depth interviews. We developed summary statistics based on the desk review. For qualitative analysis, we utilized a combination of deductive and inductive coding to identify key themes and systematically reviewed the strengths and weaknesses of each theme. The desk review captured 59 institutions offering public health training across India. The majority of training programs were graduate level degrees including Master of Public Health and Master of Science degrees. Key factors impacting these programs included collaborations, mentorship, curriculum standardization, tuition and funding, and student demand for public health education and careers. Collaborations and mentorship were highly valued but varied in quality across institutions. Curricula lacked standardization but also contained substantial flexibility and innovation as a result. Public sector programs were perceived to be affordable though fees and stipends varied across institutions. Further development of career opportunities in public health is needed. Public health education and training in India have a strong foothold. There are numerous opportunities for continued expansion and strengthening of this field, to support a robust multi-disciplinary public health workforce that will contribute towards achieving the sustainable development goals.

An integrated optimization approach for passenger flow control strategy and metro train scheduling considering skip-stop patterns in special situations

In many special situations (epidemic, emergencies, large-scale events, etc.) of the metro, it is a considerably real issue on how to control passenger flow density and improve operational efficiency synchronously under the control requirements of specific the load factor. This paper aims to propose an integrated optimization approach for jointly researching passenger flow control and train scheduling with skip-stop patterns. Besides, the passenger dynamic demand and train occupancy rate are considered. A mixed integer programming model is formulated to minimize the unused capacity of trains and the total number of waiting passengers at platforms and outside stations. Through the comprehensive analysis of the relationship between passengers and trains, the numerical experiments are implemented in Beijing Metro Line 1 to illustrate the validity of the proposed method. Finally, discussion and comparison of the performance of integration optimization strategies with diverse scenarios are given. The results show that, under the optimized combination strategy, the average boarding rate of all stations can increase from 88.09% to 94.69% by the proposed approach, the number of waiting behaviors of passengers can be declined by 92.65%, and the average travel time of passengers can be reduced by 7.22%.

Research on Information Fusion Preview Control for Freight Train Speed Tracking

Background: With the increase in the speed and capacity of freight trains, the safety, energy efficiency and intelligent operation of train operation have become increasingly important. Automatic freight trains operation can replace manual operation with automated control systems, which can guarantee the safety of train operation, and improve operational efficiency and reduce operational energy consumption. Objective: Solve the problem of tracking accuracy and stability deterioration caused by multi-information of freight train. Methods: Global navigation satellite system, inertial navigation system and speed measuring motor are selected to construct a speed fusion measurement model by using loosely coupled integrated navigation and improved entropy weight method. Calculate the information quantity of performance index and control quantity in preview control, get the controlled quantity of information fusion optimal preview control. Results: The average tracking error of the multi-source information fusion preview controller is 0.038m/s, which is 49% lower than that of the control experiment. Conclusion: Multi-source information fusion preview controller can effectively reduce the tracking error of freight train speed tracking system and improve the accuracy of automatic freight trains operation.

Influence of Recovery Mode on the Maximum Number of Intervals Until Exhaustion During an Aerobic Interval Training Session.

Varela-Sanz, A, Sánchez-Otero, T, Tuimil, JL, Boullosa, D, and Iglesias-Soler, E. Influence of recovery mode on the maximum number of intervals until exhaustion during an aerobic interval training session. J Strength Cond Res 37(9): e510-e520, 2023-We analyzed work capacity, cardiometabolic, perceptual, and neuromuscular responses to an aerobic interval training (AIT) running session until exhaustion, with active (AR) vs. passive recovery (PR). Eight well-trained male endurance runners (36.88 ± 7.14 years, 58.22 ± 3.39 ml·kg -1 ·minute -1 ) randomly completed, after familiarizations and the University of Montreal Track Test (UMTT), 2 AIT track running sessions until exhaustion consisting in 2-minute bouts at 100% of maximum aerobic speed (MAS), with 2 minutes of recovery at 80% of the velocity associated to the second ventilatory threshold (vVT 2 ) (i.e., AR), or no exercise (i.e., PR). Oxygen consumption (V̇O 2 ), heart rate (HR), blood lactate [La], rating of perceived exertion (RPE), and countermovement jump (CMJ) were continuously monitored during sessions. The level of statistical significance was set at p ≤ 0.05. PR resulted in longer time to exhaustion during sessions (13.9 vs. 11.6 bouts, p = 0.045), but lower HR ( p < 0.01) when compared with AR. Time spent over 90% of maximum oxygen consumption (V̇O 2max ), blood lactate concentrations, neuromuscular performance, and RPE did not differ between AR and PR ( p > 0.05). Thus, PR allowed runners to perform more work intervals and, therefore, to accumulate a greater volume. On the other hand, when training goals are focused on reaching a higher chronotropic stress (i.e., higher HR) during the training session, athletes would obtain more benefits from AR. This study also demonstrates that the current volume recommendations for AIT are far below (54-64.5%) the maximum training capacity of well-trained runners.

Reducing passenger waiting time in oversaturated metro lines with passenger flow control policy

Oversaturation during peak hours brings about severe challenges for metro operations management in megacities. It can deteriorate passengers’ service level experience and increase the safety risk at congested platforms. This paper focuses on designing an online passenger flow control policy to manage passenger flow in each origin-destination (OD) pair so that the total passenger waiting time during the research horizon can be minimized. Suppose that the OD demand information reveals sequentially over time, we formulate the online passenger flow control problem as stochastic dynamic programming (DP). An efficient online adaptive policy is designed to guide the real-time flow control decisions at each stage. To evaluate the performance of our approach, we exploit the realistic transit data from the Beijing metro system to carry out a series of numerical experiments. The computational results show that our approach can significantly reduce the expected total passenger waiting time as well as alleviate metro station congestion compared with the first-come-first-serve (FCFS) policy. The benefits of our approach are obtained by exploiting the reusable nature of train capacity to transport more passengers during rush hours.

Assessing the knowledge and practices of primary healthcare workers on malaria diagnosis and related challenges in view of COVID-19 outbreak in a Nigerian Southwestern metropolis.

The clinical features of COVID-19 and malaria are interrelated. Due to the similarity of symptoms between the two disease states, patients can be incorrectly diagnosed with the other ailment in areas with limited health resources. There is a dearth of knowledge of co-infection between COVID-19 and malaria from healthcare providers' perspective. Hence, this study assessed the ability of primary healthcare workers to diagnose malaria infection correctly from COVID-19 infection. A multistage sampling technique was used to select health care workers who were directly involved in malaria case management at 261 government-owned primary health facilities in Oyo State. Socio-demographic characteristics of respondents, knowledge and practices, COVID-19 differential diagnosis and challenges that healthcare workers face regarding malaria diagnosis were obtained using a standardized electronic structured questionnaire. Descriptive statistics, bivariate and multivariate analysis were conducted on data collected and significant results were interpreted at a 5% level of significance. A good percentage of the respondents (81.6%, 74.3%) had good knowledge about malaria and COVID-19. However, the knowledge gained did not translate to practice, as majority (86.2%) of respondents had poor malaria diagnosis practices. Practices relating to COVID-19 differential diagnosis in 69.7% of respondents were also poor. Most of the respondents attributed poor practices to the unavailability of Malaria Rapid Diagnostic Test (mRDT), inadequate training and continuous capacity improvement. Only 12.3% of the respondents have not had any form of training on malaria diagnosis and treatment in the last five years. Harmonization of regular trainings and continuous on-the job capacity building is essential to improve case identification, diagnosis and management of both ailments. Also, uninterrupted supplies of essential commodities such as mRDT in laboratories will reduce missed opportunities for malaria diagnosis.