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To control the rapid spread of COVID-19, we consider deploying a set of Unmanned Aerial Vehicles (UAVs) to form a quarantine barrier such that anyone crossing the barrier can be detected. We use a charging pile to recharge UAVs. The problem is scheduling UAVs to cover the barrier, and, for any scheduling strategy, estimating theminimum number of UAVs needed to cover the barrier forever. We propose breaking the barrier into subsegments so that each subsegment can be monitored by a single UAV. We then analyze two scheduling strategies, where the first one is simple to implement and the second one requires fewer UAVs. The first strategy divides UAVs into groups with each group covering a subsegment. For this strategy, we derive a closed-form formula for the minimum number of UAVs. In the case of insufficient UAVs, we give a recursive function to compute the exact coverage time and give a dynamic-programming algorithm to allocate UAVs to subsegments to maximize the overall coverage time. The second strategy schedules all UAVs dynamically. We prove a lower and an upper bound on the minimum number of UAVs. We implement a prototype system to verify the proposed coverage model and perform simulations to investigate the performance.
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Remote work has been gaining in popularity for years, even before the COVID-19 pandemic began. Along with its perceived benefits, remote work often results in individuals spending long hours at a computer or on the phone. Consequently, remote workers may find that a large portion of their day is spent sitting without taking any kind of break, especially those for physical activity. The purpose of this action research study was to explore proven strategies that enable remote workers to take active breaks during their workday. Data was collected from longtime remote workers during Cycle 1 research through 11 semi-structured interviews and document analyses. Data analysis led to 12 themes that responded to the research questions. Along with the literature and a focus group, these findings informed the action step, which was designed, executed, and evaluated in Cycle 2. The action step involved four longtime remote workers sharing their lived experiences around remote work, breaks, and activity through a podcast series. These podcasts were consumed by 16 new remote workers who answered qualitative survey questions to determine the impact of the podcasts on their break taking during their workdays. The research found that a remote worker's work environment, degree of autonomy, and break options influence how they fit in breaks during their workdays. The findings suggest that remote workers need consistent organizational support;that having autonomy to manage their workdays is critical for remote workers;and all breaks "are not created equal". (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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With the adjustment of China's epidemic prevention and control guidelines regarding coronavirus disease of 2019(COVID-19), the preoperative evaluation and timing of surgery for patients after COVID-19 infection have become the focus of attention for both healthcare workers and patients. Based on the latest study and related clinical experience, Peking Union Medical College Hospital (PUMCH) has therefore compiled this multidisciplinary, evidence-based recommendation for concise, individualized, and practical preoperative evaluation and timing of surgery for patients after COVID-19 infection. The recommendations emphasize patients' COVID-19 infection history, the severity of symptoms, and medical/physiologic recovery status during preoperative evaluation. The determination of appropriate length of time between recovery from COVID-19 and surgery/procedure should take into account of patients' underlying health conditions, the severity of the COVID-19 infection course, and the types of surgery and anesthesia scheduled, to minimize postoperative complications. The recommendations are intended to aid healthcare workers in evaluating these patients, scheduling them for the optimal timing of surgery, and optimizing perioperative management and postoperative recovery.Copyright © 2023, Peking Union Medical College Hospital. All rights reserved.
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Based on Canadian Labour Force Survey data, we estimate the differential effect of the COVID-19 pandemic on seven labour market outcomes, and separate between recent and established immigrants relative to domestic-born Canadians. We also use Recentered Influence Function (RIF) unconditional quantile regressions to estimate the differential effects across the distribution of outcomes. We find that the pandemic had an adverse effect on the labour market outcomes for all workers, and that the adverse effects were generally larger for immigrants and especially recent immigrants as well as for immigrants at the bottom of the outcome distributions. The adverse effects were generally larger at the earliest waves of the pandemic, and for recent immigrants who were female, less educated, and those with child responsibilities, and for jobs at greater risk of contact with the pandemic.
Sur la base des données de l'Enquête sur la population active du Canada, nous estimons l'effet différentiel de la pandémie de COVID-19 sur sept résultats sur le marché du travail, séparément pour les immigrants récents et établis par rapport aux Canadiens nés au pays. Nous utilisons également des régressions quantiles inconditionnelles de la fonction d'influence recentrée (RIF) pour estimer les effets différentiels sur la distribution des résultats. Nous constatons que la pandémie a eu un effet négatif sur les résultats du marché du travail pour tous les travailleurs, les effets négatifs étaient généralement plus importants pour les immigrants et en particulier les immigrants récents ainsi que pour les immigrants au bas de la distribution des résultats. Les effets néfastes étaient généralement plus importants pour les premières vagues de la pandémie et pour les immigrants récents qui étaient des femmes, moins instruits, ceux qui avaient des responsabilités envers les enfants et pour les emplois les plus à risque d'être en contact avec la pandémie.
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AIM: To explore the nursing workforce allocation in intensive care units (ICUs) of COVID-19-designated hospitals during the epidemic peak in China. DESIGN: A nationwide cross-sectional online survey. METHODS: A total of 37 head nurses and 262 frontline nurses in 37 ICUs of COVID-19-designated tertiary hospitals located in 22 cities of China were surveyed. The self-reported human resource allocation questionnaire was used to assess the nursing workforce allocation. RESULTS: The average patient-to-nurse ratio was 1.89 ± 1.14, and the median working hours per shift was 5 h. The top four majors of front-line nurses in ICUs were respiratory (31.30%), lemology (27.86%), intensive care (21.76%) and emergency (17.18%). We also found that a smaller average patient-to-nurse ratio (odds ratio [OR]: 0.328, 95% CI: 0.108, 1.000), longer average weekly rest time per person (OR: 0.193, 95% CI: 0.051, 0.729) and larger proportion of 6-9 working years (OR: 0.002, 95% CI: 0.001, 1.121) decreased the occurrence of nursing adverse events.
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Over the last three COVID-19 effective years, it was evident that healthcare has been the most sensitive sector to electricity failures. Therefore, if well developed and implemented, a microgrid system with an integrated energy storage system (ESS) installed in hospitals has great potential to provide an uninterrupted and low-energy cost solution. In this article, we target to show the importance of the installed ESS against the problems that will arise from power outages and energy quality problems in hospitals. Besides, it aims to construct an energy management system (EMS) based on the scheduling model to meet the lowest cost of a system containing solar panels, microturbine, gas boiler, and energy storage units that are repurposed lithium-ion batteries from electric vehicles and thermal storage tank. EMS is a mixed-integer linear program to meet the hospital's electricity, heating, and cooling demands with the lowest cost for every hour. The established scheduling model is run for a hospital in Antioch, Turkiye, with 197 beds, 4 operating rooms, 2 resuscitation units, and 9 intensive care units for every hour based on the data in 2019. With the EMS, approximately 25% savings were achieved compared to the previous energy cost. Furthermore, as the result of the net present value calculation, the payback period of the proposed system is estimated to be approximately seven years.
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The article concerns the potential influence of employees' dynamic capabilities on the performance of entire organization, which operates in crisis caused by Black Swan event. It is the expansion of job performance model based on employees' dynamic capabilities, proposing the possibility of translating the positive influence of those capabilities onto entire organization and underlining the importance of employees' dynamic capabilities during crisis within organization. Based on literature analysis, the shape of the amended model is proposed, in which employees' dynamic capabilities influence organizational performance through elements of the original model (person-job fit, work motivation, job satisfaction, work engagement and job performance), and additional ones: person-organization fit, person-supervisor fit. The proposed model is empirically verified based on the sample of 1160 organization operating in Poland, Italy and USA during an active wave of COVID-19 pandemic (which is an example of Black Swan event). The results obtained using path analysis confirmed that employees' dynamic capabilities indeed influence organizational performance of organizations operating in crisis caused by Black Swan event through elements proposed in the model.
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The COVID-19 disease has caused a drastic stoppage in the construction industry as a result of quarantines. For this reason, this study focuses on the workforce scheduling problem when working under COVID labor distancing constraints, and additional costs derived from deviation hours or hiring new employees that managers must assume on a project due to circumstances. A multi-objective mixed integer linear programming model was developed and solved using weighting and epsilon constraint methods to evaluate workforce scheduling and the mentioned COVID costs. The first objective function corresponds to the sum of the total extra hours; the second objective function represents the total non-worked but paid hours. Two sets of experiments are presented, the first based on a design of experiments that seeks to determine the relationship between the proposed objective functions and a methodology to determine the cost of considering COVID constraints. The second set of experiments was applied in a real company, where the situation without COVID vs with COVID, and without allowing extra hours vs with COVID allowing extra hours were compared. Obtained results showed that hiring additional employees to the man-crew leads the company to increase the extra hours cost up to 104.25%, being more convenient to keep a workforce baseline and to pay extra hours costs. Therefore, the mathematical model could represent a potential tool for decision-making in the construction sector, regarding the effects of COVID-19 costs on workforce scheduling construction projects. Consequently, this work contributes to the construction industry by quantifying the impact of COVID-19 constraints and the associated costs, offering a proactive approach to address the challenges posed by the COVID-19 pandemic for the construction sector.
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Introduction An "unscheduled absence" refers to an occurrence when an employee does not appear for work and the absence was not approved in advance by an authorized supervisor. Daily unscheduled absences need to be forecasted when doing staff scheduling to maintain an acceptable risk of being unable to run all anesthetizing locations and operating rooms planned. The number of extra personnel to be scheduled needs to be at least twice as large as the mean number absent. In an earlier historical cohort study, we found that our department's modeled risks of being unavailable unexpectedly differed among types of anesthesia practitioners (e.g., anesthesiologists and nurse anesthetists) and among weekdays (i.e., Mondays, Fridays, and workdays adjacent to holidays versus other weekdays). In the current study, with two extra years of data, we examined the effect of the coronavirus COVID-19 pandemic on the frequency of unscheduled absences. Methods There were 50 four-week periods studied at a large teaching hospital in the United States, from August 30, 2018 to June 29, 2022. The sample size of 120,687 person-assignment days (i.e., a person assigned to work on a given day) included 322 anesthesia practitioners (86 anesthesiologists, 88 certified registered nurse anesthetists, 99 resident and fellow physicians, and 49 student nurse anesthetists). The community prevalence of COVID19 was estimated using the percentage positive among asymptomatic patients tested before surgery and other interventional procedures at the hospital. Results Each 1% increase in the prevalence of COVID-19 among asymptomatic patients was associated with a 1.131 increase in the odds of unscheduled absence (P < 0.0001, 99% confidence interval 1.086 to 1.178). Using an alternative model with prevalence categories, unscheduled absences were substantively more common when the COVID-19 prevalence exceeded 2.50%, P [Formula: see text] 0.0002. For example, there was a 1% unscheduled absence rate among anesthesiologists working Mondays and Fridays early in the pandemic when the prevalence of COVID-19 among asymptomatic patients was 1.3%. At a 1% unscheduled absence rate, 67 would be the minimum scheduled to maintain a <5.0% risk for being unable to run all 65 anesthetizing locations. In contrast, there was a 3% unscheduled absence rate among nurse anesthetists working Mondays and Fridays during the Omicron variant surge when the prevalence was 4.5%. At a 3% unscheduled absence rate, 70 would be the minimum scheduled to maintain the same risk of not being able to run 65 rooms. Conclusions Increases in the prevalence of COVID-19 asymptomatic tests were associated with more unscheduled absences, with no detected threshold. This quantitative understanding of the impact of communicable diseases on the workforce potentially has broad generalizability to other fields and infectious diseases.
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A core part of the rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several legal, medical, and ethical requirements and optimizations, for example, patient's preferences and operator's work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation's needs. In this paper, we present a two-phase solution to rehabilitation scheduling based on Answer Set Programming, which proved to be an effective tool for solving practical scheduling problems. We first present a general encoding and then add domain-specific optimizations. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution as well as the impact of our domain-specific optimizations.
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The global supply chain disruption by the COVID-19 pandemic is difficult, if not impossible, to estimate as over 94% of the top 1000 fortune companies were badly affected. The need for building resilient supply chains to mitigate the effect of such disruptions is rising rapidly than ever before across the global business spectrum. Building resilience in the automotive spare parts (ASPs) supply chain is critically important as any disruption to automotive spares supply chain will affect the operations of the logistics sector, the backbone of global supply chains. This research work contributes to improving the resilience of the automotive spare parts supply chain by proposing a Viable Supply Chain (VSC) framework design that incorporates Additive Manufacturing (AM) enabled trucks in the automotive spares supply chain network. Based on the proposed model, conceptual case models are developed and tested with proposed AM enabled truck manufacturing closer to end customer. A heuristic approach called shortest time heuristic is also proposed to solve the routing and scheduling of an AM enabled truck to deliver customers' orders of the spare parts through an online platform. Importantly, the study demonstrate how additive manufacturing can help the ASPs industry to switch from the existing practice of make-to-stock to a more efficient inventory management and cost saving make-to-order model while also achieving resilience and sustainability in by providing a source of spares support for discontinued models of vehicles.
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Background: The loss of normalcy during the COVID-19 pandemic affected operation services in health facilities, leading to a reduction in the number of elective surgeries. The pandemic-related modifications in surgical residency programs gave rise to a chance to investigate effective learning strategies that help reduce burnout. Objectives: To investigate the effects of the COVID-19 epidemic on general surgeons' burnout, surgical education and training in the Qassim Region of Saudi Arabia. Methods: This cross-sectional study involved general surgery doctors in the hospitals of the Qassim region in Saudi Arabia. Results: The COVID-19 patient care had a detrimental effect on the role of examining patients on rounds among females (adjOR = 0.260, 95%CI: 0.084-0.809;p = 0.020) and males (adjOR = 0.426, 95% CI: 0.232-0.780;p = 0.006). COVID-19 patient care had a negative impact on the number of days off in a month among females (adjOR = 0.159, 95% CI: 0.029-0.875;p = 0.035). Equally, COVID-19 patient care had a negative impact on meeting ACGME's minimum requirements (adjOR = 0.163, 95% CI: 0.042-0.634;p = 0.009) as noted by the specialist. Lastly, COVID-19 patient care had a negative impact as expressed by the specialist who was concerned the pandemic had made one less prepared for the future (adjOR = 0.074, 95% CI: 0.007-0.739;p = 0.027). Conclusions: COVID-19 patient care had a negative relationship with the operation volume on the role of examining patients on rounds, the likelihood of not meeting the ACGME's minimum requirements and burnout concerns. The specialist is more concerned with matters regarding meeting the ACGMEs and burnout concerns which would make the general surgery doctors less prepared for the future.
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The purpose of resource scheduling is to deal with all kinds of unexpected events that may occur in life, such as fire, traffic jam, earthquake and other emergencies, and the scheduling algorithm is one of the key factors affecting the intelligent scheduling system. In the traditional resource scheduling system, because of the slow decision-making, it is difficult to meet the needs of the actual situation, especially in the face of emergencies, the traditional resource scheduling methods have great disadvantages. In order to solve the above problems, this paper takes emergency resource scheduling, a prominent scheduling problem, as an example. Based on Vague set theory and adaptive grid particle swarm optimization algorithm, a multi-objective emergency resource scheduling model is constructed under different conditions. This model can not only integrate the advantages of Vague set theory in dealing with uncertain problems, but also retain the advantages of adaptive grid particle swarm optimization that can solve multi-objective optimization problems and can quickly converge. The research results show that compared with the traditional resource scheduling optimization algorithm, the emergency resource scheduling model has higher resolution accuracy, more reasonable resource allocation, higher efficiency and faster speed in dealing with emergency events than the traditional resource scheduling model. Compared with the conventional fuzzy theory emergency resource scheduling model, its handling speed has increased by more than 3.82 times.
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Federated learning is a machine learning method that can break the data island. Its inherent privacy-preserving property has an important role in training medical image models. However, federated learning requires frequent communication, which incur high communication costs. Moreover, the data is heterogeneous due to different users' preferences, which may degrade the performance of models. To address the problem of statistical heterogeneity, we propose FedUC, an algorithm to control the uploaded updates for federated learning, where a client scheduling method is made on the basis of weight divergence, update increment, and loss. We also balance the local data of the clients by image augmentation to mitigate the impact of the non-independently identically distribution. The server assigns compression thresholds to the clients based on the weight divergence and update increment of the models for gradient compression to reduce the wireless communication costs. Finally, based on the weight divergence, update increment and accuracy, the server dynamically assigns weights to the model parameters for the aggregation. Simulation and analysis utilizing a publicly available chest disease dataset containing COVID-19 are compared with existing federated learning methods. Experimental results show that our proposed strategy has better training performance in improving model accuracy and reducing wireless communication costs.
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In order to meet the health and safety needs of our attendees and staff, the 2021 International Conference on Agriculture Science and Water Resource (ASWR2021) which was scheduled to be held in Guangzhou, China, was held virtually online during January 22nd-25th, 2021. Being different from the traditional gatherings we all know, this virtual conference allows us to connect in new ways while keeping expenditure saving and maintaining social distancing. Presentations of presenter from different countries are accessible to hundreds of researchers effectively. Closely related to life, agricultural science and water resource have always been hot research topics and are gaining more and more attention from various countries. Agricultural production depends to a large extent on limited resources such as soil, water, nutrients and energy. At the same time, the misconduct usage of water resources brings about the increasingly prominent environmental problems. Under such condition, ASWR2021 would lay a platform for the interaction between experts and scholars and engineering technicians in the related fields to jointly promote the challenge aspects and discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developmentsand discuss future developments in this field. More than 60 individuals attended this online conference via cloud platform for video and audio Zoom. The conference was divided into two parts: keynote speeches and oral presentations. In the keynote speeches part, three keynote speeches with allocated time about 45 minutes each were delivered by Prof. Jiří Jaromír Klemeš, Brno University of Technology, Czech Republic;Prof. Defu Zhang, Xiamen University, China and Assoc. Prof. Wenchao Li, Hebei Agricultural University, China. Their insightful speeches gave our participants great inspiration. In the oral presentations part, experts and practitioners interested in Agriculture Science and Water Resource were given about 10 minutes to present their oral presentations to discuss state-of-the-art research results, perspectives of future developments, and innovative applications of their research. The conference ASWR2021 focuses on the discussion of the various aspects of agriculture, water resource, animal science, plant science and soil science. Some submitted manuscripts within the scope of the conference, which representing the advanced studies, were selected as the excellent papers and complied in the Conference Proceedings. Every accepted paper has undergone peer review process arranged by the Editorial Committee. At least two independent reviewers should review and approve a paper. If reviewers had different opinion on it, more reviewers would be selected to help make a final decision. We believe that the Proceedings will serve as an important research source to provide recent development and information related to agriculture, water resources and environment protection. On behalf of the Organizing Committee, we would like to take this opportunity to express our sincere gratitude to all authors and presenters. We are also grateful to the Technical Program Committee members and reviewers as well as all the colleagues from IOP publisher. It is the joint efforts of everyone that makes the conference a great success. Committee of ASWR2021 List of Committee Members are available in this Pdf.
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The COVID-19 pandemic brings many unexpected disruptions, such as frequently shifting markets and limited human workforce, to manufacturers. To stay competitive, flexible and real-time manufacturing decision-making strategies are needed to deal with such highly dynamic manufacturing environments. One essential problem is dynamic resource allocation to complete production tasks, especially when a resource disruption (e.g. machine breakdown) occurs. Though multi-agent methods have been proposed to solve the problem in a flexible and agile manner, the agent internal decision-making process and resource uncertainties have rarely been studied. This work introduces a model-based resource agent (RA) architecture that enables effective agent coordination and dynamic agent decision-making. Based on the RA architecture, a rescheduling strategy that incorporates risk assessment via a clustering agent coordination strategy is also proposed. A simulation-based case study is implemented to demonstrate dynamic rescheduling using the proposed multi-agent framework. The results show that the proposed method reduces the computational efforts while losing some throughput optimality compared to the centralised method. Furthermore, the case study illustrates that incorporating risk assessment into rescheduling decision-making improves the throughput. © 2023 Informa UK Limited, trading as Taylor & Francis Group.
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The world today is suffering from a huge pandemic COVID-19 that has infected 106M people around the globe causing 2.33M deaths, as of February 9, 2021. To control the disease from spreading more and to provide accurate healthcare to existing patients, detection of COVID-19 at an early stage is important. As per the World Health Organization, diagnosing pneumonia is a common way of detecting COVID-19. In many situations, a chest X-ray is used to determine the type of pneumonia. However, writing a report for every chest X-ray becomes a tedious and time-taking task for physicians. We propose a novel method of creating reports from chest X-rays images automatically via a deep learning model using image captioning with an attention mechanism employed through CNN–LSTM architecture. On comparing the model that does not use an attention mechanism with our approach, we found that accuracy was increased from 80% to 87.5%. In conclusion, we found that results generated with attention mechanism are better, and the report thus produced can be utilized by doctors and researchers worldwide to analyze new X-rays in lesser time. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.