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This paper examines post-communist managerial ethics during the emergence and transition of charismatic leadership in two privately owned media holdings in Russia and Kyrgyzstan. These media holdings were bootstrapped in the 1990s and 2000s by people without management experience and connections. This paper argues that Weberian charismatic leadership was a necessary leadership style to start a private business for people without links to elite networks. However, once firms establish themselves on the market, charisma fades and yields itself to a legal-rational leadership style. In particular, the paper compares and contrasts the managerial ethics issues arising from the loyalty-based leader–follower relations in the charismatic leadership phase and the legal-rational phase of a firm's development and maturation. While the legal-rational phase brings positive changes to workload management and employees' rights for vacation and p/maternity leave, task delegation remains an unsolved issue. Ambiguous career advancement criteria of the legal-rational phase replace rapid career progression of junior and middle managers during the charismatic phase. By examining the dynamics of managerial ethics transformation, this study adds to the literature on post-communist leadership, management and governance. Recommendations are provided for privately owned firms on how to advance managerial ethics to attract and retain qualified talent. [ABSTRACT FROM AUTHOR]

Organs-at-risk contouring is time consuming and labour intensive. Automation by deep learning algorithms would decrease the workload of radiotherapists and technicians considerably. However, the variety of metrics used for the evaluation of deep learning algorithms make the results of many papers difficult to interpret and compare. In this paper, a qualitative evaluation is done on five established metrics to assess whether their values correlate with clinical usability. A total of 377 CT volumes with heart delineations were randomly selected for training and evaluation. A deep learning algorithm was used to predict the contours of the heart. A total of 101 CT slices from the validation set with the predicted contours were shown to three experienced radiologists. They examined each slice independently whether they would accept or adjust the prediction and if there were (small) mistakes. For each slice, the scores of this qualitative evaluation were then compared with the Sørensen-Dice coefficient (DC), the Hausdorff distance (HD), pixel-wise accuracy, sensitivity and precision. The statistical analysis of the qualitative evaluation and metrics showed a significant correlation. Of the slices with a DC over 0.96 (N = 20) or a 95% HD under 5 voxels (N = 25), no slices were rejected by the readers. Contours with lower DC or higher HD were seen in both rejected and accepted contours. Qualitative evaluation shows that it is difficult to use common quantification metrics as indicator for use in clinic. We might need to change the reporting of quantitative metrics to better reflect clinical acceptance. [ABSTRACT FROM AUTHOR]

Generative Artificial Intelligence is set to revolutionize healthcare delivery by transforming traditional patient care into a more personalized, efficient, and proactive process. Chatbots, serving as interactive conversational models, will probably drive this patient-centered transformation in healthcare. Through the provision of various services, including diagnosis, personalized lifestyle recommendations, dynamic scheduling of follow-ups, and mental health support, the objective is to substantially augment patient health outcomes, all the while mitigating the workload burden on healthcare providers. The life-critical nature of healthcare applications necessitates establishing a unified and comprehensive set of evaluation metrics for conversational models. Existing evaluation metrics proposed for various generic large language models (LLMs) demonstrate a lack of comprehension regarding medical and health concepts and their significance in promoting patients' well-being. Moreover, these metrics neglect pivotal user-centered aspects, including trust-building, ethics, personalization, empathy, user comprehension, and emotional support. The purpose of this paper is to explore state-of-the-art LLM-based evaluation metrics that are specifically applicable to the assessment of interactive conversational models in healthcare. Subsequently, we present a comprehensive set of evaluation metrics designed to thoroughly assess the performance of healthcare chatbots from an end-user perspective. These metrics encompass an evaluation of language processing abilities, impact on real-world clinical tasks, and effectiveness in user-interactive conversations. Finally, we engage in a discussion concerning the challenges associated with defining and implementing these metrics, with particular emphasis on confounding factors such as the target audience, evaluation methods, and prompt techniques involved in the evaluation process. [ABSTRACT FROM AUTHOR]

This paper presents findings from a qualitative study conducted in Ontario, Canada, exploring healthcare professionals' perceptions of barriers and solutions for implementing Web-Based Reminiscence Therapy (WBRT) in an institutionalized settings for dementia care during the COVID-19 pandemic. The study identified five major barriers, including the lack of on-boarding/educational training, need for technology availability and technical support, limited attention span of persons with dementia (PWD), availability of multi-sensory features, and time constraints due to staff workload. Seven major themes emerged related to proposed solutions/suggestions: (1) involving younger generations, (2) focusing on technology training, (3) integrating with other digital platforms, (4) adding narratives/descriptions to recollect memories, (5) ensuring accessibility, (6) adding QR codes for retrieving information, and (7) combining digital/traditional reminiscence methods. These findings provide valuable insights for implementing WBRT to facilitate dementia care and for the future refinement of its application. [ABSTRACT FROM AUTHOR]

This paper discusses the objective reasons for the growing need for qualified medical services, especially in the context of pandemics, wars, disasters, and shortages of medical staff. The directions of development of robotics in medicine are analyzed, leading to the conclusion that the use of intelligent collaborative (i.e., interacting with humans) robotics in medicine is possible and appropriate. Robot assistants can perform many auxiliary operations, which will help relieve the workload of middle and junior medical staff, and, if necessary, replace them when performing such operations. The article outlines some of the main directions in which the authors believe the development of collaborative medical robotics should occur. [ABSTRACT FROM AUTHOR]

This paper aims to highlight how to reduce medication errors through the implementation of human factors science to the design features of medication containers. Despite efforts to employ automation for increased safety and decreased workload, medication administration in hospital wards is still heavily dependent on human operators (pharmacists, nurses, physicians, etc.). Improving this multi-step process requires its being studied and designed as an interface in a complex socio-technical system. Human factors engineering, also known as ergonomics, involves designing socio-technical systems to improve overall system performance, and reduces the risk of system, and in particular, operator, failures. The incorporation of human factors principles into the design of the work environment and tools that are in use during medication administration could improve this process. During periods of high workload, the cognitive effort necessary to work through a very demanding process may overwhelm even expert operators. In such conditions, the entire system should facilitate the human operator's high level of performance. Regarding medications, clinicians should be provided with as many perceptual cues as possible to facilitate medication identification. Neglecting the shape of the container as one of the features that differentiates between classes of medications is a lost opportunity to use a helpful characteristic, and medication administration failures that happen in the absence of such intentional design arise from "designer error" rather than "user error". Guidelines that define a container's shape for each class of medication would compel pharmaceutical manufacturers to be compatible and would eliminate the confusion that arises when a hospital changes the supplier of a given medication. [ABSTRACT FROM AUTHOR]

Accurate prediction provides a number of important benefits for research and decision-making. Occupational burnout is intertwined with individual, cultural, and social factors, the resolution of which requires methods that can deal with large amounts of data. The application of such methods capable of dealing with large datasets is a relatively novel research area in social science. For this purpose, this article presents insights into machine learning methods, mainly related to prediction tasks. A brief review of these techniques in burnout domain was applied. It is shown that the choice of a method depends on the presence of certain dependent variables. This paper also presents a comparison between novel and traditional approaches, which shows that the appropriateness of a technique depends on the aim of the research. The theoretical and practical implications of using machine learning methods in this context is also presented in the paper. It is found that a gap in the study of burnout exists which requires the attention of social work researchers. Through machine learning techniques, new theoretical models of burnout can be created. These algorithms can also provide new approaches to create data-driven interventions. Burnout monitoring systems supported by machine-learning algorithms can also be used in recruitment processes and to supervise employees. Applying machine learning methods in reducing burnout can also provide socio-economic benefits such as help to reduce employee turnover and improve general working conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-start randomized heuristic for solving real-life crew rostering problems in airlines. The paper describes realistic constrains, regulations, and rules that have not been considered in the literature so far. Our algorithm is designed to provide quality solutions satisfying these real-life specifications while, at the same time, it aims at balancing the workload distribution among the different crewmembers. Thus, our approach promotes corporate social responsibility by distributing the workload in a fair way and avoiding that some crewmembers get unnecessarily overstressed. Despite its importance in real-life applications, these aspects have seldom been considered in the crew scheduling literature, where most solving approaches refer to simplified models and are tested on non-realistic benchmarks. The experimental tests show that our algorithm is capable of generating feasible quality solutions to real-life crew rostering problems in just a few seconds. These times are orders of magnitude lower than the times currently employed by some airlines to obtain a single feasible solution, since the 'optimal' solutions provided by most commercial software usually require additional adjustments in order to meet all the real-life specifications. [ABSTRACT FROM AUTHOR]

Background: Running overuse injuries (ROIs) occur within a complex, partly injury-specific interplay between training loads and extrinsic and intrinsic risk factors. Biomechanical risk factors (BRFs) are related to the individual running style. While BRFs have been reviewed regarding general ROI risk, no systematic review has addressed BRFs for specific ROIs using a standardized methodology. Objective: To identify and evaluate the evidence for the most relevant BRFs for ROIs determined during running and to suggest future research directions. Design: Systematic review considering prospective and retrospective studies. (PROSPERO_ID: 236,832). Data Sources: PubMed. Connected Papers. The search was performed in February 2021. Eligibility Criteria: English language. Studies on participants whose primary sport is running addressing the risk for the seven most common ROIs and at least one kinematic, kinetic (including pressure measurements), or electromyographic BRF. A BRF needed to be identified in at least one prospective or two independent retrospective studies. BRFs needed to be determined during running. Results: Sixty-six articles fulfilled our eligibility criteria. Levels of evidence for specific ROIs ranged from conflicting to moderate evidence. Running populations and methods applied varied considerably between studies. While some BRFs appeared for several ROIs, most BRFs were specific for a particular ROI. Most BRFs derived from lower-extremity joint kinematics and kinetics were located in the frontal and transverse planes of motion. Further, plantar pressure, vertical ground reaction force loading rate and free moment-related parameters were identified as kinetic BRFs. Conclusion: This study offers a comprehensive overview of BRFs for the most common ROIs, which might serve as a starting point to develop ROI-specific risk profiles of individual runners. We identified limited evidence for most ROI-specific risk factors, highlighting the need for performing further high-quality studies in the future. However, consensus on data collection standards (including the quantification of workload and stress tolerance variables and the reporting of injuries) is warranted. [ABSTRACT FROM AUTHOR]

Reviewer Assignment Problem (RAP) is one of the cardinal problems in Government Funding agencies where the expertise level of the referee reviewing a proposal needs to be optimised to guarantee the selection of good R&D projects. Although many solutions have been proposed for RAP in the past, none of them deals with the inherent imprecision associated with the problem. For instance, it is not possible to determine the 'exact expertise level' of a particular reviewer in a particular domain. In this paper, we propose a novel approach for assigning reviewers to proposals. To calculate the expertise of a reviewer in a particular domain, we create a type-2 fuzzy set by assigning relevant weights to the various factors that affect the expertise of the reviewer in that domain. We also create a fuzzy set of the proposal by selecting three keywords that best represent the proposal. We then use a fuzzy functions based equality operator to compute the equality of the type-2 fuzzy set of experts and the fuzzy set of proposal keywords, which is then subjected to a set of relevant constraints to optimize the solution. We consider the four important aspects: workload balancing of reviewers, avoiding Conflicts of Interest, considering individual preferences by incorporating bidding and mapping multiple keywords of a proposal. As an extension to this approach, we further consider the relative importance of each keyword with respect to the submitted proposal by using representative percentage weights to create the FUZZY sets which represent the keywords. Hence, we propose an integrated solution based on the strong mathematical foundation of fuzzy logic, comprised of all the different aspects of expertise modeling and reviewer assignment. An Expert System has also been developed for the same. [ABSTRACT FROM AUTHOR]

In this paper we apply robust optimization techniques to the shift generation problem in workforce planning. At the time that the shifts are generated, there is often much uncertainty in the workload predictions. We propose a model to generate shifts that are robust against this uncertainty. An adversarial approach is used to solve the resulting robust optimization model. In each iteration an integer nonlinear knapsack problem is solved to calculate the worst case workload scenario. We apply the approach to generate shifts in a real-life Air Traffic Controller workforce planning problem. The numerical results show the value of our approach. [ABSTRACT FROM AUTHOR]

The objective of this evaluation study is to assess a method for standard based medical data visualization. The method allows flexible and customizable visualization for ISO 13606 archetype based medical data. The chosen evaluation concept is based the Guideline for Good Evaluation Practice in Health Informatics (GEP-HI). The stages of the study were identified. Each stage got a detailed description. We also identified the participants and their required qualifications and responsibilities. The evaluation location was described in details. The evaluation metrics were defined. The questionnaires for doctors, patients and experts were developed to fulfill the requirements of the evaluation study. The study was performed in Tomsk, Russia. 30 patients and 5 doctors participated in the study. The overall performance of the users reached the expert level by the end of the study. Patients as well as medical staff stated in their comments that the usability of the system was high, and they preferred it to the previously used paper-based and computer based systems. This was also shown by the high level of satisfaction measured within our study. The visualization approach, integrated into the electronic health record, was well accepted in our pilot setting with high usability scores from patients and doctors alike. The results showed the efficiency for both modeling and visualization part of the system [ABSTRACT FROM AUTHOR]

This paper introduces a shift rostering problem that surprisingly has not been studied in the literature: the weekend shift rostering problem. It is motivated by our experience that employees' shift preferences predominantly focus on the weekends, since many social activities happen during weekends. The weekend rostering problem (WRP) addresses the rostering of weekend shifts, for which we design a problem-specific heuristic. We consider the WRP as the first phase of the shift rostering problem. To complete the shift roster, the second phase assigns the weekday shifts. This decomposition reflects how shift rosters are often created manually in practice, which makes the decomposition method proposed in this paper a more intuitive approach for business users compared to approaches without this decomposition. We believe that such approaches enable business users to effectively analyze and steer the outcomes of algorithms for shift rostering especially on criteria that are relevant to them such as those concerning weekends. We analyze and discuss effects of this two-phase approach both on the weekend shift roster and on the roster as a whole. We demonstrate that our first-phase weekend rostering heuristic is effective both on generated instances and real-life instances. For situations where the weekend shift roster is one of the key determinants of the quality of the complete roster, our two-phase approach is shown to be effective. [ABSTRACT FROM AUTHOR]

Order sets are a critical component in hospital information systems that are expected to substantially reduce physicians' physical and cognitive workload and improve patient safety. Order sets represent time interval-clustered order items, such as medications prescribed at hospital admission, that are administered to patients during their hospital stay. In this paper, we develop a mathematical programming model and an exact and a heuristic solution procedure with the objective of minimizing physicians' cognitive workload associated with prescribing order sets. Furthermore, we provide structural insights into the problem which lead us to a valid lower bound on the order set size. In a case study using order data on Asthma patients with moderate complexity from a major pediatric hospital, we compare the hospital's current solution with the exact and heuristic solutions on a variety of performance metrics. Our computational results confirm our lower bound and reveal that using a time interval decomposition approach substantially reduces computation times for the mathematical program, as does a K -means clustering based decomposition approach which, however, does not guarantee optimality because it violates the lower bound. The results of comparing the mathematical program with the current order set configuration in the hospital indicates that cognitive workload can be reduced by about 20.2% by allowing 1 to 5 order sets, respectively. The comparison of the K -means based decomposition with the hospital's current configuration reveals a cognitive workload reduction of about 19.5%, also by allowing 1 to 5 order sets, respectively. We finally provide a decision support system to help practitioners analyze the current order set configuration, the results of the mathematical program and the heuristic approach. [ABSTRACT FROM AUTHOR]

Calculating resistance-training volume in programs focused on muscle hypertrophy is an attempt to quantify the external workload carried out, then to estimate the dose of stimulus imposed on targeted muscles. The volume is usually expressed in some variables that directly affected the total training work, such as the number of sets, repetitions, and volume-load. These variables are used to try to quantify the training work easily, for the subsequent organization and prescription of training programs. One of the main uses of measures of volume quantification is seen in studies in which the purpose is to compare the effects of different training protocols on muscle growth in a volume-equated format. However, it seems that not all measures of volume are always appropriate for equating training protocols. In the current paper, it is discussed what training volume is and the potentials and shortcomings of each one of the most common ways to equate it between groups depending on the independent variable to be compared (e.g., weekly frequency, intensity of load, and advanced techniques). [ABSTRACT FROM AUTHOR]

Flexible and self-adaptive stream join processing plays an important role in a parallel shared-nothing environments. Join-Matrix model is a high-performance model which is resilient to data skew and supports arbitrary join predicates for taking random tuple distribution as its routing policy. To maximize system throughputs and minimize network communication cost, a scalable partitioning scheme on matrix is critical. In this paper, we present a novel flexible and adaptive scheme partitioning model for stream join operator, which ensures high throughput but with economical resource usages by allocating resources on demand. Specifically, a lightweight scheme generator, which requires the sample of each stream volume and processing resource quota of each physical machine, generates a join scheme; then a migration plan generator decides how to migrate data among machines under the consideration of minimizing migration cost while ensuring correctness. We do extensive experiments on different kinds of join workloads and the evaluation shows high competence comparing with baseline systems on benchmark data and real data. [ABSTRACT FROM AUTHOR]

Justice court delay is found to be consistently related to pending workload per judge, as well, output per judge (productivity) is found to be strongly related to demand pressure (total available workload) and size of court. The aim of this paper is to investigate among the causes of different performances in courts, highlighting possible anomalies (outliers) among the judges' behaviour of a court due to the distribution of the workload among court sections and judges. The statistical methods considered are classical and robust variants of Partial Least Squares regression. [ABSTRACT FROM AUTHOR]

The elastic task model, a significant development in scheduling of real-time control tasks, provides a mechanism for flexible workload management in uncertain environments. It tells how to adjust the control periods to fulfill the workload constraints. However, it is not directly linked to the quality-of-control (QoC) management, the ultimate goal of a control system. As a result, it does not tell how to make the best use of the system resources to maximize the QoC improvement. To fill in this gap, a new feedback scheduling framework, which we refer to as QoC elastic scheduling, is developed in this paper for real-time process control systems. It addresses the QoC directly through embedding both the QoC management and workload adaptation into a constrained optimization problem. The resulting solution for period adjustment is in a closed-form expressed in QoC measurements, enabling closed-loop feedback of the QoC to the task scheduler. Whenever the QoC elastic scheduler is activated, it improves the QoC the most while still meeting the system constraints. Examples are given to demonstrate the effectiveness of the QoC elastic scheduling. [ABSTRACT FROM AUTHOR]

Abstract Active database systems extend functionality of traditional database systems with powerful mechanisms of triggers (or active rules) support. Triggers provide a uniform and convenient base that can be used for realization of internal DBMS functions, such as support of integrity constraints, representations, access authorization, statistics gathering, monitoring and notifications and for higher efficiency of external applications. Among representative examples of external applications that can be based on the usage of properties of active DBMS are data-intensive expert systems and workflow management systems. Today, the majority of industrial relational DBMS already support triggers, while XML DBMS, which are comparatively new, lack such functionality. Expansion of the XML DBMS application field and its usage in constructing complex application system stimulates appearance of new research works aimed at extending the functionality of XML DBMS by trigger support. In this paper, the authors define a special type of triggers for XML DBMS—XML triggers responding to data retrieval—and propose methods for their implementation. The paper also discusses examples of applications where XML query triggers occur to be useful and gives review of existing research works in this area. [ABSTRACT FROM AUTHOR]

This paper studies the effects of complementarities in work attendance on absenteeism. It investigates the case where workers, who turn up for work, cover the workload of absent colleagues. Thus, externalities arise from workers being absent from work. The effort workers exert in the workplace is negatively related to worker turnout. Attendance levels of workers are strategic complements, as workers are more likely to turn up when turnout is high. Firms increase wages and improve working conditions in response. In contrast to the more standard shirking literature, the firm sets employment levels higher. Workers may benefit from draconian firing rules. [ABSTRACT FROM AUTHOR]

This paper describes a problem faced by CS Energy's Swanbank Power Station in the Australian state of Queensland. It involved the personnel scheduling (rostering) of staff with multiple skill levels at the power station. Such a problem can be classified using the six stage construction process proposed by Ernst et al. We assume that the three processes of ‘demand modelling,’ ‘shift starting times’ and ‘task scheduling’ are specified. We are concerned with the essential processes of ‘day off scheduling,’ ‘line of work construction’ and ‘shift assignment to staff’ with requirements to maintain multiple skills. Several other authors have reported results for staff with hierarchical skills while the methods proposed in this paper are for non-hierarchical skill sets. The paper describes a set covering approach to the multi-skilled rostering problem. We propose a number of solution strategies for the set covering approach and give a comparison of the results. [ABSTRACT FROM AUTHOR]

Ethical conflicts (ECs), dilemmas auditors face when personal values or professional obligations clash with their actions, pose significant challenges to the auditing profession, potentially influencing turnover intention (TI). This study addresses a knowledge gap in the related research by focusing on two critical EC triggers: workload (WL) and perceived auditor ethical failure (PAEF: ethical sensitivity), which refers to auditors' perceptions of ethical violations within their profession. Grounded in role theory and ethical climate theory, our study investigates the impact of WL and PAEF on ECs and explores how these conflicts mediate the relationship between PAEF/WL and TI. Additionally, we examine the moderating role of organisational ethical climate (EtC) in these relationships. Using a survey of auditors and employing partial least squares structural equation modelling (PLS-SEM), our results reveal that both WL and PAEF increase ECs, thus escalating TI. The findings suggest that a firm's perceived EtC can negatively moderate the relationship between PAEF and ECs. The study underlines the importance of fostering a strong EtC to manage ECs effectively and provides valuable implications for professionals, regulators, and academics seeking to improve ethical practices within auditing firms and to reduce auditor turnover. [ABSTRACT FROM AUTHOR]

In healthcare, integration of artificial intelligence (AI) holds strong promise for facilitating clinicians' work, especially in clinical imaging. We aimed to assess the impact of AI implementation for medical imaging on efficiency in real-world clinical workflows and conducted a systematic review searching six medical databases. Two reviewers double-screened all records. Eligible records were evaluated for methodological quality. The outcomes of interest were workflow adaptation due to AI implementation, changes in time for tasks, and clinician workload. After screening 13,756 records, we identified 48 original studies to be incuded in the review. Thirty-three studies measured time for tasks, with 67% reporting reductions. Yet, three separate meta-analyses of 12 studies did not show significant effects after AI implementation. We identified five different workflows adapting to AI use. Most commonly, AI served as a secondary reader for detection tasks. Alternatively, AI was used as the primary reader for identifying positive cases, resulting in reorganizing worklists or issuing alerts. Only three studies scrutinized workload calculations based on the time saved through AI use. This systematic review and meta-analysis represents an assessment of the efficiency improvements offered by AI applications in real-world clinical imaging, predominantly revealing enhancements across the studies. However, considerable heterogeneity in available studies renders robust inferences regarding overall effectiveness in imaging tasks. Further work is needed on standardized reporting, evaluation of system integration, and real-world data collection to better understand the technological advances of AI in real-world healthcare workflows. Systematic review registration: Prospero ID CRD42022303439, International Registered Report Identifier (IRRID): RR2-10.2196/40485. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is revolutionizing the field of biomedical research and treatment, leveraging machine learning (ML) and advanced algorithms to analyze extensive health and medical data more efficiently. In headache disorders, particularly migraine, AI has shown promising potential in various applications, such as understanding disease mechanisms and predicting patient responses to therapies. Implementing next-generation AI in headache research and treatment could transform the field by providing precision treatments and augmenting clinical practice, thereby improving patient and public health outcomes and reducing clinician workload. AI-powered tools, such as large language models, could facilitate automated clinical notes and faster identification of effective drug combinations in headache patients, reducing cognitive burdens and physician burnout. AI diagnostic models also could enhance diagnostic accuracy for non-headache specialists, making headache management more accessible in general medical practice. Furthermore, virtual health assistants, digital applications, and wearable devices are pivotal in migraine management, enabling symptom tracking, trigger identification, and preventive measures. AI tools also could offer stress management and pain relief solutions to headache patients through digital applications. However, considerations such as technology literacy, compatibility, privacy, and regulatory standards must be adequately addressed. Overall, AI-driven advancements in headache management hold significant potential for enhancing patient care, clinical practice and research, which should encourage the headache community to adopt AI innovations. [ABSTRACT FROM AUTHOR]

The US Department of Veterans Affairs has been acquiring store and forward digital diabetic retinopathy surveillance retinal fundus images for remote reading since 2007. There are 900+ retinal cameras at 756 acquisition sites. These images are manually read remotely at 134 sites. A total of 2.1 million studies have been performed in the teleretinal imaging program. The human workload for reading images is rapidly growing. It would be ideal to develop an automated computer algorithm that detects multiple eye diseases as this would help standardize interpretations and improve efficiency of the image readers. Deep learning algorithms for detection of diabetic retinopathy in retinal fundus photographs have been developed and there are needs for additional image data to validate this work. To further this research, the Atlanta VA Health Care System (VAHCS) has extracted 112,000 DICOM diabetic retinopathy surveillance images (13,000 studies) that can be subsequently used for the validation of automated algorithms. An extensive amount of associated clinical information was added to the DICOM header of each exported image to facilitate correlation of the image with the patient's medical condition. The clinical information was saved as a JSON object and stored in a single Unlimited Text (VR = UT) DICOM data element. This paper describes the methodology used for this project and the results of applying this methodology. [ABSTRACT FROM AUTHOR]

Purpose: The aim was to critically appraise, synthesise and present the evidence of medication errors amongst hospitalised patients in Middle Eastern countries, specifically prevalence, nature, severity and contributory factors. Methods: CINAHL, Embase, Medline, Pubmed and Science Direct were searched for studies published in English from 2000 to March 2018, with no exclusions. Study selection, quality assessment (using adapted STROBE checklists) and data extraction were conducted independently by two reviewers. A narrative approach to data synthesis was adopted; data related to error causation were synthesised according to Reason's Accident Causation model. Results: Searching yielded 452 articles, which were reduced to 50 following removal of duplicates and screening of titles, abstracts and full-papers. Studies were largely from Iran, Saudi Arabia, Egypt and Jordan. Thirty-two studies quantified errors; definitions of 'medication error' were inconsistent as were approaches to data collection, severity assessment, outcome measures and analysis. Of 13 studies reporting medication errors per 'total number of medication orders'/ 'number of prescriptions', the median across all studies was 10% (IQR 2–35). Twenty-four studies reported contributory factors leading to errors. Synthesis according to Reason's model identified the most common being active failures, largely slips (10 studies); lapses (9) and mistakes (12); error-provoking conditions, particularly lack of knowledge (13) and insufficient staffing levels (13) and latent conditions, commonly heavy workload (9). Conclusion: There is a need to improve the quality and reporting of studies from Middle Eastern countries. A standardised approach to quantifying medication errors' prevalence, severity, outcomes and contributory factors is warranted. [ABSTRACT FROM AUTHOR]

Cloud computing harmonizes and delivers the ability of resource sharing over different geographical sites. Cloud resource scheduling is a tedious task due to the problem of finding the best match of resource-workload pair. The efficient management of dynamic nature of resource can be done with the help of cloud workloads. Till cloud workload is deliberated as a central capability, the resources cannot be utilized in an effective way. In literature, very few efficient resource scheduling policies for energy, cost and time constraint cloud workloads are reported. This paper presents an efficient cloud workload management framework in which cloud workloads have been identified, analyzed and clustered through K-means on the basis of weights assigned and their QoS requirements. Further scheduling has been done based on different scheduling policies and their corresponding algorithms. The performance of the proposed algorithms has been evaluated with existing scheduling policies through CloudSim toolkit. The experimental results show that the proposed framework gives better results in terms of energy consumption, execution cost and time of different cloud workloads as compared to existing algorithms. [ABSTRACT FROM AUTHOR]

When working in safety critical environments, such as railway signalling, technologies are often used to assist the operator in order to make safe decisions as well as to increase efficiency. Keeping mental workload at manageable levels is one of the most critical factors to ensure that tasks can be completed in a safe and effective manner. Numerous methods have been used to measure mental workload, but none of these have managed to show the use of distinct resource pools. This paper therefore measures the levels of usage of different cognitive processes when typical tasks are carried out in two railway signalling environments. To that end, the Multiple Resource Questionnaire is used. The aims of this paper are twofold: First, it establishes the value of the Multiple Resource Questionnaire as a technique in human factors. Second, it investigates and compares the cognitive resources required to carry out the railway signalling task in two different signalling environments. [ABSTRACT FROM AUTHOR]

Rapid growth in data, maximum functionality requirements and changing behavior in the database workload tends the workload management to be more complex. Organizations have complex type of workloads that are very difficult to manage by humans and even in some cases this management becomes impossible. Human experts take long time to get sufficient experience so that they can manage the workload efficiently. The versatility in workload due to huge data size and user requirements leads us towards the new challenges. One of the challenges is the identification of the problematic queries and the decision about these, i.e. whether to continue their execution or stop. The other challenge is how to characterize the workload, as the tasks such as configuration, prediction and adoption are fully dependent on the workload characterization. Correct and timely characterization leads managing the workload in an efficient manner and vice versa. In this scenario, our objective is to produce a workload management strategy or framework that is fully adoptive. The paper provides a summary of the structure and achievements of the database tools that exhibit Autonomic Computing or self-* characteristics in workload management. We have categorized the database workload tools to these self-* characteristics and identified their limitations. Finally the paper presents the research done in the database workload management tools with respect to the workload type and Autonomic Computing. [ABSTRACT FROM AUTHOR]

The author reflects on the success of the "Neuroscience Peer Review Consortium" (NPRC). He states that the move of the authors and publishers to revised manuscript together with its first round of reviews would speed up the review process and reduce the work for reviewers and editors. He adds that the members of the NPRC to extend the life of the Consortium to continue forward on a year-to-year basis.

Physically demanding work carried out during long workdays affects women's health. In rural and agrarian societies, women perform a variety of domestic and productive tasks, often from dawn to dusk, with little or no leisure time. This paper presents the results of a survey of indigenous women in six rural communities in the Ecuadorian highlands. It was conducted to measure the amount of time women spend on physically demanding work in the context of food security, parity outcomes, and access to prenatal health care. The findings demonstrate that these women work very long workdays and also experience food insecurity and poor access to prenatal health care. [ABSTRACT FROM AUTHOR]

Identifying the temporal variations in mental workload level (MWL) is crucial for enhancing the safety of human-machine system operations, especially when there is cognitive overload or inattention of human operator. This paper proposed a cost-sensitive majority weighted minority oversampling strategy to address the imbalanced MWL data classification problem. Both the inter-class and intra-class imbalance problems are considered. For the former, imbalance ratio is defined to determine the number of the synthetic samples in the minority class. The latter problem is addressed by assigning different weights to borderline samples in the minority class based on the distance and density meaures of the sample distribution. Furthermore, multi-label classifier is designed based on an ensemble of binary classifiers. The results of analyzing 21 imbalanced UCI multi-class datasets showed that the proposed approach can effectively cope with the imbalanced classification problem in terms of several performance metrics including geometric mean (G-mean) and average accuracy (ACC). Moreover, the proposed approach was applied to the analysis of the EEG data of eight experimental participants subject to fluctuating levels of mental workload. The comparative results showed that the proposed method provides a competing alternative to several existing imbalanced learning algorithms and significantly outperforms the basic/referential method that ignores the imbalance nature of the dataset. [ABSTRACT FROM AUTHOR]

Background: The allocation of resident physicians to clinical rotations presents a complex challenge that requires balancing multiple objectives with the goals of providing optimal patient care, maintaining adequate departmental staffing, and maximizing residents' training experience. While adhering to governing guidelines and training regulations, these physicians must comply with curricular milestones and educational goals for progression that must be achieved within specific time constraints. This manuscript reports on how to create annual master rotation schedules to meet the training requirements for 60 residents, while addressing the requirements detailed above. Methods: Trainees in the field of Emergency Medicine have to meet variable essential annual curricula requirements. Methods of preparing rotations in different Emergency Departments are presented which usually need early planning to ensure the best coordination and number allocation among the different internal and external collaborative departments. This off-institution and off-service external rotation is an educational necessity regulated by the Saudi Commission of Health Sciences to maximize residents' exposure and meet the expected educational milestones unique to Emergency Medicine training. Results: We report how to create annual master rotation schedules to meet the training requirements for 60 Emergency Medicine residents, while maintaining steady adequate departmental staffing and accommodating the different external rotation capacities, a task that is usually handled by the chief residents and program director. Although some parts of this process can be performed by using scheduling software or with particular decision support management systems, many steps are still filtered and checked manually. External circumstances mandate changes in schedules that require last-minute changes which may overrule training restrictions and make them unfeasible. Conclusion: To create such an agile schedule, we maintain a standardized template with preset blocks and rotations. Residents can choose the most suitable track that meets their preference for year-long rotation blocks. Thus, we minimize the individual variability in the preset allocations, guarantee an even distribution, give equal chances to each resident to accommodate and approximate their individual preferences, and decrease the overall workload and time consumed annually. [ABSTRACT FROM AUTHOR]

Drawing on the Job Demands-Resources (JD-R) theory, this study examines the relationship between workplace incivility and workers' psychological well-being. A related aim is to examine the link between workers' religiosity and their well-being, with workplace incivility moderating this nexus. Data were collected from 247 employees identified from private sectors (in Jordan and the UAE) via online-survey questionnaire. Factor analysis and hierarchical moderated multiple regression models were used to test the hypotheses. Study results show that workers' religiosity is positively and significantly associated with their psychological well-being, while workplace incivility is negatively (but insignificantly) associated with workers' psychological well-being. In addition, and contrary to our expectations and prior studies, our results suggest that workplace incivility strengthens the direct relationship between religiosity and well-being. The mechanism of this intersection may propose that rude and uncivil treatments positively predict self-blame, something that may lead the targets to become more religious to get recovery from different types of incivility and stressful life events. This study highlights the contextual applicability and possible extension of the JD-R theory through extending its model to religiosity and well-being of employees in a diverse cultural context in the Middle East. [ABSTRACT FROM AUTHOR]

In this paper, we study a planning and scheduling problem for unrelated parallel machines. There are n jobs that have to be assigned and sequenced on m unrelated parallel machines. Each job has a weight that represents the priority of the corresponding customer order, a given due date, and a release date. An Automated Guided Vehicle is used to transport at maximum Load jobs into a storage space in front of the machines in a given period of time. We consider t consecutive periods. We are interested in minimizing the total weighted tardiness of the jobs across the periods. This measure is important when we are interested in a good on-time delivery performance. We present an appropriate mixed integer program. To solve this NP-hard problem, we develop a heuristic methodology based on decomposition and variable neighborhood search (VNS). The proposed approaches are assessed using randomly generated problem instances. We compare them with a simple heuristic based on decomposition and list scheduling using the Apparent Tardiness Cost dispatching rule. The results demonstrate that the heuristic approach based on VNS performs comparably to the mixed integer program while having reasonable solution times and outperforms the simple heuristic and a genetic algorithm (GA) from previous research. [ABSTRACT FROM AUTHOR]

Introduction Chronic pressure at work has debilitating impact on healthcare employers (e.g. reduced productivity, high costs, poor patient care) and on female healthcare employees (e.g. sickness, dysfunction). This paper highlights relationship at work as the key occupational source of work-stress which is organisational in nature. Methods A cross-sectional study (n = 230) was conducted using the Pressure Management Inventory on several female dominated health professions within a large public hospital. Analysis of variance was used to show relationship between sources and outcome of pressure. Linear regressions were used to predict which sources of pressure (IV) was linked to the outcomes of occupational pressure (DV). Results The number one source of occupational pressure is Relationships at work (i.e. with supervisors), and not workload. 'Relationship' is also the key predictor of several negative outcomes of pressure at work. Analysis of variance showed significant differences in two sources of pressures, i.e. Workload ( P = 0.04) and Home-work balance ( P = 0.03). Conclusion This paper provides insights into the occupational pressure of women health professionals by highlighting the organisational sources of pressure and the implications for preventing occupational dysfunction secondary to stress at work. [ABSTRACT FROM AUTHOR]

This paper presents a new model for project portfolio selection, paying specific attention to competence development. The model seeks to maximize a weighted average of economic gains from projects and strategic gains from the increment of desirable competencies. As a sub-problem, scheduling and staff assignment for a candidate set of selected projects must also be optimized. We provide a nonlinear mixed-integer program formulation for the overall problem, and then propose heuristic solution techniques composed of (1) a greedy heuristic for the scheduling and staff assignment part, and (2) two (alternative) metaheuristics for the project selection part. The paper outlines experimental results on a real-world application provided by the E-Commerce Competence Center Austria and, for a slightly simplified instance, presents comparisons with the exact solution computed by CPLEX. [ABSTRACT FROM AUTHOR]

This paper discusses postulated advantages of parallel job designs compared with sequential designs in business processes. Until now, the literature suggests an overall dominance of parallel designs. An analysis of relevant application domains, in which parallel tasks are prevalent in order to accelerate processes, offers two remarkable insights: (1) parallel tasks can but do not have to speed-up business processes; (2) coordination efforts may reduce or even invert potential performance gains. Thus, this paper thoroughly evaluates interdependencies between parallel designs and coordination efforts through simulation experiments. Several process patterns for order processing in a real-world example will be examined. The results offer noteworthy characteristics of parallel designs which extent the existing knowledge: (a) gains from parallelization are reduced with increasing process variability, (b) small increments in coordination efforts neutralize gains from parallelization, (c) in high work load situations, the resource capacity for the coordination task is a severe bottleneck, and (d) if the findings (a)–(c) are carefully considered, only the parallelization of multiple tasks leads to significant performance gains. [ABSTRACT FROM AUTHOR]

Minimizing total tardiness in the model of an identical parallel-machine with nonpreemptive jobs of the worker assignment scheduling problem is further studied in this paper. More complicated simulation processes are established for this purpose. The specific worker assignment scheduling problem is solved in two parts of job scheduling and worker assignment. The S_PT, E_DD, S_lack (SES) heuristic is used for the job scheduling part while the largest marginal contribution (LMC) procedure is used to for the worker assignment part and then minimizes the total tardiness. From the new simulations conducted, the heuristics developed have again shown very convincing results quite efficiently. [ABSTRACT FROM AUTHOR]

Contracts are complex to understand, represent and process electronically. Usually, contracts involve various entities such as parties, activities and clauses. An e-contract is a contract modeled, specified, executed and enacted (controlled and monitored) by a software system (such as a workflow system). Workflows are used to automate business processes that govern adherence to the e-contracts. E-contracts can be mapped to inter-related workflows, which have to be specified carefully to satisfy the contract requirements. Most workflow models do not have the capabilities to handle complex inter/intra relationships among entities in e-contracts. An e-contract does not adhere to activity/task oriented workflow processes, thus generating a gap between a conceptual model of e-contract and workflow. In this paper, we describe conceptual modeling of e-contracts and present a business process model for e-contract enactment. The enactment of e-contracts necessitates dynamic generation and initiation of workflows during the e-contract execution, besides the static workflows. EREC business process model facilitates an integrated approach to e-contracts enactment. Our methodology is illustrated by means of a case study conducted using Financial Messaging Solution contract for banking transactions. [ABSTRACT FROM AUTHOR]

The employee tour scheduling problem involves the determination of both work hours of the day and workdays of the week for each employee. This problem has proven difficult to solve optimally due to its large size and pure integer nature. During the last decade, numerous approaches for modeling and solving this problem have been proposed. In this paper, employee tour scheduling literature published since 1990 is reviewed and classified. Solution techniques are classified into ten categories: (1) manual solution, (2) integer programming, (3) implicit modeling, (4) decomposition, (5) goal programming, (6) working set generation, (7) LP-based solution, (8) construction and improvement, (9) metaheuristics, and (10) other methods. The objective is to identify broad classifications, present typical mathematical models, compare the different methods, and identify future research directions. [ABSTRACT FROM AUTHOR]

Makespan minimization on identical parallel machines is a classical scheduling problem. We consider the online scenario where a sequence of n jobs has to be scheduled non-preemptively on m machines so as to minimize the maximum completion time of any job. The best competitive ratio that can be achieved by deterministic online algorithms is in the range [1.88, 1.9201]. Currently no randomized online algorithm with a smaller competitiveness is known, for general m. In this paper we explore the power of job migration, i.e. an online scheduler is allowed to perform a limited number of job reassignments. Migration is a common technique used in theory and practice to balance load in parallel processing environments. As our main result we settle the performance that can be achieved by deterministic online algorithms. We develop an algorithm that is $$\alpha _m$$ -competitive, for any $$m\ge 2$$ , where $$\alpha _m$$ is the solution of a certain equation. For $$m=2$$ , $$\alpha _2 = 4/3$$ and $$\lim _{m\rightarrow \infty } \alpha _m = W_{-1}(-1/e^2)/(1+ W_{-1}(-1/e^2)) \approx 1.4659$$ . Here $$W_{-1}$$ is the lower branch of the Lambert W function. For $$m\ge 11$$ , the algorithm uses at most 7 m migration operations. For smaller m, 8 m to 10 m operations may be performed. We complement this result by a matching lower bound: No online algorithm that uses o( n) job migrations can achieve a competitive ratio smaller than $$\alpha _m$$ . We finally trade performance for migrations. We give a family of algorithms that is c-competitive, for any $$5/3\le c \le 2$$ . For $$c= 5/3$$ , the strategy uses at most 4 m job migrations. For $$c=1.75$$ , at most 2.5 m migrations are used. [ABSTRACT FROM AUTHOR]

It is necessary for one senior executive (she) to monitor her project manager (he) who conducts early research stage followed by a later development stage in new product development. In this paper, we analyze two monitoring mechanisms: (1) the idea information-based monitoring (IM) mechanism wherein the senior executive engages one supervisor to monitor the project manager's idea information; (2) the effort-based monitoring (EM) mechanism wherein the senior executive engages another supervisor to monitor the project manager's effort. Within the framework of uncertainty theory, we first present two classes of bilevel uncertain principal-agent monitoring models, and then derive their respective optimal incentive contracts. We find that the senior executive should set the incentive term as high as possible to motivate each supervisor to monitor the project manager's idea information and effort no matter how much the design idea value is. We also find that EM mechanism can always dominate IM mechanism when the monitoring costs are equal. Moreover, comparing with a no monitoring scenario, we identify two values of monitoring: the value of monitoring idea information and the value of monitoring effort. Our results show that adopting IM and EM mechanisms can improve the senior executive's profits obtained in the no monitoring scenario when the revenue uncertainty is sufficiently low. The results also indicate that the value of monitoring idea information decreases as the risk aversion level of the project manager improves, while the value of monitoring effort shows the opposite feature. [ABSTRACT FROM AUTHOR]

Introduction: For open and endoscopic inguinal hernia surgery, it has been demonstrated that low-volume surgeons with fewer than 25 and 30 procedures, respectively, per year are associated with significantly more recurrences than high-volume surgeons with 25 and 30 or more procedures, respectively, per year. This paper now explores the relationship between the caseload and the outcome based on the data from the Herniamed Registry.Patients and Methods: The prospective data of patients in the Herniamed Registry were analyzed using the inclusion criteria minimum age of 16 years, male patient, primary unilateral inguinal hernia, TEP or TAPP techniques and availability of data on 1-year follow-up. In total, 16,290 patients were enrolled between September 1, 2009, and February 1, 2014. Of the participating surgeons, 466 (87.6 %) had carried out fewer than 25 endoscopic/laparoscopic operations (low-volume surgeons) and 66 (12.4 %) surgeons 25 or more operations (high-volume surgeons) per year.Results: Univariable (1.03 vs. 0.73 %; p = 0.047) and multivariable analysis [OR 1.494 (1.065-2.115); p = 0.023] revealed that low-volume surgeons had a significantly higher recurrence rate compared with the high-volume surgeons, although that difference was small. Multivariable analysis also showed that pain on exertion was negatively affected by a lower caseload <25 [OR 1.191 (1.062-1.337); p = 0.003]. While here, too, the difference was small, the fact that in that group there was a greater proportion of patients with small hernia defect sizes may have also played a role since the risk in that group was higher. In this analysis, no evidence was found that pain at rest [OR 1.052 (0.903-1.226); p = 0.516] or chronic pain requiring treatment [OR 1.108 (0.903-1.361); p = 0.326] were influenced by the surgeon volume. As confirmed by previously published studies, the data in the Herniamed Registry also demonstrated that the endoscopic/laparoscopic inguinal hernia surgery caseload impacted the outcome. However, given the overall high-quality level the differences between a "low-volume" surgeon and a "high-volume" surgeon were small. That was due to the use of a standardized technique, structured training as well as continuous supervision of trainees and surgeons with low annual caseload. [ABSTRACT FROM AUTHOR]

This study considers how public sector organisations respond to the effects of austerity. While some organisations take advantage of austerity to increase the workload of employees with little or no engagement with employees, others encourage dialogue by increasing employee and union engagement. Drawing on a systematic analysis of 26 articles, the study finds that austerity policies have negative consequences for public sector employees and presents employee voice as a potential mitigator of the negative consequences. This study is one of the first to review the small but growing literature on the effects of austerity on work and employment relationship. [ABSTRACT FROM AUTHOR]

Background: In March 2020, when public health stay home orders began in order to halt the spread of COVID-19, child care as an industry was drastically and abruptly impacted. This public health emergency highlighted the weaknesses in the child care system in the United States. Objective: This study investigated the changes in operations cost, child enrollment and attendance, and state and federal support that occurred during the first year of the COVID-19 pandemic among both center-based and home-based child care programs. Methods: A total of 196 licensed centers and 283 home-based programs across Iowa participated in an online survey as a part of the 2020 Iowa Narrow Costs Analysis. This mixed method study utilizes qualitative analysis of response as well as descriptive statistics and pre- post comparison testing. Results: Analysis of qualitative and quantitative data revealed that the COVID-19 pandemic had a marked impact on child care enrollment, the operational cost of child care, the availability of child care, and a variety of other areas including staff workload and mental health. In many instances, participants shared that state and federal COVID-19 relief funds were critical. Conclusion: Although state and federal COVID-19 relief funds were critical for child care providers in Iowa during the pandemic, results suggest similar financial support will be necessary beyond the pandemic to sustain the workforce. Policy suggestions are made for how to continue to support the child care workforce in the future. [ABSTRACT FROM AUTHOR]

This paper provides a survey on task models to characterize real-time workloads at different levels of abstraction for the design and analysis of real-time systems. It covers the classic periodic and sporadic models by Liu and Layland et al., their extensions to describe recurring and branching structures as well as general graph- and automata-based models to allow modeling of complex structures such as mode switches, local loops and also global timing constraints. The focus is on the precise semantics of the various models and on the solutions and complexity results of the respective feasibilty and schedulability analysis problems for preemptable uniprocessors. [ABSTRACT FROM AUTHOR]

Multi-workday vehicle routing problem (MW-VRP) is concerned with planning daily delivery routes of vehicles over consecutive workdays. During the planning period, daily delivery routes can be the same or different. In this paper, we study two policies for assigning delivery workers to vehicles. Firstly, workers are preassigned to vehicles at the beginning of the considered period. In each workday, the same worker-vehicle pairings are followed. Secondly, workers are postassigned to vehicles based on the vehicles' carried loads. This policy usually results in different daily worker-vehicle pairings. These two policies are evaluated according to the total travel distance of the vehicles and the physical workloads imposed on the workers during the planning period. The result shows that the worker-vehicle post-assignment policy is superior to the other policy in both aspects. [ABSTRACT FROM AUTHOR]

List-based workflow scheduling has received much research attention and been implemented in many existing workflow computing systems as more and more scientific and engineering applications need to exploit task parallelism for performance improvement. In this paper, we propose two task-ranking mechanisms and one task allocation method for the two major steps in list-based workflow scheduling. The proposed task-ranking approaches are based on innovative ideas of remaining workload and hybrid ranking, in contrast to the single path-oriented concept widely used in existing methods. The investigation of task allocation points out an important aspect, amount of available resources, which was not considered seriously in previous research. The proposed approaches were evaluated extensively with a series of simulation experiments and compared to existing widely used task-ranking and allocation methods. The experimental results show that our approaches has potential to outperform existing methods significantly in many cases, but no one single method can always achieve the best performance, which indicates a promising direction for future research work. [ABSTRACT FROM AUTHOR]