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Summary: Quality of Service (QoS) regulates and controls network resources by setting priorities for specific data types. Many clustering algorithms are used to cluster cloud workloads, most of which are static. However, the lack of dynamic algorithms is seen in the face of huge databases that are real‐time and according to the existing clustering conditions. Additionally, fair allocation of tasks on servers and efficient resource utilization pose challenges. In this research, two solutions are proposed to improve the quality of service: the first solution uses the chameleon dynamic algorithm, a method to improve service quality. The chameleon algorithm has been able to show significant performance due to its high accuracy in detecting the smallest distance between clusters. This dynamic algorithm outperforms static algorithms with classification accuracy and response speed, which are the most important parameters of service quality. The second part of the proposed solution is to use the Scheduling Model using Cloud Data Centers (SM‐CDC) system to select the best service provider based on the clustering done in the previous step. A SM‐CDC technique is developed to handle cloud storage center tasks that are stored in electronic devices. According to the comparison with existing scheduling policies, SM‐CDC offered 36% decrease on response time, 50% reduction on cost of resources, and 40% improvement on QoS Satisfaction. [ABSTRACT FROM AUTHOR]

Unmanned Ariel Vehicles (UAVs) are interconnected to perform specific tasks through self-routing and air-borne communications. The problem of automated navigation and adaptive grouping of the vehicles results in improper task completion and backlogs. To address this issue, a Particle Swarm Optimization-dependent Multi-Task Assignment Model (PSO-MTAM) is introduced in this article. The swarms are initialized for the available linear groups towards the destination. This article addressed the subject of UAVs using a multi-task assignment paradigm to increase task completion rates and handling efficiency. The different swarm stages are verified for the task progression, resulting in completion at the final stage. In this completion process, the first local best solution is estimated using the completion and assignment rate of a single task. The second local best solution relies on reaching the final stage. The global solution is identified depending on the convergence of the above solutions in task progression and handling density. The swarm positions are immediately identified, and the synchronous best solutions generate the final global best. The backlog-generating solutions are revisited by reassigning or re-initializing the swarm objects. The proposed model's performance is analyzed using task handling rate, completion ratio, processing time, and backlogs. Improving the handling rate is essential for this validation, necessitating solution and position updates from the intermediate UAVs. With varying task densities and varying degrees of convergence, the iterations continue until completion. There is an 11% increase in the task handling rate and a 12.02% increase in the completion ratio with the suggested model. It leads to a 10.84% decrease in processing time, a 9.91% decrease in backlogs, and a 12.7% decrease in convergence cost. [ABSTRACT FROM AUTHOR]

This paper focuses on the problem of computation offloading in a high-mobility Internet of Vehicles (IoVs) environment. The goal is to address the challenges related to latency, energy consumption, and payment cost requirements. The approach considers both moving and parked vehicles as fog nodes, which can assist in offloading computational tasks. However, as the number of vehicles increases, the action space for each agent grows exponentially, posing a challenge for decentralised decision-making. The dynamic nature of vehicular mobility further complicates the network dynamics, requiring joint cooperative behaviour from the learning agents to achieve convergence. The traditional deep reinforcement learning (DRL) approach for offloading in IoVs treats each agent as an independent learner. It ignores the actions of other agents during the training process. This paper utilises a cooperative three-layer decentralised architecture called Vehicle-Assisted Multi-Access Edge Computing (VMEC) to overcome this limitation. The VMEC network consists of three layers: the fog, cloudlet, and cloud layers. In the fog layer, vehicles within associated Roadside Units (RSUs) and neighbouring RSUs participate as fog nodes. The middle layer comprises Mobile Edge Computing (MEC) servers, while the top layer represents the cloud infrastructure. To address the dynamic task offloading problem in VMEC, the paper proposes using a Decentralized Framework of Task and Computational Offloading (DFTCO), which utilises the strength of MADRL and NOMA techniques. This approach considers multiple agents making offloading decisions simultaneously and aims to find the optimal matching between tasks and available resources. [ABSTRACT FROM AUTHOR]

Tax management is a strategy carried out by company management to manage tax payments so as not to exceed the amount that should be paid. The reason this research was conducted is because there are still many people or companies that apply tax. There are several internal company factors that affect tax management such as Profitability, Leverage, Company Size, and Capital Intensity. This study aims to determine and prove the effect of profitability, leverage, company size, and capital intensity individually and simultaneously on tax management in manufacturing companies listed on the Indonesia Stock Exchange for a period of 4 years, namely 2019-2022. The type of research used is quantitative research. The type of data used is secondary data obtained from www.idx.co.id and the company website. The population in this study are manufacturing sector companies listed on the Indonesia Stock Exchange from 2019-2022. The sampling method used was purposive sampling technique with a total sample size of 58 companies. Data processing techniques using multiple linear regression statistical analysis with the help of the SPSS Version 25 program. The results show that profitability has a negative and significant influence on tax management, leverage and company size have no influence on tax management, and capital intensity has a positive and significant influence on tax management. Simultaneously, profitability, leverage, company size and capital intensity have an influence on tax management. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices and other IT forms in almost every area of human existence has resulted in an enormous influx of data that must be managed and stored. One viable solution to this issue is to store and handle massive amounts of data in cloud environments. Real-time data analysis has always been critical. However, it becomes even more crucial as technology and the IoT develop, and new applications emerge, such as autonomous cars, smart cities, and IoT devices for healthcare, agriculture, and other industries. Given the massive volume of data, moving to a remote cloud is time-consuming and produces severe network congestion, rendering cloud administration and rapid data processing difficult. Fog computing provides close-to-device processing at the network’s periphery, and fog computing can analyze data in near real-time. However, the increased amount of IoT gadgets and data they produce is a formidable challenge for fog nodes. Task offloading may enhance fog computing by offloading the excess data to other nodes for processing due to the restricted resources in the fog. Management of tasks and resources must be optimized in fog devices. This review article overviews related works on task offloading in IoT-Fog-Cloud Environment. In addition, we discuss about fog networks and Software-defined network (SDN) applications and challenges in fog offloading.

Advanced CRM and Task Management Software is a software wherein all the types of employees and there work can be managed and monitored of an organization. The organization may be any type of organization running a business where some people work in office and perform tasks or the people or employees who work on leads to gather business for the organization. This software will be very simple to operate and extremely easy to understand and use. With the help of this software the Employer or Admin becomes completely free from the monotonous controlling job. The software will provide them the live working information of employees at their palm top (ie. On mobile), Tablet, Laptop or Desktop Any Time and Any Where. The most important aspect is that the Employer gets more time to be utilised on business growth and developmental activities. [ABSTRACT FROM AUTHOR]

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Ethics has long been, and continues to be, a central topic among marketing scholars and practitioners. When providing complex services—multiple interactions over time that are predicated on the evolving needs of customers—service providers face ethical dilemmas, which are often resolved by engaging an ethics committee (EC). Despite the prevalence of ECs, research on service providers' preference to engage with an EC is sparse. This study examines whether the role that health care providers play, as either task manager or relationship manager, makes a difference in their preference for engaging with and utilizing an EC for resolving ethical dilemmas. Results based on 1,440 observations collected from health care service providers show that service providers' task or relationship management role, as well as prior experience with an ethics consultation, influences their preference both for engaging an EC and for having the EC prescribe a specific outcome to resolve an ethical dilemma. This study extends prior work on conceptual models examining ethical decision-making processes in marketing. [ABSTRACT FROM AUTHOR]

Task management tools allow people to record, track, and manage task-related information across their work and personal contexts. As work contexts have shifted amid the COVID-19 pandemic, it has become important to understand how these tools are continuing or failing to support peoples’ work-related and personal needs. In this article, we examine and probe practices for managing task-related information across the work–life boundary. We report findings from an online survey deployed to 150 information workers during Summer 2019 (i.e., pre-pandemic) and 70 information workers at the same organization during Summer 2020 (i.e., mid-pandemic). Across both survey cohorts, we characterize these cross-boundary task management practices, exploring the central role that physical and digital tools play in managing task-related information that arises at inopportune times. We conclude with a discussion of the opportunities and challenges for future productivity tools that aid people in managing task-related information across their personal and work contexts. [ABSTRACT FROM AUTHOR]

In this paper, we address the challenge of limited resources in Internet of Things (IoT) devices by proposing a solution based on digital twin in distributed edge computing networks. Edge computing is a promising approach that moves computing resources closer to the network’s edge to reduce response times in IoT applications. However, simply offloading tasks from IoT devices to edge computing does not accelerate user control. To enhance task performance and improve user management experience, we introduce optimized task scheduling and virtual object management based on a digital twin concept. Our system incorporates virtualization, synchronization, visualization, and simulation functionalities to provide digital twin capabilities. Additionally, we develop a user-friendly web application with a graphical user interface (GUI) for intuitive management of edge computing services. To support our approach, we implement an edge computing supervisor that generates virtualized objects such as edge gateways, IoT devices, and services. These virtual objects serve as resources for creating tasks. Using our proposed digital twin platform, users can dynamically create new tasks based on demand, easily deploy and execute tasks in specific locations, and dynamically allocate edge network resources according to task requirements. An optimized task scheduling mathematical model is presented to compare task scheduling done with and without optimization. Further, the edge computing and digital twin based optimized task scheduling method is integrated with Federated Learning for collaborative learning and privacy preserved computation of sensors sensitive data. We demonstrate the effectiveness of our system by generating tasks for data collection related to indoor environment for prediction of Predicted Mean Vote (PMV) for thermal comfort index of smart homes occupants using HTTP and IoTivity-based devices in distributed edge computing networks. These tasks are properly delivered and executed on the expected edge gateways, showcasing the successful integration of our digital twin platform with edge computing networks. Further, the optimized task scheduling has improved the overall performance of the proposed system, keeping in view latency and processing time.

The ever-increasing demand for services of end-users in the Internet of Things (IoT) often causes great congestion in the nodes dedicated to serving their requests. Such nodes are usually placed at the edge of the network, becoming the intermediates between the IoT infrastructure and Cloud. Edge nodes offer many advantages when adopted to perform processing activities that are realized close to end-users, limiting the latency in the provision of responses. In this article, we attempt to solve the problem of the potential overloading of edge nodes by proposing a mechanism that always keeps free space in their queue to host high-priority processing tasks. We introduce a proactive, self-healing mechanism that utilizes the principles of Fuzzy Logic, in combination with a non-parametric statistical method that reveals the trend of nodes’ loads as depicted by the incoming tasks and their capability to serve them in the minimum possible time. Through our approach, we manage to ensure the uninterrupted service of high-priority tasks, taking into consideration the demand for tasks as well. Based on this approach, we ensure the fastest possible delivery of results to the requestors while keeping the latency for serving high-priority tasks at the lowest possible levels. A set of experimental scenarios is adopted to evaluate the performance of the suggested model by presenting the corresponding numerical results.

In a distributed computing system, there are limited resources, which needs to be utilized effectively. Then for improving QoS Fog computing paradigm is an effective way, with suitable allocations. Thus, different resource scheduling and optimization algorithms exist. However, still, there is a scope to improve bandwidth, latency, energy consumption, and total communication cost in the Fog environment. In this work investigation is done to show significance of task management in such resource constrained environment. Various heuristics and meta-heuristic algorithms are evaluated using simulations, to show the task placement and their impacts by using 5 different Montage datasets from work flow sim tool kit for Fog-Computing environment. Then QoS parameters like cost, makespan, and energy consumptions are computed for various state-of-the-art techniques like Min-max, PSO, GA, ACO, and BLA. This shows the behaviour of these techniques with such different tasks and allocation environment configurations. Evaluated result parameters are collected and presented in the result section. This work shows the effectiveness of heuristics and meta-heuristics techniques to manage the tasks and their allocations in the Fog environment.

Cloud technology is a worldwide storage architecture in which consumers use resources that are continuously provided, such as computing, storage, and networking. Computing has led to internet servers being content in electronic platforms that are distributed by numerous consumers. The main issue with the data center is dealing with the millions of ideas that come in regularly. As a result, the Scheduling Model (SM) utilizing the cloud datacenters (SM-CDC) technique is developed in this research to handle cloud storage centers' tasks that are stored in electronic devices. The task is spread across all present virtual machines, and the online data is secured from overloaded. The data distribution is established by considering the value and state of the smart device, which aids in the fair allocation of tasks and effective use of resources. Task load (TL) involves managing these demands, and the workload input should be distributed evenly and consistently among the numerous processors. According to the findings of the trial, SM-CDC has the best performance in the terms of least finishing time of 16.76 ms, a high computation offloading rate of 93%, a minimum reaction time of 8 ms, a minimum CPU time of 5.25 ms. [ABSTRACT FROM AUTHOR]

Background: Poor leadership and teamwork in cardiopulmonary resuscitation (CPR) are associated with poor patient outcomes. Problem: Frontline nursing staff frequently identify patients in cardiac arrest but may not have the initial leadership and teamwork skills to organize their initial rescue response. Approach: The Five-4-Life Quality Improvement (QI) program was pilot tested in a pediatric unit within a 510-bed acute care hospital in 2 phases: first, an educational program focused on leadership, team dynamics, and CPR skills, followed by sustaining interventions in the unit. Video recordings of 12 mock codes (4 pre-, 4 post-, 4 follow-up) were analyzed by trained observers. Outcomes: Descriptive statistical tests indicated a significant improvement in leadership, teamwork, and task management scores pre- and post-program, and sustained after the program. Conclusion: Implementing the Five-4-Life QI program is feasible in improving leadership, teamwork, and task management of first responding frontline nurses. [ABSTRACT FROM AUTHOR]

The ever-increasing demand for services of end-users in the Internet of Things (IoT) often causes great congestion in the nodes dedicated to serving their requests. Such nodes are usually placed at the edge of the network, becoming the intermediates between the IoT infrastructure and Cloud. Edge nodes offer many advantages when adopted to perform processing activities that are realized close to end-users, limiting the latency in the provision of responses. In this article, we attempt to solve the problem of the potential overloading of edge nodes by proposing a mechanism that always keeps free space in their queue to host high-priority processing tasks. We introduce a proactive, self-healing mechanism that utilizes the principles of Fuzzy Logic, in combination with a non-parametric statistical method that reveals the trend of nodes' loads as depicted by the incoming tasks and their capability to serve them in the minimum possible time. Through our approach, we manage to ensure the uninterrupted service of high-priority tasks, taking into consideration the demand for tasks as well. Based on this approach, we ensure the fastest possible delivery of results to the requestors while keeping the latency for serving high-priority tasks at the lowest possible levels. A set of experimental scenarios is adopted to evaluate the performance of the suggested model by presenting the corresponding numerical results. [ABSTRACT FROM AUTHOR]

Abstract Background Non-technical skills (NTS) are important for the proper functioning of emergency medical ambulance crews but have hardly been researched in the conditions of clinical pre-hospital care. The primary objective of this study, therefore, is to describe the use of NTS in practice. The secondary objective is to compare if the performance of NTS varies according to the type of case. Methods In this multicentric observational study the modified Team Emergency Assessment Measure (TEAM) score was used to assess the performed NTS of two or more crews on site. The evaluation consisted of leadership, teamwork and task management, rated by a field supervisor. The study observations took place in real clinical pre-hospital emergency medical cases when two or more crews were dispatched between October 2019 and August 2020. The sample size was determined by researchers prior to the study to at least 100 evaluated events per each of the three participating emergency medical services. The results are presented as median and interquartile range. The internal reliability, consistency and validity of test items and results were evaluated. The Kruskal–Wallis test and the post hoc Mann-Whitney U test with Bonferroni correction were used for multiple comparisons of three groups. Results A total of 359 events were evaluated. Surprisingly, the median value for all eight items was as high as 3.0 with a similar interquartile range of 1.0. There were no differences observed by case type (CPR vs. TRAUMA vs. MEDICAL) except from item 1. A post hoc analysis revealed that this difference is in favour of a higher rated performance of non-technical skills in CPR. Conclusions The overall result of the performance of non-technical skills can be regarded as very good and can serve for further evaluations. The crews achieved better parameters of NTS in leadership in resuscitation situations in comparison with general medical events. Trial Registration The study is registered at Clinical Trials under the ID: NCT04503369 .

Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.

Cloud computing is a global storage framework whereby users use the tools, including the computation, storage, network, etc., that are provided automatically. The computational has led the cloud data centers to be stored in digital services that many consumers distribute. The biggest problem with cloud data centers is handling the millions of users’ continuous proposals. Therefore, in this paper, Adaptive Scheduling Algorithm Based Task Loading (ASA-TL) has been proposed to manage cloud data centers’ task to be stored in digital devices. ASA is implemented in which the task is shared between all the current virtual servers,and the cloud data are protected from overloading. The data assignment is made by taking account of the importance and status of the digital device that helps to assign task fairly and use them efficiently. TL manages these requests efficiently, and the task input must be equally and reliably allocated between various processors. The experimental result suggests that ASA-TL achieves the highest performance assessment measurements, including response time, processing time in the data center and overall expense.

All forms of formal education were abruptly shifted into online-learning when Covid-19 hit globally. This unexpected change has forced education practitioners to adapt and conduct their work remotely in order to minimize the virus spread. However, sudden changes are seldom arduous and work from home came with its many challenges. This research seeks to examine the working conditions and job requirements, the time and task management, work related stress, performance at work lecturer’s experience during online-learning and their inclination towards work from home in the future. This research is an exploratory study and the data is analyzed with a statistic descriptive method to describe the phenomenon. Using online questionnaire, a total of 120 lecturers responded. The study discovers that lecturers find the conditions of work from home to be acceptable, they are able to organize their task and time to some extent, and it does not affect their work performance and does not have a negative impact on their stress level. In the future, the lecturers will feel neutral about work from home after the pandemic subsidence. More are leaning towards going back to work in office settings, but open to do hybrid-working, which is a combination of work from office and work from home.

Data/tasks of Industrial Internet of Things (IIoT) are extremely private and valuable to computing. Edge computing facilitates IIoT services by offering platforms of supportive facilities and functionalities. However, the convergence of intelligent edge computing and IIoT platforms has been held back by the need to develop systems for maintaining security and privacy. In this article, we propose a new framework for securely and confidentially sending, storing, and computing IIoT tasks. This framework employs a lightweight encryption scheme alongside modified ElGamal encryption and digital signature schemes. We analyzed the robustness of this framework in terms of security and privacy, and assessed its performance through simulations. Ultimately, the proposed framework outperformed the existing models in terms of time complexity, latency, and energy consumption. [ABSTRACT FROM AUTHOR]

Using the giant parallelism of photons to develop a new type of computer with more processor bits and data, the ternary optical computer, with the birth of the ternary optical computer SD 16 physical machine, the team built a monitoring system structure for task management, It is mainly used to deal with various complex data problems and provide parallel services for computing requests of multiple users. The storage of data in the monitoring system has also become a crucial issue. Based on the existing achievements of the team, this paper designs a data storage scheme for the management software of the ternary optical computer monitoring system, and finally summarizes and looks forward to the next research direction of the ternary optical computer data storage. [ABSTRACT FROM AUTHOR]

The purpose of making this thesis is to provide solutions to the problems of PT. Access Komunikasi Indonesia for control and ensure product marketing activities can run according to the target and able to provide reports in real time as well as make it easier for employees to access them. The data collection method used to obtain user needs is the method of observation, interviews and literature study. The technology that we use for building this website is Laravel framework, Angular JS, and Database using MySQL. The development method used is SDLC Model. The conclusion of this thesis is that the existence of a web-based sales report system is expected to help PT. Akses Komunikasi Indonesia for getting product marketing reporting results easily, quickly, and accurately. [ABSTRACT FROM AUTHOR]

Background: Non-technical skills (NTS) are important for the proper functioning of emergency medical ambulance crews but have hardly been researched in the conditions of clinical pre-hospital care. The primary objective of this study, therefore, is to describe the use of NTS in practice. The secondary objective is to compare if the performance of NTS varies according to the type of case.Methods: In this multicentric observational study the modified Team Emergency Assessment Measure (TEAM) score was used to assess the performed NTS of two or more crews on site. The evaluation consisted of leadership, teamwork and task management, rated by a field supervisor. The study observations took place in real clinical pre-hospital emergency medical cases when two or more crews were dispatched between October 2019 and August 2020. The sample size was determined by researchers prior to the study to at least 100 evaluated events per each of the three participating emergency medical services. The results are presented as median and interquartile range. The internal reliability, consistency and validity of test items and results were evaluated. The Kruskal-Wallis test and the post hoc Mann-Whitney U test with Bonferroni correction were used for multiple comparisons of three groups.Results: A total of 359 events were evaluated. Surprisingly, the median value for all eight items was as high as 3.0 with a similar interquartile range of 1.0. There were no differences observed by case type (CPR vs. TRAUMA vs. MEDICAL) except from item 1. A post hoc analysis revealed that this difference is in favour of a higher rated performance of non-technical skills in CPR.Conclusions: The overall result of the performance of non-technical skills can be regarded as very good and can serve for further evaluations. The crews achieved better parameters of NTS in leadership in resuscitation situations in comparison with general medical events.Trial Registration: The study is registered at Clinical Trials under the ID: NCT04503369 . [ABSTRACT FROM AUTHOR]

Led by a premise that effective cooperative tasks play a vital role in enhancing Cognitive Engagement (CE) and task achievement in ESL/EFL teaching, this study investigates the effects of Cooperative Learning (CL) on Omani Bachelor of Education (B. Ed.) students' CE and task achievement. This quasiexperimental study has three objectives: (i) To find out the level of awareness of CE skills required during EFL learning sessions; (ii) To examine whether there are any statistically significant differences between the mean scores of students' responses taught through CL strategies and taught in normal setting; and (iii) To investigate whether there are significant differences among EFL students' CE levels attributed to their gender. A total of 36 B. Ed. students were assigned to Experimental Group and Control Group with 18 students in each. Analytical results of the study found that (a) The overall CE levels of the students was low at the mean score of 2.20; (b) On statistical grounds, significant differences were found at the level of <0.01 between the mean scores of CE responses of students taught through CL and those who were taught in a normal setting; and (c) No statistically significant differences were found at the level of <0.05 between mean scores of students' CE responses attributed to their gender. We have discussed a number of pedagogical implications emerging from the findings of this study for raising students' awareness, enhancing teachers' roles and responsibilities, effective task designing and developing engaging instructional materials. [ABSTRACT FROM AUTHOR]

Background: Globally, motor vehicle accidents (MVAs) cause around 1.35 million deaths annually. Distracted driving, a risk factor for MVA, includes diversion of attention from driving because of use of mobile phone. Objectives: The aim of this study was to determine prevalence of mobile phone use and to explore task management strategies, risk perception and attitude towards mobile phone use while driving among Qassim University students. Methods: An online cross-sectional survey among 212 randomly selected medical (n = 83) and engineering students (n = 129) of Qassim University, Saudi Arabia, through semi-structured, self-administered questionnaire, designed using Google forms. The survey was conducted from February to March 2020. Results: The overall prevalence of mobile phone use while driving was 93.4% (medical students: 96.4%; engineering students: 91.5%). Around 49.5% participants 'often' or 'always' used mobile phone while driving. Among task management strategies, 169 (79.7%) participants were 'likely' or 'very likely' to lower their driving speed while 90 (42.5%) were 'likely' or 'very likely' to increase control over the steering while using mobile phone during driving. Regarding risk perception, 173 (81.6%) participants thought that they were 'unlikely' or 'very unlikely' to have MVA on looking at phone continuously for more than 2 s, and 185 (87.3%) participants thought that they were 'unlikely' or 'very unlikely' to have MVA by texting or browsing while driving. Thirty-six (17%) participants reported MVA because of distraction by mobile phone use while driving. Conclusion: High prevalence of mobile phone use during driving and low perceived risk of experiencing MVA because of mobile phone use was found among Qassim University students. Creating awareness on risks of mobile phone use while driving is recommended.

At present, most logistics systems, especially those dedicated to e-commerce, are based on artificial intelligence techniques to offer better services and increase outcomes. However, the variety and complexity of resource allocation, as well as task scheduling, denote that dynamic environments have still great challenges to overcome. So advanced models based on strong algorithms are required. Introducing advanced models into scheduling solutions is a promising way to enhance logistics efficiency. As a result, managing system resources remain essential to optimize task scheduling respecting the interactive impacts, and logistics systems requirements. In response to these challenges, in this paper, a powerful solution based on a Long short-term memory (LSTM) model is proposed to optimize resource allocation and to enhance task scheduling in a smart logistics framework. This paper explores some of the most important scheduling techniques and hypothesizes that deep learning techniques might be able to afford accurate approaches. The proposed smart logistics model lays on strong techniques, for that, experimental simulations were conducted using various project instances. The validation tests demonstrated competitive results with important performance rates i.e.: accuracy of 92,44% with a precision of 93,83, a recall of 95.18%, F1-score of 94,92%, and an AUC of 88,17%. These results reveal the proof-of-principle for using LSTM models for effective and truthful logistics operations.

Objectives The establishment of integrated logistic support (ILS) information systems for submarine equipment plays an important role in maintaining and improving submarine combat capability. Based on this, a design method of submarine equipment ILS information systems is proposed.Methods First of all, the overall design solution is determined based on a functional requirements analysis and the key points of the system architecture design. Then, the task management module, data collection, and distribution module are taken as a cut-in point to explain the design idea. In the design of the task management module, the design method entailing module function division, and display and business logic separation when the information is highly connected, are analyzed. In the design of the data collection and distribution module, taking the fault record information as an example, the data interaction method between the overall ILS information database and the ILS information database at each depot level is discussed.Results Based on the above design ideas, the design and development of submarine equipment ILS information systems have been completed. The operation of the system shows that it meets the design objectives and user requirements, and effectively improves the informationization level of military support management.Conclusions The design scheme of submarine equipment ILS information systems proposed is reasonable and feasible. The data collected in the process of system operation also provides strong support for subsequent big data analysis and intelligent application.

Along with the increasing emergence of smart watch devices, new interaction techniques between these devices and smart phones have been proposed to leverage their availability. However, there are few existing studies on enhancing smart watches' current functions: working as an information processing system for users' phones in terms of both browsing and handling short information. In this work, we introduce a framework for handling tasks between the smart phone and the smart watch, including task delegation and task management, that enables the smart watch to temporarily handle lightweight jobs from the smart phone. By utilizing this framework, people can save valuable phone screen space for more complicated or important tasks. Moreover, considering users' multi-tasking behaviors, we also provide some example situations where users use both their phone and smart watch to handle different kinds of tasks simultaneously. The user study illustrates that lightweight jobs can be efficiently handled by users on the watch. [ABSTRACT FROM AUTHOR]

Objective: This case study illustrates the use of natural language processing for identifying administrative task categories, prevalence, and shifts necessitated by a major event (the COVID-19 [coronavirus disease 2019] pandemic) from user-generated data stored as free text in a task management system for a multisite mental health practice with 40 clinicians and 13 administrative staff members.Materials and Methods: Structural topic modeling was applied on 7079 task sequences from 13 administrative users of a Health Insurance Portability and Accountability Act-compliant task management platform. Context was obtained through interviews with an expert panel.Results: Ten task definitions spanning 3 major categories were identified, and their prevalence estimated. Significant shifts in task prevalence due to the pandemic were detected for tasks like billing inquiries to insurers, appointment cancellations, patient balances, and new patient follow-up.Conclusions: Structural topic modeling effectively detects task categories, prevalence, and shifts, providing opportunities for healthcare providers to reconsider staff roles and to optimize workflows and resource allocation. [ABSTRACT FROM AUTHOR]

This article presents an excerpt from a research which had as main objective to analyze the process of managing mathematical tasks of an Early Years Elementary School group of teachers, before and after a formative process. The investigation of a qualitative approach in the form of action-research was developed through a training group, involving the researcher and Early Years Elementary School teacher in a public school.The data were produced through face-to-face training meetings, semi-structured interviews, sequences of tasks planned by the teachers and direct observation of the class. Task management is understood in this work as a process that involves the management of the planning, implementation and evaluation of mathematical tasks. Specifically,for this article, we explain the analysis developed about the planning management process. The analyzed data show that, although the teachers recognize the importance of planning, they demonstrate difficulties in planning due to the lack of mastery of didactic-mathematical knowledge, revealing that they study little or do not study, and usually refute content that they have difficulties.

Spatial crowdsourcing is an effective and novel method. In crowdsourcing systems, a centralized platform is traditionally used to allocate tasks and select workers. Centralized platforms always face following challenges: 1) How to ensure the rationality of tasks allocating; 2) How to ensure the payments of workers in the system when dishonest requesters exist; 3) How to ensure the maximum number of tasks are assigned. 4) How to ensure the integrity and reliability of the centralized platform. To solve these problems, this article proposed a distributed blockchain-based crowdsourcing framework - TSWCrowd (Task Select Worker Crowd). In this framework, tasks are sorted according to specific rules, thus tasks with higher priority are assigned to workers earlier. Workers who are available for a task will be selected and return a result. Then the deployed smart contracts will pay the basic payment automatically. At the same time, relevant contracts also calculate and pay the quality payment according to the proposed quality reward formulation. The proposed TSWCrowd framework on-chain involves a public dataset and uses solidity to compile the smart contracts. The framework was deployed on a local private blockchain. The decentralization property of the blockchain ensures the reliable assignment of tasks. Task-select-worker (TSW) algorithm sorts tasks to ensure reliability. In this paper, the proposed framework was compared with the ABCrowd auction mechanism on-chain and the VCG mechanism off-chain. The results show that the average distance is shorter and the payment is higher, thus reaches the reasonability, reliability and availability.

Studies on media multitasking behavior and task-switching performance have yielded mixed results. The present preliminary study (N = 40) focused on task management, and not performance, by allowing participants to choose the switching frequency between tasks all along the experiment. This design revealed two different patterns of organization, with many participants choosing no variation at all and sticking to this organization throughout the entire experiment. We found no impact of organization type on performance. The participants who switched more often scored higher on polychronicity (i.e., preference for multitasking) and media multitasking scales. We did not find any relationship between executive functioning and task organization or media multitasking scores, but this result should be regarded with caution given the limited sample size. In broad terms, individuals tend to declare a media multitasking tendency in line with their spontaneous organization on multiple tasks, with more sequentially organized participants reporting lower media multitasking behavior. These results point to a seemingly global individual strategy to approach multiple tasks.

Lean and agile methods are now widely used in various fields. Seeing their success, management consultancy firms also want to take advantage of using them. In this case study, the company selects the appropriate task management tool for their operation, the kanban board, and implements it. The implementation process is supported by quantitative analysis. In order to track the introduction progress of the kanban board, a key performance index is defined: the board activity, which is the number of operations performed on the board in a given time period. Based on the evaluation of over 26 weeks’ data, board activity proved to be an appropriate indicator of the kanban board’s reception, operation, and stability. The individual indicators examined, however, were not found to be suitable for performance appraisal.

Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence. Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process, a total of 120 relevant studies have been identified, and then the most relevant 70 studies were selected to include as part of the current study. The proposed study shows the effect of task assignment in crowdsourcing, such as if a task is assigned to an appropriate worker or an inappropriate worker, what will be the consequences. The study also highlights crowdsourcing usage in the field of software engineering. All the existing task assignment methods used for assigning the task to make crowdsourcing activity more effective have been analyzed. The study also highlights all the available crowdsourcing platforms used for a variety of task to be performed. The study concludes by identifying the issues regarding the task assignment and to specify the methods for enhancement in the assignment of tasks for future research in crowdsourcing. [ABSTRACT FROM AUTHOR]

Abstract Background Many healthcare databases have been routinely collected over the past decades, to support clinical practice and administrative services. However, their secondary use for research is often hindered by restricted governance rules. Furthermore, health research studies typically involve many participants with complementary roles and responsibilities which require proper process management. Results From a wide set of requirements collected from European clinical studies, we developed TASKA, a task/workflow management system that helps to cope with the socio-technical issues arising when dealing with multidisciplinary and multi-setting clinical studies. The system is based on a two-layered architecture: 1) the backend engine, which follows a micro-kernel pattern, for extensibility, and RESTful web services, for decoupling from the web clients; 2) and the client, entirely developed in ReactJS, allowing the construction and management of studies through a graphical interface. TASKA is a GNU GPL open source project, accessible at https://github.com/bioinformatics-ua/taska. A demo version is also available at https://bioinformatics.ua.pt/taska. Conclusions The system is currently used to support feasibility studies across several institutions and countries, in the context of the European Medical Information Framework (EMIF) project. The tool was shown to simplify the set-up of health studies, the management of participants and their roles, as well as the overall governance process.

The purpose of this study is to create an application that can provide benefits to PT Intisoft Mitra Sejahtera in managing the tickets delivered by the client, arrange program modification policies, and can see the assessment of the performance of employees who work on the ticket and facilitate the client in obtaining quick information about obstacles which they experience by turning it into a ticket on the application. The research method used is by conducting an analysis (walking system survey, interview, and observation) and design method (UML), testing method (Black box testing). The analysis is carried out by conducting field studies and conducting interviews with the Director of PT Intisoft Mitra Sejahtera along with related divisions in the plan for making this application. The results achieved are web and android applications that function to manage work assignments and assessments that can help oversee, and document problems that exist in the company and also assess employee performance in handling reports of problems reported by the client.