Job Execution Research Articles

Noise Exposure and Job Stress – a Structural Equation Model in Textile Industries

Noise exposure is one of the most important physical agents in the workplace which can induce job stress in several ways. The aim of this study was to model the interactions between independent and mediating variables and job stress using structural equation modeling. In this study, Weinstein’s noise sensitivity scale, noise annoyance questionnaire, Health and Safety Executive (HSE) job stress questionnaire and job satisfaction scale were used. To assess worker’s noise exposure, the 8-hours equivalent continuous A-weighted sound pressure level (LAeq;8 h), was measured based on ISO 9612 (2009). To achieve the aims of study, the structural equation model was run using R software 3.4.1 and Cytoscape software 3.6.0. Based on the results, while there was a direct positive correlation of noise exposure on total job stress, there were also indirect positive effects through job satisfaction and noise sensitivity as mediator variables. Using hearing protective devices negatively affected total job stress through a direct pathway and an indirect pathway when job satisfaction was a mediator variable. Regarding the total effect of noise exposure and using hearing protection devices on job stress subscales, it can be concluded that noise exposure and using hearing protection devices had greatest effect on colleagues support and demand, respectively. It can be concluded that noise exposure and lack of hearing protective devices have a significant positive effect on job stress among workers of a textile industry. In addition to the direct effect, this factor can induce job stress through noise sensitivity, job satisfaction and noise annoyance. Therefore, measures which can decrease any of the mentioned factors, also can alleviate job stress.

Knowledge-based checkpointing strategy for spot instances in cloud computing

The Amazon EC2 offers spot-priced virtual machines (VMs) at a reduced price compared to on-demand and reserved VMs. However, Amazon EC2 can terminate these VMs anytime due to the spot price and demand fluctuation. Using spot VMs results in a longer execution time and disrupts service availability. Users can use fault-tolerant techniques such as checkpointing, migration, and job duplication to mitigate the unreliability of spot VMs. In this paper, a knowledge-based checkpointing strategy is proposed to minimize the overall checkpointing overhead during the execution of jobs. The proposed scheme uses real-time price history to decide when to take a checkpoint. Results show that the proposed approach can significantly reduce the turnaround time by 18% compared to Hourly Checkpointing Strategy and 9% compared to Rising-Edge Checkpointing Strategy. One can also achieve 54% to 78% reliability with a cost saving of 78% for the workload used with the described approach.

SLearn: A Case for Task Sampling Based Learning for Cluster Job Scheduling

The ability to accurately estimate job runtime properties allows a scheduler to effectively schedule jobs. State-of-the-art online cluster job schedulers use history-based learning, which uses past job execution information to estimate the runtime properties of newly arrived jobs. However, with fast-paced development in cluster technology (in both hardware and software) and changing user inputs, job runtime properties can change over time, which lead to inaccurate predictions. In this paper, we explore the potential and limitation of real-time learning of job runtime properties, by proactively sampling and scheduling a small fraction of the tasks of each job. Such a task-sampling-based approach exploits the similarity among runtime properties of the tasks of the same job and is inherently immune to changing job behavior. Our analytical and experimental analysis of 3 production traces with different skew and job distribution shows that learning in space can be substantially more accurate. Our simulation and testbed evaluation on Azure of the two learning approaches anchored in a generic job scheduler using 3 production cluster job traces shows that despite its online overhead, learning in space reduces the average Job Completion Time (JCT) by 1.28×, 1.56×, and 1.32× compared to the prior-art history-based predictor.

MANAGERIAL PERFORMANCE MODEL OF INDONESIAN GOVERNMENT

This study aims to determine the Managerial Performance Model of Regional Heads in Indonesia. This research is a quantitative research using WarPls 07 to process data. This research was conducted on the Government Indonesia. The sampling technique used is probability sampling technique . The sample is calculated using Slovin with an error rate of 5% where 230 respondents were selected . Test results The Effect of Managerial Performance on Political Will Perception , Servant Leadership on Political Will Perception , Public Service Motivation on Political Will Perception , Political Will Perception on Executive Job Satisfaction, Public Service Motivation Against Executive Job Satisfaction, Managerial Performance Against executive job satisfaction Through Political Will Perception, Servant Leadership Against executive job satisfaction through Political Will Perception and Public Service Motivation executive job satisfaction Through Political Will Perception has positive and significant influence , only Managerial Performance on Executive Job Satisfaction that shows Not there is positive and significant influence.This study can concluded that almost all variables have influence to performance managerial

Fast DRL-based scheduler configuration tuning for reducing tail latency in edge-cloud jobs

Edge-cloud applications are rapidly prevailing in recent years and pose the challenge of using both resource-strenuous edge devices and elastic cloud resources under dynamic workloads. Efficient resource allocation on edge-cloud jobs via cluster schedulers (e.g. Kubernetes/Volcano scheduler) is essential to guarantee their performance, e.g. tail latency, and such allocation is sensitive to scheduler configurations such as applied scheduling algorithms and task restart/discard policy. Deep reinforcement learning (DRL) is increasingly applied to optimize scheduling decisions. However, DRL faces the conundrum of achieving high rewards at a dauntingly long training time (e.g. hours or days), making it difficult to tune the scheduler configurations online in accordance to dynamically changing edge-cloud workloads and resources. For such an issue, this paper proposes EdgeTuner, a fast scheduler configuration tuning approach that efficiently leverages DRL to reduce tail latency of edge-cloud jobs. The enabling feature of EdgeTuner is to effectively simulate the execution of edge-cloud jobs under different scheduler configurations and thus quickly estimate these configurations’ influence on job performance. The simulation results allow EdgeTuner to timely train a DRL agent in order to properly tune scheduler configurations in dynamic edge-cloud environment. We implement EdgeTuner in both Kubernetes and Volcano schedulers and extensively evaluate it on real workloads driven by Alibaba production traces. Our results show that EdgeTuner outperforms prevailing scheduling algorithms by achieving much lower tail latency while accelerating DRL training speed by an average of 151.63x.

Shifting perspectives: How scrutiny shapes the relationship between CEO gender and acquisition activity

AbstractResearch SummarySeveral upper echelons studies have found that firms led by female executives are less likely to engage in risky endeavors than those led by male top executives. We argue that conceptualizing female CEOs as universally conservative decision‐makers may paint too simplistic a picture and that the impact of CEO gender on strategic decision‐making may vary significantly depending on the given situation CEOs are experiencing. We integrate executive job demands and gender research to propose that scrutiny will exhibit differential effects on female and male CEOs' acquisition activity. We show that in high‐scrutiny contexts, the difference between male and female CEO acquisition activity disappears. In contrast, in low‐scrutiny contexts, the difference between male and female CEOs' acquisition activity is exaggerated.Managerial SummarySubstantial research has shown that female executives acquire at a lower rate than male executives. We argue that viewing female CEOs as universally conservative decision‐makers may paint too simplistic a picture and that the impact of CEO gender on strategic decision‐making may vary significantly depending on the given situation CEOs are experiencing. In particular, we argue and find that in high‐scrutiny contexts, the difference between male and female CEO acquisition activity disappears. This research suggests that managers should consider the impact of environmental context—especially the role of scrutiny—when considering the risk propensity of female leaders.

Balanced-DRL: A DQN-Based Job Allocation Algorithm in BaaS

Blockchain as a Service (BaaS) combines features of cloud computing and blockchain, making blockchain applications more convenient and promising. Although current BaaS platforms have been widely adopted by both industry and academia, concerns arise regarding their performance, especially in job allocation. Existing BaaS job allocation strategies are simple and do not guarantee load balancing due to the dynamic nature and complexity of BaaS job execution. In this paper, we propose a deep reinforcement learning-based algorithm, Balanced-DRL, to learn an optimized allocation strategy in BaaS based on analyzing the execution process of BaaS jobs and a set of job scale characteristics. Following extensive experiments with generated job request workloads, the results show that Balanced-DRL significantly improves BaaS performance, achieving a 5% to 8% increase in job throughput and a 5% to 20% decrease in job latency.

A Systematic Review of the Factors That Influence Teachers’ Occupational Wellbeing

Teachers belong to a high-demand occupational group and experience work-related challenges and discretely diverse emotional turmoils of varying intensity while teaching and interacting with students. These experiences often result in high stress levels that contribute to burnout and, consequently, a breach of teachers’ occupational wellbeing. Promoting positive teacher wellbeing substantially influences teaching quality, with a flow-on effect on student wellbeing and academic development. This literature review utilised a framework to systematically explore the factors that impact the occupational wellbeing of kindergarten, primary, and secondary schoolteachers. Thirty-eight (38) studies from an initial 3766 peer-reviewed articles sourced from various databases (CINAHL, Emcare, PychINFO, Scopus, ERIC, and PsycARTICLES) were utilized for this systematic review. Four major factors were identified, including personal capabilities, socioemotional competence, personal responses to work conditions, and professional relationships. Findings highlight the importance of teachers’ occupational wellbeing in dealing with numerous challenges and competing demands, with the need for a high level of self-efficacy for instruction and behavioural management being critically significant. Teachers require adequate organisational support to successfully carry out their roles with stronger resilience and efficient job execution. Teachers also need to have social–emotional competence to be able to create a high-quality classroom environment and a conducive atmosphere that supports healthy teacher–student relationships, reduces stress and increases the occupational wellbeing of teachers. Collaborating with other relevant stakeholders such as parents, colleagues, and a school’s leadership team is critical for creating a positive work environment. A good workplace has the potential to contribute to teachers’ occupational wellbeing and provide a supportive platform for student learning and engagement. This review clearly points to the beneficial effects of prioritising teachers’ occupational wellbeing and its intentional inclusion in the professional development plan of practising teachers. Finally, while primary school teachers and secondary school teachers share many similarities in terms of the challenges they face, there are also some differences in how these challenges impact their wellbeing, and these warrant further investigation.

Automatic Migration-Enabled Dynamic Resource Management for Containerized Workload

Containerized workloads are gaining traction due to microservices architecture adaptation in many fields, including healthcare, finance, Internet of Things, and smart cities. Modern data centers are containerized to facilitate this growing demand. Most of the existing resource allocation methods for data centers used efficient scheduling algorithms to place the containers using static computing resources. These static resource allocation techniques are not energy efficient and do not help maximize data center utilization. Dynamic resource allocation and a migration-enabled placement method can reduce energy utilization while improving the utilization of the available computing infrastructure. This article presents and evaluates a novel dynamic resource management system that uses active migrations to minimize energy utilization to serve containerized workloads and improve data center utilization. Our approach uses a deep learning method to estimate the job execution time and then employs an unsupervised learning method to identify similar jobs. Similar jobs are placed and migrated to achieve energy efficiency and better utilization of the available data center infrastructure. Our proposed system is evaluated and compared with the existing state-of-the-art baseline methods. The proposed solution reduces the energy consumption from <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 1.18$</tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\times 2.35$</tex-math></inline-formula> compared to the baseline methods while maintaining similar performance.

An AI-Driven Intelligent Traffic Management Model for 6G Cloud Radio Access Networks

This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Further, a cloud-based traffic management is employed to schedule and allocate resources among user job requests according to the associated traffic states to minimize latency and maximize bandwidth utilization. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it is effective in minimizing the worse effect of traffic congestion and improves bandwidth utilization and reduces job execution latency up to 17.07% and 18%, respectively.

A combined priority scheduling method for distributed machine learning

Algorithms and frameworks for distributed machine learning have been widely used in numerous artificial intelligence engineering applications. A cloud platform provides a large number of resources at a lower cost and is a more convenient method for such applications. With the rapid development of containerization, native cloud combinations based on Docker and Kubernetes have provided effective resource support for distributed machine learning. However, native Kubernetes does not provide efficient priority or fair resource scheduling strategies for distributed machine learning in computationally intensive and time-consuming jobs, which easily leads to resource deadlock, resource waste, and low job execution efficiency. Therefore, to utilize the execution order between multiple jobs in distributed machine learning as well as the dependencies between multiple tasks for the same job, considering intra- and inter-group scheduling priorities, a combined priority scheduling method is proposed for distributed machine learning based on Kubernetes and Volcano. Considering the user priority, task priority, longest wait time, task parallelism, and affinity and non-affinity between the parameter server and worker nodes, a combined priority scheduling model of inter- and intra-job priority is proposed, which is mapped into a scheduling strategy of inter- and intra-group priorities of pods, enabling the efficient scheduling and training of distributed machine learning. The experiment results show that the proposed method achieves preferential resource allocation for urgent, high parallelism, and high-priority jobs with high-priority users and improves the job execution efficiency. The affinity and anti-affinity settings among pods reduce the time of information interaction between the parameter server and worker nodes to a certain extent, thereby improving the job completion efficiency. This group scheduling strategy alleviates the problems of resource deadlock and waste caused by insufficient resources in cloud computing.

Upper Echelon Employment: A Review of the Fundamental Questions Related to the Executive Labor Market

The executive labor market (ELM) is a topic of interest that spans several academic fields. The outcomes of the ELM, including executive selection, succession, and compensation, are important considerations as they influence executive decision making and other organizational outcomes. Yet, a comprehensive framework of ELM dynamics currently does not exist. To address this shortcoming, we reviewed the existing literature using five fundamental questions: (1) What executive jobs are available? (2) Who is available to fill executive jobs, and what is valued on the ELM? (3) Who signals interest in the executive jobs? (4) Who gets offered/accepts the executive job, and what is the agreed-upon compensation? (5) How do status and social capital influence ELM outcomes? By answering these questions, our review provides a framework for integrating existing research on the ELM while suggesting avenues for future research.

Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments

The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNNbased schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.

Culture and the way of granting job autonomy: Goal or execution?

AbstractResearchers have assumed that Westerners exercise higher job autonomy than Easterners. However, recent studies have reported mixed and even contradictory findings. The authors distinguish between two types of job autonomy, namely goal and execution autonomy, to examine the relevant cultural differences. The former denotes participation in setting work goals and making plans for meeting those goals, while the latter denotes the ability to complete tasks flexibly. Four studies with a total sample of 1192 participants working in financial or insurance companies were conducted. Study 1a generated items for a new measure of the two types of job autonomy and explored its factor structure. Studies 1b and 1c verified its construct validity and predictive capacity. Study 2 confirmed the structural and metric equivalence of the measure between samples from the United Kingdom and China. The results of Study 2 suggested that the Chinese workers were likely to have high execution autonomy but low goal autonomy, whereas the British workers tended to have high goal autonomy but low execution autonomy. The theoretical and practical implications of job autonomy in cross‐cultural contexts are discussed.

Automatic Preprocessing Pipeline for White Matter Functional Analyses of Large-Scale Databases.

Recently, increasing evidence suggests that fMRI signals in white matter (WM), conventionally ignored as nuisance, are robustly detectable using appropriate processing methods and are related to neural activity, while changes in WM with aging and degeneration are also well documented. These findings suggest variations in patterns of BOLD signals in WM should be investigated. However, existing fMRI analysis tools, which were designed for processing gray matter signals, are not well suited for large-scale processing of WM signals in fMRI data. We developed an automatic pipeline for high-performance preprocessing of fMRI images with emphasis on quantifying changes in BOLD signals in WM in an aging population. At the image processing level, the pipeline integrated existing software modules with fine parameter tunings and modifications to better extract weaker WM signals. The preprocessing results primarily included whole-brain time-courses, functional connectivity, maps and tissue masks in a common space. At the job execution level, this pipeline exploited a local XNAT to store datasets and results, while using DAX tool to automatic distribute batch jobs that run on high-performance computing clusters. Through the pipeline, 5,034 fMRI/T1 scans were preprocessed. The intraclass correlation coefficient (ICC) of test-retest experiment based on the preprocessed data is 0.52 - 0.86 (N=1000), indicating a high reliability of our pipeline, comparable to previously reported ICC in gray matter experiments. This preprocessing pipeline highly facilitates our future analyses on WM functional alterations in aging and may be of benefit to a larger community interested in WM fMRI studies.

형사절차상 통지 대상자와 권리행사자의 범위에 대한 문제점과 개선방안

In the case of voluntary accompanying or arrest by an investigative agency, certain facts or rights are required to be notified so that the accompanying person does not interfere with the safety, condition, or future exercise of defense rights, and the notified person is prescribed at each stage of criminal proceedings. In this way, notifying a person who has a certain relationship with the companion is to secure the principle of disclosure of criminal proceedings and the legitimacy of criminal proceedings.&#x0D; However, in the case of voluntary accompanying, it is not reasonable to stipulate that only the police officer's job execution law is notified and not stipulated in other laws. Therefore, even in the case of voluntary accompanying, the freedom of the companion's body is limited, so in order to guarantee the right to defend, it must be revised to notify the subject such as personal arrest. The person who has received the notification must be able to claim various rights, etc. for the parties. However, it is unreasonable because the subject of the notification and the claimant do not currently match. The subjects of the current notification are “lawyers, legal representatives, spouses, immediate relatives, siblings,” and “family members, cohabitants, and employers” can only request arrest, binding pride, or bail. This should be viewed as an inconsistent problem of the subject of notification or the claimant. The subject of the notification is to guarantee the party's right to defend, so it is necessary to expand the scope.&#x0D; If there is a lawyer, the scope of the subject should be notified to the lawyer, and in the case of minors, etc., the legal representative should be notified, and in the case of marriage, the spouse should be notified. Next, relatives should also be revised to be the subject of notification. In addition, since there is no person to notify, the person behind the fact-marriage or foreign worker must notify the cohabitant, and it is reasonable to revise it to notify the employer in case there is no cohabitant. It is considered that the subject of the notification is reasonable to amend to 'lawyers, legal representatives, spouses, relatives, cohabitants, and employers'.

Creative thinking and brain network development in schoolchildren.

Fostering creative minds has always been a premise to ensure adaptation to new challenges of human civilization. While some alternative educational settings (i.e., Montessori) were shown to nurture creative skills, it is unknown how they impact underlying brain mechanisms across the school years. This study assessed creative thinking and resting-state functional connectivity via fMRI in 75 children (4-18 y.o.) enrolled either in Montessori or traditional schools. We found that pedagogy significantly influenced creative performance and underlying brain networks. Replicating past work, Montessori-schooled children showed higher scores on creative thinking tests. Using static functional connectivity analysis, we found that Montessori-schooled children showed decreased within-network functional connectivity of the salience network. Moreover, using dynamic functional connectivity, we found that traditionally-schooled children spent more time in a brain state characterized by high intra-default mode network connectivity. These findings suggest that pedagogy may influence brain networks relevant to creative thinking-particularly the default and salience networks. Further research is needed, like a longitudinal study, to verify these results given the implications for educational practitioners. RESEARCH HIGHLIGHTS: Most executive jobs are prospected to be obsolete within several decades, so creative skills are seen as essential for the near future. School experience has been shown to play a role in creativity development, however, the underlying brain mechanisms remained under-investigated yet. Seventy-five 4-18 years-old children, from Montessori or traditional schools, performed a creativity task at the behavioral level, and a 6-min resting-state MR scan. We uniquely report preliminary evidence for the impact of pedagogy on functional brain networks.

VM queuing optimal scheduling in cloud using heuristic ant colony optimal based multi-objective genetic approach

The usages of cloud based applications are increasing tremendously. The cloud computing task distribution is an unknown polynomial time issue that is challengeable to find the optimal solution. In solve above mentioned issue with large amount user’s job requests, heuristic ant colony optimal based multi-objective genetic (HACOMOG) approach based job allocation and resource optimization is proposed. Utilization basis scheduler recognizes the task order and optimal resources to be scheduled. The primary contribution of the proposed technique is to develop several online techniques to find solution for the virtual machines (VM) Packing problem sharing-aware and for performing a comprehensive number of studies in order to assess their efficiency with online sharing algorithms. The proposed algorithm considers the utilization basis scheduler output and identified the optimzed task allocation technique based on job execution time, MakeSpan and throughput. The experimental outcomes show that the proposed HACOMOG Algorithm reduces 0.70 seconds job execution time (JET), 0.13 MakeSpan and improve 1.98 throughput on given parameters for 100, 200, and 500 tasks with conventional methodologies.

Personal Computer Virtual Voice Assistant

Information and communication technologies have a long history but constantly evolving. Because of the rapid progress of technology, everyone now has a personal AI assistant. On our smartphones, most of us have it in the form of Siri, Bixby, or Google Assistant. Voice-based personal assistants are becoming increasingly popular, making our lives easier. This paper-based intelligent voice assistant can manage and update information, such as emails, calendar events, files, and to-do lists. An attempt was made to develop an "intelligent personal voice assistant using Python" to help with voice-activated device control, information extraction, and desktop job execution.

Occupational self-efficacy, job crafting and job satisfaction in newcomer socialization: a moderated mediation model

PurposeDrawing on job crafting theory and the social cognitive theory of work satisfaction, the purpose of this study is to examine the impacts of task, relational and cognitive crafting on job satisfaction and how occupational self-efficacy (OSE) and hierarchical organizational culture affect the execution and outcome of job crafting behaviors.Design/methodology/approachData were collected at four time points among Chinese media professionals (N = 198) during three consecutive months. Regression analysis and the bootstrap method were used for hypothesis testing.FindingsOnly cognitive crafting was found to mediate the positive relationship between OSE and job satisfaction. In a hierarchical organizational culture, the positive effects of OSE on task, relational and cognitive crafting became stronger, while the effect of task crafting on job satisfaction became negative. The authors also identified a conditional negative effect of OSE on job satisfaction via task crafting. The mediating effect of cognitive crafting was consistent despite the levels of hierarchical culture.Practical implicationsTo retain new hires, organizations should look for efficacious individuals and encourage the individuals to cognitively craft individuals' jobs, especially in a hierarchical culture. Moreover, job crafting interventions should be carefully designed in this type of culture.Originality/valueThis research identifies the three dimensions of job crafting as mediators that link OSE to job satisfaction and reveals the role of hierarchical culture in moderating these relationships.