Multiple Jobs Research Articles

Levels of actigraphy-derived physical activity among Polish nurses: factors associated with the prevalence of selected metabolic disorders.

Numerous studies demonstrate a relationship between physical activity and the development of non-communicable diseases. Nurses play a crucial role in the healthcare system, and their demanding work can have an impact on their health. The objective of this cross-sectional study was to assess physical activity in relation to factors predisposing to the occurrence of specific metabolic disorders among Polish nurses. The measurements included physical activity level using ActiGraph GT3X, body weight composition using Tanita MC-980, body mass index, waist circumference, blood pressure using Welch Allyn 4200B, lipid profile, and fasting blood glucose using CardioChek PA. The results indicate that nearly one-third (31.75%) of the total sample of studied nurses do not meet the criteria for the minimum amount of physical activity of at least moderate intensity. Furthermore, over half of the surveyed nurses (55.5%) were classified as overweight or obese based on BMI, and almost half (42.86%) had abdominal obesity. The regression model, employing linear regression, revealed that factors predisposing to selected metabolic disorders were age, engaging in multiple jobs, and the number of steps per day. There is a pressing need to implement comprehensive and supportive initiatives to improve the overall health condition of nurses in Poland through increased physical activity. Activating and supporting this professional group is an investment that benefits not only the nurses themselves but also the healthcare system and the entire nation.

Sequencing a tri-criteria multiple job classes and customer orders problem on a single machine by using heuristics and simulated annealing method

Sequencing a tri-criteria multiple job classes and customer orders problem on a single machine by using heuristics and simulated annealing method

GEqO: ML-Accelerated Semantic Equivalence Detection

Large scale analytics engines have become a core dependency for modern data-driven enterprises to derive business insights and drive actions. These engines support a large number of analytic jobs processing huge volumes of data on a daily basis, and workloads are often inundated with overlapping computations across multiple jobs. Reusing common computation is crucial for efficient cluster resource utilization and reducing job execution time. Detecting common computation is the first and key step for reducing this computational redundancy. However, detecting equivalence on large-scale analytics engines requires efficient and scalable solutions that are fully automated. In addition, to maximize computation reuse, equivalence needs to be detected at the semantic level instead of just the syntactic level (i.e., the ability to detect semantic equivalence of seemingly different-looking queries). Unfortunately, existing solutions fall short of satisfying these requirements. In this paper, we take a major step towards filling this gap by proposing GEqO, a portable and lightweight machine-learning-based framework for efficiently identifying semantically equivalent computations at scale. GEqO introduces two machine-learning-based filters that quickly prune out nonequivalent subexpressions and employs a semi-supervised learning feedback loop to iteratively improve its model with an intelligent sampling mechanism. Further, with its novel database-agnostic featurization method, GEqO can transfer the learning from one workload and database to another. Our extensive empirical evaluation shows that, on TPC-DS-like queries, GEqO yields significant performance gains-up to 200x faster than automated verifiers-and finds up to 2x more equivalences than optimizer and signature-based equivalence detection approaches.

Analyzing Structural Heterogeneity of Informal Sector Employment in Kenya

The informal sector accounts for 83 per cent of total employment in Kenya. This study sought to understand duality and its driving factors to inform policy on enhancing decent work in the informal sector in Kenya. Using a multinomial logit analysis, the study established that workers are motivated to engage in more than one job in the informal sector due to its structure, to get more earnings and to meet the standard of living. Indeed, employees with low skills, less income and those residing in rural areas are likely to have two jobs in the informal sector. Further, workers in formal full time employment are less likely to engage in duality compared to their counterparts who have no contract. Women and youth are more likely than men to work on multiple jobs in the informal sector.

Desired work-leisure balance in a partial equilibrium job search model with multiple job holding

Abstract Work-leisure balances are beneficial to society. A partial equilibrium job search model is developed to explain desired work-leisure tradeoffs for single-job holders and multiple-job holders. Significant work-leisure mismatches are found: 63% of the observations underwork by an average of 17 hours per week, while 37% overwork by 8.5 hours. The value of leisure is approximately four times the average hourly real wage when a single job is held, and it drops by one-third when multiple jobs are held. Models ignoring possibilities of multiple jobholding overstate the elasticity of leisure and understate the value of leisure.

Examining the extent, forms and factors influencing multiple job holding among medical doctors, professional nurses and rehabilitation therapists in two South African provinces: a cross-sectional study

ObjectiveMultiple job holding (MJH), or working in more than one paid job simultaneously, is a common characteristic of health labour markets. The study examined the extent (prevalence), forms and factors...

More Students Going Into Petroleum Engineering Programs as Research Work Tilts to Alternative Energy

Oil companies are eagerly hiring petroleum engineering graduates, but the steep falloff in petroleum research funding is forcing a shift to alternative energy work. On the plus side, those connected with programs said most seniors have lined up jobs well in advance of graduation and some are getting multiple offers. Companies are competing for a small pool of talent, with 660 seniors expected to graduate this year, down from a peak of 2,615 in 2017, according to the annual SPE survey of colleges with petroleum engineering programs by Lloyd Heinze, professor emeritus at Texas Tech, who predicts that number will drop to 572 in 2025 based on the number of juniors. After that, enrollment looks likely to rise. There has been a 13% increase in the number of freshman and sophomore students, which is attributed to the strong demand for petroleum engineers. “Probably word of mouth is seniors getting jobs, and junior and sophomore internships is a big factor in the rise,” Heinze said. Another plus is that undergraduate enrollment is up at seven of the 10 largest programs surveyed in the US and the Middle East. “Almost everyone is up,” said Mashhad Fahes, an associate professor at The University of Oklahoma (OU), who reports that nearly all those approaching graduation have jobs in hand, often with multiple job offers. Internships were so widely available that OU had no takers for a summer program created to provide an alternative to paid internships a couple years back when those opportunities were scarce. Similar evaluations of the job market were offered by sources from Texas A&M University and The University of Texas Permian Basin (UTPB). In all cases, the jobs were mostly in oil and gas exploration and production. “They know that if they get a degree from our program, they can easily find a job in the area,” said Ahmed Kamel, an associate professor who is the department coordinator at UTPB. UTPB, which now has more PE students than OU and LSU, draws a large percentage of its students from west Texas who have grown up around the oil business before enrolling, and Kamel said many are working in the industry while in college. In addition to knowing the business, he said they offer another benefit: “Oil and gas companies want people from here who will stay here.” The program, started in 2011 as part of the business school, is now part of UTPB’s College of Engineering offering degrees in mechanical, chemical, and civil engineering. Its growth plan includes a petroleum engineering graduate program in the future, which will require increasing its research work. Kamel said that it currently has the research support it needs for teaching undergrads.

Management in Sport Climbing

Sports management in Indonesia is faced with a lack of adequate attention and investment in sports development. The budget allocated for sports development is still limited, including in terms of coaching athletes and improving sports facilities. This can make it difficult to achieve excellence in national competitions and develop the potential of high-achieving athletes. The Rock-Climbing Federation (FPTI) PENGCAB Jember district is experiencing quite serious problems in organizational management. This is evidenced by several elements of management that are not fulfilled, such as limited funds, multiple jobs in management, senior athletes changing residence, inadequate facilities and infrastructure. The aim of this research is to examine the management functions within FPTI PENGCAB Jember district as follows: Planning, Organizing, Mobilizing and Controlling. This research uses a qualitative approach with data collection methods in the form of observation, interviews and documentation. The research objects in this study are organizational administrators, coaches and athletes. The data analysis process uses data triangulation 1) data reduction, 2) data presentation, and 3) drawing conclusions. Research findings include: 1) Management planning (FPTI) meets the categorization criteria, namely clear who, what, when, where and how to do it; 2) Organization and management (FPTI) meets the categorization criteria, namely comprehensive management, work mechanisms, no written job descriptions, and routine activities discussed at the beginning of the year; 3) Driving, management (FPTI) meets the categorization criteria, namely complete management, work mechanisms, and there are routine activities. The research conclusions show that: (1) Planning: management of the Indonesian rock-climbing federation is very good; (2) Organization: the management of the Indonesian rock-climbing sports federation is quite good; and (3) Actuating: the management of the Indonesian rock-climbing sports federation is quite good. (4) Control: The Indonesian Rock-Climbing Sports Association is running quite well. Keywords: Sport Climbing, Sports management, Indonesian rock-climbing

Unrelated parallel batch processing machine scheduling with time requirements and two-dimensional packing constraints

In various complex manufacturing systems such as wafer fabrication, ceramic processes, and additive manufacturing, a single machine often performs multiple distinct jobs simultaneously, requiring non-overlapping positions in the 2D-plane. As the demand for manufacturing with two-dimensional (2D) packing constraints continues to rise, there is an urgent need to address the integrated scheduling of batch processing machines (BPMs) and 2D packing. However, only a limited number of studies have explored BPM scheduling problems with 2D packing constraints. In this study, we focus on the scheduling of unrelated parallel BPMs to process jobs with non-identical sizes, unequal release dates, and due dates. To model the machine’s capacity, we move beyond the traditional one-dimensional knapsack approach and consider it as a 2D rectangle, taking into account the 2D packing constraints. It is crucial to ensure that no overlapping or stacking occurs within the machine’s capacity under these constraints. To address this problem, we propose a mixed integer linear programming model as the initial approach. Additionally, we develop an adaptive large neighborhood search (ALNS) heuristic specifically designed for handling large instances. The ALNS incorporates eleven removal operators and seven insertion operators, offering a comprehensive exploration and improvement of solutions. To tackle the packing procedure, we adopt two distinct packing patterns, namely Skyline and Open Space. These patterns play a significant role in optimizing the packing of jobs within the machine’s capacity. Furthermore, we conduct extensive computational experiments, generating 48 categories of instances with varying job attributes. These experiments serve to compare the performance of the two packing patterns and validate the effectiveness of our ALNS approach. In conclusion, our research addresses the critical gap in BPM scheduling problems with 2D packing constraints. By introducing a mixed integer linear programming model and developing the ALNS heuristic with diverse operators, coupled with the adoption of different packing patterns, we present a comprehensive approach to tackle this complex problem. Computational experiments are conducted to compare packing patterns and verify the performance of our ALNS method.

A lightweight multi-modality medical image semantic segmentation network base on the novel UNeXt and Wave-MLP

Medical images sometimes contain diseased regions that are different sizes and. shapes, which makes it difficult to accurately segment these areas or their edges. However, directly coupling CNN and MLP to construct global and local dependency. models may also cause significant computational complexity issues. In this paper, a. unique, lightweight UNeXt network segmentation model for medical images based on. dynamic aggregation tokens was proposed. Firstly, the Wave Block module in Wave-MLP was introduced to replace the Tok-MLP module in UNeXt. The phase term in Wave Block can dynamically aggregate tokens, improving the segmentation accuracy of the model. Secondly, an AG attention gate module is added at the skip connection to suppress irrelevant feature representations in the sampling path of the encoding. network, thereby reducing computational costs and paying attention to noise and artifacts. Finally, the Focal Tversky Loss was added to handle both binary and multiple classification jobs. Quantitative and qualitative experiments were conducted on two public datasets: COVID-19 CT and BraTS 2018 MRI. The Dice score, Precision score, recall score, and Iou score of the proposed model on the COVID-19 dataset were 0.928, 0.867, 0.916, and 0.940, respectively. On BraTS 2018, the Dice scores of the ET, WT, and TC categories were 0.933, 0.925, and 0.918, respectively, and the HD scores were 1.595, 2.348, and 1.549, respectively. At the same time, the model is lightweight and has a considerably decreased training time with GFLOPs and Params of 0.52 and 0.76, respectively. The proposed lightweight model is superior to other existing methods in terms of segmentation accuracy and computing complexity according to experimental data.

Occupational Stress and Job Satisfaction: Differences Among Racial/Ethnic and Immigration Groups.

The current study aimed to investigate the factors contributing to long-term care workers' job satisfaction by examining the association between occupational stress (including job resources and job demands) and job satisfaction, and the differences among racial/ethnic and immigration groups. Data of 2,763 nursing assistants were retrieved from the National Nursing Assistant Survey. Guided by the job demands-resources model, we found that high job resources and low job demands contributed to job satisfaction. Our findings also suggest that occupational stress works differently based on race/ethnicity and immigration status. Long-term care providers looking to recruit and retain workers should include multiple job resources tailored to their diverse workforce. Designing existing workplace environments to promote job resources and reduce job demands is a useful way to lessen the adverse effects of occupational stress within the workplace. [Journal of Gerontological Nursing, 49(11), 42-52.].

Performance Analysis of Work Stealing Strategies in Large-Scale Multithreaded Computing

Distributed systems use randomized work stealing to improve performance and resource utilization. In most prior analytical studies of randomized work stealing, jobs are considered to be sequential and are executed as a whole on a single server. In this article, we consider a homogeneous system of servers where parent jobs spawn child jobs that can feasibly be executed in parallel. When an idle server probes a busy server in an attempt to steal work, it may either steal a parent job or multiple child jobs. To approximate the performance of this system, we introduce a Quasi-Birth-Death Markov chain and express the performance measures of interest via its unique steady state. We perform simulation experiments that suggest that the approximation error tends to zero as the number of servers in the system becomes large. To further support this observation, we introduce a mean field model and show that its unique fixed point corresponds to the steady state of the Quasi-Birth-Death Markov chain. Using numerical experiments, we compare the performance of various simple stealing strategies as well as optimized strategies.

Overwork Impacts on Low-Wage Workers: Insights from the Food Manufacturing Sector in Oregon and Washington

Unstable scheduling refers to such employer practices as last-minute schedule changes, lack of advance notice, requiring employees to be on-call, split shifts, “clopening,” and variable hours and shift times. Evidence has shown that such practices can lead to underwork, or involuntary part-time hours, particularly for service-sector workers. But another, less-explored dimension of unstable scheduling practices—and the focus of this analysis—is overwork. Overwork stems from practices that can limit the ability of workers to get adequate rest and can heighten the bodily and emotional hazards that workers experience. We identify the following forms of unstable scheduling practices that constitute overwork: • Long work days of over eight hours/day • Long work weeks of over 40 hours/week and/or over five days/week • Long stretches of over seven days without days off • Lack of sufficient breaks during shifts These multiple dimensions of unstable scheduling are sometimes layered together. For instance, workers can experience both optional and mandatory overtime, which can extend both their work day and/or their work week. When workers are assigned mandatory overtime, they face risk of penalty if they refuse to work these extended hours, including potential loss of employment. In addition, workers may experience combinations of overwork, underwork, or other unstable scheduling practices within one job, or as they move between multiple jobs to cobble together full-time, year-round employment, especially if working in an industry with high turnover and widespread seasonality. These combinations compound the strain that workers experience.

The weakness of common job contacts

Many people obtain job information from friends and acquaintances. However, one factor influencing labor-market outcomes that is ignored in the literature is the presence of overlapping friendship circles in social networks. We find that overlapping friendship networks produce correlated information flows, resulting in an increased probability of two events: either receiving redundant job offers or receiving no job offers at all. Consequently, people with common contact networks exhibit worse employment prospects even if they have the same number of information providers and compete with the same number of people for vacancies. In quantitative terms, the impact of overlapping friendship circles rivals that of the number of direct contacts and contacts’ contacts. This implies that the results in Calvó-Armengol (2004) only apply for networks where people’s friends are neither connected nor have common contacts. Because overlapping friendship circles are a crucial aspect of strong relationships, our findings uncover an alternative mechanism behind “The Strength of Weak Ties” (Granovetter, 1973): their ability to maintain independence in job information flows. We further show that people with common job contacts earn lower incomes on average. However, conditional on being employed, their expected wage is higher because they can take advantage of the multiple job offers received by selecting the one with the highest pay.

Double truth: employment insecurity and gender inequality in Japan’s neoliberal promotion of side jobs

The ‘Work Style Reform’ (WSR) initiative, spearheaded by the late former Prime Minister Shinzō Abe, has advocated for the adoption of ‘fukugyō’ (‘side jobs’) as an additional source of income for workers. While this initiative is often uncritically viewed as a possible solution to insecure employment, especially for women employed in low-paying, temporary positions, this article argues that the WSR’s promotion of fukugyō, reinforces patriarchal norms rather than challenging them. Furthermore, it critiques the neoliberal ideology that underpins the WSR initiative, which portrays underpriviledged groups of workers, such as working women in non-regular employment earning less than their male counterparts, as ‘flexible’, ‘autonomous’ and ‘entrepreneurial’, capable of juggling multiple jobs while fulfiling their domestic duties. The article claims that the WSR’s promotion of fukugyō lacks sufficient legal safeguards and social welfare support for fukugyō workers, the majority of whom are not recognised as ‘workers’ under Japan’s labour law. As a result, it could exacerbate the problems of employment insecurity and gender inequality in Japan.

Autonomy in Local Digital News: An Exploration of Organizational and Moral Psychology Factors

ABSTRACT This mixed-methods study examines autonomy and shifts in the evolving digital news industry. Autonomous agency of news workers is an essential indicator of how journalism work is fulfilling its role as the Fourth Estate in American democracy. This work responds to calls in media ethics, media sociology and moral ecology to better understand how organizational structure and individual moral psychology factors influence the levels at which digital news workers exhibit autonomy within their digital news organizations. Using participant observation, a unique survey and respondent interviewing, I worked for one year with a digitally native hyper-local news organization to examine how organizational structure, leadership, socialization, and individual factors influenced autonomy levels in the staff. Results found overall high levels of autonomy in the editorial staff, but with many constraints such as financial pressures, perceived lack of time, a high pressure to perform multiple jobs and duties outside of the scope of typical legacy journalism job descriptions.

Developing and Validating Japanese Versions of Psychological Safety Scale, Knowledge Sharing Scale and Expressed Humility Scale

Organizational research has increased in the contemporary, digitalized and global society. Mainly researchers in Western countries conducted empirical, organizational research in the past. These studies have investigated combinations of psychological safety, knowledge sharing and leaders’ expression of humility because these variables have crucial roles in organizational functions. Japanese research on these variables has been scarce due to the lack of scales for assessing these variables in Japanese. From a methodological perspective, research conducted in Western and other cultures where most people understand English requires multicultural validation studies. Therefore, we developed Japanese versions of scales assessing psychological safety, knowledge sharing and expressed humility and assessed their structural validity, internal consistency and convergent validity. The results indicated factor structures and inter-correlations between the scales consistent with previous research. This work is novel because of its large samples across multiple job types in contemporary work organizations. We expect this study to make methodological and theoretical contributions to future research.

An Incentive Auction for Heterogeneous Client Selection in Federated Learning

Federated Learning (FL) is a new distributed machine learning (ML) approach which enables thousands of mobile devices to collaboratively train artificial intelligence (AI) models using local data without compromising user privacy. Although FL represents a promising computing paradigm, such training process can not be fully realized without an appropriate economic mechanism that incentivizes the participation of heterogeneous clients. This work targets social cost minimization, and studies the incentive mechanism design in FL through a procurement auction. Different from existing literature, we consider a practical scenario of FL where clients are selected and scheduled at different global iterations to guarantee the completion of the FL job, and capture the distinct feature of FL that the number of global iterations is determined by the local accuracy of all participants to balance between computation and communication. Our auction framework <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> first decomposes the social cost minimization problem into a series of winner determination problems (WDPs) based on the number of global iterations. To solve each WDP, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> invokes a greedy algorithm to determine the winners, and a payment algorithm for computing remuneration to winners. Finally, <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> returns the best solution among all WDPs. We carried out theoretical analysis to prove that <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> is truthful, individual rational, computationally efficient, and achieves a near-optimal social cost. We further extend our model to consider multiple FL jobs with corresponding budgets and propose another efficient algorithm <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> to solve the extended problem. We conduct large-scale simulations based on the real-world data and testbed experiments by adopting FL frameworks FAVOR and CoCoA. Simulation and experiment results show that both <inline-formula><tex-math notation="LaTeX">$A_{FL}$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$A_{FL-M}$</tex-math></inline-formula> can reduce the social cost by up to 55&#x0025; compared with state-of-the-art algorithms.

Analisis Sistem Pengendalian Piutang Terhadap Piutang Tak Tertagih pada PT. Abadi Sakti Mitra Mandiri

Uncollectible accounts are one of the causes of losses in the company because they can hinder the process of entering finance into the company. Required control of good accounts receivable so that the company does not experience losses. This study aims to determine the application of a COSO-based internal control system for accounts receivable at PT. Abadi Sakti Mitra Mandiri. The method used in this study uses a grounded research approach. Grounded research is a research method on facts and uses comparative analysis with the aim of establishing concepts, proving theories, and developing theories, collecting, and analyzing data at the same time. Based on the results of research conducted, the internal control system implemented by PT. Abadi Sakti Mitra Mandiri has not been good enough. This is indicated by the increase in the level of bad debts caused by the low level of internal control in the elements of control activity, namely the existence of multiple jobs so that the risk assessment in the provision of receivables becomes ineffective. Therefore, it is necessary to have additional employees in marketing and selling products as well as a special section for the analysis of receivables, so that the receivables control process can run effectively and efficiently. The company should also provide fines to customers who delay payment of their receivables.

Interference-aware opportunistic job placement for shared distributed deep learning clusters

Distributed deep learning frameworks facilitate large deep learning workloads. These frameworks support sharing one GPU device among multiple jobs to improve resource utilization. Modern deep learning training jobs consume a large amount of GPU memory. Despite that, sharing GPU memory among jobs is still possible because a training job has iterative steps that its memory usage fluctuates over time. However, resource sharing also introduces the risk of job performance degradation. Co-located jobs sharing a GPU device may suffer from different levels of interference, mainly caused by memory oversharing. How to improve resource utilization while maintaining good job performance is a novel challenge for job placement strategies. This paper studies the job placement problem. We propose an opportunistic memory sharing model to describe the time-varying job memory requirements. Based on this model, we introduce an Opportunistic Job Placement Problem (OJPP) for shared GPU clusters that seek job placement configurations using a minimum number of GPU devices and guarantee user-defined performance requirements at the same time. We propose a greedy algorithm and a heuristic algorithm with computational complexities of O(nlog⁡n) and O(n2log⁡n), respectively, to solve the problem. We also propose an online adjustment algorithm with the computational complexity of O(nlog⁡n) to perform updates to job placement configurations in runtime. A machine-learning-based interference prediction method is used to prepare accurate interference estimations. Extensive experiments are conducted on a GPU cluster to verify the correctness and effectiveness of our algorithms. Compared with standalone training jobs on dedicated clusters, the proposed approach reduces resource consumption by 46% in a shared cluster, while guaranteeing over 92.97% of the job performance, in terms of average job completion time.