Changes In Working Conditions Research Articles

Improved GNN based on Graph-Transformer: A new framework for rolling mill bearing fault diagnosis

The structure of the rolling mill system is complex and the operating conditions are changeable. Therefore, the interdependence between the data needs to be fully considered in the fault diagnosis of the rolling mill. Although graph neural network (GNN) is a powerful architecture based on non-Euclidean spatial data, the current method is difficult to represent the long-range dependence of rolling mill fault vibration signals. Simply increasing the depth of GNN is not enough to expand the receptive field of the model, because the larger GNN model may have the problem of gradient disappearance or transition smoothing. In order to solve the above problems, an improved graph neural network based on Graph-Transformer is proposed to diagnose the health status of rolling mill. This method first performs sliding maximum sampling on the spectrum of the original vibration signal to improve the frequency resolution and reduce the feature dimension. Second, the relationship between fault features is characterized by constructing affinity graph. Finally, the long-range dependency between paired features is learned through the readout module and the self-attention mechanism in Graph-Transformer and the diagnostic results are output by the classifier. The experimental results on the rolling mill platform show that this method can not only adapt to the changing working conditions of the rolling mill but also achieve excellent performance in the case of sample imbalance and strong noise.

CIS2N: Causal independence and sparse shift network for rotating machinery fault diagnosis in unseen domains

Intelligent fault diagnosis (IFD) based on deep learning (DL) has demonstrated its powerful performance to promote the reliability and safe operation of rotating machinery. In industrial applications, working condition changes frequently, leading to models’ performance degeneration due to out-of-distribution (OOD) samples, such as vibration signals with unseen rotating speeds, loads, etc. To address this issue, domain generalization fault diagnosis (DGFD) has attracted increasing attention. However, mainstream DGFD methods are implemented from statical dependence, which fail to excavate intrinsic causal mechanisms and have limited performance and reliability. From a causal view, the causal independence and sparse shift network (CIS2N) is proposed to diagnose via causal mechanisms. Specially, a structural causal model (SCM) representing causal relations of features in IFD is given. Based on the SCM, it is derived that features are jointly independent and feature deviation between intervened sample pairs is sparse. The dependence among features is measured via the covariance matrices constructed by high-level features mapped by random Fourier features (RFF). Meanwhile the L1-distance between pairs of features with the same causal features (intervened pair) is calculated and optimized to realize shift sparsity. The effectiveness of the proposed CIS2N on DGFD is demonstrated on two real helical gearbox datasets.

An adaptive approximation‐free control for leader‐follower tracking of multi‐agent systems with unknown nonlinearity and disturbance

AbstractDue to variable and complex work environments, nonlinearities, uncertainty and disturbances are inevitable in multi‐agent systems. Approximation‐free control can address these issues without involving approximators, such as fuzzy logic systems and neural networks. However, some issues like the singularity problem caused by the signals exceeding the preset boundary in changing work conditions still remain. This paper proposes an adaptive and reliable approximation‐free control, which comprises a novel singularity compensator and a modified transforming function. The proposed control scheme performs better in terms of convergence rate and overshoot, avoids issues relating to singularity, and has added flexibility in terms of parameter choice. The proposed control law adapts to changes in operating conditions and nonlinearities—the efficacy of which is demonstrated using simulations.

Open-plan office employees’ perceived mental and social well-being

PurposeOver the past century, office designers have adapted to changing work conditions across various industries by creating workspaces that cater to the needs of modern workers. As a result, a range of office layouts have been developed, from traditional private offices to open-plan setups. The choice of layout can significantly impact communication, social interaction and overall performance. This study aims to explore the social and mental well-being of employees working in open-plan offices in the public sector in South Africa.Design/methodology/approachThe research was conducted in the Johannesburg Metropolitan Municipality, South Africa, focusing on five governmental departments. Using purposive sampling, employees working in open-plan offices were identified. A structured online interview form with open-ended questions was distributed to all selected employees between May and September 2021, resulting in 54 responses. Thematic content analysis using ATLAS.ti was used to analyze the data.FindingsThe findings suggest that while open-plan offices can enhance teamwork, communication, accessibility and inclusivity, their design can negatively affect employees’ perceptions of social and mental health in the workplace. Specifically, open-plan designs are perceived to compromise privacy, shorten attention spans, lower productivity and increase stress levels. Interestingly, absenteeism was not reported by employees in this study, which contradicts previous research and requires further investigation.Practical implicationsThese findings can be valuable for human resource managers, facilities managers and office space designers.Originality/valueThis research contributes to the ongoing conversation surrounding the impact of open-plan offices on the mental and social well-being of employees in the public sector within the South African context. The contribution of this paper thus lies in the South-African context and the public sector angle plus a more in-depth insight on well-being through an extensive set of 54 interviews.

A novel domain adaptive method for gearbox fault diagnosis using maximum multiple-classifier discrepancy network

Abstract Domain adaptive gearbox fault diagnosis methods have made impressive achievements for the past several years. However, most of the traditional domain adaptive methods have significant limitations under fluctuating operating conditions. The acquired acceleration signals will result in different signal vibration spectra and peak vibration amplitudes due to the different working conditions between the source and target domains. There is an obvious discrepancy between the distribution of fault samples in the source domain and the target domain, which in turn makes it difficult to classify the target domain samples with fuzzy fault category boundaries. Therefore, how to measure the discrepancy between two distributions has been an important research direction in machine learning. A good metric helps to discover better features and build better models. In this paper, a novel domain adaptive method for gearbox fault diagnosis using maximum multiple-classifier discrepancy network (MMCDN) is proposed. The sparse stack autoencoder (SSAE) is used by the MMCDN as a feature extractor for fault feature extraction, and a kind of composite distance is adopted for domain discrepancy measurement of source and target domain features for domain alignment. Then the extracted features are input into a three-classifier of the model for adversarial training. The trained model classifiers have high performance in fault classification. The combination of domain adaptation and multi-classifier discrepancy output can effectively solve the impact of working condition changes and the misclassification problem for fuzzy samples with class boundaries. Experimental validation with two planetary gearbox datasets shows that the MMCDN has more favorable diagnostic accuracy and performance than other methods.

Mediating power of career adaptability between decent work and quality of life: study with tourism independent workers

The promotion of decent work plays a central role in the transformation of tourism into a sustainable activity. The literature on this topic has multiplied, but it is imperative to pay more attention to workers in the potentially most vulnerable sectors, such as those without employment contracts and without formal higher education. The aim of this study on independent workers in tourism is to investigate how they perceive their work as decent and to analyze their quality of life. In addition, the study aims to analyze the mediating effect of career adaptability on the relationship between decent work and quality of life. The sample consists of 228 tourism workers in Portugal, mainly women, aged between 19 and 71 years. The results support the hypothesis that decent work can improve the quality of life of workers in the tourism industry. They also show that decent work is associated with a greater likelihood that workers are able and feel confident to adapt to changing tasks, to engage in continuous training and to manage their careers, which has a positive impact on their quality of life. The importance of prioritizing decent work and psychosocial resources to cope with the changing working and working conditions of the independent workers in the tourism sector in Portugal is discussed.

Plataformas digitales del trabajo de cuidado doméstico remunerado en Colombia: el caso de Hogarú

This article explores how digital platforms have changed working conditions and labor relations in domestic and care work in recent years. The study focuses on Hogarú, a digital platform in Colombia. Through a qualitative study involving interviews and focus groups, we analyze Hogarú’s business model, along with the changes in working conditions for domestic workers compared to traditional employment methods. The study highlights two main findings. First, digital platforms can enhance domestic workers’ bargaining power by raising awareness of their rights, professionalizing their work, promoting physical meeting spaces with other workers, providing ongoing training, and ensuring fundamental rights. Second, while digital platforms can shift the traditional labor relationship from one where the domestic worker directly offers care services to one managed by the company, power imbalances and low recognition of this work persist. This article offers insights into digital labor platforms from the perspective of the workers involved.

Analysis of the Covid-19 pandemic on preferences of transport modes

The Covid-19 pandemic has affected the entire world and changed many aspects of daily life, including transportation behaviour and preferences. This study examines the situation of transportation behaviour and preferences before and after the Covid-19 pandemic process through a survey study. A total of 471 people participated in the survey, and 30 questions were posed to participants regarding transportation modes preference, daily usage habits, public transportation usage rate, income status, perceived risk level of transportation systems, preferences before and after the pandemic process, and more. The answers were classified, evaluated and statistically analysed. The results showed that users were significantly affected by the Covid-19 process and changed their transportation mode preferences. The use of buses in urban journeys decreased, and the use of private vehicles significantly increased after the Covid-19 process. Hygiene was the key factor in travel, followed by the vehicle occupancy rate, ventilation, fare and travel time factors. The perceived risk levels of public transportation systems were ranked as metrobus, minibus (paratransit), bus, metro, tram and ferry. The study suggests that future transportation system designs should consider user preferences, as well as changes in education and working conditions, and pandemic conditions.

Некоторые размышления о цифровизации как о новом социально-технологическом феномене развития человеческой цивилизации

The article attempts to comprehend and reveal the significance of digitalization as the next stage of the industrial revolution affecting the further development of human civilization. Philosophical views on the role of digital technologies in the life of society are explored as arguments, a causal relationship between scientific discoveries and their impact on changing working conditions is given, linguistic, mathematical and cybernetic approaches are used to comprehensively understand the changes taking place in the world and the country.

Exploring the Work Motivational Factors of Generation Z Workforce in Selected BPO Companies: Basis For Job Satisfaction

This qualitative study explored the work motivational factors of the Generation Z workforce. Herzberg’s Approach guided the study in relating situations where Generation Z participants perceived themselves as motivated (job satisfaction) with their work and, equally, in circumstances where they felt demotivated (job dissatisfaction). Hygiene factors (extrinsic) identified for motivating and satisfying this generation at work included a conducive work environment, favorable policies, supportive leaders and teammates, and a competitive salary. For the intrinsic motivating factors, Generation Z participants expressed a desire for continuous personal and career growth, as well as aspirations for job promotions. Regarding demotivators, Generation Z participants perceived issues related to salary disputes, changes in work conditions like remote work (WFH), undesirable interactions with teammates, and unsupportive leaders. They also mentioned lack of rewards and recognition in terms of employee engagement from their company. Additionally, they were demotivated by the absence of training and development opportunities. This study recommended programs for BPO companies aimed at motivating and satisfying Generation Z in the workplace. These included work schedule options, recognition and rewards, and training and development. Work schedule options encompassed flexible scheduling and compressed work weeks, while recognition and rewards consisted of flexible reward options and employee-driven recognition programs. Finally, training and development programs focused on technology utilization, leadership, and team building. Implementing these programs would assist BPO companies in adapting to Generation Z behavior, thereby retaining them in the workforce.

Identification of Milling Cutter Wear State under Variable Working Conditions Based on Optimized SDP

Traditional data-driven tool wear state recognition methods rely on complete data under targeted working conditions. However, in actual cutting operations, working conditions vary, and data for many conditions lack labels, with data distribution characteristics differing between conditions. To address these issues, this article proposes a method for recognizing the wear state of milling cutters under varying working conditions based on an optimized symmetrized dot pattern (SDP). This method utilizes complete data from source working conditions for representation learning, transferring a generalized milling cutter wear state recognition model to varying working condition scenarios. By leveraging computer image processing features, the vibration signals produced by milling are converted into desymmetrization dot pattern images. Clustering analysis is used to extract template images of different wear states, and differential evolution algorithms are employed to adaptively optimize parameters using the maximization of Euclidean distance as an indicator. Transfer learning with a residual network incorporating an attention mechanism is used to recognize the wear state of milling cutters under varying working conditions. The experimental results indicate that the method proposed in this paper reduces the impact of working condition changes on the mapping relationship of milling cutter wear states. In the wear state identification experiment under varying conditions, the accuracy reached 97.39%, demonstrating good recognition precision and generalization ability.

Insights from end-of-career general practitioners on changing working conditions and generational differences: considerations for future strategies.

The landscape of general practice has experienced notable transformations in recent decades, profoundly influencing the working conditions of general practitioners (GPs). This study aimed to examine the most salient changes affecting GPs' daily practices. Through semi-structured qualitative interviews with 15 end-of-career GPs, the study explored how these changes affected work organization, equipment, working hours, work-life balance, job satisfaction, training, patient relationships, and reputation. The interviews revealed that these changes were perceived as barriers, opportunities, or a complex interplay of both for general practice. While the interviewed GPs valued technological advancements and reported positive developments in working conditions, challenges included a gradual reduction in the range of tasks, growing administrative burdens, and less practical training for young physicians. Other changes, such as new doctor-patient dynamics, the transition from single to group practice, and differing professional expectations of the younger generation, were seen as both challenging and strengthening for general practice. By combining these factors and trade-offs observed by end-of-career GPs in our study over the past few decades with general societal changes, we provide ideas for the design of future framework conditions in general practice that might enhance the attractiveness of the profession. These insights offer key considerations that can guide future strategies for general practice and medical education.

Modeling and Optimization of an Enhanced Soft Sensor for the Fermentation Process of Pichia pastoris.

This paper proposes a novel soft sensor modeling approach, MIC-TCA-INGO-LSSVM, to address the decline in performance of soft sensor models during the fermentation process of Pichia pastoris, caused by changes in working conditions. Initially, the transfer component analysis (TCA) method is utilized to minimize the differences in data distribution across various working conditions. Subsequently, a least squares support vector machine (LSSVM) model is constructed using the dataset adapted by TCA, and strategies for improving the northern goshawk optimization (INGO) algorithm are proposed to optimize the parameters of the LSSVM model. Finally, to further enhance the model's generalization ability and prediction accuracy, considering the transfer of knowledge from multiple-source working conditions, a sub-model weighted ensemble scheme is proposed based on the maximum information coefficient (MIC) algorithm. The proposed soft sensor model is employed to predict cell and product concentrations during the fermentation process of Pichia pastoris. Simulation results indicate that the RMSE of the INGO-LSSVM model in predicting cell and product concentrations is reduced by 47.3% and 42.1%, respectively, compared to the NGO-LSSVM model. Additionally, TCA significantly enhances the model's adaptability when working conditions change. Moreover, the soft sensor model based on TCA and the MIC-weighted ensemble method achieves a reduction of 41.6% and 31.3% in the RMSE for predicting cell and product concentrations, respectively, compared to the single-source condition transfer model TCA-INGO-LSSVM. These results demonstrate the high reliability and predictive performance of the proposed soft sensor method under varying working conditions.

Research on structural damage identification method of aircraft aluminium alloy skin based on power spectral entropy

Skin is an important structural component which covers the surface of aircraft widely, the structural health of skin has a great impact on the overall safety of aircraft. Due to the harsh operating environment, complex and changing working conditions, and high-intensity service process of aircraft, it is easy to cause damage to the skin. Thus, the in-situ health monitoring for aircraft skin is an effective approach to ensure its safety and reliability. Thanks to the ability of Lamb waves to propagate over long distances and recognize small-scale defects, they have shown great potential in the health monitoring of thin-walled structures and have received widespread attention. However, the traditional Lamb wave-based damage identification methods generally suffer from the problems of large number of sensors and low efficiency. Therefore, a skin damage identification method based on power spectral entropy is proposed in this paper, which can estimate the damage extent by extracting the nonlinear component increase caused by damage, and the experiments validate that the proposed method has higher sensitivity and computational efficiency than the existing other entropy-based methods.

The Impact of Motherhood on Women's Career Progression: A Scoping Review of Evidence-Based Interventions.

(1) Background: Despite the progress made by women in the workplace, mothers still face systemic barriers that prevent them from advancing professionally. This "motherhood penalty" involves a variety of discriminatory practices and experiences that mothers can face at work, including being held to stricter standards regarding salary and recruitment. Despite ongoing research on the association between motherhood and career outcomes, few studies specifically explore how motherhood impacts career advancement and, consequently, access to leadership. This scoping review seeks to gain an understanding of how motherhood impacts women's career progression, and how interventions can address the underrepresentation of mothers in leadership. (2) Methods: Following the PRISMA-ScR framework, we analyzed 52 articles from 2010 to 2022, drawn from 10 databases. (3) Results: The results showed both negative and positive impacts of motherhood on career progression, affecting mothers' attitudes, feelings, and behaviors and yielding changes in interpersonal relationships and work conditions. Intersectionality is highlighted, urging a nuanced examination of challenges faced by mothers from a diversity of backgrounds. Recommendations for interventions include individual and institutional efforts, comprising societal support structures, organizational policy changes, and cultural shifts. (4) Conclusions: This scoping review offers an updated perspective on a classic challenge, providing practical insights for a more inclusive and structural understanding of the career trajectories of working mothers.

Interpretable Siamese dual attention enhancement transfer compound diagnostic model for unbalanced samples

The intelligent transfer diagnosis model is used to address the issue of feature drift caused by the changing working conditions of rotating parts in engineering. However, few models can perform transfer diagnosis on multiple unbalanced samples of rotating parts simultaneously, and even fewer models can visually enhance the domain-invariant features, making them more interpretable. To address these issues, we propose a novel interpretable Siamese dual attention enhancement transfer compound diagnosis model for unbalanced samples. The model can diagnose multiple rotating parts simultaneously and consists of a channel feature attention enhancement (CFAE) network, a fragment feature attention enhancement (FFAE) network, and a Siamese feature fusion (SFF) network. The CFAE network enhances features of different convolutional channels, the FFAE network improves segment features in various frequency domains, and the SFF network extracts domain-invariant features of diverse rotating components under varying working conditions. The model is validated using bearing fault data collected under different loads and planetary gear fault data obtained at varying speeds. Its diagnostic accuracy remains above 96.4%, and the diagnostic variance is controlled within 1.0%. The model has good interpretability for imbalanced sample domain-invariant features, providing an effective tool for interpretable transfer diagnosis in this compound engineering situation.

Assessment of the Higher Education System as a Structural Component of the Region's Staffing

The relevance of the study lies in the fact that at present, in the conditions of the economic crisis, a shortage of highly qualified and professional personnel is becoming noticeable, which is caused by the unstable situation in the regions, trends in rapidly changing working conditions. It becomes clear that for a stable socio-economic system, regions and organizations within it need to be prepared for structural changes. The purpose of the study is to assess the higher education system as a structural component of the staffing of the region. Objectives: to analyze the dynamics of registered and liquidated organizations; to identify the ratio of indicators of the total population and the economically active population; to consider the per capita indicators of monetary incomes of the population of the Kursk region; to investigate the number of students enrolled in bachelor's, specialty and master's degree programs; to systematize the list of educational institutions of higher education located in the Kursk region; to present the disproportions of demand and supply of human resources by types of economic activity and in the professional and qualification context. Methodology. This article uses empirical research methods in the study and analysis of statistical data. Results. In the course of the study, the analysis of the current state of the higher education system as a structure-forming component of the staffing of the region was carried out, the disproportions of demand and supply of human resources by types of economic activity and in the professional and qualification context were revealed. Conclusions. Based on the conducted research, it can be concluded that the problem of supply and demand in the labor market is currently acute, since the development of the region's economy depends on the solution of personnel issues. One of the possible solutions to the personnel "hunger" within organizations can be step-by-step cooperation with universities and students of the desired field of training: internships, internships, round tables, business games and other methods of interaction between educational organizations and partner organizations.

Numerical study of wall cooling effects on diesel spray combustion characteristics under different ambient conditions

The effects of wall cooling on diesel spray combustion characteristics and engine performance are varied under different environmental conditions and the reasons remain to be further analyzed. The systematic explanation of its influence laws can effectively help the cooling system design and fault isolation under changing working conditions. Therefore, the effect mechanism of wall temperature on the diesel spray combustion characteristics under different ambient temperature and pressure conditions is numerically revealed through the combination of CFD simulation and chemical reaction kinetics analysis. During the calculation model validation, the simulation results showed good agreement with the experimental results and good adaptability to the change in wall temperature. The results show that high ambient temperature and pressure lead to the acceleration of the reaction rate, and high pressure also slows down the fuel diffusion, which eventually causes the fuel to undergo more reaction processes before hitting the wall and being in the relatively late stage of the low-temperature reaction process. In the process of low-temperature reaction and heat accumulation, the effect of temperature drop in the late stage of the reaction is less than that in the early stage due to more heat accumulation before cooling. This ultimately leads to diesel ignition being delayed by low wall temperatures under low ambient temperature and pressure conditions, while diesel spray at high ambient temperatures and pressures has a stronger anti-interference ability to the wall temperature.

Auto-adaptive metastructure for active tunable ultra-low frequency vibration suppression

Metastructures provide a new way to solve the problem of low-frequency vibration suppression because of their unique low-frequency bandgap characteristics. Traditional metastructures usually suffer from the problem of narrow bandgap at low frequencies, which cannot tolerant to uncertainties in manufacturing process of metastructures and cannot adapt to changing working conditions. One way to overcome the shortcomings is to design metastructures with tunable bandgaps. However, it is still a challenge to realize an auto-adaptive metastructure with a highly efficient tunability. In this work, we propose an active metaplate consisting of a host plate and a periodic array of local resonators that can be collectively and actively tuned using stepping motor. We demonstrate numerically and experimentally that the proposed active metaplate can realize continuous tunable ultra-low frequency bandgap. By introducing an auto-adaptive control strategy, it can realize auto-adaptive vibration attenuation at ultra-low frequencies. The proposed active metaplate has promising application prospects since it can achieve real-time active vibration suppression by using only a few active components.

ПОДхОДы К УПРАВЛЕНИЮ ОРГАНИЗАЦИОННыМИ И ВНЕОРГАНИЗАЦИОННыМИ СТРЕССАМИ В УСЛОВИЯх ГИГ-ЭКОНОМИКИ И ПРЕКАРИЗАЦИИ ТРУДОВых ОТНОШЕНИЙ

Precarization is one of the most important trends in the modern development of the labor market both in Russia and abroad. At the same time, the relevance of managing organizational and non-organizational stresses, which are facilitated by both numerous external factors and changing working conditions, is increasing. This article provides an analysis of the current state of procarization, as well as the results of studies of the causes of precarization, conducted with the involvement of the leaders of 46 service organizations, and studies of the correlation of labor stress and precarization. The main conclusion of the article is the thesis that in order to solve the problems associated with the development of precarization, an integrated approach and the involvement of multi-level actors are needed: the state, entrepreneurs, managers and workers themselves.