Unstable Working Conditions Research Articles

Influence of unstable working conditions on friction torque of angular contact ball bearings

Influence of unstable working conditions on friction torque of angular contact ball bearings

Between luck and stigma: gendered experiences of precarity in higher education in the UK and the US

ABSTRACT Through a phenomenological research design, this study explores experiences of precarity among early-career academics in the UK and the USA higher education sectors. These contexts, while similar, exhibit structural differences as pioneers of neoliberalization in the Global North. Conducting semi-structured interviews with 20 early-career academics in both contexts, the study delves into shared and distinct experiences of precarity, manifesting in unstable working conditions, uncertain futures, and competitive job markets dominated by temporary positions. The findings illuminate the role of emotions and gendered experiences in navigating precarity. Accordingly, informants in both contexts expressed being lucky to have academic positions even when they are temporary ones embedded in gendered hierarchies, while informants in the USA further highlighted being stigmatized. The study argues that the experiences of precarity unsettle meritocratic ideals in both cases, whereby individual success and achievements become irrelevant and excluded, and gendered inequalities are overshadowed.

Söndrade kroppar, invasiv bemanning och prekära anställningar. Intersektionella perspektiv på försörjning, lönearbete och social reproduktion

While wage labor has been celebrated as a means of women’s emancipation within Swedish gender equality policy, unequal workplaces, the prevalence of sexual harassment, precarious working conditions, and work-related health issues indicate that wage labor also entails serious problems for many women. This article examines how unstable working conditions and work organizations built on precarious labor and flexible staffing strategies impact women’s well-being. Through feminist intersectional perspectives on social reproduction, the analysis sheds light on the risks posed by current work models to workers’ health, work capacity and liveability. The article is based on survey responses among retail workers. Their responses show that working life in retail is characterized not only by precarious employment conditions but also by invasive staffing strategies. The consequences are precarious livelihoods, stress, and limited possibilities to leisure time and social relationships. The lack of opportunities to recover highlights how workers’ bodies bear the brunt of companies’ pursuit of precarization. Building on theories of social reproduction and an analysis of working conditions in retail, we call for a deeper examination of the role of wage labor in feminist strategies.

Роль професійного вигорання в системі управління конфліктами персоналу сучасних підприємств

The article is devoted to considering the concepts of “emotional burnout syndrome” and “professional burnout” and determining their commonalities and differences in essence. It characterizations individuals’ reactions to systematic stress, which necessarily occurs during intense work, unstable working conditions, etc. These reactions include exhaustion, dehumanization (depersonalization, feelings of cynicism), and reduced work capacity. The following types of reactions of individuals to stress factors are additionally characterized: emotional imbalance, pessimistic attitude towards life, distancing, and disorganization. The common logic of behavioral reactions to stress and professional and emotional burnout symptoms is determined. The influence of emotional and professional burnout on personality conflict is determined. It is noted that behavioral reactions that are formed under the influence of a state of professional or emotional burnout can form factors of conflict of various types. Understanding the 12 stages (phases) of burnout is crucial for being informed and prepared. These stages, such as ‘Forced bringing to the point of no return’, ‘Overtime work ‘, ‘Closing’ of conflicts and needs’, ‘Suppression’ of conflicts and needs’, ‘Rethinking values ‘, ‘Activation of denial of problems’, ‘Retreat’, ‘Observation of changes in behavior ‘, ‘Loss of personality ‘, ‘Inner emptiness ‘, ‘Depression ‘, and ‘Complete exhaustion ‘, provide a roadmap for recognizing and managing burnout. The need to combine the efforts of the organization’s management and personnel in managing emotional and professional burnout is emphasized. The proposed strategy of personnel development in the field of management of emotional and professional burnout. Proposed methods of psychological self-help for personnel are unchanged regardless of their functional, professional, or role affiliation. Among such methods are effective time management in professional and personal spheres; organization and redistribution of professional workload; quality rest and sleep; proactive, empathetic, and assertive communication; breathing practices; practicing self-training techniques to control the emotional and volitional sphere; development of critical, creative, and positive thinking; self-development and time for hobbies; attention to one’s health. Keywords: emotional burnout, professional burnout, conflicts, stress, personnel, methods of psychological self-help.

Multiple-criteria analysis of the employee satisfaction level at healthcare facilities during the pandemic

ABSTRACT Due to the pandemic, the entire staff employed in healthcare institutions were directly exposed to longer working hours and unstable working conditions. Based on these facts, the research in this article aims to find out more about employees’ satisfaction with working conditions and management’s engagement in preserving their health and safety. Thus, a multiple-criteria decision analysis (MCDA) model based on the analyzed attitudes and opinions of employees was formed to reveal the interdependencies between working conditions resulting from the undertaken management activities, employee satisfaction and workplace safety. The results show that both nurses and drivers were least satisfied with working conditions during the pandemic while indicating those workplaces to be the most endangered. Additionally, the proposed model can be successfully applied to any type of organization to identify insufficiently effective management activities, address them accordingly and thereby improve the level of employee satisfaction.

A TLOT train gearbox fault diagnosis method based on ridge extraction under variable speed conditions

Owing to the rapidly varying working conditions of urban rail trains, the rotational speed conditions constantly shift in a short time span. As a key component of the running gear, the gearbox generates non-stationary vibration signals, making it challenging to monitor its health status. To address this challenge, a tacholess order tracking method (TLOT) based on ridge extraction method is proposed in this paper. This method determines the optimal search starting point using the time-frequency Gini coefficient and extracts the time-varying gearbox meshing frequency components from the time-frequency representation results. Furthermore, the TLOT method is utilized to process the strongly time-varying gearbox signal. In the order domain, multiple frequency components are extracted to reconstruct the signal. Simulation and experimental results demonstrate that the proposed method achieves a superior ridge extraction effect on gearbox experimental signals. It accurately converts unstable signals into angular domain stable signals, enhancing the energy aggregation of time-varying unstable signals in the order domain. This approach addresses the problem of weak harmonic component extraction from gearbox signals and effectively reduces interference in the resonance band, realizing fault diagnosis of the gearbox under unstable working conditions.

A process knowledge-based hybrid method for univariate time series prediction with uncertain inputs in process industry

In process industry, accurate and efficient univariate time series prediction (TSP) of key process parameters is crucial for optimizing production processes. However, noisy data and varying working conditions introduce uncertainties that hinder the modeling process. Data-driven methods have been proposed to address input-uncertain TSP models, but they often fail to obtain sufficient and high-quality industrial data for model establishment in complex and dynamic production process. The paper presents a process knowledge-based hybrid method for univariate TSP that handles noisy inputs in different working conditions. Primarily, a knowledge-embedded deep kernel network (KE-DKN) model is proposed that approximates the outputs to the knowledge space in noisy industrial environment, by embedding instantiated process knowledge as soft constraints into the conventional DKN network. Further, a two-stage training schema is designed for KE-DKN to reduce the interference superposition on the predicted variable from random noisy data and operational interferences. Moreover, the pre-trained parameters transferring is conducted from stable working conditions to mitigate the low modeling efficiency in unstable working conditions. In two cases from process industry, the proposed method delivers higher prediction accuracy and robustness than baseline methods in both working conditions, and the knowledge transfer significantly enhances the modeling efficiency in unstable working conditions.

Labor Market and Class Struggle from the Perspective of Marxist Political Economy

Within the viewpoint of Marxist political economy, inequalities in the labor market are regarded as products of class struggle under the capitalist system. These inequalities reflect the relatively disadvantaged position of the working class, who often face low wages, unstable working conditions, and exploitation. These injustices not only reflect economic structures but also deeply influence the construction of social status and power relations. Studying the organizational forms of the working class within this theoretical framework holds significant importance. Labor unions, as organizations representing workers' interests, undertake the responsibility of advocating for better working conditions and wages. Union organizations engage in collective actions, such as strikes and demonstrations, demonstrating the solidarity and strength of the working class, challenging the authority of the capitalist system.

Comparative analysis of thermal and non-thermal discharge modes on ultra-lean mixtures in an optically accessible engine equipped with a corona ignition system

Radio-Frequency corona-based ignition systems are currently being investigated by the engine research community for their capability to ensure robust combustion at challenging operating conditions such as lean mixture and/or high exhaust gas recirculation (EGR) dilution. The low-temperature plasma (LTP) produced by the corona discharge accelerates the early flame development thanks to the production of combustion precursors like radical and excited species. Plasma benefits can be maintained with extensive management of the control parameters, preventing the transition from the non-thermal discharge mode to a thermal one.However, under ultra-lean conditions, where the ignition probability significantly decreases, the LTP discharge may not be suitable for promoting the combustion process because of insufficient thermal energy transfer and scarcity of available fuel, which is essential for radical production. Therefore, it may be necessary to increase the volume in which the plasma interacts to ensure robust ignitions and stable flame propagations. For this reason, the present work aims to assess and quantify the differences, in terms of combustion performance, between a corona igniter operating at LTP mode and with electric arcs occurring, at ultra-lean operating conditions. In arc-mode, the igniter produces volumetric discharges, facilitating extensive mixing of the mixture and resulting in advantageous effects on kernel formation and propagation. Tests were carried out in a single-cylinder optical access engine under low load and low speed, using gasoline as fuel. Close to the leanest stable limit found, the LTP mode shows a lower cycle-to-cycle variability. Above such a threshold, the thermal discharge can guarantee stable combustion events while the LTP discharge leads to unstable working conditions. Indicating, imaging, and pollutant emission analysis are carried out to evaluate the effects of thermal and non-thermal plasma modes on combustion characteristics and their environmental impact.

Remaining useful life prediction of lithium-ion batteries based on peak interval features and deep learning

Anticipating the lifespan of a lithium-ion battery is a challenging task due to the unstable external working conditions and complex internal response. To enhance the accuracy of the remaining usable life prediction of lithium-ion batteries, this study proposes a feature extraction method that relies on the peak value of the incremental capacity (IC) curve for a data-driven prediction approach. This method can significantly reduce the amount of data required for training the model. Furthermore, the grey correlation method (GCM) is employed to select features that have a high correlation with the target value, thereby further reducing the amount of data required for training the model. It is explored how well characteristics may be extracted from various peak intervals. The time series prediction-capable long short-term memory (LSTM) network is used to create the data-driven model. Finally, the experimental findings demonstrate that the proposed data-driven model's prediction error is smaller than 1.18 %. The proposed data-driven in this work is found to have a superior prediction impact on the same data set in different deep learning methods when compared to other data-driven approaches.

Damping Effect on Instability of Pressure Relief Valve Used for Cargo Venting System

A mathematical model for predicting an unstable working condition called cycling of a pressure relief valve controlled by weight and spring is proposed. The valve is connected to a reservoir used to store liquid cargo by a long pipe. A transient, compressible and one-dimensional fluid dynamic equation in the pipe is numerically solved, with boundary conditions given by the valve and reservoir dynamics. The effect of valve moving part damping coefficient is analysed, which can be changed by adding dampers. Installing a damper may solve the cycling problem for weight-loaded and spring-loaded pressure relief valves. The damping coefficient of the installed damper should be carefully selected, especially for spring-loaded valves. Too large damping coefficient may cause overpressure in the reservoir when the valve opens and too low pressure when the valve closes. After all, installing damper is a theoretically possible way to prevent cycling occurrence when the pipe length is too long in some practical situations.

Postdoctoral researchers' perspectives on working conditions and equal opportunities in German academia.

Postdoctoral researchers (postdocs) are an essential component of the scientific workforce in German universities and research institutions and play a vital role in advancing knowledge and innovation. However, the experiences of postdocs and other early career researchers (ECRs) indicate that working conditions pose a significant challenge to the pursuit of a long-term research career in Germany-particularly for international scientists and those from marginalized groups. We examine how unstable working conditions as well as insufficient structural support for equal opportunities and diversity are significant obstacles for the career development of ECRs in German academia. We discuss these issues with the aid of an extensive survey recently conducted and published by PostdocNet, a target-group network representing the interests of postdocs across Germany's Max Planck Society. The survey drew responses from 659 postdoctoral researchers working at the Max Planck Society and represents one of the few datasets of postdoctoral researchers' perspectives in Germany. Building on these findings, we suggest actions at governmental, institutional, and individual levels to improve the working conditions of postdoctoral researchers in Germany.

A NARX network optimized with an adaptive weighted square-root cubature Kalman filter for the dynamic state of charge estimation of lithium-ion batteries

Due to the high nonlinearities and unstable working conditions, accurately estimating the state of charge (SOC) by the battery management system (BMS) is a major challenge in ensuring the safety and reliability of lithium-ion batteries in electric vehicles. This paper presents a deep learning network, a nonlinear autoregressive model with exogenous inputs (NARX) network with a closed-loop architecture and transfer learning mechanism, which is optimized using a proposed adaptive weighted square-root cubature Kalman filter (AWSCKF) with a moving sliding window and an adaptive weighing coefficient for SOC estimation of lithium-ion batteries. The proposed AWSCKF method is established through square-root and cubature updates to optimize the statistical value of the state estimate, error covariance, and measurement noise covariance matrices, with the ability to incorporate high nonlinearities to filter out the noise, stabilize, and optimize the final SOC. To evaluate the effectiveness of the optimized NARX network and verify the proposed AWSCKF method, battery tests are carried out using a lithium cobalt oxide battery at various charge-discharge rates and a lithium nickel cobalt manganese oxide battery at temperatures of 0 and 45 °C under five complex working conditions. The SOC accuracy of lithium-ion batteries is enhanced by the hybrid method estimation process, which is based on sensitivity analysis and adaptation to various working conditions. The comprehensive results show that the proposed NARX-AWSCKF model achieves the overall best mean absolute error, root mean square error, and mean absolute percentage error values of 0.07293%, 0.0912%, and 0.40356%, respectively, under various complex conditions. By effectively utilizing battery domain knowledge for real-world BMS applications, the proposed model outperforms other existing methods in terms of high effectiveness, robustness, and potential to boost the NARX performance.

Power generation analysis of organic Rankine cycle system based on maglev vortex rotor

ABSTRACT As one of the key indicators of the Organic Rankine cycle (ORC), it is of great significance to analyze power generation effectively and directly. Different from the traditional mechanical contact ORC system and in order to reduce the influence of component friction, unstable working conditions, and other factors on power generation analysis, this paper establishes the corresponding power generation model based on the maglev vortex rotor ORC system, and uses R245fa, R123, and R601 as working fluids to carry out relevant simulation and experiments. The simulation results are in accordance with the model calculation formula, and the differences between simulation results and the reference values are less than 0.45 kW (R245fa), 0.61 kW (R601), and 0.29 kW (R123), respectively, which are in line with the theoretical power generation of the maglev rotor ORC system. Furthermore, the experimental results show that under different variable conditions, the difference between the system power generation and the net output power is less than 0.1 kW, and the corresponding RMS and standard deviation are less than 0.1 and 0.05, respectively. Therefore, the maglev rotor ORC system can effectively reduce the influence of component friction loss, and its corresponding net output power and power generation are almost equal.

A hierarchical adversarial multi-target domain adaptation for gear fault diagnosis under variable working condition based on raw acoustic signal

Acoustic-based diagnosis (ABD) relied on air-coupled measurement is received great attention in recent years due to its ability in overcoming the limitation of vibration-based diagnosis on contact measurement. However, most of the ABD approaches are only focus on a stable working condition for analysis and research. Main challenge of ABD task is the unstable working condition including variable load and speed in real-industrial scenarios, which involve variation in amplitude and phase of acoustic signal to cause domain shift problem for fault pattern detection. In this condition, to perform ABD task for meeting the requirement of scalability in dealing with multiple targets under continuously variable working condition, a novel hierarchical adversarial multiple-target domains adaptation (HAMT-DA) learning framework is proposed in this paper. Following the naturally solution idea, a target-compress adversarial adaptation (TCAA) mechanism is developed in the first-level of framework to compress multiple-target domains by minimizing the feature distribution discrepancy over all domains, and narrow the divergence between source and target domains concurrently. Then, domain invariant feature refining (DIFR) strategy is designed to take responsibility for second-level domain adaptation in the intermediate of framework by refining domain invariant attribute and encoding the domain invariant feature into high-level semantic representation space. Subsequently, a class-separate adversarial adaptation (CSAA) method is explored in the roof of the hierarchical framework for the exploration of fault-discriminative property by forming third-level domain adaptation. Thus, the entire HAMT-DA learning framework can be obtained for single source to multiple-target (SSMT) cross-domain ABD task. The experiment result in variable working conditions indicate that the proposed method has more than 94% average diagnosis performance for gear fault patterns in five cross-domain validation tasks and achieve the highest recognition accuracy among four verifications, which is about 7% average improvement than that of the optimal comparison method. It verifies the superiority of our method on robustness and generalization for acoustic-based gear fault diagnosis under SSMT setting.

Dynamic Behavior and Double-Parameter Self-Adaptive Stability Control of a Gear Transmission System

This paper proposes a new nonlinear transverse-torsional coupled model for single-stage gear transmission system, by taking transmission error, time-varying meshing stiffness, backlash, bearing clearances and the self-adaptive double-parameter control module into account. The nonlinear differential governing equation of system motion is derived and solved by applying variable step-size Runge–Kutta numerical integration method. The system’s nonlinear dynamic characteristics and stability are investigated systematically by a bifurcation diagram of the Poincaré map and parameter stability region. Firstly, the velocity bifurcation diagrams have shown that, under the same damping ratio and backlash and with the increase of control parameter [Formula: see text], the route to chaos in the subcritical velocity region is first experienced from crisis to periodic doubling, and to crisis again, but the route that reverts to periodic motion in the super-critical velocity region is not affected. Additionally, the backlash is found to be the key parameter to affect the route to chaos as well. With the increase of the backlash, the crisis becomes the unique route to chaos in sub-critical region no matter what the [Formula: see text] is, but the increase of [Formula: see text] could change the route that reverts to periodic motion from 3T-periodic attractor to 2T-periodic attractor. Secondly, with the increase of the control parameter [Formula: see text], the system starts to enter the chaotic motion and exit the chaos state at different critical points and through different routes. Besides, the unstable region could shrink dramatically and the route to crisis is suppressed as well with the increase of damping ratio. Thirdly, the motion stability region analysis established in full range of double-parameter and velocity provides a mathematical reference model and is stored in control module, which could be utilized to make the control module seek a nearest parameter set automatically that could make the motion stable again in the quickest way under unstable working condition. Finally, according to global motion stability diagram, the forbidden zones that cannot make the system motion stable by adjusting single control parameter are revealed, which has remarkable guiding value during the practical operation especially under the manual adjusting working condition.

Energy consumption mode identification and monitoring method of process industry system under unstable working conditions

Process industry systems under unstable working conditions are prone to potential anomalies, deviating from the original transition trajectory, and taking longer than expected to return to stability due to persistent disturbances from uncertainties and experience-based regulation errors. The energy waste caused by this situation has not received sufficient attention, and cannot be addressed by existing energy consumption monitoring methods. Herein, an energy consumption mode (ECM) identification and monitoring method under unstable working conditions is proposed, consisting of ECM identification model and multi-mode dynamic monitoring model, focusing on the variation rules of the correlation between energy consumption and other states of the system. In the ECM identification stage, the ECM correlation parameters that reflect the comprehensive production information are selected. Then, given the transfer characteristics of ECM, a Hidden Semi-Markov Model (HSMM) is constructed to fit the migration between modes and the duration within modes. The Variational Bayesian Gaussian Mixture Model is introduced to improve the HSMM, which solves the problem of lacking prior knowledge of ECM and achieves the automatic classification and online identification of ECM. In the dynamic monitoring stage of multi-ECMs, a series of dynamic kernel principle component analysis models are established, and the corresponding monitoring thresholds are set for each ECM. By calculating the maximum of the posteriori probability and the mode thresholds, the ECMs under unstable conditions can be accurately identified and automatically monitored. Compared with previous methods, the proposed method reduces the false detection rate and missed detection rate of abnormal ECM identification to 1.04% and 1.31% in the actual slag grinding production process, which proves its effectiveness.

Design and Investigation of a Dynamic Auto-Adjusting Ejector for the MED-TVC Desalination System Driven by Solar Energy.

Ejectors have been widely used in multi-effect distillation, thermal vapor compression (MED-TVC) desalination systems due to their simple structures and low energy consumption. However, traditional fixed geometry ejectors fail to operate under unstable working conditions driven by solar energy. Herein, a dynamic auto-adjusting ejector, equipped with a needle at the nozzle throat, is proposed to improve the ejector's performance under changeable operating conditions. A two-dimensional computational fluid dynamics (CFD) model is built to analyze the performance and flow field of the ejector. It is found that the achievable entrainment ratio gradually increases as the needle approaches the nozzle, and the entrainment ratio of the ejector is relatively stable, varying slightly between 1.1-1.2 when the primary pressure changes from 2.5 to 4 bar. Besides, the performance comparison between the proposed ejector and the traditional ejector is studied under the same primary pressure range. The entrainment ratio of the designed ejector was 1.6 times higher than that of the conventional ejector at a primary pressure of 2.5 bar. Furthermore, the average entrainment ratio of the designed ejector is 1.14, as compared to 0.84 for the traditional ejector. Overall, the proposed auto-adjusting ejector could be potentially used in MED-TVC desalination systems under variable conditions.

Job change in the conditions of precarious work (on the example of the Republic of Bashkortostan permanent residents)

Job and profession change are basic forms of social and labour mobility. Unstable working conditions are typical for less developed in socio-economic terms regions. The purpose of the study is to identify the degree of influence of precarious employment and other socio-demographic and economic factors on the probability of job change among Bashkortostan residents. The empirical basis of the study is a sociological survey of the Republic of Bashkortostan population. According to the results obtained, such factors as working in a private enterprise, self-employment, short work experience and the presence of a temporary employment contract increase the likelihood of job change. Income satisfaction, official wage and the absence of credit debt reduce the probability of job change. Respondents who want to improve their level of education and qualifications, as well as those who do not work within their specialty, tend to change their place of work more often. The conducted research has shown that changing a job, as a rule, does not contribute to improving the financial situation and working conditions of respondents.

The Outcomes of Organizational Fairness among Precarious Workers: The Critical Role of Anomie at the Work

Precarious workers usually have some of the most unstable working conditions in any country. In this research, we firstly investigated the influence of organizational fairness on the emotional exhaustion and leave intentions of Peruvian precarious workers. Then, we tested the mediating role of anomie at work in the relationship between organizational fairness and the emotional exhaustion and leave intentions of precarious workers. To identify the impact of organizational fairness on these workers, we conducted a cross-sectional survey among 206 precarious workers in Peru. Our results showed that treating precarious workers fairly reduced their emotional exhaustion and leave intentions. Furthermore, we found that anomie at work mediate the relationship between organizational fairness and the emotional exhaustion and the relationship between organizational fairness and leave intentions of precarious workers. We provide empirical evidence of the critical role of organizational fairness in the reduction of emotional exhaustion and leave intentions among precarious workers. Examining the consequences of precarious workers’ perceptions of organizational fairness is needed for owners and managers of companies to have a clear understanding of how precarious workers’ working environments may shape their attitudes and work behaviors.