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The purpose of the study is to research the current state of fathers' involvement in childcare during parental leave and to assess attitudes of Russian population towards possible measures that can expand the use of parental leave by fathers in Russia.

The authors conducted a survey of Russian parents with children under the age of 18 months in 2022. The sample accounts for 1,000 people; the survey covered almost all Russian regions.

The authors found that the ideal workload of fathers is not expected to exceed a third of the total parental workload. Russian parents are not ready to admit dissatisfaction with the existing distribution of workload during parental leave. However, an egalitarian demand for greater involvement of fathers in parental responsibilities is forming, and an interest in transforming the parental leave policy is emerging.

The value of the study consists of assessing the effectiveness of measures that may have a beneficial effect on the use of parental leave by fathers, as well as identifying consequences of the possible introduction of mandatory parental leave for fathers.

This study developed new measures of the routine and non-routine task contents of managerial, professional, technical, and clerical occupations from a workload perspective. Here, we present a comparative analysis of the workload structures of state and industrial sector employees.

Our method involves detailed descriptions of work processes and an element-wise time study. We collected and analysed data to obtain a workload structure that falls within three conceptual task categories: (i) non-routine analytic tasks, (ii) non-routine interactive tasks and (iii) routine cognitive tasks. A total of 2,312 state and industrial sector employees in Kazakhstan participated in the study. The data were collected using a proprietary web application that resembles a timesheet.

The study results are consistent with the general trend reported by previous studies: the higher the job level, the lower the occupation’s routine task content. In addition, the routine cognitive task contents of managerial, professional, technical, and clerical occupations in the industrial sector are higher than those in local governments. The work of women is also more routinary than that of men. Finally, vthe routine cognitive task contents of occupations in administrative units are higher than those of occupations in substantive units.

Our study sought to address the challenges of using the task-based approach associated with measuring tasks by introducing a new measurement framework. The main advantage of our task measures is a direct approach to assessing workloads consisting of routine tasks, which allows for an accurate estimation of potential staff reductions due to the automation of work processes.

Recently, physical crafting has been found to positively affect emotional exhaustion through workload. However, the role of cognitive crafting in this process remains unexamined. To address this research gap, this study examined the relationship between cognitive crafting and emotional exhaustion, as well as whether cognitive crafting moderates the positive indirect effects of physical crafting on emotional exhaustion through workload.

The data were collected through an Internet survey conducted with 2,143 Japanese employees, and path regression analysis was conducted to analyze the data.

The results show that cognitive crafting was negatively correlated with emotional exhaustion, weakened the relationship between workload and emotional exhaustion and weakened the indirect effects of physical crafting on emotional exhaustion.

The practical implications of these findings suggest that practitioners should encourage the improvement of cognitive crafting. Implementation of job crafting interventions and customer participation could be effective in enhancing cognitive crafting.

The study provides a deeper understanding of how cognitive crafting influences emotional exhaustion and how it influences the process through which physical crafting influences emotional exhaustion, aligning with the transactional model. The results reiterate the importance of cognitive crafting, an aspect that has received little attention since the introduction of the job demands-resources (JD-R) model of job crafting.

As data centres grow in size and complexity, traditional air-cooling methods are becoming less effective and more expensive. Immersion cooling, where servers are submerged in a dielectric fluid, has emerged as a promising alternative. Ensuring reliable operations in data centre applications requires the development of an effective control framework for immersion cooling systems, which necessitates the prediction of server temperature. While deep learning-based temperature prediction models have shown effectiveness, further enhancement is needed to improve their prediction accuracy. This study aims to develop a temperature prediction model using Long Short-Term Memory (LSTM) Networks based on recursive encoder-decoder architecture.

This paper explores the use of deep learning algorithms to predict the temperature of a heater in a two-phase immersion-cooled system using NOVEC 7100. The performance of recursive-long short-term memory-encoder-decoder (R-LSTM-ED), recursive-convolutional neural network-LSTM (R-CNN-LSTM) and R-LSTM approaches are compared using mean absolute error, root mean square error, mean absolute percentage error and coefficient of determination (R²) as performance metrics. The impact of window size, sampling period and noise within training data on the performance of the model is investigated.

The R-LSTM-ED consistently outperforms the R-LSTM model by 6%, 15.8% and 12.5%, and R-CNN-LSTM model by 4%, 11% and 12.3% in all forecast ranges of 10, 30 and 60 s, respectively, averaged across all the workloads considered in the study. The optimum sampling period based on the study is found to be 2 s and the window size to be 60 s. The performance of the model deteriorates significantly as the noise level reaches 10%.

The proposed models are currently trained on data collected from an experimental setup simulating data centre loads. Future research should seek to extend the applicability of the models by incorporating time series data from immersion-cooled servers.

The proposed multivariate-recursive-prediction models are trained and tested by using real Data Centre workload traces applied to the immersion-cooled system developed in the laboratory.

Working remotely in a COVID-19-induced lockdown has been challenging for both organisations and their employees; studies report that job demands changed, and teleworkers experienced increased burnout. This paper explores the negative employee outcomes that this work arrangement brings along and offers possible solutions to counter such negative outcomes since they could be detrimental to the much-touted future of work.

The study adopted a time-lagged longitudinal design and collected two-waved data from 403 quaternary sector employees. The data were analysed using structural equation modelling and model-21 in PROCESS macro for SPSS.

Findings affirm that employees experienced increased job demands during this crisis. Employees reported an increase in turnover intention because of burnout caused by increased job demands. However, increased task interdependence alone did not have any effect on turnover intention. The perceived organisational task support (POTS) was found to forestall the negative effect of job demands on burnout, and employee resilience (ER) buffered the burnout and turnover intention relationship.

Providing remote work task support and boosting resilience among employees will help in doing away with the negative effects of teleworking. However, managers shall prioritise reducing job demands for teleworkers.

The linkage between work factors and turnover intention is well established. Drawing on the event system theory and using the COVID-19 context, the present study added to the existing knowledge by studying the role of job demands (workload pressure and task interdependence) on turnover intention through the mediation of burnout. The study goes beyond the existing literature by accounting for POTS as a first-level moderator between job demands and burnout relationship, and ER as a second-level moderator between burnout and turnover intention relationship.

This paper examines the impact of the COVID-19 pandemic on the mental health and wellbeing of academic and professional Higher Education (HE) staff in the UK.

A mixed-method survey questionnaire was sent to almost 300 UK HE staff to secure qualitative and quantitative data to enable data triangulation.

The study found an adverse impact on academic and professional staff's mental health and wellbeing, further resulting in stress and anxiety. Several reasons for the increased stress and anxiety levels were identified, but social isolation and the increased workload were the most commonly reported. The most affected groups by the pandemic were females, younger staff, full-timers and those with disabilities or caring responsibilities.

This study offers a range of strategies to support staff's mental health and wellbeing; as such, it is of great interest to policymakers to inform their decisions of similar crisis events in the future. It also addresses some of the COVID-19 areas of research interest for the UK parliament.

The study's originality derives from exploring the pandemic's impact on UK HE staff's mental health and wellbeing by including professional staff's experiences alongside those of academics. It also expands the scant evidence concerning the pandemic's impact on HE staff in the UK.

The study aims to explore the effectiveness of performance-oriented practices like high-performance work practices (HPWPs) in higher education (HE), given its explicit focus on performance these days.

The study uses hierarchical linear modeling using statistical package for social sciences (SPSS 22.0) to test the hypotheses. An intertwined framework of the ability–motivation–opportunity (AMO) model and the job demand-resources (JD-R) model was proposed. The study considered strategic hiring, recognition and participatory decision-making as ability, motivation and opportunity-enhancing practices respectively. Further, the study addressed the impact of institutional level moderators, like administrative workload (AWL) and support staff (SS).

The findings based on the responses of 385 faculties and 443 students from 36 Indian institutes, indicated that HPWPs enhanced the education performance (EP) of HE institutes. Further, results revealed that both AWL and SS had differential effects on the relationship between HPWPs and EP. Contrary to authors’ expectations, SS showed a negative effect of the relationship between HPWPs and EP.

The increased AWL was debilitating the beneficial effects HPWPs. The negative interaction effect of SS sheds light on the hidden issues surrounding SS in HE institutes. Based on findings, the study offered important theoretical and practical implications.

To the best of authors’ knowledge, the impact of innovative human resource (HR) practices in academia remains relatively under-researched, and the current study is an attempt to fill this void.

This study aims to minimize the transportation-related cost in distribution while utilizing a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach. An increased cost in distribution is a major problem for many companies due to the absence of efficient planning methods to overcome operational challenges in distinct distribution networks. The problem addressed in this study is to minimize the transportation-related cost in distribution while using a heterogeneous fixed fleet to deliver distinct demand at different geographical locations with a proper workload balancing approach which has not gained the adequate attention in the literature.

This study formulated the transportation problem as a vehicle routing problem with a heterogeneous fixed fleet and workload balancing, which is a combinatorial optimization problem of the NP-hard category. The model was solved using both the simulated annealing and a genetic algorithm (GA) adopting distinct local search operators. A greedy approach has been used in generating an initial solution for both algorithms. The paired t-test has been used in selecting the best algorithm. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet compositions of the heterogeneous fleet. Results were analyzed using analysis of variance (ANOVA) and Hsu’s MCB methods to identify the best scenario.

The solutions generated by both algorithms were subjected to the t-test, and the results revealed that the GA outperformed in solution quality in planning a heterogeneous fleet for distribution with load balancing. Through a number of scenarios, the baseline conditions of the problem were further tested investigating the alternative fleet utilization with different compositions of the heterogeneous fleet. Results were analyzed using ANOVA and Hsu’s MCB method and found that removing the lowest capacities trucks enhances the average vehicle utilization with reduced travel distance.

The developed model has considered both planning of heterogeneous fleet and the requirement of work load balancing which are very common industry needs, however, have not been addressed adequately either individually or collectively in the literature. The adopted solution methodologies to solve the NP-hard distribution problem consist of metaheuristics, statistical analysis and scenario analysis are another significant contribution. The planning of distribution operations not only addresses operational-level decision, through a scenario analysis, but also strategic-level decision has also been considered.

The planning of distribution operations not only addresses operational-level decisions, but also strategic-level decisions conducting a scenario analysis.

We empirically assessed the antecedents of subjective well-being at work for French permanent employees.

The methodology includes qualitative and quantitative data analyses. In the first phase, interviews elicited the antecedents of subjective well-being at work among permanent French employees. In the second phase, a questionnaire survey was used to confirm the relevance of the antecedents uncovered in the first phase.

We found 14 distinct elements that influence French employees’ subjective well-being at work: corporate culture, job dissonance, relationships with colleagues, achievement, professional development, relationships with superiors, status, workload, perks, feedback, workspace, diversity and pay. Moreover, we identified discrete antecedents for the three components of subjective well-being at work: work achievement and relationships with superiors and colleagues for positive emotions at work, job dissonance and workload for negative emotions at work and organizational culture and professional development for satisfaction with one’s work.

The original contribution of this study is to have unpacked the black box of the antecedents of subjective well-being in the French workplace and to have uncovered discriminant predictors for each of the three components of subjective well-being at work. Furthermore, we specifically linked each of these three components with their most significant antecedents.

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.