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This study aims to answer the following question: Why might home-based work duties be perceived by female academics as extreme?

We employed a qualitative research method through semi-structured interviews with 33 female academics from three public universities selected from amongst 26 public institutions of higher education in Egypt. Thematic analysis was subsequently used to determine the main ideas in the transcripts.

We find that the sudden implementation of home-based work makes the academic duties of female academics extreme. Moreover, the following four factors help explain the extremity/intensity of the home-based work of female academics: mental and physical fatigue resulting from WFH, the inability to adequately meet family commitments when working from home (WFH), poor resources for home-based work and reduced ability to focus on the obstacles facing them in their academic career.

This paper contributes by filling a gap in human resources management and higher education in which empirical studies on female academics WFH and extreme academic duties have been limited so far.

This paper aim to analyse the motives behind the commitment of nurses to their profession despite their intense job duties during the COVID-19 pandemic.

The empirical sample comprises of 35 semi-structured interviews with public sector hospital nurses in under-researched contexts of Egypt and Peru.

Three types of motives were found to play a critical role in nurses’ commitment to their profession despite the difficulties associated with extreme work conditions. These factors include cultural (religious values, governmental coercion), contextual (limited education, organisational support) and personal (good nurse identity, submissive nature) dimensions.

This paper is one of the pioneering works to link existing literature streams on career commitment, extreme jobs, extreme context and management under disruptions (particularly COVID-19) by analysing these aspects in the under-researched Peruvian and Egyptian contexts.

The present study used the job-demands and resources (JD-R) framework to understand how the training is transferred to an extreme working context through the analysis of job and personal resources (social support from the leader and colleagues and adaptability). Specifically, the authors tested the mediating role of motivation to transfer in the relationship (1) between the perceived support from the supervisor and colleagues and performance after training and (2) between adaptability and performance in an extreme context of the pandemic crisis – the first peak of COVID-19 in Portugal. Further, an inspection of the factors that predicted knowledge transfer and adaptability under an extreme context was carried out.

To do so, necessary training about the new safety rules regarding the pandemic crisis of COVID-19 was implemented in a healthcare institution as a strategy to help healthcare workers deal with the increasing uncertainty and complexity that was threatening their work. It consisted of three sessions (each with one hour of training) regarding procedures, rules and safety norms. The training occurred in May 2020. Overall, 291 healthcare workers participated in the study and answered one online questionnaire one week after training completion.

The results showed that the motivation to transfer had a significant indirect effect on the relationship between colleagues' and supervisors' support and performance and between adaptability and performance. Additionally, complementary analyses showed that the mediations depended on the levels of self-efficacy in such a way that the indirect relationships were stronger when self-efficacy was higher. Thus, adaptability and support, both from colleagues and the supervisor, are determining factors for knowledge transfer and resultant performance in extreme contexts, such as the COVID-19 pandemic crisis. Lastly, the results showed that the most significant predictors of transference were self-efficacy and the motivation to transfer the learned knowledge. On the other hand, self-efficacy, peer support and the opportunity to use the knowledge were the most significant predictors of adaptability.

These findings provide support for the role of employee motivation to transfer as a mechanism connecting both perceived support and adaptability to performance outcomes under extreme working contexts.

This study, conducted in the middle of the COVID-19 pandemic context – an extreme and uncertain working context – shows the relevance of both job and individual factors to predict employees' adaptability to such contexts.

The purpose of this research is to test the effectiveness of integrating Grasshopper 3D and measuring attractiveness by a categorical based evaluation technique (M-MACBETH) for building energy simulation analysis within a virtual environment. Set of energy retrofitting solutions is evaluated against performance-based criteria (energy consumption, weight and carbon footprint), and considering the preservation of the cultural value of the building, its architectural and spatial configuration.

This research addresses the building energy performance analysis before and after the design of retrofitting solutions in extreme climate environments (2030–2100). The proposed model integrates data obtained from an advanced parametric tool (Grasshopper) and a multi-criteria decision analysis (M-MACBETH) to score different energy retrofitting solutions against energy consumption, weight, carbon footprint and impact on architectural configuration. The proposed model is tested for predicting the performance of a traditional timber-framed dwelling in a historic parish in Lisbon. The performance of distinct solutions is compared in digitally simulated climate conditions (design scenarios) considering different criteria weights.

This study shows the importance of conducting building energy simulation linking physical and digital environments and then, identifying a set of evaluation criteria in the analysed context. Architects, environmental engineers and urban planners should use computational environment in the development design phase to identify design solutions and compare their expected impact on the building configuration and performance-based behaviour.

The unavailability of local weather data (EnergyPlus Weather File (EPW) file), the high time-resource effort, and the number/type of the energy retrofit measures tested in this research limit the scope of this study. In energy simulation procedures, the baseline generally covers a period of thirty, ten or five years. In this research, due to the fact that weather data is unavailable in the format required in the simulation process (.EPW file), the input data in the baseline is the average climatic data from EnergyPlus (2022). Additionally, this workflow is time-consuming due to the low interoperability of the software. Grasshopper requires a high-skilled analyst to obtain accurate results. To calculate the values for the energy consumption, i.e. the values of energy per day of simulation, all the values given per hour are manually summed. The values of weight are obtained by calculating the amount of material required (whose dimensions are provided by Grasshopper), while the amount of carbon footprint is calculated per kg of material. Then this set of data is introduced into M-MACBETH. Another relevant limitation is related to the techniques proposed for retrofitting this case study, all based on wood-fibre boards.

The proposed method for energy simulation and climate change adaptation can be applied to other historic buildings considering different evaluation criteria and context-based priorities.

Context-based adaptation measures of the built environment are necessary for the coming years due to the projected extreme temperature changes following the 2015 Paris Agreement and the 2030 Agenda. Built environments include historical sites that represent irreplaceable cultural legacies and factors of the community's identity to be preserved over time.

This study shows the importance of conducting building energy simulation using physical and digital environments. Computational environment should be used during the development design phase by architects, engineers and urban planners to rank design solutions against a set of performance criteria and compare the expected impact on the building configuration and performance-based behaviour. This study integrates Grasshopper 3D and M-MACBETH.

This research is based on the idea that interior elements leave a wide variety of impressions on their occupants and that some interiors are likely to have more positive impressions than others. These impressions are especially prevalent when an individual cannot leave their homes for extended periods. The architectural elements of an interior where people are isolated can mitigate the adverse psychological effects.

The study was conducted by surveying individuals under lockdown because of the COVID-19 pandemic. A total of 140 participants completed three different scales (GAD-7, K10, FTB Scale) to measure mental health problems often experienced in isolated and confined environments. Their responses were then associated with the interior environments of the participants.

Statistically significant relationships were identified between the reported interiors and the results of the psychological evaluations. The level of psychological distress was associated with Volume and Visual Variety factors. Susceptibility to generalized anxiety disorder was associated with Visual Variety and Airiness factors. Finally, free time boredom was associated with Volume, Visual Variety, and Airiness factors. The Furniture and Clutter factor did not significantly contribute to any of the psychological evaluations.

The study was performed in response to the severe lockdown measures taken in response to the COVID-19 pandemic. It successfully highlighted the need for a rethinking of interior design approaches regarding the design for isolated and confined environments.

Aged-care work has become an extreme form of work. Anti-violence HRM, comprising practices to combat workplace violence, is important in an industry with widespread violence. In this paper, we employ social exchange theory to better understand the effect of anti-violence HRM and trust in the manager on perceived nurse and PCA cynicism working in Australian aged care facilities and their subsequent intention to leave.

This study used a mixed method with two stages. Stage 1 comprised semi-structured interviews with 10 managers and 50 nurses and PCAs working in Australian aged care facilities. Stage 2 comprised a survey of nurses and PCAs with a total of 254 completed responses in Time 1 (first wave) and 225 completed responses in Time 2 (second wave).

We tested three hypotheses and reported that interestingly anti-violence HRM was positively associated with organisational cynicism. Organisational cynicism mediated the relationship between anti-violence HRM and intention to leave. Worker trust in the manager moderated the relationship between anti-violence HRM practices and organisational cynicism, such that high levels of trust in the manager increased the effect of anti-violence HRM practices to reduce organisational cynicism and subsequently reduce intention to leave.

We find evidence that in aged care, workers' trust in their managers is critical for effectual anti-violence HRM. We argue that implementation of HRM practices may be more complex in extreme work settings. It is crucial to study HRM in situ and understand the root of social exchange(s) as a foundation for HRM to influence employee attitudes and behaviour.

This research aims to propose a model for the complex decision-making involved in the ecological restoration of mega-infrastructure (e.g. railway engineering). This model is based on multi-source heterogeneous data and will enable stakeholders to solve practical problems in decision-making processes and prevent delayed responses to the demand for ecological restoration.

Based on the principle of complexity degradation, this research collects and brings together multi-source heterogeneous data, including meteorological station data, remote sensing image data, railway engineering ecological risk text data and ecological restoration text data. Further, this research establishes an ecological restoration plan library to form input feature vectors. Random forest is used for classification decisions. The ecological restoration technologies and restoration plant species suitable for different regions are generated.

This research can effectively assist managers of mega-infrastructure projects in making ecological restoration decisions. The accuracy of the model reaches 0.83. Based on the natural environment and construction disturbances in different regions, this model can determine suitable types of trees, shrubs and herbs for planting, as well as the corresponding ecological restoration technologies needed.

Managers should pay attention to the multiple types of data generated in different stages of megaproject and identify the internal relationships between these multi-source heterogeneous data, which provides a decision-making basis for complex management decisions. The coupling between ecological restoration technologies and restoration plant species is also an important factor in improving the efficiency of ecological compensation.

Unlike previous studies, which have selected a typical section of a railway for specialized analysis, the complex decision-making model for ecological restoration proposed in this research has wider geographical applicability and can better meet the diverse ecological restoration needs of railway projects that span large regions.

The purpose of this work is to design the wire beam electrode (WBE) of P110 steel and study its corrosion behavior and mechanism under high temperature and pressure.

Packaging materials of the new type P110 steel WBE and high pressure stable WBE structure were designed. A metallurgical microscope (XJP-3C) and scanning electron microscopy (EV0 MA15 Zeiss) with an energy dispersive spectrometer were used to analyze the microstructure and composition of the P110 steel. The electrochemical workstation (CS310, CorrTest Instrument Co., Ltd) with a WBE potential and current scanner was used to analyze the corrosion mechanism of P110 steel.

According to the analysis of Nyquist plots at different temperatures, the corrosion resistance of P110 steel decreases with the increase of temperature under atmospheric pressure. In addition, R_p of P110 steel under high pressure is maintained in the range of 200 ∼ 375 Ωcm², while that under atmospheric pressure is maintained in the range of 20 ∼ 160 Ωcm², indicating that the corrosion products on P110 steel under high pressure is denser, which improves the corrosion resistance of P110 steel to a certain extent.

The WBE applied in high temperature and pressure environment is in blank. This work designed and prepared a WBE of P110 steel for high temperature and pressure environment, and the corrosion mechanism of P110 steel was revealed by using the designed WBE.

The present study provides a comprehensive review of the advancements in five active heating modes for cold-proof clothing as of 2021. It aims to evaluate the current state of research for each heating mode and identify their limitations. Further, the study provides insights into the optimization of intelligent temperature control algorithms and design considerations for intelligent cold-proof clothing.

This article presents a classification of active heating systems based on five different heating principles: electric heating system, solar heating system, phase-change material (PCM) heating system, chemical heating system and fluid/air heating system. The systems are analyzed and evaluated in terms of heating principle, research advancement, scientific challenges and application potential in the field of cold-proof clothing.

The rational utilization of active heating modes enhances the thermal efficiency of cold-proof clothing, resulting in enhanced cold-resistance and reduced volume and weight. Despite progress in the development of the five prevalent heating modes, particularly with regard to the improvement and advancement of heating materials, the current integration of heating systems with cold-proof clothing is limited to the torso and limbs, lacking consideration of the thermal physiological requirements of the human body. Additionally, the heating modes of each system tend to be uniform and lack differentiation to meet the varying cold protection needs of various body parts.

The effective application of multiple heating modes helps the human body to maintain a constant body temperature and thermal equilibrium in a cold environment. The research of heating mode is the basis for realizing the temperature control of cold-proof clothing and provides an effective guarantee for the future development of the intelligent algorithms for temperature control of non-uniform heating of body segments.

The integration of multiple heating modes ensures the maintenance of a constant body temperature and thermal balance for the wearer in cold environments. The research of heating modes forms the foundation for the temperature regulation of cold-proof clothing and lays the groundwork for the development of intelligent algorithms for non-uniform heating control of different body segments.

The present article systematically reviews five active heating modes suitable for use in cold-proof clothing and offers guidance for the selection of heating systems in future smart cold-proof clothing. Furthermore, the findings of this research provide a basis for future research on non-uniform heating modes that are aligned with the thermal physiological needs of the human body, thus contributing to the development of cold-proof clothing that is better suited to meet the thermal needs of the human body.

This paper aims to clarify the resistance degree of group road logistics to flood disaster resilience. The paper measures the resilience of group road logistics by establishing network structure model. The purpose of this study is to improve the resilience of road log.

This paper adopts Delphi method to collect data, interviews mainly flood management experts and supply chain risk management experts, and then analyzes the data through the network structure model combined with interpretative structure model (ISM) and analytical network process (ANP).

The results show that flood frequency and drainage systems are the main factors affecting the resilience of road transport logistics in urban areas. These research results provide useful guidance for the effective planning and design of urban road construction and infrastructure.

However, the main factors affecting the resilience of road transport logistics are likely to change with the development of factors such as climate, economy and environment. Therefore, in future work, the authors' research will focus on the further application of this evaluation method.

The results show that the impact of flooding on the four dimensions of road logistics resilience varies. This shows that in deciding what intervention measures are to be taken to improve the resilience of the road network to flooding, various measures need to be considered.

This paper provides a more scientific analysis of the risk management ability of the road network in the face of floods. In addition, it also provides a useful reference for urban road planners.

This paper addresses a clear need to study how to build models to improve the resilience of road logistics in flood risk.