During the COVID-19 crisis, the apparel industry faced many challenges. Aggressive cost-cutting strategies became a top priority, and in turn, these influenced stressors and adversely affected ...business sustainability. This study examines the impact of aggressive strategies during the COVID-19 pandemic on business sustainability in the apparel industry of Sri Lanka. Further, it investigates whether the relationship between aggressive cost-cutting strategies and business sustainability was mediated by employee stress, considering aggressive cost reduction strategies and workplace environmental changes. This was a cross-sectional study with data collected from 384 employees in the apparel industry in Sri Lanka. Structural Equation Modelling (SEM) was applied to analyze the direct and indirect effects of aggressive cost reduction strategies and workplace environmental changes on sustainability with mediating effects of stress. Aggressive cost reduction strategies (Beta = 1.317, p = 0.000) and environmental changes (Beta = 0.251, p = 0.000) led to an increase in employee stress but did not affect business sustainability. Thus, employee stress (Beta = -0.028, p = 0.594) was not a mediator in the relationship between aggressive cost-cutting strategies and business sustainability; business sustainability was not a dependent variable. The findings proved that managing workplace stress, particularly improving stressful working environments and aggressive cost reduction strategies, can enhance employee satisfaction. Thus, managing employee stress could be beneficial for policymakers to focus on the area(s) required to retain competent employees. Moreover, aggressive strategies are unsuitable to apply during crisis to enhance business sustainability. The findings provide additional knowledge to the existing literature, enabling employees and employers to predict causes of stress and serve as a significant knowledge base for further studies.
Background
Hand eczema is a common inflammatory skin disorder. Health care providers need continuously updated information about the management of hand eczema to ensure best treatment for their ...patients.
Objectives
To update the European Society of Contact Dermatitis guideline on the diagnosis, prevention, and treatment on of hand eczema.
Method
The Guideline Development Group (GDG) was established on behalf of the ESCD. A call for interest was launched via the ESCD website and via the ESCD members' mailing list. Appraisal of the evidence for therapeutic and preventive interventions was applied and a structured method of developing consensus was used and moderated by an external methodologist. The final guideline was approved by the ESCD executive committee and was in external review on the ESCD webpage for 1 month.
Results
Consensus was achieved for several statements and management strategies.
Conclusion
The updated guideline should improve management of hand eczema.
Machining is widely used to produce machine components for various applications in automotive, aerospace, and marine industries. The numbers of machining operations have greatly increased over the ...last few decades with the increase in the demand for machine elements and innovations in the manufacturing field. Mineral-based Metalworking Fluids (MWFs) are frequently used in bulk during a cutting process to obtain dimensional accuracy and better surface quality on machined elements, but researchers have documented several adverse health and environment effects of MWFs. As a sustainable alternative to mineral oil, vegetable oil has shown better performance during operations without any harmful effects on human health or ecological systems. Herein, the health and environmental effects of the mineral oil-based MWFs are reviewed in detail, and sustainable application possibilities for process optimization in machining using vegetable oil based nanofluids are highlighted.
•Industrial applicability and functional advantages of MWFs are discussed.•Drawbacks and hazardous properties of the mineral based MWFs are highlighted.•Significant of vegetable oil based MWFs for sustainable machining is emphasized.•Vegetable oil based MWF formulation challenges and solutions are reviewed.•Future prospects of Green MWFs for industrial sustainability is explained.
Pest outbreaks, harmful algal blooms and population collapses are extreme events with critical consequences for ecosystems. Therefore, understanding the ecological mechanisms underlying these extreme ...events is crucial. We evaluated theoretical predictions on the size scaling and variance of extreme population abundance by combining (i) the generalized extreme value (GEV) theory and (ii) the resource‐limited metabolic restriction hypothesis for population abundance. Using the phytoplankton data from the L4 station in the English Channel, we showed a negative size scaling of the expected value of maximal density, whose confidence interval included the predicted metabolic scaling (α = −1) supporting theoretical predictions. The role of resources and temperature in the distribution of the size–abundance pattern and residuals was well characterized by the GEV distribution. This comprehensive modelling framework will allow to elucidate community structure and fluctuations and provide unbiased return times estimates, thereby improving the prediction accuracy of the timing of the population outbreaks.
Pest outbreaks, harmful algal blooms and population collapses are extreme events with critical consequences for ecosystems, highlighting the importance of deciphering the driving ecological mechanisms underlying extreme events.By combining the generalized extreme value (GEV) theory from statistics and the hypothesis of a resource‐limited metabolic restriction to population abundance, we showed the link between maximum population fluctuations and its distribution.
The design of artificial molecular machines often takes inspiration from macroscopic machines. However, the parallels between the two systems are often only superficial, because most molecular ...machines are governed by quantum processes. Previously, rotary molecular motors powered by light and chemical energy have been developed. In electrically driven motors, tunnelling electrons from the tip of a scanning tunnelling microscope have been used to drive the rotation of a simple rotor in a single direction and to move a four-wheeled molecule across a surface. Here, we show that a stand-alone molecular motor adsorbed on a gold surface can be made to rotate in a clockwise or anticlockwise direction by selective inelastic electron tunnelling through different subunits of the motor. Our motor is composed of a tripodal stator for vertical positioning, a five-arm rotor for controlled rotations, and a ruthenium atomic ball bearing connecting the static and rotational parts. The directional rotation arises from sawtooth-like rotational potentials, which are solely determined by the internal molecular structure and are independent of the surface adsorption site.
► We give a simple description of statistical learning principles for modeling and prevision. ► We make an overview of recent and promising learning methods for ecological modeling. ► We analyze a ...real application, illustrating some models’ outputs, and comparing their prediction results.
In this paper we present a general overview of several supervised machine learning (ML) algorithms and illustrate their use for the prediction of mass mortality events in the coastal rocky benthic communities of the NW Mediterranean Sea. In the first part of the paper we present, in a conceptual way, the general framework of ML and explain the basis of the underlying theory. In the second part we describe some outstanding ML techniques to treat ecological data. In the third part we present our ecological problem and we illustrate exposed ML techniques with our data. Finally, we briefly summarize some extensions of several methods for multi-class output prediction.
Over the past decade, the artificial neural networks domain has seen a considerable embracement of deep neural networks among many applications. However, deep neural networks are typically ...computationally complex and consume high power, hindering their applicability for resource-constrained applications, such as self-driving vehicles, drones, and robotics. Spiking neural networks, often employed to bridge the gap between machine learning and neuroscience fields, are considered a promising solution for resource-constrained applications. Since deploying spiking neural networks on traditional von-Newman architectures requires significant processing time and high power, typically, neuromorphic hardware is created to execute spiking neural networks. The objective of neuromorphic devices is to mimic the distinctive functionalities of the human brain in terms of energy efficiency, computational power, and robust learning. Furthermore, natural language processing, a machine learning technique, has been widely utilized to aid machines in comprehending human language. However, natural language processing techniques cannot also be deployed efficiently on traditional computing platforms. In this research work, we strive to enhance the natural language processing traits/abilities by harnessing and integrating the SNNs traits, as well as deploying the integrated solution on neuromorphic hardware, efficiently and effectively. To facilitate this endeavor, we propose a novel, unique, and efficient sentiment analysis model created using a large-scale SNN model on SpiNNaker neuromorphic hardware that responds to user inputs. SpiNNaker neuromorphic hardware typically can simulate large spiking neural networks in real time and consumes low power. We initially create an artificial neural networks model, and then train the model using an Internet Movie Database (IMDB) dataset. Next, the pre-trained artificial neural networks model is converted into our proposed spiking neural networks model, called a spiking sentiment analysis (SSA) model. Our SSA model using SpiNNaker, called SSA-SpiNNaker, is created in such a way to respond to user inputs with a positive or negative response. Our proposed SSA-SpiNNaker model achieves 100% accuracy and only consumes 3970 Joules of energy, while processing around 10,000 words and predicting a positive/negative review. Our experimental results and analysis demonstrate that by leveraging the parallel and distributed capabilities of SpiNNaker, our proposed SSA-SpiNNaker model achieves better performance compared to artificial neural networks models. Our investigation into existing works revealed that no similar models exist in the published literature, demonstrating the uniqueness of our proposed model. Our proposed work would offer a synergy between SNNs and NLP within the neuromorphic computing domain, in order to address many challenges in this domain, including computational complexity and power consumption. Our proposed model would not only enhance the capabilities of sentiment analysis but also contribute to the advancement of brain-inspired computing. Our proposed model could be utilized in other resource-constrained and low-power applications, such as robotics, autonomous, and smart systems.
A comprehensive analysis of the archival literature on sustainable (i.e., socioeconomic, and environmental friendly) machining of advanced aerospace materials and composites has been performed. ...Specifically, the paper focuses on the techniques to improve the machinability of difficult to cut materials (Steel alloys, Ni-based super alloys, Ti-based alloys, and composites) frequently consume in aerospace manufacturing industry. The current industrial requirement on high-performance sustainable machining of advanced super alloys and composites have been addressed for the machining process optimization to gain reasonable profit margin. Here, the specific interest areas are formulation of high-performance vegetable oil-based metalworking fluid (MWF), health and environmental conscious machining of difficult to cut materials and future perspectives on biodegradable MWFs on machining advanced aerospace materials and composites. The proposed approaches of the sustainable and cleaner production for the above-mentioned areas involves the occupational health and safety, minimum waste (i.e., effluent) generation, elimination of the environment pollution (i.e., MWF usage and disposal phases) and high-performance machining. Additionally, the influence of tribological properties of vegetable oil based MWFs on thermophysical characteristics of difficult to cut materials have been critically reviewed. The study presented in the paper is timely valuable due to the rapid increment of the demand on sustainable machining requirement in difficult to cut materials. Moreover, the presented comprehensive analysis, proposed suggestions and recommendations will help the next generation scientists to find the recent advances as well as future avenues of research on high performance sustainable machining of advanced aerospace materials and composites (i.e., category of difficult to cut materials) to ensure process optimization and industrial sustainability.
•Sustainable machining of aerospace materials and composites are reviewed.•Vegetable oils for machining aerospace materials and composites are highlighted.•Vegetable oil based MWF formulation challenges and solutions are explained.•Limitations of vegetable oils on machining aerospace materials are discussed.•Future perspective of vegetable oils for high performance machining are revealed.
In the aftermath of a disaster, such as earthquake, flood, or avalanche, ground search for survivors is usually hampered by unstable surfaces and difficult terrain. Drones now play an important role ...in these situations, allowing rescuers to locate survivors and allocate resources to saving those who can be helped. The aim of this study was to explore the utility of a drone equipped for human life detection with a novel computer vision system. The proposed system uses image sequences captured by a drone camera to remotely detect the cardiopulmonary motion caused by periodic chest movement of survivors. The results of eight human subjects and one mannequin in different poses shows that motion detection on the body surface of the survivors is likely to be useful to detect life signs without any physical contact. The results presented in this study may lead to a new approach to life detection and remote life sensing assessment of survivors.
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