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The outset of the COVID-19 pandemic caused disruptions of all forms in the supply chain globally for almost two and a half years. This study identifies various challenges in the effective functioning of the existing supply chain during COVID-19. The focus is to see the disruptions impacting the energy storage supply chains.

The procedure entails a thorough analysis of scholarly literature pertaining to various supply chain interruptions, confirmed and verified by experts working in an energy storage company in India. These experts also confirmed the occurrence of more disruptive factors during their interviews and questionnaire survey. Moreover, this process attempts to filter out the relevant causal disruption factors in an energy storage company by using the integrated approach of qualitative and quantitative methodologies.

The results provide practical insights for the company management in planning and devising new strategies to manage supply chain disruptions. Supply chains for companies in other industry sectors can also benefit from the proposed framework and results in making them more robust to counter future disastrous events.

The study provides an easily adaptable decision framework to different industries by closely examining supply chain disruptions and identifying associated causes for building a robust supply chain focused on the energy storage sector. It examines four disruption dimensions and investigates possible outcomes and impacts of disruptions.

The purpose of this paper is to identify different types of resource and capability configurations among Hungarian family and nonfamily firms and explore which compositions can be considered competitive. In a rivalrous, dynamic world, understanding which sets of resources and capabilities lead to a higher level of competitiveness is vital.

This paper is based on a quantitative competitiveness survey carried out between November 2018 and July 2019 in Hungary. The authors used the Firm Competitiveness Index (FCI) to measure competitiveness and the resource-based view (RBV) approach to understand which configurations of resources and capabilities are responsible for a higher level of competitiveness based on 32 variables. An exploratory factor and cluster analysis were conducted to analyze the ownership's effect on firm competitiveness. The final sample size contained 111 companies, of which 53 were identified as family and 58 as nonfamily firms.

Factor analysis reveals five factors determining resources and capabilities: “operational,” “leadership,” “knowledge management,” “transformation” and “networking.” Based on these factors, the cluster analysis identified five groups in terms of types of family and nonfamily firms: “Lagging capabilities,” “Knowledge-based leadership,” “Innovativeness and transformation-oriented management,” “Relationship-oriented management” and “Business operation-oriented management.” Results show that nonfamily businesses focus on operational and leadership capabilities, reaching a higher FCI than family businesses, which are likely to invest more in their networking, transformation and knowledge management capabilities.

By defining the different configurations family and nonfamily firms rely on to reach competitiveness, the paper applies an essential element to the Hungarian and Middle Eastern European contexts of family business research. The findings contribute to developing family business literature and point out specific resources and capabilities family firms should focus on to shift toward reaching a higher level of professionalization and competitiveness. The characterization of different types of competitiveness comparing family and nonfamily firms enables the firms to assess customized implications.

This paper aims to examine the relationship between employee dynamic capabilities (EDCs), innovative work behavior (IWB) and job performance (JP) among nurses in public hospitals in Iraq. It also determines the mediating role of IWB in the relationship between EDC and JP.

This study uses structural equation modeling via partial least squares (PLS-SEM) and bootstrapping estimation to analyze the model. A total of 425 respondents participated in the survey, though only 372 participants completed all the items in the questionnaire. Hence, 372 responses were analyzed after data screening.

This study indicates that EDC has a positive correlation with IWB among nurses in public hospitals in Iraq. It also shows that IWB has a positive relationship with JP. It further demonstrates that IWB has a positive mediating effect on the relationship between EDC and JP among nurses in Iraq’s public hospitals.

The implication of this study is that EDC can significantly predict IWB, while the latter is a significant predictor of JP among nurses in public hospitals in Iraq. Besides, the relationship between EDC and JP is significantly mediated by IWB. However, as a cross-sectional investigation showing a single snapshot in time, this study has some limitations in terms of establishing strict causality among the variables. Rather than extensive actual data, the findings are based on a log compiled by a key respondent. The findings are obtained from a survey conducted in one country, but certain variables, such as EDC, may have varying implications across different countries. Hence, future research can focus on a diverse range of countries, which could be used to ascertain the findings’ generalizability.

This study advances our understanding of the direct effect of the exploration and exploitation variables as well as the mediating effect of IWB in the relationship between EDC and JP among nurses in public hospitals in Iraq.

Although digital platforms have become important to organizations and society, little is known about how platforms evolve over time. This is particularly true for early-stage platforms provided by entrepreneurial firms competing in nascent markets. This study aims to investigate the relationship between a platform provider's mission and the evolution of its digital platform.

This study conducted an exploratory, multi-case study of startups in the emerging health/fitness wearables market over the period 2007 to 2016.

This study emerged two organizational mission constructs – consistency and specificity – and two evolutionary dynamics of digital platforms – unity and evolution rate. It also considered unity and evolution rate in terms of features created by the platform provider and features connected by external parties. This study found relationships between aspects of mission consistency and platform unity and identified relationships between aspects of mission specificity and platform evolution rates.

This study formalized findings into a set of theoretical propositions, thereby enriching the understanding of the relationship between organizational mission and digital platform evolution in nascent markets. This study provides new constructs and relationships that can be tested and refined in future research.

This conceptual article presents a schematic of rat maternal behavior and niche stress epigenetic effects as a case study that is then aligned with current evolutionary concepts, which raises new questions regarding immigrant assimilation and niche dynamics.

The necessary background material for rat maternal and niche(s) stress factors are incorporated into a recursive, test-operate-test (rTOT), information-only-transfer, schematic (Patterson, 2023), which is an extension and refinement of the test-operate-test-exit (TOTE) schematic of Miller et al. (1960).

The generated epigenetic rTOT demonstrates the fundamental evolutionary unit of the flexible organism within its niche(s). The rTOT also confirms that epigenetic processes, epigenetic inheritance and phenotype plasticity are significant conceptual tools for understanding evolution. The teleology of rat adaptations for niche fitness via maternal behavior has been demonstrated. Sterling's (2011) allostasis, or predictive homeostasis, is extended to include species-niche(s) interaction(s) that are governed by recursive information feedback loops that function via self-organized criticality (SOC) for species and niche(s). Use of a rat model for biosocial issues in humans is strengthened.

Epigenetic rTOT only covers the species side of the evolutionary unit. Niche(s) require(s) a separate rTOT schematic. The information modeled does not include the entire system producing epigenetic effects but models a substantial portion of it.

Epigenetic rTOT demonstrates the utility of phenotypic plasticity, epigenetics and epigenetic inheritance as explanations for inheritable behavior patterns. rTOT is a useful computational model for evolutionary issues. The issues involved in niche modeling using an rTOT schematic are briefly reviewed.

When the demonstrated epigenetic model of rat genetics and inherited behavior are applied to the issues of immigrant enclaves, epigenetic complications for the difficulties of assimilation into the culture within which the enclaves are embedded become apparent. However, the questions raised must be addressed with extreme care to avoid cultural imperialism. Such cultural issues must be modeled with an rTOT application that covers the materials involved. The limitations of human Learning III restrictions when attempting to model Learning IV issues are addressed. Research into the means by which abuse and trauma are maintained by epigenetic means is urgently needed.

The rTOT schematic visualizes rat maternal behavior and stress epigenetic effects that produce inheritable behavior patterns, which answers Jablonka's (2017) request for new computational modeling representations. The concept of allostasis, or predictive homeostasis, (Sterling, 2011) is extended to the niche(s) of the organism under study so that allostasis becomes a fully cybernetic concept governed by SOC for both the organism and its niche(s). This new case study confirmed evolutionary effects of epigenetics, epigenetic inheritance and phenotypic plasticity. Niche control of organism evolution is presented. Epigenetic applications for immigrant assimilation issues have been suggested and niche dynamic questions have been raised.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

Loneliness and social isolation are risk factors for morbidity and mortality and a growing health concern. Peer support is an intervention that has shown efficacy in the wider health-care arena. However, little is known about its effectiveness when working with adults and adolescents experiencing these issues. The purpose of this study is to conduct a scoping review on loneliness and social isolation in adult and youth populations.

A preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews was conducted. Five databases, Web of Science, Academic Search Complete, Embase, PsycINFO and Scopus were searched for peer-reviewed literature published in English from inception to 2023.

The search yielded 2,402 articles of which (N = 12) met the inclusion criteria for this review. Peer support interventions for loneliness and social isolation are a relatively new topic of interest for academics. However, it is apparent that loneliness and social isolation cut across populations experiencing various health and social issues. More experiential research is needed to evidence the effectiveness of peer support in these areas with both adults and adolescents. Emerging areas of interest include the use of technology to deliver interventions and the use of technology to facilitate peer support implementation with populations difficult to reach.

Peer support may be a promising social intervention to help those experiencing loneliness and social isolation associated with a range of health conditions. However, further research needs to be undertaken in this emerging area of practice.

Loneliness and social isolation are of increasing concern. To the best of the authors’ knowledge, this is the first scoping review conducted in this area

The sudden COVID-19 epidemic in 2019 has frustrated China's overall economy, and the implementation and development of the National Fitness Program has encountered huge obstacles. At a new historical starting point, in order to realize the dream of becoming a powerful country in sports, it is necessary to transform the successful experience gained since the reform and opening up into regular understanding and systematic theories, so as to make a theoretical response to the new contradictions and challenges faced in development and give full play to the National Fitness has comprehensive values and multiple functions in improving people's health, promoting people's all-round development, promoting economic and social development and demonstrating the country's cultural soft power.

Taking the topic of national fitness as an example, this paper sets out from the three dimensions of knowledge input, knowledge output and knowledge production, using citation analysis, social network analysis, co-word analysis and cluster analysis, to measure the characteristics and knowledge structure of interdisciplinary knowledge exchange.

China's national fitness is still in the primary development stage, and the strong boost of the national top-level policy is the biggest driving force of its development, driven by the policy together with the settlement of many major events, constantly improving and enriching the wings. The main body of knowledge production on the topic of national fitness is mainly colleges and universities, with low participation of government and enterprises, high degree of cooperation among authors, obvious interdisciplinary characteristics and strong application of research themes.

This study provides a strong theoretical basis for the promotion of the Healthy China strategy. Especially under the influence of COVID-19, this paper can contribute to the comprehensive value and multimodal functions of national fitness in improving the health of people, promoting economic and social development and demonstrating the soft power of national culture.

Advancement in optimization of VLSI circuits involves reduction in chip size from micrometer to nanometer level as well as fabrication of a billions of transistors in a single die where global routing problem remains significant with a trade-off of power dissipation and interconnect delay. This paper aims to solve the increased complexity in VLSI chip by minimization of the wire length in VLSI circuits using a new approach based on nature-inspired meta-heuristic, invasive weed optimization (IWO). Further, this paper aims to achieve maximum circuit optimization using IWO hybridized with particle swarm optimization (PSO).

This paper projects the complexities of global routing process of VLSI circuit design in mapping it with a well-known NP-complete problem, the minimum rectilinear Steiner tree (MRST) problem. IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes. Further, the proposed approach is hybridized with PSO, and a comparative analysis is performed with geosteiner 5.0.1 and existing PSO technique over minimization, consistency and convergence against available benchmark.

This paper provides high performance–enhanced IWO algorithm, which keeps in generating low MRST value, thereby successful wire length reduction of VLSI circuits is significantly achieved as evident from the experimental results as compared to PSO algorithm and also generates value nearer to geosteiner 5.0.1 benchmark. Even with big VLSI instances, hybrid IWO with PSO establishes its robustness over achieving improved optimization of overall wire length of VLSI circuits.

This paper includes implications in the areas of optimization of VLSI circuit design specifically in the arena of VLSI routing and the recent developments in routing optimization using meta-heuristic algorithms.

This paper fulfills an identified need to study optimization of VLSI circuits where minimization of overall interconnected wire length in global routing plays a significant role. Use of nature-based meta-heuristics in solving the global routing problem is projected to be an alternative approach other than conventional method.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.