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The purpose of this study is to campare the corrosion behavior of Az91 films and bulk sample, in the objective to provide reference for the corrosion resistance improvement of Mg alloys.

AZ91 films with various thickness values are produced by magnetron sputtering technique, and the corrosion behavior was characterized by immersion tests and electrochemical measurements.

The AZ91 films exhibited a preferred orientation with basal planes parallel to the surface and increased densification with the increase of thickness, and a superior corrosion resistance for the AZ91 films compared with the bulk sample.

The preferred (0002) basal planes in AZ91 films benefited the corrosion resistance and the nanoscale AZ91 films facilitated the development of a dense passivation film. Consequently, AZ91 film exhibited a superior corrosion resistance.

Workforce agility (WFA) is an emergent research topic in volatile times. However, there is a lack of research in understanding the leadership dimension that triggers such an attribute in organizations. Our study aims to understand the impact of workforce agility on empowering leadership behavior and employee performance dimensions (task performance, contextual performance and counterproductive work behavior).

We collected data from 236 employees using reliable, validated scales and conducted various statistical analyses.

Our results demonstrated that WFA (1) partially mediated the relationship between empowering leadership and contextual performance (CP), (2) has not mediated the relationship between empowering leadership and counterproductive behavior (CWB) and (3) mediated the relationship between empowering leadership and task performance (TP).

Our research has practical implications for management practitioners. It suggests hiring and developing an agile workforce through appropriate training and development programs can significantly impact organizational performance. Furthermore, it provides insights into building leadership capabilities that sustain workforce agility practices, empowering leaders to make informed decisions.

Our research fills a significant gap in the existing literature by exploring the effects of WFA on leadership and performance. This novel approach provides a fresh perspective on the dynamics of organizational behavior, making it a valuable addition to the field.

This study aims to develop a novel thermal modeling strategy to simulate electron beam powder bed fusion at part scale with machine-varying process parameters strategy. Single-bead and part-scale experiments and modeling were studied. Scanning strategies were described by the process controlling functions that enabled modeling.

The finite element analysis thermal model was used along with the powder bed fusion with electron beam experiments. The proposed strategy involves dividing a part into smaller sections and creating meso-scale models for each subsection. These meso-scale models take into consideration the variable process parameters, including power and velocity of the moving heat source, during part building. Subsequently, these models are integrated to perform partscale simulations, enabling more realistic predictions of thermal accumulation and resulting distortions. The model was built and validated with single-bead experiments and bulky parts with different features.

Single-bead experiments demonstrated an average error rate of 6%–24% for melt pool dimension prediction using the proposed meso-scale models with different scanning control functions. Part-scale simulations for three different geometries (cantilever beams with supports, bulk artifact and topology-optimized transfer arm) showed good agreement between modeled temperature changes and experimental deformation values.

This study presents a novel approach for electron beam powder bed fusion modeling that leverages meso-scale models to capture the influence of variable process parameters on part quality. This strategy offers improved accuracy for predicting part geometry and identifying potential defects, leading to a more efficient additive manufacturing process.

This study aims to analyze the convergence of environmental degradation clubs in the Association of Southeast Asian Nations (ASEAN). In addition, this study also analyzes the influence of renewable energy and foreign direct investment (FDI) on each club as an intervention to change the convergence pattern in each club.

This study analyzes the club convergence of environmental degradation in an effort to find out the distribution of environmental degradation reduction policies. This study uses club convergence with the Phillips and Sul (PS) convergence methodology because it considers multiple steady-states and is robust. This study uses annual panel data from 1998 to 2020 and ASEAN country units with ecological footprints as proxies for environmental degradation. After obtaining the club results, the analysis continued by analyzing the impact of renewable energy and FDI on each club using panel data regression and the Stochastic Impacts by Regression on Population, Affluence and Technology model specification.

Based on club convergence, ASEAN countries can be grouped into three clubs with two divergent countries. Club 1 has an increasing pattern of environmental degradation, while Club 2 and Club 3 show no increase. Club 1 can primarily apply renewable energy to reduce environmental degradation, while Club 2 requires more FDI. The authors expect policymakers to take into account the clubs established to formulate collaborative policies among countries. The result that FDI reduces environmental degradation in this study is in line with the pollution halo hypothesis. This study also found that population has a significant effect on environmental degradation, so policies to regulate population need to be considered. On the other hand, increasing income has no effect on reducing environmental degradation. Therefore, the use of renewable energy and FDI toward green investment is expected to intensify within ASEAN countries to reduce environmental degradation.

This research is by far the first to apply PS Club convergence to environmental degradation in ASEAN. In addition, this study is also the first to analyze the influence of renewable energy and FDI on each club formed, considering the need for renewable energy use that has not been maximized in ASEAN.

In today’s rapidly evolving business landscape, innovation is the cornerstone for every organization. Knowledge management (KM) is crucial for developing sustainable competitive advantage by fostering innovation. This study aims to identify the key drivers of KM in the context of digital transformation through qualitative research.

This study employs a qualitative approach based on in-depth interviews with senior KM officers, including chief knowledge officers and directors who spearhead KM in their respective organizations. This research identifies four key dimensions, shedding new light on the drivers of KM in the context of digital transformation.

This study’s findings reveal that the integration of important drivers from the lens of social-technical system (STS) theory is categorized into the four dimensions of KM, namely, motivation, technology, people interaction and organizational drivers. These factors jointly impact and design the effectiveness of KM in the digital age.

This study makes a unique contribution to the field of digital transformation. It presents a conceptual framework from the lens of the STS theory that encompasses four critical dimensions of KM: motivation, technology, people interaction and organizational dimensions, each with sub-codes. This framework can be utilized by practitioners and scholars alike.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

Based on the stressor-detachment theory, this study aims to investigate the effect of daily customer mistreatment on proactive service performance and ego depletion, mediated by psychological detachment inhibition during the evening. Additionally, this study endeavors to investigate the dual moderating role of prosocial motivation.

A time-lagged, diary daily survey involving 74 participants over 8 consecutive workdays was conducted to test the hypotheses.

The findings indicate that the psychological detachment inhibition during the evening of Day t mediates the impact of Day t’s customer mistreatment on Day t + 1’s proactive service performance and ego depletion. Furthermore, although prosocial motivation was found to intensify the impact of customer mistreatment on psychological detachment inhibition, it alleviated the negative association between psychological detachment inhibition and proactive service performance.

When employees experience customer mistreatment, hospitality managers should not only provide emotional reassurance and resolve any related issues promptly but also encourage employees to engage in activities that distract them and help them to relax and recharge, especially for those who exhibit high prosocial motivation. Moreover, hiring employees with high prosocial motivation is recommended for hospitality organizations to enable them to maintain high service performance.

This study focuses on psychological detachment inhibition during the evening linking within-person design and daily spill-over impact, enriching the mechanisms through which the repercussions of daily customer mistreatment extend beyond the immediate workday and affect individuals’ outcomes. This study also expands upon the existing literature by clarifying the dual aspects – both detrimental and beneficial – of prosocial motivation.

Given the growing interest in the topic of knowledge management (KM) in family firms (FFs) and the subsequent increasing number of papers published, this study aims to review the field to identify and analyze the main themes and trends.

This study applies bibliometric techniques to a sample of 146 papers published from 2007 to 2023 and their 8,126 unique cited references. Bibliometric coupling is performed on the sample papers to explore the current intellectual structure of the field of KM in FFs, whereas cocitations analysis is performed to investigate the different literature streams that served as roots for the development of such a field.

Bibliographic coupling reveals that sample papers can be grouped into four clusters, and, through papers content analysis, the author identifies their core themes as knowledge sharing, innovation, knowledge-based dynamic capabilities and intellectual capital. Cocitation analysis of the cited references revealed four main clusters that can be considered the literature streams that served as roots for the development of the field, i.e. knowledge-based view, socioemotional wealth, strategic management and social capital (as a theory and as a resource).

This study contributes to the literature on KM in FFs by extending prior systematic review efforts with bibliometric analyses and combining these results to highlight connections between the main research themes around which scholars have debated (i.e. the clusters identified through bibliometric coupling) and their theoretical foundations (i.e. the clusters identified through cocitation analysis). This study also has practical implications by synthesizing and informing managers about FFs’ advantages and weaknesses in the KM process.

Ultracapacitors find extensive applications in various fields because of their high energy density and long cycling periods. However, due to the movement of ions and the arrangement patterns on rough/irregular electrode surfaces during the charge and discharge process of ultracapacitors, the parameters of ultracapacitors usually change with the variation of operating conditions. The purpose of this study is to accurately and quickly identify the parameters of ultracapacitors.

A variable forgetting factor recursive least square (VFFRLS) algorithm is proposed in this paper for online identifying the equivalent series resistance and capacitance C of ultracapacitors. In this work, a real-time error-based strategy is developed to adaptively regulate the value of the forgetting factor of traditional forgetting factor recursive least square (FFRLS) algorithm. The strategy uses the square of the average time autocorrelation estimation of the prior error and the posterior error between the predicted output and the actual output as the adjustment basis of forgetting factors.

Experiments were conducted using the proposed scheme, and the results were compared with the estimation results obtained by the recursive least squares (RLS) algorithm and the traditional FFRLS algorithm. The maximum root mean square error between the estimated values and actual values for VFFRLS is 3.63%, whereas for FFRLS it is 9.61%, and for RLS it is 19.33%.

By using the proposed VFFRLS algorithm, a relatively high precision can be achieved for the online parameter estimation of ultracapacitors. Besides, the dynamic balance between parameter stability and tracking performance can be validated by dynamically adjusting the forgetting factor.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.