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Ball grid array (BGA) package is prone to failure issues in a thermal vibration-coupled environment, such as deformation and fracture of solder joints. To predict the minimum equivalent stress of solder joints more accurately and optimize the solder joint structure, this paper aims to compare the machine learning method with response surface methodology (RSM).

This paper introduced a machine learning algorithm using Grey Wolf Optimization (GWO) Support Vector Regression (SVR) to optimize solder joint parameters. The solder joint height, spacing, solder pad diameter and thickness were the design variables, and minimizing the equivalent stress of solder joint was the optimization objective. The three dimensional finite element model of the printed circuit board assembly was verified by a modal experiment, and simulations were conducted for 25 groups of models with different parameter combinations. The simulation results were employed to train GWO-SVR to build a mathematical model and were analyzed using RSM to obtain a regression equation. Finally, GWO optimized these two methods.

The results show that the optimization results of GWO-SVR are closer to the simulation results than those of RSM. The minimum equivalent stress is decreased by 8.528% that of the original solution.

This study demonstrates that GWO-SVR is more precise and effective than RSM in optimizing the design of solder joints.

The purpose of this paper is to actively calibrate power density to match the application requirements with as small an actuator as possible. So, this paper introduces shape memory alloy to design variable stiffness elements. Meanwhile, the purpose of this paper is also to solve the problem of not being able to install sensors on shape memory alloy due to volume limitations.

This paper introduces the design, modeling and control process for a variable stiffness passive ankle exoskeleton, adjusting joint stiffness using shape memory alloy (SMA). This innovative exoskeleton aids the human ankle by adapting the precompression of elastic components by SMA, thereby adjusting the ankle exoskeleton’s integral stiffness. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

Using SMA as the driving force for stiffness modification in passive exoskeletons introduces several distinct advantages, inclusive of high energy density, programmability, rapid response time and simplified structural design. In the course of experimental validation, this ankle exoskeleton, endowed with variable stiffness, proficiently executed actions like squatting and walking and it can effectively increase the joint stiffness by 0.2 Nm/Deg.

The contribution of this paper is to introduce SMA to adjust the stiffness to actively calibrate power density to match the application requirements. At the same time, this paper constructs a mathematical model of SMA to achieve a dynamic stiffness adjustment function.

The existing literature on the effects of capital mobility and financial development on sustainable trade development in sub-Saharan African (SSA) countries has been centered on production-based carbon emissions without investigating consumption-based or trade-adjusted carbon emissions. The purpose of this paper is to examine the effects of capital mobility and financial development on sustainable trade development, specifically trade-adjusted carbon emissions in SSA economies.

The study employed the novel GMM-PVAR estimator and the Drisc-Kraay fixed effect panel corrected standard error (PCSE) dynamic ordinary least squares (DOLS) and the fully modified least squares (FMOLS) approaches on panel data from 46 sub-Saharan African (SSA) countries over the period 1992–2021.

The study established that capital mobility has a significant positive effect on sustainable trade development in SSA in the long run. Further, the empirical results reveal that the link between financial development and sustainable trade development is significantly positive in the long run. Moreover, the results suggest that capital mobility and financial development have predictive power on sustainable trade development.

The findings of the study imply that policymakers ought to pay equal attention to capital mobility and financial development when developing sustainable trade development policies.

The existing literature has been centered on production-based carbon emissions, without specifically considering sustainable trade development (consumption-based carbon emissions). To the best of our knowledge, this is the first study to examine the effect of capital mobility and financial development on sustainable trade development in SSA countries context.

This study aims to improve detection efficiency of fluorescence biosensor or a graphene field-effect transistor biosensor. Graphene field-effect transistor biosensing and fluorescent biosensing were integrated and combined with magnetic nanoparticles to construct a multi-sensor integrated microfluidic biochip for detecting single-stranded DNA. Multi-sensor integrated biochip demonstrated higher detection reliability for a single target and could simultaneously detect different targets.

In this study, the authors integrated graphene field-effect transistor biosensing and fluorescent biosensing, combined with magnetic nanoparticles, to fabricate a multi-sensor integrated microfluidic biochip for the detection of single-stranded deoxyribonucleic acid (DNA). Graphene films synthesized through chemical vapor deposition were transferred onto a glass substrate featuring two indium tin oxide electrodes, thus establishing conductive channels for the graphene field-effect transistor. Using π-π stacking, 1-pyrenebutanoic acid succinimidyl ester was immobilized onto the graphene film to serve as a medium for anchoring the probe aptamer. The fluorophore-labeled target DNA subsequently underwent hybridization with the probe aptamer, thereby forming a fluorescence detection channel.

This paper presents a novel approach using three channels of light, electricity and magnetism for the detection of single-stranded DNA, accompanied by the design of a microfluidic detection platform integrating biosensor chips. Remarkably, the detection limit achieved is 10 pm, with an impressively low relative standard deviation of 1.007%.

By detecting target DNA, the photo-electro-magnetic multi-sensor graphene field-effect transistor biosensor not only enhances the reliability and efficiency of detection but also exhibits additional advantages such as compact size, affordability, portability and straightforward automation. Real-time display of detection outcomes on the host facilitates a deeper comprehension of biochemical reaction dynamics. Moreover, besides detecting the same target, the sensor can also identify diverse targets, primarily leveraging the penetrative and noninvasive nature of light.

The few previous researches on the impact of calf compression garments (CG) on running performance while assessing physiological and perceptual factors. Therefore, this study investigated how the clothing pressure of two types of Calf CG, CG1 and CG2, affects muscle fatigue and activation during running.

Five healthy amateur runners(three female and two male)were recruited for a 30-min running trial. They wear a Calf CG on their right leg (CG group), but not on their left leg(CON group). After obtaining the clothing pressure of Calf CG on the gastrocnemius lateral head (GL), gastrocnemius medial head (GM) and tibialis anterior(TA) of the right leg, surface electromyography (sEMG)of four muscles of GL, GM, TA and rectus femoris (RF) of the left and right legs were measured during running, and heart rate, cardiopulmonary rate, and human RPE were also measured. Blood bleed oxygen before and after the running trial were measured. The root mean square (RMS) of the characteristic values was selected as an index for the analysis of sEMG signals, and the data were analyzed using statistical and computational methods.

The results showed that the indexes of heart rate, blood oxygen, and RPE were significantly increased, indicating that the subjects had reached the fatigue level. The comparison of mean clothing pressure at GL, GM and TA locations reveals that the TA location consistently exhibits the highest pressure for both types of CG. When wearing CG1, the mean clothing pressure at the GL and GM test points is greater than that of CG2(CG1-GL = 0.2059 kPa > CG2-GL = 0.148 kPa; CG1-GM = 0.1633 kPa > CG2-GM = 0.127 kPa). This is attributed to the double-layered fabric on the sides of CG1, which precisely covers the GL and GM areas, thereby resulting in higher mean clothing pressure at these locations compared to CG2. Conversely, the mean clothing pressure at the TA location for CG1 is lower than that for CG2(CG1-TA = 0.3852 kPa < CG2-TA = 0.426 kPa). The pressure exerted by the CG1 on the lower limb test areas has both positive and negative effects, though neither are statistically significant. The pressure exerted by CG2 alleviates fatigue at the directly affected locations GL and GM, but exerts excessive pressure on TA, resulting in a negative effect. Additionally, CG2 pressure alleviates fatigue at the indirectly affected location RF on the same side. Based on the specific clothing pressure data, it is concluded that when the pressure at the GM location is 0.127 kPa, 30 min of running has a fatigue-relieving effect. However, the pressure should not be excessively high, at 0.1633 kPa it exhibits an insignificant adverse effect. At the TA location, a garment pressure mean between 0.3852 and 0.426 kPa does not alleviate fatigue after 30 min of running, and the negative effect becomes more pronounced as the pressure increases. The pressure exerted by the CG at GL, GM, TA and RF locations shows significant changes from the previous time period during the 15–18 min interval after running. Therefore, in the design of CG, attention should be paid to the changes in clothing pressure effects on muscles during this specific time period.

The few previous researches on the impact of calf compression garments (CG) on running performance while assessing physiological and perceptual factors. Therefore, this study investigated how the clothing pressure of two types of Calf CG, CG1 and CG2, affects muscle fatigue and activation during running.

To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs.

This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science.

Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field.

This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

This research aims to investigate the impact of corporate digital transformation (CDT) on stock market activity.

A data set of Chinese listed manufacturing enterprises from 2012–2021 is used as a research sample.

This research finds that CDT can promote stock market activity. This study validates two potential mechanisms: increasing financial performance and mitigating information asymmetry. This study further suggests that internal control and analyst coverage can strengthen the impact of CDT on stock market activity.

The research exhibits certain limitations that should be considered in future research. Because the findings are based on the Chinese context, the applicability and generalizability of the findings to other environments may be limited. This research enriches the literature on the determinants of stock market activity from a technological perspective and incrementally contributes to understanding the impact of CDT on stock markets. After analyzing two opposing perspectives on the economic consequences of CDT, the favorable effect of CDT on stock market activity is proven based on the resource-based view and agency theory. This research extends the literature on the relationship between CDT and investor behavior, demonstrating that investors perceive CDT as beneficial. The results provide evidence that CDT can increase financial performance and improve the information environment, leading to increased investor attention and enhanced trading activity.

This research has incremental practical implications for enterprises and regulatory authorities to comprehend the economic consequences of CDT in developing countries. First, enterprises should increase their digital investments to improve their performance and decrease information asymmetry. Furthermore, enterprise managers should strengthen information systems to adapt to the process of CDT and train employees on digital skills. Second, regulatory authorities should provide comprehensive digital policies and programs supported by tax incentives, subsidies and digital infrastructure projects (Wang et al., 2023).

This research strengthens the debate on the market impact of CDT. Unlike prior literature, this study explores the influence of CDT on stock market activity for the first time, enriching the literature on CDT and stock market activity. Furthermore, the outcomes guide regulatory authorities to actively support CDT and expedite the digital upgrading of manufacturing industries to promote stock market activity.

Many waste-to-energy (WTE) plants are constructed and operated using the public-private partnership (PPP) mode in China. However, risk events of PPP WTE incineration projects sometimes occur. This study aims to clarify the relationship of risks in China's PPP WTE incineration projects and identify the key risks accordingly and risk transmission paths.

A risk list of PPP WTE incineration projects was obtained based on literature analysis. Moreover, a hybrid approach combining fuzzy sets, decision-making trial and evaluation laboratory (DEMATEL) and interpretive structural modeling (ISM) was developed to analyze the causality of risks, explore critical risks and reveal the risk transmission paths. The quantitative analysis process was implemented in MATLAB.

The results show that government decision-making risk, government credit risk, government supervision behavior risk, legal and policy risk, revenue and cost risk and management capacity risk are the critical risks of PPP WTE incineration projects in China. These critical risks are at different levels in the risk hierarchy and often trigger other risks.

Currently, there is a lack of exploration on the interaction between the risks of PPP WTE incineration projects. This study fills this gap by examining the key risks and risk transfer pathways of PPP WTE incineration projects from the perspective of risk interactions. The findings can help the public and private sectors to systematically understand the risks in PPP WTE incineration projects, thus enabling them to identify the risks that need to be focused on when making decisions and to optimize risk prevention strategies. The proposed hybrid approach can offer methodological ideas for risk analysis of other types of PPP projects.

This study aims to propose and examine a conceptual model that shows how green performance measurement systems (GPMS) mediate the relationship between green intellectual capital components and environmental performance.

The research surveyed 407 Vietnamese publicly listed companies to gather empirical data. Then, the exploratory factor analysis (EFA) and structural equation modeling (SEM) are used to examine the degree of emphasis firms place on using GPMS to transform green intellectual capital into firm value.

The results indicate that both green human capital and green organizational capital have a direct positive impact on environmental performance. On the contrary, the influence of green social capital on environmental performance was found to be indirect through the mediation of GPMS.

GPMS can be considered a tool that helps managers renew, develop and synchronize their systems to take advantage of green resources in environmental performance improvement.

The effective assimilation of GPMS within industrial entities holds the potential to mitigate air pollution and hazardous waste, thereby ameliorating social conditions for both employees and the neighboring community. Besides that, proficient implementation of GPMS enhances collaborative efforts within the industrial sphere, yielding collective societal benefits.

This study emphasizes the importance of aligning green intellectual capital with appropriate control mechanisms, such as performance measurement systems, to maximize the benefits derived from these capital resources. The findings provide insights for organizations seeking to enhance their environmental performance and sustainability practices by effectively using their intellectual and social capital while implementing robust measurement systems.

The purpose of this study is to examine the association between audit committee expertise and asymmetric information in the US equity market.

The authors use measures of information asymmetry for 705 US firms (5,260 firm-year observations) over the period from 2007 to 2018, and use the theory of expertise (Ericsson and Smith, 1991) to examine the association between audit committee financial expertise and information asymmetry. The authors use multiple econometric approaches such as firm fixed-effect regression and two-stage ordinary least squares regression to control for possible endogeneity and reverse causality and find that the results remain the same.

The authors find that the existence of an audit committee with financial expertise is negatively and significantly associated with information asymmetry. The authors further provide empirical evidence through which audit committee financial expertise affects the firm’s informational environment. Additional analysis supports the argument that the audit committee’s financial expertise enhances the firm’s informational environment by increasing (decreasing) analyst following (dispersion).

One limitation to consider, like most studies on audit committees, is that the authors do not examine the actual role performed by the audit committee. The authors focus on the characteristics stipulated by the Sarbanes–Oxley Act 2002 and stock exchange rules regarding the financial expertise of audit committee members only.

This study is useful to policy makers, standard setters, investors, activists, managers, lenders and various stakeholders who rely on the financial statements of firms with an expert audit committee on board. The outcome of this study promotes recruiting audit committees with financial expertise due to the assumed benefits of this trait to the US firm.

The results of this study are not event-dependent and therefore have persistent effects, which is important to the evaluation of the usefulness of a regulation. This study promotes recruiting audit committees with financial expertise on boards because of the assumed benefits to the firm and investors.

This study is the first to document that financial expertise of audit committee characteristics is not only negatively related to the magnitude of information asymmetry but also driven by the financial expertise of audit committee members rather than chairs.