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This study aims to investigate the association between certifications in the Bangladeshi readymade garment (RMG) sector and diverse firm resources, contributing to Bangladesh’s competitive advantage.

The study conducted a quantitative content analysis of 366 Bangladeshi RMG firm websites, using Barney’s (1991) resource-based theory (RBT) framework. Pearson correlation and linear regression analyses were used to explore the research questions.

The findings reveal significant positive impacts of certifications on all firm resource categories (physical, human, organizational knowledge and learning, general organizational and financial) under the RBT framework. Certifications correlate positively with resources, from small to medium, and with various factors, though some negative correlations were identified.

The study improves comprehension of apparel manufacturers’ certifications and their association with firm resources, offering valuable insights for stakeholders on long-term competitive advantages. Yet, limitations should be considered, including size-dependent variations and reliance on self-reported website data.

This study represents a pioneering effort, concentrating on Bangladesh’s RMG sector and offering a unique perspective on the implications of certifications for firm resources within emerging economies.

Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

The aim of this paper was to examine the interrelationships between Maslow’s motivated needs in relation to employees’ personal and workplace spirituality experiences.

Regression analysis using a cross-sectional, convenience sampling approach of 158 US employees responding to survey questions using a Qualtrics survey related to their demographics, motivated needs, daily spirituality experiences, workplace spirituality, work spirituality experiences and work-related flow.

Correlation analyses provided support for Maslow’s hierarchy of needs in relation to employees’ daily spiritual experiences, workplace spirituality, work spirituality experiences and work-related flow. Regression analyses also identified the specific Maslow needs that served as predictive factors in relation to employees’ personal and workplace spirituality. Findings and conclusions are also discussed in relation to employees and organizations.

Currently, there are no correlation studies that have examined workplace spirituality as an ethical behavior in the workplace and Maslow’s hierarchy of needs. This correlation gap is notable because further examination of Maslow’s hierarchy of needs as a theoretical framework in relation to employees’ spirituality can be particularly valuable for contemporary work settings. Consider that today’s work environment is faced with dynamic and unique factors, and each of these factors not only changes the work environment but also they significantly drive or minimize employee motivation. Three such factors include new generations of employees with unique values entering the workforce and the great resignation and quiet quitting.

The study identifies that Maslow’s belonging, esteem and self-transcendence are related positively to employees’ spiritual experiences in the workplace. When these needs are fulfilled in the work environment, employees may be more likely to engage in spiritual practices at work, such as participating in yoga, prayer and meditation and in fulfilling one’s motivated needs and spirituality, employees are able to pursue their true purpose in the workplace.

This study extends the literature regarding understanding the value of workplace spirituality as a positive outcome for the employees and organizations.

This study aims to analyze the export competitiveness of Bangladesh's apparel industry by identifying the specific product categories that help sustain its export comparative advantage.

Compound annual growth rate (CAGR) and market share (MS) are calculated between 2011 and 2020 at the two- and four-digit level apparel product categories within the harmonized system (HS) to analyze the industry’s growth and export dominance. Trade competitiveness (TC) at the four-digit level, revealed comparative advantage (RCA) and normalized revealed comparative advantage (NRCA) at the two-, four- and six-digit-level apparel product categories are computed for the same 10-year period to investigate the industry’s export competitiveness. Major export destinations of the top 5 exporting product categories are identified to understand the factors facilitating the industry’s growth. A non-parametric Spearman rank correlation analysis evaluated the association between the RCA and NRCA indices.

Among the 34 product categories at the four-digit level, 29 consistently demonstrated an export comparative advantage, as did 34 out of 217 six-digit level sub-categories. In contrast, 12 sub-categories at the six-digit level consistently exhibited a comparative disadvantage in Bangladesh's export competitiveness. Furthermore, the TC measure identified 28 categories at the four-digit level with a robust comparative advantage. 30 categories displayed a positive CAGR, and Bangladesh asserted significant market dominance over 26 product categories at the four-digit level.

This study's implications are significant for various stakeholders in Bangladesh and other apparel-exporting industries, encompassing government entities, industry officials, policymakers, investors, researchers and students. Nevertheless, limitations arise from the study's reliance on RCA and NRCA as competitiveness indicators, particularly its adoption of a macro-level approach for measurement without exploring a micro-level perspective. This constitutes a notable constraint in the study's analytical framework.

This study contributed novelty and enrichment to the existing academic literature by identifying distinct apparel product categories that contribute to the industry's growth.

The prospect of Sub-Saharan Africa (SSA) as an apparel-sourcing base for US fashion companies has been a growing heated debate among academia, industry practitioners and policymakers. This study aims to evaluate SSA countries’ readiness to serve as an alternative sourcing destination to Asia for US fashion companies, focusing on comparing the similarities and differences of US apparel imports from these two regions at the product level.

This study was based on a statistical analysis of detailed product features and assortment information of thousands of apparel items at the stock-keeping unit level sold by US retailers between January 2021 and December 2023.

US fashion companies seemed to leverage SSA countries as suppliers of “niche products,” such as those relatively simple and basic apparel categories containing African cultural elements and targeting the luxury and premium market segment. However, the range of apparel products available for US fashion companies to source from the SSA region remained significantly more limited than those from Asia. Also, US apparel imports from SSA countries were primarily made of cotton and polyester, with less use of other fiber types, including nylon, rayon, viscose, wool and those made from recycled textile materials.

The study’s findings provided fresh insights into why US fashion companies sourced from SSA countries and the specific types of products they were sourcing, going beyond existing studies based on macro trade statistics. The results also deepened the understanding of SSA countries’ competitiveness as an apparel-sourcing destination and their potential to serve as an alternative to sourcing from Asia, particularly from a unique product perspective.

Bitcoin (BTC) is significantly correlated with global financial assets such as crude oil, gold and the US dollar. BTC and global financial assets have become more closely related, particularly since the outbreak of the COVID-19 pandemic. The purpose of this paper is to formulate BTC investment decisions with the aid of global financial assets.

This study suggests a more accurate prediction model for BTC trading by combining the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) model with the artificial neural network (ANN). The DCC-GARCH model offers significant input information, including dynamic correlation and volatility, to the ANN. To analyze the data effectively, the study divides it into two periods: before and during the COVID-19 outbreak. Each period is then further divided into a training set and a prediction set.

The empirical results show that BTC and gold have the highest positive correlation compared with crude oil and the USD, while BTC and the USD have a dynamic and negative correlation. More importantly, the ANN-DCC-GARCH model had a cumulative return of 318% before the outbreak of the COVID-19 pandemic and can decrease loss by 50% during the COVID-19 pandemic. Moreover, the risk-averse can turn a loss into a profit of about 20% in 2022.

The empirical analysis provides technical support and decision-making reference for investors and financial institutions to make investment decisions on BTC.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

Whilst a modest number of investigations have been undertaken concerning nanofluids (NFs), the exploration of fluid flow under exponentially stretching velocities using NFs remains comparatively uncharted territory. This work presents a distinctive contribution through the comprehensive examination of heat and mass transfer phenomena in the NF ND–Cu/H₂O under the influence of an exponentially stretching velocity. Moreover, the investigation delves into the intriguing interplay of gyrotactic microorganisms and convective boundary conditions within the system.

Similarity transformations have been used on PDEs to convert them into dimensionless ODEs. The solution is derived by using the homotopy analysis method (HAM). The pictorial notations have been prepared for sundry flow parameters. Furthermore, some engineering quantities are calculated in terms of the density of motile microbes, Nusselt and Sherwood numbers and skin friction, which are presented in tabular form.

The mixed convection effect associated with the combined effect of the buoyancy ratio, bioconvection Rayleigh constant and the resistivity due to the magnetization property gives rise to attenuating the velocity distribution significantly in the case of hybrid nanoliquid. The parameters involved in the profile of motile microorganisms attenuate the profile significantly.

The current simulations have uncovered fascinating discoveries about how metallic NFs behave near a stretched surface. These insights give us valuable information about the characteristics of the boundary layer close to the surface under exponential stretching.

The novelty of the current investigation is the analysis of NF ND–Cu/H₂O along with an exponentially stretching velocity in a system with gyrotactic microorganisms. The investigation of fluid flow at an exponentially stretching velocity using NFs is still relatively unexplored.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.

In the dynamic realm of energy storage, lithium-ion batteries stand out as a frontrunner, powering a myriad of devices from smartphones to electric vehicles. However, efficient heat management is crucial for ensuring the longevity and safety of these batteries. This paper aims to delve into the process of lithium-ion battery heat management systems, exploring how cutting-edge technologies are used to regulate temperature and optimize performance. In addition, computational fluid dynamics (CFD) studies take center stage, offering insights into the intricate thermal dynamics within these powerhouses.

In this study, thermal behavior of pouch type lithium-ion battery cell has been investigated by using CFD method. Result of different discharge rates have been evaluated by using multi-scale multi-dimensional (MSMD) battery model. By using MSMD Model 0.5C, 1C, 2C, 3C and 5C discharge rates are compared in equivalent circuit model (ECM) and NTGK empirical models by monitoring averaged surface temperature on battery body wall. In addition, on NTGK model, air cooling effect has been studied with the 0.1 m/s, 0.2 m/s and 0.5 m/s air, velocities.

Results shows that higher discharge rate causes higher temperature on battery zones and air cooling is effective to obtain the lower zone temperatures. Also, ECM model gives higher temperature than NTGK model on battery zone.

When the literature is evaluated, comparison of the models used in battery cooling (ECM and NTGK) has never been done before. Within the scope of this study, model comparison was made. At the same time, the time step has always been ignored in the literature. In this study, both time step and forced convection conditions were considered when comparing the models.