Exploring the synergy of logistics, finance, and technology on innovation.

As global environmental challenges intensify, manufacturing firms face increasing pressure to innovate sustainably. Green innovation, characterized by the development of environmentally friendly products, processes, and technologies, has become essential for firms striving to remain competitive. This study aims to investigate the influence of key factors-green logistics, green finance, and green technology-on green innovation within manufacturing firms, while exploring the mediating role of green technology in these relationships.

Does gender diversity mediate the relationships of diversity beliefs and workplace happiness?

Though its intensity varies across cultures, practicing diversity beliefs has become increasingly prevalent in contemporary business organizations. Traditionally, diversity encompasses various aspects such as gender, education, religion, language, age, ethnicity, culture and personality orientation. The current study has demonstrated to identify the mediating effects of gender diversity on diversity beliefs and workplace happiness.

Public health concern-driven insights and response of low- and middle-income nations to the World health Organization call for cervical cancer risk eradication.

The heart shattering impact afflicted by the notorious cervical cancer is rising rapidly as it emerges as the second most prevalent cancer among women in the developing countries. There was an anticipated 604,127 observed reports and 341,831 fatalities reported worldwide in 2020. The mortality rate was 7.

CottonFabricImageBD: An image dataset characterized by the percentage of cotton in a fabric for computer vision-based garment recycling.

The utilization of computer vision techniques has significantly enhanced the automation processes across various industries, including textile manufacturing, agriculture, and information technology. Specifically, in the domain of textile manufacturing, these techniques have revolutionized the detection of fiber defects and the quantification of cotton content in fabrics. Traditionally, the assessment of cotton percentages was a labor-intensive and time-consuming process that relied heavily on manual testing methods.

Effect of Chinese outward FDI on youth unemployment in sub-Saharan Africa.

This paper investigates the effect of Chinese outward foreign direct investment (FDI) on youth unemployment in sub-Saharan Africa (SSA) by using a modified labour demand model to identify the investment sources that are helpful for reducing youth unemployment in the region. The model is applied to a panel of 42 countries for the period 2003-2021 using random-effect, and generalized method of moment (GMM) estimators for robustness check. Our results suggest that Chinese FDI has direct short-term reducing effect on youth unemployment in SSA.

Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network.

Objective: Early diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality of life. The objective of this study is to present an automated analysis and classification system for breast cancer using clinical markers such as tumor shape, orientation, margin, and surrounding tissue. The novelty and uniqueness of the study lie in the approach of considering medical features based on the diagnosis of radiologists.

Unlocking the path to environmental sustainability: navigating economic policy uncertainty, ICT, and environmental taxes for a sustainable future.

The study aims to gauge the impact of economic policy uncertainty, ICT, and environmental tax on environmental sustainability, which is measured by carbon emission and ecological footprint in a panel of 22 nations from 1997 to 2021. The present study has implemented the advanced panel data estimation techniques, including continuously updated fully modified (CUP-FM) and continuously updated bias-corrected (CUP-BC), dynamic seemingly unrelated regressions (DSUR), and nonlinear autoregressive distributed lagged (NARDL) in documenting the elasticities of target variables. Moreover, the directional causality has been tested through the D-H causality test.

The role of ICT investment, digital financial inclusion, and environmental tax in promoting sustainable energy development in the MENA region: Evidences with Dynamic Common Correlated Effects (DCE) and instrumental variable-adjusted DCE.

His research investigates the interplay among investment in Information and Communication Technology [ICT], digital financial inclusion, environmental tax policies, and their impact on the progression of sustainable energy development within the Middle East and North Africa [MENA] region. Recognizing the distinctive hurdles impeding sustainable energy advancement, effective policy formulation and implementation in MENA necessitate a comprehensive understanding of these variables. Employing a Dynamic Common Correlated Effects [DCE] model alongside an instrumental variable-adjusted DCE approach, this study explores the relationship between ICT investment, digital financial inclusion, environmental tax, and sustainable energy development.

Unveiling the power of education, political stability and ICT in shaping technological innovation in BRI nations.

Technological innovation is a critical element of economic and environmental sustainability; thus, the promotion of technological innovation in the economy has gained an apex among policy makers. The study's impetus is to measure the effect of investments in information and communication technology (ICT), education, and political stability on technical innovation in BRI countries for 2004-2020. In the process of documenting the empirical nexus through the implementation of novel panel techniques commonly known as Dynamic Seemingly Unrelated Regressions (SUR), continuously updated fully modified" (Cup-FM) and continuously updated bias-corrected (Cup-BC).

SleepBoost: a multi-level tree-based ensemble model for automatic sleep stage classification.

Neurodegenerative diseases often exhibit a strong link with sleep disruption, highlighting the importance of effective sleep stage monitoring. In this light, automatic sleep stage classification (ASSC) plays a pivotal role, now more streamlined than ever due to the advancements in deep learning (DL). However, the opaque nature of DL models can be a barrier in their clinical adoption, due to trust concerns among medical practitioners.

Exploring post-COVID-19 health effects and features with advanced machine learning techniques.

COVID-19 is an infectious respiratory disease that has had a significant impact, resulting in a range of outcomes including recovery, continued health issues, and the loss of life. Among those who have recovered, many experience negative health effects, particularly influenced by demographic factors such as gender and age, as well as physiological and neurological factors like sleep patterns, emotional states, anxiety, and memory. This research aims to explore various health factors affecting different demographic profiles and establish significant correlations among physiological and neurological factors in the post-COVID-19 state.

Future Industrial Applications: Exploring LPWAN-Driven IoT Protocols.

The Internet of Things (IoT) will bring about the next industrial revolution in Industry 4.0. The communication aspect of IoT devices is one of the most critical factors in choosing the device that is suitable for use.

Investigating Rhythmicity in App Usage to Predict Depressive Symptoms: Protocol for Personalized Framework Development and Validation Through a Countrywide Study.

Background: Understanding a student's depressive symptoms could facilitate significantly more precise diagnosis and treatment. However, few studies have focused on depressive symptom prediction through unobtrusive systems, and these studies are limited by small sample sizes, low performance, and the requirement for higher resources. In addition, research has not explored whether statistically significant rhythms based on different app usage behavioral markers (eg, app usage sessions) exist that could be useful in finding subtle differences to predict with higher accuracy like the models based on rhythms of physiological data.

After-meal blood glucose level prediction for type-2 diabetic patients.

Type 2 Diabetes, a metabolic disorder disease, is becoming a fast growing health crisis worldwide. It reduces the quality of life, and increases mortality and health care costs unless managed well. After-meal blood glucose level measure is considered as one of the most fundamental and well-recognized steps in managing Type 2 diabetes as it guides a user to make better plans of their diet and thus control the diabetes well.

Social capital and health beliefs: Exploring the effect of bridging and bonding social capital on health locus of control among women in Dhaka.

This cross-sectional study examined if social capital affects women's health attributions. The study used the Internet Social Capital Scale (ISCS) and Multidimensional Health Locus of Control (MHLC) Scale to measure Social Capital and Health Locus of Control. A predefined 38-item questionnaire was used to survey 485 purposively selected women.

Empowering renewable energy consumption through public-private investment, urbanization, and globalization: Evidence from CS-ARDL and NARDL.

This study examines the interrelationship among public-private investment, urbanization, globalization, and renewable energy consumption in the BIMSTEC nations for 1995-2021. The study implemented linear and nonlinear frameworks to document the magnitudes of explanatory variables on REC. Referring to the study findings with CSD, CIPS, CADF, and PCT disclosed the presence of cross-sectional dependency; variables are integrated after the first difference, i.

An Automated Decision Support System to Analyze Malignancy Patterns of Breast Masses Employing Medically Relevant Features of Ultrasound Images.

An automated computer-aided approach might aid radiologists in diagnosing breast cancer at a primary stage. This study proposes a novel decision support system to classify breast tumors into benign and malignant based on clinically important features, using ultrasound images. Nine handcrafted features, which align with the clinical markers used by radiologists, are extracted from the region of interest (ROI) of ultrasound images.

Computer vision based deep learning approach for toxic and harmful substances detection in fruits.

Formaldehyde (CH₂O) is one of the significant chemicals mixed with different perishable fruits in Bangladesh. The fruits are artificially preserved for extended periods by dishonest vendors using this dangerous chemical. Such substances are complicated to detect in appearance.

Virtual screening of flavonoids as potential RIPK1 inhibitors for neurodegeneration therapy.

Background: Global prevalence of neurodegenerative diseases such as Alzheimer's disease and Parkinson's disease is increasing gradually, whereas approvals of successful therapeutics for central nervous system disorders are inadequate. Accumulating evidence suggests pivotal roles of the receptor-interacting serine/threonine-protein kinase 1 (RIPK1) in modulating neuroinflammation and necroptosis. Discoveries of potent small molecule inhibitors for RIPK1 with favorable pharmacokinetic properties could thus address the unmet medical needs in treating neurodegeneration.

Consistent patterns of common species across tropical tree communities.

Trees structure the Earth's most biodiverse ecosystem, tropical forests. The vast number of tree species presents a formidable challenge to understanding these forests, including their response to environmental change, as very little is known about most tropical tree species. A focus on the common species may circumvent this challenge.

A Bengali news and public opinion dataset from YouTube.

Along with the traditional news publishing policies, news agencies now share news over the internet since people nowadays prefer reading news online. Moreover, news media maintain YouTube channels to publish visual stories. Readers comment to share their opinions below the corresponding news item.

Nexus between energy efficiency, green investment, urbanization and environmental quality: Evidence from MENA region.

Environmental protection holds a paramount position in the pursuit of sustainable development. The existing body of literature has extensively examined various driving forces for environmental enhancement, including renewable energy sources, innovation, and governmental interventions. This study aims to assess the impact of green investment, energy efficiency, and urbanization on achieving environmental sustainability in the MENA region during the period 2004-2019.

BdSL47: A complete depth-based Bangla sign alphabet and digit dataset.

Sign Language Recognition (SLR) is crucial for enabling communication between the deaf-mute and hearing communities. Nevertheless, the development of a comprehensive sign language dataset is a challenging task due to the complexity and variations in hand gestures. This challenge is particularly evident in the case of Bangla Sign Language (BdSL), where the limited availability of depth datasets impedes accurate recognition.