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This study aims to explore the underdog brand biography dimensions that emerging-country consumers identify with (Study 1) and attempts to uncover the effects of these dimensions on brand affinity and purchase intention moderated by self-identity and brand trust (Study 2).

Study 1, using data from 359 young Indians, reveals three underlying dimensions integral to underdog brand biography in emerging markets. Study 2 uses an experimental setup with a single-factorial design among 332 young Mexican consumers to investigate the direct effects of three identified underdog brand biography dimensions on purchase intention, mediated by brand affinity and moderated by consumer self-identity and brand trust.

Study 1 reveals three dimensions underlying underdog brand biographies: unfavorable circumstances, striving in adversities and passion, and persistent will to succeed. Study 2 reveals that consumers with higher self-identity demonstrate greater purchase intentions for an underdog brand than a top dog one.

The results indicate that marketers can successfully use underdog narratives to influence consumer decision-making, thereby increasing brand affinity and purchase intention.

This study delineates the link between different dimensions of underdog brand biographies with brand affinity and purchase intention in emerging countries and builds on the understanding of the moderating role played by self-identity and brand trust.

The unique nature, complicated design, hazardous activities and complex work environment involved in the high-rise construction projects constitute significant risks worldwide. In the Indian context, construction safety management in high-rise construction projects is crucial due to the presence of significant occupational risks and hazards at the workplace. Occupational hazards lead to accidents that severely affect human health and result in substantial financial losses.

The study aims to present a hybrid risk assessment method (RAM) and the technique for order of preference by similarity to ideal solution (TOPSIS) method to detect and evaluate occupational risks in different construction activities through a questionnaire survey approach.

Aroundsix types of construction activities and corresponding ten risks are identified and evaluated during the study. Based on the calculation of risk scores, the findings imply that “roof work activities,” “finishing work,” “mechanical, electrical and plumbing work (MEP)” are hazardous construction activities, while, among the corresponding ten risks, “workers falling from height” is the most prominent risk among the majority of activities. Other risks include “risk due to fire and electric accidents” and “struck by falling objects,” which are the major risks in high-rise construction projects.

Theoriginality of the paper lies in its activity-based risk assessment and ranking of hazards in high-rise construction projects. By integrating theoretical insights with practical applications, the study attempts to enhance occupational safety and reduce accidents on construction sites, thereby significantly contributing to both academia and industry practices.

This study aims to elucidate the dyeing kinetics and thermodynamic relationships of CI Reactive Red 24 (RR24) on cotton fabrics, achieve the recycling of inorganic salts and water resources and obtain comprehensive data on color parameters, fastness and other characteristics of fabrics dyed with the recycled dyeing residual wastewater.

The dyeing wastewater obtained through advanced oxidation technology was used as a medium for dyeing cotton fabrics with RR24. The absorbance value of the dyeing residue served as an evaluation index, and the relevant kinetic and thermodynamic parameters were calculated based on this absorbance. The color parameters and fastness of the fabric samples were measured to compare the performance of different dyeing media.

Dyeing cotton with RR24 in both media follows pseudo-second-order kinetics. When dyeing with wastewater media, the dye adsorption in the first 45 min increased by 0.082–1.29 g/kg compared with conventional dyeing. Furthermore, the half-dyeing time was shortened by 4.19–11.99 min and the equilibrium adsorption amount was reduced by 0.277–0.302 g/kg. The adsorption of RR24 on cotton fabrics conformed to the Freundlich model. Fabrics dyed using recycled wastewater exhibit a deeper color, with reduced red light and enhanced blue light, resulting in an overall deeper apparent color.

These dyeing kinetics and thermodynamic properties are beneficial for comprehending and interpreting the dyeing performance and behavior of reactive dyes in dyeing wastewater. They lay a theoretical foundation for the treatment and recycling of dyeing wastewater.

This study aims to investigate the effects of process input parameters (welding current, welding time, electrode pressure and holding time) on the output responses (nugget diameter, peak load and indentation) that control the mechanical properties and quality of the joints in dissimilar resistance spot welding (RSW) for the third generation of advanced high-strength steel (AHSS) quenching and partitioning (Q&P980) and (SPFC780Y) high-strength steel spot welds.

Design of experiment approach with two level factors and center points was adopted. Destructive peel and shear tensile strengths were used to measure the responses. The significant factors were determined using analysis of variance implemented by Minitab 18 software. Finally, multiresponse optimization was carried out using the desirability function analysis method.

Holding time was the most significant factor influencing nugget diameter, whereas welding current had the greatest impact on peak load and indentation. Multiresponse optimization revealed that the optimal settings were a welding current of 12.5 KA, welding time of 18 cycles, electrode pressure of 420 Kgf and holding time of 10 cycles. These settings produced a nugget diameter of 8.0 mm, a peak load of 35.15 KN and an indentation of 22.5%, with a composite desirability function of 0.764.

This study provides an effective approach for multiple response optimization to the mechanical behavior of RSW joints, even though there have been few studies on the third generation of AHSS joints and none on the dissimilar joints of the materials used in this study.

This study uses a comprehensive literature review and analysis of recent research, policy documents and program evaluations related to Supplemental Nutrition Assistance Program Education (SNAP-Ed) and policy, systems and environmental (PSE) strategies. Key sources include peer-reviewed articles, the 2025 SNAP-Ed Plan Guidance and case studies of successful PSE interventions. The Social-Ecological Model serves as a framework to organize and analyze the multilevel impacts of PSE interventions. The method involves identifying relevant information, synthesizing key themes and patterns and critically examining the potential impact of PSE strategies on nutrition security and health equity.

This paper examines the evolution of the SNAP-Ed from direct nutrition education to a comprehensive approach integrating PSE change strategies. It aims to analyze the rationale, implementation and potential impact of PSE approaches in SNAP-Ed on nutrition security and health equity in the USA. The study explores how these strategies address social determinants of health, promote sustainable population-level changes in nutrition environments, and their capacity to reduce health disparities in low-income communities. It seeks to identify challenges, opportunities and future research directions in implementing PSE strategies within SNAP-Ed.

The review reveals that PSE strategies in SNAP-Ed show promise in creating sustainable, population-level changes in nutrition environments and health outcomes. Successful examples, such as healthy corner store initiatives and workplace wellness programs, demonstrate the potential of PSE approaches to improve access to healthy food options and physical activity opportunities. The integration of PSE strategies has enhanced SNAP-Ed’s capacity to address social determinants of health and promote health equity. However, challenges including resource constraints, political opposition and the need for cross-sector collaboration persist. The effectiveness of PSE interventions relies heavily on community engagement, partnerships and supportive policies.

The findings underscore the importance of adopting comprehensive, multilevel approaches in nutrition education and obesity prevention programs. For SNAP-Ed implementers, this implies a need to develop expertise in PSE strategies, foster cross-sector partnerships and engage communities in intervention design and implementation. Policymakers should consider increasing support and resources for PSE approaches within SNAP-Ed and similar programs. Public health practitioners can use these insights to design more effective, equitable interventions that address root causes of nutrition insecurity. The study also highlights the need for improved evaluation methods to assess the long-term impact of PSE strategies on population health outcomes.

This study highlights the importance of addressing social determinants of health, such as poverty and access to healthy food options, to promote equitable health outcomes. It underscores the potential of community-driven, multilevel interventions in building a more just and equitable food system accessible to all.

This paper provides a comprehensive analysis of the shift toward PSE strategies in SNAP-Ed, offering valuable insights into the program’s evolution and its potential to address complex public health challenges. By examining both successes and challenges, it contributes to the growing body of evidence on the effectiveness of multilevel interventions in promoting nutrition security and health equity. The study’s emphasis on the role of community engagement and partnerships in PSE implementation offers practical guidance for program planners and policymakers.

Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

The purpose of this paper is to recognize and prioritize the critical drivers (CDs) essential for establishing a sustainable foxnut supply chain (SFNSC) aligned with the sustainable development goals (SDGs) set forth by the United Nations. The objective is to make a meaningful contribution to the longevity and well-rounded sustainability of the foxnut industry by scrutinizing pivotal factors that endorse triple bottom line (TBL) sustainability aspect throughout the supply chain.

A systematic approach, integrating literature reviews and government reports, identified potential CDs for a sustainable foxnut supply chain. Expert opinions refined the list with the help of fuzzy-Delphi method (FDM), and the final CDs were analyzed with fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) to establish their causal relationships and hierarchical importance.

The study identifies the top three CDs for a SFNSC: “Branding of the product”, “The Global increase in demand” and “Value addition of the foxnut”. Moreover, “Storage infrastructure”, “Mechanized processing” and “Proper transportation facilities” also contribute to the sustainability of the foxnut supply chain.

The results hold significance for various stakeholders in the foxnut industry, encompassing producers, policymakers and researchers. The identified CDs can guide decision-making and resource allocation to improve the sustainability of the foxnut supply chain. The study's framework and methodology can also be applied to other industries to promote sustainable practices and achieve SDGs.

This study enhances understanding of CDs for an SFNSC. FDM and F-DEMATEL techniques analyze causal relationships and rank key factors. The SFNSC model may help other major foxnut producers to become more sustainable.

Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

The purpose of this study is to investigate the usage of digital technologies (DTs) in improving the mental health of workers on construction sites.

A mixed research approach was used in the study, which comprised a questionnaire survey and two phases of semi-structured interviews. Purposive sampling was used to determine the interviewees and respondents of the questionnaire survey. Weighted mean rating (WMR) and manual content analysis were used to rank and evaluate the collected data.

The findings of this study revealed bipolar disorder, anxiety disorders, attention-deficit/hyperactivity disorder, obsessive-compulsive disorder, work-related stress and depression as the six most significant mental disorders (MDs) among the construction workforce and 30 causes for them. Moreover, 27 symptoms were related to the six most significant MDs, and sweating was the most significant symptom among them. Despite that, 16 DTs were found to be suitable in mitigating the causes for the most significant MDs.

There are numerous studies conducted on the application of DTs to construction operations. However, insufficient studies have been conducted focusing on the application of DTs in improving the mental health of workers at construction sites. This study can thus influence the use of DTs for tackling the common causes for MDs by bringing a new paradigm to the construction industry.

In the present-day highly customer-conscious service environment, supply chain management has become a critical component of health-care industry, helping in fulfilling patient expectation, optimizing inventory and automating departmental activities. Supplier selection is one of the crucial elements of health-care supplier chain, establishing mutually beneficial relationships with the reliable suppliers that provide the most value of money. Health-care supplier selection with feasible sets of alternatives and conflicting criteria can be treated as a multi-criteria decision making (MCDM) problem. Among the MCDM methods, grey relational analysis (GRA) appears as a potent tool due to its simple computational steps and ability to deal with imprecise data. The purpose of this paper is to explore the applicability of a newly developed MCDM tool for solving a health-care supplier selection problem.

In GRA, the distinguishing coefficient (ξ) plays a contributive role in final ranking of the alternative suppliers and almost all the past researchers have considered its value as 0.5. In this paper, a newly developed MCDM tool, i.e. dynamic GRA (DGRA), is adopted to evaluate the relative performance of 25 leading pharmaceutical suppliers for a health-care unit based on nine important financial metrics. Instead of static value of ξ, DGRA treats it as a dynamic variable dependent on grey relational variator and ranks the health-care suppliers using their computed rank product scores.

Based on rank product scores and developed exponential curve, DGRA classifies all the suppliers into reliable, moderately reliable and unreliable clusters, helping the health-care unit in identifying the best performing suppliers for subsequent order allocation. Among the reliable suppliers, alternatives A₂ and A₁₁ occupy the top two positions having almost the same performance with respect to the considered financial metrics.

Application of DGRA along with determination of the most reliable suppliers would help in effectively adopting multi-sourcing strategy to increase resilience while diversifying the supply portfolio, thereby enabling the health-care unit to minimize chances of sudden disruption in the supply chain. It can act as an intelligent decision-making framework aiding in solving health-care supplier selection problems considering perceived risks and dynamic input data.