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We propose a machine learning based methodology to deal with data collected from a mobile application asking users their opinion regarding fashion products. Based on different machine learning techniques, the proposed approach relies on the data value chain principle to enrich data into knowledge, insights and learning experience.

Online interaction and the usage of social media have dramatically altered both consumers’ behaviors and business practices. Companies invest in social media platforms and digital marketing in order to increase their brand awareness and boost their sales. Especially for fashion retailers, understanding consumers’ behavior before launching a new collection is crucial to reduce overstock situations. In this study, we aim at providing retailers better understand consumers’ different assessments of newly introduced products.

By creating new product-related and user-related attributes, the proposed prediction model attends an average of 70.15% accuracy when evaluating the potential success of new future products during the design process of the collection. Results showed that by harnessing artificial intelligence techniques, along with social media data and mobile apps, new ways of interacting with clients and understanding their preferences are established.

From a practical point of view, the proposed approach helps businesses better target their marketing campaigns, localize their potential clients and adjust manufactured quantities.

The originality of the proposed approach lies in (1) the implementation of the data value chain principle to enhance the information of raw data collected from mobile apps and improve the prediction model performances, and (2) the combination consumer and product attributes to provide an accurate prediction of new fashion, products.

The corona virus disease 2019 (COVID-19) pandemic had devastating impact on sporting activities, education and global health. Given the impact of the pandemic-related restrictions and closed fitness centers and other sports facilities, the coping strategies adopted by athletes while training at home to continue their training remain an important question. The purpose of this review is to examine the findings of key studies focusing on the impact of the pandemic on sport training.

A review was conducted on Google Scholar, Scopus and PubMed to identify articles on physical activity and sport training during the COVID-19 pandemic. Eligibility criteria included peer-reviewed empirical and quantitative studies. The selected articles were reviewed using contextual analysis.

The COVID-19 pandemic had devastating impact on sports activities globally. Studies evaluating the influence of the pandemic on sports training have revealed abysmal decline in training volume and general physical fitness, limited access to facilities and equipment and significant reduction in training load. The damage of the pandemic on the sporting world should serve as a guide for proactive steps that should be taken to prevent recurrence of a similar calamity.

This paper highlights important lessons to be learned from the lockdown imposed by the COVID-19 pandemic by stakeholders in sport, including the importance of improvisation of sports facilities by utilizing available spaces at home and neighborhood for physical training.

This study aims to analyze the factors affecting tea-producing farmers' intention to use e-commerce exchanges (ECEs) to sell their products, combining the technology acceptance model (TAM) theory and barrier factors.

The authors use the generalized structural equation modeling (GSEM) to analyze the intermediate model that is built on TAM.

Research results show that perceived usefulness (PU) and perceived ease of use (PEU) significantly influence farmers' intention to use ECEs to sell their products. However, knowledge and information barriers hinder farmers' intention to use such ECEs.

Encouraging farmers to use ECEs is the most helpful solution for agricultural economic development in the context of the Covid-19 pandemic.

From an academic perspective, this is the first study that combines the TAM theory of Davis (1989) and barrier factors to analyze farmers' intention to use ECEs. The findings are valuable references for policymakers to propose strategies for agricultural economic development during the current pandemic. At the same time, the empirical results obtained from this study provide good orientations for agricultural economic development 4.0 in the future.

This article aims at - exploring and prioritizing the barriers to adoption of digitization in supply chains (SCs), categorizing them into sustainability triple bottom line (STBL) based upon their direct impact and suggesting digital technologies to address each barrier.

A five-phase methodology is used which consists of an exploration of 44 barriers to the adoption of digitization in SCs, analysis of 44 barriers for mean, standard deviation and Cronbach alpha based on questionnaire-based feedback of 25 experts, extraction of 10 most significant barriers through 05 experts, followed by categorization of the barriers into STBL referring to their direct impact on STBL, prioritization of ten barriers using Fuzzy Technique for Order Performance by Similarity to Ideal Solution and recommendation of digital technologies to address each barrier.

While all the barriers considered in this study significantly impede the adoption of digitization in SCs, lack of top management commitment (B1) is found to be most crucial while lack of culture toward use of information and communication technology required for digitization (B3) has minimum impact. Large investment in digital infrastructure (B6), difficulty in integration of cyber physical systems (CPSs) on varied platforms (B8) and lack of experts having knowledge of digital technologies (B2) are equally important barriers requiring more attention while adopting digitization in SCs.

This study is mainly based on feedback from 25 seasoned experts; a wider cross section of experts will give more insight.

The outcomes are very significant for organizations looking to adopt digitization in their SCs. Simultaneous consideration to all the barriers becomes impractical hence prioritization of same will be useful for the SC managers to benchmark their preparedness and decide strategies for the adoption of digitization with due consideration toward the impact of barriers on STBL. The digital technologies recommended will further aid in planning the digital strategies to address each barrier.

A unique approach to explore, analyze, prioritize and categorize the barriers to adoption of digitization in SCs is used to provide a deeper understanding of factors deterring the same. It implies that a supportive top management along with systematic allocation of finances plays a crucial role. The importance of availability of digital experts for integrating CPSs on a single platform is also highlighted. The digital technologies recommended will further assist the organizations toward adoption of digitization in SCs with due consideration to STBL.

Digital supply chains require nascent technologies like blockchain and Internet of Things (IoT). There is a need to develop a roadmap for the implementation of these technologies, as they require a huge amount of resources and infrastructure. The purpose of this paper is to analyze the challenges of implementing blockchain-IoT integrated architecture in the green supply chain and develop strategies for the same.

After a thorough literature survey of Scopus-indexed journals and books, 37 barriers were identified, which were then brought down to 15 barriers after confirming with industry and academic experts using the Delphi method. Using the total interpretive structural modeling (TISM) method and cross-impact matrix multiplication applied to classification (MICMAC) analysis, the barriers were modeled, and finally, strategies were formulated using a concept map to handle the barriers in the blockchain-IoT integrated architecture for a green supply chain.

This paper presents the research on barriers that can be considered for incorporating blockchain and IoT in the green supply chain. It was found from the TISM model that environmental concerns are Level-1 barriers and need to be addressed by developing appropriate technology and allocating funds for the same. An integrated ecosystem with blockchain and IoT is developed.

The focus of this study was on the challenges of blockchain and IoT; hence, it is required to extend the research and find challenges for different industries and also analyze the criteria using other multi-criteria decision-making (MCDM) methods. Further research is required for the integration of blockchain-IoT with supply chain functions.

The transformation of a traditional supply chain into a green supply chain is possible with the integration of technologies. This research work and the strategies developed are useful to managers and practitioners working on technology implementation. Planning resources and addressing key barriers is possible with the concept maps and architecture developed.

Green supply chain management (SCM) is gaining importance in industry as well as the academic sector due to government Policies and norms worldwide for reducing emissions and encouraging environment-friendly production systems. Incorporating blockchain and IoT in a green supply chain will further digitize and increase transparency in supply chains.

We have done a categorization of all barriers based on the expert survey by academicians and industry experts from industries in India. The concept map helps in identifying possible solutions for the challenges and initiatives to be taken for the smooth integration of technologies in the green supply chain.

This study’s fundamental objective is to assess climate change impact on reference evapotranspiration (ET_o) patterns in Egypt under the latest shared socioeconomic pathways (SSPs) of climate change scenarios. Additionally, the study considered the change in the future solar radiation and actual vapor pressure and predicted them from historical data, as these factors significantly impact changes in the ET_o.

The study utilizes data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze reference ET_o. Six models are used, and an ArcGIS tool is created to calculate the monthly average ET_o for historical and future periods. The tool considers changes in actual vapor pressure and solar radiation, which are the primary factors influencing ET_o.

The research reveals that monthly reference ET_o in Egypt follows a distinct pattern, with the highest values concentrated in the southern region during summer and the lowest values in the northern part during winter. This disparity is primarily driven by mean air temperature, which is significantly higher in the southern areas. Looking ahead to the near future (2020–2040), the data shows that Aswan, in the south, continues to have the highest annual ET_o, while Kafr ash Shaykh, in the north, maintains the lowest. This pattern remains consistent in the subsequent period (2040–2060). Additionally, the study identifies variations in ET_o , with the most significant variability occurring in Shamal Sina under the SSP585 scenario and the least variability in Aswan under the SSP370 scenario for the 2020–2040 time frame.

This study’s originality lies in its focused analysis of climate change effects on ET_o, incorporating crucial factors like actual vapor pressure and solar radiation. Its significance becomes evident as it projects ET_o patterns into the near and distant future, providing indispensable insights for long-term planning and tailored adaptation strategies. As a result, this research serves as a valuable resource for policymakers and researchers in need of in-depth, region-specific climate change impact assessments.

The purpose of this study is to introduce a new type of sensor which uses microwave metamaterials and direct-coupled split-ring resonators (DC-SRRs) to measure the dielectric properties of solid materials in real time. The sensor uses a transmission line with a bridge-type structure to measure the differential frequency, which can be used to calculate the dielectric constant of the material being tested. The study aims to establish an empirical relationship between the dielectric properties of the material and the frequency measurements obtained from the sensor.

In the proposed design, the opposite arm of the bridge transmission line is loaded by DC-SRRs, and the distance between DC-SRRs is optimized to minimize the mutual coupling between them. The DC-SRRs are loaded with the material under test (MUT) to perform differential permittivity sensing. When identical MUT is placed on both resonators, a single transmission zero (notch) is obtained, but non-identical MUTs exhibit two split notches. For the design of differential sensors and comparators based on symmetry disruption, frequency splitting is highly useful.

The proposed structure is demonstrated using electromagnetic simulation, and a prototype of the proposed sensor is fabricated and experimentally validated to prove the differential sensing principle. Here, the sensor is analyzed for sensitivity by using different MUTs with relative permittivity ranges from 1.006 to 10 and with a fixed dimension of 9 mm × 10 mm ×1.2 mm. It shows a very good average frequency deviation per unit change in permittivity of the MUTs, which is around 743 MHz, and it also exhibits a very high average relative sensitivity and quality factor of around 11.5% and 323, respectively.

The proposed sensor can be used for differential characterization of permittivity and also as a comparator to test the purity of solid dielectric samples. This sensor most importantly strengthens robustness to environmental conditions that cause cross-sensitivity or miscalibration. The accuracy of the measurement is enhanced as compared to conventional single- and double-notch metamaterial-based sensors.

Business models (BMs) are becoming increasingly crucial for value creation in the healthcare sector. The study explores the conceptualization and application of BM concepts within the healthcare sector and investigates their evolution in emerging economies (EEs) and developed economies (DEs). This study aims to uncover these two contexts' shared characteristics and unique variances through a comparative analysis.

The paper systematically investigates and consolidates the literature on healthcare by employing the antecedents, decisions and outcomes (ADO) framework and finally examines 71 shortlisted articles published between 2003 and 2022.

The recognition of the BM within healthcare is increasing, both in EEs and DEs. EEs prioritize value creation and capture through cost efficiency, while DEs focus on innovation. Key theories employed include a resource-based view, the network theory and the theory of innovation. Case studies are commonly used as a methodology. Further research is needed to explore the decisions and outcomes of BMs.

The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

Beyond comparing and highlighting gaps in BMs between EEs and DEs, benchmarking DE's healthcare business models (HBMs) helps healthcare organizations in EEs align their practices, mitigate risks and establish efficient healthcare systems tailored to their specific contexts. The study adopts stringent filtration and keyword criteria, potentially excluding relevant research. Future researchers are encouraged to broaden their selection criteria to encompass a more extensive range of relevant studies.

The study analyzes HBMs using an SLR framework perspective and provides practical implications for academicians and practitioners to enhance their decision-making.

One of the sources for the increase in the carbon footprint on the earth is the manufacturing of cement, which causes a severer environmental impact. Abundant research is going on to diminish CO₂ content in the atmosphere by appropriate utilization of waste by-products of industries. Alkali-activated slag concrete (AASC) is an innovative green new concrete made by complete replacement of cement various supplementary cementitious raw materials. Concrete is a versatile material used in different fields of structures, so it is very important to study the durability in different exposures along with the strength. The purpose of this paper is to study the performance of AASC by incorporating quartz sand as fine aggregate under different exposure conditions.

The materials for this innovative AASC are selected based on preliminary studies and literature surveys. Based on numerous trials a better performance mix proportion of AASC with quartz sand is developed with 1:2:4 mix proportion, 0.8 alkali Binder ratio, 19 M of NaOH and 50% concentration of Na₂SiO₃. Subsequently, AASC cubes are prepared and exposed for 3, 7, 14, 28, 56, 90, 112, 180, 252 and 365 days in ambient, acid, alkaline, sulfate, chloride and seawater and tested for compressive strength. In addition, to study the microstructural characteristics, scanning electron microscope (SEM), energy dispersive X-ray analysis and X-ray diffraction analysis was also performed.

Long-term performance of AASC developed with quartz sand is very good in the ambient, alkaline environment of 5% NaOH and seawater with the highest compressive strength values of 51.8, 50.83 and 64.46, respectively. A decrease in compressive strengths was observed after the age of 14, 56 and 112 days for acid, chloride and sulfate exposure conditions, respectively. SEM image shows a denser microstructure of AASC matrix for ambient, alkaline of 5% NaOH and seawater.

The proposed AASC is prepared with a mix proportion of 1:2:4, so the other proportions of AASC need to verify. In general plain, AASC is not used in practice except in few applications, in this work the effect of reinforced AASC is not checked. The real environmental exposure in fields may not create for AASC, as it was tested in different exposure conditions in the laboratory.

The developed AASC is recommended in practical applications where early strength is required, where the climate is hot, where water is scarce for curing, offshore and onshore constructions exposed to the marine environment and alkaline environment industries like breweries, distilleries and sewage treatment plants. As AASC is recommended for ambient air and in other exposures, its implementation as a construction material will reduce the carbon footprint.

The developed AASC mix proportion 1:2:4 is an economical mix, because of low binder content, but it exhibits a higher early age compressive strength value of 45.6 MPa at the age of 3 days. The compressive strength increases linearly with age from 3 to 365 days when exposed to seawater and ambient air. The performance of AASC is very good in the ambient, alkaline environment and seawater compared to other exposure conditions.

This study aims to investigate the mechanical properties of sustainable recycled polypropylene (rPP) composite materials integrated with spherical silicon carbide (SiC) particles.

A representative volume element (RVE) analysis is employed to predict the Young’s modulus of rPP filled with spherical-shaped SiC at varying volume percentages (i.e. 10, 20 and 30%).

The investigation reveals that the highest values of Young’s modulus, tensile strength, flexural strength and mode 1 frequency are observed for the 30% rPP/SiC samples, exhibiting increases of 115, 116, 62 and 15%, respectively, compared to pure rPP. Fractography analysis confirms the ductile nature of pure rPP and the brittle behavior of the 30% rPP/SiC composite. Moreover, the RVE method predicts Young’s modulus more accurate than micromechanical models, aligning closely with experimental results. Additionally, results from ANSYS simulation tests show tensile strength, flexural strength and frequency within a 10% error range when compared to experimental data.

This study contributes to the field by demonstrating the mechanical enhancements achievable through the incorporation of sustainable materials like rPP/SiC, thereby promoting environmentally friendly engineering solutions.