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This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries.

Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers. A reference dataset contains comprehensive information regarding mechanical behavior of the lignocellulosic fibers was compiled from previous experimental investigations on mechanical properties for eight different fiber materials. Data encompass three key factors: Density of 0.9–1.6 g/cm³, Diameter of 5.9–1,000 µm, and Microfibrillar angle of 2–49 deg were utilized. Initially, fuzzy clustering technique was utilized for the data. For validating proposed model, ultimate tensile strength and elongation at break were predicted and then examined against unseen new data that had not been used during model development.

The output results demonstrated remarkably accurate and highly acceptable predictions results. The error analysis for the proposed method was discussed by using statistical criteria. The stacked model proved to be effective in significantly reducing level of uncertainty in predicting the mechanical properties, thereby enhancing model’s reliability and precision. The study demonstrates the robustness and efficacy of the stacked method in accurately estimating mechanical properties of lignocellulosic fibers, making it a valuable tool for material scientists and engineers in various applications.

Cellulosic fibers are essential for biomaterials to enhance developing green sustainable bio-products. However, such fibers have diverse characteristics according to their types, chemical composition and structure causing inconsistent mechanical performance. This work introduces an integrated artificial intelligence schemes to enhance accurately predicting the mechanical properties of cellulosic fibers towards boosting their reliability for more sustainable industries. Fuzzy clustering and stacked method approach were utilized to predict the mechanical performance of the fibers.

Quality assessment of textile products is of prime concern to intimately meet consumer demands. The dilemma faced by textile producers is to figure out the stability among quality criteria and efficiently deal with target specifications. Hence, the basic devotion is to attain the optimum value product which entirely satisfies the views and perceptions of consumers. Selection of best fabric among several alternatives in the presence of contradictory measures is a disputing problem in multicriteria decision-making.

In the current study, the analytic hierarchy process (AHP) and preference ranking organization method for enrichment evaluation (PROMETHEE) are proficiently used to solve the problem in selection of branded woven shawls. AHP method verifies comparative weights of the criteria selection, while the ranking of fabric alternatives grounded on specific net-outranking flows is executed through PROMETHEE II method.

The collective AHP and PROMETHEE approaches are applied for the useful accomplishment of grading of branded shawls based on multicriteria weights, used for effective selection of fabric materials in the textile market.

In the apparel industry, fabric and garment manufacturers often rely on hit-and-trial methods, leading to significant wastage of valuable resources and time, in achieving the desirable fabric qualities. The implementation of the findings can assist apparel manufacturers in streamlining their fabric selection processes based on multiple criteria. By adopting this method, industry players can make informed decisions, ensuring a balance between quality standards and consumer expectations, thereby enhancing both product value and market competitiveness.

The methods of Visual PROMETHEE and AHP are assimilated to offer a complete method for the selection and grading of fabrics with reference to multiple selection criteria.

This study aims to integrate the resource-based view (RBV) with other theories that consider external factors necessary to respond successfully to dynamic and uncertain entrepreneurial business conditions.

The paper introduces an multi-criteria decision-making (MCDM) approach, utilizing the axial-distance-based aggregated measurement (ADAM) method with weights determined by the preference selection index (PSI) method, to rank eight European countries based on the Global Entrepreneurship Monitor (GEM) data. Additionally, the paper extends the existing entrepreneurial ecosystem taxonomy (EET), offering an additional classification.

The performed analysis emphasizes the importance and necessity of involving different dimensions of EE in assessing the countries' entrepreneurship performance, which facilitates creating adequate policy measures.

The crucial limitations are assessments based only on the GEM data from a particular period, possibly leading to a certain bias. Future research should involve data from various resources to increase the results' reliability.

The ranking results and country classification obtained using the ADAM-based approach and two distinct taxonomies served as the basis for formulating tailored policy recommendations, aiming to formulate tailored policy implications for increasing the number of new entrepreneurs and improving innovativeness, sustainability and internationalization of existing entrepreneurs for each group of countries.

Composite materials have revolutionized the aerospace industry by offering superior structural qualities over traditional elements. This study aims to focus on the development and testing of bamboo natural fiber-based composites enhanced with SiO₂ nanoparticles.

The investigation involved fabricating specimens with varying nanoparticle compositions (0, 10 and 20%) and conducting tensile, flexural, impact and fracture toughness tests. Results indicated significant improvements in mechanical properties with the addition of nanoparticles, particularly at a 10% composition level.

This study underscores the potential of natural fiber composites, highlighting their environmental friendliness, cost-effectiveness and improved structural properties when reinforced with nanoparticles. The findings suggest an optimal ratio for nanoparticle integration, emphasizing the critical role of precise mixing proportions in achieving superior composite performance.

The tensile strength, flexural strength, impact resistance and fracture toughness exhibited notable enhancements compared with the 0 and 20% nanoparticle compositions. The 10% composition showed the most promising outcomes, showcasing increased strength across all parameters.

This study aims to provide a scientific framework for the selection of suitable substation technology in an electrical power distribution network.

The present paper focuses on adopting an integrated multi-criteria decision-making approach using the Delphi method, analytic hierarchy process (AHP) and technique for order preference by similarity to ideal solution (TOPSIS). The AHP is used to ascertain the criteria weights, and the TOPSIS is used for choosing the most fitting technology among choices of air-insulated substation, gas-insulated substation (GIS) and hybrid substation, to guarantee educated and supported choice.

The results reveal that the GIS is the most preferred technology by area experts, considering all the criteria and their relative preferences.

The current research has implications for public and private organizations responsible for the management of electricity in India, particularly the distribution system as the choice of substations is an essential component that has a strong impact on the smooth functioning and performance of the energy distribution in the country. The implementation of the chosen technology not only reduces economic losses but also contributes to the reduction of power outages, minimization of energy losses and improvement of the reliability, security, stability and quality of supply of the electrical networks.

The study explores the impact of substation technology installation in terms of its economic and environmental challenges. It emphasizes the need for proper installation checks to avoid long-term environmental hazards. Further, it reports that the economic benefits should not come at the cost of ecological degradation.

The present study is the first to provide a decision support framework for the selection of substation technologies using the hybrid AHP-TOPSIS approach. It also provides a cost–benefit analysis with short-term and long-term horizons. It further pinpoints the environmental issues with the installation of substation technology.

This study aims to examine how the thickness of layers and printing speed impact the energy absorption capacity of honeycomb structures through drop-weight experiments. In addition, the effect of printing orientation on the resulting microstructure and mechanical performance was targeted to be examined.

In this paper, after manufacturing test specimens using fused deposition modeling technique with three distinct layer thicknesses (0.16 mm, 0.20 mm and 0.28 mm) and printing speeds (40 mm/min, 50 mm/min and 70 mm/min), drop weight tests were carried out. Then to see the effect of printing orientation on mechanical performance, three-point-bending tests were performed and damage mechanisms were comparatively examined through scanning electron microscopy.

An increase in layer thickness from 0.16 mm to 0.28 mm resulted in a notable 37% decrease in the impact resistance of the printed part. In addition, increasing the printing speed from 50 to 70 mm/min reduced the energy absorption capacity of the printed part by approximately 36.5%. Moreover, in terms of printing direction, transversely printed specimens showed 10% lower flexural strength than longitudinally printed specimens. Finally, scanning electron microscopy (SEM) observation showed that internal defects were more prominent in transversely printed specimens, resulting in premature failure. Furthermore, delamination was also detected in transversely printed specimens as another damage mechanism accelerating material failure.

It is seen that the effect of printing parameters on the fundamental mechanical properties including tensile strength, strain at break, ductility and elastic modulus were studied by various researchers. However, to the best of authors’ knowledge, the effect of printing speed and layer thickness on the energy absorption of polylactic acid based hexagonal honeycomb was not encountered. In addition, in-depth SEM analysis to discover the influence of printing direction significantly contributes to the literature.

The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local communities impacted by disasters.

Through a bibliometric methodology (co-citation, bibliographic coupling and co-word analysis), this study presents significant themes in the past, current and future predictions on the role of KM in disaster management. In this review paper, 437 publications were retrieved from the Web of Science and analyzed through VOSviewer software to visualize and explore the knowledge map on the subject domain.

Findings suggest that the significant themes derived are centralized to disaster preparedness during disaster and disaster postrecovery. This review presents a state-of-art bibliometric analysis of the crucial role of KM in building networks and interconnection among relevant players and stakeholders involved in disaster management.

The main implication of this study is how the authorities, stakeholders and local community can integrate the KM system within the three stages of disasters and the crucial role of technologies and social media in facilitating disaster management.

To the best of the authors’ knowledge, this is the first study to present a bibliometric analysis in mapping KM’s past, present and future trends in disaster management.

Effective knowledge management has played a crucial role in propelling the green transformation of organisations and industries. Nevertheless, its underutilisation in the real estate sector has impeded the progress of green transformation. Therefore, the purpose of this paper is to offer a theoretical and practical analysis of the green transformation of the real estate industry through the lens of knowledge management and to provide a valuable reference to facilitate the industry’s green transformation.

This study entailed applying induction and deduction method, using China’s real estate industry as a typical case, and collecting and analysing the public data, corporate reports and literature of China’s real estate industry. On this basis, the authors conducted an in-depth analysis of the mechanisms through which the green transformation has empowered the sustainable development of China’s real estate industry, as well as the critical role of knowledge management.

The study has revealed that the primary challenges encountered by China’s real estate industry during the green transformation have stemmed from significant disparities in the knowledge base among different industry entities, the complexities related to knowledge integration and the “difficulty” of applying green knowledge across the entire life cycle. To address these issues, the authors recommend several strategic actions, including creating a dedicated green knowledge platform for the real estate industry, establishing a knowledge-sharing mechanism, enhancing knowledge acquisition on both the supply and demand sides and intensifying the focus on the application of green knowledge within the real estate industry.

This research holds considerable theoretical and practical significance concerning the comprehension and promotion of knowledge management’s role in the green transformation of China’s real estate industry. These insights can be applied to significantly enhance the theoretical framework of knowledge management, and the research outcomes provide substantial support for propelling the green transformation in China’s real estate industry and contributing to the sustainable development of the overall economy in China.

From a knowledge management perspective, this study introduces a series of solutions and recommendations, presenting new research ideas and pathways for advancing the green transformation of the real estate industry. In addition to guiding the industry’s sustainable development, it also significantly contributes to enhancing the theoretical framework of knowledge management.

This study aims to develop a system dynamics (SD) simulation model to forecast the performance of the Indonesian halal industry to verify whether decision-making has been properly executed to increase the contribution of the success factors.

This study establishes a SD-based model using three subsystems, namely, the halal assurance and certification process, the government and the export–import subsystem.

The best scenario is the third scenario or the combined scenario of providing facilities to accelerate the micro-, small- and medium-sized enterprises (MSMEs), carrying out halal certification, reducing the time for the certification process, increasing the number of the halal auditor and increasing the awareness from enterprises (both MSMEs and large enterprises) to carry out halal certification because of the internal and external motivations.

First, the demand for certification of the type of food product is acquired using the number of SMEs and large companies. Second, the model does not include the government budget as a constraint.

This study provides essential insights into implementing the best policies that can increase the performance of the halal industry.

This study revealed that relevant policy scenarios could be built after simulating and analyzing each scenario’s effect on the halal industry’s performance.

This study will enrich the scientific insight related to institutional theory and resource-based view, as those theories identify success factors associated with the performance of the halal food industry. This study will also enrich the scientific insight related to system dynamic methodology, as it is used to model the performance of the halal industry.

The present work aims to prepare biocomposites blend based on linear low density polyethylene/ starch without using harmful chemicals to improve the adhesion between two phases. Also, the efficiency of essential oils as green plasticizers and natural antimicrobial agents were evaluated.

Barrier properties and biodegradation behavior of linear low density polyethylene/starch (LLDPE/starch) blends plasticized with different essential oils including moringa oleifera and castor oils wereassessed as a comparison with traditional plasticizer such as glycerol. Biodegradation behavior forLLDPE/starch blends was monitored by soil burial test. The composted samples were recovered then washed followed by drying, and weighting samples after 30, 60, and 90 days to assess the change in weight loss. Also, mechanical properties including retention values of tensile strength and elongation at break were measured before and after composting. Furthermore, scanning electron microscope (SEM) was used to evaluate the change in the morphology of the polymeric blends. In addition to, the antimicrobial activity of plasticized LLDPE/starch blends films was evaluated using a standard plate counting technique.

The results illustrate that the water vapor transition rate increases from 2.5 g m⁻² 24 h⁻¹ for LLDPE/5starch to 4.21 g m⁻² 24 h⁻¹ and 4.43 g m⁻² 24 h⁻¹ for castor and moringa oleifera respectively. Also, the retained tensile strength values of all blends decrease gradually with increasing composting period. Unplasticized LLDPE/5starch showed highest tensile strength retention of 91.6% compared to the other blends that were 89.61, 88.49 and 86.91 for the plasticized LLDPE/5starch with glycerol, castor and M. oleifera oils respectively. As well as, the presence of essential oils in LLDPE/ starch blends increase the inhibition growth of escherichia coli, candida albicans and staphylococcus aureus.

The objective of this work is to develop cost-effective and environmentally-friendly methods for preparing biodegradable polymers suitable for packaging applications.