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The aim of this study is to present an overview of sustainable practices in the development of waterproof breathable fabrics for garments. It aims to provide insights into the current state of academic research in this domain and identify and analyze major sustainable trends in the field.

This study conducts a thorough examination of research publications sourced from the Scopus database spanning the years 2013–2023 by employing a systematic approach. The research utilizes both descriptive analysis and content analysis to identify trends, notable journals and leading countries in sustainable waterproof breathable fabric development.

The study reveals a notable increase in studies focusing on sustainable approaches in the development of waterproof breathable fabrics for garments. Descriptive analysis highlights the most prominent journal and leading country in terms of research volume. Content analysis identifies four key trends: minimizing chemical usage, developing easily degradable materials, creating fabrics promoting health and well-being and initiatives to reduce energy consumption.

The main limitation of this research lies in its exclusive reliance on the Scopus database.

The insights derived from this study offer practical guidance for prospective researchers interested in investigating sustainable approaches to developing waterproof breathable fabric for garments. The identified trends provide a foundation for aligning research endeavors with contemporary global perspectives, facilitating the integration of sustainable methodologies into the garment industry.

This systematic literature review contributes original insights by synthesizing current research trends and outlining evolving sustainable practices in the development of waterproof breathable fabrics. The identification of key focus areas adds a novel perspective to existing knowledge.

The purpose of this paper is to analyse the development and evolution of industrial innovation ecosystems of Around-Tongji Knowledge Economy Circle from the three levels mentioned above, focusing on knowledge-producing populations, core populations and service-supporting populations, and to further develop this research framework by combining with the latest developments.

Based on the five-helix theory and economic census statistical data, this paper adopts geographic information system technology and examines the characteristics of the industrial innovation ecosystem and the synergistic evolution process in Around-Tongji knowledge economy circle.

The knowledge product populations lead the development of industries in Around-Tongji Knowledge Economy Circle. It contributes political capital output for the government. It innovates community cooperation and governance mode, and it improves the natural ecological environment. In the face of the changes and challenges in the development environment, the future development must be recognised from the height of the iterative development of the interaction mode between university knowledge production and economic and social development.

Based on the five-helix theory and economic census statistical data, this paper examines the characteristics of the industrial innovation ecosystem and the synergistic evolution process in Around-Tongji Knowledge Economy Circle. It further expands the research framework used to develop a synergistic evolution model, which reveals the interactive and synergistic relationship among the populations and the evolution characteristics of the entire industrial innovation ecosystem. This paper also provides useful perspectives for the study of the industrial innovation ecosystem.

This study aims to examine the role of big data analytics (BDA) powered by artificial intelligence (AI) in improving sustainable performance (SP) through green supply chain collaboration (GSCC), sustainable manufacturing (SM) and environmental process integration (EPI).

Data was collected from 249 supply chain professionals working at various manufacturing firms, and hypotheses were tested through a quantitative method using PLS-SEM with the help of SmartPLS version 4 to validate the measurement model.

This study identified that BDA-AI significantly and positively affects GSCC, SM and EPI. Similarly, the results showed that GSCC significantly and positively affects SP. At the same time, SM and EPI have an insignificant effect on SP. The GSCC found a significant relationship between BDA-AI and SP for mediation. However, SM and environmental performance integration did not mediate the relationship between BDA and AI and SP.

This research evaluated a second-order model and tested SP in conjunction with the dynamic capability theory in the manufacturing industry of Pakistan. Therefore, this research could be beneficial for researchers, manufacturers and policymakers to attain sustainable goals by implementing the BDA-AI in the supply chain.

Auxetics metamaterials show high performance in their specific characteristics, while the absolute stiffness and strength are much weaker due to substantial porosity. This paper aims to propose a novel auxetic honeycomb structure manufactured using selective laser melting and study the enhanced mechanical performance when subjected to in-plane compression loading.

A novel composite structure was designed and fabricated on the basis of an arrowhead auxetic honeycomb and filled with polyurethane foam. The deformation mechanism and mechanical responses of the structure with different structural parameters were investigated experimentally and numerically. With the verified simulation models, the effects of parameters on compression strength and energy absorption characteristics were further discussed through parametric analysis.

A good agreement was achieved between the experimental and simulation results, showing an evidently enhanced compression strength and energy absorption capacity. The interaction between the auxetic honeycomb and foam reveals to exploit a reinforcement effect on the compression performance. The parametric analysis indicates that the composite with smaller included angel and higher foam density exhibits higher plateau stress and better specific energy absorption, while increasing strut thickness is undesirable for high energy absorption efficiency.

The results of this study served to demonstrate an enhanced mechanical performance for the foam filled auxetic honeycomb, which is expected to be exploited with applications in aerospace, automobile, civil engineering and protective devices. The findings of this study can provide numerical and experimental references for the design of structural parameters.

Auxetic sandwich structures are gaining attention because of the negative Poisson’s ratio effect offered by these structures. Re-entrant core was one configuration of the auxetic structures. There is a growing concern about the design and behavior of re-entrant cores in aerospace, marine and protection applications. Several researchers proposed various designs of re-entrant core sandwiches with various materials. The purpose of this study is to review the most recent advances in re-entrant core sandwich structures. This review serves as a guide for researchers conducting further research in this wide field of study.

The re-entrant core sandwich structures were reviewed in terms of their design improvements, impact and quasi-static crushing responses. Several design improvements were reviewed including 2D cell, 3D cell, gradient, hierarchical and hybrid configurations. Some common applications of the re-entrant core sandwiches were given at the end of this paper with suggestions for future developments in this field.

Generally, the re-entrant configuration showed improved energy absorption and impact response among auxetic structures. The main manufacturing method for re-entrant core manufacturing was additive manufacturing. The negative Poisson’s ratio effect of the re-entrant core provided a wide area of research.

Generally, re-entrant cores were mentioned in the review articles as part of other auxetic structures. However, in this review, the focus was solely made on the re-entrant core sandwiches with their mechanics.

The purpose of this study is investigate the transient aerodynamic characteristics of high-speed vehicle with body roll motion under crosswind condition to improve aerodynamic stability.

An overset mesh was used to simulate the rolling motion of the vehicle body. A wind tunnel experiment was conducted to validate the numerical method.

The results revealed that the vehicle’s aerodynamic characteristics changed periodically with the body’s periodic motion. In the absence of crosswind, the pressure distribution on the left and right sides of the vehicle body was symmetrical, and the speed streamline flowed to the rear of the vehicle in an orderly manner. The maximum aerodynamic lift observed in the transient simulation was −0.089, which is approximately 0.70 times that of the quasi-static simulation experiment. In addition, the maximum aerodynamic side force observed in the transient simulation was 0.654, which is approximately 1.25 times that of the quasi-static simulation experiment.

The aerodynamic load varies periodically with the vehicle body’s cyclic motion. However, the extreme values of the aerodynamic load do not occur when the vehicle body is at its highest or lowest position. This phenomenon is primarily attributed to the mutual interference of airflow viscosity and the hysteresis effect in the flow field, leading to the formation of a substantial vortex near the wheel. Consequently, the aerodynamic coefficient at each horizontal position becomes inconsistent during the periodic rolling of the vehicle body.

This paper aims to better control the mechanical properties and functional properties of NiTi alloy.

NiTi alloy samples with equal atomic ratio were formed by selective laser melting (SLM). X-ray diffraction (XRD), differential scanning calorimetry (DSC), scanning electron microscopy and tensile testing methods were used to study the effects of different laser power and scanning speed on the densification behavior, phase transformation characteristics and mechanical properties of NiTi alloy.

Compared with the laser power, the variation of the keyhole effect caused by the change of scanning speed is more intense, which has a greater effect on the densification behavior of SLM NiTi alloy. The effect of the laser power on the phase transition temperature is small. The increase of scanning speed weakens the burning degree of Ni element, so phase transition temperature decreases. The results of DSC test and tensile test show that the scanning velocity can significantly change the phase transition temperature, martensite twins reorientation and stress–strain behavior of SLM NiTi alloy.

This study provides a potential method to regulate the mechanical properties and functional properties of NiTi shape memory alloy in the future and NiTi alloys formed by SLM with good elongation were obtained because the Supercellular crystal structure formed during the nonequilibrium solidification of SLM and the superfine precipitates dispersed in the alloy prevented the dislocation formation.

316 L stainless steel alloy is potentially the most used material in the selective laser melting (SLM) process because of its versatility and broad fields of applications (e.g. medical devices, tooling, automotive, etc.). That is why producing fully functional parts through optimal printing configuration is still a key issue to be addressed. This paper aims to present an entirely new framework for simultaneously reducing surface roughness (SR) while increasing the material processing rate in the SLM process of 316L stainless steel, keeping fundamental mechanical properties within their allowable range.

Considering the nonlinear relationship between the printing parameters and features analyzed in the entire experimental space, machine learning and statistical modeling methods were defined to describe the behavior of the selected variables in the as-built conditions. First, the Box–Behnken design was adopted and corresponding experimental planning was conducted to measure the required variables. Second, the relationship between the laser power, scanning speed, hatch distance, layer thickness and selected responses was modeled using empirical methods. Subsequently, three heuristic algorithms (nonsorting genetic algorithm, multi-objective particle swarm optimization and cross-entropy method) were used and compared to search for the Pareto solutions of the formulated multi-objective problem.

A minimum SR value of approximately 12.83 μm and a maximum material processing rate of 2.35 mm³/s were achieved. Finally, some verification experiments recommended by the decision-making system implemented strongly confirmed the reliability of the proposed optimization methodology by providing the ultimate part qualities and their mechanical properties nearly identical to those defined in the literature, with only approximately 10% of error at the maximum.

To the best of the authors’ knowledge, this is the first study dealing with an entirely different and more comprehensive approach for optimizing the 316 L SLM process, embedding it in a unique framework of mechanical and surface properties and material processing rate.

The concept of a smart city is becoming more closely linked to tourism destinations. The relationship between the environment, sustainability and tourism has become more interrelated. An eco-friendly city environment can influence tourists’ decisions, but tourist flows can also negatively impact the local ecosystem by increasing energy consumption, air pollution and environmental sustainability. In this research letter, the authors reverse this perspective, moving into smart city trajectories, highlighting that increasing tourism in cities drives the city to advance a green transition to respond to the imbalance generated by large flows of tourists that increase the users-density of the city. Thus, the purpose of this study is to investigate the symbiotic relationship between increasing tourism in cities and the imperative for a green transition.

This study builds on the generalized method of moment approach and an 11-year panel data set covering 30 Italian cities to investigate the relationship between tourism flow and the cities’ green practices embedded in the smart environment dimension.

This study provides empirical evidence that a large flow of tourists in cities positively affects the cities’ green transition, contributing to the advancement of smart tourism and smart city debate.

The study focuses only on one country, and although its results may be generalizable to the European Union, it is not representative at a global level and requires further research.

This study offers practical implications underscoring the benefits of large tourism inflow in managing and implementing smart city projects.

Tourism and overtourism may lead to a city reaction regarding green practices, which can have a multiplier benefit on the urban environment.

To the best of the authors’ knowledge, this is one of the first study attempt to investigate the tourism flow as a driver of smart city implementation, considering the city’s reaction to tourism and overtourism conditions in the city that would lead to the ecological and sustainable collapse of a city.

The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.