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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.

As an advanced manufacturing method, additive manufacturing (AM) technology provides new possibilities for efficient production and design of parts. However, with the continuous expansion of the application of AM materials, subtractive processing has become one of the necessary steps to improve the accuracy and performance of parts. In this paper, the processing process of AM materials is discussed in depth, and the surface integrity problem caused by it is discussed.

Firstly, we listed and analyzed the characterization parameters of metal surface integrity and its influence on the performance of parts and then introduced the application of integrated processing of metal adding and subtracting materials and the influence of different processing forms on the surface integrity of parts. The surface of the trial-cut material is detected and analyzed, and the surface of the integrated processing of adding and subtracting materials is compared with that of the pure processing of reducing materials, so that the corresponding conclusions are obtained.

In this process, we also found some surface integrity problems, such as knife marks, residual stress and thermal effects. These problems may have a potential negative impact on the performance of the final parts. In processing, we can try to use other integrated processing technologies of adding and subtracting materials, try to combine various integrated processing technologies of adding and subtracting materials, or consider exploring more efficient AM technology to improve processing efficiency. We can also consider adopting production process optimization measures to reduce the processing cost of adding and subtracting materials.

With the gradual improvement of the requirements for the surface quality of parts in the production process and the in-depth implementation of sustainable manufacturing, the demand for integrated processing of metal addition and subtraction materials is likely to continue to grow in the future. By deeply understanding and studying the problems of material reduction and surface integrity of AM materials, we can better meet the challenges in the manufacturing process and improve the quality and performance of parts. This research is very important for promoting the development of manufacturing technology and achieving success in practical application.

This study aims to improve the complexity of chaotic systems and the security accuracy of information encrypted transmission. Applying five-dimensional memristive Hopfield neural network (5D-HNN) to secure communication will greatly improve the confidentiality of signal transmission and greatly enhance the anticracking ability of the system.

Chaos masking: Chaos masking is the process of superimposing a message signal directly into a chaotic signal and masking the signal using the randomness of the chaotic output. Synchronous coupling: The coupled synchronization method first replicates the drive system to get the response system, and then adds the appropriate coupling term between the drive The synchronization error and the coupling term of the system will eventually converge to zero with time. The synchronization error and coupling term of the system will eventually converge to zero over time.

A 5D memristive neural network is obtained based on the original four-dimensional memristive neural network through the feedback control method. The system has five equations and contains infinite balance points. Compared with other systems, the 5D-HNN has rich dynamic behaviors, and the most unique feature is that it has multistable characteristics. First, its dissipation property, equilibrium point stability, bifurcation graph and Lyapunov exponent spectrum are analyzed to verify its chaotic state, and the system characteristics are more complex. Different dynamic characteristics can be obtained by adjusting the parameter k.

A new 5D memristive HNN is proposed and used in the secure communication

To develop electrode materials for supercapacitor with superior electrochemical performance and simple preparation process, the purpose of this study is to prepare flexible CC/NiS/a-NiS electrodes with self-supporting structure by loading hydrothermally synthesized a-NiS particles along with nano-NiS on carbon cloth by electroplating method.

The effects of current densities, temperatures and pH values on the loading amount and uniformity of the active substances during the plating process were investigated on the basis of optimization of surface morphology, crystalline structure and electrochemical evaluation as the cyclic voltammetry curves, constant current charge–discharge curves and AC impedance.

The a-NiS particles on CC/NiS/a-NiS were mostly covered by the plated nano-NiS, which behaved as a bulge and provided a larger specific surface area. The CC/NiS/a-NiS electrode prepared with the optimized parameter exhibited a specific capacitance of 115.13 F/g at a current density of 1 A/g and a Coulomb efficiency of 84% at 5 A/g, which is superior to that of CC/NiS electrode prepared by electroplating at a current density of 10 mA/cm², a temperature of 55°C and a pH of 4, demonstrating its fast charge response of the electrode and potential application in wearable electronics.

This study provides an integrated solution for the development of specifically structured NiS-based electrode for supercapacitor with simple process, low cost and high electrochemical charge/discharge performance, and the simple and easy-to-use method is also applicable to other electrochemically active composites.

This study examines how informal business networks achieve marketing goals in socially uncertain contexts. Drawing from multiple historical sources, Shangbangs, a type of business network that thrived in pre-1949 China, are analyzed.

The Critical Historical Research Method (CHRM) undergirds a study of Shangbangs’ historicity (i.e. their socio-historically embedded multiplicity, including organizational forms, activities and connotations.

As informal regional, professional, project-based, special-product-based or mixed marketing networks, Shangbangs relied on “flexible specialization” and coupled multiple business needs to market goods and services, business organizations, specific social values and, when necessary, to debrand business rivals.

This analysis extends theories about marketing networks by probing their subtypes, diverse marketing activities, multipronged channels and relationship building with social entities (including underground societies, business associations and guilds) in response to pre-1949 China’s market uncertainties. Substantiating an alternative approach to “flexible specialization” and marketing innovations within the pre-1949 Chinese economy shows how a parallel theoretical framework can complement western-based marketing theories.

This first comprehensive analysis of Shangbangs, an innovative historical Chinese marketing network outside the conventional market-corporate dichotomy, can inform theory building for marketing strategy-making and management conditioned by social contexts.

Despite the knowledge transfer between projects has received increasing attention from scholars, few scholars still conduct comprehensive research on inter-project knowledge transfer from both horizontal and vertical perspectives. Besides, knowledge transfer is affected by multiple antecedent conditions, and these factors should be combined for analysis. Therefore, this paper aims to explore the key factors influencing knowledge transfer between projects using the fuzzy-set qualitative comparative analysis (fsQCA) method from both horizontal and vertical perspectives and how these factors combine to improve the effectiveness of knowledge transfer (EKT) between projects.

First, nine factors affecting knowledge transfer between projects were identified, which were from the four dimensions of subject, relationship, channel, and context, namely temporary nature (TN), time urgency (TU), transmit willingness (TW), receive willingness (RW), trust (TR), project-project transfer channels (PPC), project-enterprise transfer channels (PEC), organizational atmosphere (OA), and motivation system (MS). Then, the source of the samples was determined and the data from the respondents was collected for analysis. Following the operation steps of the fsQCA method, variable calibration, single condition necessity analysis, and configuration analysis were carried out. After that, the configurations of influencing factors were obtained and the robustness test was conducted.

The results of the fsQCA method show that there are five configurations that can obtain better EKT between projects. Configuration 3 (∼TN * ∼TU * TW * RW * TR * ∼PPC * PEC * MS) has the highest consistency, indicating that it has the highest degree of the explanatory variable subset. Configuration 1 (∼TN * ∼TU * TW * RW * PEC * OA * MS) has the highest coverage, meaning that this configuration can explain most cases. Also, the five configurations were divided into three types: vertical transfer, horizontal-vertical transfer, and channel-free transfer category.

Firstly, this study explores the key factors influencing knowledge transfer between projects from four dimensions, which presents the logical chain of influencing factors more clearly. Then, this study divided the five configurations obtained into three categories according to the transfer direction: vertical, horizontal-vertical, and channel-free transfer, which gives implications to focus on both horizontal knowledge transfer (HKT) and (VKT) when studying knowledge transfer between projects. Lastly, this study helps to realize the exploration of combined improvement strategies for EKT, thereby providing meaningful recommendations for enterprises and project teams to facilitate knowledge transfer between projects.

The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

For privacy protection, federated learning based on data separation allows machine learning models to be trained on remote devices or in isolated data devices. However, due to the limited resources such as bandwidth and power of local devices, communication in federated learning can be much slower than in local computing. This study aims to improve communication efficiency by reducing the number of communication rounds and the size of information transmitted in each round.

This paper allows each user node to perform multiple local trainings, then upload the local model parameters to a central server. The central server updates the global model parameters by weighted averaging the parameter information. Based on this aggregation, user nodes first cluster the parameter information to be uploaded and then replace each value with the mean value of its cluster. Considering the asymmetry of the federated learning framework, adaptively select the optimal number of clusters required to compress the model information.

While maintaining the loss convergence rate similar to that of federated averaging, the test accuracy did not decrease significantly.

By compressing uplink traffic, the work can improve communication efficiency on dynamic networks with limited resources.

High-resolution remote sensing images possess a wealth of semantic information. However, these images often contain objects of different sizes and distributions, which make the semantic segmentation task challenging. In this paper, a bidirectional feature fusion network (BFFNet) is designed to address this challenge, which aims at increasing the accurate recognition of surface objects in order to effectively classify special features.

There are two main crucial elements in BFFNet. Firstly, the mean-weighted module (MWM) is used to obtain the key features in the main network. Secondly, the proposed polarization enhanced branch network performs feature extraction simultaneously with the main network to obtain different feature information. The authors then fuse these two features in both directions while applying a cross-entropy loss function to monitor the network training process. Finally, BFFNet is validated on two publicly available datasets, Potsdam and Vaihingen.

In this paper, a quantitative analysis method is used to illustrate that the proposed network achieves superior performance of 2–6%, respectively, compared to other mainstream segmentation networks from experimental results on two datasets. Complete ablation experiments are also conducted to demonstrate the effectiveness of the elements in the network. In summary, BFFNet has proven to be effective in achieving accurate identification of small objects and in reducing the effect of shadows on the segmentation process.

The originality of the paper is the proposal of a BFFNet based on multi-scale and multi-attention strategies to improve the ability to accurately segment high-resolution and complex remote sensing images, especially for small objects and shadow-obscured objects.

Big data marketing analytics (BDMA) has been discovered to be a key contributing factor to developing necessary marketing capabilities. This research aims to investigate the impact of the technology and information quality of BDMA on the critical marketing capabilities by differentiating between firms with low and high perceived market performance.

The responses were collected from marketing professionals familiar with BDMA in North America (N = 236). The analysis was done with partial least squares-structural equation modelling (PLS-SEM).

The results indicated positive and significant relationships between the information and technology quality as exogenous constructs and the endogenous constructs of the marketing capabilities of marketing planning, implementation and customer relationship management (CRM) with mainly moderate effect sizes. Differences in the path coefficients in the structural model were detected between firms with low and high perceived market performance.

This research indicates the critical role of technology and information quality in developing marketing capabilities. The study discovered heterogeneity in the sample population when using the low and high perceived market performance as the source of potential heterogeneity, the presence of which would likely cause a threat to the validity of the results in case heterogeneity is not considered. Thus, this research builds on previous research by considering this issue.