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This study adopts the elaboration likelihood model and configuration perspectives to explore the internal mechanisms underlying the influence of live streaming on consumer trust building and purchase intention.

This study invited 757 experienced live streaming e-commerce users from Chinese platforms such as TikTok and RED, who participated in survey by filling questionnaires collected online. The research employed a mixed-method approach using SEM and fsQCA. SEM was utilized to analyze quantitative data to determine the direct and mediated relationships within product trust, while fsQCA served as a complement to identify the combinations of conditions that enhance product trust.

The findings reveal three important insights. Firstly, in the context of live streaming e-commerce, both product characteristics and streamer characteristics significantly influence consumers' trust in products. The para-social interaction plays a partial mediating role in the relationship between streamer characteristics and product trust. Secondly, four distinct paths are identified that contribute to enhancing product trust in live streaming e-commerce. Thirdly, PSI emerging as a core condition across all four paths, underscores the importance for merchants to foster positive social interactions with consumers beyond the live streaming environment.

This study enhances understanding of the dynamic live streaming e-commerce industry, offering insights into consumer behavior and practical guidance for merchants seeking to build engaged, trustworthy customer relationships.

Scholars and practitioners alike are paying attention to entrepreneurial orientation (EO) as an antecedent of the financial performance of SMEs. Other factors foster and improve SMEs' financial performance. This paper aims to shed the light on other two different strategic orientations that may help enhance SMEs' financial performance in addition to EO, namely; market orientation (MO) and brand orientation (BO).

The three different important strategic orientations are explored through two different studies. The first study was conducted to determine the different effects of the three orientations on SMEs' financial performance. Data were collected using a questionnaire among a convenient sample (131) of business owners/managers, and next PLS-SEM was used for data analysis. The financial performance of firms in the second study is hypothesized to be an outcome of a combination of different strategic orientations; therefore, the fsQCA method is applied to explore the causal recipes of those orientations.

The paper concluded that the three different strategic orientations are collectively, of paramount importance to strategic managers of SMEs.

The brand, market and EOs have been discussed discretely in previous studies and this study attempted to provide managers/owners of SMEs with a holistic view of the three different orientations and the amalgamation among them to be beneficial for better financial performance.

This study analyses the direct and indirect impacts of risks on the service-oriented construction supply chain and its resilience during disruptions.

We utilised the service-dominant logic, contingency and information processing theories to identify service-oriented construction supply chain characteristics and risk behaviours during turbulent times.

Our analysis of 285 construction companies with a strong service orientation revealed that the proposed risk model explains a 33.6% variance in supplier performance, 46.4% operational performance, 47.1% customer satisfaction and 46.5% financial performance. Our findings highlight the importance of effectively monitoring risks in service-oriented construction supply chains and examining complex networks in which risk variables impact construction supply chain performance.

This study examines the influence mechanisms between risks and actors’ performance in construction supply chains, taking a service-oriented perspective.

Previous studies emphasise the risks that construction companies encounter from disruptions, such as maintaining operations and enhancing performance. Nevertheless, the research still needs to establish the transmission mechanism of the simultaneous impact (direct and indirect) of all forms of risk on supply chain performance.

A transmission tower usually experiences bolt loosening under long-term alternating cyclic load, which may lead to collapse of the tower in extreme operating conditions. The paper aims to propose a data-driven identification method for bolt looseness of complicated tower structures based on reduced-order models and numerical simulations to perceive and evaluate the health state of a tower in operation.

The equivalent stiffnesses of three types of bolt joints under various loosening scenarios are numerically determined by three-dimensional finite element (FE) simulations. The order of the FE model of a tower structure with bolt loosening is reduced by means of the component modal synthesis method, and the dynamic responses of the reducer-order model under calibration loads are simulated and used to create the dataset. An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed.

An identification model for bolt looseness of the tower structure based on convolutional neural networks driven by the acceleration sensors is constructed and the applicability of the model is investigated. It is shown that the proposed method has a high identification accuracy and strong robustness to data noise and data missing. Meanwhile, the method is less dependent on the number and location of sensors and is easier to apply in real transmission lines.

This paper proposes a data-driven identification method for bolt looseness of a complicated tower structure based on reduced-order models and numerical simulations. Non-linear relationships between equivalent stiffness of bolted joints and bolt preload depicting looseness are obtained and reduced-order model of tower structure with bolt looseness is established. Finally, this paper investigates applicability of identification model for bolt looseness.

By incorporating the defect feature information, an ML-based linkage between defects and fatigue life unaffected by the time scale is developed, the primary focus is to quantitatively assess and elucidate the impact of different defect features on fatigue life.

A machine learning (ML) framework is proposed to predict the fatigue life of LPBF-built Hastelloy X utilizing microstructural defects identified through nondestructive detection prior to fatigue testing. The proposed method combines nondestructive micro-computerized tomography (micro-CT) technique to comprehensively analyze the size, location, morphology and distribution of the defects.

In the test set, SVM-based fatigue life prediction exhibits the highest accuracy. Regarding the defect information, the defect size significantly affects fatigue life, and the diameter of the circumscribed sphere of the largest defect has a critical effect on fatigue life.

This comprehensive approach provides valuable insights into the fatigue mechanism of structural materials in defective states, offering a novel perspective for better understanding the influence of defects on fatigue performance.

This study aims to enhance singularity detection in non-stationary signals by introducing the frugal wavelet transform (FrugWT), a novel variation of the wavelet transform.

The frugal wavelet transform, based on a modified first-level discrete wavelet transform decomposition, is compared with traditional discrete wavelet transform. The performance of these transforms is evaluated using signals derived from finite element analysis of a functionally graded tapered beam made of porous material.

The frugal wavelet transform significantly outperforms the discrete wavelet transform in detecting singularities within the analyzed signals. It offers more accurate detection of singularities and local abrupt changes, demonstrating its effectiveness for signal analysis.

This paper contributes to the field by proposing the relative frugal wavelet transform as a novel enhancement of the frugal wavelet transform. It provides a significant improvement in detecting subtle singularities in one-dimensional signals, with potential applications in advanced signal processing and analysis across various scientific domains such as electrical engineering, automotive, aerospace engineering, civil engineering, marine engineering and medical signal processing.

This study aims to focus on the main materials used in consolidation processes of illuminated paper manuscripts and leather binding.

For each material, chemical structure, chemical composition, molecular formula, solubility, advantages, disadvantages and its role in treatment process are presented.

This study concluded that carboxy methyl cellulose, hydroxy propyl cellulose, methyl cellulose, cellulose acetate, nanocrystalline cellulose, funori, sturgeon glue, poly vinyl alcohol, chitosan, chitosan nanoparticles (NPs), gelatin, aquazol, paraloid B72 and hydroxyapatite NPs were the most common and important materials used for the consolidation of illuminated paper manuscripts. For the leather bindings, hydroxy propyl cellulose, polyethylene glycol, oligomeric melamine-formaldehyde resin, acrylic wax SC6000, pliantex, paraloid B67 and B72, silicone oil and collagen NPs are the most consolidants used.

Illuminated paper manuscripts with leather binding are considered one of the most important objects in libraries, museums and storehouses. The uncontrolled conditions and other deterioration factors inside the libraries and storehouses lead to degradation of these artifacts. The brittleness, fragility and weakness are considered the most common deterioration aspects of illuminated paper manuscripts and leather binding. Therefore, the consolidation process became vital and important to solve this problem. This study presents the main materials used for consolidation process of illuminated paper manuscripts and leather bindings.

To enhance the fit, comfort and overall satisfaction of lower body attire for online shoppers, this study introduces a reclassification method of the lower body profiles of young females in complex environments, which is used in the framework of remote clothing mass customization.

Frontal and lateral photographs were collected from 170 females prior, marked as size M. Employing a salient object detection algorithm suitable for complex backgrounds, precise segmentation of body profiles was achieved while refining the performance through transfer learning techniques. Subsequently, a skeletal detection algorithm was employed to delineate distinct human regions, from which 21 pivotal dimensional metrics were derived. These metrics underwent clustering procedures, thus establishing a systematic framework for categorizing the lower body shapes of young females. Building upon this foundation, a methodology for the body type combination across different body parts was proposed. This approach incorporated a frequency-based filtering mechanism to regulate the enumeration of body type combinations. The automated identification of body types was executed through a support vector machine (SVM) model, achieving an average accuracy exceeding 95% for each defined type.

Young females prior to being marked as the same lower garment size can be further subdivided based on their lower body types. Participants' torso types were classified into barrel-shaped, hip-convex and fat-accumulation types. Leg profile shapes were categorized into slender-elongated and short-stocky types. The frontal straightness of participants’ legs was classified as X-shaped, I-shaped and O-shaped types, while the leg side straightness was categorized based on the knee hyperextended degree. The number of combinations can be controlled based on the frequency of occurrence of combinations of different body types.

This methodological advancement serves as a robust cornerstone for optimizing clothing sizing and enabling remote clothing mass customization in E-commerce, providing assistance for body type database and clothing size database management as well as strategies for establishing a comprehensive remote customization supply chain and on-demand production model.