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The application of steel fiber reinforced concrete (SFRC) beams is limited in practice, partially due to the lack of accurate shear strength prediction models. This study aims to develop a reliable shear strength prediction model for SFRC beams.

In this study, an artificial neural network was employed to predict the shear strength of SFRC beams, utilizing a comprehensive database of 562 experimental studies. Multiple neural networks were established with varying hyperparameters, and their performance was evaluated using statistical parameters.

The neural network with 11 neurons showed superior results than other networks. The performance evaluation, efficiency and accuracy of the selected neural network were examined using margin of deviation, k-fold cross-validation, Shapley analysis, sensitivity analysis and parametric analysis. The proposed artificial neural network model accurately predicts the shear strength and outperforms other existing equations.

This research contributes to overcoming the limitations of existing prediction models for shear strength of SFRC beams without stirrups by developing a highly accurate model based on ANN. Utilizing a comprehensive database and rigorous evaluation techniques enhances the reliability and applicability of the proposed model in practical engineering applications.

The present study aims to investigate factors influencing Gen-Z consumers' “green food purchase intention” and “healthy lifestyle”. Guided by the attribution theory, “perceived usefulness of green food”, “food safety concerns” (internal attributes), “perceived threat of environmental problems” and “green peer influence” (external attributes) are considered the predictors of “attitude towards green food”, which eventually lead to a healthy lifestyle and green food purchase intention. Besides, “fear of pandemic recurrence” and “greenwash” are tested as moderators.

The Structural Equation Modelling-Partial Least Squares (PLS-SEM) technique was employed for the model testing. An online questionnaire survey was conducted among Gen-Z Chinese adults, which yielded 556 complete, valid responses.

The findings indicate that “consumers' attitudes towards green food” are positively influenced by “perceived threat of environmental problems”, “perceived usefulness of green food”, “concerns about food safety”, and the influence of “green peers”. In addition, results revealed that “attitude toward green food” exerts a positive effect on “healthy lifestyle” and “green food purchase intention”. The study supports the moderating role of “perceived greenwash” in the relationship between “attitude” and “intention to purchase green food”. However, there was no evidence to support the moderating effect of “fear of pandemic recurrence” in relation to a “healthy lifestyle”.

This study is a pioneer in utilizing the attribution theory to predict the drivers of a “healthy lifestyle” and the “intention to purchase green foods”. Furthermore, this study predicted the moderating influence of “fear of pandemic recurrence” on the relationship between attitude and “healthy lifestyle”, a link that has not been tested in previous research. Furthermore, it introduces a novel examination of the moderating effect of “perceived greenwash” on the relationship between “attitudes” and “purchase intentions”.

This paper examines the effect of managerial interlocking networks (MINs) on firm risk spillover by using a sample of Chinese A-share listed firms.

Applying the complex network approach, we build managerial interlocking networks (MINs) and leverage degree centrality to quantify a manager’s network position. To gauge firm risk spillover, we utilize the conditional autoregressive value at risk (CAViaR) model to compute the value-at-risk. Subsequently, we employ ordinary least squares to investigate the influence of MINs on firm risk spillover.

Our research uncovers a direct correlation between a firm risk spillover and the status of network positions within managerial interlocking networks; namely, the more central the position, the greater the risk spillover. This increase is believed to be due to central firms in MINs having greater connectedness and influence. This fosters a similarity in decision-making across different firms through interfirm managerial communication, thus amplifying the risk spillover. Economic policy uncertainty (EPU) and Guanxi culture furtherly intensify the effects of MINs. Additional analysis reveals that the impact of MINs on the firm risk spillover is significantly noticeable in non-state-owned enterprises, while good corporate governance diminishes the risk spillover prompted by MINs.

Our findings offer fresh insights into the interfirm risk outcome associated with MINs and extend practical guidelines for attenuating firm risk spillover with a view toward mitigating systemic risk.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

Body language is an integral part of interpersonal communication and exchange, which can convey rich emotions, intentions and information. However, how anchor’s body language works in live-streaming e-commerce (LSE) has yet to receive adequate attention. Based on dual systems theory of decision-making, this paper aims to explore the impact of anchor’s body language on the performance of LSE from the perspective of customer engagement behavior and to examine the moderating role of anchor’s relational social interaction.

The authors confirmed the theoretical model through empirical analysis of structured data from 1,415 actual livestreaming rooms from Douyin, as well as unstructured data of 418,939 min of video and audio, 1,985,473 words of text and 423,302 keyframe images.

The study found that anchor’s body language has a significant positive effect on the performance of LSE, and customer engagement behavior plays a partially mediating role. The moderating effect suggests that anchor’s relational social interaction and body language have substitution effects in enhancing customer engagement behavior and the performance of LSE, which reveals the substitution relationship between anchor’s verbal and nonverbal interactions in LSE.

This study is one of the earlier literature focusing on anchor’s body language, and the findings provide practical references for enhancing customer engagement behavior and achieving performance growth in LSE.

The purpose of this study is to investigate the influence of rotational speed on the oil film stability of the hydrostatic rotary table having double rectangular oil pads. The oil film stability is evaluated based on the oil film stiffness under constant load condition and the displacement response amplitude of the oil film under disturbance load condition.

The oil film stability theoretical equations of the double rectangular oil cavity are deduced such as oil film stiffness, damping and dynamic equations. A simulation model is developed to analyze the relationship among oil film temperature, oil film pressure fields and oil film stability. The user-defined function programs are used to control the rotational speed, lubricant viscosity and oil film thickness during the simulation. In addition, an experimental rig is built to test the simulation results.

This study shows that oil film stability decreases with increasing rotational speed under constant load and disturbance load. The trend of oil film stability decreased slowly within 30 r/min, and then rapidly. However, since the hydrodynamic pressure effect, the decrease rate of stability is mitigated under constant load and high rotational speeds.

The conclusions can provide a theoretical basis for improving the oil film stability of machines with similar hydrostatic support structure.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-07-2024-0267/

This study aims to explore the dynamic interplay of key resources (i.e. research and development (R&D), advertising and exports) in affecting the performance of USA manufacturing firms. Specifically, the authors examine the dynamic impact of joint resources and predict differential effect scales contingent on firm capabilities.

This study presents a combined multiple regression analysis (MRA)-multilayer perceptron (MLP) neural network modeling and investigates the complex interlinkage of capabilities, resources and performance. As an innovative approach, the MRA-MLP model investigates the effect of capabilities under the combinatory deployment of joint resources.

This study finds that the impact of joint resources and synergistic rents is not uniform but rather distinctive according to the combinatory conditions and that the pattern is further shaped by firm capabilities. Accordingly, besides signifying the contingent aspect of capabilities across a range of resource combinations, the result also shows that managerial sophistication in adaptive resource control is more than a managerial ethos.

The proposed analytic process provides scientific decision support tools with control mechanisms with respect to deploying multiple resources and setting actionable goals, thereby presenting pragmatic benchmarking options to industry managers.

Using the theoretical underpinnings of the resource-based view (RBV) and resource orchestration, this study advances knowledge about the complex interaction of key resources by presenting a salient analytic process. The empirical design, which portrays holistic interaction patterns, adds to the uniqueness of this study of the complex interlinkages between capabilities, resources and shareholder value.

This study’s aim is to introduce a high-performance sorbent for the removal of both anionic (Congo red; CR) and cationic (methylene blue; MB) dyes from aqueous solutions.

Sodium silicate is adopted as a substrate for GO and AgNPs with positive charge are used as modifiers. The synthesized nanocomposite is characterized by FTIR, FESEM, EDS, BET and XRD techniques. Then, some of the most effective parameters on the removal of CR and MB dyes such as solution pH, sorbent dose, adsorption equilibrium time, primary dye concentration and salt effect are optimized using the spectrophotometry technique.

The authors successfully achieved notable maximum adsorption capacities (Qmax) of CR and MB, which were 41.15 and 37.04 mg g⁻¹, respectively. The required equilibrium times for maximum efficiency of the developed sorbent were 10 and 15 min for CR and MB dyes, respectively. Adsorption equilibrium data present a good correlation with Langmuir isotherm, with a correlation coefficient of R2 = 0.9924 for CR and R2 = 0.9904 for MB, and kinetic studies prove that the dye adsorption process follows pseudo second-order models (CR R2 = 0.9986 and MB R2 = 0.9967).

The results showed that the proposed mechanism for the function of the developed sorbent in dye adsorption was based on physical and multilayer adsorption for both dyes onto the active sites of non-homogeneous sorbent.

The as-prepared nano-adsorbent has a high ability to remove both cationic and anionic dyes; moreover, to the high efficiency of the adsorbent, it has been tried to make its synthesis steps as simple as possible using inexpensive and available materials.

Tacit knowledge in frontline operations is primarily reflected in the holders’ intuition about dynamic systems. Despite the implicit nature of tacit knowledge, the understanding of complex systems it encapsulates can be displayed through formalization methods. This study seeks to develop a methodology for formalizing tacit knowledge in a dynamic delivery system.

This study employs a structured survey to gather experiential knowledge from dispatchers engaged in last-mile delivery operations. This knowledge is then formalized using a value function approximation approach, which transforms tacit insights into structured inputs for dynamic decision-making. We apply this methodology to optimize delivery operations in an online-to-offline pharmacy context.

The raw system feature data are not strongly correlated with the system’s development trends, making them ineffective for guiding dynamic decision-making. However, the system features obtained through preprocessing the raw data increase the predictiveness of dynamic decisions and improve the overall effectiveness of decision-making in delivery operations.

This research provides a foundational framework for studying sequential dynamic decision problems, highlighting the potential for improved decision quality and system optimization through the formalization and integration of tacit knowledge.

This approach proposed in this study offers a method to preserve and formalize critical operational expertise. By embedding tacit knowledge into decision-making systems, organizations can enhance real-time responsiveness and reduce operational costs.

This study presents a novel approach to integrating tacit knowledge into dynamic decision-making frameworks, demonstrated in a real-world last-mile delivery context. Unlike previous research that focuses primarily on explicit data-driven methods, our approach leverages the implicit, experience-based insights of operational staff, leading to more informed and effective decision-making strategies.

Rubber-plastic double-layer bush water-lubricated bearings have demonstrated superior performance, while research on their vibration characteristics remains limited. This paper aims to investigate the lubrication and vibration properties of these bearings by experiments and examine the effect of rubber-to-plastic bush thickness ratio on bearing performance.

A water-lubricated journal bearing test rig is constructed, and three bearings with different bush thickness ratios are fabricated. Bush deformation under various loads is measured, and the friction coefficient and axis trajectory under different operating conditions are tested. The vibration responses of the bearings under directional harmonic excitation are studied. The influences of rotational speed, load and rubber-to-plastic bush thickness ratio on the bearing’s lubrication and vibration properties are analyzed.

The friction coefficient of the bearing initially decreases rapidly and subsequently increases gradually as the rotational speed or load increases. The bearing with a thicker rubber bush shows lower displacement amplitudes in its axis trajectory. Under a 45° directed excitation, significant oscillations are observed in the vertical displacement, while the horizontal displacement remains stable. The damping effect of the bearing with a thicker rubber bush is more pronounced.

This paper present the influence of rubber-to-plastic bush thickness ratio on bearing lubrication and vibration performance. The results are valuable for the design of this type of bearing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0469/