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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.

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.

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.

With respect to severe working conditions such as heavy load and impact, this paper aims to investigate the friction reduction and anti-wear performance of kaolin and molybdenum dialkyl dithiophosphate (MoDDP) composite lubricant additives to improve the lubrication effect of a single additive.

A four-ball friction test was carried out to determine the optimal concentration of kaolin and organic molybdenum additives and the tribological properties of the kaolin/MoDDP composite lubricant additives. A ring block test of composite lubricant additives was designed to investigate its lubrication performance under the severe working conditions of low speed, heavy load and impact.

The results showed that the optimal addition mass fractions of kaolin and MoDDP were 4.0 and 1.5 Wt.%, respectively, when kaolin and MoDDP were used as single lubricant additives. Compared with the single additive, the 4.0 Wt.% kaolin/1.5 Wt.% MoDDP composite lubricant additive showed excellent friction reduction and anti-wear effects under heavy load and impact conditions. Physicochemical analysis of the wear surface revealed that the lamellar kaolin additive and MoDDP had excellent synergistic effects, and the friction process promoted the generation of lubricant films containing a chemically reactive layer of MoS₂, MoO₂, FeS₂ and Fe₂O₃ and a physically adsorbent layer containing SiO₂ and Al₂O₃, which play important roles in anti-wear and friction reduction.

The excellent friction reduction and anti-wear effects of lamellar silicate minerals and the excellent antioxidant properties and good synergistic effects of molybdenum were comprehensively used to develop the composite additives with great lubricating properties.

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

The purpose of this paper is to combine triboelectric nanogeneration technology with ball bearing structure to achieve energy collection and fault monitoring.

In this paper, according to the rotation mode of ball bearings, the freestanding mode of triboelectric nanogeneration is selected to design and manufacture a novel triboelectric nanogeneration device Rolling Ball Triboelectric Nanogenerator (RB-TENG) which combines rotary energy collection with ball bearing fault self-sensing.

The 10,000s continuous operation experiment of the RB-TENG is carried out to verify its robustness. The accurate feedback relationship between the RB-TENG and rotation velocity can be demonstrated by the fitting comparison between the theoretical and experimental electrical signal periods at a certain time. By comparing the output electrical signals of the normal RB-TENG and the rotor spalling RB-TENG and polytetrafluoroethylene (PTFE) balls with different degrees of wear at 500 r/min, it can be concluded that the RB-TENG has an ideal monitoring effect on the radial clearance distance of bearings. The spalling fault test of the RB-TENG stator inner ring and rotor outer ring is carried out.

Through coupling experiments of rotor spalling fault of the RB-TENG and PTFE balls fault with different degrees of wear, it can be seen that when rotor spalling fault occurs, balls wear has a greater impact on the normal operation of the RB-TENG, and it is easier to identify. The fault self-sensing ability of the RB-TENG can be obtained, which is expected to provide an effective scheme for monitoring the radial wear clearance distance of ball bearings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0295/

This research focuses on a very important research question of determining the appropriate feature selection methods for software defect prediction. The study is centered on the creation of a new method that would enable the identification of both positive and negative selection criteria and the handling of ambiguous information in the decision-making process.

To do so, we develop an improved method by extending the WASPAS assessment in the context of bipolar complex fuzzy sets, which leads to the bipolar complex fuzzy WASPAS method. The approach also uses Einstein operators to increase the accuracy of aggregation and manage complicated decision-making parameters. The methodology is designed for the processing of multi-criteria decision-making problems where criteria have positive and negative polarities as well as other ambiguous information.

It is also shown that the proposed methodology outperforms the traditional weighted sum or product models when assessing feature selection methods. The incorporation of bipolar complex fuzzy sets with WASPAS improves the assessment of selection criteria by taking into account both positive and negative aspects of the criteria, which contributes to more accurate feature selection for software defect prediction. We investigate a case study related to the identification of feature selection techniques for software defect prediction by using the bipolar complex fuzzy WASPAS methodology. We compare the proposed methodology with certain prevailing ones to reveal the supremacy and the requirements of the proposed theory.

This research offers the first integrated framework for handling bipolarity and uncertainty in feature selection for software defect prediction. The combination of Einstein operators with bipolar complex fuzzy sets improves the DM process, which will be useful for software engineers and help them select the best feature selection techniques. This work also helps to enhance the overall performance of software defect prediction systems.