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The aim of this paper is to review the literature on the prediction of cutting tool life. Tool life is typically estimated by predicting the time to reach the threshold flank wear width. The cutting tool is a crucial component in any machining process, and its failure affects the manufacturing process adversely. The prediction of cutting tool life by considering several factors that affect tool life is crucial to managing quality, cost, availability and waste in machining processes.

This study has undertaken the critical analysis and summarisation of various techniques used in the literature for predicting the life or remaining useful life (RUL) of the cutting tool through monitoring the tool wear, primarily flank wear. The experimental setups that comprise diversified machining processes, including turning, milling, drilling, boring and slotting, are covered in this review.

Cutting tool life is a stochastic variable. Tool failure depends on various factors, including the type and material of the cutting tool, work material, cutting conditions and machine tool. Thus, the life of the cutting tool for a particular experimental setup must be modelled by considering the cutting parameters.

This submission discusses tool life prediction comprehensively, from monitoring tool wear, primarily flank wear, to modelling tool life, and this type of comprehensive review on cutting tool life prediction has not been reported in the literature till now. The future suggestions provided in this review are expected to provide avenues to solve the unexplored challenges in this field.

Construction organizations must maintain a productive workforce without sacrificing their health and safety. The global construction sector loses billions of dollars yearly to poor health and safety practices. This study aims to investigate benefits derivable from using wearable technologies to improve construction health and safety. The study also reports the challenges associated with adopting wearable technologies.

The study adopted a quantitative design, administering close-ended questions to professionals in the Nigerian construction industry. The research data were analysed using descriptive and inferential statistics.

The study found that the critical areas construction organizations can benefit from using WSDs include slips and trips, sensing environmental concerns, collision avoidance, falling from a high level and electrocution. However, key barriers preventing the organizations from adopting wearable technologies are related to cost, technology and human factors.

The time and cost lost to H&S incidents in the Nigerian construction sector can be reduced by implementing the report of this study.

Studies on WSDs have continued to increase in developed countries, but Nigeria is yet to experience a leap in the research area. This study provides insights into the Nigerian reality to provide directions for practice and theory.

When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.

First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.

The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.

We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.

The study aims to examine how different types of green brand ritual (GBR) influence customer engagement behavior and the mediation mechanisms and boundary conditions of the positive and negative pathways.

The study conducts two online experiments to collect data from a total of 940 consumers in China. Hypotheses are tested by independent samples t-test, two-way ANOVA and Hayes' PROCESS model.

Different kinds of GBR have different effects on customer engagement behavior. Internal GBR is more likely to play a positive role by inciting connectedness to nature. External GBR is more likely to play a negative role by inciting psychological resistance. This dual effect is especially pronounced for warm brands rather than competent brands.

The study pioneers the brand ritual into the field of interactive marketing and enriches its dual effect research. Additionally, the study figures out whether the category of brand ritual can trigger negative effect.

Inappropriate brand rituals are worse than no rituals at all. The results provide guidance for green companies to design effective brand rituals to strengthen the connection with consumers. Green brands should describe brand rituals in vivid detail and consciously lead consumers to immerse themselves in them.

Laser fading, commonly used in the denim industry, is a computer-controlled, dry, ecological finishing method whereas conventional methods include high water, energy and time consumption. Resolution and pixel time are crucial parameters of laser source influencing the effect of laser treatment. The purpose of this study is to determine the optimum laser parameters of CO₂ laser followed by enzyme washing and to compare the tensile strength and color values of laser-treated denim fabric with that of conventional enzyme-faded.

Two different indigo-dyed, sulfur bottom-indigo-dyed and only indigo-dyed organic cotton denim fabrics with different unit weights, were lasetreated with different laser parameters and then subjected to 10 min enzyme washing. Tensile strength, abrasion resistance, and change in fabric unit weight were tested. CIE (L*a*b*, ΔE*, h°, C*) color values, color strength (K/S), yellowness and whiteness indexes were measured to identify the color differences. Color fastness tests including washing, rubbing, light, water and perspiration fastness were investigated.

Most effective laser fading in terms of good mechanical properties and color values was obtained at 40 dpi resolution and 300 µs pixel time.

Conventional enzyme fading of denim fabrics is a wet process and requires a long process time of 40–45 min and high temperatures, leading to high energy and water consumption. Laser fading, on the other hand, is a dry and ecological method, but causes a decrease in mechanical properties of the fabric, and an increase in yellowness. In this study, unlike the similar studies in the literature, denim fading was carried out by a combination of laser treatment followed by only 10 min enzyme washing in order to eliminate or minimize the drawbacks of the denim fading, such as high energy and water consumption for enzyme fading and decrease in mechanical properties of the fabric and increase in yellowness for laser fading. This method was applied to two different dyed denim fabrics, sulfur (bottom) and indigo (top) and laser process conditions were optimized to achieve the desired fading effects compared to conventional enzyme fading.

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.

Prefabricated building (PB) has high technical requirements and is susceptible to safety accidents, and its construction occupational health and safety (OHS) problems should not be ignored. To promote the better development of PB, this study aims to measure their construction safety management level and propose corresponding countermeasures.

By systematically combing the relevant literature, this study extracts the influencing factors that appear frequently in several studies and categorizes them according to six dimensions: people, materials and components, technology, mechanical equipment, environment and system. Combining expert opinions, the measurement index system, including 6 primary indexes and 24 secondary indexes, is constructed. The structural entropy weight (SEW) method is applied to calculate the index weights. The cloud matter element (CME) model based on the weights is constructed to determine the level of construction occupational health and safety management (COHSM). A project case of a training building is used to verify it. The results obtained from the model are compared with those from other measurement models to verify the feasibility of the model in measuring the level of COHSM for PB.

The calculated weights show that technology is the most important for the COHSM of PB. The management level of the project in terms of people, materials and components, technology, machinery and equipment, environment and system is Level II good. The overall safety management level is also Level II, which is good. The model of this study is consistent with other model measurements. The methodology of this study yields reasonable and realistic results.

This study is the first to include occupational health dimensions in the research on the construction safety management of PB, which not only covers the key elements in traditional construction safety management but also considers the impact of the construction process, material use and technology of PB on safety management, making the measurement index system more scientific. Meanwhile, the introduction of the CME model based on the SEW method effectively solves the deficiencies of the traditional method in dealing with ambiguity and uncertainty and provides practitioners with more accurate and comprehensive measurement results. It helps practitioners formulate a more scientific management plan in combination with the actual situation and provides a guiding idea and practical path for the COHSM of similar projects.

Establishing and developing digital science and education evaluation platforms (DSEEPs) have several practical implications for the development of China's science, technology and education. Identifying and analyzing the key factors influencing DSEEP user experience (UX) can improve the users' willingness to use the platform and effectively promote its sustainable development.

First, a literature survey, a five-element model of UX and semi-structured interviews were used in this study to develop a DSEEP UX-influencing factor model, which included five dimensions and 22 influencing factors. Second, the model validity was verified using questionnaire data. Finally, the key influencing factors were identified and analyzed using a fuzzy decision-making trial and evaluation laboratory (fuzzy-DEMATEL) method.

Fourteen influencing factors, including diverse information forms and comprehensive information content, are crucial for the DSEEP UX. Its optimization path is “‘Function Services’ → ‘Information Resources’ → ‘Interaction Design’ → ‘Interface Design’ and ‘Visual Design’.” In this regard, platform managers can take the following measures to optimize UX: strengthening functional services, improving information resources, enhancing the interactive experience and considering interface effects.

This study uses a combination of qualitative and quantitative research methods to determine the key influencing factors and optimization path of DSEEP UX. Optimization suggestions for UX are proposed from the perspective of platform managers, who provide an effective theoretical reference for innovating and developing a DSEEP.

The purpose of this study is to assess the effects of fused filament fabrication (FFF) printing parameters on the surface quality and dimensional accuracy of FFF-fabricated copper green parts using the appropriate filaments. The orthogonal experiments were implemented and the errors in length, width and height were measured and analyzed. The results of range analysis and variance analysis indicated the orders of effect factors. Dissolvent debinding combined with thermal debinding was adopted to remove the binders inside the green part by calculating debinding rate. The influence mechanism of sintering temperatures on the microstructure and shrinkage was elaborated.

The extrusion-based FFF in manufacturing copper parts can overcome shortcomings for high reflectivity and heat dissipation in laser powder bed fusion process at cost-saving and materials saving. This study makes an attempt to prepare copper/binder composite filaments through mixing, extrusion and flowability evaluation.

The results showed that the suitable composite filaments applied for FFF should balance rigidity and plasticity. The combination of printing speed and heating temperature impacts on the surface quality significantly, and the major factor in determining the dimensional accuracy is layer thickness. Two-stage debinding procedure was beneficial for binder removal and sintering process. The higher sintering temperature results in less voids, sizes shrinkage and densified microstructure, which is attributed to the occurrence of sintering neck among the fused copper powders.

The self-prepared copper/binder composite filaments were successfully manufactured using the FFF process. This study provides unique approach and print guidance for fabricating complex structures of pure copper components.

The purpose of this study proposes a strategy based on vehicle kinematics, dynamics and fusion estimation. The estimation signal of vehicle driving state is crucial for vehicle driving safety and stability control, and the issue of fault-tolerant reconstruction estimation of vehicle driving state under the failure of yaw rate or lateral acceleration sensors is a significant research topic.

A strategy based on vehicle kinematics, dynamics and fusion estimation is proposed. To address the issue of inaccurate calculation of tire forces because of sensor failure, a method combining adaptive sliding mode observer, genetic algorithm and particle swarm optimization algorithm is proposed to accurately calculate tire forces, and the Square Root Cubature Kalman Filter algorithm is used to reconstruct the estimation of vehicle driving state under sensor failure. To improve the accuracy of fault-tolerant reconstruction estimation of vehicle driving state, an error-weighted multi-method fusion estimation strategy for vehicle driving state is proposed. A fast terminal sliding mode control algorithm is proposed to control the stability of the fault-tolerant reconstruction estimation signal of vehicle driving state.

Simulation results show that the proposed fault-tolerant reconstruction estimation algorithm for vehicle driving state can accurately estimate the actual driving state of the vehicle and stably participate in the vehicle stability control system, achieving fault-tolerant reconstruction estimation and control of vehicle driving state under sensor failure.

The problem of vehicle motion state estimation under yaw velocity sensor fault or lateral acceleration sensor fault is solved, and fault tolerance control under sensor state is realized.