The growing share of renewable energy sources in recent years has been driven by the development of national legislation in various countries aiming to reduce carbon dioxide emissions originating from fossil fuels. Sustainable growth of national economies therefore requires the search for novel green technologies. Biomass has recently been used as a supplementary fuel to coal. The literature describes the synergetic effect in the technical context of combustion in the power engineering sector. In the presented research, five types of biomass dust were added to coal dust. The selected explosion indices were determined using a 20 L sphere apparatus, in accordance with EN 14034 standards. The results demonstrate the impact of biomass on the course of dust–air explosions. A synergetic effect was observed and explained. Certain types of biomass were found to be characterized by a higher explosion pressure rise (15-17 % or 0.88-1.28 bar) and higher maximum explosion pressure rates (16-148 % or 57-143 bar/s) than those obtained for the samples tested separately. The results indicate that the implementation of biomass for co-combustion always requires a revision of the existing process safety measures designed for coal combustion.

This study investigates the relationship between competitive pressures, technological adoption, and organizational dynamics in the manufacturing sector, with a focus on Serbian firms. Using data from the European Manufacturing Survey (EMS) of 146 manufacturing firms, Structural Equation Modeling (SEM) was employed to analyze the influence of competitive factors on the adoption of advanced manufacturing technologies, including Production Control Systems, Automation and Robotics, Efficiency Technologies, and Additive Manufacturing and Simulation Tools. The findings highlight the pivotal role of Production Control Systems in enhancing operational efficiency and strategic decision-making, demonstrating a significant positive effect on both the Organization of Production (β = 0.384, p = 0.001) and Production Management/Controlling (β = 0.500, p < 0.001). In contrast, other technologies did not show statistically significant effects, indicating more selective adoption patterns. Barriers such as financial constraints, technical complexity, and limited organizational readiness were found to restrict broader integration of automation, efficiency, and additive technologies. These results underscore that firms tend to prioritize technologies that offer immediate and auditable operational benefits. Practical implications include the need for workforce upskilling programs, managerial training in digital transformation, innovation hubs for lower-risk piloting, and supportive policy frameworks (e.g., financial incentives, regulatory sandboxes). Collectively, these measures can strengthen absorptive capacity, reduce resistance to change, and foster resilience, sustainability, and competitiveness through targeted technological integration and organizational alignment.

New energy vehicle manufacturers usually choose to cooperate with technology suppliers to improve their products’ market competitiveness. For this purpose, this paper constructs a single and dual sales-channel supply chain system comprising a technology supplier and a new energy vehicle manufacturer. Within this framework, the technology supplier acts as the leader in a Stackelberg game, while the new energy vehicle manufacturer acts as the follower. Based on intelligent driving cooperation cases between Huawei and Chang’an, BYD, and Seres, three cooperation modes—“technology introduction + own sales channel (Model A)”, “cooperative R&D + own sales channel (Model B)”, and “technology introduction + channel support (Model C)”—are examined to analyze cooperation mode selection strategies for the new energy vehicle manufacturer and the technology supplier. The study found that when the profit ratio of technology supplier to the new energy vehicle manufacturer (referred to as the revenue sharing coefficient) meets certain conditions, the optimal cooperation mode of the new energy vehicle manufacturer and technology supplier is Model C. Additionally, the profit obtained by the technology supplier under Model A is always the smallest, while the profit size between the other two models depends on the revenue sharing coefficient. Moreover, five situations describe the profit outcomes of the new energy vehicle manufacturer across the three cooperation modes, which are affected by the revenue sharing coefficient and the proportion of the technology supplier’s R&D investment cost. This study addresses a gap in research on cooperation modes within the new energy vehicle sector, and the conclusions obtained can provide valuable theoretical insights for new energy vehicle manufacturers and technology suppliers when selecting cooperation strategies.

This study addresses a critical challenge in industrial big data analytics for smart manufacturing: conventional machine learning methods often fail to account for data discontinuities caused by scrapped defective intermediates in multi-stage production processes, inadvertently treating non-conforming products as qualified during model training. We propose a novel process-aware data analytics framework specifically designed for process industries, featuring: (1) intelligent attribute partitioning based on information flow discontinuity points, and (2) an ensemble modelling approach combining Random Forest and C5.0 Decision Tree algorithms to generate interpretable prediction rules with quantified feature importance rankings. Validated using real-world production data from a Chinese rail steel manufacturer, our methodology demonstrates superior performance by explicitly incorporating process-specific data correlations. The proposed solution effectively mitigates information distortion caused by scrapped intermediates while maintaining operational interpretability – a crucial requirement for industrial implementation. The research results increased the accuracy rate of the test set of the random forest experiment from 88.39 % to 92.69 %, and the accuracy rate of the test set of the decision tree experiment from 71.89 % to 79.15 %. Additionally, the experimental results verify that, compared with the traditional methods, our framework has better applicability in capturing product quality in the manufacturing industry when process attributes are considered.

As blockchain increasingly demonstrates advantages in enhancing consumer trust and reducing collaborative production costs in food supply chains, more food companies are developing blockchain-based supply chain management platforms. However, in a complex and competitive supply chain environment, food companies face critical operational management challenges in selecting appropriate blockchain R&D approaches and determining optimal R&D levels. This study examines two competing food companies deciding on their optimal food production and blockchain R&D levels. We first established a benchmark model without blockchain adoption. Then, we constructed supply chain operation models for both independent and collaborative blockchain R&D scenarios. By comparing equilibrium decisions across different models, we derived the optimal blockchain R&D model and operational strategies in food supply chains. Furthermore, we extended our analysis to consider asymmetric food substitution scenarios. Our findings revealed that independent blockchain R&D tends to increase equilibrium food production and is more suitable for premium food supply chains. Conversely, collaborative blockchain R&D significantly enhanced overall supply chain profitability. As spillover effects increase, food companies are likely to favor independent blockchain R&D to achieve higher R&D levels and stronger market competitiveness. Additionally, we demonstrated that blockchain R&D levels are influenced by food substitutability and quality credibility.

The production and sales of electric vehicles have been increasing in line with the gradual adaptation of consumers to electric vehicles, driven by various government policies and subsidies over the past decade. As a result, the deployment of charging facilities, which are essential for supporting electric vehicles, has also been extensive. However, despite the rapid development in scale, charging facilities face challenges such as low utilization during off-peak hours and excessive congestion during peak hours, leading to resource wastage and a diminished user charging experience. To address these issues, this study proposes a spatio-temporal prediction model for charging station load. The model introduces a global spatial enhancement module to simultaneously learn short-range and long-range spatial dependencies in the data, resulting in improved prediction accuracy. The aim of this research is to provide practical guidance in terms of conserving charging resources and enhancing user charging experience based on spatio-temporal prediction effectiveness.

With the rapid development of the global manufacturing industry, an efficient and accurate quality control system has become key to enhancing competitiveness. Ultrasonic Nondestructive Testing (NDT), as an efficient means of quality inspection, plays a crucial role in improving manufacturing quality through the precision of its data analysis. This study aims to explore the application of ultrasonic NDT data in manufacturing quality control by integrating machine learning technologies, with a specific focus on the Wavelet Neural Network optimized by Genetic Algorithms (GA-WNN). This study achieved significant prediction and evaluation results by applying a GA-WNN to quality control in manufacturing. Compared to traditional Wavelet Neural Network (WNN) models, the GA-WNN more effectively identifies and predicts potential quality issues, especially in noisy data and complex production environments, demonstrating higher accuracy and stability. When predicting possible defect types in the manufacturing process, the GA-WNN showed a notable improvement in accuracy over other models. Additionally, in quality stability evaluation, GA-WNN was able to capture production fluctuations more accurately, providing more valuable results for decision-making. The methodologies and discoveries of this study offer new perspectives and tools for quality control in manufacturing and the analysis of ultrasonic NDT data, presenting broad application prospects.

This study provides an in-depth analysis of a new financial model for cloud warehouses and evaluates the associated credit risk within the context of supply chain financing, focusing on the intelligent transformation in this field. Concurrently, an optimization problem was derived from the evaluation issue, with the whale optimization algorithm (WOA) used to identify a reasonable default point and distance. To simplify the identification of these points, we enhanced the traditional WOA, resulting in an improved version, the IWOA, which demonstrated very good optimization performance. The IWOA's optimization capabilities were applied to determine the optimal ratio of short- and long-term debt coefficients, identifying the default point in the Kealhofer, McQuown, and Vasicek (KMV) credit monitoring model, replacing fixed values and yielding more precise results. Furthermore, this study introduces a novel analytical approach to credit risk measurement, advancing the development of related theories and methods. Accurate analysis of financial stability and risk is crucial in industrial sectors, including engineering and manufacturing. The simulation using specific data revealed that the IWOA-KMV model exhibited better and faster optimization capabilities, with greater discrimination ability compared to the KMV model. Overall, this study examines the risk factors in the cloud warehouse financing model, offers an improved version of the WOA, introduces a modified IWOA-KMV model to create a scientific, practical credit risk assessment framework, and provides guidance for risk control in cloud warehouse financing, a novel financing service.

As the third largest greenhouse gas emission industry across the globe, the transportation industry dominates a major position in the total carbon emission. Multimodal transportation has incomparable advantages over single-modal transportation, and choosing transportation routes and modes under the background of carbon peaking and carbon neutrality is crucial. The uncertainties of demand and transportation time were described using the maximum regret value and Lyapunov central limit theorem, respectively, and a robust optimization model for cold chain multimodal transportation routes considering hybrid uncertainty of carbon emissions was established. Then, a dual-pheromone ant colony algorithm was designed, and the improved niche genetic algorithm was nested into the ant colony algorithm to solve the model. Results showed that the changes in transportation time and node transfer time lead to the changes in transportation cost and transportation scheme, and the robust optimization of the multimodal transportation route under hybrid uncertainties is affected by the regret value constraint and the fluctuation range of uncertain factors, resulting in the increase in transportation cost and carbon emission cost. Therefore, the decision-makers of cold chain multimodal transportation must predict the influence of uncertain factors, choose the appropriate maximum regret value, and pay attention to the mixed time window constraints of transportation time and node transfer time to reduce costs and improve efficiency.

The CNC dry turning of Inconel 625 alloy was investigated in this study. Turning was performed using different feeds, corner radii, and insert types. The dimensions and the arithmetic mean surface roughness were measured before and after turning. The influence of the input parameters and the process modelling were evaluated using a full factorial design of experiments. Prediction models were developed, and process optimization was carried out to simultaneously maximize dimensional accuracy and surface quality. The optimal values of the input parameters were identified for the wiper insert, a feed rate of 0.1 mm/rev, and a corner radius of 0.8 mm. Under these conditions, the optimal deviation from the specified dimension was 0.2 mm, while the surface roughness was 0.297 µm. During the confirmation phase, the mean percentage errors were 0.9 % for surface roughness and 3.45 % for dimensional deviation. The percentage errors observed in the confirmation experiments, all of which were below 5 %, demonstrate the feasibility of using the proposed approach for modelling and optimizing the turning of Inconel 625 alloy.