Abstract: Aflatoxin B1 is one of the most toxic mycotoxins contaminating animal feed, and bentonite clays are widely used as adsorbents to reduce its bioavailability. The influence of particle size on bentonite adsorption performance, particularly regarding cost-effectiveness of fine fractionation, remains underexplored. This study investigated natural bentonite from the Bijelo Polje deposit (Montenegro) containing ~55% montmorillonite and its size fractions: <0.200 mm (~72% smectite), <0.037 mm (~75% smectite), and <0.005 mm (~91% smectite), obtained by sieving and centrifugation. Fractions were characterized by laser diffraction, chemical composition, cation exchange capacity, and quantitative XRD (Rietveld refinement). In vitro AFB1 adsorption (2–50 mg/L initial concentration, pH 3.0, 0.02% w/v adsorbent) simulated monogastric gastrointestinal conditions. Particle size reduction progressively increased smectite content, CEC (44–70 meq/100 g), and purity, reducing heavy metals to undetectable levels. All purified fractions achieved satisfactory AFB1 binding (>90% at 4 mg/L). The finest <0.005 mm fraction exhibited the highest maximum adsorption capacity (qmax ≈ 240 mg/g) due to superior specific surface area and site accessibility. However, only the <0.005 mm fraction meets EU regulatory requirements (Commission Implementing Regulation (EU) No 1060/2013) for AFB1-binding feed additives (≥70% dioctahedral smectite, low accompanying minerals, >90% binding), as coarser fractions retain excess quartz and calcite. Extensive fractionation, despite higher costs, is essential for regulatory-compliant, high-performance natural bentonite adsorbents.

Abstract:

The accelerating climate emergency demands urgent transformation of the built environment, a sector simultaneously responsible for substantial greenhouse gas emissions and highly vulnerable to climatic impacts. Rising global temperatures, extreme weather events, and increasing resource demands necessitate a holistic rethinking of building design, materials, and urban planning. This paper explores the potential of artificial intelligence (AI) as a catalyst for energy efficiency, circularity, and resilience in buildings. AI-driven tools—from generative design and predictive maintenance to digital twins and automated material flow analytics—enable optimization across the design, construction, operation, and end-of-life phases, reducing both operational and embodied carbon. Crucially, AI lowers entry barriers for startups, fostering an entrepreneurial ecosystem capable of delivering innovative, adaptive, and sustainable solutions in civil engineering. By integrating technological, regulatory, financial, and behavioral strategies, AI-enabled startups can accelerate the transition to a low-carbon, circular, and climate-resilient built environment. This paper argues that coordinated, multi-disciplinary action is essential: without it, the sector risks perpetuating vulnerable, carbon-intensive infrastructure; with it, civil engineering can evolve into a dynamic driver of sustainable innovation.

Abstract:

Injection molding processes generate large volumes of heterogeneous process data that reflect complex and dynamic manufacturing conditions, directly influencing product quality. Conventional quality control approaches based on fixed statistical thresholds and operator experience often fail to ensure stable and secure operation under such variability, leading to increased defect rates and reduced process reliability. Moreover, insufficient consideration of data integrity and process stability can compromise the robustness of data-driven manufacturing systems. This study proposes a secure machine learning framework for defect detection and quality enhancement in injection molding processes. The framework integrates systematic data preprocessing, correlation analysis, and an unsupervised learning approach based on a Gaussian Mixture Model (GMM) to achieve reliable defect classification. Key process parameters contributing to quality deviations are identified through statistical correlation analysis, and process data are clustered into one normal group and three abnormal groups corresponding to different defect types without requiring labeled datasets. By emphasizing data integrity, stable clustering behavior, and resilience to process variability, the proposed framework enhances manufacturing security and reduces the risk of misclassification. Experimental results using real injection molding process data demonstrate effective defect reduction, improved process parameter optimization, and enhanced operational efficiency. By enhancing defect detectability and reducing misclassification risks, the proposed framework supports safer and more human-centric manufacturing environments by improving operator trust, decision confidence, and preventive intervention capability.

Abstract: Eletromobility is increasingly recognized as a cornerstone of sustainable transport, yet its adoption remains uneven across regions. Addressing these challenges requires planning approaches that extend beyond urban centers and operate at the national scale to ensure equitable and coordinated deployment of fast-charging networks. This study develops an integrated framework that combines geospatial analysis, multi-criteria decision-making (MCDM), and power system evaluation to identify and prioritize fast-charging sites. The framework incorporates spatial suitability, socioeconomic and demand-driven priorities, and grid readiness under projected electric vehicle (EV) penetration scenarios. Its applicability is demonstrated through a national-scale case study of Costa Rica, encompassing the country’s socioeconomic conditions, electric grid, and national road network (NRN). The results demonstrate that such a strategy is both feasible and essential to supporting inclusive EV adoption across diverse territorial contexts, from metropolitan corridors to peripheral regions. While grounded in Costa Rica, the framework is designed to be transferable, providing a practical tool for other countries seeking to align EV infrastructure planning with short-, mid-, and long-term sustainability and decarbonization objectives in the private transport sector.

Abstract: The article considers issues related to improving the surface characteristics of titanium Gr2 using one of the lightest, cheapest and ecological methods - electrospark deposition (ESD) with low pulse energy and with ultradisperse electrodes TiB2-TiAl with nanosized additives of NbC and ZrO2. By means of profilometric, metallographic, XRD, SEM and EDS methods, the change in the geometric characteristics, composition, structure, micro- and nanohardness of the coatings as a function of the electrical parameters of the ESD regime has been studied. The results show that the use of TiB2-TiAl electrodes and low pulse energy allows the formation of dense, continuous and uniform coatings, demonstrating a significant reduction in roughness and inherent irregularities and structural defects of electrospark coatings, as well as the synthesis of newly formed wear-resistant phases and amorphous-nanocrystalline structures. The obtained coatings have crystal-line-amorphous structures, with newly formed intermetallic and wear-resistant phases with minimal defects, with roughness, thickness and microhardness, which can be controlled within the ranges Ra =1.8-3.5 µm, δ= 8-20 µm, HV=9-13 GPa, respectively. Energy pulse parameters have been defined to produce of coatings with a predetermined thickness and roughness, maximum increased hardness and with the most favorable characteristics in terms of abrasive and corrosion resistance.

Abstract: The fastest growth in electricity demand in the industrialized world will likely come from the broad adoption of artificial intelligence (AI) - accelerated by the rise of generative AI models such as OpenAI’s ChatGPT. The global “data center arms race” is driving up power demand and grid stress, which creates local and regional challenges because people in the area understand that the additional data center-related electricity demand is coming from faraway places, and they will have to support the additional infrastructure while not directly benefiting from it. So, there is an incentive for the data center operators to manage the fast and unpredictable power surges internally so that their loads appear like a constant baseload to the electricity grid. Such high-intensity and short-duration loads - which can have very high tariffs - can be served by Hybrid Energy Storage Systems (HESS) combining multiple types of storage technologies. Each storage type can respond to different timescales of load variations, enabling fast power balancing and reduced battery degradation. Properly designed HESS architectures, and their optimal operations, will make AI data center loads as baseloads, which will help the data center operators avoid high-tariff electricity. This review paper presents an overview of current energy storage technologies, their classifications, and recent performance data, focusing on their suitability for AI-driven computing. Technical requirements such as fast response, modularity, and long cycle life - that are critical for sustainable and reliable data center operations - are discussed. Special attention is given to Northern Virginia, the world’s largest data center hub, where rapid load growth and grid limitations highlight the urgent need for hybrid storage deployment. The paper concludes by identifying key challenges, research gaps, and future directions for integrating hybrid energy storage with on-site generation to optimally manage the high variability in the data center load and build sustainable, low-carbon, and intelligent AI data centers.

Abstract: National economic growth drives rapid infrastructure development, with civil engineering technology shifting from manual to AI and computer-based approaches due to the mature development of the Internet. Against this backdrop, this study applies computer technologies—specifically multimodal data—to enhance project rigor and safety. Taking an intelligent Trombe wall in a specific temperature area as a case study, we use multimodal data analysis and a multi-view algorithm to establish a systematic analysis process. Experimental results show that the four temperature measurement points of the Trombe wall reach maximum temperatures of 25.7°C (point 1), 24.7°C (point 2), and 25.1°C (points 3 and 4). These findings indicate that multimodal data analysis can effectively improve the quality of civil construction.

Abstract: Climate change will pose greater challenges to occupational safety and health, and advanced approaches for risk and reliability modeling that can include sustainability perspectives are needed. This scoping review aims to map and synthesize the current methodologies on modeling OSH risks in the context of climate change and sustainability. We systematically searched the Scopus, Web of Science, PubMed, ERIC, and other databases following the PRISMA-ScR guidelines for studies published from 2010 to 2025. Seventeen studies met the inclusion criteria and were categorized into four thematic groups: the reliability engineering and probabilistic models, climate-related occupational risk models, hybrid and AI-based models, and the sustainability-oriented reviews. Results The study indicates a methodological shift from deterministic toward probabilistic and systems-based toward data-driven approaches, while heat stress is identified as the key climate-related hazard. Sustainability considerations are increasingly embedded within the risk frameworks, linking safety outcomes with productivity, labor capacity, and resilience. Still, the integration is uneven, and key performance metrics are underreported. The advanced computational methods, such as Bayesian networks and machine learning techniques, along with hybrid models, show promise but need further validation in a wide range of occupational settings. CONCLUSION This review stresses the need for more integrative, transparent, and harmonized modeling practices, providing support to climate-resilient and sustainable OSH management systems.

Abstract: Weed management is a critical component of soybean production. Efficient weed con-trol can improve both yield and crop quality. However, conventional spraying tech-niques exhibit low pesticide utilization and contribute to environmental pollution. To address these issues, this study proposes a deep learning–based precision target spray-ing method. A lightweight YOLOv5-MobileNetv3-SE model was developed by modi-fying the backbone feature extraction network and incorporating an attention mecha-nism. Field images of weeds were collected to construct a dataset, and the detection performance of the model was subsequently evaluated. A grid-based matching spray-ing algorithm was developed to synchronize target detection with spray actuation. The system time delay, including image processing delay, communication and control de-lay, and spray deposition delay, was analyzed and measured, and a time-delay com-pensation strategy was implemented to ensure accurate spraying. Experimental results demonstrated that the improved model achieved an mAP@0.5 of 86.9%, a model size of 7.5 MB, and a frame rate of 38.17 frames per second. Experimental results showed that weed detection accuracy exceeded 92.94%, and spraying accuracy exceeded 85.88% at forward speeds of 1–4 km/h. Compared with conventional continuous spraying, pesticide reduction rates reduced 79.0%, 72.5%, 55.8%, and 48.6% at weed coverage rates of 5%, 10%, 15%, and 20%, respectively. The proposed method provides a practi-cal approach for precise herbicide application, effectively reducing chemical usage and minimizing environmental impact.

Abstract: This work investigates how internal flow in a steadily fed sessile water droplet on a heated substrate shifts between buoyancy-driven and Marangoni-driven convection. Using COMSOL Multiphysics 6.2, two regimes are simulated: one with only buoyancy forces and one that also includes a temperature dependent surface-tension gradient at the free surface. At substrate temperatures near 30^◦C, the droplet develops a single circulation cell typical of buoyancy-controlled flow. As the substrate temperature increases towards 40^◦C, strong Marangoni stresses appear, producing multiple counter-rotating vortices and increasing the characteristic velocity by roughly an order of magnitude. This clear transition to Marangoni-dominated transport enhances internal mixing, redistributes heat, and modifies the evaporation pattern at the interface. The results identify the temperature range where Marangoni forces overtake buoyancy and provide quantitative guidance for engineering thermally driven droplet flows in microfluidics, thermal management, and heat-assisted deposition processes.

Abstract: This study explores the use of machine learning to predict high-cycle fatigue (HCF) be-havior and fatigue crack growth rate (FCGR) in Co-Cr-Mo alloys manufactured through laser powder bed fusion. Two machine learning (ML) models: extreme gradient boosting (XGB) and deep neural networks (DNN), are implemented to estimate HCF and FCGR across three distinct scanning strategies. The raw datasets for HCF and FCGR are taken from previously performed experiments. The HCF dataset is augmented using a Gaussian Mixture Model, while the FCGR dataset is used in its raw form. Following hyperparameter optimization, both models exhibited quite similar accuracy on validation datasets. Their performance was assessed during testing using mean squared error (MSE) and R2 scores. The XGB model demonstrated higher accuracy in HCF predictions by achieving higher R2 scores. In contrast, the DNN model performed better in FCGR predictions and yielded higher R2 scores compared to XGB. The good agreement with the experimental dataset shows that these two ML techniques are effective in predicting HCF and FCGR behavior.

Abstract: In this work, hydrogenated amorphous silicon carbide (a-SiCx​:H) and hydrogenated amorphous silicon oxide (a-SiOx​:H) films with similar optical bandgaps (Eg​), refractive indices (n), and extinction coefficients (k) were fabricated using pulse-wave modulation plasma technology by controlling the plasma turn-on and turn-off time ratio (ton​​/toff​). These films were placed at the 1/4 position of the p/i and i/n interfaces of hydrogenated amorphous silicon (a-Si:H) p-i-n solar cells to investigate their influence on solar cell performance. Experimental results showed that when deviations in Eg​, n, and k were less than 0.2%, 1.4%, and 4.1%, respectively, placing a-SiCx​:H and a-SiOx​:H films at the p/i and i/n interfaces increased the open-circuit voltage (Voc​​) and decreased the short-circuit current due to valence band (ΔEv​) or conduction band (ΔEc​​) offsets. The reduction in cell fill factor (FF) and efficiency (η) caused by placing a-SiCx​:H and a-SiOx​:H films at the p/i interface was greater than that caused by placing them at the i/n interface. Placing the a-SiCx​:H film at the p/i interface significantly improved the Voc​​. Due to the n-type doping effect of oxygen atoms, the a-SiOx​:H film exhibited the lowest FF and η at the p/i interface; however, when placed at the i/n interface, it yielded FF and η values second only to the a-Si:H standard reference cell. Appropriately placing the a-SiCx​:H film at the p/i interface and the slightly n-type a-SiOx​:H film at the i/n interface can effectively improve the Voc​​, FF, and η of p-i-n solar cells.

Abstract: A wide range of factors influences the dynamic and complex environment that is the commodity market. The most significant of these are external influences, such as political decisions and weather conditions, which cannot be directly controlled. Nevertheless, specific characteristics and price behaviors are exhibited by individual commodities, which manifest through seasonal patterns and characteristic fluctuations. This study aimed to analyses the day-ahead electricity market and identify the key factors influencing electricity price formation. Particular focus was given to the impact of meteorological variables and the interrelationships between the prices of other commodities, such as natural gas, coal and oil. A basic forecast of electricity prices in the day-ahead market was provided by a simple predictive model that was developed based on the findings. The results highlight the interconnectedness of energy markets and confirm that external factors play a crucial role in shaping electricity prices.

Abstract: Laser Powder Bed Fusion (LPBF) of 316L stainless steel is highly sensitive to laser power, scan speed, and beam size, which makes property prediction challenging especially when only small, scattered experimental datasets are available. Traditional machine-learning models trained directly on such limited data often struggle with overfitting and poor generalization. In this study, we present a lightweight, physics-Guided surrogate modelling framework designed specifically for small-data LPBF environments. Starting from 74 literature-reported microhardness measurements, we create an expanded training set using a cluster-aware Kernel Density Estimation (KDE) strategy that generates new samples only within physically meaningful regions of the P–v–spot space. A SAFE_DIST constraint ensures that surrogate points do not become near-duplicates of actual experiments, while a ±3 HV noise model preserves realistic hardness variability seen in LPBF studies. To incorporate first-order thermal behaviour without resorting to computationally expensive simulations, we construct three analytical descriptors: an energy-density proxy, a Rosenthal-inspired thermal-gradient indicator, and a thermo-mechanical efficiency (TME) metric. Together, these features improve interpretability and guide the model toward thermally consistent predictions. Ensemble regressors trained solely on the surrogate dataset demonstrate strong predictive capability on unseen real measurements, achieving an independent real-only test R² of 0.84. A strict real-only leave-one-out cross-validation (LOOCV) yields a conservative R² of 0.64, consistent with the inherent scatter of LPBF microhardness data. When trained on the full augmented dataset, the model achieves an overall R² of 0.91, reflecting the smooth, physically coherent nature of the surrogate space. The resulting process maps and learning-curve trends align closely with established LPBF thermal–microstructural behaviour, confirming that the framework learns underlying physics rather than memorizing datapoints. Overall, this work provides a simulation-free, data-efficient, and thermally grounded approach for predicting microhardness in LPBF 316L, offering a practical foundation for rapid parameter exploration, process optimization, and extension to other materials and LPBF responses.

Abstract: This study examines cyber vulnerabilities affecting critical infrastructure along NATO’s 2 eastern flank, with a focus on industrial control systems and operational technology. It 3 addresses how hybrid threats exploit legacy protocols and interoperability gaps across 4 mixed-generation IIoT environments, increasing the likelihood of disruptive events. We 5 propose an AI-enabled framework that links cyber resilience engineering to European 6 regulatory and operational requirements through two components: a Unified Compli- 7 ance Framework that maps legal obligations to implementable technical controls, and 8 an AI-enabled Cyber Resilience Index that consolidates detection, operational continuity, 9 governance, and supply-chain risk into a single scoring model. The methodology combines 10 regulatory-control mapping, OT-specific gap analysis, and engineering validation of real- 11 time constraints, supported by a digital-twin testing environment used to evaluate resilience 12 under representative adversarial scenarios. Results from the simulation-based evaluation 13 show consistent improvements in detection and response stability across tested scenar- 14 ios and provide an auditable evidence model for continuous assurance. The framework 15 supports risk-informed governance and investment decisions by translating compliance 16 objectives into measurable service-level targets and operational resilience indicators, while 17 promoting time-deterministic architectures, federated learning, and explainable AI for 18 accountable deployment in industrial settings

Abstract:

Coal and gas outbursts constitute a major hazard for mining safety, which is critical for the sustainable development of China’s energy industry. Rapid, accurate and reliable prediction is pivotal for preventing and controlling outburst incidents. Nevertheless, the mechanisms driving coal and gas outbursts involve highly complex influencing factors. By examining the attributes of these factors and their association to outburst intensity, four major geological and environmental indicators were identified. This study developed a machine learning-based prediction model for outburst risk. Five algorithms were evaluated: K-Nearest Neighbors (KNN), Back Propagation Neural Network (BPNN), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost). Model optimization was performed via Bayesian hyperparameter (BO) tuning. Model performance was assessed by the Receiver Operating Characteristic (ROC) curve; the optimized XGBoost model demonstrated strong predictive performance. To enhance model transparency and interpretability, the SHapley Additive exPlanations (SHAP) method was implemented. The SHAP analysis identified geological structure was the most important predictive feature, providing a practical decision-support tool for mine executives to prevent and control outburst incidents.

Abstract: Existing experimental results are sometimes difficult to guide the design of a water mist fire extinguishing system ascribing to many factors that affect the fire extinguishing per-formance of water mist. This paper sums up the factors and combs the logical relation-ships between them based on fire extinguishing mechanism of water mist and existing literature. Direct influence factors on fire extinguishing performance are analyzed em-phatically by the model of movement, heat transfer and mass transfer of water mist in the flame zone. The results show that the velocity and diameter of water mist entering the flame zone can be determined according to the temperature difference and the height of flame without considering the action of the flame plume. The water mist will enter the flame zone from the top and the periphery of the flame when the plume effect cannot be ignored. And the maximum heat absorption power of the water mist entering through the two ways should be obtained when determining the velocity and diameter of the water mist. This research can serve as a theoretical basis for the design of a water mist fire ex-tinguishing system.

Abstract: As new energy technology systems grow increasingly complex, corporate innovation activities in photovoltaics, energy storage, wind power, and energy management exhibit distinct distributed characteristics. Cross-enterprise collaboration and cross-regional innovation have become pivotal drivers for industrial upgrading. However, the lengthy new energy industrial chain, numerous participants, and high technical coupling lead to significant knowledge stickiness in knowledge transfer processes, thereby impairing collaborative innovation efficiency.To address this issue, this paper constructs a probabilistic resource allocation optimization model for distributed innovation networks in new energy enterprises. First, Least Squares Support Vector Machine (LSSVM) is employed to predict the success rate of collaborative innovation tasks. Model parameters are jointly optimized using grid search and Particle Swarm Optimization (PSO) under RBF, Polynomial, and Sigmoid kernel functions to adapt to different types of technological coupling and knowledge stickiness structures.Subsequently, a Gaussian Copula is introduced to characterize the risk dependency structure among technological maturity, external market volatility, and R&D collaboration complexity between innovation nodes. This enables the calculation of conditional value at risk (CoVaR) to quantify collaborative innovation risks under high uncertainty in new energy technologies. During resource allocation, a cost-loss model based on Bayesian decision theory is established, and the simulated annealing algorithm is employed to solve for the optimal combination of R&D and collaborative resources.Simulation results demonstrate that under conditions of rapid new energy technology iteration and significant knowledge stickiness, this method reduces resource waste by 17%–23% compared to traditional empirical allocation approaches while enhancing cross-enterprise innovation collaboration efficiency. This study provides a quantifiable and interpretable decision-making method for distributed innovation management in the new energy industry, holding significant implications for building an efficient industrial collaborative innovation ecosystem.

Abstract: This study investigates critical research gaps in procurement management challenges faced by Chinese contractors in international engineering–procurement–construction (EPC) projects under the Belt and Road Initiative (BRI), with a particular focus on sustainability-oriented outcomes. It examines: (1) prevalent procurement inefficiencies, such as communication delays and material shortages, encountered in international EPC projects; (2) the role of supply chain INTEGRATION in enhancing procurement performance; (3) the application of social network analysis (SNA) to reveal inter-organizational relationships in procurement systems; and (4) the influence of stakeholder collaboration on achieving efficient and sustainable procurement processes. The findings demonstrate that effective supply chain integration significantly improves procurement efficiency, reduces delays, and lowers costs, thereby contributing to more sustainable project delivery. Strong collaboration and transparent communication among key stakeholders—including contractors, suppliers, subcontractors, and designers—are shown to be essential for mitigating procurement risks and supporting resilient supply chain operations. SNA results highlight the critical roles of central stakeholders and their relational structures in optimizing resource allocation and enhancing risk management capabilities. Evidence from case studies further indicates that Chinese contractors increasingly adopt sustainability-oriented practices, such as just-in-time inventory management, strategic supplier relationship management, and digital procurement platforms, to reduce inefficiencies and environmental impacts. Overall, this study underscores that supply chain INTEGRATION, combined with robust stakeholder collaboration, is a key enabler of sustainable procurement and long-term competitiveness for Chinese contractors in the global EPC market.

Abstract: Non-thermal plasma is a promising technology for odor abatement from agricultural and domestic waste. However, its widespread application is often limited by the inherent trade-off between energy efficiency and processing capacity in conventional reactors. To address this challenge, we propose a novel multi-needle-to-cylinder dielectric barrier discharge reactor integrated with a deflector ring. By synergistically optimizing the electrode topology and modulating the flow field, this reactor achieves enhanced removal of complex ammonia–sulfur odor mixtures. The underlying mechanisms were elucidated through computational fluid dynamics (CFD) simulations coupled with systematic performance evaluation. Experimental results demonstrate that an 8-needle electrode configuration provides the optimal balance between discharge density and energy efficiency. CFD simulations further reveal that the deflector ring effectively suppresses gas bypass and promotes recirculation vortices downstream, thereby extending the residence time significantly. Mechanistic studies indicate that the removal of recalcitrant inorganic sulfides (e.g., CS₂ and H₂S) follows a synergistic mass-transfer–reaction controlled process, which is markedly improved by flow field optimization. In contrast, organic sulfides are governed primarily by chemical kinetics and show little dependence on flow variations. Under an extremely short residence time of 0.57 s (corresponding to a flow rate of 2.0 m³/h) and an ultra-low specific energy input of 6.26 J/L, the system achieved nearly complete removal of organic sulfides. Even for challenging inorganic sulfides, removal efficiencies reached 80.9% for H₂S and 45.3% for CS₂, with negligible byproduct formation. By effectively coordinating discharge characteristics with flow dynamics, this study provides both theoretical insight and technical support for the development of next-generation, energy-efficient, high-throughput industrial odor control systems.