Search Results (3,033)

The advancement of polymeric materials for orthopedic applications has enabled the development of lightweight, adaptable structures that support patient-specific solutions. This study focuses on the design, fabrication, and mechanical characterization of additively manufactured (AM) polymeric polylactic acid (PLA) components produced via Material Extrusion (MEX), commonly known as Fused Filament Fabrication (FFF). By optimizing geometric configurations and process parameters, these structures demonstrate enhanced flexibility, energy absorption, and load distribution, making them well-suited for orthopedic products and assistive devices. A comprehensive mechanical testing campaign was conducted to evaluate the elasticity, ductility, and strength of FFF-fabricated samples under tensile and three-point bending loads. Key process parameters, including nozzle diameter, layer thickness, and printing orientation, were systematically varied, and their influence on mechanical performance was recorded. The results reveal that these parameters affect mechanical properties in a complex, interdependent manner. To better understand these relationships, an automated routine was developed to calculate the experimental mechanical response, specifically, stiffness and strength. This methodology enables an automated evaluation of the output, considering parameter ranges for future applications. The outcome of the analysis of variance (ANOVA) of the experimental investigation reveals that the printing orientation has a strong impact on the mechanical anisotropy in FFF, while layer thickness and nozzle diameter demonstrate moderate-to-weak importance. Thereafter, the experimental findings were applied on an innovative orthopedic wrist splint design to be fabricated by means of FFF. The most suitable mechanical properties were selected to test the mechanical response of the designed components under operational bending loading by means of linear elastic finite element (FE) analysis. The computational results indicated the importance of employing the actual mechanical properties derived from the applied printing process parameters compared to data sheet values. Hereby, an additional parameter to adjust the mechanical response is the product’s design topology. Finally, this framework lays the foundation for future training of neural networks to optimize specific mechanical responses, reducing reliance on conventional trial-and-error processes and improving the balance between orthopedic product quality and manufacturing efficiency. Full article

High and variable moisture in wood logs limits their use in continuous carbonization reactors. Artificial drying emerges as a solution to homogenize the moisture of the raw material, optimizing the process, increasing yield, and improving the quality of charcoal. This study aimed to develop an experimental fixed-bed drying system for logs, evaluating the effects of cutting layout (40 cm, 20 cm, and split), diameter class (>12 cm, 12.1–14 cm, 14.1–16 cm, and 16.1–18 cm), and residence time (30, 60, and 90 min) at 300 °C. Split logs showed higher heating and drying rates, positively impacting efficiency. However, split and 20 cm logs subjected to 90 min of drying underwent combustion, indicating operational limits for these layouts under the tested conditions. The heartwood and sapwood regions of split logs heated more rapidly, resulting in higher drying rates and moisture loss, directly affecting drying efficiency. Split logs dried for 60 min showed the best drying efficiency and greatest moisture reduction, making this the most recommended treatment. This study not only demonstrates the technical feasibility of artificial drying of logs for continuous carbonization but also establishes fundamental guidelines for the development of more efficient, safe and sustainable industrial technologies in the charcoal production sector. Full article

This study examines the impact of Environmental, Social, and Governance (ESG) strategies on reducing air pollution in the West Kazakhstan region, a major hub for Kazakhstan’s oil and gas industry. A spatial analysis of atmospheric emissions reveals an uneven distribution of emission sources, predominantly concentrated in the northern industrialized part of the region, where the Karachaganak oil and gas condensate field is located. The ESG model of Karachaganak Petroleum Operating b.v. (KPO), implemented as an integrated management system based on Global Reporting Initiative (GRI) standards, is compared with the ESG strategies of leading oil and gas companies in Kazakhstan and globally, aligning with current international research trends. The analysis underscores the interdependence of technological and social aspects in the transition to a low-carbon economy, confirming the importance of integrating the environmental, social, and governance components of ESG into a unified strategic planning framework for sustainable development. Using econometric modeling, the study establishes a relationship between ESG indicators and the reduction in atmospheric pollution and provides a forecast for emission reductions by 2030. The key measures proposed to improve regional air quality are linked to long-term decarbonization strategies within the context of the sustainable development of the entire region. The proposed algorithm for implementing ESG principles helps to identify the concentration of functions and associated risks at different management levels within Highly Polluting Enterprises (HPEs) and optimizes business processes by focusing efforts on air pollution mitigation. The findings are applicable to other countries, as oil and gas producers worldwide face a number of common air pollution challenges. Full article

Urban freight pickup-and-delivery services operate in road networks where travel times are highly variable due to congestion, incidents, and operational restrictions. Such variability threatens the punctuality of deliveries and complicates the design of reliable service schedules. This paper examines an urban pickup-and-delivery vehicle routing problem with soft time windows under travel-time uncertainty and provides an empirical comparison of robust and deterministic planning approaches on a real road network. The problem is formulated as a time-dependent pickup-and-delivery VRP with soft time windows, where link travel times are represented by a finite set of scenarios calibrated from observed network conditions. The objective function combines four components that are central to urban freight operations: total travel time, total distance, and penalties for earliness and lateness relative to customer time windows. This structure captures the trade-off between routing efficiency and service quality. On this basis, a robust model is constructed that optimises tour plans with respect to scenario-based worst-case or risk-aggregated costs, while a standard deterministic model minimises the same objective using nominal (average) travel times only. An empirical study on a real urban network compares the deterministic and robust solutions with respect to delivery punctuality, tour length, and time-window violations across a range of demand and variability settings. The results show that robust routing systematically reduces the frequency and magnitude of late deliveries at the expense of only moderate increases in planned distance and travel time. Although energy use and emissions are not modelled explicitly, the improved reliability and reduced need for reactive re-routing indicate a potential to support more reliable and resource-efficient urban freight operations in the context of sustainable city logistics. Full article

As the global cement industry moves toward energy efficiency and intelligent manufacturing, refined control of key processes like precalciner outlet temperature is critical for improving energy use and product quality. The precalciner’s outlet temperature directly affects clinker calcination quality and heat consumption, so developing a high-accuracy prediction model is essential to shift from empirical to intelligent control. This study proposes a TCN-BiLSTM hybrid neural network model for the accurate prediction and regulation of the outlet temperature of the decomposition furnace. Based on actual operational data from a cement plant in Guangxi, the Spearman correlation coefficient method is employed to select feature variables significantly correlated with the outlet temperature, including kiln rotation speed, high-temperature fan speed, temperature A at the middle-lower part of the decomposition furnace, temperature B of the discharge from the five-stage cyclone, exhaust fan speed, and tertiary air temperature of the decomposition furnace. This method effectively reduces feature dimensionality while enhancing the prediction accuracy of the model. All selected feature variables are normalized and used as input data for the model. Finally, comparative experiments with RNN, LSTM, BiLSTM, TCN, and TCN-LSTM models are performed. The experimental results indicate that the TCN-BiLSTM model achieves the best performance across major evaluation metrics, with a Mean Relative Error (MRE) as low as 0.91%, representing an average reduction of over 1.1% compared to other benchmark models, thereby demonstrating the highest prediction accuracy and robustness. This approach provides high-quality predictive inputs for constructing intelligent control systems, thereby facilitating the advancement of cement production toward intelligent, green, and high-efficiency development. Full article

Reducing farmland abandonment and improving land resource utilization efficiency are critical pathways for safeguarding national food security. This study aims to identify the mechanism through which Land Consolidation (LC) affects farmers’ abandonment behavior at the land parcel scale, providing empirical evidence for improving LC policies and optimizing abandonment governance strategies. Using micro-survey data from 5014 land parcels in Sichuan Province collected in 2024, this study employs Probit, IV-Probit, and other econometric models to conduct empirical analysis, combining mechanism tests and heterogeneity analysis to systematically evaluate the suppression effects of LC. The results show that: (1) On the whole, LC significantly inhibits farmers’ abandonment behavior, with a notable decrease in the probability of abandonment for renovated land parcels. (2) The mechanism analysis indicates that LC alleviates farmers’ resource constraints and labor bottlenecks by expanding parcel size, operational scale, and improving the degree of land parcel consolidation, thereby reducing abandonment risk. (3) The heterogeneity analysis reveals that LC shows stronger suppression effects on abandonment behavior in flat land parcels, remote land parcels, and among ordinary farmers. In conclusion, LC is not only an essential measure for improving land quality and agricultural production efficiency but also a key policy tool for reducing farmers’ abandonment, stabilizing land use, and ensuring food security. Future efforts should promote targeted consolidation strategies, strengthening differentiated governance for varying land attributes and farmer types to achieve accurate and efficient abandonment management. Full article

Power transformers are critical components of power grids, and their operational status characterization and fault diagnosis are crucial for power system reliability. Oil quality assessment is a crucial method for determining transformer status, and the detection of impurity particles in oil has historically been a key approach. However, recent field tests have revealed the presence of numerous impurity particles less than 5 μm in transformer oil. Current power standards do not address these micron-sized particles, and their sources and mechanisms of action are largely unresolved. Therefore, this paper designed a localized overheating experiment, incorporating microflow imaging technology, to investigate the generation patterns of impurity particles under localized overheating and their quantitative correlation with heat. Field oil samples were also collected and tested to further explore the potential application of these micron-sized particles in transformer overheating assessment. The research results show that insulating oil can decompose and produce impurity particles at temperatures as low as 140 °C. When the temperature is below 140 °C, the number of particles at different heat levels is not significantly different from that of the non-overheated oil sample. However, when the temperature exceeds 140 °C, the number of particles increases significantly with increasing heat. Among the generated particles, particles with a diameter of less than 5 μm account for over 50% of the total number, and their number increases significantly with increasing heat. Their morphology is characterized by a smooth, regular, and spherical shape. Field test results of overheated oil samples are consistent with laboratory tests. Micron-sized particles are highly sensitive to changes in overheating conditions and have the potential to be used as a new characteristic parameter of transformer overheating conditions. In summary, this paper reveals the formation mechanism of impurity particles in insulating oil under localized overheating conditions. It was found that insulating oil can also decompose and generate impurity particles at 140 °C, with the pyrolysis products mainly consisting of particles smaller than 5 μm in diameter, which are not currently considered a concern in existing standards. Further research indicates that these micron-sized particles exhibit high sensitivity to changes in overheating conditions, demonstrating potential application value as a novel characteristic parameter of transformer overheating. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ²H, δ¹⁸O, δ¹³C and δ¹⁵N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ²H, δ¹⁸O), carbon and nitrogen isotopes (δ¹³C, δ¹⁵N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023–2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil–plant–atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ¹⁸O covaried strongly with relative humidity (RH; r = −0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ¹⁸O (leaf–stem), RH, ET, and soil matric potential at 60 cm (Soil₆₀) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July–August 2024 drought (IDI_0–100 > 90; RH < 60%, ET > 40 mm wk⁻¹, Soil₆₀ > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ¹³C increased (less negative) with drought (r = −0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ¹⁵N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil₆₀; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture. Full article

The accelerating integration of Artificial Intelligence (AI) in Industrial Automation has established Reinforcement Learning (RL) as a transformative paradigm for adaptive control, intelligent optimization, and autonomous decision-making in smart factories. Despite the growing literature, existing reviews often emphasize algorithmic performance or domain-specific applications, neglecting broader links between methodological evolution, technological maturity, and industrial readiness. To address this gap, this study presents a bibliometric review mapping the development of RL and Deep Reinforcement Learning (DRL) research in Industrial Automation and robotics. Following the PRISMA 2020 protocol to guide the data collection procedures and inclusion criteria, 672 peer-reviewed journal articles published between 2017 and 2026 were retrieved from Scopus, ensuring high-quality, interdisciplinary coverage. Quantitative bibliometric analyses were conducted in R using Bibliometrix and Biblioshiny, including co-authorship, co-citation, keyword co-occurrence, and thematic network analyses, to reveal collaboration patterns, influential works, and emerging research trends. Results indicate that 42% of studies employed DRL, 27% focused on Multi-Agent RL (MARL), and 31% relied on classical RL, with applications concentrated in robotic control (33%), process optimization (28%), and predictive maintenance (19%). However, only 22% of the studies reported real-world or pilot implementations, highlighting persistent challenges in scalability, safety validation, interpretability, and deployment readiness. By integrating a review with bibliometric mapping, this study provides a comprehensive taxonomy and a strategic roadmap linking theoretical RL research with practical industrial applications. This roadmap is structured across four critical dimensions: (1) Algorithmic Development (e.g., safe, explainable, and data-efficient RL), (2) Integration Technologies (e.g., digital twins and IoT), (3) Validation Maturity (from simulation to real-world pilots), and (4) Human-Centricity (addressing trust, collaboration, and workforce transition). These insights can guide researchers, engineers, and policymakers in developing scalable, safe, and human-centric RL solutions, prioritizing research directions, and informing the implementation of Industry 5.0–aligned intelligent automation systems emphasizing transparency, sustainability, and operational resilience. Full article

This study introduces an innovative Honey Badger Optimization (HBO) designed to address the Optimal Power Flow (OPF) challenge in electrical power systems. HBO is a unique population-based searching method inspired by the resourceful foraging behavior of honey badgers when hunting for food. In this algorithm, the dynamic search process of honey badgers, characterized by digging and honey-seeking tactics, is divided into two distinct stages, exploration and exploitation. The OPF problem is formulated with objectives including fuel cost minimization and voltage deviation reduction, alongside operational constraints such as generator limits, transformer settings, and line power flows. HBO is applied to the IEEE 30-bus test system, outperforming existing methods such as Particle Swarm Optimization (PSO) and Gray Wolf Optimization (GWO) in both fuel cost reduction and voltage profile enhancement. Results indicate significant improvements in system performance, achieving 38.5% and 22.78% better voltage deviations compared to GWO and PSO, respectively. This demonstrates HBO’s efficacy as a robust optimization tool for modern power systems. In addition to the single-objective studies, a multi-objective OPF formulation was investigated to produce the complete Pareto front between fuel cost and voltage deviation objectives. The proposed HBO successfully generated a well-distributed set of trade-off solutions, revealing a clear conflict between economic efficiency and voltage quality. The Pareto analysis demonstrated HBO’s strong capability to balance these competing objectives, identify knee-point operating conditions, and provide flexible decision-making options for system operators. Full article

This study examines the organization of offshore bunkering operations with the aim of improving their economic and logistical efficiency. A mathematical model is proposed that minimizes the total cost of fleet refueling while accounting for technical limitations of vessels, service time windows, and external operational constraints. The formulation extends classical vehicle routing approaches by incorporating fixed and variable costs as well as penalties for delays. A case study based on the Sea of Okhotsk fleet illustrates the application of the model to ten client vessels and four bunkering ships. Using mixed-integer programming combined with heuristic route construction, optimal routing solutions were obtained and tested under varying fuel prices, demand volumes, and fleet sizes. In a stylized one-day case study with ten client vessels located within a 100 km radius around Magadan, the results indicate that reducing the number of active bunkering vessels from four to three can lower overall operating costs while maintaining service quality, yielding indicative savings of approximately 12–18% relative to a simple sequential baseline policy in which bunkering vessels serve customers in a fixed order and the client set is partitioned roughly equally among vessels. The proposed approach provides a practical framework for decision-makers to enhance planning, resource allocation, and operational reliability in marine fuel supply chains. Full article

This systematic review assesses indoor air quality (IAQ) in tropical residences (Köppen Af/Am/Aw), explicitly linking IAQ to ventilation from in situ monitoring and, when relevant, occupant surveys (surveys synthesized qualitatively). This focus is warranted by the scarcity of tropical, housing-specific evidence. Searches were performed exclusively in Google Scholar (25 August 2024–5 August 2025; English/Spanish) under PRISMA, with documented queries/filters; eligible studies reported residential settings, tropical climate, and IAQ–ventilation linkage. Results show a regulatory mosaic with few binding residential limits and heterogeneous protocols that hinder comparison. Robust patterns include cooking-related particle peaks, penetration of traffic dust, humidity-driven VOC/formaldehyde emissions, and mold growth under deficient hygrothermal control. CO₂ is a useful operational indicator of ventilation yet insufficient for risk assessment without PM and VOC monitoring. Evidence supports source control, cross-ventilation and/or on-demand extraction/outdoor-air supply, humidity management, and filtration/purification to avoid particle ingress during ventilation. Reporting of sensor performance (calibration, drift, RH/T effects) is inconsistent, and targeted evaluations of TVOC/formaldehyde and window screens (mesh) are scarce. We conclude that tropical residential IAQ management requires multi-parameter, continuous monitoring, standardized reporting, and trials integrating ventilation, dehumidification, and filtration under real occupancy, alongside adaptive regulation and passive tropical design augmented by light mechanical support and informed occupant behavior. Full article

This paper presents Hy-LIFT (Hybrid LLM-Integrated Fault Diagnosis Toolkit), a multi-stage framework for interpretable and data-efficient fault diagnosis in 5G/6G networks that integrates a high-precision interpretable rule-based engine (IRBE) for known patterns, a semi-supervised classifier (SSC) that leverages scarce labels and abundant unlabeled logs via consistency regularization and pseudo-labeling, and an LLM Augmentation Engine (LAE) that generates operator-ready, context-aware explanations and zero-shot hypotheses for novel faults. Evaluations on a five-class, imbalanced Dataset-A and a simulated production setting with noise and label scarcity show that Hy-LIFT consistently attains higher macro-F1 than rule-only and standalone ML baselines while maintaining strong per-class precision/recall (≈0.85–0.93), including minority classes, indicating robust generalization under class imbalance. IRBE supplies auditable, high-confidence seeds; SSC expands coverage beyond explicit rules without sacrificing precision; and LAE improves operational interpretability and surfaces potential “unknown/novel” faults without altering classifier labels. The paper’s contributions are as follows: (i) a reproducible, interpretable baseline that doubles as a high-quality pseudo-label source; (ii) a principled semi-supervised learning objective tailored to network logs; (iii) an LLM-driven explanation layer with zero-shot capability; and (iv) an open, end-to-end toolkit with scripts to regenerate all figures and tables. Overall, Hy-LIFT narrows the gap between brittle rules and opaque black-box models by combining accuracy, data efficiency, and auditability, offering a practical path toward trustworthy AIOps in next-generation mobile networks. Full article

The production of reliable turbo machinery, particularly gas turbine blades, is a major global challenge. This capability serves as a key indicator of a nation’s industrial base, technological prowess, and comprehensive strength. Critical components in aircraft engines and gas turbines operate under extreme conditions, including high temperatures, high pressures, and substantial mechanical stresses. Consequently, there is a growing urgency to develop cost-effective and time-efficient repair strategies to enhance engine performance and efficiency. However, many mission-critical parts, especially high-pressure (HP) blades, are prone to severe damage. Moreover, taking equipment offline for blade maintenance and repair is a time-consuming process. It is also highly costly to restore these essential components to full functionality. Since 1996, researchers have focused on applying laser metal deposition (LMD) additive manufacturing technology for high-performance repair and remanufacturing of aerospace engines and industrial gas turbine (IGT) blades. Empirical studies have demonstrated that depositing a high-quality, erosion-resistant protective coating on the leading edge of HP blades effectively extends the service life of turbine blades in both aircraft engines and industrial gas turbines. This study systematically outlines the technical workflow of the proposed methodology and provides a concise perspective on emerging development trends. Full article