Search Results (21,889)

SAG grinding mills represent critical energy-intensive operations in copper concentrators, accounting for 30%–50% of total plant energy consumption. The accurate prediction of mill power draw and production rate under varying operational conditions is essential for real-time control, production planning, and energy management. This study presents a comprehensive comparison of ML algorithms for modeling Production and Power in a Chilean copper mining industry. Deterministic and stochastic models were fitted and validated using industrial data from a Chilean copper operation. More representative models were re-estimated and subsequently evaluated under different operating regimes to examine their predictive performance under aggregated conditions of the feeding variables. This procedure allowed for the identification of the modeling approaches that provide the most robust performance across varying operational regimes. The results show that XGB achieved the best predictive performance, with test RMSE and R² values of 87.98 and 97.35% for SAG Production, and 431.11 and 95.11% for SAG Power, respectively. Stochastic approaches provided complementary uncertainty quantification, supporting risk-informed decision making under variable operating conditions. The analysis by operational regime indicates that XGB presents better fit in the Thick hydraulic regime, for both responses’ variables, which could be explained why a dense pulp operation provides more predictable grinding dynamics. The comparative analysis reveals trade-offs between model complexity, interpretability, computational requirements, and predictive performance, offering practical guidance for selecting appropriate modeling frameworks based on specific operational objectives and data availability in mineral processing applications. Full article

Despite the significant potential of ocean wave energy, the high cost of the generated power remains a major challenge. This highlights the need for innovative conceptual designs that enhance energy conversion while maintaining comparable implementation and installation costs. Recently, the concept of Variable-Shape Buoy Wave Energy Converters (VSB WECs) was introduced that uses flexible buoy material. While many studies have demonstrated the improved performance of VSB WECs compared to Fixed-Shape Buoy Wave Energy Converters (FSB WECs) through numerical simulations, analytical validation is essential to support these findings. This paper presents an analytical derivation of the theoretical limit of power absorption for VSB WECs using the complex-conjugate criteria for the heave motion. In this study, a multi-degree-of-freedom (multi-DoF) VSB WEC model is developed using a thin spherical shell representation, incorporating Rayleigh–Ritz and Love approximations under the assumptions of small deformations and axisymmetric vibration. Hydrodynamic coefficients are computed using a Boundary Element Method (BEM) software. The variation in the theoretical power absorption limit with Young’s modulus is analyzed across a range of elastic materials. As a validation step, the derived theoretical limit criterion is applied to the standard reduced-order single-DoF model of an FSBWEC, successfully yielding the exact theoretical limit reported in the literature. Full article

Pile-supported geosynthetic-reinforced embankments, as effective foundation improvements, are being used increasingly often in the construction of highway and railway engineering at present. The geosynthetic-reinforced load transfer platform in the horizontal direction was simulated to the thin plate, and then the differential equation of the curved surface and the nonlinear foundation model were used to solve the analytical expression of the post-construction settlement of the reinforced area, and the engineering example was used to verify it. Furthermore, a finite element model was developed to simulate the settlement. The analysis utilized a static general step and incorporated a linear elastic–perfectly plastic model with the Mohr–Coulomb failure criterion. The numerical result of 19.7 mm was consistent with the theoretical prediction of 20.1 mm, demonstrating a mere 2.0% relative error and substantiating the validity and accuracy of the theoretical model. The analysis examined how bending stiffness, the subgrade reaction coefficient, pile spacing, and embankment height affect post-construction settlement. The results demonstrate that the settlement increases with larger pile spacings or lower values of the subgrade reaction coefficient and bending stiffness. Notably, the settlement increases with embankment height only until a critical height—calculated from the bearing capacity of the inter-pile soil—is exceeded. Based on this, it was found that the subgrade reaction coefficient was identified as the most influential parameter, followed by pile spacing and then bending stiffness. These findings lead to practical recommendations for engineering practice. Full article

Mill relining manipulator is essential maintenance equipment used to replace liners in a grinding mill. However, its excessive structural weight significantly constrains maneuverability and operational efficiency. To address this problem, this paper proposed a lightweight design framework for the manipulator’s upper arm, integrating improved multiple operating conditions topology optimization with size optimization. Firstly, a finite element model of the manipulator was established in ANSYS Workbench 2022R2. The loads under the corresponding operating conditions were extracted and applied to the finite element model of the upper arm to perform multi-condition finite element simulations. Secondly, a mathematical model for multi-condition topology optimization was developed using the variable density method combined with the Analytic Hierarchy Process (AHP), and the weight coefficients for each operating condition were determined. Finally, a combined response surface methodology (RSM) and genetic algorithm (GA) approach was employed to optimize the structural parameters of the upper arm. A response surface model with maximum equivalent stress and maximum deformation as the response variables was constructed, and the Pareto optimal set was obtained using the non-dominated sorting genetic algorithm (NSGA-II) to determine the optimal structural design. Quasi-static load tests were conducted on a scaled prototype to verify the reliability of the numerical optimization results. The results demonstrate that the optimized upper arm satisfies the strength and stiffness requirements while achieving a 12% mass reduction (2463 kg), confirming the effectiveness and engineering applicability of the proposed lightweight design methodology. Full article

Unmanned Aerial Vehicles (UAVs) have been key platforms to perform sensing, analytics and automation across intelligent transportation, construction, smart agriculture, logistics and defense. Generative AI (GenAI) accelerates intelligence of UAVs by creating synthetic data, simulating environments and improving learning with restricted data conditions. When integrated with digital twin and AI frameworks, GenAI enables advanced design, modeling, adaptation and making a decision. In this paper, we survey recent advances for generative AI-enabled UAVs systems and applicable scenarios. Then, we categorize four applicable research branches using generative AI-enabled UAVs for intelligent transportation systems, digital twin and smart infrastructure, smart agriculture, last-mile logistics and delivery. Full article

Environmental pollutants, lifestyle factors, and intrinsic metabolism can amplify reactive oxygen and nitrogen species (ROS/RNS) generation beyond antioxidant capacity. The resulting oxidative stress damages macromolecules, perturbs redox signaling, and may accelerate biological aging. This review synthesizes evidence published mainly in 2020–2025 on how major pollutant classes (air pollutants, metals, pesticides, nanoparticles, and micro-/nanoplastics) induce ROS through shared nodes mitochondrial electron transport disruption, NADPH oxidase activation, and redox cycling/Fenton chemistry and how these signals propagate to epigenetic remodeling (DNA methylation, histone modifications, and non-coding RNAs). To move beyond descriptive cataloging, we grade the strength of evidence by study context (cell culture, animal models, human observational studies, and clinically oriented biomarker research), highlight convergent findings and unresolved controversies, and specify key methodological limits. We then compare oxidative-stress biomarker platforms by analytical specificity, pre-analytical susceptibility, and translational readiness, distinguishing validated markers from exploratory redox-epigenetic and multi-omics signatures. Finally, we discuss how exposomics and AI-assisted multi-omics integration may support biomarker discovery while emphasizing current constraints (confounding, batch effects, and limited prospective validation) that must be addressed for clinical translation. Full article

Background/Objectives: Acute pancreatitis (AP) lacks disease-modifying pharmacotherapy. Neuroimmune, serotonergic, and redox-regulated pathways may modulate inflammatory amplification and acinar injury, although pharmacovigilance data link some psychotropic drug classes to AP risk. This review synthesized controlled rodent studies evaluating neuromodulatory interventions with serotonergic, stress-axis, or ferroptosis-linked targets in experimental AP. Methods: PubMed, Scopus, eLIBRARY.ru, and Elicit were searched in January 2026, supplemented by Google Scholar audit and citation chasing. Eligible studies were controlled in vivo rodent experiments using validated AP models with quantitative outcomes. Intervention timing was classified a priori as a primary analytic variable. Risk of bias was assessed with SYRCLE. A prespecified audit showed that no subset met the criteria for quantitative pooling because of heterogeneity in model class, compounds, timing, outcome definitions, units, and sampling timepoints. Mechanism-stratified qualitative synthesis was therefore performed. The protocol was registered on OSF (doi: 10.17605/OSF.IO/CZXDJ). Results: Nine studies (1992–2023) yielded 410 outcome rows across three mechanistic strands. Serotonergic modulation (5-HT₂/5-HT₂A-focused; six studies) reduced serum amylase/lipase (−37% to −65% vs. disease controls) and histological injury, with receptor-selectivity data supporting 5-HT₂A-mediated mechanisms. Stress-axis modulation with thiadiazine L-17 reduced 7-day mortality in two severe models (from 50–70% to 30%). Olanzapine attenuated ferroptosis-linked injury via off-target antioxidant activity independent of serotonergic receptors. All interventions were prophylactic, peri-induction, or very early post-induction; no delayed therapeutic-window studies were identified. Most SYRCLE domains were unclear, particularly allocation concealment and blinding-related procedures. Conclusions: Neuromodulatory pathways modulate experimental AP in rodents, but evidentiary strength differs across mechanistic strands. Inference is constrained by absent therapeutic-window testing, heterogeneous endpoints, and reporting deficits. The findings support mechanism-level target prioritization rather than clinical repurposing. Full article

Tropical forests, despite their critical environmental and socio-economic roles, remain highly vulnerable to deforestation, forest degradation, and climate-related disturbances. There is a growing demand for robust and transparent forest monitoring systems, particularly under REDD+, the Paris Agreement’s Enhanced Transparency Framework (ETF), and emerging climate-finance mechanisms. Conventional approaches based on field inventories and traditional remote sensing are often constrained by limited or uneven field data, persistent cloud cover, complex forest conditions, and limited institutional and technical capacity. This review examines how artificial intelligence (AI) and machine learning (ML) are being integrated into remote sensing–based tropical forest monitoring to address these structural constraints. Using a semi-systematic synthesis of peer-reviewed studies, complemented by operational platforms and grey literature, the review assesses AI/ML approaches, remote sensing datasets, and applications relevant to national and large-scale monitoring. Evidence is synthesized across five analytical dimensions: AI/ML model families and workflows, multi-sensor datasets and training resources, operational monitoring platforms, application domains (including deforestation, degradation, and biomass/carbon estimation), and cross-cutting technical, institutional, and governance barriers. The review finds that AI/ML-enabled remote sensing, particularly those combining optical, radar, and LiDAR time series within cloud-based platforms, has substantially improved the automation, scalability, and speed of tropical forest monitoring. However, effective and equitable adoption remains constrained by limitations in training and validation data, dependence on proprietary platforms and data, uneven technical capacity, and unresolved governance and ethical challenges. Emerging solutions, including open and representative training datasets, platform-agnostic processing infrastructures, long-term capacity building, and inclusive data-governance frameworks, are identified as critical enablers of credible and nationally owned AI/ML-enabled forest-monitoring systems. The review highlights that AI/ML can play a transformative role in supporting climate mitigation, biodiversity conservation, and informed decision-making. This potential, however, depends on transparent data governance arrangements, long-term capacity building, and platform-agnostic infrastructures that support national ownership. Full article

As a solid-state welding technique, friction stir welding (FSW) has many advantages over conventional fusion welding. Its applications in the manufacturing and joining of parts in aerospace, automotive, and shipbuilding have significantly increased. Friction heat generation is the fundamental driver of the FSW process. It governs material flow, microstructural evolution, mechanical properties, and residual stresses. Understanding the effect of heat generated on the joint quality is essential for process parameter optimization, ensuring defect-free welds and high-quality joints. Thus, evaluating the thermal history of the FSW process is a key requirement for effective analysis. This comprehensive review critically discusses research studies published over the past three decades (1991–2025) that have examined different approaches to predict and measure heat generation in FSW. A total of 136 highly relevant articles were selected from the Scopus database and systematically analyzed. The effects of various welding parameters on heat generation, microstructural evolution, and joints’ mechanical properties have been reported. Different heat generation prediction and measurement techniques, such as analytical models, finite element models (FEM), and experimental methods have been discussed in terms of their feasibility, accuracy, advantages, disadvantages, and cost. The evolution, state of the art of analytical models and FEM over the last three decades are analyzed and future research directions are outlined. Finally, the correlation between process parameters, heat generated, microstructural development, and mechanical performance of the welded joints for various workpiece materials is investigated. This review provides a critical and comparative perspective that highlights the strengths and limitations of each method, offering practical guidance for researchers and industry practitioners. Full article

Maritime traffic complexity (MTC) is a term that has gained increased importance in the last decade in the maritime safety domain. It is a concept for understanding navigational safety and operational challenges in congested maritime environments. Although research interest in MTC has grown, it is a concept that remains fragmented, with various interpretations of definitions, indicators, and modeling approaches present in the literature. This study presents a comprehensive literature review and bibliometric analysis to synthesize the current state of research on MTC as a scientific construct and clarify its conceptual foundations from an analytical perspective. In accordance with PRISMA guidelines and systematic literature review (SLR) methodology, relevant studies were identified and screened across major scientific databases. A detailed analysis was conducted on 40 scientific publications. The findings indicate that most existing MTC models rely mainly on Automatic Identification System (AIS) data and corresponding derived metrics. MTC is primarily assessed through geometric vessel–vessel interactions, relative motion parameters, and collision-risk indicators. Bibliometric analysis demonstrates a rapid increase in scientific interest in this topic since 2015, with research concentrated in several leading journals. The study identifies a significant methodological limitation in current frameworks, which often overlook the heterogeneity of marine traffic, environmental conditions, vessel reliability, and human factors. Therefore, this study highlights the need for a more comprehensive MTC evaluation framework that incorporates operational, geographical constraint-based, environmental, and behavioral variables alongside traditional AIS-based metrics. Full article

Sitka spruce (Picea sitchensis (Bong.) Carr.) is among the most commercially important tree species in European and North American forestry, and genetic improvement programmes are therefore essential for promoting its productivity and sustainability. This research emphasises the significance of the breeding programmes. The primary objective of this study was to provide more reliable information on family selection for the improvement programme of Sitka spruce by accounting for competition and environmental heterogeneity effects. Analyses in the present study were carried out on historical inventory data of height (HT) and diameter at breast height (DBH) from a half-sib progeny trial of Sitka spruce in Ireland. Tree measurement data were collected at ages 6, 12, 15 and 20 years. A mixed linear model incorporating spatial and competition terms was applied to estimate genetic parameters of the Sitka spruce population. The direct genetic effects of each family on its own phenotypes and the competition effect on its neighbour’s phenotype were examined over time. The study demonstrated an analytical approach for assessing both genetic as well as environmental aspects of competition in a Sitka spruce progeny trial. The combined model integrating competition and spatial terms (model CS) improved model fit compared with the basic model, which only included the random effects of genetic and experimental design factors (model B), with an AIC difference of up to 3609 between them. Residual error obtained from model CS was usually smaller than from model B, with the greatest reduction of 85%. Furthermore, model CS generally improved the estimation of heritability for growth traits, by up to 241, when compared with model B. In addition, genetic differences in competitive ability among families were also observed. Families with favourable combinations of direct genetic and competitive breeding values were suggested for selection in subsequent cycles of the breeding programme, i.e., families with relatively high direct genetic breeding value but low and consistent competitive breeding value over time. This work develops a practical framework to inform future family selection for Sitka spruce improvement programmes. Full article

The performance, safety, and long-term durability of electric vehicle (EV) battery systems are strongly governed by the chemical stability and thermophysical properties of their constituent materials. In response to the limitations of conventional lithium-based batteries—particularly with respect to thermal stability, material sustainability, and degradation under high operational loads—this study presents a thermodynamic and electrochemical modeling framework for evaluating alternative battery materials relevant to electric vehicle energy storage systems. Xenon difluoride (XeF₂) and zirconium carbide (ZrC) are proposed as functional battery components and comparatively analyzed based on chemical stability, bond enthalpy, mass–capacity relationships, and energy density characteristics. Analytical modeling is employed to investigate voltage–capacity–mass interactions over a wide operating range (3–48 V and 100–1000 mAh), representing diverse EV operating scenarios, including high-load and elevated-temperature conditions. In addition, temperature-dependent degradation behavior and cycle life performance are assessed using logarithmic degradation models and Arrhenius-based life cycle formulations. The results indicate that ZrC, with a high total bond enthalpy of 561 kJ mol⁻¹, demonstrates superior energy density, reduced material mass requirements, and enhanced resistance to thermal degradation, making it particularly suitable for high-temperature and long-life EV battery applications. In contrast, XeF₂ exhibits stable electrochemical performance under moderate temperature and capacity conditions but shows increased sensitivity to thermal effects at higher operating ranges, suggesting potential applicability in balanced-performance EV battery configurations. Overall, the proposed modeling framework provides a systematic approach for assessing alternative battery materials under electric vehicle-relevant operating conditions and offers guidance for future experimental validation, material selection, and battery design aimed at improving safety, durability, and sustainability in next-generation electric vehicle energy storage systems. Full article

This paper presents an analytical framework for the preliminary design of stringer-stiffened composite panels subjected to low-velocity impact. The formulation combines First-Order Shear Deformation Theory with a two-degree-of-freedom spring–mass model, while the super-stringer is represented as a Euler–Bernoulli beam whose bending contribution is transferred to the skin mid-surface through the parallel axis theorem. This provides a computationally efficient tool for rapid parametric assessment of stiffened configurations at the early design stage. To support laminate selection, a Specific Impact Energy Index (SIEI) is introduced to rank configurations according to their elastic energy storage efficiency relative to the product of skin and stringer thicknesses. The tool is validated against both published experimental results and a finite element dynamic explicit model, demonstrating a good approximation of the impact response. It is then applied to identify the optimum laminate configuration for a super-stringer case study within the design space considered. Full article

This paper derives a closed-form expression for the expected discounted value of aggregate cash flows when arrival times follow a Poisson process but both the time horizon and the number of arrivals are finite. The result provides a tractable analytical formula for the expected discounted sum under simultaneous constraints on time and arrival counts. We show that the expression converges to the well-known infinite-horizon and infinite-arrival results as limiting cases. Numerical illustrations demonstrate the behavior of the formula under different parameter values. The result can be interpreted as the valuation of a discounted compound Poisson process with finite constraints and may be useful in stochastic modeling and risk-analysis applications. The proposed formula provides a simple analytical tool for evaluating discounted losses or revenues in finite risk portfolios. Full article

Tequila is frequently stored or aged in polymer containers and oak casks, which can enable the migration of phthalates such as di-(2-ethylhexyl) phthalate (DEHP), an endocrine-disrupting chemical. We quantified DEHP in tequila (55% ethanol) stored in LDPE tanks, HDPE jerry cans, PET carboys, and French oak casks with and without thermal treatment during long-term storage/aging (up to 18 and 11 months, respectively). Monthly samples were extracted and analyzed by GC–MS. Migration kinetics were evaluated using empirical exponential/sigmoidal models and an analytical solution of Fick’s second law for a semi-infinite slab. In plastics, DEHP increased nonlinearly and was best described by a modified Gompertz model, exhibiting a lag phase up to 42 days (~month 2), maximum transfer rates (Rmax) up to 0.82 µg L⁻¹ day⁻¹, and late-time concentrations near 120 µg L⁻¹. The non-toasted oak cask previously used for wine showed exponential behavior, reaching ~185 µg L⁻¹ and fitting the Minchev–Minkov model, whereas the toasted cask showed minimal transfer. Although concentrations remained below a reference safety limit (1500 µg kg⁻¹), the results indicate that food-contact plastics and commonly used oak casks are not risk-free under prolonged contact, supporting model-based forecasting for quality control. Full article