Search Results (2,294)

As product complexity and customization levels continue to rise in high-end manufacturing, optimizing and controlling multistage manufacturing processes (MMPs) presents growing challenges. However, existing MMP research has largely focused on optimizing relatively fixed process routes, while limited attention has been paid to the route selection problem itself, particularly the global selection of process routes under real-world conditions where MMPs stages are mutually coupled and characterized by uncertainty. Therefore, the present study focuses on the fundamental challenge of process route decision-making for complex products within MMPs. A hybrid decision model is developed that incorporates expert knowledge and explicitly quantifies uncertainty arising from decision inconsistency and linguistic ambiguity. The proposed model consists of three main components: expert weighting, criterion weighting, and comprehensive ranking of process schemes. Expert and criterion weights are derived using the Enhanced Analytic Hierarchy Process (EAHP) to address inconsistency in expert judgments, while the ranking of alternatives is performed using a novel Combined Compromise Solution (CoCoSo) rule within an Interval Type-2 Fuzzy Sets (IT2FS) linguistic environment. Furthermore, the effectiveness of the proposed framework is validated through a case study on the multistage manufacturing process of compact aerospace heat exchangers. The results demonstrate that the proposed approach provides effective decision support for selecting robust process schemes during the initial planning phase of MMPs. Full article

Background: In high-dimensional, small-sample omics studies such as metabolomics, feature selection not only determines the discriminative performance of classification models but also directly affects the reproducibility and translational value of candidate biomarkers. However, most existing methods primarily optimize classification accuracy and treat stability as a post hoc diagnostic, leading to considerable fluctuations in selected feature sets under different data splits or mild perturbations. Methods: To address this issue, this study proposes FRL-TSFS, a feature selection framework synergistically driven by filter-based Robust Rank Aggregation and L1-sparse modeling. Five complementary filter methods—variance thresholding, chi-square test, mutual information, ANOVA F test, and ReliefF—are first applied in parallel to score features, and Robust Rank Aggregation (RRA) is then used to obtain a consensus feature ranking that is less sensitive to the bias of any single scoring criterion. An L1-regularized logistic regression model is subsequently constructed on the candidate feature subset defined by the RRA ranking to achieve task-coupled sparse selection, thereby linking feature selection stability, feature compression, and classification performance. Results: FRL-TSFS was evaluated on six representative metabolomics and gene expression datasets under a mildly perturbed scenario induced by 10-fold cross-validation, and its performance was compared with multiple baselines using the Extended Kuncheva Index (EKI), Accuracy, and F1-score. The results show that RRA substantially improves ranking stability compared with conventional aggregation strategies without degrading classification performance, while the full FRL-TSFS framework consistently attains higher EKI values than the other feature selection schemes, markedly reduces the number of selected features to several tens of metabolites or genes, and maintains competitive classification performance. Conclusions: These findings indicate that FRL-TSFS can generate compact, reproducible, and interpretable biomarker panels, providing a practical analysis framework for stability-oriented feature selection and biomarker discovery in untargeted metabolomics. Full article

Home energy management systems have emerged as a crucial solution for enhancing energy efficiency, reducing carbon emissions, and facilitating the integration of renewable energy sources into homes. To fully realize their potential, these systems’ performance must be optimized, which involves addressing multiple objectives, such as minimizing costs and environmental impact. The Pareto frontier is a tool widely adopted in multi-objective optimization within home energy management systems’ operation, where a range of optimal solutions are produced. This study uses the Pareto curve to optimize the operational performance of home energy management systems, considering the state health of the battery to determine the best answer among the optimal solutions in the curve. The main reason for considering the state of health is the effects of the battery’s operation on the performance of energy systems, especially for long-term optimization outcomes. In this study, the performance of the battery is measured through a physical model named PyBaMM that is tuned based on swarm intelligence techniques, including the Whale Optimization Algorithm, Grey Wolf Optimization, Particle Swarm Optimization, and the Gravitational Search Algorithm. The proposed framework automatically identifies the optimal solution out of the ones in the Pareto curve by comparing the performance of the battery through the tuned physical model. The effectiveness of the proposed algorithm is demonstrated for a home, including four distinct energy carriers along with a 12 V 128 Ah LFP chemistry Li-ion battery module, where the overall cost and carbon emissions are the metrics for comparisons. Implementation results show that tuning the physical model based on the Whale Optimization Algorithm reaches the highest accuracy compared to the other methods. Moreover, considering the state of health of the battery as the selecting criterion will improve home energy management systems’ performance, particularly in long-term operation models, because it guarantees a longer battery lifespan. Full article

Asoil loosening device is designed to overcome the poor soil disturbance performance observed during residual film recovery, thereby effectively improving residual film recovery rates. Based on soil properties measured in cotton fields, a discrete element method was developed to simulate the interaction between the soil and the soil loosening device. A comparative analysis of the soil angle of repose and soil firmness was conducted to validate the accuracy of the soil discrete element model. Simulation experiments were conducted to analyze the effects of forward speed on soil particle velocity, soil particle forces, and forces on the soil loosening device. A theoretical analysis was performed to examine how forward speed and soil penetration depth affect the soil disturbance coefficient. Using this coefficient as the evaluation metric, a Central Composite Design experiment was carried out. Using the soil disturbance coefficient as the evaluation criterion, a central composite design experiment was carried out to identify the optimal parameter set: a forward speed of 6 km/h and a tillage implement penetration depth of 108 mm. Under these optimized conditions, the standard deviation of the soil disturbance coefficient was measured at 1.92%, which satisfies the operational requirements. The results offer useful insights for the design improvement of tillage implements. Full article

Current research on disaster-adaptive resilience predominantly focuses on urban systems, with insufficient attention paid to the unique scale of traditional settlements and their formation mechanisms and pathways to systemic realization remain significantly understudied. There is also a lack of multi-dimensional coupling analysis and innovative methods tailored to the specific contexts of rural areas. To address this, this study innovatively introduces ant colony bionic intelligence, drawing on its characteristics of swarm intelligence, positive feedback, path optimization, and dynamic adaptation to reframe emergency decision-making logic in human societies. An evaluation model for disaster-adaptive resilience is constructed based on these four dimensions as the criterion layer. The weights of dimensions and indicators are determined using a combined AHP–entropy weight method, enabling a comprehensive assessment of settlement resilience. Taking Fenghuang Ancient Town as an empirical case, the research utilizes methods such as field surveys, questionnaire surveys, and GIS data analysis. The results indicate that (1) the overall resilience evaluation score of Fenghuang Ancient Town is 3.408 (based on a 5-point scale); (2) the path optimization dimension contributes the most to the overall resilience, with road redundancy design (C21) being the core driving factor; within the positive feedback mechanism dimension, soil and water conservation projects (C15) provide the fundamental guarantee for village safety; (3) based on these findings, hierarchical planning strategies encompassing infrastructure reinforcement, community capacity enhancement, and ecological risk management are proposed. This study verifies the applicability of the evaluation model based on ant colony bionic intelligence in assessing the disaster resilience of traditional settlements, revealing a new paradigm of “bio-intelligence-driven” resilience planning. It successfully translates ant colony behavioral principles into actionable planning and design guidelines and governance tools, providing a replicable method for resilience evaluation and enhancement for traditional settlements in ecological barrier areas such as the Qinling Mountains. Full article

The huge impact of construction on the environment is becoming increasingly apparent, and it is unacceptable to many engineers and designers. A growing interest in sustainable construction has been observed for several years. This is especially true for commercial buildings, where achieving an appropriate standard is often the main criterion for investment. Many current publications deal with the topic of energy related to building use. In contrast, knowledge of the so-called embodied carbon footprint is not yet widespread but increasingly important in the context of low-carbon construction. The study created six different building types by juxtaposing different construction variants with different facade variants. The analysis was given to the “cradle to grave” phases, i.e., A1–A4, B4–B5 and C1–C4. Module D (material recycling) is omitted, as well as phases B1–B3 and B6–B7 related to use, maintenance, repair and energy and water consumption. Phases B1–B3 refer to maintenance repair and use activities that are the responsibility of the building manager, so they are taken as estimates at the concept stage. Phase B6 and B7 were excluded from the study, due to the fact that they are not responsible for the embodied carbon footprint, but the operational one. It was assumed that the values for B6 would be shown independently in the building’s energy performance and the final values would be comparable. The purpose of the study was to verify the factors that have the greatest impact on the results of the embodied environmental footprint. The study showed that changes in the building’s design and facade have the greatest impact on the embodied carbon footprint. Furthermore, not only the quantity of materials used but also their durability is crucial, so using durable finishes to minimize the need for repair and replacement can play a key role in reducing the building’s embodied carbon footprint. Differences between the variants reached approximately 107 kg CO₂e/m² (about 15%). The comparison of impact categories further indicates that solutions optimized for global warming potential are not necessarily favorable in other environmental dimensions. Finally, the relatively moderate spread between the most and least favorable variants within the analyzed scope indicates that material substitution alone is insufficient to achieve deep decarbonization of office buildings. Comprehensive strategies addressing material selection, durability, service life and design for disassembly and reuse are therefore required. Full article

In manufacturing processes, both material processing methods and the resulting microstructure play a fundamental role in determining material behavior during component fabrication and subsequent service conditions. Materials produced by additive manufacturing exhibit a unique microstructure due to the rapid heating and solidification cycles inherent to the process, distinguishing them from conventionally cast counterparts and leading to differences in mechanical and functional properties. This article presents problems related to the longitudinal turning of Ti6Al4V titanium alloy elements produced by the casting and powder laser sintering (DMLS) methods. The authors made an attempt to establish a procedure for determining the optimal parameters of finishing cutting while minimizing the specific cutting force, taking into account the criterion of machined surface quality. In the course of the experiments, the influence of the cutting data on the cutting force values, surface roughness parameters, and chip shape was examined. The material hardening state during machining and the variability of the specific cutting force as a function of the cross-sectional shape of the cutting layer were also tested. The authors presented a practical application of the proposed optimization algorithm. It was found that by changing the shape of the cross-section of the cutting layer, it was possible to carry out the turning process with significantly reduced specific cutting force (from 2300 N/mm² to 1950 N/mm²) without deteriorating the surface roughness. Full article

Background/Objectives: The university stage is a critical period for consolidating dietary habits that influence future health. University canteens therefore play a key role in providing menus aligned with nutritional recommendations. As menu composition shapes students’ access to healthy food, its evaluation also has equity implications. This study aimed to apply a newly designed questionnaire—the COMUNI questionnaire—intended to provide a rapid, user-friendly, and transferable method for evaluating the dietary quality of lunch menus offered in university canteens. Methods: Two versions of the 13-item COMUNI questionnaire were developed: COMUNI-1 for single-option menus and COMUNI-2 for menus offering multiple first- and second-course choices. The tool evaluates the frequency of key food groups, the availability of water and wholegrain bread, and the variety of foods and culinary techniques. To test the questionnaire, it was applied to 34 menu templates from university residences, colleges, and cafeterias. Results: 85.3% of menus showed deficient dietary quality, and 14.7% were rated as improvable; none achieved an optimal score. Menus managed by catering companies obtained significantly higher scores than those under direct management. Most frequently shortcomings included insufficient offerings of vegetables, legumes, fish, and wholegrain bread, alongside a frequent presence of refined carbohydrate sources and fried or ultra-processed foods. Conclusions: Universities should incorporate adherence to dietary recommendations as a key criterion in food-service procurement. The COMUNI questionnaire provides a simple and operational tool for assessing menu quality, supporting both diagnosis and monitoring of university food-service, once formally validated. Its use may also help identify structural disparities in access to healthy foods across campus settings, supporting more equitable food-service policies. Full article

The construction of rational absolute nodal coordinate formulation (RANCF) elements is usually based on a linear transformation of non-uniform rational B-spline (NURBS) geometry. However, this linear transformation can lead to property transfer issues, which greatly reduce the modeling efficiency, especially for conic sections. To overcome this limitation, we first analyze the geometric constraints of conic sections and derive a unique defining equation in rational parametric form. A corresponding degree-elevation formula is also obtained. Using these results, we propose a direct definition method for RANCF elements that explicitly exploits the analytic properties of conic sections. The method provides fast and accurate expressions for the nodal coordinates and weights, and thus enables efficient modeling of RANCF elements for conic-section configurations. We also mitigate the arbitrariness in element definition by introducing, for the first time, the concept of a mapping factor K, which characterizes the mapping between the physical space and the parameter space. Based on this mapping factor, we establish a parameterization procedure for RANCF conic-section elements. An evaluation criterion for K is further proposed and used to define the optimal mapping factor K_opt, which yields an optimal parameterization and allows the construction of K_opt elements. Numerical examples demonstrate that, in large-deformation analyses of flexible systems, the proposed elements can achieve a given accuracy with fewer elements than conventional approaches. Full article

This paper focuses on mixed seeds of Vicia villosa and Avena sativa, with their discrete element model and contact parameters being systematically calibrated and validated to provide reliable theoretical support for the structural design and parameter optimization of the air-assisted seed delivery system. The physical properties of both seed types, including triaxial dimensions, density, moisture content, Poisson’s ratio, and shear modulus, were first measured. The Hertz–Mindlin (no slip) contact model and the multi-sphere aggregation method were employed to construct the discrete element models of Vicia villosa and Avena sativa, with preliminary calibration of the intrinsic model parameters. Poisson’s ratio, elastic modulus, collision restitution coefficient, static friction coefficient, and rolling friction coefficient between the seeds and PLA plastic plate were determined through uniaxial compression, free fall, inclined sliding, and inclined rolling tests. Each test was repeated five times, and the calibration criterion for contact parameters was based on minimizing the relative error between simulation and experimental results. Based on this, experiments on the packing angle of mixed seeds, steepest slope, and a three-factor quadratic rotational orthogonal combination were conducted. The inter-seed collision restitution coefficient, static friction coefficient, and rolling friction coefficient were set as the experimental factors. A total of 23 treatments were designed with repetitions at the center point, and a regression model was established for the relative error of the packing angle with respect to each factor. Based on the measured packing angle of 28.01° for the mixed seeds, the optimal contact parameter combination for the mixed seed pile was determined to be: inter-seed collision restitution coefficient of 0.312, static friction coefficient of 0.328, and rolling friction coefficient of 0.032. The relative error between the simulated packing angle and the measured value was 1.32%. The calibrated inter-seed contact parameters were further coupled into the EDEM–Fluent gas–solid two-phase flow model. Simulations and bench verification tests were carried out under nine treatment combinations, corresponding to three fan speeds (20, 25, and 30 m·s⁻¹) and three total transport efficiencies (12.5, 17.5, and 22.5 g·s⁻¹), with the consistency coefficient of seed distribution in each row being the main evaluation variable. The results showed that the deviation in the consistency coefficient of seed distribution between the simulation and experimental measurements ranged from 1.24% to 3.94%. This indicates that the calibrated discrete element model for mixed seeds and the EDEM–Fluent coupled simulation can effectively reproduce the air-assisted seed delivery process under the conditions of Vicia villosa and Avena sativa mixed sowing, providing reliable parameters and methodological support for the structural design of seeders and DEM-CFD coupled simulations in legume–grass mixed sowing systems. Full article

Identifying structural damage in high-dimensional systems remains a major challenge due to the curse of dimensionality and the inherent sparsity of real-world damage scenarios. Traditional Bayesian or optimization-based approaches often become computationally intractable when applied to structures with a large number of uncertain parameters, where only a few members are actually damaged. To address this problem, this study proposes a Machine Learning and Widely Applicable Information Criterion (WAIC) based Bayesian framework for efficient and accurate damage identification in high-dimensional systems. In the proposed approach, an ML is first trained using simulated modal responses under randomly generated damage patterns. The ML predicts the most likely damaged members by measured responses, effectively reducing the high-dimensional search space to a small subset of candidates. Subsequently, a WAIC is employed to estimate the model combined by these candidates, while automatically selecting the optimal damage model. By combining the localization capability of ML with the uncertainty quantification of Bayesian inference, the proposed method achieves high identification accuracy with significantly reduced computational cost of model selection. Numerical experiments on a high-dimensional truss system demonstrate that the method can accurately locate and quantify multiple damages even under noise contamination. The results confirm that the hybrid framework effectively mitigates the curse of dimensionality and provides a robust solution for structural damage identification in large-scale structural systems. Full article

Additive Manufacturing (AM) using Fused Layer Modelling (FLM) often results in polymer components with limited and highly anisotropic mechanical properties, exhibiting structural weaknesses in the layer direction (Z-direction) due to low interlaminar adhesion. The main objective of this work was to investigate and quantify these mechanical limitations and to develop strategies for their mitigation. Specifically, this study aimed to (1) characterize the anisotropic behavior of unreinforced Polycarbonate (PC) components, (2) evaluate the effect of continuous, unidirectional (UD) carbon fiber tape reinforcement on mechanical performance, and (3) validate experimental findings through Finite Element Method (FEM) simulations to support predictive modeling of reinforced FLM structures. Methods involved experimental tensile and 3-point bending tests on specimens printed in all three spatial directions (X, Y, Z), validated against FEM simulations in ANSYS Composite PrepPost (ACP) using an orthotropic material model and the Hashin failure criterion. Results showed unreinforced samples had a pronounced anisotropy, with tensile strength reduced by over 70% in the Z direction. UD tape integration nearly eliminated this orthotropic behavior and led to strength gains of over 400% in tensile and flexural strength in the Z-direction. The FEM simulations showed very good agreement regarding initial stiffness and failure load. Targeted UD tape reinforcement effectively compensates for the weaknesses of FLM structures, although the quality of the tape–matrix bond and process reproducibility remain decisive factors for the reliability of the composite system, underscoring the necessity for targeted process optimization. Full article

This paper proposes a risk-based, multi-objective approach to identify a solution, referred to as the fairness improvement plan, that enhances the fairness of photovoltaic (PV) curtailment, primarily applied to mitigate overvoltage issues in both balanced and unbalanced low-voltage distribution networks with high PV penetration. The proposed approach considers the uncertainty of loads, PV generation, and slack bus voltage. Relative Distance Measure (RDM) interval arithmetic is employed to represent these uncertainties while accounting for correlations among uncertain quantities, and the Pareto Simulated Annealing (PSA) method is used to generate a set of efficient fairness improvement plans. The Hurwicz criterion for measuring risk, which accounts for a decision maker’s risk preference, is incorporated in the interval TOPSIS technique to identify the fairness improvement plan, selected from a set of efficient plans, that minimizes the risk of financial losses and the risk of unfairness of PV’s active power curtailment. The numerical results obtained show that the proposed approach improves the insight and the understanding of the fairness improvement planning under uncertainty. They also highlight the effectiveness of incorporating decision makers’ risk preferences and their trade-off preferences between fairness and cost in developing the optimal fairness improvement plan under uncertainty in low-voltage distribution networks with high PV penetration. Full article

Under the dual challenges of energy supply demand imbalance and the efficient operation of underground gas storage (UGS) facilities, this study investigated the mechanical behavior of reservoir rocks and optimal production pressure differential in a depleted gas reservoir in China under multi-cycle injection-production. For the first time, we reveal the mechanical degradation mechanism of hydration and cyclic fatigue for three typical lithologies in depleted sandstone reservoirs. Rock mechanics tests were conducted to analyze the effects of lithology, water saturation, and cyclic loading on mechanical properties, and appropriate failure criteria were evaluated. The main findings are as follows: (1) Under a confining pressure of 45 MPa, the peak strength of fine sandstone was the highest at 160.13 MPa, and the peak strength of argillaceous sandstone was the lowest at 114.92 MPa. The strength increased approximately linearly with confining pressure. (2) Increasing water saturation significantly weakened rock strength, particularly in argillaceous sandstone due to hydration effects. At 45% water saturation, its strength decreased by 37.38%. while Young’s modulus and Poisson’s ratio remained relatively unaffected. (3) Rock strength progressively degraded with the number of loading cycles. Siltstone showed the most significant degradation, with a strength reduction of 28.50% after 200 cycles. The damage induced by cyclic loading was less severe than that caused by hydration. (4) Among five failure criteria evaluated, the Mogi–Coulomb criterion demonstrated superior predictive capability by incorporating three-dimensional principal stress effects, showing closest agreement with the experimental data. We further established a depth-dependent production pressure differential profile and proposed a lithology-specific injection-production strategy. These findings provide theoretical foundations for optimizing injection-production strategies and sand control measures in depleted reservoir UGS systems. Full article

The removal of topsoil from agricultural land and the use of low-quality fuel to produce fired clay bricks affect the environment, disturbing the ecological balance and contributing to climate change. This study has attempted to produce sustainable concrete hollow blocks by replacing OPC with a combination of supplementary cementitious materials (SCMs) (5–25% fly ash) optimally (10% silica fume and 5% recycled aggregate fine dust). Furthermore, 100% of the developed sustainable sand was added instead of natural sand. Based on the results, the highest compressive strength, 7.6 MPa, was achieved in the mix 15FASFRAHB with the combination SCMs (15% fly ash + 10% silica fume + 5% recycled aggregate fine dust), slightly higher (2.7%) than that of the reference mix NAHB*’s value of 7.4 MPa. All hollow block mixes also satisfied the tensile strength criterion (10–15% of f’c of NAHB*). This showed that they reached the acceptable strength limit for building hollow blocks. In addition, the SCMs effectively reduce the permeability coefficient (k) of sustainable concrete hollow block mixes. However, a direct correlation between the permeability coefficient (k) and compressive strength was not maintained. Finally, the best overall mix from this study, 15FASFRAHB, was with an optimal 30% SCMs and 100% sustainable sand. By using developed sustainable concrete hollow blocks in place of fired clay bricks (6.48 × 10⁷ tons of CO₂ emission), 1.2 × 10⁹ tons of natural sand can be saved. Full article