Search Results (4,568)

Precision Livestock Farming (PLF) requires real-time control systems that connect high-level Decision Support Systems with resource-constrained edge devices. This paper presents a hybrid Multi-Agent System (MAS) architecture for swine precision feeding designed to address the trade-off between semantic interoperability and real-time operational efficiency. The proposed Controlling Module uses a dual-layer communication strategy: a lightweight character-delimited TCP/IP protocol ensures deterministic performance for embedded controllers, while an XML-serialized format that maps to the FIPA Agent Communication Language preserves semantic interoperability. A custom serialization/deserialization algorithm was developed to process this XML structure within LabVIEW while avoiding the overhead typically associated with generic DOM/SAX parsers. The architecture was validated in a 120 h laboratory test that combined a Digital Twin simulation of 50 virtual feeders with Hardware-in-the-Loop testing of key sensing components. Under these test conditions, no communication failures were observed, all simulated network interruptions were recovered from, and the system operated with a modest resource footprint, including an average CPU use of 15% and a peak memory use of 350 MB. The platform also processed 2590 consumption events without reported data loss during the validation period. These results indicate that the proposed hybrid MAS architecture is a feasible solution for integrating interoperable decision support and deterministic edge control in PLF applications. Full article

The transition towards sustainable manufacturing necessitates complex optimization that integrates economic goals with environmental factors, such as energy consumption and greenhouse gas emissions. This research addresses the critical challenge of optimizing the Incoming Quality Control (IQC) policy for raw material batches. The primary objective is formulated as a multi-criteria control problem that jointly minimizes the weekly final product cost, carbon footprint, and energy consumption. To handle sequential decision making under uncertainty, we adopt a scalarized reinforcement learning (RL) reward that combines these objectives into a single value function and explores different trade-offs through alternative weight configurations. To effectively handle the uncertainty in incoming quality and the sequential decision making required for dynamic control, the optimization problem is modeled as a Bayesian Adaptive Markov Decision Process (BAMDP). To maintain computational tractability despite the continuous belief space inherent in the BAMDP formulation, we employ a Deep Q-Network (DQN) architecture acting as an approximate dynamic programming solver. The Bayesian framework represents model uncertainty explicitly, updates beliefs as new inspection evidence becomes available, and allows prior domain knowledge on supplier quality to be incorporated into the learning process. The BAMDP formulation is used to learn a set of adaptive inspection policies that adjust the IQC strategy over time to achieve conflicting goals: reducing inspection costs while maintaining standard quality, minimizing energy consumption, and lowering CO₂-equivalent emissions. The goal is to find robust policies that balance these trade-offs under different quality and demand conditions. This methodology aligns with the principles of Industry 5.0 by leveraging advanced artificial intelligence (AI) methods, such as reinforcement learning (RL), coupled with a stochastic simulation of the production system, based on a geometric/physical model of the component’s tolerance chains, to support decision-makers in designing and assessing sustainable IQC strategies. Comparative simulations on the case study, including a benchmark against ISO 2859-1 sampling plans, confirm that this dynamic and risk-aware optimization paradigm can reduce overall cost, energy use, and environmental impact across various quality conditions, while preserving outgoing quality. Full article

The rapid proliferation of low-altitude unmanned aerial vehicle (UAV) applications has made autonomous identification technology critical for flight safety and collaborative operations. In this paper, we propose and systematically analyze an autonomous identification scheme based on Bluetooth Low Energy (BLE) technology. We formulate a comprehensive system model that integrates link budget, packet collision, identification success probability, and power consumption. By incorporating safety interval constraints and a three-channel integrated reception probability, we employ an exhaustive search algorithm to optimize monitoring strategy parameters, thereby achieving an optimal trade-off between the Recognition Success Rate (RSR) and power consumption. Simulation results indicate that, at a PHY 1 Mbps rate, the optimal monitoring strategy theoretically approaches the Target Level of Safety (TLS) requirements for civil UAVs under the defined model assumptions, with a power consumption of 19.24 mW and an Average First Identification Delay (AFID) of 105 ms. Furthermore, simulation analysis verifies the scheme’s feasibility under dynamic topology, interference, and multi-UAV scenarios, providing a solid theoretical and technical reference for the practical implementation of autonomous UAV identification. Full article

Contrast detection autofocus (CDAF) performance in industrial machine vision is shaped by platform capability as well as by the focus measure and search strategy. CDAF is analyzed through a platform capability framework and a unified frame-level transaction chain across three platforms: a capability upper-bound platform (P1), a bridging platform (P2), and an industrial black-box platform (P3). In experiments covering six scene categories, four initial conditions, five fixed-rule strategies, and 30 repetitions per condition, the dominant observable tail on P3 is localized after control submission, in the command-to-actuation segment. On P2, controlled one-factor perturbations using a physically calibrated sample position mismatch intensity (σ_align) and an actuation chain variability coordinate (λ_act) reproduce the main P3 degradation directions, providing a mechanism-level account in terms of sample position mismatch and command-to-actuation variability. Platform capability sets the reachable performance boundary, within which strategies trade speed, final quality, and failure risk. On P3, S1–S4 form the main engineering trade-off band, whereas S5 shows condition-dependent upper-quantile quality gains without a stable frontier advantage. The resulting deployment logic combines capability tiering, segment-wise bottleneck localization, and strategy band selection and treats CDAF as a capability-conditioned speed–quality–risk trade-off rather than a platform-independent strategy ranking. Full article

The rapid growth of environmental, social, and governance (ESG) investing has intensified the debate regarding whether ESG-oriented investment strategies exhibit performance patterns that differ from those of conventional investments, particularly during periods of market disruption. This study examines the risk-adjusted performance of ESG-oriented and non-ESG exchange-traded funds (ETFs) across market regimes surrounding the COVID-19 shock. The analysis classifies 28 passively managed ETFs into four sustainability-based categories and evaluates their performance using factor-based asset pricing models derived from the Fama–French framework. Additional analyses assess benchmark-relative performance using the S&P 500 and MSCI World indices and consider alternative ETF classifications based on investment mandates. The study estimates regime-specific regressions for the pre-COVID, COVID, and post-COVID periods. The results indicate that performance patterns vary across market regimes and ETF categories. Non-ESG ETFs tend to underperform on a risk-adjusted basis during the pre-COVID period, although this effect disappears thereafter. ESG-oriented ETFs generally exhibit limited evidence of abnormal performance, while factor exposures vary across regimes, reflecting changes in sector composition and macro-financial conditions. The findings suggest that, in addition to ESG orientation, market regimes and sectoral exposures play an important role in explaining differences in ETF performance. Full article

The inclusion of the cement industry into China’s national carbon emissions trading system in 2025 has fundamentally altered the compliance environment for high-emission enterprises, transforming carbon allowances from passive regulatory instruments into dynamic assets whose management directly affects financial performance. We develop a multi-scenario carbon asset management decision model tailored to the intensity-based benchmarking mechanism adopted by the national market. The model centres on the quota surplus-deficit variable EA4, which is computed from enterprise-level emission intensity relative to the industry benchmark, and decomposes the management problem into sequential selling and buying subproblems linked by coupled decision boundaries. A systematic parameter framework is constructed, and the model is applied to two cement enterprises—Enterprise A, a leading producer with a clear allowance surplus, and Enterprise B, a mid-tier producer operating near the benchmark boundary—through historical backtesting over the 2024–2025 period. Three principal findings emerge. First, the intensity benchmarking mechanism creates a dual-leverage effect whereby a 1.4% improvement in emission intensity (from 0.8112 to 0.8000 t/t) increases the quota surplus by 27%, a nonlinearity not captured by conventional compliance-cost models. Second, the model-driven strategy outperforms traditional experience-based approaches by 36.8% (baseline scenario, +95.20 vs. +69.58 MRMB) and 37.3% (risk scenario, −44.55 vs. −71.08 MRMB), with the improvement rate remaining consistent across both enterprises, suggesting that trading timing outweighs instrument selection in determining compliance cost outcomes. Third, dynamic CEA–CCER allocation captures an incremental 2.33 MRMB through the exploitation of a transient price inversion, a gain invisible to single-instrument strategies. Sensitivity analysis confirms that the relative advantage is robust to carbon price variations (±30%) and CCER offset caps (2–10%), while emission intensity and carry-over allowances represent the most consequential parameters for strategy direction, with EA4 crossing zero near the industry benchmark (I ≈ 0.85). The framework provides actionable decision support for cement and other high-emission enterprises navigating the unified carbon market, and contributes a quantitative methodology to the emerging field of environmental management accounting. This study contributes to Sustainable Development Goal 13 (Climate Action), Goal 7 (Affordable and Clean Energy), and Goal 9 (Industry, Innovation, and Infrastructure) by providing operational tools for decarbonisation in carbon-intensive industries. Full article

The Dense-Residual-Connected Transformer (DRCT) has established a new state-of-the-art in single image super-resolution by mitigating the information bottleneck in deep networks. However, its feature aggregation mechanism relies on a suboptimal Static Addition strategy, where residual features are scaled by a fixed, learnable scalar, regardless of the image content. This content-agnostic approach treats high-frequency textures and low-frequency noise indiscriminately, limiting the model’s representational capability. To address this, we propose a Frequency-Aware Adaptive Fusion Gate (FAFG) to replace the static scaling. Unlike spatial-only gating mechanisms, FAFG integrates the Discrete Cosine Transform (DCT) to explicitly perceive the frequency distribution of feature maps. By decomposing features into frequency components, our gate acts as an intelligent valve, dynamically amplifying valid structural details while suppressing redundant background noise. Extensive experiments on standard benchmarks demonstrate that our proposed FAFG-integrated model consistently outperforms the static-scaling and other state-of-the-art methods. Specifically, our method achieves a significant PSNR improvement of 0.31 dB on the texture-rich Urban100 dataset at $\times 4$ scale. Visual results further confirm that our frequency-aware gating mechanism effectively recovers sharper edges and fine textures, providing a superior trade-off between reconstruction accuracy and model complexity. Full article

Poor consolidations and the strength–conductivity trade-off limit the performance of copper alloys fabricated by laser powder bed fusion (LPBF). To address this, this study developed a strategy combining the response surface methodology (RSM) with direct ageing treatment (DAT) to achieve a favorable strength–conductivity synergy. The results showed that under the optimal process parameters, a high relative density of 99.25% (8.95 g/cm³ for theoretical density) was obtained. After direct ageing treatment at 490 °C for 60 min, the CuCrZr exhibited an ultimate tensile strength of 399.31 MPa and a thermal conductivity of 326.53 W/(m·K). To reveal the underlying mechanisms, this study employed a combination of systematic characterization via high-resolution transmission electron microscopy (HRTEM) and quantitative modeling. HRTEM characterized the uniformly dispersed nanoscale body-centered cubic (BCC) Cr precipitates that form semi-coherent interfaces with the face-centered cubic (FCC) Cu matrix, showing a crystallographic misorientation of approximately 10.5° intermediate between the classic Nishiyama–Wassermann and Kurdjumov–Sachs orientation relationships. Quantitative modeling indicates that the high strength arises from a synergistic effect: coherent strain fields exerted by the precipitates effectively pin retained dislocations, coupling Orowan and dislocation strengthening. Meanwhile, solute precipitation reduces lattice distortion, restoring notable thermal conductivity. Full article

This paper investigates the role of cross-border trade in shaping participation in global value chains (GVCs) in developing and emerging economies over the period 2000–2022. It tests the central hypothesis that greater trade intensity enhances integration into fragmented global production systems. Using panel data methods, the analysis examines the effects of trade openness alongside foreign direct investment, logistics performance, GDP per capita, and domestic value added. The results provide strong evidence that trade openness is the dominant driver of GVC participation, with a robust and economically meaningful elasticity. Domestic value added is also positively associated with GVC integration, suggesting that deeper global engagement can coincide with increased domestic value creation. GDP per capita exerts a weaker but significant effect, while foreign direct investment and logistics performance do not show direct statistical significance in the preferred specification. These findings highlight trade as the primary transmission mechanism linking national economies to global production networks, while also pointing to a complementary role of domestic capabilities. At the same time, increased reliance on cross-border trade may heighten exposure to external shocks, underscoring a key policy trade-off. The study concludes that effective GVC integration requires balancing openness with strategies that strengthen resilience and value capture. Full article

The selective conversion of syngas (CO + H₂) to higher alcohols (C₂₊OH) via Fischer–Tropsch synthesis (FTS) is a strategically important but challenging process, requiring catalysts that can simultaneously sustain C–C chain growth and preserve C–O bonds in reactive intermediates. Over the past decade (2015–2025), perovskite-type complex oxides with the formula ABO₃ have emerged as powerful precatalysts for this application, with LaCoO₃ attracting particular attention due to its structural flexibility, controllable reducibility, and the unique catalytic role of the La₂O₃ phase formed upon reduction. This review systematically covers recent advances in synthesis strategies for LaCoO₃ and substituted perovskites, including sol–gel, co-precipitation, mechanochemical, and template-assisted (KIT-6, SBA-15) methods; effects of A-site (Sr) and B-site (Cu, Ga, Ni, Mn) substitution on reducibility, active phase dispersion, and product selectivity; alkali promotion and its interaction with the perovskite-derived active phase; mechanistic understanding of the alcohol-forming pathway, including the Co⁰/Co³⁺ bifunctional site concept, CO insertion mechanism, and the role of La₂O₃ in suppressing the Boudouard reaction; and catalyst stability and deactivation pathways under FTS conditions. Original data from LaCoO₃ catalysts prepared by co-precipitation with ethylene glycol (LCO-1: S_KOH = 90%, Y_KOH = 57 mg·g⁻¹·h⁻¹) and via citrate/KIT-6 template synthesis (LCO/KIT-6: Y_KOH = 80 mg·g⁻¹·h⁻¹, S_BET = 220 m²/g) at 240 °C and 2 MPa serve as the primary experimental reference throughout. Key challenges, including the surface area–selectivity trade-off, long-term stability under industrial conditions, and opportunities in CO₂ hydrogenation, are critically discussed. Full article

This paper proposes a mathematical co-design framework for synchronous Boost DC-DC converters and their PI voltage controllers. In contrast to the conventional sequential design approach, where the power stage is sized first and the controller is tuned afterward, the proposed method treats the converter and the controller as a single coupled design problem. A nonlinear averaged model of the synchronous boost converter operating in continuous conduction mode is considered, explicitly incorporating the inductor series resistance, the capacitor equivalent series resistance, and the on-state resistances of the active switches. In addition, a simplified but physically interpretable loss model is included in order to capture inductor copper loss, capacitor ESR loss, semiconductor conduction loss, and switching loss. Based on this formulation, the joint design of the power stage and the PI controller is cast as a constrained multi-objective optimization problem whose decision variables include the inductance, capacitance, switching frequency, and controller gains. The optimization criteria account for output-voltage ripple, settling time, total losses, and current stress, while practical constraints related to duty cycle, current limits, ripple bounds, and closed-loop feasibility are enforced. The proposed framework makes it possible to compute Pareto-efficient designs and to reveal trade-offs that remain hidden under classical decoupled design procedures. Numerical case studies are structured to compare the proposed co-design strategy with a conventional sequential-design baseline. An optional technology-aware extension is also considered, allowing the influence of different semiconductor classes, such as Si, SiC, and GaN, to be assessed through technology-dependent loss and switching-frequency assumptions. The results indicate that the proposed framework provides a mathematically grounded and practically useful basis for integrated converter–controller synthesis in nonideal power electronic systems. Full article

Adverse weather conditions, particularly thunderstorms, are the primary cause of flight delays and safety threats, accounting for approximately 58.7% of irregular flights in 2025. Traditional static rerouting methods often fail to adapt to the non-linear evolution of convective weather. This paper proposes a high-fidelity dynamic rerouting framework to enhance flight safety and efficiency. In the perception layer, a RainNet deep learning model is employed for short-term recursive nowcasting of radar reflectivity, which is subsequently transformed into Dynamic Avoidance Zones (DAZ) via clustering and convex hull algorithms. In the decision layer, a two-stage improved Genetic Algorithm (GA) is developed to solve the rerouting path. The first stage generates initial collaborative solutions under a receding-horizon framework, while the second stage applies a “path-straightening” module to reduce cumulative turning angles and curvature fluctuations. The comparative results in actual scenarios demonstrate a distinct dual-advantage over baseline methodologies. Compared to sampling-based strategies, the proposed model reduces the path length by 14.79%. Furthermore, when compared to heuristic algorithms, it actively trades a negligible 1% distance margin to achieve a massive 92.7% reduction in the cumulative turning angle. With a maximum single turn of only 32.51°, the trajectory completely eliminates sawtooth jitter and redundant detours. Ultimately, this research provides essential technical support for improving air traffic management efficiency and reducing controller workload during severe weather events. Full article

Algorithmic prediction models are increasingly used to support decision-making in high-stakes environments, including emergency departments (ED). However, aggregate performance metrics may obscure systematic differences in classification errors or calibration across subgroups. This study presents a stage-wise, multi-metric, and interpretable fairness auditing framework for ED prediction. The framework compares mitigation strategies across data-, model-, and decision-level interventions, evaluates subgroup fairness using complementary classification and calibration criteria including equalized odds difference (EOD) and expected calibration error (ECE) disparity, and incorporates interpretability analyses based on SHapley Additive exPlanations (SHAP) and the calibration adjustment difference (CAD) to characterize changes in feature-contribution patterns and subgroup-specific probability adjustments after mitigation. The framework was applied to 126,819 ED encounters from MIMIC-IV-ED using measurements recorded within the first 2 h after arrival, and penalized logistic regression and random forest models were compared under reweighting, reduction, and multicalibration. Baseline AUROC values were 0.748 ± 0.028 for random forest and 0.746 ± 0.028 for penalized logistic regression. Reduction and multicalibration largely preserved discrimination performance, whereas reweighting was associated with reduced AUROC and AUPRC. Reweighting most clearly reduced EOD-based classification disparity, particularly for age, yielding reductions of 80.6% in random forest and 86.4% in penalized logistic regression. By contrast, multicalibration most consistently reduced ECE-based calibration disparity for sex and age but did not consistently improve EOD-based classification disparity. In the interpretability analyses, SHAP indicated that data- and model-level mitigation altered feature-contribution patterns, whereas CAD showed that decision-level mitigation produced subgroup-specific probability adjustments that varied in direction and magnitude across groups. These findings reveal trade-offs among discrimination performance, classification fairness, and calibration fairness, indicating that fairness mitigation should be guided by a clearly defined target fairness objective. Pre-deployment fairness auditing should therefore combine complementary fairness metrics with interpretability analyses to evaluate both subgroup-level outcomes and unintended changes in model behavior. Full article

Extracellular vesicles (EVs), naturally secreted by cells as nanoscale lipid bilayer structures, have become a research hotspot in biomedicine owing to their excellent biocompatibility, low immunogenicity, and inherent ability to cross biological barriers. This review systematically summarizes recent advances in EVs as natural nanomaterials. The biogenesis mechanisms of EVs are outlined, followed by a comparative analysis of the advantages and limitations of mainstream isolation and purification methods, including ultracentrifugation, size-exclusion chromatography, and microfluidic technologies. The core guiding role of the MISEV 2023 guidelines in standardizing EV characterization is highlighted. Engineering strategies to enhance EV therapeutic efficacy—including parental cell modification, post-isolation physicochemical tailoring, and hybrid vesicle construction—are then reviewed, followed by a comparative analysis of mainstream isolation technologies, emphasizing the trade-offs between purity and yield. Distinct from conventional descriptive reviews, this article establishes a strong biomimetic framework to scrutinize engineering strategies, including parental cell genetic modification, post-isolation physicochemical tailoring, and the fabrication of hybrid bio-synthetic vesicles. The design principles governing targeted delivery, drug-loading physics, and in vivo pharmacokinetic stability are critically evaluated through the lens of biomimetic nanotechnology. Furthermore, we identify critical research gaps and technical bottlenecks impeding clinical translation, offering a forward-looking perspective on the evolution of EVs from natural messengers into standardized precision medicine platforms. Full article

Incorporating drinking water treatment sludge (DWTS) into unfired bricks provides a promising approach for large-scale utilization with low carbon emission. However, the complex effects of material composition and curing strategy on unfired brick performance are difficult to optimize through conventional trial-and-error methods. Therefore, in this study machine learning (ML) combined with Pareto front analysis was introduced to develop a multi-objective optimization of both compressive strength and cost. Among the tested models, the random forest (RF) and extreme gradient boosting (XGBoost) exhibited the best generalization performance for predicting compressive strength and cost based on the 5-fold cross validation, respectively. SHapley Additive exPlanation (SHAP) analysis revealed that early water immersion followed by standard curing strongly enhanced compressive strength and DWTS proportion had the greatest negative influence on cost. Pareto optimization identified a trade-off scheme with the predicted compressive strength of 15.5 MPa and a negative cost of −2.4 RMB. The measured compressive strength of this optimal sample was 15.08 MPa, close to the predicted value and much higher than that of the reference sample. Scanning electron microscopy (SEM) and thermogravimetry analysis (TGA) results further confirmed abundant hydration products in the optimal sample. This study highlights the potential of ML to guide DWTS utilization in unfired bricks while balancing compressive strength and cost. Full article