Search Results (4,413)

Hybrid renewable energy systems, which combine photovoltaic panels, wind turbines, batteries, generators, and grid connections, require careful sizing to balance economic performance, renewable integration, and supply reliability. In this context, this study proposes a deep reinforcement learning (DRL)-based sensitivity analysis framework in which the admissible energy contributions from the diesel generator and the grid are treated as explicit design-control parameters. The objective is to simultaneously minimize the levelized cost of energy, minimize the loss of power supply probability, and maximize the renewable energy fraction. A sensitivity analysis was conducted across different HRES configurations, load profiles, and tau/gamma values. The performance of the DRL approach was compared with that of multi-objective particle swarm optimization and the non-dominated sorting genetic algorithm II under the same study setting. The results indicate that DRL can identify competitive trade-offs, especially under standard load conditions, while also providing insight into how admissible backup-energy constraints reshape techno-economic and reliability compromises. The best trade-offs were observed around intermediate tau and gamma values, suggesting that moderate backup-energy margins are more favorable than extreme values. These findings should be interpreted within the scope of a simulation-based study and provide comparative design-oriented evidence rather than universally transferable design rules. Full article

Bone tissue engineering requires biomaterials capable of simultaneously supporting regeneration and preventing infection. Platelet-rich fibrin (PRF) has been widely used due to its autologous origin and growth factor release, but its rapid resorption limits its clinical applications. Albumin-PRF (Alb-PRF) membranes were developed to improve stability, and their combination with carbonated nanostructured hydroxyapatite (nCHA) may further reinforce osteoconductive properties. In this proof-of-concept study, we fabricated Alb-PRF, Alb-nCHA-PRF, and Alb-nCHA-PRF + doxycycline (DOX) membranes and characterized their physicochemical, antimicrobial, and biological performance in vitro. Membrane stability was monitored for up to 14 days; DOX incorporation and release were evaluated by autofluorescence and spectrophotometry; antimicrobial activity was assessed against E. faecalis and S. aureus; and MG-63 osteoblast-like cells were used to test cytocompatibility, proliferation, mineralization, and alkaline phosphatase (ALP) activity. The release of 27 cytokines and growth factors was quantified by multiplex immunoassay. Alb-PRF exhibited morphological integrity and an enhanced trophic secretome, and supported proliferation and late mineralization. nCHA incorporation reduced cell proliferation and secretome output, while DOX conferred sustained antibacterial activity and enhanced early ALP expression even with attenuated cytokine release, positively impacting mineralization, when compared to nCHA alone. These preliminary results provide preliminary feasibility evidence that Alb-PRF can be engineered as a multifunctional scaffold combining antimicrobial and regenerative functions, though some trade-offs indicate the need for dose optimization and validation with in vivo models. Full article

Plant metabolism is essential for coordinating growth, development, and defense under changing environmental conditions. Plants continuously adjust their metabolic pathways to balance resource allocation between growth and immune responses. Under stress, metabolic reprogramming redirects energy and resources toward the production of defense compounds and activation of immune signaling pathways. These changes involve complex interactions among primary metabolism, specialised metabolites, and regulatory networks, including calcium signaling, reactive oxygen species, and phytohormones. Advances in metabolomics and multi-omics technologies have improved understanding of the metabolic control of plant immunity; however, knowledge remains fragmented, and an integrated framework linking metabolism, development, and defense is still emerging. This review examines plant immunometabolism by highlighting the dynamic relationships between metabolic networks and immune functions during development and stress. It discusses pathways that influence growth, stress-induced metabolic shifts linked to defense, and how signaling interacts with metabolism. Progress in metabolomics, transcriptomics, proteomics, and computational modeling that supports systems-level analysis of plant metabolism is also summarized. In addition, potential applications in agriculture and biotechnology, including metabolic engineering, genome editing, and metabolomics-based breeding, are considered in relation to crop resilience. By integrating metabolism, signaling, and systems biology, this review provides a broad perspective on how metabolic reprogramming shapes the growth–defense trade-off in plants and outlines future directions for developing climate-resilient crops. Full article

This study examines how institutional investors allocate trading across the earnings announcement cycle and whether industry trading concentration strengthens that activity. The analysis is motivated by two complementary ideas: public disclosures can increase the value of investors’ prior information, and even sophisticated investors face costly information processing. These perspectives imply that institutional trading need not be concentrated only before disclosure and may be strongest after earnings announcements, when investors combine newly released public information with prior firm- and industry-specific signals. Using daily institutional trading data from Ancerno, we find that institutional net trading is positively related to earnings surprises before, during, and after earnings announcements, with the strongest relation occurring in the post-announcement period. We also document a clear asymmetry: trading is strongly related to positive earnings surprises across all three stages, whereas trading related to negative earnings surprises is concentrated mainly after disclosure. In addition, industry trading concentration strengthens the relation between institutional trading and earnings news across the announcement cycle, especially for positive surprises. These findings provide an integrated view of institutional information processing around a major recurring disclosure event, show that the timing of institutional trading is informative about how earnings news is incorporated into prices, and support the view that industry specialization is linked to stronger earnings-related trading. Full article

With the rapid penetration of renewable energy, grid-connected microgrids have become a cornerstone of low-carbon power systems, while also posing major challenges for coordinated scheduling under coupled economic and environmental goals. The resulting dispatch problem is highly nonlinear and high-dimensional, featuring tight operational constraints and conflicting cost–emission trade-offs that often undermine the efficiency and reliability of conventional optimization methods, thereby limiting overall economic productivity. This paper presents an adaptive economic–environmental dispatch framework for grid-connected microgrids formulated as a multi-objective optimization problem that simultaneously minimizes operating cost and environmental protection cost. To navigate the rugged and constrained search landscape, we develop an enhanced metaheuristic termed the Lévy–Triangular Walk Dung Beetle Optimizer (LTWDBO). The LTWDBO integrates (i) chaotic population initialization to improve diversity and feasibility coverage, (ii) a geometry-inspired triangular walk operator to strengthen local exploitation, and (iii) an adaptive Lévy-flight strategy to boost global exploration, achieving a robust exploration–exploitation balance over the entire optimization process, representing a process innovation in metaheuristic-driven dispatch optimization. The proposed method is validated on a representative grid-connected microgrid comprising photovoltaic generation, wind turbines, micro gas turbines, and battery energy storage. Comparative experiments against representative baselines (DBO, WOA, TDBO, and NSGA-II) demonstrate that the LTWDBO achieves consistently better solution quality. Our LTWDBO attains the lowest optimal objective value of 255,718.34 Yuan, compared with 357,702.68 Yuan (DBO), 347,369.28 Yuan (TDBO), and 3,854,359.36 Yuan (WOA). The LTWDBO also yields the best average objective value of 673,842.24 Yuan, an improvement of over 1,001,813.10 Yuan (DBO). Full article

As the integrated energy market evolves toward a multi-stakeholder coexistence model, balancing economic efficiency, user well-being, and system-level sustainability among interacting stakeholders has become a key challenge, particularly in the rapidly developing regional integrated energy markets in China. Thus, to satisfy user comfort and energy substitution requirements while achieving cost-effective electricity and heating supply, this study proposes a Stackelberg game-based market trading framework involving an integrated energy producer (IEP), an integrated energy operator (IEO), and a load aggregator (LA). First, the integrated energy market framework and transaction modes are established, and the profit models of IEP and IEO are formulated. Considering users’ energy substitution behavior, user comfort is quantified to explicitly reflect user welfare in market decision making, and a consumer surplus model is developed for LA participating in market transactions. Second, a Stackelberg game framework is constructed to coordinate the strategies of all participants by incorporating source–load energy flows, and the equilibrium solution is proven to be unique and solvable using quadratic programming. Finally, a case study based on historical data from Hebei Province, China, is conducted to validate the proposed strategy. The results demonstrate that the proposed method effectively coordinates the interests of all stakeholders, enhances demand response capability without reducing user comfort, and improves economic benefits for both supply and demand sides in regional integrated energy markets. Full article

Ports play a pivotal role in global trade but are also associated with significant environmental and social challenges. Despite growing research on green ports, existing studies remain fragmented, with limited integration between technological, environmental, and governance perspectives within the blue economy framework. This review examines the transition from green port initiatives toward integrated blue-economy-oriented port systems by synthesizing recent advances in sustainable maritime infrastructure, smart port technologies, renewable energy integration, and policy frameworks. The analysis reveals three major findings. First, ports are increasingly evolving into energy-integrated hubs, with leading examples adopting shore power systems, renewable energy microgrids, and hydrogen-based infrastructure, thereby contributing to emissions reductions. Second, digitalization through artificial intelligence, IoT, and data-driven logistics significantly enhances operational efficiency, reduces energy consumption, and improves real-time decision-making. Third, effective governance frameworks that combine regulatory measures and incentive-based instruments are critical to accelerating sustainability transitions while ensuring economic competitiveness. In addition, the review highlights the growing integration of biodiversity conservation, marine pollution mitigation, and community engagement into port management strategies, reflecting a shift toward ecosystem-based approaches. Overall, the findings demonstrate that ports are transitioning from conventional logistics hubs into integrated socio-technical systems that enable low-carbon maritime transport while supporting inclusive and resilient coastal development. Full article

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

The accelerated low-carbon transition of power systems and the widespread integration of Electric Vehicles (EVs) present both severe operational challenges and substantial flexible regulation potential for Active Distribution Networks (ADNs). This paper provides an integrative review of the coordinated control and multi-market bidding mechanisms for EV-centric Technical Virtual Power Plants (TVPPs). Moving beyond descriptive surveys, this review systematically synthesizes the fragmented literature across three critical dimensions: (1) the physical-economic bidirectional mapping, which considers nonlinear power flow constraints and node voltage limits within the TVPP framework; (2) multi-market coupling mechanisms, evolving from unilateral energy bidding to coordinated participation in carbon trading and ancillary services; and (3) real-time control strategies, critically evaluating the trade-offs between optimization techniques (e.g., Model Predictive Control) and cutting-edge artificial intelligence approaches (e.g., Deep Reinforcement Learning) in mitigating battery degradation. Furthermore, a transparent review methodology is adopted to ensure literature rigor. By explicitly outlining the boundaries between TVPPs, Commercial VPPs (CVPPs), and EV aggregators, this paper identifies core unresolved trade-offs among aggregation fidelity, market complexity, and communication latency, providing evidence-backed pathways for future engineering demonstrations and V2G applications. Full article

Urban sprawl constantly reconfigures the land use pattern, and such transformations may significantly modify regional carbon stocks. Utilizing Tai’an City as the study site, this research established a comprehensive integrated Patch-generating Land Use Simulation (PLUS), Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), and Optimal Parameters-based Geographical Detector (OPGD) system to reconstruct carbon storage shifts from 2000 to 2020, project its reaction to four diverse development trajectories in 2030, and investigate the drivers underlying spatial disparities. The results indicate a persistent decline in carbon storage throughout the past two decades, with peak concentrations primarily gathered in mountain regions dominated by forest and grassland, whereas lesser amounts were grouped in urban and suburban areas defined by built-up land. Compared to 2020, the projected carbon stock in 2030 drops by 1,803,966 t under the natural growth trajectory and by 2,417,778 t under the high-quality economic growth pathway, whereas it rises by 47,326 t under cultivated land conservation and by 7679 t under ecological conservation. Elevation represents the most crucial driver among the selected variables in clarifying the spatial fluctuation of carbon storage (q = 0.3985), followed by slope (0.3323), mean annual temperature (0.2382), and the Normalized Difference Vegetation Index (NDVI) (0.1219). The synergy between elevation and NDVI produces the highest integrated explanatory power (q = 0.4906). These outcomes imply that constraining construction land growth while protecting agricultural and ecological land is vital for preserving and enhancing regional carbon sink potential. Full article

Coking coal flotation is a typical nonlinear, multi-variable, and multi-objective process in which concentrate quality and combustible matter recovery must be balanced under fluctuating feed and operating conditions. To improve both predictive reliability and decision support, this study proposes an integrated data-driven framework that combines particle swarm optimization-back propagation (PSO-BP) prediction, SHapley Additive exPlanations (SHAP) based interpretation, Non-dominated Sorting Genetic Algorithm II (NSGA-II) optimization, and entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (Entropy-TOPSIS) decision-making. After three-sigma outlier screening, 2000 valid distributed control system (DCS) samples were retained for model development and temporal holdout evaluation, and an additional 200 later-period industrial samples were used for independent validation. The data were partitioned chronologically, with months 1–4, month 5, and month 6 used for training, validation, and temporal holdout testing, respectively, while the months 7–8 dataset was reserved for later-period validation. The results show that PSO-BP consistently outperformed conventional BP under both temporal holdout and later-period validation. SHAP analysis identified raw coal ash and collector dosage as the dominant factors for product-quality prediction, while collector dosage and frother dosage contributed most strongly to tailing heat of combustion. NSGA-II further revealed the trade-off among clean coal ash, clean coal sulfur, and tailing heat of combustion, and Entropy-TOPSIS converted the Pareto-optimal candidate set into a practically balanced operating recommendation. Sensitivity and robustness analyses indicated acceptable stability of both the optimization process and the final decision result. Overall, the proposed framework provides an interpretable prediction–optimization–decision workflow for coking coal flotation and offers a practical basis for future DCS-assisted intelligent regulation. Full article

Previous approaches to flexible-lined channel design typically isolate least-cost cross-section optimization from parameter uncertainty, or restrict reliability analysis to specific cases, limited failure modes, and proprietary codes. This paper presents VegyRap, an open-source QGIS-based plugin with an intuitive graphical user interface that unites these traditionally disjointed, sequential tasks into a single computational framework. The tool guides designers sequentially through: (i) terrain-driven longitudinal profile optimization using dynamic programming; (ii) least-cost cross-sectional optimization for riprap and vegetated linings; and (iii) multi-mode probabilistic reliability analysis coupled with dual risk–cost Pareto optimization. To seamlessly handle the stochastic behavior of uncertain variables, the framework features built-in statistical distributions and allows users to flexibly evaluate up to four distinct failure modes: overtopping, erosion, sedimentation, and near-critical flow oscillation. The framework’s capabilities are demonstrated through nine diverse design examples, incorporating benchmark validations against published studies and a comprehensive real-world case study in Wadi Al-Arja, Saudi Arabia. Results highlight that for vegetated channels, a hierarchical two-phase design logic is essential to satisfy both establishment-phase stability (Class E) and long-term conveyance (Class B). While benchmark comparisons show VegyRap achieves consistent cost reductions of 10–15% over traditional methods, the case study demonstrates that deterministic least-cost solutions can carry non-negligible failure probabilities. By utilizing marginal efficiency analysis to identify cost-effective enhancements, the integrated Pareto-based dual optimization produces transparent trade-off surfaces, empowering practitioners to transition from a single least-cost solution to a defensible, risk-calibrated preferred alternative. Full article

The selection of bridge construction systems significantly influences the sustainability of infrastructure projects, encompassing both economic and environmental dimensions. This study presents a comparative assessment of three hybrid fuzzy Multi-Criteria Decision-Making (MCDM) techniques, Fuzzy AHP–VIKOR, Fuzzy TOPSIS, and Fuzzy BWM–VIKOR, for choosing optimum bridge construction system during the preliminary design phases. Each method was applied consistently, integrating project-specific criteria and construction alternatives. The comparison extended beyond the final rankings to assess computational efficiency, sensitivity to input variations, ease of implementation, and stability. Expert opinions were gathered using semi-structured interviews and questionnaires to reflect the practical circumstances of bridge engineering in the field. The results show distinct strengths and trade-offs among the techniques, offering valuable insights for researchers and industry professionals alike. This study contributes to the knowledge base by explaining how different fuzzy MCDM methods are used in real-world bridge construction projects. These outcomes improve the methodological rigor of decision science and support more robust decision-making frameworks in bridge engineering. Full article

In insurance portfolios, classifying customers without a prior history at a given company is particularly challenging due to the absence of historical behavior, extreme class imbalance, heavy-tailed loss distributions, and strict operational constraints. Traditional machine learning approaches, including the baseline methodology proposed in previous studies, typically optimize global predictive accuracy and therefore fail to capture business-critical outcomes, especially the identification of high-risk clients. This study extends the existing approach by evaluating two complementary business-aware classification strategies: (i) a balanced bagging ensemble specifically designed to handle class imbalance and maximize expected profit under explicit customer-omission constraints, and (ii) a lightweight Transformer-based architecture capable of learning richer feature representations. Both approaches incorporate the asymmetric financial cost structure of insurance and operate under operational selection limits. The empirical analysis is conducted on a proprietary large-scale auto insurance dataset comprising 51,618 customers and is complemented by validation on nine synthetic datasets to assess robustness. Model performance is evaluated using statistical tests (ANOVA, Friedman, and pair-wise comparisons) together with business-oriented metrics. The results show that both proposed approaches consistently outperform the baseline methodology (p < 0.001) in terms of profit, with the ensemble offering a better balance of performance and efficiency, while the Transformer shows stronger robustness and generalization under data perturbations. The balanced ensemble provides the most favourable trade-off between predictive performance, robustness, interpretability, and computational efficiency, making it suitable for deployment in regulated insurance environments, while the Transformer achieves competitive results and exhibits stronger generalization under data perturbations. The proposed approach aligns machine learning with actuarial portfolio optimization by explicitly integrating profit-driven objectives and operational constraints, offering two practical and scalable solutions for risk-based decision-making in real-world insurance settings. Full article

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 10⁴ points/cm² while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging. Full article