Search Results (13,206)

The transition toward unmanned and hybrid vessels increases the requirements for diagnosability and continuity of power supply to critical consumers under physical and information-layer faults. The aim of this study is to develop a probabilistic digital twin of a shipboard microgrid that combines nonlinear state estimation, Bayesian mode inference, adaptive measurement-quality accounting, and risk-aware power reconfiguration. Field validation was performed using a long-term operational measurement program. The database comprises 12 classes of operating and fault events, with 30 field records per class, yielding 360 thirty-minute records, 180 h of observations, and 162,360 time samples at a 4 s sampling interval. Three diagnostic and control methods were applied to the same field records, producing 1080 paired algorithmic evaluations. Relative to the deterministic digital twin, the proposed method reduced voltage RMSE by 37.3%, SOC RMSE by 32.7%, and power RMSE by 30.1%, while reducing ENS by 13.3%. Mean fault-detection delay did not differ significantly from that of the deterministic approach; however, the missed-detection rate decreased from 12.7% to 7.3%. Compared with threshold-based protection, detection latency and recovery time were reduced by 25.7% and 30.8%, respectively. The field evidence therefore supports the proposed architecture as a supervisory state-estimation, diagnosis, and energy-reconfiguration layer. Because the field records originate from one operator and predominantly conventional tanker vessels, transfer to hybrid and unmanned platforms requires prospective onboard validation on the target vessel classes. Full article

Artificial intelligence has become increasingly important in research on Disassembly Sequence Planning (DSP). AI-based methods can now solve more complex problems and consider several economic, environmental and operational objectives. However, most studies still focus on optimizing the disassembly process after the recovery strategy and target components have already been selected. This study reviews AI-supported disassembly planning in remanufacturing and identifies the main research gaps and future research directions. The review combines bibliometric mapping of 241 publications with a qualitative analysis of 35 selected studies. Of these studies, 23 directly address DSP, seven concern related disassembly problems, and five focus on remanufacturing planning or scheduling outside DSP. The results show progress in metaheuristic optimization, reinforcement learning, digital twins, knowledge graphs, computer vision, large language models and robotic planning. At the same time, the analysis identified several recurring limitations that can be grouped into three broader dimensions: product-state knowledge and adaptability, validation and transferability, and decision scope and strategic-operational integration. The reviewed studies show that uncertainty, adaptive planning, digital twins and multi-objective optimization have already received considerable attention, but their application remains limited by predefined product representations, case-specific validation and weak integration between product-level recovery assessment and operational disassembly planning. The findings suggest that future research should complement further algorithmic development with more integrated, adaptive and knowledge-supported decision-support approaches that connect recovery assessment with the planning of the required disassembly process. Full article

Urban regeneration has become a central focus in global urban studies, increasingly linked to the dual imperatives of sustainability and urban resilience. According to Christopher Alexander’s theories of living structure and pattern language, traditional built environments emerge through progressive adaptation rather than top-down design. Moreover, historic urban fabrics evolve through continuous refinement of spatial solutions that simultaneously respond to environmental, functional, and social needs. Therefore, this study situates urban regeneration within a theoretical framework that recognizes traditional settlements as complex adaptive systems rather than static heritage. Chinese urban villages (Chengzhongcun) and Spanish Suelo Urbano no Consolidado (SUNC, Unconsolidated Urban Land) areas represent two contrasting forms of urban socio-spatial systems engulfed by urban expansion, both characterized by dense, historically rooted morphologies and incomplete infrastructure. As Chinese urban villages shift from a demolition–reconstruction model toward more sustainable regeneration approaches, this study compares Beijing’s Cuigezhuang and Málaga’s El Perchel through spatial analysis and stakeholder surveys. Alexander would argue that the demolition–reconstruction model disrupts the evolutionary processes that generate coherent urban fabrics. The research evaluates how differing planning systems foster or constrain sustainable development and social-spatial resilience in regeneration processes. Urban resilience is explicitly defined here as the capacity of urban communities and spatial structures to adapt to change, withstand pressures, and maintain social cohesion, accessibility, and local identity, while ecological and economic resilience are not measured in this study. Findings demonstrate that Spain’s incremental, participatory approach—anchored in Planes Especiales de Reforma Interior (PERI, special plans for inner urban renewal) and land readjustment (equitable distribution of costs and benefits) mechanisms—significantly outperforms China’s state-led demolition-based model in supporting long-term sustainability, heritage integrity, community cohesion, and spatial continuity. Spain’s legally embedded participation and in situ rehabilitation strategies offer transferable lessons for China’s evolving sustainable and resilience-oriented regeneration paradigm. Full article

Confined enzyme catalysis is a promising approach for pharmaceutical synthesis because it can combine high selectivity, catalytic efficiency and mild reaction conditions. This review examines recent advances in multiscale enzyme confinement for pharmaceutical applications, with emphasis on atomic-scale active-site regulation, molecular-scale immobilization and transport control and multi-enzyme cascade organization. We discuss how confined microenvironments modulate enzyme electronic states, conformational dynamics, substrate accessibility, local reaction conditions and intermediate transfer, thereby influencing catalytic activity, stability and stereoselectivity. Representative applications across major enzyme classes are discussed, with a focus on the synthesis of chiral drug intermediates and complex pharmaceutical molecules. The review also considers process-level sustainability, including process mass intensity (PMI) and the E-factor, together with limitations related to mass transfer, support preparation, long-term stability, scale-up and support material burdens. Overall, integrating multiscale confinement with spatially organized cascade catalysis offers a promising route toward more efficient and potentially more sustainable pharmaceutical manufacturing, although its net environmental benefit must be established through system-level assessment. Full article

Sepsis-induced acute lung injury (SI-ALI) is a severe complication of sepsis characterized by dysregulated inflammatory responses and impaired immune homeostasis. Growing evidence indicates that extracellular vesicles (EVs), particularly exosomes, are important mediators of intercellular communication. Despite the heterogeneity of infectious sources underlying sepsis, EVs can regulate macrophage polarization and functional reprogramming by transferring diverse bioactive cargo. Consequently, EVs are involved in the pathophysiological progression of SI-ALI arising from sepsis of different etiologies. However, the mechanisms through which distinct EV cargos regulate macrophage function and contribute to SI-ALI pathogenesis remain incompletely understood. To address these issues, this review summarizes how different EV subtypes and their cargos, including RNAs, proteins, lipids, and DNA, modulate macrophage functional states through multiple signaling pathways. The effect of such processes further contributes to inflammatory reaction, immune balance, and tissue regeneration in acute lung injury caused by damage to the SI-ALI. Particularly, the EV-mediated modulation of macrophage function goes beyond the rigid M1/M2 dichotomy, being rather based on the dynamic functional repertoire involving both pro-inflammatory response and immune regulation as well as tissue regeneration. The article finally concludes with EV-based treatment approaches aimed at cargo delivery or blocking and the main problems related to translational medicine. Overall, the review article identifies the macrophage regulatory network controlled by EVs, thus helping to understand immunopathogenesis of SI-ALI as well as laying the theoretical foundation for developing EV-based precision medicine. Full article

Ensuring reliable and efficient communication under high-delay conditions remains a major challenge for Network Digital Twin (NDT) systems operating across heterogeneous networks. This paper experimentally evaluates the Transmission Control Protocol (TCP), User Datagram Protocol (UDP), QUIC, Stream Control Transmission Protocol (SCTP), the DTN7 Bundle Protocol Version 7 (BPv7) implementation, and the NASA Interplanetary Overlay Network (ION) using a controlled delay-generation environment. Performance is assessed through throughput, bandwidth utilization, transmission time, transfer time, End-to-End Completion Time (ECT), and packet inter-arrival variability. The results show that conventional feedback-driven transport protocols suffer significant performance degradation as network delay increases, whereas UDP maintains high throughput but does not guarantee reliable delivery. In contrast, BPv7-based communication mechanisms, particularly ION using the Licklider Transmission Protocol convergence layer (ION-LTP), achieve superior end-to-end responsiveness under the evaluated long-delay conditions. To enable unified comparison across heterogeneous protocol families, this paper introduces ECT as a protocol-independent application-level metric. The experimental benchmark demonstrates that no single communication mechanism is universally optimal, highlighting the need for adaptive communication strategies in NDT systems. Based on the observed protocol trade-offs, a conceptual Artificial Intelligence (AI)-assisted Deep SARSA framework is presented as a potential approach for dynamic protocol selection under varying network conditions. Its implementation, agent training, and experimental validation are reserved for future work. Full article

Parametric insurance products for tropical cyclone risk transfer typically rely on maximum wind speed (MWS) as the only trigger parameter. While interpretable and widely used, it nevertheless leaves a substantial portion of loss variance unexplained. This paper proposes and evaluates two dual-parameter extensions of the cat-in-a-grid parametric framework, combining MWS with either the radius of maximum wind speed (RMW) or minimum central pressure (MCP). Two integration strategies are examined: a stratified approach, in which events are partitioned by the secondary parameter and separate MWS-based loss functions are fitted within each stratum, and a multivariable approach, in which both parameters enter a bivariate polynomial loss function directly without event partitioning. Four dual-parameter configurations are evaluated against a single-metric MWS baseline using 5-fold cross-validation (CV) on a large stochastic catalog for Jamaica, across four training data strategies. Results show that RMW improves predictive accuracy over the MWS baseline under most training strategies, although the multivariable extension underperforms the baseline under binned training. MCP, by contrast, offers only a marginal benefit that disappears under binned training, consistent with its strong correlation with MWS. In addition, results also show that multivariable models require IQR-filtered raw training to realize their potential, while stratified models perform best with binned training. Full article

Reliable operation of hydro turbine-generator units (HTGUs) is central to safe, flexible, and efficient hydropower generation. State-of-the-art approaches to condition monitoring, fault diagnosis, and early fault detection increasingly rely on artificial intelligence (AI)-driven methods. However, labeled fault data remain scarce and industrial validation of these AI methods remains limited. This paper presents a systematic, algorithm-focused review of predictive maintenance (PdM) for HTGUs. Using a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-guided literature search, we map data-driven and AI-enabled methods from classical machine learning through deep Autoencoders and hybrids to Transformers and state-space models (SSMs), and we compare them with respect to their interpretability, data requirements, and industrial deployability. Relative to plant-wide sensing surveys and generator-centric AI reviews, the contribution is an HTGU-wide assessment of which method families are validated on operational plants versus rotating-machinery benchmarks. Across the reviewed studies, several general patterns emerge: hydropower-specific work is dominated by unsupervised anomaly detection, classical machine learning approaches remain a strong, plant-validated baseline, and high accuracies of recent Transformer/SSM architectures should be interpreted as architectural potential on related assets, not as demonstrated HTGU deployability. We translate the identified gaps into a short-/medium-/long-term roadmap toward transferable, explainable, and industrially validated PdM. Full article

This study presents a closed-loop digital–physical workflow for manufacturing patient-specific anatomical models and surgical guides for maxillofacial reconstruction. The workflow combines medical-image segmentation, additive manufacturing of anatomical models, surgeon-led physical simulation, re-digitization of the modified models, landmark-based and surface-based alignment with the original CT-derived anatomy, with FFD used as a limited supporting approach for localized geometry updating, CAD design, and fabrication of sterilizable guides. Technical feasibility was examined in two detailed mandibular reconstruction cases involving temporomandibular joint prosthesis implantation. The workflow was subsequently used in adapted forms in eight clinically heterogeneous mandibular reconstruction cases related to oncological treatment. These cases are reported as supplementary clinical implementations and were neither treated as direct replications of the complete closed-loop methodology nor pooled for quantitative comparison. In the two detailed cases, the guides were verified on the anatomical models, used intraoperatively, and followed by postoperative imaging providing qualitative clinical confirmation of implant positioning. The study did not include a direct CT-based control workflow or standardized geometric deviation analysis. Therefore, operating-time and cost effects are reported as case-specific observations and an illustrative scenario rather than comparative evidence. The main contribution is the physical-to-digital feedback step, which allows surgeon-introduced modifications of a printed model to be transferred back to the digital geometry before final guide design. The results support the technical feasibility and adaptability of the workflow, while prospective quantitative validation remains necessary. Full article

Pediatric health research must translate evidence into decisions that affect children’s development, safety, function, and long-term well-being. Scientific progress can still fail in two opposing ways: claims may exceed their evidentiary support, or research programs may remain productive while repeatedly refining an established signal without resolving decision-relevant uncertainty. These risks are amplified by developmental heterogeneity, ethical constraints, surrogate or short-term outcomes, long follow-up, and caregiver-mediated implementation. This concept paper formalizes these failures as Evidence Drift (ED) and Causal Maturation Drift (CMD). ED is a claim-level failure in which a finding moves into a stronger or different inferential domain without an adequate bridge. CMD is a trajectory-level failure in which research continues to accumulate within an established domain after a signal is sufficiently characterized, without proportionate progression toward temporal, causal, comparative, long-term, or implementation evidence. Inferential Fidelity is proposed as the governing principle linking claim calibration to purposeful uncertainty reduction. Applications are illustrated through early childhood caries microbiome research, vitamin D and childhood caries, silver diamine fluoride, pediatric biomarker and omics pipelines, and artificial intelligence prediction studies. Two preliminary eight-item appraisal frameworks are introduced: the Evidence Drift Assessment Scale (EDAS) for claims and the Causal Maturation Drift Assessment Scale (CMDAS) for literature trajectories. These formative frameworks prioritize item-level profiles; any standardized index is secondary and non-diagnostic. A phased validation program includes content validation, cognitive testing, multi-assessor reliability, hypothesis-based construct testing, bibliometric trajectory mapping, and evaluation of practical utility. The framework offers investigators, reviewers, funders, guideline panels, and policymakers a structured approach to determining whether conclusions remain within evidentiary boundaries and whether research activity reduces the uncertainties that matter most to children and families. Transferability beyond pediatric research requires empirical testing. Full article

Alginate is a biodegradable and renewable natural polysaccharide that has been extensively investigated for the design of macromolecular delivery systems. This review presents an overview of alginate-based microparticles regarding the relationship between molecular structure, gelation behavior, and functional performance. The impacts of main structural parameters (mannuronic to guluronic acid (M/G) ratio, molecular weight, and block distribution) are discussed comprehensively. Particular attention is given to Ca²⁺-mediated ionic gelation, including egg-box junction zone formation and the development of three-dimensional hydrogel networks. Different production strategies such as external and internal gelation, emulsification, and microfluidic approaches are evaluated in terms of their impact on particle morphology and network homogeneity. In addition, the effects of formulation and process parameters on encapsulation efficiency, mechanical stability, and mass transfer behavior are analyzed. Furthermore, release mechanisms are discussed in relation to network structure and polymer-solute interactions. The environmental significance of alginate-based systems is also emphasized as sustainable alternatives to synthetic polymeric carriers. Full article

Pharmaceutical pollutants have emerged as a critical class of aquatic micropollutants due to their continuous release, persistence, and potential impacts on ecosystems and human health. Conventional wastewater treatment systems are often insufficient to achieve complete removal, necessitating the development of advanced oxidation processes (AOPs), such as Fenton and Fenton-like systems. These processes rely on the generation of reactive oxygen species (ROS), including hydroxyl radicals (•OH), superoxide species, singlet oxygen, and, in some heterogeneous systems, high-valent iron-oxo intermediates, which collectively enable the degradation of structurally diverse and recalcitrant pharmaceutical compounds. Recent advances have highlighted the pivotal role of nanomaterials as catalysts in enhancing Fenton-based processes. Nanostructured catalysts, including iron-based nanoparticles (NPs), metal oxides, carbon-based materials, and bimetallic composites, offer high surface area, tunable redox properties, and improved electron transfer, leading to enhanced catalytic efficiency and mineralization rates. Importantly, the integration of green synthesis approaches using plant extracts, microorganisms, and biopolymers provides environmentally benign routes for nanomaterial fabrication while introducing functional surface groups that improve catalytic performance. Mechanistically, pharmaceutical degradation in Fenton systems involves complex pathways driven by multiple ROS species, including •OH, superoxide radicals, and singlet oxygen, leading to the formation of intermediate products and eventual mineralization. However, challenges such as NP aggregation, metal leaching, incomplete mineralization, and potential toxicity of intermediates remain critical considerations. This review critically evaluates the occurrence of pharmaceutical pollutants, the fundamentals of Fenton and Fenton-like processes, and the design and application of green-synthesized nanomaterials as efficient catalysts. It further explores degradation mechanisms, operational parameters, and sustainability considerations, highlighting future directions for scalable, environmentally responsible water treatment technologies. Full article

The cost–performance relationship in mutual funds is a longstanding open question in financial economics, particularly when costs are assumed to exert a single, linear effect on returns. This study proposes an integrated causal machine learning framework to revisit this question using a panel of Hungarian open-ended public investment funds across all major asset classes—equity, bond, absolute yield, misc, money market, real estate, and commodity—covering 2017–2024. Six machine learning algorithms are benchmarked for return prediction, and Double Machine Learning, with fund-level cluster-robust inference and year fixed effects, is applied to estimate the effect of the Total Expense Ratio (TER) on next-year returns, under the identifying assumptions stated in the paper, while flexibly controlling for a set of observed fund-level confounders (size, NAV dynamics, volatility, past and cumulative performance, and fund age) without imposing a linear functional form. To move beyond average effects, a Causal Forest model—tuned using an out-of-fold, effect size-neutral selection criterion—estimates heterogeneous treatment effects across funds, and SHAP-based interpretation uncovers the mechanisms underlying this heterogeneity. The results show that, once the outcome is measured in the year following the one in which TER is observed and panel dependence is properly accounted for, the average TER effect is not robustly different from zero at the full-sample level; where a statistically robust effect emerges, it is negative rather than positive, concentrated in equity and absolute-yield funds, and largely confined to the period after 2022, which coincided with the war in Ukraine, rising interest rates, and heightened market volatility, although the research design does not identify which, if any, of these developments drove the change. Average-effect models are shown to conceal this heterogeneity, and the results are further shown to be sensitive to two methodological choices that might otherwise appear secondary—the timing convention linking cost and return, and the criterion used to select among competing heterogeneous-effects specifications—underscoring the importance of making such choices explicit. These findings demonstrate the added value of combining predictive and causal machine learning, together with identification-robust and panel-robust inference, for uncovering heterogeneity that conventional econometric approaches overlook and offer a transferable methodological template for causal machine learning applications in finance and other high-dimensional decision-making domains. Full article

Gel polymer electrolytes (GPEs) have emerged as promising electrolytes for lithium metal batteries owing to their high ionic conductivity, mechanical flexibility, and reduced risk of electrolyte leakage. However, PAN-based GPEs still suffer from limited ion transport caused by the semi-crystalline structure of PAN chains. In this work, polyacrylonitrile (PAN)/poly(methyl methacrylate) (PMMA)/lithium aluminum titanium phosphate (LATP)/lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) gel polymer electrolytes were fabricated via direct ink writing (DIW) 3D printing, where PMMA was introduced to regulate the PAN matrix and enhance Li⁺ transport. The results reveal that PMMA incorporation effectively reduces PAN crystallinity, increases the amorphous fraction, and modifies the local functional-group environment of the polymer matrix, while LATP fillers further improve ionic transport and mechanical stability. The optimized PPM8:2 gel polymer electrolyte delivers a room-temperature ionic conductivity of 4.22 × 10⁻⁴ S cm⁻¹, a Li⁺ transference number of 0.624, and an electrochemical stability window of 4.75 V. When applied in LiFePO₄|Li batteries, it maintains a discharge capacity of approximately 150 mAh g⁻¹ after 100 cycles at 0.1 C with excellent rate capability and cycling stability. This work provides an effective approach to developing PAN-based gel polymer electrolytes for high-performance lithium metal batteries. Full article

The reuse of expired pharmaceuticals as corrosion inhibitors offers a sustainable strategy for reducing pharmaceutical waste while providing environmentally friendly alternatives to conventional inhibitors. In this work, the corrosion inhibition performance of expired ibrutinib (EIB) for P110 carbon steel in 1.0 M HCl was investigated using electrochemical techniques, mass loss measurements, surface characterization, and computational approaches. Electrochemical impedance spectroscopy (EIS) revealed a progressive increase in charge-transfer resistance with increasing inhibitor concentration, while potentiodynamic polarization (PDP) measurements demonstrated that EIB acts as a mixed-type inhibitor with a predominant anodic effect. At 1000 mg L⁻¹, inhibition efficiencies of 88.7%, 96.8%, and 93.3% were obtained from EIS, PDP, and mass loss measurements, respectively. SEM analysis confirmed the formation of a compact and homogeneous protective film on the steel surface, significantly reducing corrosion damage and surface roughness. Density functional theory (DFT), Natural Bond Orbital (NBO), Monte Carlo (MC), and molecular dynamics (MD) simulations demonstrated strong adsorption of EIB on the Fe(110) surface through nitrogen- and oxygen-containing active centers, while radial distribution function analysis confirmed the contribution of chemisorption. The excellent agreement between the experimental and theoretical results demonstrates that expired ibrutinib is an efficient and sustainable corrosion inhibitor for P110 carbon steel in acidic environments and represents a promising approach for the valorization of expired pharmaceutical products. Full article