Search Results (6,305)

We propose Adaptive Inference, a portfolio management framework extending Active Inference to non-stationary financial environments. The framework integrates inference, control, and execution under endogenous uncertainty, modeling investment decisions as coupled dynamics of belief updating, preference encoding, and action selection rather than optimization over fixed objectives. In this approach, portfolio behavior is governed by the expected free energy (EFE) minimization, showing that classical valuation models emerge as limiting cases when epistemic components vanish. Using train–test evaluation on the ARKK Innovation ETF (2015–2025), we identify a Passivity Paradox: frozen belief transfer outperforms naive adaptive learning. A Professional Agent achieves a Sharpe ratio of 0.39 while its adaptive counterpart degrades to $-0.28$, reflecting belief contamination when learning from policy-dependent signals. Crucially, the architecture is not designed to generate alpha but to perform endogenous risk management that mitigates overtrading under regime ambiguity and distributional shift. Adaptive Inference Agents maintain long exposure most of the time while tactically reducing positions during high-entropy periods, implementing uncertainty-aware passive investing. All agents reduce realized volatility relative to ARKK Buy-and-Hold (43.0% annualized). Cross-asset validation on the S&P 500 ETF (SPY) shows that inference-guided risk shaping achieves a positive Entropic Sharpe Ratio (ESR), defined as excess return per unit of informational work, thereby quantifying the economic value of information under thermodynamic constraints on inference. Full article

Detecting distributional changes in multivariate sensor networks is a fundamental task for monitoring complex systems such as industrial processes, structural health monitoring, and large-scale Internet of Things infrastructures. Despite significant progress, most existing change-point detection methods either operate on high-dimensional observations in a black-box manner or provide limited insight into how inter-sensor dependencies evolve over time, thereby restricting their practical utility in safety-critical applications. In this work, we propose an interpretable change detection framework based on the Cauchy–Schwarz (CS) divergence. By extending CS divergence to conditional distributions over sensor variables, the proposed method detects distributional shifts through changes in sensor-wise conditional relationships. This design enables reliable change detection while simultaneously providing transparent sensor-level explanations of detected changes. Extensive experiments on synthetic data, generic multivariate sensor time series, and a large-scale industrial process benchmark demonstrate that the proposed method achieves competitive or superior detection performance compared to representative baselines, while offering fine-grained interpretability for practical sensor monitoring systems. Full article

Background and Objectives: Burnout is highly prevalent among emergency and acute care healthcare workers (HCWs), yet biological correlates remain debated because candidate biomarkers are strongly shaped by circadian timing, shift work, sleep loss, and overlapping affective symptoms. We mapped post-2018 evidence of biological biomarkers assessed alongside validated burnout measures in emergency department (ED), emergency medical services (EMS), and related acute care settings. Specifically, we asked whether reproducible biological correlates of burnout can be identified in emergency and acute-care healthcare workers when biomarker endpoint class and sampling context are systematically considered. Materials and Methods: We conducted a systematic scoping review with evidence mapping (PRISMA-ScR). PubMed/MEDLINE and the MDPI platform were searched for English-language studies published from 2018 onward (through January 2026). Eligible quantitative studies enrolled ED/EMS or acute care HCWs, assessed burnout using validated instruments, and reported at least one biological biomarker. Evidence was charted by biomarker domain and endpoint class (basal measures, stress reactivity paradigms, and chronic indices such as hair-based markers). Results: Overall, 19 studies were included in mapping/synthesis. Biomarker selection clustered around the hypothalamic–pituitary–adrenal axis (cortisol; n = 10/19), with fewer studies focused on autonomic function (heart rate variability; n = 2/19) and immune–inflammatory markers (n = 2/19), and single-study coverage for oxidative stress (n = 1/19), cardiometabolic candidates (n = 1/19), cellular aging (n = 1/19), neuroglial/multi-system candidates (n = 1/19), and feasibility-oriented multi-marker designs (n = 1/19). Reported associations with burnout were heterogeneous in direction and magnitude, but were more interpretable when endpoint class, timing anchors, and shift/sleep-related covariates were explicitly reported. Rates of confounder adjustment were low across studies (e.g., only 3/19 reported multivariable adjustment, and none systematically measured sleep or circadian factors), substantially limiting interpretability. Conclusions: The 2018+ literature does not support a single reproducible biomarker for burnout in emergency and acute care workforces. Evidence instead suggests multi-system dysregulation that is highly sensitive to endpoint class, sampling timing, and contextual confounding. Future studies should prioritize timing-anchored repeated-measures protocols across shift and recovery windows, jointly model sleep/circadian factors and depressive symptoms, and evaluate multi-marker panels and intervention responsiveness. Full article

Horizontal well drilling is the mainstream technology for developing deep oil and gas resources. Engineering practice has demonstrated that hydraulic oscillators can solve the problem of the backing pressure of pipe strings and improve drilling efficiency. However, the design of excitation parameters for hydraulic oscillators is currently largely based on idealized friction models and does not fully consider the nonlinear characteristics of friction between the drill string and the formation, resulting in a lack of quantitative basis for parameter selection under different operating conditions. A series of laboratory friction tests was conducted to systematically characterize the dependence of interfacial friction behavior on sliding velocity across different combinations of drill string materials, drilling fluid systems, and rock lithologies. Based on the experimentally determined velocity–friction relationships, a drill string dynamic model incorporating a hydraulic oscillator was developed in which nonlinear frictional effects at the interface were explicitly represented. Using this modeling framework, parametric simulations were carried out to examine how variations in excitation amplitude and excitation frequency influence drag reduction performance under diverse operating conditions. The simulation results indicate that the contribution of drill string material to overall drag reduction effectiveness is comparatively limited, whereas drilling fluid type plays a dominant regulatory role. Oil-based drilling fluids significantly enhance drag reduction performance relative to water-based systems and exhibit greater responsiveness to adjustments in excitation parameters. Rock lithology exerts a pronounced influence on the effectiveness of drag reduction. When water-based drilling fluids are used, the overall performance ranks from highest to lowest as limestone, shale, and sandstone. In contrast, under oil-based drilling fluid conditions, the relative ordering shifts to shale, followed by sandstone, and then limestone. Excitation amplitude is the dominant parameter in enhancing drag reduction capability, and in most cases, its incremental effect exceeds that of excitation frequency; however, under certain specific operating conditions, increasing the excitation frequency can provide additional drag reduction benefits. Based on the above findings, a hydraulic oscillator excitation parameter design method was proposed that matches drilling conditions and formation characteristics by distinguishing between different drilling fluid environments and lithologies, with amplitude as the primary control parameter and frequency as a supplementary parameter. This method provides a theoretical foundation for the design of output parameters of hydraulic oscillators operating under diverse working conditions. Full article

This study developed and evaluated an integrated experimental–computational framework to quantify coconut-oil transport and marbling stabilization in soy protein concentrate (SPC) during static holding and co-extrusion with a cooling die. Temperature-sweep rheology and Differential Scanning Calorimetry (DSC) identified the main gelation transition at 65–78 °C, with oil shifting gelation to higher temperatures and increasing enthalpy, supporting an exit/cooling target of 70–75 °C. Static drop tests at 100 °C for 60 s were analyzed by depth-resolved imaging and coupled with a single-phase CFD model to inversely calibrate an effective diffusion coefficient for coconut oil in SPC (D_ref = 4.86 × 10⁻¹⁸ m²/s). A viscosity-coupled fractional Stokes–Einstein relationship then gave temperature-dependent effective diffusivities of 1.89 × 10⁻¹⁸ to 4.86 × 10⁻¹⁸ m²/s over 60–100 °C, indicating reduced oil mobility during cooling. Additional static time-temperature comparisons suggested limited redistribution beyond ~50 s. Co-extrusion simulations and product imaging further indicated that staged hot-zone residence followed by rapid cooling can help stabilize oil domains into marbling-like structures. The framework can support selection of cooling-die temperatures, residence times, and oil-injection conditions. Future work should extend the framework by linking marbling microstructure with sensory performance, oxidative stability, and sensitivity analysis of key transport parameters. Full article

Civil engineering faces the dual challenge of addressing climate change and managing construction and demolition waste (CDW). While existing analyses often focus solely on the mechanical characteristics of recycled materials, there is a significant gap in research on integrating these technical advancements with climate-resilient infrastructure and public policies that encourage circularity. This article offers a detailed review of the technical possibilities for materials derived from CDW, shifting the focus from “low-value recycling” to higher value-added uses. We analyze progress in this area over the past decade (2015–2025), specifically exploring the role of Building Information Modeling (BIM), Artificial Intelligence (AI), and advanced pretreatment processes (such as carbonation and alkaline activation) in improving material properties. A unique contribution of this work is the creation of a conceptual framework connecting materials science to global sustainability indicators and urban resilience strategies. Our findings show that, while technical feasibility is well established, the transition to a circular economy is hampered by the absence of standardized environmental metrics and effective public policies. This review summarizes these interdisciplinary trajectories and presents a plan for engineers and policymakers to transform construction and demolition waste (CDW) from a problem into a strategic resource for climate-adaptable urban development. Full article

This qualitative study examined how a 21-day integrated program fosters interoceptive awareness and mind–body integration among urban adults in mainland China (n = 11). The intervention combined daily nasal breathing regulation, spontaneous mandala making, and descriptive journaling, complemented by weekly group sharing. Using a cultural–psychological lens, we investigated how an inward–turning tradition in Chinese culture shapes embodied experience and meaning–making. Applying Interpretative Phenomenological Analysis to diaries, drawings, and focus-group data, we identified three interrelated processes: (1) the refinement of bodily attention; (2) a shift from deliberate control to natural immersion; and (3) the symbolization of feeling through artistic expression and social resonance. Findings indicate that systematic engagement in the “breath–mandala” intervention heightened sensitivity to chest-centered embodied sensations and promoted the integration of bodily experience into personal narratives; a non-goal-directed, relaxed practice style facilitated the transition from control to absorption, activating self-regulatory mechanisms; and non-evaluative awareness deepened flow while supporting cognitive reorganization and reflective capacity. The study delineates a core pathway by which breath-triggered interoceptive work operates within mind–body interventions, offering a theoretical basis and practical direction for tailored regulation programs across diverse populations. Full article

In this work, a study of the chemical composition of the compound Sb₂(S_1−xSe_x)₃ used in thin-film solar cell fabrication, based on correlating data obtained from XRD and XPS analyses, is presented. This approach enables us to propose a mathematical expression for evaluating stoichiometric variations in the material, showing how the variable x evolves as a function of the diffraction angle 2θ. To establish this model, we analyzed the most intense diffraction peak, corresponding to the (221) plane. To validate the proposed method, a series of Sb₂(S_1−xSe_x)₃ thin films with different compositions were synthesized using RF-magnetron sputtering followed by conventional heat treatments in a controlled-atmosphere reaction furnace. The XRD results reveal a systematic 2θ shift in the crystalline diffraction peaks toward the positions of the binary precursor phases—from Sb₂Se₃ to Sb₂S₃—caused by the increased sulfur content during synthesis. XPS measurements confirm the presence of Sb, Se, and S, and high-resolution spectra indicate a decrease in selenium content as the sulfur fraction increases. These results allowed us to elucidate the stoichiometric behavior of antimony sulfoselenide Sb₂(S_1−xSe_x)₃ using trend curves fitted to the characterization data. Full article

This review systematically evaluates the interdisciplinary convergence of artificial intelligence (AI) and polymer science in cancer therapy. Beyond mere description, we provide an integrated framework spanning synthetic optimization, biocompatibility prediction, and the design of tumor microenvironment (TME)-responsive carriers. We highlight how AI algorithms (ML, DL, and RNNs) transform traditional trial-and-error methods into a data-driven paradigm, enabling precise spatiotemporal drug release and individualized pharmacokinetic modeling. Crucially, this work addresses the critical gap between computational modeling and clinical realization by providing a balanced critical analysis of current bottlenecks, including the “small data” challenge, publication bias, and regulatory hurdles. We conclude with a roadmap for AI-guided precision oncology, shifting the focus from predictive accuracy to mechanistic interpretability and prospective in vivo validation. Full article

The Spanish Public University System (SUPE) assigns student placements through a multi-step application process governed by legal criteria. Analyzing how students move between different degree programs during this process is crucial for universities to optimize and plan their academic offerings. This paper analyzes a decade of student pre-registration data (2014–2023) to track evolving preferences and mobility between degrees. We model this process as a directed graph, mapping student traffic and studying the formation of directional communities within the degree network. A significant challenge is the weakly connected and poorly conditioned nature of these graphs, which impedes standard community detection algorithms. Extending prior work that relied on manually set thresholds for pruning edges, we propose a novel adaptive pruning algorithm that requires no manual intervention. Applying this method to annual data improves community detection performance and reveals gradual shifts in student preferences and demand, particularly in response to new degrees. These insights provide a valuable decision-making tool for higher education institutions, helping them refine their degree offerings in response to evolving trends. Full article

Organic solar cells require molecular materials with broad absorption and proper energy-level alignment to maximize photon harvesting and charge transport; in this context, this work focuses on the computational design and characterization of π-conjugated push–pull chromophores, providing an integrated evaluation of their electronic, thermodynamic, and optoelectronic properties for photovoltaic applications. The chromophores were optimized using DFT/ b3lyp/6-31g+(d,p) in Gaussian16, incorporating solvation effects through the CPCM model. Electronic, thermodynamic, and optical properties were investigated using DFT and TD-DFT/CAM-B3LYP/6-311+G(d,p), including the calculation of absorption and emission spectra, first hyperpolarizability, and two-photon absorption. The STQ-PP chromophores exhibit differentiated optoelectronic responses, with DTTQ-DPP-1 showing an energy gap of 0.82–0.86 eV, stabilized LUMO levels between −2.50 and −2.61 eV, high electronic polarizability, and optical absorption extended beyond 800 nm, favoring the harvesting of low-energy photons, whereas DTTQ-DPP displays a gap close to 2.70 eV and absorption predominantly localized in the UV region, associated with potentially inferior photovoltaic performance. Compared with commercial donor materials, DTTQ-DPP-1 exhibits a red-shifted absorption into the NIR and a smaller gap, indicating enhanced low-energy photon capture; its structural stability and increased rigidity further support its photovoltaic viability. Full article

Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial information. Recent advances in artificial intelligence (AI), particularly large language models and automated information-extraction systems, create new opportunities to accelerate financial analysis; however, their outputs remain probabilistic, context-dependent, and potentially error-prone, making governance and verification essential. This article proposes an AI-assisted value investing framework that integrates automated extraction, valuation modeling, explainability, and human-in-the-loop (HITL) supervision into a unified decision-support architecture. The framework is organized into three layers: (i) a data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling layer for automated key performance indicator (KPI) computation, forecasting support, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer for traceability, verification, model-risk control, and analyst oversight. A central contribution of the paper is the operational characterization of prompt literacy as a determinant of analytical reliability, showing that structured, context-aware prompts materially affect extraction correctness, usability, and verification effort. The framework is evaluated through a case study using Rivanna AI on three large U.S. beverage firms—namely, The Coca-Cola Company, PepsiCo, and Keurig Dr Pepper—selected as a controlled, information-rich setting for comparative analysis. The results indicate that the proposed workflow can reduce end-to-end analysis time from approximately 25–40 h in a traditional manual process to approximately 8–12 h in an AI-assisted setting, including citation/source verification, unit and period reconciliation, and review of key valuation assumptions. Rather than eliminating analyst effort, AI shifts it from manual information processing toward verification, adjudication, and interpretation. Overall, the findings position AI not as an autonomous decision-maker, but as a governed reasoning accelerator whose effectiveness depends on structured human guidance, traceability, and disciplined validation. In value investing, a discipline traditionally grounded in labor-intensive fundamental analysis and disciplined intrinsic value estimation, AI introduces the potential to scale analytical coverage and accelerate evidence synthesis. However, AI systems in financial contexts are probabilistic, context-sensitive, and inherently dependent on human interaction, raising critical questions about reliability, governance, and operational integration. This article proposes a structured framework for AI-driven value investing that preserves the foundational principles of intrinsic value, margin of safety, and economic reasoning, while redesigning the analytical workflow through automation, explainability, and human-in-the-loop (HITL) supervision. The proposed architecture integrates three layers: (i) an AI-enabled data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling and valuation layer combining automated KPI computation, machine learning forecasting, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer ensuring traceability, verification, and model risk control. A central contribution of this work is the operational characterization of prompt literacy, namely the ability to formulate structured, context-aware requests to AI systems, as a critical determinant of system reliability and analytical correctness. Through a focused case study using an AI-assisted analysis platform (Rivanna AI) on three U.S. beverage firms, we provide evidence that structured prompt formulation can improve extraction consistency, reduce verification overhead, and increase workflow efficiency in a human-supervised setting. In this setting, analysis time decreased from a manual range of approximately 25–40 h to 8–12 h with AI assistance and HITL validation, while preserving traceability and decision accountability. The reported hour savings should be interpreted as conservative estimates from the initial deployment phase; additional efficiency gains are expected as operational maturity increases, driven by learning-economy effects. The findings position AI not as an autonomous decision-maker but as a probabilistic reasoning accelerator whose effectiveness depends on structured human guidance, verification discipline, and prompt-driven interaction. These results redefine the role of the financial analyst from manual data processor to reasoning architect, responsible for designing, guiding, and validating AI-assisted analytical workflows. Full article

Battery-powered edge systems must operate under tight energy budgets while facing growing computational demand from rapidly evolving edge workloads. Field-programmable gate arrays (FPGAs) offer middle ground when optimized for energy, especially flash-based FPGAs due to inherent low-power characteristics. Microchip flash-based SoC FPGAs further expose ultra-low-power (LP) modes including fabric Flash*Freeze (F*F), processor sleep and selectable standby clocks. Combining these modes with HW/SW partitioning and clock-frequency scaling can reduce energy for low-duty-cycle workloads; however, selecting an energy-efficient operating point in this multidimensional design space is non-trivial. This work explores the design space by measuring and analyzing LP modes across three architectural approaches (SW, co-design, and HW) under frequency scaling on a Microchip Smartfusion2 platform, using a low-duty-cycle heart-rate monitoring workload. Measurements indicate that, for low-duty-cycle workloads, total energy is dominated by the idle phase and is minimized by combining fabric-F*F with processor sleep. The results further show that main-clock downscaling reduces active-phase current but has limited impact on idle consumption once F*F and sleep are applied, while standby-clock selection trades idle current against LP entry/exit latency. Event-rate scaling further shows that the energy-optimal operating point can shift with duty cycle. We provide measurement-based guidelines for duty-cycle-aware energy-efficient operating point selection in similar flash-based SoC platforms. Full article

Operational (short-term) planning in open-pit mining is a critical phase for ensuring grade control and production stability, particularly in complex geological environments. While long-term plans define the strategic goals, they often overlook shift-level variability and operational constraints of a shovel-truck system. This paper presents an optimization model based on a genetic algorithm (GA) for shift-by-shift operational planning. The model integrates real-world technological constraints of the equipment used, including fixed shift capacity (2000 t) and various constraints characteristic of active mining locations. The fitness function is designed to minimize the deviations from the targeted quality range for iron (Fe: 47–50%) and silica (SiO₂: ≤11%), while ensuring rational use of mineral reserves. The model was tested on a case study involving eight limonite ore open pits over a period of one production year (1000 shifts). The results show that the GA-generated plan reaches quality requirements in 98.1% of all shifts. This GA approach provides more balanced mining operations and confirms and ensures the achievement of goals from long-term plans, reducing the reliance on large-scale homogenization stockpiles. The developed tool is implemented in Excel/VBA and offers a practical framework for mining engineers to work with. Full article

A new spiral nematode species, Scutellonema curcumae sp. n., was identified from the rhizosphere of turmeric (Curcuma longa L.) in the Western Highlands of Vietnam. Integrative taxonomical analysis, combining detailed morphology and molecular characterization (ITS, 28S D2–D3 rDNA, and COI mtDNA), confirmed its distinctiveness. Scutellonema curcumae sp. n. is characterized by a unique combination of a spiral body, a hemispherical lip region with four annuli, a robust stylet, and a rounded tail with a prominent scutellum, forming a highly divergent lineage within the genus. Beyond its description, this study reveals a significant inverse correlation between nematode population density and the phytochemical quality of the host. High infestation levels were associated with a marked decline in total curcuminoid content. Notably, lower nematode density favored a specific shift in the curcuminoid profile, with bisdemethoxycurcumin levels increasing by up to 250%. These phytochemical alterations directly influenced the therapeutic potential of the rhizomes: lower infestation levels resulted in significantly enhanced antioxidant capacity (lower SC₅₀ values) and cytotoxic activity (lower IC₅₀ against HepG2 and A549 cell lines). This work represents the first report of a Scutellonema species associated with turmeric in Vietnam and underscores its detrimental impact on the medicinal and nutraceutical value of the crop. Our findings suggest that effective nematode management is crucial not only for yield protection but as a strategic intervention in precision agriculture to optimize the secondary metabolite profiles of medicinal plants. Full article