Search Results (22,816)

Financial time series are heterogeneous, nonstationary, and dispersed across institutions that cannot share raw data. While federated learning enables collaborative modeling under privacy constraints, fixed architectures struggle to accommodate cross-market drift and device-resource diversity; conversely, existing neural architecture search techniques presume centralized data and typically ignore communication, latency, and privacy budgets. This paper introduces FedRegNAS, a regime-aware federated NAS framework that jointly optimizes forecasting accuracy, communication cost, and on-device latency under user-level $(\epsilon ,\delta )$-differential privacy. FedRegNAS trains a shared temporal supernet composed of candidate operators (dilated temporal convolutions, gated recurrent units, and attention blocks) with regime-conditioned gating and lightweight market-aware personalization. Clients perform differentiable architecture updates locally via Gumbel-Softmax and mirror descent; the server aggregates architecture distributions through Dirichlet barycenters with participation-weighted trust, while model weights are combined by adaptive, staleness-robust federated averaging. A risk-sensitive objective emphasizes downside errors and integrates transaction-cost-aware profit terms. We further inject calibrated noise into architecture gradients to decouple privacy leakage from weight updates and schedule search-to-train phases to reduce communication. Across three real-world equity datasets, FedRegNAS improves directional accuracy by 3–7 percentage points and Sharpe ratio by 18–32%. Ablations highlight the importance of regime gating and barycentric aggregation, and analyses outline convergence of the architecture mirror-descent under standard smoothness assumptions. FedRegNAS yields adaptive, privacy-aware architectures that translate into materially better trading-relevant forecasts without centralizing data. Full article

This paper explores what heritage conservation might become when it listens differently—when it opens itself to relational, situated, and community-led practices of care. Beginning with the provocation “Museums? I don’t think this is for us. Museums are far too clever for us,” voiced in the context of an ecomuseum, I interrogate the assumptions that underpin conventional heritage conservation: expert authority, linear temporality, and the desire to stabilize. Drawing on new materialism theories, I question the disciplinary logics that produce heritage as a human centred practice that look at objects as static and conservation as a neutral act. In contrast, I present ecomuseums not as policy model but as conceptual disruption—territories of care that emerge from entanglements of memory and place, becoming, therefore, an active force that are engaged in sustainable practices. In thinking with ecomuseum practices, I consider how conservation would look if shifted from colonial to liberative practices, from control to attention, from fixity to fluidity. I explore conservation as a field of relations—affective and unfinished. Finally, I offer a call for heritage practitioners to reimagine conservation not as the act of keeping things the same, but as an ongoing negotiation with change in a pluriversal world. Full article

Accurate 3D reconstructions of architecture, engineering, and construction AEC structures using UAV photogrammetry are often hindered by occlusions, excessive image overlaps, or insufficient coverage, leading to inefficient flight paths and extended mission durations. This work presents a BIM-aware, autonomous UAV trajectory generation framework wherein a compact, geometrically valid viewpoint network is first derived as a foundation for path planning. The network is optimized via Integer Linear Programming (ILP) to ensure coverage of IFC-modeled components while penalizing poor stereo geometry, GSD, and triangulation uncertainty. The resulting minimal network is then sequenced into a global path using a TSP solver and partitioned into battery-feasible epochs for operation on active construction sites. Evaluated on two synthetic and one real-world case study, the method produces autonomous UAV trajectories that are 31–63% more compact in camera usage, 17–35% shorter in path length, and 28–50% faster in execution time, without compromising coverage or reconstruction quality. The proposed integration of BIM modeling, ILP optimization, TSP sequencing, and endurance-aware partitioning enables the framework for digital-twin updates and QA/QC monitoring, accordingly, offering a unified, geometry-adaptive solution for autonomous UAV inspection and remote sensing. Full article

This paper proposes a safety-constrained interaction control scheme for robotic manipulators by integrating model predictive control (MPC) and active disturbance rejection control (ADRC). The proposed method is specifically designed for manipulators with complex nonlinear dynamics. To ensure that the control system satisfies safety constraints during human–robot interaction, MPC is incorporated into the impedance control framework to construct a model predictive impedance controller (MPIC). By exploiting the prediction and constraint-handling capabilities of MPC, the controller provides guaranteed safety throughout the interaction process. Meanwhile, ADRC is employed to track the target joint control signals generated by the MPIC, where an extended state observer is utilized to compensate for dynamic modeling errors and nonlinear disturbances within the system, thereby achieving accurate trajectory tracking. The proposed method is validated through both simulation and real-world experiments, achieving high-performance interaction control with safety constraints at a 2 ms control cycle. The controller exhibits active compliant interaction behavior when the interaction stays within the constraint boundaries, while maintaining strict adherence to the safety constraints when the interaction tends to violate them. Full article

Based on existing models, this paper incorporates some key ecological factors, thereby obtaining a class of eco-epidemiological models that can more objectively reflect natural phenomena. This model simultaneously integrates disease dynamics within the prey population and the Beddington–DeAngelis functional response, thus achieving an organic combination of ecological dynamics, epidemic transmission, and spatial movement under time-varying environmental conditions. The proposed framework significantly enhances ecological realism by simultaneously accounting for spatial dispersal, predator–prey interactions, disease transmission within prey species, and seasonal or temporal variations, providing a comprehensive mathematical tool for analyzing complex eco-epidemiological systems. The theoretical results obtained from this study can be summarized as follows: Firstly, the existence and uniqueness of globally positive solutions for any positive initial data are rigorously established, ensuring the well-posedness and biological feasibility of the model over extended temporal scales. Secondly, analytically tractable sufficient conditions for uniform population persistence are derived, which elucidate the mechanisms of species coexistence and biodiversity preservation even under sustained epidemiological pressure. Thirdly, by employing innovative applications of differential inequalities and fixed point theory, the existence and uniqueness of a positive spatially homogeneous periodic solution in the presence of time-periodic coefficients are conclusively demonstrated, capturing essential rhythmicities inherent in natural systems. Fourthly, through a sophisticated combination of the upper and lower solution method for parabolic partial differential equations and Lyapunov stability theory, the global asymptotic stability of this periodic solution is rigorously established, offering a powerful analytical guarantee for long-term predictive modeling. Beyond theoretical contributions, these research findings provide actionable insights and quantitative analytical tools to tackle pressing ecological and public health challenges. They facilitate the prediction of thresholds for maintaining ecosystem stability using real-world data, enable the analysis and assessment of disease persistence in spatially structured environments, and offer robust theoretical support for the planning and design of wildlife management and conservation strategies. The derived criteria support evidence-based decision-making in areas such as controlling zoonotic disease outbreaks, maintaining ecosystem stability, and mitigating anthropogenic impacts on ecological communities. A representative numerical case study has been integrated into the analysis to verify all of the theoretical findings. In doing so, it effectively highlights the model’s substantial theoretical value in informing policy-making and advancing sustainable ecosystem management practices. Full article

Real-time, reliable forecasting of water quality (WQ) is a critical component of sustainable environmental management. A key challenge, however, is modeling time-varying uncertainty (heteroscedasticity), particularly during disruptive events like storms where predictability decreases dramatically. Standard probabilistic models often fail in these high-stakes scenarios, producing forecasts that are either too conservative during calm periods or dangerously overconfident during volatile events. This paper introduces the Gated Hybrid–Mixture Density Network (GH-MDN), an architecture explicitly designed for adaptive uncertainty estimation. Its core innovation is a dedicated gating network that learns to adaptively modulate the prediction interval width in response to a domain-relevant, event-precursor signal. We evaluate the GH-MDN on both synthetic and real-world WQ datasets using a rigorous cross-validation protocol. The results demonstrate that our gated model provides robust calibration and trustworthy adaptive coverage; specifically, it successfully widens prediction intervals to capture extreme events where standard benchmarks fail. We further show that common aggregate metrics such as CRPS can mask over-confident behavior during rare events, underscoring the need for evaluation approaches that prioritize calibration. This science-informed approach to modeling heteroscedasticity prioritizes reliable risk coverage over aggregate error minimization, marking a critical step towards the development of more trustworthy environmental forecasting systems. Full article

In recent years, there has been growing interest in discrete probability distributions due to their ability to model the complex behavior of real-world count data. In this paper, a new discrete mixture distribution based on two Weibull components is introduced, constructed using the general discretization approach. Several important statistical properties of the proposed distribution, including the survival function, hazard rate function, alternative hazard rate function, moments, quantile function, and order statistics are derived. It was concluded from the descriptive measures that the discrete mixture of two Weibull distributions transitions from being positively skewed with heavy tails to a more symmetric and light-tailed form. This demonstrates the high flexibility of the discrete mixture of two Weibull distributions in capturing a wide range of shapes as its parameter values vary. Estimation of the parameters is performed via maximum likelihood under Type II censoring scheme. A simulation study assesses the performance of the maximum likelihood estimators. Furthermore, the applicability of the proposed distribution is demonstrated using two real-life datasets. In summary, this paper constructs the discrete mixture of two Weibull distributions, investigates its statistical characteristics, and estimates its parameters, demonstrating its flexibility and practical applicability. These results highlight its potential as a powerful tool for modeling complex discrete data. Full article

Many countries around the world repeatedly suffer from natural disasters such as earthquakes, tsunamis, floods, and hurricanes due to geographical factors, including plate boundaries, tropical cyclone zones, and coastal regions. Representative examples include Hurricane Katrina, which struck the United States in 2005, and the Great East Japan Earthquake in 2011. Both were large-scale disasters that occurred in developed countries and caused enormous human and economic losses regardless of disaster type or location. As the occurrence of such catastrophic events remains inevitable, establishing effective preparedness and rapid response systems for large-scale disasters has become an urgent global challenge. One of the critical issues in disaster response is the rapid estimation of the number of affected individuals required for effective rescue operations. During large-scale disasters, terrestrial communication infrastructure is often rendered unusable, which severely hampers the collection of situational information. If the population within a disaster-affected area can be estimated without relying on ground-based communication networks, rescue resources can be more appropriately allocated based on the estimated number of people in need, thereby accelerating rescue operations and potentially reducing casualties. In this study, we propose a population-estimation system that remotely senses radio signals emitted from smartphones in disaster areas using Low Earth Orbit (LEO) satellites. Through numerical analysis conducted in MATLAB R2023b, the feasibility of the proposed system is examined. The numerical results demonstrate that, under ideal conditions, the proposed system can estimate the number of smartphones within the observation area with an average error of 2.254 devices. Furthermore, an additional evaluation incorporating a 3D urban model demonstrates that the proposed system can estimate the number of smartphones with an average error of 19.03 devices. To the best of our knowledge, this is the first attempt to estimate post-disaster population using wireless LAN signals sensed by LEO satellites, offering a novel remote-sensing-based approach for rapid disaster response. Full article

In the field of remote sensing, tracking highly maneuvering targets is challenging due to its rapidly changing patterns and uncertainties, particularly under non-Gaussian noise conditions. In this paper, we consider the problem of tracking highly maneuvering targets without using preset parameters in non-Gaussian noise. We propose a multi-domain intelligent state estimation network (MIENet). It consists of two main models to estimate the key parameter for the Unscented Kalman Filter, enabling robust tracking of highly maneuvering targets under various intensities and distributions of observation noise. The first model, called a fusion denoising model (FDM), is designed to eliminate observation noise by enhancing multi-domain feature fusion. The second model, called a parameter estimation model (PEM), is designed to estimate key parameters of target motion by learning both global and local motion information. Additionally, we design a physically constrained loss function (PCLoss) that incorporates physics-informed constraints and prior knowledge. We evaluate our method on radar trajectory simulation and real remote sensing video datasets. Simulation results on the LAST dataset demonstrate that the proposed FDM can reduce the root mean square error (RMSE) of observation noise by more than 60%. Moreover, the proposed MIENet consistently outperforms the state-of-the-art state estimation algorithms across various highly maneuvering scenes, achieving this performance without requiring adjustment of noise parameters under non-Gaussian noise. Furthermore, experiments conducted on the real-world SV248S dataset confirm that MIENet effectively generalizes to satellite video object tracking tasks. Full article

Federated learning (FL) enables collaborative model training without centralizing raw data, but its application to large-scale vision models remains constrained by high communication cost, data heterogeneity, and privacy risks. Furthermore, in real-world applications such as autonomous driving and healthcare, model updates can inadvertently expose sensitive information even without direct data sharing. This highlights the need for frameworks that balance privacy, efficiency, and accuracy. The current approach to addressing information exposure involves encrypting data by incorporating additional encoding. However, such approaches to encrypting data significantly increase communication costs. In this paper, we propose Federated Share-A Low-Rank Adaptation with Differential Privacy (FedSA-LoRA-DP), a parameter-efficient and privacy-preserving federated learning framework. The framework combines selective aggregation of low-rank parameters with Differential Privacy (DP), ensuring that only lightweight components are shared while formally bounding individual data influence. Since DP simply perturbs the numeric values of existing parameters without altering their dimensionality or structure, it does not increase communication cost. This design allows FedSA-LoRA-DP to provide strong privacy guarantees while maintaining communication efficiency and model accuracy. Experiments on CIFAR-100, MNIST, and SVHN datasets demonstrate that the proposed framework achieves accuracy comparable to non-private counterparts, even under heterogeneous non-independent and identically distributed data and partial client participation. These results demonstrate that integrating differential privacy into low-rank adaptation enables privacy-preserving and communication-efficient federated learning without sacrificing model performance across heterogeneous environments. Full article

Accurate multi-energy load forecasting is essential for the low-carbon, efficient, and resilient operation of park-level Integrated Energy Systems (PIESs), where cooling, heating, and electricity networks interact closely and increasingly incorporate renewable energy resources. However, forecasting in such systems remains challenging due to complex cross-energy coupling, high-dimensional feature interactions, and pronounced nonlinearities under diverse meteorological and operational conditions. To address these challenges, this study develops a novel three-stage hybrid forecasting framework that integrates Recursive Feature Elimination with Cross-Validation (RFECV), a Multi-Task Long Short-Term Memory network (MTL-LSTM), and Random Forest (RF). In the first stage, RFECV performs adaptive and interpretable feature selection, ensuring robust model inputs and capturing meteorological drivers relevant to renewable energy dynamics. The second stage employs MTL-LSTM to jointly learn shared temporal dependencies and intrinsic coupling relationships among multiple energy loads. The final RF-based residual correction enhances local accuracy by capturing nonlinear residual patterns overlooked by deep learning. A real-world case study from an East China PIES verifies the superior predictive performance of the proposed framework, achieving mean absolute percentage errors of 4.65%, 2.79%, and 3.01% for cooling, heating, and electricity loads, respectively—substantially outperforming benchmark models. These results demonstrate that the proposed method offers a reliable, interpretable, and data-driven solution to support refined scheduling, renewable energy integration, and sustainable operational planning in modern multi-energy systems. Full article

Cardiovascular disease remains the world’s leading cause of death globally, and there is continuing interest in adjunct, diet-based strategies that may support vascular health alongside guideline-directed pharmacotherapy. Anthocyanin-rich berries are one such option: they are widely consumed, generally safe, and can provide substantial amounts of polyphenols in habitual diets. This narrative review focuses on two anthocyanin-rich species, blueberries (Vaccinium spp.) and haskap/blue honeysuckle (Lonicera caerulea L.), and examines the extent to which their intake may influence vascular endothelial function and cardiometabolic risk markers. For blueberries, which are typically dominated by malvidin- and delphinidin-based anthocyanins together with flavonols, phenolic acids and stilbenes such as pterostilbene, randomized controlled trials and meta-analyses have reported improvements in flow-mediated dilation, with modest effects on blood pressure and arterial stiffness in at-risk populations. Haskap berries, characterized by high levels of cyanidin-3-O-glucoside (C3G) and enriched in iridoids and vitamin C, have been studied mainly in cell and animal models, with early human data suggesting potential effects on vascular function, blood pressure and physical performance. Across both berries, emerging evidence indicates that vascular actions are mediated largely by gut- and host-derived phenolic metabolites rather than by transient circulating parent anthocyanins. We synthesize current knowledge on the phytochemical composition of blueberries and haskap, on molecular pathways implicated in endothelial protection (including NO/eNOS signaling, NRF2-mediated antioxidant defense, NF-κB-driven inflammation, lipoprotein metabolism and platelet activation), and on clinical outcomes related to vascular and cardiometabolic health. On this basis, we outline a mechanistic hypothesis that combined blueberry–haskap interventions could provide additive or synergistic effects on vascular function. This hypothesis is currently supported primarily by preclinical and indirect clinical evidence and should be regarded as hypothesis-generating, highlighting priorities for future mechanism-aware trials rather than constituting a practice-changing recommendation. Full article

Humanity faces converging crises of climate change, biodiversity loss, inequality, and social fragmentation. These challenges are usually treated as technical or policy problems, yet their persistence suggests deeper causes in the paradigms through which human beings understand themselves and act in the world. Systems thinking highlights that paradigms shape perception, motivation, and institutions, but it does not specify which paradigms best support sustainability. This article develops a conceptual framework to examine how paradigms of human nature have shifted historically and how these shifts influence sustainability outcomes. Using a comparative synthesis of wisdom traditions (Greek, Islamic, Christian, Jewish, Hindu, Confucian, and Daoist) together with modern and late-modern frameworks, the study identifies key differences in how human faculties and values are ordered, and how these differences manifest in ecological and social outcomes. A paradigm–perception–intention–action–impact feedback model is introduced to explain how worldviews propagate into institutions and outcomes, and how inversions contribute to ecological overshoot, inequality, and dislocation. The article contributes a synthesized map of paradigms across traditions, a causal schema linking paradigm shifts to sustainability outcomes, practice-oriented design principles, and a research agenda for testing the framework in sustainability transitions. Re-examining paradigms is argued to be a critical leverage point for durable sustainability. Full article

This study presents the design and implementation of a real-time flight-path tracking control system for a quadrotor unmanned aerial vehicle (UAV) capable of accurately following a mobile ground target under dynamic and uncertain environmental conditions. The proposed framework integrates classical fixed-gain PID regulation executed on Pixhawk with its built-in adaptive mechanisms, namely autotuning, hover-throttle learning, and dynamic harmonic notch filtering, to enhance robustness under communication latency and disturbances. No machine learning PID tuning is used on Pixhawk; adaptive features are estimator based rather than ML based. The proposed system addresses critical challenges in trajectory tracking, including real-time delay compensation between the UAV and rover, external perturbations, and the requirement to maintain stable six-degree-of-freedom (DOF) control of altitude, yaw, pitch, and roll. A dynamic mathematical model, formulated using ordinary differential equations with embedded delay elements, is developed to emulate real-world flight behavior and validate control performance. Experimental evaluation demonstrates robust path-tracking accuracy, attitude stability, and responsiveness across diverse terrains and weather conditions, achieving a mean positional error below one meter and effective resilience against an 8.2 ms communication delay. Overall, this work establishes a scalable, computationally efficient, and high-precision control framework for UAV guidance and cooperative ground-target tracking, with potential applications in autonomous navigation, search-and-rescue operations, infrastructure inspection, and intelligent surveillance. The term “delay-aware” in this work refers to the explicit modeling of the measured 8.2 ms end-to-end delay and the use of Pixhawk’s estimator-based adaptive mechanisms, without any machine learning-based PID tuning. Full article

This article combines computational analysis and historical interpretation to reassess the genre of the Gospel of Mark. Drawing on prototype theory and J. Z. Smith’s comparative method, we model Mark’s linguistic and stylistic profile against a large corpus of ancient Greek texts—including tragedies, biographies, historiographies, novels, and Septuagint (LXX) single-person narratives. Using supervised and unsupervised clustering, the study shows that Mark consistently aligns with the LXX corpus rather than with the Greco-Roman genres traditionally proposed. Even when segmented into smaller textual units (prologues, epilogues, or 1000-word chunks), the Gospel remains anchored in the scriptural prototype of divinely commissioned figures such as Moses, Joseph, or Esther. The results suggest that Mark’s genre is best described as a scriptural narrative of divine agency: a continuation of Israel’s storytelling tradition reimagined within the Greek-speaking world of the first century. Full article