Heavy metals, radionuclides, and rare earth elements in drinking water treatment sludge: Source apportionment and health risk implications.

• Introduces a novel methodological framework that integrates CBS-based multi-agent path finding with probabilistic model checking. • Employs an MDP model to represent multi-agent path execution process, refines model by proposed adjustment solutions, and leverages PRISM for formal verification. • Evaluate the reliability of multi-agent path execution and the robustness of adjustment solutions under stochastic environments. Multi-agent pathfinding and its reliable execution in stochastic environments represent a critical challenge for real-world applications, demanding both the planning of efficient paths and the formal assurance of safe, conflict-free operation. This paper introduces a novel methodology framework to address this dual requirement. To maximize operational efficiency, we introduce a strategy for optimal goal allocation for team collaboration, integrating it with the conflict-based search algorithm to minimize the total move counts required for mission completion. The second component is an integrated verification process grounded in probabilistic model checking. We model the multi-agent path execution process under stochastic uncertainties using a Markov decision process. By leveraging the probabilistic model checker and probabilistic computation tree logic, the framework formally verifies critical safety properties, ensuring conflict-free and deadlock-free path execution. Furthermore, it evaluates the effectiveness of proposed behavioral constraints designed to mitigate stochastic delays, thereby verifying the overall system safety. By fusing multi-agent planning, probabilistic reasoning, and formal logic-based verification, the proposed framework establishes a foundation amenable to natural extension for addressing multi-agent decision-making and uncertainty estimation. Case study results demonstrate that our methodology effectively selects the pathfinding solution with the minimum move count while significantly enhancing overall system safety through these formally verified behavioral constraints.

Can aging effects in Li-ion cells be quantified with electrochemical impedance spectroscopy under varying state of charge and temperature? Applying inferential statistics

Adaptive overlap penalization and probabilistic modeling in hypergraph influence maximization

Explaining urban flood susceptibility under rainfall uncertainty through probabilistic modeling and interpretable machine learning

Floating offshore wind turbines (FOWTs) are increasingly exposed to tropical cyclone (TC) hazards, which impose severe cyclic and transient loads on structural components. The complex marine environment leads to continuous corrosion-fatigue (CF) deterioration, gradually weakening the typhoon resistance and increasing system fragility over time. This study proposes a probabilistic framework for evaluating the reliability and vulnerability of FOWT flange bolted connections under typhoon conditions while accounting for CF-induced degradation. The framework integrates long-term environmental characterization, TC-induced wind-wave field modeling, a probabilistic CF model, and structural dynamic analysis. It is applied to assess the vulnerability and risk of a semi-submersible wind turbine. The results indicate that CF degradation leads to a reduction of flange-connection bending and axial resistances to approximately 20% and 40%, respectively. The system reliability declines rapidly under the combined effects of CF and typhoon loading, with flange connections exhibiting pronounced susceptibility to coupled cyclic and transient extremes. Moreover, the failure-probability distribution progressively shifts toward lower load levels with increasing deterioration, indicating that typhoon-induced stress amplification markedly reduces the structural safety. The cumulative system risk rises monotonically throughout service life, emphasizing the importance of accounting for long-term CF deterioration in resilience-oriented maintenance strategies for FOWTs operating in typhoon-prone regions. • Developed a probabilistic framework for FOWT typhoon–CF vulnerability. • Integrated wind–wave modeling with corrosion–fatigue crack evolution. • Coupled resistance degradation with dynamic typhoon response analysis. • Applied multi-mode reliability and risk assessment for flange connections.

Late Quaternary loess-paleosol sequences in the Armenian highlands represent key terrestrial archives for reconstructing past climate variability. Related proxy data are essential both for understanding the environmental and cultural history of the Caucasus area - a global biodiversity hotspot and archaeological key region - and for benchmarking Earth system models. However, robust quantitative paleoclimate records remain scarce for this climatically and topographically complex area. Here, we present a combined approach integrating (1) stable isotope analysis (δ 18 O, δ 13 C) of land snail shells with transfer functions build on modern calibration datasets and (2) probabilistic climatic niche modeling. For the latter, assemblage-weighted climatic optima are derived from species-specific response curves based on modern species distribution data. Our results reveal predominantly xerophilous faunas associated with colder glacial phases, and mesophilous high-grass to forest-steppe assemblages during interstadial and interglacial intervals. δ 18 O shell was used to reconstruct δ 18 O precipitation signals, which in this study area strongly correlate with temperature. Growing season temperature estimates, based on modern empirical relationships, suggest a mean difference of ∼4.9 °C between glacial minima and interglacial maxima, while precipitation reconstructions from climatic niche modeling suggest a shift from ∼511 mm to ∼770 mm. This study provides the first mollusk-based quantitative reconstructions of Late Quaternary temperature and precipitation in the Caucasus area, demonstrating the potential of integrated mollusk proxies as powerful tools for resolving glacial-interglacial climate dynamics. • Land snail shell isotopes and climatic niche modeling provides new insights into Late Quaternary climatic conditions. • First mollusk-based quantitative reconstructions of temperature and precipitation for the Southern Caucasus. • Palaeoclimate reconstructions indicate glacial-interglacial growing season temperature contrasts of up to ∼5 °C. • PDF-based climatic niche modeling indicates a shift from ∼511 mm during glacial minima to ∼770 mm during interglacial maxima.

Classification of infrared PV faults via diffusion-based data balancing and improved pooling vision transformer

Modern complex socio-technical systems demand systemic risk analysis approaches that can holistically address the interdependencies between human, technological, and organizational components. Traditional models often fall short in capturing the dynamic and emergent nature of these interactions. This study introduces a novel, integrated risk analysis framework grounded in the Safety-II paradigm, which emphasizes understanding how systems succeed under varying conditions rather than focusing solely on failure. The proposed methodology combines the Functional Resonance Analysis Method (FRAM) with Bayesian Networks to overcome FRAM’s qualitative limitations and enable quantitative assessment of performance variability. The framework is further enriched by integrating complementary techniques, including Monte Carlo Simulation and canonical probabilistic models. This holistic toolkit enables a rigorous and scalable approach for modelling uncertainty and systemic variability across complex operational environments. The methodology is demonstrated through a case study of seaport operations, a representative example of a complex socio-technical system. The results show that the integrated Safety-II-informed framework improves the quantification of systemic risk and enhances the capacity to manage complexity and uncertainty in real-world settings.

A probabilistic model of tsunami debris transport using stochastic reflection angle in collision dynamics

The accelerating transition toward sustainable transportation has led to a rapid deployment of Plug-in Hybrid Electric Vehicles (PHEVs), introducing significant operational challenges for active distribution networks, particularly in terms of voltage regulation and network flexibility. High and spatially concentrated charging demand, combined with stochastic vehicle behavior, can substantially reduce voltage headroom and compromise grid integrity if not properly managed. To address these challenges, this paper proposes a novel Active Distribution Network Management (ADNM) framework based on the Voltage Network Flexibility Index (VNFI) for coordinated PHEV charging and discharging. The VNFI is employed as an actionable steering signal to identify voltage-critical buses and time periods, enabling flexibility-aware scheduling decisions under strict network-security constraints. Stochastic PHEV arrival, departure, and energy demand are modeled using probabilistic distributions, and the proposed framework is implemented and validated through a high-fidelity MATLAB–OpenDSS co-simulation on a modified IEEE 33-bus distribution system. Numerical results demonstrate that the VNFI-driven coordination improves the voltage flexibility margin by up to 44.2% and reduces total power losses by 29.4% compared with a conventional TOU-based charging strategy. Moreover, even under 100% PHEV penetration, the maximum voltage deviation remains within 0.055 p.u., confirming the robustness and scalability of the proposed approach. The results highlight the effectiveness of VNFI-based management in transforming PHEVs into flexibility resources for future smart grid operations. • The study examines the operational challenges posed by electric vehicles (EVs) on distribution systems, focusing on voltage control and power losses. • A probabilistic model for aggregating plug-in hybrid electric vehicles (PHEVs) is proposed, based on parameters derived from the National Household Travel Survey (NHTS). • The concept of voltage flexibility is introduced, with the evaluation of a newly proposed index to assess it. • A smart charging/discharging approach is developed for optimal PHEV scheduling within active distribution networks (ADNs), addressing power demand throughout different hours. • A nonlinear optimization method for mixed integers is used to formulate and solve the scheduling problem. • The proposed method is tested on an IEEE 33-bus distribution system, demonstrating its effectiveness and validating the proposed index against previously reported strategies.

Objective. Sparse-view computed tomography (SVCT) reduces radiation exposure, but it inevitably leads to severe streak artifacts, which become even more pronounced in the presence of metallic implants. Most existing methods address SVCT and metal artifact reduction (MAR) separately, making the joint SVCT and MAR (SVMAR) problem suboptimal. While a few methods have been proposed to tackle SVMAR jointly, most are supervised and require large paired datasets that are difficult to obtain in clinical practice. To overcome these limitations, we propose a self-supervised framework that leverages a denoising diffusion probabilistic model and an implicit neural representation (INR) to address the SVMAR problem.Approach. First, an INR is optimized to produce an initial reconstruction using sparse-view measurements by sampling only rays outside the metal trace. This initial estimate is then used to accelerate the reverse diffusion process. During reverse diffusion, we alternately perform: (i) a MAR step, where diffusion priors guide the inpainting of metal-trace regions in the measurements using Poisson blending; and (ii) an SVCT step, where data fidelity is enforced by refining the INR with both diffusion priors and the MAR-corrected sinograms obtained from the MAR step.Main result. Experiments on both simulation and clinical datasets show that the proposed method reconstructs high-quality images without requiring large paired datasets. Quantitative evaluations demonstrated that the proposed method outperformed existing methods including IndudoNet+ on the OOD dataset across 160-, 80-, and 40-view settings, with peak signal-to-noise ratio (PSNR) improving from 36.28 to 45.39 dB, 32.69-44.22 dB, and 31.37-41.90 dB, and structural similarity index measure (SSIM) increasing from 0.955 to 0.988, 0.920-0.983, and 0.906-0.973, respectively.Significance. By leveraging diffusion priors within a self-supervised INR framework, the method provides a practical and generalizable solution for real-world SVMAR scenarios where ground-truth images are unavailable.

Alzheimer's disease (AD) is one of the most prevalent neurodegenerative disorders. Recent statistical surveys and studies indicate that AD is poised to become a major global health burden in the coming decades. Among current strategies, leveraging multimodal imaging data for early diagnosis and classification of AD has emerged as a promising approach. However, most existing methods primarily focus only on improving classification accuracy, while overlooking the importance of extracted imaging features and the underlying relationships among them. In this study, we propose a novel Multimodal-Aware Bi-Objective Competitive Mechanism Estimation of Distribution Algorithm (MABOC-EDA). The proposed MABOC-EDA incorporates both the individual significance of imaging features and their inter-modality interactions during the evolutionary process, two data structures were employed to represent them, which were updated according to a predefined update strategy. These structures were then used to construct a probabilistic model, from which the optimal feature subset was derived. The proposed method achieved classification accuracies and area under the ROC curve of 95.74%/97.25%,87.23%/89.93%, and 86.54%/88.76% for the AD vs. CN, AD vs. MCI, and CN vs. MCI classification tasks, respectively. Moreover, the brain regions that contribute most significantly to classification and those exhibiting strong correlations in the context of AD diagnosis. The correlated brain regions identified by proposed method provide insights that may facilitate the discovery of complementary biomarkers across different imaging modalities.

Accurate tractography mapping of the optic radiations is essential to avoiding post-operative visual field deficits in patients undergoing temporal lobe surgery. This literature review highlights the current landscape of MRI tractography methods that are used to delineate the optic radiations (ORs), with a particular focus on the anterior segment known as Meyer's loop. By synthesizing findings from the past decade of research on probabilistic and deterministic tractography models, this literature review underscores the limitations of traditional DTI-based approaches and the need for methodologically tailored protocols that consider anatomical complexity and tract curvature. We performed a systematic review following PRISMA guidelines to compare DTI-based deterministic and probabilistic tractography methods. Twelve studies were included in this scoping literature review. Low-threshold probabilistic tractography (≤1%) emerged as the most anatomically accurate and reliable method for delineating the ORs because it placed the ML anterior border close to dissection-based ground truth and preserved the curved anterior trajectory that deterministic streamlining tends to truncate. However, this approach has a high computational cost which may make it difficult to use in clinical settings. Emerging deterministic techniques such as anatomically constrained tractography, MAGNETic tractography, and parallel transport tractography demonstrate a significant improvement in mapping the full anterior extent and volume of the ORs compared to current deterministic approaches. These methods offer promising, clinically applicable advancements in OR tractography studies.

Introducing probabilistic models into photonic neural networks, harnessing high-throughput and low-latency performance of photons, holds great promise for Bayesian inference. Photonic spiking neurons with stochasticity are essential to realize probabilistic computing. However, existing probabilistic neurons lack intrinsic stochasticity and rely on external entropy sources, adding architectural complexity that impedes high-density integration. Here, we report the first compact on-chip photonic spiking neuron with inherent stochasticity based on a novel phase-change material SbTe9, featuring an active footprint of only 1.5 μm2. This neuron enables stable and tunable probabilistic firing behaviors, arising from the intrinsic fluctuations of the melting point and temperature of the SbTe9 layer driven by microstructure evolution during non-equilibrium melting and crystallization. Leveraging this stochasticity, the neuron enables the Bayesian inference achieving 98.67% accuracy with uncertainty quantification for breast cell diagnosis, and demonstrates remarkable tolerance to hardware synaptic variations (0.47% reduction, ten times smaller) and input noise (4.28% reduction at 15% noise, over twofold smaller) compared with deterministic neurons. Based on the novel volatile phase-change material, this neuron establishes a transformative pathway toward the development of large-scale, low-complexity and high-performance on-chip photonic neuromorphic computing systems.

ABSTRACT Circadian rest-activity rhythms, or a person’s consistency in their daily activity and rest patterns, can be quantified using various parametric and non-parametric methods. However, these methods are seldom applied to understand symptoms in cancer survivorship. This study examined associations between various rest-activity rhythm parameters and cancer-related fatigue and quality of life. Adult cancer survivors (n = 31) were enrolled in a 12-week randomized clinical trial of individualized nutrition counseling with or without time-restricted eating. Participants wore actigraphy watches continuously for 3–7 days at baseline, 6 weeks, and 12 weeks. Rest-activity parameters were derived using cosinor models, traditional non-parametric methods, and probabilistic hidden Markov models (HMMs). Participants completed the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) questionnaire, including physical, social, emotional, and functional well-being subscales; fatigue; and a quality-of-life total score. Mixed models revealed that stronger rest-activity rhythms were generally associated with lower fatigue and greater well-being in several dimensions. Specifically, intradaily variability and the HMM-derived rhythm index were positively associated with emotional well-being (p = 0.040 and p = 0.037, respectively, medium effect sizes). Earlier daily activity peaks, identified through both parametric and non-parametric methods, were linked to greater emotional well-being (e.g. cosinor peak time and emotional well-being, p = 0.006, large effect size; start hour of the most active 10 hours, p = 0.004, large effect size). There was no effect of group (time-restricted eating vs. control) on any of the rest-activity parameters. Findings suggest that rest-activity rhythm parameters, including those derived from novel HMMs, may serve as useful biomarkers of fatigue and various dimensions of well-being, supporting further research in larger samples and potential clinical application.

The influence of spatially non-uniform reinforcement corrosion on the performance and survivability of flexural reinforced concrete elements is investigated. The distribution of the actual cross-sectional areas of reinforcing bars after corrosion exposure is studied experimentally. The data are obtained using three-dimensional scanning followed by statistical analysis. To quantify local section weakening, a coefficient of corrosion non-uniformity is introduced, and a probabilistic distribution model is proposed, allowing the design minimum cross-section of corroded reinforcement to be determined. It is shown that using the average corrosion level leads to an overestimation of the load-bearing capacity of flexural reinforced concrete elements. Accounting for the localized nature of corrosion changes the degradation path of structural performance and leads to a reduction in survivability. The obtained relationships make it possible to move from evaluating residual strength at a single point in time to an integral assessment of survivability, reflecting the degree to which the structure retains its performance during damage accumulation.

Stroke-related dysphagia is influenced by brain damage location and cognitive impairment, but its mechanisms remain unclear. In this study, we aimed to clarify the mechanisms by which brain damage causes dysphagia and cognitive function in 246 patients with stroke using a probabilistic neural network model. Dysphagia was classified as mild (oral intake with liquid and diet modifications) or severe (unable to take food orally, requiring tube feeding). Atlas-based segmentation applied to brain MRI data delineated 121 anatomically-defined regions, including 116 Automated Anatomical Labeling regions (AAL116), brainstem level 1, and white matter level 4 of the Automated Talairach Atlas Labels (ATAL), and the total score of five cognitive items on the Functional Independence Measure was used to evaluate cognitive function. Classifying dysphagia by severity and evaluating cognitive function resulted in improvements in prediction accuracy and reduced the number of predictor variables. In addition, after adding evaluations of cognitive function in both the severe and mild dysphagia groups, evaluations of the brainstem, which had remained a final predictor variable in the analysis of brain regions only, no longer remained. The results highlight the importance of integrating neurological imaging and cognitive assessment in the diagnosis and rehabilitation of dysphagia after stroke.

MultiSP deciphers tissue structure and multicellular communication from spatial multi-omics data.

This study presents the largest regional-scale stable isotope investigation of human and faunal remains spanning the Bronze and Iron Ages (c. 2400-100 BCE) in the Eastern Adriatic to date. New δ13C and δ15N data from 51 individuals were integrated with previously published results (n = 233) to explore long-term and region-specific dietary trends, with a focus on the emergence of millet as a dietary component. Results reveal an inland-coastal contrast: while coastal populations predominantly consumed C3-based diets, inland groups show consistently higher δ13C values indicative of direct millet intake from the Middle/Late Bronze Age onwards. Statistical analyses confirm strong effects of both period and location on δ13C values, with most inland populations exhibiting more positive signatures particularly during the Middle/Late Bronze Age transition and again in the Iron Age. A probabilistic two-endmember mixing model estimates millet contributions reaching up to ~ 40% of dietary protein in these inland groups, suggesting episodic but significant integration of C4 crops into subsistence systems. In contrast, faunal baselines reflect purely C3 diets, indicating that the C4 signal in humans derives from direct millet consumption. These findings help refine the chronology of millet adoption in the eastern Adriatic, pushing its significant dietary incorporation back to the Middle/Late Bronze Age transition, and highlight fluctuating patterns of C4 plant consumption through the Bronze and Iron Ages, likely shaped by ecological conditions and local adaptive strategies rather than uniform cultural change.