To evaluate the caries detection performance of a commercially available AI-system (Nostic) and human raters for radiographic caries detection using tooth sections as reference. Radiographs and corresponding tooth sections from extracted teeth (548 approximal and 91 cervical sites) were assessed by three dentists and AI-system (graded/probabilistic outputs). Performance versus reference was quantified using ROC/PR analyses, calibration metrics, and threshold-based measures with bootstrapped confidence intervals. Rater-AI-system agreement/disagreement subsets were analysed. For early dentine lesions (D3), four decision approaches (rater-only, AI-system-only, rater-OR-AI-system, rater-AND-AI-system) were compared using a weighted clinical-loss analysis across varying false-positive penalties. For approximal enamel lesions (D1/2), AI-system was more sensitive than raters (0.60 vs 0.42) with slightly lower specificity (0.80 vs 0.84) and modest discrimination (AUC 0.70 vs 0.64). For approximal dentine lesions (D3/4), discrimination was high (AUC 0.90 AI-system vs 0.86 rater); The AI-system was more sensitive (0.84 vs 0.72) while raters were more specific (0.88 vs 0.94). For cervical dentine lesions (D3/4), both performed well (AI-system: Se/Sp 0.92/0.84; rater: 0.97/0.77; AUC 0.91 vs 0.87). In D3 decision strategies, rater-only prioritised specificity (Se/Sp 0.60/0.85), AI-system-only prioritised sensitivity (0.75/0.77), OR reduced false negatives (0.84/0.73), AND reduced false positives (0.51/0.89), with AND yielding the lowest clinical loss at higher false-positive penalties. The AI-system provides complementary information that becomes clinically relevant when integrated into structured human-AI-system decision rules. Context-dependent use may support minimally invasive caries management. Combining human assessment with AI-system may improve preventive and operative decision-making by balancing false negatives and false positives.

Problem anger after experiencing trauma is a common mental health issue. Shift is a new cognitive-behavioral based app, co-designed with users and experts, that works by prompting users to report their current anger state, and delivering personalized support to manage anger accordingly. The aim of this study is to investigate whether prompting users to engage with Shift modules tailored to their in-the-moment anger state, as compared to no prompt, reduces near-term anger intensity. We also aim to investigate whether this effect is moderated by the anger state, or days in the study. Finally, we aim to evaluate the acceptability of the intervention. A micro-randomized trial will be implemented with 65 adults with problem anger and a history of trauma exposure. For 30days, four times a day, each participant will be prompted to self-report their current anger state and will be micro-randomized to be shown a Shift module tailored to their in-the-moment anger state. Post intervention surveys, and app use and engagement indices will be analyzed to evaluate the acceptability of the intervention. Self-report assessments of anger intensity will be analyzed to optimize decision rules for a just-in-time adaptive intervention (JITAI) to manage problem anger. This will be the first digital trial using micro-randomization to optimize a JITAI for problem anger in a population who have experienced trauma, and one of the few micro-randomized trials to optimize digital mental health tools to manage emotional dysregulation. Australia and New Zealand Clinical Trials Registry ACTRN12624001400550.

Identifying utility maximizers and regret minimizers in zero-energy house adoption by using individual-specific heterogeneous alternative decision rules

We present an extended formulation of our base framework FedT4T-Evo ; a Federated Learning approach that systematically evaluates utility-driven client strategies under resource limitations. To address challenges in distributed learning systems, including resource constraints and non- cooperative behaviors, we model client interactions through the Iterated Prisoner’s Dilemma. Our framework enables clients to adapt decision rules based on prior interactions and resource availability, optimizing both individual utility and contributions to the global optimization target. To further extend the natural perspective, we present a novel evolutionary selection algorithm that simulates ecological dynamics over populations of client strategies, providing a instinctive mechanism for the emergence and persistence of cooperation. Applied to benchmark tasks, our experimental results showed that the framework offers an effective approach for gaining insights into Federated Learning systems through the lens of cooperation theory. • A cooperation-theoretic FL framework is proposed to natively study and foster collaborative client behaviors. • Resource-aware mechanisms are introduced to adapt to heterogeneity and constraints in real-world FL environments. • Evolutionary dynamics are integrated to simulate ecological patterns of cooperation across FL clients.

Spatial flavor metabolomics: probing food flavor distribution with MALDI-MSI technology.

Cost-Effectiveness Analysis of the Messenger RNA Test (Aptima®) Compared With the Polymerase Chain Reaction DNA Test for Human Papillomavirus Screening From the Colombian Perspective.

ABSTRACT Identifying linear distribution patterns in large scale, complex building groups are essential for analysing regional spatial structures and improving spatial governance. This paper presents a new approach that integrates semantic and spatial-scale analysis to address issues, such as incomplete semantic refinement, potential omissions during recognition, and inaccuracies in extraction. We construct a multi-dimensional feature model combining semantic attributes, basic spatial factors (direction and shape), and extended spatial factors (location and spacing) to form a comprehensive, quantifiable feature set. A progressive constrained clustering strategy is developed to adapt dynamically to variations in semantics, morphology, and density across patches, enabling accurate delineation of recognition scope. Automatic recognition is achieved by integrating pattern combination generation with decision rules, based on extracted parameters and clustering results. Experiments in the study area show that the proposed method achieved a recall of 100% under the present evaluation protocol and an accuracy above 91.6%, with strong performance under complex spatial scales and mixed semantic conditions. It helps reveal multi-level (global and local) distribution patterns, shows consistency with human visual interpretation, and supports knowledge discovery. This study offers a valuable reference for geospatial clustering, linear pattern recognition, and map generalization, supporting improved assessment of urban environmental changes.

ABSTRACT This article examines intercultural bargaining dynamics in Chile by analyzing the strategies Indigenous representatives employ within formal state decision-making spaces. Rather than focusing on factors explaining the lack of Indigenous recognition, this study investigates how Indigenous-non-Indigenous interactions operate across different contexts. We analyze three cases: the Constitutional Convention (2021–2022), the National Council of Cultures, Arts, and Heritage, and four Communal Councils. Through interviews with 52 participants and document analysis, we identify four key dimensions shaping these interactions: power asymmetries, institutional frameworks, negotiation strategies, and epistemological disputes. Findings reveal significant variation across institutional levels. At the national level (Constitutional Convention), epistemological and symbolic debates were prominent, the National Council of Cultures showed intermediate levels of symbolic negotiation focused on discussions of interculturality, while local councils exhibited more pragmatic dynamics. Indigenous representatives employed diverse strategies including alliance-building, repetition, pedagogy, social mobilization, adapting tactics to specific institutional contexts and decision-making rules. The study demonstrates that intercultural negotiation patterns are highly sensitive to institutional settings, with national spaces emphasizing recognition debates while local contexts integrate Indigenous issues into daily governance. These findings underscore the importance of understanding how institutional frameworks and decision-making rules shape Indigenous political agency and intercultural relations in contemporary Chile.

Surface anomalies such as dust accumulation and bird droppings on photovoltaic (PV) panels can significantly reduce their energy production and lead to inefficient maintenance decisions. This paper proposes a vision-based deep learning framework for the automatic detection of PV panel surface conditions and validates the detected anomalies using real inverter-level energy production data. Unlike conventional studies focusing solely on detection performance, the proposed approach introduces a unified and physically interpretable framework that directly links image-based anomaly detection with inverter-level energy performance and decision-oriented PV maintenance. An EfficientNetB3-based model is trained using a two-stage transfer learning strategy on a publicly available Kaggle dataset and evaluated using standard classification metrics. The trained model is then deployed and validated at a 1 MW solar power plant located at Karaman, Türkiye. Classification results obtained from field images are systematically linked with inverter-associated hourly energy production measurements. Following panel cleaning and natural rainfall, an approximately 12.5% increase in inverter-level hourly energy production is observed for the analyzed PV group (120 panels, ~270 Wp), corresponding to an increase from 23.2 to 26.1 kWh. In addition, the study introduces an energy–water–sustainability-aware cleaning decision framework tailored for arid and semi-arid regions where water scarcity and deep groundwater extraction present critical constraints. The framework defines a quantitative decision rule in which panel cleaning is performed only when the expected recoverable energy exceeds the energy cost of water extraction and cleaning. Overall, the proposed approach enables accurate surface anomaly detection while supporting sustainability-aware, resource-efficient and data-driven maintenance decisions for PV power plant operation.

The stochastic dominance method has been extensively applied in finance and economics. However, recent research indicates that the traditional integer-degree stochastic dominance methods are too coarse in some cases. To solve this limitation, several researchers have put forward the theory of fractional-degree stochastic dominance. In this paper, we propose a consistent test for the ( 1 + c ) th-degree stochastic dominance based on a quantile decision rule. In addition, we obtain several asymptotic properties of the test statistic. We also develop a valid bootstrap procedure for estimating its critical values and powers. Simulations show that our proposed test has good performance and its type-I error converges to the nominal level when two populations are identical. Finally, we demonstrate the broad applicability of our method through two empirical studies. One is to identify a critical risk aversion threshold for left-tail crash risk in the Chinese equity market. Another is to establish fractional-degree dominance in Chinese household incomes across urban-rural and cross-regional dimensions.

Clinical practice guidelines recommend hepatocellular cancer (HCC) surveillance in patients with cirrhosis from any etiology and those with chronic hepatitis B virus (HBV) infection and additional risk factors. However, HCC incidence varies across groups. Several risk stratification models using clinical factors and/or biomarkers have been derived to facilitate tailored HCC surveillance. Although risk stratification models are used for patients with hepatitis B, few have been sufficiently validated in patients with cirrhosis. Indeed, many unanswered questions related to the development, validation, and impact evaluation of risk stratification models must be addressed before widespread implementation can be recommended. The National Cancer Institute’s Translational Liver Cancer (TLC) Consortium was established to advance research focused on risk stratification and early detection of liver cancer. The TLC convened a multidisciplinary group, including clinicians, scientists, biostatisticians, and technology experts from the United States, Asia, and Europe, to provide a framework for the development, validation, and implementation of risk stratification models. The framework defines 4 phases of risk stratification model development and validation: phase 1 —development and internal validation, phase 2 —decision rule development, phase 3 —external validation, and phase 4 —impact evaluation. The group also defined a set of recommendations to improve the rigor of development and validation of HCC risk stratification strategies. This framework can inform best practices and highlight necessary steps for endorsement by practice guidelines and regulatory agencies, highlighting a path toward implementation in clinical practice.

Escalating insecticide resistance in mosquito vectors threatens the durability of vector-borne disease control and increasingly constrains the effectiveness of core interventions. This resistance is a multilayered adaptive phenotype arising from the combined action of target-site substitutions that reduce insecticide sensitivity, transcriptional and enzymatic upregulation of detoxification systems that enhance xenobiotic metabolism, cuticular and behavioral changes that limit exposure and penetration, and transporter-mediated efflux, with additional modulation by microbiota and local environmental conditions that shape phenotypic expression in the field. Current integrated vector management (IVM) strategies aim to mitigate resistance through operationally guided deployment of dual-active-ingredient or synergist-treated nets, indoor residual spraying with rotations or mixtures, integration of larval source management and habitat modification, and incorporation of nonchemical tools such as Wolbachia releases and genetic control, supported by routine resistance surveillance. However, much of the existing evidence remains fragmented, with an overreliance on a narrow set of insecticide classes and a limited number of genetic markers, variable phenotyping and performance metrics across settings, and insufficient prospective linkage between molecular signals and intervention impact under real transmission ecologies. Multi-omics frameworks provide a route to move beyond single-locus screening toward network-level reconstruction of resistance biology, enabling discovery of predictive biomarkers, pathway signatures, and metabolic readouts that can be translated into actionable diagnostics and locally optimized decision rules. Looking forward, omics-enabled precision surveillance integrated with field-deployable assays, standardized benchmarks, and model-informed adaptive management could support closed-loop resistance mitigation in which operational choices are continuously refined to preserve long-term intervention efficacy within IVM programs.

Supply chains are increasingly multi-layered and interdependent, and this complexity makes coordination across echelons a persistent challenge. Local optimization often backfires: a retailer's attempt to reduce stockouts can trigger larger fluctuations upstream, producing the well-known bullwhip effect. Earlier studies have provided partial responses—reinforcement learning improves local policies, contract theory offers theoretical incentive alignment, and system dynamics clarifies structural causes—but taken separately, these approaches fall short of resolving multi-level coordination under uncertainty. In this study, the authors introduce the Hybrid Multi-Level Coordination Framework (HMLCF), which brings together multi-agent reinforcement learning for adaptive decision-making, contractual mechanisms to align decentralized incentives, system-dynamics modules to capture lead-time pipelines, and interpretable models that distill policies into transparent decision rules.

Do we say what we do? Testing cheap talk to reduce hypothetical bias in moral choice experiments.

The aim of this study is to prospectively compare the diagnostic performance of the PECARN, CATCH, and CHALICE clinical decision rules in pediatric patients presenting with minor head trauma, within a rural secondary-level emergency department characterized by limited resources, high patient volume, and restricted access to specialist physicians. The study was designed as a prospective observational study. The PECARN, CATCH, and CHALICE clinical decision rules were applied to the patients included in the study, and the data were recorded. The data included in the study were statistically analyzed using the SPSS (Statistical Package for Social Sciences; SPSS Inc., Chicago, IL) 27 program. A value of p < 0.05 was considered statistically significant. A total of 1,032 patients were included in the study. The PECARN did not miss any of the 15 patients with pathological brain CT and had 100% sensitivity and 52.1% specificity. CATCH failed to detect pathological brain CT in 1 patient and had 93.3% sensitivity and 53.3% specificity. CHALICE failed to detect pathological brain CT in 2 patients, with 86.7% sensitivity and 87.8% specificity. Overall, PECARN was more successful in detecting pathological brain CT, but it also had a high false-positive brain CT rate. PECARN criteria are more sensitive than other criteria in predicting brain CT pathology, but they lead to a large number of brain CT scans. While the CHALICE criteria are beneficial in reducing unnecessary brain CT scans, they may miss positive cases.

Standard oncology regimens often follow a dose-optimization paradigm: intensify a single agent until efficacy emerges or toxicity intervenes. Yet many targets sit in compensatory tumor-host networks that reroute around pathway pressure, while incremental efficacy may plateau as dose rises and toxicity escalates. Clinical reality is further complicated by heterogeneous patient states, including suppressed antigen presentation (e.g., β2-microglobulin loss or antigen-processing/presentation machinery defects), immature or poorly trafficking dendritic cells, and symptom/toxicity burdens that drive dose delays or reductions and erode relative dose intensity. These factors contribute to late-stage monotherapy failures and leave clinicians with a binary choice-more of the same therapy or a complete stop-when the actionable question is often whether a small, precisely timed change could restore the preconditions under which standard-of-care (SoC) therapy works best. Evidence-steered medicine (ESM) proposes a conservative adjunctive framework that adds only tiny, single-pulse exposures ("micro-adjuncts") without delaying or displacing SoC. Each micro-adjunct is pre-specified with a directional mechanistic hypothesis and read out by short-horizon proxies at 48-96 h (e.g., HLA-ABC and TAP1/2 for antigen presentation; CD86/CCR7 for antigen-presenting-cell activation and trafficking readiness; hs-CRP for systemic inflammatory tone; HRV and symptom scores for host readiness). ESM enforces three safety-first gates: a mechanistic sign-consistency check; a transportability/overlap screen to avoid out-of-support exposure; and a banded decision rule that acts only when the pessimistic bound of the expected proxy effect exceeds a preset threshold under strict exposure caps, otherwise defaulting to NO-STEP. Governance features-drug-drug interaction matrices, order-set blocks, one-click rollback, and degrade-to-rules when calibration drifts-aim to convert combination experimentation into an auditable, risk-minimizing bedside layer. This review synthesizes the rationale for micro-dosed, order-aware adjuncts, summarizes implementable class-level candidates using approved agents and routine assays (with investigational items restricted to protocol settings), and outlines an evaluation plan embedded in care that prioritizes feasibility, safety, and mechanistic informativeness while protecting SoC delivery.

This calculator quantifies the risk of traumatic brain injury in infants who have sustained a head injury. It may assist clinicians in decision-making regarding whether or not to obtain imaging in infants, particularly those less than 12 months of age, where the broader PECARN head injury decision rule may be less applicable.

The inequality-adjusted incremental cost-effectiveness ratio.

One of the primary goals of individualized treatment rule (ITR) methodology is to identify optimal decision rules using clinical predictors. While functional data has become increasingly available in biomedical research, there has been limited work on incorporating functional data into ITR estimation, particularly in observational studies. In this paper, we propose a novel approach that integrates outcome-weighted learning (OWL) with reproducing kernel Hilbert space to determine optimal treatment regimes involving functional data. Furthermore, to address the issue of data piling, we employ the distance-weighted discrimination classifier instead of traditional support vector machines. We establish the theoretical consistency of the decision functional estimator with its risk bound. Extensive simulations and the analysis of the Alzheimer's Disease Neuroimaging Initiative dataset demonstrate the superior performance of our method compared to existing OWL approaches. The results highlight critical factors in Alzheimer's Disease progression and reveal limitations of the original OWL method in this context.

Integrating metagenomics into legume breeding: A breeder-centered roadmap from core microbiomes to precision inoculation.