Search Results (11,611)

Objectives: To validate claims-based algorithms for identifying thyroid eye disease (TED) cases and assess whether machine learning can improve case identification in a large, linked real-world dataset. Methods: Using a large, linked database from Komodo Health^® and Academy IRIS^® Registry, we evaluated six rule-based algorithms incorporating Graves’ disease (GD), eye symptoms and signs. The IRIS Registry’s curated data, based on confirmed TED diagnoses from medical notes, served as the reference standard. Additionally, we developed supervised machine learning models using demographic, diagnostic, procedural, and medication data. Feature selection was performed using recursive feature elimination to rank predictive codes and construct a simplified, interpretable model. Cross-validation was used to assess model performance and compare performance with the rule-based algorithms. Results: The rule-based algorithms demonstrated a trade-off between sensitivity and specificity, with some achieving high specificity but limited sensitivity. Algorithm 1 had the highest sensitivity (48.7%) but lower specificity (59.9%) and PPV (75.8%). Algorithms 2–5 demonstrated higher specificity (87.2–93.5%) but lower sensitivity (17.8–27.0%). Algorithm 6 improved sensitivity (33.4%) compared to Algorithms 2–5 while maintaining high specificity (86.8%) and a strong PPV (86.7%). Machine learning models demonstrated similar trade-offs. One model achieved improved specificity (77.2%) with sensitivity of 49.3%, outperforming Algorithm 1 in specificity while matching its sensitivity. Another model maximized specificity (91.7%) and PPV (89.8%) at a reduced sensitivity of 28.5%. These results highlight the flexibility of machine learning models in adjusting performance to address different research objectives. Conclusions: This study evaluated existing rule-based algorithms for identifying TED cases in claims data, revealing trade-offs between sensitivity and specificity. Machine learning models provide additional flexibility, allowing performance to be tailored to specific research use cases. While no single method consistently outperformed others across all metrics, both rule-based and machine learning approaches demonstrated value in improving TED case identification using real-world data sources. Full article

Background: Multi-objective optimization (MOO) is widely used in engineering design and energy systems to represent trade-offs through Pareto fronts. Yet practical deployment requires moving from a non-dominated set to an implementable preferred design, and this decision step is often treated implicitly. Many studies equate decision support with improved Pareto front generation or visualization, while decision-maker preferences are assumed, weakly specified, or not elicited from stakeholders. Methods: A two-phase scoping evidence synthesis with PRISMA-informed reporting was adopted to map the literature and synthesize explicit Pareto-front decision-support mechanisms. Phase 1 produced a broad evidence map of how Pareto-front decision support is framed and clustered studies by primary contribution, while Phase 2 conducted a focused synthesis of explicit Pareto-front decision-support methods using refined searches in Scopus and SpringerLink. Results: Phase 1 mapped 46 studies; only 10 reported an explicit reproducible Pareto front solution-selection mechanism. Phase 2 included 17 studies and identified four method families: post hoc scoring and ranking, compromise aggregation, interactive preference-guided exploration, and preference elicitation and learning. Conclusions: The literature remains dominated by Pareto front generation and exploration rather than reproducible final solution selection; future work should strengthen preference elicitation, transparency, sensitivity analysis, and uncertainty-aware recommendation stability. Full article

Wi-Fi Channel State Information (CSI)-based indoor localization enables high-precision positioning, but its deployment across multiple environments faces two major challenges: privacy concerns from centralizing CSI data, and severe statistical heterogeneity (non-IID) arising from the strong environment-dependency of CSI. This heterogeneity creates a stability–plasticity trade-off in federated learning—maintaining precision in known environments (stability) while adapting to unseen domains (plasticity). To address this trade-off, we propose AdaFed-LDR, which combines server-side Confidence-Weighted Adaptive Aggregation with client-side Layerwise Dynamics Regularization (LDR). The aggregation recalibrates client contributions based on feature covariance changes, while LDR imposes depth-dependent constraints—stronger constraints on shallow layers to preserve environment-agnostic features and weaker constraints on deeper layers to allow environment-specific adaptation. Evaluated across 8 indoor environments using Leave-One-Out Cross-Validation and 5 random seeds, AdaFed-LDR achieved a mean localization error (MLE) of 0.41 cm in known environments, corresponding to an 88.2% reduction compared with FedAvg. In domain generalization to unseen environments, AdaFed-LDR achieved an MLE of $218.2\pm 2.8$ cm, demonstrating an improvement over FedPos ($257.6\pm 14.04$ cm). With one adaptation sample per reference point, MLE improved to 21 cm. Ablation experiments confirmed that combining the two proposed components achieved the highest improvement (83.9%) compared with applying them individually, supporting AdaFed-LDR as a reproducible approach to the stability–plasticity trade-off in federated CSI-based localization. Full article

Background: Artificial intelligence (AI) is transitioning from experimental pilots to core public health functions such as disease surveillance, resource planning, and analysis of social and structural determinants of health. Yet, health data collection and stewardship remain fragmented across the globe; some jurisdictions still rely on paper-based systems, while others operate noninteroperable digital systems that can exacerbate inequities. Treating health data as a global good therefore requires governance that enables innovation while protecting rights, safety, and trust. This study aims to develop a conceptual meso-level capability framework that translates responsible AI principles into organizational practices for public health agencies. Methods: We developed the framework using a targeted narrative synthesis of contemporary governance guidance and documented early implementation experiences, purposively selected to represent major strands of current practice and debate. A structured expert panel consultation (n = 9) was subsequently conducted to assess the face validity and content validity of the proposed framework domains. Results: We propose the Public Health Responsible AI Capability (PH-RAIC) framework, which adapts principles of transparency, accountability, fairness, ethics, and safety to institutional realities faced by public health agencies. PH-RAIC identifies four interdependent capability domains: (1) strategic governance and alignment; (2) data and infrastructure stewardship; (3) participatory design, equity, and public engagement; and (4) lifecycle oversight, learning, and decommissioning. All four domains achieved Content Validity Index (CVI) values ≥ 0.85 in the expert panel consultation. The framework is presented as a conceptual, meso-level model that has undergone preliminary expert validation but requires further empirical testing in real-world agency settings. Conclusions: PH-RAIC links these domains to example practices, diagnostic questions, and illustrative measurement indicators to help agencies navigate efficiency–equity trade-offs and strengthen legitimacy and accountability in AI-enabled public health systems. It offers a validated conceptual basis for future empirical testing and operational readiness tools. Full article

Cancer continues to present a profound challenge due to high mortality and the inherent limitations of conventional treatments, including suboptimal targeting, systemic toxicity, and difficulty in overcoming physiological barriers. Micro/nanorobots (MNRs) offer a promising enhanced precision and efficacy in cancer therapy. This review systematically analyzes recent advancements in MNR applications, establishing a consistent framework that interlinks their diverse material compositions, propulsion strategies, and therapeutic functions. We critically compare various materials (inorganic, organic/polymeric, and biological/hybrid materials), elucidating their respective trade-offs in biocompatibility, biodegradability, and stimulus responsiveness. This paper further examines both internal (chemical and biological) and external (magnetic, light, and ultrasound) propulsion mechanisms, highlighting their strengths in overcoming biological barriers and enabling complex in vivo navigation, while also discussing their inherent limitations in control, fuel dependency, and tissue penetration. We then synthesize the therapeutic capabilities of MNRs across targeted drug delivery, phototherapy, radiotherapy, and immunotherapy, emphasizing common advantages like enhanced tumor specificity and reduced systemic side effects. A forward-looking perspective was also provided on the remaining challenges, particularly focusing on in vivo controllability, long-term biosafety, manufacturing scalability, and the significant hurdles in clinical translation. By offering a more critical and integrated analysis, this review underscores the immense potential of MNRs to revolutionize personalized precision cancer treatment, while candidly addressing the complex obstacles that must be surmounted for their successful clinical adoption. Full article

China’s steel decarbonization is a key sustainability challenge because cleaner production routes must be evaluated not only by their mitigation potential, but also by their implications for industrial continuity, cost affordability, resource security, and transition manageability. This study develops a national-scale soft-linked sustainability assessment framework that translates policy-conditioned macro signals into a multi-period, multi-objective optimization model of steelmaking-route transition from 2025 to 2050. Three policy environments are examined: carbon-control pressure, electricity-cost support for electrified routes, and their combined application. The model evaluates route portfolios by cumulative system cost, emissions, and transition adjustment intensity, linking mitigation with affordability and implementation feasibility. Results show that policy environments do not shift pathways uniformly; instead, they reshape the feasible trade-off frontier and alter which route combinations emerge as plausible compromise solutions. Across scenarios, scrap-based electric arc furnace steelmaking (Scrap-EAF) becomes the central medium-term route, while blast furnace–basic oxygen furnace steelmaking (BF-BOF) contracts but remains residual. Hydrogen-based direct reduced iron–electric arc furnace steelmaking (H₂-DRI-EAF) expands under favorable conditions, but does not become dominant by 2050 under the baseline national-scale parameterization. Overall, this study contributes to sustainability-oriented industrial transition analysis by showing how policy-conditioned environments reshape route feasibility, transition sequencing, affordability–mitigation trade-offs, and the practical manageability of China’s steel-sector decarbonization. Full article

Population aging poses growing social and economic challenges, yet effective psychological interventions targeting ageism remain limited. The present research examined whether future-aged-self perspective taking increases self–other overlap with older adults and promotes prosocial behavioral responses toward them, and whether these effects depend on decision-making context. In Study 1 (N = 160), participants completed a perspective-taking task followed by a Dictator Game. Individuals who imagined their future aged self reported greater self–other overlap with older adults and allocated more resources to older, compared with younger, targets. Study 2 (N = 143) extended this investigation using a Welfare Trade-Off Task that manipulated gain versus loss framing. Participants in the future-aged-self condition again reported higher self–other overlap and stronger intentions to communicate with older adults. They also showed higher welfare trade-off ratios favoring older adults under gain-framed conditions, whereas no significant group difference emerged under loss framing. These findings suggest that future-aged-self perspective taking can enhance young adults’ prosocial responses toward older adults, but that its effectiveness is contingent on situational framing. Temporal-self interventions may be most effective when prosocial action is framed as allocating potential gains rather than accepting explicit personal losses. Full article

Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertainty-calibrated safety-gating wrapper that estimates online failure risk and routes control among policy execution, pause-and-reobserve, and a fallback planner. Using a cleaned and consistently aggregated benchmark pipeline, we evaluate two long-horizon manipulation tasks in NVIDIA Isaac Sim 5.0 under lighting, texture, occlusion, sensor, and combined shifts. Relative to an ungated VLA baseline, calibrated gating improves mean shifted success from 57.5% to 77.2% and reduces aggregate expected calibration error from 0.303 to 0.100. The largest success gains occur under occlusion and combined shift, including improvements from 48.3% to 85.2% on the drawer task and from 59.4% to 87.8% on clutter sort. The results also expose a systems trade-off: an aggressive uncalibrated threshold baseline attains stronger raw success and collision metrics, but requires nearly twice as many interventions per shifted episode (21.6 vs. 11.5). The main contribution is, therefore, an empirical characterization of the reliability–intervention trade-off created by calibrated supervision, not a claim that the calibrated supervisor is universally the best terminal controller. We frame calibrated gating as a better-calibrated, lower-intervention supervisor that materially improves robustness relative to an ungated VLA while revealing the open problem of mapping calibrated risk into efficient intervention policies. Additional threshold-sensitivity, signal-diagnostic, overhead, and residual-failure analyses show that the selected operating point is meaningful but not universal: the calibrated risk threshold captures most shifted failures in retrospective logs, yet residual contacts still arise during pause and fallback states. These findings provide controlled simulation evidence for trustworthy VLA supervision under distribution shift and clarify the reliability–intervention frontier that future embodied-control systems must navigate. Full article

While multilane roundabouts follow geometric design standards, they often overlook motorcycle-dominated traffic behavior. This study evaluates lane-reduction strategies to create safer and more inclusive urban corridors in mixed-traffic conditions, focusing on a case study in Southern Thailand. High-resolution unmanned aerial vehicle (UAV) trajectory data were analyzed using the Macroscopic Fundamental Diagram (MFD), Cell Transmission Model (CTM), and Time-To-Collision (TTC) frameworks under three configurations: full lane availability, partial inner-lane closure, and full inner-lane closure. Results indicate progressive deterioration in performance under restricted-lane conditions. Under full closure, total flow decreased by 31%, and average travel time increased by 43%. The MFD curve shifted toward higher critical densities, indicating earlier congestion onset, while CTM results revealed longer discharge times, queue spillback, and increased merging friction. Conversely, safety outcomes (TTC) improved significantly: extreme rear-end conflicts were reduced by 48%, and severe lane-change conflicts were nearly eliminated (99%). Behavioral evidence suggests that full closure constrains motorcycles to a single circulating path, reducing erratic filtering and promoting more stable interactions. Overall, this study identifies a systemic trade-off between safety and efficiency, highlighting how geometric interventions catalyze behavioral adaptation. The findings highlight how geometric constraints shape collective behavior in motorcycle-dominated roundabouts and demonstrate the value of an integrated UAV-based framework as a vital tool for inclusive urban management, providing the granular data needed to balance safety and mobility in complex traffic landscapes. Full article

Digital Elevation Model (DEM) resolution plays a critical role in hydraulic flood modelling by influencing inundation accuracy, spatial precision and computational efficiency. However, limited studies have simultaneously evaluated both inundation accuracy and computational performance across multiple DEM resolutions in event-based urban flood modelling. This study aims to evaluate the impact of DEM spatial resolution on the performance of HEC-RAS 1D and 2D models in simulating an event-based urban flood that occurred on 3 March 2025. A 1 m LiDAR-derived DEM was resampled to 2 m, 5 m, 8 m, 10 m, 20 m, 25 m, and 30 m resolutions to assess the effects of terrain generalization on hydraulic response. Simulated inundation extents were validated against observed flood areas derived from aerial imagery, and computation time was recorded for each scenario. Results reveal a clear trade-off between spatial accuracy and computational demand. In the 1D simulations, deviation from observed inundation increased from 0.76 ha at 1 m to 2.50 ha at 30 m, while computation time remained relatively stable. The 2D simulations were more sensitive to DEM resolution, with deviation increasing from 0.33 ha to 3.12 ha and longer runtimes at finer resolutions. Among the evaluated scenarios, the 10 m DEM provided the most balanced performance in both 1D and 2D models. For rapid assessment and operational flood management, where computational efficiency and timely decision-making are critical, a 1D modelling approach combined with a 10 × 10 m DEM is recommended as a practical and efficient solution. Full article

Reinforced concrete (RC) slabs with internal voids are increasingly used to improve material efficiency; however, their residual structural performance after fire exposure remains insufficiently understood. This study presents a numerical investigation of RC slabs with different void geometries using a three-dimensional nonlinear Finite Element (FE) model. A sequential thermal–structural approach was adopted, in which fire exposure was simulated through transient thermal analysis, and the resulting spatial distribution of maximum temperatures was used to assign residual material properties to each FE based on its local peak temperature, followed by structural analysis under ambient conditions. A parametric study was conducted on seven slab configurations, including two solid slabs and five voided slabs with spherical, elliptical, ellipsoidal, capsule, and biaxial capsule geometries. To ensure a consistent evaluation, two reference solid slabs were considered: a 230 mm thick slab to enable comparison under identical geometric conditions, and a 160 mm thick slab representing equivalent concrete volume to assess material efficiency. Fire exposure was applied according to the ISO 834 standard fire curve for durations of 30, 60, and 90 min. The results indicate that voided slabs exhibit higher deflections than the solid slab of identical thickness due to reduced stiffness, while achieving comparable performance relative to the solid slab with equivalent concrete volume. These findings highlight the trade-off between structural stiffness and material efficiency under increasing fire exposure time. Full article

Recycled aggregates (RAs) from construction and demolition waste (CDW) provide substantial circular-economy benefits, yet their elevated porosity, adhered mortar, and heterogeneity typically impair the mechanical performance and durability of recycled aggregate concrete (RAC). This PRISMA 2020-compliant systematic review synthesises 2180 records (2015–2026) to evaluate advanced strategies for enhancing RA quality prior to structural use. This paper critically compares removal-based treatments (mechanical, thermal, acid cleaning) with strengthening and densification approaches, including accelerated carbonation, pozzolanic and nano-silica coatings, polymer impregnation, microbial-induced calcium carbonate precipitation (MICP), and modified mixing methods such as triple-stage mixing (TSMA). Evidence shows that while all RA types (including recycled fine aggregate (RFA), recycled coarse aggregate (RCA), and their combination (RFCA)) can slightly reduce compressive strength and 30% replacement serves as a critical threshold, beyond this, strength loss accelerates, particularly in RCA and RFCA mixes. However, accelerated carbonation and TSMA consistently refine the interfacial transition zone, reduce water absorption by 17–30%, and recover 85–94% of natural aggregate concrete strength. Bio-deposition reduces water absorption by 13–21%, while acid/silica fume treatments improve late-age strength but carry environmental trade-offs. This review formulates a practice-oriented implementation framework for structural-grade RAC. Sustainability analyses indicate that carbonated RA can achieve net-positive CO₂ abatement when under low-carbon energy supply. A mechanistic schematic is presented to synthesise treatment-to-pore-structure/durability pathways across the four principal treatment routes, and a quantitative synthesis plot compares water absorption reductions across all treatment types using 13 data points drawn from included studies. A structured treatment comparison evaluates the energy intensity, industrial scalability, CO₂ footprint, and technology readiness level for each strategy. The remaining challenges include a lack of hybrid treatment studies, limited real-scale durability data, and insufficient mechanistic models linking treatment to pore structure evolution. This review recommends harmonised durability-based criteria and updates to standards (e.g., BS 8500, EN 12620) to support the scalable deployment of treated RA. Full article

Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM_2.5 and r = 0.86 for PM₁₀, and low error levels (RMSE ≈ 2.1–2.2 µg/m³). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency < 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems. Full article

Urban water scarcity and climate change pose significant challenges for sustainable development, particularly in rapidly expanding metropolitan areas. In cities like Casablanca, these pressures also threaten the preservation of cultural heritage sites such as traditional public bathhouses (Hammams). This study investigates how Hammams can integrate sustainable water management solutions in alignment with Sustainable Development Goal 11 (SDG 11), focusing on the treatment and reuse of graywater. This study compares three graywater treatment systems, a Membrane Bioreactor (MBR), a Sequencing Batch Reactor (SBR), and a Moving Bed Biofilm Reactor (MBBR), evaluated through literature review and dimensioning calculations, and also integrates an existing treatment plant in Berlin that functions as a real-scale laboratory. The comparison is based on a set of technical, economic, and environmental criteria used for comparative engineering design assessment and evaluation for the selected Hammam water reuse applications. All systems are technically feasible but show distinct trade-offs. The SBR has the lowest energy demand and highest cost savings, the MBBR offers a compact and simple design, and the MBR provides the highest effluent quality at a higher energy cost. Heat recovery provides a significant thermal energy recovery potential but is reported separately from the electrical energy demand of the treatment systems. Full article

Nuclear energy offers a zero-carbon solution to emission challenges, yet nuclear power plant design is constrained by spatial limitations and complex nonlinear parameter interactions. This study develops a hybrid genetic multi-objective optimization framework, NHGA-NSGA-II, by integrating refined NHGA strategies with the NSGA-II technique. Applied to a reactor primary loop system, the framework reveals a fundamental trade-off between system miniaturization (mass/volume) and passive safety (natural circulation and MDNBR). Pareto analysis indicates that Optimization Plan 3 corresponds to the most favorable representative trade-off identified under the present modeling assumptions, optimization settings, and constraint framework, achieving a 20% gain in natural circulation capacity and a 5.9% safety improvement with only a 9.2% cost increase, thereby illustrating a balanced relationship among passive safety, compactness, and economic efficiency within the current scope of the study. The proposed framework offers an effective tool for high-dimensional nonlinear optimization in nuclear engineering. Full article