Search Results (5,049)

City-level indicators are difficult to ground across heterogeneous statistical systems in the Global South, where large language model (LLM) agents accelerate multilingual source discovery but risk unsupported values and fabricated execution reports. We present TerriScan, a doctrine-governed multi-agent system built while producing a 142-indicator matrix for ten emerging centralities in eight countries. A versioned charter separates production, model review, deterministic validation and non-delegable human decisions. We evaluate it as a four-month longitudinal design case study with one instrumented four-day period; its lot evidence is stratified, and six lots required substantive interception. During 15–19 July 2026, eight defect classes were registered, each with an identifiable corrective and no recorded intra-class recurrence; exposure denominators were published where countable; and seven earlier qualifying corrections predating the register are reported. The arbiter origin recurred across classes; the exhaustive hash-resolution control remained planned. No unsupported value detected by recorded controls remained in the engraved matrix. The system stopped when work required unrecorded human decisions, but our audit found its absence rule unenforced—181 of 210 absence-state cells named no consulted source. We claim no minimal or universal architecture, but show how incident records, deterministic controls and decision boundaries make urban data production auditable and capable of principled refusal. Full article

Electronic toll collection (ETC) gantries generate transaction records, yet existing anomaly-detection approaches often depend on manual labels or external information, limiting multiview inconsistency ranking under restricted supervision. We propose consistency-aware weakly supervised anomaly sensing for ETC (CAWS-ETC), combining monetary, temporal, structural-pattern, and contextual-semantic evidence and transferring high-confidence references to Light Gradient Boosting Machine (LightGBM). Evaluation used 14,510,847 transactions from 2525 gantries. On joint-transfer benchmarks, CAWS-ETC achieved area under the precision–recall curve (AUPRC) values of 0.9354 for rule-aligned interventions and 0.9018 for rule-orthogonal challenges, versus 0.7609 and 0.7377 for Isolation Forest. A blinded audit of 1200 unmodified transactions by two independent reviewers yielded 777 determinate labels; CAWS-ETC achieved a sampling-weighted AUPRC of 0.7946, while Isolation Forest showed higher ranking point estimates on this temporal-only subset. Post hoc attribution showed that direct rule-created and high-confidence probabilistic labels were identical after the 0.90/0.10 selection, and matched LightGBM models produced essentially identical rankings. Thus, capability beyond direct rule activation arose primarily from discriminative transfer rather than measurable gains from probabilistic aggregation or posterior-confidence weighting. Because all experiments used one day from one provincial network, multi-day, seasonal, and cross-region generalizability remain unverified. Full article

Background/Objectives: Cardiopulmonary bypass (CPB) induces a systemic inflammatory response that may contribute to postoperative organ dysfunction. However, the effects of pulsatile and non-pulsatile CPB on the inflammatory response remain incompletely understood. This study investigated the perioperative dynamics of interleukin (IL)-1β, IL-18, and IL-18 binding protein (IL-18BP), as pre-specified secondary inflammatory biomarker analysis of a prospective, multicentric, randomized trial (ISRCTN11243508), in patients undergoing cardiac surgery with pulsatile or non-pulsatile CPB and evaluated their relationships with routine inflammatory and myocardial injury biomarkers. Methods: In this study 142 patients undergoing elective cardiac surgery were randomized to pulsatile (Group P) or non-pulsatile (Group NP) CPB, and 129 were analyzed (pulsatile, n = 63; non-pulsatile, n = 66). Blood samples were collected before surgery and at five postoperative time points. Plasma concentrations of IL-1β, IL-18, and IL-18BP were measured by ELISA. Leukocyte count, C-reactive protein (CRP), procalcitonin (PCT), high-sensitivity cardiac troponin I (hsTnI), and N-terminal pro-B-type natriuretic peptide (NT-proBNP) were also analyzed. The trial was powered for its primary outcome; for these secondary outcomes the achieved sample size had 80% power to detect between-group ratios of geometric means of approximately 1.27 (IL-18), 1.41 (IL-18BP) and 1.38 (IL-1β). Results: Cardiac surgery with CPB induced a marked postoperative inflammatory response in both groups: leukocyte count, CRP, PCT, IL-18, IL-18BP, hsTnI and NT-proBNP increased significantly after surgery, whereas IL-1β remained essentially unchanged. In the pre-specified confirmatory comparison of postoperative peak concentrations, no biomarker differed significantly between groups. The 95% confidence intervals of this comparison exclude a pulsatile-flow effect on peak IL-18 larger than a 15% reduction or 19% increase, and on peak IL-18BP larger than a 26% reduction or 20% increase. A supporting mixed model for repeated measures showed no differential trajectory over time for any biomarker; its confidence intervals for the average postoperative effect were narrower (approximately ±10% for IL-18 and −9% to +15% for IL-18BP). The only exception was the leukocyte count, which was approximately 10% higher throughout the postoperative period in Group NP, without a differential trajectory shape. Conclusions: This pre-specified secondary analysis of a randomized trial found no evidence that pulsatile CPB reduces the early postoperative inflammatory or myocardial-injury response compared with non-pulsatile CPB for any of the eight biomarkers studied, apart from a modest difference in leukocyte count that did not survive correction for multiple comparisons. The confidence intervals obtained exclude only moderate-to-large between-group differences in IL-18 and IL-18BP; smaller true differences remain compatible with the data and cannot be ruled out at this sample size. These findings should not be interpreted as evidence of biochemical equivalence between pulsatile and non-pulsatile CPB, but they do not support a clinically important early anti-inflammatory advantage of pulsatile perfusion in this population. Full article

High-rise construction requires both geometric setting-out and monitoring of structural movement. This paper presents a simulation-based decision framework for adaptive construction control. The numerical experiment is a synthetic uncertainty-propagation test: a prescribed storey-level deformation profile is treated as the input signal, while 500 Monte Carlo realisations represent repeated coordinate solutions affected by independent horizontal coordinate noise and a campaign-common reference-frame component. The Monte Carlo procedure does not simulate structural mechanics, raw Global Navigation Satellite System (GNSS) observables, satellite geometry or a full GNSS network adjustment; its purpose is to quantify how coordinate-level uncertainty affects threshold exceedance and decision-zone assignment. The same decision logic can be supplied by conventional geodetic techniques, provided that they deliver displacement estimates in a common reference frame; in a future field implementation, periodic GNSS ties could therefore be complemented by total-station and/or optical/laser-plummet observations. A four-zone decision rule combines the observed deformation magnitude with a transfer-risk indicator and is exercised on a synthetic 248 m, 62-storey benchmark geometry. Under the stated stress-test assumptions, the adopted correction model reduces the mean residual deformation on intervention storeys to approximately 1.1–3.3 mm. These values are outputs of the synthetic benchmark, not demonstrated field performance. The study therefore evaluates the internal consistency and uncertainty sensitivity of the decision logic and defines requirements for future observation-level and field validation. Full article

In large-scale plant construction projects, integrating Building Information Modeling (BIM) with construction schedules is essential for detailed planning and advanced practices such as Advanced Work Packaging (AWP), yet a granularity mismatch remains between schedule activities and object-level BIM components. This study proposes a method that combines lightweight axis-aligned bounding box (AABB) geometry with piping-specific semantic rules to derive object-level connectivity for installation grouping and sequencing. The method was implemented as a custom Autodesk Navisworks add-in and evaluated using an industrial process piping system comprising 705 physical objects. Geometric adjacency relationships were refined through semantic false-positive filtering, achieving 99.02% edge-level precision and 98.37% port-count agreement. The validated connectivity was then used to restructure 20 initial semantic partitions into 16 dimensionally feasible installation groups. A candidate installation sequence was subsequently generated by prioritizing equipment-connected main-line piping and maintaining continuity along connected piping routes. The results demonstrate that lightweight geometry combined with piping-specific semantic constraints can reliably support object-level installation planning without requiring predefined installation grouping or sequencing information in the BIM model. The proposed method provides an intermediate planning structure that links detailed BIM objects with broader construction schedule activities and supports subsequent detailed 4D BIM planning. Full article

Neonatal sepsis remains a major cause of morbidity and mortality worldwide, while early diagnosis is challenging due to nonspecific clinical manifestations and the limited accuracy of currently available biomarkers. Serum amyloid A (SAA), a rapidly responding acute-phase protein, has emerged as a potential adjunctive biomarker. This review searched PubMed/MEDLINE, the Cochrane Library, and the Directory of Open Access Journals to identify original studies published between 2000 and 2026 evaluating SAA’s diagnostic value in neonatal sepsis. Study quality was assessed using QUADAS-2. Following screening of 281 records, 13 studies comprising 1827 neonates were included. Due to methodological heterogeneity and limited availability of comparable diagnostic accuracy data, findings were synthesized qualitatively. Reported diagnostic performance was highly variable, with specificity generally higher than sensitivity and area under the receiver operating characteristic curve values ranging from approximately 0.60 to above 0.90. In studies evaluating kinetic patterns, SAA demonstrated earlier rise and more rapid decline compared with C-reactive protein. Relatively high negative predictive values were observed in some studies of preterm infants with late-onset sepsis, suggesting potential utility as a rule-out marker in selected contexts. Despite substantial heterogeneity, SAA appears promising as an adjunctive biomarker, although further standardized prospective studies are required before routine clinical implementation. Full article

Early-stage selection of pile foundation systems is associated with considerable uncertainty due to limited site information and the need to consider multiple geotechnical, structural, and construction-related factors simultaneously. This study proposes a rule-based decision-support framework, named GeoSupport Decision Platform (GSDP), for transparent and systematic evaluation of pile foundation alternatives across varying engineering conditions. A multicriteria scoring procedure is introduced into the proposed framework to evaluate the recommended pile alternative for the application. The implemented procedure combines compatibility scores and weighting coefficients based on engineering judgement and related to soil type, groundwater conditions, frost depth, load level, vibration/noise restrictions, and site accessibility. The ranked pile alternative list, closely linked to the suitability scores, is an outcome of the framework. The applicability of the GSDP framework is illustrated through two case studies based on construction sites in Kokshetau and Astana, Kazakhstan, representing different geotechnical and construction conditions. For the Kokshetau construction site, the GSDP framework recommends driven piles with a suitability score of 0.975 as the most suitable foundation alternative, followed by bored piles and micropiles, with suitability scores of 0.905 and 0.845, respectively. In contrast, for the Astana case, bored/CFA piles are ranked first with a suitability score of 0.970, followed by micropiles (0.825) and driven piles (0.775). In both cases, the highest-ranked pile alternative corresponds to the pile foundation system adopted at the respective construction site. The results show that the framework generates transparent, interpretable recommendations through a weighted scoring procedure and provides brief explanations that align with the engineering solution chosen for the case study. The contrasting rankings, which demonstrate the ability of the framework to respond to site-specific conditions rather than systematically favoring a single pile type, indicate the GSDP’s practical engineering significance for the preliminary selection of pile foundations when site investigation data are limited. Full article

Background/Objectives: Clinical prediction models may support staged triage in acute pelvic pain, but simplified integer scores can lose information and destabilise point assignments. We evaluated a tiered clinical–ultrasound prediction pathway and the stability of its simplified score within an ultrasound-documented acute-pelvic-pain cohort. Methods: This single-centre retrospective cohort included 4800 encounters in 4320 women. Encounters from 2013 to 2019 formed the development cohort (n = 3360), and patient-separated encounters from 2020 to 2022 formed the temporal-evaluation cohort (n = 1440). M1 contained clinical and laboratory predictors without imaging variables, M2 added ultrasound descriptors, and M3 added cross-sectional-imaging descriptors. Multiple imputation, ridge logistic regression, patient-level cluster bootstrap, calibration, decision-curve analysis, reader reproducibility and score-stability analyses were used. Results: Urgent pathology occurred in 20.0% of development and 18.0% of temporal encounters. Temporal AUC was 0.841 for M1 and 0.845 for M2 (difference 0.004; 95% CI 0.001–0.009). Because inclusion required documented ultrasound, this difference estimates incremental information from the selected ultrasound descriptors within an already ultrasound-documented pathway and does not estimate the pathway-level benefit of performing ultrasound. The locked integer rule classified 2.9% as low risk, with 99.6% sensitivity and 3.5% specificity. Categorisation accounted for 0.057 of the total 0.060 AUC loss, and 798 distinct score cards arose in 800 bootstrap resamples. Conclusions: Continuous M2 retained good temporal discrimination, but the small M1-M2 difference does not establish whether ultrasound improves pathway-level triage or can safely alter subsequent management. The integer score had low rule-out yield and highly variable point assignments; independent evaluation should prioritise local calibration, clinical utility and score-card stability before implementation. Full article

The tourism industry faces increasing demand for personalized travel services alongside environmental sustainability requirements. Recent developments in generative artificial intelligence and large language models provide mechanisms for assisting travelers with itinerary planning, although their integration with tourism data services and sustainability criteria remains under investigation. This paper presents the design and technical evaluation of a conversational agent for sustainability-aware tourism planning in a Technology Readiness Level (TRL) 4 experimental environment. The system combines large language model-based interaction with an external flight information service to generate structured itineraries covering transportation, accommodation, and activities. Sustainability considerations include externally supplied flight emissions information and qualitative recommendation rules for other itinerary components. The controlled evaluation examines functional correctness, natural language processing, external service coordination, response time, and the inclusion of sustainability information. The results indicate that the components can be integrated under the evaluated conditions, while also identifying limitations related to heterogeneous data sources, environmental impact estimation, and the absence of real-world user deployment. The findings concern technical feasibility and do not demonstrate behavioral change or reductions in trip-related emissions. Full article

This study investigates the development of an AI-driven document authentication framework for Anti-Money Laundering (AML) and Know Your Customer (KYC) verification environments. Conventional manual inspection and rule-based verification techniques often fail to detect sophisticated forged identity documents containing subtle visual or semantic manipulations. To address this limitation, the proposed framework combines handcrafted forensic feature extraction, OCR-driven semantic analysis, rule-based semantic field extraction and Random Forest classification to identify inconsistencies within identity documents captured under realistic mobile imaging conditions. Experimental evaluation was conducted using selected MIDV-2020 identity document subsets consisting of Albanian identity cards, Latvian passports, and Slovakian identity cards. The proposed framework achieved a recall rate of 92.31% and an overall accuracy of 84.85% on the held-out test set, while maintaining interpretable forensic feature analysis suitable for regulated AML/KYC environments. The results demonstrate that lightweight and explainable machine learning approaches can provide effective forged-document detection without requiring computationally intensive deep learning architectures. Full article

Symmetry and asymmetry appear together in aero-engine prognostics and health management. A monitoring system should preserve a common decision structure across operating scenarios, while sensor evidence becomes asymmetric under changing operating conditions and fault modes. This study formulates the National Aeronautics and Space Administration (NASA) Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) turbofan benchmark as a four-level health-state assessment problem using remaining useful life (RUL) thresholds of 100, 50 and 15 cycles, and proposes a neural evidential reasoning (ER) framework with decision-structure symmetry. FD001–FD004 share the same health-state space, threshold map, reliability-discounted ER operator and final argmax rule, whereas feature selection, evidence transformation and reliability parameters are estimated independently for each subset. Sliding statistical descriptors and training-fold-only minimum-redundancy maximum-relevance (mRMR) selection retain six compact sensor-derived features. FD001 and FD003 therefore use six inputs; FD002 and FD004 additionally retain the same three operating-setting variables and use nine inputs. Boundary soft labels and auxiliary ordinal/RUL supervision exploit ordered degradation information near adjacent-state boundaries. Evaluation uses engine-disjoint validation, three random seeds, identical subset-specific inputs for every comparator, ensemble/neural/ordinal baselines, component ablation and calibration/error-detection analyses. Across the four subsets, the proposed model achieves the highest mean three-metric average (Avg3), 0.664 (standard deviation 0.007), with Accuracy 0.816 (0.006), Macro-F1 0.582 (0.010) and Balanced Accuracy 0.594 (0.005). It is statistically comparable to histogram gradient boosting, random forest and a plain multilayer perceptron, and significantly exceeds the tested compact-input cumulative ordinal, Feature Transformer and correlation-graph attention controls after Holm correction. Fixed-probe diagnostics identify the five-cycle window as the best overall short-history setting. The mRMR top-6 interface reduces the sensor-feature dimension by 97.1% while retaining 98.2% and 97.0% of full-pool Avg3 on FD002 and FD004, respectively. Unknown mass ranks erroneous predictions above correct predictions on every subset (area under the receiver operating characteristic curve (ROC-AUC) 0.749–0.810), including the highest value on the most heterogeneous FD004 subset. These results support a compact and auditable evidence interface that combines competitive classification with source-wise reliability and uncertainty information. Full article

The second-life utilization of retired power batteries is critically constrained by high cell-to-cell variability in capacity and internal resistance, which severely reduces the usable capacity of repacked modules. To overcome this challenge, this paper presents a multi-stage screening and grouping strategy that balances accuracy with practical efficiency. The method comprises four progressive steps: static Euclidean distance-based pre-screening, 0.1C low-rate reference capacity calibration, 0.5C operating-condition re-screening, and serpentine sorting for final grouping. A total of 389 retired 18650 ternary lithium-ion batteries from a single batch were studied. First, 89 cells were pre-screened using voltage–internal resistance Euclidean distance, from which 16 cells were selected for 0.1C calibration to establish a low-rate reference capacity baseline. Subsequently, 52 cells were re-screened from the remaining 300 and tested at a 0.5C rate. Finally, the 52 cells were assembled into 13 groups via serpentine sorting and uniformly calibrated to 50% SOC. A benchmark conversion coefficient β, defined as the ratio of the mean 0.5C capacity to the mean 0.1C capacity, and a comprehensive consistency index (CQI) were established for evaluation. Results show that the mean 0.1C capacity is 2835.2 mAh with β = 0.9681. After serpentine grouping, the capacity range across the 13 groups is only 16.69 mAh, with a coefficient of variation of 0.0407%—significantly outperforming random grouping—and the CQI reaches 0.985. The proposed method reduces the total capacity testing time from approximately 21.9 days to about 2.5 days, improving efficiency by approximately 88%. In contrast to prior work focusing solely on algorithmic improvements, this study, for the first time, integrates static outlier exclusion, small-sample-rate mapping, and serpentine balanced grouping into a closed-loop engineering workflow, providing a deterministic, rule-based solution for the entire screening-to-grouping pipeline in second-life applications. The method requires neither complex instrumentation nor sophisticated algorithms and exhibits strong robustness against common measurement errors, offering an economical, reliable, and easily replicable engineering solution for retired battery second-life utilization. Full article

Identifying distributed photovoltaic generation, battery energy storage, and load-dominant behaviour remains challenging when transformer-area measurements are sparse, operating patterns overlap, and reference labels are incomplete. We develop a knowledge-guided multimodal learning framework that combines raw electrical sequences with temporal, statistical, frequency-domain, and contextual descriptors. A knowledge-guided label library reconciles archived operating records, expert rules, and clustering-based screening, while transfer learning and WGAN-based augmentation are used to improve learning under limited and imbalanced data. The proposed framework jointly encodes raw electrical sequences, engineered descriptors, and contextual information and integrates their complementary representations through attention-based multimodal fusion. On a held-out real-only test set with independently verified labels, the proposed framework achieved 93.8% accuracy and a macro-F1 of 93.8%, while retaining 90.4% accuracy when 30% of the inputs were randomly masked. These results support further evaluation of knowledge-guided multimodal learning for transformer-area resource identification, although broader cross-region and cross-utility validation is required before operational deployment. Full article

Post-well conformance assessment requires identifying the realized locations where an as-drilled trajectory changes between its principal inclination regimes; planned breakpoints are targets, not observations. We formulate this task as sparse event localization using domain-informed multiscale inclination features, planned-trajectory deviations, event-specific LightGBM models, a well-level Drop-presence model, and exact Build–Hold or Build–Hold–Drop decoding. The operational scope is restricted to completed, conventional single-cycle trajectories containing exactly one Build, exactly one Hold, and at most one Drop; build-only, repeated-transition, seven-section, and online trajectories are excluded. We evaluated 63 field wells, including 25 with Drop, by fixed five-fold well-level cross-validation. At the prespecified 60 m tolerance, the detector achieved a macro-F1 of 0.806 (95% confidence interval, 0.737–0.869), versus 0.616 for a derivative rule, 0.567 for change-point dynamic programming, and 0.615 for segmented regression. Its paired advantage over the strongest classical comparator was 0.190 (0.111–0.268). Build, Hold, and Drop F1 values were 0.984, 0.857, and 0.577. Drop-presence ranking was strong (average precision, 0.944), but only 15 of 22 emitted true Drops were localized within 60 m. All seven localization failures were late and spanned 115.9–347.5 m; they were associated with lower ranks of the labeled station score and weaker early inclination decline. On 47 matched wells, planned anchors achieved 0.281, versus 0.809 for the detector; sensitivity analysis over 324 anchor-rule configurations did not close this gap. Exact decoding guaranteed valid event order but produced the same predictions as greedy selection; thus, its demonstrated contribution on this dataset is output validity rather than localization-accuracy improvement. Validation was limited to 63 wells from a single competition-supplied field dataset; external multi-field generalization remains untested. Full article

Coal spontaneous combustion is both a mine-safety hazard and a source of uncontrolled greenhouse gas emissions, since seam fires burn underground for years and force premature panel sealing and resource sterilisation. Early detection is therefore a sustainability problem as well as a safety one, yet the gas ratios used to detect it, above all Graham’s index, are rarely assessed against periods in which no heating develops. Reported detection rates and lead times are consequently uninterpretable, because any alarm rule can look prescient by alarming continuously. We propose a false-alarm-controlled protocol in which every method is reduced to a continuous alarm score, its threshold fixed on validation episodes, and the test and field data reserved for evaluation. On 18 field episodes from three coal mines, a carbon-monoxide threshold of 11 ppm detected all three confirmed fires (95% CI 0.29 to 1.00) with no false alarms among fifteen event-free records and a median lead time of 276 h, while Attention–LSTM models also detected all three but raised false alarms in six and two of the fifteen event-free records, respectively, while Graham’s index detected none of the three. This ordering among the single-channel methods is unchanged across four persistence durations and three false-alarm targets. Achieved false-alarm rates nonetheless departed from the target when thresholds were transferred, indicating that alarm calibration does not carry across monitoring settings. Full article