Search Results (7,366)

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

Background: We developed and externally validated a model for estimating the probability of clinical pregnancy after in vitro fertilization and fresh embryo transfer using routinely available laboratory and multimodal ultrasonographic measures. Methods: This two-center retrospective study included 451 women in the development cohort and 229 women in the external validation cohort. Eleven candidate predictors were evaluated using Group LASSO-penalized logistic regression with stratified 10-fold cross-validation. Continuous predictors were assessed for nonlinearity using restricted cubic splines, and the complete model-development procedure was repeated in 1000 bootstrap resamples. Model performance was assessed by discrimination, calibration, overall prediction error, and decision curve analysis. Results: The final model included female age, serum anti-müllerian hormone (AMH), endometrial thickness, endometrial volume, and vascularization flow index (VFI). The apparent area under the receiver operating characteristic curve (AUC) was 0.864 (95% confidence interval [CI], 0.826–0.898) in the development cohort; the optimism-corrected AUC was 0.842. The optimism-corrected calibration intercept, calibration slope, Brier score, and maximum absolute calibration error (Emax) were 0.002, 1.051, 0.163, and 0.045, respectively. In external validation, the AUC was 0.810 (95% CI, 0.750–0.868), the calibration intercept was −0.106, the calibration slope was 0.900, the Brier score was 0.177, and Emax was 0.070. Decision curve analysis indicated net benefit over selected threshold probabilities. Conclusions: The model showed good discrimination and generally satisfactory calibration. It may support individualized counseling before fresh embryo transfer, but prospective validation in larger and more diverse populations is required. Full article

Background/Objectives: Antibiotic residues in aquatic environments reflect interacting physicochemical, climatic, and microbiological processes. This study characterized selected antibiotics in wastewater and surface water associated with the Danube River near Novi Sad, Serbia, and evaluated explainable stacked machine-learning models for concentration prediction. Methods: Thirty-six samples collected during summer and autumn 2024 were analyzed using SPE-HPLC-MS/MS. Artificial neural network, random forest, support vector machine, XGBoost, stacked linear, and stacked random forest (STACK-RF) models were developed using environmental/physicochemical variables or presumptive resistant bacterial taxa. Models were evaluated by fivefold cross-validation, complementary error metrics, Holm-adjusted Diebold–Mariano tests, XGBoost Gain, and SHAP analysis. Results: All target antibiotics were detected at least once. Azithromycin was most prevalent (75.0%), followed by sulfamethoxazole (58.3%), trimethoprim, and ciprofloxacin (52.8% each), while wastewater generally exhibited broader antibiotic profiles and higher concentrations than surface water. Standalone algorithms showed weak-to-moderate performance, whereas STACK-RF achieved the highest numerical accuracy for all environmental/physicochemical models (R² = 0.745–0.913) and the available microbial-taxa models (R² = 0.819–0.945), with consistently lower prediction errors. However, most pairwise differences were not significant after Holm correction. Influential environmental predictors were compound-specific and included COD, BOD₅, pH, turbidity, electrical conductivity, water temperature, and relative humidity. Leading bacterial predictors included Klebsiella pneumoniae, Escherichia coli, Citrobacter freundii, and Aeromonas veronii. Conclusions: The results provide a proof-of-concept for machine-learning-assisted antibiotic prediction. Explainable STACK-RF modeling captured nonlinear, antibiotic-specific associations among residues, water-quality conditions, and microbial indicators. It may complement targeted chemical monitoring and support hypothesis generation, although larger, externally validated datasets are required before broader application. Full article

Retrofit condition monitoring of industrial manipulators should distinguish actual mechanical anomalies from signal changes produced by payload, speed, program phase, and transient motion. This study presents a hybrid detector for a six-axis machine-tending robot using a forearm-mounted inertial measurement unit and an auditable two-stage decision architecture. Engineered descriptors support two branches: a Random Forest estimates similarity to reviewed abnormal patterns, while a PCA representation measures context-compatible geometric novelty. The branch scores are combined via a linear fusion coefficient selected on grouped validation runs. Operating context selects a pre-validated, controlled context-compatible PCA reference and modifies the final decision through a bounded threshold correction; it does not update the nominal model online. An H-of-K persistence rule converts repeated window-level exceedances into event-level maintenance evidence. All data-dependent transformations are fitted after complete physical acquisition runs have been assigned to training, validation, or held-out testing. In the run-grouped archive, the complete adaptive hybrid achieved 97.1 ± 0.8% accuracy and an F1-score of 0.96 ± 0.01 across 18 held-out runs. The resulting framework prioritizes leakage-controlled validation, constrained adaptation, and computationally modest retrofit deployment; further developments will enable online adaptation. Full article

Background: Pressure injuries are common in elderly and immobile patients and are associated with substantial clinical burden and mortality. This study evaluated an exploratory multidimensional prognostic index for in-hospital mortality and wound-related clinical outcomes in patients with advanced pressure injuries receiving negative pressure wound therapy (NPWT). Methods: This retrospective, single-center cohort study included 86 patients with stage III–IV pressure injuries treated with NPWT in a palliative care unit. The Pressure Injury Prognostic Index incorporated age, Nutritional Risk Screening 2002 (NRS-2002), Karnofsky Performance Scale (KPS), and Braden Scale scores. The primary outcome was in-hospital mortality, and the secondary outcome was a pragmatically defined favorable wound-related clinical outcome. Mortality discrimination was evaluated using receiver operating characteristic analysis, direct AUC comparisons, and bootstrap-based assessment of internal optimism. Sensitivity analyses examined alternative NRS-2002 weighting values. Results: Twenty-seven patients (31.4%) died during hospitalization. Each 10-point increase in the index was associated with higher odds of in-hospital mortality (OR 1.71, 95% CI: 1.23–2.37; p = 0.001). The index yielded an AUC of 0.768 (95% CI: 0.659–0.871), with an optimism-corrected bootstrap AUC of 0.765. Its discrimination did not differ significantly from age alone (AUC 0.773; DeLong p = 0.890). The post hoc threshold of 192 yielded 77.8% sensitivity and 72.9% specificity. Mortality was higher among patients with scores ≥192 (56.8% vs. 12.2%), whereas favorable wound-related clinical outcomes were less frequent (45.9% vs. 79.6%). Conclusions: The Pressure Injury Prognostic Index was associated with in-hospital mortality and wound-related clinical outcomes and may provide a multidimensional summary of clinical vulnerability in patients already receiving NPWT. However, it did not demonstrate superior mortality discrimination compared with age alone. The findings remain exploratory, and external validation, formal calibration assessment, and evaluation of clinical utility are required before routine clinical application. Full article

Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive subtype with limited therapeutic targets. We retrospectively evaluated the expression of phosphatase and tensin homolog (PTEN) and transmembrane serine protease 4 (TMPRSS4) by immunohistochemistry in formalin-fixed, paraffin-embedded tumor tissues from 145 patients with histologically confirmed TNBC, and we analyzed associations with clinicopathological features, Fudan immunohistochemistry-based subtypes, and clinical outcomes. Clinicopathological analyses included all 145 cases. Exploratory survival analyses included 59 patients with complete, verifiable follow-up and outcome data (median follow-up, 46 months), during which three deaths and eight progression events occurred. PTEN-retained expression was associated with worse overall survival (OS; log-rank p = 0.030), whereas TMPRSS4-positive expression was associated with worse progression-free survival (PFS; log-rank p = 0.034). TMPRSS4 expression showed a nominal association with Fudan subtype in the unadjusted omnibus analysis (p = 0.025), which was considered exploratory after Benjamini–Hochberg correction (q = 0.379). The PTEN and TMPRSS4 staining categories were not significantly associated. In an exploratory four-group survival analysis, patients with concurrent PTEN-retained and TMPRSS4-positive expression (PTEN⁺/TMPRSS4⁺) showed the lowest OS and PFS estimates, patients with PTEN loss and TMPRSS4 negativity (PTEN⁻/TMPRSS4⁻) showed the highest estimates, and the two single-positive groups showed intermediate estimates. Independent transcript-level validation restricted to TNBC in The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) did not reproduce these protein-level associations. These protein-level findings should therefore be considered exploratory and hypothesis-generating and warrant confirmation in larger, adequately powered cohorts using standardized immunohistochemistry with complete treatment data. Full article

Torque characteristic surfaces in nonlinear hydraulic turbine models are typically derived from model tests. However, model–prototype discrepancies can reduce the accuracy of dynamic simulations of hydropower units. To improve hydraulic turbine modeling accuracy, this study uses measured operational data to perform a multi-stage correction of the torque characteristic surface. The method calibrates the input–output mapping of the original surface through sequential parameter estimation, with parameters fixed after each stage. Polynomial, Gaussian kernel and Sigmoid functions are combined with six port sequences to construct 18 correction schemes, with the parameters at each stage optimized using particle swarm optimization. The results show that correction accuracy and the preferred sequence depend on the function form. For the studied unit, the Gaussian kernel with the “guide-vane opening–unit torque–unit speed” sequence yields the lowest weighted composite error, reducing it by 80.76% relative to the original model. The corrected data in the normal operating region are further used to construct the zero-opening and zero-unit-speed boundaries, which are combined with the runaway-speed boundary to reconstruct the full-operating-range torque characteristic surface using a backpropagation neural network (BPNN). The resulting NRMSE and NMaxAE are 0.54% and 1.58%, respectively. The corrected model is then embedded in the hydropower unit for multi-condition validation under primary frequency regulation. The mean RMSE and MAE of active power decrease by 45.10% and 50.46%, respectively, while the mean accuracy of the response regulation magnitude increases to 99.27%. The prediction error of guide-vane opening is also reduced. These results demonstrate that the proposed method effectively reduces model–prototype discrepancies and improves the accuracy of dynamic prediction under primary frequency regulation. Full article

This study decodes the BDS-3 PPP-B2b binary data broadcast by BDS satellites using a self-developed software decoder, RT-B2b (Real-Time PPP-B2b). The orbit, clock corrections, and differential code biases (DCB)—collectively referred to as State Space Representation (SSR) corrections—are extracted from the decoded messages. Subsequently, precise satellite orbits and clock offsets are recovered in real time by integrating the SSR corrections with the broadcast ephemeris. The characteristics and accuracy of the SSR corrections are first evaluated. The performance of real-time PPP is then assessed through both static and kinematic experimental schemes, with particular emphasis on the kinematic positioning of a marine buoy. The results demonstrate that the developed software decoder can reliably decode the PPP-B2b SSR corrections and derive accurate orbit and clock products. In terms of positioning performance, real-time PPP achieves centimeter-level accuracy of 4–5 cm in static terrestrial scenarios and decimeter-level accuracy of 1–2 dm in the kinematic marine buoy scenario. This study validates the reliability of the PPP-B2b real-time service in marine environments, thereby providing a valuable reference for marine precise positioning and the practical application of high-accuracy positioning information. Full article

Background/Objectives: Parkinson’s disease (PD) is a prevalent neurodegenerative disorder, and its comorbid peripheral neuropathy (PN) severely worsens patient prognosis. The diagnostic gold standard, nerve electrophysiological examination, has critical limitations, including invasiveness. This study aimed to explore the diagnostic value of high-frequency ultrasound combined with shear wave elastography (SWE) for PD-associated PN. Methods: Intra- and inter-observer reproducibility of ultrasound parameters (cross-sectional area [CSA], shear wave velocity [SWV]) of the median and common peroneal nerves was validated in 30 PD patients. A case–control study was conducted in 40 PD patients (stratified into PD-alone and PD-PN groups via the electrophysiological gold standard) and 40 healthy controls. Correlation analysis, logistic regression, and receiver operating characteristic (ROC) curve analysis were performed to assess diagnostic efficacy. Results: All measured parameters showed good reproducibility (intraclass correlation coefficient [ICC] > 0.855). Median nerve longitudinal SWV and common peroneal nerve SWV increased sequentially across the control, PD-alone, and PD-PN groups (after age adjustment using analysis of covariance (ANCOVA), p < 0.001), and showed weak-to-moderate statistically significant correlations with median nerve motor conduction latencies after Bonferroni correction for multiple comparisons. Age and common peroneal nerve SWV were identified as independent risk factors for PD-PN, while median nerve longitudinal SWV showed a positive but non-significant association in the multivariable model. A combined model incorporating age, median nerve longitudinal SWV, and common peroneal nerve SWV achieved an area under the ROC curve (AUC) of 0.938 (sensitivity 93.75%, specificity 91.67%; bootstrap-corrected AUC = 0.906). Conclusions: SWE-derived SWV has favorable diagnostic value for PD-associated PN with good reproducibility. The combination of age and multi-nerve SWV further improves diagnostic efficiency, providing a reliable non-invasive imaging biomarker for early PD-PN diagnosis. However, given the observational design and significant age imbalance between groups, residual confounding cannot be fully excluded, and prospective studies with age-matched cohorts are needed for definitive confirmation. Full article

A point-of-care pathogen-microscopy readout must signal when a sample falls outside what its detector was trained on. PathoSafe is an 816-byte int8-projection low-rank Mahalanobis out-of-distribution (OOD) head reading a frozen detector’s penultimate feature. It reaches a cross-site area under the receiver operating characteristic curve (AUROC) of 0.980 and multi-source AUROC of 0.964 (parasitic-egg-dominated), within 0.01 of a 16,896-byte full-precision dense baseline, and adds a measured 30.76 µs and 816 bytes of read-only memory (ROM) constants on an STM32H743 development board, so abstention adds only a small measured cost on-chip. The advantage is the trunk feature rather than the integer form. Certification is the harder problem and carries our central result: slide clustering silently breaks the standard independent-sample certificate, since distribution-free risk control assumes exchangeable samples that patch-clustered medical data do not supply. On a degraded-input risk certificate, the naive cell-level version holds on only 20% of leakage-free re-splits, whereas the correct slide-level one is valid at 0.266 with its width set by the slide count rather than by any bound we evaluate. The deployed threshold inherits this less severely. A Hoeffding–Bentkus budget gives an illustrative count of 38 independent slides under the stated assumptions (35 when five seeds are used), which is about $1.3\times$ what this benchmark provides. We release that leakage-free slide-disjoint benchmark: an in-distribution malaria task with cross-site (BBBC041) and multi-source (SIPaKMeD, parasitic egg, white blood cells) regimes. Full article

Correct packet delivery does not guarantee correct semantic interpretation when endpoint meanings for the same learned codeword diverge. CoSMA DAI is proposed for mismatch detection, protected confirmation, task preservation and semantic repair. Channel-conditioned global evidence, semantic class local evidence, temporal dynamics, channel context and protected probe evidence are fused by a causal machine-learned mismatch belief. A transmitter-derived task belief preserves the downstream decision while repair is pending and BDIx agents select guarded intentions for probing, fallback and resynchronisation. Evaluation uses 30 held-out drift seeds, 300 matched null streams and 300 degrading channel controls. Six referenced sequential monitors receive the same conditioned payload score. CoSMA DAI obtains 100.00 percent balanced accuracy, precision, recall, F1 score and Matthews correlation coefficient with zero observed matched null false alarms. Its aggregate delay is 5.62 slots, compared with 11.58 slots for the other zero false alarm method. The task-preservation belief maintains 94.73 percent task accuracy through every divergence scenario, above the quantised accuracy ceiling of 0.919 of the semantic path, because it is derived from the unquantised transmitter latent. A task-label-only control confirms that this accuracy is secured by the preservation belief alone, independently of the detector, so task preservation and mismatch detection are decoupled by design and detectors are compared on residual functional semantic outage, outage duration and semantic reconstruction fidelity, which measure the restoration of the semantic representation itself. Without repair, the residual semantic outage is 73.69 percent at 15.97 dB reconstruction fidelity, whereas CoSMA DAI reduces it to 0.73 percent over 6.62 slots at 21.74 dB. Under five declared parity tiers, in which multivariate and supervised baselines receive the identical features, training seeds, protected probe and candidate budget, the protected confirmation stage reduces false repair for every detector to which it is attached. Zero-shot evaluation over 7 unseen mismatch families and 5 unseen link models retains full detection with zero observed false repair in 6 of the 7 families and on every link and identifies receiver-side decoder drift as a condition the present observation model cannot detect. The learned belief is validated at slot level with an area under the receiver operating characteristic curve of 0.99997 and a class overlap of 0.00039, leave-one-mechanism-out and cross-channel retraining are reported, behaviour is characterised down to the practical detection boundary and scaling to 64-dimensional representations with 2048-entry codebooks is demonstrated. The task-belief mechanism is shown to be economical only for small closed-set output spaces and the channel-conditioning tables are shown to reduce to 6 cells without loss. Every comparator is additionally retuned on the same development budget, paired bootstrap intervals and signed-rank tests are reported over the shared streams, auxiliary traffic and radio energy are normalised per correct decision, authentication of the task belief is specified and charged and transfer to MNIST, Fashion-MNIST, CIFAR-10 and CIFAR-100 is demonstrated without retraining, including on a convolutional VQ-VAE representation with a jointly learned 512-entry codebook, where foreground segmentation and localisation are restored to within the quantisation limit while a class decision cannot serve either task. The control traffic share is 23.59 percent, which is 12.62 percent lower than the monitor value. The additional semantic side information increases radio energy to 0.393 mJ per stream and reduces control-adjusted resource efficiency to 6.203 source-equivalent bits per channel use. The results therefore establish reliable detection and semantic repair within the principal comparison, with comparator-specific delay advantages and without claiming task-accuracy, semantic-rate or energy superiority. Full article

To address the problems of uneven illumination distribution, missing edge details, and noise interference in underground coal mine images, a mine image enhancement algorithm based on local perception and multi-domain guided fusion is proposed. First, the algorithm constructs a dynamic nonlinear luminance mapping function based on local luminance features to adaptively adjust the enhancement amplitude. This addresses the issues of overexposure in strong light areas and missing details in dark areas caused by uneven illumination. Second, an adaptive gradient enhancement strategy is introduced to construct a gradient weight matrix. This matrix dynamically allocates enhancement weights according to local luminance differences, thereby suppressing noise and sharpening edges while maintaining luminance balance, achieving the collaborative optimization of luminance and details. Finally, a saturation stretching module based on color drift perception is designed to correct color deviations. Combined with a non-local means (NLM) denoising mechanism in the YUV space, it further improves the overall perceptual quality and color naturalness of the images. Extensive experimental validations were conducted on the public Low-Light(LOL) test set and a self-built coal mine image dataset. Quantitative evaluation results show that the proposed method achieves the best overall performance in both full-reference metrics (e.g., Peak Signal-to-Noise Ratio(PSNR), Structural Similarity Index Measure(SSIM)) and no-reference metrics (e.g., Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE)). Compared with the evaluated methods, it more effectively improves image contrast, preserves structural details, and suppresses noise. Ultimately, this method effectively improves the luminance uniformity, contrast, and edge detail resolution of images in low-light environments, providing a feasible theoretical reference for downstream tasks such as image enhancement and target detection in coal mine intelligent monitoring systems. Full article

Winter food conditions can shape behavioral investment, yet measured food mass and short-term foraging behavior need not covary. We quantified the potentially available food dry mass (FD), foraging-attempt rate (FAR), and 15 min behavioral time allocation of wintering black-necked cranes (Grus nigricollis Przevalski, 1876) in five habitats across three sampling stages in Linzhou County, XiZang. Food was sampled in eight independent plots per stage × habitat combination, and 600 valid focal observations were analyzed under a working-independence assumption. Habitat, sampling stage, and their interaction were associated with both FD and FAR (all nominal p < 0.001 in the fixed-effects models). Unplowed farmland consistently had the highest FD and FAR, whereas riverbank and shallow-water areas generally had the lowest values. Foraging accounted for 59.2–75.3% of observed time. Because different plots and partly different valleys were sampled among stages, these stage-associated contrasts combine temporal and spatial variation. Plot-level FD–FAR relationships varied among habitats and stages, showing that measured dry mass and a 1 min behavioral rate were not interchangeable indicators of food accessibility, foraging success, or energetic payoff. Future work should revisit fixed plots, retain individual–flock–plot–session identifiers, and quantify food identity, burial depth, hydrological conditions, successful ingestion, and detection-corrected habitat use. Full article

Background/Objectives: Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disease, and alterations in cholesterol metabolism may contribute to motor neuron vulnerability. This study investigated CYP7B1 rs121908613 and CYP46A1 rs754203 in relation to ALS susceptibility in a Brazilian case–control study. Methods: The study included 115 patients with ALS and 115 controls. Both variants were genotyped using TaqMan^® allelic discrimination assays. Genetic association analyses were performed under codominant, dominant, recessive, and overdominant inheritance models using Firth penalized logistic regression, with p-values adjusted for multiple testing using the Holm procedure. Sex-stratified analyses and a formal genotype-by-sex interaction test were also performed. Survival and in silico analyses were conducted as complementary exploratory analyses. Results: Genotyping of CYP7B1 rs121908613 revealed no allelic variability. Given the extremely low frequency of this variant in the general population, this finding should be interpreted as a negative result. For CYP46A1 rs754203, the overdominant model showed a nominal association with ALS (OR = 1.94, 95% CI: 1.14–3.34; nominal p = 0.015), but this association did not remain statistically significant after Holm correction (adjusted p = 0.148). In the male subgroup, the overdominant model remained significant after Holm correction (adjusted p = 0.046). However, the genotype-by-sex interaction test was not statistically significant (OR = 2.46, 95% CI: 0.84–7.32; p = 0.101), indicating insufficient evidence to support a sex-specific genetic effect. Hardy–Weinberg equilibrium (HWE) analysis revealed that the ALS group was deviated, therefore associations should be interpreted with caution. (exact p = 0.012). Survival analyses showed no statistically significant differences among CYP46A1 rs754203 genotypes. In silico analyses identified sequence-dependent structural and regulatory predictions that require experimental validation. Conclusions: CYP46A1 rs754203 showed exploratory association signals, including an overdominant effect in the male subgroup after multiple-testing correction. Additionally, the absence of a statistically significant genotype-by-sex interaction did not support sex-dependent evidence. These findings should be considered preliminary and hypothesis-generating and require replication in larger and well-characterized cohorts, together with experimental functional validation. 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