Search Results (848)

This paper addresses the need for compact, low-cost soil moisture sensors operating at low microwave frequencies that can provide accurate, texture-aware (i.e., sensitive to different soil particle-size distributions such as sand and loam) characterization of soil permittivity and moisture content. Conventional techniques and many existing microwave resonator sensors are constrained by limited penetration depth, relatively large or complex structures, and calibration procedures that do not robustly account for different soil textures and moisture ranges. A dual-port microstrip square split ring resonator (SRR) sensor on Rogers RO3010 (Rmit University, Melbourne, Australia) is designed for operation at 1.3 GHz and analyzed using full-wave 3D electromagnetic simulations. The structure employs a T-shaped feedline and a shunt quarter-wavelength matching section to achieve strong field confinement in the sensing region and effective impedance matching. Soil is modeled as sandy and loamy superstrates over practical agricultural moisture ranges, with their complex permittivities drawn from reference datasets. Empirical calibration models are then developed, including polynomial curve fitting between resonance frequency shift and real permittivity, machine-learning-based calibration using resonance frequency and transmission loss features, and multiple linear regression linking moisture content to both real and imaginary permittivity components. The sensor exhibits a resonance frequency shift of about 115 MHz over 0–30% moisture for sand and 0–40% for loam, with a maximum sensitivity of 3.4%. Calibration models achieve mean absolute error below 1.22%, root mean square error under 1.58%, and coefficients of determination R² > 0.98 for both soil textures. These results demonstrate that a compact 1.3 GHz square SRR sensor with data-driven calibration, i.e., empirical models learned from simulated and measured S-parameters, enables sensitive, reproducible, and texture-aware soil moisture estimation suitable for agricultural and environmental monitoring. Full article

The role of land use/land cover (LULC) dynamics in predisposing slopes to shallow landsliding is widely acknowledged but seldom translated into operational susceptibility modelling. Most data-driven approaches still treat LULC as a static factor, neglecting the legacy effects of recent transitions. This study presents a methodological framework to quantify the influence of multi-temporal LULC changes on shallow landslide initiation and to incorporate this information into susceptibility mapping. The procedure was tested in the Metropolitan City of Messina (formerly known as the Province of Messina), Southern Italy, a representative Mediterranean area repeatedly affected by rainfall-triggered slope failures. Freely available LULC maps from 1990 to 2006 were processed through post-classification change detection to identify dominant land cover trajectories. Preliminary analyses within buffer areas showed higher landslide indices (LI, LAI) and Frequency Ratios for some transition classes, suggesting a potential role of LULC changes. These findings motivated the comparison between a static LULC configuration and a dynamic one incorporating the detected transitions within a Frequency Ratio susceptibility model. The dynamic model did not improve the mean Area Under the Curve (AUC) compared to the static model (0.7774 vs. 0.7742), and the observed reduction in variability across five independent random splits (standard deviation 0.012 vs. 0.064) should be considered preliminary. The proposed workflow, based entirely on open data and GIS-based processing, offers a transparent and reproducible methodology for integrating LULC transitions into dynamic susceptibility maps. The use of higher-resolution input data could potentially reduce the scale mismatch and improve the detection of fine-scale transitions, supporting more effective landslide risk mitigation and evidence-based land planning. Full article

Background/Objective: The EXTRACT registry cohort of 504 patients undergoing transvenous lead extraction (TLE) has been previously reported in terms of periprocedural safety and short-term outcomes. Data on long-term mortality and its predictors in this population remain limited. The study aimed to identify risk factors for overall mortality at three years following TLE. Methods: This is a secondary, long-term follow-up analysis of the previously published EXTRACT registry cohort of 504 consecutive patients who underwent TLE between 2016 and 2022. We conducted a univariate analysis to predict mortality risk following TLE, followed by Cox regression with cross-validation to identify independent predictors of mortality over a 3-year follow-up period; Brier score assessed prediction accuracy for mortality; and decision curve analysis evaluated the clinical usefulness of the model. Results: Mortality data were completed for 487 patients at three-year follow-up. Long-term outcomes of TLE showed that patients with CIED infection (p = 0.0091), higher NYHA class (p < 0.0001), arterial hypertension (p = 0.0068), previous stroke (p = 0.0092), upgraded to condition pacing post-TLE (p = 0.0092), and higher CHA₂DS₂-VA score (p = 0.0026) had markedly higher mortality at three-year follow-up. Increased mortality was independently associated with estimated glomerular filtration rate (eGFR) and the CHA₂DS₂-VA score. Risk increased incrementally with each 1 mL/min/1.73 m² decrease in eGFR (hazard ratio, 0.99; 95% CI, 0.98–0.99), and each 1-point increase in CHA₂DS₂-VA conferred a 12% higher mortality rate (hazard ratio, 1.12; 95% CI, 1.02–1.23). Conclusions: In this prospective registry, 3-year mortality after TLE was driven predominantly by baseline clinical risk rather than procedural factors. Renal dysfunction and a higher CHA₂DS₂-VA score independently predicted death, supporting the incorporation of kidney function and global vascular risk into pre-extraction assessment and post-extraction management. Full article

Reliable estimation of liquid accumulation in gas-well tubing is important for characterizing liquid-loading conditions and supporting engineering assessment. Traditional mechanistic approaches commonly depend on an extensive set of wellbore descriptors and empirical parameters, while also requiring complicated solution procedures. This work addresses these constraints through a data-driven predictive framework that couples ensemble feature selection with ant-colony-optimized support vector regression (ACO-SVR). A majority-voting scheme was applied to 107 production-test records collected from a gas field. The scheme combined linear regression, grey relational analysis, random-forest mean decrease in impurity, the Pearson correlation coefficient, and SHAP attribution, and selected seven dominant factors from 11 candidate variables: casing pressure, tubing pressure, tubing depth, reservoir mid-depth, daily gas production, daily water production, and wellhead temperature. Ant colony optimization subsequently determined the SVR hyperparameters. Evaluation with 32 held-out well samples produced a root-mean-square error of 165.73 m, a mean absolute error of 103.26 m, a coefficient of determination of 0.94, and a mean relative error of 2.11%. Repeated five-fold cross-validation further yielded an average $R^2$ of $0.91\pm 0.04$ and an RMSE of $181.6\pm 24.8$ m, indicating moderate variability across alternative data partitions. Relative to the untuned SVR, ACO-SVR lowered the root-mean-square error and mean absolute error by approximately 27.0% and 31.1%, respectively. Its mean relative error was also 3.66 percentage points below that of the PLATA model. The resulting framework provides accurate prediction of tubing liquid accumulation height from a small sample and offers quantitative information for liquid-loading assessment under the investigated operating conditions. Full article

The rapid development of Industry 4.0 brings significant benefits to intralogistics through increased flexibility, interoperability, and real-time responsiveness. However, the growing complexity of industrial environments creates challenges for logistics process adaptation and the integration of emerging technologies such as voice-driven control of heterogeneous robotic and IoT systems. Open-source solutions offer a promising approach by enabling vendor independence and cost-effective deployment. This paper investigates the use of the OPIL cyber–physical middleware, developed within the Horizon 2020 L4MS initiative, combined with convolutional neural networks for speech denoising and keyword recognition, to enable robust voice-controlled intralogistics in demanding noisy industrial environments. A voice-controlled logistics system was designed, deployed, and empirically evaluated on the OPIL platform. The results confirm the technical feasibility of OPIL as an open and modular alternative to proprietary Industry 4.0 platforms. They further demonstrate the feasibility of integrating AI-based keyword spotting and voice interaction into environments using an open middleware architecture. The study also identifies key limitations, including reduced performance for non-native speakers and mild speech over-suppression at high signal-to-noise ratios. To support reproducibility and external validation, the complete network configurations, training procedure, and deployment data are provided. Full article

Background: Direct oral anticoagulants (DOACs) are widely used among hospitalized patients, yet real-world inpatient bleeding data remains relatively sparse. Most evidence derives from randomized trials enrolling ambulatory, clinically stable patients, or those being treated for a single specific indication such as acute venous thromboembolism (VTE), which may not reflect the risk profile of acutely ill inpatients with multimorbidity, procedural exposures, and fluctuating renal function. This study describes the incidence and types of bleeding events in a real-world inpatient cohort receiving apixaban or rivaroxaban at a single tertiary care center. Methods: We conducted a single-center, retrospective cohort study of 867 hospital encounters involving apixaban (n = 722, 83.3%) or rivaroxaban (n = 145, 16.7%) between 2019 and 2021. The primary outcome was a non-adjudicated, non-standardized composite of any documented bleeding (gastrointestinal bleeding, hematuria, intracranial hemorrhage, vaginal bleeding, or epistaxis) during the index hospitalization; International Society on Thrombosis and Haemostasis (ISTH) major bleeding or clinically relevant non-major (CRNM) bleeding definitions were not applied. Secondary outcomes included individual bleeding subtypes, hemoglobin drop ≥2 g/dL, transfusion requirement, ICU/CCU admission, vasopressor use, and bleeding-related mortality. All comparisons are unadjusted and descriptive. Between-group comparisons used Fisher’s exact test and the Mann–Whitney U test. Results: Bleeding was documented in 13 encounters (1.5% overall; apixaban 1.39%, rivaroxaban 2.07%); given reliance on clinical documentation rather than systematic adjudication, true incidence may be higher. The composite outcome was driven predominantly by gastrointestinal bleeding (n = 10, 76.9% of events). No hemoglobin drop ≥2 g/dL, transfusion, ICU/CCU admission, vasopressor use, or bleeding-related mortality occurred in either group. No statistically significant between-agent difference was observed (p = 0.47); all comparisons are unadjusted and descriptive. Median length of stay (LOS) differed between drug groups (8.6 vs. 6.2 days; p = 0.006), an exploratory finding confounded by patient class imbalance. Conclusions: In this single-center, retrospective cohort, bleeding was documented in 1.5% of encounters; ascertainment limitations mean true incidence may be higher. Non-adjudicated, non-standardized outcome definitions and the small number of events limit comparability with the literature and preclude definitive inference. The absence of a statistically significant between-agent difference should not be interpreted as evidence of equivalence. These findings are hypothesis-generating and should not be used for comparative inference. Prospective, multicenter studies using ISTH-standardized outcomes, indication-level data, and formal adjudication are needed. Full article

Background: Gender disparities have been reported in Emergency Medicine (EM) residency milestone attainment, with some studies showing male residents achieving higher competency-based ratings than female residents despite similar clinical exposure. Prior research suggests subjective faculty evaluation and implicit bias may contribute to these differences. The influence of resident self-assignment of patients on milestone attainment remains unclear, and the potential contribution of differential patient exposure through self-selection has not been directly investigated. Objectives: To determine whether gender differences exist in the types of patients or acuity levels that EM residents self-assign to determine if this could potentially explain the differences seen in EM milestone achievement across genders. Methods: Design and Setting: Retrospective chart review of EM resident patient selection at the Mayo Clinic Arizona Emergency Department from September 2018 through December 2022. Patients: The study was conducted at a single academic emergency department with resident and attending physician staffing. The study was determined to be IRB exempt. Data Collection: Epic electronic medical record data included resident and attending gender, patient demographics, chief complaint, Emergency Severity Index (ESI) triage level, vital signs, method of arrival, and procedures performed. Statistical Analysis: Continuous variables were compared using Student’s t-test or Wilcoxon rank-sum test; categorical variables were compared using chi-square test. Significance was set at p < 0.05. Analyses were performed using SAS version 9.4. Results: A total of 14,279 cases were analyzed. No significant differences were observed between female and male residents in patient chief complaint, age, race, gender, means of arrival, attending gender (p = 0.10872), or total procedures performed (p = 0.23102). Female residents saw patients with higher initial respiratory rates (p = 0.04313) and trended non-significantly toward higher-acuity ESI levels 1–2 (p = 0.07214). Conclusion: When residents self-assign patients in the emergency department, no meaningful gender differences exist in patient selection, procedural exposure, or gender attending assignment. These findings suggest that observed gender disparities in milestone attainment are unlikely driven by clinical exposure or patient selection, supporting that other factors may contribute to these differences. Full article

Background and Objectives: Stenting isolated Medina 0,0,1 ostial side-branch lesions can result in geographic miss or protrusion into the main vessel. Drug-coated balloon (DCB) angioplasty avoids a permanent implant, but lesion-specific outcome data remain limited. We assessed procedural performance, bailout stenting, and 6- and 12-month outcomes after an intended DCB-only strategy. Materials and Methods: In this retrospective, single-center observational cohort study, we screened 170 patients who underwent coronary DCB treatment at a single center between 1 January 2023 and 31 July 2025. Thirty-one patients with de novo isolated Medina 0,0,1 lesions were included. The primary endpoint was 12-month target lesion failure (TLF), defined as cardiovascular death, target-vessel myocardial infarction, or clinically driven target lesion revascularization (TLR). Results: DCB-only strategy success was achieved in 29/31 patients (93.5%), post-DCB angiographic success in 29/31 (93.5%), and study-defined overall procedural success in 31/31 (100%). Final TIMI grade 3 flow after DCB was present in 30/31 patients; no flow-limiting dissection occurred. Two patients required bailout stenting, one because of deterioration to TIMI grade 2 flow and one because of residual stenosis >30% with recoil. Thirty patients were alive with documented follow-up at 12 months; one patient died from a non-cardiovascular cause at month 1. No TLF was recorded within 12 months. One clinically driven TLR for target-lesion restenosis occurred at month 20, outside the prespecified window. Conclusions: In this selected cohort, an intended DCB-only strategy avoided stent implantation in most patients and had a low bailout rate. The findings support procedural feasibility but do not establish comparative efficacy. Larger prospective studies are required. Full article

This study presents an integrated surface diagnostic investigation and a diagnostic-driven restoration of the marble sculpture Real Infante Carlo Tito di Borbone (1775), attributed to Giuseppe Sanmartino and preserved at the Royal Palace of Caserta (Italy). Despite the artistic relevance of the sculptor, scientific investigations of his works remain limited. The aim of this work is to characterize surface coatings, identify alteration processes, and support conservation strategies through a multi-analytical approach. The results reveal that the sculpture is composed of calcitic marble covered by an altered organic surface film. Spectroscopic and chromatographic data indicate the presence of lipidic compounds consistent with natural wax, likely associated with previous conservation treatments. An epoxy-based adhesive was also identified, suggesting past restoration interventions. Microbiological analyses detected fungal and bacterial species potentially involved in surface alteration processes. The integration of analytical data enabled the development of a targeted restoration strategy. Cleaning procedures were defined based on the chemical nature of the surface coatings, while structurally stable previous restorations were preserved. A biocide treatment was applied to mitigate biological colonization. This study highlights the importance of surface-oriented diagnostics in understanding coating materials and guiding conservation interventions, providing a methodological framework applicable to marble artworks. Full article

Purpose: Excessive drill advancement after cortical breakthrough is a potential safety concern in orthopaedic procedures. We developed a data-driven approach to estimate the drill-tip position relative to the far cortex prior to breakthrough using time-series thrust force and spindle torque signals. Methods: Drilling experiments were performed on 268 porcine cortical bone specimens at a constant feed rate of 0.5 mm/s. A long short-term memory network was trained to estimate the drill-tip position from filtered force and torque signals. The reference position was derived from breakthrough timing confirmed by high-speed imaging and the programmed feed rate. Performance was evaluated using mean absolute error within the −2 to +2 mm peri-breakthrough interval. Two post hoc analyses examined whether model performance exceeded an elapsed-time baseline and whether pre-breakthrough force patterns were more consistent when expressed relative to breakthrough position than to drilling onset time. Results: The combined-input LSTM achieved an MAE of 0.20 mm, compared with 0.23 mm for force alone and 0.24 mm for torque alone. Among the representative architectures evaluated, LSTM showed the lowest regression error. A signal-blind time-only baseline yielded an MAE of 0.54 mm. The association between cortical thickness and force-decline onset was weaker when expressed in spatial coordinates relative to breakthrough than when expressed as time from drilling onset (R² = 23% vs. 74%). These findings suggest that force and torque signals contained information associated with proximity to breakthrough beyond that provided by average drilling duration alone. Conclusion: Converting sensor-derived resistance patterns into spatially anchored positional information may support proactive strategies such as controlled deceleration before penetration. The proposed approach represents a step toward exemplifying the emerging concept of surgeon-assisting Physical AI. Full article

A fixed-odds contract on short-horizon price direction has a positive expected value only when its win probability exceeds the break-even rate implied by the payout ratio. A deployable predictor must also produce signals at a sufficiently stable rate. We formulate this setting as selective prediction with a coverage constraint and combine a five-seed gradient-boosting ensemble over a 90-dimensional causal feature panel with daily adaptive quantile thresholds, each estimated from the preceding 14 to 28 days of model scores, with parameters selected on training data alone. Configurations are frozen after three chronological pseudo-out-of-sample folds and evaluated on a held-out period from 1 January to 10 June 2026, and the whole procedure is then repeated on a quarterly re-freezing cadence over seven successive windows. Across BTC and ETH at 5- and 10-min horizons, with a payout of 0.8 and a 55.56% break-even rate, the models execute 10.4 to 11.0 trades per day, and all four selective win rates exceed break-even. Under a dependence-aware block bootstrap, three of four remain significant, and within a 32-test confirmatory family, two survive Holm–Bonferroni correction. Coverage stays inside the operational band in 26 of 28 re-frozen windows. Compared under one execution protocol, a fixed calibration slice drifts out of band while a trailing window does not, and adaptive conformal inference (ACI) matches the proposed rule on coverage when its step size is tuned but not otherwise, whereas an outcome-driven conformal controller reduces coverage by more than an order of magnitude. The expected value is insensitive to exchange fees, which consume under 5% of the measured edge, and sensitive to the payout term. Under matched feature sets, training pools, and coverage, most of the apparent cross-asset difference does not persist. This paper presents a proof of concept for the framework rather than making any claim about cryptocurrency predictability. Full article

We propose a data-driven procedure, based on convolutional variational autoencoders, to identify the presence of signal pulses in long time series. The dataset consists of synthetic waveforms, each composed of non-Gaussian noise and a log-normal-shaped signal of variable intensity, with a length of 10,000 samples. The model heavily compresses the input waveforms, allowing a direct study of such a reduced representation. After training for 150 epochs on 7500 waveforms, a region in the latent space where the network encodes time-series presenting only background noise emerges, allowing, in turn, to tag as candidates for containing a signal those falling outside. When applied to a test dataset of freshly generated waveforms, 100% of events with signal amplitudes well above the baseline noise are correctly labelled, and this fraction only decreases for amplitudes comparable with accidental noise pulses. This approach was designed to fully exploit the measurements in dual-phase Liquid Argon Time Projection Chambers, as the one of the Recoil Directionality experiment, built in the context of the Darkside project. Full article

Background: Early identification of patients who will remain sputum-culture-positive at two months could support risk-adapted tuberculosis (TB) monitoring. We developed a compact baseline score from routine admission data measured before anti-TB therapy. Methods: The cohort comprised 155 adults (62 culture-positive and 93 culture-negative at two months). Random Forest ranking and correlation clustering were used as exploratory selection steps. The complete development pipeline was evaluated by repeated fully nested cross-validation; a secondary conditional analysis held the full-cohort-selected candidate set fixed. Results: The fully nested pipeline yielded a mean test-fold AUC of 0.70 ± 0.09 (fold-level percentile interval 0.53–0.85). Conditional on full-cohort predictor preselection, mean test-fold AUC was 0.74 ± 0.08, and the patient-level mean repeated out-of-fold score AUC was 0.75 (bootstrap 95% CI 0.67–0.83). At provisional thresholds of ≥2, ≥3 and ≥4 points, conditional procedure sensitivity/specificity were 0.90/0.39, 0.80/0.60 and 0.57/0.74, respectively. The conditional repeated out-of-fold Brier score was 0.200, with calibration intercept approximately 0.00 and slope 0.74. Conclusions: TB-CAST is an exploratory score with moderate internal discrimination. The fully nested result is the primary internal estimate for the complete data-driven pipeline; the conditional results and provisional thresholds require prospective external validation before clinical use. Full article

Domain-specific information systems often maintain their data model, rule base, and application infrastructure as separate artifacts, complicating maintenance and pre-deployment verification. This study investigates whether these artifacts can be unified in a verifiable ontology-to-code pipeline without changing the expected classifications. The proposed Model-Driven Architecture uses the Business Application Builder framework and a Web Ontology Language 2 Description Logic ontology to represent domain structure, classification rules, and generation metadata. HermiT verifies consistency, satisfiability, and subsumption under open-world semantics before code generation. The generator produces persistence, business-logic, data-transfer, and presentation layers, while the generated Java application evaluates stored records under closed-world semantics and resolves overlapping categories using ontology-declared priorities. In a Serbian research-evaluation case study, the generated system reproduced the M30 and M33 classifications of an established Jess implementation. An internal secondary experiment generated and executed a prenatal-diagnosis application; all six runtime classifications matched the HermiT entailments and expected outcomes. The public artifact independently reproduces the ontology-level experiments but excludes the proprietary generator and generated source code. The results support the feasibility of ontology-driven generation for static-classification systems, whereas arithmetic risk computation and temporal event processing remain better suited to complementary procedural technologies. No performance superiority is claimed. Full article

Background: Trigeminal neuralgia is a chronic neuropathic pain disorder characterized by severe recurrent facial pain and substantial impairment in quality of life. Although microvascular decompression (MVD) is an established surgical treatment for medically refractory cases, routine clinical data integrating clinical characteristics, previous interventional treatment history, and early postoperative outcomes remain limited. This study aimed to describe the clinical profile and early postoperative outcomes of patients undergoing MVD for trigeminal neuralgia in a single-center retrospective cohort. Methods: This single-center, single-surgeon retrospective cohort study included 62 adult patients who underwent MVD at Pamukkale University Hospitals between 4 September 2019 and 9 July 2026. Demographic and clinical characteristics, previous interventional procedures, trigeminal nerve division involvement, vascular compression status, surgical opening approach, postoperative Barrow Neurological Institute (BNI) pain intensity scores, visual analog scale (VAS) pain scores, wound-related complications, and exploratory perioperative inflammatory markers were retrospectively collected. Favorable postoperative outcome was defined as BNI Grade I–II. Results: The mean age was 51.9 ± 12.8 years, 66.1% were female, and vascular compression was identified in 87.1%. A previous interventional procedure was documented in 35.5%. Favorable BNI outcomes occurred in 74.2% (95% CI, 62.1–83.4%), and complete pain relief (VAS = 0) in 88.7% (95% CI, 78.5–94.4%). Wound-related complications occurred in 9.7%. No evaluated baseline characteristic was significantly associated with either pain outcome. Paired analyses showed no significant perioperative change in CRP (p = 0.353), whereas NLR increased significantly (p = 0.001). Conclusions: MVD was associated with favorable early postoperative pain outcomes in most patients. However, the retrospective design, modest sample size, and symptom-driven longer-term follow-up limit conclusions regarding prognostic determinants and durability of pain relief. Full article