Search Results (14,167)

Due to the depletion of global crude oil reserves and the unstable socio-political conditions in the regions with the highest production, there is a growing need to reduce oil consumption, more than 50% of which is associated with the transportation sector. One approach to decreasing oil demand is the incorporation of bio-additives into petroleum-derived fuels. However, modifications to fuel composition inevitably alter engine operating characteristics. Consequently, adjustments to the control system settings are required to maintain optimal engine performance. This study investigates the application of an artificial neural network to model the influence of bio-additive concentration on the operating parameters of a spark-ignition engine under varying control system configurations. Measurements were conducted over an engine speed range of 1200–6000 rpm. Key engine operating parameters were identified, relevant control settings requiring correction were selected, and experimental measurements were carried out on an engine test bench. The resulting dataset was evaluated for accuracy and statistical correlation. An artificial neural network model was subsequently designed, trained, and optimized using the dataset. For comparison, alternative models based on polynomial approximation and Partial Least Squares (PLS) were also constructed. The neural model achieved a coefficient of variation in relative root mean square error (CVRMSE) in the Leave-One-Condition-Out (LOCO) procedure of 7.04%, whereas the polynomial and PLS models exhibited errors of 8.52% and 7.39% accordingly. The findings of this study demonstrate the suitability and effectiveness of artificial intelligence methods for addressing the operational problem under investigation. Full article

Background and clinical significance: Gestational deciduosis is a benign, progesterone-related ectopic decidual reaction that commonly involves pelvic and peritoneal surfaces and usually regresses after delivery. Although generally asymptomatic, diffuse peritoneal deciduosis may present as multiple nodular implants and closely mimic peritoneal carcinomatosis, mesothelioma, or miliary tuberculosis, creating a challenging intraoperative differential diagnosis. Prompt histopathological assessment is essential to prevent unnecessary staging procedures or oncological treatment. Case presentation: A 30-year-old primigravida at 40 weeks’ gestation underwent unplanned intrapartum cesarean section after a premature rupture of membranes, the induction of labor, and failed progression. Multiple friable, gray–yellow subcentimetric nodules were incidentally observed on the uterine serosa, adnexa, omentum, visceral intestinal peritoneum, and appendix. The macroscopic appearance raised concern for peritoneal carcinomatosis and abdominal miliary tuberculosis. No ascites or intra-abdominal masses were identified. CA-125 was elevated (207.7 U/mL), Quantiferon testing was indeterminate, and postoperative chest computed tomography showed no pulmonary disease. Histological examination showed ectopic decidual cells with abundant eosinophilic cytoplasm in a perivascular and septal distribution. The cells showed diffuse vimentin, CD10, and progesterone-receptor expression and lacked epithelial and mesothelial marker expression, supporting diffuse peritoneal deciduosis. The postoperative course was uncomplicated, and pelvic ultrasonography at 40 days postpartum showed no residual lesions. Conclusions: Diffuse peritoneal deciduosis should be considered when disseminated peritoneal nodules are encountered during pregnancy or cesarean delivery. In this setting, elevated CA-125 is nonspecific and may heighten concern for tuberculosis or malignancy without distinguishing among these entities. Correlation of the clinical context, operative findings, morphology, and targeted immunohistochemistry is essential to avoid unnecessary oncological treatment or extensive staging procedures. Full article

The soil-biosphere nexus is a critical component embedded in the concepts of soil security and ecosystem services (ES) and is directly linked to several global challenges identified by the United Nations (UN). Although soil security has been proposed as a policy framework, a major problem is that there are no standard procedures to assess the five dimensions of soil security: capability, condition, capital, connectivity, and codification in relation to the soil-biosphere nexus. This study proposes a land cover change matrix, disaggregated by soil type (“biophysical soil security matrix”), as a tool to evaluate the biophysical soil security continuum and its temporary changes, integrated with ES valuation. The matrix was tested using the state of Minnesota (MN) as a case study. Although the dominant soil orders in MN possess high natural capability, widespread human-caused land degradation has dramatically lowered their actual biophysical condition and has fueled massive soil decarbonization. Historic land degradation in MN due to human activity totaled 98,516 km² through 2024, with nearly 480 km² of anthropogenically degraded land created between 2001 and 2024. Based on carbon emissions alone, we estimate that historic land degradation in MN has resulted in total social costs of nearly $50B (U.S. dollars, B = billion = 10⁹) through 2024, with about 10% of this total social cost being realized between 2001 and 2024. Trends like this threaten soil security by directly eroding the soil’s capacity to sustain ES. Based on the analyses and results, this study recommends conducting both soil-centric and human-centric analyses of soil security to ensure the sustainable use of soil. 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

Background/Objectives: Burn patients are particularly vulnerable to microbial colonization and healthcare-associated microorganisms due to extensive skin barrier disruption, prolonged hospitalization, immune imbalance, and frequent invasive procedures. This study aimed to characterize the epidemiological profile and temporal trends of clinically relevant microorganisms isolated from patients admitted to a burn unit over an 11-year period. Methods: This retrospective observational study analyzed microbiological records from burn patients at a Brazilian tertiary hospital (2014–2024), including only the first isolate per patient, clinical episode, and specimen type. Microorganisms were categorized by taxon, group, and specimen sources. Temporal trends were evaluated using regression-based count models and nonparametric tests, while dominant taxa and distribution shifts were identified via epidemiological ranking, Pareto distribution, and heatmap analyses. Results: A total of 6769 clinically relevant microbial isolates were analyzed. Gram-negative bacteria predominated (60.6%), followed by Gram-positive bacteria (28.9%) and fungi (10.5%). The most frequently identified taxa were Acinetobacter spp. (17.3%), coagulase-negative staphylococci (13.7%), and Pseudomonas spp. (12.6%). Eight taxa accounted for 79.5% of all isolates, demonstrating a highly concentrated microbiological profile. Temporal analyses revealed a significant increase in Gram-positive bacterial isolates and a decline in fungal isolates throughout the study period. The distribution of microorganisms varied significantly according to clinical specimen type. Conclusions: The burn unit microbiological profile was dominated by Gram-negative pathogens and exhibited important temporal shifts over the 11-year period. These findings highlight the value of continuous local surveillance to inform infection prevention measures, and support institution-specific antimicrobial stewardship programs. Full article

White tea is rich in polyphenols with antioxidant potential, but information on Zheng’an white tea remains limited. This study optimized water-based ultrasound-assisted extraction (UAE) using single-factor experiments and a Box–Behnken design and evaluated the antioxidant activity of the resulting extract. Tea-leaf microstructure, polyphenolic composition, and antioxidant capacity were assessed using scanning electron microscopy (SEM), liquid chromatography–mass spectrometry (LC–MS), in vitro radical-scavenging assays, and a Caenorhabditis elegans model, respectively. The optimized conditions within the investigated experimental domain were a solid-to-liquid ratio of 1:50 (g/mL), an ultrasonic time of 50 min, and an ultrasonic power of 118 W, yielding a total polyphenol content of 37.93 ± 0.17 mg GAE/g DW. SEM revealed marked disruption of tea-leaf cellular structures after UAE. LC–MS enabled the putative annotation of 25 representative polyphenols spanning phenolic acids, flavan-3-ols, procyanidins, flavonols and their glycosides, and related derivatives. At 1.5 mg/mL, the extract exhibited ABTS and DPPH radical-scavenging rates of 81.68 ± 3.23% and 85.43 ± 2.31%, respectively. In C. elegans, 100 μg/mL extract increased catalase activity by 86.03% and decreased malondialdehyde content by 46.40% (p < 0.01). These findings show that the optimized aqueous UAE procedure recovered a polyphenol-rich extract with antioxidant activity in the tested chemical assays and C. elegans model. Full article

Bleeding in Gaucher disease (GD) is not fully explained by thrombocytopenia and coagulation disorders. Previous studies have shown impaired agonist-induced cluster of differentiation (CD) 62P (CD62P/P-selectin) responses, but their persistence over time is unknown. This retrospective longitudinal observational study characterized platelet activation and secretion responses over time and factors associated with persistent abnormalities. Whole-blood flow cytometry studies from 333 patients with GD with at least two assessments were analyzed. Platelet activation complex-1 (PAC1), CD62P, and CD63 responses were categorized longitudinally. Patients contributed 949 visits over a median follow-up of 2.3 years. Persistent CD62P abnormality was most frequent (92/333, 27.6%), compared with PAC1 (45/329, 13.7%) and CD63 (9/328, 2.7%). PAC1 abnormalities were more often dynamic, whereas CD62P abnormalities were frequent and persistent, most often involving thrombin receptor-activating peptide 6 (TRAP-6) and cross-linked collagen-related peptide (CRP-XL). Lower platelet count was independently associated with persistent CD62P abnormality, although approximately half of affected patients had platelet counts ≥150 × 10⁹/L. Treatment throughout follow-up was associated with lower odds of persistent CD62P and CD63 abnormalities. Impaired CD62P expression was the predominant persistent abnormality, consistent with preferential impairment of α-granule secretion. Platelet function changed over time, supporting reassessment in patients with bleeding manifestations and before procedures when previous testing is remote or clinical status has changed. Full article

Advanced multispectral and hyperspectral remote sensing supports mineral and alteration mapping, but its imagery, specialist expertise and validation requirements may remain inaccessible to artisanal and small-scale mining operators during first-pass evaluation. This study evaluates how much defensible geological reconnaissance information can be extracted from rendered Google Earth imagery without interpreting display color as mineralogical evidence. Google Earth Pro RGB exports displaying Airbus Pléiades imagery were processed using a reproducible workflow combining preprocessing, RGB–HSV transformation, color-class delineation, spatial-pattern assessment and lineament analysis. Image-derived classes were treated as color-defined surface indicators and evaluated against documented geological and structural evidence. Two Chilean IOCG-related reference cases represented contrasting geometries: Farellon displayed narrow, structurally aligned patterns, whereas El Morado displayed a broader, discontinuous corridor-scale distribution. Two underexplored Chilean targets displayed corresponding linear and patchy patterns. A documented skarn occurrence served as a contrasting mineral-system case. Kabul Koh and Ziarat Malik Karkam in Balochistan’s porphyry Cu–Au-prospective Chagai magmatic belt were used to test cross-regional and cross-mineral-system applicability. Across the cases, the workflow delineated contrasting surface-pattern geometries and their spatial relationships with interpreted structures without inferring mineral identity or deposit type from the RGB–HSV classes alone. The method therefore provides a low-cost, constraint-conditioned reconnaissance and target-prioritization procedure for arid copper-prospective terrains, supporting preliminary reassessment of existing or abandoned artisanal workings. Full article

We propose a lightweight cone-based Region of Interest (ROI) filtering method for camera–LiDAR fusion on distributed edge systems. Multiple Neural Processing Unit (NPU) nodes perform camera inference, and a central edge board combines their detections with LiDAR point clouds. The relative rotation is obtained from IMU quaternions under a common attitude reference and aligned sensor axes, while camera Field of View (FOV) parameters define the viewing rays. The method avoids a separate offline extrinsic-rotation estimation procedure, but it requires initial alignment, a measured translation vector, and timestamp-based synchronization. Because the rotation follows from the attitude streams rather than from a per-pair calibration session, a camera node can be added or re-aimed without a new calibration session, which lowers the setup cost of extending the system to further viewpoints. A cone membership test replaces four plane-normal dot products with a forward sign test and a squared angular cosine comparison that reuse the same axis–point dot product; on the same hardware, the mean per-camera ROI-filtering and clustering latency decreases from 6.02 to 4.46 ms, a 25.9% reduction. An adaptive threshold tightens the ROI boundary using the angular separation between neighboring detections. Across six overlap events in a parking scenario, pair-level separation succeeds in 2/6 cases (33.3%) with Pyramid and 5/6 cases (83.3%) with Cone+Adp. These preliminary results indicate improved ROI point selection for the tested configurations, rather than a general increase in intrinsic spatial separation capability. Full article

Accurate identification of constitutive parameters is essential for reliable finite element simulations of sheet metal forming processes. Conventional inverse identification techniques, such as Finite Element Model Updating (FEMU), generally require iterative optimization procedures involving numerous finite element simulations, resulting in high computational cost. This work proposes a machine learning framework based on a bidirectional Long Short-Term Memory (Bi-LSTM) neural network for the identification of combined isotropic–kinematic hardening parameters from a custom-designed reverse bending test. A synthetic dataset comprising 5000 finite element simulations was generated by systematically varying the parameters of the Swift isotropic hardening law and the Armstrong–Frederick kinematic hardening law using Sobol sampling. The resulting force–displacement histories were used to train the Bi-LSTM network to predict five constitutive parameters (K, ${\sigma }_0$, n, C, and $\gamma$) directly from force–displacement responses. To improve the physical admissibility of the predictions, a physically constrained loss function was introduced by penalizing negative values of the kinematic hardening parameters during training. The proposed model achieved coefficients of determination, $R^2$ above 0.98 for the isotropic and kinematic hardening parameters while reducing the number of physically inadmissible predictions from approximately 30 to a single case. Experimental validation was performed on DP500 and DP780 advanced high-strength steels, where finite element simulations using the Bi-LSTM predicted parameters reproduced the experimentally measured springback angles with good agreement. The proposed methodology provides near-instantaneous constitutive parameter identification after training, eliminating the need for computationally expensive iterative optimization while maintaining high predictive accuracy and physical consistency. Full article

This study examines how accounting professionals’ self-reported forensic-auditing capabilities relate to their attitudinal readiness to recognize suspicious anti-money-laundering (AML) indicators, using a transparent hybrid design that combines classical psychometrics, structural equation modeling (SEM), and leakage-controlled explainable machine learning (ML). A cross-sectional survey collected 700 valid responses from public accountants in Lima, Peru, with a 30-item, five-point Likert instrument (19 forensic-auditing items in three dimensions and 11 AML-recognition items). Reliability was high (Cronbach’s α = 0.935; McDonald’s ω = 0.935), but average variance extracted was below 0.50 in every block (0.329–0.449), and Fornell–Larcker testing showed that skills-and-knowledge and AML recognition were not discriminantly distinct (r = 0.673 > √AVE = 0.651/0.649). Responses showed a pronounced ceiling (51% of answers were the maximum), and 86 respondents (12.3%) answered all 30 items identically; removing them lowered the forensic-auditing–AML association from r = 0.731 to 0.630 and explained variance from 53.8% to 40.3%. Skills-and-knowledge remained the strongest predictor in SEM and HC3-robust regression (β = 0.478 and 0.425). Under a leakage-free protocol, ensemble models reached ROC-AUC ≈ 0.86 on held-out data, but threshold tuning did not improve F1 test, and item-level attributions were unstable (Spearman ρ = 0.28). Forensic-auditing capabilities are positively associated with declared AML-recognition readiness, driven by applied skills and knowledge; the evidence is attitudinal and correlational, and should not be read as real detection capability. Because professional experience, seniority, sector, and prior AML training were not measured, the reported associations may be partly confounded by unobserved professional background, and the dominance of skills-and-knowledge is therefore advanced as tentative, pending resolution of the skills-and-knowledge/AML-recognition discriminant-validity overlap. A second, procedural contribution is that the study reports the data-quality screening, the failed validity tests, and the explanation-stability diagnostics that survey-based forensic-accounting research rarely makes visible. Full article

This study aims to contribute to Sustainable Development Goal 4 by addressing two urgent gaps in cognitive assessment practice: unequal access to assessment and to the information it can provide. Access remains constrained by unequal student-to-professional ratios, while the breadth of information obtained depends partly on the resources available for more extensive assessment procedures. Static and dynamic traditions have addressed these challenges from different directions. Advances in test engineering have enabled self-administered multidomain cognitive batteries that reduce administration demands while dynamic approaches have shown weaker associations with situational and socioeconomic factors. We propose integrating these complementary strengths within a single process, using rapidly repeatable cognitive tests to preserve a broad static measure while generating short-interval trajectories examined through longitudinal modeling. The approach was tested in 428 students aged 12–16 years across ten administrations over 7–10 days. Longitudinal modeling characterized individual trajectories, while predictive-validity analyses showed that the mean across administrations explained up to 3.2 times as much variance in school achievement as the initial score. These findings suggest a route toward cognitive assessment that is less dependent on one-to-one professional administration while providing broader information about students’ performance level and change across repeated opportunities. Full article

The present paper deals with the possibility of using near-infrared spectral data (900–1700 nm) for classification of wheat kernels in two classes—healthy and Fusarium-diseased. Hyperspectral images of a certain number of kernels from each class are captured and a representative spectral characteristic for each of the objects is extracted from them. After preprocessing of spectral characteristics and extraction of informative wavelengths, a neural network is trained and tested for the needs of the classification procedure. Based on the classification results, an optimal set of informative wavelengths is determined, which can successfully be used for assessment of Fusarium disease in wheat kernels. Full article

Conventional manual microscopic examination for ship ballast-water microalgae detection suffers from cumbersome classification procedures, subjective viability assessment, and low counting efficiency, making it difficult to accomplish multi-task detection simultaneously. To address these issues, this study takes Tetraselmis chui and Phaeodactylum tricornutum as research objects and constructs a trinity detection model integrating species classification, viability discrimination, and automatic counting based on the YOLOv8m network, combining the color and morphological features of neutral red vital staining. A four-class microalgae annotated dataset was established, and the model was trained with an input resolution of 800 × 800 pixels. The optimal confidence threshold (0.383) was determined on the validation set by maximizing the F1-score and then applied to test-set evaluation. The experimental results demonstrate that on 156 independent test-set images, the proposed model achieves an average precision of 95.9% and mAP@0.5 of 96% across the four microalgae classes, with the highest precision of 97.3% for live Phaeodactylum tricornutum. Under the optimal confidence threshold, the average counting accuracy on the test set reaches 92.3%. Visualization tests confirm that the model can simultaneously accomplish species identification, viability determination, and cell counting in a single inference, providing a technical reference and algorithmic support for intelligent ballast-water microalgae monitoring. Full article