Search Results (3,998)

This study investigates the thermodynamic and experimental aspects of producing a chromium–manganese ligature under high-temperature smelting conditions using low-grade iron–manganese ore and ferrosilicochrome (FeSiCr) dust as both a reducing agent and a chromium source. Thermodynamic modeling of the multicomponent Fe–Cr–Mn–Si–Al–Ca–Mg–O system was carried out using the HSC Chemistry 10 and FactSage 8.4 software packages to substantiate the temperature regime, reducing agent consumption, and conditions for the formation of a stable metal–slag system. The calculations indicated that efficient reduction of manganese oxides and formation of the metallic phase are achieved at a smelting temperature of 1600 °C with a reducing agent consumption of approximately 50 kg. Experimental smelting trials conducted in a laboratory Tammann furnace under the calculated parameters confirmed the validity of the thermodynamic predictions and demonstrated the feasibility of obtaining a concentrated chromium–manganese ligature. The resulting metallic product exhibited a high total content of alloying elements and had the following chemical composition (wt.%): Fe 35.41, Cr 41.10, Mn 8.15, and Si 4.31. SEM–EDS microstructural analysis revealed a uniform distribution of chromium and manganese within the metallic matrix, indicating stable reduction behavior and favorable melt crystallization conditions. The obtained results demonstrate the effectiveness of an integrated thermodynamic–experimental approach for producing chromium–manganese ligatures from low-grade mineral raw materials and industrial by-products and confirm the potential applicability of the proposed process for complex steel alloying. Full article

Accurate braking-distance prediction for heavy-duty multi-axle trucks remains challenging due to the large gross vehicle weight, tandem-axle interactions, and strong transient load transfer during emergency braking. Recent studies on tire–road friction estimation, commercial-vehicle braking control (EBS/AEBS), and weigh-in-motion (WIM) sensing have highlighted that unmeasured vertical-load dynamics and time-varying friction are key sources of prediction uncertainty. To address these limitations, this study proposes an integrated sensing–simulation–AI framework that combines real-time axle-load estimation, full-scale robotic braking tests, fused road-friction sensing, and physics-consistent machine-learning modeling. A micro-electro-mechanical systems (MEMS)-based load-angle sensor was installed on the leaf-spring panel linking tandem axles, enabling the continuous estimation of dynamic vertical loads via a polynomial calibration model. Full-scale on-road braking tests were conducted at 40–60 km/h under systematically varied payloads (0–15.5 t) using an actuator-based braking robot to eliminate driver variability. A forward-looking optical friction module was synchronized with dynamic axle-load estimates and deceleration signals, and additional scenarios generated in a commercial ASM environment expanded the operational domain across a broader range of friction, grade, and loading conditions. A gradient-boosting regression model trained on the hybrid dataset reproduced measured stopping distances with a mean absolute error (MAE) of 1.58 m and a mean absolute percentage error (MAPE) of 2.46%, with most predictions falling within ±5 m across all test conditions. The results indicate that incorporating real-time dynamic axle-load sensing together with fused friction estimation improves braking-distance prediction compared with static-load assumptions and purely kinematic formulations. The proposed load-aware framework provides a scalable basis for advanced driver-assistance functions, autonomous emergency braking for heavy trucks, and infrastructure-integrated freight safety management. All full-scale braking tests were carried out at approximately 60% of the nominal service-brake pressure, representing non-panic but moderately severe braking conditions, and the proposed model is designed to accurately predict the resulting stopping distance under this prescribed braking regime rather than to minimize the absolute stopping distance itself. Full article

Conservation of historic buildings has long relied on traditional, reactive methods that address deterioration only after it occurs, often leading to irreversible damage. This study introduces an innovative approach that integrates Digital Twin (DT) technology with advanced machine learning algorithms to enable predictive and data-driven conservation. Focusing on Nottingham Cathedral, a Grade II listed Gothic Revival building, this research developed a 3D Historic Building Information Model (HBIM) enhanced with real-time environmental monitoring of temperature, humidity, and air quality. The collected data were analysed using MATLABR2024a to train and evaluate several predictive algorithms, including Long Short-Term Memory (LSTM), Backpropagation Neural Network (BPNN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Nonlinear Autoregressive Exogenous (NARX) models. The NARX model achieved the highest accuracy (Root Mean Square Error (RMSE) = 0.19) in forecasting indoor environmental conditions. Findings indicate that maintaining an indoor temperature increase of 4–6 °C can effectively reduce relative humidity below 60%, minimising deterioration risks. The study demonstrates how integrating DT and machine learning offers a proactive framework for environmental optimisation and long-term preservation of heritage assets, moving conservation practice from reactive restoration toward predictive conservation. Full article

Background/Objectives: This study evaluated the diagnostic accuracy of cervical cytology, CINtec^® (p16/Ki-67 dual staining), and PD-L1 immunohistochemistry, individually and in combination with high-risk HPV (HR-HPV) testing, for identifying histologically confirmed cervical lesions ranging from CIN1 to invasive carcinoma. Methods: We conducted a prospective cross-sectional study including 114 patients who underwent cervical cytology, CINtec^®, PD-L1 staining, HPV genotyping, and histopathologic confirmation at a tertiary clinical center between September 2024 and September 2025. Sensitivity, specificity, PPV, NPV, and ROC performance were calculated for each test across lesion categories. Multivariable logistic regression models incorporating HR-HPV status were used to assess added predictive value. Results: All tests showed poor performance for CIN1 (cytology AUC 0.488; CINtec^® 0.374; PD-L1 0.366). Diagnostic accuracy improved markedly with lesion severity. For CIN3, CINtec^® demonstrated the highest discriminative ability (AUC 0.826), with cytology and PD-L1 also performing well (AUC 0.820 and 0.753). Cytology achieved the strongest ROC performance for CIN2+ (AUC 0.937), CIN3+ (0.913), and invasive carcinoma (0.887). PD-L1 consistently showed lower accuracy across categories. Cytology + HR-HPV demonstrated the highest AUC across all lesion categories. Conclusions: Cytology and CINtec^® exhibited strong diagnostic accuracy for high-grade lesions, while PD-L1 showed limited utility as an independent screening marker. Combining cytology with HR-HPV testing enhanced predictive performance across all lesion categories. These findings support the continued use of cytology-based triage and highlight CINtec^® as a valuable adjunct for high-grade disease detection. Because this study used a high-prevalence referral cohort, specificity may be overestimated and not representative of population-based screening. Full article

Human epidermal growth factor receptor 2 (HER2) is a molecular target of interest in prostate cancer due to its association with poor prognosis and its potential role in androgen receptor signaling. However, earlier clinical trials investigating HER2-targeted therapies, including antibodies and small molecules, have shown limited efficacy. More recent studies using the HER2 antibody-drug conjugate (ADC) trastuzumab deruxtecan (T-DXd) suggest potential therapeutic benefit in prostate cancer. However, its effective utilization requires a HER2 IHC scoring system that accurately represents HER2 expression patterns unique to prostate cancer, which is currently not established. We have developed a modified HER2 IHC scoring system that, unlike the breast and gastrointestinal tumor HER2 IHC grading scales, considers the distinct spatiotemporal expression of HER2 in prostate tumors. In this commentary, we discussed two patients with metastatic prostate cancer who were classified as HER2 IHC 3+ using our prostate cancer-specific scoring system and who demonstrated meaningful clinical responses and responded to treatment with T-DXd. We further review the historical evolution of HER2 testing in prostate cancer, as well as factors that may have contributed to the failure of previous clinical trials targeting HER2 in prostate tumors. Our aim is to highlight the need for developing a standardized HER2 IHC grading model in prostate cancer, which could improve the predictive value of HER2 IHC expression, enabling a more accurate identification of patients likely to benefit from HER2-targeted ADCs. Full article

Surgical resection combined with chemotherapy offers the best chance of survival in pancreatic ductal adenocarcinoma (PDAC), but many will experience recurrence and early mortality. We examined soluble AXL (sAXL), a blood protein, for its ability to predict 6-month mortality after resection and compared it to CA19-9. Fifty-four patients with PDAC who underwent tumour resection were analyzed to assess biomarker performance and identify optimal cut-off levels. The cut-off for sAXL was 40.26 ng/mL (sensitivity 0.729; specificity 0.643), while it 253.3 U/mL for CA19-9 (sensitivity 0.591; specificity 0.621). Patients with sAXL > 40.26 ng/mL had a non-significant trend toward worse survival (log-rank p = 0.088). Univariate Cox regression revealed that high tumour grade (3 + 4) and positive resection margin significantly predicted early mortality. Multivariate Cox regression showed that sAXL > 40.26 ng/mL remained associated with 6-month mortality (hazard ratio 2.42, bootstrap 95% CI 1.15–5.65, p = 0.020), independent of high tumour grade (hazard ratio 4.02, bootstrap 95% CI 1.68–13.2, p = 0.002). These findings suggest that a preoperative blood test (sAXL) has utility for predicting futile surgery beyond the current standard, CA19-9, and can be incorporated into larger models to assist in risk stratification and follow-up planning. Full article

Background: Current histopathology- and molecular-based gold standards for diagnosing adult diffuse gliomas (ADGs) have inherent limitations in reproducibility and interobserver concordance, while being time-intensive and resource-demanding. Although hyperspectral imaging (HSI)-based computer-aided pathology shows potential for automated diagnosis, it often yields suboptimal accuracy due to the lack of complementary spatial and structural tumor information. This study introduces a multimodal fusion framework integrating HSI with routinely acquired preoperative magnetic resonance imaging (MRI) to enable automated, high-precision ADG diagnosis. Methods: We developed the Hyperspectral Attention Fusion Network (HAFNet), incorporating residual learning and channel attention to jointly capture HSI patterns and MRI-derived radiomic features. The dataset comprised 1931 HSI cubes (400–1000 nm, 300 spectral bands) from histopathological patches of six major World Health Organization (WHO)-defined glioma subtypes in 30 patients, together with their routinely acquired preoperative MRI sequences. Informative wavelengths were selected using mutual information. Radiomic features were extracted with the PyRadiomics package. Model performance was assessed via stratified 5-fold cross-validation, with accuracy and area under the curve (AUC) as primary endpoints. Results: The multimodal HAFNet achieved a macro-averaged AUC of 0.9886 and a classification accuracy of 98.66%, markedly outperforming the HSI-only baseline (AUC 0.9267, accuracy 87.25%; p < 0.001), highlighting the complementary value of MRI-derived radiomic features in enhancing discrimination beyond spectral information. Conclusions: Integrating HSI biochemical and microstructural insights with MRI radiomics of morphology and context, HAFNet provides a robust, reproducible, and efficient framework for accurately predicting 2021 WHO types and grades of ADGs, demonstrating the significant added value of multimodal integration for precise glioma diagnosis. Full article

Background: Postpartum depression and anxiety are common in the perinatal period and can adversely affect maternal functioning and infant outcomes. Self-esteem is a relevant psychosocial factor, yet evidence from Eastern Europe remains limited. Objectives: To describe self-esteem levels among postpartum Romanian women, examine correlational associations between self-esteem and postpartum depression/anxiety symptoms, and assess whether these associations persist after adjustment for sociodemographic and clinical covariates, across two maternity centers in a cross-sectional design. Methods: This cross-sectional study included 201 postpartum women recruited consecutively during their initial postpartum hospitalization from two public maternity hospitals in Western Romania (Bihor, n = 100; Timiș, n = 101) during 2024–2025. Participants completed the Rosenberg Self-Esteem Scale (RSES), Edinburgh Postnatal Depression Scale (EPDS), Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder-7 (GAD-7). Associations were assessed with χ² tests (categorical comparisons), Pearson correlations, and multivariable linear regression models including center and selected sociodemographic/obstetric covariates. Results: Self-esteem showed a strong inverse correlation with postpartum depressive symptoms (RSES–EPDS: r = −0.542 overall; r = −0.537 in Bihor; r = −0.552 in Timiș; all p < 0.001). Negative correlations were also observed with anxiety (RSES–GAD-7: r = −0.400; p < 0.001) and PHQ-9 depressive severity (r = −0.370; p < 0.001). Stratified analyses indicated graded symptom burden across self-esteem categories, with higher EPDS risk proportions among women with moderate/low self-esteem within each center (χ²p ≤ 0.039). In adjusted models, EPDS (B = −0.37; p < 0.001) and GAD-7 (B = −0.15; p = 0.021) remained independently associated with lower RSES, alongside study center (Timiș vs. Bihor: B = −1.08; p = 0.043) and educational attainment. Conclusions: Lower self-esteem co-occurs with postpartum depressive symptoms and, secondarily, anxiety in Romanian women. While the cross-sectional design precludes causal inference, these robust correlational associations support the potential value of self-esteem assessment for early psychosocial risk identification. Longitudinal research is needed to establish temporal relationships and evaluate whether self-esteem can prospectively predict postpartum mental health outcomes. Full article

The exponential growth and complexity of legal agreements pose significant Big Data challenges and strategic risks for modern organizations, often overwhelming traditional, manual contract management workflows. While AI has enhanced legal research, most current applications require extensive domain-specific fine-tuning or substantial annotated data, and Large Language Models (LLMs) remain susceptible to hallucination risk. This paper presents an AI-based Agreement Management System that addresses this methodological gap and scale. The system integrates a Python 3.1.2/MySQL 9.4.0-backed centralized repository for multi-format document ingestion, a role-based Collaboration and Access Control module, and a core AI Functions module. The core contribution lies in the AI module, which leverages zero-shot learning with OpenAI’s GPT-4o and structured prompt chaining to perform advanced contractual analysis without domain-specific fine-tuning. Key functions include automated metadata extraction, executive summarization, red-flag clause detection, and a novel feature for natural-language contract modification. This approach overcomes the cost and complexity of training proprietary models, democratizing legal insight and significantly reducing operational overhead. The system was validated through real-world testing at a leading industry partner, demonstrating its effectiveness as a scalable and secure foundation for managing the high volume of legal data. This work establishes a robust proof-of-concept for future enterprise-grade enhancements, including workflow automation and predictive analytics. Full article

Structural steel reuse can gain large embodied-carbon savings, yet it is still not widely adopted since approval depends on the quality of the evidence, how uncertainty is handled, and if the design requirements are followed, not just on resistance. Reclaimed members frequently lack dependable documentation regarding material grade, loading history, boundary conditions, connection status, and degradation. For reuse decisions, conservative default assumptions protect safety but frequently eliminate qualified reuse options. This research examines data-driven and physics-informed computational methods from a decision-making standpoint, contending that their significance resides in facilitating an auditable approval process, not in supplanting deterministic verification. We differentiate feasibility, acceptability, and approval as distinct engineering phases. Data-driven models are thought of as tools for quickly screening candidates, surrogate evaluation, inverse reasoning, and stock-to-demand matching. Their goal is to reduce the list of candidates and prioritize evidence collection. Physics-informed approaches are examined as admissibility filters that impose restrictions of equilibrium, compatibility, stability, and plausible boundary-condition envelopes; therefore, minimizing mechanically invalid predictions under partial information. Next, we consider uncertainty quantification and explainability to be essential for reuse decisions. We suggest practical outputs for approval packages, such as resistance bounds within specified assumption envelopes, sensitivity rankings of decision-critical unknowns, low-support flags, and evidence actions for conditional acceptance. This document is organized into a process from audit to approval. It also states the open issues in reuse-specific datasets, standardized evidence capturing, decision-relevant validation under degradation, and regulatory acceptance. The resulting framework clarifies how advanced computational tools can enable adaptable, conservative, and transparent steel reuse in practice. Full article

In the production of clad rolled plates from asymmetric sandwich-type slab for pipeline applications, achieving both target mechanical properties and high geometric flatness remains a critical challenge due to differential thermal stresses between the dissimilar steel layers during accelerated cooling. This study aims to develop an optimal cooling schedule for a 25 mm thick clad plate, comprising a X70-grade steel base layer and an AISI 316L cladding, to ensure required strength and minimal bending. A comprehensive approach was employed, integrating a 3D finite element model (Ansys) for simulating thermoelastic stresses with a CatBoost machine learning model trained on industrial data to predict heat transfer coefficients accurately. A parametric analysis of cooling strategies was conducted. Results showed that a standard cooling strategy caused unacceptable bending of plate after cooling exceeding 130 mm. An optimized strategy featuring delayed activation of the lower cooling headers (on the cladding side) created a compensating thermoelastic moment, successfully reducing bending to approximately 20 mm while maintaining the base layer’s requisite mechanical properties. The findings validate the efficacy of the combined FEM-machine learning methodology and propose a viable, industrially implementable cooling strategy for high-quality clad plate production. Full article

Background: Accumulating evidence demonstrates that inflammatory responses play a critical role in tumor progression, invasion, and metastasis. In recent years, systemic immune-inflammatory (SII) markers have emerged as valuable tools for evaluating tumor grade and prognosis in solid tumors. To assess SII scores associated with neoplasm-related grading and staging in order to investigate the incidence rate in SII levels and cancer-related aggressiveness. Methods: The present review was recorded in Figshare system with id no. 10.6084/m9.figshare.31029121. Observational studies recording grading and staging differences (advanced III–IV vs. early 0–II) in SII values among cancer patients were included in the present review. Frequencies were collected among cancer patients, along with low and high SII levels and related grading and staging scores. Results: A total of ten studies were included in the present review. Considering cancer grading classification and SII levels, our data showed significant associations not recorded in all the selected studies. Additionally, high SII values appeared to be significantly associated with advanced-stage cancer (stages III–IV), while lower SII values were associated with an increased probability of early-stage tumors (stages 0–II), (p < 0.05). Conclusions: The SII marker has shown potential predictive value in cancer. However, the SII is a relatively novel index in which a direct causal effect on tumor initiation or subsequent disease progression remains to be comprehensively investigated. Full article

The durability of the carbon fiber-reinforced polymer (CFRP)–concrete interface is a critical indicator for assessing the service life of composite structures in cold regions. This study systematically investigates the normal bond behavior under coupled deicing salt and freeze–thaw cycles through single-sided salt-frost tests on 126 specimens. The influence of surface roughness, number of freeze–thaw cycles, concrete strength grade, and CFRP material type was systematically evaluated. The results demonstrate that bond behavior is positively correlated with surface roughness, with the f₂ interface exhibiting optimal performance and increasing the ultimate capacity by up to 76.61% compared to the smooth interface. CFRP cloth showed superior bond retention compared to CFRP plates, which experienced a bond strength loss rate up to 26.90% higher than cloth specimens after six cycles. A critical performance threshold was identified between six and eight cycles, where the failure mode transitioned from cohesive adhesive failure to brittle interfacial debonding. Concrete matrix strength had a negligible effect compared to the dominant environmental damage. A two-parameter prediction model based on cycle count and roughness was established with high accuracy. SEM analysis confirmed that epoxy resin cracking, fiber–matrix debonding, and microcrack propagation in the concrete surface layer were the fundamental causes of macroscopic mechanical degradation. These findings provide a theoretical foundation for optimizing interface treatment and predicting the structural integrity of CFRP-strengthened systems in salt-frost regions. Full article

Since land use considerably affects the spatial variation of PM_2.5 levels, it is crucial to predict PM_2.5 concentrations under future land use changes. However, prior research has primarily concentrated on meteorological factors influencing PM_2.5 predictions, while neglecting the effect of land use configurations. Consequently, in our study, a novel Patch-generating Land Use Simulation–Land Use Regression (PLUS-LUR) method was developed by integrating the PLUS model’s dynamic prediction capability with the LUR model’s spatial interpretation strength. The incorporation of landscape indices as key variables was essential for predicting PM_2.5 concentrations. First, the random forest-optimized LUR method was trained with PM_2.5 datasets from the Pearl River Delta (PRD) monitoring stations and multi-source spatial datasets. We assessed the modeling accuracy with and without considering landscape indices using the test dataset. Subsequently, the PLUS approach was applied to forecast land use as well as associated landscape indices in 2028. Based on these projections, grid-scale influencing factors were input into the previously constructed LUR model to forecast future PM_2.5 distributions at a grid scale. The results reveal a spatial pattern with higher PM_2.5 levels in central areas and lower levels in peripheral regions. Furthermore, the PM_2.5 concentrations in the PRD are all below the Grade II threshold of the China Ambient Air Quality Benchmark in 2028. Notably, the predictions incorporating landscape indices demonstrate higher accuracy and reliability compared to those excluding them. These results provide methodological support for future PM_2.5 assessment and land use management. Full article