Search Results (3,988)

It is widely recognized that benthic sediment plumes generated by deep-sea mining may pose significant potential risks to ecosystems, yet their dispersion behavior remains difficult to predict with accuracy. In this study, we combined laboratory experiments with three-dimensional numerical simulations using the Environmental Fluid Dynamics Code (EFDC) to investigate the dispersion of sediment plumes composed of particles of different sizes. Laboratory experiments were conducted with deep-sea clay samples from the western Pacific under varying conditions for plume dispersion. Experimental data were used to capture horizontal diffusion and vertical entrainment through a Gaussian plume model, and the results served for parameter calibration in large-scale plume simulations. The results show that ambient current velocity and discharge height are the primary factors regulating plume dispersion distance, particularly for fine particles, while discharge rate and sediment concentration mainly control plume duration and the extent of dispersion in the horizontal direction. Although the duration of a single-source release is short, continuous mining activities may sustain broad dispersion and result in thicker sediment deposits, thereby intensifying ecological risks. This study provides the first comprehensive numerical assessment of deep-sea mining plumes across a range of particle sizes with clay from the western Pacific. The findings establish a mechanistic framework for predicting plume behavior under different operational scenarios and contribute to defining threshold values for discharge-induced plumes based on scientific evidence. By integrating experimental, theoretical, and numerical approaches, this work offers quantitative thresholds that can inform environmentally responsible strategies for deep-sea resource exploitation. Full article

Background/Objectives: Frequent users (FUs) are patients who repeatedly attend Emergency Departments (EDs). This study aims to identify the clinical and social characteristics of FUs in a Local Health Authority in Rome and to quantify and compare the variation in the probability of being FU attributable to General Practitioners (GPs) and Local Health Districts (LHDs). Methods: The Healthcare Emergency Information System and an automated database of Lazio Region residents were used for the collection of data on the patients’ socioeconomic status, GP, LHD and chronic diseases. Different FU thresholds (attendances ≥4, 5, 7 or 10) were used for descriptive analyses. Univariate logistic analysis and a multilevel logistic model were performed for inferential analyses. Results: A total of 89,036 individuals attended at least one of the 13 EDs included in the study. Mental illness was present in 2.6% of non-FUs compared with 7.6% of FUs with ≥4 attendances. The OR of being FU increased with higher clinical complexity. GP appeared to play an important role in determining FU behavior, while no significant effect was found on the LHD level. Conclusions: This study identified potential risk factors predictive of disproportionate ED use and may help policymakers address the FU phenomenon. Full article

Background: Operational cycle detection underpins a range of important tasks, such as predictive maintenance, energy consumption prediction, and energy management for mobile equipment in mining. Yet, no review has investigated the landscape of methods that segment mobile mining vehicle telemetry into discrete operating modes—a task termed operational cycle detection. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, Scoping Review extension (PRISMA-ScR) framework, we searched The Lens database on 27 June 2025, for records published between 2000 and 2025 that apply cycle detection to mobile mining vehicles. After de-duplication and two-stage screening, 20 empirical studies met all criteria (19 diesel, 1 electric-drive). Due to the sparse research involving battery electric vehicles (BEVs) in mining, three articles performing cycle detection on heavy-duty vehicles in a similar operational context to mining are synthesized. Results: Early diesel work used single-sensor thresholds, often achieving >90% site-specific accuracy, while recent studies increasingly employ neural networks using multivariate datasets. While the cycle detection research on mining BEVs, even supplemented with additional heavy-duty BEV studies, is sparse, similar approaches are favored. Conclusions: Persisting gaps in the literature include the absence of public mining datasets, inconsistent evaluation metrics, and limited real-time validation. Full article

Bone drilling is a widely encountered process in orthopedic surgeries and keyhole neuro surgeries. We are developing a sensor-integrated smart end-effector for drilling for robotic surgical applications. In manual surgeries, surgeons assess tool wear based on experience and force perception. In this work, we propose a machine-learning (ML)-based tool condition monitoring system based on multi-sensor data to preempt excessive tool wear during drilling in robotic surgery. Real-time data is acquired from the six-component force sensor of a collaborative arm along with the data from the temperature and multi-axis vibration sensor mounted on the bone specimen being drilled upon. Raw data from the sensors may have noises and outliers. Signal processing in the time- and frequency-domain are used for denoising as well as to obtain additional features to be derived from the raw sensory data. This paper addresses the challenging problem of identification of the most suitable ML algorithm and the most suitable features to be used as inputs to the algorithm. While dozens of features and innumerable machine learning and deep learning models are available, this paper addresses the problem of selecting the most relevant features, the most relevant AI models, and the optimal hyperparameters to be used in the AI model to provide accurate prediction on the tool condition. A unique framework is proposed for classifying tool wear that combines machine learning-based modeling with multi-sensor data. From the raw sensory data that contains only a handful of features, a number of additional features are derived using frequency-domain techniques and statistical measures. Using feature engineering, we arrived at a total of 60 features from time-domain, frequency-domain, and interaction-based metrics. Such additional features help in improving its predictive capabilities but make the training and prediction complicated and time-consuming. Using a sequence of techniques such as variance thresholding, correlation filtering, ANOVA F-test, and SHAP analysis, the number of features was reduced from 60 to the 4 features that will be most effective in real-time tool condition prediction. In contrast to previous studies that only examine a small number of machine learning models, our approach systematically evaluates a wide range of machine learning and deep learning architectures. The performances of 47 classical ML models and 6 deep learning (DL) architectures were analyzed using the set of the four features identified as most suitable. The Extra Trees Classifier (an ML model) and the one-dimensional Convolutional Neural Network (1D CNN) exhibited the best prediction accuracy among the models studied. Using real-time data, these models monitored the drilling tool condition in real-time to classify the tool wear into three categories of slight, moderate, and severe. Full article

Background/Objectives: Baricitinib, a selective Janus kinase (JAK) 1 and 2 inhibitor, has recently emerged as a therapeutic option for patients with severe alopecia areata (AA). The aim of this study was to identify clinical and biological predictors of early therapeutic response to baricitinib in patients with AA in real-world clinical practice. Methods: A retrospective cohort study was conducted including patients with AA initiating baricitinib between January 2022 and January 2025. Patients were stratified into early responders and non-early responders. Univariate and multivariate logistic regression analyses were performed to assess factors independently associated with early therapeutic response. Results: A total of 44 patients with AA treated with baricitinib were included, the majority being female (65.9%, 29/44), with a mean age of 37.3 years (SD 16.1). Early responders accounted for 34.1% (15/44) of the cohort. In multivariate analysis, early response to baricitinib was independently associated with a lower baseline Severity of Alopecia Tool (SALT) score, shorter disease duration, and elevated erythrocyte sedimentation rate (ESR) at baseline (p < 0.05). Receiver Operating Characteristic (ROC) curve analyses were performed to determine optimal thresholds for predicting early therapeutic response: ESR ≥ 9 mm/h, baseline SALT score ≤ 60%, and disease duration ≤ 7 years. Conclusions: Baseline stratification using easily obtainable clinical and laboratory parameters may help identify patients most likely to benefit from initiating treatment with baricitinib. Our findings support the existence of a therapeutic window of opportunity in AA, particularly in patients with lower disease burden, shorter disease duration, and elevated ESR values. Full article

Background/Objectives: Anaplastic thyroid cancer (ATC) is an aggressive thyroid cancer subtype with a poor prognosis. The Controlling Nutritional Status (CONUT) score, reflecting both immune and nutritional status, is a prognostic marker in several malignancies; however, its utility in ATC has not been established. We aimed to evaluate the predictive value of the pretreatment CONUT score in ATC and compare its prognostic utility with that of other nutritional indices, including the Prognostic Nutritional Index (PNI) and Geriatric Nutritional Risk Index (GNRI). Methods: We retrospectively reviewed clinical characteristics, laboratory parameters, and survival outcomes of 156 patients with ATC at our institution between January 2004 and May 2024. Based on survival analysis, patients were categorized into low- and high-risk groups based on each nutritional index (CONUT score, PNI, GNRI) using optimal cut-off values. One-year survival differences were evaluated using Kaplan–Meier curves and log-rank test. Independent predictors of 1-year mortality were identified using multivariable Cox proportional hazards regression. Results: Optimal thresholds were 3, 42, and 102 for the CONUT score, PNI, and GNRI, respectively. Patients with CONUT scores ≥ 3 exhibited significantly higher 1-year mortality, compared with those with scores < 3. Multivariable analysis revealed that CONUT score ≥ 3, PNI ≤ 42, and GNRI ≤ 102 were independently associated with increased 1-year mortality risk. Incorporation of CONUT score ≥ 3 into the baseline prediction model significantly enhanced its discriminatory performance. Conclusions: These findings underscore the prognostic value of pretreatment immuno-nutritional assessment and support the integration of the CONUT score into early risk stratification strategies for patients with ATC. Full article

We tested the longitudinal invariance of seven short versions of the Center for Epidemiological Studies Depression Scale (CES-D) in maternal caregivers, following recent analytic recommendations for ordered categorical responses. Data for this study were drawn from the Longitudinal Studies in Child Abuse and Neglect (LONGSCAN) consortium, based on responses from 427 maternal caregivers across five waves corresponding to their children’s ages: 4, 6, 12, 14, and 16 years. We employed a comprehensive approach using differences in two approximate fit indices (CFI and RMSEA), the chi-square difference test (χ²), and a sensitivity analysis based on predicted response differences. Only one version demonstrated full invariance across all levels, while the others showed only partial evidence for loading or threshold invariance. These findings highlight concerns regarding the use of brief CES-D versions in longitudinal research, particularly over extended time periods. They also underscore the need to reassess whether item content aligns with current definitions of depressive syndrome. Our results suggest that evaluating the longitudinal invariance of short depression measures is essential to ensure the validity of conclusions about changes over time. Full article

Background: Current clinical guidelines recommend 52 ng/L as the abnormal baseline cutoff in high-sensitivity cardiac troponin (hs-cTn) algorithms for the rapid diagnosis of acute myocardial infarction (AMI). Though abnormal, this threshold is not AMI-specific, leading to extensive workups for many non-AMI chest pain patients, overutilization of resources, and emergency department (ED) overcrowding. Hence, the performance of this baseline abnormal cutoff was compared against the refined new thresholds for rapid AMI diagnosis in ED chest pain patients. Methods: We included ED chest pain patients with hs-cTnT and hs-cTnI levels simultaneously measured and clinical outcomes adjudicated by cardiologists. We performed receiver operating characteristics (ROC) analyses across various thresholds for diagnostic performance, including sensitivity, specificity, negative and positive likelihood ratios, and predictive values. Statistical analysis was carried out using Graphpad Prism 10, with p < 0.05 considered as significant. Results: In our study, 17 patients were adjudicated as AMI, and 682 patients were ruled out for AMI. In 15/17 AMI cases, baseline hs-cTn values far exceeded 52 ng/L. Notably, among non-AMI individuals, 140 (hs-cTnT) and 91 (hs-cTnI) also exceeded this cutoff. ROC analyses identified optimal abnormal cutoffs of 82 ng/L for hs-cTnT and 122 ng/L for hs-cTnI, which improved specificity without compromising sensitivity. Post-discharge follow-up at 1, 3, and 12 months for cardiovascular events supported these revised thresholds. Conclusions: Increasing the baseline abnormal value from 52 ng/L to 82 ng/L for hs-cTnT and to 122 ng/L for hs-cTnI in care pathways could reduce false positives with the potential to decrease unnecessary testing and alleviate long stays in the ED and resource management. Larger, diverse cohort studies are warranted to validate these findings. Full article

Background: Ovarian cancer remains a major contributor to cancer-related morbidity and mortality worldwide. Primary cytoreductive surgery is the cornerstone of treatment, and accurate preoperative assessment of tumor resectability is critical to guiding optimal therapeutic strategies in patients with advanced tubo-ovarian cancer. Methods: A narrative review about the role of ultrasound for assessing tumor spread and prediction of tumor resectability was performed. Results: The ISAAC study represents the largest prospective multicenter trial to date comparing the diagnostic performance of ultrasound (US), computed tomography (CT), and whole-body diffusion-weighted magnetic resonance imaging (WB-DWI/MRI) in predicting non-resectability, using surgical and histopathological findings as the reference standard. Key strengths of the study include the use of standardized imaging and intraoperative reporting protocols across ESGO-accredited high-volume oncologic centers. All three imaging modalities were performed within four weeks prior to surgery by independent, blinded expert operators. US demonstrated diagnostic accuracy comparable to that of CT and WB-DWI/MRI. The study also defined modality-specific thresholds for the Peritoneal Cancer Index (PCI) and Predictive Index Value (PIV), offering quantitative tools to support surgical decision-making. A noteworthy secondary finding was patient preference: in a cohort of 144 participants who underwent all three imaging modalities, nearly half preferred US, while WB-DWI/MRI was the least favored due to discomfort and examination duration. Conclusions: The ISAAC study represents a significant advancement in imaging-based prediction of surgical non-resectability in tubo-ovarian cancer. Its findings suggest that, in expert hands, ultrasound can match or even surpass cross-sectional imaging for preoperative staging, supporting its integration into routine clinical practice, particularly in resource-constrained settings. Full article

Background: Functional lumbar segmental instability (FLSI) is a clinically significant subtype of nonspecific low back pain, characterized by impaired motor control during mid-range spinal motion. Despite its prevalence, diagnostic approaches remain fragmented, and no single clinical test reliably captures its complexity. This scoping review aims to synthesize current evidence on the reliability, validity, subclassification, and predictive value of manual tests used in the evaluation of FLSI, and to identify conceptual and methodological gaps in the literature. Methods: A structured search was conducted across five databases (PubMed, Scopus, Web of Science, CINAHL, Embase) between May and August 2025. Twenty-four empirical studies and eleven foundational conceptual sources were included. Data were charted into five thematic domains: conceptual frameworks, diagnostic accuracy, reliability, subclassification models, and predictive value. Methodological appraisal was performed using QUADAS and QAREL tools. Results: The Passive Lumbar Extension Test (PLET) demonstrated the most consistent reliability and clinical utility. The Prone Instability Test (PIT) and Posterior Shear Test (PST) showed variable performance depending on protocol standardization. Subclassification models distinguishing functional, structural, and combined instability achieved high inter-rater agreement. Screening tools for sub-threshold lumbar instability (STLI) showed preliminary feasibility. Predictive validity of manual tests for rehabilitation outcomes was inconsistent, suggesting the need for multivariate models. Conclusions: Manual tests can support the clinical evaluation of FLSI when interpreted within structured diagnostic frameworks. Subclassification models and composite test batteries enhance diagnostic precision, but standardization and longitudinal validation remain necessary. Future research should prioritize protocol harmonization, integration of sensor-based technologies, and stratified outcome studies to guide individualized rehabilitation planning. Full article

Chronic dermatological conditions such as acne vulgaris, androgenetic alopecia (AGA), and alopecia areata (AA) affect hundreds of millions worldwide and contribute substantially to quality-of-life impairment. Despite the availability of systemic retinoids, anti-androgens, and JAK inhibitors, therapeutic responses remain heterogeneous and relapse is common, underscoring the need for biologically grounded stratification. Over the past decade, large genome-wide association studies and functional analyses have clarified disease-specific and cross-cutting mechanisms. In AA, multiple independent HLA class II signals and immune-regulatory loci such as BCL2L11 and LRRC32 establish antigen presentation and interferon-γ/JAK–STAT signalling as central drivers, consistent with clinical responses to JAK inhibition. AGA is driven by variation at the androgen receptor and 5-α-reductase genes alongside WNT/TGF-β regulators (WNT10A, LGR4, RSPO2, DKK2), explaining follicular miniaturisation and enabling polygenic risk prediction. Acne genetics highlight an immune–morphogenesis–lipid triad, with loci in TGFB2, WNT10A, LGR6, FASN, and FADS2 linking follicle repair, innate sensing, and sebocyte lipid metabolism. Barrier modulators such as FLG and OVOL1, first described in atopic dermatitis, further shape inflammatory thresholds across acne and related phenotypes. Together, these findings position genetics not as an abstract catalogue of risk alleles but as a map of tractable biological pathways. They provide the substrate for patient-stratified interventions ranging from JAK inhibitors in AA, to endocrine versus morphogenesis-targeted strategies in AGA, to lipid- and barrier-directed therapies in acne, while also informing cosmetic practices focused on barrier repair, sebaceous balance, and follicle health. Full article

Forests store substantial amounts of aboveground biomass (AGB) and play a critical role in the global carbon cycle. Optical remote sensing offers long-term, large-scale monitoring capabilities; however, spectral saturation in high-biomass regions limits the accuracy of AGB estimation. Although radar and LiDAR data can mitigate the saturation problem, optical imagery remains irreplaceable for continuous, multi-decadal monitoring from regional to global scales. Nevertheless, quantitative analyses of nationwide optical saturation thresholds and compensation mechanisms are still lacking. In this study, we integrated high-accuracy AGB estimates from the Global Ecosystem Dynamics Investigation (GEDI) L4A product, Sentinel-2 optical imagery, and topographic variables to develop a 200 m resolution Light Gradient Boosting Machine (LightGBM) machine learning model for forests in China. Stratified error analysis, locally weighted scatterplot smoothing (LOWESS) curves, and SHapley Additive exPlanations (SHAP) were employed to quantify optical saturation thresholds and the compensatory effects of topographic features. Results showed that estimation accuracy declined markedly when AGB exceeded approximately 300 Mg·ha⁻¹. Red and red-edge bands saturated at around 80 Mg·ha⁻¹, while certain spectral indices delayed the threshold to 100–150 Mg·ha⁻¹. Topographic features maintained stable contributions below 300 Mg·ha⁻¹, providing critical compensation for AGB prediction in high-biomass areas. This study delivers a high-resolution national AGB dataset and a transferable analytical framework for saturation mechanisms, offering methodological insights for large-scale, long-term optical AGB monitoring. Full article

Sediment erosion under turbulent wave action is a highly dynamic process shaped by the interaction between wave properties and sediment characteristics. Despite extensive empirical research, the underlying mechanisms of wave-induced erosion remain insufficiently understood, particularly regarding the threshold energy required for particle mobilization and the factors governing displacement patterns. This study employed a custom-built wave flume and a 3D-printed sampler to examine sediment behavior under controlled wave conditions. Rounded glass beads, chosen to eliminate the influence of particle shape, were used as sediment analogs with a similar specific gravity to natural sand. Ten experiments were conducted to systematically assess the effects of particle size, particle number, input voltage (wave power), and water depth on sediment response. The results revealed that (1) only a fraction of particles were mobilized, with the remainder forming stable interlocking structures; (2) the number of displaced particles increased with particle size, particle count, and water depth; (3) a threshold wave power is required to initiate erosion, though buoyancy under shallow conditions reduces this threshold; and (4) wave steepness, rather than voltage or wave height alone, provided the strongest predictor of sediment displacement. These findings highlight the central role of wave steepness in erosion modeling and call for its integration into predictive frameworks. The study concludes with methodological limitations and proposes future research directions, including expanded soil types, large-scale flume testing, and advanced flow field measurements. Full article

Agricultural management in Europe faces increasing pressure to reduce its environmental footprint. Implementing precision agriculture for weed management could offer a solution and minimize the use of chemical products. High spatial resolution imagery from real time kinematic (RTK) unmanned aerial vehicles (UAV) in combination with supervised convolutional neural network (CNNs) models have proven successful in making location specific treatments. This site-specific advice limits the amount of herbicide applied to the field to areas that require action, thereby reducing the environmental impact and inputs for the farmer. To develop performant CNN models, there is a need for sufficient high-quality labelled data. To reduce the labelling effort and time, a new labelling method is proposed whereby image subsection pairs are labelled based on their relative differences in weed pressure to train a CNN ordinal regression model. The model is evaluated on detecting weed pressure in potato (Solanum tuberosum L.). Model performance was evaluated on different levels: pairwise accuracy, linearity (Pearson correlation coefficient), rank consistency (Spearman’s (r_s) and Kendal (τ) rank correlations coefficients) and binary accuracy. After hyperparameter tuning, a pairwise accuracy of 85.2%, significant linearity (r_s = 0.81) and significant rank consistency (r_s = 0.87 and τ = 0.69) were found. This suggests that the model is capable of correctly detecting the gradient in weed pressure for the dataset. A maximum binary accuracy and F1-score of 92% and 88% were found for the dataset after thresholding the predicted weed scores into weed versus non-weed images. The model architecture allows us to visualize the intermediate features of the last convolutional block. This allows data analysts to better evaluate if the model “sees” the features of interest (in this case weeds). The results indicate the potential of ordinal regression with relative labels as a fast, lightweight model that predicts weed pressure gradients. Experts have the freedom to decide which threshold value(s) can be used on predicted weed scores depending on the weed, crop and treatment that they want to use for flexible weed control management. Full article

This study develops an innovative unmanned aerial vehicle (UAV)-based intelligent system for winter wheat yield prediction, addressing the inefficiencies of traditional manual counting methods (with approximately 15% error rate) and enabling quantitative analysis of water–fertilizer interactions. By integrating an enhanced Faster Region-Based Convolutional Neural Network (Faster R-CNN) architecture with multi-source data fusion and machine learning, the system significantly improves both spike detection accuracy and yield forecasting performance. Field experiments during the 2022–2023 growing season captured high-resolution multispectral imagery for varied irrigation regimes and fertilization treatments. The optimized detection model incorporates ResNet-50 as the backbone feature extraction network, with residual connections and channel attention mechanisms, achieving a mean average precision (mAP) of 91.2% (calculated at IoU threshold 0.5) and 88.72% recall while reducing computational complexity. The model outperformed YOLOv8 by a statistically significant 2.1% margin (p < 0.05). Using model-generated spike counts as input, the random forest (RF) model regressor demonstrated superior yield prediction performance (R² = 0.82, RMSE = 324.42 kg·ha⁻¹), exceeding the Partial Least Squares Regression (PLSR) (R² +46%, RMSE-44.3%), Least Squares Support Vector Machine (LSSVM) (R² + 32.3%, RMSE-32.4%), Support Vector Regression (SVR) (R² + 30.2%, RMSE-29.6%), and Backpropagation (BP) Neural Network (R²+22.4%, RMSE-24.4%) models. Analysis of different water–fertilizer treatments revealed that while organic fertilizer under full irrigation (750 m³ ha⁻¹) conditions achieved maximum yield benefit (13,679.26 CNY·ha⁻¹), it showed relatively low water productivity (WP = 7.43 kg·m⁻³). Conversely, under deficit irrigation (450 m³ ha⁻¹) conditions, the 3:7 organic/inorganic fertilizer treatment achieved optimal WP (11.65 kg m⁻³) and WUE (20.16 kg∙ha⁻¹∙mm⁻¹) while increasing yield benefit by 25.46% compared to organic fertilizer alone. This research establishes an integrated technical framework for high-throughput spike monitoring and yield estimation, providing actionable insights for synergistic water–fertilizer management strategies in sustainable precision agriculture. Full article