Search Results (978)

Bovine tuberculosis (bTB) remains endemic in southern Italy, particularly in Sicily, posing challenges for disease control and eradication. This study evaluated the diagnostic agreement between the intradermal tuberculin test (IDT), interferon-gamma release assay (IFN-γ), and post-mortem lesion detection in 1801 cattle from 44 herds in the province of Palermo during 2024–2025. Diagnostic agreement between IDT and IFN-γ was substantial in 2024 (κ = 0.731; 95% CI: 0.64–0.82; n = 991) and decreased to moderate in 2025 (κ = 0.453; 95% CI: 0.33–0.57; n = 572), reflecting a higher proportion of IFN-γ positive and inconclusive animals. An increase in inconclusive results for IFN-γ was observed in 2025, but the available data did not allow investigation of the factors underlying this difference. Agreement between ante-mortem tests and post-mortem lesion detection was poor in a selected subgroup of slaughtered animals, potentially subject to verification bias, highlighting the limitations of single diagnostic tools and the need for a complementary approach combining immunological and pathological methods. These findings support the interpretation of the complementary roles of IDT and IFN-γ within herd-level bTB surveillance, highlight the need for standardized protocols to manage inconclusive IFN-γ results, and underline the complementary contribution of post-mortem inspection within integrated surveillance strategies in endemic settings. Official confirmation of bovine tuberculosis relies on an integrated evaluation combining diagnostic results, post-mortem findings, and epidemiological investigations. Given the absence of a validated gold standard applicable to all animals in this observational dataset, the study was designed as a diagnostic agreement study rather than a formal accuracy assessment. Full article

Background: Streptococcus suis serotype 2 is a major zoonotic pathogen responsible for severe systemic infections in pigs and humans, including septicemia, meningitis, and high mortality outcomes. Cefquinome, a fourth-generation β-lactam antibiotic widely used in veterinary medicine, is commonly applied for the treatment of S. suis infections. However, optimized dosing strategies remain insufficiently defined, particularly under conditions of varying bacterial burden, inoculum size, and reduced susceptibility or resistance phenotypes. These factors may significantly alter pharmacodynamic responses and compromise the predictive value of conventional MIC-based approaches. Objectives: This study aimed to characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of cefquinome against S. suis serotype 2 using an integrated ex vivo serum time-kill experiments and semi-mechanistic PK/PD modeling. A secondary objective was to evaluate optimized dosing regimens across different inoculum levels and susceptibility phenotypes, including a cefquinome-resistant mutant. Methods: Cefquinome pharmacokinetics following intramuscular administration at 2 and 4 mg/kg in piglets were described using a two-compartment model. Dose proportionality, exposure linearity, and clearance parameters were assessed. Ex vivo serum time-kill experiments were conducted using a parental strain and a cefquinome-resistant mutant (M1) under normal-inoculum (NI), high-inoculum (HI), and mutant/resistant (MS) conditions. A semi-mechanistic PK/PD model incorporating logistic bacterial growth, sigmoidal Emax killing, nutrient limitation, and a time-delay function was developed to describe dynamic bacterial responses. Model parameters (k₀, k_max, EC₅₀) were estimated using nonlinear least-squares regression (Scientist v2.0), and simulations were performed by integrating time-varying PK input functions. Results: Cefquinome demonstrated linear pharmacokinetics with dose-proportional increases in Cmax and AUC between 2 and 4 mg/kg, with comparable clearance across doses. Ex vivo studies revealed time-dependent antibacterial activity with a pronounced inoculum effect. Higher bacterial burdens significantly reduced bactericidal efficiency and promoted regrowth during declining drug exposure. No tested concentrations achieved ≥3-log₁₀ killing in HI or MS conditions, whereas the NI group achieved a maximal reduction of 3.5-log₁₀ CFU/mL. MIC values in serum and medium were consistent (0.03, 0.06, and 0.24 µg/mL for NI, HI, and MS, respectively), indicating minimal protein binding influence. The semi-mechanistic model accurately described observed bacterial dynamics (R² > 0.99; MSC > 1.5), capturing delayed drug effects, inoculum-dependent growth suppression, and regrowth phenomena. Growth rates were reduced under serum conditions, reflecting nutrient limitation. Importantly, inoculum size exerted a stronger impact on pharmacodynamic outcomes than resistance phenotype, as reflected by reductions in kmax and increases in EC₅₀ under HI conditions. Although %T>MIC exceeded conventional β-lactam targets (>40%) in most regimens, MIC-based indices poorly correlated with observed dynamic killing responses. Conclusions: Cefquinome exhibited time-dependent antibacterial activity against S. suis serotype 2, strongly modulated by inoculum size and reduced susceptibility. The developed semi-mechanistic PK/PD model provided robust prediction of bacterial time-kill behavior and outperformed MIC-based metrics in guiding dose optimization. Simulation results support 2 mg/kg every 24 h for normal infections and 2 mg/kg every 12 h for high-inoculum or less susceptible infections, emphasizing the value of model-informed dosing strategies for optimizing β-lactam therapy. Full article

Limited inclusion of ethnically diverse and socioeconomically disadvantaged populations in pain research undermines external validity, generalizability, and equity. This comprehensive review synthesized evidence on effective strategies to recruit and retain these populations in pain studies. We searched Medline, Web of Science, Embase, Scopus, and CENTRAL (12 April 2025) and included studies in which ethnically diverse and socioeconomically disadvantaged participants comprised ≥75% of the sample. Quantitative data were pooled using random-effects meta-analyses of proportions, with prespecified subgroup analyses, and qualitative findings were integrated through thematic synthesis. Certainty of evidence was evaluated using GRADE. Eighteen studies (n = 4611; 11 experimental, 5 observational, 2 qualitative), primarily from the United States and involving chronic pain, met inclusion criteria. Overall enrollment among ethnically diverse and socioeconomically disadvantaged groups was 37% (95% CI 18–58%), with significantly higher enrollment in observational studies (84%, 95% CI 78–90%) than in experimental trials (22%, 95% CI 10–36%; p < 0.001). Overall retention was 77% (95% CI 64–88%) and did not differ significantly by study design. Statewide disease-clinic networks, purposive community-leader engagement, and snowball sampling produced the highest enrollment, whereas medical-record screening yielded the lowest enrollment but the highest retention. Compensation and reminder strategies were similarly effective for retention. Thematic synthesis highlighted trust, culturally and linguistically tailored communication, hybrid and flexible visit schedules, transportation assistance, and clinician engagement as key facilitators. GRADE certainty was low for enrollment and moderate for retention. Community-engaged recruitment strategies, clinician referrals, culturally tailored materials, hybrid procedures, and modest incentives can substantially improve participation of ethnically diverse and socioeconomically disadvantaged populations in pain research. Standardized CONSORT-style reporting of recruitment/retention flowcharts according to strategy and ethnicity/socioeconomic status is essential to refine evidence-based strategies in the future. Full article

The incidence of fungal infections, particularly those caused by Candida species, has significantly risen among immunocompromised patients in recent years. Aim: This study evaluated the species distribution, antifungal susceptibility patterns, and virulence gene profiles of Candida species isolated from critically ill ICU patients while comparing the diagnostic performance of conventional phenotypic isolation media with molecular genotypic identification methods. Materials and Methods: A total of 211 clinical specimens were obtained from 200 hospitalized patients at Menoufia University Hospitals, Egypt. Candida species were phenotypically identified using cornmeal agar supplemented with Tween 80 (CTA) and CHROMagar^TM Candida (CMA) and were molecularly confirmed at the species level by sequencing PCR-amplified ITS regions. Antifungal susceptibility profiles to fluconazole, voriconazole, and amphotericin B were evaluated using the disk diffusion method. Furthermore, real-time PCR was deployed to detect genes encoding adherence factors and Secreted Aspartyl Proteinase. Results: Candida species were recovered from 45 out of 211 clinical specimens. The most frequently isolated species was Candida albicans (15/45, 33.3%), followed by C. parapsilosis (9/45, 20.0%), C. glabrata (7/45, 15.5%), C. tropicalis (7/45, 15.5%), C. krusei (4/45, 8.8%), and C. famata (3/45, 6.7%). Among the recovered Candida isolates, the percentage rates of the ALS1, ALS3, HWP1, and SAP4 genes were 40%, 42.22%, 44.4%, and 37.78%, respectively. HWP1 was the most frequently detected pathogenicity factor, whereas SAP4 was the least prevalent. Conclusions: In the studied immunocompromised cohort, non-albicans Candida species constituted a substantial proportion of clinical isolates. Notable antifungal resistance patterns were also observed, emphasizing the value of integrated phenotypic and genotypic identification for tailored management. Furthermore, virulence-associated genes (ALS1, ALS3, HWP1, and SAP4) were detected in isolates of several Candida species, highlighting their presence in clinical strains. Full article

Understanding building operational carbon dioxide (CO₂) emissions is essential for sustainable urban planning, yet variations in emissions across building types remain poorly characterized. This study integrated top-down and bottom-up approaches to estimate building-scale CO₂ emissions, capturing fine-scale emission patterns while maintaining consistency with aggregate energy statistics. Taking Guiyang as a case study, electricity consumption was simulated using the Designer’s Simulation Tool (DeST), while natural gas (NG) and liquefied petroleum gas (LPG) consumption were disaggregated using an area-proportional allocation method. Emission factors were then applied to estimate monthly CO₂ emissions. Results showed that monthly building CO₂ emissions ranged from 0.81 to 1.09 million tons. Significant spatial disparities were observed, with core districts contributing more than 22% of total emissions, whereas peripheral districts accounted for only approximately 6%. Residential buildings produced the highest total emissions, averaging 433 thousand tons per month, while shopping malls showed the highest emission intensity, reaching 8.08 kg/m² in July. The different building types and different seasons had different emissions, with residential buildings showing higher emissions in winter, whereas hotels and shopping malls experienced higher emissions during summer. The proposed framework extends existing approaches, providing reliable building CO₂ data for urban low-carbon planning and targeted mitigation. Full article

Soil erosion is a major environmental issue that threatens watershed ecological security and the sustainable use of land resources. Quantifying its spatiotemporal variability and associated environmental controls is important for sustainable land use planning and watershed management. To characterize the spatiotemporal variation in CSLE-simulated soil erosion and the relative explanatory contributions of environmental variables in the Qingyi River Basin, this study integrated rainfall, soil type, digital elevation model, land use, and vegetation coverage data for six observation years from 2000 to 2025 with GIS spatial analysis and the Chinese Soil Loss Equation (CSLE). Geodetector and CatBoost–SHAP were further applied to evaluate the explanatory contributions and interaction patterns of the selected environmental variables on the simulated erosion results. The results showed the following: (1) Woodland and cropland dominated the land use structure of the basin, while construction land increased from 90.95 km² to 164.21 km². Land use patterns differed markedly between the upstream and downstream areas, with woodland and grassland dominating the upstream area and cropland and construction land accounting for higher proportions in the downstream area. (2) Across the six observation years, the mean soil erosion modulus ranged from 135.74 to 378.39 t·km⁻²·a⁻¹, indicating generally low erosion levels, with the highest value occurring in 2015 and the lowest in 2025. (3) Soil erosion intensity was mainly characterized by slight and mild erosion, which together accounted for more than 95% of the basin area, whereas areas of moderate erosion and above were mainly concentrated in downstream mountainous areas and along both sides of river valleys. (4) The explanatory analysis showed that elevation, land use, and vegetation coverage made relatively high contributions to the spatial variability in the CSLE-simulated erosion results. Topographic and vegetation-related variables showed higher explanatory contributions in the upstream area, whereas land use showed a higher contribution in the downstream area. These findings provide a quantitative basis for soil erosion monitoring, the identification of priority areas for soil and water conservation, sustainable land use optimization, and region-specific watershed management in the Qingyi River Basin. Full article

Background: The global prevalence of oral disease including dental caries varies significantly across different regions, and South Africa is no exception. Although dental caries is a major public health concern, it is largely preventable through the promotion of proper oral hygiene practices and screening. The study aimed to assess the oral health status of school children within selected primary schools in the Tshwane District, South Africa. Methods: The study used a quasi-experimental design employing a quantitative research approach, structured into three phases: a pre-test assessment, the implementation of the intervention, and a post-test evaluation. A total of 390 primary school learners from five selected schools underwent dental screening and were included in the study on a voluntary basis. Data analysis was conducted using IBM SPSS version 30, incorporating both descriptive and inferential statistical tools. Results: Prior to screening, 75.9% of participants exhibited dental caries, while only 24.1% showed no signs of tooth decay. Following the intervention, improvements in oral hygiene status were observed among participants. The proportion of children presenting with high plaque scores decreased markedly from 19.0% at baseline to 1.8% post-intervention. Similarly, the prevalence of poor oral hygiene declined from 30.1% pre-intervention to 16.2% after the intervention, indicating an overall enhancement in hygiene practices. The proportion of dmft in primary teeth recorded 37.3% pre intervention, higher than post intervention (23.2%). Additionally, the record of DMFT in permanent teeth (49.2%) pre-intervention compared to the post intervention (40.8%). The study reported a similar percentage on the treatment during pre and post intervention (75.9%). Conclusions: Dental screening intervention yielded promising short-term results, particularly in plaque reduction and caries detection. However, its long-term success hinges on integrating school efforts with parental support and improving access to follow-up dental care. A comprehensive, multi-stakeholder approach remains essential to ensure lasting oral health benefits for children. Full article

With the increasing frequency of extreme-heat events, there is an urgent need to identify the key drivers of the urban thermal environment at fine spatial scales. Focusing on urban blocks within Beijing’s Fifth Ring Road, this study integrates Landsat 8 imagery acquired from March 2020 to February 2021 with multi-source data on land cover, buildings, population, pollution, and topography. LightGBM–SHAP, a theory-informed directed acyclic graph (DAG), CausalForestDML, and cross-fitted g-computation were employed to investigate predictive associations, Q25–Q75 average total treatment effects, and block-level responses to prespecified urban-morphology intervention scenarios for seasonal land surface temperature (LST). The within-season SHAP analyses consistently placed building height (BH) and building density (BD) among the relatively important predictors, whereas the predictive patterns of the normalized difference vegetation index (NDVI) and the proportion of impervious surfaces (ID) were more season-specific. The autumn and winter models also assigned relatively high within-season importance to the digital elevation model (DEM), PM_2.5, and CO₂ emission proxy. Because the four seasonal models differed in predictive performance and LST distributions, these cross-seasonal patterns were interpreted qualitatively rather than as direct comparisons of absolute SHAP values or rank positions. Causal-effect estimation further indicated that, under the primary DAG and identification assumptions, the Q25–Q75 point estimates were positive for BD and negative for BH in all four seasons. In summer, the Q25–Q75 effects of NDVI and ID were −0.772 and +1.548 °C, respectively. Intervention-scenario analysis further indicated that the estimated responses varied across blocks and seasons, emphasizing the importance of considering baseline urban conditions and common support when interpreting potential planning effects. Additional spatially blocked validation yielded lower predictive performance than random validation, while significant positive residual spatial autocorrelation remained in all four seasons. These findings may inform the local evaluation of season- and context-specific surface-temperature mitigation strategies within the observed-support range, but they should not be interpreted as universal planning prescriptions. Full article

Aim: Shared decision-making is increasingly recognized as a cornerstone of person-centered care. The aim of this study to assess the level of adults’ involvement in shared decision-making regarding dialysis modality choice and to identify its factors. Design: A cross-sectional correlational design was used. Methods: Data were collected from adults with advanced chronic kidney disease (CKD) at two Saudi hospitals between July and December 2024. Shared decision-making was measured using the Arabic version of the 9-item Shared Decision-Making Questionnaire. Perceived health status was assessed using a single item from the Kidney Disease Quality of Life questionnaire. Education and support were assessed using two dichotomous items. Data were analyzed using descriptive statistics, t-tests, ANOVA, and multiple regression analysis. Results: A total of 294 participants were included. The mean shared decision-making score was 64.7%. The highest mean item scores were observed for clarifying that a decision was required (4.65 ± 1.861) and assisting patients in understanding the available information (4.60 ± 1.682). A multiple regression analysis examined the contributions of attendance at a kidney treatment options education class and perception of health to shared decision-making perception among participants. The final model was statistically significant, F(2, 291) = 16.60, p < 0.001, and accounted for 10.2% of the variance in shared decision-making perception (R² = 0.102, adjusted R² = 0.096). Attendance at a kidney treatment options education class was a significant positive predictor in both steps (B = 0.436, SE = 0.086, β = 0.282, p < 0.001), and perception of health contributed uniquely in the final model (B = 0.056, SE = 0.025, β = 0.127, p = 0.024). Education-class attendance exerted a stronger unique effect than perception of health. Together, these two factors explained a modest but statistically significant portion of the variance in shared decision-making perception. Conclusions: Adults with advanced CKD in Saudi Arabia reported moderate-to-high levels of perceived shared decision-making, although collaborative deliberation and joint treatment selection remained limited. Attendance at a kidney treatment options education class showed the strongest unique association with higher shared decision-making perception, followed by perceived health status. Although these factors together explained only a modest proportion of the variance, the findings underscore the value of structured education and support in promoting patient involvement in treatment decisions. Further research should evaluate whether integrating structured kidney treatment options education into pre-dialysis care pathways improves person-centered care and patient engagement in modality selection. Full article

Accurately understanding the complex interactions among ecosystem services (ESs) and their driving mechanisms across multiple temporal and spatial scales is essential for effective ecological governance and regional sustainable development. To address the limited simultaneous consideration of long-term dynamics, cross-scale differences, and nonlinear driver effects in previous ES studies, this study develops an integrated multi-temporal and multi-scale framework to reveal the spatiotemporal dynamics and scale-dependent patterns of ES interactions and bundles and further identify their key drivers and threshold effects. The InVEST model was used to quantify six categories of ESs, while their trade-offs/synergies and bundles were examined across the 2 km, 5 km, and 10 km grids and county scales from 1990 to 2020. The XGBoost–SHAP model was further employed to identify the key drivers of ESs and their associated thresholds at each spatial scale. The main conclusions are as follows: (1) Six ESs exhibited spatial heterogeneity and broadly consistent declining trends across different spatial scales. From 1990 to 2020, soil retention (SR) showed the largest decrease across the four spatial scales (14.56–14.57%), followed by water yield (WY; 11.20–11.29%) and food production (FP; 7.52%), while landscape aesthetics (LA) declined the least (1.36%). (2) Interaction patterns among ESs were broadly consistent across spatial scales. Synergies were mainly observed among habitat quality (HQ), SR, carbon storage (CS), and LA, with Spearman correlation coefficients generally ranging from 0.49 to 0.92 (mostly p < 0.001), whereas trade-offs were predominantly observed between FP and other ESs. (3) ES bundles varied across spatial scales, with 8, 6, 6, and 5 bundle types identified across the four spatial scales, respectively; however, within a given spatial scale, their spatial distributions remained relatively stable across the four study periods. Transitions among different bundle types were also observed. (4) The relative importance of driving factors varied substantially among ESs, time periods, and spatial scales. The areal proportions of different landscape types were the primary drivers of habitat quality, carbon storage, food production, and landscape aesthetics, whereas soil retention and water yield were mainly influenced by biophysical indicators. The major drivers consistently exhibited relatively stable threshold effects across different temporal and spatial contexts. These findings deepen the understanding of multiscale interactions among ESs and their driving mechanisms and provide a scientific basis and decision-making support for the sustainable development of China’s coastal cities and other coastal regions worldwide. Full article

Reliable modelling of monthly air temperature is relevant to station-scale climate assessment and the evaluation of meteorological data-driven models. However, station-scale monthly meteorological datasets often contain correlated and partially redundant predictors because thermal, moisture, precipitation, wind, and seasonal variables are jointly controlled by atmospheric and seasonal forcing. This study conducts an integrated comparative analysis of established regression and machine learning models for monthly mean air temperature modelling in Zonguldak, a humid coastal province in the Western Black Sea Region of Türkiye. Monthly meteorological observations from 2000 to 2022 were used to evaluate eight primary regression and machine-learning models: Partial Least Squares regression, Ridge, Lasso, ElasticNet, Support Vector Regression, Random Forest, Gradient Boosting, and Extreme Gradient Boosting. Ordinary Least Squares (OLS) and Huber regression were additionally included as reference models. The analysis retained the original meteorological predictors and jointly evaluated predictive accuracy, model stability, ablation sensitivity, and model-specific predictor relevance. Reduced-predictor and seasonality-only scenarios were examined to distinguish direct thermal reconstruction from broader climatological predictability. Model performance was assessed using repeated nested cross-validation, bootstrap summaries of performance variability, supplementary rolling-origin validation, and Wilcoxon signed-rank tests with Holm correction. Although the full-predictor models achieved high predictive accuracy, this performance largely reflected the direct thermal information contained in minimum and maximum air temperature. When these thermal predictors were excluded, MAE increased to approximately 1.13–1.22 °C and $R^2$ decreased to approximately 0.93–0.94. The seasonality-only scenario yielded MAE values of approximately 1.27–1.34 °C and R² values of approximately 0.92, indicating that the annual cycle accounted for a substantial proportion of monthly temperature predictability. The additional non-thermal meteorological predictors provided only limited improvement beyond the strong seasonal baseline. Overall, model performance depended on the predictor information available, and no single model family showed a consistent advantage across the evaluated scenarios. These findings highlight the importance of considering predictive accuracy together with model stability and predictor dependence in data-limited station-scale temperature modelling. Full article

This study presents a small-scale application-oriented vehicle vision-sharing prototype for blocked-view observation in vehicle-following scenarios. The system was implemented using two DuckieBot vehicles and integrates socket-based image transmission, JPEG image compression, You Only Look Once (YOLO)-based object detection, road-plane-aligned image fusion, and fuzzy proportional–integral–derivative (fuzzy-PID) motor control. These modules are adopted as existing techniques and integrated for prototype-level experimental evaluation rather than proposed as new perception, compression, fusion, or control algorithms. Experiments were conducted under controlled small-scale indoor conditions. JPEG compression was quantitatively evaluated using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), encoded file size, and processing time. Q75 provided a mean PSNR of 39.8777 dB, a mean SSIM of 0.970522, and an average encoded size of 27.78 KB, representing a practical trade-off between reconstructed image quality and encoded data size. The YOLOv8n obstacle detector achieved a precision of 0.9724, a recall of 0.9571, an mAP@0.5 of 0.9851, and an mAP@0.5:0.95 of 0.8585 on an independent test set. Image-fusion evaluation showed that α = 0.60 produced the highest global mean PSNR, whereas α = 0.90 produced the highest global mean SSIM, indicating that the preferred blending coefficient depends on the selected image-quality criterion. A system-level ablation further distinguished shared-view visualization from a warning-only configuration, with the expected obstacle information presented in all 35 positive trials and no false alarms observed in 10 negative trials. The vehicle-following experiment verified the functional operation of the complete perception-to-control pipeline. The results should be interpreted within the controlled miniature-vehicle setting and should not be directly generalized to full-scale vehicles or real-road advanced driver assistance systems. Full article

Accurate identification of land subsidence types is essential for effective urban risk management, yet remains challenging due to the superposition of deformation signals at different spatial scales and the complexity of urban environments. In this study, land subsidence types in Fuzhou were identified through an integrated framework combining multi-scale deformation analysis and ensemble learning. Ground deformation time-series measurements were derived from 66 Sentinel-1A synthetic aperture radar (SAR) observations acquired between January 2018 and June 2023 using the time-series interferometric synthetic aperture radar (TS-InSAR). Deformation values in decorrelated areas were subsequently reconstructed using regression models driven by multi-source geological, hydrological, land-use, and urban features, resulting in a spatially continuous deformation field. A Fast Fourier Transform (FFT)-based Butterworth filtering approach was then applied to separate regional-scale and local-scale subsidence signals. Based on the extracted local deformation patterns and discriminative auxiliary features, land subsidence was classified into five categories: farmland-related subsidence, linear infrastructure-related subsidence, low-lying stratum-related subsidence, land-use transition-related subsidence, and older building area-related subsidence. Three ensemble learning models, XGBoost, CatBoost, and LightGBM, were implemented for subsidence type classification. All models achieved satisfactory performance, among which LightGBM exhibited the best overall performance. The classification results reveal pronounced differences in spatial distribution and deformation intensity among subsidence types. Farmland-related subsidence occupies the largest proportion of the affected area but is characterized by relatively moderate deformation rates, whereas older building area-related subsidence, despite its limited spatial extent, exhibits the highest deformation intensity. This study demonstrates the potential of ensemble learning for land subsidence type classification. Full article

This paper addresses fault diagnosis in nonlinear systems where reliable mathematical models are unavailable and only input–output measurements are accessible. The proposed methodology consists of three stages. First, a data-driven identification stage is performed using an Adaptive Neuro-Fuzzy Inference System (ANFIS) to capture the nonlinear dynamics of the system from fault-free sensor data. This procedure yields a set of convex Takagi–Sugeno (TS) models representing the system dynamics. In the second stage, fault detection is achieved using zonotopic proportional–integral (PI) observers with convex structures. Robustness against parametric uncertainty and sensor noise is ensured through an ${\mathcal{H}}_{\infty }$ formulation expressed as a set of linear matrix inequalities (LMIs). Finally, fault isolation is carried out using a fault signature matrix (FSM). The zonotopic framework provides adaptive set-based residual bounds that act as adaptive thresholds for fault detection, while structured residual activation patterns enable reliable fault isolation. The proposed approach is evaluated on a continuous stirred tank reactor (CSTR) under sensor faults and incipient process faults in the presence of measurement noise and compared with representative data-driven methods. Results demonstrate improved diagnostic accuracy and reduced false-alarm rates while maintaining timely fault detection and reliable isolation. Full article

Background: Neurological disease may lead to malnutrition through disease-related complications, underscoring the need for accurate muscle assessment. This study aims to evaluate an AI-based tool for quantifying and characterizing muscle ultrasound images, comparing its performance with the usual techniques of muscle mass and function. Methods: This was a prospective, open-label, longitudinal observational study of 117 adults with neurological disorders at high nutritional risk, designed to evaluate nutritional status and clinical evolution. The clinical assessment integrated anthropometry, bioelectrical impedanciometry, handgrip strength, dysphagia testing, and rectus femoris quadriceps ultrasound. Ultrasound images were evaluated through an AI-based platform to extract muscle quantity (rectus femoris muscle area (RFMA) and rectus femoris muscle thickness (RFMT) and quality biomarkers (percentage of low-echogenicity areas (Mi), interpreted as muscle; percentage of medium-echogenicity areas (FATi), interpreted as intramuscular fat). Patients were followed for two years to record mortality. Results: The sample included 117 adults with neurological disorders (52.1% women), with a mean age of 63.01 (16.14) years. A total of 77 patients (65.8%) had a condition with direct neuromuscular involvement. According to Global Leadership Initiative on Malnutrition (GLIM) criteria, 73 patients (62.4%) had malnutrition, while 30 patients (25.6%) had severe malnutrition. There were no differences in muscle mass parameters, but patients with neuromuscular involvement (NM) had lower values of percentage of Mi (NM: 42.44 (9.09%) vs. 47.36 (7.74)%; p < 0.01), and higher values of FATi (41.91 (5.71)% vs. 39.15 (4.89)%). The prevalence of mortality was 26 patients (22.2%). In the multivariate analysis, FATi (above median) (OR = 5.11 (IC95%: 1.26–20.67)) increased risk of death, adjusted by age, neuromuscular involvement, sex, and Mi. Conclusions: Patients with neuromuscular disorders showed a markedly lower proportion of Mi and a higher presence of FATi compared to those with non-neuromuscular conditions. Mortality was associated with greater FATi on AI-based ultrasound analysis. These findings suggest AI-enhanced imaging captures clinically relevant tissue alterations with potential prognostic value; however, given the observational data and heterogeneity of neurological conditions, these implications should be interpreted cautiously. Full article