Search Results (12,910)

Low back pain (LBP) is a highly prevalent musculoskeletal problem and a leading cause of disability worldwide. From a pathophysiological perspective, the contribution of inflammation to LBP is being increasingly recognized. In this literature review, we aim to provide an overview of the role of inflammation as a mediator of LBP while summarizing clinical studies investigating the potential role of anti-inflammatory treatments in the management of LBP. Although often controversial, the available evidence suggests an important role of inflammation in the pathogenesis of LBP, which can be further translated into novel therapeutic targets. Both anti-tumor necrosis factor (anti-TNF) and anti-nerve growth factor (anti-NGF) agents hold the potential of blocking inflammation and pain pathways in patients with chronic LBP. TNF inhibitors have been tested mostly in small trials with mixed results, and their long-term efficacy remains to be proven. Anti-NGF agents have demonstrated stronger and consistent efficacy in randomized controlled trials, but safety concerns compromise their widespread use. The potential role of other anti-inflammatory molecules is currently under investigation. Presently, the routine use of TNF or NGF inhibitors is not supported in radiculopathy or chronic LBP. However, novel anti-inflammatory therapies introduced in the rheumatology field appear to be promising for specific subsets of patients suffering from chronic, refractory LBP, with a complementary role as therapeutic tools, after the unsuccessful outcome of the conservative approach. Full article

Background: The majority of injuries among older adults occur due to unexpected and sudden incidents in the home environment. This study aimed to develop a protocol for the design of the health belief model-based program for preventing unintentional home injuries in older adults and to evaluate the effectiveness of the program. Methods: The study proposed in this protocol, Safety and Home Injury Prevention for Seniors (SHIPs), is a single-blind, parallel-group, randomized controlled trial. A total of 54 Korean older adults (≥65 years) will be randomly assigned to either (1) the intervention group (n = 27), which will receive the SHIPs program, or (2) the control group (n = 27), which will attend four lecture-only sessions. The efficacy of the program will be assessed via tests performed at baseline, 1 week after program completion, and 1 month after program completion, and analyses of the changes in injury occurrences, risk factors, preventive behaviors, beliefs about safety and injury prevention, psychological health, physiological function, and health-related quality of life. Expected Results: The SHIPs intervention is expected to reduce home injuries and enhance awareness and preventive behaviors among community-dwelling older adults. It may also improve their physical and psychological health and overall quality of life. Conclusions: The SHIPs intervention may serve as an effective community-based strategy to promote injury prevention and improve the overall well-being of older adults. Full article

The ageing of polymer-modified bitumen (PMB) significantly affects its rheological performance and service life in asphalt pavements. In this study, experimental data PMB 25/55–60 aged under a modified Thin Film Oven Test (TFOT) were restructured into a tidy dataset and analyzed using machine learning techniques. The input variables included temperature, angular frequency, and ageing condition, while the output variable was the complex shear modulus (G*). Two state-of-the-art regression models, Random Forest (RF) and Gradient Boosting Regressor (GBR), were trained and evaluated. Performance assessment revealed that GBR outperformed RF, achieving R² = 0.992, MAE = 1.07 × 10⁶ Pa, and RMSE = 2.04 × 10⁶ Pa, compared to RF with R² = 0.962. Condition-wise analysis further confirmed the robustness of GBR across different TFOT scenarios. Feature importance analysis identified temperature as the dominant factor influencing rheological behavior, followed by frequency and ageing condition. These findings demonstrate the potential of gradient boosting approaches for accurately predicting the rheological properties of aged PMB, providing a reliable tool for performance evaluation and supporting the development of predictive frameworks for pavement materials. Full article

The rural population in semi-arid areas of Mexico suffers from poverty levels that hinder a dignified life, leading to migration and abandonment of their resources. This is exacerbated by climate change (droughts and high temperatures), which negatively impacts crops. While farmers attempt to adapt, their strategies are insufficient. A low-cost Complementary Agriculture (AgriCom) model was designed, using local resources to produce prickly pear (Opuntia ficus-indica Mill.) and corn (Zea mays L.), while simultaneously conserving regional germplasm of Opuntia spp. A randomized block design with three replications was used. Each block included seven varieties, with 125 plants per variety. Corn was grown as a monocrop in the same experimental site. Graphical analysis, analysis of variance with mean comparison test in RStudio, a profitability analysis, and a Land Equivalent (ELU) analysis were performed. The varieties Verdura, Atlixco, and Rojo Liso showed higher yield, internal rate of return, and net present value; their benefit–cost ratios were 7.97, 6.35, and 6.82, respectively. The ELU was greater than 1.0 when combining the prickly pear varieties. Agroclimatic conditions did not allow the corn to complete its phenological cycle, and its ELU was zero. Seventy prickly pear genotypes, with three replicates each, representing eight Opuntia species, were collected and integrated into the periphery of the production unit. This model was accepted by the Climate Action Platform for Agriculture in Latin America and the Caribbean (PLACA) for implementation in other communities. Full article

Wine phenolics serve as robust chemical signatures correlated to grape variety, processing, and regional identity. This study explores the potential of machine learning algorithms, combined with the phenolic profiles of Albanian wines, to classify them according to grape variety. Geographic origin analysis was conducted as a preliminary exploration. The dataset of phenolic compounds included white and red wines, spanning the 2017 to 2021 vintages. Using five supervised algorithms—Support Vector Machine (SVM), Random Forest, XGBoost, Logistic Regression, and K-Nearest Neighbors—a high classification accuracy was achieved, with SVM reaching 100% under Leave-One-Out Cross-Validation (LOOCV). To address class imbalance, the Synthetic Minority Over-sampling Technique (SMOTE) and stratified cross-validation were applied. Random Forest feature importance consistently highlighted trans-Fertaric acid and Procyanidin B3 as dominant discriminants. Parallel coordinates plots demonstrated clear varietal patterns driven by phenolic differences, while PCA and hierarchical clustering confirmed unsupervised grouping consistent with wine type and maceration level. Permutation testing (1000 iterations) confirmed the non-randomness of model performance. These findings show that a small set of phenolic markers can offer high classification accuracy, supporting chemically based wine authentication. Although the dataset is relatively small, thorough cross-validation, non-redundant modeling, and chemical interpretability provide a solid foundation for scalable methods. Future work will expand the dataset and explore sensor-based phenolic measurement to enable rapid authentication in wine. Full article

Introduction: Long-term heart failure and mortality after catheter ablation for premature ventricular contraction (PVC) remain underexplored. Methods: We retrospectively analyzed 4195 adults who underwent PVC ablation in a nationwide claims database. To address class imbalance, we used synthetic minority over-sampling technique (SMOTE) and random over-sampling examples (ROSE). Five supervised algorithms were compared: logistic regression, decision tree, random forest, XGBoost, and LightGBM. Discrimination was assessed by stratified five-fold cross-validation using the area under the receiver operating characteristic curve (ROC AUC). Because rare events can bias ROC, we also examined precision–recall (PR) curves. Results: For predicting three-year heart failure, LightGBM with ROSE achieved the highest ROC AUC at 0.822. For three-year mortality, logistic regression with ROSE and LightGBM with ROSE showed balanced performance with ROC AUCs of 0.886 and 0.882. Pairwise DeLong tests indicated that these leading models formed a high-performing cluster without significant differences in ROC AUC. Age, prior heart failure, malignancy, and end-stage renal disease were the most influential predictors by model explainability analysis. Discussion: Addressing class imbalance and benchmarking modern learners against a transparent logistic baseline yielded robust, clinically interpretable risk stratification after PVC ablation. These models are suitable for integration into electronic health record dashboards, with external validation and local threshold optimization as next steps. Full article

Previous researches have proved that the synchronized use of inexpensive RGB images, image processing, and machine learning (ML) can accurately identify crop stress. Four Machine Learning Image Modules (MLIMs) were developed to enable the rapid and cost-effective identification of sugar beet stresses caused by water and/or nitrogen deficiencies. RGB images representing stressed and non-stressed crops were used in the analysis. To improve robustness, data augmentation was applied, generating six variations on each image and expanding the dataset from 150 to 900 images for training and testing. Each MLIM was trained and tested using 54 combinations derived from nine canopy and RGB-based input features and six ML algorithms. The most accurate MLIM used RGB bands as inputs to a Multilayer Perceptron, achieving 96.67% accuracy for overall stress detection, and 95.93% and 94.44% for water and nitrogen stress identification, respectively. A Random Forest model, using only the green band, achieved 92.22% accuracy for stress detection while requiring only one-fourth the computation time. For specific stresses, a Random Forest (RF) model using a Scale-Invariant Feature Transform descriptor (SIFT) achieved 93.33% for water stress, while RF with RGB bands and canopy cover reached 85.56% for nitrogen stress. To address the trade-off between accuracy and computational cost, a bargaining theory-based framework was applied. This approach identified optimal MLIMs that balance performance and execution efficiency. Full article

Abnormal gas emissions are one of the main risk factors evoking coal mine gas accidents. How to accurately and efficiently predict gas emissions and identify the risk of gas anomalies has become a key issue in coal mine safety management. This study takes the Yuqia No.1 Mine of Qinghai Energy Group as the research object, collecting environmental variable data such as the gas emission quantity of the mining face, coal seam depth, coal seam thickness, coal seam gas content, temperature, wind speed, and respirable dust concentration. Multi-parameter data fusion, gray correlation degree analysis, least square support vector machine (LS-SVM), random forest (RF), back propagation neural network (BPNN), and other methods were adopted in this paper to explore the prediction accuracy and risk factors of mine gas emissions. The results show the following: (1) The correlation coefficients between coal seam depth, coal seam thickness, coal seam gas content, daily progress, daily output, and wind speed and gas emission quantity are 0.955, 0.975, 0.963, −0.912, 0.983, and 0.681, respectively, showing a significance level of 0.01, and are used as external input characteristic quantities for gas emission quantity prediction. (2) For the LS-SVM model, the root mean square error (RMSE) and mean absolute error (MAE) values on the training set were 0.015 and 0.012, respectively, while the corresponding test errors were 0.216 and 0.094, which represent the lowest among all models. The R² values for the training and test sets were 0.993 and 0.951, respectively, indicating higher predictive accuracy compared to the other three benchmark models. (3) According to the comprehensive correlation degree, the top five factors that had a greater impact on the amount of gas emissions were, successively, as follows: coal seam thickness (0.8896), coal seam gas content (0.8849), daily output (0.6456), coal seam depth (0.6258), and wind speed (0.5578). The research results can provide a reference for high-precision prediction of gas emissions. Full article

Phytase releases phosphorus from phytate and may confer extra-phosphoric benefits in sows. We tested whether Escherichia coli-derived 6-phytase during late gestation and lactation improves sow and litter outcomes. In a randomized complete block trial, 186 TN70 sows received a phytase-free positive control (adequate Ca and available P) or Ca- and P-reduced diets with 500, 1500, or 2500 FTU/kg. Outcomes included sow body condition, lactation feed intake and feed conversion ratio (FCR), farrowing duration and blood glucose, piglet weaning performance and diarrhea scores, maternal serum Ca and P (farrowing, weaning), and piglet glutathione peroxidase (GPx) and superoxide dismutase (SOD; day 14). Phytase increased lactation intake by 4.4–5.6%; farrowing duration was shorter at all doses (−24.2, −23.8, and −14.8 min; up to −8.1%). Litter weaning weight rose by 6.1–8.2%, and piglet average daily gain increased by 9.1% at 2500 FTU/kg. Maternal Ca and P increased dose-responsively, especially at weaning (Ca +73% at 500–1500 FTU/kg; +140% at 2500; P +55%, +59%, +118%). Diarrhea counts declined at selected doses (e.g., scores 1–2: −17% at 500 FTU/kg), and piglet SOD decreased with dose (−8.6% to −39.3%); GPx showed modest modulation. Sow body weight, backfat, and the weaning-to-estrus interval were unchanged. In Ca- and P-reduced diets, conventional and super-dosed phytase enhanced mineral bioavailability and peripartum efficiency, supporting heavier litters without compromising sow condition. Full article

Background and Objectives: Dynamic nutrient profiling represents a paradigm shift in personalized nutrition, integrating real-time nutritional assessment with individualized dietary recommendations through advanced algorithmic approaches, biomarker integration, and artificial intelligence. This comprehensive systematic review and meta-analysis examines the current state of dynamic nutrient profiling methodologies for personalized diet planning, evaluating their effectiveness, methodological quality, and clinical outcomes. Methods: Following PRISMA 2020 guidelines, we conducted a comprehensive search of electronic databases (PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar) from inception to December 2024. The protocol was prospectively registered in PROSPERO (Registration: CRD42024512893). Studies were systematically screened using predefined inclusion criteria, quality was assessed using validated tools (RoB 2, ROBINS-I, Newcastle–Ottawa Scale), and data were extracted using standardized forms. Random-effects meta-analyses were performed where appropriate, with heterogeneity assessed using I² statistics. Publication bias was evaluated using funnel plots and Egger’s test. Results: From 2847 initially identified records plus 156 from additional sources, 117 studies met the inclusion criteria after removing 391 duplicates and systematic screening, representing 45,672 participants across 28 countries. Studies employed various methodological approaches: algorithmic-based profiling systems (76 studies), biomarker-integrated approaches (45 studies), and AI-enhanced personalized nutrition platforms (23 studies), with some studies utilizing multiple methodologies. Meta-analysis revealed significant improvements in dietary quality measures (standardized mean difference: 1.24, 95% CI: 0.89–1.59, p < 0.001), dietary adherence (risk ratio: 1.34, 95% CI: 1.18–1.52, p < 0.001), and clinical outcomes including weight reduction (mean difference: −2.8 kg, 95% CI: −4.2 to −1.4, p < 0.001) and improved cardiovascular risk markers. Substantial heterogeneity was observed across studies (I² = 78–92%), attributed to methodological diversity and population characteristics. AI-enhanced systems demonstrated superior effectiveness (SMD = 1.67) compared to traditional algorithmic approaches (SMD = 1.08). However, current evidence is constrained by practical limitations, including the technological accessibility of dynamic profiling systems and equity concerns in vulnerable populations. Additionally, the evidence base shows geographical concentration, with most studies conducted in high-income countries, underscoring the need for research in diverse global settings. These findings have significant implications for shaping public health policies and clinical guidelines aimed at integrating personalized nutrition into healthcare systems and addressing dietary disparities at the population level. Conclusions: Dynamic nutrient profiling demonstrates significant promise for advancing personalized nutrition interventions, with robust evidence supporting improved nutritional and clinical outcomes. However, methodological standardization, long-term validation studies exceeding six months, and comprehensive cost-effectiveness analyses remain critical research priorities. The integration of artificial intelligence and multi-omics data represents the future direction of this rapidly evolving field. Full article

This paper presents a data-driven decision-support framework for distribution system operations using logistic regression (LR) on the Voltage Margin Index (VMI). Treating VMI as the sole explanatory feature, the proposed two-stage workflow first fits an inferential LR model to establish statistical significance and perform valid statistical inference on the coefficients. Next, it trains a performance-optimized LR classifier with class-balanced sample weighting to produce calibrated violation probabilities. LR maps VMI to violation probability and analytically converts a calibrated probability threshold into an operator-ready VMI decision boundary. Applying 5-fold group cross-validation to 8816 node-level samples generated from a 22.9 kV Jeju Island model yields performance- and safety-oriented probability thresholds (θ_opt = 0.7891, θ_safe = 0.6880), which correspond to VMI decision boundaries VMI_DB,opt = 0.7893 and VMI_DB,safe = 0.8101. On an unseen 20% test set, the LR classifier achieves 99.94% accuracy (F₁ = 0.9977) under θ_opt and 100% recall under θ_safe. A random forest (RF) benchmark confirms comparable accuracy (=99.72%) but lacks analytical invertibility and transparency. This framework offers distribution system operators (DSOs) and virtual power plant (VPP) operators clear, evidence-based criteria for routine planning and risk-averse decision-making, and it can be applied directly to any distribution system with node-level voltage measurements and known regulation limits. Full article

Background: Circadian rhythm disruption caused by shift work alters metabolic and hormonal pathways, which accelerates chronic disease onset, leading to decreased quality and quantity of life. This study aimed to determine whether a practical lifestyle intervention emphasizing nutrition timing and recovery habits could mitigate the metabolic and psychological effects of night-shift work. We conducted a randomized, open-label, crossover trial with two 8-week periods. Methods: Female healthcare workers (n = 13) aged 18–50 years with a body mass index (BMI) between 27 and 40 kg/m² and working predominantly night shifts (≥30 h/week for ≥6 months) were randomized. During the 8-week intervention phase, participants received daily text messages with guidance on food, sleep/rest, and physical activity and were provided with whey protein isolate powder and grain-based snack bars to consume during work shifts. The program targeted improved nutrient timing, adequate protein intake, and structured rest without formal exercise training, allowing evaluation of dietary and behavioral effects feasible for this population. Total caloric (~30 kcal/kg lean mass) and protein (2 g/kg lean mass) needs were measured, along with sleep/rest goals of 6–8 h/24 h. Primary outcome measures were change in visceral fat percentage (VF%) by DXA and mental/physical quality of life (RAND SF-12). Secondary outcomes included fasting triglycerides, ALT, blood glucose, LDL, actigraphy, and fecal microbiome. Mixed-design two-way ANOVA was conducted to assess the effects of group (immediate [IG] and delayed [DG]), time (baseline, 8-week crossover, and week 16), and Group $\times$ Time (GxT) interactions, and Bonferroni correction was applied to post hoc t-tests. Results: Eleven participants completed the study. Both groups increased dietary protein intake (p < 0.001), and a GxT interaction for VF% (p = 0.039) indicated DG reduced VF% to a greater extent (−0.335 ± 0.114% (p = 0.003) vs. 0.279 ± 0.543% (p = 0.158)). Mental and physical QOL, objectively measured physical activity and sleep, serum lipids and inflammatory markers, and fecal microbiota remained unchanged (p > 0.05 for all GxT). Conclusions: The findings suggest that targeted nutrition and recovery strategies can modestly improve dietary intake and visceral fat; however, consistent with prior work, interventions without structured exercise may be insufficient to reverse broader metabolic effects of circadian disruption. This trial was registered at ClinicalTrials.gov, identifier: NCT06158204, first registered: 28 November 2023. Full article

This paper presents the results of numerical modeling and vibration testing of a nanosatellite’s optical payload, aimed at assessing its mechanical stability under the mechanical impacts of launch. The purpose of the study is to compare finite element modeling (FEM) data with experimental testing to refine the computational model and improve the reliability of mechanical stability predictions. The methodology included an FEM analysis with an average damping coefficient, an adapter blank test, a resonance study with a low-level sinusoidal run, random vibration tests, and a sinusoidal pulse test. The FEM results showed an average yield margin of safety MoS = 2.5 with a minimum MoS = 1.8 in the primary mirror mount area. The adapter blank test confirmed the absence of natural resonances in the operating range. The resonance study revealed modes in the 300–1340 Hz range, with the most pronounced peaks in the secondary mirror bracket (520–600 Hz) and the electronics unit (1030–1100 Hz). A comparison of the root mean square (RMS) acceleration values between calculations and tests revealed discrepancies due to the heterogeneous nature of the damping. The values of ζ determined by the half-power method varied from 0.9% to 4.8%, which confirms the dependence of the damping properties on the frequency and localization of the modes. The obtained results confirmed the structural integrity of the payload, allowed for the localization of structural elements, and substantiated the need to consider actual damping coefficients in FEM models. The presented data can be used to optimize the design and improve mechanical stability during payload integration into the satellite platform. Full article

This study investigates the influence of variable electrode positions on image reconstruction in capacitively coupled electrical impedance tomography (CC-EIT) of the human thorax. Images were reconstructed by an adversarial neural network trained on a synthetic dataset generated using a tomographic model that included a wearable elastic band with 32 electrodes attached. Dataset generation was conducted using a previously developed numerical phantom of the thorax, combined with a newly developed algorithm for random selection of electrode positions based on physical limitations resulting from the elasticity of the band and possible position inaccuracies while putting the band on the patient’s chest. The thorax phantom included the heart, lungs, aorta, and spine. Four training and four testing datasets were generated using four different levels of electrode displacement. Reconstruction was conducted using four versions of neural networks trained on the datasets, with random ellipses included and noise added to achieve an SNR of 30 dB. The quality was assessed using pixel-to-pixel metrics such as the root-mean-square error, structural similarity index, 2D correlation coefficient, and peak signal-to-noise ratio. The results showed a strong negative influence of electrode displacement on reconstruction quality when no samples with displaced electrodes were present in the training dataset. Training the network on the dataset containing samples with electrode displacement allowed us to significantly improve the quality of the reconstructed images. Introducing samples with misplaced electrodes increased neural network robustness to electrode displacement while testing. Full article

Bioavailable phosphorus is essential for sustaining high crop productivity, yet excessive inorganic P fertilization often leads to P accumulation in stable soil forms, reducing utilization efficiency. Straw serves as an organic P source and enhances P availability by stimulating microbial activity. However, systematic studies on how organic P inputs (straw returning) and inorganic P fertilizers regulate soil bioavailable P through microbial and enzymatic processes remain limited. A 16-year field experiment was carried out in a rice–wheat rotation system, including five fertilization treatments: no fertilization (CK), optimized fertilization (OPT), increased N (OPTN), increased P (OPTP), and optimized fertilization combined with straw mulching/returning (OPTM). This study evaluates the impacts of long-term organic and inorganic P sources on soil P fractions, extracellular enzyme activities, and the composition of microbial communities, alongside their collective contributions to crop yield. In this study, based on soil samples collected in 2023, we found that fertilization led to significant increases in Citrate-P and HCl-P, enhanced the activities of β-1,4-glucosidase (BG), β-D-cellobiosidase (CBH), and β-1,4-N-acetylglucosaminidase (NAG), and altered both microbial diversity and co-occurrence network complexity. The OPTM treatment showed the highest yield and improved microbial diversity and network complexity, with Enzyme-P, Citrate-P, and HCl-P increasing by 62.64%, 11.24%, and 9.49%, and BG, CBH, and NAG activities rising by 22.74%, 40.90%, and 18.09% compared to OPT. Mantel tests and random forest analyses revealed significant associations between microbial community and biochemical properties, while partial least squares path modeling (PLS-PM) indicated that inorganic P source enhanced yield primarily through altering soil P dynamics and enzymatic processes, while microbial communities under organic P source acted as key mediators to increase crop productivity. These findings deepen insights into how microbial communities and enzymatic stoichiometry synergistically regulate phosphorus bioavailability and wheat yield, providing a theoretical basis for sustainable fertilization practices in rice–wheat rotation systems. Full article