Search Results (2,577)

South African irrigation schemes face critical challenges of water scarcity, infrastructure deterioration, and limited monitoring capacity, threatening agricultural productivity and food security. This scoping review systematically analyses 59 peer-reviewed publications (2000–2025) on sensor-based and acoustic irrigation monitoring technologies in South Africa, using transformer-based natural language processing (Sentence-BERT embeddings), unsupervised Machine Learning (UMAP dimensionality reduction, HDBSCAN clustering), and geospatial mapping applied to literature retrieved from Web of Science and Scopus. Results show that water quality monitoring (42.4% of studies) and remote sensing (25.4%) dominate the national research landscape, while soil moisture sensing and modelling remain comparatively limited. Notably, no peer-reviewed studies applying acoustic monitoring technologies to irrigation were identified, representing a critical gap despite proven international applications for leak detection (95–98% accuracy), widespread infrastructure aging (over 50% of schemes exceeding 30 years), and reported water losses of 30–60% in poorly managed systems. Reported experimental water savings range from 15% to 30%, yet applications remain largely confined to pilot-scale implementations concentrated within a limited number of Water Management Areas. Persistent adoption barriers include infrastructure unreliability, financial inaccessibility, limited digital literacy, and weak institutional coordination. The review recommends: (i) expanding research coverage across underrepresented regions and Water Management Areas; (ii) strengthening extension support and technical training to enable broader adoption; and (iii) integrating low-cost sensor networks with predictive, data-driven irrigation advisory systems. These priorities aim to support scalable, context-sensitive irrigation modernisation under increasing water scarcity pressures. Full article

Ti alloys are widely used in aerospace and biomedical fields due to their high mechanical properties under severe loading. Interest in additively manufactured Ti6Al4V has increased, but further research is needed to fully characterize their properties. This work compares the effects of surface properties, internal defects, microstructure, hardness, and Hot Isostatic Pressing (HIP) or Vacuum Heat Treatment (VHT) on the fatigue behavior of Ti6Al4V produced by Selective Laser Melting (SLM) and Electron Beam Melting (EBM). Printing parameters and post-processing were optimized to achieve high density and minimal porosity, providing a solid basis for realistic fatigue comparisons. Samples were characterized in terms of microstructure (optical microscopy and SEM), mechanical properties (hardness mapping), surface texture (confocal microscopy), and internal defects (image-based analysis). Uniaxial fatigue limits were determined by a Dixon-Mood staircase method, and failed specimens were analyzed for fracture surfaces and defect areas. Applied load on flaws was evaluated to identify root causes of fatigue failure. Results showed that fatigue of as-printed samples is governed by surface roughness, while machined specimens are controlled by internal defect size. Machining increased the fatigue limit roughly threefold, and HIP further improved it by 10–20% by reducing internal porosity. In conclusion, with properly optimized melting parameters, both EBM and SLM produce similar mechanical performance at comparable roughness, supporting their use for structural components. Full article

Rotator cuff injuries are among the most common musculoskeletal conditions that affect shoulder function and can ultimately impact quality of life. While physical therapy is essential in the care of rotator cuff injuries, the ideal dose of therapeutic exercises continues to be a significant clinical dilemma because of the generalized nature of rehabilitation protocols. This pilot study proposes a machine learning approach to personalize rehabilitation using surface electromyography (sEMG) data collected from eight healthy individuals by testing four key shoulder movements: scaption, internal rotation, external rotation, and external rotation at 90° abduction. In this research, the XGBoost algorithm was used to model muscle activation patterns by achieving a high predictive accuracy (R² = 0.5325; MSE = 0.0084 μV²). Because sEMG reliably measures superficial muscle activity, a linear programming model was used to divide a 60 min therapy session in a way that increases activation of superficial muscles (such as deltoid and trapezius) while reducing strain on deep muscles (such as supraspinatus and infraspinatus). Three optimization scenarios were tested by reflecting a different clinical goal: prioritizing superficial muscles, minimizing deep muscle strain, or balancing both. Optimized time allocations assigned more time to external rotation at 90° abduction and scaption. This research demonstrates the potential for data-driven methods to transform rotator cuff rehabilitation through personalized and evidence-based treatment plans. The results enhance clinical practice by enabling adaptive rehabilitation planning and show that machine learning can support decision-making in complex muscle activation analysis with strong performance and low latency. Full article

Surface quality of machined wood-based panels plays a key role in subsequent processing and product performance; however, its formation during CNC edge milling remains insufficiently understood, particularly for materials with different structural characteristics, including recycled content. This study investigates the influence of feed per tooth, milling strategy, and material structure on surface quality during CNC edge milling of particleboards manufactured from alternative lignocellulosic resources. Six board variants were experimentally produced and machined on a five-axis CNC machining center Morbidelli m100 using a single-edge milling cutter, with feed per tooth varied at three levels and both climb and conventional milling strategies applied. Surface quality was evaluated using a non-contact 3D optical profilometer Keyence VR-6000, and roughness (R_a) and waviness (W_z) parameters were analyzed. The results showed that surface roughness increased with increasing feed per tooth for all materials, with an increase of approximately 30%–70%. Statistical analysis confirmed a significant effect of feed per tooth and material type, while milling strategy and its interaction with material were not statistically significant. Materials with higher surface heterogeneity (CVR_a) showed increased roughness and greater sensitivity to feed. A statistically significant positive relationship was found between surface heterogeneity (CVR_a) and roughness sensitivity (ΔR_a), indicating that materials with higher surface heterogeneity (CVR_a), which likely reflects variability in their internal structure, are more sensitive to changes in feed per tooth. Full article

To investigate concrete drying shrinkage in high-altitude environments, moisture evaporation and shrinkage rates were examined under combined curing regimes of four temperatures (40 °C, 20 °C, 0 °C, −10 °C) and three relative humidities (RH40%, RH60%, RH80%). Curing temperature and humidity primarily regulate shrinkage deformation by altering the internal moisture evaporation rate. Both evaporation and shrinkage rates exhibited a rapid initial increase, followed by deceleration, and finally stabilization with increasing age. A strong positive correlation was observed between these two parameters. The high-temperature and low-humidity condition (40 °C, RH40%) induced the most severe shrinkage. Four machine learning algorithms (XGBoost, RF, ANN, and KNN) were used to construct prediction models. After hyperparameter optimization and cross-validation, the RF models exhibited superior generalization and robustness (test set R² > 0.94). The model accurately captures the complex non-linear relationship between environmental parameters and shrinkage. SHAP analysis on the optimal models identified the moisture evaporation rate as the primary driving factor. The analysis quantified the non-linear contributions of temperature and age, alongside the inhibitory effect of humidity. The study verified the consistency between data-driven models and physical mechanisms. This study elucidates the shrinkage mechanism under extreme conditions. It provides a reliable reference for crack control and life prediction in high-altitude engineering. Full article

Background: To explore the correlation between spatial dose distribution and post-implant urinary toxicity, aiming to assist decision making in low-dose-rate (LDR) treatment planning, thereby improving patient outcomes. Methods: Eighty-five prostate LDR patients with >12-month follow-up were included. Patient-reported urinary toxicity was collected prospectively using the International Prostate Symptom Score (IPSS) questionnaire, from before implant (baseline) to post-implant follow-up. Patients were then grouped into those whose symptom scores returned to ≤2 points above baseline by 12 months (no long-term toxicity) vs. those who did not (long-term toxicity). A total of 106 features were extracted for each patient, including principal components of dose-volume histograms (DVHs) from multiple prostate subzones, the whole prostate and urethra, along with baseline IPSS, implantation characteristics, and additional DVH indicators for the prostate and the urethra. A machine learning (ML) model incorporating backward feature selection algorithm was developed to predict long-term toxicity status, using a shuffle-and-split validation strategy for model evaluation during feature selection. A univariate statistical analysis was conducted on the model’s selected features. Results: Out of 85 patients, 41 (48%) had long-term urinary toxicity. Seven features were selected during model training, including baseline IPSS and six dosimetric features from several prostate subzones primarily located in the posterior prostate. The model achieved a high mean area under the receiver operating characteristic curve (AUC) of 0.81, with a balanced sensitivity and specificity of 0.78 by adjusting the probability threshold. In univariate analysis, only baseline IPSS and one selected dose feature were significantly correlated with long-term toxicity with AUC < 0.71. Conclusions: The proposed ML model, integrating baseline IPSS and spatial dosimetric features, effectively predicts long-term urinary toxicity after prostate LDR. This approach offers a practical method for risk stratification, allowing clinicians to identify patients at elevated risk and prioritize them for targeted preventative measures and closer follow-up. Full article

Machine components are subject to a wide range of damage and failure processes, and their correct identification is essential for reliable operation, effective maintenance, and accurate diagnosis. However, a persistent gap exists between morphology-based classification systems, used in international standards, and the mechanism-based interpretations developed in tribology and mechanics. This review analyzes the evolution and current practice of damage classification for machine components, with emphasis on rolling bearings as a representative case. The study is based on a structured analysis of international standards (e.g., ISO 15243), complemented by tribological literature and manufacturers’ documentation. The review focuses on how several damage modes such as spalling, pitting, and fretting are defined, interpreted, and applied in practice. The results highlight systematic ambiguities arising from the separation between visual descriptors and underlying failure mechanisms, particularly in the case of fatigue-related surface damage. Through selected case studies, the review demonstrates how reliance on morphology alone may obscure causal interpretation and lead to inconsistent diagnosis. The study further discusses emerging trends, including digital damage atlases and artificial-intelligence-based diagnostic tools, emphasizing how inconsistencies in standardized terminology may affect their reliability. It is concluded that morphology-based standards should be regarded as complementary diagnostic tools rather than substitutes for mechanical analysis. A closer alignment between standardized terminology and mechanistic understanding is necessary to improve failure diagnosis, support engineering education, and enhance the reliability of machine components. Full article

Early-age shrinkage is a critical factor governing the dimensional stability and cracking susceptibility of ultra-high-performance concrete (UHPC). However, accurate prediction of UHPC shrinkage remains challenging due to the strong nonlinear interactions among mixture parameters, curing conditions, and hydration-induced internal moisture evolution, particularly when only limited experimental data are available. In this study, a systematic experimental program was conducted to investigate the influence of the binder-to-sand ratio, water-to-binder ratio, polypropylene fiber dosage, and curing environment on both early drying shrinkage and autogenous shrinkage of UHPC. Based on the experimental results, a structured dataset covering all shrinkage test data was constructed to support data-driven modeling. To improve prediction reliability under small-sample conditions, a Bayesian-optimized Extreme Gradient Boosting (BO-XGBoost) framework was developed and benchmarked against several conventional machine learning models, including Backpropagation Neural Networks (BPNNs), Random Forest (RF), and Support Vector Machines (SVMs). Shrinkage test data from other literature validated the prediction accuracy of this model, demonstrating its rationality and practicality. In addition, the Shapley Additive Explanations (SHAP) method was employed to quantitatively interpret the contribution and interaction mechanisms of key variables affecting shrinkage behavior. The results show that the BO-XGBoost model achieves the highest prediction accuracy and stability among the evaluated algorithms. SHAP analysis further reveals that curing age and curing environment dominate drying shrinkage, whereas autogenous shrinkage is primarily governed by the curing age and water-to-binder ratio. The interaction analysis also identifies the coupled effects between low water-to-binder ratio and extended curing age. The proposed framework not only improves prediction robustness for UHPC shrinkage under limited data conditions but also provides interpretable insights into the mechanisms governing early-age deformation. These findings offer a data-driven basis for optimizing UHPC mixture design and mitigating early-age cracking risks in engineering applications. Full article

Cardiovascular disease (CVD) risk assessment in older adults requires models that are accurate, clinically interpretable, and able to retain performance in independent populations. This study developed an interpretable machine-learning framework for CVD risk stratification in individuals aged 65 years and older using routinely available clinical factors and a selected biochemical extension and then evaluated its performance in a substantially larger independent external cohort. Model development used a development cohort of 100 patients (Almaty, age ≥ 65) with leakage-free nested cross-validation and out-of-fold (OOF) probabilities. Three internally evaluated configurations were compared: a clinical logistic regression baseline (LR clinical), a biomarker-augmented logistic regression (LR selected), and a nonlinear random forest on the selected feature set (RF selected). Discrimination was assessed using ROC-AUC and PR-AUC; probabilistic accuracy using Brier score and log loss. Calibration was examined using OOF calibration curves with sigmoid calibration for selected models. Decision-analytic utility and exploratory operational thresholds were assessed using Decision Curve Analysis (DCA), yielding a three-tier scale with thresholds t__low = 0.23 and t__high = 0.40. In nested cross-validation, LR clinical achieved ROC-AUC 0.9425 ± 0.0188 and PR-AUC 0.9574 ± 0.0092 with Brier 0.1004 ± 0.0215 and log loss 0.3634 ± 0.0652; LR selected performed worse, while RF selected showed competitive discrimination. External validation on an independent cohort (n = 695) showed retained discrimination (ROC-AUC 0.8355; PR-AUC 0.9376) with acceptable probabilistic accuracy (Brier 0.1131; log loss 0.3760), and recalibration (intercept + slope) slightly improved probability metrics. Explainability analyses (odds ratios, permutation importance, SHAP) consistently identified heredity, BMI, physical activity, and diabetes as influential model-associated factors, with clinically plausible directionality. The results suggest that an interpretable model trained on a small geriatric cohort can retain meaningful predictive performance on a substantially larger external cohort, supporting the potential value of transparent risk stratification in older adults, while broader prospective and multi-center validation remains necessary before routine clinical implementation. Full article

Background: Esophageal cancer (EC) is an aggressive malignancy with low survival rates, making accurate prognosis critical for guiding treatment decisions. Traditional prognostic methods, while essential, often lack precision and comprehensive data insights. This study aims to apply machine learning (ML) approaches to investigate EC prognosis by identifying key factors associated with 5-year survival. Methods: Multiple ML algorithms—Random Forest (RF), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), AdaBoost, and Naïve Bayes—were applied to a dataset from the SEER database. Model development included exploratory data analysis, internal validation, and 5-fold cross-validation. Traditional survival analysis methods, such as Cox regression and Kaplan–Meier (KM) analysis, were integrated to further explore relationships between key predictor and outcome variables. Additionally, time-series analysis was conducted to examine survival trends over time and identify influencing factors. Results: RF demonstrated the highest predictive performance among the models tested. Key prognostic factors identified included surgery, summary stage, tumor size, metastasis, AJCC M stage, and age. An exploratory analysis of temporal trends further showed changes in survival outcomes across diagnosis years. Conclusions: The findings highlight the potential of ML approaches to analyze prognostic patterns in EC. Integrating ML models with traditional statistical analyses helped identify key prognostic factors such as surgery, summary stage, and metastasis, while the exploratory temporal analysis provided additional context regarding survival trends over time. While promising, further external validation and addressing time-series challenges are necessary. Overall, this study demonstrates the potential of ML to support the identification of prognostic factors in EC and may contribute to more informed clinical decision-making. Full article

Time-series machine learning models represented by long short-term memory (LSTM) networks provide an effective way to obtain high-precision sound speed profiles (SSPs) quickly and at low cost, which can meet the practical application requirements of underwater sonar systems. However, in sea areas with frequent strong internal solitary waves, the large-amplitude sound speed anomalies caused by them will seriously interfere with model learning in the form of strong outlier features, resulting in a sharp drop in SSP prediction accuracy and significant degradation of the generalization stability and robustness of the model. To address this problem, this paper proposes a time-series SSP prediction method based on a bidirectional long short-term memory (Bi-LSTM) network. First, Empirical Orthogonal Function (EOF) decomposition is used to realize the low-dimensional feature representation of SSPs, and then the bidirectional time-series feature capture capability of Bi-LSTM is used to predict the SSP sequence with large disturbances caused by strong internal solitary waves. Multiple groups of comparative experiments based on the measured temperature chain data in the continental slope area of the South China Sea show that the Bi-LSTM model has a significant improvement in prediction accuracy and robustness compared with the classical LSTM model. Among them, the Bi-LSTM model with EOF decomposition achieves a correlation coefficient of 0.995 and an average Root Mean Square Error (RMSE) as low as 0.387 m/s. Under the condition of internal solitary wave disturbance, the classical LSTM is difficult to effectively capture the large abrupt change in sound speed, while the proposed Bi-LSTM model can still achieve accurate prediction of the SSP in the disturbance section, and has both the feature recognition and evolution prediction capabilities for the strongly nonlinear internal solitary wave process. This method provides effective technical support for the rapid and large-scale reconstruction of the sound speed field under the disturbance of strong internal solitary waves. Full article

With the decreasing availability of high-quality mineral resources and the increasing complexity of ore properties, efficient and sustainable flotation technology has become a research focus in the field of mineral processing. To optimize the taper angle of the air distributor in a KYF flotation machine, numerical simulation was used in this study to investigate its influence on the internal flow field, gas-phase characteristics, structural pressure distribution, and stirring power consumption. The results show that the peak turbulent kinetic energy and gas holdup are concentrated in the shear zone between the impeller and stator. Under the +5° condition, the peak turbulent kinetic energy is the lowest, while its vertical distribution is the most uniform. The peak gas holdup in the impeller–stator region reaches 19.2%, and the number of efficient bubbles with a diameter of 0.5 mm reaches 3.8 × 10⁶ per m³, which is significantly higher than under the other conditions. During stable operation, this condition exhibits the lowest stirring power consumption at 126.0 W, which is 7.557% and 4.255% lower than under the −5° and 0° conditions, respectively. The optimal taper angle is therefore determined to be +5°. However, the associated large pressure gradient on the impeller surface may accelerate blade wear, indicating that surface strengthening measures should be considered to balance performance and durability. Full article

Selecting appropriate computational methods for organic crystals becomes particularly challenging for systems containing heavy halogens like bromine, whose complex electronic structures and diverse non-covalent interactions challenge approximate methods. Here we benchmark periodic DFT (PBE-D3BJ), CrystalExplorer (CE17/CE21), DFTB3-D3BJ, and PM7 against experimental stability data for 14 chlorine- and bromine-containing polymorphs across six CSD families. Chlorine systems show method-consistent performance, but bromine introduces large lattice energy variations (>10 kJ/mol) and, in several cases, qualitatively wrong stability rankings. Crucially, low mean absolute errors do not ensure correct thermodynamic ordering, and no semi-empirical method proves universally reliable for bromine. Only PBE-D3BJ achieves perfect experimental agreement. These results position bromine-containing crystals as exceptionally sensitive benchmarks and emphasize internal validation against experiment or reference DFT as essential before large-scale studies—particularly timely as machine-learning potentials emerge. Full article

Introduction: Periprosthetic joint infections (PJI) are among the most serious and costly complications in orthopedic surgery, significantly affecting patient prognosis and healthcare systems. Despite rigorous aseptic measures, intraoperative contamination of sterile fields, instruments, and air remains a persistent source of potential infection. This study investigates the relationship between the microbial contamination of sterile fields during arthroplasty and postoperative inflammatory markers, with the objective of determining whether the contamination of sterile fields correlates with the presence of periprosthetic joint infection (PJI). Material and Methods: This prospective observational study included 33 patients undergoing total hip or knee arthroplasty in a university-affiliated orthopedic center. Intraoperative samples were collected from sterile fields and equipment to detect microbial contamination, while postoperative monitoring involved the C-reactive protein (CRP); erythrocyte sedimentation rate (ESR); leukocyte count; temperature; and wound assessment on days 1, 3 and 7. All patients received 48 h of prophylactic cefuroxime. Statistical analysis was conducted using the International Business Machines (IBM) Statistical Product and Service Solutions (SPSS) software for Windows, version 30.0 (IBM Corporation, Armonk, New York, United States of America) with significance set at p ≤ 0.05. Results: Postoperative inflammatory markers showed distinct patterns depending on the isolated microorganism, with Proteus vulgaris and Staphylococcus hominis ssp. consistently associated with higher CRP and leukocyte values, indicating a more intense systemic response. Staphylococcus epidermidis was the most frequently isolated species but showed moderate inflammatory profiles, suggesting its potential role in subclinical colonization. A strong correlation between CRP on day 3 and leukocyte count (r = 0.81) confirms their combined utility in the early detection of infectious complications, while ESR appeared less dynamic and more complementary in nature. Discussion: This study highlights the significant role of intraoperative contamination and microbial virulence in shaping the postoperative inflammatory response after arthroplasty. Elevated CRP and leukocyte levels, particularly on day 3, were closely associated with pathogens known for biofilm formation and chronic infections. Despite prophylactic antibiotic use, confirmed infections still occurred, suggesting the need to reassess current protocols and enhance intraoperative contamination control. Conclusions: Pathogen presence in sterile fields during arthroplasty increases the risk of periprosthetic joint infections, often without early clinical symptoms. CRP on day 3 and leukocyte count were the most reliable early indicators of persistent inflammation. Full article

To address the issues of high cutting temperatures and severe tool wear during titanium alloy machining, this study proposes a hybrid surface modification strategy combining micro-textures and multicomponent titanium nitride (TiN)-based coatings on cemented carbide tools. Using YG8 cemented carbide as the substrate, micro-dimple textures were fabricated by fiber laser, and three coatings with different architectures (TiAlSiN, TiSiN/TiAlN, and TiSiN/TiAlSiN/TiAlN) were deposited via multi-arc ion plating technology. Based on a two-factor (texture diameter and texture spacing) and three-level orthogonal experiment, the evolution behaviors of surface morphology, phase composition, and mechanical properties of the textured multicomponent TiN-based coatings were systematically characterized and comparatively analyzed. The results reveal that: compared to the monolithic-structured TiAlSiN coating, the TiSiN/TiAlSiN/TiAlN and TiSiN/TiAlN composite coatings with multilayered composite structures can effectively relieve the residual stress inside the film–substrate system, and significantly suppress the phenomena of coating cracking and localized spallation caused by irregular protrusions of the recast layer at the micro-texture edges. X-ray diffraction (XRD) and crystallite size analyses indicate that the amorphous Si₃N₄ phase promoted by the Si element in the composite coatings effectively impedes the growth of TiN columnar crystals, achieving significant grain refinement. Mechanical property tests confirm that the existence of multicomponent composite interfaces effectively hinders dislocation movement. Among them, the textured TiSiN/TiAlSiN/TiAlN composite coating exhibits the optimal comprehensive performance; its microhardness, nanohardness, and H/E ratio (characterizing the resistance to plastic deformation) are increased by 17.94%, 8%, and approximately 45%, respectively, compared to those of the textured TiAlSiN coating. This study deeply elucidates the synergistic strengthening and toughening mechanisms between micro-texture parameters and the internal structures of the coatings, providing important theoretical guidance and experimental data support for the surface design of long-lifespan tools oriented towards the high-efficiency machining of titanium alloys. Full article