Search Results (39,563)

Backgrounds/Objectives: Atrial fibrillation (AF) confers a fivefold greater risk of acute ischaemic stroke (AIS) relative to normal sinus rhythm. Among patients with AF-related AIS (AFAIS), recurrence is common: AFAIS rate is sixfold higher in secondary versus primary prevention patients. Guidelines recommend oral anticoagulation for primary and secondary prevention on the basis of CHA₂DS₂-VASc. However, guideline adherence is poor for secondary prevention. This is, in part, because the predictive value of CHA₂DS₂-VASc has not been ascertained with respect to recurrence: patients with and without previous stroke were not routinely differentiated in validation studies. We put forth a protocol to (1) validate, and (2) update CHA₂DS₂-VASc for secondary prevention, aiming to deliver a CPR that better captures 90-day recurrence risk for a given AFAIS patient. Overwhelmingly poor quality of reporting has been deplored among published clinical prediction rules (CPRs). Combined with the fact that machine learning (ML) and deep learning (DL) methods are rife with challenges, registered protocols are needed to make the CPR literature more validation-oriented, transparent, and systematic. This protocol aims to lead by example for prior planning of primary and secondary analyses to obtain incremental predictive value for existing CPRs. Methods: The Virtual International Stroke Trials Archive (VISTA), which has compiled data from 38 randomised controlled trials (RCTs) in AIS, was screened for patients that (1) had an AF diagnosis, and (2) were treated with vitamin K antagonists (VKAs) or without any antithrombotic medication. This yielded 2763 AFAIS patients. Patients without an AF diagnosis were also retained under the condition that they were treated with VKAs or without any antithrombotic medication, which yielded 7809 non-AF AIS patients. We will validate CHA₂DS₂-VASc for 90-day recurrence and secondary outcomes (7-day recurrence, 7- and 90-day haemorrhagic transformation, 90-day decline in functional status, and 90-day all-cause mortality) by examining discrimination, calibration, and clinical utility. To update CHA₂DS₂-VASc, logistic regression (LR), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP) models will be trained using nested cross-validation. The MLP model will employ transfer learning to leverage information from the non-AF AIS patient cohort. Results: Models will be assessed on a hold-out test set (25%) using area under the receiver operating characteristic curve (AUC), calibration curves, and F1 score. Shapley additive explanations (SHAP) will be used to interpret the models and construct the updated CPRs. Conclusions: The CPRs will be compared by means of discrimination, calibration, and clinical utility. In so doing, the CPRs will be evaluated against each other, CHA₂DS₂-VASc, and default strategies, with test tradeoff analysis performed to balance ease-of-use with clinical utility. Full article

Fragile X syndrome (FXS, OMIM#300624) is the most common inherited cause of X-linked intellectual disability and behavior difficulties. In 99% of cases, it is caused by the pathological expansion (>200 repeats, full mutation -FM) of the CGG trinucleotide located at the 5′ UTR of the FMR1 (Fragile X Messenger Ribonucleoprotein 1) gene, leading to the lack of production of the FMRP. Clinical manifestations are well known in boys but are sometimes overlooked in girls, who may remain underdiagnosed. Premutation (PM) populations (55–200 repeats) may present other medical issues, such as FXPOI or FXTAS. Mosaic conditions, such as a combination of PM and FM lines in the same patient, may lead to milder phenotypes. With the improvement of genetic testing, information regarding the exact number of CGG triplet repeats and methylation status could help explain milder phenotypes in patients who may produce some FMRP. Chromosome X preferential inactivation (XCI) in FXS women can also play a role in clinical severity. We present four non-related families who were followed up in our FXS clinic. Some of their members showed FM on Southern blot, but had milder symptoms than expected. To rule out size mosaicism, a RT-PCR was performed, giving a different and more consistent molecular diagnosis. When mosaicism was not present, methylation status was performed, excluding full methylation. For females, XCI showed preferential inactivation in one case. Revisiting old molecular diagnoses should be considered in clinical practice, especially for patients with a milder phenotype than expected from their molecular reports. This personalized follow up may change their former diagnosis, prognosis, and expectations. Full article

Portfolio optimization is a cornerstone of modern financial decision-making, traditionally based on the mean–variance model introduced by Markowitz. However, this framework relies on restrictive assumptions—such as normally distributed returns and symmetric risk preferences—that often fail in real-world markets, particularly in volatile and non-Gaussian environments such as cryptocurrencies. To address these limitations, this paper proposes a novel multi-objective model that combines expected return maximization, mean absolute deviation (MAD) minimization, and entropy-based diversification into a unified optimization structure: the Mean–Deviation–Entropy (MDE) model. The MAD metric offers a robust alternative to variance by capturing the average magnitude of deviations from the mean without inflating extreme values, while entropy serves as an information-theoretic proxy for portfolio diversification and uncertainty. Three entropy formulations are considered—Shannon entropy, Tsallis entropy, and cumulative residual Sharma–Taneja–Mittal entropy (CR-STME)—to explore different notions of uncertainty and structural diversity. The MDE model is formulated as a tri-objective optimization problem and solved via scalarization techniques, enabling flexible trade-offs between return, deviation, and entropy. The framework is empirically tested on a cryptocurrency portfolio composed of Bitcoin (BTC), Ethereum (ETH), Solana (SOL), and Binance Coin (BNB), using daily data over a 12-month period. The empirical setting reflects a high-volatility, high-skewness regime, ideal for testing entropy-driven diversification. Comparative outcomes reveal that entropy-integrated models yield more robust weightings, particularly when tail risk and regime shifts are present. Comparative results against classical mean–variance and mean–MAD models indicate that the MDE model achieves improved diversification, enhanced allocation stability, and greater resilience to volatility clustering and tail risk. This study contributes to the literature on robust portfolio optimization by integrating entropy as a formal objective within a scalarized multi-criteria framework. The proposed approach offers promising applications in sustainable investing, algorithmic asset allocation, and decentralized finance, especially under high-uncertainty market conditions. Full article

Self-lubricating polymer composites based on polydicyclopentadiene (PDCPD) were reinforced with carbon nanomaterials to evaluate the effect of filler type and loading on their mechanical and tribological performance. Four carbon forms were introduced: carbon nanotubes (0.3 and 0.5 wt.%), carbon fibers (5 and 10 wt.%), flake graphite (5 and 10 wt.%) and dusty graphite (5 and 10 wt.%). Tensile tests showed that carbon fibers—and graphite-filled matrices reached ~50 MPa tensile strength, while the addition of carbon nanotubes resulted in a reduction in strength by half compared to the pure resin, indicating poor compatibility of carbon nanotubes with the matrix. The highest compressive strength, ~90 MPa, was obtained for PDCPD containing 5 wt.% carbon fibers. Tribological behavior was evaluated in a pin-on-disk configuration under dry sliding. All fillers lowered the coefficient of friction; the most pronounced, three-fold reduction was achieved with both graphite variants. The combined high load-bearing capacity and greatly reduced friction of the graphite and carbon fibers modified systems highlight their potential as self-lubricating bearing materials capable of replacing conventional metal or oil-lubricated components. Full article

Introduction: Acquired brain injury (ABI) produces significant cognitive, motor, and psychosocial impairments that affect people’s daily functioning. Rehabilitation programs increasingly combine physical activity with neuropsychological strategies for greater effectiveness. Purpose: The aim of this study was to analyze the effects of neurophysiotherapy based on physical activity and neuropsychological rehabilitation on cognitive and psychosocial functioning in individuals with an acquired brain injury (ABI). Method: A total of 19 individuals between the ages of 24 and 89 years (M ± SD: age = 59.26 ± 19.01) belonging to the Acquired Brain Injury Association of Málaga (ADACEMA) participated in this study. A quasi-experimental design with pre- and post-test measures and multiple experimental groups was used. The instruments used were the digit subtest of the Barcelona Test, the Five Digit Test (FDT), the Tower of Hanoi, the modified six-element subtest of the Behavioural Assessment of the Dysexecutive Syndrome, the Trail Making Test (TMT), the WHOQOL-BREF, and the Profile of Mood States (POMS) questionnaire. The Kruskal–Wallis H, Mann–Whitney U, and Wilcoxon tests were used to analyze the data. Results: The results obtained showed a positive effect of physical activity (PA) combined with neuropsychological rehabilitation on working memory, planning, emotional well-being, personal relationships, depressive symptoms, and overall quality of life. Conclusions: The findings suggest that combining neurophysiotherapeutic physical-activity-based rehabilitation with other neuropsychological interventions may be a promising approach to improving executive functioning, emotional well-being, and quality of life in people with an ABI. These preliminary results highlight the potential value of multidisciplinary programs in post-injury recovery, although further studies with larger and more homogeneous samples are needed to confirm these effects. Full article

The integration of renewable energy systems and electrified transportation requires advanced energy storage solutions capable of providing both high energy density and fast dynamic response. Hybrid energy storage systems offer a promising approach by combining complementary battery chemistries, exploiting their respective strengths while mitigating individual limitations. This study presents the design, modeling, and optimization of a hybrid energy storage system composed of two high-energy lithium nickel manganese cobalt batteries and one high-power lithium titanate oxide battery, interconnected through a triple dual-active multi-port converter. A nonlinear model predictive control strategy was employed to optimally distribute battery currents while respecting constraints such as state of charge limits, current bounds, and converter efficiency. Equivalent circuit models were used for real-time state of charge estimation, and converter losses were explicitly included in the optimization. The main contributions of this work are threefold: (i) verification of the model predictive control strategy in diverse applications, including residential renewable energy systems with photovoltaic generation and electric vehicles following the World Harmonized Light-duty Vehicle Test Procedure driving cycle; (ii) explicit inclusion of the power converter model in the system dynamics, enabling realistic coordination between batteries and power electronics; and (iii) incorporation of converter efficiency into the cost function, allowing for simultaneous optimization of energy losses, battery stress, and operational constraints. Simulation results demonstrate that the proposed model predictive control strategy effectively balances power demand, extends system lifetime by prioritizing lithium titanate oxide battery during transient peaks, and preserves lithium nickel manganese cobalt cell health through smoother operation. Overall, the results confirm that the proposed hybrid energy storage system architecture and control strategy enables flexible, reliable, and efficient operation across diverse real-world scenarios, providing a pathway toward more sustainable and durable energy storage solutions. Full article

Accurately determining the modulus of each structural layer remains a key challenge in asphalt pavement design, construction quality control, and bearing capacity assessment. This study introduces an ensemble model combining a genetic algorithm-optimized backpropagation neural network (GA-BP) and a convolutional neural network (CNN) to back-calculate the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures. Using a dynamic deflection basin database created by our research team, we built a dataset of 1,552,000 pavement structure samples with Falling Weight Deflectometer (FWD) data. Based on this dataset, we developed regression models, including a backpropagation (BP) neural network, GA-BP, and CNN, to perform the back-calculation of dynamic modulus values. Performance testing revealed that the CNN model outperformed both the GA-BP and BP models in terms of accuracy and stability, as indicated by evaluation metrics (R², MAE, RMSE, MAPE), with the following ranking: CNN > GA-BP > BP. Nonetheless, the maximum relative error across all three models remained notable. To address this, an ensemble model combining GA-BP and CNN was created, significantly enhancing the accuracy and stability of the back-calculation results. The proposed ensemble model was tested on-site with FWD data to estimate the dynamic modulus of asphalt pavement layers. The results demonstrated strong agreement with actual pavement performance and high consistency with numerical outcomes from three-dimensional (3D) dynamic finite element method simulations. These findings suggest that the GA-BP and CNN ensemble approach offers a reliable method for back-calculating the dynamic modulus of asphalt pavement layers over rubblized old cement concrete structures. Full article

Background/Objectives: The aims of this study are to define the roles of the neurotrophic tyrosine receptor kinase genes NTRK1, NTRK2 and NTRK3 (NTRK1/2/3) in CRC and to determine the clinicopathological, molecular, cancer signalling and potential predictive significances of NTRK1/2/3 expression in CRC, irrespective of NTRK gene fusion. Methods: Standard statistical tests in SPSS were utilised to interrogate the associations and correlations between NTRK1/2/3 expression and clinicopathological, molecular and genomic features in two CRC cohorts. NTRK1/2/3 expression deregulation was also investigated using correlation and regression analyses. Furthermore, gene set enrichment analysis (GSEA) and pathway/drug ontology enrichment analysis (POEA/DOEA) were utilised to interrogate the enrichment of cancer signalling pathways, as well as NTRK and other tyrosine kinase inhibitor response in the CRC cohorts. Results: Whilst NTRK1 expression was higher in the CRC subset with microsatellite instability, NTRK2/3 expression was preferentially overexpressed in the microsatellite stable subsets. Moreover, there was differential NTRK1/2/3 expression with respect to clinicopathological and molecular/genomic indices. In addition, this study demonstrated that NTRK1/2/3 expression was deregulated by a combination of copy number alterations (NTRK2), aberrant methylation (NTRK1/2/3) and potentially and cryptic gene fusion (NTRK3). Furthermore, GSEA and POEA demonstrated that NTRK1/2/3-high CRC subsets exhibited enrichment of and cross-talks among the NTRK signalling pathways, as well as of known cancer signalling pathways. The GSEA and DOEA showed that NTRK signalling was enriched for kinase inhibitors responses, representing evidence that NTRK1/2/3 expression may serve as biomarkers for multiple kinase inhibitors, including entrectinib—the tissue-agnostic kinase inhibitor for cancers with NTRK gene fusions. Conclusions: The results demonstrated that fusion-negative NTRK signalling may be active in CRC and may contribute to the molecular pathogenesis and biology of the disease. The results also demonstrated that the NTRK1/2/3 expression may be predictive multiple kinase inhibitors. Full article

Meeting the growing demand for vegetable oil while promoting agricultural sustainability in Northeast China requires developing high-yield, high-oil-content soybean varieties. We present the comprehensive development and evaluation of Heinong 551, an innovative soybean variety created through an integrated approach of conventional breeding methods and radiation-induced mutation techniques. The breeding program began with hybridization between Heinong 44 (the maternal parent) and Hefeng 47 (the paternal parent), followed by targeted exposure to ⁶⁰Co gamma radiation at 130 Gy to induce beneficial mutations. Using systematic selection protocols over five generations from 2012 to 2016, we identified superior lines that underwent rigorous multi-location testing across seven sites in Heilongjiang Province during 2020–2021. Field evaluation results showed consistent performance, with Heinong 551 achieving average yields of 2901 kg/ha and 3142 kg/ha in those years, representing significant gains of 10. 6% and 11.0. 0% compared to standard control varieties. The cultivar maintained stable phenological traits with a reliable 120-day maturation period and demonstrated strong environmental adaptability across different growing conditions. Biochemical analysis revealed excellent nutritional value, with 39.45% crude protein and 21.69% crude fat, reaching a combined protein–fat percentage of 61.14%. Quality tests confirmed superior seed integrity, with sound seed rates over 97% and minimal pest or disease damage. Disease resistance assessments showed moderate tolerance to gray leaf spot while maintaining excellent overall plant health, with no signs of viral infections or nematode infestations during testing. Heinong 551 has received official approval for cultivation in Heilongjiang Province’ s second accumulated temperature zone, characterized by thermal units ≥ 2550 °C above a 10 °C threshold. This represents significant progress in high-oil soybean variety development, illustrating the success of combining traditional breeding methods with modern mutation technology. Full article

Addressing the core issue of rock mass failure and deformation induced by local water-induced uneven expansion in expansive soft rock tunnels, this study systematically analyzes the stress–displacement response of the rock mass under various working conditions. This analysis integrates physical model testing with FLAC3D 6.0 numerical simulation and covers four typical expansion zone configurations (vault, spandrel, haunch, invert) as well as multiple stages of stress loading. Leveraging the mathematical analogy between heat conduction and fluid seepage and combining it with a thermo-hydraulic coupling approach, the FLAC3D temperature field module precisely simulates the moisture-induced stress field. This overcomes the limitations of traditional tools for direct moisture field simulation and enables quantitative assessment of how localized expansion impacts tunnel lining failure. The study reveals that horizontal expansion zones significantly increase the risk of shear failure in tunnel structures. Expansion zones at the tunnel crown and base (invert) pose critical challenges to overall safety and exhibit a pronounced nonlinear relationship between stress loading and displacement. This research deepens the theoretical understanding of the interaction between localized non-uniform expansion and the surrounding rock mass and provides crucial technical guidance for optimizing tunnel support systems and improving disaster monitoring and prevention measures. 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: Tuberculosis (TB) remains a global health challenge due to long treatment durations, poor adherence, and growing drug resistance. Inhalable co-amorphous systems (COAMS) offer a promising strategy for targeted pulmonary delivery of fixed-dose combinations, improving efficacy and reducing systemic side effects. Methods: Our in-house-developed machine learning (ML) tool identified two promising API-API combinations for TB therapy, rifampicin (RIF)–moxifloxacin (MOX) and RIF–ethambutol (ETH). Physiologically based pharmacokinetic (PBPK) modeling was used to estimate therapeutic lung doses of RIF, ETH, and MOX following oral administration. Predicted lung doses were translated into molar ratios, and COAMS of RIF-ETH and RIF-MOX at both model-predicted (1:1) and PBPK-informed ratios were prepared by spray drying and co-milling, followed by comprehensive physicochemical and aerodynamic characterization. Results: RIF-MOX COAMS could be prepared in all molar ratios tested, whereas RIF-ETH failed to result in COAMS for therapeutically relevant molar ratios. Spray drying and ball milling successfully produced stable RIF-MOX formulations, with spray drying showing superior behavior in terms of morphology (narrow particle size distribution; lower Sauter mean diameter), aerosolization performance (fine particle fraction above 74% for RIF and MOX), and dissolution. Conclusions: This study demonstrated that PBPK modeling and ML are useful tools to develop COAMS for pulmonary delivery of active pharmaceutical ingredients (APIs) routinely applied through the oral route. It was also observed that COAMS may be less effective when the therapeutic lung dose ratio significantly deviates from the predicted 1:1 molar ratio. This suggests the need for alternative delivery strategies in such cases. Full article

Free chlorine residual is the most widely adopted disinfectant residual in water supply systems. Chlorine is usually applied at treatment works, but it decays as water flows and spends time within the network. Chlorine decay is the result of a bulk and a wall decay component. Bulk decay may be considered invariable through the pipe network (it only depends on water composition) and is often characterized at the entrance to the system through bottle tests, which measure chlorine evolution over time in a laboratory environment to then adjust a model (dependent on one or more coefficients) that represents its behavior. Previous studies have acknowledged that the bulk decay coefficient varies widely and that free chlorine measurements are subject to measurement errors, but they have not quantified the impact of these errors on the bulk decay coefficient. The aim of this paper is to provide a methodology that statistically fits chlorine’s bulk decay coefficient based on bottle test results, with appropriate management of uncertainty effects. The proposal is to use state estimation techniques, which combine free chlorine measurements and system knowledge (in this case, a first-order bulk decay model) to provide the most likely chlorine behavior and its associated uncertainty. This approach goes one step beyond previous studies, which report only a single value of the bulk decay coefficient without accounting for randomness, and thus fail to assess true variability, leading to unrepresentative comparisons. Results for water samples from different sources demonstrate the importance of controlling the fitting process through state estimation to understand and compare the bulk decay coefficient. Full article

Artificial intelligence is playing an increasingly important role in education. Learning path recommendation is one of the key technologies in artificial intelligence education applications. This paper applies knowledge space theory and fuzzy set theory to study the construction of consistent fuzzy competence spaces and their application to learning path recommendation. With the help of the outer fringe of fuzzy competence states, this paper proves the necessary and sufficient conditions for a fuzzy competence space to be a consistent fuzzy competence space and designs an algorithm for verifying consistent fuzzy competence spaces. It also proposes methods for constructing and reducing consistent fuzzy competence spaces, provides learning path recommendation algorithms from the competence perspective and combined with a disjunctive fuzzy skill mapping, and constructs a bottom-up gradual and effective learning path tree. Simulation experiments are carried out for the construction and reduction in consistent fuzzy competence spaces and for learning path recommendation, and the simulation studies show that the proposed methods achieve significant performance improvement compared with related research and produce a more complete recommendation of gradual and effective learning paths. The research of this paper can provide theoretical foundations and algorithmic references for the development of artificial intelligence education applications such as learning assessment systems and intelligent testing systems. Full article

This study reports the development of polyvinylidene fluoride (PVDF) membranes decorated with a copper(II) complex (CuL) for the removal of organic pollutants from wastewater. Using Drimaren Red X-6BN (DRX-6BN) as a probe, the PVDF membrane with the lowest CuL loading (PVDF/PDA/CuL-4) reached an adsorption capacity of 19.78 mg/g at 300 min, with removal of up to 50% DRX-6BN. Kinetic analysis favored Elovich (R² > 0.9928; RMSE < 0.4489) and the pseudo-second-order model (R² > 0.9540; RMSE < 1.1388), consistent with chemisorption. Intraparticle diffusion occurred in two steps. In the presence of 20 mg/L of hydrogen peroxide (H₂O₂), the removal was >80% within 180 min at higher CuL loadings (PVDF/PDA/CuL-40). In oily wastewater, PVDF/PDA/CuL-4 achieved ~100% COD removal in 120 min with H₂O₂, whereas pristine PVDF achieved 38.5%. Storage stability tests demonstrated the preservation of catalytic and separation performance for at least three months. All tests were conducted at pH ≈ 6.0 and a temperature of 25 °C. In contrast to many catalytic membranes, these membranes operate at near-neutral pH and ambient temperature in the absence of radiation. The results highlight PVDF membranes decorated with CuL as a robust and sustainable approach for the treatment of oily effluents, particularly by combining Fenton-like processes under mild conditions. Full article