Search Results (18,817)

To enhance the accuracy and robustness of lithium-ion battery state-of-health (SOH) prediction, this study proposes a dual-mode intelligent optimization BP neural network model (AO–AVOA–BP) which integrates the Aquila Optimizer (AO) and the African Vulture Optimization Algorithm (AVOA). The model leverages the global search capabilities of AO and the local exploitation strengths of AVOA to achieve efficient and collaborative optimization of network parameters. In terms of feature construction, eight key health indicators are extracted from voltage, current, and temperature signals during the charging phase, and the optimal input set is selected using gray relational analysis. Experimental results demonstrate that the AO–AVOA–BP model significantly outperforms traditional BP and other improved models on both the NASA and CALCE datasets, with MAE, RMSE, and MAPE maintained within 0.0087, 0.0115, and 1.095%, respectively, indicating outstanding prediction accuracy and strong generalization performance. The proposed method demonstrates strong generalization capability and engineering adaptability, providing reliable support for lifetime prediction and safety warning in battery management systems (BMS). Moreover, it shows great potential for wide application in the health management of electric vehicles and energy storage systems. Full article

Whole-body electromyostimulation (WB-EMS) is a time-efficient, joint-friendly, and highly customizable training technology that particularly attracts sportspeople and athletes looking to enhance performance, accelerate regeneration, and prevent injuries with WB-EMS. Based on a systematic review of the literature, the present evidence map aimed to provide an overview of outcomes addressed by WB-EMS in exercising cohorts of different levels. In summary, the search identified 34 research projects with 39 studies and 43 publications that addressed 79 outcome categories (e.g., isometric strength) with more than 300 single outcomes (e.g., isometric strength of leg extensors). Thirty-one studies focused on performance-related outcomes, four studies addressed regeneration-related outcomes, and eight studies reported outcomes related to anthropometry. A further 14 studies reported health- and safety-related outcomes. Twenty-five of the 31 studies that reported performance parameters addressed strength, ten power, 18 jumping, ten sprinting, six agility, six endurance, five anaerobic power, and one each flexibility or balance, and five studies reported sport-specific performance outcomes (e.g., shot velocity). Apart from outcomes concerning injury prevention or sport-specific complaints, there are in particular evidence gaps relating to the acute effects of WB-EMS on regeneration, particularly with respect to muscle recovery. Semiprofessionals/professionals were rarely addressed, and if so, primarily cohorts from team sports were evaluated, while no study focused on elite strength, endurance, or precision sports athletes. Full article

Objective: This scoping review identifies and analyzes factors influencing the effectiveness of orthodontic mini-implants and temporary anchorage devices in orthodontic treatments, including clinical applications, success rates, and associated complications. Methods: A systematic search was conducted across EBSCOhost, Ovid Medline, PubMed, Scopus, and Web of Science for peer-reviewed, English-language human studies published between 2013 and 2023 that examined determinants of mini-implants/temporary anchorage devices success or failure. Inclusion/exclusion criteria were predefined, and screening was performed in duplicate. Thirty-six studies met criteria. Results: Placement site and peri-implant oral hygiene/soft-tissue health were the most consistent contributors to success. Optimal sites varied by indication, supporting individualized planning. Greater implant length generally improved stability but must be balanced against anatomic limits and patient comfort. Temporary anchorage devices supported diverse movements (e.g., molar distalization; posterior/anterior intrusion). Findings for loading protocol, patient age, bone quality, and operator experience were mixed, reflecting heterogeneity in primary stability, force magnitude/vector, and outcome definitions. Conclusion: Mini-implants/temporary anchorage devices success is multifactorial. Emphasis on site-specific selection, hygiene management, appropriate implant dimensions, and patient-specific modifiers can optimize outcomes and minimize complications. Future studies should report standardized outcomes and explicit loading parameters to enable granular analyses of movement-specific biomechanics and evidence-based decision-making. Full article

In complicated scenarios, due to the low precision of float solutions and poor reliability of fixed solutions, it is challenging to achieve a balance between accuracy and reliability of the integer least squares (ILS) estimation. To address this dilemma, the best integer equivariant (BIE) estimation, which makes a weighted sum of all possible candidates, has recently been attached great importance. The BIE solution approaches the float solution at a low ILS success rate, maintaining positioning reliability. As the success rate increases, it converges to the fixed solution, facilitating high-precision positioning. Furthermore, the posterior variance of BIE estimation provides the capability of reliability evaluation. However, in environments with a limited number or a deficient configuration of available satellites, there is a sharp decline in the strength of the GNSS precise positioning model. In this case, the exactness of weight allocation for integer candidates in BIE estimation will be severely compromised by unmodeled errors. When the ambiguity is incorrectly fixed, the wrongly determined optimal candidate is probably assigned an excessively high weight. Therefore, the BIE solution in a weak GNSS model always exhibits a significant positioning error consistent with the fixed solution. Moreover, the posterior variance of BIE estimation approximately resembles that of a fixed solution, losing error warning ability. Consequently, the BIE estimation may exhibit lower reliability compared to the ILS estimation employing a validation test with a loose acceptance threshold. To improve the positioning performance in weak GNSS models, a BIE ambiguity resolution (AR) method based on Tikhonov regularization is proposed in this paper. The method introduces Tikhonov regularization into the least squares (LS) estimation and the ILS ambiguity search, mitigating the serious impact of unmodeled errors on the BIE estimation under weak observation conditions. Meanwhile, the regularization factors are appropriately selected by utilizing an optimized approach established on the L-curve method. Simulation experiments and field tests have demonstrated that the method can significantly enhance the positioning accuracy and reliability in weak GNSS models. Compared to the traditional BIE estimation, the proposed method achieved accuracy improvements of 73.6% and 69.3% in the field tests with 10 km and 18 km baselines, respectively. Full article

Background: The potential impact of maternal SARS-CoV-2 infection during pregnancy on the neurodevelopment of offspring has raised considerable concern. Emerging studies have evaluated various developmental domains in exposed infants, yet findings remain inconsistent. Objective: To synthesize current evidence regarding neurodevelopmental outcomes in infants born to mothers with confirmed SARS-CoV-2 infection during pregnancy. Methods: We conducted a narrative review following PRISMA guidelines. A literature search was performed in PubMed, Cochrane, and ScienceDirect using keywords including “COVID-19”, “pregnancy”, “neurodevelopment”, and “SARS-CoV-2”. Nineteen studies were included. Data were extracted regarding study design, sample size, timing of exposure, age at assessment, developmental tools used, and key findings. Study quality was assessed using the Newcastle–Ottawa Scale. Results: Among 19 included studies, 12 reported at least some neurodevelopmental delays, particularly in motor and language domains. However, these delays were generally mild, domain-specific, and often not statistically significant. Seven studies, most of which were high-quality and low-risk, reported no significant differences between exposed and unexposed groups. Assessment tools and follow-up durations varied widely, limiting comparability. Conclusions: Current evidence does not support a consistent association between in utero SARS-CoV-2 exposure and an unfavorable neurodevelopmental outcome up to 24 months. However, heterogeneity in methods and short-term follow-up warrant further high-quality longitudinal research. Full article

Background: Chronic low back pain markedly impairs quality of life and imposes a significant economic burden on public health. The complex pathophysiology of chronic low back pain arises from the complex anatomical configuration of the lumbar region, which includes a diverse array of structures. Consequently, etiologies may involve intervertebral disc degeneration, facet joint osteoarthritis, spinal stenosis, spondylosis, and spondylolisthesis. Therapeutic interventions for chronic low back pain are equally varied, ranging from pharmacological treatments to physiotherapy, kinetotherapy, balneotherapy, and image-guided local injectable procedures such as prolotherapy. Prolotherapy is a regenerative injection technique designed to stimulate the body’s healing processes by applying a regenerative treatment (typically dextrose), which aims to modulate neurogenic inflammation and diminish nociceptive signaling. Methods: A systematic review of the literature was performed in alignment with the PRISMA guidelines (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Studies published within the last ten years evaluating the effects of prolotherapy on pain reduction in individuals with chronic low back pain were included, following a search across six databases. Results: The review revealed several studies evaluating the influence of prolotherapy on pain in chronic low back pain patients. Findings were heterogeneous, with some studies indicating significant pain reduction and others showing minimal or no improvement. Conclusions: The current evidence regarding the efficacy of prolotherapy for pain relief in chronic low back pain remains inconclusive, highlighting the necessity for further in-depth research. Continued and updated investigations into prolotherapy’s role are imperative for enhancing the quality of life of affected patients. Full article

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and exploitation within the search space. Despite its innovative approach, the Honey Badger Algorithm (HBA) faces challenges such as slow convergence rates, an imbalanced trade-off between exploration and exploitation, and a tendency to become trapped in local optima. To address these issues, we propose an enhanced version of the Honey Badger Algorithm (HBA), namely the Multi-Strategy Honey Badger Algorithm (MSHBA), which incorporates a Cubic Chaotic Mapping mechanism for population initialization. This integration aims to enhance the uniformity and diversity of the initial population distribution. In the mining and honey-seeking stages, the position of the honey badger is updated based on the best fitness value within the population. This strategy may lead to premature convergence due to population aggregation around the fittest individual. To counteract this tendency and enhance the algorithm’s global optimization capability, we introduce a random search strategy. Furthermore, an elite tangential search and a differential mutation strategy are employed after three iterations without detecting a new best value in the population, thereby enhancing the algorithm’s efficacy. A comprehensive performance evaluation, conducted across a suite of established benchmark functions, reveals that the MSHBA excels in 26 out of 29 IEEE CEC 2017 benchmarks. Subsequent statistical analysis corroborates the superior performance of the MSHBA. Moreover, the MSHBA has been successfully applied to four engineering design problems, highlighting its capability for addressing constrained engineering design challenges and outperforming other optimization algorithms in this domain. Full article

The search for bioactive compounds with antioxidant properties is critical in combating oxidative stress-related diseases and advancing novel therapeutic agents. Azo dyes, traditionally used in textiles, food, and cosmetics, have recently attracted attention due to their emerging biological activities, including antioxidant potential. In this study, we synthesized and characterized 267 azo dyes derived from natural phenolic cores such as salicylic acid, syringol, and 5,6,7,8-tetrahydro-2-naphthol. Eighteen of these compounds are novel. Structural characterization was performed using NMR, UV-Vis, IR spectroscopy, and mass spectrometry. Antioxidant activity was assessed using in vitro assays with ABTS radical scavenging method. SAR analysis revealed that dyes derived from syringol and 5, 6, 7, 8-tetrahydro-2-naphthol showed the most consistent and potent antioxidant activity. Notably, azo dyes bearing fluoro and nitro substituents in the para position exhibited the lowest IC₅₀ values, highlighting the influence of electron-withdrawing groups and substitution patterns on antioxidant behavior. This work establishes a precedent for SAR-driven evaluation of azo dyes using ABTS and supports their further exploration as functional antioxidant agents in medicinal chemistry. Full article

The application of predictive and explainable artificial intelligence to bioinformatics data such as single nucleotide polymorphism (SNP) information is attracting rising attention in the diagnosis of various diseases. However, there are few reviews available on the recent progress of genetic artificial intelligence for the early diagnosis of gastrointestinal disease (GID). The purpose of this study is to complete a systematic review on the recent progress of genetic artificial intelligence in GID. The source of data was ten original studies from PubMed. The ten original studies were eligible according to the following criteria: (participants) the dependent variable of GID or associated disease; (interventions/comparisons) artificial intelligence; (outcomes) accuracy, the area under the curve (AUC), and/or variable importance; a publication year of 2010 or later; and the publication language of English. The performance outcomes reported varied within 79–100 for accuracy (%) and 63–98 for the AUC (%). Random forest was the best approach (AUC 98%) for the classification of inflammatory bowel disease with 13 single nucleotide polymorphisms (SNPs). Similarly, random forest was the best method (R-square 99%) for the regression of the gut microbiome SNP saturation number. The following SNPs were discovered to be major variables for the prediction of GID or associated disease: rs2295778, rs13337626, rs2296188, rs2114039 (esophageal adenocarcinoma); rs28785174, rs60532570, rs13056955, rs7660164 (Crohn’s disease early intestinal resection); rs4945943 (Crohn’s disease); rs316115020, rs316420452 (calcium metabolism); rs738409_G, rs2642438_A, rs58542926_T, rs72613567_TA (steatotic liver disease); rs148710154, rs75146099 (esophageal squamous cell carcinoma). The following demographic and health-related variables were found to be important predictors of GID or associated disease besides SNPs: age, body mass index, disease behavior, immune cell type, intestinal microbiome, MARCKS protein, smoking, and SNP density/number. No deep learning study was found even though deep learning was used as a search term together with machine learning. Genetic artificial intelligence is effective and non-invasive as a decision support system for GID. Full article

Chronic kidney disease (CKD) is a progressive global health burden often diagnosed in late stages due to reliance on invasive and centralized blood and urine tests. Saliva, as a non-invasive diagnostic fluid, has emerged as a promising alternative for assessing renal function. This scoping review aims to evaluate the diagnostic accuracy of salivary biomarkers compared to traditional methods, and to explore the potential of emerging biosensing technologies for CKD detection and monitoring. Methods: A comprehensive literature search was conducted in PubMed/MEDLINE, Scopus, Web of Science, and Cochrane Library up to 1 July 2025, following the PRISMA-ScR guidelines. Studies involving adult CKD patients and healthy controls that assessed the diagnostic performance of salivary biomarkers against validated reference standards (e.g., serum creatinine, eGFR) were included. A total of 29 eligible studies were selected after applying predefined inclusion and exclusion criteria. Results: Salivary creatinine and urea were the most frequently assessed biomarkers and demonstrated strong correlations with serum levels (AUCs up to 1.00; sensitivity and specificity frequently >85%). Several studies reported high diagnostic potential for novel salivary markers such as Trimethylamine N-oxide (TMAO), cystatin C, and amino acids. Technological innovations, including electrochemical biosensors and ATR-FTIR spectroscopy, showed promise for enhancing sensitivity and enabling point-of-care testing. However, heterogeneity in sampling protocols and limited data for early-stage CKD were notable limitations. Conclusions: Salivary diagnostics, supported by biosensor technologies, offer a feasible and non-invasive alternative for CKD screening and monitoring. Standardization, broader clinical validation, and integration into dental workflows are key to clinical implementation. Full article

Background/Objectives: The purpose of this review was to determine the impact of osteoporosis on outcomes after surgery for cervical deformity. Cervical deformity involves abnormal curvature or misalignment of the cervical spine, often resulting in a significant loss of quality of life and requiring surgical correction. While osteoporosis has been associated with hardware failure including screw loosening and cage migration in spine surgery, its role in cervical deformity remains unclear. Existing studies report mixed findings with regard to postoperative sequelae in patients with osteoporosis undergoing surgical correction of cervical deformity. Methods: A systematic review using PRISMA guidelines and MeSH terms involving spine surgery for cervical deformity and osteoporosis was performed. The Medline (PubMed) database was searched from 1990 to August 2022 using the following terms: “osteoporosis” AND “cervical” AND (“outcomes” OR “revision” OR “reoperation” OR “complication”). This review focused on radiographic outcomes, as well as post-operative complications. Results: Eight studies were included in the final analysis. Three papers assessed risk factors for the development of post-operative distal junctional kyphosis (DJK), but only one found osteoporosis as a predictor for DJK. Although three studies found that osteoporosis was not significantly associated with the incidence of surgical complications, one highlights osteoporosis as a predictor of complications at 90 days postoperatively (p < 0.001) and another associates osteoporosis with overall poor outcomes (p = 0.021). Furthermore, one study assessing the relationship between osteoporosis and reoperation found no association. Conclusions: Overall, our systematic review suggests that in patients undergoing surgery for cervical deformity, osteoporosis is not predictive of the need for reoperation or the development of postoperative complications, such as DJK, dysphagia, superficial infection, and others. These findings highlight the need for further study regarding the role of osteoporosis in surgical correction of cervical deformity. Full article

Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article

Objective: this systematic review with a meta-analysis aimed to evaluate the available body of published peer-reviewed randomized controlled trial (RCT) studies on the effects of different doses and types of strength training (ST) on body composition, physical performance, and protein or calcium intake in older people with osteosarcopenia. Method: a systematic literature search was conducted between July 2024 and August 2025 using five databases: PubMed, Medline, CINAHL Complete, Scopus, and Web of Science. PRISMA, TESTEX, RoB 2, and GRADE tools assessed methodological quality and certainty of evidence. Hedge’s g effect sizes were calculated for the abovementioned variables for the meta-analysis. Results: the protocol was registered in PROSPERO (code: CRD42025643858). Of 141 registers, seven RCTs with 349 participants were included. Seven overall and two subgroup meta-analyses showed significant increases in skeletal muscle mass index (SMMI; p < 0.01), maximal isometric handgrip strength (MIHS; p = 0.03), and protein intake (p = 0.03). There were no significant differences in bone mineral density (BMD), body fat percentage (BFP), gait speed, and calcium intake. However, meta-analysis by subgroups showed significant decreases in BFP (p = 0.01) in favor of elastic band training versus resistance training, with no significant differences in BMD. Conclusions: ST in older people with osteosarcopenia conditions increases SMMI, MIHS, and protein intake. Full article

Background: Dental implants have become integral in restoring partially or completely edentulous patients due to their reported long-term success. While titanium remains the primary material for implants and abutments due to its mechanical properties and biocompatibility, zirconia has emerged as a promising alternative, especially for aesthetic regions. This systematic review aimed to assess whether zirconia abutments present a rational alternative to titanium in modern implantology, focusing on their mechanical and clinical performances. Method: The workflow used for this review included the PRISMA checklist. The eligibility criteria included various study types, with a preference given to clinical trials. The search strategy employed the PICO model, including a large number of relevant studies, and online research was carried on the online databases PubMed and Scopus, with “implant” AND “abutment” AND “zirconia” and “zirconia abutment” AND “mechanical properties” used as search strings. Results: Six clinical studies were included with an adequate follow-up and patient cohort; they suggest that while zirconia abutments offer improved aesthetics and biological integration, concerns persist regarding their mechanical properties, particularly regarding their fatigue resistance and connection stability. In vitro studies have revealed differences between titanium and zirconia abutments, with the latter showing greater susceptibility to fatigue-induced deformation and fretting wear. The clinical outcomes, however, demonstrate favourable long-term performance, with zirconia abutments promoting healthy soft tissue conditions. CAD/CAM technologies enable the precise customization of zirconia abutments, enhancing their compatibility and aesthetic outcomes. Conclusions: Although this review faces limitations due to the scarcity of comparative studies and varied methodologies, it underscores the potential of zirconia abutments in implantology. In conclusion, while zirconia abutments offer promising advantages, the careful consideration of patient-specific factors and the long-term outcomes is warranted for their optimal utilisation in implant-supported prostheses. Full article

Background: Occupational risk prevention is a critical discipline for ensuring safe working conditions and minimizing accidents and occupational diseases. With the rise of artificial intelligence (AI) and machine learning (ML), these approaches are increasingly utilized for predicting and preventing workplace hazards. This systematic review aims to identify, evaluate, and synthesize existing literature on the use of AI algorithms for detecting and predicting hazardous environments and occupational risks in the workplace, focusing on predictive modeling and prevention strategies. Methods: A systematic literature review was conducted following the PRISMA 2020 protocol, with minor adaptations to include conference proceedings and technical reports due to the topic’s emerging and multidisciplinary nature. Searches were performed in IEEE Digital Library, PubMed, Scopus, and Web of Science, with the last search conducted on 1 August 2024. Only peer-reviewed articles published from 2019 onwards and written in English were included. Systematic literature reviews were explicitly excluded. The screening process involved duplicate removal (reducing 209 initial documents to 183 unique ones), a preliminary screening based on titles, abstracts, and keywords (further reducing to 92 articles), and a detailed full-text review. During the full-text review, study quality was assessed using six quality assessment (QA) questions, where articles receiving a total score below 4.5 or 0 in any QA question were excluded. This rigorous process resulted in the selection of 61 relevant articles for quantitative and qualitative analysis. Results: The analysis revealed a growing interest in the field, with a clear upward trend in publications from 2021 to 2023, and a continuation of growth into 2024. The most significant contributions originated from countries such as China, South Korea, and India. Applications primarily focused on high-risk sectors, notably construction, mining, and manufacturing. The most common approach involved the use of visual data captured by cameras, which constituted over 40% of the reviewed studies, processed using deep learning (DL) models, particularly Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Conclusions: The study highlights current limitations, including an over-reliance on visual data (especially challenging in low-visibility environments) and a lack of methodological standardization for AI-based risk detection systems. Future research should emphasize the integration of multimodal data (visual, environmental, physiological) and the development of interpretable AI models (XAI) to enhance accuracy, transparency, and trust in hazard detection systems. Addressing long-term societal implications, such as privacy and potential worker displacement, necessitates transparent data policies and robust regulatory frameworks. Full article