Search Results (66,435)

Background: To effectively promote physical activity (PA) participation in preschool-aged children with disabilities (PACD), a comprehensive understanding of the associated factors is necessary. Consequently, this study aims to examine the factors influencing PA participation in PACD using the socio-ecological model. Methods: The Disability Status Survey 2020 in South Korea has been used for the current study. PACD aged below five years were selected, resulting in 5825 children. Variables were selected across each level of the socio-ecological model: (1) intrapersonal level (child’s sex, chronic condition, disability level, perceived health), (2) interpersonal level (siblings’ status, grandparents’ status, satisfaction with number of friends), (3) organizational level (enrollment in physical therapy, enrollment in occupational therapy), and (4) environmental level (residential area, government support). The decision tree analysis was conducted using chi-square automatic interaction detection to examine multi-level associated factors of PA participation in PACD. Results: The decision tree analysis produced a three-level model with six terminal nodes. In the study sample, 48.4% of participants reported participating in PA. The most common location for PA was at home, with balance exercises being the most frequent type of activity, followed by stretching. Frequent barriers to regular PA participation included a lack of professionals who are knowledgeable about disabilities and the absence of disability-specific programs. The decision tree analysis identified living with grandparents, the child’s sex, having a sibling, and enrollment in physical therapy as common predictors of PA participation in PACD. Conclusions: This study’s strength lies in its thorough examination of the socio-ecological factors influencing PA participation in PACD. The associated factors span both intrapersonal and interpersonal levels. To enhance PA participation among PACD, interventions should target these levels. Full article

This research paper focuses on dimensionality reduction, which is a major subproblem in any data processing operation. Dimensionality reduction based on principal components is the most used methodology. Our paper examines three heuristics, namely Kaiser’s rule, the broken stick, and the conditional number rule, for selecting informative principal components when using principal component analysis to reduce high-dimensional data to lower dimensions. This study uses 22 classification datasets and three classifiers, namely Fisher’s discriminant classifier, logistic regression, and K nearest neighbors, to test the effectiveness of the three heuristics. The results show that there is no universal answer to the best intrinsic dimension, but the conditional number heuristic performs better, on average. This means that the conditional number heuristic is the best candidate for automatic data pre-processing. Full article

Background: The issue of digital piracy is increasingly prevalent, with its proliferation further fueled by the widespread use of social media outlets such as WhatsApp, Snapchat, Instagram, Pinterest, and X. These platforms have become hotspots for the unauthorized sharing of copyrighted materials without due recognition to the original creators. Current techniques for digital watermarking are inadequate; they frequently choose less-than-ideal locations for embedding watermarks. This often results in a compromise on maintaining critical relationships within the data. Purpose: This research aims to tackle the growing problem of digital piracy, which represents a major risk to rights holders in various sectors, most notably those involved in entertainment. The goal is to devise a robust watermarking approach that effectively safeguards intellectual property rights and guarantees rightful earnings for those who create content. Approach: To address the issues at hand, this study presents an innovative technique for digital video watermarking. Utilizing the 2D-DWT along with the KAZE feature detection algorithm, which incorporates the Accelerated Segment Test with Zero Eigenvalue, scrutinize and pinpoint data points that exhibit circular symmetry. The KAZE algorithm pinpoints a quintet of stable features within the brightness aspect of video frames to act as central embedding sites. This research selects the chief embedding site by identifying the point of greatest intensity on a specific arc segment on a circle’s edge, while three other sites are chosen based on principles of circular symmetry. Following these procedures, the proposed method subjects videos to several robustness tests to simulate potential disturbances. The efficacy of the proposed approach is quantified using established objective metrics that confirm strong correlation and outstanding visual fidelity in watermarked videos. Moreover, statistical validation through t-tests corroborates the effectiveness of the watermarking strategy in maintaining integrity under various types of assaults. This fortifies the team’s confidence in its practical deployment. Full article

This study aimed to evaluate the virulence of 36 clinical isolates estimated as blood culture contaminants (BCCs). MALDI-TOF MS classified all isolates as coagulase-negative staphylococci (CoNS) with the highest percentage of S. epidermidis (77.78%). All tested strains formed biofilms with greater ability at room temperature than 37 °C. CoNS were sensitive to vancomycin (0% resistance) and had relatively low resistance to linezolid and rifampicin (8.33 and 22.22% resistance). The highest resistance was observed for penicillin (94.44%). Moreover, we observed the transfer of antibiotic resistance genes from the tested CoNS to S. aureus and even to E. coli, although with lower efficiency. CoNS in planktonic form were completely combated by antiseptics after 10 and 60 s exposition, and activity against biofilms was time-dependent. The complete elimination of biofilms was observed after a 180 s exposure to Kodan and CITROclorex, and this exposure to Rivanol and Octenidyne showed still viable cells (>0.9 log CFU/mL). Our findings showed that a careful selection of antiseptics and extending the exposure time before blood collection can reduce the occurrence of blood culture contamination. However, our most important finding is the indication that CoNS naturally occurring on human skin and mucous membranes exhibit antibiotic resistance, and what is more, determinants of antibiotic resistance are transferred to both closely related Gram-positive bacteria and phylogenetically distant Gram-negative bacteria. Thus, our findings shed new light on CoNS—they indicate the necessity of their control due to the effective transfer of mobile genetic elements harboring antibiotic resistance genes, which may contribute to the spread of resistance genes and deepening the antibiotic crisis. Full article

Urban sprawl and excessive reliance on motorization have led to many urban problems. The balance of supply and demand in the real estate market, as well as price fluctuations, also face many challenges. Urban rail transit not only alleviates traffic congestion and air pollution, but also significantly reduces residents’ commuting time, broadens urban accessibility, and reshapes the decision-making basis for residents when choosing residential locations. This study takes the 1st, 2nd, 3rd, 4th, 8th, 11th, and 13th metro lines that have been opened in Qingdao City as examples. It selects 12,924 residential samples within a 2 km radius along the rail transit lines. By using GIS spatial analysis tools and the multi-scale geographically weighted regression (MGWR) model, it analyzes the spatial differentiation characteristics of housing prices along the rail transit lines and the reasons and mechanisms behind them. The empirical results show that housing prices decrease to varying degrees with the increase in the distance from the rail transit. For every additional 1 km from the rail transit station, the housing price increases by 0.246%. Through model comparison, it was found that MGWR has a better fitting degree than the traditional ordinary least squares method (OLS) and the previous geographically weighted regression model (GWR), and reveals the spatial heterogeneity of the influence of urban rail transit on housing prices. Different indicator elements have different effects on housing prices along these lines. The urban rail transit factor in the location characteristics has a positive impact on housing prices, and has a significant negative correlation in some areas. The significant influence range of the distance to the nearest metro station on housing prices is concentrated within a radius of 373 m, and the effect decays beyond this range. The total floors, building area, green coverage rate, property management fee, and the distance to hospitals and parks in the neighborhood and structural characteristics have spatial heterogeneity. Analyzing the areas affected by the urban rail transit factor, it was found that the double location superposition effect, the networked transportation system, and the agglomeration of urban functional axes are important reasons for the significant phenomena in some local areas. This research provides a scientific basis for optimizing the sustainable development of rail transit in Qingdao and formulating differentiated housing policies. Meanwhile, it expands the application of the MGWR model in sustainable urban spatial governance and has practical significance for other cities to achieve sustainable urban development. Full article

Geese (Anser cygnoides) are popular worldwide with consumers for their unique meat quality, egg production, foie gras, and goose down; however, the key genes that influence geese growth remain elusive. To explore the mechanism of geese growth, a total of 500 Zhedong White geese were raised; four high-weight (HW) and four low-weight (LW) male geese were selected to collect carcass traits and for further transcriptomic and metabolomic analysis. The body weight and average daily gain of HW geese were significantly higher than those of the LW geese (p-value < 0.05), and the yields of the liver, gizzard, glandular stomach, and pancreas showed no significant difference between the HW and the LW group (p-value > 0.05). Compared with the LW geese, 19 differentially expressed genes (DEGs) (i.e., COL11A2, COL22A1, and TF) were detected in the breast muscle from the HW geese, which were involved in the PPAR signaling pathway, adipocytokine signaling pathway, fatty acid biosynthesis, and ferroptosis. A total of 59 differential accumulation metabolites (DAMs), which influence the pathways of glutathione metabolism and vitamin B6 metabolism, were detected in the breast muscle between the HW and LW geese. In the liver, 106 DEGs (i.e., THSD4, CREB3L3, and CNST) and 202 DAMs were found in the livers of the HW and LW groups, respectively. DEGs regulated the pathways of the TGF-beta signaling pathway, pyruvate metabolism, and adipocytokine signaling pathway; DAMs were involved in pyrimidine metabolism, nitrogen metabolism, and phenylalanine metabolism. Correlation analysis between the top DEGs and DAMs revealed that in the breast muscle, the expression levels of COL11A2 and COL22A1 were positively correlated with the content of S-(2-Hydroxy-3-buten-1-yl)glutathione. In the liver, the expression of THSD4 was positively correlated with the content of 2-Hydroxyhexadecanoic acid. In addition, one DEG (LOC106049048) and four DAMs (mogrol, brassidic acid, flabelline, and L-Leucyl-L-alanine) were shared in the breast muscle and liver. These important results contribute to improving the knowledge of goose growth and exploring the effective molecular markers that could be adopted for Zhedong White goose breeding. Full article

The density-based spatial clustering of applications with noise (DBSCAN) is able to cluster arbitrarily structured datasets. However, the clustering result of this algorithm is exceptionally sensitive to the neighborhood radius (Eps) and noise points, and it is hard to obtain the best result quickly and accurately with it. To address this issue, a parameter-adaptive DBSCAN clustering algorithm based on the Sparrow Search Algorithm (SSA), referred to as SSA-DBSCAN, is proposed. This method leverages the local fast search ability of SSA, using the optimal number of clusters and the silhouette coefficient of the dataset as the objective functions to iteratively optimize and select the two input parameters of DBSCAN. This avoids the adverse impact of manually inputting parameters, enabling adaptive clustering with DBSCAN. Experiments on typical synthetic datasets, UCI (University of California, Irvine) real-world datasets, and image segmentation tasks have validated the effectiveness of the SSA-DBSCAN algorithm. Comparative analysis with DBSCAN and other related optimization algorithms demonstrates the clustering performance of SSA-DBSCAN. Full article

Percutaneous ventricular assist devices are utilized in cases of cardiogenic shock following acute myocardial infarction (AMI). However, the mechanism underlying the beneficial effects of LV unloading in AMI remains unclear. This study aimed to examine the impact of LV unloading on cardiac function, heart failure markers, and protein degradation (autophagy and ubiquitin–proteasome system: UPS) post AMI in rats. Nine-week-old male Lewis rats were randomized into non-AMI, AMI, non-AMI with LV unloading, and AMI with LV unloading groups. LV unloading was achieved through heterotopic heart–lung transplantation. Rats were euthanized 2 and 14 days after the procedure. Cardiac functional assessment was performed using Langendorff heart perfusion. RT-PCR and Western blot analyses were conducted using the LV myocardium. The rate pressure product was comparable between the non-AMI with LV unloading group and the AMI with LV unloading at 14 days. The atrial natriuretic factor tended to be suppressed by LV unloading. LV unloading had reducing effects on the expressions of p62, selectively degraded during autophagy, both 2 and 14 days after AMI. There was no effect on the parameters for the UPS. LV unloading has a mitigating effect on the deterioration of cardiac function following AMI. Autophagy, which was suppressed by AMI, was ameliorated by LV unloading. Full article

Due to the rapid proliferation of social media and online reviews, the accurate identification and classification of controversial texts has emerged as a significant challenge in the field of natural language processing. However, traditional text-classification methodologies frequently encounter critical limitations, such as feature sensitivity and inadequate generalization capabilities. This results in a notably suboptimal performance when confronted with diverse controversial content. To address these substantial limitations, this paper proposes a novel controversial text-detection framework based on stacked ensemble learning to enhance the accuracy and robustness of text classification. Firstly, considering the multidimensional complexity of textual features, we integrate comprehensive feature engineering, i.e., encompassing word frequency, statistical metrics, sentiment analysis, and comment tree structure features, as well as advanced feature selection methodologies, particularly lassonet, i.e., a neural network with feature sparsity, to effectively address dimensionality challenges while enhancing model interpretability and computational efficiency. Secondly, we design a two-tier stacked ensemble architecture, which not only combines the strengths of multiple machine learning algorithms, e.g., gradient-boosted decision tree (GBDT), random forest (RF), and extreme gradient boosting (XGBoost), with deep learning models, e.g., gated recurrent unit (GRU) and long short-term memory (LSTM), but also implements the support vector machine (SVM) for efficient meta-learning. Furthermore, we systematically compare three hyperparameter optimization algorithms, including the sparrow search algorithm (SSA), particle swarm optimization (PSO), and Bayesian optimization (BO). The experimental results demonstrate that the SSA exhibits a superior performance in exploring high-dimensional parameter spaces. Extensive experimentation across diverse topics and domains also confirms that our proposed methodology significantly outperforms the state-of-the-art approaches. Full article

Background and Objectives: Arteriovenous malformations (AVMs) are abnormal connections between arteries and veins, lacking a normal capillary network. Seizures are a common clinical manifestation in patients with brain AVMs, ranking as the second most frequent presentation. The objective of this study was to evaluate the dynamics of seizure activity in patients with brain AVMs following surgical treatment. Materials and Methods: This study included 27 patients with brain AVMs who underwent microsurgical AVM resection for symptomatic epilepsy. All surgical interventions were performed at JSC “National Centre for Neurosurgery” between 2008 and 2020. Results: Over an average follow-up period of 98.07 ± 45.6 months, 82 patients with brain AVMs underwent open microsurgical resection at the National Centre for Neurosurgery. Among them, 27 patients presented with seizures and had complete follow-up information, qualifying them for inclusion in this study. The participants had a mean age of 32.59 ± 9.06 years, with 13 of them being women. The Spetzler–Martin grading system was used to classify the AVMs: 6 patients had grade 1, 13 had grade 2, 7 had grade 3, and 1 had grade 4. More than half of the patients experienced generalized seizures. Microsurgical removal of the AVMs resulted in seizure remission for all patients. Only one patient experienced postoperative hemorrhage during the follow-up period. Additionally, one patient developed acute postoperative anemia, which resolved with a favorable outcome. Conclusions: Microsurgical resection of brain AVMs, when performed with careful patient selection, leads to a significant reduction in seizure activity. It is a safe and effective treatment option for symptomatic epilepsy associated with brain AVMs. Full article

Flash floods pose a significant threat to Bangladesh; in particular, on 20 August 2024, the Feni district experienced a major flash flood, affecting more than 550,000 people and causing widespread damage. To effectively mitigate the impacts of flash floods, it is essential to conduct a comprehensive flash flood vulnerability assessment, incorporating multiple triggering factors. This study aims to assess flash flood vulnerability in the Feni District through a unique approach, integrating various dimensions of vulnerability. The study utilizes a geospatial methodology, employing the formula of vulnerability developed by UNESCO-IHE. Four dimensions of vulnerability were analyzed: social, physical, economic, and environmental. For each dimension, specific variables were selected to assess exposure, susceptibility, and resilience. Principal Component Analysis (PCA) was used to assign weights to these variables. The geospatial layers of influencing vulnerability factors were integrated together to create flash flood vulnerability maps of four dimensions. These were then overlaid to generate a composite flash flood vulnerability map. The analysis revealed a distinct spatial distribution of vulnerability across Feni District. In terms of environmental vulnerability due to flash flood, about 14% of the total area falls into the very highly vulnerable zone, whereas 13%, 8% and 5% of the study area were found to be very highly vulnerable regarding social, economic and physical aspects, respectively. The composite flash flood vulnerability map identified key vulnerability hotspots, with the most vulnerable unions (the smallest administrative unit in Bangladesh) being Feni Pourashava (68% very high), Sonagazi Paurashava (40% very high), and Nawabpur (32% very high), while the least vulnerable areas were Jailashkara (58% very low), Anandapur (81% very low), and Darbarpur (82% very low). The results show that the Feni District’s flash flood susceptibility varies significantly throughout the region, which provide crucial insights for policymakers and local authorities in order to identify vulnerability hotspots, prioritize interventions in vulnerable areas, enhance flash flood resilience, and implement adaptive strategies. Full article

Chlorpyrifos is one of the most toxic organophosphate pesticides, prompting its ban in Europe in 2020. Consequently, developing a detection method that is both selective and sensitive is essential for protecting human health and the environment. In this study, we report for the first time a fluorescent probe based on an oxime for the direct detection of chlorpyrifos. 9-fluorenone oxime, upon deprotonation with a phosphazene base, undergoes a nucleophilic attack on chlorpyrifos, resulting in a significant alteration of its fluorescence properties. Following careful optimization, the method demonstrated excellent linearity (R² = 0.98) over a concentration range of 350 to 6980 μg/L, with a limit of detection of 15.5 μg/L. Furthermore, the probe was successfully applied to chlorpyrifos detection in water samples, yielding satisfactory results. This approach effectively overcomes the stability limitations of enzyme-based fluorescent sensors, offering a robust and innovative solution for the detection of organophosphate pesticides. Full article

Watermelon is a widely cultivated fruit and vegetable that is native to Africa and has become one of the world’s important summer fruits. Watermelon seed vigor has a critical impact on watermelon planting and yield, and seed water content is a key factor in maintaining vigor during seed storage and germination. In this study, reflectance and transmittance spectral data from hyperspectral imaging were fused to improve the detection accuracy of moisture content in watermelon seeds. First, watermelon seed samples with different water content gradients were prepared by dividing all 456 selected watermelon seeds into 10 groups and drying them in a drying oven at 60 °C for 0, 3, 5, 10, 15, 20, 25, 30, 40, and 50 min. Reflectance and transmission spectra of 456 watermelon seeds were collected by a hyperspectral imaging system, and the single spectral data were subsequently used to build PLSR and LSSVR models for quantitative analysis of watermelon seed moisture content. Model performance is enhanced by Competitive Adaptive Reweighted Sampling (CARS), Unrelated Variable Elimination (UVE), and primary and intermediate data fusion methods. Primary data fusion improves model predictions compared to single models based on reflectance and transmission spectra. The intermediate data fusion of the feature spectral data of reflectance and transmittance selected by the CARS algorithm improves the prediction effect of the model more obviously, in which the model with the best prediction accuracy is Raw-CRAS-LSSVR, whose $R_P^2$ and RMSEP are 0.9149 and 0.0144, respectively, which improves the prediction effect of the model built by a single full-spectrum datum by 5.72%. This study demonstrates that hyperspectral reflectance and transmission imaging techniques combined with data fusion can effectively detect watermelon seed moisture content quickly and with high accuracy. Full article

Background: Aging and its pathologies, particularly sleep problems, are increasingly affecting industrialized societies. This study aims to determine the effectiveness of different cognitive interventions for the treatment of insomnia or poor sleep quality in community-dwelling older people. Methodology: A systematic review was carried out from November 2023–July 2024 according to the standards of the Preferred Reporting Items for Systematic Review and Meta-Analyses in the databases. The following databases were consulted: Pubmed, Web of Science, and ClinicalTrials.gov. The studies included patients with sleep problems or insomnia over 60 years of age. To evaluate the quality of the studies, the Critical Appraisal Skills Program Spanish (CASPe) guide was used. Results: Nine clinical trials with intervention groups and control groups belonging to the last 10 years were selected. They were analyzed, and the results were verified via questionnaires, scales, sleep diaries, and objective measures. In general, the implementation of the interventions improved the quality of sleep and symptoms of insomnia. Conclusions: Cognitive interventions have been found to be safe and useful for the treatment of insomnia and poor sleep quality in older people. Furthermore, they are feasible in terms of cost effectiveness and can be easily implemented by primary care teams. Full article

Nickel-containing orthodontic archwires, particularly those made of nickel-titanium (NiTi) and stainless steel (SS), play a crucial role in orthodontic treatment using the fixed technique due to their mechanical properties. However, concerns regarding nickel-induced allergic reactions, cytotoxicity, and metal ion release, especially nickel-related ones, persist. This narrative review aims to explore recent findings on nickel release from orthodontic appliances, building upon prior systematic reviews by analyzing both in vitro and in vivo studies under various environmental conditions. The databases Web of Science, Scopus, and PubMed were searched for relevant studies that examined the relationship between nickel ion release from nickel-containing archwires and various environmental conditions. The studies found indicate that while metal ion release occurs during short-term treatment, the levels are lower than harmful thresholds, with factors such as pH, corrosion, length of treatment, and environmental influences affecting release rates. Despite this, long-term studies are few and are usually conducted only in an in vitro or in vivo environment, but not both. To establish causal relationships regarding metal ion release, in vivo monitoring of ions like Ni is critical, with further research needed to assess its prolonged effects. Furthermore, collaborative efforts among practitioners, researchers, and regulatory bodies are vital for developing evidence-based guidelines for orthodontic material selection, prioritizing patient safety and addressing metal ion release risks. Full article