Large-Scale Method for Identifying the Relationships between Racket Properties and Playing Characteristics

Childhood obesity is a growing worldwide epidemic that requires a biopsychosocial treatment approach to achieve a healthy lifestyle. This study on children in a weight management program examines the relationship between obesity, disease complications, and social determinants of health. We expect that children with higher degrees of obesity are more likely to live in areas lacking access to healthy food and have similar behavioural and socioeconomic characteristics. Program participants were identified by neighbourhood food access status based on their home address. The prevalence of comorbidities in the participants was analyzed according to neighbourhood food accessibility. Multivariate regressions evaluated the association between participants' health outcomes and their sociodemographic and geographical characteristics. A total of 283 (98.3%) participants had a BMI ≥95th percentile for their age and sex and 68 (23.6%) lived in neighbourhoods with limited food access. Almost a third (Adj. R2 = 0.3302; p < 0.01) of the variability in study population's BMI was driven by sociodemographic factors, self-reported eating and physical activity behaviours, and had a positive relationship with access to healthy food. Nonetheless, HbA1c had a negative relationship with access to healthy food given the limited variation in the sample of participants with HbA1c levels indicating diabetes. Children living in neighbourhoods with limited food access had higher BMIs than other program participants. Thus, it is critical to identify children with limited neighbourhood food accessibility and promote societal and legislative change to improve access to healthy food.

Identifying Clusters of College Students at Elevated Health Risk Based on Eating and Exercise Behaviors and Psychosocial Determinants of Body Weight

In the entity space, the independent individuals under the same metapopulation have similar behavior characteristics and biological characteristics, widely used in commercial promotion, medical treatment and education, and whether independent individuals under the same metapopulation in cyberspace have similar behavioral characteristics is unknown. We use Chinese massive online user behavior data, through the neural network algorithm in machine learning, study the metapopulation category identification of online user based on the online fingerprint constructed by click-software behavior, and give some application scenarios. The result show that the independent individuals under the same metapopulation in cyberspace have similar behavioral characteristics, and the metapopulation category of online user can be identified uniquely based on online fingerprint. Respectively, under the six demographic information dimensions of online user, which are social strata, age stage, region, educational background, gender and household registration, identify metapopulation category of online user based on online fingerprint. A large number of experiments show that the average identification accuracy can reach more than 89.93%.

The mechanisms behind the formation and propagation of meteotsunamis and seiches—anomalous water waves generated by meteorological events that share similar spatial, temporal, and behavioral characteristics with seismically-generated tsunamis—are poorly understood. Small-scale ground tilt (microradians-nanoradians) triggered by these anomalous water waves in Lake Michigan is investigated with an array of 10 near-shore (< 100 km) seismometers installed from 2012-2014 by calculating tilt from seismic recordings and comparing it with data from relatively nearby (10s of km) NOAA water level stations in Holland and Ludington, Michigan. After determining an appropriate period band from 15 minutes to four days to isolate these events, we show that potential meteotsunami and seiche activity on eastern Lake Michigan can be deduced from long-term trends (multiple weeks) in tilt and water level time series. Exploratory in nature, the methods and observations presented in this paper serve as an initial step in quantifying the potential for nearshore seismometers to monitor shoreline inundation. Integrating these observations with meteorological data could provide insights into the origins of these hazards by linking them to the specific weather events that generated them. This approach offers a novel method for improving the understanding and potential future mitigation of meteotsunami and seiche impacts in the Great Lakes region.

Trichotillomania (TTM) is a body-focused repetitive disorder affecting as much as 0.5 to 2% of the population, with women four times more likely to be affected than men. This disorder causes impairment in daily function and significant distress. A potential animal model for this disorder is the inbred C57BL/6J mouse which displays clinical signs and behavioral characteristics similar to those described for people affected by this disorder. Because alcohol-preferring P rats also display similar clinical signs and behavioral characteristics, it was hypothesized that this selectively bred stock could be an additional animal model. In this study, 112 female P rats were recorded on digital media for 15 min after being sprayed with a mist of water and assessed for grooming patterns—oral, manual, and scratching. Significant elevations in scratching and oral grooming behavior were predictive of the future development of skin lesions. These findings suggest that P rats may be an additional model to study TTM, with the advantage of increased genetic variation (i.e., non-inbred) which mirrors the human population. The use of this model may help to identify preventative and therapeutic interventions for humans and other animals with similar body-focused repetitive disorders.

This study aimed to estimate the latent frailty trajectories and identify corresponding predictors (sociodemographic, HIV-related, comorbidities, and behavioral) among a cohort of people with HIV (PWH). Longitudinal observational study using latent class growth modeling. Nine hundred and seventy-six PWH aged 40 years and older with frailty measured from at least two visits within the ACTG HAILO cohort were included. Frailty components included weakness, physical activity, weight loss, exhaustion, and slowness. Latent class growth models were estimated to capture change in frailty over time; multinomial logistic regression was used to estimate associations between predictors and frailty trajectory class. At baseline, participants were M = 51.5 years old ( SD = 7.5), 81% male ( n = 783), 48% White non-Hispanic ( n = 461), and 20% Hispanic ( n = 195). Latent class growth models identified three frailty trajectories: sustained robustness ( n = 811; 83%), worsening frailty ( n = 79; 8%), and frailty improvement ( n = 86; 9%). Older age, race, sex at birth, select comorbidities (cardiovascular disease, depression, type 2 diabetes), and behavioral characteristics (physical activity, smoking, and alcohol) were associated with fluctuations in frailty trajectories over time ( P < 0.05). Modifiable factors such as managing comorbidities and promoting physical activity present ideal opportunities for future interventions to prevent or slow the progression of frailty.

Elevated blood pressure (BP) in childhood is a predictor of adult hypertension. We characterized existing blood pressure (BP) percentile trajectories in children aged 3-9 years and examined factors associated with BP trajectory and elevated BP among 1040 participants in a prospective birth cohort study in Durham, North Carolina (2005-2011). We obtained BP measurements from medical records and demographic, social, and behavioral characteristics from questionnaires. We identified systolic blood pressure (SBP) percentile trajectories using group-based trajectory modeling as well as diastolic blood pressure (DBP) percentiles as a secondary outcome. We then calculated associations between trajectory group and covariates of interest using multinomial logistic regression. We also estimated the association of these covariates with elevated BP using generalized estimating equations. We identified four distinct SBP percentile trajectories (lower stable, lower increasing, higher stable, and higher decreasing) and three distinct DBP percentile trajectories (lower increasing, higher decreasing, and higher stable). Birthing parent race and ethnicity, pre-pregnancy BMI, relationship status, educational attainment, and gestational diabetes status were significantly associated with offspring SBP percentile trajectory group assignment. Offspring sex assigned at birth, birthing parent race and ethnicity, pre-pregnancy BMI, smoking, relationship status, educational attainment, and chronic hypertension status were significantly associated with offspring DBP percentile trajectory group assignment. Infant sex, preterm birth, birthing parent race and ethnicity, pre-pregnancy BMI, and chronic hypertension status were significantly associated with offspring elevated BP. Our results enhance understanding of pediatric SBP and DBP patterns and assist in identifying factors that contribute to elevated BP in children. Our research contributes to understanding of pediatric BP patterns leveraging percentiles which account for age, sex, and height. We identified four distinct SBP and three distinct DBP trajectories in children aged 3-9 years. We identified associations of several social, demographic, and clinical characteristics with childhood BP trajectories and elevated BP. Elevated blood pressure in childhood is a leading precursor for adverse cardiovascular outcomes in adults. Our research may assist in targeted interventions for high-risk groups.

Companion dogs with spontaneous malignancies are clinically relevant models in which to study the corresponding human diseases and potential therapies. In both dogs and people, non-Hodgkin's lymphoma (NHL) is the most common hematopoietic malignancy. Diffuse large B-cell lymphoma (DLBCL) is the most common NHL subtype in dogs and people, sharing similar biologic, behavioral, genetic, and molecular characteristics in both species. One such molecular characteristic is the constitutive activation of the canonical NF-κB pathway, which in health regulates the expression of target genes that control cellular proliferation, survival, and immune and inflammatory responses as well as multidrug resistance. We found that canine and human DLBCL patients share similar NF-κB activity profiles. Using the cell-permeable NBD peptide, which blocks NF-κB signaling, we inhibited constitutive NF-κB activity and induced apoptosis of primary canine malignant B cells in vitro. In addition, we found that NBD peptide administration to dogs with relapsed B-cell lymphoma inhibited the expression of NF-κB target genes and reduced tumor burden. In this chapter, we describe our methods for processing canine malignant lymphoid tissue. We also describe our methods for treating the lymphocytes isolated from this tissue with NBD peptide and evaluating constitutive canonical NF-κB activity in these cells via immunoblot and electrophoretic mobility shift assay (EMSA). We highlight the nuances of working with canine primary cells.

Recently, e-education around the world is developing rapidly and the main problem is the timely provision of students with quality educational information. A significant impetus for this is the global epidemic of covid-19. The problem of implementing e-education cannot be solved without analysing the large flow of information coming into the information environment of e-education from participants in the educational process – students, teachers, administration, etc. In this environment, there are a large number of different types of data, both structured and unstructured, which are difficult to process by traditional statistical methods. The aim of the study is to show that the development and implementation of successful e-learning systems requires the use of new technologies that would allow the storage and processing of large data streams. Large amounts of disk space are required to store large data. It is shown that to solve this problem it is expedient to use cluster technology NAS (Network Area Storage), which allows to store information of educational institutions on NAS - servers and to have access to them from the Internet. To process and personalize Big Data in the e-learning environment, it is proposed to use technologies MapReduce, Hadoop, NoSQL and others. The article provides examples of the use of these technologies in the cloud environment. These technologies in e-learning make it possible to achieve flexibility, scalability, accessibility, security, confidentiality and ease of use of educational information. Another important problem of e-learning is the discovery of new, sometimes hidden, relationships in big data, new knowledge (data mining), which can be used to improve the educational process and increase the efficiency of its management. To classify electronic educational resources, identify patterns (patterns) of students with similar psychological, behavioural and intellectual characteristics, the development of individualized curricula in the article it is proposed to use methods of big data analysis. The article shows that to date, many software applications have been developed for big data mining. These software products can be used for classification, clustering, regression and network analysis of educational information. The application of these methods in e-education will allow teachers to receive timely information about students, to respond quickly to any changes in the learning process, to make timely changes to educational content. The obtained results of the research are offered to be used for development of recommendations at creation of electronic courses in higher and secondary educational institutions of Ukraine.

As a public infrastructure service, remote sensing data provided by smart cities will go deep into the safety field and realize the comprehensive improvement of urban management and services. However, it is challenging to detect criminal individuals with abnormal features from massive sensing data and identify groups composed of criminal individuals with similar behavioral characteristics. To address this issue, we study two research aspects: pickpocketing individual detection and pickpocketing group identification. First, we propose an IForest-FD pickpocketing individual detection algorithm. The IForest algorithm filters the abnormal individuals of each feature extracted from ticketing and geographic information data. Through the filtered results, the factorization machines (FM) and deep neural network (DNN) (FD) algorithm learns the combination relationship between low-order and high-order features to improve the accuracy of identifying pickpockets composed of factorization machines and deep neural networks. Second, we propose a community relationship strength (CRS)-Louvain pickpocketing group identification algorithm. Based on crowdsensing, we measure the similarity of temporal, spatial, social and identity features among pickpocketing individuals. We then use the weighted combination similarity as an edge weight to construct the pickpocketing association graph. Furthermore, the CRS-Louvain algorithm improves the modularity of the Louvain algorithm to overcome the limitation that small-scale communities cannot be identified. The experimental results indicate that the IForest-FD algorithm has better detection results in Precision, Recall and F1score than similar algorithms. In addition, the normalized mutual information results of the group division effect obtained by the CRS-Louvain pickpocketing group identification algorithm are better than those of other representative methods.

This systematic review evaluated the effects of simulated gastric acid (SGA) on surface and mechanical properties of dental ceramics to answer the research question: Does immersion in SGA alter the surface roughness, microhardness, fracture strength, and color of dental ceramics? A literature search was conducted in PubMed, Scopus, Web of Science, Embase, Open Gray, and Google Scholar, in February 2025. Independent reviewers selected and analyzed in vitro studies assessing one or more outcomes-roughness, microhardness, fracture strength, or color-after acid exposure. Data were extracted independently, and risk of bias was assessed using the RoBDEMAT tool. Out of 2,111 studies, 22 were included. Ceramics analyzed were lithium disilicate (n = 15), polymer-infiltrated feldspathic (n = 11), zirconia (n = 11), feldspathic (n = 8), zirconia-reinforced lithium silicate (n = 7), and leucite-reinforced (n = 4). Surface roughness changes were material-dependent, with feldspathic ceramics most affected and zirconia most stable. Microhardness generally decreased across ceramics. Fracture strength remained mostly unchanged, except for occasional reductions. Polymer-infiltrated ceramics and zirconia showed the greatest color stability. Specimen shapes and dimensions varied across studies, reflecting differences in study aims and testing protocols. Most studies showed appropriate outcome reporting, statistical analysis, and test standardization (>90%). However, operator blinding, when applicable, was not reported in any of the included studies. Randomization (> 87%) and sample size calculation (50%) were also frequently missing. Surface, mechanical, and optical properties of ceramics were affected by acid exposure, varying by material. Zirconia and polymer-infiltrated ceramics show greater stability. Methodological heterogeneity limits conclusions, underscoring the need for standardized protocols and clinical validation.

Several criminal profiling systems have been developed to assist the Law Enforcement Agencies in solving crimes but the techniques employed in most of the systems lack the ability to cluster criminal based on their behavioral characteristics. This paper reviewed different clustering techniques used in criminal profiling and then selects one fuzzy clustering algorithm (Expectation Maximization) and two hard clustering algorithm (K-means and Hierarchical). The selected algorithms were then developed and tested on real life data to produce "profiles" of criminal activity and behavior of criminals. The algorithms were implemented using WEKA software package. The performance of the algorithms was evaluated using cluster accuracy and time complexity. The results show that Expectation Maximization algorithm gave a 90.5% clusters accuracy in 8.5s, while K-Means had 62.6% in 0.09s and Hierarchical with 51.9% in 0.11s. In conclusion, soft clustering algorithm performs better than hard clustering algorithm in analyzing criminal data.&#x0D; Keywords: Clustering Algorithm, Profiling, Crime, Membership value

How do therapists administer the rapid exchange grip test? A survey

The objective of this document was to standardise published cardiopulmonary exercise testing (CPET) protocols for improved interpretation in clinical settings and multicentre research projects. This document: 1) summarises the protocols and procedures used in published studies focusing on incremental CPET in chronic lung conditions; 2) presents standard incremental protocols for CPET on a stationary cycle ergometer and a treadmill; and 3) provides patients' perspectives on CPET obtained through an online survey supported by the European Lung Foundation. We systematically reviewed published studies obtained from EMBASE, Medline, Scopus, Web of Science and the Cochrane Library from inception to January 2017. Of 7914 identified studies, 595 studies with 26 523 subjects were included. The literature supports a test protocol with a resting phase lasting at least 3 min, a 3-min unloaded phase, and an 8- to 12-min incremental phase with work rate increased linearly at least every minute, followed by a recovery phase of at least 2-3 min. Patients responding to the survey (n=295) perceived CPET as highly beneficial for their diagnostic assessment and informed the Task Force consensus. Future research should focus on the individualised estimation of optimal work rate increments across different lung diseases, and the collection of robust normative data.

Testing for electromagnetic compatibility (EMC) in the clinical environment introduces a host of complex conditions not normally encountered under laboratory conditions. In the clinical environment, various radio-frequency (RF) sources of electromagnetic interference (EMI) may be present throughout the entire spectrum of interest. Isolating and analyzing the impact from the sources of interference to medical devices involves a multidisciplinary approach based on training in, and knowledge of, the following: operation of medical devices and their susceptibility to EMI; RF propagation modalities and interaction theory; spectrum analysis systems and techniques (preferably with signature analysis capabilities) and calibrated antennas; the investigation methodology of suspected EMC problems, and testing protocols and standards. Using combinations of standard test procedures adapted for the clinical environment with personnel that have an understanding of radio-frequency behavior increases the probability of controlling, proactively, EMI in the clinical environment, thus providing for a safe and more effective patient care environment.