Physical Clusters Research Articles

Associations between family factors and physical activity clustering in preschool children: a cross-sectional study

ObjectiveThis study aimed to examine the association between family factors and physical activity (PA) clustering in preschool children.MethodsPreschoolers’ PA and sedentary behavior (SB) were assessed consecutively for 7 days using ActiGraph accelerometers based on the cutoff counts developed by Pate et al. Information about children, their parents, and their families was collected using questionnaires. We developed a two-step approach to clustering PA both inside and outside of kindergarten. The Euclidean distance metric was utilized to distinguish between groups, while the Schwarz–Bayesian criterion was applied to identify the most optimal sub-group model. A one-way ANOVA was used to assess the clustering effect, and logistic regression was used to analyze the influencing factors of different clustering.ResultsWe collected data from 291 preschool children aged 3 to 6 years and divided them into three clusters—Inactive (50.2%), Active in kindergarten (26.8%), and Active outside kindergarten (23.0%)—with significant differences in PA and SB, revealing distinct temporal and spatial clustering patterns (silhouette coefficient = 0.3, p < 0.05). Furthermore, preschooler activity levels correlated significantly with factors including gender (OR = 0.35, 95% CI: 0.19–0.66), age (OR = 1.05, 95% CI: 1.00–1.10), birth weight (OR = 1.79, 95% CI: 1.16–2.76), paternal age (OR = 1.01, 95% CI: 1.00–1.02), and maternal income (OR = 0.68, 95% CI: 0.48–0.96).ConclusionThis study shows that the family environment or parents significantly influence the PA of preschool children. Older fathers may promote preschool children’s PA through greater educational focus and financial stability, while higher maternal income can provide more opportunities and resources for preschool children to engage in active lifestyles. Thus, it is suggested that families providing more attention and exercise opportunities for preschool children’s education can help improve their PA levels in the future.

Initial Symptom Severity and Recovery of Sport-Related Concussion in Team Versus Individual Sport Athletes.

Recovery after sport-related concussion is variable, and potential differences between team vs individual sport athletes are not fully understood. In a cohort of athletes with concussions, we sought to compare these groups across (1) symptom severity score, (2) individual symptom cluster scores, and (3) recovery metrics. A retrospective, cohort study of 13 to 23-year-old athletes treated at a regional sport concussion center between November 2017 and April 2022 was conducted. Athletes were categorized into team vs individual sport athletes, with additional classification of individual sports based on strong vs minimal team elements (ie, the degree of interdependence displayed by athletes). The primary outcomes were symptom severity score, measured by either the Sport Concussion Assessment Tool-5th Edition or the Immediate Post-Concussion Assessment and Cognitive Testing, and physical, cognitive, emotional, and sleep symptom cluster scores. Secondary outcomes were recovery metrics, including time to return-to-learn, symptom resolution, and return-to-play. Of the 1051 athletes, 954 (90.8%) were in team sports and 97 (9.2%) in individual sports. In multivariable linear regression, individual sport athletes had higher emotional symptom severity compared with team sport athletes (β = 0.09 [0.01, 0.17], P = .034) when adjusting for sex, attention-deficit/hyperactivity disorder, psychological disorders, time to clinic, on-field evaluation, and Immediate Post-Concussion Assessment and Cognitive Testing vs Sport Concussion Assessment Tool-5th Edition scale. There were no significant differences in return-to-learn (P = .104), symptom resolution (P = .941), or return-to-play (P = .279) on univariate regressions. In the current single-center, pilot study of athletes with a sport-related concussion, individual sport athletes exhibited more emotional symptoms than team sport athletes. These preliminary data lend early support that individual sport athletes may be more at risk of emotional symptoms than team sport athletes after a sport-related concussion. Clinicians may use these findings to identify athletes who may be particularly vulnerable to emotional symptoms.

Heat shock responsive genes in Brassicaceae: genome-wide identification, phylogeny, and evolutionary associations within and between genera.

Heat stress affects the growth and development of Brassicaceae crops. Plant breeders aim to mitigate the effects of heat stress by selecting for heat stress tolerance, but the genes responsible for heat stress in Brassicaceae remain largely unknown. During heat stress, heat shock proteins (HSPs) function as molecular chaperones to aid in protein folding, and heat shock transcription factors (HSFs) serve as transcriptional regulators of HSP expression. We identified 5002 heat shock related genes, including HSPs and HSFs, across 32 genomes in Brassicaceae. Among these, 3347 genes were duplicated, with segmented duplication primarily contributing to their expansion. We identified 466 physical gene clusters, including 240 homogenous clusters and 226 heterogeneous clusters, shedding light on the organization of heat shock related genes. Notably, 37 genes were co-located with published thermotolerance quantitative trait loci, which supports their functional role in conferring heat stress tolerance. This study provides a comprehensive resource for the identification of functional Brassicaceae heat shock related genes, elucidates their clustering and duplication patterns and establishes the genomic foundation for future heat tolerance research. We hypothesise that genetic variants in HSP and HSF genes in certain species have potential for improving heat stress tolerance in Brassicaceae crops.

Examining physical activity clustering using machine learning revealed a diversity of 24-hour step-counting patterns

BackgroundPhysical activity is a crucial aspect of health benefits in the public society. Although studies on the temporal physical activity patterns might lead to the protocol for efficient intervention/program, a standardized procedure to determine and analyze the temporal physical activity patterns remains to be developed. Here, we attempted to develop a procedure to cluster 24-hour patterns of physical activity as step counts measured with an accelerometer-based wearable sensor.MethodsThe 1 Hz step count data was collected by a hip-worn triaxial accelerometer from 42 healthy participants, comprising 35 males and 7 females, at the Sendai Oroshisho center in 2008. This is a cross sectional study using unsupervised machine learning, specifically the kernel k-means algorithm with the global alignment kernel was applied on a total of 815 days from 42 participants, and 6 activity patterns were identified. Further, the probability of each 24-hour step-counting pattern was calculated for every participant and used for spectral clustering of step-behavioral patterns.ResultsWe could identify six 24-hour step-counting patterns and five daily step-behavioral clusters. We could further identify five step-behavioral clusters, all-day dominant (21 participants), all-day + bi-phasic dominant (8 participants), bi-phasic dominant (6 participants), all-day + evening dominant (4 participants), and morning dominant (3 participants). When the amount of physical activity was categorized into tertile groups reflecting highly active, moderately active, and low active, each tertile group consisted of different proportions of six 24-hour step-counting patternsConclusionsOur study introduces a novel approach using an unsupervised machine learning method to categorize daily hourly activity, revealing six distinct step counting patterns and five clusters representing daily step behaviors. Our procedure would be reliable for finding and clustering physical activity patterns/behaviors and reveal diversity in the categorization by a traditional tertile procedure using total step amount.

Research on control and management of smart grid optical network based on optical transmission control Protocol (OTN) technology

Smart grids can improve the operational efficiency and reliability of power systems, and optical transmission control protocol (OTN) technology is widely used in the field of power communication. This article focuses on the control and management of smart grid optical networks, aiming to achieve efficient control and management of smart grid optical networks through OTN technology. This article proposes a smart grid OTN management system based on Optical Transmission Control Protocol (OTN) technology. This system achieves the management and optimization of power OTN business by researching functions such as container management and service orchestration, pod and physical node management, application and cluster status monitoring, application logs and cluster logs. In this system, the sources of data are very rich. Data can be obtained from databases, file streams and Flow network. This can ensure that the system receives comprehensive data support for better business analysis and decision-making. This article further studies the optimization methods of optical network control for power OTN services to improve network reliability and reduce risk levels. The experimental results indicate that the use of OTN technology can improve the operational efficiency and reliability of the smart grid, and achieve remote monitoring and control of the smart grid.

Artificial Intelligence Predictions of Biomass Power of an Installed Waste Water Treatment Plant

In the current study, the power generations obtained from gas turbines of an installed waste water treatment plant were predicted, utilizing artificial intelligence method consisting of artificial neural network (ANN). In this regards, a cumulative of 445 data, found in the power generation data cluster and found in the physical and chemical data clusters has been used in the predictions based on the artificial intelligence association method. Each instant data of these total 445 data corresponds to daily average power generation (P) obtained from gas turbines of the facility and corresponds to physical and chemical parameters including the temperature (T), degree of acidity (pH), conductivity (σ), as well as the daily total volumetric flow of the waste gas to be burned at the gas generator (Q). Accordingly, the best prediction obtained by ANN approach was concluded to generate the statistical accuracy results corresponding to 6.1279% mean absolute percentage error (MAPE), 2.1540 MWh/day root mean square error (RMSE), and 0.9730 correlation coefficient (R) for power generation parameter.

A - 24 Acute SCAT-3 Symptom Clusters and Risk for Prolonged Recovery from Concussion in College Athletes

Abstract Purpose More severe symptomatology post-concussion is predictive of prolonged recovery; however, it is unclear whether certain symptom clusters are associated with increased risk for protracted recovery. This study examined the relationship between five symptoms clusters on the SCAT-3 and risk for prolonged recovery from sports-related concussion. Method 1444 (F = 619) collegiate athletes completed post-concussion neuropsychological evaluation within 24 to 48 hours following injury. SCAT-3 scores were separated into clusters based on previous research (Cognitive, Physical, Emotional, Insomnia, Ungrouped). Given that the clusters consisted of different numbers of items, an average item score was created for each cluster. Athletes were separated into two groups based on number of days to return-to-play, using 28 days as the cutoff. Logistic regression was conducted with recovery type (Typical/Prolonged) as the outcome, and the five SCAT-3 symptom clusters as predictors. Results Only the SCAT-3 Physical cluster was significantly associated with recovery (p < 0.001). Specifically, athletes endorsing higher acute physical symptoms were at increased risk for prolonged return-to-play, such that a one-point increase in average item score on the SCAT-3 Physical symptom cluster was associated with 90.81% increased risk (O/R = 1.91) for prolonged recovery. Conclusions Athletes endorsing more severe physical symptoms 24 to 48 hours post-concussion were at significantly increased risk for prolonged recovery, though other symptom clusters did not infer the same risk. Findings suggest differential risk associated with symptom presentation; athletes endorsing significant physical symptoms in the acute phase of injury would likely benefit from targeted treatment to ameliorate these symptoms and, potentially, reduce risk for complicated recovery.

Associations between Sleep and Physical Activity Behavior Clusters and Epigenetic Age Acceleration in Mexican Adolescents.

Epigenetic aging, a marker of biological aging measured by DNA methylation, may be affected by behaviors, including sleep and physical activity. However, investigations of physical activity and sleep with epigenetic aging among pediatric populations are scant and have not accounted for correlated behaviors. The study population included 472 Mexico City adolescents (52% female). Blood collection and 7-day wrist actigraphy (Actigraph GTX-BT) occurred during a follow-up visit when participants were 14.5 (2.09) years. Leukocyte DNA methylation was measured with the Infinium MethylationEPIC array after bisulfite conversion, and 9 epigenetic clocks were calculated. Sleep vs wake time was identified through a pruned dynamic programing algorithm, and physical activity was processed with Chandler cut-offs. Kmeans clustering was used to select actigraphy-assessed physical activity and sleep behavior clusters. Linear regression analyses were used to evaluate adjusted associations between the clusters and epigenetic aging. There were 3 unique clusters: "Short sleep/high sedentary behavior", "Adequate sleep duration and late timing/low moderate or vigorous physical activity (MVPA)", and "Adequate sleep duration/high MVPA". Compared to the "Adequate duration/high MVPA", adolescents with "Adequate duration and late sleep timing/low MVPA" had more accelerated aging for the GrimAge clock (β = 0.63;95% CI 0.07, 1.19). In pubertal-stratified analyses, more mature adolescents in the "Adequate duration and late sleep timing/low MVPA group" had accelerated epigenetic aging. In contrast, females in the "Short sleep/high sedentary" group had decelerated epigenetic aging for the Wu pediatric clock. Associations between behavior clusters and epigenetic aging varied by pubertal status and sex. Contrary results in the Wu clock suggest the need for future research on pediatric-specific clocks.

Physical Heart Failure Symptom Clusters Predictive of Delay in Seeking Treatment.

Recent research has focused on the relationship between heart failure (HF) symptom clusters and outcomes, including mortality, hospitalization, functional status, and quality of life. No known studies to date have explored the role of physical HF symptom clusters and delays in seeking treatment. Describe physical symptom clusters in a population of HF patients and determine if a specific cluster is predictive of delay in seeking treatment for HF symptoms. We analyzed combined data from two studies (n = 406) collected during acute HF hospitalization. The Heart Failure Somatic Awareness Scale quantified physical HF symptoms. Delay, measured in days, was collected from the medical record and confirmed by interview. Hierarchical agglomerative clustering techniques determined physical HF symptom clusters. Hierarchical multiple regression analysis was computed to explore predictors of delay. Participants were primarily White, male sex older adults. Three physical HF symptom clusters were identified: discordant, edema-related symptoms, and dyspnea-related symptoms. Hierarchical multiple regression analysis revealed in Step 1 that age was a significant predictor of delay. Our findings provide valuable insight into the role of physical symptom clusters on delay in persons with HF. Through agglomerative hierarchical clustering techniques, we found three physical HF symptom clusters that were then used to determine differences in cluster membership by demographic and clinical variables. Significant age differences were noted by cluster membership with youngest older adults in a discordant symptom cluster.

Symptom clusters and their impacts on the quality of life of patients with lung cancer receiving immunotherapy: A cross-sectional study.

The objective of this study was to identify symptom clusters in lung cancer patients receiving immunotherapy and explore their impact on the quality of life of patients. Immunotherapy is widely used in lung cancer; however, there is little understanding of symptom clusters and their impacts on the quality of life of this population. Cross-sectional study. The survey contained the Memorial Symptom Assessment Scale (MSAS), Quality of Life Questionnaire-Lung Cancer 43 and a self-designed General Information Evaluation Form. Symptom clusters were identified using exploratory factor analysis (EFA) based on the symptom scores. Spearman correlation analysis was performed to evaluate the associations between each symptom cluster and the patients' quality of life. Multiple linear regression analysis was employed to examine the impact of the symptom clusters on quality of life. This study adhered to the STROBE guidelines. In total, 240 participants completed the survey. Five symptom clusters were identified and named according to their characteristics: emotional-related symptom cluster, lung cancer-related symptom cluster, physical symptom cluster, skin symptom cluster and neural symptom cluster. All symptom clusters, except for the neural symptom cluster, had a significantly detrimental impact on patient quality of life. Lung cancer patients undergoing immunotherapy experience a range of symptoms, which can be categorized into five clusters. These symptom clusters have a negative impact on patients' quality of life. Future research should focus on developing interventions for each symptom cluster and their influencing factors. In the data collection phase, lung cancer patients undergoing immunotherapy were recruited to participate in the survey.

The change of symptom clusters in gastric cancer patients during the perioperative period: a longitudinal study.

The purpose of this study was to describe the number, type and trajectory of symptom clusters during the perioperative period in patients with gastric cancer at four different time points. The study also aimed to identify the changes and consistency of these symptom clusters over time. This was a longitudinal study. This study was conducted in a tertiary cancer hospital with 205 patients with gastric cancer. The M.D. Anderson Symptom Inventory Gastrointestinal Cancer Module was used to assess the incidence and severity of symptom clusters. Exploratory factor analysis was used to extract symptom clusters. The study identified four symptom clusters in patients with gastric cancer during the perioperative period: gastrointestinal symptom cluster, physical symptom cluster, psychological symptom cluster, and sleep disturbance symptom cluster. These clusters were observed across two to four time points. The findings of this study provide scientific evidence for medical staff and researchers to better understand the symptoms of patients with gastrointestinal cancer during the perioperative period. These findings can help develop individualized interventions for managing symptoms. Gastric cancer patients suffered from various symptom clusters, which lasted from one day before surgery to one month after surgery. They should be given careful consideration by clinical staff.

Exploring the impacts of ecommerce activities on the spatial resilience of warehouse clubs: the role of retail agglomeration

PurposeAs ecommerce becomes more prevalent, traditional brick-and-mortar retailers such as warehouse clubs (WCs) face the challenging task of maintaining and growing their customer base. This study aims to unravel the combined impact of retail agglomeration and ecommerce activities on consumer foot traffic (also referred to as “footprint”) at WC stores, placing an emphasis on the locational strategies adopted by WCs in this evolving retail landscape.Design/methodology/approachMobile-based customer foot traffic data for Costco, a major U.S. WC chain, is sourced for our analysis. We use Principal Component Analysis (PCA) to identify dimensions of general merchandise (GM) and narrow-range merchandise (NM) retail agglomeration. Two-stage least squares (2SLS) regressions are used to explore how the intensity of ecommerce activities and WC locational choices within retail agglomerations impact WC foot traffic.FindingsOur analysis highlights a notable decline in WC store visits attributable to both GM and NM ecommerce activities, with GM ecommerce presenting a more significant competitive challenge to WCs. Regarding retail agglomerations, proximity to GM clusters that include a diverse range of supercenters, department stores, and club stores, is associated with an increase in WC customer visits within their vicinity. In contrast, the influence of NM agglomerations is mixed; clusters adjacent to grocery stores lead to higher WC customer traffic compared to those focused on other specialized stores. These findings underscore the strategic importance of location in mitigating the adverse effects of ecommerce competition. Additionally, our study uncovers intricate dynamics between GM and NM retail clusters and ecommerce activities, demonstrating varied impacts on WC customer footprint.Research limitations/implicationsAccess to customer footprint data illustrates the potential of this data source for retail decision making and researchers. Our analysis is limited to one chain, notably Costco.Practical implicationsOur findings underscore the need for retailers to adeptly navigate the evolving retail landscape, including the confluence between physical and digital retail environments, to secure future success. In particular, our results emphasize the benefits of locating stores within mixed retail agglomerations and underline the need to consider the broader retail landscape in location decisions.Social implicationsThe rise of ecommerce in the U.S. has reshaped consumer behavior and altered local shopping districts’ communal dynamics. This change may spur policy interventions to help physical stores compete with online retailers, emphasizing the importance of retail diversity and community-centric environments to sustain communal retail interactions amidst digital advancements.Originality/valueThe paper makes use of a unique dataset to provide a first assessment of the combined effects of retail agglomeration and ecommerce activities on consumer foot traffic for WC retailers. Thus, this paper provides insights into the impacts on consumer shopping behavior from the dynamic interactions between physical retail clusters and online shopping behaviors.

Deciphering the Diversity in Bacterial Transporters That Salvage Queuosine Precursors.

Queuosine (Q) is a modification of the wobble base of tRNA harboring GUN anticodons with roles in decoding accuracy and efficiency. Its synthesis is complex with multiple enzymatic steps, and several pathway intermediates can be salvaged. The only two transporter families known to salvage Q precursors are QPTR/COG1738 and QrtT/QueT. Analyses of the distribution of known Q synthesis and salvage genes in human gut and oral microbiota genomes have suggested that more transporter families remain to be found and that Q precursor exchanges must occur within the structured microenvironments of the mammalian host. Using physical clustering and fusion-based association with Q salvage genes, candidate genes for missing transporters were identified and five were tested experimentally by complementation assays in Escherichia coli. Three genes encoding transporters from three different Pfam families, a ureide permease (PF07168) from Acidobacteriota bacterium, a hemolysin III family protein (PF03006) from Bifidobacterium breve, and a Major Facilitator Superfamily protein (PF07690) from Bartonella henselae, were found to allow the transport of both preQ0 and preQ1 in this heterologous system. This work suggests that many transporter families can evolve to transport Q precursors, reinforcing the concept of transporter plasticity.

Effect of K+ Force Fields on Ionic Conductivity and Charge Dynamics of KOH in Ethylene Glycol.

Predicting ionic conductivity is crucial for developing efficient electrolytes for energy storage and conversion and other electrochemical applications. An accurate estimate of ionic conductivity requires understanding complex ion-ion and ion-solvent interactions governing the charge transport at the molecular level. Molecular simulations can provide key insights into the spatial and temporal behavior of electrolyte constituents. However, such insights depend on the ability of force fields to describe the underlying phenomena. In this work, molecular dynamics simulations were leveraged to delineate the impact of force field parameters on ionic conductivity predictions of potassium hydroxide (KOH) in ethylene glycol (EG). Four different force fields were used to represent the K+ ion. Diffusion-based Nernst-Einstein and correlation-based Einstein approaches were implemented to estimate the ionic conductivity, and the predicted values were compared with experimental measurements. The physical aspects, including ion-aggregation, charge distribution, cluster correlation, and cluster dynamics, were also examined. A force field was identified that provides reasonably accurate Einstein conductivity values and a physically coherent representation of the electrolyte at the molecular level.

Symptom clusters and their impact on quality of life in multiple myeloma survivors: Secondary analysis of BMT CTN 0702 trial.

Autologous haematopoietic cell transplantation (autoHCT) and continuous post-transplant maintenance therapy are the standard of care in transplant-eligible multiple myeloma (MM) patients. We sought to describe symptom burden and identify symptom clusters occurring in MM patients after autoHCT using data from the BMT CTN 0702 randomized controlled trial comparing the outcomes of three treatment interventions after an autoHCT in 758 MM patients. We analysed individual transplant-related symptoms assessed via the FACT-BMT questionnaire at enrolment and annually for 4-year post-autoHCT. We also described the effect the individual symptoms and symptom clusters have on quality of life (QoL). We identified three stable symptom clusters: malaise symptom cluster (lack of energy, feeling ill, having pain, experiencing nausea, loss of appetite), physical symptom cluster (having skin problems, tremors, worsening eyesight, change in taste, shortness of breath, frequent colds) and emotional symptom cluster (feeling sad, being nervous, experiencing sleep problems). Malaise and emotional symptom clusters have a greater impact on QoL than the physical symptoms cluster. Identifying these symptoms warrant additional support in terms of psychosocial support, in addition to treatment of the physical symptoms themselves.

An Exploratory Analysis of Physical Examination Subtypes in Pediatric Athletes With Concussion.

Pediatric athletes with concussion present with a variety of impairments on clinical assessment and require individualized treatment. The Buffalo Concussion Physical Examination is a brief, pertinent clinical assessment for individuals with concussion. The purpose of this study was to identify physical examination subtypes in pediatric athletes with concussion within 2 weeks of injury that are relevant to diagnosis and treatment. Secondary analysis of a published cohort study and clinician consensus. Three university-affiliated sports medicine centers. Two hundred seventy children (14.9 ± 1.9 years). Orthostatic intolerance, horizontal and vertical saccades, smooth pursuits, vestibulo-ocular reflex, near-point convergence, complex tandem gait, neck range of motion, neck tenderness, and neck spasm. Correlations between independent variables were calculated, and network graphs were made. k -means and hierarchical clustering were used to identify clusters of impairments. Optimal number of clusters was assessed. Results were reviewed by experienced clinicians and consensus was reached on proposed subtypes. Physical examination clusters overlapped with each other, and no optimal number of clusters was identified. Clinician consensus suggested 3 possible subtypes: (1) visio-vestibular (horizontal and vertical saccades, smooth pursuits, and vestibulo-ocular reflex), (2) cervicogenic (neck range of motion and spasm), and (3) autonomic/balance (orthostatic intolerance and complex tandem gait). Although we identified 3 physical examination subtypes, it seemed that physical examination findings alone are not enough to define subtypes that are both statistically supported and clinically relevant, likely because they do not include symptoms, assessment of mood or cognitive problems, or graded exertion testing.

Training of specialists in physical rehabilitation in the context of digitalization of higher education

The scientific research highlights the peculiarities of using digital potential in the programs of teaching physical rehabilitation in higher education. It is noted that due to the ongoing military operations in Ukraine, the need for rehabilitation specialists will only grow, therefore the educational system should intensify the training of specialists in the required field of knowledge. It is possible to achieve the required level of training of physical rehabilitation specialists under the condition of operational and integration of innovative training programs that are focused on the active use of digital potential. Rehabilitation is interpreted as a complex process that requires the use of synergistic approaches that form appropriate theoretical and methodological guidelines in educational strategies. The physical rehabilitation cluster combines human-dimensional and technological-organizational components. Digital skills for physical rehabilitation specialists are already established in the competency paradigm of these educational specialties. It is noted that the status of digital skills in educational training in physical rehabilitation is gradually transforming, moving from the soft-skills segment to the fundamental characteristics of professional competencies.

Enhancing Feature Extraction in Sensor Fault Detection

The large amount of data provided by sensors and sensor networks deployed in industrial plants presents an opportunity for advancing process monitoring and informed asset maintenance management. Data-driven anomaly detection systems can be developed for both process anomalies and monitoring systems but the approach must be tailored to effectively capture and categorize relevant features, establishing a robust approach. The use of multi-source signals facilitates the retention of maximal information without reducing the number of available features, as sensor signals may show high degrees of correlation even if the sensors measure different variables across different equipment. The scope of the paper is to execute a preliminary analysis of industrial data, obtaining a logical clustering from a physical one using Canonical Correlation Analysis. This data mining methodology is employed to gather highly correlated data for training an Artificial Neural Network aimed at identifying sensor anomalies. A benchmark is provided by comparing the performance of the ANN trained using logical clusters rather than physical clusters. This comparison serves to evaluate the effectiveness and advantages of utilizing logical clusters for anomaly detection and classification.

Examination of race-based traumatic stress symptom networks in Black adults in the United States: A network analysis.

In the United States, racism is theorized to exert its negative effects on Black individuals' mental health by triggering a response known as "race-based traumatic stress" (RBTS), a multidimensional construct comprising seven clusters of symptoms that can occur following exposure to race-based traumatic events (e.g., racial discrimination, racist incidents): depression, intrusion, anger, hypervigilance, physical symptoms, (low) self-esteem, and avoidance. However, little is known about which symptoms and clusters are strongest and most influential in the maintenance of RBTS. Network analysis is a powerful tool for understanding the etiology of traumatic stress, but it has not yet been applied to the examination of this construct. The present study aimed to identify the symptoms most central to RBTS and examine associations between symptoms and symptom clusters. Participants (N = 1,037) identified as Black, and lived in the United States (Mage = 45.12 years, range: 18-82 years) and completed the Race-Based Traumatic Stress Symptom Scale-Short Form (RBTSSS-SF). Regularized partial correlation networks were estimated using R/RStudio. The cluster- and item-level networks demonstrated adequate centrality stability, CS = .44. The depression and physical symptoms clusters were the most central nodes in the cluster network. Feelings of meaninglessness, experiencing mental images of the event, and physical trembling were the most central items within the item-level network. These findings offer insights and implications for assessing and treating symptoms of RBTS in Black adults in the United States who are exposed to race-based traumatic events.

Innovative Business Effort in a Mediterranean Region, Same Characteristics and/or Same Spatial Distribution?

Business innovation is fundamental for sustained economic growth at the regional level. Knowing the common characteristics of innovative companies and their location is essential to carry out appropriate economic policies. To this end, we have carried out a double analysis: one grouping of companies according to characteristics and another by geolocation. This study focused on one of Spain’s 17 autonomous communities, the Comunitat Valenciana, a region characterised by significant industrial diversity. Our results show, among other things, that size is not a differentiating factor when it comes to innovation, and that there is a positive relationship between physical clustering and productivity.