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BACKGROUND: The use of blood flow restriction training in clinical rehabilitation is becoming more common, in part due to technological advances and increased interest in research and medicine. OBJECTIVE: To explore the global trend and development prospects of clinical rehabilitation-related blood flow restriction training application, identify research gaps, and propose future research directions. METHODS: Web of Science, PubMed, and Elsevier were searched for literature related to clinical rehabilitation using blood flow restriction training published from 2001 to 2022. A total of 515 articles were included for further review. Bibliometrics, exploratory factor analysis and cluster analysis were used to reveal the emerging research fields and topics. RESULTS AND CONCLUSION: First of all, the overall research trend of the number of published articles shows a steady rise. The United States is the main research area in this field, and universities contribute the most to journals. Secondly, a core group of authors, led by Loenneke JP, Abe T and Dankel SJ, has been formed, with less collaboration among highly productive authors. Finally, research on blood flow restriction training focuses on skeletal muscle rehabilitation, cognitive functional activity, and chronic disease rehabilitation in clinical rehabilitation, with particular attention to the application of blood flow restriction in the recovery of symptoms such as muscle atrophy, knee pain, and joint effusion after anterior cruciate reconstruction surgery. Theoretical research on low-pressure blood flow restriction that is effective in relieving musculoskeletal symptoms has been hot in recent years, which can systematically guide the musculoskeletal rehabilitation in clinical practice. In the future, clinicians will extend follow-up periods, register larger and more diverse sample sizes, and use randomization techniques to determine appropriate prescribing indications for rehabilitating patients, ensure clinical safety, and quantitatively analyze and develop rehabilitation protocols with the maximum effect at the minimum effective dose for different clinical populations. [ABSTRACT FROM AUTHOR]

The Ogallala Aquifer, located in the Central Plains of the United States, is essential for agricultural irrigation and public water supply. Indiscriminate pumping from the aquifer has caused several negative impacts, such as deterioration of water quality and depletion of groundwater levels, which urgently demand better management. This paper applies hierarchical cluster analysis (HCA) and artificial neural networks (ANNs) for predicting annual groundwater levels in 403 wells of the Ogallala Aquifer. First, the methodology employed HCA to cluster homogeneous wells based on the time series of groundwater levels. Then, the study calibrated an ANN model for each cluster (composed of one or more wells) using previous annual values of groundwater levels as input. The HCA results showed a particular pattern in the spatial distribution of the 30 found clusters, revealing that the Ogallala Aquifer holds higher groundwater levels in the western part, which gradually decrease, advancing to the east. The ANN models provided proper predictions even for wells outside of the calibration data set. This investigation concludes that the integration of HCA and ANN enabled single models to accurately forecast annual groundwater levels for sets of wells in the Ogallala Aquifer. [ABSTRACT FROM AUTHOR]

Providing adequate water supply to the growing number of urban residents will be a challenge faced by many utility managers throughout the remainder of this century. Though traditionally water managers have looked towards supply-based solutions (e.g., expanding reservoirs), recent trends indicate a shift towards demand-side management (e.g., encouraging conservation behaviors). A major part of successfully implementing demand management strategies is understanding the community-specific attitudes and beliefs that may influence uptake of conservation behaviors. Here, we present results from a study aimed at understanding these community-specific attitudes and beliefs towards water conservation. In particular, we leverage survey data from three cities in the Southwestern United States and a state-of-the-art clustering algorithm to determine seven key archetypes of water consumers. These archetypes can be used to determine demand management strategies that might have greater (or lesser) success. This study provides transferable archetypes of consumer attitudes towards water conservation, as well as a novel interdisciplinary methodology that combines social survey data with unsupervised machine learning. [ABSTRACT FROM AUTHOR]

Since December 2019, COVID-19 has been raging worldwide. To prevent the spread of COVID-19 infection, many countries have proposed epidemic prevention policies and quickly administered vaccines, However, under facing a shortage of vaccines, the United States did not put forward effective epidemic prevention policies in time to prevent the infection from expanding, resulting in the epidemic in the United States becoming more and more serious. Through "The COVID Tracking Project", this study collects medical indicators for each state in the United States from 2020 to 2021, and through feature selection, each state is clustered according to the epidemic's severity. Furthermore, through the confusion matrix of the classifier to verify the accuracy of the cluster analysis, the study results show that the Cascade K-means cluster analysis has the highest accuracy. This study also labeled the three clusters of the cluster analysis results as high, medium, and low infection levels. Policymakers could more objectively decide which states should prioritize vaccine allocation in a vaccine shortage to prevent the epidemic from continuing to expand. It is hoped that if there is a similar epidemic in the future, relevant policymakers can use the analysis procedure of this study to determine the allocation of relevant medical resources for epidemic prevention according to the severity of infection in each state to prevent the spread of infection. [ABSTRACT FROM AUTHOR]

Mobility is a critical element of one's quality of life regardless of one's age. Although the challenges for women are more significant than those for men as they age, far less is known about the gender differences in mobility patterns of older adults, especially in the United States (US) context. This paper reports on a study that examined potential gender gaps in mobility patterns of older adults (aged 65 years and over) in the US by analyzing data from the 2017 National Household Travel Survey. Elderly respondents were first classified into one of six clusters based on socio-demographic variables. A Structural Equation Model (SEM) was then estimated and showed that gender gaps existed in the mobility patterns of the elderly, and the differences were diverse across the different clusters. The most substantial gender gap was found in the Senior Elder with Medical Condition(s) cluster, followed by the High-income Workers cluster and the Middle-income Urban Residents cluster. In contrast, females in the Low-Income Single Elder cluster enjoyed statistically significant positive mobility differences with their male counterparts. Our results also found that female elderly in the Senior Elder with Medical Condition(s) and the Low-income Family Elder clusters suffered most after the cessation of driving, with the largest mobility gender gap in the Middle-income Urban Resident cluster. This study will help transportation planners and policymakers understand gender and other socio-demographic differences in elderly mobility. Thus, it will facilitate the development of measures to improve elderly mobility and reduce gender gaps by recognizing and addressing specific target groups' mobility characteristics and needs rather than treating the elderly as a single potential user group. [ABSTRACT FROM AUTHOR]

Background: Little is known about the extent of exposure to metals and metal mixtures among midlife women.Objectives: We assessed exposure to multiple metals in the Study of Women's Health Across the Nation (SWAN), a multi-site, multi-racial/ethnic cohort of women at midlife.Methods: We measured urinary concentrations of 21 metals (arsenic, barium, beryllium, cadmium, cobalt, chromium, cesium, copper, mercury, manganese, molybdenum, nickel, lead, platinum, antimony, tin, thallium, uranium, vanadium, tungsten and zinc) using high-resolution inductively coupled plasma-mass spectrometry among 1335 white, black, Chinese and Japanese women aged 45-56 years at the third SWAN annual visit (1999-2000). Least squared geometric mean concentrations were compared across race/ethnicity, education, financial hardship, smoking, secondhand smoking, seafood intake and rice intake groups. Overall exposure patterns of multiple metals were derived using k-means clustering method.Results: The percentage of women with detectable concentrations of metals ranged from 100% for arsenic, cesium, molybdenum and zinc, to less than 5% for platinum; 15 metals had detection rates of 70% or more. Asian women, both Chinese and Japanese, had higher urinary concentrations of arsenic, cadmium, copper, mercury, molybdenum, lead and thallium, compared with other race/ethnic groups, independent of sociodemographic, lifestyle, dietary, and geographic characteristics. Seafood and rice intake were important determinants of urinary arsenic, cesium, mercury, molybdenum and lead levels. Two distinct overall exposure patterns- "high" vs. "low" -- were identified. Women in the "high" overall exposure pattern were more likely to be Asians, current smokers, and to report high consumption of seafood and rice. Black women were less likely to have the high exposure pattern.Conclusions: Metal exposure of midlife women differs by racial/ethnic, sociodemographic, lifestyle, dietary, and geographic characteristics. Asian women may be experiencing the highest exposures to multiple metals compared with other racial/ethnic groups in the United States. [ABSTRACT FROM AUTHOR]

• The adaptive irrigation behaviors might worsen the water scarcity in the CRB. • Clustering identified bold, prudent, and forward-looking farmer classes. • We indicated specific parameters for uncertainty reduction in human systems. The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer's water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers' adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. By conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers' adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling. [ABSTRACT FROM AUTHOR]

The construct validity of the Job Content Questionnaire (JCQ) psychological demands scale in relationship to physical demands has been inconsistent. This study aims to test quantitatively and qualitatively whether the scale validity differs by occupation. Hierarchical clustering analyses of 10 JCQ psychological and physical demands items were conducted in 61 occupations from two datasets: one of non-faculty workers at a university in the United States (6 occupations with 208 total workers) and the other of a Belgian working population (55 occupations with 13,039 total workers). The psychological and physical demands items overlapped in 13 of 61 occupation-stratified clustering analyses. Most of the overlaps occurred in physically-demanding occupations and involved the two psychological demands items, ‘work fast’ and ‘work hard’. Generally, the scale reliability was low in such occupations. Additionally, interviews with eight university workers revealed that workers interpreted the two psychological demands items differently by the nature of their tasks. The scale validity was occupation-differential. Practitioner Summary: The JCQ psychological job demands scale as a job demand measure has been used worldwide in many studies. This study indicates that the wordings of the ‘work fast’ and ‘work hard’ items of the scale need to be reworded enough to differentiate mental and physical job demands as intended, ‘psychological.’ [ABSTRACT FROM AUTHOR]

This study aimed to evaluate a complete nutritional composition in the seeds Quercus virginiana to compare this nutritional composition with three Mediterranean Quercus species. We analyzed the seed morphometry, proximate composition, phytochemicals, and antioxidant capacity. The seed of Q. virginiana presented the smaller seed size and weight, while Q. suber presented the highest values. Moreover, Q. virginiana seeds showed the highest amounts of sugar and total lipids, digestibility, energy, palmitic acid, and stearic acid. On the other hand, Q. virginiana seeds showed the lowest values of linoleic acid. Moreover, Q. coccifera seeds presented the highest total phenolics and flavonoids contents and antioxidant activity. The clustering analysis revealed a significant similarity in seed morphometry and nutritional composition between the Mediterranean Q. ilex and Q. suber , grouping with the American Q. virginiana , but to a considerable distance; by contrast, the Mediterranean Q. coccifera was the most distant in the clustering analysis. The content of phenolics and flavonoids and digestibility value were the variables that contributed to the separation to a greater extent in the clustering of the four species. The nutritional and biological activity assessment of plant seed may be considered as an essential mission to find new sustainable sources and novel chemical agents. In this sense, Quercus seeds may be an alternative and a competitive food source for the agri-food industry.