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The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Most critics focus on the pain and suffering of the first-generation Vietnamese immigrants depicted in Lan Cao's Monkey Bridge. This paper explores how Cao strategically uses the philosophy of karma in Vietnamese Buddhism to provide a method for alleviating their suffering in this novel. It argues that she employs karma to investigate the origins of the adversity and trauma experienced by the first-generation Vietnamese immigrants, including the pro-American attitude of the early Vietnamese authorities during the Vietnam War, the imperialistic actions of the United States, and the resulting karmic consequences. In addition, they demonstrate, through actions like forming "hui", a way to change their fate and heal their trauma for later generations of Vietnamese immigrants, emphasizing positive transformation of karma. This paper suggests that the Buddhist philosophy of karma provides an effective strategy for Vietnamese American immigrants to reflect on the Vietnam War, overcome adversity, and heal their own trauma. [ABSTRACT FROM AUTHOR]

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

Simple Summary: In spite of significant reductions in the numbers of animals entering shelters and being killed there over the last decades, the beliefs that companion animals must be under the direct control of a human guardian, and that capturing and often killing animals is morally justifiable and even necessary, continue to guide animal sheltering practices in the United States. Looking at the historical origins of animal control and sheltering in the US reveals a high level of consistency in ideologies and practices across almost 150 years. In this commentary, two scholars on animal sheltering in the US examine the historical legacies of animal control and sheltering in the US to show how they are problematic and dysfunctional and need to be abandoned to improve the lives of companion animals and the human communities of which they are a part. This paper examines the legacies of the emergence of the animal control and sheltering industry in the United States and their impact on contemporary public animal shelters. While decades of gradual reform have helped substantially reduce the number of animals entering shelters and being killed there, contemporary animal sheltering largely continues to follow the path set when animal sheltering developed in the late 1800s and early 1900s. Three key interrelated legacies of the pound model of early animal control and sheltering enduringly shape sheltering today: (1) the institutional culture of animal shelters grounded in the logics of caging and killing; (2) the lack of visibility and transparency, especially within government shelters; and (3) the economic logics of the pound model, including the disparities in sheltering resources across communities. Examining the origins of animal control and sheltering and identifying the specific legacies of this pound model within contemporary government-funded shelters improves understanding of why such shelters in the US have developed with a particular set of practices and ideologies, and thus provides an important footing for envisioning and enacting radical changes in animal sheltering. [ABSTRACT FROM AUTHOR]

Concussions, a prevalent public health concern in the United States, often result from mild traumatic brain injuries (mTBI), notably in sports such as American football. There is limited exploration of smart-textile-based sensors for measuring the head impacts associated with concussions in sports and recreational activities. In this paper, we describe the development and construction of a smart textile impact sensor (STIS) and validate STIS functionality under high magnitude impacts. This STIS can be inserted into helmet cushioning to determine head impact force. The designed 2 × 2 STIS matrix is composed of a number of material layered structures, with a sensing surface made of semiconducting polymer composite (SPC). The SPC dimension was modified in the design iteration to increase sensor range, responsiveness, and linearity. This was to be applicable in high impact situations. A microcontroller board with a biasing circuit was used to interface the STIS and read the sensor's response. A pendulum test setup was constructed to evaluate various STISs with impact forces. A camera and Tracker software were used to monitor the pendulum swing. The impact forces were calculated by measuring the pendulum bob's velocity and acceleration. The performance of the various STISs was measured in terms of voltage due to impact force, with forces varying from 180 to 722 N. Through data analysis, the threshold impact forces in the linear range were determined. Through an analysis of linear regression, the sensors' sensitivity was assessed. Also, a simplified model was developed to measure the force distribution in the 2 × 2 STIS areas from the measured voltages. The results showed that improving the SPC thickness could obtain improved sensor behavior. However, for impacts that exceeded the threshold, the suggested sensor did not respond by reflecting the actual impact forces, but it gave helpful information about the impact distribution on the sensor regardless of the accurate expected linear response. Results showed that the proposed STIS performs satisfactorily within a range and has the potential to be used in the development of an e-helmet with a large STIS matrix that could cover the whole head within the e-helmet. This work also encourages future research, especially on the structure of the sensor that could withstand impacts which in turn could improve the overall range and performance and would accurately measure the impact in concussion-causing impact ranges. [ABSTRACT FROM AUTHOR]

A traditionally salient topic of empirical investigation in the sociology of religion, this paper seeks to offer a recent investigation into the intergenerational transmission of religion and the parental forms of religious engagement that predict adult engagement with religion. The study of this paper explores the intergenerational transmission of religion, focusing on the parental forms of religious identity and engagement that influence religious identity, beliefs, and practices in adulthood. By analyzing the 2018 GSS dataset in the United States with multiple regression analyses, I found strong parental and childhood influences on adult religiosity, religious service attendance, and belief in God. Indeed, this engagement often mirrors parental engagement for these variables. However, while paternal religious identity often predicts these religious variables, I found that the religious identity and engagement of parents generally do not predict religious identity in adulthood. Ultimately, while these results generally show strong predictive mechanisms of intergenerational transmission, they also illustrate that these relationships are variably dependent on the form of parental and adult religious engagement, and which parent participates or is associated with that engagement. [ABSTRACT FROM AUTHOR]

This study explores the relationship between religiosity, spirituality, and loneliness in the United States, using the 2018 General Social Survey to assess their interactions against a backdrop of declining traditional religious affiliation and a rise in "spiritual but not religious" identification. It examines religion and spirituality's capacity to counteract loneliness, a condition with significant health implications. The analysis uncovers complex relationships between aspects of religious life and loneliness, showing no mitigating role of spirituality when controlling for other factors, with complex and varied negative relationships of religious service attendance and self-rate religiosity to different aspects of loneliness. Yet, any potentially protective effect of religion varies, with minority religious groups reporting feelings of increased loneliness. These findings underscore religion and spirituality's nuanced roles in emotional well-being, indicating that their benefits (or not) against loneliness are complex, varied, and depend on the aspect of religion or loneliness observed, along with non-religious factors. The paper contributes to the literature on societal loneliness, changing religious and spiritual engagements, and highlighting the need for a comprehensive approach to explore the role of religion and spirituality in understanding loneliness specifically, and personal and social well-being more generally. [ABSTRACT FROM AUTHOR]

The African Growth Opportunity Act (AGOA) has been a crucial trade and development initiative, offering preferential access to qualified Sub-Saharan African (SSA) countries to the United States market since its enactment in 2000. This paper presents a comprehensive review of scholarly articles and policy reports that analyze the impact of AGOA on the economic performance of SSA countries. Employing various econometric methods and data analysis techniques, researchers have investigated the effects of AGOA on trade flows, foreign direct investment (FDI) inflows, employment, economic growth, and poverty levels. The findings reveal that AGOA has positively affected the region's trade, particularly in apparel, textiles, and agriculture. However, its influence on promoting export diversification and attracting FDI is nuanced, with substantial heterogeneity among the beneficiary countries and industries within each country. While some SSA countries have experienced substantial export growth and FDI inflows, others have not fully leveraged the benefits of AGOA due to absorptive capacity constraints and governance challenges. AGOA's effectiveness in promoting broad-based employment, GDP growth, and poverty reduction remains an active area of inquiry, necessitating further research to understand the policy's sustained impact and inform future trade policy designs for SSA countries. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to review the data supporting current endoscopic surgical techniques for the spine and the potential challenges and future of the field. The origins of endoscopic spine surgery can be traced back many decades, with many important innovations throughout its development. It can be applied to all levels of the spine, with many robust trials supporting its clinical outcomes. Continued clinical research is needed to explore its expanding indications. Although the limitations of starting an endoscopic program can be justified by its cost effectiveness and positive societal impact, challenges facing its widespread adoption are still present. As more residency and fellowship programs include endoscopy as part of their spine training, it will become more prevalent in hospitals in the United States. Technological advancements in spine surgery will further propel and enhance endoscopic techniques as they become an integral part of a spine surgeon's repertoire. [ABSTRACT FROM AUTHOR]

Alcohol use disorder (AUD) is a significant contributor to morbidity and mortality in the United States. It contributes to over 140,000 annual deaths, to over 200 related diseases and health conditions globally, and accounts for 5.1% of the global disease burden. Despite its substantial impact, AUD remains undertreated, marked by a scarcity of approved medications. This paper explores the current treatment landscape and novel strategies for both alcohol withdrawal syndrome and AUD. Promising results, including the use of psychedelics alongside psychotherapy, noninvasive neural-circuit-based interventions, phosphodiesterase-4 inhibitors, and GLP-1 receptor agonists, have emerged from recent studies. While these advancements show potential, further research is crucial for a comprehensive understanding of their effectiveness. The clear shortage of approved medications and other treatment modalities underscores the pressing need for ongoing research. [ABSTRACT FROM AUTHOR]

Pooled sample testing is an effective strategy to reduce the cost of disease surveillance in human and animal medicine. Testing pooled samples commonly produces matched observations with dichotomous responses in medical and epidemiological research. Although standard approaches exist for one-to-one paired binary data analyses, there is not much work on one-to-two or one-to-N matched binary data in the current statistical literature. The existing Miettinen's test assumes that the multiple observations from the same matched set are mutually independent. In this paper, we propose exact and asymptotic tests for one-to-two matched binary data. Our methods are markedly different from the previous studies in that we do not rely on the mutual independence assumption. The emphasis on the interdependence of observations within a matched set is inherent and attractive in both human health and veterinary medicine. It can be applied to all kinds of diagnostic studies with a one-to-two matched data structure. Our methods can be generalized to the one-to-N matched case. We discuss applications of the proposed methods to the environmental testing of salmonella in the United States. [ABSTRACT FROM AUTHOR]

The essential role of nicotinamide adenine dinucleotide+ (NAD+) in redox reactions during oxidative respiration is well known, yet the coenzyme and regulator functions of NAD+ in diverse and important processes are still being discovered. Maintaining NAD+ levels through diet is essential for health. In fact, the United States requires supplementation of the NAD+ precursor niacin into the food chain for these reasons. A large body of research also indicates that elevating NAD+ levels is beneficial for numerous conditions, including cancer, cardiovascular health, inflammatory response, and longevity. Consequently, strategies have been created to elevate NAD+ levels through dietary supplementation with NAD+ precursor compounds. This paper explores current research regarding these therapeutic compounds. It then focuses on the NAD+ regulation of IL-13 signaling, which is a research area garnering little attention. IL-13 is a critical regulator of allergic response and is associated with Parkinson's disease and cancer. Evidence supporting the notion that increasing NAD+ levels might reduce IL-13 signal-induced inflammatory response is presented. The assessment is concluded with an examination of reports involving popular precursor compounds that boost NAD+ and their associations with IL-13 signaling in the context of offering a means for safely and effectively reducing inflammatory response by IL-13. [ABSTRACT FROM AUTHOR]

Wildfires are becoming more frequent due to the global climate change. Large amounts of greenhouse gases emitted by wildfires can lead to increases in extreme climate events. Accurately estimating the greenhouse gas carbon dioxide (CO2) emissions from wildfires is important for mitigation of climate change. In this paper, we develop a novel method to estimate wildfire CO2 emissions from the relationship between local CO2 emissions and XCO2 anomalies. Our method uses the WRF-Chem assimilation system from OCO-2 XCO2 retrievals which coupled with Data Assimilation Research Testbed (DART). To validate our results, we conducted three experiments evaluating the wildfire CO2 emissions over the conterminous United States. The four-month average wildfire emissions from July to October in 2015∼2018 were estimated at 4.408 Tg C, 1.784 Tg C, 1.514 Tg C and 2.873 Tg C, respectively. Compared to the average of established inventories CT2019B, FINNv1.5 and GFASv1.2 fire emissions, our estimates fall within one standard deviation, except for 2017 due to lacking of OCO-2 XCO2 retrievals. These results suggest that the regional carbon assimilation system, such as WRF-Chem/DART, using OCO-2 XCO2 retrievals has a great potential for accurately tracking regional wildfire emissions. [ABSTRACT FROM AUTHOR]

The electrification of personal transportation holds great potential for lowering greenhouse gas emissions and reducing climate change. The promise of electric vehicles (EVs) to serve these goals has resulted in a broad range of supporting policies aimed at encouraging widespread EV adoption at both the state and federal levels in the United States and around the world. While the EV revolution and prospects of a world with ubiquitous EVs are impacting various industries and many aspects of daily life, strategic interactions between the power grid and EVs are crucial for a successful energy transition. However, managing the interplay between EVs and the power grid remains a challenge. Motivated by that tension, this paper surveys a variety of solutions, policies, and incentives that are focused on effectively managing EV charging behaviors. The paper's objective is to explore these tools to ensure that EV owners have ultimate control over their personal vehicles while simultaneously allowing the power grid to mitigate adverse network impacts. Furthermore, this paper examines the role of charging infrastructure technology and its strategic placement in facilitating the seamless integration of EVs into the grid. Additionally, the paper highlights financial mechanisms associated with EV integration and discusses the consequences of these mechanisms. [ABSTRACT FROM AUTHOR]

Small and medium commercial buildings account for nearly half of the energy consumed by commercial buildings in the United States. While energy retrofits can significantly reduce building energy consumption, buildings' owners often lack the capital and experience to perform detailed energy audits and retrofit assessments. The purpose of this paper is to introduce a low-investment, bottom-up and simplified methodology for identifying energy retrofit opportunities that benefit the owners of small and medium sized office buildings In particular, the paper addresses small and medium commercial buildings on a university campus as a proof-of-concept for other owner-operators that have small and medium commercial facilities in their portfolio. The methodology consists of an eight-step framework using publicly-available and simplified tools. While energy audits and retrofit opportunity assessments are not new, a low-cost methodology for owner-operators of small and medium commercial buildings to analyze energy consumption and identify retrofit opportunities represents a contribution to knowledge. A medium office building on a university campus in Arizona served as a case study to validate the methodology. The case study showed a maximum potential energy reduction of an estimated 50%, but the figure varies based on the types of retrofit (deep versus light), energy conservation measures selected and implemented, invested resources, and interactive effects between measures. This methodology is extensible to other owner-operators that have building utility data and would like to perform retrofit opportunity assessments themselves. [ABSTRACT FROM AUTHOR]

Background: As the field of sensor-based rehabilitation continues to expand, it is important to gain a comprehensive understanding of its current research landscape. This study aimed to conduct a bibliometric analysis to identify the most influential authors, institutions, journals, and research areas in this field. Methods: A search of the Web of Science Core Collection was performed using keywords related to sensor-based rehabilitation in neurological diseases. The search results were analyzed with CiteSpace software using bibliometric techniques, including co-authorship analysis, citation analysis, and keyword co-occurrence analysis. Results: Between 2002 and 2022, 1103 papers were published on the topic, with slow growth from 2002 to 2017, followed by a rapid increase from 2018 to 2022. The United States was the most active country, while the Swiss Federal Institute of Technology had the highest number of publications among institutions. Sensors published the most papers. The top keywords included rehabilitation, stroke, and recovery. The clusters of keywords comprised machine learning, specific neurological conditions, and sensor-based rehabilitation technologies. Conclusions: This study provides a comprehensive overview of the current state of sensor-based rehabilitation research in neurological diseases, highlighting the most influential authors, journals, and research themes. The findings can help researchers and practitioners to identify emerging trends and opportunities for collaboration and can inform the development of future research directions in this field. [ABSTRACT FROM AUTHOR]

Simple Summary: In this position paper, we aim to summarize state-of-the-art treatments for patients with leiomyosarcomas in order to identify knowledge gaps and current unmet needs, thereby guiding the community to design innovative clinical trials and basic research and close these research gaps. This white paper arose from a leiomyosarcoma research meeting in October 2020 hosted by the National LeioMyoSarcoma Foundation (NLMSF) and Sarcoma Patients EuroNet (SPAEN). As leiomyosarcoma patients are challenged by the development of metastatic disease, effective systemic therapies are the cornerstone of outcome. However, the overall activity of the currently available conventional systemic treatments and the prognosis of patients with advanced or metastatic disease are still poor, making the treatment of this patient group challenging. Therefore, in a joint effort together with patient networks and organizations, namely Sarcoma Patients EuroNet (SPAEN), the international network of sarcoma patients organizations, and the National LeioMyoSarcoma Foundation (NLMSF) in the United States, we aim to summarize state-of-the-art treatments for leiomyosarcoma patients in order to identify knowledge gaps and current unmet needs, thereby guiding the community to design innovative clinical trials and basic research and close these research gaps. This position paper arose from a leiomyosarcoma research meeting in October 2020 hosted by the NLMSF and SPAEN. [ABSTRACT FROM AUTHOR]

Weigh-in-motion (WIM) systems are essential for efficient transportation and monitoring parameters such as vehicle number, speed, and weight to ensure regulatory compliance and enhance road safety. Recently, WIM measurements using the Glass Fiber Reinforced Polymer Fiber Bragg Grating (GFRP-FBG) sensors have shown robustness and effectiveness. However, the accuracy of weight evaluation using the WIM systems based on GFRP-FBG sensors can be significantly influenced by the vehicle-wandering effect, which introduces uncertainties in wheel position determination and weight calculations. This paper assessed the impact of vehicle wandering on the accuracy of a WIM measurement system based on GFRP-FBG sensors by utilizing a new hybrid sensor-camera system that integrates roadside cameras and in-pavement GFRP-FBG sensors. The detailed methodology and framework of the developed hybrid system are introduced, followed by field testing on Highway I-94 in the United States. The field testing results indicate that by using the hybrid system, the wheel load detection accuracy of the WIM system based on GFRP-FBG sensors can be controlled to be a Type I or Type III WIM according to the ASTM 1318E-09 standard, with an average accuracy ranging from 87.83% to 94.65%. At the same time, when the wander distance is less than or equal to 9 cm, the developed WIM system proves to be very cost-effective as it only comprises two GFRP-FBG sensors, one temperature FBG sensor, and one camera. These findings indicate the practical potential to enhance the accuracy of WIM systems based on GFRP-FBG sensors designed for highways for low-coast, reliable, and accurate measurements by addressing vehicle wandering effects. [ABSTRACT FROM AUTHOR]

At the beginning of the COVID-19 pandemic, the entire world was waiting for a medical solution (for example, vaccines) in order to return to normality. Sanitary restrictions changed our consumption behaviors and feelings. Therefore, this paper analyzes the stochastic properties of consumer sentiment during the COVID-19 episode and the appearance of vaccines against the virus in December 2020 in the United States of America. This study adds a new dimension to the literature because it is the first research paper that uses advanced methodologies based on fractional integration and fractional cointegration analysis to understand the statistical properties of these time series and their behavior in the long term. The results using fractional integration methodologies exhibit a high degree of persistence, finding behavior of mean reversion during the pandemic episode. Therefore, the shock duration in consumer sentiment will be transitory, recovering to its previous trend in the short run. Focusing on the cointegrating part, we arrive at two main conclusions. First, an increase in total vaccination produces a positive reaction or impact on the behavior of consumers. On the other hand, an increase in new COVID-19 cases negatively affects the behavior of the consumer. [ABSTRACT FROM AUTHOR]

Applying the behavioral model of health services utilization to data from the United States, this study examined immigrant children's use of physician and dentist visits, hospital care, and prescribed medication. We employed data describing 9759 immigrant parents and children who participated in 2021's National Survey of Children's Health. Logistic regression results negatively linked physician visits to child health, child age, Asian children, fathers, lower parent education, lower family income, uninsured children, and parent's U.S. residence under 5 years. Dentist visits were positively associated with child age, girls, Hispanic children, parent education, family income, public/private health insurance coverage, and U.S.-born children, but such use was associated negatively with Asian children. Hospital use was positively associated with poor child health, Black children, children of "other" race/ethnicity, younger parent age, enrolled in health insurance, and parent's U.S. residence under 5 years. The use of prescribed medication was negatively associated with Asian children, younger child age, lower parent education, uninsured child, and lack of English proficiency. The paper's conclusion suggests policymakers expand Medicaid and CHIP eligibility among immigrant children and suggests community education to foster awareness of children's physical and oral health needs and of Medicaid, CHIP, and prescription assistance programs. The conclusion calls for healthcare providers and social workers to accommodate and respect immigrants' traditional health-related beliefs, showing cultural competence. [ABSTRACT FROM AUTHOR]

The United States has witnessed a concerning surge in the incidence of diseases like Coal Workers' Pneumoconiosis (CWP), despite numerous efforts aimed at prevention. This study delves into the realm of respiratory health by investigating the deposition of dust particles within the respiratory tract and lungs. By analyzing particles of varying sizes, shapes, velocities, and aerodynamic diameters, we aim to gain a comprehensive understanding of their impact on deposition patterns. This insight could potentially drive changes in dust exposure protocols within mining environments and improve monitoring practices. The interplay of several critical factors, including particle characteristics and an individual's breathing patterns, plays a pivotal role in determining whether particles settle in the lungs or are exhaled. This paper provides a comprehensive literature review on Respirable Coal Mine Dust (RCMD), with a specific focus on examining particle deposition across different regions of the airway system and lungs. Additionally, we explore the utility of Computational Fluid Dynamics (CFD) in simulating particle behavior within the respiratory system. Predicting the precise behavior of dust particles within the respiratory airway poses a significant challenge. However, through numerical simulations, we aspire to enhance our understanding of strategies to mitigate total lung deposition by comprehensively modeling particle interactions within the respiratory system. [ABSTRACT FROM AUTHOR]

Soil moisture is a key factor in ecosystems that profoundly affects carbon, nitrogen, and water cycles on land surfaces, vegetation growth, and climate change. Consequently, numerous scholars have researched and authored scientific literature on soil moisture and related topics. Using the Web of Science database, we conducted a bibliometric analysis of 60,581 papers published in the field of soil moisture between 2000 and 2022. The findings revealed the following trends. (1) The number of publications on soil moisture has consistently increased in the 21st century at an increasing rate. For instance, although the annual increase was only 94 publications in 2005, it surged to 321 publications in 2020. (2) The United States (US), China, and developed European countries emerged as primary research institutions and authors. The US occupies a leading position in soil moisture research, boasting the highest number of publications and total citations in the field, whereas China ranks second in both publications and total citations. (3) Regarding international collaboration, the US has established close partnerships with numerous international research institutions. However, China's international cooperation in this field requires improvement. (4) The Journal of Hydrology holds the top position in terms of both the total number of published articles and citations. Research on water resources ranked first in terms of its H-index. (5) Keyword analysis highlighted several current research hotspots, including the coupled covariance effect of soil moisture and land surface environmental factors in the context of climate change, soil moisture utilization rate, crop yield, influence mechanism of soil moisture on soil ecosystem structure, and development of high-precision soil moisture data products. In conclusion, this study provides a systematic review of the research hotspots and trends in soil moisture studies in the 21st century. The objective is to offer a comprehensive reference to aid in understanding the evolutionary patterns of soil moisture research in multiple dimensions. [ABSTRACT FROM AUTHOR]

The retrieval of soil moisture (SM) using the Global Navigation Satellite System-Reflectometry (GNSS-R) technique has become a prominent topic in recent years. Although prior research has reached a spatial resolution of up to 9 km through the Cyclone Global Navigation Satellite System (CYGNSS), it is insufficient to meet the requirements of higher spatial resolutions for hydrological or agricultural applications. In this paper, we present an SM downscaling method that fuses CYGNSS and SMAP SM. This method aims to construct a dataset of CYGNSS observables, auxiliary variables, and SMAP SM (36 km) products. It then establishes their nonlinear relationship at the same scale and finally builds a downscale retrieval model of SM using the eXtreme Gradient Boosting (XGBoost) algorithm. Focusing on the southern United States, the results indicate that the SM downscaling method exhibits robust performance during both the training and testing processes, enabling the generation of a CYGNSS SM product with a 1 day/3 km resolution. Compared to existing methods, the spatial resolution is increased threefold. Furthermore, in situ sites are utilized to validate the downscaled SM, and spatial correlation analysis is conducted using MODIS EVI and MODIS ET products. The CYGNSS SM obtained by the downscaling model exhibits favorable correlations. The high temporal and spatial resolution characteristics of GNSS-R are fully leveraged through the downscaled method proposed. Furthermore, this work provides a new perspective for enhancing the spatial resolution of SM retrieval using the GNSS-R technique. [ABSTRACT FROM AUTHOR]

Investigating the determinants of environmental sustainability has become a very attractive and popular area of research in response to the United Nations' sustainable development goals (SDGs) and COP26 targets. Against this backdrop, this paper aims to explore the effects of renewable energy, technological innovation, and the insurance market on environmental sustainability in the United States (US). This work contributes to the extant body of knowledge by exploring the effect of the insurance market on the load capacity factor (LC), specifically regarding the US. The LC tracks a certain ecological threshold by simultaneously comparing biocapacity and ecological footprint (EF), thereby providing a comprehensive empirical analysis of ecological sustainability determinants. However, this proxy includes the combined attributes of the demand and supply sides of ecological sustainability. Using the recently developed autoregressive distributed lag method, this research reveals that the insurance market adversely affects the LC in the US. The overall outcomes highlight the positive role of renewable energy, technological innovation, and the insurance market in achieving the SDGs and COP26 targets. Policy recommendations for policymakers concerning promoting renewable energy, green innovation activities, the green insurance market, and ecological regulations are also discussed. [ABSTRACT FROM AUTHOR]

This paper analyzes the stochastic properties of consumer sentiment to understand how they affected the luxury sector in the United States before and after COVID-19. The results were derived using fractional integration methodologies and suggest that, before the pandemic episode, both variables were expected to be mean reverting and the shocks were transitory, having similar behavior. However, after the appearance of COVID-19, results suggest that consumer sentiment recovered before the luxury sector. Results from the use of cointegration methodologies show that the effects of COVID-19 disappeared in the short-run. Finally, the sentiment of consumers acts as a leading indicator of the behavior of the luxury sector according to wavelet analysis. Thus, an increase in consumer sentiment implies an increase of 3.6% in the luxury sector. [ABSTRACT FROM AUTHOR]

Building energy codes are essential tools for achieving energy efficiency in buildings. However, the full energy savings potential of these codes can only be realized if buildings are constructed in compliance with them. Therefore, evaluating building energy code compliance is crucial in bridging the gap between the energy efficiency requirements set by energy codes and the actualized energy savings achieved. An energy code compliance evaluation serves as a mechanism to assess construction practices, evaluate the effectiveness of code enforcement, identify gaps in compliance, and guide strategies for improvement through training and education. Conducting code compliance evaluation activities involves field studies that require careful design and significant resources. Historically, more emphasis has been placed on developing and adopting building energy codes, while efforts to evaluate compliance have been relatively limited and lacking consistent approaches. The passage of the 2009 American Recovery and Reinvestment Act (ARRA), which mandated that states create plans for achieving 90% compliance within eight years, stimulated the need for an energy code compliance evaluation. As a result, federal, state, and local governments, and utilities have invested in the development of methodologies and tools for code compliance evaluation studies. This paper reviews the code compliance evaluation studies conducted in the United States over the past three decades. It describes and compares the methodologies and metrics used to assess building energy code compliance, summarizes the general elements and steps involved in the evaluation process, and discusses common issues in these studies. Over time, code compliance evaluation methodologies have evolved from isolated development within individual states, regions, and utilities, to widely accepted protocols applicable across different states and local jurisdictions. There has been a transition in compliance metrics, shifting from historical compliance rates to energy-consumption-oriented approaches. [ABSTRACT FROM AUTHOR]

This paper investigates the Gen Z counter-demographic of the religious nones on college and university campuses by focusing on BIPOC students and the stories that they tell about why they actively engage in evangelical campus ministries during their college years. This is carried out by being attentive to the racially segregated campus ministry context and the preponderance of "white spaces" in colleges and universities, including in campus ministries. Data for this study come from the Landscape Study of Chaplaincy and Campus Ministry (LSCCM 2019–2022) in the United States. Like other students in campus ministries, we find that BIPOC students who are "churched" with a Christian upbringing seek out campus ministries that function as a "home away from home", where they can find authentic belonging—genuine connections and acceptance among like-minded Christians. For BIPOC students, however, this search for authentic belonging included a search for a campus ministry where they could be "safe and seen" for both their ethnoracial and Christian selves. [ABSTRACT FROM AUTHOR]

Stroke is a significant disability and death cause worldwide and is conventionally defined as a neurological impairment relating to the intense focal harm of the central nervous system (CNS) by vascular causative components. Although the applicability of robotic rehabilitation is a topic with considerable practical significance because it has produced noticeably higher improvements in motor function than regular (physical and occupational) therapy and exempted the therapists, most of the existing bibliometric papers were not focused on stroke survivors. Additionally, a modular system is designed by joining several medical end-effector devices to a single limb segment, which addresses the issue of potentially dangerous pathological compensatory motions. Searching the Web of Science database, 31,930 papers were identified, and using the VOSviewer software and science mapping technology, data were extracted on the most prolific countries, the connections between them, the most valuable journals according to certain factors, their average year of publication, the most influential papers, and the most relevant topical issues (bubble map of term occurrence). The most prolific country in the analyzed field and over the entire period evaluated (1975–2022) is the United States, and the most prolific journal is Neurorehabilitation and Neural Repair, observing a marked increase in the three periods of scientific interest for this field. The present paper assesses numerous scientific publications to provide, through statistical interpretation of the data, a detailed description of the use of robotic rehabilitation in stroke survivors. The findings may aid scientists, academics, and clinicians in establishing precise goals in the optimization of the management of stroke survivors via robotic rehabilitation, but also through easier access to scientifically validated literature. [ABSTRACT FROM AUTHOR]

This paper utilizes narrative inquiry to examine the effect of COVID-19 on political resistance, focusing on education as a key site. Based on survey and interview data the paper considers parents' perspectives about the impacts of COVID-19 and racial inequalities in their children's schooling. Two narrative types are constructed and analyzed: consensus narratives and parenting narratives that refute an overarching, manufactured political narrative in the United States of "divisiveness" about race and education, while also identifying the layers and complexities of individual parents' everyday lives raising and educating children. [ABSTRACT FROM AUTHOR]

The number of COVID-19 fatalities fluctuated widely across United States (US) counties. The number of deaths is stochastic. When the average number of deaths is equal to the dispersion, the distribution is the usual Poisson. When the average number of deaths is higher than the dispersion, the distribution is an intervened Poisson. When the average number of deaths is lower than the dispersion, the distribution is an incidence-rate-restricted Poisson (IRRP) type. Because dispersion of COVID-19 fatalities in some counties is higher than the average number of fatalities, the underlying model for the chance-oriented mechanism might be IRRP. Understanding where this overdispersion or volatility exists and the implications of it is the topic of this research. In essence, this paper focuses on the number of COVID-19 fatalities that fluctuated widely across United States (US) counties and develops an incidence-rate-restricted Poisson (IRRP) to understand where this overdispersion or volatility exists and the implications of it. [ABSTRACT FROM AUTHOR]

The noctuid moth soybean looper (SBL), Chrysodeixis includens (Walker) is an economically important pest of soybeans (Glycine max (L.) Merr.) in the southeastern United States. It has characteristics that are of particular concern for pest mitigation that include a broad host range, the capacity for annual long-distance flight, and resistance in some populations to important pesticides such as pyrethroids and chitin synthesis inhibitor. The biology of SBL in the United States resembles that of the fellow noctuid fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), a major pest of corn and several other crops. FAW exhibits a population structure in that it can be divided into two groups (host strains) that differ in their host preferences but are broadly sympatric and exhibit incomplete reproductive isolation. In this paper, strategies used to characterize the FAW strains were applied to SBL to assess the likelihood of population structure in the United States. Evidence is presented for two SBL strains that were defined phylogenetically and display differences in the proportions of a small set of genetic markers. The populations exhibit evidence of reproductive barriers sufficient to allow persistent asymmetry in the distribution of mitochondrial haplotypes. The identified molecular markers will facilitate studies characterizing the behaviors of these two populations, with relevance to pest mitigation and efforts to prevent further dispersal of the resistance traits. [ABSTRACT FROM AUTHOR]

Researchers have long puzzled over suicidal behavior. In this paper, we posit that when people are unable to attract mates given unfavorable sex ratios, suicide rates increase. Sex ratio, the proportion of males in a population, is linked to a variety of behaviors, including marriage stability, violence, depression, and infidelity. We test whether suicide rates are associated with county-level sex ratios utilizing data from 1999 to 2018, controlling for a variety of factors known to be associated with suicide risk. We find that sex ratio is associated with suicide rates, where a greater proportion of males in a county (age 35–74) is associated with an increased rate of suicide for these males. Mediation analyses show that these effects are mediated by male marriage rates. Counter to predictions, male-biased sex ratios also tend to be associated with increased female suicide rates for women aged 35 to 74, and this effect is mediated by the unmarried sex ratio (i.e., when there are more unmarried men compared to unmarried women in a county, there is increased female suicide). Overall, these results suggest that male-biased sex ratios are associated with suicide rates for both men and women, but the mediators vary. [ABSTRACT FROM AUTHOR]

Research has shown that climate change creates warmer temperatures and drier conditions, leading to longer wildfire seasons and increased wildfire risks in the United States. These factors have, in turn, led to increases in the frequency, extent, and severity of wildfires in recent years. Given the danger posed by wildland fires to people, property, wildlife, and the environment, there is an urgent need to provide tools for effective wildfire management. Early detection of wildfires is essential to minimizing potentially catastrophic destruction. To that end, in this paper, we present our work on integrating multiple data sources into SmokeyNet, a deep learning model using spatiotemporal information to detect smoke from wildland fires. We present Multimodal SmokeyNet and SmokeyNet Ensemble for multimodal wildland fire smoke detection using satellite-based fire detections, weather sensor measurements, and optical camera images. An analysis is provided to compare these multimodal approaches to the baseline SmokeyNet in terms of accuracy metrics, as well as time-to-detect, which is important for the early detection of wildfires. Our results show that incorporating weather data in SmokeyNet improves performance numerically in terms of both F1 and time-to-detect over the baseline with a single data source. With a time-to-detect of only a few minutes, SmokeyNet can be used for automated early notification of wildfires, providing a useful tool in the fight against destructive wildfires. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a devastating impact on the world at large with over 750 million cases and almost 7 million deaths reported thus far. Of those, over 100 million cases and 1 million deaths have occurred in the United States of America (USA). The mental health of the general population has been impacted by several aspects of the pandemic including lockdowns, media sensationalism, social isolation, and spread of the disease. In this paper, we examine the associations that social isolation and COVID-19 infection and related death had with the prevalence of anxiety and depression in the general population of the USA in a state-by-state multiple time-series analysis. Vector Error Correction Models are estimated and we subsequently evaluated the coefficients of the estimated models and calculated their impulse response functions for further interpretation. We found that COVID-19 incidence was positively associated with anxiety across the studied period for a majority of states. Variables related to social isolation had a varied effect depending on the state being considered. [ABSTRACT FROM AUTHOR]

This paper theoretically studies the interaction between an informed borrower and an uninformed lender facing possible default of a loan application. The lender is motivated to invest cognitive resources before making a lending decision. If the regulatory fine is weak, it is impossible for a bad-debt borrower to fully disclose his situation in the application. In this case, when the likelihood of a bad debt is low, the borrower always claims that nothing in the application is wrong. Otherwise, the borrower randomizes between full disclosure and information suppression. The transaction cost of the lender's pre-lending cognition increases with the default probability, as the default probability is small and decreases thereafter. Evidence from a peer-to-peer lending platform with 816,274 observations between 2012 and 2015 in the United States is largely consistent with our model implications. [ABSTRACT FROM AUTHOR]

The authors review the efforts made in the last five years to implement Demand Response (DR) programs, considering and studying several models and countries. As motivation, climate change has been a topic widely discussed in the last decades, namely in the power and energy sectors. Therefore, it is crucial to substitute non-renewable fuels with more environment-friendly solutions. Enabling Distributed Generation (DG), namely using renewable resources such as wind and solar, can be part of the solution to reduce the greenhouse effects. However, their unpredictable behavior might result in several problems for network management. Therefore, the consumer should become more flexible towards this new paradigm where the generation no longer follows the demand requests. With this, Demand Response (DR) concept is created as part of this solution. This paper studies the European Union and United States' current status, with over 50 references. [ABSTRACT FROM AUTHOR]

In this paper, we analyzed a dataset of over 2000 crypto-assets to assess their credit risk by computing their probability of death using the daily range. Unlike conventional low-frequency volatility models that only utilize close-to-close prices, the daily range incorporates all the information provided in traditional daily datasets, including the open-high-low-close (OHLC) prices for each asset. We evaluated the accuracy of the probability of death estimated with the daily range against various forecasting models, including credit scoring models, machine learning models, and time-series-based models. Our study considered different definitions of "dead coins" and various forecasting horizons. Our results indicate that credit scoring models and machine learning methods incorporating lagged trading volumes and online searches were the best models for short-term horizons up to 30 days. Conversely, time-series models using the daily range were more appropriate for longer term forecasts, up to one year. Additionally, our analysis revealed that the models using the daily range signaled, far in advance, the weakened credit position of the crypto derivatives trading platform FTX, which filed for Chapter 11 bankruptcy protection in the United States on 11 November 2022. [ABSTRACT FROM AUTHOR]

One important parameter in the Black–Scholes option pricing model is the implied volatility. Implied volatility surface (IVS) is an important concept in finance that describes the variation of implied volatility across option strike price and time to maturity. Over the last few decades, economists and financialists have long tried to exploit the predictability in the IVS using various parametric models, which require deep understanding of financial practices in the area. In this paper, we explore how a data-driven machine learning method, symbolic regression, performs in identifying the implied volatility surface even without deep financial knowledge. Two different approaches of symbolic regression are explored through a simulation study and an empirical study using a large panel of option data in the United States options market. [ABSTRACT FROM AUTHOR]

Simple Summary: Liver cancer continues to exhibit increasing incidence and mortality worldwide. Subsequently, there has been a concomitant effort to development novel therapeutics that effectively target and treat these malignancies. This paper aims to provide a thorough review of the available immunotherapy treatments that target liver cancer, with a specific focus on the role and potential therapeutic strategies available to the interventional radiologist. The underlying biochemical and molecular mechanisms underlying these diseases are discussed to highlight the specific targets for immunotherapy, the clinical efficacy and safety of available pharmaceutical treatments are analyzed, and future advancements in the field, such as combination therapies and personalized medicine, are explored as they pertain to the immunotherapy of liver cancer. Liver cancer, including hepatocellular carcinoma and intrahepatic cholangiocarcinoma, is increasing in incidence and mortality across the globe. An improved understanding of the complex tumor microenvironment has opened many therapeutic doors and led to the development of novel pharmaceuticals targeting cellular signaling pathways or immune checkpoints. These interventions have significantly improved tumor control rates and patient outcomes, both in clinical trials and in real-world practice. Interventional radiologists play an important role in the multidisciplinary team given their expertise in minimally invasive locoregional therapy, as the bulk of these tumors are usually in the liver. The aim of this review is to highlight the immunological therapeutic targets for primary liver cancers, the available immune-based approaches, and the contributions that interventional radiology can provide in the care of these patients. [ABSTRACT FROM AUTHOR]

This paper investigates the effects of coronavirus disease 2019 (COVID-19) on the performance of arbitrage trading in the energy market using daily data covering the period between 1 January 2015 and 5 December 2021. The investigation was achieved by utilizing a parametric pairs-trading model, where pairs of energy-related securities, including futures, stocks and ETFs traded in the United States, are formed. The empirical results suggest that the out-of-sample performances of pair trading declined sharply in the face of COVID-19. Dividing the whole data sample into two sub-samples, we found that the strategy performed well before COVID-19 but yielded poor results in the pandemic era. The analysis presented in this paper could serve as a benchmark for arbitrage-based trading models in the energy market during the pandemic. [ABSTRACT FROM AUTHOR]

The proliferation of inverter-based distributed energy resources (IBDERs) has increased the number of control variables and dynamic interactions, leading to new grid control challenges. For stability analysis and designing appropriate protection controls, it is important that IBDER models are accurate. This paper focuses on the accurate estimation and parameter calibration of DER_A, a recently proposed aggregated IBDER model. In particular, we focus on the parameters of the reactive power–voltage regulation module. We formulate the problem of parameter tuning as a non-linear least square minimization problem and solve it using the Levenberg–Marquardt (LM) method. The LM method is primarily chosen due to its flexibility in adaptively selecting between the steepest descent and Gauss–Newton methods through a damping parameter. The LM approach is used to minimize the error between the actual measurements and the estimated response of the model. Further, the computational challenges posed by the numerical calculation of the Jacobian are tackled using a quasi-Newton root-finding approach. The proposed method is validated on a real feeder model in the northeastern part of the United States. The feeder is modeled in OpenDSS and the measurements thus obtained are fed to the DER_A model for calibration. The simulation results indicate that our approach is able to successfully calibrate the relevant model parameters quickly and with high accuracy, with a total sum of square error of 3.57 × 10 − 7 . [ABSTRACT FROM AUTHOR]

The events of January 6th were a clear example of threats to American democracy. De-democratization is a process that preceded Trump's election and that can still be seen in the United States and around the world. Social theorist Charles Tilly wrote about how becoming a democracy is not a unidirectional, one-time event or goal, but a non-linear process. This paper analyzes developments in the United States that signal rises and falls in the level of democracy over the last several decades. It discusses Donald Trump's rise to power, the insurrection on January 6th, 2021, and the state of inclusion of ethnoracial minorities in the United States. It uses Tilly's proposed processes of democratization and de-democratization. This more nuanced understanding of democracy and state–society relations helps avoid celebratory stances about the promise of electoral politics as well as pessimistic assessments about the imminent arrival of fascism and authoritarianism. [ABSTRACT FROM AUTHOR]

Researchers' interest in finding practical applications for virtual reality (VR) and augmented reality (AR) technologies has increased as new devices have become cheaper and more accessible, being used in entertainment, healthcare, and rehabilitation fields, among others. This study aims to provide an overview of the current state of scientific literature related to VR, AR, and physical activity (PA). A bibliometric analysis of studies published between 1994 and 2022 was conducted using The Web of Science (WoS), applying the traditional bibliometric laws and using the VOSviewer software for data and metadata processing. The results revealed an exponential increase in scientific production between 2009 and 2021 (R2 = 94%). The United States (USA) was the country/region with the most relevant co-authorship networks (72 papers); the most prolific author was Kerstin Witte, and the most prominent was Richard Kulpa. The most productive journal's core was composed of high-impact and open access journals. A great thematic diversity was found according to the most used keywords by the co-authors, highlighting concepts such as rehabilitation, cognition, training, and obesity. Then, the research related to this topic is in an exponential development phase, with great interest in the rehabilitation and sports sciences fields. [ABSTRACT FROM AUTHOR]

As our planet faces more frequent and severe environmental threats due to climate change (including threats to biodiversity), environmental justice will be essential to ensure that the costs and burdens of combating these threats are shared equally, borne by all people worldwide in a fair and equitable manner. If the past is any indicator, however, environmental problems—and their "solutions"—disproportionately affect poor communities and communities of color, including Indigenous communities. Despite these past injustices, Indigenous lands, which make up only 20 percent of the Earth's territory, contain 80 percent of the world's remaining biodiversity—evidence that Indigenous peoples are among the most effective stewards of the environment. A primary reason for this remarkable statistic is the use and practice of Indigenous Traditional Ecological Knowledge; ecological wisdom which has been passed down for generations and has been shown to strengthen community resilience in response to the multiple stressors of global environmental change. While the United States government has been slow to acknowledge the value of Traditional Ecological Knowledge, it has recently begun to incorporate that knowledge into environmental policy in response to the worldwide climate crisis. Continuing the integration of Traditional Ecological Knowledge into government environmental policy will ensure that such policies will be more effective at the federal, state, and local levels and more equitable in their application. Western scientists, government officials, and global leaders need to build trusting and co-equal relationships with Indigenous communities by actively listening to all cultures and respecting the many kinds of knowledge systems required to conserve the natural world and all living beings. This paper will address how incorporating Traditional Ecological Knowledge into U.S. policy would help safeguard the environment from further biodiversity loss and other ecological destruction, and advance environmental justice to ensure the fair treatment of all. [ABSTRACT FROM AUTHOR]

Frequency analysis of tornadoes is very important for risk analysis and disaster control. In this paper, the annual frequency of tornadoes that occurred in the United States from 1967 to 2016 is analyzed. The simple analysis shows that frequencies of tornadoes of different sites are spatially correlated and over-dispersed. To explain the two characteristics of the data, the Bayesian hierarchical model is proposed. For comparison purposes, the Bayesian model with negative binomial distribution, Poisson distribution, Polya distribution, and first-order, non-negative, integer-valued autoregressive model with Bell innovations(BL-INAR(1)) are considered to fit the frequency of tornado. The distribution parameters of all sites are assumed to be spatially correlated, and the corresponding Bayesian hierarchical models were established. MCMC (Markov Chain Monte Carlo) method is applied to parameter estimations and relative statistical inference. By comparison of the analysis results, the negative binomial distribution is recommended to analyze the overdispersion and spatial correlation among the sites of the data. The comparison between the simulated frequencies based on the proposed model and the actual frequencies also verifies that the proposed method is a better model for the data. [ABSTRACT FROM AUTHOR]

A smart city is a city that binds together technology, society, and government to enable the existence of a smart economy, smart mobility, smart environment, smart living, smart people, and smart governance in order to reduce the environmental impact of cities and improve life quality. The first step to achieve a fully connected smart city is to start with smaller modules such as smart homes and smart buildings with energy management systems. Buildings are responsible for a third of the total energy consumption; moreover, heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Even though connected thermostats are widely available, they are not used as intended since most people do not have the expertise to control this device to reduce energy consumption. It is commonly set according to their thermal comfort needs; therefore, unnecessary energy consumption is often caused by wasteful behaviors and the estimated energy saving is not reached. Most studies in the thermal comfort domain to date have relied on simple activity diaries to estimate metabolic rate and fixed values of clothing parameters for strategies to set the connected thermostat's setpoints because of the difficulty in tracking those variables. Therefore, this paper proposes a strategy to save energy by dynamically changing the setpoint of a connected thermostat by human activity recognition based on computer vision preserving the occupant's thermal comfort. With the use of a depth sensor in conjunction with an RGB (Red–Green–Blue) camera, a methodology is proposed to eliminate the most common challenges in computer vision: background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance on human detection. Moreover, a Recurrent Neural Network (RNN) is implemented for human activity recognition (HAR) because of its data's sequential characteristics, in combination with physiological parameters identification to estimate a dynamic metabolic rate. Finally, a strategy for dynamic setpoints based on the metabolic rate, predicted mean vote (PMV) parameter and the air temperature is simulated using EnergyPlus™ to evaluate the energy consumption in comparison with the expected energy consumption with fixed value setpoints. This work contributes with a strategy to reduce energy consumption up to 15% in buildings with connected thermostats from the successful implementation of the proposed method. [ABSTRACT FROM AUTHOR]

The existing process for the preparation of cladded rebars is too complicated for large-scale industrial production. Therefore, this paper proposes a 55#/316L rebar preparation method based on vacuum hot rolling. The microstructure and mechanical properties of the composite interface of the rebar, along with the connecting technique, were studied using transmission electron microscopy, X-ray diffraction, and Vickers hardness testing. The obtained results showed that the minimum thickness of the 55#/316L rebar cladding was 0.25 mm, which was twice that of the M 329M/M 329-11 design standard used in the United States of America. Due to the diffusion of carbon, large numbers of second-phase particles were precipitated on the stainless-steel side, which resulted in intergranular chromium depletion. After multi-pass hot rolling, the minimum bonding strength of the composite interface reached 316.58 MPa, which was considerably higher than the specified value of 210 MPa. In addition, we designed three different types of rebar connection joints: sleeve, groove-welded, and bar-welded. According to the tensile test, the bar-welded joint had higher yield strength (385 MPa) and tensile strength (665 MPa) than the base rebar (376.6 MPa and 655 MPa), as well as a very high corrosion resistance. [ABSTRACT FROM AUTHOR]

Childhood obesity is a public health concern in the United States. Consequences of childhood obesity include metabolic disease and heart, lung, kidney, and other health-related comorbidities. Therefore, the early determination of obesity risk is needed and predicting the trend of a child's body mass index (BMI) at an early age is crucial. Early identification of obesity can lead to early prevention. Multiple methods have been tested and evaluated to assess obesity trends in children. Available growth charts help determine a child's current obesity level but do not predict future obesity risk. The present methods of predicting obesity include regression analysis and machine learning-based classifications and risk factor (threshold)-based categorizations based on specific criteria. All the present techniques, especially current machine learning-based methods, require longitudinal data and information on a large number of variables related to a child's growth (e.g., socioeconomic, family-related factors) in order to predict future obesity-risk. In this paper, we propose three different techniques for three different scenarios to predict childhood obesity based on machine learning approaches and apply them to real data. Our proposed methods predict obesity for children at five years of age using the following three data sets: (1) a single well-child visit, (2) multiple well-child visits under the age of two, and (3) multiple random well-child visits under the age of five. Our models are especially important for situations where only the current patient information is available rather than having multiple data points from regular spaced well-child visits. Our models predict obesity using basic information such as birth BMI, gestational age, BMI measures from well-child visits, and gender. Our models can predict a child's obesity category (normal, overweight, or obese) at five years of age with an accuracy of 89%, 77%, and 89%, for the three application scenarios, respectively. Therefore, our proposed models can assist healthcare professionals by acting as a decision support tool to aid in predicting childhood obesity early in order to reduce obesity-related complications, and in turn, improve healthcare. [ABSTRACT FROM AUTHOR]

Background: Over the past decade, many studies in the field of transcranial direct current stimulation (tDCS) in stroke have been published in scholarly journals. However, a scientometric analysis focusing on tDCS after stroke is still missing. The purpose of this study is to deliver a bibliometric analysis to investigate the global hotspots and frontiers in the domain of tDCS in stroke from 2012 to 2021. Methods: Articles and reviews related to tDCS in stroke were retrieved and obtained from the Web of Science core collection database from 2012 to 2021. Data visualization and analysis were conducted by using CiteSpace, VOSviewer, and Microsoft Excel 2019. Results: Finally, 371 publications were included in the scientometric analysis, including 288 articles and 83 reviews. The results showed that the number of publications per year increased from 15 to 68 in the last 10 years. Neurosciences was the main research hotspot category (n = 201). Frontiers in Human Neuroscience was the most published journal with 14 papers. The most productive author, institution, and country were Fregni F (n = 13), the League of European Research Universities (n = 37), and the United States of America (n = 98), respectively. A burstness analysis of keywords and the literature indicated that current studies in the field of tDCS in stroke focused on poststroke aphasia, tDCS combined with robotic therapy, and anatomical parameters. Conclusion: The research of tDCS in stroke is predicted to remain a research hotspot in the future. We recommend investigating the curative effect of other different tDCS closed-loop rehabilitation methods for different stroke dysfunctions. In conclusion, this bibliometric study presented the hotspots and trends of tDCS in stroke over the last decade, which may help researchers manage their further studies. [ABSTRACT FROM AUTHOR]

This paper extends the conditional autoregressive range (CARR) model to the multivariate CARR (MCARR) model and further to the two-stage MCARR-return model to model and forecast volatilities, correlations and returns of multiple financial assets. The first stage model fits the scaled realised Parkinson volatility measures using individual series and their pairwise sums of indices to the MCARR model to obtain the fitted volatilities. Then covariances are calculated to construct the fitted variance–covariance matrix of returns which are imputed into the stage-two return model to capture the heteroskedasticity of assets' returns. We investigate different choices of mean functions to describe the volatility dynamics. Empirical applications are based on the Standard and Poor 500, Dow Jones Industrial Average and Dow Jones United States Financial Service Indices. Results show that the stage-one MCARR models using asymmetric mean functions give better in-sample model fits than those based on symmetric mean functions. They also provide better out-of-sample volatility forecasts than those using CARR models based on two robust loss functions. We also find that the stage-two return models with constant means and multivariate Student-t errors give better in-sample fits than the Baba–Engle–Kraft–Kroner generalised autoregressive conditional heteroskedasticity models. The estimates and forecasts of value-at-risk (VaR) and conditional VaR based on the best MCARR-return models for each asset are provided and tested using backtests to confirm the accuracy of the VaR forecasts. [ABSTRACT FROM AUTHOR]