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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]

In this paper, we demonstrate the ways in which the schooling of refugee youth in the United States reflects ongoing coloniality in education. Drawing on data collected in a case study, conducted between 2013 and 2016, as part of a larger ongoing ethnography of a Southwest United States District school's response to refugee students, we show how the enactment of policies, pedagogies, and practices within schools reinforce the government's control over refugee students and their families. In schools, the students are kept out of certain school spaces, marginalized in remedial courses, and denied academic opportunities and integrated support services. Using empirical data, we demonstrate how the restriction of the students' movement in and around schools is embedded within the larger limitations embedded in coloniality and assimilation. We situate our analysis within the tensions and interactions between coloniality, assimilation, and neoliberalism as articulated in studies within anthropology and sociology, migration studies, critical refugee studies, and cultural studies. We conclude with a call for the decolonization of education and offer a practical starting point in refugee education. [ABSTRACT FROM AUTHOR]

This paper demonstrates the macro dynamics of housing and disability by comparing pressures on a broad range of issues related to housing and disability across states. Despite the growing population of adults with disabilities in the United States, research on the national-level relationship between housing and disability has been largely absent and little attention has been paid to accessibility, housing affordability, or the prevalence of involuntary movement and relocation for adults with disabilities. Using national data from the 2020 American Community Survey (ACS), we examine the multi-dimensional nature of housing, through cost burdens, upkeep, and maintenance, renting vs. ownership, and types of housing prevalent in the United States, and argue that this creates a paradox of livability vs. affordability for adults with disabilities. These state-level trends in the cost and livability of housing environments by state show that housing conditions and housing costs are inversely associated for adults with disabilities. These macro-level conditionalities are not uniform but vary by housing type and location across the country, creating specific paradoxes in each state. We assert that housing itself is one of the key mechanisms that can support or hinder the long-term well-being of persons with disabilities living in the community, through the presence or absence of both physical space and facilities in the home. Ultimately, housing conditions and affordability need to be considered primary factors in the study and support of persons with disabilities and these considerations must be tailored to state and local housing dynamics to meet persons with disabilities where they live. [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]

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]