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The forest products industries play a vital role in the economic, social, and environmental well-being of the Lake States in the United States. While various economic contribution analyses of forest products industries have been conducted to highlight the importance of such industries to regional economies, little effort has yet been made to parse out the contribution of activities in the value chain. The value chain is a series of steps involved in producing goods or services. This study used a matrix decomposition approach to estimate the economic contribution along the value chain through multiple pathways of four forest resource-based industries using wood as inputs: biomass power generation, sawmills, paper mills, and the construction of new single-family residential structures in the Lake States. The direct and indirect economic output values in 2017 resulting from the construction of new single-family residential structures were $19.1 billion, sawmills were $2.5 billion, paper mills were $17.6 billion, and the biomass power generation industry was $759 million. Of the direct and indirect economic output contributed by each industry, the highest percentage of output attributable to the logging industry was observed from the sawmills industry (12%), followed by biomass power generation (9%), paper mills (1.4%), and the construction of new single-family residential structures (<1%), respectively. The percentage of total economic output attributable to the stumpage industry in the region followed a similar trend as commercial logging for all value-chain industries. The relative economic contribution of the value-chain industries to the total economic contribution of the final industry varied based on whether the industry was a primary or secondary forest products industry and the pathways used for sourcing wood inputs. [ABSTRACT FROM AUTHOR]

Listeria monocytogenes is the third most deadly foodborne pathogen in the United States. The bacterium is found in soil and water, contaminating raw food products and the processing environment, where it can persist for an extended period. Currently, testing of food contact and non-food contact surfaces is performed using an array of sampling devices and endpoint technologies, offering various levels of sensitivity, cost, user skill, and time to detection. Paper-based microfluidic devices (µPADs) are a rapid detection platform amenable to low-cost, user-friendly, and portable diagnostics. In this study, we developed and evaluated a µPAD platform specific for the colorimetric detection of the Listeria genus following recovery from food contact and non-food contact surfaces. For detection, four colorimetric substrates specific for the detection of β-glucosidase, two broths selective for the detection of Listeria spp., and a nonselective broth were evaluated to facilitate detection of Listeria spp. The limit of detection and time to detection were determined by using pure bacterial cultures. After 8 h enrichment, L. monocytogenes (102 Colony Forming Units (CFU)/coupon) was detected on every surface. After 18 h enrichment, L. monocytogenes (102 CFU/coupon) was detected on all surfaces with all swabbing devices. This study demonstrated the ability of the µPAD-based method to detect potentially stressed cells at low levels of environmental contamination. [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]

Large-span, post-tensioned (PT) beams play a crucial role in maximizing the benefits of post-tensioning techniques. Bonded and unbonded systems are prevalent, with the latter being more widespread in the United States. While bonded systems are advantageous for creating long spans when multiple tendons are grouped in ducts, limited studies in the literature exist on their demolition. With a case study, this paper addresses the unique challenge of demolishing large-span-bonded, post-tensioned beams that occurs due to a building's functional change. Emphasizing insights for engineers, it explores the use of cutting and dismantling methods, thereby considering the presence of prestressed cables. The demolition process is distinctive due to the presence of numerous prestressed cables along the beams, necessitating a specialized and cautious cutting approach. This is accomplished through the use of a drilling technique that selectively distresses the tendons, ensuring they are not all affected simultaneously. An intriguing observation discussed in this paper pertains to the occurrence of horizontal cracks accompanied by loud sounds following the drilling process, thereby offering insights from the design perspective of PT systems. This paper details an innovative method for safely demolishing large-span, bonded PT beams using ground-penetrating radar and computer models to navigate structural complexities and ensure nearby structures' safety. [ABSTRACT FROM AUTHOR]

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]

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]

Technologies that capture and analyze movement patterns for diagnostic or therapeutic purposes are a major locus of innovation in the United States. Several studies have evaluated their measurement properties in different conditions with variable findings. To date, the authors are not aware of any systematic review of studies conducted to assess the concurrent validity of pressure-sensing walkway technologies. The results of such an analysis could establish the body of evidence needed to confidently use these systems as reference or gold-standard systems when validating novel tools or measures. A comprehensive search of electronic databases including MEDLINE, Embase, and Cumulative Index to Nursing and Allied Health Literature (CINAHL) was performed. The initial search yielded 7670 papers. After removing duplicates and applying study inclusion/exclusion criteria, 11 papers were included in the systematic review with 10 included in a meta-analysis. There were 25 spatial and temporal gait parameters extracted from the included studies. The results showed there was not a significant bias for nearly all spatiotemporal gait parameters when the walkway system was compared to the reference systems. The findings from this analysis should provide confidence in using the walkway systems as reference systems in future studies to support the evaluation and validation of novel technologies deriving gait parameters. [ABSTRACT FROM AUTHOR]

In an increasingly mobile world, the integration of immigrants and displaced individuals is an important factor in creating cohesive and inclusive societies. Integration has different dimensions; this scoping review examines the conceptualization and measurement of economic integration among immigrants and refugees in the United States. Quantitative peer-reviewed journal papers measuring or conceptualizing the economic integration of first-generation documented adult immigrants or refugees in the United States, as well as relevant conceptual or theory papers on this topic, were included in the review. The search strategy included an online search of the Web of Science Core Collection, PsycINFO, Applied Social Sciences Index and Abstracts (ASSIA), and EconLit. Additional search strategies included scanning the reference lists of studies identified as relevant in the initial database search. An analysis of 72 studies included in the review using a data extraction table reveals seven key domains of economic integration: income and economic security, employment and occupational categories, assets and use of financial services, neighborhood and housing, health, education, and use of public assistance. Income and economic security emerged as the most common indicators of integration in the reviewed studies. Notably, less than half of the reviewed publications had a multidimensional approach to defining or measuring economic integration, and the majority of studies were focused on immigrants, with a smaller proportion dedicated to refugees. This review emphasizes the need for comprehensive frameworks in assessing economic integration among immigrants and refugees, reflecting the multifaceted nature of their economic integration experiences. [ABSTRACT FROM AUTHOR]

Manufacturing of a product such as a corrugated cardboard box (CCB) includes the extraction of a variety of raw materials in addition to supply chain efforts to get the raw materials to the industry. Conducting a LifeCycle Assessment (LCA) gives the carbon emission of each phase of the product and a quantitative estimate of the overall product carbon footprint and its effect on the environment. This gives impetus to recommendations for improving the phases of the lifecycle to minimize carbon emissions. The proposed waste management method in this paper is the "reuse" method instead of recycling or landfilling the CCB and, in so doing, focusing only on reducing carbon emissions in the manufacturing phase. The paper examines if the incremental cost of reusing the CCBs is less than the environmental and economic cost of reducing the extraction and supply chain of raw materials. This paper uses LCA to evaluate the carbon emission in each phase of the lifecycle of a typical 1 kg corrugated cardboard box in the United States. Carbon emission for the proposed "reuse" phase is also calculated, and the results are compared. This paper also explores the economic feasibility of the proposed "reuse" method that incentivizes the general population to reuse the CCBs instead of recycling or landfilling them. Economic tools such as willingness-to-pay vs. marginal cost curves and benefit-cost analyses are used to evaluate economic feasibility. The results indicate that the "reuse" method for CCBs is economically and environmentally feasible. It also supports the approach of using analytics, economics, and LCA to create a model that can be used for other products and processes as an evaluative process to determine if businesses can benefit from the reduction (or removal) of material extraction costs from the supply chain. [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]

This study provides a comprehensive analysis of global patent trends in battery recycling, focusing on secondary batteries and related technologies across Korea, China, and the United States. The methodology involved collecting data from various patent databases, followed by quantitative analysis to identify technology trends and guide future development. The research employed statistical tools to analyze patent activities, including the frequency and scope of patent filings, and comparative analysis to highlight differences between countries. This study reveals distinct emphases on technologies such as lithium-ion and waste battery recycling, highlighting notable differences in patent activities among key companies and countries. China's large number of patents in battery manufacturing processes contrasts with the USA's focus on electrochemical cell construction and storage systems, while Korea shows significant activity in waste battery technology. The novelty of this paper lies in its detailed comparative analysis of patent trends across these three major economies, providing insights into the technological focuses and priorities of each country. The study also identifies key challenges, such as the need for consistent innovation and broader geographic coverage in Korea, enhancing patent influence and international presence in China, and ensuring high patent quality and fostering innovation in lagging sectors in the United States. Addressing these challenges through enhanced collaboration, increased R&D investments, and supportive policies is crucial for strengthening the global position and driving further innovation in the battery recycling sector. [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]

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]

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]

Community gardens offer broad research opportunities and analytical resources encompassing urban planning to environmental sustainability, food systems, and social capital. However, little is known about the knowledge structure and research development related to community gardens. This study presents an in-depth bibliometric performance analysis and visual scientific mapping analysis of the literature on community gardens by examining 487 published papers selected from the Web of Science database. The results indicated a considerable rise in research papers in this subject area from 2012 onwards, with most contributions from the United States. Studies from high-income countries accounted for 93.22%, and 38 countries have cooperated 167 times in this field. "Health" is the most frequent keyword, and the terms "ecosystem services" has been gaining popularity over the last five years. A combination of co-citation clustering and keyword co-occurrence clustering analysis identified three major research themes in the field of community gardens: "ecosystem services and disservices", "multidimensional association", and "sustainable garden systems". The development of ecosystem value assessment frameworks, the establishment of region-wide soil monitoring databases, accounting for the cost-effectiveness of nature-based solutions, the integration of garden systems into smart cities, and the integration of water management into regulation will be important future research directions regarding community gardens. Overall, this study provides scholars with a systematic and quantitative understanding of community gardens. [ABSTRACT FROM AUTHOR]

International trade is a critical part of the United States economy. Land border crossings between the United States and Mexico accounts for a large proportion of the USD 779 billion in trade between these two countries. Monitoring and managing the operations of these land border crossings is critical for ensuring efficient trade and providing appropriate security. This paper examines the opportunity to use connected vehicle data to monitor the travel time delay of passenger vehicles crossing the border for system level assessment across 26 border crossing locations over an analysis period of 25 days in August 2020. A sample size of 51,341 trips from the US to Mexico and 41,708 trips from Mexico to the US were used in this study. Furthermore, 97% trips to the US and 76% trips to Mexico experienced delays. The average delay was 34 min for trips to the US compared to only 2 min for trips to Mexico. In terms of the predictability of border crossing times, there was also substantial variation by direction. The interquartile range of vehicle delay from the US to Mexico was 2 min, while the interquartile range of delay for vehicles travelling from Mexico to the US was 46 min. Border crossings were also ranked using four performance metrics—trip counts, median delay, delayed trip counts and total delays in vehicle hours. Methods for summarizing delay trends by time of the day and day of the week to identify time windows of interest are also presented. Land border crossing operations have a significant influence on security and economic efficiency. We believe the techniques presented in this paper provide a scalable methodology for providing near real-time factual data on border crossing delays that provide important information for land border transport-managing stakeholders to make informed management decisions that balance security and economic efficiency. [ABSTRACT FROM AUTHOR]

In the United States (US), tick-borne diseases (TBDs) have more than doubled in the past fifteen years and are a major contributor to the overall burden of vector-borne diseases. The most common TBDs in the US—Lyme disease, rickettsioses (including Rocky Mountain spotted fever), and anaplasmosis—have gradually shifted in recent years, resulting in increased morbidity and mortality. In this systematic review, we examined climate change and other environmental factors that have influenced the epidemiology of these TBDs in the US while highlighting the opportunities for a One Health approach to mitigating their impact. We searched Medline Plus, PUBMED, and Google Scholar for studies focused on these three TBDs in the US from January 2018 to August 2023. Data selection and extraction were completed using Covidence, and the risk of bias was assessed with the ROBINS-I tool. The review included 84 papers covering multiple states across the US. We found that climate, seasonality and temporality, and land use are important environmental factors that impact the epidemiology and patterns of TBDs. The emerging trends, influenced by environmental factors, emphasize the need for region-specific research to aid in the prediction and prevention of TBDs. [ABSTRACT FROM AUTHOR]

Simple Summary: Oral cancer affects the mouth and throat areas. It is a major cause of death for older people in developed countries. This review looked at how living in rural areas influences oral cancer trends in these countries. The studies from these countries showed increasing rates of oral cancer in rural areas of the US, Australia, Canada, and Europe. Older people are more affected by these cancers than younger groups. The main risk factors are tobacco use, alcohol consumption, and HPV infections. People in rural areas often do not know much about HPV-related cancers. They also tend to use more tobacco and alcohol than city dwellers. Even in developed countries, living in rural areas can lead to shorter lifespans for oral cancer patients. This is mainly because it is harder for them to access specialized cancer treatment centres and advanced medical care. In summary, where people live can significantly impact their chances of surviving oral cancer, even in wealthy nations. Oral cancer is the general term used to describe cancers of the oral cavity and oropharyngeal region. These cancers are one of the leading causes of death in elderly residents within the Organisation for Economic Co-operation and Development (OECD) member countries in the 21st century. This scoping review was carried out to assess the influence of rurality on oral cancer trends and patterns among OECD member countries. Four online databases (Medline, PubMed, Scopus, and CINAHL) were searched for studies that reported on oral cancer trends in rural and remote areas in OECD member countries. A total of 1143 articles were obtained initially; among them, 995 papers were screened to include 18 articles for this scoping review. Studies have reported increasing incidence and prevalence in the United States, Australia, Canada, and European countries wherein risk factors such as tobacco, alcohol, and human papilloma virus (HPV) infections were associated with oral and oropharyngeal cancers. Awareness among people living in rural areas about HPV-related cancers was very low, while rates of tobacco and alcohol abuse were noted to be rising more rapidly than among their urban counterparts. Furthermore, the ageing population was most affected compared to the younger age groups of people with oral and oropharyngeal cancer that are prevalent in these regions. Overall, despite living in developed countries, rurality was noted to be a significant factor in the lower life expectancy of oral cancer patients, mainly due to the limited accessibility to tertiary cancer care centres and advanced medical care. [ABSTRACT FROM AUTHOR]

Simple Summary: Patients with pediatric liver cancers hepatoblastoma and hepatocellular carcinoma very often develop lung metastases. These cancers can present with lung metastases and are at higher risk of relapse. Although cisplatin is very effective at clearing lung metastases, they can still relapse. Therefore, there is an urgent need to develop therapeutic approaches to prevent the development of lung metastases in patients with pediatric liver cancers. In this paper, we show that the metastatic microenvironment of HBL and HCC patients contains a heterogeneous cell population that formed tumor clusters. We found that both fresh primary tumors and generated primary cell cultures had increased the expression of HDAC1, a histone deacetylase, and the transcription factor Sp5. Sp5 and HDAC1 work in tandem by transporting HDAC1 to the promoters of genes and changing their expression. We analyzed the effects of the HDAC inhibitor, SAHA, on the metastasis-initiating cells in combination with cisplatin. We found that HDAC inhibition increases the efficacy of cisplatin to eliminate these metastasis-initiating cells. The pediatric liver cancers, hepatoblastoma and hepatocellular carcinoma, are dangerous cancers which often spread to the lungs. Although treatments with cisplatin significantly improve outcomes, cisplatin may not eliminate metastasis-initiating cells. Our group has recently shown that the metastatic microenvironments of hepatoblastoma contain Cancer Associated Fibroblasts (CAFs) and neuron-like cells, which initiate cancer spread from liver to lungs. In this study, we found that these cells express high levels of HDAC1; therefore, we examined if histone deacetylase inhibition improves cisplatin anti-proliferative effects and reduces the formation of tumor clusters in pediatric liver cancer metastatic microenvironments. Methods: New cell lines were generated from primary hepatoblastoma liver tumors (hbl) and lung metastases (LM) of HBL patients. In addition, cell lines were generated from hepatocellular neoplasm, not otherwise specified (HCN-NOS) tumor samples, and hcc cell lines. Hbl, LM and hcc cells were treated with cisplatin, SAHA or in combination. The effect of these drugs on the number of cells, formation of tumor clusters and HDAC1-Sp5-p21 axis were examined. Results: Both HBL and HCC tissue specimens have increased HDAC1-Sp5 pathway activation, recapitulated in cell lines generated from the tumors. HDAC inhibition with vorinostat (SAHA) increases cisplatin efficacy to eliminate CAFs in hbl and in hcc cell lines. Although the neuron-like cells survive the combined treatments, proliferation was inhibited. Notably, combining SAHA with cisplatin overcame cisplatin resistance in an LM cell line from an aggressive case with multiple metastases. Underlying mechanisms of this enhanced inhibition include suppression of the HDAC1-Sp5 pathway and elevation of an inhibitor of proliferation p21. Similar findings were found with gemcitabine treatments suggesting that elimination of proliferative CAFs cells is a key event in the inhibition of mitotic microenvironment. Conclusions: Our studies demonstrate the synergistic benefits of HDAC inhibition and cisplatin to eliminate metastasis-initiating cells in pediatric liver cancers. [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]

Crop yield prediction is essential for tasks like determining the optimal profile of crops to be planted, allocating government resources, effectively planning and preparing for aid distribution, making decisions about imports, and so on. Crop yield prediction using remote sensing data during the growing season is helpful to farm planning and management, which has received increasing attention. Information mining from multichannel geo-spatiotemporal data brings many benefits to crop yield prediction. However, most of the existing methods have not fully utilized the dimension reduction technology and the spatiotemporal feature of the data. In this paper, a new approach is proposed to predict the yield from multispatial images by using the dimension reduction method and a 3D convolutional neural network. In addition, regions with similar crop yields should have similar features learned by the network. Thus, metric learning and multitask learning are used to learn more discriminative features. We evaluated the proposed method on county-level soybean yield prediction in the United States, and the experimental results show the effectiveness of the proposed method. The proposed method provides new ideas for crop yield estimation and effectively improves the accuracy of crop yield estimation. [ABSTRACT FROM AUTHOR]

Adhesively bonded doublers and adhesively bonded repairs are extensively used to extend the operational life of metallic aircraft structures. Consequently, this paper focuses on the tools needed to address sustainment issues associated with both adhesively bonded doublers and adhesively bonded repairs to (metallic) aircraft structures, in a fashion that is consistent with the building-block approach mandated in the United States Air Force (USAF) airworthiness certification standard MIL-STD-1530D and also in the United States (US) Joint Services Structural Guidelines JSSG-2006. In this context, it is shown that the effect of biaxial loads on cohesive crack growth in a bonded doubler under both constant amplitude fatigue loads and operational flight loads can be significant. It is also suggested that as a result, for uniaxial tests to replicate the cohesive crack growth seen in adhesively bonded doublers and adhesively bonded repairs under operational flight loads, the magnitude of the applied load spectrum may need to be continuously modified so as to ensure that the crack tip similitude parameter in the laboratory tests reflects that seen in the full-scale aircraft. [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]

Accelerating economic globalization is a major driver of the transfer of embodied pollutant emissions from trade. China and the United States are currently the largest importers and exporters of agricultural products, respectively, and are also major producers and consumers of these products. This paper aims to analyze and compare the patterns of embodied agricultural carbon emissions (ACE) in the two countries, which is crucial for understanding how trade influences the transfer of such emissions. In this study, we calculated the embodied ACE of China and the United States from the perspectives of production and consumption for the years 1970, 1980, 1990, 2000, 2010, and 2016 by establishing a multi-regional input–output (MRIO) model. Additionally, we employed the Logarithmic Mean Divisia Index (LMDI) decomposition method to analyze the driving factors behind the changes in embodied ACE over time. The findings indicated that the embodied ACE associated with imports and exports in China and the United States followed a pattern of increase and subsequent decrease during the period 1970–2016, with net imports escalating from −18.79 million tons and −3.62 million tons to 40.35 million tons and 51.22 million tons, respectively. This study identified two main factors contributing to the reduction in embodied ACE in both countries: the declining intensity of embodied ACE per unit of traded products and the diminishing proportion of the primary industry. The growth in GDP per capita, population expansion, and an increase in the proportion of agricultural products in international trade are predicted to promote an increase in embodied ACE imports and exports in both countries. This paper advocates for the reduction of embodied ACE through the continuous promotion of research and application of energy-saving and emission-reduction technologies, an optimized industrial structure, and the implementation of relevant energy-saving and emission-reduction policies. [ABSTRACT FROM AUTHOR]

Asset management decision support tools determine which action (maintenance, rehabilitation, or reconstruction) is applied to each facility in a transportation network and when. Sophisticated tools recognize uncertainties and consider emerging priorities. However, these tools are often computationally complex and lack transparency, the models are difficult to evaluate, and the outputs are challenging to validate. This paper explores computational complexity, transparency, and realism in transportation asset management decision support tools to better understand how to select the right tools for a particular context. Descriptions of how state departments of transportation in the United States make use of optimization in their mandated transportation asset management plans to make decisions are used to understand the needs of states. This qualitative analysis serves as a review of the goals and practices of state agencies. An existing asset management tool is then used to demonstrate the tradeoffs involved in accurately capturing the decision-making process and complexity. The results provide examples of strategies that agencies can use when selecting decision support tools and for researchers and tool developers working toward developing the right tool for an application. [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]

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]

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]

Kidney transplantation is a life-saving intervention for end-stage renal disease; yet, the persistent gap between organ demand and supply remains a significant challenge. This paper explores the escalating discard rates of deceased donor kidneys in the United States to assess trends, discard reasons, demographical differences, and preservation techniques. Data from the Scientific Registry of Transplant Recipients from 2010 to 2021 was analyzed using chi-squared tests for trend significance and logistic regression to estimate odds ratios for kidney discard. Over the last decade, discard rates have risen to 25% in 2021. Most discarded kidneys came from extended criteria donor (ECD) donors and elevated kidney donor profile index (KDPI) scores. Kidney biopsy status was a significant factor and predictor of discard. Discard rates varied greatly between Organ Procurement and Transplantation Network regions. Of reasons for discard, "no recipient located" reached a high of 60%. Additionally, there has been a twofold increase in hypothermic machine perfusion (HMP) since 2010, with transportation difficulties being the main reason for the discard of perfused kidneys. Our findings suggest a need to recalibrate organ utilization strategies, optimize the use of lower-quality kidneys through advanced preservation methods, and address the evolving landscape of organ allocation policies to reduce kidney discard rates. [ABSTRACT FROM AUTHOR]

Since 2012, twenty-four states have legalized adult-use recreational cannabis. To varying degrees, state laws allow for the production, distribution, retail sale, and on-site consumption of cannabis in licensed businesses. Accompanying cannabis, some legalization has acknowledged that black and brown communities were disproportionately impacted by the war on drugs. To address this, social equity provisions have been a component of their public policies related to cannabis legalization. These provisions include measures to expunge cannabis-related criminal records and create social equity set-asides for cannabis business licenses. This paper's research question asks if, under its current structure and implementation, recreational cannabis laws achieve social equity goals. The methods for the analysis apply content analysis to public policy documents and quantify recreational cannabis licensing outcomes. These methods were applied to a comparative analysis of social equity provisions in state and local cannabis laws applicable to large U.S. cities (2020 population > 600,000). This analysis focuses on the characteristics of set-asides for social equity licenses to sell recreational cannabis. The analysis examines the scope of social equity policies, their administration and implementation, and the characteristics of licensees. The findings from the analysis are used to identify a model policy framework and generate recommendations to strengthen social equity outcomes in recreational cannabis licensing. [ABSTRACT FROM AUTHOR]

This research utilizes the Long-Term Pavement Performance database, focusing on devel-oping a predictive model for flexible pavement performance in the Southern United States. Analyzing 367 pavement sections, this study investigates crucial factors influencing asphaltic concrete (AC) pavement deterioration, such as structural and material components, air voids, compaction density, temperature at laydown, traffic load, precipitation, and freeze–thaw cycles. The objective of this study is to develop a predictive machine learning model for AC pavement wheel path cracking (WpCrAr) and the age at which cracking initiates (WpCrAr) as performance indicators. This study thoroughly investigated three ensemble machine learning models, including random forest, extremely randomized trees (ETR), and extreme gradient boosting (XGBoost). It was observed that XGBoost, optimized using Bayesian methods, emerged as the most effective among the evaluated models, demonstrating good predictive accuracy, with an R2 of 0.79 for WpCrAr and 0.92 for AgeCrack and mean absolute errors of 1.07 and 0.74, respectively. The most important features influencing crack initiation and progression were identified, including equivalent single axle load (ESAL), pavement age, number of layers, precipitation, and freeze–thaw cycles. This paper also showed the impact of pavement material combinations for base and subgrade layers on the delay of crack initiation. [ABSTRACT FROM AUTHOR]

This paper simulates a hypothetical pan-coronavirus vaccine that confers immediate sterilizing immunity against all SARS-CoV-2 variants. Simulations used a SEIIS (susceptible, exposed, infective, immune, susceptible) spreadsheet model that ran two parallel subpopulations: one that accepted vaccination and another that refused it. The two subpopulations could transmit infections to one another. Using data from the United States (US), the simulated vaccine was tested against limiting factors such as vaccine hesitancy, slow vaccination distribution, and the development of high-transmission variants. The vaccine was often successful at reducing cases, but high-transmission variants and discontinuation of non-pharmaceutical interventions (NPIs) such as masking greatly elevated cases. A puzzling outcome was that if NPIs were discontinued and high-transmission variants became common, the model predicted consistently higher rates of disease than are actually observed in the US in 2024. However, if cumulative exposure to virus antigens increased the duration of immunity or decreased the infectivity of the virus, the model predictions were brought back into a more realistic range. The major finding was that even when a COVID-19 vaccine always produces sterilizing immunity against every SARS-CoV-2 variant, its ability to control the epidemic can be compromised by multiple common conditions. [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]

Background: The Patient Protection and Affordable Care Act (ACA) established the Hospital Quality Initiative in 2010 to enhance patient safety, reduce hospital readmissions, improve quality, and minimize healthcare costs. In response, this study aims to systematically review the literature and conduct a meta-analysis to estimate the average cost of procedure-specific 30-day risk-standardized unplanned readmissions for Acute Myocardial Infarction (AMI), Heart Failure (HF), Pneumonia, Coronary Artery Bypass Graft (CABG), and Total Hip Arthroplasty and/or Total Knee Arthroplasty (THA/TKA). Methods: Eligibility Criteria: This study included English language original research papers from the USA, encompassing various study designs. Exclusion criteria comprise studies lacking empirical evidence on hospital financial performance. Information Sources: A comprehensive search using relevant keywords was conducted across databases from January 1990 to December 2019 (updated in March 2021), covering peer-reviewed articles and gray literature. Risk of Bias: Bias in the included studies was assessed considering study design, adjustment for confounding factors, and potential effect modifiers. Synthesis of Results: The review adhered to PRISMA guidelines. Employing Monte Carlo simulations, a meta-analysis was conducted with 100,000 simulated samples. Results indicated mean 30-day readmission costs: USD 16,037.08 (95% CI, USD 15,196.01–16,870.06) overall, USD 6852.97 (95% CI, USD 6684.44–7021.08) for AMI, USD 9817.42 (95% CI, USD 9575.82–10,060.43) for HF, and USD 21,346.50 (95% CI, USD 20,818.14–21,871.85) for THA/TKA. Discussion: Despite the financial challenges that hospitals face due to the ACA and the Hospital Readmissions Reduction Program, this meta-analysis contributes valuable insights into the consistent cost trends associated with 30-day readmissions. Conclusions: This systematic review and meta-analysis provide comprehensive insights into the financial implications of 30-day readmissions for specific medical conditions, enhancing our understanding of the nexus between healthcare quality and financial performance. [ABSTRACT FROM AUTHOR]

Contact network models are recent alternatives to equation-based models in epidemiology. In this paper, the spread of disease is modeled on contact networks using bond percolation. The weight of the edges in the contact graphs is determined as a function of several variables in which case the weight is the product of the probabilities of independent events involving each of the variables. In the first experiment, the weight of the edges is computed from a single variable involving the number of passengers on flights between two cities within the United States, and in the second experiment, the weight of the edges is computed as a function of several variables using data from 2012 Kenyan household contact networks. In addition, the paper explored the dynamics and adaptive nature of contact networks. The results from the contact network model outperform the equation-based model in estimating the spread of the 1918 Influenza virus. [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]

We aim to provide a comprehensive systematic analysis of scholarly publications in the field of board compensation in financial sectors extending through the years 1987 to 2021. Hence, the most notable themes, theories, and contributions to the literature are identified, and research developments over time are evaluated. With the identification of a final sample of 87 research papers indexed in Scopus, we identify research gaps to provide insight into future research following a systematic method. The results revealed that the United States of America received the broadest research interest, along with cross-country research. While the literature lacked to provide investigations for other countries of the world. Although the effect of compensation on organizational outcomes (performance and grow) is still unclear in the literature, several factors have been introduced as key drivers of the compensation, including the country's level of development, the development of equity markets, the development of banking system, its dependence on foreign capital, collective rights empowering labor, the strength of a country's welfare institutions, employment market forces, and social order and authority relations. On a theoretical level, agency theory has been most popular in the literature, along with providing multiple theoretical frameworks with agency theory as a slack resources theory, managerial talent theory, and managerial power theory. This is the first research to our knowledge that used a systematic review (SR) of literature to give a complete and comprehensive evaluation of the literature on board compensation in the financial sector. The current study documents the flow of literature on the board's compensation in the financial sectors over 24 years and establishes future research opportunities. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive forest mapping system using a customized drone payload equipped with Light Detection and Ranging (LiDAR), cameras, a Global Navigation Satellite System (GNSS), and Inertial Measurement Unit (IMU) sensors. The goal is to develop an efficient solution for collecting accurate forest data in dynamic environments and to highlight potential wildfire regions of interest to support precise forest management and conservation on the ground. Our paper provides a detailed description of the hardware and software components of the system, covering sensor synchronization, data acquisition, and processing. The overall system implements simultaneous localization and mapping (SLAM) techniques, particularly Fast LiDAR Inertial Odometry with Scan Context (FASTLIO-SC), and LiDAR Inertial Odometry Smoothing and Mapping (LIOSAM), for accurate odometry estimation and map generation. We also integrate a fuel mapping representation based on one of the models, used by the United States Secretary of Agriculture (USDA) to classify fire behavior, into the system using semantic segmentation, LiDAR camera registration, and odometry as inputs. Real-time representation of fuel properties is achieved through a lightweight map data structure at 4 Hz. The research results demonstrate the effectiveness and reliability of the proposed system and show that it can provide accurate forest data collection, accurate pose estimation, and comprehensive fuel mapping with precision values for the main segmented classes above 85%. Qualitative evaluations suggest the system's capabilities and highlight its potential to improve forest management and conservation efforts. In summary, this study presents a versatile forest mapping system that provides accurate forest data for effective management. [ABSTRACT FROM AUTHOR]

Among the tools useful for the management of neuropathic pain, ultrasound presents several advantages, shown by the literature. We assessed the scientific production about neuropathic pain and ultrasound from different points of view: general topics, journal categories, geographical origin and lexical analysis. We searched papers on PubMed using the Medical Subject Headings "neuropathic pain" AND "ultrasound". We collected data about the journals where the papers were published, the country of the affiliation of the first author. For the lexical analysis, we evaluated the presence of selected words in the papers, and we built a graph representing the connections among words and papers. The papers were focused on the use of ultrasound as a diagnostic tool and guide for the therapy, assessing its application in different diseases such as Morton's neuroma and piriformis syndrome. The most represented journal category was anesthesia while the most common country the United States of America. The lexical analysis confirmed the importance of ultrasound for diagnosis of specific disease and treatment of pain. The described approaches provide a multiperspective evaluation of the literature and may support the interpretation of the information contained by the papers. [ABSTRACT FROM AUTHOR]

Various institutions such as universities and corporations strive to commercialize technologies produced through R&D investment. The ideal way to commercialize technology is to transfer it, recognizing the value of the developed technology. Technology transfer is the transfer of technology from R&D entities, such as universities, research institutes, and companies, to others, with the advantage of spreading research results and maximizing cost efficiency. In other words, if enough technology is transferred, it can be commercialized. Although many institutions have various support measures to assist in transferring technology, there is no substitution for quantitative, objective methods. To solve this problem, this paper proposes a technology transfer prediction model based on the information found in patents. However, it is not realistic to include the information from all patents in the quantitative, objective method, so patterns related to technology transfer must be identified to select the appropriate patents that can be used in the predictive model. In addition, a method is needed to address the insufficient training data for the model. Training data are limited because some technology transfer information is not disclosed, and there is little technology transferred in new technology fields. The technology transfer prediction model proposed in this paper searches for hidden patterns related to technology transfer by imaging the patent information, which can also be applied to image analysis models. Furthermore, augmenting the data can solve the problem of the lack of learning data for technology transfer. To examine whether the proposed model can be used in real industries, we collected patents related to artificial intelligence technology registered in the United States and conducted experiments. The experimental results show that the models trained by imaging patent information performed excellently. Moreover, it was shown that the data augmentation technique can be used when there are insufficient data for technology transfer. [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]

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 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]

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]

Background: Type 1 diabetes (T1D) is a devastating autoimmune disease, and its rising prevalence in the United States and around the world presents a critical problem in public health. While some treatment options exist for patients already diagnosed, individuals considered at risk for developing T1D and who are still in the early stages of their disease pathogenesis without symptoms have no options for any preventive intervention. This is because of the uncertainty in determining their risk level and in predicting with high confidence who will progress, or not, to clinical diagnosis. Biomarkers that assess one's risk with high certainty could address this problem and will inform decisions on early intervention, especially in children where the burden of justifying treatment is high. Single omics approaches (e.g., genomics, proteomics, metabolomics, etc.) have been applied to identify T1D biomarkers based on specific disturbances in association with the disease. However, reliable early biomarkers of T1D have remained elusive to date. To overcome this, we previously showed that parallel multi-omics provides a more comprehensive picture of the disease-associated disturbances and facilitates the identification of candidate T1D biomarkers. Methods: This paper evaluated the use of machine learning (ML) using data augmentation and supervised ML methods for the purpose of improving the identification of salient patterns in the data and the ultimate extraction of novel biomarker candidates in integrated parallel multi-omics datasets from a limited number of samples. We also examined different stages of data integration (early, intermediate, and late) to assess at which stage supervised parametric models can learn under conditions of high dimensionality and variation in feature counts across different omics. In the late integration scheme, we employed a multi-view ensemble comprising individual parametric models trained over single omics to address the computational challenges posed by the high dimensionality and variation in feature counts across the different yet integrated multi-omics datasets. Results: the multi-view ensemble improves the prediction of case vs. control and finds the most success in flagging a larger consistent set of associated features when compared with chance models, which may eventually be used downstream in identifying a novel composite biomarker signature of T1D risk. Conclusions: the current work demonstrates the utility of supervised ML in exploring integrated parallel multi-omics data in the ongoing quest for early T1D biomarkers, reinforcing the hope for identifying novel composite biomarker signatures of T1D risk via ML and ultimately informing early treatment decisions in the face of the escalating global incidence of this debilitating disease. [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 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]