Forecast of CO2 Emissions From the U.S. Transportation Sector: Estimation From a Double Exponential Smoothing Model

This study examines whether the decreasing trend in U.S. CO2 emissions from the transportation sector since the end of the 2000s will be shown across all states in the nation for 2012‒2021. A double exponential smoothing model is used to forecast CO2 emissions for the transportation sector in the 50 states and the U.S., and its findings are supported by the validity test of pseudo out-of-sample forecasts. We conclude that the decreasing trend in transportation CO2 emissions in the U.S. will continue in most states in the future

Modeling Investments in County and Local Roads to Support Agricultural Logistics

Investments in local roads in North Dakota to support agricultural logistics are estimated with a detailed model that predicts flows from 1,406 crop-producing zones to 317 elevators and plants, and forecasts improvements and maintenance costs for paved and unpaved roads. The study finds that (1) the average farm-to-market trip distance has increased from 12 miles in 1980 to 26 miles in 2009, (2) the estimated resurfacing cost per mile for agricultural distribution routes is 40% greater than for non-agricultural routes, and (3) the estimated cost to maintain acceptable service levels on county and local roads is roughly double historical funding levels

The Sasang Constitution as an Independent Risk Factor for Metabolic Syndrome: Propensity Matching Analysis

The Sasang Constitutional Medicine is a traditional Korean customized medicine that classifies people into four types: Tae-eumin (TE), Soyangin (SY), Soeumin (SE), and Taeyangin. The aim of this study was to show whether the Sasang Constitution (SC) could be an independent risk factor for the metabolic syndrome (MS). Totally, 3,334 subjects from 24 Korean medicine clinics participated in this study. A one-way ANOVA for the continuous variables and a chi-square test for the prevalence of MS were conducted. A logistic regression was conducted to calculate the propensity score and the odds ratios (ORs). The prevalence for MS in TE, SY, and SE was 50.6%, 30.9%, and 17.7% (P<0.001) before matching, and 36.7%, 28.6% and 28.2% (P=0.042) after matching, respectively. The TE was associated with an increased OR for MS compared with the SE and SY in both crude (OR 4.773, 95% CI 3.889–5.859, and OR 2.292, 95% CI 1.942–2.704, resp.) and matched groups (OR 1.476, 95% CI 1.043–2.089, and OR 1.452, 95% CI 1.026–2.053, resp.). This study reveals that the SC, especially the TE type, could be considered as a risk element for MS even in people with otherwise similar physical characteristics

Blockchain-Enabled Federated Learning: A Reference Architecture Design, Implementation, and Verification

This paper presents an innovative reference architecture for blockchain-enabled federated learning (BCFL), a state-of-the-art approach that amalgamates the strengths of federated learning and blockchain technology. This results in a decentralized, collaborative machine learning system that respects data privacy and user-controlled identity. Our architecture strategically employs a decentralized identifier (DID)-based authentication system, allowing participants to authenticate and then gain access to the federated learning platform securely using their self-sovereign DIDs, which are recorded on the blockchain. Ensuring robust security and efficient decentralization through the execution of smart contracts is a key aspect of our approach. Moreover, our BCFL reference architecture provides significant extensibility, accommodating the integration of various additional elements, as per specific requirements and use cases, thereby rendering it an adaptable solution for a wide range of BCFL applications. Participants can authenticate and then gain access to the federated learning platform securely using their self-sovereign DIDs, which are securely recorded on the blockchain. The pivotal contribution of this study is the successful implementation and validation of a realistic BCFL reference architecture, marking a significant milestone in the field. We intend to make the source code publicly accessible shortly, fostering further advancements and adaptations within the community. This research not only bridges a crucial gap in the current literature but also lays a solid foundation for future explorations in the realm of BCFL.Comment: 14 pages, 15 figures, 3 table

Development and Validation of a Personality Assessment Instrument for Traditional Korean Medicine: Sasang Personality Questionnaire

Objective. Sasang typology is a traditional Korean medicine based on the biopsychosocial perspectives of Neo-Confucianism and utilizes medical herbs and acupuncture for type-specific treatment. This study was designed to develop and validate the Sasang Personality Questionnaire (SPQ) for future use in the assessment of personality based on Sasang typology. Design and Methods. We selected questionnaire items using internal consistency analysis and examined construct validity with explorative factor analysis using 245 healthy participants. Test-retest reliability as well as convergent validity were examined. Results. The 14-item SPQ showed acceptable internal consistency (Cronbach's alpha = .817) and test-retest reliability (r = .837). Three extracted subscales, SPQ-behavior, SPQ-emotionality, and SPQ-cognition, were found, explaining 55.77% of the total variance. The SPQ significantly correlated with Temperament and Character Inventory novelty seeking (r = .462), harm avoidance (r = −.390), and NEO Personality Inventory extraversion (r = .629). The SPQ score of the So-Eum (24.43 ± 4.93), Tae-Eum (27.33 ± 5.88), and So-Yang (30.90 ± 5.23) types were significantly different from each other (P < .01). Conclusion. Current results demonstrated the reliability and validity of the SPQ and its subscales that can be utilized as an objective instrument for conducting personalized medicine research incorporating the biopsychosocial perspective

Interactive Spatiotemporal Analysis of Oil Spills Using Comap in North Dakota

The aim of the study is to analyze the oil spill pattern from various types of incidents and contaminants to determine the extent that incident data can be used as a baseline to prevent hazardous material releases and improve response activities at a state level. This study addresses the importance of collecting and sharing oil spill incidents as well as analytics using the data. Temporal, spatial and spatiotemporal analysis techniques are employed for the oil-spill related environmental incidents observed in the state of North Dakota, United States of America, from 2000 to 2014, as a result of the oil boom. Specifically, spatiotemporal methods are used to examine how the patterns of environmental incidents in North Dakota, which vary with the time of day, the day, the month, and the season. Results indicate that there were critical spatial and time variations in the distribution of environmental incidents. Application of spatiotemporal interaction visualization techniques, called comap has the potential to help planners and decision makers formulate policy to mitigate the risks associated with environmental incidents, improve safety, and allocate resources

Strategic Global Logistics Management for Sourcing Road Oil in the U.S.

The demand for asphalt and road oil heavily leverages local supply because the product is a hot binder of aggregates that form the final mix needed to pave roads. This paper discusses the supply chain characteristics of crude oil feedstock by considering the overall logistics of sourcing heavy crude oil domestically, or importing it from international trading partners. Heavy crude oil is a source of asphalt and road oil production. The study examines critical global and domestic logistics factors such as customs, regulations, security, environmental compliance, and natural events that will affect costs, schedules, and risks. The study provides a framework for decision-making in sourcing the feedstock. The study helps global logisticians and transportation managers improve strategic design and planning towards efficient sourcing

Understanding CO_2 capture mechanisms in aqueous hydrazine via combined NMR and first-principles studies

Aqueous amines are currently the most promising solution for large-scale CO_2 capture from industrial sources. However, molecular design and optimization of amine-based solvents have proceeded slowly due to a lack of understanding of the underlying reaction mechanisms. Unique and unexpected reaction mechanisms involved in CO_2 absorption into aqueous hydrazine are identified using ^1H, ^(13)C, and ^(15)N NMR spectroscopy combined with first-principles quantum-mechanical simulations. We find production of both hydrazine mono-carbamate (NH_2-NH-COO^−) and hydrazine di-carbamate (^−OOC-NH-NH-COO^−), with the latter becoming more populated with increasing CO_2 loading. Exchange NMR spectroscopy also demonstrates that the reaction products are in dynamic equilibrium under ambient conditions due to CO_2 exchange between mono-carbamate and di-carbamate as well as fast proton transfer between un-protonated free hydrazine and mono-carbamate. The exchange rate rises steeply at high CO_2 loadings, enhancing CO_2 release, which appears to be a unique property of hydrazine in aqueous solution. The underlying mechanisms of these processes are further evaluated using quantum mechanical calculations. We also analyze and discuss reversible precipitation of carbamate and conversion of bicarbonate to carbamates. The comprehensive mechanistic study provides useful guidance for optimal design of amine-based solvents and processes to reduce the cost of carbon capture. Moreover, this work demonstrates the value of a combined experimental and computational approach for exploring the complex reaction dynamics of CO_2 in aqueous amines