Behavioral Economic Assessment of Alcohol and Cigarette Demand in Smokers With Alcohol Use Disorder

Background and Objectives: Behavioral economic purchase tasks are widely used to assess drug demand in substance use disorder research. Comorbid alcohol use is common among cigarette smokers and associated with greater difficulty in quitting smoking. However, demand for alcohol and cigarettes in this population has not been fully characterized. The present study addressed this gap by examining alcohol and cigarette demand among treatment-seeking smokers with alcohol use disorder (AUD).Methods: Alcohol and cigarette demand was assessed among 99 smokers with AUD. We conducted Principal Component Analysis (PCA) and correlational analyses on the demand indices.Results: Participants showed higher demand for alcohol than for cigarettes, as evidenced lower elasticity (resistance to increasing price) and higher Omax (maximum response output for drug). PCA revealed a two-factor structure (Persistence and Amplitude) for both alcohol and cigarette demand indices. Cigarette-related demand indices were positively correlated with nicotine dependence, but alcohol-related demand indices were not associated with alcohol dependence, suggesting dissociation between alcohol demand and use behaviors.Discussion and Conclusions: Our results suggest that smokers with AUD were more resistant to price elevations in relation to reducing alcohol consumption as compared to cigarette consumption, suggesting preferential demand for alcohol over cigarettes. However, it is unclear how acute substance exposure/withdrawal impacts the demand indices.Scientific Significance: Potentially differential alcohol and cigarette demands among smokers with AUD should be considered in the concurrent treatment of smoking and alcohol.

Effect of green sand mixture with dextrin additives on mechanical properties of aluminum 6351

Quality of cast produced from green sand mold is been influenced by mold properties which includes green compression strength, permeability, etc. In this work the green sand used for casting of aluminum 6351 alloy specimens were made by mixing in varied percentage proportions; bentonite clay, dextrin additive and moisture content with local silica sand considering the need for most effective proportions of these mixtures to enhance green sand production of aluminum 6351 alloy products. A 3 factor, 3 level (33) design of experiment (DOE) was made for this research work using Optimal (custom) design of Design-Expert 10 software which gave 20 runs. Cylindrical specimens for green sand test were prepared according to standard per run. This was in order to study effects of bentonite clay, dextrin additive and moisture content of the green molding sand used for casting per mold this aluminum 6351 alloy. Prepared sand specimens were individually subjected to basic sand test like green sand strength and permeability test and also cast specimens per mold achieved were subjected to mechanical property test to achieve results which become the Response output of the study. These experimental results were optimized for the purpose of achieving most effective proportions of the mixtures to give effective results and from the optimal validation values, 5% water content, 12% bentonite and 8.85182% dextrin organic additive was found to be the optimized solution that gave the most effective hardness at (40.4GSS and 112PN) while 3% water, 12% bentonite clay and 9% dextrin additive gave most effective toughness at (41.9GSS and 96.10PN).

Rwanda

The COVID-19 pandemic is having an adverse impact on Rwanda’s economy, despite a sizeable policy response. Output in 2020 is projected to contract by 0.2 percent, compared to an 8 percent increase expected pre-pandemic. The government’s early actions helped contain the spread of the virus and mitigate its economic impact, supported by financing from Rwanda’s development partners, including from the IMF under the RCF. With the number of infections contained, the authorities are gradually easing up containment measures.

Modeling and Optimization of Solar Flat Plate Collector

Solar flat plate collector (SFPC) is a heat exchanger that transforms radiant solar energy into thermal energy in the form of heated fluid. The performance of SFPC is very much dependent on operating/input and response/output parameter which mainly affects the efficiency of SFPC. This chapter presented the modeling and optimization of SFPC system parameters (solar radiation [I], wind velocity [V], ambient temperature [Ta], and Inlet Temperature [Ti]) for SFPC. Modified-fuzzy set theory with MOOSRA (M-FST-MOOSRA) was employed to optimize the SFPC system. Based on results, trail no. 14 (i.e., I = 825 W/m2, V = 1.4 m/s, Ta = 28.8oC, and Ti = 66.4oC) gave highest RPI among the other trail nos. and shows the optimal setting which results in higher efficiency and better performance for the SFPC. Further, parametric analysis is also done to determine the most important parameter followed by analysis of variance (ANOVA) analysis. Last, confirmatory test are conducted to verify and validate the proposed method with the experimental results.

Distinct Chemotaxis Protein Paralogs Assemble into Chemoreceptor Signaling Arrays To Coordinate Signaling Output

ABSTRACT Most chemotactic motile bacteria possess multiple chemotaxis signaling systems, the functions of which are not well characterized. Chemotaxis signaling is initiated by chemoreceptors that assemble as large arrays, together with chemotaxis coupling proteins (CheW) and histidine kinase proteins (CheA), which form a baseplate with the cytoplasmic tips of receptors. These cell pole-localized arrays mediate sensing, signaling, and signal amplification during chemotaxis responses. Membrane-bound chemoreceptors with different cytoplasmic domain lengths segregate into distinct arrays. Here, we show that a bacterium, Azospirillum brasilense, which utilizes two chemotaxis signaling systems controlling distinct motility parameters, coordinates its chemotactic responses through the production of two separate membrane-bound chemoreceptor arrays by mixing paralogs within chemotaxis baseplates. The polar localization of chemoreceptors of different length classes is maintained in strains that had baseplate signaling proteins from either chemotaxis system but was lost when both systems were deleted. Chemotaxis proteins (CheA and CheW) from each of the chemotaxis signaling systems (Che1 and Che4) could physically interact with one another, and chemoreceptors from both classes present in A. brasilense could interact with Che1 and Che4 proteins. The assembly of paralogs from distinct chemotaxis pathways into baseplates provides a straightforward mechanism for coordinating signaling from distinct pathways, which we predict is not unique to this system given the propensity of chemotaxis systems for horizontal gene transfer. IMPORTANCE The assembly of chemotaxis receptors and signaling proteins into polar arrays is universal in motile chemotactic bacteria. Comparative genome analyses indicate that most motile bacteria possess multiple chemotaxis signaling systems, and experimental evidence suggests that signaling from distinct chemotaxis systems is integrated. Here, we identify one such mechanism. We show that paralogs from two chemotaxis systems assemble together into chemoreceptor arrays, forming baseplates comprised of proteins from both chemotaxis systems. These mixed arrays provide a straightforward mechanism for signal integration and coordinated response output from distinct chemotaxis systems. Given that most chemotactic bacteria encode multiple chemotaxis systems and the propensity for these systems to be laterally transferred, this mechanism may be common to ensure chemotaxis signal integration occurs.

Hyperspectral Anomaly Detection via Dictionary Construction-Based Low-Rank Representation and Adaptive Weighting

Anomaly detection (AD), which aims to distinguish targets with significant spectral differences from the background, has become an important topic in hyperspectral imagery (HSI) processing. In this paper, a novel anomaly detection algorithm via dictionary construction-based low-rank representation (LRR) and adaptive weighting is proposed. This algorithm has three main advantages. First, based on the consistency with AD problem, the LRR is employed to mine the lowest-rank representation of hyperspectral data by imposing a low-rank constraint on the representation coefficients. Sparse component contains most of the anomaly information and can be used for anomaly detection. Second, to better separate the sparse anomalies from the background component, a background dictionary construction strategy based on the usage frequency of the dictionary atoms for HSI reconstruction is proposed. The constructed dictionary excludes possible anomalies and contains all background categories, thus spanning a more reasonable background space. Finally, to further enhance the response difference between the background pixels and anomalies, the response output obtained by LRR is multiplied by an adaptive weighting matrix. Therefore, the anomaly pixels are more easily distinguished from the background. Experiments on synthetic and real-world hyperspectral datasets demonstrate the superiority of our proposed method over other AD detectors.

Role of Metaheuristic Optimization in Portfolio Management for the Banking Sector

In this chapter, the importance of optimization technique, more specifically metaheuristic optimization in banking portfolio management, is reviewed. Present work deals with interactive bank marketing campaign of a specific Portugal bank, taken from UCI dataset archive. This dataset consists of 45,211 samples with 17 features including one response/output variable. The classification work is carried out with all data using decision tree (DT), support vector machine (SVM), and k-nearest neighbour (k-NN), without any feature optimization. Metaheuristic genetic algorithm (GA) is used as a feature optimizer to find only 5 features out of the 16 features. Finally, the classification work with the optimized feature shows relatively good accuracy in comparison to classification with all feature set. This result shows that with a smaller number of optimized features better classification can be achieved with less computational overhead.

Neurocircuitry of Affective, Cognitive, and Regulatory Systems

This chapter provides a review and synthesis of the neurocircuitry involved in affect and cognition and their interactions as it relates to regulatory functions. Cognition and emotion are considered together taking a more integrated, functional perspective. The chapter first gives an overview regarding structure and function of key brain regions, that is, prefrontal and cingulate regions, insula, and subcortical regions, as well as other temporal-parietal-occipital regions. Following this overview, the chapter proceeds with summarizing key neuroscientific findings as organized by cognitive processes and their relevance for emotion. The choice of processes reflects the key stages involved in responding to a stimulus, from the time of sensory input to behavioral response/output, namely perception, learning and memory central executive functions, cognitive appraisal, and reappraisal. The overall aim of the chapter is to provide a better understanding of cognitive-emotional interactions at the neurocircuit level.