An Overview of the Connection between Hoarding Disorder and Substance Use Disorder

This essay explores the connections between Hoarding Disorder and Substance Use Disorder,two distinct mental health conditions with clear diagnostic criteria in the DSM-5.Hoarding Disorder,marked by excessive possession accumulation and difficulties in letting go,affects 2-6%of the global population,leading to severe consequences.Potential causes include genetic predisposition,neural issues,and traumatic experience.Substance Use Disorder is influenced by more societal factors.Both disorders share a comorbidity in Attention-Deficit/Hyperactivity Disorder(ADHD).Network analysis shows a strong direct relationship between Hoarding Disorder and ADHD,while social influences increase ADHD prevalence in Substance Use Disorder individuals.Moreover,both disorders benefit from Cognitive Behavioral Therapy(CBT).Exposure and Ritual Prevention effectively address hoarding symptoms,while CBT for Substance Use Disorder focuses on the interplay between thoughts,emotions,and actions.Despite being classified as a mental illness in DSM-5,Hoarding Disorder receives less attention than Substance Use Disorder.This essay aims to raise awareness of Hoarding Disorder by highlighting its commonalities with Substance Use Disorder in terms of causes,comorbidities,and treatment approaches.

A preliminary study on the theory and method of comprehensiveregionalization of cryospheric services

As one of the five most important spheres affecting climate systems globally, the cryosphere not only exerts crucial impacts on natural environment, but also plays a key role in guaranteeing the goods and services provision of the oasis in the cold and arid regions. However, there is a lack of synthesized knowledge about the contributions and profitability on socio-economic aspects of the cryospheric services (CSs). To cope with this key and urgent sustainable development issues, this study constructs a comprehensive regionalization research system that integrates the natural and socio-economic aspects for cryospheric characteristics with emphasis on interdisciplinary approaches. Guided by the supply- demand equilibrium model and the service maximization model, the research system consists of establishing a classification system for CSs, identifying the spatial distribution pattern of CSs, and constructing the comprehensive regionalization of CSs, which finally divides the research area into relatively independent, complete, and organically linked CS units of multiple spatial levels. By setting up a bridge between the CSs with socio-economic needs, it has profound and practical significance and implications for implementing the sustainable utilization strategies and macroeconomic policymaking for global environmental protection.

Ground Ozone Level Prediction Using Machine Learning

Because of the increasing attention on environmental issues, especially air pollution, predicting whether a day is polluted or not is necessary to people’s health. In order to solve this problem, this research is classifying ground ozone level based on big data and machine learning models, where polluted ozone day has class 1 and non-ozone day has class 0. The dataset used in this research was derived from the UCI Website, containing various environmental factors in Houston, Galveston and Brazoria area that could possibly affect the occurrence of ozone pollution [1]. This dataset is first filled up for further process, next standardized to ensure every feature has the same weight, and then split into training set and testing set. After this, five different machine learning models are used in the prediction of ground ozone level and their final accuracy scores are compared. In conclusion, among Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine (SVM), the last one has the highest test score of 0.949. This research utilizes relatively simple methods of forecasting and calculates the first accuracy scores in predicting ground ozone level;it can thus be a reference for environmentalists. Moreover, the direct comparison among five different models provides machine learning field an insight to determine the most accurate model. In the future, Neural Network can also be utilized to predict air pollution, and its test scores can be compared with the previous five methods to conclude the accuracy of Neuron Network.

The Game Theoretical Approach for Multi-phase Complex Systems in Chemical Engineering

This paper explores the application of noncooperative game theory together with the concept of Nash equilibrium to the investigation of some basic problems on multi-scale structure, especially the meso-scale structure in the multi-phase complex systems in chemical engineering. The basis of this work is the energy-minimization-multi-scale(EMMS) model proposed by Li and Kwauk(1994) and Li, et al.(2013) which identifies the multi-scale structure as a result of ‘compromise-in-competition between dominant mechanisms' and tries to solve a multi-objective optimization problem. However,the existing methods often integrate it into a problem of single objective optimization, which does not clearly reflect the ‘compromise-in-competition' mechanism and causes heavy computation burden as well as uncertainty in choosing suitable weighting factors. This paper will formulate the compromise in competition mechanism in EMMS model as a noncooperative game with constraints, and will describe the desired stable system state as a generalized Nash equilibrium. Then the authors will investigate the game theoretical approach for two typical systems in chemical engineering, the gas-solid fluidization(GSF) system and turbulent flow in pipe. Two different cases for generalized Nash equilibrium in such systems will be well defined and distinguished. The generalize Nash equilibrium will be solved accurately for the GSF system and a feasible method will be given for turbulent flow in pipe. These results coincide with the existing computational results and show the feasibility of this approach, which overcomes the disadvantages of the existing methods and provides deep insight into the mechanisms of multi-scale structure in the multi-phase complex systems in chemical engineering.