The cultural psychology of obesity: diffusion of pathological norms from Western to East Asian societies

We examine the accelerating worldwide obesity epidemic using a mathematical model relating a cognitive hypothalamic-pituitary-adrenal axis tuned by embedding cultural context to a signal of chronic, structured, psychosocial threat. The obesity epidemic emerges as a distorted physiological image of ratcheting social pathology involving massive, policy-driven, economic and social 'structural adjustment' causing increasing individual, family, and community insecurity. The resulting, broadly developmental, disorder, while stratified by expected divisions of class, ethnicity, and culture, is nonetheless relentlessly engulfing even affluent majority populations across the globe. The progression of analogous epidemics in affluent Western and East Asian socieities is particularly noteworthy since these enjoy markedly different cultural structures known to influence even such fundamental psychophysical phenomena as change blindness. Indeed, until recently population patterns of obesity were quite different for these cultures. We attribute the entrainment of East Asian societies into the obesity epidemic to the diffusion of Western socioeconomic practices whose imposed resource uncertainties and exacerbation of social and economic divisions constitute powerful threat signals. We find that individual-oriented 'therapeutic' interventions will be largely ineffective since the therapeutic process itself (e.g. relinace on drug treatments) embodies the very threats causing the epidemic

Machine Learning Applications for Dynamic Security Assessment in presence of Renewable Generation and Load Induced Variability

abstract: Large-scale blackouts that have occurred across North America in the past few decades have paved the path for substantial amount of research in the field of security assessment of the grid. With the aid of advanced technology such as phasor measurement units (PMUs), considerable work has been done involving voltage stability analysis and power system dynamic behavior analysis to ensure security and reliability of the grid. Online dynamic security assessment (DSA) analysis has been developed and applied in several power system control centers. Existing applications of DSA are limited by the assumption of simplistic load profiles, which often considers a normative day to represent an entire year. To overcome these aforementioned challenges, this research developed a novel DSA scheme to provide security prediction in real-time for load profiles corresponding to different seasons. The major contributions of this research are to (1) develop a DSA scheme incorporated with PMU data, (2) consider a comprehensive seasonal load profile, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA. The ML algorithms that will be the focus of this study include decision trees (DTs), support vector machines (SVMs), random forests (RFs), and multilayer neural networks (MLNNs). This thesis describes the development of a novel DSA scheme using synchrophasor measurements that accounts for the load variability occurring across different seasons in a year. Different amounts of solar generation have also been incorporated in this study to account for increasing percentage of renewables in the modern grid. To account for the security of the operating conditions different ML algorithms have been trained and tested. A database of cases for different operating conditions has been developed offline that contains secure as well as insecure cases, and the ML models have been trained to classify the security or insecurity of a particular operating condition in real-time. Multiple scenarios are generated every 15 minutes for different seasons and stored in the database. The performance of this approach is tested on the IEEE-118 bus system.Dissertation/ThesisMasters Thesis Electrical Engineering 201

Exploring the trend of New Zealand housing prices to support sustainable development

The New Zealand housing sector is experiencing rapid growth that has a significant impact on society, the economy, and the environment. In line with the growth, the housing market for both residential and business purposes has been booming, as have house prices. To sustain the housing development, it is critical to accurately monitor and predict housing prices so as to support the decision-making process in the housing sector. This study is devoted to applying a mathematical method to predict housing prices. The forecasting performance of two types of models: autoregressive integrated moving average (ARIMA) and multiple linear regression (MLR) analysis are compared. The ARIMA and regression models are developed based on a training-validation sample method. The results show that the ARIMA model generally performs better than the regression model. However, the regression model explores, to some extent, the significant correlations between house prices in New Zealand and the macro-economic conditions

Hybrid integration of multilayer perceptrons and parametric models for reliability forecasting in the smart grid

The reliable power system operation is a major goal for electric utilities, which requires the accurate reliability forecasting to minimize the duration of power interruptions. Since weather conditions are usually the leading causes for power interruptions in the smart grid, especially for its distribution networks, this paper comprehensively investigates the combined effect of various weather parameters on the reliability performance of distribution networks. Specially, a multilayer perceptron (MLP) based framework is proposed to forecast the daily numbers of sustained and momentary power interruptions in one distribution management area using time series of common weather data. First, the parametric regression models are implemented to analyze the relationship between the daily numbers of power interruptions and various common weather parameters, such as temperature, precipitation, air pressure, wind speed, and lightning. The selected weather parameters and corresponding parametric models are then integrated as inputs to formulate a MLP neural network model to predict the daily numbers of power interruptions. A modified extreme learning machine (ELM) based hierarchical learning algorithm is introduced for training the formulated model using realtime reliability data from an electric utility in Florida and common weather data from National Climatic Data Center (NCDC). In addition, the sensitivity analysis is implemented to determine the various impacts of different weather parameters on the daily numbers of power interruptions

Networks of idealization, stalking and planning of the violence reflected in university students of the center of Mexico

El artículo parte de la premisa de que una red neuronal supone un aprendizaje que el presente trabajo se propuso develar como asimetrías entre narrativas de violencia sexista. Se realizó un estudio retrospectivo, descriptivo e interpretativo con una selección intencional de estudiantes en una Institución de Educación Superior (IES) pública del centro de México. Se develó una estructura contemplativa, considerando los pesos sinápticos que explicaron la relación entre la entrada, procesamiento y salida de unidades de idealización, contemplación y planificación del conflicto. Se sugieren líneas de investigación relativas a la estructura de relaciones entre las categorías.A neural network supposes a learning that the present work set out to reveal as asymmetries between narratives of sexist violence. A retrospective, descriptive and interpretive study was carried out with an intentional selection of students in a public Institution of Higher Education (IES) in central Mexico. A contemplative structure was revealed, considering the synaptic weights that explained the relationship between the entry, processing and exit of units of idealization, contemplation and conflict planning. Research lines related to the structure of relationships between the categories are suggested

Risk-based security assessment for operating electric power systems

The power system is a widespread and complex network whose complete behavior, at present, still remains partially characterized. Power systems have operated in most cases reliably, but conservatively with the help of many deterministic techniques that rely heavily on the modeling of system components and the associated dynamics. Now, with increasing competition and growing demand, the power system, however, has been shifting from a deterministically regulated system to a competitive and uncertain market environment. Power utilities are required to have a comprehensive knowledge of the risks as well as benefits in their transmission operations. Our interest is motivated by this need of the industry to provide a method to quantify the risk of operating a power system with consideration to the probabilistic nature of system behaviors. The objective of this dissertation is to develop a foundation of risk-based bulk power system security assessment that leads to the definition, calculation, and application of the risk in operating electric power systems. The work includes three parts of risk assessments: transmission line thermal overload, voltage insecurity, and composite risk assessments. Both the probability of insecurity problems and their cost consequences are measured such that an expected monetary impact is given as the measurement of risk. This quantitative measurement of thermal, voltage, and composite risk is helpful for the operator to trade off the benefits and costs in the competitive utility environment. For making this economic tradeoff, several decision criteria, including both deterministic and probabilistic strategies, from conservative to greedy preference, are introduced to aid the operator to make operating decisions. This research establishes a bridge between power system security and economics by the index of risk that is compatible with the economic results of market-based electricity trading. Both the method to quantify the risk and the ways to apply it in decision-making make contributions to the power industry

New Classifier Design for Static Security Evaluation Using Artificial In-telligence Techniques

This paper proposes evaluation and classification classifier for static security evaluation (SSE) and classifica-tion. Data are generated on (30, 57, 118 and 300) bus IEEE test systems used to design the classifiers. The implementation decision tree methods on several IEEE test systems involved appropriateness SSE and classi-fication by using four algorithms of DT’s. Empirically, with the present of FSA, the implementation results indicate that these classifiers have the capability for system security evaluation and classification. Lastly, FSA is efficient and effective approach for real-time evaluation and classification classifier design

Forecasting Trends in Food Security: a Reservoir Computing Approach

Early warning systems are an essential tool for effective humanitarian action. Advance warnings on impending disasters facilitate timely and targeted response which help save lives, livelihoods, and scarce financial resources. In this work we present a new quantitative methodology to forecast levels of food consumption for 60 consecutive days, at the sub-national level, in four countries: Mali, Nigeria, Syria, and Yemen. The methodology is built on publicly available data from the World Food Programme's integrated global hunger monitoring system which collects, processes, and displays daily updates on key food security metrics, conflict, weather events, and other drivers of food insecurity across 90 countries (https://hungermap.wfp.org/). In this study, we assessed the performance of various models including ARIMA, XGBoost, LSTMs, CNNs, and Reservoir Computing (RC), by comparing their Root Mean Squared Error (RMSE) metrics. This comprehensive analysis spanned classical statistical, machine learning, and deep learning approaches. Our findings highlight Reservoir Computing as a particularly well-suited model in the field of food security given both its notable resistance to over-fitting on limited data samples and its efficient training capabilities. The methodology we introduce establishes the groundwork for a global, data-driven early warning system designed to anticipate and detect food insecurity.Comment: 22 pages, 11 figures, typo in acknowledgements correcte

Detecting Political Framing Shifts and the Adversarial Phrases within\\ Rival Factions and Ranking Temporal Snapshot Contents in Social Media

abstract: Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model is developed to predict the emergence of viral hashtag breakouts, using network features. Next, another model is developed to detect and compare individual and organizational accounts, by using a set of domain and language-independent features. The third model exposes contentious issues, driving reactionary dynamics between opposing camps. The fourth model develops community detection and visualization methods to reveal underlying dynamics and key messages that drive dynamics. The final model presents a use case methodology for detecting and monitoring foreign influence, wherein a state actor and news media under its control attempt to shift public opinion by framing information to support multiple adversarial narratives that facilitate their goals. In each case, a discussion of novel aspects and contributions of the models is presented, as well as quantitative and qualitative evaluations. An analysis of multiple conflict situations will be conducted, covering areas in the UK, Bangladesh, Libya and the Ukraine where adversarial framing lead to polarization, declines in social cohesion, social unrest, and even civil wars (e.g., Libya and the Ukraine).Dissertation/ThesisDoctoral Dissertation Computer Science 201