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"Cognitive Models for Sustainable Environment reviews the fundamental concepts of gathering, processing and analyzing data from batch processes, along with a review of intelligent and cognitive tools that can be used. The book is centered on evolving novel intelligent/cognitive models and algorithms to develop sustainable solutions for the mitigation of environmental pollution. It unveils intelligent and cognitive models to address issues related to the effective monitoring of environmental pollution and sustainable environmental design. As such, the book focuses on the overall well-being of the global environment for better sustenance and livelihood. The book covers novel cognitive models for effective environmental pollution data management at par with the standards laid down by the World Health Organization. Every chapter is supported by real-life case studies, illustrative examples and video demonstrations that enlighten readers" --

Front Cover -- Cognitive Data Models for Sustainable Environment -- Cognitive Data Models for Sustainable Environment: Cognitive Data Science in Sustainable Computing -- Copyright -- Dedication -- Contents -- Contributors -- Preface -- 1 - Multidimensional controlling properties of biofabricated silver-nanoparticles on different mosquito species -- 1. Introduction -- 2. Silver nanoparticles synthesis -- 2.1 Plant-mediated synthesis -- 2.2 Microorganism-mediated synthesis -- 2.3 Animal products - mediated synthesis -- 3. Silver nanoparticles application on mosquito -- 3.1 AgNPs application on mosquito larvae -- 3.2 AgNPs application on mosquito pupae -- 3.3 AgNPs application on mosquito egg and adult -- 4. Research gaps -- 5. Conclusion -- Appendix A. Supplementary data -- References -- 2 - Machine learning-enabled cognitive approaches for handling IoT-based environmental data -- 1. Introduction -- 2. Cognitive IoT data-processing framework -- 3. Solution and technology overview -- 3.1 Data collection -- 3.2 Data storage -- 3.3 Data flow orchestration -- 3.4 Data analysis -- 3.5 Data visualization -- 3.6 Action -- 4. Experimental results and discussion -- 5. Last note -- 6. Future directions -- Appendix A. Supplementary data -- References -- 3 - Evolution of sustainable environment: a cognitive outlook -- 1. Introduction -- 2. Classification of smart materials -- 2.1 Piezoelectric substances -- 2.2 Thermosensitive substances -- 2.3 pH-sensitive substances -- 2.4 Chromogenic substances -- 2.5 Hydrogels -- 2.6 Magnetoresponsive substances -- 2.7 Optical fiber -- 2.8 Active and passive substances -- 3. Properties of smart materials -- 4. Application of smart materials -- 4.1 Application in the field of nanotechnology and acoustics -- 4.2 Application in the field of piezoelectric and electrochromic device.

Cover -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Preface -- Acknowledgments -- Editors -- Contributors -- Chapter 1 Big Data for Smart Education -- 1.1 Introduction -- 1.1.1 Big Data (BD) -- 1.1.1.1 Big Data Analytics (BDA) -- 1.1.2 Big Data Architecture Aimed at Learning Analytics -- 1.1.3 Role of Big Data in Smart Education -- 1.2 Fruition of Smart Learning -- 1.2.1 Insight of Smart Learning -- 1.2.2 Smart Learning Environment -- 1.2.3 Denotation of Smart in Smart Learning -- 1.2.4 Smart Learner -- 1.3 Framework of Smart Education -- 1.3.1 Smart Pedagogy -- 1.3.2 Smart Learning Environments -- 1.3.3 Technical Architecture of a Smart Education Environment -- 1.3.4 3-Tier Architecture of Smart Computing -- 1.3.5 Key Function of Smart Computing -- 1.4 Big Data Subsequent Revolution in Education -- 1.4.1 Big Data is Making Education Smarter -- 1.5 Technique Educators Are Improving in Learning Process -- 1.5.1 Measure, Monitor, and Respond -- 1.5.2 Epitomize Learning Experience -- 1.5.3 Designing New Courses -- 1.6 Big Data Components in Smart Education -- 1.7 Big Data Tools in Smart Education -- 1.8 Big Data Applications for Smart Education -- 1.8.1 Higher Education Analysis -- 1.8.2 Student Engagement -- 1.8.3 Bookstore Effectiveness -- 1.9 How BD and Education Could Work Together to Benefit Student Success -- 1.9.1 Customized Curricula Aimed at Improved Learning Outcome -- 1.9.2 Big Data to Expand Student's Performance -- 1.9.3 New Paths of Learning Potentials -- 1.10 Big Data Analytics Consequensces in Advanced Education -- 1.11 Opportunities along with Challenges of Big Data in Smart Education -- 1.12 Conclusion: Challenge of Simplifying Smart Education -- References -- Chapter 2 Big Data Analytics Using R for Offline Voltage Prediction in an Electric Power System -- 2.1 Introduction.

AbstractAimsBrain-derived neurotrophic factor (BDNF) levels may be associated with alcohol use disorders (AUD) and alcohol consumption, correlate with sleep disturbance and be influenced by sex differences and sex hormones. These associations have not been examined in a single sample accounting for all these factors.MethodsData from 190 participants (29.4% female) with AUD were utilized. Sleep quality, craving intensity, depression, anxiety and alcohol consumption were assessed using the Pittsburgh Sleep Quality Index (PSQI), Penn Alcohol Craving Scale (PACS), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Timeline Follow Back for 90 days(TLFB 90). Inventory of Drug Taking Situations (IDTS) assessed the tendency to drink in positive/negative emotional states. Serum BDNF (sBDNF) and plasma sex hormones (estrogen, progesterone, testosterone, FSH and SHBG) were measured. Pearson correlation analyses were used to examine the association between sBDNF and these measures in the entire sample and in men and women separately. Higher order interaction effects between these factors were evaluated for their association with sBDNF using a backward selection model.ResultsNo significant correlations between sBDNF levels and sex hormones, PSQI, PHQ-9, PACS, IDTS scores and alcohol consumption were found (all P-values > 0.05). sBDNF levels were negatively correlated with GAD-7 scores in men (r = −0.1841; P = 0.03). When considering all quadratic and two-way interactions among PSQI, PHQ-9, GAD-7, mean and max drinks/day, number of drinking days, heavy drinking days, and sex no higher order moderating effects of sBDNF levels were found.ConclusionOur study revealed no significant associations between sBDNF and alcohol measures, sleep, depression and sex hormones suggesting limited utility as a biomarker.