Synthesis, characterization and effects of citric acid and PVA on magnetic properties of CoFe2O4

Cobalt ferrite (CoFe2O4) particles were synthesized by sol-gel method using metal nitrates, citric acid (CA) and polyvinyl alcohol (PVA). X-ray diffraction (XRD), high resolution scanning electron microscopy (HR-SEM), thermogravimetry/differential scanning calorimetry analysis and vibrating sample magnetometer were used to study the structural, thermal and magnetic properties of the CoFe2O4 powder. XRD results indicate that the resultant particles have crystalline, pure single phase spinel structure. From HR-SEM images, a systematic decrease in particle size is observed with an increase in PVA concentration, along with addition of CA. CA at various concentrations of PVA significantly enhance the magnetic properties of the materials

Wurtzite ZnSe quantum dots: synthesis, characterization and PL properties

A facile method for synthesis of monodispersed, starch-capped ZnSe nanoparticles at room temperature is being reported. The nanoparticles exhibited strong quantum confinement effect with respect to the bulk ZnSe. The transmission electron microscopy image indicated that the particles were well dispersed and spherical in shape. The X-ray diffraction analysis showed that the ZnSe nanoparticles were of the wurtzite structure, with average particle diameter of about 3.6 nm. The Fourier transform infrared spectrum confirmed the presence of starch as passivating agent

Implementation of a heart disease risk prediction model using machine learning

Cardiovascular disease prediction aids practitioners in making more accurate health decisions for their patients. Early detection can aid people in making lifestyle changes and, if necessary, ensuring effective medical care. Machine learning (ML) is a plausible option for reducing and understanding heart symptoms of disease. The chi-square statistical test is performed to select specific attributes from the Cleveland heart disease (HD) dataset. Support vector machine (SVM), Gaussian Naive Bayes, logistic regression, LightGBM, XGBoost, and random forest algorithm have been employed for developing heart disease risk prediction model and obtained the accuracy as 80.32%, 78.68%, 80.32%, 77.04%, 73.77%, and 88.5%, respectively. The data visualization has been generated to illustrate the relationship between the features. According to the findings of the experiments, the random forest algorithm achieves 88.5% accuracy during validation for 303 data instances with 13 selected features of the Cleveland HD dataset

Epidemiology of Untreated Psychoses in 3 Diverse Settings in the Global South: The International Research Program on Psychotic Disorders in Diverse Settings (INTREPID II).

IMPORTANCE: Less than 10% of research on psychotic disorders has been conducted in settings in the Global South, which refers broadly to the regions of Latin America, Asia, Africa, and Oceania. There is a lack of basic epidemiological data on the distribution of and risks for psychoses that can inform the development of services in many parts of the world. OBJECTIVE: To compare demographic and clinical profiles of cohorts of cases and rates of untreated psychoses (proxy for incidence) across and within 3 economically and socially diverse settings in the Global South. Two hypotheses were tested: (1) demographic and clinical profiles of cases with an untreated psychotic disorder vary across setting and (2) rates of untreated psychotic disorders vary across and within setting by clinical and demographic group. DESIGN, SETTING, AND PARTICIPANTS: The International Research Program on Psychotic Disorders in Diverse Settings (INTREPID II) comprises incidence, case-control, and cohort studies of untreated psychoses in catchment areas in 3 countries in the Global South: Kancheepuram District, India; Ibadan, Nigeria; and northern Trinidad. Participants were individuals with an untreated psychotic disorder. This incidence study was conducted from May 1, 2018, to July 31, 2020. In each setting, comprehensive systems were implemented to identify and assess all individuals with an untreated psychosis during a 2-year period. Data were analyzed from January 1 to May 1, 2022. MAIN OUTCOMES AND MEASURES: The presence of an untreated psychotic disorder, assessed using the Schedules for Clinical Assessment in Neuropsychiatry, which incorporate the Present State Examination. RESULTS: Identified were a total of 1038 cases, including 64 through leakage studies (Kancheepuram: 268; median [IQR] age, 42 [33-50] years; 154 women [57.5%]; 114 men [42.5%]; Ibadan: 196; median [IQR] age, 34 [26-41] years; 93 women [47.4%]; 103 men [52.6%]; Trinidad: 574; median [IQR] age, 30 [23-40] years; 235 women [40.9%]; 339 men [59.1%]). Marked variations were found across and within settings in the sex, age, and clinical profiles of cases (eg, lower percentage of men, older age at onset, longer duration of psychosis, and lower percentage of affective psychosis in Kancheepuram compared with Ibadan and Trinidad) and in rates of untreated psychosis. Age- and sex-standardized rates of untreated psychoses were approximately 3 times higher in Trinidad (59.1/100 000 person-years; 95% CI, 54.2-64.0) compared with Kancheepuram (20.7/100 000 person-years; 95% CI, 18.2-23.2) and Ibadan (14.4/100 000 person-years; 95% CI, 12.3-16.5). In Trinidad, rates were approximately 2 times higher in the African Trinidadian population (85.4/100 000 person-years; 95% CI, 76.0-94.9) compared with the Indian Trinidadian (43.9/100 000 person-years; 95% CI, 35.7-52.2) and mixed populations (50.7/100 000 person-years; 95% CI, 42.0-59.5). CONCLUSIONS AND RELEVANCE: This analysis adds to research that suggests that core aspects of psychosis vary by historic, economic, and social context, with far-reaching implications for understanding and treatment of psychoses globally

Deep Learning Applications and Intelligent Decision Making in Engineering

Deep learning includes a subset of machine learning for processing the unsupervised data with artificial neural network functions. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. When applied to engineering, deep learning can have a great impact on the decision-making process.Deep Learning Applications and Intelligent Decision Making in Engineering is a pivotal reference source that provides practical applications of deep learning to improve decision-making methods and construct smart environments. Highlighting topics such as smart transportation, e-commerce, and cyber physical systems, this book is ideally designed for engineers, computer scientists, programmers, software engineers, research scholars, IT professionals, academicians, and postgraduate students seeking current research on the implementation of automation and deep learning in various engineering disciplines.Глубокое обучение включает в себя подмножество машинного обучения для обработки неконтролируемых данных с помощью функций искусственной нейронной сети. Основным преимуществом глубокого обучения является обработка больших объемов данных для лучшего анализа и самоадаптивные алгоритмы для обработки большего объема данных. Применительно к инженерному делу глубокое обучение может оказать огромное влияние на процесс принятия решений. Приложения глубокого обучения и интеллектуальное принятие решений в инженерии - это ключевой справочный источник, который обеспечивает практическое применение глубокого обучения для улучшения методов принятия решений и создания интеллектуальных сред. Освещая такие темы, как интеллектуальный транспорт, электронная коммерция и киберфизические системы, эта книга идеально предназначена для инженеров, специалистов по информатике, программистов, разработчиков программного обеспечения, научных работников, ИТ-специалистов, академиков и аспирантов, которые ищут актуальные исследования по внедреИспользуемые программы Adobe Acroba

Smartphone-Operated Wireless Chemical Sensors: A Review

Wireless chemical sensors have been developed as a result of advances in chemical sensing and wireless communication technology. Because of their mobility and widespread availability, smartphones have been extensively combined with sensors such as hand-held detectors, sensor chips, and test strips for biochemical detection. Smartphones are frequently used as controllers, analyzers, and displayers for quick, authentic, and point-of-care monitoring, which may considerably streamline the design and lower the cost of sensing systems. This study looks at the most recent wireless and smartphone-supported chemical sensors. The review is divided into four different topics that emphasize the basic types of wireless smartphone-operated chemical sensors. According to a study of 114 original research publications published during recent years, market opportunities for wireless and smartphone-supported chemical sensor systems include environmental monitoring, healthcare and medicine, food quality, sport, and fitness. The issues and illustrations for each of the primary chemical sensors relevant to many application areas are covered. In terms of performance, the advancement of technologies related to chemical sensors will result in smaller and more lightweight, cost-effective, versatile, and durable devices. Given the limitations, we suggest that wireless and smartphone-supported chemical sensor systems play a significant role in the sensor Internet of Things

Smartphone-Operated Wireless Chemical Sensors: A Review

Wireless chemical sensors have been developed as a result of advances in chemical sensing and wireless communication technology. Because of their mobility and widespread availability, smartphones have been extensively combined with sensors such as hand-held detectors, sensor chips, and test strips for biochemical detection. Smartphones are frequently used as controllers, analyzers, and displayers for quick, authentic, and point-of-care monitoring, which may considerably streamline the design and lower the cost of sensing systems. This study looks at the most recent wireless and smartphone-supported chemical sensors. The review is divided into four different topics that emphasize the basic types of wireless smartphone-operated chemical sensors. According to a study of 114 original research publications published during recent years, market opportunities for wireless and smartphone-supported chemical sensor systems include environmental monitoring, healthcare and medicine, food quality, sport, and fitness. The issues and illustrations for each of the primary chemical sensors relevant to many application areas are covered. In terms of performance, the advancement of technologies related to chemical sensors will result in smaller and more lightweight, cost-effective, versatile, and durable devices. Given the limitations, we suggest that wireless and smartphone-supported chemical sensor systems play a significant role in the sensor Internet of Things

Gas phase synthesis of isopropyl chloride from isopropanol and HCl over alumina and flexible 3-D carbon foam supported catalysts

Abstract Isopropyl chloride synthesis from isopropanol and HCl in gas phase over ZnCl₂ catalysts supported on Al₂O₃ as well as flexible carbon foam was studied in a continuous reactor. A series of catalytic materials were synthesised and characterised by BET, XPS, SEM, TEM, XRD and NH₃-TPD methods. Catalytic activity tests (product selectivity and conversion of reactants) were performed for all materials and optimal reaction conditions (temperature and feedstock flow rates) were found. The results indicate that the highest yield of isopropyl chloride was obtained over 5 wt.% ZnCl₂ on commercial Al₂O₃ (No. II) (95.3%). Determination of product mixture compositions and by-product identification were done using a GC-MS method. Carbon foam variant catalyst, 5 wt.% ZnCl₂/C, was found to perform best out of the carbon-supported materials, achieving ∼75% yield of isopropyl chloride. The kinetic model describing the process in a continuous packed bed reactor was proposed and kinetic parameters were calculated. The activation energy for the formation of isopropyl chloride reaction directly from isopropanol and HCl was found to be ∼58 kJ/mol