The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems using IoT, Big Data, and Machine Learning

The quality of air is closely linked with the life quality of humans, plantations, and wildlife. It needs to be monitored and preserved continuously. Transportations, industries, construction sites, generators, fireworks, and waste burning have a major percentage in degrading the air quality. These sources are required to be used in a safe and controlled manner. Using traditional laboratory analysis or installing bulk and expensive models every few miles is no longer efficient. Smart devices are needed for collecting and analyzing air data. The quality of air depends on various factors, including location, traffic, and time. Recent researches are using machine learning algorithms, big data technologies, and the Internet of Things to propose a stable and efficient model for the stated purpose. This review paper focuses on studying and compiling recent research in this field and emphasizes the Data sources, Monitoring, and Forecasting models. The main objective of this paper is to provide the astuteness of the researches happening to improve the various aspects of air polluting models. Further, it casts light on the various research issues and challenges also.Comment: 30 pages, 11 figures, Wireless Personal Communications. Wireless Pers Commun (2023

Neuropsychological effects and attitudes in patients following electroconvulsive therapy

Miriam Feliu1,2, Christopher L Edwards1,2,3, Shiv Sudhakar4, Camela McDougald1, Renee Raynor5, Stephanie Johnson6, Goldie Byrd7, Keith Whitfield8, Charles Jonassaint8, Heather Romero1, Lekisha Edwards1, Chante’ Wellington1, LaBarron K Hill9, James Sollers, III9, Patrick E Logue11Department of Psychiatry and Behavioral Sciences; 2Duke Pain and Palliative Care Center; 3Department of Medicine, Division of Hematology; 4Drexel University Medical School; 5Brain Tumor Center, Duke University Medical Center, Durham, NC, USA; 6Science Directorate, American Psychological Association; 7Department of Biology, North Carolina A&T State University; 8Department of Psychology, Duke University; 9Department of Psychology, The Ohio State UniversityAbstract: The current study examined the effects of electroconvulsive therapy (ECT) on neuropsychological test performance. Forty-six patients completed brief neuropsychological and psychological testing before and after receiving ECT for the treatment of recalcitrant and severe depression. Neuropsychological testing consisted of the Levin Selective Reminding Test (Levin) and Wechsler Memory Scale-Revised Edition (WMS-R). Self-report measures included the Beck Depression Inventory (BDI), the Short-Term Memory Questionnaire (STMQ), and several other measures of emotional functioning and patient attitudes toward ECT. The mean number of days between pre-ECT and post-ECT testing was 24. T-test revealed a significant decrease in subjective ratings of depression as rated by the BDI, t(45) = 9.82, P < 0.0001 (Pre-BDI = 27.9 ± 20.2; post-BDI = 13.5 ± 9.7). Objective ratings of memory appeared impaired following treatment, and patients’ self-report measures of memory confirmed this decline. More specifically, repeated measures MANOVA [Wilks Lambda F(11,30) = 4.3, p < 0.001] indicated significant decreases for measures of immediate recognition memory (p < 0.005), long-term storage (p < 0.05), delayed prose passage recall (p < 0.0001), percent retained of prose passages (p < 0.0001), and percent retained of visual designs (p < 0.0001). In addition, the number of double mentions on the Levin increased (p < 0.02). This study suggests that there may be a greater need to discuss the intermittent cognitive risks associated with ECT when obtaining informed consent prior to treatment. Further that self-reports of cognitive difficulties may persist even when depression has remitted. However, patients may not acknowledge or be aware of changes in their memory functioning, and post-ECT self-reports may not be reliable.Keywords: electroconvulsive therapy, neuropsychology, attitudes, memor

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Not AvailableA study was conducted to investigate the effect of chemical fertilizer, organic manure and two cropping systems viz., pearlmillet - clusterbean - castor rotation and upland rice - lentil sequence on microbial quotient (MQ), metabolic quotient, specific enzyme activity (dehydrogenase, arylsulfatase and urease) and microbial biomass carbon (MBC) in long-term (1821 years) field experiments in Entisols of semi-arid region of Gujarat and Inceptisols of sub-humid region of Varanasi. Higher MQ values were recorded in Entisols than Inceptisols. MQ ranged from 4.00-5.08 and 1.00-1.85 % across soil layers in Entisols and Inceptisols, respectively. Metabolic quotient values ranged from 0.11-0.23 and 0.04-0.07 across soil layers in Entisols and Inceptisols respectively. The specific enzyme activity of dehydrogenase was maximum in Entisols, whereas, arylsulfatase and urease activity was recorded more in Inceptisols. Higher specific enzyme activity reflects greater microbial activity and microbial biomass turnover. Agro-ecosystem in sub-humid region resulted in 1.5 fold higher geometric mean of enzymes (GMea) than in semi-arid region. The application of 50% RDNF (recommended dose of N-fertilizer) + 50% RDN FYM (farm yard manure) in Entisols and 50% N (FYM) + 50% RDF in Inceptisols improved microbiological activities (GMea) at both the sites. The concentration of soil organic carbon (SOC) and MBC were significantly correlated with GMea in both agro-ecosystems. In conclusion, integrated sources of nutrients with inclusion of FYM and 50 % reduction of fertilizers improved the microbiological activities in both Inceptisols and Entisols.Indian Council of Agricultural Research, New DelhiIndian Council of Agricultural Research (ICAR