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Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.

Protein dynamics on the microsecond (μs) time scale were investigated by temperature-jump fluorescence spectroscopy as a function of temperature in two variants of a thermophilic alcohol dehydrogenase: W87F and W87F:H43A. Both mutants exhibit a fast, temperature-independent μs decrease in fluorescence followed by a slower full recovery of the initial fluorescence. The results, which rule out an ionizing histidine as the origin of the fluorescence quenching, are discussed in the context of a Trp49-containing dimer interface that acts as a conduit for thermally activated structural change within the protein interior.

The present work investigates the fatigue, fracture toughness, and dynamic mechanical analysis of carboxyl-terminated acrylonitrile butadiene (CTBN) rubber-toughened epoxy composites reinforced with surface-modified pineapple fiber and micro boron carbide (B4C). This research aims to determine the effects of the inclusion of CTBN and surface modification on pineapple fiber and B4Cto form a composite with the epoxy matrix. A surface treatment with an amino silane (3-Aminopropyl) trimethoxysilane (APTMS) on natural fibers and filler microparticles is performed. The silanization was carried out in an aqueous solution. The natural fiber epoxy composites were fabricated using the hand lay-up technique and further characterization was performed. According to the results, CTBN rubber up to 3 vol. % shows enchantment in fatigue life cycles by 97% for composite designation N3(Fiber + CTBN). The DMA properties of the composites are enhanced after being reinforced with the treated pineapple fiber reinforcement. The glass transition temperature was found to be increased up to 86 °C for the N4 (Fiber + CTBN + B4C) composite designation; it is nearly 32% improvement. Likewise, the N4composite classification indicates a fracture toughness of 32.2 MPa and an energy release rate of 1.224 MJ/m2. The fractography images revealed better microparticle interfacial bonding in the matrix material. These composites with great fatigue life counts, dynamic mechanical behaviour and good resistance to fracture were highly demanded for mini-aircraft, hybrid vehicle, and structural applications. [ABSTRACT FROM AUTHOR]

Background: Waste produced in the course of health-care activities carries a higher potential for infection and injury than any other type of waste. Hence, to avoid the hazards caused by hospital waste, the Government of India issued notification on bio-medical waste (BMW) (management and handling) Rules 1998 which was amended in 2016 under Environment (protection) Act. These new rules fill up the gaps in the old rules to regulate disposal of various categories of BMW. Aim: To assess the awareness and practices toward dental health-care waste management among dentists of Nellore city. Material and Methods: A cross sectional study in Nellore City was conducted among 204 private dental practitioners using a close ended questionnaire. The questionnaire was distributed in the dental clinics by the investigator and collected back the same or consecutive day. Data were analyzed using SPSS version 22 (IBM Corp, Armonk, NY, USA). Chi-square test was used, and P < 0.05 was considered statistically significant. Results: The study results showed that there was no statistically significant association between the responses by dentists on majority of the questions relating to BMW Management and Handling rules. 86.6% of clinicians and 78.1% of academicians responded that they were aware of the BMW rules. 87.8% and 84.4% of clinicians and academicians, respectively, were aware of different categories of waste, whereas 97.1% and 93.8%, respectively, reported that they knew about different color codings of BMW and these responses were not statistically significant (P = 0.05). Majority of the dentists (85.3%) were aware of the category of wastes, but not aware of the color coding followed for the same. Majority of the respondents did not segregate the waste generated in their working place, 85.8% used chair side bins for disposal. Conclusion: Though most of the dentists were aware of the BMW management rules, majority of them practiced inappropriate waste disposal techniques.

The Global Household Air Pollution (HAP) Measurements database, commissioned by the World Health Organization, provides an organized summary of data reported in the literature describing HAP microenvironments, methods and measurements. As of June 2018, the database contains measurements from 43 countries obtained from 196 studies published through 2016. The database includes information useful for understanding the range of household and personal air pollution measurements that have been collected in a country, as well as characteristics of the cooking environment, including primary cooking fuel type, stove type, heating fuel type and kitchen location. Quantitative particulate matter (PM) of various size fractions and/or carbon monoxide (CO) exposure measurements included in the database can be aggregated and analyzed to generate summary statistics (e.g. average sub-national, national, regional and global HAP exposures) to assess temporal and spatial relationships. The quantitative PM exposure measurements in the database have been used in global predictive modeling of HAP-PM2.5 exposures (“Global Estimation of Exposure to Fine Particulate Matter (PM2.5) from Household Air Pollution” (Shupler et al., 2018) [1])

Pneumonia is both a treatable and preventable disease but remains a leading cause of death in children worldwide. Household air pollution caused by burning biomass fuels for cooking has been identified as a potentially preventable risk factor for pneumonia in low- and middle-income countries. We are conducting a randomised controlled trial of a clean energy intervention in 3200 households with pregnant women living in Guatemala, India, Peru and Rwanda. Here, we describe the protocol to ascertain the incidence of severe pneumonia in infants born to participants during the first year of the study period using three independent algorithms: the presence of cough or difficulty breathing and hypoxaemia (≤92% in Guatemala, India and Rwanda and ≤86% in Peru); presence of cough or difficulty breathing along with at least one World Health Organization-defined general danger sign and consolidation on chest radiography or lung ultrasound; and pneumonia confirmed to be the cause of death by verbal autopsy. Prior to the study launch, we identified health facilities in the study areas where cases of severe pneumonia would be referred. After participant enrolment, we posted staff at each of these facilities to identify children enrolled in the trial seeking care for severe pneumonia. To ensure severe pneumonia cases are not missed, we are also conducting home visits to all households and providing education on pneumonia to the mother. Severe pneumonia reduction due to mitigation of household air pollution could be a key piece of evidence that sways policymakers to invest in liquefied petroleum gas distribution programmes.