Crop Diseases Identification Using Deep Learning in Application

This comprehensive review paper explores the profound impact of deep learning in the context of agriculture, with a specific focus on its crucial role in crop disease analysis and management. Deep learning techniques have exhibited remarkable potential to revolutionize agricultural practices, enhancing efficiency, sustainability, and resilience. The introductory section sets the stage by emphasizing the significant role of deep learning in agriculture, offering insights into its transformative applications, including disease detection, yield prediction, precision agriculture, and resource optimization. Subsequent sections delve into the fundamental aspects of deep learning, beginning with an exploration of its relevance and its practical implementations in crop disease detection. These discussions illuminate the essential techniques and methodologies that drive this technology, stressing the critical importance of data quality, model generalization, computational resources, and cost considerations. The paper also addresses ethical and environmental concerns, emphasizing the imperative of responsible and sustainable deep learning applications in agriculture. Furthermore, the document outlines the limitations and challenges faced in this field, encompassing data availability, ethical considerations, and computational resource accessibility, offering valuable insights for future research and development. This paper underscores the immense potential of deep learning to revolutionize agriculture by improving disease management, resource allocation, and overall sustainability. While persistent challenges exist, such as data quality and accessibility, the promise of harnessing deep learning to address global food security challenges is exceptionally encouraging. This comprehensive review serves as a foundational resource for ongoing research and innovation within the agricultural domain

Mikrosfere ropinirol hidroklorida za polagano oslobađanje: Utjecaj procesnih parametara

An emulsion solvent evaporation method was employed to prepare microspheres of ropinirole hydrochloride, a highly water soluble drug, by using ethylcellulose and PEG with the help of 32 full factorial design. The microspheres were made by incorporating the drug in a polar organic solvent, which was emulsified using liquid paraffin as an external oil phase. Effects of various process parameters such as viscosity of the external phase, selection of the internal phase, surfactant selection and selection of stirring speed were studied. Microspheres were evaluated for product yield, encapsulation efficiency and particle size. Various drug/ethylcellulose ratios and PEG concentrations were assayed. In vitro dissolution profiles showed that ethylcellulose microspheres were able to control release of the drug for a period of 12 h.Mikrosfere ropinirol hidroklorida, ljekovite tvari vrlo dobro topljive u vodi, pripravljene su metodom isparavanja otapala, koristeći etilcelulozu i PEG te 32 potpuno faktorijalno dizajniranje. Mikrosfere su pripravljene na sljedeći način: otopina ljekovite tvari u polarnom organskom otapalu emulgirana je s tekućim parafinom kao vanjskom uljnom fazom. Ispitivan je utjecaj različitih procesnih parametara poput viskoznosti vanjske faze, vrste interne faze i površinski aktivne tvari te brzine miješanja. Za pripravljene mikrosfere određeno je iskorištenje, učinkovitost inkapsuliranja i veličina čestica. Isprobavani su različiti odnosi ljekovite tvari i etilceluloze te koncentracija PEG-a. In vitro pokusi su pokazali da je oslobađanje ljekovite tvari kontrolirano tijekom 12 h

Effects of the WHO Labour Care Guide on cesarean section in India: a pragmatic, stepped-wedge, cluster-randomized pilot trial

Cesarean section rates worldwide are rising, driven by medically unnecessary cesarean use. The new World Health Organization Labour Care Guide (LCG) aims to improve the quality of care for women during labor and childbirth. Using the LCG might reduce overuse of cesarean; however, its effects have not been evaluated in randomized trials. We conducted a stepped-wedge, cluster-randomized pilot trial in four hospitals in India to evaluate the implementation of an LCG strategy intervention, compared with routine care. We performed this trial to pilot the intervention and obtain preliminary effectiveness data, informing future research. Eligible clusters were four hospitals with >4,000 births annually and cesarean rates ≥30%. Eligible women were those giving birth at ≥20 weeks' gestation. One hospital transitioned to intervention every 2 months, according to a random sequence. The primary outcome was the cesarean rate among women in Robson Group 1 (that is, those who were nulliparous and gave birth to a singleton, term pregnancy in cephalic presentation and in spontaneous labor). A total of 26,331 participants gave birth. A 5.5% crude absolute reduction in the primary outcome was observed (45.2% versus 39.7%; relative risk 0.85, 95% confidence interval 0.54-1.33). Maternal process-of-care outcomes were not significantly different, though labor augmentation with oxytocin was 18.0% lower with the LCG strategy. No differences were observed for other health outcomes or women's birth experiences. These findings can guide future definitive effectiveness trials, particularly in settings where urgent reversal of rising cesarean section rates is needed. Clinical Trials Registry India number: CTRI/2021/01/03069