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The field experiment was conducted during winter (rabi) 2020–21 and rainy (kharif) season of 2021–22 at College of Agriculture (University of Agricultural Sciences, Dharwad), Vijayapura, Karnataka to evaluate efficacy of bio-rationals against citrus leaf miner (Phyllocnistis citrella Stainton). The experiment was conducted in a randomized block design (RBD) with 8 treatments, viz. bio digester solution (10%); pongamia leaf extract (5%); Prosopis juliflora leaf extract (5%); Beauveria bassiana (2 × 108 conidia/g) (1 kg/ha); Bacillus thuringiensis 8 L (1 L/ha); neem-based insecticide (10000 ppm) (1.5 L/ha); thiamethoxam 25 wg (125 ml/ha) (standard check); and untreated control (water spray), replicated thrice. Among the selected bio-rationals, two application of neem (Azadirachta indica)-based insecticide recorded significantly lesser number of live mines/shoot followed by bio-digester solution (10%) and these treatments were found superior in controlling citrus leaf miner on acid lime [Citrus aurantiifolia (Christm.) Swingle]. The pongamia leaf extract, Bacillus thuringiensis 8 L and Beauveria bassiana were proved to be moderately affective against this pest. The bio-rationals found effective can be used in integrated management of citrus leaf miner on acid lime.

The manufacturing of ballistic impact-resistant (BIR) body armours has evolved over the years with the aim of reducing their weight and enhancing their energy-absorbing capacity upon ballistic impacts. The incorporation of nanoparticles into advanced BIR body armour systems is considered one of the promising techniques. The methods employed in incorporating various nanoparticles in the manufacturing of textile-based body armour systems face a research gap in the optimisation of the associated parameters. This article discusses the mechanism involved in the energy absorption of composites and nanocomposites upon ballistic impact. The current review article highlights the chemical, physical, and mechanical properties of various nanoparticles incorporated into BIR body armour systems. BIR nanocomposites consisting of carbon nanotubes, graphene nanoplatelets, nano-silica, nanoclays, nano-alumina, etc., have been discussed herein. In addition, the significance of various techniques for the dispersion of these nanoparticles was also highlighted. Various methods, such as sol–gel, PVD, CVD, thermal spray, and electroless methods for coating the nanoparticles on the surface of the fibre/fabric were also discussed.

Background: This literature review examines some key articles addressing the theme “Transformations for achieving SDGs: Innovate, Integrate, Implement.” The articles provide insights into the concerns, strategies, and evaluation processes related to sustainable development goals (SDGs) with a focusing on India. Objective: The primary objectives of this literature review are to analyze the approaches highlighted in the selected articles and to identify common themes and deviations in the strategies proposed for achieving SDGs. Methodology: A systematic review of the selected 11 articles was conducted to fetch relevant information regarding innovations, integration approaches, and implementation strategies for achieving SDGs. Every article was scrutinized for key themes, methodologies, and their findings. The analysis considered multiple perspectives presented in the literature, including case studies, literature reviews, and working papers. Results: The literature review showed a multifaceted understanding of the challenges and opportunities associated with achieving SDGs. Ahmad Mohd Khalid, Seema Sharma & Amlendu Kumar Dubey (2020) emphasized the specific concerns of developing countries, providing a case study for India. Srivastava (2018) advocated for standardized evaluation processes, highlighting a case study focused on India’s progress. Rosário and Dias (2022) explored the impact of digital transition on sustainability through a detailed literature review. Additionally, insights from the Journal of Sustainability Science and Management and the working paper by Bajpai and Biberman (2020) discussed the integration of SDGs in the ICT sector. Conclusion: The findings suggests a variety in the pursuit of SDGs, with a diverse range of strategies and considerations. Innovations, integration efforts, and implementation methodologies vary across contexts and different sectors. The challenges discussed underscore the need for specific approaches, stressing the importance of standardized evaluation processes. Overall, the literature highlights the complexity of achieving SDGs, necessitating a comprehensive and adaptive framework that integrates innovative solutions, interdisciplinary collaboration, and effective implementation strategies.

Background: Cancer being a deadly disease, cancer victims face great challenges with respect to their routines, roles and relationships. The usage of other means to cure illness is called complementary and alternative medicine or CAM. This study is done to determine the proportion and usage pattern of CAM for cancer treatment and to assess the perception towards CAM among cancer patients. Methodologys: It was a Descriptive cross-sectional study, carried out among cancer patients admitted to tertiary care hospitals attached to a medical college. The interview schedule was prepared which consists of four sections including the socio-demographic details, details about the diagnosis and treatment of cancer among the study participants, details about the usage pattern of Complementary medicine, and the perception of cancer victims towards Complementary medicine for cancer treatment. Data will be entered and analyzed by using the statistical software Statistical Package for Social Sciences (SPSS) Version 11.5. Results: Out of 105 participants majority of cancer patients,74 (70%) of them were of the age group between 40- 70 years. 65% of the patients had addictions among which 60 (90.9%) were addicts for more than 10 years. The majority of the patients underwent radiotherapy 85 (81.0%), followed by Chemotherapy 72 (68.6%), The number of patients that have used CAM in our study was found to be 12 (11.4%). Many of them chose Ayurveda as their type of CAM 5 (41.7%) followed by home remedies usage 4 (25%). 39 (38.5%) believe that there should be more evidence that CAM works while 38 (33.8%) don’t need any evidence about it. 45 (42.9%) feel that CAM treatment does not work in the treatment of cancer while 35 (33.3%) remained neutral in this aspect. 85 (81.0%) are satisfied with present cancer treatment. 78 (74.3%) agreed that Modern medicine is most effective for treatment of cancer. Conclusion: The usage of CAM in our study was found to be minimal. The majority of the participants are satisfied with modern medicine and most of the patients are satisfied with allopathic treatment.

Power switching in smart grid has been identified as the key issue in literature. There exist number of models to handle this problem which consider the residual energy of grids in performing power switching. However, the performances of the models are not up to the expected rate. To handle this issue, an efficient Requirement Optimization based Power Switching Model (ROPSM) is presented in this article. The model focused on optimizing the selection of power grids towards maintaining the power stability in smart grids. To perform this, the model monitors the incoming voltage level and maintains the voltage production capability of various grids at all the duty cycles. Based on that, the method computes Power Requirement Support (PRS) value for various grid units. According to the value of PRS, a subset of grids are selected to optimize the power stability and allowed to regulate the power. The proposed model improves the performance of power stability in smart grid environment.

The power stability in power distribution systems are well studied. There exist numbers of models towards maintaining the power stability which consider the residual energy of PV systems. However, there are not efficient in maintaining the power stability and suffer to achieve higher efficiency in potential maximization. Towards maintaining higher potential maximization, an efficient Optimized Solar Potential Maximization Model (OSPMM) is presented in this article. The model considers the factors like Mean Voltage Generation, Mean Voltage Supply and Residual Voltage as the key in the selection of PV system towards power stability. To perform this, the method monitors the incoming voltage produced by various PV systems and collects such factors mentioned above. According to the factors identified, the method estimates Potential Maximization Support (PMS) for various PV systems. Based on the value of PMS, the method selects the PV system towards maintaining power stability. The proposed approach improves the performance of solar potential maximization and power stability maximization.

Millions of people throughout the globe suffer from diabetes mellitus, a debilitating illness that increases the risk of severe complications and early death. To take preventative measures and tailor treatment to each individual's needs, it is essential to identify diabetes early and estimate risk accurately. This research provides a data-driven strategy for predicting diabetes based on SVM models. This work uses a large dataset, including clinical and demographic data from a wide range of people, including those with and without diabetes, to conduct our analysis. A prediction model that divides people into diabetes and non-diabetic groups based on their input attributes is constructed using the SVM algorithm. Engineers use feature selection and other engineering methods to improve the model's efficacy and readability. The results of the research show that the SVM algorithm is capable of producing reliable predictions of diabetes risk. Measures of the model's efficacy include its sensitivity to false positives, specificity in identifying true positives, and area under the Receiver Operating Characteristics curve (AUC-ROC). In addition, feature significance analysis improves the model's interpretability by illuminating the most critical risk variables for diabetes. The accuracy and interpretability of the proposed SVM-based diabetic prediction model are promising, making it a valuable tool for healthcare practitioners and policymakers to identify those at high risk of developing diabetes and modify preventative measures and interventions appropriately.

A field trial was conducted during 2016–17, 2017–18, 2018–19 and 2019–20 at the Main Horticultural Research and Extension Centre, Bagalkot under Northern Dry Zone-3 of the Agroclimatic zones of Karnataka with an intention to study the response of moringa (Moringa oleifera L.) crop to different organic sources of nutrients. There were five nutrition treatments, viz. T1, 100% RDN through FYM; T2, 100% RDN through vermicompost; T3, 100% RDN through neem cake; T4, 1/3 RDN through FYM+1/3 RDN through vermicompost+1/3 RDN through neem cake and; T5, 50: 125: 30 kg NPK+20 tonnes FYM/ha (RDF) and replicated four times. The treatment T4 recorded significantly higher green pod yield (87.99 q/ha) which was on par with RDF (83.83 q/ha) and T3 (82.66 q/ha). However, most of the growth (except height) and yield contributing parameters, in all the organic treatments, were on par with RDF. Application of organic manures helped in improving available nutrients status, including micronutrients in soil. Among the different organic manures, the effect of FYM and VC in increasing the population of rhizosphere microorganisms was more than other treatments. Net return was found more in T4 (`124446/ha) followed by T3 (`118179/ha) and RDF (`114079/ha). It was found that organic manure applied in equal proportion (T4) and 100% substitution of N through neem cake (T3) was found better in getting higher yield and returns.

The rapid developments in wireless multimedia sensor networks (WMSNs) have increased the demand for an efficient method of safeguarding multimedia data from attackers. As data are transmitted over a wireless medium, the authentication process needs to be provided with some efficient detection and prevention methods. The Sybil attack is one of the most common and involves replicating the identity of an original node in the network and behaving like a true node in order to retrieve/destroy information using this fake identity. An efficient enhanced random password comparison technique is proposed to detect and prevent Sybil attacks. The results of simulations indicate that the proposed method detects this type of attack more efficiently than existing methods. In addition to early detection, our application increases the throughput and reduces the average delay with an enhanced true detection rate. The identification of this malicious activity in its initial phases increases the efficiency of the system in terms of the data transmission process.

Abstract Background Inflammatory diseases are the vast array of disorders caused by inflammation. During most inflammatory events, many cytokines expressions were modulated, and one such cytokine is tumor necrosis factor-alpha (TNF-α). TNF-α is mainly secreted by monocytes and macrophages. Notably, it has been proposed as a therapeutic target for several diseases. The anti-TNF biology approach is mainly based on monoclonal antibodies. The fusion protein and biosimilars are prevalent in treating inflammation for decades. Only a few small molecule inhibitors are available to inhibit the expression of TNF-α, and one such promising drug was thalidomide. Therefore, the study was carried out to design thalidomide-based small molecule inhibitors for TNF-α. The main objective of our study is to design thalidomide analogs to inhibit TNF-α using the insilico approach. Results Several thalidomide analogs were designed using chemsketch. After filtration of compounds through ‘Lipinski rule of 5’ by Molinspiration tool, as a result, five compounds were selected. All these compounds were subjected to molecular docking, and the study showed that all five compounds had good binding energy. However, based on ADMET predictions, two compounds (S3 and S5) were eliminated. Conclusions Our preliminary results suggest that S1, S2, S4 compounds showed potential ligand binding capacity with TNF-α and, interestingly, with limited or no toxicity. Our preliminary investigation and obtained results have fashioned more interest for further in vitro studies.