SRLG: To Finding the Packet Loss in Peer to Peer Network

We introduce the ideas of watching methods (MPs) and watching cycles (MCs) for distinctive localization of shared risk connected cluster (SRLG) failures in all-optical networks. An SRLG failure causes multiple links to interrupt at the same time due to the failure of a typical resource. MCs (MPs) begin and finish at identical (distinct) watching location(s).They are constructed such any SRLG failure leads to the failure of a unique combination of methods and cycles. We tend to derive necessary and ample conditions on the set of MCs and MPs required for localizing associate single SRLG failure in a capricious graph. We determine the minimum range of optical splitters that area unit needed to watch all SRLG failures within the network. Extensive simulations area unit won�t to demonstrate the effectiveness of the planned watching technique

Effectiveness of WhatsApp Agro - Advisory Service by the Scientist of KVK of Cauvery Delta Zone

Nowadays usage of Social media is increasing rapidly in the 21st century. WhatsApp is the most popular social media which is used by a large number of people. The present study was focused on how effectively KVK's farmers use WhatsApp groups for agricultural purposes. Three districts of Cauvery Delta Zone Nagapattinam, Thiruvarur, and Karaikal KVK WhatsApp groups were purposively selected for the study. Totally 120 farmers were selected for primary data, and from each district, 40 farmers were selected by the Simple Random Sampling method. Data were analyzed by descriptive statistics. It reported that the overall effectiveness of the farmer's use of WhatsApp groups was at the medium level (74.17%) indicating that farmers were using the group for their agricultural activities. The easiness of understanding information on Whatsapp was found to be good, and image and video-based information, in addition to textual information aided in a better understanding of information. Based on the study recommended that is important to be placed on disseminate weather and market information to aid indecision making

Treatment of synthetic textile wastewater containing dye mixtures with microcosms

The aim was to assess the ability of microcosms (laboratory-scale shallow ponds) as a post polishing stage for the remediation of artificial textile wastewater comprising two commercial dyes (basic red 46 (BR46) and reactive blue 198 (RB198)) as a mixture. The objectives were to evaluate the impact of Lemna minor L. (common duckweed) on the water quality outflows; the elimination of dye mixtures, organic matter, and nutrients; and the impact of synthetic textile wastewater comprising dye mixtures on the L. minor plant growth. Three mixtures were prepared providing a total dye concentration of 10 mg/l. Findings showed that the planted simulated ponds possess a significant (p < 0.05) potential for improving the outflow characteristics and eliminate dyes, ammonium-nitrogen (NH4-N), and nitrate-nitrogen (NO3-N) in all mixtures compared with the corresponding unplanted ponds. The removal of mixed dyes in planted ponds was mainly due to phyto-transformation and adsorption of BR46 with complete aromatic amine mineralisation. For ponds containing 2 mg/l of RB198 and 8 mg/l of BR46, removals were around 53%, which was significantly higher than those for other mixtures: 5 mg/l of RB198 and 5 mg/l of BR46 and 8 mg/l of RB198 and 2 mg/l of BR46 achieved only 41 and 26% removals, respectively. Dye mixtures stopped the growth of L. minor, and the presence of artificial wastewater reduced their development

Multi-view united transformer block of graph attention network based autism spectrum disorder recognition

IntroductionAutism Spectrum Disorder (ASD) identification poses significant challenges due to its multifaceted and diverse nature, necessitating early discovery for operative involvement. In a recent study, there has been a lot of talk about how deep learning algorithms might improve the diagnosis of ASD by analyzing neuroimaging data.MethodTo overrule the negatives of current techniques, this research proposed a revolutionary strategic model called the Unified Transformer Block for Multi-View Graph Attention Networks (MVUT_GAT). For the purpose of extracting delicate outlines from physical and efficient functional MRI data, MVUT_GAT combines the advantages of multi-view learning with attention processes.ResultWith the use of the ABIDE dataset, a thorough analysis shows that MVUT_GAT performs better than Mutli-view Site Graph Convolution Network (MVS_GCN), outperforming it in accuracy by +3.40%. This enhancement reinforces our suggested model’s effectiveness in identifying ASD. The result has implications over higher accuracy metrics. Through improving the accuracy and consistency of ASD diagnosis, MVUT_GAT will help with early interference and assistance for ASD patients.DiscussionMoreover, the proposed MVUT_GAT’s which patches the distance between the models of deep learning and medical visions by helping to identify biomarkers linked to ASD. In the end, this effort advances the knowledge of recognizing autism spectrum disorder along with the powerful ability to enhance results and the value of people who are undergone

Conservation agriculture: A pathway to achieving sustainable development goals

Conservation agriculture (CA) is an approach to optimize farm and watershed performance. It integrates local and national economic systems while considering societal, environmental and institutional frameworks. This approach engages value chains supported by public, private and civil sectors. CA seeks to harmonize the use of natural resources with population needs, employing sustainable intensification to meet human demands effectively and preventing the loss of arable land. Conservation agriculture directly influences all sustainable development goals (SDGs) by leveraging the core principles of minimum soil disturbance, permanent soil cover and crop rotation. Conservation agriculture can prove to be a viable option for meeting the targets of the sustainable agenda. This practice supports environmental, social and economic justice, which creates a holistic developmental route that supports the burgeoning population. Conservation agriculture relies on a knowledge-based strategy to reduce production costs, enabling farmers to adopt new technologies more readily. While CA demonstrates significant benefits across scales, its adoption remains constrained by socioeconomic factors and limited mechanization in the smallholder context. Advancing CA requires a multidisciplinary, participatory research paradigm coupled with policy support, institutional support and capacity building for farmers. CA offers a sustainable framework that ensures sustainable intensification and environmental stewardship in the long term