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YouTube faces a global crisis with the dissemination of false information and hate speech. To counter these issues, YouTube has implemented strict rules against uploading content that includes false information or promotes hate speech. While numerous studies have been conducted to reduce offensive English-language content, there's a significant lack of research on Sinhala content. This study aims to address the aforementioned gap by proposing a solution to minimize the spread of violence and misinformation in Sinhala YouTube videos. The approach involves developing a rating system that assesses whether a video contains false information by comparing the title and description with the audio content and evaluating whether the video includes hate speech. The methodology encompasses several steps, including audio extraction using the Pytube library, audio transcription via the fine-tuned Whisper model, hate speech detection employing the distilroberta-base model and a text classification LSTM model, and text summarization through the fine-tuned BART-Large- XSUM model. Notably, the Whisper model achieved a 48.99\\% word error rate, while the distilroberta-base model demonstrated an F1 score of 0.856 and a recall value of 0.861 in comparison to the LSTM model, which exhibited signs of overfitting.

Bunchy top disease of banana caused by Banana bunchy top virus (BBTV, genus Babuvirus family Nanoviridae ) is one of the most important constraints in production of banana in the different parts of the world. Six genomic DNA components of BBTV isolate from Kandy, Sri Lanka (BBTV-K) were amplified by polymerase chain reaction (PCR) with specific primers using total DNA extracted from banana tissues showing typical symptoms of bunchy top disease. The amplicons were of expected size of 1.0–1.1 kb, which were cloned and sequenced. Analysis of sequence data revealed the presence of six DNA components; DNA-R, DNA-U3, DNA-S, DNA-N, DNA-M and DNA-C for Sri Lanka isolate. Comparisons of sequence data of DNA components followed by the phylogenetic analysis, grouped Sri Lanka-(Kandy) isolate in the Pacific Indian Oceans (PIO) group. Sri Lanka-(Kandy) isolate of BBTV is classified a new member of PIO group based on analysis of six components of the virus.

Chickpea ( Cicer arietinum L.) plants showing typical symptoms of infection by a phytoplasma that causes phyllody disease have been commonly observed in recent years in parts of south India. The symptoms included pale green leaves, bushy appearance due to excessive stunting of shoots, reduced internodal length and excessive axillary proliferation. The causal agent of the phyllody disease was identified based on symptoms, amplification of 16S rDNA of the phytoplasma by polymerase chain reaction (PCR) from infected samples, as well as by sequencing and phylogenetic analysis. First round PCR and nested-PCR protocols were standardized for improved efficiency and reliability of the diagnostic protocols. Using the primers P1/P7 and R16F2n/R16R2, 1,800 bp and 1,200 bp size products were amplified in first round PCR and nested-PCR protocols, respectively. The PCR product was cloned and sequenced and compared with the reference phytoplasma sequences from the database (NCBI). The Indian chickpea phyllody phytoplasma 16S rDNA sequences shared the highest nucleotide identity (>98%) with the 16S rII group phytoplasma candidates, also infecting chickpea from Australia and Pakistan. This is the first report of a phytoplasma of the 16SrII-group infecting chickpea from India. The genetic similarities and the potential threat of this new disease to chickpea cultivation in India are discussed.

The COmpact detectoR for the Eic (CORE) Proposal was submitted to the EIC \"Call for Collaboration Proposals for Detectors\". CORE comprehensively covers the physics scope of the EIC Community White Paper and the National Academies of Science 2018 report. The design exploits advances in detector precision and granularity to minimize size. The central detector includes a 3Tesla, 2.5m solenoid. Tracking is primarily silicon. Electromagnetic calorimetry is based on the high performance crystals. Ring-imaging Cherenkov detectors provide hadronic particle identification.