Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model

According to Global Electricity Review 2022, electricity generation from renewable energy sources has increased by 20% worldwide primarily due to more installation of large green power plants. Monitoring the renewable energy assets in those large power plants is still challenging as the assets are highly impacted by several environmental factors, resulting in issues like less power generation, malfunctioning, and degradation of asset life. Therefore, detecting the surface defects on the renewable energy assets would facilitate the process to maintain the safety and efficiency of the green power plants. An innovative detection framework is proposed to achieve an economical renewable energy asset surface monitoring system. First capture the asset's high-resolution images on a regular basis and inspect them to detect the damages. For inspection this paper presents a unified deep learning-based image inspection model which analyzes the captured images to identify the surface or structural damages on the various renewable energy assets in large power plants. We use the Vision Transformer (ViT), the latest developed deep-learning model in computer vision, to detect the damages on solar panels and wind turbine blades and classify the type of defect to suggest the preventive measures. With the ViT model, we have achieved above 97% accuracy for both the assets, which outperforms the benchmark classification models for the input images of varied modalities taken from publicly available sources

A Resilient Power Distribution System using P2P Energy Sharing

The adoption of distributed energy resources (DERs) such as solar panels and wind turbines is transforming the traditional energy grid into a more decentralized system, where microgrids are emerging as a key concept. Peer-to-Peer (P2P) energy sharing in microgrids enhances the efficiency and flexibility of the overall system by allowing the exchange of surplus energy and better management of energy resources. This work analyzes the impact of P2P energy sharing for three cases - within a microgrid, with neighboring microgrids, and all microgrids combined together in a distribution system. A standard IEEE 123 node test feeder integrated with renewable energy sources is partitioned into microgrids. For P2P energy sharing between microgrids, the results show significant benefits in cost, reduced energy dependence on the grid, and a significant improvement in the system's resilience. We also predicted the energy requirement for a microgrid to evaluate energy resilience for the control and operation of the microgrid. Overall, the analysis provides valuable insights into the performance and sustainability of microgrids with P2P energy sharing.Comment: arXiv admin note: text overlap with arXiv:2212.0231

Modelling of the Electric Vehicle Charging Infrastructure as Cyber Physical Power Systems: A Review on Components, Standards, Vulnerabilities and Attacks

The increasing number of electric vehicles (EVs) has led to the growing need to establish EV charging infrastructures (EVCIs) with fast charging capabilities to reduce congestion at the EV charging stations (EVCS) and also provide alternative solutions for EV owners without residential charging facilities. The EV charging stations are broadly classified based on i) where the charging equipment is located - on-board and off-board charging stations, and ii) the type of current and power levels - AC and DC charging stations. The DC charging stations are further classified into fast and extreme fast charging stations. This article focuses mainly on several components that model the EVCI as a cyberphysical system (CPS)

Samanantar: The Largest Publicly Available Parallel Corpora Collection for 11 Indic Languages

We present Samanantar, the largest publicly available parallel corpora collection for Indic languages. The collection contains a total of 49.7 million sentence pairs between English and 11 Indic languages (from two language families). Specifically, we compile 12.4 million sentence pairs from existing, publicly-available parallel corpora, and additionally mine 37.4 million sentence pairs from the web, resulting in a 4x increase. We mine the parallel sentences from the web by combining many corpora, tools, and methods: (a) web-crawled monolingual corpora, (b) document OCR for extracting sentences from scanned documents, (c) multilingual representation models for aligning sentences, and (d) approximate nearest neighbor search for searching in a large collection of sentences. Human evaluation of samples from the newly mined corpora validate the high quality of the parallel sentences across 11 languages. Further, we extract 83.4 million sentence pairs between all 55 Indic language pairs from the English-centric parallel corpus using English as the pivot language. We trained multilingual NMT models spanning all these languages on Samanantar, which outperform existing models and baselines on publicly available benchmarks, such as FLORES, establishing the utility of Samanantar. Our data and models are available publicly at https://indicnlp.ai4bharat.org/samanantar/ and we hope they will help advance research in NMT and multilingual NLP for Indic languages.Comment: Accepted to the Transactions of the Association for Computational Linguistics (TACL

LEARN: A multi-centre, cross-sectional evaluation of Urology teaching in UK medical schools

OBJECTIVE: To evaluate the status of UK undergraduate urology teaching against the British Association of Urological Surgeons (BAUS) Undergraduate Syllabus for Urology. Secondary objectives included evaluating the type and quantity of teaching provided, the reported performance rate of General Medical Council (GMC)-mandated urological procedures, and the proportion of undergraduates considering urology as a career. MATERIALS AND METHODS: LEARN was a national multicentre cross-sectional study. Year 2 to Year 5 medical students and FY1 doctors were invited to complete a survey between 3rd October and 20th December 2020, retrospectively assessing the urology teaching received to date. Results are reported according to the Checklist for Reporting Results of Internet E-Surveys (CHERRIES). RESULTS: 7,063/8,346 (84.6%) responses from all 39 UK medical schools were included; 1,127/7,063 (16.0%) were from Foundation Year (FY) 1 doctors, who reported that the most frequently taught topics in undergraduate training were on urinary tract infection (96.5%), acute kidney injury (95.9%) and haematuria (94.4%). The most infrequently taught topics were male urinary incontinence (59.4%), male infertility (52.4%) and erectile dysfunction (43.8%). Male and female catheterisation on patients as undergraduates was performed by 92.1% and 73.0% of FY1 doctors respectively, and 16.9% had considered a career in urology. Theory based teaching was mainly prevalent in the early years of medical school, with clinical skills teaching, and clinical placements in the later years of medical school. 20.1% of FY1 doctors reported no undergraduate clinical attachment in urology. CONCLUSION: LEARN is the largest ever evaluation of undergraduate urology teaching. In the UK, teaching seemed satisfactory as evaluated by the BAUS undergraduate syllabus. However, many students report having no clinical attachments in Urology and some newly qualified doctors report never having inserted a catheter, which is a GMC mandated requirement. We recommend a greater emphasis on undergraduate clinical exposure to urology and stricter adherence to GMC mandated procedures

Biochemical and <i>in silico</i> analysis of the binding mode of erastin with tubulin

Erastin (ERN) is a small molecule that induces different forms of cell death. For example, it has been reported to induce ferroptosis by disrupting tubulin subunits that maintain the voltage-dependent anion channels (VDACs) of mitochondria. Although its possible binding to tubulin has been suggested, the fine details of the interaction between ERN and tubulin are poorly understood. Using a combination of biochemical, cell-model and in silico approaches, we elucidate the interactions of ERN with tubulin and their biological manifestations. After confirming ERN's antiproliferative efficacy (IC50, 20 ± 3.2 M) and induction of cell death in the breast cancer cell line MDA-MB-231, the binding interactions of ERN with tubulin were examined. ERN bound to tubulin in a concentration-dependent manner, disorganizing the structural integrity of the protein, as substantiated via the tryptophan-quenching assay and the aniline-naphthalene sulfonate binding assay, respectively. In silico studies based on molecular docking revealed a docking score of −5.863 kcal/mol, suggesting strong binding interactions of ERN with tubulin. Additionally, molecular dynamics simulation and Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) analyses evinced the binding free energy (ΔGbinding) of −31.235 kcal/mol, substantiating strong binding affinity of ERN with tubulin. Ligplot analysis showed hydrogen bonding with specific amino acids (Asn A226, Thr A223, Gln B247 and Val B355). QikProp-based ADME (absorption, distribution, metabolism and excretion) assessment showed considerable therapeutic potential for ERN. Communicated by Ramaswamy H. Sarma</p