This study investigates the relationship between deep learning (DL) model accuracy and expert agreement in classifying crash narratives. We evaluate five DL models -- including BERT variants, USE, and a zero-shot classifier -- against expert labels and narratives, and extend the analysis to four large language models (LLMs): GPT-4, LLaMA 3, Qwen, and Claude. Our findings reveal an inverse relationship: models with higher technical accuracy often show lower agreement with human experts, while LLMs demonstrate stronger expert alignment despite lower accuracy. We use Cohen's Kappa and Principal Component Analysis (PCA) to quantify and visualize model-expert agreement, and employ SHAP analysis to explain misclassifications. Results show that expert-aligned models rely more on contextual and temporal cues than location-specific keywords. These findings suggest that accuracy alone is insufficient for safety-critical NLP tasks. We argue for incorporating expert agreement into model evaluation frameworks and highlight the potential of LLMs as interpretable tools in crash analysis pipelines.

Comorbidity carries significant implications for disease understanding and management. The genetic causes for comorbidity often trace back to mutations occurred either in the same gene associated with two diseases or in different genes associated with different diseases respectively but coming into connection via protein-protein interactions. Therefore, human interactome has been used in more sophisticated study of disease comorbidity. Human interactome, as a large incomplete graph, presents its own challenges to extracting useful features for comorbidity prediction. In this work, we introduce a novel approach named Biologically Supervised Graph Embedding (BSE) to allow for selecting most relevant features to enhance the prediction accuracy of comorbid disease pairs. Our investigation into BSE's impact on both centered and uncentered embedding methods showcases its consistent superiority over the state-of-the-art techniques and its adeptness in selecting dimensions enriched with vital biological insights, thereby improving prediction performance significantly, up to 50% when measured by ROC for some variations. Further analysis indicates that BSE consistently and substantially improves the ratio of disease associations to gene connectivity, affirming its potential in uncovering latent biological factors affecting comorbidity. The statistically significant enhancements across diverse metrics underscore BSE's potential to introduce novel avenues for precise disease comorbidity predictions and other potential applications. The GitHub repository containing the source code can be accessed at the following link: https://github.com/xihan-qin/Biologically-Supervised-Graph-Embedding.

Blind spots created by the driver-side B-pillar impair the ability of the driver to assess their surroundings accurately, significantly contributing to the frequency and severity of vehicular accidents. Vehicle manufacturers are unable to readily eliminate the B-pillar due to regulatory guidelines intended to protect vehicular occupants in the event of side collisions and rollover incidents. Furthermore, assistance implements utilized to counteract the adverse effects of blind spots remain ineffective due to technological limitations and optical impediments. This paper introduces mechanisms to quantify the obstruction caused by the B-pillar when the head of the driver is facing forward and turning 90 degrees, typical of an over-the-shoulder blind spot check. It uses the metrics developed to demonstrate the relationship between B-pillar width and the obstruction angle. The paper then creates a methodology to determine the movement required of the driver to eliminate blind spots. Ultimately, this paper proposes a solution, the Blind Spot Eliminator, and demonstrates that it successfully decreases both the obstruction angle and, consequently, the required driver movement. A prototype of the Blind Spot Eliminator is also constructed and experimented with using a mannequin to model human vision in a typical passenger vehicle. The results of this experiment illustrated a substantial improvement in viewing ability, as predicted by earlier calculations. Therefore, this paper concludes that the proposed Blind Spot Eliminator has excellent potential to improve driver safety and reduce vehicular accidents. Keywords: B-pillar, driver vision, active safety, blind spots, transportation, crash avoidance, side-view assist.

To make stock market listing attractive for startups, leading stock exchange BSE has decided to launch a new platform next month to list the new-age companies. This platform will facilitate the listing of companies in sectors like IT, ITES, bio-technology and life sciences, 3D printing, space technology and e-commerce. Besides, the platform will aid in listing of companies from hi-tech defence, drones, nano technologies, artificial intelligence, big data, virtual reality, e-gaming, robotics, genetic engineering, among other sectors. In order to provide further incentive to startups, the exchange has announced BSE startup platform at its SME (small and medium enterprise) segment. "We will launch the platform on July 9," an exchange official said.

The Bombay Stock Exchange and the National Stock Exchange are exploring the opportunity to venture beyond the current businesses and have, along with other market infrastructure companies, sought SEBI's permission to form a separate entity to take on these businesses. "Stock exchanges (SEs) are seeking a one-time regulatory approval to venture into business areas outside SEBI's purview," sources close to the development told BusinessLine. "They have sought SEBI's permission to engage in activities or businesses that are unrelated to, or not identical to, those of an SE or of clearing corporations (CCs) or their core business, through a separate legal entity," they added. The proposal was made to the newly-formed SEBI committee headed by former Reserve Bank of India Deputy Governor R Gandhi, which has been mandated to review norms for stock exchanges, depositories and CCs. The BSE and the NSE did not respond to an e-mail from BusinessLine seeking their views.

The Bombay Stock Exchange and the National Stock Exchange are exploring the opportunity to venture beyond the current businesses and have, along with other market infrastructure companies, sought SEBI's permission to form a separate entity to take on these businesses. "Stock exchanges (SEs) are seeking a one-time regulatory approval to venture into business areas outside SEBI's purview," sources close to the development told BusinessLine. "They have sought SEBI's permission to engage in activities or businesses that are unrelated to, or not identical to, those of an SE or of clearing corporations (CCs) or their core business, through a separate legal entity," they added. The proposal was made to the newly-formed SEBI committee headed by former Reserve Bank of India Deputy Governor R Gandhi, which has been mandated to review norms for stock exchanges, depositories and CCs. The BSE and the NSE did not respond to an e-mail from BusinessLine seeking their views.

Stock market major BSE on Monday said it has launched an artificial intelligence mechanism to track "news related to listed companies" on digital media. According to BSE, the artificial intelligence mechanism based on a systemic solution has been envisaged to deepen its regulatory oversight on newer channels of communication. "The primary objective of verification is that the mechanism will detect and mitigate potential risks of market manipulation, rumour, and reduce information asymmetry arising from it on digital media platforms, including social media," the BSE said in a statement. "It provides accurate information involving listed companies and BSE through the exchange website for the benefit of investors." According to the regulated stock exchange, the solution employs an advanced level combination of statistical modeling and big data analytics.