World Top Scientists Awards

  Winter road safety is a major challenge in snowy and icy regions, where rapidly changing surface conditions can lead to severe traffic accidents. Winter Road Surface Condition Recognition (WRSCR) aims to automatically identify road states such as snow-covered, icy, wet, or dry surfaces using visual data. By integrating image-to-image translation techniques , this research introduces an intelligent and robust framework that enhances perception accuracy under harsh winter environments. 🧠 Core Concept: Image-to-Image Translation Image-to-image translation is a deep learning approach that transforms images from one domain to another while preserving essential structural features. In this context, it converts complex winter road images into enhanced or normalized representations, reducing visual noise caused by snow glare, low contrast, fog, and lighting variations ❄️✨. 📸 Data Acquisition and Preprocessing High-resolution road images are collected from vehicle-mounted cameras u...

  The Most Shared Article Awards celebrate outstanding articles that spark conversations, inspire minds, and travel far across digital platforms 🌍. These awards recognize content that readers not only enjoy but feel compelled to share, discuss, and recommend within their professional and social networks 🤝. By honoring such impactful work, the awards highlight the true power of ideas in the age of connectivity 📡. 🌟 Purpose of the Award 🔹 Recognizing Digital Influence This award acknowledges articles that achieve exceptional reach through shares on social media, academic platforms, blogs, and online communities 📈. 🔹 Encouraging Knowledge Dissemination It motivates authors to create meaningful, accessible, and engaging content that bridges knowledge gaps and reaches diverse audiences 🎯. 📝 Eligibility Criteria 🔹 Quality & Originality Articles must demonstrate originality, clarity, and relevance, presenting ideas that resonate with a wide readership ✨. 🔹 Shareab...

  Blast-induced air overpressure is a critical safety concern in mining, construction blasting, and defense operations 💣. Accurate prediction of air overpressure helps prevent structural damage, environmental impact, and human discomfort 🏗️👷‍♂️. Traditional empirical models often fail to capture complex nonlinear interactions between blast parameters and atmospheric conditions 🌍. Explainable Artificial Intelligence (XAI) emerges as a powerful solution by combining predictive accuracy with interpretability 🤖✨. 🧠 Role of Explainable Artificial Intelligence (XAI) Explainable AI focuses on transparent machine learning models that reveal how and why predictions are made 🔍📊. Unlike black-box models, XAI techniques such as SHAP, LIME, and decision trees allow engineers to understand the influence of input variables on air overpressure outcomes 🧩. This enhances trust, reliability, and regulatory acceptance of AI-driven blast prediction systems ✔️. 💥 Blast-Induced Air Overpre...

 Laparoscopic surgery demands exceptional hand–eye coordination, spatial awareness, and precision. However, access to commercial laparoscopic simulators is often limited due to high costs and infrastructure requirements 💰. A low-cost, do-it-yourself (DIY) laparoscopic simulator offers an innovative and inclusive solution, enabling surgical trainees to practice essential skills in a safe, repeatable, and stress-free environment 🧠👐. 🧩 2. Concept & Design Principles The simulator is designed to replicate the core ergonomics of minimally invasive surgery while remaining affordable and easy to construct 🛠️. Key design elements include: 📦 A compact training box simulating the abdominal cavity 🔦 Internal lighting for clear visualization 📷 A basic camera or smartphone for video feedback 🎯 Entry ports mimicking trocar placement The focus is on functionality over complexity , ensuring effective skill transfer without unnecessary technical barriers. 🔧 3. Asse...

  Rapid urbanization has intensified air pollution, with nitrogen dioxide (NO₂) emerging as a major threat to public health. Emitted mainly from vehicles, power plants, and industrial activities, NO₂ contributes to respiratory illnesses and environmental degradation. Urban green spaces, when designed strategically using tree–shrub–grass (TSG) vegetation structures , act as natural air purifiers 🌬️🍃, offering a sustainable solution to mitigate NO₂ pollution. 🌲 Role of Trees in NO₂ Reduction Trees form the upper canopy layer and play a dominant role in pollutant removal. Their large leaf surface area efficiently absorbs NO₂ through stomata and traps particulate matter on leaf surfaces. Tall trees also alter wind flow patterns, slowing air movement and allowing pollutants to settle and disperse safely. Species with dense foliage and rough leaf textures enhance NO₂ capture, making urban streets and parks healthier environments 🌳💨. 🌿 Contribution of Shrubs as Pollution Barri...

  The Most Liked Article Awards recognize outstanding written works that resonate deeply with readers across digital and academic platforms. These awards honor authors whose articles generate exceptional appreciation through likes, shares, and positive engagement. Beyond popularity, the award reflects clarity of thought, originality, relevance, and emotional connection with audiences worldwide 🌍✨. 🎯 Recognizing Reader-Driven Excellence This award highlights content that captures attention, sparks discussion, and inspires communities. It values reader voice , making engagement a powerful indicator of quality and influence 👍💬. 📊 Measuring Digital Impact Likes, reactions, and interactions are used as benchmarks to evaluate how effectively an article connects with its audience in the digital era 📱📈. 📝 Eligibility Criteria ✍️ Content Quality Articles must demonstrate originality, structured presentation, and meaningful insights supported by facts or experience 🔍🧠. 🌐...

  Graves’ disease is an autoimmune thyroid disorder marked by excessive production of thyroid hormones (hyperthyroidism). This hormonal surge accelerates metabolism, profoundly influencing body composition, appetite, and dietary behavior. Understanding these changes at the time of diagnosis is crucial for effective clinical management and nutritional intervention. 🌡️🧬 ⚖️ Alterations in Body Composition Newly diagnosed Graves’ disease patients often experience significant body composition shifts compared with euthyroid (normal thyroid function) individuals. 🧍‍♂️ Lean Mass Reduction Despite increased energy intake, patients frequently show loss of skeletal muscle mass , driven by heightened protein catabolism. This muscle wasting can lead to early fatigue and reduced physical strength. 💪⬇️ ⚖️ Fat Mass Changes Total body fat is often reduced due to increased lipolysis; however, fat distribution may vary, sometimes masking weight loss and complicating diagnosis. 🔥 🍽️ Ea...

Hydration heat plays a critical role in determining the durability, strength, and thermal performance of cement composites. In energy-efficient cement systems—especially those incorporating supplementary cementitious materials (SCMs)—accurate prediction of hydration heat is essential to prevent thermal cracking and reduce carbon emissions. Traditional experimental and empirical models are often time-consuming and limited in adaptability. 🚧 This has led to the emergence of Machine Learning (ML) as a powerful alternative for predictive modeling in sustainable construction. 🧠 Role of Machine Learning in Hydration Heat Prediction Machine learning techniques enable data-driven prediction by learning complex nonlinear relationships between material composition and thermal behavior. ML models can process large datasets involving cement chemistry, curing conditions, and admixture proportions to forecast hydration heat with higher accuracy and efficiency. ⚡ This approach significantly redu...

  🌐 Introduction to Wireless Sensor Networks (WSNs) Wireless Sensor Networks (WSNs) consist of tiny, battery-powered sensor nodes that cooperatively monitor physical or environmental conditions such as temperature, pressure, or motion. These nodes communicate wirelessly and are often deployed in remote or inaccessible areas, making energy efficiency a critical design challenge 🔌. One of the most energy-consuming operations in WSNs is neighbor discovery , where nodes identify nearby nodes to establish communication links. 🔍 Neighbor Discovery: Concept and Challenges Neighbor discovery is the process by which sensor nodes periodically wake up to detect and communicate with neighboring nodes 📶. Traditional discovery protocols rely on synchronized schedules or frequent wake-ups, leading to excessive energy consumption, idle listening, and reduced network lifetime ⏳. The challenge lies in achieving fast discovery while minimizing power usage , especially in large-scale, asynch...

  The Hydrogen Evolution Reaction (HER) is a cornerstone process in sustainable energy technologies, enabling the conversion of water into clean hydrogen fuel 🔋💧. Developing cost-effective, high-performance, and durable electrocatalysts is essential to replace precious metal catalysts like platinum. Transition-metal-based composites have emerged as promising alternatives due to their tunable electronic structures and abundant active sites. 🔹 2. Graphitic Carbon Nitride (g-C₃N₄): A Functional Support 🌿 Graphitic carbon nitride (g-C₃N₄) is a metal-free, polymeric semiconductor known for its high nitrogen content, chemical stability, and layered structure . Key advantages include: Rich nitrogen sites for electron donation ⚛️ Large surface area enhancing catalyst dispersion Excellent corrosion resistance under electrochemical conditions These features make g-C₃N₄ an ideal support material for boosting catalytic activity. 🔹 3. Cobalt Diselenide (CoSe₂): An Active HER ...