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Showing posts from November, 2025

๐Ÿฆด๐Ÿ”ฉ Accuracy of Robotic-Guided Pedicular Screw Insertion in Thoracolumbar Spinal Surgery

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  Robotic-guided surgical systems are transforming spinal care by enhancing precision, safety, and surgeon confidence. In thoracolumbar spinal surgery , pedicular screw placement is a vital step for stabilizing the spine in cases like trauma, deformity, spinal stenosis, or degenerative conditions. The accuracy of screw placement is crucial, as millimeter-level deviations can affect nerves, spinal cord, and overall patient outcomes. ๐Ÿค–๐Ÿฅ ๐Ÿ”น1️⃣ Importance of Accurate Pedicular Screw Placement Spinal Stability ๐Ÿ›ก️ Correct alignment provides strong biomechanical support. Neurological Safety ⚡ Prevents nerve root or spinal cord injuries. Reduced Revision Surgeries ๐Ÿ” Accurate positioning lowers complications and need for corrections. ๐Ÿ”น2️⃣ Role of Robotic Guidance in Spinal Surgery Preoperative Planning๐Ÿง ๐Ÿ“Š Robots use CT/MRI data for 3D visualization ensuring precise screw trajectory mapping. Intraoperative Navigation๐Ÿ“ Real-time guidance reduces hand-related tr...

⚗️ Waste-to-Energy Potential of Petroleum Refinery Sludge

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  ๐Ÿ”ฅ Statistical Optimization • ๐Ÿค– Machine Learning • ๐Ÿ’ฐ Life Cycle Cost Models Petroleum refinery sludge (PRS) has long been seen as an industrial nuisance, yet modern environmental engineering is transforming it into a valuable energy reservoir . With growing global energy demands and sustainability goals, converting PRS into usable fuel forms is becoming a strategic pathway toward a circular economy. Below is a structured overview of this multidimensional research domain. ๐ŸŒ✨ 1️⃣ Waste-to-Energy (WtE) Conversion Potential ⚡ 1.1 Composition & Characterization ๐Ÿงช Chemical constitution of PRS—rich in hydrocarbons, metals, and organics Thermochemical properties influencing calorific value Initial preprocessing requirements (drying, homogenization) 1.2 Conversion Routes ๐Ÿ”ฅ♻️ Pyrolysis : transforming sludge into bio-oil, syngas, and char Gasification : generating combustible gas mixtures Anaerobic digestion (for partially biodegradable fractions) Compa...

๐ŸŒฟ In Vitro Evaluation of Sugar-Conjugated Thienopyrimidinone Derivatives: A Glimpse Into Neuroprotection & Antioxidant Mastery ๐Ÿง ✨

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  ๐Ÿ”ฌ 1. Introduction to Thienopyrimidinone Framework ๐ŸŒŸ 1.1. Molecular Backbone Thienopyrimidinones represent a hybrid heterocyclic scaffold , fusing sulfur-rich thiophene with a biologically potent pyrimidinone ring. This dualistic structure grants them impressive electron-donating and receptor-binding qualities, making them ideal candidates for neurological research. ๐Ÿฌ 1.2. Sugar Conjugation: A Sweet Advantage By grafting carbohydrate moieties onto the core framework, researchers enhance solubility, cell permeability, metabolic stability , and even target selectivity , giving these molecules a “biological passport” for smoother cellular entry. ๐Ÿงช 2. In Vitro Screening Approaches ๐Ÿงซ 2.1. Antioxidant Metrics Studies often employ DPPH, ABTS, FRAP , and ROS-scavenging assays to quantify how effectively these derivatives neutralize reactive oxygen species. Sugar-linking tends to amplify redox-quenching potential , shielding cells from oxidative harm. ๐Ÿงฌ 2.2. Neuroprotect...

⚡๐Ÿง  Comparative Study of Neuroevolution and Deep Reinforcement Learning for Voltage Regulation in Power Systems

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Voltage regulation is crucial for ensuring the stability, reliability, and efficiency of modern power systems ✨. With increasing renewable integration, load variations, and distributed generators, traditional control strategies often fall short. This study explores two cutting-edge AI-based methods — Neuroevolution and Deep Reinforcement Learning (DRL) — to enhance intelligent control and dynamic voltage regulation. ๐ŸŒ⚡ ๐Ÿ”น 1️⃣ Introduction to Smart Voltage Regulation ๐ŸŒฉ️ Challenges in modern power systems ๐ŸŒฑ Impact of renewables and decentralized grids ๐Ÿค– Need for AI-driven voltage control ๐Ÿ”น 2️⃣ Neuroevolution (NE) ๐Ÿงฌ ⚙️ Concept Evolutionary algorithms + Neural networks Learns control strategies through biological-inspired mutation & selection ⭐ Strengths No gradient requirement Efficient for searching optimal network architectures Handles non-linear dynamic environments well ⚠️ Limitations High computational effort Slower convergence ...

⭐ Research Excellence Award – A Celebration of Scientific Brilliance

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  The Research Excellence Award is a prestigious global recognition designed to honour outstanding researchers, innovators, and academicians who demonstrate exceptional contributions to scientific knowledge and societal development ๐ŸŒ✨. This accolade acknowledges those whose work, discoveries, and publications create a transformative impact and inspire future generations of scholars. ๐Ÿ“Œ Objectives of the Award ๐ŸŽฏ Promoting Innovation & Creativity This award encourages groundbreaking research ideas, innovative methodologies, and problem-solving approaches that push the boundaries of science, technology, medicine, and humanities. ๐ŸŒฑ Encouraging Knowledge Growth By celebrating high-impact research, the award strengthens academic excellence and motivates researchers to pursue meaningful discoveries that benefit society and global industries. ๐Ÿ… Eligibility & Selection Criteria ๐Ÿงช Research Output & Publications Nominees are evaluated based on peer-reviewed publica...

Calcium Phosphates for Bone Tissue Regeneration — Influence of Synthesis Method on Physicochemical and Biological Properties ๐Ÿฆด๐Ÿ”ฌ

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  ๐ŸŒŸ Introduction Bone injuries, fractures, and degenerative disorders require advanced regenerative solutions. Calcium Phosphates (CaPs) have emerged as a revolutionary class of biomaterials due to their biocompatibility, osteoconductivity, and chemical similarity to natural bone minerals . Their effectiveness in tissue repair depends largely on the synthesis technique , which modifies their structure, composition, and biological interactions. ๐Ÿ”ง Synthesis Methods of Calcium Phosphates 1️⃣ Wet Chemical Precipitation ๐Ÿ’ง Uses aqueous solutions for forming CaP crystals. Enables control over crystal size, morphology, and stoichiometry . Produces highly bioactive and nano-structured materials ideal for scaffolds. 2️⃣ Sol-Gel Method ๐Ÿงช Generates materials at low temperatures with high purity. Provides homogeneous porosity and enhanced surface area , beneficial for cell attachment and drug delivery. 3️⃣ Hydrothermal Synthesis ♨️ Involves high pressure and te...

๐ŸŒŠ In Situ Water Quality Monitoring for the Assessment of Algae and Harmful Substances with Consideration of Uncertainties

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  ๐Ÿ” 1. Introduction to In Situ Water Quality Monitoring In situ water quality monitoring refers to the real-time, on-site measurement of chemical, physical, and biological parameters within natural water bodies ๐ŸŒฟ. This method eliminates the delays, distortions, and contamination risks associated with laboratory sampling. Through compact sensors and autonomous devices, researchers and environmental agencies can detect changes in water composition with remarkable immediacy ⏱️. ๐Ÿงช 2. Monitoring of Algae and Harmful Substances ๐ŸŸข 2.1 Algal Detection Excessive algae—especially harmful algal blooms (HABs) —pose threats to ecosystems, water supply, and aquatic life. In situ sensors, such as fluorometers, identify: Chlorophyll-a concentrations ๐ŸŒฑ Phycocyanin for cyanobacteria ๐ŸŒ Turbidity shifts indicating bloom proliferation These tools help forecast bloom events and implement mitigation strategies before they escalate. ☣️ 2.2 Detection of Harmful Substances Water...

๐ŸŒฑ Predictive Fermentation Control of Lactiplantibacillus plantarum Using Deep Learning CNNs

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  ๐Ÿ”ฌ 1. Introduction to Smart Fermentation Fermentation is no longer just a traditional biochemical process—it has evolved into a smart, data-driven ecosystem. Lactiplantibacillus plantarum , a versatile probiotic used in food, beverage, and nutraceutical industries, requires precise monitoring to maintain quality and consistency. Deep Learning, especially Convolutional Neural Networks (CNNs), empowers researchers to predict fermentation behavior with unmatched accuracy ๐Ÿš€. ๐Ÿค– 2. Role of Deep Learning in Microbial Fermentation Deep Learning transforms raw fermentation data into actionable intelligence. CNNs excel at pattern recognition, making them ideal for analyzing time-series sensor readings, microbial growth curves, pH shifts, and metabolite trends. This predictive capability enables faster decision-making and reduces dependency on manual testing ๐Ÿ“ˆ. ๐Ÿ” 2.1 Convolutional Neural Networks (CNNs) Identify hidden patterns in fermentation datasets Handle complex nonlinear...