Technology Scientists

  Despite soaring R&D costs, the efficiency of drug development remains stagnant, indicating the need for smarter research collaboration strategies. Traditional approaches that rely solely on knowledge-based metrics such as technology similarity or spillover risk fall short of leveraging biological data essential for drug discovery. Addressing this gap, this research presents a comprehensive framework for selecting drug development partners by incorporating protein interaction predictions into collaboration decision-making. By utilizing a heterogeneous network of diseases, drugs, and proteins, and applying advanced knowledge graph embedding models, the framework enhances the accuracy of identifying potential collaborative partners who can significantly contribute to drug innovation. Limitations of Current Collaboration Models in Drug R&D Conventional models for choosing research collaborators often prioritize knowledge characteristics like similarity or complementarity, ...

With the global depletion of fossil fuel resources and escalating environmental challenges, the need for clean, sustainable energy sources has become more urgent than ever. Among various renewable energy options, wind energy has gained significant traction due to its eco-friendly nature and abundance. However, the inherent intermittency and unpredictability of wind power pose critical challenges for its integration into power grids. Accurate forecasting of wind energy is, therefore, essential for grid stability, power system reliability, and efficient energy management. This research investigates a comprehensive wind prediction framework based on real-world data from Penmanshiel wind farm in Scotland, offering both deterministic and uncertainty-based forecasts over multiple time horizons. Challenges in Wind Power Grid Integration Despite the rapid growth of wind energy installations worldwide, large-scale grid integration remains a technical challenge. Wind's variable nature lea...

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  Additive manufacturing has emerged as a game-changing technique in the fabrication of microelectronics, offering unparalleled freedom in material selection and device architecture. Unlike traditional cleanroom-based methods that are constrained to flat, two-dimensional topologies, additive processes—especially direct ink writing (DIW)—enable the incorporation of complex-shaped particles into flexible substrates. This research showcases a novel approach for creating flexible ultraviolet (UV) sensors by combining tetrapodal zinc oxide (t-ZnO) particles and carbon nanotubes. Through a simple yet effective DIW method followed by laser milling, the binder polymer is selectively removed, revealing functional particles that deliver high UV responsivity and mechanical flexibility. The result is a highly adaptable, printable sensor suitable for a wide range of applications. Material Innovation in Printable Electronics The core strength of this work lies in the use of tetrapodal ZnO (t-...

Osteoarthritis is a progressive joint disease primarily caused by the degeneration of articular cartilage. Effective and lasting treatments for cartilage defects remain limited due to the tissue’s avascular nature and poor regenerative capacity. However, advances in biomaterials and microrobotic systems present innovative opportunities in regenerative medicine. This study explores the use of biodegradable microrobots formed from sodium alginate (SA), gelatin methacrylate (GelMA), and iron oxide nanoparticles (Fe₃O₄) to deliver chondroitin sulfate (CS) for cartilage regeneration. These microrobots leverage magnetic field manipulation for targeted delivery, offering a minimally invasive and efficient strategy to treat cartilage damage. Fabrication of Biodegradable Microrobots The development of microrobots capable of targeted drug delivery relies heavily on the optimization of their structure and composition. In this study, spherical microcapsules approximately 450 μm in diameter were...

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Technology Scientists Awards =================== Website : technologyscientists.com Nomination Link : https://technologyscientists.com/award-nomination/?ecategory=Awards&rcategory=Awardee To Contact : support@technologyscientists.com

  Outstanding Contribution Award The Outstanding Contribution in Research Award honors individuals who have demonstrated exceptional dedication, innovation, and long-term impact in their respective research fields. This prestigious award recognizes researchers whose groundbreaking work has significantly advanced knowledge, inspired new directions, and made lasting contributions to science, technology, or social progress. Through sustained excellence and leadership, these individuals serve as role models and thought leaders in the global research community. Technology Scientists Awards =================== Website : technologyscientists.com Nomination Link : https://technologyscientists.com/award-nomination/?ecategory=Awards&rcategory=Awardee To Contact : support@technologyscientists.com #OutstandingContributionAward #ResearchExcellence #ScientificAchievement #InnovationInResearch #GlobalResearchImpact #LeadingResearcher #AcademicRecognition #ResearchLeadership #...

  The integration of patient sentiment analysis into smart healthcare frameworks has emerged as a transformative approach in enhancing pain management strategies. By leveraging cutting-edge deep learning techniques and facial expression analysis, researchers are developing systems that can accurately interpret the emotional and pain responses of patients in real time. This advancement supports medical professionals in delivering more empathetic and tailored care, especially for patients who may have difficulty verbalizing their pain. The proposed pain sentiment recognition system presents a significant step forward in this domain, offering a multi-phase architecture that combines facial detection, deep learning-based feature extraction, emotion modeling, and performance optimization to create a robust and reliable tool for modern medical environments. Deep Learning Techniques for Facial Feature Extraction Facial expression analysis has become a pivotal element in emotion and pai...

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In recent years, deep learning has revolutionized diagnostic approaches in medical imaging, including dentistry. This study builds upon prior efforts to enhance caries detection by addressing two key limitations: limited classification granularity and a narrow focus on local lesions. Leveraging advanced object detection architectures—YOLO-v8, YOLO-v9, and YOLO-NAS—the study introduces a novel framework capable of full-tooth instance detection with fine-grained classification based on the International Caries Detection and Assessment System (ICDAS). Furthermore, two correction strategies integrating background knowledge were developed to improve model stability and accuracy under real-world clinical conditions. Tooth Instance-Based Detection Using YOLO Models Traditional approaches often localize caries within a limited area of interest, which undermines comprehensive diagnosis. This research innovates by implementing a full-tooth instance detection method where every tooth in an in...

Thermal Lens Spectroscopy (TLS) is a sensitive photothermal technique widely used for studying heat transfer in molecular systems. This research introduces an optimized TLS setup where geometric calibration is employed to improve measurement precision. Through this enhanced approach, the photothermal behavior of carboxylic acids—specifically those used as modulators in metal-organic frameworks (MOFs)—is explored. The study focuses on both non-fluorinated (formic acid, acetic acid) and fluorinated derivatives (difluoroacetic acid, trifluoroacetic acid), uncovering new insights into how structural and electronic variations influence thermal responses under variable laser power. Photothermal Response of Carboxylic Modulators Carboxylic acids serve as essential modulators in MOF synthesis due to their ability to fine-tune the structural and chemical properties of frameworks. In this work, the thermal lens response of formic acid, acetic acid, and their fluorinated analogs is quantitativ...

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 In the era of Industry 4.0 , the demand for real-time, data-driven insights in chemical reaction engineering is rapidly growing. A pivotal aspect of this transformation lies in the creation and continuous refinement of digital twins—virtual replicas of physical systems that depend heavily on accurate, mechanistic modeling. Traditional methods of model derivation are time-consuming and prone to human error, especially when faced with noisy or complex data. The integration of reinforcement learning into this modeling process offers a promising pathway toward automation, accuracy, and scalability. This study introduces a novel workflow that leverages reinforcement learning to generate interpretable reactor models from raw experimental data, demonstrating the next evolution in process automation and model intelligence. Reinforcement Learning in Mechanistic Model Derivation Reinforcement learning (RL) presents a paradigm shift in how models can be generated and refined in chemical reac...

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  Future space exploration increasingly targets small celestial bodies such as asteroids, but these missions confront substantial challenges due to unknown environmental dynamics and limited prior knowledge. Ensuring a safe and precise landing on these irregular bodies requires highly intelligent and adaptable autonomous systems. The presented study proposes a robust adaptive guidance framework that synergistically combines meta-reinforcement learning with Monte Carlo Tree Search (MCTS) to meet these demands. The approach aims to accelerate the learning process while enhancing adaptability to uncertain, dynamic asteroid environments. Through simulations, this methodology demonstrates high robustness and reliability, making it a promising direction for next-generation space missions. Meta-Reinforcement Learning for Space Robotics Meta-reinforcement learning (meta-RL) is central to the proposed guidance strategy, as it enables rapid generalization across varying asteroid environme...

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  This research explores a cutting-edge solution to the cooperative output regulation problem in multi-agent systems through a novel energy-dependent intermittent event-triggered compensator . Unlike traditional time-triggered or continuous communication strategies, this method introduces an intelligent mechanism that monitors the real-time error energy to determine whether communication is necessary. It flexibly segments the system's operational area into three regions—defined by a safety boundary and an intermittence boundary—allowing the agents to adapt their communication patterns based on situational demand. The strategy not only ensures system stability and regulation convergence but also significantly reduces communication overhead, which is crucial for bandwidth-limited and energy-constrained networks. Energy-Dependent Intermittent Compensator Design The heart of this research lies in designing an intermittent compensator that dynamically governs agent communication by asse...

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  Monoclinic n-type bismuth vanadate (BiVO₄) has emerged as a benchmark photoanode material for solar-driven photoelectrochemical (PEC) water splitting due to its ideal band alignment with water redox potentials, ease of synthesis, and high charge extraction efficiency. Despite these advantages, BiVO₄ suffers from several intrinsic limitations such as a relatively wide band gap, low electronic conductivity, and high recombination rates that hinder its practical PEC performance. Addressing these challenges requires a fundamental understanding of how to engineer the material’s electronic and optical properties—especially through doping and defect manipulation. This research investigates the effects of non-metal doping and intrinsic vacancies, paving the way for performance-optimized BiVO₄ photoanodes. Electronic Structure Tuning through Nitrogen Doping The substitution of nitrogen into the BiVO₄ lattice significantly alters its electronic structure, leading to a reduction in band ...

  Passive acoustic monitoring is a critical tool for studying marine mammals, especially in vast oceanic environments where direct observation is challenging. With the rising volume of acoustic data, automated detectors—often powered by deep learning models—have become essential. These detectors commonly use spectrograms to convert sound into a visual time-frequency representation, which helps identify species-specific vocal patterns. This study explores the limitations of relying solely on traditional spectrograms and investigates whether alternative or combined acoustic representations can improve the accuracy of identifying orca (Orcinus orca) vocalizations amid environmental noise. Acoustic Representation Techniques for Marine Bioacoustics Acoustic signals are inherently complex, often requiring sophisticated processing techniques to extract meaningful patterns. This study evaluates nine different acoustic representations—magnitude, mel, and constant-Q transform (CQT) spectr...

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The advancement of wearable electronics hinges on the development of smart materials that seamlessly combine mechanical flexibility with functional properties like conductivity, self-healing, and biocompatibility. Hydrogels, with their water-rich, soft polymer networks, have emerged as ideal candidates for these applications. However, achieving both high ionic conductivity and mechanical robustness in a single hydrogel remains a formidable challenge. In response, this study introduces a novel sodium alginate-based hydrogel—OSA-g-P(AA-co-MBA)/Li+—engineered using a Hofmeister effect-driven dual-crosslinking strategy. This innovative method facilitates the integration of multiple performance metrics including elasticity, conductivity, and antibacterial activity, positioning the hydrogel as a multifunctional platform for flexible, next-generation wearable sensors. Hofmeister Effect-Driven Dual-Crosslinking Mechanism The unique dual-crosslinking mechanism employed in the synthesis of OSA-...