Technology Scientists

  Cardiovascular health monitoring has become a key focus in medical research due to the rising prevalence of heart-related diseases. Traditional methods often lack continuous monitoring capabilities, creating a need for advanced wearable devices. A newly proposed wearable optic-fiber sensor, designed with a W-shaped microfiber encapsulated in PDMS, offers high sensitivity and weak pressure sensing for real-time pulse monitoring. This innovative approach not only enhances early detection of cardiovascular risks but also promotes preventive healthcare strategies in healthy individuals. Sensor Design and Working Principle The proposed sensor integrates a W-shaped microfiber into a polydimethylsiloxane (PDMS) film, optimizing the structure for high sensitivity to small pressure variations. By effectively transmitting optical signals in response to pressure changes, the sensor captures detailed pulse characteristics. Its compact design makes it suitable for wearable applications wit...

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  Artificial intelligence (AI) is transforming healthcare and nursing by improving decision-making, enhancing patient safety, and optimizing clinical workflows. Nursing students, as future healthcare professionals, must be equipped with the necessary skills and knowledge to apply AI in clinical practice. However, the readiness of nursing students to adopt and integrate AI in their future roles depends heavily on structured education and training. This study highlights the significance of AI-focused courses in shaping nursing students’ readiness for medical artificial intelligence and their ability to adapt to technological advancements in healthcare. Significance of AI in Nursing Education The rapid adoption of AI in healthcare necessitates curriculum integration at the undergraduate nursing level. Traditional nursing programs often emphasize clinical and humanistic aspects of care but may lack focus on technological literacy. By introducing dedicated AI courses, institutions ca...

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

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

The rapid proliferation of electronic devices, fueled by shorter product lifecycles and growing consumer demand, has led to a significant surge in electronic waste (e-waste). Governments across the globe have responded with recycling regulations aimed at mitigating environmental and health hazards, promoting sustainable resource use, and fostering a circular economy. While these measures address urgent ecological concerns, they also impose compliance costs on businesses, potentially influencing their strategic priorities. This study investigates how such regulations affect the R&D and marketing expenditures of firms in the electronics sector, with an emphasis on balancing environmental compliance and corporate innovation. E-Waste Regulations and Corporate Resource Allocation E-waste recycling laws dictate how electronic products are collected, processed, and disposed of, requiring firms to invest in compliance mechanisms. While these initiatives advance environmental goals, they ...

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

  Breast cancer remains one of the most prevalent and life-threatening diseases worldwide, necessitating accurate and reliable diagnostic methods for early detection. This research introduces a novel diagnostic framework for breast cancer detection using 3D tomosynthesis mammography. By integrating radiomic features with deep learning-derived features from an attention-based 3D autoencoder, the study addresses the challenges of diagnostic accuracy, feature reproducibility across imaging scanners, and clinical interpretability. The proposed method represents a significant step forward in computer-aided diagnosis, offering improved performance through feature harmonization, dimensionality reduction, and ensemble learning approaches. Dataset and Feature Extraction Techniques The study utilized a comprehensive dataset of 6672 cases classified into normal, benign, and malignant categories. Radiomic features were extracted from the images and harmonized using ComBat to mitigate scanne...

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

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

  The oil and gas industry presents a complex matrix of operations where safety is critical due to the inherent risks of fires, explosions, and toxic releases. Despite the deployment of sophisticated safety management systems, incidents continue to occur, underlining the limitations of traditional hazard evaluation and response frameworks. Historical HAZOP reports are valuable repositories of operational learnings, offering a rich source of countermeasures. However, the industry struggles with the sheer volume of these countermeasures, which makes manual analysis impractical. To address this gap, this study proposes an NLP-based framework using the BERTopic algorithm to categorize and automate the interpretation of HAZOP countermeasures, significantly improving the efficiency and reach of safety systems during the design phase. Leveraging Natural Language Processing for Safety Data Interpretation The advent of Natural Language Processing (NLP) has introduced novel possibilities ...

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

In the era of advanced electronics and stealth technologies , the demand for materials that are not only ultrathin and lightweight but also exhibit exceptional functional integration is rising rapidly. Electromagnetic interference (EMI) shielding, thermal camouflage, and gas barrier capabilities are critical in various applications ranging from aerospace to wearable electronics. This research introduces an innovative approach in the design of Poly(ethylene furandicarboxylate) (PEF)-MXene/liquid metal (LM) composite films. By crafting a tailored multilayer heterostructure using electrospinning and electrostatic spraying, the study demonstrates a new pathway to engineer multifunctional materials that meet the increasing demands for performance, adaptability, and sustainability. Microstructural Design and Fabrication Strategy The composite films developed in this study utilize a layered architecture where each constituent is selected for its targeted functional contribution. The PEF nan...

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

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

  Effective waste management plays a vital role in improving public health, sanitation, and environmental sustainability, especially in underprivileged communities. This study focuses on six municipalities in the Birbhum district of West Bengal, aiming to estimate population growth, organic waste generation, and the resulting biomethane production potential up to the year 2051. By using a combination of traditional forecasting methods like Arithmetic and Geometric Progression for population and advanced models such as ARIMA and LSTM for methane estimation, the research provides a data-driven foundation for renewable energy planning. The high percentage of organic waste in municipal solid waste (57%–64%) highlights the untapped potential for clean energy generation through biomethane, offering a sustainable solution for both environmental and socio-economic upliftment. Population Growth Projections and Waste Generation Trends Accurate forecasting of population growth is essential...

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