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1990 articles for “D’Alembert’s principle” (ranking capped at the first 2,000 matches — narrow the search to see the rest)
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Secure Forge: Deepfake Image Detection Using Vision Transformers
Abstract: Deepfake technologies have become a major risk to the credibility and trustworthiness of digital visual information. Using powerful generative models like GANs and autoencoders, deepfakes can generate highly realistic fake videos and images, resulting in misinformation, identity theft, and public loss of trust in digital media. Classic Convolutional Neural Networks (CNNs) while being highly effective in initial-stage, deepfake detection tend to be limited by their local receptive fields and dependency …
Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 12, Issue 3, 2025 · pp. 32–45 Read article
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Melanoma Skin Cancer Detection Using Deep Learning
Abstract: Cancer as one of the major diseases rank in the World is still very challenging to diagnose and treat hence need for the technological advancements. Chemotherapy, radiation, as well as surgery therapies have several drawbacks including non-selective action, damage to healthy tissues, and multi-drug resistance. Smart nano-theranostics, an advanced integration of nanotechnology with diagnostic and therapeutic modalities, offers a next-generation approach for precision oncology. Thus, the development of multifunctional nanoparticles …
Published in Research and Reviews: Journal of Oncology and Hematology · Vol. 14, Issue 2, 2025 Read article
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Development of Cost-Effective Nanocarriers for Targeted Drug Delivery in Cancer Therapy in India
Abstract: The growing challenge of cancer in India demands the development of novel treatment approaches that are efficient and less expensive in alleviating disparities related to healthcare access and optimizing outcomes. Among the possible options, the drug delivery systems utilizing nanocarriers have been developed and they provide greater therapeutic targeting, reduced systemic toxicity, and decreased treatment costs over time. In this research, we assess the degrees of awareness, affordability, and acceptability …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 15, Issue 3, 2025 · pp. 1–7 Read article
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Reinventing Drug Delivery Through Cutting-Edge Nanotechnology
Abstract: Nanotechnology has emerged as a revolutionary platform in the field of drug delivery, offering novel strategies for the precise, efficient, and controlled delivery of therapeutic agents. The integration of nanocarriers, such as liposomes, polymeric nanoparticles, dendrimers, micelles, and inorganic nanoparticles, has significantly enhanced drug solubility, stability, bioavailability, and targeted delivery to specific tissues or cells. Targeted drug delivery systems based on nanotechnology minimize off-target effects, reduce systemic toxicity, and improve …
Published in Trends in Drug Delivery · Vol. 12, Issue 3, 2025 · pp. 44–61 Read article
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Integrative Network Biology Analysis of GSE6011 Uncovers Molecular Signatures in Duchenne Muscular Dystrophy
Abstract: Duchenne muscular dystrophy (DMD) is a rare, severe neuromuscular disorder demonstrated by progressive skeletal muscle deterioration and premature mortality. Despite advances in supportive care, no definitive cure exists, highlighting the need to explore novel molecular targets. The current study aimed to uncover key dysregulated genes and molecular pathways in DMD through a dataset-specific network biology approach. Publicly available microarray data (GSE6011) from DMD quadriceps muscle biopsies of 22 patients and …
Published in Research and Reviews : Journal of Computational Biology · Vol. 14, Issue 3, 2025 · pp. 36–48 Read article
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Depression Detection Using Machine Learning: A Comprehensive Review
Abstract: Depression remains one of the most prevalent mental health conditions globally, yet it frequently goes undiagnosed due to the reliance on subjective evaluation methods. With the growing availability of digital behavioral data and significant progress in machine learning (ML), new possibilities have emerged for the automated detection of depression. This review offers a detailed examination of recent advancements in ML-driven approaches to identifying depressive symptoms. It covers a range of …
Published in Journal of Advanced Database Management & Systems · Vol. 12, Issue 3, 2025 · pp. 27–32 Read article
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Pathways in Drug Discovery and Development: From Molecular Targets to Market Approval
Abstract: A complicated, multidisciplinary, and resource-intensive process, the discovery and development of new pharmacological drugs is essential to the advancement of contemporary medicine. Finding and optimising lead chemicals comes after a disease-relevant biological target has been identified and validated. Through preclinical research in animal models, these leads are thoroughly assessed for toxicity, pharmacokinetics, safety, and efficacy. Clinical trials, which are carried out in several stages to evaluate safety, efficacy, ideal dosage, …
Published in Research & Reviews: A Journal of Drug Design & Discovery · Vol. 13, Issue 1, 2026 · pp. 01–04 Read article
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TPLO-M: A Dual-Purpose Solution for CCL and MPL in Dogs
Abstract: Cranial Cruciate Ligament (CCL) rupture and Medial Patellar Luxation (MPL) are common orthopedic conditions affecting the canine stifle joint, particularly in small and toy breeds and larger athletic dogs. When these conditions occur together, they pose a significant surgical challenge due to the combined biomechanical abnormalities. Traditional surgical approaches, such as Tibial Plateau Leveling Osteotomy (TPLO) for CCL insufficiency and corrective procedures for MPL, do not adequately address both conditions …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article
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A Review on Applications of Artificial Intelligence (AI) in Parkinsons’s Disease Diagnosis and Treatment and Its Future Challenges
Abstract: Parkinson’s disease (PD) is a long-term, progressive neurodegenerative disorder that mainly occurs in people older than 60 years, affecting nearly 1% of this population. It is chiefly marked by the loss of dopaminergic neurons in the substantia nigra, a crucial brain region responsible for controlling motor functions. The resultant dopamine deficiency significantly disrupts motor control, manifesting in clinical symptoms such as tremors, bradykinesia, muscle rigidity, and postural instability. While PD …
Published in Research and Reviews: A Journal of Neuroscience · Vol. 15, Issue 3, 2025 · pp. 1–15 Read article
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Kinematic and Dynamic Modelling of a 6-DOF Robotic Manipulator for Industrial Applications
Abstract: The rapid evolution of industrial automation has intensified the need for highly accurate, flexible, and intelligent robotic systems capable of operating in dynamic and demanding environments. Among these systems, six-degree-of-freedom (6-DOF) robotic manipulators have emerged as a versatile solution due to their superior dexterity, large workspace, and human-arm-like motion capabilities. This research focuses on the comprehensive kinematic and dynamic modelling of a 6-DOF robotic manipulator designed for various industrial tasks …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 3, Issue 2, 2025 · pp. 27–32 Read article
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Early Alzheimer's Disease Detection Using Deep Ensemble Learning and MRI Image Analysis
Abstract: Early detection of Alzheimer's disease (AD) is crucial to slowing cognitive decline and enabling timely clinical interventions. Traditional diagnostic methods, including cognitive tests and single-model classifiers, have limited sensitivity during early stages of the disease. This paper presents a deep ensemble learning approach that integrates multiple convolutional neural networks (CNNs) for accurate Alzheimer's disease detection using structural Magnetic Resonance Imaging (MRI) data. The proposed framework utilizes ResNet50, VGG16, and DenseNet121 …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 1, 2026 · pp. 1–9 Read article
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A Comprehensive Review on Federated Learning in Disease Detection
Abstract: Healthcare data, which is frequently dispersed among various organisations, has enormous potential to improve predictive analytics and illness identification. However, there are substantial privacy & legal obstacles to sharing this private data for centralised model training. Federated Learning is a paradigm shift that allows several organisations to work together to build a global model without disclosing raw patient information. Federated Learning uses a larger dataset to provide more reliable insights …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 1, 2026 · pp. 1–21 Read article
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PREDICTIVE MAINTENANCE IN SEMICONDUCTOR SYSTEMS: INSIGHTS FROM MACHINE INTELLIGENCE AND DATA-DRIVEN METHODS
Abstract: With the fast-paced development of semiconductor technology comes the need to focus on device reliability, or how long devices will function and the likelihood of devices having operational issues. Predicting failures and avoiding downtime with the implementation of timely, actionable, and data-driven maintenance strategies are essential to insure devices function sustainably within predetermined performance levels. The implementation of predictive maintenance within artificial intelligence and machine learning technologies will provide the …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 1, 2026 · pp. 1–9 Read article
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A Comparative Study of Lipid Profile in Pre-Dialysis and Post-Dialysis End-Stage Renal Disease Patients
Abstract: Background: Chronic kidney disease (CKD) represents a major public health concern in India, with its prevalence increasing at an alarming rate. Individuals with CKD are at a substantially higher risk of developing cardiovascular disease (CVD), which remains the leading cause of mortality in this population. Dyslipidemia is particularly pronounced among patients with end-stage renal disease (ESRD); however, limited data are available regarding the impact of hemodialysis on lipid profile alterations. …
Published in Research and Reviews: A Journal of Medicine · Vol. 16, Issue 1, 2026 · pp. 25–31 Read article
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The Use of Digital Tools in Assessing Language and Literacy Development in Early Childhood Centres in Tamale Nanton District, Ghana
Abstract: The study investigated the use of digital tools in assessing language and literacy development in early childhood centres in the Tamale Nanton-district, Ghana. Specifically, the study examined the extent to which facilitators integrate digital tools in teaching practices, assessed facilitators’ levels of digital literacy skills, and explored the factors influencing the successful integration of digital tools in teaching. A total of 113 facilitators (39 males and 74 females) were selected …
Published in International Journal of Children · Vol. 3, Issue 1, 2026 · pp. 17–29 Read article
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Polymer Composite-Enhanced Anaerobic Digestion of Kitchen Waste for Optimised Biogas Production: Process Design, Parametric Study, and Simulation Analysis
Abstract: There is an urgent need for decentralised sustainable biogas production from organic waste due to the rising amount of food waste in cities. The application of polymer composite material including HDPE tanks, PVC pipes, and FRP secondary containment systems presents a promising option compared to conventional MS digester fabrication in terms of durability, excellent thermal insulation capability, light weight, and easier installation. This study explores the possibility of adopting a …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 803–818 Read article
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ANN-Based Adaptive Rotor Current Control for DFIG Wind Systems: A Comparative Dynamic Analysis
Abstract: The variability of rotor current management in Doubly Fed Induction Generator (DFIG)-based wind energy conversion systems is crucial for maintaining stability in power extraction under fluctuating wind and grid circumstances. Traditional proportional-integral (PI) controllers, despite their ease of use, frequently exhibit diminished performance when faced with parameter uncertainty, nonlinear behaviors, and rapid wind fluctuations.This paper presents an adaptive rotor current control strategy, which is an Artificial Neural Network (ANN)-based approach …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 1, 2026 · pp. 41–53 Read article
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An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study
Abstract: As the Earth’s climate enters a state of unprecedented volatility, the traditional methods of ecological observation—characterized by delayed reporting and fragmented data—are no longer sufficient. This study investigates the paradigm shift toward AI-driven IoT (KSK Approach)-based decision-making frameworks as the primary frontier in climate science. By deploying a "planetary nervous system" of interconnected sensors—measuring everything from soil moisture in the Sahel to glacial melt rates in the Arctic—we generate a …
Published in International Journal of Climate Conditions · Vol. 3, Issue 1, 2026 · pp. 1–10 Read article
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A Constraint-Driven Generative Design Methodology for Modular Actuated Robotic Components in Decentralized Manufacturing
Abstract: This work formalizes a constraint-driven generative design framework for actuator-integrated robotic components intended for decentralized additive manufacturing. Conventional topology optimization typically prioritizes structural efficiency while treating actuator integration, modular interfaces, and fabrication constraints as secondary considerations. In contrast, the proposed methodology encodes these requirements as first-order geometric and mechanical constraints prior to automated material redistribution. The framework defines preserved actuator geometry, bounded design envelopes, representative loading abstractions, and manufacturability-aware domains …
Published in International Journal of Robotics and Automation in Mechanics · Vol. 4, Issue 1, 2026 · pp. 1–12 Read article
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Improving Dataset Integrity Through Automated Data Cleaning Techniques
Abstract: High-quality data is a fundamental requirement in data science for producing trustworthy analytical insights and effective machine learning models. Problems, including incomplete records, inconsistent entries, duplicate observations, and anomalous values, can severely reduce the accuracy and robustness of predictive systems. As modern datasets continue to expand in both volume and structural complexity, relying on manual data cleaning methods become time-consuming and error-prone, highlighting the growing importance of automated data preprocessing …
Published in International Journal of Data Structure Studies · Vol. 4, Issue 1, 2026 · pp. 40–45 Read article