Browse Articles
Discover research articles across all indexed journals
Research on cloud-edge-end distributed collaborative computing based on deep reinforcement learning
Annulative Skeletal Diversification of Pyrimidines to Expanded Heteroaromatic Space
A dual mode approach based efficient relay-aided device-to-device communication in 5G mm-Wave cellular network
Abstract Relay aided device-to-device (D2D) communication has the potential to increase the capacity and coverage of the network thereby enhancing the quality of service (QoS). Thus, we propose a dual mode scheme (direct and relay mode) for a single cell where D2D and cellular users co-exist in an underlaying 5G mm-Wave cellular network which minimizes the interference. Direct mode operates at 2 GHz carrier frequency while relay mode employs full duplex amplify and forward (FDAF) relay strategy at 28 GHz frequency. The closed form expressions of performance metrices namely, spectral efficiency (SE) and energy efficiency (EE) are derived using stochastic geometry as a tool for both the modes to evaluate the system performance. Expressions for probability distribution function (PDF) and cumulative distribution function (CDF) are also derived. Simulation results suggest that the relay mode exhibits better performance than the direct mode in terms of SE and EE. With an increase in the D2D transmit power; the EE gradually increases to around 180 Mbps/J. The average data rate also increases to around 135 Kbps at D2D power of 250 mW with pathloss attenuation of 2.5. Further, simulation results validate the efficacy of the proposed scheme. Also, comparison of the proposed method with the existing methods depicts better performance of the proposed system.
MTHFR and ABCB1 polymorphisms associated with toxicity in hematological malignancies patients receiving high dose methotrexate
Interfacial and Crystalline <i>Gauche</i> OCH <sub>2</sub> –CH <sub>2</sub> O Layers in Biodegradable and Recyclable Polyethylene-like Polyesters Detected by Nuclear Magnetic Resonance
Alzheimer-related individual factors modulate effects of transcranial direct current stimulation strength on white matter integrity in mild cognitive impairment
Abstract Transcranial direct current stimulation (tDCS) is a promising non-invasive intervention for mild cognitive impairment (MCI). This prospective study investigated the relationship between optimized electrical field (EF) strength of tDCS and white matter (WM) microstructural changes in 55 individuals with MCI. Magnetic resonance imaging (MRI)-based computational modeling was used to optimize EF strength targeting the left dorsolateral prefrontal cortex (DLPFC). Diffusion tensor imaging (DTI) assessed WM integrity through fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD). Higher EF strength was significantly associated with increased FA and reduced MD and RD in specific left-lateralized tracts, including the anterior thalamic radiation, corticospinal tract, inferior fronto-occipital fasciculus, and inferior longitudinal fasciculus. These EF-dependent WM changes were moderated by Alzheimer’s disease (AD)-related factors. Greater WM plasticity was observed in Aβ-positive individuals, APOE ε4 non-carriers, and BDNF Met non-carriers. Moreover, APOE ε4 status significantly moderated the relationship between EF strength and executive function; in non-carriers, stronger EF strength was associated with improved Stroop performance, potentially reflecting enhanced WM integrity in the right superior longitudinal fasciculus. However, no significant associations were observed between EF-sensitive tracts and short-term cognitive changes in the full sample, suggesting that structural modifications may precede functional improvements or require longer follow-up. These findings emphasize the importance of individual AD-related factors in shaping neuromodulatory responses. They also support the need for longitudinal, sham-controlled studies to clarify the clinical implications of EF strength in personalized tDCS for MCI.
Optimization and physicochemical & antioxidant properties of leek (Allium porrum L.) dried by foam-mat drying
Refining the Substrate-Cofactor Disposition Model of Hyoscyamine 6β-Hydroxylase Catalysis Using Hyoscyamine Analogs
A systematic review and meta-analysis to assess the effect of hidden hunger on glycaemic control in patients with type 2 diabetes
Contextual priority attention enables linear time sequence modeling in transformers
Ginkgetin reverses cisplatin resistance in cervical cancer by regulating the Nrf2/HO-1 signaling pathway to induce ferroptosis
ME-BYO index as a formative construct for healthy ageing: a cross-sectional PLS-SEM analysis
Antifungal, antioxidant, and antibacterial activities of C. macrostachyus plant leaves extracts using different solvents
Experimental study of CO2 sequestration and H2 generation potential through mineral carbonation in Saudi red mud
An artificial intelligence-based diagnosis system for the identification of helminth parasitic infections in mithun and allied bovines
Abstract This study presents a novel deep learning approach addressing the critical shortage of veterinary expertise in India’s North Eastern Hill (NEH) region through automated identification of parasitic infections in livestock. We developed a Convolutional Neural Network (CNN) architecture capable of analyzing both standard and microscopic images to identify and classify 16 distinct parasitic species. The model comprises four convolutional layers (32, 64, 128, 256 filters) with ReLU activation and MaxPooling for efficient feature extraction, followed by Dense layers and a Softmax classifier. The model was trained on a comprehensive dataset of over 5,334 annotated images, achieving 96% accuracy after 30 training epochs. To evaluate stability, it was trained ten times, yielding an average accuracy of 0.9616 ± 0.0024 (95% CI: [0.9601, 0.9630]), Macro F1 of 0.9527 ± 0.0021, and Weighted F1 of 0.9598 ± 0.0019, demonstrating consistent performance. A PHP-based web interface enables real-time predictions and adaptable deployment across hardware and cloud platforms. This system offers a scalable and accessible diagnostic tool for enhancing parasite detection and livestock health monitoring.