A facile and scalable thermal exfoliation–deposition method was developed to synthesize 2D g-C3N4/CuO heterojunction photocatalysts with controlled morphology. By tuning the g-C3N4 loading, the carbon nitride morphology transitions from dispersed two-dimensional (2D) nanosheets to bulk-like three-dimensional (3D) structures, which significantly influences the optical properties and photocatalytic performance of the composites. The photocatalysts were evaluated under solar irradiation for the degradation of carbendazim (CBZ) and methylene blue (MB). The g-C3N4/CuO composites exhibited significantly enhanced photocatalytic activity compared to individual g-C3N4 and CuO, due to efficient charge separation at the heterojunction. Optimal photocatalytic performance was achieved at 15
Semi-active suspension systems in electric vehicles equipped with in-wheel motors (IWMs) face significant challenges in simultaneously achieving optimal ride comfort and vehicle traction. Dynamic vibration absorbers (DVAs) and magnetorheological (MR) dampers offer promising solutions, yet conventional controllers such as skyhook control exhibit limitations in adapting to varying road excitations and mass conditions. Time delays inherent in damper and controller responses further complicate real-world performance, underscoring the need for a more intelligent and adaptive control strategy. An adaptive neuro-fuzzy inference system (ANFIS) controller is developed for vibration control of a semi-active suspension system utilizing a hybrid annular radial magnetorheological (HARMR) damper as the semi-active actuator. The ANFIS controller integrates neural network learning capability with fuzzy logic reasoning to continuously adjust the damping characteristics of the HARMR damper in response to road excitations. Controller performance is benchmarked against a conventional skyhook controller and a passive system. The effects of time delays in the HARMR damper and controllers are explicitly evaluated against an ideal delay-free condition, and robustness is assessed across varying sprung-to-unsprung mass ratios. The proposed ANFIS controller significantly enhances dynamic performance metrics including vehicle body deflection, tire deflection, sprung mass acceleration, tire dynamic force, and motor dynamic force. Body acceleration is reduced by up to 18
Assessment of white matter integrity is critical for predicting functional recovery after ischemic stroke. However, conventional magnetic resonance imaging (MRI) cannot capture tract-specific microstructural changes, and diffusion tensor imaging (DTI) is limited by prolonged acquisition times. This study aimed to synthesize fractional anisotropy (FA) maps from routinely acquired T1-weighted (T1) MRI using 2.5D inputs within a generative adversarial network (GAN) framework. Specifically, our primary objective was to evaluate the relative efficacy of a proposed transfer learning strategy compared to single-domain training approaches. T1–FA paired data from 375 cognitively normal participants (832 images) from the Alzheimer’s Disease Neuroimaging Initiative served as the non-lesion dataset, while longitudinal MRI data from 69 ischemic stroke patients (236 images) were from a single-center cohort. Three models were evaluated: the non-lesion-trained (NLT) model trained on non-lesion data, the lesion-trained (LT) model trained on stroke data, and the NLT model further fine-tuned on the stroke dataset (NLT + LF). Model performance was evaluated using voxel-wise errors (mean absolute error (MAE) and root mean square error (RMSE)), structural similarity (peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM)), spatial overlap (Dice coefficient (Dice)), and distributional similarity (Kullback–Leibler divergence). Bonferroni-corrected paired t-tests showed that NLT + LF showed significantly better performance than NLT across all evaluated regions, including the whole brain, white matter, and lesions (all p < 0.001). Compared with LT, NLT + LF showed superior performance for all metrics at p < 0.001, except for lesion-region Dice and RMSE, which remained significant at p < 0.01. The preservation of lesion-relevant features and anatomical fidelity, together with the capture of degeneration patterns, accompanied these gains. Overall, the proposed NLT + LF approach improved lesion-specific representation and established that high-fidelity FA maps can be reliably synthesized from T1 MRI. This transfer learning framework offers a practical alternative to DTI for clinical stroke assessment.
Land administration in Nigeria continues to face critical challenges, including document inconsistencies, opaque verification procedures, slow processing times, and persistent administrative malpractice associated with centralized and conventional, non-automated record-management systems. These systemic weaknesses create opportunities for double allocation, unauthorized alterations, and hidden registry manipulation, highlighting the need for more secure and transparent digital approaches. This paper proposes BERBLOM, a blockchain-enabled land ownership management system tailored to Nigerian land-registry workflows. The system tokenizes land parcels as ERC-4907 assets and stores supporting documents in the InterPlanetary File System (IPFS) to enhance data integrity and reduce single points of failure. BERBLOM supports practical use cases such as government plot allocation, peer-to-peer property transfers, and an on-chain leasing mechanism that automatically reverts ownership upon lease expiration. The framework operates on a permissioned Hyperledger Besu network using the Quorum Byzantine Fault Tolerance (QBFT) consensus protocol, selected for its Byzantine fault tolerance, predictable gas-free operation, and scalability under controlled conditions. Experimental evaluation indicates that QBFT maintains stable throughput and manageable latency as the number of validator nodes increases from 4 to 32. A Flutter-based decentralized application (DApp) enables real-time interaction through event-driven synchronization. While human-driven corruption and malpractice cannot be fully eliminated, the proposed prototype demonstrates how blockchain-based registries can reduce opportunities for hidden technical manipulation and improve on-chain accountability. It is important to note that the primary performance benefits of BERBLOM are realized after tokenization. Initial land registration remains dependent on legally mandated, human-driven governmental approvals; however, post-registration operations such as peer-to-peer transfers and on-chain leasing can be executed without intermediaries and complete within seconds. Consequently, the performance analysis focuses on the lifecycle stages where blockchain automation provides the greatest impact.
Cardiotoxicity remains the leading driver of drug attrition; however, its prediction remains suboptimal when conventional hERG assays and animal models are used. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) offer a human-relevant alternative that aligns with the CiPA initiative and ICH E14/S7B guidelines. This study validated an integrated platform that combines high-purity hiPSC-CMs with Artificial Intelligence (AI) to enhance the accuracy of predicting drug-induced proarrhythmic risk. Phenotypic characterization of the hiPSC-CMs demonstrated high cardiac differentiation efficiency (cTnT + > 95
