The performance of ZnO-based dye-sensitised solar cells (DSSCs) remains significantly limited by poor dye uptake, dye aggregation, and rapid recombination. Here, we demonstrate a solvation-assisted strategy using dodecylbenzene sulfonic acid (DBSA) to regulate the nucleation, dispersion and surface chemistry of ZnO during co-precipitation. Three DBSA-functionalised ZnO photoanodes (DZO1, DZO2, DZO3) were synthesised by varying the thermal treatment (RT, 150 °C, and 400 °C). XRD confirmed the wurtzite ZnO phase, with crystallite size increasing from 18 nm (DZO1) to 32 nm (DZO3). FTIR verified DBSA anchoring via sulfonate–Zn interactions and the modulation of surface hydroxyl groups. Optical analysis revealed a direct band gap reduction from 3.20 eV (DZO1) to 3.13 eV (DZO3), consistent with thermally induced crystallite growth. Dye-sensitised films showed a controlled bathochromic shift (527–533 nm) attributed to varying degrees of J-aggregation. DZO1 exhibited the lowest dye aggregation and moderate dye loading (0.86 nmol/mg), whereas DZO3 showed the highest dye loading (1.30 nmol/mg) but stronger aggregation. The DSSC device based on DZO1 delivered the highest experimental power conversion efficiency of 1.78
Fetal abnormality detection plays a critical role in prenatal care, as early identification of potential risks enables timely intervention and informed decision-making. However, existing approaches are constrained by scarce labeled data for rare conditions, anatomical variability across gestational stages, and inconsistent imaging quality in resource-limited settings. Conventional segmentation models often struggle with accurate multi-region delineation, fine boundary precision, and adaptability to diverse clinical environments. The Federated Biometric UNet++ (FB-SeUNet++) is introduced as a federated learning framework specifically designed for multistructure fetal abnormality detection. Unlike centralized approaches, FB-SeUNet++ enables collaborative training across hospitals and clinics without exposing sensitive patient data. Its core methodology integrates an enhanced U-Net++ architecture, where the encoder employs Diverse Scale Depthwise Convolution (DSDW) and wavelet pooling for multi-scale feature extraction, while the decoder incorporates Vision Eagle Attention to refine segmentation. Segmentation masks of fetal regions such as the head, abdomen, brain, thorax, and femur are subsequently processed using the Curvature-Based Least Squares and Zhang-Suen Thinning algorithms to obtain precise biometric measurements. These measurements are combined with a threshold-based classification strategy to differentiate between normal and abnormal conditions. Experimental evaluation on benchmark datasets demonstrates that FB-SeUNet++ achieves a Dice coefficient of 0.99 and accuracy of 99.4 %, outperforming state-of-the-art methods by 4-6 % across multiple metrics. By uniting multi-structure segmentation, accurate biometric measurement, and federated learning, FB-SeUNet++ provides a robust, generalizable, and privacy-preserving framework that enhances automated prenatal diagnostics and supports smaller clinics with limited data resources.
Information technology pertaining to medicine and healthcare is included in the subsection of health technology known as health information technology. It permits the secure exchange of health information between payers, quality monitors, providers, and consumers as well as the administration of health information across computerized systems. The IoMT, gathers medical data through sensors and sends it to distant computers so that physicians may analyze it and determine whether a disease has been detected early. Conversely, there is always a threat on using wireless communication and the user’s private data can be targeted by the attackers. In this paper, BOD-BLOAT: Security aware Bayesian optimized Deep learning model for BLOAT Prevention in IoT Healthcare. Natural language processing (NLP) models, such as sentence segmentation, tokenization, word stemming, and stop word removal, are used to pre-process the collected input datasets in order to eliminate unnecessary information. Following preprocessing, Dilated RNN-Bilstm is used to extract the features, and Bayesian optimization is used to pick the features. The Mobilenet network, which is based on deep learning, is employed to detect attacks. Lastly, deep learning-based Ghostnet is utilized to classify and identify the various types of attacks. UNBDS-NB-15 and KDD99 are the two datasets used for performance analysis. According to the classification results, the suggested BOD-BLOAT model outperforms current models in terms of attack detection accuracy. This approach can be deployed in real-time IoMT systems with improved attack detection and reduced latency. MATLAB has been used to simulate the suggested BOD-BLOAT approach. The Proposed BO-BLOAT method improves the overall accuracy of the proposed BOD-BLOAT, LRO-S, GOL, H-CIoT, BDLT-IoMT is 1.16
Plants play a vital role in living organisms. However, daily changes in the environment can significantly impact plant growth with respect to their surroundings. Exposure to pathogens, environmental disturbances, or unfavorable conditions make plants vulnerable to diseases. The rapid spread of diseases affects the healthy growth of plants, resulting in the reduction of products quality and quantity. To mitigate these issues, it is necessary to monitor the plant growth and to identify the plant diseases at an earlier stage. However, the manual detection of plant diseases is an unreliable method as it is a time-consuming and error-prone process. Analyzing the subjected plant leaves through the advancements in image processing has paved the way to engage the researchers in the development of automated solutions for disease identification. In addition, image acquisition, data collection, and the development of advanced state-of-the-art approaches are the major research tools contributed by various researchers. Therefore, this paper delves into the latest methodologies by concentrating on these research contributions. In this regards, the available research papers published from 2018 to 2024 are considered. This study also incorporates some of the challenges and research topics for future plant leaf disease analysis by emphasizing the critical role of high-performance computing. Moreover, it offers valuable insights for researchers and practitioners, providing a wide-ranging understanding of the current state-of-the art approaches in the field of plant leaf disease analysis by highlighting their pros and cons.
A mobile ad hoc network (MANET) is a group of wireless mobile nodes that create a network without the assistance of a standard support provider or central administrator. Packet transmission in a MANET is enabled by collecting intermediary equipment between the sender and the recipient. As a result of efficient packet transmission, quality of service is improved, including faster throughput, reduced latency, and assured delivery. However, challenges such as frequent topology changes and unpredictable traffic patterns caused by dynamic node mobility and uneven data distribution can significantly degrade network performance. To overcome these problems, an Enhanced QoS-aware Routing approach using an Optimized Deep Learning framework (ERODE) has been proposed for effective routing and data transmission in MANETs. The ERODE approach integrates Adaptive Neuro-Fuzzy Inference System (ANFIS) for retransmission control and Tyrannosaurus Optimization Algorithm (TOA) for energy-efficient routing. By optimizing parameters including throughput, end-to-end (E2E) delay, Network Lifetime (NL), and packet delivery ratio (PDR), the ERODE method improves network performance and reliability under dynamic conditions. The proposed method achieves an EC of 31.56 J, while the existing AFB-GPSR, FLSTMTLAR, and OFC-TR systems attain 35 J, 40 J, and 45 J, respectively. In terms of PDR, the ERODE technique outperforms AFB-GPSR, FLSTMTLAR and OFC-TR by 5.01
