Muhammad Rauf

Underwater Wireless Sensor Networks (UWSNs) face significant communication and performance challenges due to their harsh and dynamic underwater environment. To address these issues, efficient and robust communication models are essential. This research proposes an energy-aware deployment approach and introduces the Controlled Transmission enhanced by hybrid model scheme with Differential Evolution, Whale Optimization, and Chaotic Adaptation (CT-DEWCA) for relay selection. This model enhances node placement by implementing a “strategic dual transmission” approach, which employs CT for short-distance data transfer and utilizes the DEWCA hybrid algorithm to select optimal relay nodes for long-distance communication. The strategy optimizes energy allocation between storage and regular nodes. Simulations show that the CT-DEWCA model outperforms conventional Vector-Based Forwarding (VBF), Hop-by-Hop Vector-Based Forwarding (HH-VBF), Depth-Based Routing (DBR), and, with recent work, Energy-Efficient Artificial Fish Swarm-based Clustering Cognitive Intelligence Protocol (EAFSCCIP). The results show an improvement by an average of 5.4% in packet delivery ratio, 20% reduction in cumulative energy consumption, 6% increase in throughput, and 12% lower packet-loss rate on average compared to the other protocols. This research offers a robust solution for enhancing UWSNs performance and longevity by addressing critical energy efficiency issues and reliable data transmission in underwater environments. The proposed approach demonstrates the utility and potential of bio-inspired optimization techniques to tackle the unique challenges of underwater communication networks.

Background/Objectives: Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization capabilities of models. This study introduces a novel CART-ANOVA (Cartesian-ANOVA) hyperparameter tuning framework, which differs from traditional optimization methods by systematically integrating statistical significance testing (ANOVA) with the Cartesian product of hyperparameter values. This approach ensures robust and precise parameter tuning by evaluating the interaction effects between hyperparameters, such as batch size and learning rate, rather than relying solely on grid or random search. Additionally, it implements seven distinct classification schemes for brain tumors, aimed at improving diagnostic accuracy and robustness. Methods: The proposed framework employs a ResNet18-based knowledge transfer learning (KTL) model trained on a primary dataset, with 20% allocated for testing. Hyperparameters were optimized using CART-ANOVA analysis, and statistical validation ensured robust parameter selection. The model’s generalization and robustness were evaluated on an independent second dataset. Performance metrics, including precision, accuracy, sensitivity, and F1 score, were compared against other pre-trained CNN models. Results: The framework achieved exceptional testing accuracy of 99.65% for four-class classification and 98.05% for seven-class classification on the source 1 dataset. It also maintained high generalization capabilities, achieving accuracies of 98.77% and 96.77% on the source 2 datasets for the same tasks. The incorporation of seven distinct classification schemes further enhanced variability and diagnostic capability, surpassing the performance of other pre-trained models. Conclusions: The CART-ANOVA hyperparameter tuning framework, combined with a ResNet18-based KTL approach, significantly improves brain tumor classification accuracy, robustness, and generalization. These advancements demonstrate strong potential for enhancing diagnostic precision and informing effective treatment strategies, contributing to advancements in medical imaging and AI-driven healthcare solutions.

Industries strive to prevent ecologically destructive actions in their supply chains. At the same time, the optimization of their resources is a major concern for industries to minimize carbon emissions, boost sustainable practices, and improve a country’s long-term economic development. Therefore, the objective of this study is to examine the impact of Green Supply Chain Management (GSCM) methods on operational performance with the mediation of technological innovation, in the context of Pakistani manufacturing firms. The partial least square-structural modeling (PLS-SEM) method is adopted in this paper. Data were gathered from 223 different manufacturing firms in Pakistan and then analyzed among these variables. The data show good validity and reliability, and structural model explains 61% of the variance in operational performance and 45.4% of the variance in technical innovation, demonstrating its predictive validity. The R-square criteria classify R-square entities of 0.67, 0.33, and 0.19 as considerable, moderate, and weak, respectively. It is demonstrated that all the f-square values are greater than 0.020 and 0.35, indicating a significant effect on the model’s validity. The findings of this study reveal that GSCM practices have a significantly positive effect on both technological innovation and operational performance. Technological innovation has a direct influence on operational performance and has a partial mediating effect on the relationship between GSCM practices and operational performance. Therefore, this research offers managers insight into the importance of technological innovation and GSCM practice adoption to achieve competitive advantages. It further provides the groundwork for managers, practitioners, and environmental management researchers to emphasize the value of GSCM practice in improving operational performance.

We propose the design of a novel fractal antenna that is both unique and performance-driven. Two important antenna design features, miniaturization and wideband operation, are combined in this work. A ring-shaped antenna is designed using the well-known fractal geometry. This hybrid geometry is a fusion of meander and Koch curve shapes. The geometrical construction of the proposed antenna is compared to the standard Koch curve geometry. It is shown that combining the meander and Koch curve shapes increases the effective electrical length. The wider bandwidth is achieved by bringing the higher modes together. The overall dimensions of proposed meander Koch curve fractal ring antenna are 45×25×1.6mm³. The resonance frequency of the antenna is between 4.94 and 6.12GHz ( BW=21.83), which covers the entire 5GHz WLAN band. The prototype has been fabricated and experimentally verified.

The domain of underwater wireless communication (UWC) link is gaining much attention due to an increase in various underwater activities such as offshore hydrocarbon exploration, underwater unmanned vehicles (UUV), and military practices. Increased bandwidth and a reliable data link are mainly required for such activities. Both requirements of the domain are heavily affected by the highly conductive property of the seawater. This paper demonstrates the performance evaluation of radiofrequency-UWC, focusing on surface wave analysis, to propose a reliable solution for offshore activities. A constructive interference scheme can be useful due to the sharp difference in the properties of the two mediums (air and seawater). To that end, an experimental setup is created, and a corresponding finite element method (FEM) based simulation of the radio-based wireless link is run. This is because it has higher bandwidth and speed than acoustic and optical approaches. A conduction current mechanism transmits and receives data in a synthetic water tank containing a prepared conductive media (saltwater). The study of changing depths of transmitter-receiver nodes in saltwater shows that surface waves cause significant noise reception in shallow water (less than dipole length, below water level). During a series of experiments in the tank, the lowest bit error rate (BER) is observed only when the node’s submerged height was greater than dipole length. As a result, it is meant to provide a genuine data channel model. The discovery and analysis will aid in the development of a dependable underwater data link, with applications including short-range diver-to-diver communication, and UUV capability.

The performance of time-domain channel estimation deteriorates due to the presence of Gaussian mixture model (GMM) noise, which results in high mean squared error (MSE) as a challenging issue. The performance of the estimator further decreases when the complexity of the estimator is high due to the high convergence rate. In this paper, an optimized channel estimation method is proposed with low complexity and high accuracy in the GMM environment. In this channel estimation, an improved Gauss-Seidel iterative method is utilized with a minimum number of iterations. The convergence rate of the Gauss-Seidel method is improved by estimating an appropriate initial guess value when no guard bands are used in the orthogonal frequency-division multiplexing (OFDM) symbol. Simulation results provide an acceptable MSE for GMM environments, up to the probability of 5% impulsive noise component. This paper also presents the design and implementation of the proposed estimator in the NEXYS-2 FPGA platform that provides resources allocation, reconfigurability, schematic, and the timing diagram for detailed insight.