Inhealthcare monitoring, where effective Medium Access Control (MAC) protocols are required to handle both periodic and emergency traffic, Wireless Body Area Networks (WBANs) are becoming increasingly significant. Conventional slot allocation techniques often lack adaptability to changing network conditions, which results in increased energy usage, longer wait times, and decreased dependability. To overcome these constraints, this study suggests a dynamic slot allocation mechanism for WBAN MAC protocols based on a Decision Tree. The Decision Tree Traffic Adaptive MAC (DTTA-MAC) approach allocates time slots according to real-time node conditions, ensuring that emergency traffic is prioritized while maintaining stable performance for normal traffic. A dataset was generated using Castalia and OmNeT++ to simulate realistic WBAN scenarios, incorporating parameters such as energy levels, latency, buffer size, throughput, and signal quality. Simulation results show that the proposed DTTA-MAC achieves up to approximately 34% reduction in energy consumption and about 89% reduction in average packet delay compared to existing MAC protocols, particularly under emergency traffic conditions. This work provides a portable and interpretable substitute for Reinforcement Learning (RL) techniques. To support real-world healthcare applications, future research will investigate expanding the model to larger networks, integrating it with cutting-edge machine learning techniques, and bolstering security and privacy.
This study investigates the effect of different hole arrangements in rows and columns on the energy absorption of thin-walled tubes. Due to their simple geometry and manufacturability, holed thin-walled tubes are well-suited for application in energy-absorbing systems. The study concentrates on energy absorption (EA), specific energy absorption (SEA), initial crushing force (ICF), and ratio of initial crushing force to mean crushing force (MCF) via strategically located perforations in ST37 tubes. Finite element (FE) simulations and empirical experiments were employed to investigate the effects of various geometric configurations. To compare the performance of the hole patterns, the RC-5-10 configuration (5 rows, 10 columns), which showed the highest energy absorption among the tested samples, was compared with a simple tube without perforations. The findings revealed that the RC-5-10 had a higher SEA compared to the Simple tube. On the other hand, the peak crushing force (PCF) value in the RC-510 tube decreases by 3.4% as compared to simple tubes. These results further highlight the potential of perforated thin-walled tubes for energy absorption applications for various automotive safety and protective structural applications. Overall, this investigation provides insights into the influence of geometric design parameters on energy-absorbing mechanisms through both experimental and numerical analysis.
Modified Buton Asphalt (MBA) is a refined binder produced by combining petroleum bitumen with natural rock bitumen, abundantly available on Buton Island, Indonesia. This study investigates the tensile behavior and stability performance of asphalt mixtures containing MBA under short-term immersion in water and Na2SO4 solution for 1, 3, and 7 days. Core samples extracted from field pavements were subjected to Indirect Tensile (IDT) tests to evaluate moisture susceptibility and tensile integrity. Key mechanical parameters-tensile strength (sigma t), tensile stiffness modulus (ET), and tensile index (TI)- were derived from the stress-strain relationships, while Marshall stability and flow were assessed to examine the structural integrity of the mixture. The results indicate that immersion duration significantly affects the bituminous matrix but not the coarse aggregate framework. After 7 days of immersion in water, the tensile strength decreased by approximately 11.6%, while immersion in Na2SO4 resulted in a 15.2% reduction compared to unconditioned specimens. Correspondingly, the tensile stiffness modulus dropped by about 9.4%, indicating minor degradation of the MBA-filler interface. Despite these reductions, Marshall stability remained above 95% of the control values, confirming the structural durability of MBA-based asphalt. Overall, MBA mixtures exhibit good resistance to moisture and salt-induced weakening, highlighting their suitability for tropical pavement applications.
The rapid expansion of 5G and upcoming 6G networks requires wireless image transmission schemes that deliver ultra-reliable, low-latency, and energy-efficient performance over highly dynamic multiuser channels. This paper presents a deep convolutional neural networks (DCNN) DenseNet-with Enhanced Marine Predator Algorithm (EMPA) and a hybrid deep learning and metaheuristic framework for wireless image transmission in non-orthogonal multiple access (NOMA)-aided massive multiple-input multiple-output (MIMO) systems. The proposed model combines a dense convolutional network (DenseNet) encoder-decoder with an enhanced marine predator algorithm (EMPA) optimizer to jointly tune channel coding power allocation, and resource scheduling under multi-user interference. DenseNet model dense feature propagation efficiently extracts multi-scale image representations, whereas EMPA adaptively minimizes transmission error and latency by optimizing NOMA power coefficients and MIMO beamforming weights. The benchmark proposed model DenseNet-EMPA against recent hybrid learning optimization paradigms, including a comparison of exit models such as deep reinforcement learning with self-optimized particle swarm joint source-channel coding (DRL-SOPSOJSCC), enhanced golden optimization algorithm integrated with deep reinforcement learning (EGOA-DRL), and improved golden jackal optimization with Bi-LSTM capsule network (IGJO-BiLSTM-CapsNet). Simulation results under realistic 5G/6G-orientated parameters show that DenseNet-EMPA achieves up to 18-24% lower bit error rate, 15% higher spectral efficiency, and 20% reduced latency compared to state-of-theart approaches, while maintaining robust performance across varying SNR, user density, and mobility profiles. The performance results demonstrate that combining deep convolutional encoders with bioinspired metaheuristics provides a powerful design paradigm for next-generation wireless adaptive image transmissions.
