As an underlayment to cellular 5G communication network, device-to-device (D2D) communications will not only boost capacity utilization and power efficiency but also provide public health and public safety services. One of the most important requirements for these businesses is to have alternate access to cellular networks in the event that they are partially or completely disrupted as a result of a natural disaster. Despite limited communication coverage and bandwidth scarcity, the 3rd Generation Partnership Project (3GPP) must have developed a new device-to-device (D2D) communication method fundamental enhanced mobile that can strengthen spectral efficiencies besides allowing direct communication of gadgets in close propinquity devoid of transitory by elevated-node B (eNB). Unfortunately, enabling data transmission on a cellular connection offers a challenge in terms of two-way radio source administration, because D2D associates recycle cellular users’ uplink radio resources, which might create interference to D2D user equipment’s (DUE) receiving channels. In this study, we concentrate on optimal cluster head selection using the binary flower pollination optimization algorithm by designing an energy-efficient lifetime-aware leisure degree adaptive routing protocol named OptCH_L-LDAR. This topology is constructed with a multi-hop obliging communication system, instructed on the way to wrap an extensive remoteness connecting source and destination. The proposed OptCH_L-LDAR is compared with three state-of-art methods such as binary flower pollination (BFP) algorithm, time division multiple access (TDMA), and data-driven technique (DDT). As a result, the proposed OptCH_L-LDAR achieves 96% of energy efficiency, 89% of lifetime, 97% of outage probability, and 98% of spectral efficiency.

This article investigates and presents the upshots observed in the brook of hybrid composites especially, the current investigation focuses on the impact of fiber composition, sequence, and stacking pattern on composite mechanical Features. Five varied stacking sequences of hybrid composites encompassing laminates are used to create four classes of fiber with jute/bamboo/glass by utilizing a conscientious hand lay‐up process with glass fiber‐laced mats as their peripheral layer. For examination, fiber sequences are arranged in the combination of GJBJG, GBJBG, GJGJG, and GBGBG, where G, J, and B refer to glass fiber, jute fiber, and bamboo fiber, respectively. The position of fiber in the core layer is kept in a perpendicular direction with respect to adjacent piles which might be jute or bamboo fiber and the best position of fiber is considered due to the stacking order. Stress and strain were linear in the load versus deflection curves, and all of the samples failed quickly, it is observed that the sample containing a higher or considerable number of bamboo fiber layers exhibited increased strain and toughness. In comparison to other samples, embolism of glass fiber as the main and covering layer expressed a higher impact on the mechanical properties of the composites is observed in this investigation. The shattered sample morphology demonstrated that the matrix and reinforcements were compatible.

Detecting Alzheimer’s disease (AD) early on allows patients to take preventative measures before the onset of irreversible brain damage, which is a critical factor in the treatment of Alzheimer’s patients. Most machine detection methods are constrained by congenital observations, although computers have been utilized in several recent research studies to diagnose AD. In AD, the hippocampus is usually the first part of the brain to be affected. Structural magnetic resonance imaging (SMRI) can be used to assist in diagnosing AD by measuring the hippocampus’s form and volume (MRI). The information encoded by these attributes is restricted and may be affected by segmentation problems. These traits are also extracted independently of the classification, which could result in lower‐than‐desired classification accuracy. Researchers in this study used structural MRI data to develop a deep learning framework for combined automatic hippocampus segmentation and AD categorization. Multi‐task deep learning (MTDL) is used to learn hippocampus segmentation simultaneously. The hyperparameter optimization of the CNN model (capsule network) for illness classification is then carried out using the deer hunting optimization (DHO) technique. ADNI‐standardized MRI datasets have been used to test the suggested method, and it is accurate. Suggested MTDL achieved 97.1% accuracy and 93.5% of Dice coefficient, whereas the proposed MTDL model achieved an accuracy of 96% for binary classification and 93% for multi‐class classification.

Thermal energy harvesting has seen a rise in popularity in recent years due to its potential to generate renewable energy from the sun. One of the key components of this process is the solar absorber, which is responsible for converting solar radiation into thermal energy. In this paper, a smart performance optimization of energy efficient solar absorber for thermal energy harvesting is proposed for modern industrial environments using solar deep learning model. In this model, data is collected from multiple sensors over time that measure various environmental factors such as temperature, humidity, wind speed, atmospheric pressure, and solar radiation. This data is then used to train a machine learning algorithm to make predictions on how much thermal energy can be harvested from a particular panel or system. In a computational range, the proposed solar deep learning model (SDLM) reached 83.22 % of testing and 91.72 % of training results of false positive absorption rate, 69.88 % of testing and 81.48 % of training results of false absorption discovery rate, 81.40 % of testing and 72.08 % of training results of false absorption omission rate, 75.04 % of testing and 73.19 % of training results of absorbance prevalence threshold, and 90.81 % of testing and 78.09 % of training results of critical success index. The model also incorporates components such as insulation and orientation to further improve its accuracy in predicting the amount of thermal energy that can be harvested. Solar absorbers are used in industrial environments to absorb the sun's radiation and turn it into thermal energy. This thermal energy can then be used to power things such as heating and cooling systems, air compressors, and even some types of manufacturing operations. By using a solar deep learning model, businesses can accurately predict how much thermal energy can be harvested from a particular solar absorber before making an investment in a system.

The main objective of this research study is to optimize the printing parameters that can be used in the FDM (fusion deposition modeling) production method to obtain the lowest production time and best printing parameter of PLA (polylactic acid) filament with the tensile test. The printing parameter that can be used in FDM machines such as extruder temperature, bed temperature, layer height, printing speed, travel speed, infill, and shell count is taken into account for optimization. In addition, the tensile specimens from ASTM (American Society for Testing and Materials) D638 standard were manufactured by PLA filament with the above‐modified printing parameters. The best printing parameters for PLA products were found by the time recorded during production and tensile test results after production. Thus, through this research, one can find the best PLA filament printing parameters and their timing.

The use of biodegradable materials in 3D printing has gained attention due to its potential in addressing environmental concerns in the manufacturing industry. This paper aims to explore the current state of research and development in sustainable 3D printing using biodegradable materials. The research found that biodegradable materials, such as bioplastics, are being increasingly used in 3D printing as an eco-friendly alternative to traditional materials. Various types of biodegradable materials have been tested, including Polylactic Acid (PLA), cellulose-based materials, and starch-based materials. One of the main advantages of using biodegradable materials in 3D printing is its potential to reduce the carbon footprint of the production process. These materials are derived from renewable resources and have a lower environmental impact compared to non-biodegradable materials, such as petroleum-based plastics. However, the use of biodegradable materials in 3D printing also presents challenges, including limited availability and higher production costs, as well as the need for specific print settings and post-processing methods. Further research is needed to optimize the use of biodegradable materials in 3D printing and to develop new materials with improved properties. Collaboration between material scientists and 3D printing manufacturers is crucial to advancing sustainable 3D printing using biodegradable materials.