Natural fiber-reinforced polymer composites are gaining in popularity due to recyclability and availability. This research investigates how oil palm shell (OPS) filler materials impact the interlaminar shear and the dynamic properties of flax fiber-reinforced hybrid composites under cryogenic circumstances. Filler materials in two different proportions (0, 2, 4, and 6 wt.% OPS) and 40 wt.% flax fibers were used to make composites. The OPS filler-filled polymeric materials were invented through typical hand lay-up. The hybrid materials were imperiled to liquid nitrogen for varying amounts of time after production (15 and 30 min). According to the findings, OPS nanoparticles can be used as natural rather than artificial fillers. Furthermore, loading 4 wt.% OPS nanoparticles into organic fabric-strengthened epoxy polymeric materials during 15 min of cryogenic settings resulted in the best interlaminar shear and dynamic performances. The storage and loss modulus of the flax/epoxy composites were improved by adding a 4% OPS nanofiller. The improvement can be ascribed to the hardness and stiffness of the additional OPS nanofillers. The 4% nano-OPS/flax/epoxy hybrid nanocomposite’s damping factor was substantially reduced compared to the flax/epoxy composites. The OPS nanofiller limits the epoxy molecular chain’s free segmental mobility, resulting in a lower damping factor and enhancing the adherence among flax fibers and the epoxy resin. The shattered specimen of the hybrid materials was investigated using a scanning electron microscope.

The capture of a target spacecraft by a chaser is an on-orbit docking operation that requires an accurate, reliable, and robust object recognition algorithm. Vision-based guided spacecraft relative motion during close-proximity maneuvers has been consecutively applied using dynamic modeling as a spacecraft on-orbit service system. This research constructs a vision-based pose estimation model that performs image processing via a deep convolutional neural network. The pose estimation model was constructed by repurposing a modified pretrained GoogLeNet model with the available Unreal Engine 4 rendered dataset of the Soyuz spacecraft. In the implementation, the convolutional neural network learns from the data samples to create correlations between the images and the spacecraft’s six degrees-of-freedom parameters. The experiment has compared an exponential-based loss function and a weighted Euclidean-based loss function. Using the weighted Euclidean-based loss function, the implemented pose estimation model achieved moderately high performance with a position accuracy of 92.53 percent and an error of 1.2 m. The in-attitude prediction accuracy can reach 87.93 percent, and the errors in the three Euler angles do not exceed 7.6 degrees. This research can contribute to spacecraft detection and tracking problems. Although the finished vision-based model is specific to the environment of synthetic dataset, the model could be trained further to address actual docking operations in the future.

Electric vehicles (EVs) are proportionally increasing day-by-day with the inclusion of upgraded technology toward considered zero carbon emission efforts. To mitigate greenhouse gas emissions from the transportation sector, grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies are getting significant attention nowadays. EVs equipped with modern technology can help to stabilize the power grids through load-balancing topology during peak hours. The improvement in EVs can support the surroundings in numerous ways, such as power grid voltage and frequency regulations, harmonics distortions, accessible solar energy implemented to the grids, and peak load stabilizations. This literature review analyzes G2V and V2G impacts in more depth, namely opportunities, improvements in strategies, operation, control, issues, and new technology adoptions. This paper emphasizes the possibilities of bringing advancements in EV technology, smooth operations between grids and EVs, fast bidirectional charging and discharging scopes, control of grids and EVs structures, issues, benefits, pitfalls, challenges, and recommendations.

Automatic brain tumor detection in MR Images is one of the basic applications of machine vision in medical image processing, which, despite much research, still needs further development. Using multiple machine learning techniques as an ensemble system is one of the solutions that can be effective in achieving this goal. In this paper, a novel method for diagnosing brain tumors by combining data mining and machine learning techniques has been proposed. In the proposed method, each image is initially pre-processed to eliminate its background region and identify brain tissue. The Social Spider Optimization (SSO) algorithm is then utilized to segment the MRI Images. The MRI Images segmentation allows for a more precise identification of the tumor region in the image. In the next step, the distinctive features of the image are extracted using the SVD technique. In addition to removing redundant information, this strategy boosts the speed of the processing at the classification stage. Finally, a combination of the algorithms Naïve Bayes, Support vector machine and K-nearest neighbor is used to classify the extracted features and detect brain tumors. Each of the three algorithms performs feature classification individually, and the final output of the proposed model is created by integrating the three independent outputs and voting the results. The results indicate that the proposed method can diagnose brain tumors in the BRATS 2014 dataset with an average accuracy of 98.61%, sensitivity of 95.79% and specificity of 99.71%. Additionally, the proposed method could diagnose brain tumors in the BTD20 database with an average accuracy of 99.13%, sensitivity of 99% and specificity of 99.26%. These results show a significant improvement compared to previous efforts. The findings confirm that using the image segmentation technique, as well as the ensemble learning, is effective in improving the efficiency of the proposed method.

Antimony trisulfide (Sb₂Se₃), a non-toxic and accessible substance, has possibilities as a material for use in solar cells. The current study numerically analyses Sb₂Se₃ solar cells through the program Solar Cell Capacitance Simulator (SCAPS). A detailed simulation and analysis of the influence of the Sb₂Se₃ layer’s thickness, defect density, band gap, energy level, and carrier concentration on the devices’ performance are carried out. The results indicate that a good device performance is guaranteed with the following values in the Sb₂Se₃ layer: an 800 optimal thickness for the Sb₂Se₃ absorber; less than 10¹⁵ cm⁻³ for the absorber defect density; a 1.2 eV optimum band gap; a 0.1 eV energy level (above the valence band); and a 10¹⁴ cm⁻³ carrier concentration. The highest efficiency of 30% can be attained following optimization of diverse parameters. The simulation outcomes offer beneficial insights and directions for designing and engineering Sb₂Se₃ solar cells.

The Sustainable Development Goals (SDGs) play an essential role, emphasizing responsible resource use, production, and consumption, including waste management. In addition, SDG 3, 7, 11, 12, and 13 are directly/indirectly related to waste management. This study aims to determine a suitable waste-to-energy (WtE) technology in Chittagong City, Bangladesh, focusing on cleaner technology. Anaerobic digestion, gasification, incineration, and landfill gas (LFG) recovery were considered as possible alternatives. Technical, economic, environmental, and social issues have been considered as necessary criteria for evaluation. An analytical hierarchy process was applied to rank these technologies based on stakeholders’ perceptions. The study found that anaerobic digestion (AD) ranked first, receiving 38% of overall weight. The second preferred technology is LFG (27%). Gasification and incineration stood at third and fourth, respectively (21% and 14%). According to a sensitivity study, the decision is only sensitive to the economy. LFG will become the most favoured solution for WtE conversion if the economy prioritizes more than 38%. Subsequently, this study’s findings will help achieve Bangladesh’s SDG agenda.