The current analysis analyzed the odds of casualties by road accidents. Hence, data were classified into tertiles for better research, and accident types were classified into five following the authority methodology: rollovers, crash, roadway departure, special accident, and car capsizing. Multi-logistic regression was employed for the data analysis. This research found that rollover was the most deadly accident, and the crash was the most probable to cause injuries.

Without a doubt, lightweight materials of high strength are in high demand in the automotive, aerospace, biomedical, and other industries that require such materials. Processing or manufacturing such materials has been a vital topic in contemporary research, as well as material development in the industry. A possible solution for the processing of lightweight materials of high strength is to target lightweight materials by nature such as magnesium and improve their mechanical properties such as stiffness, strength, and hardness. The aforementioned properties are sometimes achieved by processing soft and light materials through High-Pressure Torsion. In this work, Magnesium with Silicon Carbide nanoparticles (Mg-SiC) was strengthened and hardened through the High-Pressure Torsion (HPT) processing technique. The samples were compressed with a pressure of 6.0 GPa and twisted at the rotating speed of 1 rpm with varying numbers of turns N = 0, N = 1, N = 5 and N = 10 at a temperature of 23°C. The processed samples were prepared for the experimental investigation of microstructural characterization and hardness test examinations. Microstructural results showed that grain refinements of material can be achieved through HPT processing methods, which reduced the average grain sizes of unprocessed (N = 0) Mg alloy samples from 149.9 µm to 27.1 µm after processing ten turns. However, hardness test results do not indicate any significant improvement after one HPT processing turn although homogeneity is attained at five processing turns within the nanocomposites.

Polluted air creates health problems for people, plants and animals today due to many factors in industrial cities and power generation projects, transportation and chemical industry and others. It is for this reason that this research in bibliographic review allows us to know the different solutions to produce hydrogen through the analysis of the Scopus database and the VOSviewer tool that allows us to analyze the data, considering the variables that are artificial leaf, hydrogen, production , clean energy through seawater, graphs and tables were obtained which provide us with an analysis of the number of publications, the countries that carry out these investigations and the bibliometric maps worldwide for a global analysis. The results allow us to analyze and learn about the different solutions and materials that are used to carry out artificial photosynthesis that develops the production of hydrogen by separating water molecules with the aim of emitting zero emissions and being able to use it in different applications such as fuel, energy electrical, industrial uses and others. The purpose of this research is to allow us to make better decisions to apply this methodology according to the materials that we have in greater scope and that is a promising future for a generation of the new industry for the following years, also considering the objectives of sustainable development and finally, motivate readers to continue with these investigations and be able to apply it with institutions in charge of combating this problem.

For the past 19 years, high entropy alloys (denoted as HEAs) have piqued the interest of many researchers due to their unique properties. Sputtering High Entropy Alloys on bulk materials, on the other hand, have been widely utilized due to their low cost of manufacture. This paper reviews the most recent trends and advancements in sputtered HEA coatings, thin film research, and development. Firstly, HEA coating and thin films were introduced. Then, a look at sputtering technologies and procedures is presented, followed by a summary of recent research on sputtered HEA thin films, properties, and applications. From reviewed literature, it can be deduced that HEAs that include the full nitride element have greater mechanical and elastic properties, and HEAs generally have the potential for structural, industrial, biomedical, and energy applications. Finally, it is suggested that new stable HEA films and coating research initiatives with various materials be undertaken to widen its applications.

In this work, a mixed-integer linear programming model is formulated to allocate the appropriate orders to the right suppliers for recyclable raw materials. We modify the previous model for the supplier selection and order allocation problem for stochastic demand to cope with the supply risks of recyclable raw materials such as insufficient supply quantity, defective rate, and late delivery. The optimal solution of the mathematical model is the benchmark for small-sized problems. Then, a hybrid meta-heuristic of Particles Swarm Optimization and Grey Wolf Optimization (PSO-GWO) is proposed to search for the best solution for large-sized problems. A real-life case study of a steel manufacturer with two factories in Vietnam is presented to validate the proposed approach. Some experiments have been tested to confirm the performance of the hybrid PSO-GWO approach.

Losses experienced by the Indonesian government due to floods are predicted. It is because of the significance of population growth, closure of water catchment areas, and climate change in many regions in Indonesia. The government has tried to reduce the risk but faces insufficient funds. Therefore, new innovative funding sources are essential to overcome these limitations. One way to obtain it is through issuing Flood Catastrophe Bonds (FCB). Unfortunately, Indonesia has had no FCB price estimate until now. On the basis of this problem, this study aims to estimate the FCB price in Indonesia. The primary method used is the approximation method of the aggregate loss distribution. This method can compute the aggregate flood loss cumulative distribution function value faster. The FCB fair price estimation results are cheap because the risk of the instrument is significant. This significant risk is also proportional to the large return. Finally, further analysis shows that in Indonesia, the attachment point of the FCB has a relationship that is in line with the price, while the term of FCB does not. This research is expected to assist the Indonesian government in determining the fair price of FCB in Indonesia. This research can assist the investors in choosing FCB based on expected return, attachment point, and the term they want.

This study investigates how audit quality moderates the effect of financial performance indicators on the stock returns of Amman Stock Exchange-listed firms (ASE). The panel data analysis selected the data of 95 ASE-listed firms from 2013 through 2021. This analysis demonstrates a significant inverse relationship between a company's book value and its stock returns. A statistically negative relationship was observed between cash flow, dividends per share, and stock return. The empirical results of this study confirm the moderating influence of audit quality in the relationship between financial performance and stock return. Firstly, auditor's fees have a significant impact on the relationship between firm stock returns and EPS, BV, DPS, and cash flows (CFO). The size of the auditing firm moderates the relationship between company stock returns and EPS, DPS, and the CFO, but not with book value (BV). The auditor's opinion moderates the relationship between business stock returns and EPS, BV, and DPS but not the relationship between firm stock returns and cash flows (CFO). The study suggests that regulatory bodies like the Companies Control Department (CCD) and ASE should make sure that local audit firms in Jordan improve their audit quality to be on par with the Big 4 audit firms in order to improve their financial performance measures and stock returns.

The vehicle routing problem combined with loading of goods, considering the reduction of fuel consumption, aims at finding the set of routes that will serve the demands of the customers, arguing that the fuel consumption is directly related to the weight of the load in the paths that compose the routes. This study integrates the Fuel Consumption Heterogeneous Vehicle Routing Problem with Two-Dimensional Loading Constraints (2L-FHFVRP). To reduce fuel consumption taking the associated environmental impact into account is a classical VRP variant that has gained increasing attention in the last decade. The objective of this problem is to design the delivery routes to satisfy the customers’ demands with the lowest possible fuel consumption, which depends on the distances of the paths, the assigned vehicles, the loading/unloading pattern and the load weight. In the vehicle routing problem literature, the approximate algorithms have had great success, especially the evolutionary ones, which appear in previous works with quite a sophisticated structure, obtaining excellent results, but that are difficult to implement and adapt to other variants such as the one proposed here. In this study, we present a specialized genetic algorithm to solve the design of routes, keeping its main characteristic: the easy implementation. By contrast, the loading of goods restriction is validated by means of a GRASP algorithm, which has been widely employed for solving packing problems. With a view of confirming the performance of the proposed methodology, we provide a computational study that uses all the available benchmark instances, allowing to illustrate the savings achieved in fuel consumption. In addition, the methodology suggested can be adapted to the version of solely minimizing the total distance traveled for serving the customers (without the fuel consumption) and it is compared to the best works presented in the literature. The computational results show that the methodology manages to be adequately adapted to this version and it is capable of finding improved solutions for some benchmark instances. As for future work, we propose to adjust the methodology to consider the three-dimensional loading problem so that it adapts to more real-life conditions of the industry.

In this work, accelerated thermal aging at different temperatures was performed on high-density polyethylene (HDPE) bottles and specimens for food packaging. The degradation of HDPE was followed macroscopically by tensile and compression tests to evaluate the decrease in mechanical properties. The data obtained from these tests are used to calculate a new static damage law and the life fraction of this polymer. A prediction of the lifetime of HDPE was determined by thermogravimetric analysis (TGA) in dynamic mode. The evolution of the crystallinity rate during accelerated thermal aging was carried out using FOURIER transform infrared spectrometry (FTIR). The results obtained show a remarkable degradation of the mechanical properties in traction and compression by calculation of the static damage, and the physical and chemical properties by the analysis of the rate of crystallinity as well as the prediction by the law of Arrhenius.

COVID-19 detection through radiological examination is favoured since it is fast and produces more accurate results than the laboratory approach. However, when it has infected many people and put a strain on the healthcare system, the need for fast, automatic COVID-19 detection in patients has become critical. This study proposes to detect COVID-19 from chest X-ray (CXR) images with a machine learning approach. The main contributions of this paper are to compare two powerful deep learning models, i.e., convolutional neural networks (CNN) and the combination of CNN and Long Short-Term Memory (LSTM). In the combination model, CNN is recommended for feature extraction, and COVID-19 is classified using the features of LSTM. The dataset used in this study amounted to 4,095 CXR images, consisting of 1,400 images of normal conditions, 1,350 images of COVID-19, and 1,345 images of pneumonia. Both CNN and CNN-LSTM were executed in a similar experimental setup and evaluated using a confusion matrix. The experiment results provide evidence that the CNN-LTSM is better than the CNN deep learning model, with an overall accuracy of about 98.78%. Furthermore, it has a precision and recall of 99% and 98%, respectively. These findings will be valuable in the fast and accurate detection of COVID-19.