This paper investigates the mechanical behavior and buckling failure of SUS304 stainless steel oval square tubes with four different long/short axis ratios (1.5, 2.0, 2.5, and 3.0) under cyclic bending. The wall thickness is 0.7 mm for all oval square tubes, and cyclic bending loads are applied until buckling failure occurs. The moment-curvature relationships for all SUS304 stainless steel oval rectangular tubes eventually forms a stable elastic-plastic loop for every long/short axis ratio. Additionally, the relationships between short axis variation (the change in the length of the short axis divided by the original length of the short axis) and curvature demonstrate serrations, and a growth pattern as cycles progress. Moreover, a larger long/short axis ratio corresponds to a greater short axis variation. Regarding the curvature-number of cycles required to ignite buckling relationships, it can be observed that the four long/short axis ratios correspond to four straight lines when plotted on double logarithmic coordinates. Lastly, this study proposes theoretical equations to describe the aforementioned relationships. The theoretical analysis is compared with experimental data, revealing a close alignment between the two approaches. This indicates that the theory can reasonably describe the experimental results.

Aspiration pneumonia is a type of lung infection that occurs when food, liquid, saliva, or vomit is inhaled into the lungs instead of being swallowed into the esophagus. This condition is more common in people with difficulty swallowing, a weakened immune system, or compromised gag reflexes. When foreign materials enter the lungs, they can introduce bacteria, viruses, or fungi that lead to inflammation and infection. The severity of aspiration pneumonia depends on the type of material aspirated, the volume, and the individual's overall health. Early diagnosis and treatment are crucial for managing symptoms and preventing complications like respiratory failure or abscess formation. Understanding the risk factors, symptoms, and potential complications is key to effective prevention and management of this condition.

Pattern recognition is a branch of science whose major goal is to characterize information. The pattern refers to these units; depending on the application, signals for images may differ from waveforms. This study explains the convenience of Principle component analysis (PCA) in classifying Surface Electromyogram (SEMG) signals. PCA is a popular method for analyzing large amounts of data, so in this study, EMG data from six separate hand gestures were collected features were extracted using the time domain variables. Principle component analysis used these features as the input variables for classifying myoelectrical i.e. SEMG signal. The information gathered for correlation analysis, all datasets is rotated by class-specific Principal component (PC) matrices, and then PCA is used to classify different hand gestures.

Acinetobacter baumannii, an opportunistic extracellular pathogen is one of the major causes of nosocomial infections. Omp34, also known as Omp33-36 or Omp34, is a bacterial porin protein involved in the virulence and fitness of this pathogen by adhesion to the host cell, fibronectin. In this study, the expression of the recombinant Omp34 (rOmp34)was carried in E. coil BL21 (DE3). The immunogenicity of the rOmp34 in A. baumannii was studied in a murine sepsis model. Antibody response in mice injected with the recombinant protein was assessed using indirect ELISA. A high titer equivalent to 1.54±0.06 of specific antibody against rOmp34 was elicited in the immunized mice sera. Homogenized liver and spleen samples of the control mice challenged with A.baumannii were loaded with 8×103 and 9×103 CFU per gram tissue respectively 48 hours post-challenge as against complete clearance of A.baumannii in the immunized group. The protective immunity was achieved by challenging the mice groups with 5×LD50 of live A. baumannii. Omp34 can be nominated as an immunogen that can bring about protection against Acinetobacter baumannii.

Security has been the need of the hour in health care systems with so many electronic health records. The cloud is not assured to provide security as it is involved parallel processing and is in distributed nature. The Blockchain (BC) has been introduced in the cloud for some applications like intelligent healthcare systems since they are highly susceptible to security violations and attacks, such as forgery, tampering, privacy leakage, etc., to provide protection and security for the medical data. Thus, this paper presents a survey on the use of Blockchain (BC) technology in cloud storage for the safety of healthcare systems. At first, the security measures provided by the traditional cloud storage system are discussed, along with its limitations. Then a brief introduction of Blockchain technology in a cloud storage with its background work is presented. Finally survey of different works focused on blockchain technology in the healthcare systems is presented as a promising solution for the security issues to provide tightened and enhanced security levels over the health care systems. This survey can provide a potential solution using blockchain technology to protect healthcare data outsourced over the cloud. Evaluating and comparing the simulation experiments of presented Blockchain technology focused works can prove that integrity verification with cloud storage and medical data, data sharing with less computing complexity, and security and privacy protection are achieved. Blockchain technology and IT have led to business warfare, and countries in the Middle East have turned towards blockchain technology. Accordingly, the factors affecting the interest and approval of clients towards blockchain technology in cloud storage for the security of healthcare systems and the factors that increase the awareness of individuals towards blockchain technology were addressed in this study. United theory of Acceptance and use of technology (UTAUT) was applied to comprehend the factors affecting the community’s reaction to learning via blockchain technology to maintain this healthcare service. An online survey was conducted to obtain various data on blockchain technology in developing countries in the Middle East, and respondents were selected using a snowball sampling method. A total of 443 random responses were obtained and tested using SPSS. It was found that Acceptance of blockchain technology is factored by: Anticipation, Effort Expectancy, Social Influence (SI), Facilitate conditions, personal innovativeness (Penn), and perceived Security Risk (PSR). Results have shown that Anticipation, Effort Expectancy, Social Influence (SI), Facilitate conditions, personal innovativeness (Penn), and perceived Security Risk (PSR) have an overall of the Adoption and Acceptance of blockchain technology during the Covid-19 Pandemic, in addition to providing an overview of current trends in the field, as well as the issues of significance and compatibility with this specific community.

To follow the path of economic development, developed countries have long relied, , on the income from their natural resources. This is notably the case for Australia (minerals), Canada (oil, minerals) and the United States (oil) but also Germany, France and England (coal). However, there are recent experiences of countries which have based part of their economic development on their natural resources. Examples of Norway (petroleum), Chile (copper ore) and Botswana (diamonds) provide an illustration. Despite these success stories, empirical studies generally shown the existence of a negative relationship between their natural resource wealth and their economic growth, known as “Dutch Disease”. Generally, countries with large stocks of natural resources struggle to guarantee sustainable growth of their GDP. Unlike the others. With significant oil deposits, is Algeria one of the countries that have fallen into Dutch disease? This contribution will attempt to answer as well as to identify, if applicable, the conditions that led to the appearance of the phenomenon and to propose a solution to get out of it.

Solving Multi Attribute Decision Making (MADM) problems in uncertain environment is a challenging task. In complex group decision making, decision makers and the decision attributes are the core of the relevant activities. In a group decision making environment, the consideration of the weights of the Decision Makers is really an important task, as, it is almost impossible to gather a homogeneous group of Decision Makers (DMs) whose decision-making skills and other attributes are same or equivalent. This problem has been addressed in this work in a unique way. The uncertainty in various decisions provided by the DMs has been tackled by considering Interval Grey Numbers (IGNs). A novel method has been introduced to compare IGNs by ‘grey preference degree’. TOPSIS (Technique for order preference by similarity to ideal solution) has been used to compare the alternatives. After the ranking is done, another unique way has been presented to classify the alternatives in different groups, which will really help the decision-making agency in future to deal with the same set of alternatives. Finally, the methodology has been applied to a real supplier selection problem to illustrate the decision-making steps and the effectiveness of this method.

Conventional sensors have a relatively low degree of precision and accuracy in identifying the location of fire and may give false alarms. This research study is aimed at utilizing a framework that is an agglomeration of traditional strategy, a vision-based framework for intelligent detection and prediction of fire using convolution neural systems. The performance of the Fire Detection and Prediction Framework is compared based on the precision values under different evaluation scenarios. This research is two-phased - the first phase is used to build a fire detection framework or tool based on image processing methods. In the subsequent steps, the Deep CNN architecture will trigger an alert in case of fire data and will improve on the classification by continuous learning of fire and no fire data. The Learning Rate Finder class will be used to find out the optimal learning rate, and the accuracy of the classifier is then subsequently defined. More than 1200 plus augmented fire datasets were considered under study, and the non-fire dataset constitutes 2000 plus entries from ImageNet and Kaggle. The problem has two classes of data, i.e., fire and non-fire and smoke. The learning accuracy achieved is 90-92%, detection via a vision-based approach attained is 85%, and fire and smoke prediction attained using Convolutional Neural Network is 95%. The error rate was 0-0.5%.

Fuzzy relational inequalities with fuzzy constraints (FRI-FC) are the generalized form of fuzzy relational inequalities (FRI) in which feasible solution sets are defined as fuzzy sets; that is, fuzzy inequality replaces ordinary inequality in the constraints. Also, an FRI-FC problem is defined as an optimization problem subjected to an FRI-FC using a certain max-t-norm composition. Unlike most optimization algorithms, the main goal of the FRI-FC problems is to find super-optima; that is, near-feasible solutions (solutions with pre-specified desirable infeasibility) having better objective values than those resulted from the resolution of the similar problems with crisp (ordinary) inequality constraints. Previously, some special cases of FRI-FC problems were investigated using only one type of fuzzy relational inequalities. This paper considers the most general version of FRI-FC problems with application in the management model of wireless communication with minimum cost. In doing so, the feasible region is formed by the intersection of both types of the fuzzy relational inequalities. Firstly, some structural properties of the problem are studied and then the primary problem is converted into an equivalent problem with simple constraints. Subsequently, a PSO-based algorithm is proposed to find a super-optimum with pre-specified desirable infeasibility. The proposed algorithm is tested with different generalized FRI-FC problems defined with ten well-known continuous t-norms. Moreover, the generated solutions for these problems, are also compared with some well-known meta-heuristic methods which have been applied to many practical optimization problems.