An IoE Blockchain-Based Network Knowledge Management Model for Resilient Disaster Frameworks

The disaster area is a constantly changing environment, which can make it challenging to distribute supplies effectively. The lack of accurate information about the required goods and potential bottlenecks in the distribution process can be detrimental. The success of a response network is dependent on collaboration, coordination, sovereignty, and equal distribution of relief resources. To facilitate these interactions and improve knowledge of supply chain operations, a reliable and dynamic logistic system is essential. This study proposes the integration of blockchain technology, the Internet of Things (IoT), and the Internet of Everything (IoE) into the disaster management structure. The proposed disaster response model aims to reduce response times and ensure the secure and timely distribution of goods. The hyper-connected disaster supply network is modeled through a concrete implementation on the Network Simulation (NS2) platform. The simulation results demonstrate that the proposed method yields significant improvements in several key performance metrics. Specifically, it achieved more than a 30% improvement in the successful migration of tasks, a 17% reduction in errors, a 15% reduction in delays, and a 9% reduction in energy consumption

Breaking Free: Leakage Model-free Deep Learning-based Side-channel Analysis

Profiling side-channel analysis has gained widespread acceptance in both academic and industrial realms due to its robust capacity to unveil protected secrets, even in the presence of countermeasures. To harness this capability, an adversary must access a clone of the target device to acquire profiling measurements, labeling them with leakage models. The challenge of finding an effective leakage model, especially for a protected dataset with a low signal-to-noise ratio or weak correlation between actual leakages and labels, often necessitates an intuitive engineering approach, as otherwise, the attack will not perform well. In this paper, we introduce a deep learning approach that does not assume any specific leakage model, referred to as the multibit model. Instead of trying to learn a representation of the target intermediate data (label), we utilize the concept of the stochastic model to decompose the label into bits. Then, the deep learning model is used to classify each bit independently. This versatile multibit model can align with existing leakage models like the Hamming weight and Most Significant Bit leakage models while also possessing the flexibility to adapt to complex leakage scenarios. To further improve the attack efficiency, we extend the multibit model to simultaneously attack all 16 subkey bytes, which requires negligible computational effort. Based on our preliminary analysis, two of the four considered datasets could only be broken using a Hamming Weight leakage model. Using the same model, the proposed methods can efficiently crack all key bytes across four considered datasets. Our work, thus, signifies a significant step forward in deep learning-based side-channel attacks, showcasing a high degree of flexibility and efficiency without any presumption of the leakage model

Identification of medicinal plants effective on sinusitis native to Shiraz province in Iran

Sinusitis is one of the most infectious diseases that affect holes around the nose such as frontal ethmoid sinuses, maxillary and sphenoid. Symptoms usually include nasal congestion and obstruction, feeling of pressure or fullness in the face, anterior or posterior nasal causing discharge, headaches, fever, swelling and erythema in forehead or cheek and cough. The symptoms might be edema and mucosal congestion, nasal drainage, posterior nasal discharge, nasal septum deviation and polyps. The medicinal plants identified for instance are Amygdalus scoparia Spach, Echinophora platyloba DC., Haplophyllum perforatum L, Lavandula stoechas L, Borago officinalis, Matricaria recutita, Descurainia Sophia (L.) Schr and Haplophyllum perforatum L to treat sinusitis in Shiraz. Many of these plants have antioxidant activity and contain bioactive compounds such as flavonoids, flavonoids, polyphenols, anthocyanins, tannins and many other pharmaceutical bioactive ingredients that have effects on sinusitis. This paper aims to review the recently published papers in this topic

Detection of Extended-Spectrum beta-Lactamases among Acinetobacter Baumannii Isolated from Hospitals of Qazvin, Iran

BACKGROUND፡ Acinetobacter baumannii is a major contributor to nosocomial infections. Extended-spectrum ßlactamase (ESBL)-producing A. baumannii is spreading worldwide. We aimed to determine the frequency of ESBLencoding genes in clinical isolates of A. baumannii and to access their clonal relationship by repetitive extragenic palindromicPCR (rep-PCR). METHODS: In this descriptive cross-sectional study, 203 isolates of A. baumannii were collected from Qazvin hospitals. The Identification of isolates was performed by standard laboratory methods. To verify ESBL production, all isolates were screened by disk agar diffusion and confirmed by the combined disk method. Subsequently, ESBL-encoding genes were detected by PCR and sequencing. Possible clonal association of ESBL-producing isolates was evaluated using rep-PCR. RESULTS: Two hundred (98.5%) isolates showed reduced susceptibility to one of the antibiotics used in the ESBL screening test, of which 127 isolates (62.6%) produced ESBL. PCR results showed blaOXA-1 (20.5%) was the most prevalent gene followed by blaTEM-1 (20%), blaGES-1 (15.7%), blaCTX-M-15 (7.9%), and blaPER-1 (1.6%). Rep-PCR results revealed that ESBL-producing isolates belonged to clones A (85%), B (13.4%), and C (1.6%). CONCLUSION: Our study showed the significant presence of blaOXA-1, blaTEM-1, blaGES-1, blaCTX-M-15, and blaPER-1 genes in ESBLproducing A. baumannii isolates in the studied hospitals. This is the first report on the emergence of blaOXA-1 gene in these isolates in Iran. The use of comprehensive antimicrobial treatment guidelines based on laboratory data and appropriate infection control interventions are essential

A Different Olfactory Perception in Anosmic Patients: Evidence from Functional MRI

Olfactory system is a vital sensory system in mammals, giving them the ability to connect with their environment. Anosmia, or the complete loss of olfaction ability, which could be caused by injuries, is an interesting topic for inspectors with the aim of diagnosing patients. Sniffing test is currently utilized to examine if an individual is suffering from anosmia; however, functional Magnetic Resonance Imaging (fMRI) provides unique information about the structure and function of the different areas of the human brain, and therefore this noninvasive method could be used as a tool to locate the olfactory-related regions of the brain. In this study, by recruiting 31 healthy and anosmic individuals, we investigated the neural BOLD responses in the olfactory cortices following two odor stimuli, rose and eucalyptus, by using a 3T MR scanner. Comparing the two groups, we observed a network of brain areas being more active in the normal individuals when smelling the odors. In addition, a number of brain areas also showed an activation decline during the odor stimuli, which is hypothesized as a resource allocation deactivation. This study illustrated alterations in the brain activity between the normal individuals and anosmic patients when smelling odors, and could potentially help for a better anosmia diagnosis in the future

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

This publication is the Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering from July 6-8, 2022. The EG-ICE International Workshop on Intelligent Computing in Engineering brings together international experts working on the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolution of challenges such as supporting multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways. &nbsp

Study of Effects of Tax on Vehicle on Reducing Tehran Air Pollutants from Viewpoint of Urban Management

ABSTRACT Based on a report by The Organization for Economic Co-operation and Development (2001), approximately all environmental components are influenced by human activities. In fact, human activities cause undesirable effects on climate changes, weather and earth. The present paper aims to study effect of environmental tax on vehicle on reducing Tehran air pollutants from viewpoint of urban management. In perspective of this purpose, this paper is applicable, in terms of data collecting, this paper is descriptive. Statistical population of this paper includes people who had gone to centers of car technical examination at the last three months of 2011, sample size in accordance with statistical pollution and Morgan table has been determined 576, however, this number increased to 586 cases in this research. Moreover, Chi-square test one variable type and one way analysis of variance are tests used in this research. Findings show that individuals participating in this research -without considering their age, education, gender, social and financial class-all emphasize the role of environmental tax in reducing Tehran air pollutants. Moreover, making a proper culture and informing people about advantages of making environmental tax and its effect on reducing Tehran air pollutants have been effective as well

A laboratory study to evaluate the possibility of sulphur and phosphorous removal from iron ore concentrate by leaching

Iron ore concentrates with high grade sulfur cause several problems in the steel making process, and hence affect the concentrate price. Environmental issues such as sulfur dioxide emission during the concentrate pelletizing process and effect on the steel quality are other issues. The current study was focused on removal of sulfur from the iron ore concentrate by using the chemical leaching technique. The magnetite iron ore concentrate was chosen for this purpose. The results obtained showed that more than 90% of the total sulfur content was removed from the iron ore concentrate by chemical leaching. Effects of several parameters such as temperature, particle size and use of organic solvent on sulfur removal were investigated by a series of experiments. After optimizing the experimental conditions, it was demonstrated that with addition of sulfur, phosphorus, another important impurity was also removed from the iron ore concentrate. In addition, one of the major advantages of our proposed method was transformation of mineral pyrites to useful by-products such as elemental sulfur

Investigation of the Performance of Electro-Fenton Process in the Degradation of Acid Black 1 and Acid Blue 113 in Aquatic Environment

Azo dyes are a major environmental concern due to the presence of benzene rings in their structure. The present experimental study investigates the capability of the Electro-Fenton process as an Electrochemical advanced Oxidation Process for degrading Acid Black 1 and Acid Blue 113 in an aquatic environment. In this study, a lab-scale EF batch reactor equipped with four electrodes and a DC power supply was used for removing the dye. The effects of such operating parameters as pH, voltage, H2O2, initial dye concentration, cathode materials, and operation time were evaluated. The results showed that initial pH of the solution, initial H2O2 concentration, as well as different applied voltages and reaction times were highly effective in the dye removal efficiency of the process so that the 98% of both dyes were removed after 10 min of reaction at pH=3.0, a voltage of 20 V, and a H2O2 concentration of 100 mg/L. Removal efficiency decreased dramatically when pH was increased from 3 to 11, and voltage from 20 to 40 V. The presence of H2O2 was found to be the prerequisite to this process since the maximum dye removal obtained at an H2O2 concentration of zero was 7% for both dyes. The results of this study indicate that the Electro-Fenton method can be considered as an alternative process for the traditional treatment processes used