EFFICIENT CAMERA SELECTION FOR MAXIMIZED TARGET COVERAGE IN UNDERWATER ACOUSTIC SENSOR NETWORKS

In Underwater Acoustic Sensor Networks (UWASNs), cameras have recently been deployed for enhanced monitoring. However, their use has faced several obstacles. Since video capturing and processing consume significant amounts of camera battery power, they are kept in sleep mode and activated only when ultrasonic sensors detect a target. The present study proposes a camera relocation structure in UWASNs to maximize the coverage of detected targets with the least possible vertical camera movement. This approach determines the coverage of each acoustic sensor in advance by getting the most applicable cameras in terms of orientation and frustum of camera in 3-D that are covered by such sensors. Whenever a target is exposed, this information is then used and shared with other sensors that detected the same target. Compared to a flooding-based approach, experiment results indicate that this proposed solution can quickly capture the detected targets with the least camera movement

Protective Effect of Hesperidin against Cyclophosphamide Hepatotoxicity in Rats

The protective effect of hesperidin was investigated in rats exposed to liver injury induced by a single intraperitoneal injection of cyclophosphamide (CYP) at a dose of 150 mg kg-1. Hesperidin treatment (100 mg kg-1/day, orally) was applied for seven days, starting five days before CYP administration. Hesperidin significantly decreased the CYP-induced elevations of serum alanine aminotransferase, and hepatic malondialdehyde and myeloperoxidase activity, significantly prevented the depletion of hepatic glutathione peroxidase activity resulted from CYP administration. Also, hesperidin ameliorated the CYP-induced liver tissue injury observed by histopathological examination. In addition, hesperidin decreased the CYP-induced expression of inducible nitric oxide synthase, tumor necrosis factor-α, cyclooxygenase-2, Fas ligand, and caspase-9 in liver tissue. It was concluded that hesperidin may represent a potential candidate to protect against CYP-induced hepatotoxicity

Protective Effect of Hesperidin against Cyclophosphamide Hepatotoxicity in Rats

The protective effect of hesperidin was investigated in rats exposed to liver injury induced by a single intraperitoneal injection of cyclophosphamide (CYP) at a dose of 150 mg kg-1. Hesperidin treatment (100 mg kg-1/day, orally) was applied for seven days, starting five days before CYP administration. Hesperidin significantly decreased the CYP-induced elevations of serum alanine aminotransferase, and hepatic malondialdehyde and myeloperoxidase activity, significantly prevented the depletion of hepatic glutathione peroxidase activity resulted from CYP administration. Also, hesperidin ameliorated the CYP-induced liver tissue injury observed by histopathological examination. In addition, hesperidin decreased the CYP-induced expression of inducible nitric oxide synthase, tumor necrosis factor-α, cyclooxygenase-2, Fas ligand, and caspase-9 in liver tissue. It was concluded that hesperidin may represent a potential candidate to protect against CYP-induced hepatotoxicity

Protective Effect of Hesperidin against Cyclophosphamide Hepatotoxicity in Rats

The protective effect of hesperidin was investigated in rats exposed to liver injury induced by a single intraperitoneal injection of cyclophosphamide (CYP) at a dose of 150 mg kg-1. Hesperidin treatment (100 mg kg-1/day, orally) was applied for seven days, starting five days before CYP administration. Hesperidin significantly decreased the CYP-induced elevations of serum alanine aminotransferase, and hepatic malondialdehyde and myeloperoxidase activity, significantly prevented the depletion of hepatic glutathione peroxidase activity resulted from CYP administration. Also, hesperidin ameliorated the CYP-induced liver tissue injury observed by histopathological examination. In addition, hesperidin decreased the CYP-induced expression of inducible nitric oxide synthase, tumor necrosis factor-α, cyclooxygenase-2, Fas ligand, and caspase-9 in liver tissue. It was concluded that hesperidin may represent a potential candidate to protect against CYP-induced hepatotoxicity

Hybrid optimization algorithm for enhanced performance and security of counter-flow shell and tube heat exchangers

A shell and tube heat exchanger (STHE) for heat recovery applications was studied to discover the intricacies of its optimization. To optimize performance, a hybrid optimization methodology was developed by combining the Neural Fitting Tool (NFTool), Particle Swarm Optimization (PSO), and Grey Relational Analysis (GRE). STHE heat exchangers were analyzed systematically using the Taguchi method to analyze the critical elements related to a particular response. To clarify the complex relationship between the heat exchanger efficiency and operational parameters, grey relational grades (GRGs) are first computed. A forecast of the grey relation coefficients was then conducted using NFTool to provide more insight into the complex dynamics. An optimized parameter with a grey coefficient was created after applying PSO analysis, resulting in a higher grey coefficient and improved performance of the heat exchanger. A major and far-reaching application of this study was based on heat recovery. A detailed comparison was conducted between the estimated values and the experimental results as a result of the hybrid optimization algorithm. In the current study, the results demonstrate that the proposed counter-flow shell and tube strategy is effective for optimizing performance

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen–Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model’s decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies

Paediatrics: messages from Munich.

The aim of this article is to describe paediatric highlights from the 2014 European Respiratory Society (ERS) International Congress in Munich, Germany. Abstracts from the seven groups of the ERS Paediatric Assembly (Respiratory Physiology and Sleep, Asthma and Allergy, Cystic Fibrosis, Respiratory Infection and Immunology, Neonatology and Paediatric Intensive Care, Respiratory Epidemiology, and Bronchology) are presented in the context of the current literature

DNA damage in obesity: Initiator, promoter and predictor of cancer

Epidemiological evidence linking obesity with increased risk of cancer is steadily growing, although the causative aspects underpinning this association are only partially understood. Obesity leads to a physiological imbalance in the regulation of adipose tissue and its normal functioning, resulting in hyperglycaemia, dyslipidaemia and inflammation. These states promote the generation of oxidative stress, which is exacerbated in obesity by a decline in anti-oxidant defence systems. Oxidative stress can have a marked impact on DNA, producing mutagenic lesions that could prove carcinogenic. Here we review the current evidence for genomic instability, sustained DNA damage and accelerated genome ageing in obesity. We explore the notion of genotoxicity, ensuing from systemic oxidative stress, as a key oncogenic factor in obesity. Finally, we advocate for early, pre-malignant assessment of genome integrity and stability to inform surveillance strategies and interventions

A ZERO-TRUST-BASED IDENTITY MANAGEMENT MODEL FOR VOLUNTEER CLOUD COMPUTING

Non-conventional cloud computing models such as volunteer and mobile clouds have been increasingly popular in cloud computing research. Volunteer cloud computing is a more economical, greener alternative to the current model based on data centers in which tens of thousands of dedicated servers facilitate cloud services. Volunteer clouds offer numerous benefits: no upfront investment to procure the many servers needed for traditional data center hosting; no maintenance costs, such as electricity for cooling and running servers; and physical closeness to edge computing resources, such as individually owned PCs. Despite these benefits, such systems introduce their own technical challenges due to the dynamics and heterogeneity of volunteer computers that are shared not only among cloud users but also between cloud and local users. The key issues in cloud computing such as security, privacy, reliability, and availability thus need to be addressed more critically in volunteer cloud computing.Emerging paradigms are plagued by security issues, such as in volunteer cloud computing, where trust among entities is nonexistent. Thus, this study presents a zero-trust model that does not assign trust to any volunteer node (VN) and always verifies using a server-client topology for all communications, whether internal or external (between VNs and the system). To ensure the model chooses only the most trusted VNs in the system, two sets of monitoring mechanisms are used. The first uses a series of reputation-based trust management mechanisms to filter VNs at various critical points in their life-cycle. This set of mechanisms helps the volunteer cloud management system detect malicious activities, violations, and failures among VNs through innovative monitoring policies that affect the trust scores of less trusted VNs and reward the most trusted VNs during their life-cycle in the system. The second set of mechanisms uses adaptive behavior evaluation contexts in VN identity management. This is done by calculating the challenge score and risk rate of each node to calculate and predict a trust score. Furthermore, the study resulted in a volunteer computing as a service (VCaaS) cloud system using undedicated hosts as resources. Both cuCloud and the open-source CloudSim platform are used to evaluate the proposed model.The results shows that zero-trust identity management for volunteer clouds can execute a range of applications securely, reliably, and efficiently. With the help of the proposed model, volunteer clouds can be a potential enabler for various edge computing applications. Edge computing could use volunteer cloud computing along with the proposed trust system and penalty module (ZTIMM and ZTIMM-P) to manage the identity of all VNs that are part of the volunteer edge computing architecture

Scalable Lightweight IoT-Based Smart Weather Measurement System

The Internet of Things (IoT) plays a critical role in remotely monitoring a wide variety of different application sectors, including agriculture, building, and energy. The wind turbine energy generator (WTEG) is a real-world application that can take advantage of IoT technologies, such as a low-cost weather station, where human activities can be significantly affected by enhancing the production of clean energy based on the known direction of the wind. Meanwhile, common weather stations are neither affordable nor customizable for specific applications. Moreover, due to weather forecast changes over time and location within the same city, it is not efficient to rely on a limited number of weather stations that may be located far away from a recipient’s location. Therefore, in this paper, we focus on presenting a low-cost weather station that relies on an artificial intelligence (AI) algorithm that can be distributed across a WTEG area with minimal cost. The proposed study measures multiple weather parameters, such as wind direction, wind velocity (WV), temperature, pressure, mean sea level, and relative humidity to provide current measurements to recipients and AI-based forecasts. In addition, the proposed study consists of several heterogeneous nodes and a controller for each station in a target area. The collected data can be transmitted through Bluetooth low energy (BLE). The experimental results reveal that the proposed study matches the standard of the National Meteorological Center (NMC), with a nowcast measurement of 95% accuracy for WV and 92% for wind direction (WD)