QoS Aware Multicast Routing Protocol for Video Transmission in Smart Cities

In recent years, Software Defined Networking (SDN) has become an important candidate for communication infrastructure in smart cities. It produces a drastic increase in the need for delivery of video services that are of high resolution, multiview, and large-scale in nature. However, this entity gets easily influenced by heterogeneous behaviour of the user's wireless link features that might reduce the quality of video stream for few or all clients. The development of SDN allows the emergence of new possibilities for complicated controlling of video conferences. Besides, multicast routing protocol with multiple constraints in terms of Quality of Service (QoS) is a Nondeterministic Polynomial time (NP) hard problem which can be solved only with the help of metaheuristic optimization algorithms. With this motivation, the current research paper presents a new Improved Black Widow Optimization with Levy Distribution model (IBWO-LD)-based multicast routing protocol for smart cities. The presented IBWO-LD model aims at minimizing the energy consumption and bandwidth utilization while at the same time accomplish improved quality of video streams that the clients receive. Besides, a priority-based scheduling and classifier model is designed to allocate multicast request based on the type of applications and deadline constraints. A detailed experimental analysis was carried out to ensure the outcomes improved under different aspects. The results from comprehensive comparative analysis highlighted the superiority of the proposed IBWO-LD model over other compared methods.     

Weapons Detection for Security and Video Surveillance Using CNN and YOLO-V5s

In recent years, the number of Gun-related incidents has crossed over 250,000 per year and over 85% of the existing 1 billion firearms are in civilian hands, manual monitoring has not proven effective in detecting firearms. which is why an automated weapon detection system is needed. Various automated convolutional neural networks (CNN) weapon detection systems have been proposed in the past to generate good results. However, These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system. These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos. This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter. The proposed framework is based on You Only Look Once (YOLO) and Area of Interest (AOI). Initially, the models take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm. The proposed architecture will be assessed through various performance parameters such as False Negative, False Positive, precision, recall rate, and F1 score. The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved. Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN. It is promising to be used in the field of security and weapon detection.     

Mining Patterns from Change Logs to Support Reuse-Driven Evolution of Software Architectures

Modern software systems are subject to a continuous evolution under frequently varying requirements and changes in systems’ operational environments. Lehman’s law of continuing change demands for long-living and continuously evolving software to prolong its productive life and economic value by accommodating changes in existing software. Reusable knowledge and practices have proven to be successful for continuous development and evolution of the software effectively and efficiently. However, challenges such as empirical acquisition and systematic application of the reusable knowledge and practices must be addressed to enable or enhance software evolution. We investigate architecture change logs — mining histories of architecture-centric software evolution — to discover change patterns that 1) support reusability of architectural changes and 2) enhance the efficiency of the architecture evolution process. We model architecture change logs as a graph and apply graph-based formalism (i.e., graph mining techniques) to discover software architecture change patterns. We have developed a prototype that enables tool-driven automation and user decision support during software evolution. We have used the ISO-IEC-9126 model to qualitatively evaluate the proposed solution. The evaluation results suggest that the proposed solution 1) enables the reusability of frequent architectural changes and 2) enhances the efficiency of architecture-centric software evolution process. The proposed solution promotes research efforts to exploit the history of architectural changes to empirically discover knowledge that can guide architecture-centric software evolution.     

Predicting emotions in online social networks: challenges and opportunities

Online social networking has become a popular means of information exchange and social interactions. Users of these platforms generate massive amounts of data about their relationships, behaviors, interests, opinions, locations visited, items purchased, and subjective experiences of various aspects of life. Moreover, these platforms enable people from wide-ranging social and cultural backgrounds to synergize and interact. One interesting area of research is the emotional dimensions contained in this user-generated content, specifically, emotion detection and prediction, which involve the extraction and analysis of emotions in social network data. This study aimed to provide a comprehensive overview and better understanding of the current state of research regarding emotion detection in online social networks by performing a systematic literature review (SLR). SLRs help identify the gaps, challenges, and opportunities in a field of study through a careful examination of current research to understand the methods and results, ultimately highlighting methodological concerns that can be used to improve future work in the field. Hence, we collected and analyzed studies that focused on emotion in social network posts and discussed various topics published in digital databases between 2010 and December 2020. Over 239 articles were initially included in the collection, and after the selection process and application of our quality criteria, 104 articles were examined, and the results showed a robust extant body of literature on the text-based emotion analysis model, while the image-based requires more attention as well as the multiple modality emotion analysis.

     

Economical, green synthesis of fluorescent carbon nanoparticles and their use as probes for sensitive and selective detection of mercury(II) ions

The present article reports on a simple, economical, and green preparative strategy toward water-soluble, fluorescent carbon nanoparticles (CPs) with a quantum yield of approximately 6.9% by hydrothermal process using low cost wastes of pomelo peel as a carbon source for the first time. We further explore the use of such CPs as probes for a fluorescent Hg(2+) detection application, which is based on Hg(2+)-induced fluorescence quenching of CPs. This sensing system exhibits excellent sensitivity and selectivity toward Hg(2+), and a detection limit as low as 0.23 nM is achieved. The practical use of this system for Hg(2+) determination in lake water samples is also demonstrated successfully.     

Mucoadhesive polymeric hydrogels for nasal delivery of acyclovir

The study evaluated different mucoadhesive polymeric hydrogels for nasal delivery of acyclovir. Gels containing poly-N-vinyl-2-pyrrolidone (PVP) were prepared with crosslinking achieved by irradiation with a radiation dose of 15 kGy being as efficient as 20 kGy. Gels containing chitosan and carbopol were also evaluated. The mucoadhesive properties of gels were measured by a modification of a classical tensile experiment, employing a tensile tester and using freshly excised sheep nasal mucosa. Considering the mucoadhesive force, chitosan gel and gel prepared with 3% PVP in presence of polyethylene glycol (PEG) 600 were the most efficient. The in vitro drug release depended on the gel composition. Higher release rates were obtained from PVP gels compared to chitosan or carbopol gels. The release rate of drug from PVP gels was increased further in presence of PEG or glycerol. Histopathological investigations proved that the PVP was a safe hydrogel to be used for mucosal delivery. The PEG in gel formulations caused less damages to the nasal mucosal compared to formulation containing glycerol.     

Prediction of thermal buffer zone effectiveness in real‐size buildings—An experimental and analytical study

Global warming is caused by greenhouse gas (GHG) emissions produced from the use of fossil fuel–based energy sources. Buildings consume about 30% to 35% of the global energy use, which makes buildings a major contributor to the global warming problem. A long‐term plan has been established at the Thermal Processing Laboratory (TPL) at McMaster University to investigate the use of various renewable energy–based technologies to achieve net‐zero energy buildings (NZEB) in Canada. This paper presents results of an investigation of the effectiveness of using a thermal buffer zone (TBZ) in real‐size buildings. A TBZ is a closed passage built around the building that allows air to passively redistribute heat energy from solar radiation received on the south side throughout the building. A TBZ offers an effective solution of the overheating problem usually experienced on the south side of the building, and at the same time, it helps in reducing the heating load of the north side of the building. An experimental setup employing TBZ in a lab‐scale model of a typical building floor has been built. An analytical model of the TBZ has been developed. The experimental data has been used to validate the developed analytical model, which then was used to predict the performance of the TBZ implemented in a real‐size building floor, considering four cases. Results of the first three case studies considering the use of TBZ in cold and hot climates, with and without thermal insulation, show that the predicted effectiveness of TBZ could reach 117% and 72.5% in the winter and summer, respectively. Results of the fourth case study considering the effect of integrating a fan with the TBZ show that a fan is beneficial up to a certain fan power, beyond which the use of the fan would not be feasible. Results presented herein confirm that the TBZ is an effective means of integrating solar energy into buildings, thereby reducing buildings' fossil fuel–based energy consumption.     

Expectation Maximization Method for Effective Detection and Tracking of Object Using Machine Learning Technique for Secure Wireless Communication

Wireless Personal Communications - Object detection is one of the most important computer vision tasks that is used synonymous to object recognition which comprises the mission of identifying the...     

Performance computation and implementation of distributed controllers for reliable software-defined networks

The Journal of Supercomputing - Software-defined networks (SDNs) are designed to cover the dynamic operations of network factors and the complex role of controlling components to achieve...     

A Bioinformatics Systems Biology Analysis of the Current Oral Proteomic Biomarkers and Implications for Diagnosis and Treatment of External Root Resorption

External root resorption (ERR) is a silent destructive phenomenon detrimental to dental health. ERR may have multiple etiologies such as infection, inflammation, traumatic injuries, pressure, mechanical stimulations, neoplastic conditions, systemic disorders, or idiopathic causes. Often, if undiagnosed and untreated, ERR can lead to the loss of the tooth or multiple teeth. Traditionally, clinicians have relied on radiographs and cone beam computed tomography (CBCT) images for the diagnosis of ERR; however, these techniques are not often precise or definitive and may require exposure of patients to more ionizing radiation than necessary. To overcome these shortcomings, there is an immense need to develop non-invasive approaches such as biomarker screening methods for rapid and precise diagnosis for ERR. In this review, we performed a literature survey for potential salivary or gingival crevicular fluid (GCF) proteomic biomarkers associated with ERR and analyzed the potential pathways leading to ERR. To the best of our knowledge, this is the first proteomics biomarker survey that connects ERR to body biofluids which represents a novel approach to diagnose and even monitor treatment progress for ERR.