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Detection of anomalies in video surveillance plays a key role in ensuring the safety and security of public spaces. The number of surveillance cameras is growing, making it harder to monitor them manually. So, automated systems are needed. This change increases the demand for automated systems that detect abnormal events or anomalies, such as road accidents, fighting, snatching, car fires, and explosions in real-time. These systems improve detection accuracy, minimize human error, and make security operations more efficient. In this study, we proposed the Composite Recurrent Bi-Attention (CRBA) model for detecting anomalies in surveillance videos. The CRBA model combines DenseNet201 for robust spatial feature extraction with BiLSTM networks that capture temporal dependencies across video frames. A multi-attention mechanism was also incorporated to direct the model's focus to critical spatiotemporal regions. This improves the system's ability to distinguish between normal and abnormal behaviors. By integrating these methodologies, the CRBA model improves the detection and classification of anomalies in surveillance videos, effectively addressing both spatial and temporal challenges. Experimental assessments demonstrate that the CRBA model achieves high accuracy on both the University of Central Florida (UCF) and the newly developed Road Anomaly Dataset (RAD). This model enhances detection accuracy while also improving resource efficiency and minimizing response times in critical situations. These advantages make it an invaluable tool for public safety and security operations, where rapid and accurate responses are needed for maintaining safety. [ABSTRACT FROM AUTHOR]

Surveillance cameras have been widely used for monitoring in both private and public sectors as a security measure. Close Circuits Television (CCTV) Cameras are used to surveillance and monitor the normal and anomalous incidents. Real-world anomaly detection is a significant challenge due to its complex and diverse nature. It is difficult to manually analyze because vast amounts of video data have been generated through surveillance systems, and the need for automated techniques has been raised to enhance detection accuracy. This paper proposes a novel deep-stacked ensemble model integrated with a data augmentation approach called Stack Ensemble Road Anomaly Detection (SERAD). SERAD is used to detect and classify the four most happening road anomalies, such as accidents, car fires, fighting, and snatching, through road surveillance videos with high accuracy. The SERAD adapted three pre-trained Convolutional Neural Networks (CNNs) models, namely VGG19, ResNet50 and InceptionV3. The stacking technique is employed to incorporate these three models, resulting in much-improved accuracy for classifying road abnormalities compared to individual models. Additionally, it presented a custom real-world Road Anomaly Dataset (RAD) comprising a comprehensive collection of road images and videos. The experimental results demonstrate the strength and reliability of the proposed SERAD model, achieving an impressive classification accuracy of 98.7%. The results indicate that the proposed SERAD model outperforms than the individual CNN base models. [ABSTRACT FROM AUTHOR]

Wireless Mesh Network (WMNs) is a promising technology that has been accepted favorably by the Internet Service Providers (ISPs). ISPs use it to provide last mile broadband Internet connectivity to the end users. This popularity arises from the fact, that it inter operates well with diverse wireless systems and provides robust fault tolerance with a high degree of redundancy and reliability. Routing metrics plays a crucial part in the performance of WMN. When routing protocols are implemented, the routing metric is assigned to different paths. It calculates the optimum path to predict the best routing path. These are integrated in routing protocols to improve WMNs efficiency in terms of reliability, latency, throughput, error rate and cost. This paper compares the impact of using load balancing interference aware routing metric (LBIARM) on the performance of Multi-Channel Multi Radio WMNs (MCMR WMNs). The performance of LBIARM was evaluated by comparing it with popular standard metrics weighted cumulative expected transmission time metric and Hop Count metric. OPNET Modeler 17.5 PL1 was used as a simulation tool for implementation. Simulation results show the effectiveness of using LBIARM metric on MCMR WMNs.

Photocatalytic reduction of CO2 with CH4 through the dry reforming of methane (DRM) is an attractive approach to recycling greenhouse gases into valuable chemicals and fuels; however, this process is quite challenging. Although there is growing interest in designing efficient photocatalysts, they are less stable, and have lower photoactivity when employed for DRM reactions. Herein, we developed a noble metal-free hierarchical graphitic carbon nitride (HC3N4) loaded with cobalt (Co) for highly efficient and stable photocatalytic dry reforming of methane to produce synthesis gases (CO and H2). The performance of the newly designed Co/HC3N4 composite was tested for different reforming systems such as the dry reforming of methane, bi-reforming of methane (BRM) and reforming of CO2 with methanol–water. The performance of HC3N4 was much higher compared to bulk g-C3N4, whereas Co/HC3N4 was found to be promising for higher charge carrier separation and visible light absorption. The yield of CO and H2 with HC3N4 was 1.85- and 1.81-fold higher than when using g-C3N4 due to higher charge carrier separation. The optimized 2% Co/HC3N4 produces CO and H2 at an evolution rate of 555 and 41.2 µmol g−1 h−1, which was 18.28- and 1.74-fold more than using HC3N4 during photocatalytic dry reforming of methane (DRM), with a CH4/CO2 feed ratio of 1.0. This significantly enhanced photocatalytic CO and H2 evolution during DRM was due to efficient charge carrier separation in the presence of Co. The CH4/CO2 feed ratio was further investigated, and a 2:1 ratio was best for CO production. In contrast, the highest H2 was produced with a 1:1 feed ratio due to the competitive adsorption of the reactants over the catalyst surface. The performance of the composite was further investigated for bi-reforming methane and methanol. Using photocatalytic CO2 reduction with CH4/H2O, the production of CO and H2 was reduced, whereas significantly higher CO and H2 evolved using the BRM process involving methanol. Using methanol with CO2 and H2O, 10.77- and 1.39-fold more H2 and CO efficiency was achieved than when using dry reforming of methane. The composite was also very stable for continuous synthesis gas production during DRM in consecutive cycles. Thus, a co-assisted g-C3N4 nanotexture is promising for promoting photocatalytic activity and can be further explored in other solar energy applications. [ABSTRACT FROM AUTHOR]

Problem statement: The real world scenario have changed from the wired connection to wireless connection. Over the years software development has responded to the increasing growth of wireless connectivity in developing network enabled software. The problem arises in the wireless domain due to random packet loss in transport layer and as well as in data link layer for the end to end connection. The basic problem we have considered in this paper is to convert the real world scenario of 'Vehicular ad hoc network' into lab oriented problem by used the APS-system and study the result to achieve better performance in wireless domain. Approach: We map the real world physical problem into analytical problem and simulate that analytic problem with respect to real world scenario by Automated Position System (APS) for antenna mounted over the mobile node in 2D space. Results: We quantify the performance and the impact of the packet loss, delay, by the bit error rate and throughput with respect to the real world scenario of VANET in the MAC layer, data link layer and transport layer. Conclusion: We observe that the Directional the Antenna which is mounted over the vehicle gives less bit error in comparison to Isotropic and Discone antenna. Key words: EAPS, APS, VANET, bit error rate, throughput, antenna, performance, wireless, INTRODUCTION Vehicular Ad Hoc Networks (VANET) differ from usual Ad Hoc Networks ((MANETs) in many different aspects. First VANETs consist of highly mobile nodes moving in the same or opposite [...]

This work reports on the fabrication of an improved sensitivity metal-insulator-metal (MIM) diode. They devise an asymmetric structure diode that cascades vanadium, vanadium pentoxide, and aluminium (V-V2O5-Al) thin film layers. The MIM diode is fabricated using electron-beam lithography, sputter deposition and metal liftoff techniques. X-ray photoelectron spectroscopy analysis is performed to determine the phase composition of the V2O5 insulating thin film. Electrical characterisation of the fabricated V-V2O5-Al shows a clear high sensitivity at -316 mV that reaches -8.52 V-1 with a dynamic resistance of 5.024 kΩ. [ABSTRACT FROM AUTHOR]

Background: In recent years, there has been an increasing trend towards open-access (OA) publishing. Objective: To evaluate the current status of OA Scopus-indexed journals of ophthalmology regarding quality and volume. Design: A Cross-Sectional Study. Setting: University of Jordan Hospital, Amman, Jordan. Method: Active journals indexed by Scopus in 2017 were investigated. Data were gathered and filtered into OA or non-OA ophthalmology journals. Eight variables were adopted to compare the quality of the included journals namely: CiteScore, CiteScore Percentile, Citation Count, Scholarly Output, Percent Cited, SCImago Journal Rank (SJR), Source Normalized Impact per Paper (SNIP), and SCImago Quartiles. Result: One hundred eight ophthalmology journals were indexed by Scopus in 2017. OA journals were 29 (26.9%). We found that OA journals did not have higher impact indices, and the only index that differs between OA and non-OA ophthalmology journals was SJR. Conclusion: The increasing demand of OA publishing has not yet been linked to improved quality and citation advantage. [ABSTRACT FROM AUTHOR]