Search

Power generation from solar photovoltaic (PV) systems is employed due to its recyclable nature, limitless source of energy, low maintenance, ease of installment, and eco-friendly potential. There is a major challenge in the PV system to harvest the maximal power in varying weather scenarios which decreases the overall efficiency and causes significant power loss. Maximum power point tracking (MPPT) methods are exploited to crop the maximum power from PV strings. Conventional algorithms do not find the Global Maxima (GM) and are trapped on any Local Minima (LM). However, bio-inspired methods find the GM but take more time to search the GM. This paper presents a Corona-Virus Optimization (CVO) algorithm to harvest maximum power against seven well-known conventional and bio-inspired MPPT algorithms. The proposed technique is estimated for Uniform Irradiance (UI), Partial Shading (PS), and Complex Partial Shading (CPS) conditions. Furthermore, the comparative analysis between seven already implemented and proposed techniques shows that the CVO algorithm lessens the unwanted oscillation and quicker searching the GM. Moreover, their numerical analysis is also conducted to evaluate the stability, robustness, performance, and sensitivity of the CVO algorithm. The proposed technique gives the highest efficiencies of 99.99%, 99.98%, 99.97%, and 99.98% in all cases of UI, PS-1, PS-2, and CPS respectively when comparing with P&O, InC, DFO, CS, FFO, PSO, and ACO techniques. Real-time data from the Beijing database is also used to verify the performance of the proposed algorithm.

Dense depth information can be reconstructed from stereo images using conventional hand-crafted as well as deep learning-based approaches. While deep-learning methods often show superior results compared to hand-crafted ones, they commonly learn geometric principles underlying the matching task from scratch and neglect that these principles have already been intensively studied and were considered explicitly in various models with great success in the past. In consequence, a broad range of principles and associated features need to be learned, limiting the possibility to focus on important details to also succeed in challenging image regions, such as close to depth discontinuities, thin objects and in weakly textured areas. To overcome this limitation, in this work, a hybrid technique, i.e., a combination of conventional hand-crafted and deep learning-based methods, is presented, addressing the task of dense stereo matching. More precisely, the input RGB stereo images are supplemented by a fourth image channel containing feature information obtained with a method based on expert knowledge. In addition, the assumption that edges in an image and discontinuities in the corresponding depth map coincide is modeled explicitly, allowing to predict the probability of being located next to a depth discontinuity per pixel. This information is used to guide the matching process and helps to sharpen correct depth discontinuities and to avoid the false prediction of such discontinuities, especially in weakly textured areas. The performance of the proposed method is investigated on three different data sets, including studies on the influence of the two methodological components as well as on the generalization capability. The results demonstrate that the presented hybrid approach can help to mitigate common limitations of deep learning-based methods and improves the quality of the estimated depth maps.

Contamination of aquatic environments by pharmaceuticals used by modern societies has become a serious threat to human beings. Among them, antibiotics are of particular concern due to the risk of creating drug-resistant bacteria and, thus, developing efficient protocols for the capture of this particular type of drug is mandatory. Herein, we report a family of three isoreticular MOFs, derived from natural amino acids, that exhibit high efficiency in the removal of a mixture of four distinct families of antibiotics, such as fluoroquinolones, penicillins, lincomycins, and cephalosporins, as solid-phase extraction (SPE) sorbents. In particular, a multivariate (MTV)-MOF, prepared using equal percentages of amino acids l-serine and l-methionine, also exhibits outstanding recyclability, surpassing the benchmark material activated carbon. The good removal performance of the MTV-MOF was rationalized by means of single-crystal X-ray diffraction. These results highlight the situation of MOFs as a real and promising alternative for the capture of antibiotics from environmental matrices, especially wastewater streams.

The increasing integration of Internet of Things (IoT) technologies in consumer electronics has revolutionized various sectors, including healthcare. This evolution has led to the development of IoT-enabled consumer health devices and systems, offering benefits such as enhanced remote health monitoring and more efficient health data management. However, these advancements also pose significant security challenges, especially regarding data privacy and secure access. A critical concern is the vulnerability of current cryptographic methods to potential future quantum computing capabilities. This paper focuses on addressing these challenges by exploring the implementation of Post-Quantum Cryptography (PQC) in IoT-based consumer health electronics. Specifically, it evaluates the application of PQC methods in conjunction with Transport Layer Security 1.3 (TLS 1.3) for robust authentication in these systems. The study analyzes the performance and security efficacy of these schemes, comparing them to existing cryptographic approaches. Additionally, it delves into the practical hurdles and prospective solutions related to the deployment of post-quantum cryptographic techniques in the context of consumer health electronics, paving the way for more secure and reliable healthcare technology in the era of advanced consumer electronics.

Edge computing is a distributed architecture that features decentralized processing of data near the source/devices, where data are being generated. These devices are known as Internet of Things (IoT) devices or edge devices. As we continue to rely on IoT devices, the amount of data generated by the IoT devices have increased significantly due to which it has become infeasible to transfer all the data over to the Cloud for processing. Since these devices contain insufficient storage and processing power, it gives rise to the edge computing paradigm. In edge computing data are processed by edge devices and only the required data are sent to the Cloud to increase robustness and decrease overall network overhead. IoT edge devices are inherently suffering from various security risks and attacks causing a lack of trust between devices. To reduce this malicious behavior, a lightweight trust management model is proposed that maintains the trust of a device and manages the service level trust along with quality of service (QoS). The model calculates the overall trust of the devices by using QoS parameters to evaluate the trust of devices through assigned weights. Trust management models using QoS parameters show improved results that can be helpful in identifying malicious edge nodes in edge computing networks and can be used for industrial purposes.

Purpose: This article aims to provide an interactive model for hands on training of malware analysis. Cyberwar games are the highly stylized representation of cyber conflicts in a simulation model. Game models are helpful in understanding the phenomenon of cyber attacks as well as to evolve new techniques of detection. Cyber security trainings are generally very challenging. Cyber test beds make such trainings easy both for trainees and trainers. However, it is not feasible for each organization to build a network for the sole purpose of hands-on training for employees. Therefore, it is desirable to build an interactive environment that is interesting and free of cost as well. Design/methodology/approach: After exploring existing cyberwar games and their techniques, limitation and strengths, this paper presents a design to merge the cyber attacks into a unique model of war game for detection and analysis of malware. This research designs a malware analysis testbed using online free resources. The authors have used the platform of Cyber Defense Technology Experimental Research (DETER). This study proposed model of a testbed that supports malware reverse engineering scenarios, exercise logs and analysis to develop reverse engineering tactics. Findings: The proposed cyber testbed is an automated system that can be used as a platform to train cyber warriors. A few features of the proposed testbed are as follows: testbed provides real or seemingly real malware communication with the real world. It includes automated decisions for the detection of malicious behavior without human intervention. The author gives a design to develop free of cost mechanism for remote learning of highly technical cyber security areas, and this simulation is for malware analysis. Originality/value: Cyberwar games are built for strengthening offensive and defensive capabilities in cyberspace. For this purpose, many simulations, as well as emulation platforms, can be found. Some are free and open-source, whereas others are commercial and quite expensive. Existing testbeds have limitations in respect of cyberwar games for creating innovative scenarios. Existing literature does not offer any attack and response scenario developed for malware detection through some existing open-source and online simulation or emulation environments. This research includes an analysis of the existing platforms as well as the design of a new model for malware analysis and training.

Software-defined storage (SDS) is an abrupt technology that aims to address the explosive data proliferation and storage management complexity through the separation of control and data path, which are tightly bounded in the traditional storage area network (SAN) model. In SDS, the centralized controller manages and controls the overall storage services and operations. However, the centralized controller approach raises the scalability and single point of failure flag. In such a paradigm, the controller placement is an important activity. A random placement of controllers suffers from unpredictable delay in a large enterprise. Multiple SDS controllers mitigate the single point of failure and provide redundancy. We propose a method called controller placement based on the center of gravity (CPCG) to solve the optimal placement controllers’ locations to reduce the delay. CPCG incorporates different coordinates and analyzes their density (the number of attached devices to a switch) to suggest new optimal sites. For the least number of SDS controllers, we propose an areawise optimal placement location strategy; the areawise approach mitigates workload processing from the central controller and also provides a scalable and reliable storage system. Experiments show that the optimal placement drastically reduces delay and, hence, boosts scalability.