Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions.

The Paris Agreement, a landmark international treaty signed in 2016 to limit global warming to 2°C, has urged researchers to explore various strategies for achieving its ambitious goals. While Renewable Energy (RE) innovation holds promise, it alone may not be sufficient as critical deadlines approach. This field of research presents numerous challenges, foremost among them being the costliness of materials involved. However, emerging advancements in Machine Learning (ML) technologies provide a glimmer of hope; these sophisticated algorithms can accurately predict the output of energy systems without relying on physical resources and instead leverage available data from diverse energy platforms that have emerged over recent decades. The primary objective of this paper is to comprehensively explore various ML techniques and algorithms in the context of Renewable Energy Systems (RES). The investigation will address several vital inquiries, including identifying and evaluating existing RE technologies, assessing their potential for further advancement, and thoroughly analyzing the challenges and limitations associated with their deployment and testing. Furthermore, this research examines how ML can effectively overcome these obstacles by enhancing RES performance. By identifying future research opportunities and outlining potential directions for improvement, this work seeks to contribute to developing environmentally sustainable energy systems.

Tissue levels of suppressor of cytokine signaling-3 (SOCS-3) in mycosis fungoides.

Mycosis fungoides (MF) is a type of cutaneous T-cell lymphoma with proposed multifactorial etiology. Suppressor of cytokine signaling-3 (SOCS-3) is one of the proteins expressed in MF. Its exact role in disease pathogenesis has not yet been thoroughly investigated. This study aimed to assess the expression of SOCS-3 in patients' skin with mycosis fungoides to elucidate their possible role in the pathogenesis in MF. 30 patients with mycosis fungoides and 30 age and sex-matched healthy controls were included. After clinical examination, tissue levels of SOCS-3 were measured by ELISA. The level of expression of SOCS-3 was significantly upregulated in the lesional tissue compared to perilesional SOCS-3 level in patients' group (P < 0.001), and both levels were higher than the SOCS-3 level in control group (P < 0.001). In addition, there was a statistically significant positive correlation between lesional SOCS-3 level and itching in patients' group (P < 0.001). Regarding lesional and perilesional SOCS-3 levels in each stage, there was a significant increase in lesional SOCS-3 levels in comparison to perilesional level whether in stage Ia, Ib, and IIa; (P < 0.001), (P < 0.001) and (P < 0.001), respectively. Increased tissue levels of SOCS-3 patients with mycosis fungoides point to a role that SOCS-3 could play in its pathogenesis. Also, high levels of SOCS-3 in MF patients with itching suggest a role in the pathogenesis of this symptom. These findings may prove helpful in formulating a new treatment modality in addition to the current treatment of MF.

Robotics cyber security: vulnerabilities, attacks, countermeasures, and recommendations.

The recent digital revolution led robots to become integrated more than ever into different domains such as agricultural, medical, industrial, military, police (law enforcement), and logistics. Robots are devoted to serve, facilitate, and enhance the human life. However, many incidents have been occurring, leading to serious injuries and devastating impacts such as the unnecessary loss of human lives. Unintended accidents will always take place, but the ones caused by malicious attacks represent a very challenging issue. This includes maliciously hijacking and controlling robots and causing serious economic and financial losses. This paper reviews the main security vulnerabilities, threats, risks, and their impacts, and the main security attacks within the robotics domain. In this context, different approaches and recommendations are presented in order to enhance and improve the security level of robotic systems such as multi-factor device/user authentication schemes, in addition to multi-factor cryptographic algorithms. We also review the recently presented security solutions for robotic systems.

Cyber-physical systems security: Limitations, issues and future trends.

Typically, Cyber-Physical Systems (CPS) involve various interconnected systems, which can monitor and manipulate real objects and processes. They are closely related to Internet of Things (IoT) systems, except that CPS focuses on the interaction between physical, networking and computation processes. Their integration with IoT led to a new CPS aspect, the Internet of Cyber-Physical Things (IoCPT). The fast and significant evolution of CPS affects various aspects in people's way of life and enables a wider range of services and applications including e-Health, smart homes, e-Commerce, etc. However, interconnecting the cyber and physical worlds gives rise to new dangerous security challenges. Consequently, CPS security has attracted the attention of both researchers and industries. This paper surveys the main aspects of CPS and the corresponding applications, technologies, and standards. Moreover, CPS security vulnerabilities, threats and attacks are reviewed, while the key issues and challenges are identified. Additionally, the existing security measures are presented and analyzed while identifying their main limitations. Finally, several suggestions and recommendations are proposed benefiting from the lessons learned throughout this comprehensive review.

Security analysis of drones systems: Attacks, limitations, and recommendations.

Recently, the world witnessed a significant increase in the number of used drones, with a global and continuous rise in the demand for their multi-purpose applications. The pervasive aspect of these drones is due to their ability to answer people's needs. Drones are providing users with a bird's eye that can be activated and used almost anywhere and at any time. However, recently, the malicious use of drones began to emerge among criminals and cyber-criminals alike. The probability and frequency of these attacks are both high and their impact can be very dangerous with devastating effects. Therefore, the need for detective, protective and preventive counter-measures is highly required. The aim of this survey is to investigate the emerging threats of using drones in cyber-attacks, along the countermeasures to thwart these attacks. The different uses of drones for malicious purposes are also reviewed, along the possible detection methods. As such, this paper analyzes the exploitation of drones vulnerabilities within communication links, as well as smart devices and hardware, including smart-phones and tablets. Moreover, this paper presents a detailed review on the drone/Unmanned Aerial Vehicle (UAV) usage in multiple domains (i.e civilian, military, terrorism, etc.) and for different purposes. A realistic attack scenario is also presented, which details how the authors performed a simulated attack on a given drone following the hacking cycle. This review would greatly help ethical hackers to understand the existing vulnerabilities of UAVs in both military and civilian domains. Moreover, it allows them to adopt and come up with new techniques and technologies for enhanced UAV attack detection and protection. As a result, various civilian and military anti-drones/UAVs (detective and preventive) countermeasures will be reviewed.