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Unmanned Aerial Systems (UAVs, Drones), initially known only for their military applications, are getting increasingly popular in the civil sector as well. Over the military canvas, drones have already proven themselves as a potent force multiplier through unmanned, round-the-clock, long-range and high-endurance missions for surveillance, reconnaissance, search and rescue, and even armed combat applications. With the emergence of the Internet of Things (IoT), commercial deployments of drones are also growing exponentially, ranging from cargo and taxi services to agriculture, disaster relief, risk assessment and monitoring of critical infrastructures. Irrespective of the deployment sector, drones are often entrusted to conduct safety, time and liability critical tasks, thus requiring secure, robust and trustworthy operations. In contrast, the rise in UAVs’ demand, coupled with market pressure to reduce size, weight, power and cost (SwaP-C) parameters, has caused vendors to often ignore security aspects, thus inducing serious safety and security threats. As UAVs rely on Global Positioning System (GPS) for positioning and navigation, they can fall prey to GPS jamming and spoofing attacks. The vulnerability of GPS to spoofing has serious implications for UAVs, as victim drones using civil GPS can be misdirected or even completely hijacked for malicious intents, as already demonstrated in several academic research efforts using commercially available GPS spoofing hardware. Beside UAVs, GPS spoofing attacks are equally applicable to other GPS-dependent platforms, including manned aircraft, ground vehicles, and cellular systems. This paper conducts a comprehensive review of GPS spoofing threats, with a special focus on their applicability over UAVs and other GPS-dependent mobile platforms. It presents a novel taxonomy of GPS spoofing attacks and critically analyzes different spoofing techniques based upon placement of spoofing device, attack stealthiness, attack methodologies, and objectives of the attacker. We also discuss some of the recent experiments from open literature which utilized commercially available hardware for successfully conducting spoofing attacks.

Electro‐organic synthesis has attracted a lot of attention in pharmaceutical science, medicinal chemistry, and future industrial applications in energy storage and conversion. To date, there has not been a detailed review on electro‐organic synthesis with the strategy of heterogeneous catalysis. In this review, the most recent advances in synthesizing value‐added chemicals by heterogeneous catalysis are summarized. An overview of electrocatalytic oxidation and reduction processes as well as paired electrocatalysis is provided, and the anodic oxidation of alcohols (monohydric and polyhydric), aldehydes, and amines are discussed. This review also provides in‐depth insight into the cathodic reduction of carboxylates, carbon dioxide, CC, C≡C, and reductive coupling reactions. Moreover, the electrocatalytic paired electro‐synthesis methods, including parallel paired, sequential divergent paired, and convergent paired electrolysis, are summarized. Additionally, the strategies developed to achieve high electrosynthesis efficiency and the associated challenges are also addressed. It is believed that electro‐organic synthesis is a promising direction of organic electrochemistry, offering numerous opportunities to develop new organic reaction methods. In this review, the most recent advances in synthesizing value‐added chemicals by heterogeneous catalysis are summarized, and an overview of electrocatalytic oxidation and reduction processes as well as paired electrocatalysis is also provided.

The integration of NIST-standardized lattice-based cryptographic algorithms (ML-KEM and ML-DSA) into TLS 1.3 enables post-quantum secure authentication for Industrial IoT (IIoT) environments. This work implements these algorithms within the X.509 certificate infrastructure and evaluates their performance on resource-constrained IIoT hardware (Raspberry Pi 4). Experimental measurements covering key generation, encapsulation, decapsulation, and signature operations were obtained using a liboqs-enabled TLS 1.3 stack. The results show that PQ TLS 1.3 achieves comparable handshake latency to conventional TLS on IIoT-class gateways, with certificate size identified as the dominant overhead. These findings confirm the practicality of post-quantum authentication in IIoT systems while acknowledging limitations related to the hardware scope and simulated networking. Future work will extend validation to field deployments and additional PQC candidates.

Solar energy is a long-established technology, which has zero CO2 emissions, and provides low-cost energy for a given area of land. The concentrator photovoltaic (CPV) has been given preference over the photovoltaic due to its high efficiency. In a CPV system, most of the solar cell area has been replaced with an optical concentrator. Various parabolic trough based CPV systems have been presented where a concentration of <300 is achieved. In the current research, a design is presented to achieve a high concentration of 622×. The design consists of two stages of concentration including parabolic trough as a main concentrator and nonimaging reflective grooves as a secondary concentrator. The trough reflects the incident light towards the secondary reflector where the light is redirected over the solar cell. Design of the two-stage concentrator, ray-tracing simulation, and results are presented. The system achieved an optical efficiency of 79%. The system would also be highly acceptable in solar thermal applications owing to its high concentration.

The ever-growing ecosystem of the Internet of Things (IoT) integrating with the ever-evolving wireless communication technology paves the way for adopting new applications in a smart society. The core concept of smart society emphasizes utilizing information and communication technology (ICT) infrastructure to improve every aspect of life. Among the variety of smart services, eHealth is at the forefront of these promises. eHealth is rapidly gaining popularity to overcome the insufficient healthcare services and provide patient-centric treatment for the rising aging population with chronic diseases. Keeping in view the sensitivity of medical data, this interfacing between healthcare and technology has raised many security concerns. Among the many contemporary solutions, attribute-based encryption (ABE) is the dominant technology because of its inherent support for one-to-many transfer and fine-grained access control mechanisms to confidential medical data. ABE uses costly bilinear pairing operations, which are too heavy for eHealth’s tiny wireless body area network (WBAN) devices despite its proper functionality. We present an efficient and secure ABE architecture with outsourcing intense encryption and decryption operations in this work. For practical realization, our scheme uses elliptic curve scalar point multiplication as the underlying technology of ABE instead of costly pairing operations. In addition, it provides support for attribute/users revocation and verifiability of outsourced medical data. Using the selective-set security model, the proposed scheme is secure under the elliptic curve decisional Diffie–Hellman (ECDDH) assumption. The performance assessment and top-ranked value via the help of fuzzy logic’s evaluation based on distance from average solution (EDAS) method show that the proposed scheme is efficient and suitable for access control in eHealth smart societies.

Load balancing is a technique for identifying overloaded and underloaded nodes and balancing the load between them. To maximize various performance parameters in cloud computing, researchers suggested various load balancing approaches. To store and access data and services provided by the different service providers through the network over different regions, cloud computing is one of the latest technology systems for both end-users and service providers. The volume of data is increasing due to the pandemic and a significant increase in usage of the internet has also been experienced. Users of the cloud are looking for services that are intelligent, and, can balance the traffic load by service providers, resulting in seamless and uninterrupted services. Different types of algorithms and techniques are available that can manage the load balancing in the cloud services. In this paper, a newly proposed method for load balancing in cloud computing at the database level is introduced. The database cloud services are frequently employed by companies of all sizes, for application development and business process. Load balancing for distributed applications can be used to maintain an efficient task scheduling process that also meets the user requirements and improves resource utilization. Load balancing is the process of distributing the load on various nodes to ensure that no single node is overloaded. To avoid the nodes from being overloaded, the load balancer divides an equal amount of computing time to all nodes. The results of two different scenarios showed the cross-region traffic management and significant growth in revenue of restaurants by using load balancer decisions on application traffic gateways.

Objective: To identify various difficulties in clinical practice faced by young physical medicine and rehabilitation (PMR) fellows.Study Design: Qualitative study.Place and Duration of Study: The study was carried out at Armed Forces Institute of Rehabilitation Medierne (AFIRM) from Aug 2017 to Sep 2017.Material and Methods: The study was carried out on ten physical medicine and rehabilitation fellows, who have cleared their fellowship exams in last 5 years. The study utilized online questionnaire fohowed by in-depth; telephonic interviews until the point of data saturation. The data was analyzed through thematic analysis. The data was transcribed in six stages to observe defining meaningful patterns. It was analyzed manually to draw conclusions.Results: The most common problems observed by young fellows were difficulty in performing specialized electrodiagnostic procedures, interpretation of urodynamics results and performing certam pain procedures including supra-scapular nerve block, platelet-rich plasma (PRP) and meralgiaparesthetica block. It was followed by managing specialized population including geriatrics, cerebral palsy and amputees and counseling skills.Conclusion: The skills component and specialized population management were deficient, though, sufficiently covered in the curriculum. So training institutes must consider certam changes in the teaching and learning environment to improve the identified areas.

China has the world's largest population, going under health transition due to industrialization, urbanization, and a sedentary lifestyle. About 82% of China's disease burden is due to the prevalence of non-communicable diseases (NCDs). Physical activity (active travel) is the best preventive measure against NCDs. The Chinese government has introduced several steps to improve national fitness and overcome NCDs among the aging population. Exercise and sports play a vital role in promoting physical activity and helpful in accomplishing the national fitness level for Healthy China under the national fitness plan (NFP). The current study aimed to explore the role of national fitness plan (NFP) in promoting physical activity and health well-being preventing NCDs under built environment intervention. The study was exploratory, and the mixed-method approach was used to analyze the primary and secondary data. Quantitative content analysis was used to analyze the collected data. A purposive sampling technique was used to collect the primary data from those individuals rich in knowledge about the National Fitness Plan (NFP) of China and its role in promoting physical activity for physical fitness. The majority of the participants (32.0%) said that the national fitness plan helps promote sports environment and health. About 28.0% reported that NFP helps promote cycling and walk environments for physical activity and health promotion and preventing non-communicable diseases (NCDs). Participation in physical exercise and sports is the preferred approach to prevent different diseases. In the context of rising active living among the Chinese people, the facilities such as built environment and green parks under the national fitness plan (NFP) for sports play a crucial role in the mitigation of NCDs.

With the increased use of smartphones over the years, an immense evolution in camera technology and image enhancement applications has been observed. However, this associates significantly greater security concerns with smartphones. Cyber crime offenders often retrieve personal images from second hand smart phones and try to blackmail or harass their former users. Similar situations are often faced by users in undesirable situations like hacking or theft of smartphones. Despite deletion of images by popular image deletion tools, sensitive images can still be recovered by using widely available forensic methods and tools. This paper checks the efficacy of top five image deletion applications namely Shreddit, Android Eraser, Permanent Delete, Sdelete, and Safe Delete in perspective of permanent deletion of images for Android, against National Institute of Standards and Technology’s (NIST), Standard for Media Sanitization known as NIST 800-88. The efficiency of the aforementioned deletion applications has been analyzed by using famous image recovery forensic tools; XRY, Autopsy, and DiskDigger. The results of our forensic analysis recovered the deleted images which were ostensibly deleted by the chosen applications. Moreover, all the image locations have been identified in this research which were found to contain images’ artifacts. In addition to that, the capability of forensic tools in recovering deleted images has been discussed too. So, this research highlights weaknesses of existing deletion mechanisms. Moreover, based on the conducted forensic analysis and NIST 800-88 standard, the research proposes a model for secure deletion by setting grounds for the development of a secure image deletion application to make permanent deletion of images possible.

Water resource management and sustainable agriculture rely heavily on accurate Reference Evapotranspiration (ET o ). Efforts have been made to simplify the (ET o ) estimation using machine learning models. The existing approaches are limited to a single specific area. There is a need for ET o estimations of multiple locations with diverse weather conditions. The study intends to propose ET o estimation of multiple locations with distinct weather conditions using a federated learning approach. Traditional centralized approaches require aggregating all data in one place, which can be problematic due to privacy concerns and data transfer limitations. However, federated learning trains models locally and combines the knowledge, resulting in more generalized ET o estimates across different regions. The three geographical locations of Pakistan, each with diverse weather conditions, are selected to implement the proposed model using the weather data from 2012 to 2022 of the selected three locations. At each selected location, three machine learning models named Random Forest Regressor (RFR), Support Vector Regressor (SVR), and Decision Tree Regressor (DTR), are evaluated for local Evapotranspiration (ET) estimation and the federated global model. The feature importance-based analysis is also performed to assess the impacts of weather parameters on machine learning performance at each selected local location. The evaluation reveals that Random Forest Regressor (RFR) based federated learning outperformed other models with coefficient of determination (R 2 ) = 0.97%, Root Mean Squared Error (RMSE) = 0.44, Mean Absolute Error (MAE) = 0.33 mm day −1 , and Mean Absolute Percentage Error (MAPE) = 8.18%. The Random Forest Regressor (RFR) performance yields the local machine learning models against each selected site. The analysis results suggest that maximum temperature and wind speed are the most influential factors in Evapotranspiration (ET) predictions.