Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size.

Recently, in healthcare organizations, real-time data have been collected from connected or implantable sensors, layered protocol stacks, lightweight communication frameworks, and end devices, named the Internet-of-Medical-Things (IoMT) ecosystems. IoMT is vital in driving healthcare analytics (HA) toward extracting meaningful data-driven insights. Recently, concerns have been raised over data sharing over IoMT, and stored electronic health records (EHRs) forms due to privacy regulations. Thus, with less data, the analytics model is deemed inaccurate. Thus, a transformative shift has started in HA from centralized learning paradigms towards distributed or edge-learning paradigms. In distributed learning, federated learning (FL) allows for training on local data without explicit data-sharing requirements. However, FL suffers from a high degree of statistical heterogeneity of learning models, level of data partitions, and fragmentation, which jeopardizes its accuracy during the learning and updating process. Recent surveys of FL in healthcare have yet to discuss the challenges of massive distributed datasets, sparsification, and scalability concerns. Because of this gap, the survey highlights the potential integration of FL in IoMT, the FL aggregation policies, reference architecture, and the use of distributed learning models to support FL in IoMT ecosystems. A case study of a trusted cross-cluster-based FL, named Cross-FL, is presented, highlighting the gradient aggregation policy over remotely connected and networked hospitals. Performance analysis is conducted regarding system latency, model accuracy, and the trust of consensus mechanism. The distributed FL outperforms the centralized FL approaches by a potential margin, which makes it viable for real-IoMT prototypes. As potential outcomes, the proposed survey addresses key solutions and the potential of FL in IoMT to support distributed networked healthcare organizations.

This paper presents a numerical model regarding the passive cooling of PV panels through perforated and non-perforated heat sinks. A typical PV panel was studied in a fixed position, tilted at 45 degrees from the horizontal with the wind direction towards its backside. A challenging approach was used in order to calibrate the base case of the numerical model according to the NOCT conditions. Further validation of the accuracy of the numerical simulation consisted of a comparison between the results obtained for the base case, or heat sink, with horizontal non-perforated fins and the experiments presented in the literature. Six types of heat sink attached to the backside of the PV panel were numerically studied. The analyzed configurations focused on heat sinks with both perforated and non-perforated fins that were distributed horizontally and vertically. The CFD simulation was also conducted by modeling the air volume around the PV panel in real wind conditions. The main output parameters were the average temperature and the convective heat transfer coefficient on the front and back of the PV panel. The most important effect of cooling was achieved in low wind conditions and high levels of solar radiation. For v_air = 1 m/s, G = 1000 W/m² and ambient temperature t_air = 35 °C, the percentage of maximum power production achieved 83.33% for the base case, while in the best cooling scenario it reached 88.74%, assuring a rise in the power production of 6.49%.

The negative effect of the operating temperature on the functioning of photovoltaic panels has become a significant issue in the actual energetic context and has been studied intensively during the last decade. The very high operating temperatures of the photovoltaic panels, even for lower levels of solar radiation, determine a drop in the open-circuit voltage, with consequences over the electrical power generated and PV-conversion efficiency. The temperature effect over the efficiency of monocrystalline and polycrystalline photovoltaic panels by using a double-climatic chamber and a solar simulation device was studied experimentally for two photovoltaic panels, one monocrystalline and another polycrystalline, with the same nominal power of 30 Wp. The double-climatic chamber used is composed of two separate rooms, a cold and a hot one, while the PV panel is placed as a barrier between them. The study is focused on establishing the effect of raising the temperature of PV panels over electrical parameters: voltage, current, and power produced and for efficiency and fill factor to promote sustainable energy consumption. The findings highlight the positive impact of cooling on enhancing system efficiency, with the primary focus on quantifying its overall performance. The operating temperature is controlled by the flow of air on the backside of the PV panel inside the cold room. The level of radiation studied corresponds to a vertical integration of PV panels in building façades. The coefficient of the mean variation of the efficiency with the photovoltaic panels’ temperature was −0.52%/°C; for voltage, −0.48%/°C, and for current, +0.10%/°C.

After installing a solar panel system, the orientation problem arises because of the sun’s position variation relative to a collection point throughout the day. It is, therefore, necessary to change the position of the photovoltaic panels to follow the sun and capture the maximum incident beam. This work describes our methodology for the simulation and the design of a solar tracker system using the advantages that the orientation and efficiency of the PV panel offer due to the latitude and the number of hours of sunshine in the testing area. This proposed methodology is experimentally validated through the implementation of a single-axis solar tracker at a specific location (36.261° latitude), which allowed the incorporation of a high-availability tracking mechanism, low precision, and low cost. Based on the results, the feasibility of this type of solar tracker for latitudes close to 36° was demonstrated, as this tracking system costs less than traditional commercial systems. Furthermore, this system increased the collection efficiency compared to a fixed device. Our results provide an excellent platform for engineering technology researchers and students to study the design theory of a sun-tracking solar system.

At the end of 2019, a variation of a coronavirus, named SARS-CoV-2, has been identified as being responsible for a respiratory illness disease (COVID-19). Since ventilation is an important factor that influences airborne transmission, we proposed to study the impact of heating, ventilation and air-conditioning (HVAC) with a variable air volume (VAV) primary air system, on the dispersion of infectious aerosols, in a cardiac intensive care unit, using a transient simulation with computational fluid dynamics (CFD), based on the finite element method (FEM). We analyzed three scenarios that followed the dispersion of pathogen carrying expiratory droplets particles from coughing, from patients possibly infected with COVID-19, depending on the location of the patients in the intensive care unit. Our study provides the mechanism for spread of infectious aerosols, and possibly of COVID-19 infection, by air conditioning systems and also highlights important recommendations for disease control and optimization of ventilation in intensive care units, by increasing the use of outdoor air and the rate of air change, decreasing the recirculation of air and using high-efficiency particulate air (HEPA) filters. The CFD-FEM simulation approach that was applied in our study could also be extended to other targets, such as public transport, theaters, philharmonics and amphitheaters from educational units.