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For a reliable and risk-free power source, dye-sensitized solar cells (DSSCs) have been the subject of much research. The method is very attractive because of its cheap manufacturing cost, environmentally friendly manufacture, and competitive efficiency. This study constructed a heterojunction as a photoanode for DSSCs using a combination of TiO2 and graphene using a wet chemical technique. According to the XRD and TEM findings, the graphene sheets consist of evenly wrapped spherical nanoparticles with diameters ranging from 30 to 40 nm, and the TiO2 exhibits a tetragonal (anatase) phase. It is common for anatase to have a bandgap energy of 3.15 eV for TiO2, whereas graphene/TiO2 has a bandgap energy of 2.77 eV. With a power conversion efficiency (PCE) of 7.2%, graphene/TiO2 DSSCs outperform cells based on pure TiO2 electrodes by a margin of 2.22%. In particular, graphene's ability to absorb solar light has the potential to improve the power conversion efficiency of DSSCs by increasing the light harvest of dye molecules. In addition, graphene/TiO2 had an average electron transit time of 1.765 ms while TiO2 had an average of 2.981 ms. The research shows that DSSCs' power conversion efficiency may be greatly improved by strategically adding graphene, which can be affordably synthesized, to certain layers of the photoanode. [ABSTRACT FROM AUTHOR]

Vehicular ad-hoc networks (VANET) represent an improved iteration of Wireless Sensor Networks (WSN) with mobile sensory nodes situated within vehicles. The Vehicular Adhoc Networks hold a crucial position within smart city applications as inter-vehicle communication is deemed indispensable for maintaining the technological efficiency of the city. Despite the benefits provided by VANET, it encounters numerous challenges and drawbacks within the context of smart city applications. One such challenge pertains to the security and privacy principles of VANET. Privacy and security emerge as principal concerns associated with VANET, prompting multiple researchers to propose security solutions over the past decade. The present research endeavor focuses on elevating the quality of service (QoS) by offering an enhanced level of security for data communication. This security enhancement is achieved through the use of blockchain technology and the integration of Elliptical Curve Cryptography with secure hash functions to safeguard data communication from node to Mobile Control Unit (MCU). Furthermore, the proposed research work presents an efficient routing mechanism for data between mobile nodes and the Control Unit by employing neuro fuzzy logic to identify the optimal path from the source node to the Mobile Control Unit (MCU). The proposed work is compared with existing cryptographic methodologies as well as state-of-the-art routing path optimization algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Memetic Algorithm (MA), and Honey Bee Optimization (HBO), in order to establish its superiority in terms of computational time, throughput, packet delivery ratio, and accuracy. [ABSTRACT FROM AUTHOR]

Fog is a common weather phenomenon that can have a significant impact on agriculture. It can reduce visibility, making it difficult for farmers to work in their fields. It can also damage crops by reducing photosynthesis and increasing the risk of frost. This paper presents a system for monitoring fog in an agriculture field using a camera module. The system uses a machine learning algorithm to detect fog in images captured by the camera. A fog detector is a device or system designed to detect the presence of fog or reduce the impact of foggy conditions. If the fog level is too high, an alert is sent to the farmer. The system was evaluated in a field experiment. According to the Indian Meteorological Department, the average humidity level during the fog season (October to March) is around 90%. However, humidity levels can often reach 100% or higher, especially in the early morning hours. The results showed that the system was able to accurately detect fog with a high degree of sensitivity and specificity. The system also had a low false alarm rate. The results of this study demonstrate the potential of using camera modules for fog detection in agriculture. This system can help farmers to protect their crops from the harmful effects of fog. [ABSTRACT FROM AUTHOR]