The ability to track the trajectory or path on the sea surface remains a key measurement in the control system of an unmanned surface vehicle (USV). In this research, the designed algorithm defines the path and minimizes the disturbances to zero. Underactuated USVs are ships or boats, which operate on the water surface without a crew and the underactuated system has low actuators than its degree of freedom (DOF). The adaptive control strategy is applied to the unbounded system as the ocean due to its high performance, while the robust control system attains high performance in the bounded system. To consider the trajectory tracking of UUSV and provide an optimal control strategy for a ship in the presence of external disturbances, the study presents a controller-based on model reference adaptive control with an integrator (MRACI), which guarantees the stability of a closed-loop system. The vehicle experiences variations in the system response due to external disturbance. This abovementioned scheme reduces the variations to nearly equal to zero making the vehicle stable. First, use computer-based simulation to verify the proposed controller under two different scenarios. Then, simulation results show that the designed scheme lowers the errors and performs well.

: This paper emphasize on the sixth sense technology which is estimated to be blowout worldwide in upcoming year. It is one of the latest technology which helps to tie the physical world with the digital world. Sixth Sense technology is a technology by means of which a system could be taught to distinguish and percept, and act in response as preferred in the real world objects.To conduct the study a set of survey questions have been prepared and distributed among participants of selected groups. Therefore the result has been evaluated based on the pros and cons of the latest technology.Researches have been made on this latest and most breathtaking technology which is thus far to be publicizing in the market. Distinctive realization methodology and the potential applications and prospect of such technology is under considerations.

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Dengue is the most vital arboviral disease in humans, which is occurring in tropical and subtropical areas around the world. Dengue fever is itemized as an urban human disease as it spreads easily to urban environmental/ morphological contexts because of the uneven increase of urban population and infectious diseases as a result of climate change. Dengue epidemic cases related to climatic parameters are helpful to monitor and prevent the transmission of dengue fever. Many studies have focused on describing the clinical aspects of dengue outbreak. We bring out the epidemiological study to investigate the dengue fever development and prediction in the Karachi city. This study described the oncological treatment by statistical analysis and fractal rescaled range (R/S) method of the dengue epidemics from January 2001 to December 2020, based on the urban morphological patterns, and climatic variables including temperature and ENSO respectively. The R/S method in oncologists has been carried in two ways, basic oncological/statistical analysis and Fractal dimension adapt to the study the nature of the subtleties of dengue epidemic data, another showing the dynamics of oncological process. Climate parameters are shown that the fractal dimension value revealed a persistency behavior i.e. time series is an increasing, Fractal analysis also confirmed the anti-persistent behavior of dengue for months of September to November and the normality tests specified the robust indication of the intricacy of data. This study will be useful for future researchers working on epidemiology and urban environmental oncological fields to improve and rectify the urban infectious diseases.

This study presents a comparative analysis of classical and deep learning approaches for the classification of apple fruit quality, within the broader context of machine vision applications. Emphasizing the importance of the fruit's physical appearance in meeting market standards, the research explores the performance of classical methods such as Support Vector Machine (SVM), K-nearest neighbour (KNN), and Decision Tree (DT), in comparison to deep learning methods like Mobile-Net and Convolutional Neural Network (CNN) with self-design. A self-created dataset comprising 150 apple fruit images categorized into fresh, mid, and rotten classes are utilized, with an 80:20 training-test split. The evaluation of the approaches reveals promising results, with SVM achieving 86% accuracy, DT achieving 93%, KNN achieving 96.6%, and Mobile-Net and CNN achieving 97% accuracy. Notably, the study demonstrates the efficiency of these methods in classifying different quality classes of apple fruit. This research contributes to the existing knowledge in agricultural quality control and provides insights for researchers seeking suitable algorithms in the field of machine vision for fruit quality assessment, ultimately advancing the application of machine vision techniques in real-world scenarios.