Instagram has grown in popularity among youth and is currently the second-favorite social network in the world. One of the social media that is widely used nowadays is Instagram. According to Enterprise (2012:02), Instagram is a community shared among one member with other members from all over the world. This study examines the impact of Instagram on the physical and mental well-being of Indian youth. With the rapid proliferation of social media platforms in India, Instagram has emerged as a prominent influence on the lives and perceptions of young individuals. Through a systematic synthesis of existing literature, this review paper critically evaluates the effects of Instagram use on various dimensions of youth well-being, considering both the opportunities and challenges posed by this popular social media platform. The review explores the nuanced relationship between Instagram and youth health outcomes. Specifically, it examines the influence of Instagram on body image perceptions, self-esteem, social comparison, and mental health. The review considers the cultural context and nuances that shape the impact of Instagram within the Indian socio-cultural landscape.

This paper addresses the claim that by enabling intelligent, flexible, and autonomous decision-making at the local scale, MAS can substantially strengthen the performance, reliability, and durability of smart grid operations. We discuss the design and implementation of a MAS for smart grid management through an in-depth case study, emphasizing on particular design decisions, difficulties faced, and results achieved. Among MAS's main benefits are its capacity to guarantee grid stability, reduce overload, and maximize the utilization of resources.

This work proposes a novel combination of behavioural-tracking sensors and immersive virtual reality in a gamified proof-of-concept prototype, which demonstrates affective treatment concepts for hypervigilance symptoms. A number of limitations have been identified in current approaches, prompting more advanced techniques that efficiently target hypervigilance at an individual patient level. In response, we developed a virtual reality first-person shooter that responds to inertial user behaviour in a way that aims to combat detrimental symptoms, proposed as an exploratory investigation into innovative technology and its potential to maximise cognitive behavioural therapy outcomes for hypervigilance treatment. The prototype is evaluated through interactive user studies with 22 participants, gathering a large volume of qualitative data regarding participant experiences and opinions after use. Rigorous thematic analysis finds that participants can independently identify the cognitive behavioural therapy purpose of the intervention without prior knowledge of such intentions, and relate efficacious approaches from the literature to their own experiences. Despite prospective apprehension, themes also demonstrate widespread adherence and acceptance of such approaches to hypervigilance treatment, alongside perceived effectiveness both of experienced outcomes and future potential. These results support the validity of combining such technologies in the context of cognitive behavioural therapy interventions, such that the standard of future interventions may be improved.

In this note, the problem of data-driven saturated state feedback design for polynomial nonlinear systems is solved by means of a sum-of-squares (SOS) approach. This new strategy combines recent results in dissipativity theory and data-driven feedback control using noisy input-state data. SOS optimization is employed in this work for controller design and to deliver an estimate of the closed-loop domain of attraction under saturated feedback. Numerical examples allow the reader to verify the usefulness of our strategy, which is the first in literature to provide a datadriven and dissipativity-based approach for solving the problem of input saturation for continuous-time polynomial systems.

Wind energy conversion technologies, such as advanced wind turbine management and power station scheduling, rely on accurate wind power forecasting to optimize operations. Improving the precision of wind power forecasts is essential to minimize the volatility associated with wind power integration and enhance the competitiveness of wind energy in energy auction markets. Time series forecasting is a method used to estimate future values in a sequence based on historical data. However, traditional wind power forecasting approaches face computational time and complexity limitations. As a result, machine learning researchers have recently focused on overcoming these challenges. We propose an intelligent forecasting model that uses a bidirectional long-short-term memory network (Bi-LSTM) optimized with Bayesian optimization using Tree-structured Parzen Estimators (TPE) to improve the accuracy of the forecasts of short-and long-term wind power. Wind power forecasting (WPF) is classified into two main aspects in the proposed research: multivariate estimation (MVE), which considers input features such as wind direction, speed, temperature, and pressure. In contrast, pure time-series prediction (TSP) analyzes the historical power generation pattern over a specific period for WPF. A sixyear historical wind energy dataset from the NERL lab evaluates the performance of the proposed WPF model. The validation of the proposed method is assessed using root mean squared error (RMSE), mean absolute error (MAE), and R2 score. The findings demonstrate that the proposed TPE-optimized Bi-LSTM model achieves higher MVE and TSP accuracy than the Vanilla LSTM, Stacked LSTM, and Bi-LSTM. Finally, this study gives the findings and prospective recommendations for improving WPF based on a thorough and critical analysis of the results.

A document by Timothy Wong. Click on the document to view its contents.

We propose a novel approach to active neuromorphic sensing to rapidly focus and artificially generate contrast to improve event-based space imaging. Space imaging is the collection of timely and accurate visual data of objects in the space environment and is essential for space-based operations and infrastructure. Recently, neuromorphic event-based vision sensors have been demonstrated as effective space imaging sensors with their high-speed contrast-driven operation. We demonstrate the performance of variable focus liquid lens driven artificial contrast generation as an active sensing technique. We demonstrate an improvement to the performance of an event-based space imaging system by cycling rapidly through an optical power range to incidentally pass through and detect an optimal focus range at each oscillation. We show that our approach can improve an event-based space imaging system's source detection, sensitivity, noise filtering, and localisation error. Furthermore, we demonstrate a solution to the critical problem of accurately and rapidly focusing the event-based camera for space imaging. By improving the performance of event-based space imaging systems, this sensor's unique capabilities can be further leveraged in critical space imaging applications such as international space traffic management efforts, and other optical domains as a new approach to difficult target detection problems.

This research focuses on simulating a transformer using electromagnetic field solver software: FEMM and COMSOL Multiphysics®. These tools are used to simulate and extract the model of a three-winding transformer. The primary objective of the extracted model is to predict key magnetic parameters, such as leakage and magnetizing inductances. These inductances play a critical role in switch-mode power converter topologies, ensuring proper operation and achieving high efficiency through zero-voltage or zero-current switching. In this study, the transformer, based on an ER28/14/11 core, is simulated using both FEMM and COMSOL, with the resulting models compared to each other and experimental measurements. FEMM is a free, 2D solver, while COMSOL is a commercial, 3D solver. This research aims to evaluate the feasibility of using the noncommercial FEMM tool to simulate complex transformer core shapes. Transformations of the E, ER, and ETD core types for simulation in both 2D axis-symmetric and planar problem types are also proposed.

A document by Francesco Lelli. Click on the document to view its contents.

To assess the performance of orthomode transducers (OMTs), the back-to-back (BTB) configuration is analyzed in simple fundamentals for accurate results and clear physical insight. The axial rotation of an OMT in the BTB measurement setup is investigated, and clear facts are highlighted. Furthermore, terminating OMTâ\euro™s Common Port with a short circuit load is proposed as a low-cost and accurate evaluation technique based on detailed analysis. The presented techniques have the advantage of using only power measurements without anechoic chambers. A full-wave simulation of a published OMT operating at V-band (55 - 65) GHz is used to validate these analyses.

Differential microphone arrays (DMAs) use multiple omnidirectional microphones for synthesising higher-order microphone directivity patterns. In their most basic form, they can be used to obtain fixed-directivity or horizontally steerable beamformers that can satisfy certain constraints. We propose a vector differential microphone array (VDMA) which is frequency-and direction-invariantly steerable in three dimensions. The proposed design comprises pressure and particle velocity sensors positioned on a circular constellation in a plane and allows extracting the third-order spherical harmonic decomposition of the sound field. This decomposition can then be used to obtain spherically directioninvariant steered beams. Synthesis of a maximum directivity factor (MaxDF) directivity pattern is demonstrated. A closed-form expression for the proposed array's white noise gain (WNG) is derived. The robustness of the proposed design to noise is analysed.

This paper presents a novel framework for real-time object detection in Wireless Mesh Networks (WMNs) using AI driven drones. The system integrates YOLOv5 for object detection, a hybrid greedy and reinforcement learning-based routing algorithm for efficient data communication, and a scalable multihop mesh topology. MATLAB-based simulations evaluate energy consumption, detection accuracy, and network connectivity, showcasing significant performance improvements. The results demonstrate the system's ability to detect objects accurately, maintain robust connectivity, and optimize energy consumption, even in dynamic environments. Future work includes expanding the system to integrate 5G and satellite networks and conducting field tests.

This abstract encapsulates an exploration into the innovative potential of harnessing sparks and tungsten electrodes to generate energy akin to nuclear fission. Utilizing a mere 100,000 sparks, powered by just 2 tungsten electrodes and a 500,000-watt supply, this approach yields an impressive 3,121 MeV, signaling a substantial reduction in energy consumption compared to traditional methods. Furthermore, this research proposal reveals the ingenious workings of a machine that employs alternating current (AC) waveforms and an electromagnetic cavity chamber to create a focused electron beam, promising the controlled initiation of nuclear fission reactions with remarkable efficiency. This innovative approach presents an alluring solution to the worldâ\euro™s escalating energy needs, underpinned by a profound understanding of the machineâ\euro™s unique capabilities.

Molecular communication is a communication paradigm inspired by biological systems, where chemical signals are used to encode and transmit information. MoSK (Molecule Shift Keying) is proposed as a modulation technique that utilizes different types of signaling molecules to encode digital information. MoSK mimics natural molecular communication by leveraging the diversity of signaling molecules and their specific roles. Experimental testbeds have been developed using mi-crofluidic systems, droplet-based platforms, and biological cells to validate MoSK. A prototype platform for MoSK implementation is presented, including a transmitter with infusion and selection valves, and a fluorescence-based receiver. The receiver detects and decodes fluorescence signals emitted by Graphene Quantum Dots (GQDs), which are water-soluble and fluorescent molecular messengers. The fluorescence signals of blue-GQDs and cyan-GQDs are acquired by the receiver, and the performance of the system is evaluated in terms of synchronization, detection threshold, and symbol recognition using Principal Component Analysis (PCA). The results demonstrate the successful detection and recognition of different symbols, even at lower concentrations. PCA proves to be an efficient method for qualitative recognition of molecular messengers in MoSK-based molecular communication systems.

Vision-based Transformers have emerged as a revolutionary architecture in the field of robotics, offering significant advancements in perception, decision-making, and control. This survey aims to provide a comprehensive review of the application of Vision-based Transformers in robotic systems. We explore the fundamental principles of Transformers, highlighting their ability to handle longrange dependencies and contextual understanding, which are critical for complex robotic tasks. The paper delves into various implementations of Vision-based Transformers across different robotic domains, including object detection, autonomous navigation, manipulation, and human-robot interaction. We also discuss the challenges and limitations associated with integrating Transformers into robotic systems, such as computational demands and real-time processing constraints. Furthermore, we present a comparative analysis of Vision-based Transformers with traditional convolutional neural networks and other state-of-the-art approaches, underscoring their unique advantages. Finally, the survey identifies promising research directions and potential future applications, aiming to guide and inspire further innovations in this rapidly evolving field.

It is envisioned that 6G mobile networks will enhance and majorly empower the Industry 4.0 paradigm, evolving towards smart factories with optimized and customized services. Especially the smart factory scenario with high-capacity data communication, which requires the usage of new portions of the electromagnetic spectrum (mmWave/sub-THz), is presenting us with new challenges, both in communications and networking. This article discusses the new challenges arising from highcapacity data communications in smart factories. It proposes extensions to the current Open Radio Access Network (O-RAN) standards for 6G networks to enable further evolution of Industry 4.0 and beyond. We motivate the need for real-time functionalities in O-RAN and an extended interface to the user equipment (UE) to allow for its fine-granular control.

This paper explores the problems caused by spam messages on the internet. It talks about different types of spam, like email spam and fake advertisements on social media. The paper also looks at how spam can make things difficult for people, like draining resources and tricking them into clicking on harmful links. The paper suggests fighting against spam by enabling filters in emails, using third-party spam filtering, avoiding clicking suspicious links, reporting, and blocking and avoiding using personal or professional emails. Lastly, the paper discusses what might happen with spam in the future and the challenges we may face. Overall, the paper aims to help and understand then find ways to deal with it for a safer and smoother experience.

We present a Multiple Input Multiple Output fuzzy path tracking controller that reduced its rule-base using Hierarchical Rule Base Reduction. A stability proof and outdoor results are also presented.