The rapid spread of infectious diseases poses a significant global health challenge, requiring timely and accurate detection for effective intervention. Traditional disease detection services, such as the Centers for Disease Control and Prevention (CDC) and the World Health Organization (WHO), play a crucial role in monitoring and responding to outbreaks. However, these services are largely inaccessible to people around the world due to their high costs and resource-intensive processes because they often rely on expensive sources of data. Fortunately, satellite images are a great alternative source of data as modern satellites can provide detailed images which clearly display a region’s financial status and pollution levels: two key metrics in potential disease outbreaks. Therefore, this study aimed on developing a more affordable algorithm (SatNet) that utilizes publicly available satellite imagery to perform disease hotspot detection. The algorithm works by retrieving zoomed-in satellite images of the city inputted by the user and feeding these images into a novel, hybrid recursive convolutional neural network. This model, designed to classify regions within the images as low income, high-income, or industrial areas, was trained and tested on a custom data set consisting of 7,448 images and was able to achieve a 94.872 training accuracy and 84.183 testing accuracy. The output of this model is then used to create a detailed heatmap for the city which clearly indicates the specific regions in most danger of disease outbreaks. Overall, the affordability and accessibility of SatNet will allow governments/organizations around the world to provide their people with the healthcare they need and significantly reduce the spread of diseases in an increasingly interconnected world.

This paper sets its primary objective to  understand the transformative potential of Tiny Machine  Learning (Tiny ML) and Artificial Intelligence (AI) in  enhancing industrial efficiency. Using a research design that  juxtaposes the theoretical understanding of these technologies  with real-world applications, the methodology adopted  emphasizes three pivotal use cases, substantiated with tangible  examples. The main outcomes show the influential role of  STMicroelectronics, a leading semiconductor entity, in bridging  the gap between Tiny ML, AI, and industrial applications.  Results from in-depth examinations highlight the value of  Predictive Maintenance as evidenced by offshore wind farms,  the importance of Gesture Recognition in Human-Machine  Interaction with a focus on autonomous vehicles, and the  efficient integration of Tiny ML into IoT Sensor Networks,  notably seismic monitoring. In conclusion, this manuscript underscores the impending future where Tiny ML and AI  synergize, particularly hinting at breakthroughs in humanoid  robotics. Such advancements are anticipated to redefine the  contours of human-technology interaction impacting everyone  in everyday life.

Microwave pulses can penetrate several centimeters to decimeters into dry soils. As a result, active microwave sensors are sensitive to discontinuities in the soil profile caused by the presence of stones, rocks or distinct soil layers. Such subsurface scattering effects can disturb the retrieval of soil moisture, vegetation and other land surface properties from active microwave measurements. In this study we mapped subsurface scatterers impacting C-band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on a global scale. Users of ASCAT soil moisture data distributed by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management are recommended to use the subsurface scattering masks generated within this study.

Background: Current medical practice in most parts of the world is based on evidence-based medicine. Medical students and residents have the potential to contribute to research which can have profound impact in healthcare. To be able to perform quality research, knowledge on interpreting and even performing statistical tests is crucial. This paper focusses on introducing a free, simple, and intuitive software – JASP – which packs a lot of features and can easily be used by novice to advanced researchers and teachers alike. Methods: JASP is an open-source software with a simple graphical user interface and uses the R programming language to run its analyses. It consists of various modules. Advanced analysis including meta-analysis, network analysis, factor analysis, and structural equation modelling are supported. Results: Users can perform descriptive analyses, t-tests, analysis of variance, chi-squared tests, linear and logistic regression, mixed effects regression, time-series analyses and so forth. A drag-and-drop interface allows visualization of the analysis. Output is in the form of tables and graphs and can be directly copied and pasted in a word document. Example analyses are provided to help users understand how to perform statistical analyses using JASP. The software’s user-friendly interface and variety of statistical tests make it an accessible tool for medical students and researchers of all levels. Conclusion: Due to its simple and intuitive architecture and being free-to-use, JASP is the ideal statistical software for medical students and researchers who are looking to learn to perform and correctly interpret results of statistical analyses.

Recently, a novel power flow model called weather dependent power flow (WDPF) has been proposed to incorporate conductor temperatures in power flow calculation. It is promising to adopt WDPF model in probabilistic analysis since it provides more accurate electrical and thermal states of a power system. However, calculation of the WDPF model is more computationally expensive than that of conventional power flow model, and hence repetitive calculation of the WDPF in probabilistic analysis is quite time-consuming. To overcome this challenge, a surrogate?model-based sequential algorithm is proposed for calculation of the WDPF model with high computational efficiency and good accuracy. Compared to the method that directly learns the whole WDPF model, the proposed method only learns the power flow sub-model whereas applies a linearization method to solve the thermal resistance sub-model. The construction of the proposed method considers the learning difficulty of the sub-model and the whole WDPF model, thus ensuring the high calculation accuracy. This study is the first to conduct a probabilistic power flow analysis based on the WDPF model. Numerical results in the IEEE 30-bus, 57-bus, and 118-bus systems demonstrate the importance of adopting the WDPF model in probabilistic analysis and the efficacy of the proposed method.

This paper presents a multi-channel phase-shift LLC resonant converter that was specially optimized for fuel-cell applications and has a wide input and output voltage range. The worst-case minimum stack voltage and the battery voltage range were used to find the optimal parameters of the LLC converter. The voltage gain of the converter operating at a constant power was considered, and the algorithm for such a calculation was proposed. Current balancing was implemented using channel temperature measurements instead of expensive current measurements. A natural self-balancing mechanism was investigated for converters operating at high frequencies above the series resonance of a resonant converter. Recommendations for the converter's transformer turn ratio were illustrated for efficiency optimization. The multi-object optimization is implemented to reach the optimized design so the minimum and maximum voltage gains can be reached and, at the same time, the transformer turn ratio is kept at the minimum to reach high efficiency. An experimental prototype of a four-channel converter was presented and tested at a 5 kW power level. Active content and C-language code files for converter calculations are provided as well with this work.

Smart Contracts are a technology that leverages blockchain to automate and secure transactions. They are commonly used to manage transaction fees and mining rewards within blockchain networks, and can be programmed to execute automated transactions based on predetermined conditions. The use of smart contracts offers numerous benefits for small and medium-sized enterprises, including reduced transaction costs, improved transparency and accountability, and enhanced security and efficiency. The deployment of smart contracts in a company can also lead to increased automation of transactions, further streamlining processes and reducing the potential for human error.

The technology associated with 5G and future 6G networks is challenging for military communications in tactical scenarios. This work has selected and analyzed a group of leading technologies that allow tactical networks to evolve. The virtualization of networks with 5G helps to guarantee greater security. These developments are vital in improving the feasibility of truly network-centric operations for military applications. Software-defined networks, IoT, blockchain, Artificial Intelligence, semantic communications, or neuromorphic processors help increase communications performance. These technologies have improved ten classic use cases in the military field. This work is an analysis to serve as a starting point for new scientists to open up new lines of research. It is not merely a compilation but an analytical basis for future work.

In the year 2023, a heightened sense of curiosity and apprehension pervaded the landscape of generative artificial intelligence (AI), particularly in the wake of the unveiling of the ChatGPT product by OpenAI. This pivotal moment sparked a flurry of discussions that predominantly revolved around the role of data in shaping the trajectory of generative AI. As researchers and organizations alike delved into this innovative realm, a pronounced inclination toward investigating its potential applications emerged. Notably, organizations swiftly recognized the transformative potential of generative AI in bolstering productivity across various sectors. At the heart of these deliberations lies the profound significance of data. With data as the focal point, a compelling exploration began to unfold, with researchers keenly scrutinizing the ramifications of integrating generative AI within the domain of data and analytics. This research initiative was driven by an intrinsic desire to uncover the ways in which generative AI could be harnessed to enhance and streamline analytical processes. In this context, the present research undertook a comprehensive investigation, employing a multifaceted approach. Leveraging various social media platforms as a primary source of insights, the research embarked on a journey to discern the prevailing sentiments, concerns, and expectations surrounding generative AI tools. This was further complemented by the execution of proof-of-concept (POC) endeavors, which not only enabled hands-on experience with generative AI tools but also facilitated a nuanced comprehension of their practical implications. The culmination of these efforts yielded a series of noteworthy findings. Principally, it was discerned that enterprises stand to gain substantial benefits from embracing the capabilities of generative AI within the domain of data and analytics. The integration of generative AI tools offers the potential to revolutionize productivity, propelling organizations toward novel insights and expediting analytical processes. Concurrently, a strategic partnership with generative AI entities emerged as a salient consideration for safeguarding intellectual properties. Collaborative engagements between companies and generative AI providers became imperative to navigate the evolving landscape of data-driven innovation. In conclusion, the year 2023 ushered in a period marked by intense curiosity and apprehension surrounding generative AI, catalyzed by the introduction of ChatGPT and its ensuing discussions. The centrality of data within this discourse propelled researchers and organizations toward an exploration of generative AI’s potential applications, notably in the realm of data and analytics. Through a comprehensive research endeavor encompassing social media insights, POC experimentation, and practical insights, it became evident that the integration of generative AI could usher in transformative enhancements to productivity and analytical processes. In parallel, collaborative endeavors with generative AI entities emerged as a strategic imperative, offering a dual advantage of innovation and intellectual property protection. This research underscores the compelling need for enterprises to harness generative AI’s capabilities, thereby positioning themselves at the vanguard of data-driven progress.

For many augmented reality guidance, teleoperation, or human-robot interaction systems, accurate, fast, and robust 6 degree of freedom object pose tracking is essential. However, current solutions easily lose tracking when line-of-sight to markers is lost. In this paper we present a tracking system which matches or improves on current methods in speed and accuracy, achieving 1.77 mm and 1.51 degrees accuracy at 22 Hz. Reflective markers are segmented in infrared images using contour detection before using the known marker geometry to perform point correspondence and pose computation using novel approaches. At the same time, a new square root unscented Kalman filter is introduced which improves accuracy and flexibility by tracking the markers themselves rather than the computed pose, and enables fusion of an external inertial measurement unit. This reduces noise and makes the tracking robust to brief loss of line-of-sight. The algorithms and methods are described in detail with pseudo-code for ease of reproduction. The system is implemented in simulation and on a Microsoft HoloLens 2 using Unity for ease of entry and integration into graphical projects. The code is made available open source. Tests of the system are described, and the results analyzed .

A document by Hammad Ahmed . Click on the document to view its contents.

This article presents a novel numerical approach aimed at finding a distribution network expansion plan that prevents future congestion and voltage issues. Forecasted duration and intensity of thermal and voltage violation events are used to determine a pool of potential candidates for infrastructure (i.e., line/cable) upgrade, voltage regulator, and energy storage system installations. This is complemented with an algorithm to obtain the minimum-cost list of these candidates that solves all constraint violation events using binary linear programming. This approach is validated using the modified IEEE 33-bus network and a real 1171-bus feeder in the West of Ireland through numerous high-resolution quasi-static time series simulations. Three pools of candidates and three cost projections were considered to explore the method’s sensitivity to different scenarios. Results show that the proposed methodology is a versatile tool for designers, planners and policymakers. The methodology can ensure that the investment plan solves all forecasted violation events. Nevertheless, we show that accepting a marginal degree of violations may be acceptable and would significantly reduce investment costs.

An 8-bit asynchronous single-ended Successive Approximation Register (SAR) Analog-to-Digital Converter (ADC) for the realization of a perovskite photovoltaic (PV) sensing application is presented. Various techniques have been proposed to enhance speed and power efficiency. A bootstrap transmission gate-based sample and hold circuit is proposed to improve sampling linearity and reduce power consumption. The sample and hold circuit is configured to operate at a 90% duty cycle, hence, giving it enough time for the conversion. A two-stage latch dynamic comparator is implemented to ensure high-speed and low power consumption. The SAR ADC also implements an asynchronous SAR logic block, controlled by an internal clock, which eliminates the need for an external clock. The proposed SAR ADC was designed in a UMC 180nm CMOS technology. The SAR ADC is tested with a sinusoidal analog input of 500 mV at 10 kHz frequency and a 4 MHz input clock frequency. The SAR ADC consumes a power of 2.67 mW at a supply of 1.5 V. The proposed ADC is an important unit for application in the fast growing perovskite-based PV industry.

The convergence of blockchain technology and artificial intelligence (AI) in sports applications presents a revolutionary approach to enhancing sports experiences, optimizing performance, and personalizing user interactions. This article offers an in-depth review of the applications, algorithms, challenges, and future directions of blockchain and AI in sports applications. We delve into the use of AI algorithms and blockchain technology in sports apps to create secure, transparent platforms for transactions, analyze performance data, provide personalized insights, and foster fan engagement. The article scrutinizes the scientific underpinnings of blockchain and AI-enhanced sports apps, discussing the personalization of user experiences, performance analysis using AI and blockchain-powered tools, fan engagement strategies, ethical implications and data privacy, case studies and empirical evidence, challenges, and recommendations for further research. We underscore the potential of blockchain and AI in revolutionizing sports apps, offering tailored experiences, and optimizing user engagement. The article concludes by pinpointing areas for future research, including advanced data analytics, explainable AI models, ethical considerations, collaboration, longitudinal studies, optimization of user experiences, human-AI interaction, and generalization to diverse populations. By exploring these research avenues, the field of blockchain and AI-enhanced sports applications can continue to evolve, supporting fans, athletes, and coaches in achieving their goals and unlocking new dimensions of sports experiences.

This study elucidates the implications of blockchain technology on the product and service offerings of a multinational corporation, examined through the lens of Karl Marx’s seminal work “Capital: A Critique of Political Economy”. Blockchain’s potential to decentralize control and distribute power counteracts Marx’s critique of worker alienation in capitalist economies. My investigation indicates that blockchain’s transparency can help demystify commodity fetishism by revealing production processes, and its decentralized structure can challenge the centralization of capital, offering solutions to traditional capitalist problems. This research contributes a unique perspective on the impact of blockchain technology in global companies, suggesting a revolutionary shift not only in their operations but also in their social relations of production. The study concludes by acknowledging the potential challenges in the practical implementation of these theoretical insights and suggests future research directions to overcome these obstacles.

Federated Multi-Agent Deep Reinforcement Learning (F-MADRL) is gathering keen research interests, as it may offer efficient solutions towards meeting the extreme requirements of future wireless communication networks. By contrast to centralized Deep Reinforcement Learning (DRL) and Multi- Agent DRL (MADRL), F-MADRL enables edge devices to cooperate without sharing their private data, while reducing the delays and signaling costs inherent to centralized approaches. In this article, we explore the new opportunities brought by F-MADRL by conducting a holistic survey of its related recent works. Firstly, we categorize state-of-the-art F-MADRL approaches, based on some distinctive features such as aggregation frequency, signaling overhead and privacy level. To better illustrate the behavior and advantages of F-MADRL, it is numerically compared to its fully centralized and distributed independent DRL counterparts, through a Sub-6GHz/mmWave band association optimization problem for Internet of Things (IoT) wireless communications. Finally, we identify and discuss the open research directions and challenges, in order to spur further interest in this promising area.

A Bidirectional Quantum Teleportation protocol between two parties (Alice and Bob) is investigated in which Alice possesses arbitrary entangled coherent state and Bob preserve unknown superposed coherent state via a quantum channel (hexa- modal entangled coherent states). This assymeteric bidirectional protocol is worked out in continuous variable (coherent states) regime.

Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 150 Large Language Models (LLMs), elucidating their myriad implications ranging from social and ethical to regulatory, as well as their applicability across industries. Building on this foundation, we propose a novel functional architecture that seamlessly integrates the structured dynamics of Knowledge Graphs with the linguistic capabilities of LLMs. Validated using real-world AI news data, our architecture adeptly blends linguistic sophistication with factual rigour and further strengthens data security through Role-Based Access Control. This research provides insights into the evolving landscape of conversational AI, emphasizing the imperative for systems that are efficient, transparent, and trustworthy.