The investigations into the ‘Hedro Power Pump’ system were carried out on behalf of the inventor Jakob Sadighi from Minden (NRW/DE). The aim of the investigations was an assessment of the economic benefit for further work and cooperation with companies.

In the first part, uncompensated and unstable polynomials from 2nd order to 10th order are mentioned and it was also concluded that control loops are unstable, the change in coefficients could be seen in the list. In this part, rather taking such unpractical or impractical circuit (or novel B circuit or novel B-topology), a practical ‘buck-boost’ known circuit and its stability has been explored. The known working circuit is assumed as a black box and is declared as  equipment under test (EUT) with just input and output and the control loop stability would be investigated using compensators. The author examines the stability and analyses how the abyss of coefficients change with respect to different compensators when the MOSFET switching frequency is changed from 50 Hz to  1.999 MHz or 1999 kHz. Due to large number,this paper studies the abyss of stability and unstability, the game of coefficients, upto third order polynomials. The Bode plots of the compensators designed with P, I, PID, series PI, parallel PI, One pole one zero (1P1Z), multipoles, two pole two zero (2P2Z) and three pole three zero (3P3Z) are submitted. The known theory is also explained as summary. This paper is also the transcript of the proposed invited talk as speaker in some conference.

To provide various intelligent services in Internet of Vehicles (IoVs), such as autonomous driving, data sharing technologies enable vehicles to overcome information barriers and provide a big data foundation. Federated Learning (FL), which shares models instead of raw data, has emerged as a popular solution to address privacy concerns. However, current approaches are limited scalability and security, which are not suitable for the dynamic network topology of IoV scenarios. In this letter, we propose a Multi-Region Asynchronous Swarm Learning (MASL) framework in IoVs, which is empowered by the hierarchical blockchain and executed parallel between multiple regions. The MASL integrates identity verification and asynchronous model training while ensuring secure aggregation as well as data privacy. Through intra-regional and cross-regional sharing, the security and efficiency during large-scale data sharing in IoVs are effectively improved while alleviating the Non-IID data problem. Finally, both the simulation and hardware testbed results demonstrate that the proposed MASL framework could achieve better performances in terms of efficiency and security compared with the existing algorithms.

Federated learning (FL) emerges as a distributed training method in the Internet of Vehicles (IoVs), allowing connected and automated vehicles (CAVs) to train a global model by exchanging models instead of raw data, protecting data privacy. Due to the limitation of model accuracy and communication overhead in FL, in this paper, we propose a directed acyclic graph (DAG) blockchain-based IoV that comprises a DAG layer and a CAV layer for model sharing and training, respectively. Furthermore, a DAG blockchain-based asynchronous federated mutual learning (DAFML) algorithm is introduced to improve the model performance, which utilizes a student-teacher model in the local training and a mutual distillation method that enables models to be partially updated using mutual transferred knowledge. Moreover, to further improve the model accuracy, the personalized weight is designed to adjust the mutual distillation in the model updating. Simulation results demonstrate that the proposed DAFML algorithm outperforms other benchmarks in terms of the model accuracy and distillation ratio.

A document by Zixuan Wang . Click on the document to view its contents.

In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA). To better assess the freshness of information in computationintensive applications the criterion of age of information (AoI) is considered. In particular, the problem is formulated to minimize the average AoI of users with elastic tasks, by adjusting UAVs’ trajectory and resource allocation on both UAVs and HAP, which is restricted by the channel state information (CSI) uncertainty and multiple resource constraints of UAVs and HAP. In order to solve this non-convex optimization problem, two methods of multi-agent deep deterministic policy gradient (MADDPG) and federated reinforcement learning (FRL) are proposed to design the UAVs’ trajectory, and obtain channel, power, and CPU allocations. It is shown that task scheduling significantly reduces the average AoI. This improvement is more pronounced for larger task sizes. On one hand, it is shown that power allocation has a marginal effect on the average AoI compared to using full transmission power for all users. Compared with traditional transmission schemes, the simulation results show our scheduling scheme results in a substantial improvement in average AoI.

Sound event detection (SED) in the medical environment can be helpful in accomplishing different healthcare tasks. Due to the success of transformer encoder architectures for sound event detection, they seem to be a promising choice for detecting audio events in hospital settings. However, there are two main difficulties in detecting medical audio events with transformers. Firstly, the availability of medical audio data is extremely limited, making it difficult to effectively train a transformer model. Secondly, it is necessary for the SED model to be computationally efficient in order to be deployed in medical environments with limited resources. But, the transformer has high computational complexity because of the attention mechanism. To address these challenges, this paper introduces the Audio Spectrogram Fourier Network (ASFNet), a novel attention-free transformer encoder designed specifically for sound event detection in the medical environment. ASFNet replaces the attention operation with a simplified Fast Fourier Transform. By leveraging this approach, ASFNet outperforms the other methods, achieving a superior average mAP of 0.474 with a 16.76% relative improvement. ASFNet achieves this performance with fewer model parameters and smaller model size, making it an efficient and effective solution for medical audio event detection.

This research paper explores the integration of AI technology and Brain-Based Learning Theories in education, aiming to address a significant research gap and highlight their potential impact on cognitive development. While both AI technology and Brain-Based Learning Theories have been extensively studied and applied independently in the field of education, there is a lack of comprehensive research exploring their combined potential. This study delves into the theoretical foundations and guiding principles of brain-based learning, investigating its application in personalizing and enhancing learning experiences. It evaluates the current state of AI technology in education and examines how AI-powered brain-based learning techniques can improve student engagement, knowledge retention, and critical thinking skills. Ethical considerations and challenges associated with integrating AI into brain-based learning methodologies are acknowledged, and practical guidelines are provided for educators and decision-makers to effectively leverage AI in implementation. The research also examines the long-term effects of AI-enabled brain-based learning on educational systems, workforce readiness, and lifelong learning opportunities. Drawing on case studies and best practices from successful academic institutions, valuable insights are presented regarding the synergistic relationship between brain-based learning and AI. The paper proposes strategies for scaling and implementing AI-based brain-based learning approaches across diverse educational settings, with the aim of driving future innovations and advancements in education. Ultimately, this research sheds light on the transformative potential of AI-enabled brain-based learning, opening new avenues for educational improvement and advancement.

Infectious viruses can survive on surfaces for hours; they can be responsible for the fomite transmission mechanism of many diseases. We report the development and evaluation of a compact non-thermal plasma (NTP) flow reactor and its application for disinfection of surfaces. The concentric flow reactor produces a high concentration of reactive species by ionizing air flow passed through the reactor; the ionized jet exiting the reactor is impinged on the surface, exposing pathogens to high concentrations of reactive oxygen and nitrogen species. The reactor performance is characterized by measuring ozone concentration for various geometries and operating conditions. The ozone concentration reaches saturation at the exit for electrical power input above 100 W. The approach was tested for the inactivation of MS2 bacteriophage on surfaces with varied exposure times. We observed a 1.3 log reduction in viable virus counts at the exposure of 30 s and a 5.3 log reduction at 120 s exposure. The ozone production rates suggest that the reactor can be scaled to operate at higher flow rates, producing a concentration of reactive species sufficient for virus inactivation.

REDCap, a widely used electronic data capture tool for clinical and translational research, receives crucial support from various committees and groups within the extensive network of institutional collaborators known as the REDCap Consortium. For example, the Regulatory and Software Validation Committee defines and releases manual validation test scripts intended to aid institutions in the testing and validation efforts of their institutional REDCap deployments. Although these resources are important and valuable, the manual testing process itself has certain limitations such as time-consuming test execution and potential for inconsistent database state. To address some of the limitations of manual testing, a group of software developers within the Regulatory and Software Validation Committee wrote and released automated feature test scripts using an automated testing framework called the REDCap Cypress Test Framework. This report provides an overview of the REDCap Cypress Test Framework and its potential to improve the testing and validation of REDCap through automated testing.

The breast cancer dataset is downloaded from Kaggle for our investigation. The dataset contains 569 samples, 30 features and two classes : benign and malignant.

This article overviews OpenAI’s ChatGPT and its capability at solving classical communications and networking problems, ranging from physical layer, medium access control, queueing theory and TCP/IP. We asked ChatGPT v3.5 to solve ten classical problems in this field of science and engineering and we observed that, although ChatGPT knows the theoretical background and sounds convincing, it fails to give the final result due to intermediate mistakes in eight cases. This may be dangerous for students using ChatGPT v3.5 as an assistant in their studies of Computer Science and Engineering and should be therefore used carefully.

This paper presents a homogenized electrothermal model for high-temperature superconductor (HTS) stacks made of multiple REBCO tapes, which reduces the computational complexity of their thermal analysis. An average heat capacity and an orthotropic heat conductivity are derived for the equivalent thermal properties of the HTS coil. The proposed homogenization method is verified to produce the same results as the full model with an error below 5% for a range of scenarios, including transport losses, magnetization losses, AC frequencies from 50 to 300 Hz and a temperature range from 25 to 80 K. This new model allows for the use of a significantly coarser mesh, which reduces the computation time by an order of magnitude compared to the full model.

A new deep-learning approach, dual-domain diffu?sion model (DDDM), is proposed for sparse-view CT reconstruc?tion, which is composed of a sinogram upgrading module (SUM) and an image refining module (IRM) connected in series. In the sinogram domain, a novel degrading and upgrading framework is defined, in which SUM is trained to upgrade sparse-view sinograms step by step to reverse the degradation process of CT images caused by successive down-sampling of scanning views. In the image domain, IRM adopts an improved denoising diffusion framework to further reduce remaining artifacts and restore image details, where a skip connection from the original sparse?view sinogram is introduced to constrain the generation of details. Our DDDM shows significant improvement over deep-learning baseline models in both classical similarity metrics and perceptual loss, and has good generalization to untrained organs. We release our code at: https://github.com/YC-Markus/code-for-DDDM.

This paper tells about the review on challenges in networking for some of the techonologies existed nowadays and how it can be overcome by implementing mechanism and algorithm that suitable with it. This review has be done by reviewing other articles and thesis related with rights.

Artificial intelligence (AI) in healthcare, especially in medical imaging, faces challenges due to data scarcity and privacy concerns. Addressing these, we introduce Med-DDPM, a diffusion model designed for 3D semantic brain MRI synthesis. This model effectively tackles data scarcity and privacy issues by integrating semantic conditioning. This involves the channel-wise concatenation of a conditioning image to the model input, enabling control in image generation. Med-DDPM demonstrates superior stability and performance compared to existing 3D brain imaging synthesis methods. It generates diverse, anatomically coherent images with high visual fidelity. In terms of dice score accuracy in the tumor segmentation task, Med-DDPM achieves 0.6207, close to the 0.6531 accuracy of real images, and outperforms baseline models. Combined with real images, it further increases segmentation accuracy to 0.6675, showing the potential of our proposed method for data augmentation. This model represents the first use of a diffusion model in 3D semantic brain MRI synthesis, producing high-quality images. Its semantic conditioning feature also shows potential for image anonymization in biomedical imaging, addressing data and privacy issues. We provide the code and model weights for Med-DDPM on our GitHub repository (https://github.com/mobaidoctor/med-ddpm/) to support reproducibility.

In this work, we present the development of miniaturized, multiplexed, and connected platform for impedance spectroscopy. Designed for online measurements and adapted to wireless network architectures, our platform has been tested and optimized to be used for multi-selective chemical organic sensor nodes. Our designed circuit is built from low cost and low power consumption microelectronics components that achieve long duration operability without compromising on sensor measurement accuracy and precision. We used the well-known impedance network analyzer AD5933 (Analog Devices, Norwood, MA, USA) chip which can measure a spectrum of impedances in the range 5 Hz to 100 kHz. The proposed system is based on ESP32-C3 Microcontroller to manage the AD5933 through its I2C interface and two multiplexer components CD74HC4067 used for calibration process and data feedback. Our platform is able to interface up to 15 conductimetric sensors with real time acquisition (less than 90 ms per acquisition) and the possibility to relay the information through the network for data analysis and storage. The paper describes the microelectronics design, the impedance response over time, the measurement’s sensitivity and accuracy and the testing of the platform with embedded chemical sensors for environmental classification and recognition.

The paper introduces the main principles of spatio-temporal associative memory (STAM) inspired by learning principles in the human brain and implemented on a brain -inspired spiking neural network (SNN) architecture. A STAM is a machine learning model that is trained on a full set of spatio-temporal variables, but can be successfully recalled on only a subset of the variables measured in different time intervals. In addition, a STAM model can be further incrementally trained on a new subset of variables measured at varying times. STAM is based on spatio-temporal learning (STL), where existing spatial or other relevant information from the data is used to structure a SNN model and then temporal information is used to train the model. The model captures evolvable and explainable spatio/specro temporal patterns. A STAM model is validated through the introduced association- and generalization accuracy. Departing from traditional deep neural networks and machine learning methods, where trained models can only be recalled or incrementally trained on the same set of variables using vector-based representation, STAM opens the field of machine intelligence for the development of large-scale global spatio-temporal models that can be recalled and used locally, within the available local spatio-temporal data. Possible applications for STAM include: biological and brain signals; audio-visual data; seismic sensory data; financial and economic data; and other.