Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on the Prosumer Electricity Market Trading Model Based on the Transactive Energy Mechanism

With the increase in the proportion of renewable resources such as photovoltaics connected to the distribution network, traditional power users are gradually transforming into prosumers. Prosumers have the dual attributes of power generation and consumption, and also have a strong demand and willingness to join the electricity market. In order to improve the local absorption capacity of renewable resources and reduce the electricity cost of prosumers, first of all, this paper proposes a weakly centralized energy trading method and a decentralized energy trading method based on the transactive energy mechanism, which can help prosumers make better use of renewable resources and set appropriate electricity prices, then compares it with the traditional power trading method to prove the model can help prosumers save electricity costs, finally, proves the effectiveness of the proposed model through an example analysis.
Review of Power Grid Importance Identification and Cascading Fault under Natural Disasters Based on Complex Network Theory

Since the 21st century, global warming has caused frequent natural disasters, bringing great challenges to the stable operation of power grid. Based on the complex network theory, this paper discusses the importance identification of power grid under natural disasters from perspective of system, and analyses its application in cascading failures. Firstly, complex network models such as small-world model, scale-free model and space-time evolution model, etc. are introduced based on the dynamic and static characteristics of power grid topology. Secondly, in order to identify the importance of power grid, some identification indexes such as degree centrality, average path length, betweenness centrality and node cohesion, etc. are listed from two aspects of social network analysis and system science analysis. Some improved indexes are also elaborated. Thirdly, the advantages and disadvantages of establishing cascading failure models are compared from the perspectives of self-organized criticality theory and complex network theory. And typical cascading failure models such as hidden failure model and isolated centrality model are listed. Finally, the development trend of complex network theory in disaster emergency repair, cascading failure, energy Internet, new power system, and the introduction of artificial intelligence algorithm is discussed.
Domain Knowledge Driven Unified Architecture for IoT Device Identification using Deep Metric Representation Learning

Devices connected to the Internet of Things (IoT) are well recognized for being the root of several security issues, and as a result, they would significantly benefit from automated management. For the purpose of implementing the proper network security measures, robustly identifying devices is necessary. The performance and security of IoT devices are monitored and improved in large part thanks to device identification. Thus, methods to properly recognize IoT devices are explored. When detecting the many distinct categories of Smart home devices as indicated to differentiate devices, IoT devices confront us with both inherent difficulties and prospects. Based on important results from preceding research on comprehending the patterns of network traffic in IoT devices, an efficient machine-deep-learning IoT Network identifier approach is created in this study. In order to extract 298 distinct features of network traffic, the proposed model integrates three separate parameters such as network servers in remote, port numbers, the time between packets which is the characteristics of traffic in packet level, and the duration of the flow which is the characteristics of traffic in flowlevel. The fact that distinct devices on a network might create traffic at varying rates and this variability are considered, both during the process of training the system and while evaluating its performance. This isolates the proposed methodology from prior literature.
Nonlinear instability of the wake behind a flat plate placed parallel to a uniform stream

A theoretical study is presented on nonlinear aspects in the instability of the wake behind an aligned flat plate. This is done in the long-wavelength régime by asymptotic solutions of the unsteady Euler equations, in a rational fashion. In particular, equations are obtained that describe the nonlinear evolution with time and space of the vortex-layer interface formed just downstream of the trailing edge. The instability is found to be driven by a pressure jump across this layer, which has the same form as that arising from surface-tension effects but with a negative (destabilizing) sign. Finite-time breakdown of the solutions is conjectured and analysed. The usefully simplified case of a bounded wake is formulated, analysed and computed numerically, and comparisons are made with experiments, showing qualitative agreement.
The Effects of Structural Network Embeddedness on College Students’ intention to Volunteer Service Behavior: An Empirical Study

Volunteer service is an important carrier for college students to participate in social life, and also a symbol of social civilization and progress. Stimulating college students' voluntary service behavior can not only meet the individual emotional needs but also help to promote the whole society to improve the moral level. Through the questionnaire survey of 450 College Students in China, it reveals the influence mechanism of structural network embeddedness on the intention to volunteer service behavior, which provides a reference for more effective cultivation of college students' volunteerism. The results show that: (1) structural network embeddedness has a positive impact on the intention to volunteer service behavior, (2) emotional support has a positive impact on the intention to volunteer service behavior, and (3) structural network embeddedness can influence the intention to volunteer service behavior by influencing emotional support.
IT Project Portfolio Risks: development and validation of a reference model

IT project portfolio management has been implemented in most organizations to effectively manage and balance complex portfolios of IT projects with business strategy. Portfolio risks can affect the success of a portfolio and the achievement of its strategic intentions. While recent literature identifies potential risks in sub-areas of ITPPM, a validated comprehensive list of risk factors and an understanding of why these factors are perceived as risks by practitioners is lacking. Therefore, this study designs and then validates a reference set of risks based on practitioners’ experiences with these risks. Our validated reference set of risk factors can be used by organizations to more confidently assess the risks of their IT project portfolio.
Measuring Information Propagation and Processing in Biological Systems

aBsTracT In this chapter the authors study the propagation and processing of information in dynamical systems. Various information management systems can be represented as dynamical systems of interconnected information processing units. Here they focus mostly on genetic regulatory networks that are information processing systems that process and propagate information stored in genome. Boolean networks are used as a dynamical model of regulation, and different ways of parameterizing the dynamical behavior are studied. What are called critical networks are in particular under study, since they have been hypothesized as being the most effective under evolutionary pressure. Critical networks are also present in BLOCKINman-made BLOCKINsystems, BLOCKINsuch BLOCKINas BLOCKINthe BLOCKINInternet, BLOCKINand BLOCKINprovide BLOCKINa BLOCKINcandidate BLOCKINapplication BLOCKINarea BLOCKINfor BLOCKINfindings BLOCKINon BLOCKINthe theory of dynamical networks in this chapter. The authors present approaches of annealed approximation BLOCKINand BLOCKINfind BLOCKINthat BLOCKINavalanche BLOCKINsize BLOCKINdistribution BLOCKINdata BLOCKINsupports BLOCKINcriticality BLOCKINof BLOCKINregulatory BLOCKINnetworks. BLOCKINBased BLOCKINon Shannon BLOCKINinformation, BLOCKINthey BLOCKINthen BLOCKINfind BLOCKINthat BLOCKINa BLOCKINmutual BLOCKINinformation BLOCKINmeasure BLOCKINquantifying BLOCKINthe BLOCKINcoordination of pairwise element activity is maximized at criticality. An approach of algorithmic complexity, the normalized compression distance (NCD), is shown to be applicable to both dynamical and topological features of regulatory networks. NCD can also be seen to enable further utilization of measurement data to estimate information propagation and processing in biological networks.
Super-Resolution GPS Receiver: User's Acceleration Computation

Current commercial global navigation satellite system (GNSS) receivers cannot measure the change of Doppler shift via baseband processing. However, positioning in challenging environments demands accurate Doppler estimates to replicate the actual intermediate-frequency (IF) line-of-sight (LOS) GNSS signals. Otherwise, the instantaneous GNSS measurements, like pseudoranges and carrier phases, will be seriously distorted, restricting the upper bound of GNSS positioning. This paper proposes a brand-new super-resolution (SR) GNSS receiver that computes the user's absolute acceleration based on super-long coherent integration (S-LCI), fractional Fourier transform (FRFT), and a baseband maximum likelihood estimator (MLE). A basic nonlinear least square (NLS) navigator taken as an intuitive example shows how the proposed receiver models and generates averaging Doppler rates via the baseband-dependent gradient descent (GD) algorithm. A global positioning system (GPS) software-defined radio (SDR) processing the L5 IF data collected via a mobile cart in the real world validates the proposed technique with the Doppler rate measurement, the user's acceleration navigation solution, and the clock drift rate.
Multi-Modal Transfer Learning with DenseNet and Random Forest for Accurate Detection of Powdery Mildew in Rice & Maize Crops

The early detection and accurate diagnosis of powdery mildew in rice and maize crops are essential for ensuring optimal yield and quality. This research study proposes a Multi-Modal Transfer Learning with DenseNet and Random Forest approach for the detection and classification of powdery mildew in rice and maize crops. Our proposed method employs transfer learning, where the DenseNet model is pre-trained on a large-scale image dataset to extract useful features from the images. Subsequently, the extracted features are fed into a Random Forest classifier to achieve precise classification of the powdery mildew disease in the crops. Moreover, the vegetation index is provided as an additional input to enhance the accuracy of the classification. Data augmentation techniques are additionally employed to enhance the diversity of the training dataset and bolster the model's capacity to generalize. Experimental results demonstrate that the proposed approach attains high levels of accuracy, precision, sensitivity, and specificity for the classification of powdery mildew in rice and maize crops. The use of transfer learning and vegetation index integration, along with data augmentation, contributes to the effectiveness of our proposed approach. This research study has significant implications in the agriculture industry, as it provides a reliable and efficient means of detecting and classifying powdery mildew disease in rice and maize crops.
Measurement of shear-induced self-diffusion in concentrated suspensions of spheres

A novel technique is presented for determining the coefficient of shear-induced particle self-diffusion in concentrated suspensions of solid spheres, which relies on the fact that this coefficient can be computed from the measured variations in the time taken by a single marked particle in the suspension to complete successive circuits in a Couette device. Since this method does not involve the direct measurement of the lateral position of the marked particle, it requires a much simpler experiment than that used by Eckstein, Bailey & Shapiro (1977) which is shown to be constrained by wall effects at high particle concentration. The diffusion coefficient thus determined was found to be proportional to the product γa2, where γ is the shear rate and a the particle radius, and to have the asymptotic form 0. 5γa2ϕ2 in the dilute limit when the particle concentration ϕ → 0.
Performance Analysis of MIMO FSO Communication system over $\mathcal{M}$-distributed Fading Channel

Currently, a new statistical model named málaga or $\mathcal{M}$-distribution was proposed to model the atmospheric turbulence channel. All existing traditional statistical channel models can be derived from this. Atmospheric turbulence degrades the performance of a free space optical communication (FSO) system. Spatial diversity is a promising technique to enhance the performance of the system. In this article, an FSO system is analyzed with spatial diversity with on-off keying modulation over $\mathcal{M}$-distributed turbulent channel. A novel closed-form expression for bit error rate is derived for the proposed FSO systems over this channel and analyzed the effects of pointing errors.
Adam Optimization of Burger’s Equation Using Physics-Informed Neural Networks

In this paper, physics informed neural networks are used for numerical approximation of partial differential equations. The data which is used in the process is generated by Latin Hypercube sampling which has been discussed. Adam optimization technique has been implemented to minimize the loss for the discussed partial differential equation. The above proposed methodology has been applied for Burger’s equation and the obtained results have been discussed in section 5. Loss function graphs have also been provided to showcase the efficiency of the proposed methodologies.
Improving Lactation Curve Prediction by Incorporating Weather and Cow Behaviour

Thailand has tropical weather with both high temperatures and humidity, which can affect the milk yield. Thus, commonly used lactation curve models derived from milk yields in different geographic regions cannot fit the actual milk yield in Thailand well. This study, therefore, proposes a mixed model of the lactation curve model and a regression model to adjust the prediction with regard to the effect of weather, cow behaviour, and cow information on milk production. The performance of all mixed models is better than that of the lactation curve models. This indicates the improvements in the model prediction and may imply that weather, cow behaviour, and cow information play a part in milk production.
Precision magnetometry and imaging via quantum manipulation of spins in diamond

Precise control of quantum states is a cornerstone of quantum science and technology. Recently, a multi-level electronic spin system in a robust room-temperature solid, based on the nitrogen-vacancy (NV) color center in diamond, has emerged as a leading platform for quantum sensing as well as quantum information processing at roomtemperature. Developing new approaches to high-precision NV spin manipulation provides key insights for advancing these quantum technologies. In this thesis, I demonstrate three experimental methods for controlling NV spins with various concentrations toward high-performance magnetic field sensing and imaging. First, the wide-field optical magnetic microscopy experiment provides ensembleNV control via continuous-wave electron spin resonance and camera-based parallel spin-state readout. This microscope offers a factor of 100 larger field-of-view compared to the confocal detection size, which enables magnetic imaging of populations of living bacteria. Second, the Fourier magnetic imaging experiment demonstrates for the first time multiple-NV control using phase encoding. Pulsed magnetic field gradients encode in the NV spin phase the information about the position of the NV centers as well as the external magnetic field in the Fourier-space. This scheme allows 100-fold improvement in spatial resolution beyond the optical diffraction limit, and has higher signal-to-noise ratio than other superresolution imaging techniques when applied to NV spins. Third, the geometric phase magnetometry experiment employs single-NV control using a Berry sequence, consisting of off-resonant microwaves whose parameters vary along a cyclic path, thereby realizing 100 times larger magnetic field dynamic-range compared to the typical Ramsey-type interferometry approach. Finally, I discuss the possibilities of combining these techniques to realize various other quantum applications in future work. Thesis Supervisor: Ronald Walsworth Title: Senior Lecturer, Harvard University
Recognition of Intracranial Hemorrhage with its Subtypes from CT Images using Deep Learning Approach

Intracranial Hemorrhage (ICH) or Intracranial Bleeding is a severe problem for a person who rushes towards death unless it is recognized accurately within a short period. Commonly, it occurs within the skull or brain. Since the brain is the most crucial part of the human body and consists of soft tissue, its lesion detection, and treatment are so sensitive and also challenging within a faster time. The radiologist analyzes brain CT images to diagnose diseases consuming more time that affects a severe patient negatively. Being the process slow, non-automated, and costly, an automated deep learning approach is proposed to diagnose the problems accurately within the shortest time. For our research, the Radiological Society of North America (RSNA) dataset is considered, which consists of 752,803 training images and 121,232 test images. Normalization, windowing, and augmentation are used as preprocessing techniques on the training data, and during the model training, 30% of data are treated as validation data for hyper-tuning. After the successful completion of learning, this study has applied the test data to evaluate the performance of the proposed model, and it was observed that the proposed model performs better in the recognition of ICH with 98% accuracy.
A Novel Ransomware Virus Detection Technique using Machine and Deep Learning Methods

Malware is typically found only after the victim gets a ransom demand. Ransomware detection systems detect the threat more quickly, allowing victims to take action before irreversible damage occurs. The identification of ransomware will assist in avoiding the loss of crucial data. After an intrusion, many users never access their original data again. Data will be lost permanently if a recent backup is not created. Endpoint detection is one of the virus-defense techniques that can stop malware as soon as an attacker gains access. Hence, the sensitive data can be kept safe by protecting the data. This study proposes a system for detecting ransomware attacks that uses machine learning and deep learning approaches to analyse the malware that enters the network in order to stop these malware attacks. Each file was initially analysed and examined in a computer system to see whether it’s vulnerable to attack. When there is network communication, each and every packet is detected. The assessed packets will be checked to see whether there was any unusual behaviour in the system’s activities. The goal of this study is to determine when a harmful signal first arises and where it happens in a ransomware workflow, as well as to allow early identification and stop ransomware activities before they cause significant damage.
Performance Analysis of Electric Vehicle using BLDC Motor Drive

A study on position and speed is made on BLDC (Brushless DC motor) in this research. The study is performed based on the sensor less technology which includes background investigation. Compared to the conventional method, the reliability and the performance of BLDC motor is improved using the proposed design. In the simulation, controlling the speed of BLDC motor is performed. In simulation, the speed of the BLDC motor remains constant even when the voltage varies. Further in the real time experimentation sensor less are studied in terms of merits and demerits, further an analysis is made on design parameter, issue in implementation and the application. The study includes an Electro Motive Force sensing methods, Terminal Voltage Sensing and Terminal Current Sensing. Using microcontroller (Atmega8A) board and Electronic Speed Controller, the speed of the motor is controlled by varying the 10k potentiometer. The parameter like speed and rpm are obtained and shown in the load display.
CNN Transfer Learning for Two Stage Classification of Diabetic Retinopathy using Fundus Images

Diabetic retinopathy is a common retina disease caused by diabetes, which is increasing rapidly worldwide along with diabetic Mellitus. It is very difficult to diagnose in the beginning as it is asymptomatic, which leads to blindness. Presently, there are about 422 million diabetes patients, and it is projected that the number will be 552 million in 2030, as per a report from the World Health Organization. With the advancement in Artificial Intelligence, Deep Learning, and Computer Vision fields, many CNN-powered models have been developed for the detection and classification of DR using color fundus images. Early diagnosis of DR will increase recovery and decrease the possibility of vision loss threats. In this study, we aim to classify Diabetic Retinopathy in two stages, diseased versus healthy images. Three different pre-trained CNN models, namely, VGG16, InceptionV3, and MobileNet were deployed through transfer learning. Messidor and Messidor-2, two publicly available datasets are used in the training and testing of these CNN models. In terms of classification accuracy, the highest result of 84% was obtained using InceptionV3, whereas MobileNet and VGG16 shows 83% and 78% accuracy, respectively. The Highest 86 % precision for the healthy class and 88% sensitivity for the diseased class is shown by VGG16.
Integrity Analysis for Greedy Search Based Fault Exclusion with a Large Number of Faults

Detection and exclusion can be achieved using Solution Separation (SS), for example, in civilian aviation applications using Advanced Receiver Autonomous Integrity Monitoring (ARAIM). Global Navigation Satellite Systems (GNSS) fault modes, which are well defined with predictable probabilities of occurrence, can be efficiently and exhaustively accounted for by grouping. However, other navigation applications emerge that require multi-sensor fusion in varying operational environments. Both the total number of measurements and the number of potentially faulty measurements can increase significantly. For such applications, an exhaustive account of all modes can be challenging and can become computationally intractable. These challenges can be addressed using greedy-search-based fault exclusion. However, methods are lacking to quantify the navigation integrity performance of such approaches. In this research, we perform a theoretical analysis of the integrity of greedy-search-based fault exclusion approach. The methods to calculate the minimal detectable bias and the worst case fault direction are discussed.