Miracle-dz/UniFAR-FTUs huggingface dataset:
Liftings of formal groups and the Artinian completion of BP

Let BP denote the localization at υn, of the Brown-Peterson spectrum (associated to the prime p). There is a related ring spectrum E(n) with homotopy ring (as a quotient ring of in fact the cohomology theory E(n)*( ) is determined via a Conner-Floyd type isomorphism from on finite complexes, and moreover E(n) and BP are in the same Bousfield class (see [2, 14]). Although it is known (essentially from [17]) that BP cannot be a product of suspensions of E(n) in a multiplicative sense, D. Ravenel conjectured that such a splitting might occur after suitable completion of these spectra (see the introduction to [14]). This question was the original motivation of the present paper; however in proving Ravenel's conjecture we were naturally led to the consideration of some fundamental results in the theory of liftings of formal group laws and ‘change of ring’ results for Ext groups occurring in connection with the work of [10, 11, 12].
Temperature and magnetic field-dependent correlators of the exactly integrable (1+1)-dimensional gas of impenetrable fermions

The author shows that the finite-temperature equal-time field correlators of the exactly integrable (1+1)-dimensional gas of impenetrable fermions in a magnetic field can be expressed as the first Fredholm minor of a completely integrable linear operator. This result enables the author to derive differential equations for quantum correlators and to obtain analytical formulae for long-distance asymptotics. In particular, a formula for correlation length as a function of temperature and magnetic field is presented. His analysis reveals the presence of two length scales, which separate short-distance, intermediate and extreme asymptotic domains. The importance of the cross-over region is emphasized.
Classical Vlasov plasma description through quantum numerical methods

The Schrödinger equation describes the motion of a particle in a statistical sense. It consequently possesses the two main properties of the Vlasov equation (dynamic and statistic) and can replace this last equation provided we take sophisticated initial conditions. The scheme must be considered as a new attempt to discretize intelligently the amount of information contained in the phase space distribution and to stop, without destroying it, the flow of information which usually goes to high wavenumbers in velocity space. The method is applied to the breaking of highly nonlinear waves in a cold plasma (usually treated by the Lagrangian method) and to double beam instability. It is shown that such an Eulerian scheme works quite well with a much smaller number of discretized functions than are required in the regular Fourier—Fourier or Fourier-Hermite methods. The central point is the introduction of the phase space Wigner distribution function which is a useful mathematical tool in spite of its poor physical properties.
Why try to build try to build a co-creative poetry system that makes people feel that they have "creative superpowers"? 67-80

The paper examines co-creative writing systems, and argues that existing Large Language Models could potentially reduce human capacity. Furthermore, existing sociocultural inequalities might be exacerbated by the widespread adoption of such generative systems. The paper instead suggests a custom approach, using co-creative poetry writing as an example. The system has architectural changes from typical language models to better support poetry. It also uses rap lyrics as part of the training data in order to help reduce sociocultural bias. A high level system implementation is proposed along with some evaluation methods. Evaluation is based on expert judgement on final outputs, and user performance on language tasks associated with human creativity. The final section of the paper explores how and why alternatives to existing co-creative systems could benefit individual users as well as wider society.
Oceanic fine structure

Abstract Measurements by free fall instruments, in the San Diego Trough, the Florida Current, and the central Pacific, reveal the detailed structure of the vertical component of the oceanic temperature gradient. The temperature changes are concentrated into regions on the order of a meter thick wherein the measured gradients are often more than ten times the average gradient. The horizontal extent of the regions of high gradient is greater than 750 meters in the seasonal thermocline off San Diego, but is only a few hundred meters at depths greater than 400 meters. Fine scale measurements show that the layers of high gradient consist of even finer fluctuations in gradient which are only a few centimeters thick. Time scales of the thinnest of these regions of high gradient are of the order of five minutes. The data also yields an estimate of the entropy generation. According to the results of an idealized model relating entropy generation to the turbulent heat transport, only 240 to 700 ergs per cm.2 per se.
Real-time Pipeline Reconfiguration of P4 Programmable Switches to Efficiently Detect and Mitigate DDoS Attacks

In this work we demonstrate the integration of P4 enabled switches with high level AI techniques with the aim to improve efficiency and performance of DDoS detection and mitigation. Powerful ML-based strategies are adopted only when a suspicious behaviour is occurring in the network, and its activation is triggered by a coarser-grained and lightweight strategy fully executable in the data plane.
Modelling of an Intelligent Voice System using MI Algorithm for E-Business

The next generation will require of such commerce site with the renaissance of conventional technology to more current technology and use of advanced technology, where a large portion of e-commerce are presently created on back of such revolution and inventive breakthroughs like robotization. Most of businesses or owner(s) of these sites are sending off more unique sites rather than conventional list and static sites, so machine learning, augmented with man-made brainpower, and deep learning, which are in demand and profoundly necessary, should be integrated into the present e-commerce sites to be best and profitable for the owners of these sites. The importance of artificial intelligence in various spheres of business and life has increased as a result of the accelerated technological advancement. The use of AI in voice recognition helps organisations and individuals understand how to provide stakeholders with better services and effectively completes the task. Therefore, this work focuses on utilising regression analysis to analyse the critical factors of employing AI in speech recognition for a successful multipurpose Machine Learning platform.
Frequency Projection: A Review and its application in channel modelling

Frequency projection is an idea of leveraging the channel-related parameters in one frequency band to estimate its counterpart in the other. This phenomenon applies to many applications such as Frequency Division Duplex (FDD) systems where uplink and downlink are operated in different frequencies, exploiting frequency correlation between the underlay and overlay wireless networks for 5G/B5G Wireless Communication, multi-band chipsets, etc. This paper presents a review of the key frequency projection-based application and innovations over the last three decades. Also, its application in channel modelling of Non-Terrestial Network (NTN) is presented with simulation results. A scope for new work on this interesting topic is also argued in this contribution.
Neuromorphic Hardware Applied in the Development of Low-Power IoTs *

In the field of study on smart cities, mapping information using Internet of Things (IoT) sensing is necessary because it allows optimizing resources through the real-time integration of the consumer with the supplier. However, world IoT production capacity would have to increase by two orders of magnitude to allow a meaningful level of mapping that could be felt in our daily lives. There are several technological challenges related to increasing IoT production by two orders of magnitude, such as the energy impact we would need to deal with. This work aims to introduce the idea that part of these challenges could be solved by changing the traditional computational architecture to the so-called Neuromorphic, a computational architecture based on biological brains. The work consisted of an extensive literature review that analyzed data on the specifications of prototypes of Neuromorphic architecture devices that were published in scientific databases. As a result, it was shown that Neuromorphic devices could meet the requirements and solve some challenges related to the increase that the world needs in the production of IoT, without the need for long periods of technology development.
State Estimation for Unbalanced Three-Phase AC Microgrids Based on Mathematical Programming

State estimation (SE) helps to determine the most-likely steady-state operation of microgrids based on field measurements. It is a fundamental data processing tool responsible for supporting and increasing system visibility and filtering errors that may appear in real-time measurements, system topology, and parameters. Hence, it serves as a reliable basis for the energy management system (EMS) infrastructure. Existing SE methods are based on Newton-like algorithms rather than using specific optimization. Nevertheless, SE can always be reduced to a constrained optimization problem, with the objective of minimizing a given criterion, e.g., weighted least squares (WLS) or weighted least absolute value (WLAV). In this context, this paper presents two unbalanced three-phase AC SE methods based on mathematical programming for microgrids. The effectiveness and validity of the proposed state estimation approach are demonstrated on a real microgrid located at the State University of Campinas, in Brazil, and over one widely known IEEE test system. The method can be easily adapted to other microgrids with different configurations, distributed energy resources, and measurements. Results show that the proposed methods report high accuracy, but the state estimator based on WLS is faster than the WLAV.
Meditative State Classification Using Neuronal Multi-IMF Band Power and Complexity Features

The incredible complexity of physiological signals, along with the awareness that physiological time series contains hidden information, has sparked a surge in the interest to conduct neuroscientific research. The human brain is one of the most significant instances of intricate physiologic oscillations as recorded through Electroencephalogram (EEG). In this article, we propose a methodology for assessing the EEG chaotic levels by decomposing EEG signals into Intrinsic Mode Functions (IMFs) and extracting features like Sample Entropy (SE) and Hurst Exponent (HE). We then demonstrate how various mediation styles affect the EEG chaotic levels and also provide a framework for classifying meditative states. We conduct our research on two different types of meditation - Himalayan Yoga (HT) and Hare Krishna mantra meditation (HKT). HKT is an in-house study of a group of 16 experienced meditators who participated in a two-week mantra meditation practice. Corresponding to HT, there is a publicly accessible online dataset of 16 experienced meditators. We observed that when compared to the control group (CTR), HKT meditation demonstrated a significant increase in SE and a significantly higher drop in HE for the alpha and gamma bands, indicating enhanced human brain complexity and decreased predictability, causing the brain to be more active. We tested multiple ML classifiers with HE, SE, and Band-power (BP) features and discovered that by utilizing BP features, we could reach 100% accuracy in classifying meditative and non meditative states in both traditions ((HT) and (HKT)).
Blowing the Truth: Strengths and Weaknesses in Creating Whistleblowing System for Higher Education Institutions

Wrongdoing that occurs if it is not prevented and controlled by the system created by the organization will increasingly have a bad impact. In order to control the occurrence of wrongdoing, it is necessary to design a whistleblowing system which is expected to be able to prevent and even minimize wrongdoing behavior. It takes various relevant requirements and conditions for the creation and development of a whistleblowing system in higher education institutions. To maximize the whistleblowing function, the right organizational pattern and wellprepared system components are needed. There are various weaknesses and advantages of creating a system in higher education institutions that can be a valuable input for developing a whistleblowing system. Good corporate governance, senior management commitment, policy effectiveness, awareness of member organizations are important components that must be formed in creating an effective whistleblowing system.
Sizing of Energy Storage System Based on Time Series for providing Frequency Regulation

This paper investigates whether energy storage systems (ESS) can be used for providing frequency regulation ancillary services. We have presented two approaches for sizing storage. The first approach looks at the probability distribution function of the net imbalance and derives the size of ESS with 99.8% confidence. The second approach is based on time series in which we have defined a desired frequency time series and have estimated power compensation requirement for each time interval. In this method a detailed account of load model and capacity limits of ESS is presented. The estimated size of ESS obtained using time series based approach is compared with the size estimated using the statistical method and is shown to be significantly smaller. Results are presented for the existing grid conditions using WAFMS (wide area frequency measurement system) data and also for a future scenario with reduced grid inertia.
Contribution of Artificial intelligence to the Promotion of Mental Health

Mental stress is a normal and frequent phenomenon in human beings. Earlier stress recognition is critical for avoiding these negative impacts since prolonged stress has an adverse impact on mental health leading to anxiety, loss of sleep, or headache. Making utilize of physiological data gathered from a wearable device, present study attempts to simplify the procedure of psychological stress determination, which helps to distinguish the persons suffering from stress over healthy one. We tested our method using a dataset that was made accessible to the public. The precision of forecasting exact stress state was applied to make comparisons among effectiveness of numerous techniques of artificial intelligence (AI), including Artificial Neural Networks (ANN), Fusions of ANN with Support Vector Machines (SVM), Stack Classifying method, and Radial-basis Function (RF) Networks. The study included 3-class stress categorization method in which, results shown greatest accurate rating of 99.920percent by Stack Classifier, while RF provided the lowest preciseness of 84.462percent. Study outcome infer that the suggested models are efficient in detecting mental stress over time and show that physiological indicators could be highly relevant in identifying mental stress.
Influence of Rotor Sleeve on Multiphysics Performance for HSPMSM

The multiphysics characteristics of high-speed permanent magnet synchronous machines (HSPMSMs) can be significantly affected by the rotor sleeve. This article aims to comprehensively investigate how the electromagnetic, loss, mechanical, and thermal characteristics are influenced by the rotor sleeve. First, the change of the rotor sleeve is divided into two stages. The mechanical air gap length is fixed in the first stage, while the electromagnetic air gap length is fixed in the second stage. Besides, the influence of sleeve thickness of the alloy sleeve and carbon fiber sleeve on electromagnetic characteristics, loss characteristics, rotor stress, and temperature characteristics are obtained within these two stages. Furthermore, four sleeve design configurations with different thicknesses and materials are investigated, and a comprehensive comparison is performed. Finally, a prototype of an HSPMSM is manufactured, and the theoretical analysis is verified through performance testing of the prototype.
Partial Camera Obstruction Detection Using Single Value Image Metrics and Data Augmentation

To improve the resilience and ensure the dependability of a critical system, the measurements and the derived intelligence provided by the sensors monitoring the system need to be reliable. This is increasingly challenging. As the computer vision methods evolve, the usage of cameras as a part of monitoring solutions has increased, and, consequently, the need for reliable diagnosis strategies for those image-based sensors. This work investigates the suitability of various single-value image metrics, derived from first and second-order statistics, for detecting partial camera obstruction. The presented methodology includes using data augmentation techniques to expand a small dataset of labeled images, and a score-based selection of the best metrics for the target application. The results show that even simple first-order statistics, such as the image histogram skewness, can provide good detection results. The strategy presented could be extended and adapted for the detection of other types of physical anomalies, being particularly useful for integrity assessment in applications with limited computational resources.
Design and Implementation of Embedded Sensor Network for an Automated Radio Telescope

This paper presents a real-time implementation of an embedded sensor network for an automated radio telescope. The sensor network consists of accelerometers, digital motor temperature sensors, azimuth and elevation encoders, ambient temperature and humidity sensors, and an over the air wireless updating system for the microcontroller. A super loop software architecture was used to collect sensor data and update the control room operation. A custom PCB was designed for the sensor that houses the microcontroller, a wireless communication module, and peripheral circuit components. A laboratory test setup was built to collect sensor data and emulate the actual system operation. The test results validated the sensor network and system operation.
Enhancing the Share of Solar PV Energy in BVRIT Campus Distribution Grid using Campus Energy Monitoring System (CEMS)

The amount of energy consumed has a very substantial impact on the price of the products in industries, it effects the operation cost. As a result, the reduction of energy consumption has risen to the top priority in all places like industries and commercial application. Hence energy balance and sharing of available energy between the loads are key points to reduce the electricity bill. In this paper, a practical case study considered at BVRIT campus. It includes power availability in campus, solar peak, morning and night load peak, total demand peak, energy balance analysis are presented. Multi function meters with RS-485 communication and Raspberry pi are used for data collection in the real time. Data related to solar power generation, utilisation, peak load timings, excess power available times and power consumed from the main grid were measured and collected, will assist consumer to maintain cost effective system. Based on the analysis, it is found that for the month of March 2022, BVRIT campus has sourced 38.8% of its energy requirement from renewable energy which is a significant achievement for an academic campus in India.
Most Possible Likely Word Generation for Text based Applications using Generative Pretrained Transformer model Comparing to Long Short Term Memory Model

In contrast to long short term memory techniques, the proposed study intends to produce automatic next word generation for text-based applications while enhancing accuracy using state-of-the-art generative pretrained transformers and recurrent neural networks. Materials and Methods: On the data, which is a text file including a series of words, generative pretrained transformer models and long short term memory are used. Long short term memory model that contrasts cutting edge generative pretrained transformer models for recommendation accuracy of the next word. It has been suggested and created to use LSTM. The sample size was calculated to be 8046 for each group with a G power of 0.8. Results: When compared with a long short term memory model (70.84%) for the same dataset p=0.02(p<0.05), the accuracy in predicting the upcoming word for text editor based Applications utilizing generative pretrained transformers was greatest at 87.98% with the lowest mean error. In generating the next word for text-based Applications, the study shows that generative pretrained transformers are more accurate than long short term memory.Conclusion: The study demonstrates that when recommending the most possible word for text generation based applications, generative pretrained transformers are more accurate than long short term memory.
Energy Storage Optimization Planning for Resilience Enhancement under Extreme Events

Frequent occurrence of extreme events caused serious losses to the power system. This paper takes typhoon disasters as an example to establish the optimal planning model of energy storage system (ESS). The proposed model is a scenario-based two-stage stochastic MILP model. Fully considering the uncertainty of failure scenarios under typhoon and load, the first stage is to select the location and capacity of ESS with line hardening strategy under a limited budget; in the second stage, the charging and discharging strategy of ESS is optimized with distribution system operation strategies to minimize system losses and investment costs. The model is tested on a modified IEEE33 system. Results indicates that the combined planning of allocating ESS and hardening lines can significantly improve resilience of distribution system under extreme events.
Detection and Classification of Single Line to Ground Faults in Unbalanced Islanded Microgrids

The distribution grid of the future will have a variety of generation resources. With the help of such distributed energy resources (DERs), the grid should be able to fragment and coalesce to form microgrids as needed for enhanced resiliency. In such microgrids, the distinction between different events such as faults and topology change/load step can be challenging. Especially for single line to grounds faults in an unbalanced microgrid, where zero sequence components are already present. The proposed method analyses current waveform through time-frequency multi-resolution analysis (MRA) to extract information without processing into sequence components. It then extracts fault-features using statistical methods applied to Shannon entropy and signal energy. These features quantify the changes in the current signal while incorporating the changing physics of the system. To make the method agnostic to the DER mix and topology changes, rather than using set thresholds, it implements machine learning based algorithm using Support Vector Machines (SVM) to detect and classify fault types. The method is tested against a variety of events simulated in MATLAB Simulink.
Literature Review of Image Denoising Methods

Image denoising is a fundamental and important task in image processing and computer vision fields.A lot of methods are proposed to reconstruct clean images from their noisy versions.These methods differ in both methodology and performance.On one hand,denoising methods can be classified into local and nonlocal methods.On the other hand,they can be marked as spatial and frequency domain methods.Sparse coding and low-rank are two popular techniques for denoising recently.This paper summarizes existing techniques and provides several promising directions for further studying in the future.