Miracle-dz/UniFAR-FTUs huggingface dataset:
Developing a transcutaneous electrical nerve stimulation device for regulating hormones in PCOS patients

Numerous investigations have been carried out in later times to comprehend the impacts of Transcutaneous Electrical Nerve Stimulation (TENS) innovation on the female regenerative framework, and several contrivances have been proposed to explicate its prospective benefits. These include regulation of the hormonal secretion and living a normal lifestyle without being subjected to several symptoms of Polycystic ovarian syndrome (PCOS) through the effect of TENS. It is known that normal levels of reproductive hormones fall out of balance as an outcome. Anovulation, or the absence of ovulation, can cause little follicular cysts on the ovaries to be apparent on ultrasound. Regulation of these hormones on normal levels and reducing the amount of androgen secretion are very important in regulating the behavioral and physiological events necessary for success to enlarge. The point of this study is to test the convenience of TENS in normalizing hormone levels in ladies, considering its impacts on comes about of age, Body Mass Index (BMI) and torment resistance of members. It is theorized that inducing pain at certain points of the body using TENS significantly regulates hormone levels and decreases the androgen level.
Classification of Events Tweets Using Machine Learning

Microblogging platforms like Twitter have turned into a significant correspondence divert in the midst of crisis. The omnipresence of cell phones empowers individuals to declare a crisis they’re seeing continuously. Along these lines, more offices are keen on programmatically observing Twitter (for example calamity help associations and news organizations). Be that as it may, it’s not commonly apparent whether a singular’s words are truly pronouncing a catastrophe. Because of this many reserachers are working to use this fact of information and utilize the same. So here in this research study we have prepared and tried 9 classifiers with 3 different feature extraction technique and investigated their exhibition. That’s what the proposed exploratory outcomes showed, Transformer based architecture like BERT yield best execution with the most noteworthy exactness of 83.92%, trailed by the Ensemble voting classifier with a F1-Score of 77.90% and SVM classifier with TF-IDF at 79.41%.
Multi-Objective Optimization of High-Speed Permanent Magnet Synchronous Motor Based on Multi-Physical Field Coupling

High-speed permanent magnet synchronous motors (HSPMSMs) possess distinct attributes, including high frequency, high rotational speed, and high loss density. Thus, in-depth research on each parameter’s optimization is crucial to attain maximum performance and reliability. Based on the mechanism of multi-physical field coupling (MPFC), this paper discusses an experimental design method for optimizing a 60,000 rpm, 30 kW motor through multi-objective optimization based on generalized sample space. Special attention is paid to multi-physical field factors such as motor loss, rotor dynamics, and cogging torque. The results demonstrate a considerable improvement in the motor’s performance, including its “goodness” function and cost. Furthermore, the uniform design (UD) method utilizing a genetic algorithm demonstrates high efficiency. Ultimately, the experiment gauging performance and motor temperature rise confirmed a maximum error of 4.2% between simulated and measured results, demonstrating the optimization algorithm’s accuracy in multi-physical field coupling scenarios.
Collision Avoidance for Unmanned Surface Vehicle in Extreme Multi-Ship Encounter Situations

Unmanned surface vehicles (USVs) can encounter traffic ships while performing trajectory tracking missions. USVs need to take suitable collision avoidance (CA) action in accordance with international regulations for preventing collisions at sea (COLREGS). A novel CA approach is proposed for the problem of simultaneous collision avoidance of multiple ships. Different from the already existing CA approaches, our proposed approach can cope with extreme multi-ship encounter situations where USV is surrounded by multiple ships from all sides. Collisions can be caused by incongruous CA actions between encountering ships. So, a CA strategy is proposed with symmetric rules according to COLREGS. In general, CA velocity designed for USV is obtained by adding an offset velocity to its desired velocity. To avoid all encountering ships simultaneously, the consistent offset velocity direction (COVD) method is proposed to design the direction of offset velocity. In addition, an emergency CA module is added to further ensure that a safe distance is maintained between USV and traffic ships. The performance of the proposed CA approach is verified by physical simulations with an existing simulator. The simulation results show that multiple USVs can implement simultaneous collision avoidance and return to their respective desired trajectories when they all use the proposed CA approach.
Parallel Treasury for TRUE DAOs: Model, Indicators and Mechanism*

In response to concerns over the centralization tendency in the decentralized autonomous organizations (DAOs), TRUE autonomous organizations and operations (TAOs or TRUE DAOs) have been proposed recently. TAOs aim at spreading equitable value distribution and democratized decision-making, distinguishing them from their DAOs counterparts. This study focuses on the treasury within TAOs, which acts as a central fund pool and a crucial element in the decentralized economy (DeEco) system. First, against a backdrop of potential black swan events and other long-tail unforeseen challenges, a reference model for the intelligent treasury management of TAOs is proposed. Then, an evaluation system, namely VALID, is presented with metrics including verifiability, anti-volatility, legitimacy, inclusiveness, and decentralization. Furthermore, a novel parallel treasury management mechanism is proposed to demonstrate a virtual-real interactive closed-loop management and control paradigm of the treasury, thereby fostering the formulation and development of DeEco. This research provides a comprehensive perspective on intelligent treasury management of TAOs and their role in sustainable advancement of DeEco.
Optimizing System Costs in Local Electricity Market through Peer-to-Peer Energy Trading: Impact of Photovoltaic and Battery Storage Adoption

As the global energy landscape undergoes a paradigm shift towards decentralization and sustainability, local electricity markets have emerged as a promising avenue for efficient energy exchange among prosumers. This conference paper presents a comprehensive study focused on optimizing system costs within local electricity markets through peer-to-peer energy transactions. Our primary objective is to investigate the impact of photovoltaic and battery storage adoption on system costs, shedding light on the potential economic benefits of these technologies in diverse scenarios. The study encompasses a rigorous analysis of various cases and scenarios to provide a holistic understanding of the dynamics involved in local electricity markets. By simulating a range of scenarios, we explore the implications of different levels of PV and battery storage adoption, evaluating their capacity to mitigate system costs effectively. Our findings reveal that, in the best-case scenario, substantial cost savings of 24.42% can be achieved through the integration of PV and battery storage systems into the local electricity market. This research contributes to the growing body of knowledge on local electricity markets, emphasizing the pivotal role of peer-to-peer energy transactions in shaping the future of energy distribution. Furthermore, it provides valuable insights for policymakers, utilities, and consumers seeking to optimize their participation in decentralized energy systems while promoting sustainable energy practices.
Optimizing Light Extraction in Luminescent Solar Concentrators for Horticulture Applications via Genetic Algorithm-Based Reconstruction

Horticulture luminescent solar concentrators (HLSCs) have demonstrated their ability to accomplish spectral conversion and enhance light extraction. However, conventional HLSCs with an extruded uniform micro-cone array on the bottom surface exhibit relatively poor light extraction due to non-isotropic scattering of photons. In this study, we propose a novel HLSC design inspired by nature, which incorporates an extruded disordered micro-cone array that is optimised using the genetic algorithm. By employing Monte-Carlo ray tracing, we estimate a maximum normalised external quantum efficiency of 52.43% for our design, corresponding to a 16.4% improvement compared to a conventional HLSC with an extruded uniform micro-cone array on the bottom surface. Our disordered micro-cone array patterned surface successfully address the light trapping issues in uniform textured HLSCs, laying a solid groundwork for practical applications.
Authentication of Digital Document using Blockchain

In today's technology-driven landscape, the ease of access to information has led to a pressing concern: the proliferation of counterfeit documents. Document authentication plays a pivotal role in our daily lives, where proving our identity is crucial. Various identification documents like Aadhar cards, PAN cards, driving licenses, and passports require validation by human operators. To counteract the challenge of counterfeit documents, a highly accurate automated system has been devised. This system employs gradient optimization and domain-specific factors for matching ID documents with live face images. Addressing the scarcity of samples in numerous classes, a pair of interconnected systems with shared parameters facilitates efficient classifier strength training. The decentralized framework utilizes Ethereum blockchain-based innovation, ensuring digital document security through online storage, hashing, face verification, a peer-to-peer infrastructure, and QR code confirmation.
A Hybrid Approach for Navigation of a Solar-powered UAV in a Dynamic Urban Environment

This paper considers a scenario in which a solar-powered UAV travels in a dynamic urban environment with unknown static and moving obstacles. A framework is proposed, including a three-phase hybrid approach for this problem. Firstly, an energy-aware path planning algorithm is proposed based on the limited information of the environment. Secondly, a pure pursuit controller is applied to follow the pre-generated online path. Finally, an energy-aware reactive obstacle avoidance algorithm is used to avoid collision with unknown obstacles. The effectiveness of the proposed framework is verified based on computer simulation.
A Novel Graph Clustering Algorithm with Enhanced Security using Power Method and Homomorphic Encryption

In recent years, privacy concerns have led to an increasing demand for secure data clustering algorithms that can protect sensitive data while maintaining the accuracy of the clustering results. By using homomorphic encryption to enable safe computation on encrypted data is one interesting strategy. Homomorphic encryption approach that allows addition and multiplication to be performed on encrypted data is the Paillier cryptosystem. In this study, we present an innovative approach for using the power iteration algorithm for clustering with the Paillier cryptosystem. An effective and scalable method for grouping large data sets is the power iteration clustering algorithm. We show that by using the Paillier cryptosystem, we can perform the necessary computations for the power iteration clustering algorithm on encrypted data without revealing any sensitive information. We prove our approach can achieve excellent accuracy while maintaining data privacy by evaluating it on many benchmark datasets. Our results show that the Paillier cryptosystem can be a viable solution for privacypreserving clustering applications.
Object Detection Its Progress and Principles

Locating objects in images or videos is the major task accomplished by object detection. It has widely been used in various applications related to computer vision for the identification and localization of objects. Over the years, different techniques have been used for object detection ranging from conventional methods to deep learning-based methods. The present work describes the basic methodology of object detection algorithms and gives a broad classification of the techniques utilized for object detection. The work then further sheds light on the basic principle involved behind each technique and also reviews the different studies that have been carried out by utilizing the said object detection technique, highlighting the issue being addressed and the performance.
Reduced-Complexity Convex Optimization Method of MIMO Beampattern Synthesis

The beampattern synthesis method based on convex optimization theory can obtain a high-performance beampattern. However, the computational complexity of convex optimization will increase significantly as the number of sensors increases, which presents a great challenge to the application of convex optimization in the multiple-input multiple-output (MIMO) array. This paper decomposed the weight vector into the transmit and receive weight vectors to reduce the dimension of the weight vector. On this basis, the decomposition linear fractional semidefinite relaxation (DLFSDR) algorithm was proposed. Compared with the linear fractional semidefinite relaxation (LFSDR) beampattern synthesis method, the proposed method can significantly reduce the computational complexity of beampattern synthesis for the massive MIMO arrays. Numerical simulations demonstrated the superiority of the proposed approach in terms of computational time.
Energy Management in Community Microgrid—A Techno-economic Study Framework

This study proposes an energy management framework for community microgrids to produce energy at a reduced cost with high reliability. The techno-economic benefits of the proposed solution are also investigated. A regional community with solar and wind energies is considered as a case study. The test microgrid consists of solar photovoltaic, wind turbines, a diesel generator, battery storage, and grid connection capability. Simple performance metrics are used to assess the performance of the proposed energy management framework. Finally, results are compared with and without energy management implementation. Results show that the proposed framework reduces electricity costs by up to 18.50% compared to a baseline method. Reliability analysis reveals that the proposed methods ensure continuous power supply even during grid outages. This study also finds that the proposed energy management technique reduces fuel consumption by approximately 90.25%. This work can be valuable in identifying effective solutions for supplying reliable and cost-effective power to regional and remote areas.
Bridging the Strawberry Cultivation Gap: Federated Learning CNN for Disease Detection

Leaf infections are a persistent problem in strawberry agriculture, an agricultural sector with significant economic importance. Five primary kinds of leaf diseases significantly reduce productivity and quality. To identify and classify these disorders, this study presents a novel method that combines the image classification skills of convolutional neural networks (CNNs) with the decentralized nature of federated learning. The research can capture various illness presentations by working with five customers, each representing a distinct regional and cultural context. Several performance metrics are shown for each client in the result analysis: Py_1 performs very well with an accuracy of 0.96, closely followed by Py_5 at 0.98, while Py_2,Py_3, and Py_4 record accuracies of 0.94, 0.93, and 0.93 and so on. Federated averaging is key in moving from localized models to a global viewpoint. Py_5 was the clear winner when the averaging measures were examined. Its macro, weighted, and micro averages were 94.72%, 95.14%, and 95.14%, respectively. Py_3, in sharp contrast, falls significantly behind the other two averages, with values of 83.02%, 83.45%, and 83.52%. This work provides a strong, decentralized model for classifying strawberry leaf diseases and highlights the effectiveness of federated learning in integrating various data sources. A bright future for practical applications in agricultural diagnostics is predicted by transforming local data viewpoints into an all-encompassing global model, as shown by the constant performance metrics after federated averaging.
Method of automatic formation of the underwater manipulator motion program based on noise three-dimensional models

The paper presents the method of automatic formation of the multi-link manipulator (MM) of underwater vehicle (UV) movement program using noise three-dimensional model of underwater objects obtained by their computer vision system (CVS). A feature of the proposed method is the automatic selection of a set of elementary trajectories (straight lines and circular arcs) in the trajectory of the working tool (WT) of MM laid on the surface of a three-dimensional model of an object unknown in advance. The specified allocation takes place using the RANSAC algorithm, which allows considering a large amount of noise contained in the specified model. This makes it possible to increase the accuracy of performing underwater technological operations using MM by providing more accurate stabilization of the UV during MM operation. The conducted mathematical modeling has shown that the proposed algorithm is workable and effective in conditions of high noise of three-dimensional models and can be used for automatic performing technological operations.
A 5 Class Classification of ECG Using SRCNN and CNN Using MIT-BIH Arrhythmia Dataset

Arrhythmia detection is impossible without computer-based ECG interpretation. The purpose of the study is to present a new deep-learning technique for automatic ECG categorization that can be utilized for arrhythmia identification. Using a dataset of 23 recordings that were randomly selected from a collection of 4000 samples obtained from a heterogeneous group of patients, this work constructed a Super Resolution Convolutional Neural Network to categorize ECG into 5 classes. Based on the nature of the input data and the desired output accuracy, the right classifier is chosen. These Deep Learning results contribute to an ECG classification with strong diagnostic performance comparable to that of cardiologists. The novelty of the paper mainly focuses on the employment of a Super Resolution Neural Network for the predictive classification of arrhythmia. It has been proven that this automated classification of ECG reduces misdiagnosis and provides better accuracy. The tested model provides an accuracy of 87.3%, which is comparatively high for a network just in progressive research.
PIDPipe: A Pipeline for Digitizing P&ID with Unsupervised Symbol Localization

Digitizing Piping and Instrumentation Diagrams (P&IDs) is challenging since the information is loosely structured in the document. Mostly supervised learning has been used by researchers to extract the information from P&IDs. However, having good quality labels, particularly, for localizing the symbols that require tight bounding boxes on the symbols, is difficult. In this research work, we propose a pipeline for digitizing P&IDs called PIDPipe with an unsupervised method for symbol localization. We analyze our method on a dataset of synthetic and real P&IDs. Our method achieved an accuracy of 71.3%, 72%, 52%, 74% and 81% for text detection, text recognition, symbol localization, and solid and broken line detection, respectively. This digitized information can be used towards the automated creation of Human Machine Interfaces and Digital Twins.
An Improved Architecture for Automatic People Counting in Public Transport using Deep Learning

This paper presents an architecture assembled with computer vision algorithms to perform automatic people counting passing through a portal. The system is divided in four stages: capture, detection, tracking and counting. First, the top view of the portal is obtained with an RGB camera and analyzed using a state-of-the-art deep neural network for object detection. The tracking algorithm is activated when a person is detected and automatically identifies the movement patterns. Then, the rules for entering and leaving the portal are defined in the image area to perform the counting. The architecture is tested using videos collected from public buses. Result metrics show an average precision of 96.8% and a recall of 92.0%. In addition, a comparison with a previous counting method for this scenario is presented. With the proposed approach, an F1-Score of 94.3% is obtained, in contrast with a 89.4% presented by the compared method, proving an increase in the performance of the system. Finally, the complete system is mounted in the gate of a building.
Improving Academic Advising by Predicting a Student’s GPA given their Chosen Courses

The academic advising process is very beneficial since many students are unaware of the difficulties they may face when taking certain courses or combinations of courses in the upcoming semester. Unfortunately, it is difficult for academic staff to take into account the various factors that may affect the student’s performance given their choice of courses. We believe that historical student data can assist with this task. One can use tools such as collaborative filtering to predict the performance of a student on a set of courses given the past performances of similar students as well as the past performance of the individual. An academic advisor can then use this information to provide better advice to the student to avoid course over-loading or even course under-loading. We present one such approach in which, given a set of courses for the coming semester, we predict the semester GPA of the student if they were to take these courses. Through this advice the student may then decide to reduce their course load in order to maintain a high grade point average or increase their course load to complete their degree earlier. We use data from a Computer Science department at a University and demonstrate that, using the available demographic and course grade data, one can make reasonably accurate predictions to assist the advisor.
Why *Sarah cannot glow the light bulb? Accounting for the constructional behavior of light and sound emission verbs

This article provides an in-depth lexical-constructional account of two English verbal classes, namely light and sound emission, with special emphasis on the causative-inchoative alternation, the resultative and the intransitive motion constructions. To shed light on the kinds of constructional realization for these verb classes, I will follow and build on previous taxonomic work by Levin (1993) and Faber and Mairal (1999), which will be complemented by the analytical and explanatory tools developed by the Lexical Constructional Model (Ruiz de Mendoza and Mairal, 2007, 2008; Mairal and Ruiz de Mendoza, 2008, 2009). Levin and Rappaport Hovav (1995) show that internally caused verbs can only participate in the inchoative construction (cf. The stars twinkled in the black sky, Blood gurgled in his throat) whereas externally caused verbs can occur in the causative configuration (cf. He winked the light, She jangled her car keys). Nevertheless, a serious problem for the internal cause generalization stems from the fact that intransitive light emission verbs can also express a light produced by an entity whose surface is in contact with a natural light source (cf. The jewel sparkled in the sun).