Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Mesh Simplification: A Newton-Based Approach to Quadratic Error Metrics

Mesh simplification is an important task in the fields of computer graphics and 3D model processing. However, the traditional quadratic error measurement method may lead to the loss of geometric features, abnormal topological changes, and even unstable results in the simplified model. To solve these problems, this paper proposes an improved quadratic error measurement method based on Newton's method. As an iterative optimization algorithm, Newton's method is known for its fast convergence speed and high accuracy and is particularly suitable for solving problems involving quadratic error metric matrices. This method also fully considers the influence of different simplified vertices on the mesh simplification results. By using the Euclidean distance metric, the simplified vertices with the smallest error from the original vertices are selected, thereby making the details of the simplified model closer to the original model. Experimental results show that the quadratic error measurement method based on Newton's method can more effectively retain the detailed features of the original model at the same simplified scale. What is even more encouraging is that this method performs well when handling model simplifications with different curvature characteristics, while the simplification results are more stable. This provides strong support for further research in the field of 3D model simplification.
Inter-Harmonics Identification in Frequency Spectra by the Application of Artificial Neural Networks

Harmonic analysis using the Discrete Fourier Transform is a relevant topic in electric power quality. However, such analysis may be impaired by the presence of inter-harmonics, which is ever-growing due to the insertion of power electronic converters in the grid, and causes spectral leakage; despite the efforts of the scientific community in proposing solutions for the mitigation of this problem, there is still no consensus. This work proposes a solution based on Multilayer Perceptron Artificial Neural Networks for inter-harmonic diagnosis. The solution can properly identify and estimate the frequency of inter-harmonics, and the results for synthetic signals indicate the effectiveness of the proposed technique, with a hundred percent identification rate and a less than 1.3 percent relative error.
Indirect Assessment of Hyperechogenicity of Substantia Nigra Utilizing Sleep-based Biomarkers

Transcranial sonography of the substantia nigra (TCS-SN) may serve as a suitable test for screening groups at a high risk of developing Lewy body diseases (LBDs) such as Parkinson's disease or dementia with Lewy bodies. Although one of the most prominent early markers of these neurodegenerative disorders is the idiopathic rapid eye movement (REM) sleep behavior disorder, the relationship between TCS-SN and sleep alterations has not been fully explored. The aim of this study is to investigate whether sleep-based biomarkers could be used to stratify subjects into three groups with different echogenic areas of the substantia nigra. To achieve this goal, we enrolled 93 participants who underwent TCS-SN and 7-night actigraphy. Additionally, participants completed a sleep diary and the REM sleep behavior disorder screening questionnaire. To assess the severity of pathological echogenicity, we employed a machine learning algorithm utilizing the XGBoost algorithm. The results show that a multimodal assessment of sleep was able to predict the outcomes of TCS-SN with a balanced accuracy of 96 %. Overall, these findings underscore the potential of a comprehensive approach to model the results of TCS-SN and its implications for the prodromal diagnosis of LBDs.
Handling MEG Data Corrupted with Deep Brain Stimulation Artifacts - Some Pointers

Deep brain stimulation (DBS) is a therapeutic treatment used in pathological conditions, which is known to improve clinical state, but the effect-mechanisms are unclear. The electrophysiological study of effect-mechanisms is complicated by strong artifactual components blurring the boundary between brain activity and DBS-induced noise. To study the artifactual signatures and the effect of pre-processing steps such as spatiotemporal signal space separation (tSSS) and normalization on the DBS-corrupted MEG data, we deployed a watermelon-based phantom ‘implanted’ with DBS leads and stimulated the phantom with various DBS stimulation settings. We observe that tSSS can lead to a ‘spillover’ of artifacts between sensor types and bipolar stimulation leads to lower sub-harmonic (20 Hz) artifacts compared to monopolar stimulation. Normalized spectra, we show here, can be biased if artifactual spectral bins are chosen as the normalizing factor. Alternatively, artifact-free bins can be visually chosen or an unbiased approach such as the FOOOF method can be used.
System Identification Study to Model a Digital Twin for a Heavy-Duty Vehicle's Oil System

This paper explores the application of system identification to a lubrication system found in heavy-duty diesel engines. These engines are equipped with a variable oil pump and a cooling piston jet. The objective is to establish a dynamic model that accurately captures the relationship between the duty cycle of the valves and the resulting pressure values under normal road operating conditions to be used as a digital twin of the system. Additionally, the study aims to determine whether a simple recursive model can sufficiently describe the system with enough precision. Different linear and nonlinear models were evaluated and validated to identify the best fit for the system. Ultimately, the system was described using a Hammerstein-Wiener model, resulting in an 83.86% Normalized Root Mean Squared Error (NRMSE) for main gallery pressure and 82.69% for piston cooling jet gallery pressure.
Geology reviewed for the Falkland Islands and their offshore sedimentary basins, South Atlantic Ocean

ABSTRACT The position of the Falkland Islands adjacent to the South American continental margin belies the close association of their geology with that of South Africa. A Mesoproterozoic basement is unconformably overlain by a Silurian to Devonian succession of fluvial to neritic and shallow marine, siliciclastic strata. This is disconformably succeeded by a largely Permian succession that, near its base, includes a glacigenic diamictite and, thence, passes upwards into a succession of deltaic and lacustrine strata. The lithological succession and the character of its deformation bear striking similarities to the Cape Fold Belt and Karoo retroarc foreland basin. Swarms of Early Jurassic dykes were coeval with the Karoo magmatism and the initial break-up of Gondwana; Early Cretaceous dykes were intruded during the opening of the South Atlantic Ocean. Offshore sedimentary basins surrounding the archipelago contain Late Jurassic to Palaeogene successions and are currently the focus of hydrocarbon exploration. Best known is the North Falkland Basin, a classic failed rift. To the SE, the passive margin, Falkland Plateau Basin may also be rift-controlled, whilst the South Falkland Basin is a foreland basin created at the boundary of the South American and Scotia plates. The role of the Falkland Islands during the breakup of Gondwana remains controversial. Compelling evidence from the onshore geology favours rotation of an independent microplate from an original position adjacent to the Eastern Cape, South Africa. Alternative interpretations, justified largely from offshore geology, favour extension of the Falkland Plateau as a fixed promontory from the South American margin.
Coordinated Ride-hailing Order Scheduling and Vehicle to Grid for Autonomous Electric Vehicles Based on Independent Proximal Policy Optimization

Autonomous electric vehicles (AEVs) fleet will bring about tremendous change in the future ride-hailing system, with huge potential to increase operational efficiency in transportation networks and provide vehicle-to-grid (V2G) services in power networks. However, the uncertain spatio-temporal distribution of orders increases the dispatch difficulty. The time-varying charging and discharging prices among charging stations (CSs) exert further challenges in coordinating the order demand, V2G supply, and cost-saving purposes. This paper introduces a novel scheme for coordinated ride-hailing order scheduling and V2G for AEVs through the independent proximal policy optimization (IPPO) method. The model divides the AEV fleet into multiple parts and matches them to CSs through the optimal matching scheme. Then each CS is regarded as an agent, and through multi-agent reinforcement learning, it decides to charge and discharge corresponding parts of the vehicle. The proposed model strategically enables AEVs to charge at lower prices and discharge at higher ones while considering the balance of orders and V2G services at the same time, thus lowering operational costs and obtaining higher revenue. Numerical experiments are conducted to verify the superiority of the proposed method through a New York City zone-based power-transportation coupled network. The results validate the effectiveness of the proposed framework in better financial performance of the AEV fleet with higher computational efficiency.
Rotor Electrical Fault Detection in Induction Generators Considering Low-Frequency Oscillations

The induction generator in wind turbine application is subjected to fluctuations caused by polluted grid, tower shadow, and wind speed variations. The oscillations introduced by the mentioned factors especially the low frequency ones make the fault detection in induction generator difficult. In this paper, a model-based technique using the Unscented Kalman filter is proposed for rotor electrical fault detection of a wound rotor induction generator in wind turbine applications. The proper model of induction machine, and the proper measurements and states are introduced, and the corrections need to be considered in calculations are presented. The per-unit calculation makes the algorithm setting easier than the case of using real values. Several scenarios consisting of distorted grid voltage, tower shadow effect, variable wind speed, grid-connected and islanded mode, and low speed operations are studied. The fault detection is assessed thoroughly in the aforementioned conditions by means of simulations. The experimental results prove the effectivity of the proposed technique.
Low-Cost Fish-Based Soft Robot Development for Underwater Environment

Due to the ability to perform various maritime activities flexibly and reduce human life risks, Remotely Operated Underwater Vehicles (ROVs) are increasingly used in most offshore applications. The demand for more complex missions has increased the capabilities of ROVs in their operations. Because it is a robust and rigid system, this type of robot may not be effective in certain missions where monitoring in narrow and difficult-to-access places is required. The idea is to create a more flexible and compact three-degrees-of-freedom robot. This robot can help the offshore industry, marine biologists, and researchers in marine life investigation. The reason is that the prototype is more similar to a fish, which would avoid scaring the other specimens, thus not harming the environment. Therefore, this research project intends to develop a low-cost underwater robot modeling applied to an embedded system capable of monitoring information on the seafloor. The results are still preliminary, but they already indicate the possibility of inserting the system in a test environment closer to the real one.
Artificial Bee Colony Algorithm based Segnet Model for Structural Magnetic Resonance Image Segmentation in Alzheimer’s Disease Classification

Alzheimer’s disease is a chronic and incurable brain disorder. Alzheimer’s disease is preceded clinically by mild cognitive impairment. A prominent indicator of Alzheimer’s disease that is most commonly recognized is memory loss. Alzheimer’s disease can be identified using structural magnetic resonance imaging, which analyses the hippocampus’s volume and shape. These characteristics encode a small volume of data and are likely to cause segmentation mistakes. Hence, deep learning based feature extraction and segmentation are essential to improve the performance classification of Alzheimer’s disease. This paper proposes an Artificial Bee Colony based SegNet (ABC-SN) model for image segmentation in Alzheimer’s disease classification. The proposed framework uses the OASIS Alzheimer’s image dataset database for training, testing, and validation. The proposed ABC-SN model produces improved results of 91.2% specificity, 95.6% accuracy, 95% sensitivity, and 94% F1-score. The performance analysis shows convincingly that the proposed ABC-SN performs better segmentation compared with the existing threshold based segmentation, edge based segmentation, region based segmentation, and clustering based segmentation methods.
Identification of churn affecting factors for customer satisfaction in telecommunication industry using Deep Learning

One of the key sectors in developed nations is now telecommunications. Reduce customer turnover, sometimes referred to as customer attrition, as much as possible in order to compete in the very competitive telecommunications sector. It is an essential issue and also the major uncertainties for the huge businesses. So, the organization have plan in generating methods for predicting probable customer churn due to its direct impact over their revenues especially in the telecom sectors. Companies are working to create methods to predict probable customer churn because it has a direct impact on their revenues, particularly in the telecom industry. Since consumers are their primary source of income, businesses in the business sector are seeking for strategies to keep them. The primary contribution of research is the analysis of data on customer behavior, where customer churn is a common occurrence, and the development and validation of a churn prediction model. However, because to its capacity to analyze enormous volumes of consumer data, Deep Learning (DL) is one of the modern techniques employed in churn research. Therefore, the goal of this study is to determine whether the Recency Frequency Monetary (RFM) pattern with the DL methods have been utilized in analyzing the future and existing customer churn in the telecommunication sector.
Radiation Safety in Nuclear Power Plants: ResNet-Based Glove Image Classification

Ensuring the safety of personnel working in nuclear power plants remains a paramount concern, with a primary focus on mitigating radiation exposure risks. An essential safety measure involves monitoring and verifying that employees are wearing the appropriate gloves while engaged in their tasks. This study aims to design an advanced algorithm capable of accurately identifying the glove status of personnel upon their entry into the workspace by analyzing photographs taken at the entrance. Our classification system includes six categories: false detection, indeterminate, latex glove, white glove, red glove, and no glove. To enhance data diversity and training efficacy, we employ the mixup technique in data preprocessing. Our model of choice is a 50-layer residual network, and we utilize meticulously designed training methods for optimal results. Extensive data validation demonstrates the effectiveness of our approach, achieving an impressive accuracy rate of 92.84%. This research presents a valuable tool for enhancing radiation safety measures within nuclear power plants, with the potential to significantly reduce accident risks and uphold the well-being of plant personnel.
Wrist Movements Estimation Based on Limited Training Dataset

This paper presents a novel approach to decode multi-DOF wrist movements using a single-DOF training dataset. The main machine learning model employed is the support vector regression (SVR), with R2 as the evaluation index. Through a thorough comparison of slide window lengths, model parameters, and different machine learning models, we demonstrate the effectiveness of our proposed approach. Longer slide window lengths enhance decoding accuracy but come with increased computational complexity. Fine-tuning SVR model parameters significantly improves performance. Our findings highlight the feasibility of reducing dataset size and alleviating patient burden, offering potential advancements in assistive technologies for individuals with motor disabilities.
A Comparative Study of Derivative and Fourier Magnitude Spectrum Based CNN Models for Automatic ECG Signal Quality Assessment

Resource-constrained, wearable, and continuous health-monitoring devices require automatic electrocardiogram (ECG) signal quality assessment (SQA) (ECG-SQA) to reduce false alarms and optimize energy consumption. In this paper, the ECG signals are classified as clean and noisy. The performance and computational complexity of convolutional neural network (CNN) based ECG-SQA models are compared for the derivative of ECG (dECG) and Fourier magnitude spectrum of ECG as input. We have evaluated 19 dECG-based and 16 Fourier magnitude spectrum-ECG-based CNNs to find the optimal CNN for ECG-SQA. The CNNs are analyzed for kernel sizes 3 × 1, 4×1, 5 × 1, and 6×1; convolution layers 2, 3, and 4; dense layers 3, 4, and 5; exponential linear unit (ELU) activation function. The proposed CNN-based SQA method with Fourier magnitude spectrum-based ECG-SQA is lightweight and less computationally complex, with a model size of 852 kB and 67,697 parameters. The robustness of the CNN-based ECG-SQA method is tested with two unseen datasets, such as the PhysioNet/Computing in Cardiology Challenge 2011 (PCCC2011) database and the St. Petersburg Institute of Cardiological Technics (INCART) 12-lead arrhythmia database. The method achieved the sensitivity of 99.30% and 98.41%, respectively, and specificity of 95.40% and 99.3%, respectively, with unseen PCCC2011 and INCART datasets. For the assessment of 5 s ECG signal, the proposed method has a processing time of 62.6±12.6 ms.
Statistical Analysis of Least Mean Modulus Algorithm for Non-Gaussian Noise

This paper develops a statistical analysis of adaptive filters using Least Mean Modulus Algorithm (LMMA) for a Gaussian regressor and non-Gaussian observation noise. In the analysis, we propose using the characteristic function for the probability distribution of the non-Gaussian noise, to derive difference equations for calculating theoretical transient behavior of the filter convergence. Numerical experiments are carried out to examine the accuracy of the analysis, where simulations and theoretical calculations of filter convergence for the non-Gaussian noise are compared with those for the Gaussian noise. Good agreement is observed between simulation and theory, proving the validity of the proposed method of analysis.
Facial Recognition System using Convolutional Neural Networks (CNN)

Facial recognition technology is fast developing and has the potential to transform a variety of industries. The current study discusses the technology, techniques, and development of automatic face recognition. Also, the various Face Detection and Facial Recognition technologies, along with their application in how they are used in various sectors, are discussed in this paper. In the current study, we have experimented with machine learning techniques to detect faces and their emotional states on the Kaggle dataset. The experimental accuracy of 64.3 % is achieved using the CNN model.
Rôle des intéractions virus/ pesticides dans le déclin des abeilles

Les abeilles domestiques subissent des pertes de colonies sans precedents, ce qui pourrait a terme avoir un fort impact economique compte tenu de leur apport a l’agriculture en service de pollinisation. Ces pertes sont definies comme causees par des stress multifactoriels. Cependant, l’impact de ces stress est en general teste separement sur les individus ou les colonies. Les colonies sont connues pour concentrer les pesticides provenant de l’environnement a travers le pollen et le nectar recoltes mais aussi les pathogenes. L’etude de la co-exposition entre le thiamethoxam, un insecticide neonicotinoide, et deux virus de l’abeille, le DWV et le CBPV, a mis en evidence l’existence d’interactions entre chacun de ces virus et le pesticide. Diverses methodes de transmission des virus ont ete testees afin de s’approcher au mieux des conditions naturelles et une methode de transmission du CBPV par contact repetable a ete developpee. Une cinetique de metabolisation du thiamethoxam a ete effectuee pour la premiere fois, renseignant sur la forte probabilite d’excretion du metabolite en conditions naturelles. Lors d’experimentations en cagettes la co-exposition entre le CBPV et le thiamethoxam a fortes doses chroniques a cause des mortalites synergiques, ou une augmentation des charges virales atteignant le seuil en nombre de copies virales connu pour declencher des infections apparentes chez les abeilles. Les differences de resultats entre des abeilles provenant de colonies dans diverse conditions a souligne un effet de tolerance aux stress encore non etudie chez l’abeille domestique. En parallele, la co-exposition au sein de la colonie entre le thiamethoxam a une dose subletale aigue et le DWV a cause des premieres sorties de butinage extremement precoces chez les abeilles, ainsi que de fortes proportions de premiers vols sans retour, ce qui pourrait a terme avoir un effet devastateur sur les colonies. L’etude de la transcription de genes suggere que les effets obtenus sur les charges virales en CBPV seraient dus a un effet du thiamethoxam sur dorsal-1a, un facteur de l’immunite, et les sorties precoces a une baisse significative de la transcription de la vittelogenine, entrant en jeu dans les mecanismes de vieillissement, due au DWV. Le developpement de clones moleculaires du DWV a ete amorce, ce qui pourra a terme permettre des etudes de genetique inverse sur ce virus, qui pourront expliquer ses modes de transmission et d’infections encore inconnus a ce jour.
Error-Correcting Codes and the Power of Machine Learning: Enhancing Data Reliability

Error-correcting codes and machine learning algorithms are two fundamental pillars in ensuring the reliability and accuracy of data transmission and storage systems. Error-correcting codes have long been instrumental in detecting and correcting errors during data communication and retrieval, while machine learning techniques have emerged as powerful tools for data analysis and pattern recognition. Integrating machine learning into error correction processes presents exciting opportunities to enhance error detection and correction performance, especially in complex and dynamic environments. In this article, we provide an in-depth overview of error-correcting codes, including traditional techniques like Hamming codes, Reed-Solomon codes, Turbo codes, and Convolutional codes. We explore the challenges faced by traditional error correction methods in handling modern data complexities. Subsequently, we delve into the integration of machine learning algorithms, such as neural networks, support vector machines, and decision trees, for error correction tasks. We discuss how machine learning enables adaptive, efficient, and dynamic error correction strategies. Furthermore, we examine real-world applications of machine learning-based error correction in communication systems, data storage, DNA sequencing, and quantum computing. Additionally, we highlight the challenges that researchers and engineers need to overcome, such as computational complexity, real-time processing, and adversarial attacks. Ultimately, the collaboration between error-correcting codes and machine learning offers exciting prospects for a future where data transmission and storage systems are more secure, efficient, and adaptable. By addressing challenges and embracing innovations, we can pave the way for error-free data management in an ever-evolving data-driven world.