Miracle-dz/UniFAR-FTUs huggingface dataset:
Genesis comun de los yacimientos estratoligados y vetiformes de cobre del Jurasico Medio a Superior en la Cordillera de la Costa, Region de Antofagasta, Chile.

RESUMEN:Las rocas volcanicas basaltico-andesiticas de la Formacion La Negra (ca. 180 Ma) y las rocas plutonicas (160-140 Ma) de composicion gabrica a granitica, que las intruyen, hospedan depositos de Cu con contenidos variables de Ag, Au y Fe. Los yacimientos estratoligados de Cu± Ag, que incluyen tanto depositos estratiformes como chimeneas de brechas se hospedan preferentemente en la secuencia volcanica, mientras que los yacimientos vetiformes de Cu-Fe± Au (calcopirita-magnetita-actinolita) se restringen a los cuerpos intrusivos. Las relaciones espaciales y temporales entre ambos tipos de depositos sugieren un origen hidrotermal comun, geneticamente relacionado con los granitoides del Jurasico Medio a Superior. Esto es consistente con la composicion isotopica de los minerales de mena/ganga (d34SCDT=3,5 a 2‰; d13CPDB=3,9‰; d 18OSMOW=14‰) que revelan un origen magmatico para las soluciones hidrotermales involucradas en su genesis. La razon inicial de 87Sr/86Sr (0,70435) de las soluciones hidrotermales, inferidas de la composicion isotopica de la calcita, es ligeramente superior que la misma razon en los granitoides (0,70329-0,70387) con los cuales los depositos se relacionan geneticamente. Esto sugiere una interaccion de las soluciones hidrotermales magmaticas con fluidos de razones 87Sr/86Sr mas altas, por ejemplo aguas meteoricas equilibradas isotopicamente con rocas corticales mas antiguas. Un reservorio orogenico comun para el Pb de la mena es indicado por la composicion isotopica del Pb de los sulfuros (206Pb/204Pb 18,529; 207Pb/204Pb 15,630; 208Pb/204Pb 38,526). Los yacimientos vetiformes fueron formados, en la zona plutonica, de mas alta temperatura, a partir de soluciones magmaticas en equilibrio con magnetita y condiciones de pH neutro a ligeramente alcalino, como lo indica la presencia de magnetita asociada a los minerales de mena y albita o feldespato potasico entre los minerales de alteracion. Estas soluciones migraron hacia zonas mas superficiales y condiciones mas oxidantes, con hematita estable y menor temperatura, para dar origen a los depositos estratoligados alojados en las rocas volcanicas.
Thermal Stress Effect on 3D Integrated Circuit Performance and Reliability

The technology of the through silicon vias (TSVs) plays a significant role in 3D integrated circuits (3D ICs). TSVs have the ability to vertically facilitate enormous and short interconnects between stacked silicon dies to create 3D integrated circuits; thus, increasing their value and functionality. The fabrication of TSVs undergoes several stages and can be performed in different ways. However, the mismatch of the coefficient thermal expansion (CTE) between the material of TSV and silicon substrates has resulted in the accumulation of crack driving force and tensile hoop stress in the silicon substrate. In this paper, thermal stress, its effect on the 3D IC devices, and the solutions proposed in several studies to increase performance and reliability of 3D ICs were examined.
Advanced Fault Diagnosis in Rotating Machines Using 2D Grayscale Images with Improved Deep Convolutional Neural Networks

This paper presents a Deep Convolutional Neural Network (DCNN) model with a SoftMax classifier for the diagnosis of bearing and gear faults based on 2D grayscale images converted from raw vibration signals. The proposed model is validated and verified using a vibration dataset consisting of gear and bearing vibrations recorded under different operating conditions and fault conditions. The experimental results demonstrate that the DCNN achieves remarkable accuracies for both bearing fault diagnosis under different operating conditions and fault conditions, with high validation accuracy achieved. Furthermore, the performance of the proposed model is evaluated and compared with existing models in the literature for the same datasets, and it is concluded that the proposed DCNN is more efficient in terms of fault diagnosis accuracy, demonstrating its superiority. Therefore, the proposed high-accuracy DCNN can be an effective and accurate tool for fault diagnosis in various industrial applications.
Specific Target Contour Extraction Method for Color Images Based on Priori Information-Assisted Dual-Threshold Binary Segmentation

To meet the need for attacking against specific targets, the patrol missile should have some target identification capability to facilitate comparison and targeting of the plotted target contour with the on-board data. For the problem of recognition and capture of specific targets, based on computer vision technology, this paper proposes a method for extracting target contours of color images based on priori information assisted dual-threshold binary segmentation with the ability to recognize and extract specific target contours. Through the experiments conducted on this contour extraction method in natural and artificial environments, it is proved that the method has high accuracy and applicability for the contour extraction of specific targets, and can provide an identification method for patrol missiles to attack specific targets.
Double Branch Model Based on Discrete Wavelet Transform for Spatiotemporal Prediction

In recent years, spatiotemporal sequence prediction has received increasing attention from researchers and has a wide range of promising applications in the fields of meteorology, traffic flow prediction, and autonomous driving. However, existing spatiotemporal sequence prediction models have some problems, such as slow convergence, training difficulties, and loss of image structural and detail information. We propose a novel end-to-end two-branch spatiotemporal sequence prediction model, which has been improved on these issues. We have compared our model with current advanced models using two datasets and found that our model reached or exceeded the level of the other advanced models in several metrics.
Early Detection of Lung Disease using Multi-Class Classifiers

Lung diseases affect a significant portion of the global population. These can be potentially fatal and, if not treated right away, can have a negative impact on one's health. Early detection and prompt treatment are essential because all such diseases generate different changes in the respiratory tract and lungs. Hence, the audio of breathing sounds is quite helpful in determining the disease. The presented research work aims to predict a disease using multi-class classification, considering 5 classes at a time (i.e., Healthy, Asthma, Chronic Obstructive Pulmonary Disease (COPD), Lung fibrosis, Pneumonia). In the conducted experiment, five optimized machine learning classifiers, such as Decision Tree, Naive Bayes, Support-Vector Machines (SVM), K-Nearest Neighbors (KNN), and Discriminant, were applied to the extracted Gammatone Cepstral Coefficient (GTCC), Mel Frequency Cepstral Coefficient (MFCC), and Baseline spectral features, where the KNN classifier with all three features outperformed other classifiers by providing the highest accuracy of 99.81%.
Measuring and Evaluating Frameworks for IT Service Quality in the IT Industry: A Comparative Study

Measuring and evaluating IT service quality is essential for ensuring customer satisfaction and improving organizational performance in the IT industry. This paper presents a systematic literature review comparing the most popular frameworks for measuring and evaluating IT service quality, including ITIL, COBIT, SERVQUAL, and E-SERVQUAL. The study used a systematic approach to identify and analyze 20 articles that compared the effectiveness of these frameworks for measuring and evaluating IT service quality. The research findings show that all four frameworks effectively measure and evaluate IT service quality, but each has its strengths and weaknesses. ITIL was the most comprehensive framework, and COBIT provided a broader focus on IT governance. SERVQUAL is a well-known framework used in the service industry to assess customer perceptions of service quality. E-SERVQUAL is an extension of SERVQUAL that focuses specifically on electronic services. The study provides insights into the effectiveness of these frameworks for IT service quality management and offers recommendations for selecting the appropriate framework based on organizational needs and goals.
Diabetes Prediction Using Supervised Machine Learning Models

Diabetes is regarded as one of the most chronic illness that raises blood sugar level. Versatile disorders including kidney failure, variation in blood pressure, eye defectiveness, heart problems and other major organ failures are directly related to undiagnosed/untreated diabetes. Early identification of diabetes assisted by machine learning techniques shall provide definite health tracking even before the organ failures. The present study focuses on creation of a machine-learning model that can predict a patient's chances of developing diabetes, with the highest degree of accuracy. Two machine learning classification methods: K-Nearest Neighbors (KNN) and Logistic Regression (LR) have been proposed in this study for Pima Indians Diabetes Dataset (PIDD), to identify the diabetes at an early stage with greater accuracy. The effectiveness of the proposed algorithms were assessed using various metrices including confusion matrix, recall, precision, accuracy, and F1-measure. Accuracy is evaluated in a precise way after obtaining two distinct dataset: valid and erroneous. Results obtained through LR surpasses other algorithms with the maximum accuracy of 84.89%.
A new Ad-Hoc parallel file system for HPC environments based on the Expand parallel file system

An Ad-Hoc File System dynamically virtualizes storage on compute nodes into a fast storage volume to reduce congestion on parallel file systems used as backends in HPC environments and improve data locality. This paper presents Expand Ad-Hoc, a version of the Expand parallel file system, for use as an Ad-Hoc storage system for HPC environments. Such an update seeks to take better advantage of new storage technologies (on SSDs local to nodes, for example) and to adapt to new demands of current parallel applications (e.g., optimizing data access by analyzing data locality). The paper describes the new system’s features and presents a first evaluation comparing the performance of Expand with another Ad-Hoc File System (GekkoFS), and GPFS. The first results are quite satisfactory and demonstrate the good performance of Expand as an Ad-Hoc storage system.
Automated Audio Time Alignment for Multi-Microphone Setups: An Open-Source Approach

This study investigates audio time alignment in postproduction, examining the impact of phase shifts between signals from multiple microphones. We quantify these effects and evaluate if post-production temporal realignment can enhance audio quality. Two open-source tools were developed, using MATLAB and a real-time VST plugin in C++, to address comb filtering issues by detecting and realigning signal delays. A MUSHRA listening test provided qualitative feedback on audio preference and the effectiveness of temporal realignment.
Estimation of Tree Biomass, Carbon Stocks, and Error Propagation in Mecrusse Woodlands

We performed a biomass inventory using two-phase sampling to estimate biomass and carbon stocks for mecrusse woodlands and to quantify errors in the estimates. The first sampling phase involved measurement of auxiliary variables of living Androstachys johnsonii trees; in the second phase, we performed destructive biomass measurements on a randomly selected subset of trees from the first phase. The second-phase data were used to fit regression models to estimate below and aboveground biomass. These models were then applied to the first-phase data to estimate biomass stock. The estimated forest biomass and carbon stocks were 167.05 and 82.73 Mg·ha −1 , respectively. The percent error resulting from plot selection and allometric equations for whole tree biomass stock was 4.55% and 1.53%, respectively, yielding a total error of 4.80%. Among in- dividual variables in the first sampling phase, diameter at breast height (DBH) measurement was the largest source of error, and tree-height estimates contributed substantially to the error. Al- most none of the error was attributable to plot variability. For the second sampling phase, DBH measurements were the largest source of error, followed by height measurements and stem-wood density estimates. Of the total error (as total variance) of the sampling process, 90% was attrib- uted to plot selection and 10% to the allometric biomass model. The total error of our measure- ments was very low, which indicated that the two-phase sampling approach and sample size were effective for capturing and predicting biomass of this forest type.
Discovering Object-Centric Process Simulation Models

Process simulation assesses the impact of changing environmental parameters on a process. To obtain realistic simulation models, process mining techniques can be deployed for a log-based discovery. Such discovery techniques usually rely on a fixed case notion, falling short in capturing the entangled nature of real organizational processes as an interplay of objects and subprocesses. Yet there is a need for such methods, given the requirement for information systems to foresee and adapt to changing environments in an online setting and in a holistic manner. In this work, we approach this research need by elaborating a method for simulation model discovery that is based on the object-centricity paradigm. To implement object-centric simulation, some intrinsic challenges have to be overcome. These include, first, the parametrizable generation of sets of objects having predefined interrelations that structure possible behavior. Second, the generated objects have to be synchronized and routed through a control-flow model. We outline these challenges, describe our solution approach, and evaluate the quality of both object generation and behavior.
Incorporating Eye-Tracking and Facial Expression Recognition for Preference Prediction in Recommender Systems

Human behavior contains significant information that can greatly contribute to improving the way we interact with computer systems. Getting a better understanding of a user during their interaction with a computer system will greatly improve usability and user satisfaction. Current recommender systems lack such human behavioral indicators. Adding these indicators can be a valuable factor in improving typical recommender systems. This paper proposed a framework that incorporates behavioral indicators in recommender systems to predict user preference. The behavioral indicators chosen for this framework are eye-tracking and emotion recognition through facial expressions.
Gypsum solubility in seawater, and its application to bauxite residue amelioration

The solubilities and dissolution rates of three gypsum sources (analytical grade (AG), phosphogypsum (PG) and mined gypsum (MG)) with six MG size fractions ((mm) > 2.0, 1.0-2.0, 0.5-1.0, 0.25-0.5, 0.125-0.25, and PG > MG) due to reactivity and surface area differences, with 1:20 gypsum:solution suspensions reaching saturation within 15 s (AR) to 30 min (MG > 2.0). The ability of bauxite residue to adsorb Ca from solution was also examined. The quantity of the total solution Ca adsorbed was found to be small (5 %). These low rates of solution Ca adsorption combined with the comparatively rapid dissolution rates preclude the application of gypsum to the residue sand/seawater slurry as a method for residue amelioration. Instead, direct field application to the residue would ensure more efficient gypsum use. In addition, the formation of a sparingly soluble CaCO3 coating around the gypsum particles after mixing in a highly alkaline seawater/supernatant liquor (SNL) solution greatly reduced the rate of gypsum dissolution.
Generation of Octagonal Pyramids on Silicon Wafer for Photovoltaics by Swift Anisotropic Chemical Etching Process

A novel octagonal upright micro-pyramid structure was generated by wet chemical anisotropic etching on a monocrystalline silicon wafer (100). The primary objectives are to reduce front surface reflectance of silicon wafers, improve wettability, enhance surface morphology, and maximize the area coverage by generated octagonal pyramids. Under rigorous control and observation, the etching process’ response time was maintained precisely. The experimental outcomes show a significant decrease in the optical surface reflectance of silicon wafers, with the lowest reflectance of 8.98%, as well as enhanced surface structure, periodicity, and surface area coverage of more than 85%. The octagonal silicon pyramid was formed with a high etch rate of 0.475 um/min and a much shorter reaction time with the addition of hydrofluoric acid coupled with magnetic stirring (mechanical agitation) at 500 rpm.
Performance Assessment of Jaw Muscle Electromyography in the Detection of Temporomandibular Joint Disorder

Temporomandibular Joint Disorder (TMD), one of the most chronic muscular disorders affecting adults and adolescents whose signs and symptoms are complex. It is estimated that about 60% to 70% of the population has at least one symptom. Although it is prevalent in the general population, females are more prone to this condition. A simple electromyography test is commonly used to visualize and diagnose jaw muscles disorders such as TMD. In this study, the researchers used the wavelet decomposition technique to denoise the surface electromyographic (EMG) signal and extract the various features of the same. They were able to use the classification models to extract the results. After testing the model optimization features, the output values indicated when it came to TMD detection, the Adaboost model outperformed the other techniques. The null hypothesis is disproved by data analysis, demonstrating that there is a significant difference between the EMG activity of the control and study groups.
Elemental Images Labelling and Grouping to Minimise Disparity Error in Texture-less Regions of Holoscopic Images

Holoscopic imaging is a promising technique that captures full-colour spatial images using a single aperture. It uses a micro-lens array to view the scene at different angles and record 4D information on a two-dimensional surface, making it useful for depth estimation. However, current disparity estimation methods suffer from poor performance in texture-less regions. This paper proposes a novel method to reduce the disparity error in these regions by directly labelling and grouping elemental images from a Holoscopic image. The proposed approach involves extracting a subset of viewpoint images from the Holoscopic image and subjecting them to conventional image segmentation. Labels are then applied to the elemental images corresponding to each segmented object using viewpoint images/elemental image pixels mapping. Content-based image retrieval is also employed to improve segmentation. The proposed technique has wide applications for 3D imaging, including augmented and virtual reality, inspection, robotics, security, and entertainment.
Automated Tongue Image Segmentation in Tongue Diagnosis

In tongue diagnosis, it is difficult to maintain patient’s tongue extended for a little bit long time, especially for pediatric patients. Fast tongue image capture and automated segmentation is the crucial procedures in the tongue diagnosis system. Its accuracy affects the subsequent automatic process directly. In the paper, an automated tongue image segmentation method was proposed. One of our main contributions is the combination of the GVF (gradient vector flow) algorithm and the ellipse shape based on the least-squares ellipse fitting algorithm as seed points; and the other is the convergence analysis for proposed algorithm combined with the transform matrix algorithm was performed. Before segmentation, a face mask was proposed based on Anil K. Jain’s theory, and the initial tongue edge was extracted with log-polar transform, as a preprocessing. The experiment results indicate that our method speeds up contour convergence, ensures correctness, reduces the system’s operating expenses, and enhances the algorithm usability. This research focused on tongue diagnosis involved in image segmentation and color correction.
Fast 3D Reconstruction of Power Transmission Lines Based on Sequential Images

The 3D reconstruction can visualize the inspection scene of power transmission lines (PTLs), providing data support for PTL intelligent inspection. To address the low-efficiency and large-computation of the 3D reconstruction methods for PTL, this paper proposes an efficient reconstruction method based on the sequential images of PTLs. Firstly, collect multi-view sequential images of PTLs by the Flying-walking Power Line Inspection Robot (FPLIR). Secondly, obtain the initial sparse point clouds data by the Structure from Motion (SFM) algorithm. Thirdly, remove the redundant point clouds based on the camera trajectories to acquire the point clouds data of PTLs. Lastly, obtain the dense point clouds of PTLs data and spatial surface model by the Multiple View Stereo (MVS) algorithm. The experimental results show that our proposed method allows processing 70 images within 48 minutes, obtaining the PTL model with a mean reprojection error of about 1.2 pixels. The proposed method provides the foundation for a refined and intelligent inspection of PTL.
Strategic Prosumer-Side Energy Trading Using A Parameter Independent Convex Model: From A Discussion Toward A Case Study

Energy trading in local energy communities and markets has been receiving high attention in recent years owing to the growth of renewables and distributed resources in the prosumer-sides. To trade energy by prosumers, an energy management system (EMS) is required to not only maximize the profit but also coordinate internal resources. In this paper, the existing none-convex models for EMS are evaluated with a focus on maximizing the profit of trading. It is clarified that the existing models for EMS are not appropriate to guarantee optimality and use in practice. In this regard, a practical and convex model is addressed to enable scalability, while ensuring optimality. The proposed model is tested and compared to other existing models.
Application of Data Security Models Based on Neural Network Algorithms in Optimizing Social Network Order

Internet data is changing government management, business operations, public services, and people's lives, and driving social change. From the perspective of social management, the Internet data era has brought opportunities for innovation and development, but also serious challenges. This paper aimed to study how to use neural network algorithms. This paper analyzed and designed a data security evaluation model for the Internet, and improved the Back Propagation (BP) algorithm. This article compared and analyzed the BP algorithm and the improved BP algorithm in experiments, starting with the accuracy and time consumption of network data security evaluation. The data shows that when the number of test samples was 400, the accuracy rates of the two algorithms were 90.89% and 99.18%, respectively, and the consumption time was 3.21s and 1.56s, respectively. It is not difficult to see that the improved BP algorithm plays a more excellent performance in the network data security evaluation model.