Miracle-dz/UniFAR-FTUs huggingface dataset:
A Solar Panel Quadrilateral Feature Detection and Positioning Method for Non-cooperative Space Target

Non-cooperative target position and attitude estimation are essential technologies for the effective implementation of space target operations. However non-cooperative target lacks prior information and information interaction and require certain real-time performance with hardware limitations in space target operation. This presents significant challenges in accurately estimation the pose of non-cooperative targets so the model-based method and deep learning have certain limitations. To address the challenges in real scenarios, we utilize YOLOv7 for preliminary object detection and propose the traditional feature-based methods to tackle the important task of identifying essential characteristics for determining the position and attitude of non-cooperative spacecraft. A feature extraction method specifically tailored for spacecraft solar panels commonly observed is proposed based on line detection and clustering and the combination of length and angle measure functions. The quadrilateral structure closest to the solar panel can be extracted by the measure function. The physical simulation platform is built and based on the experimental results, YOLOv7 demonstrates good accuracy in the detection of solar panels with low real-time, the proposed solar panel position method effectively extracts and locates the features of the solar panel with minimal pixel error. It possesses a certain degree of robustness and real-time performance that can facilitate the subsequent estimation of position and attitude.
Comparison of Matrix-Rotor Induction Motor and Permanent Magnet Machine for Low-Speed High-Torque Applications

In recent years, rare-earth free machines have gained increased attention in low-speed high-torque (LSHT) applications considering rare-earth scarcity and price fluctuations. In this sense, matrix-rotor induction machines (MRIM) have shown promising results, but their suitability and competitivity when compared with the prominent permanent magnet synchronous machines (PMSM) have not been disclosed so far. This paper provides an electromagnetic analysis of MRIMs by means of 3D finite element simulations, comparing their performance to a PMSM in LSHT applications. Rated torque, efficiency and power factor of these machines are assessed and compared.
The role and responsibility of man in the evolution of the Italian Adriatic coast

Une grande partie des 1250 kilometres de la cote adriatique italienne est affectee par une erosion drastique. L'activite humaine, comme l'agriculture, la construction de routes et de voies ferrees, les interventions dans les bassins hydrographiques, l'extraction de sediments dans les rivieres et la construction de barrages a diverses fins, ont radicalement change les conditions naturelles de transport des sediments par les fleuves. La preuve en est fournie par l'evolution des embouchures qui au debut du siecle avancaient dans la mer et montrent aujourd'hui, dans la plupart des cas, des signes inquietants d'erosion. Au cours des cinquante dernieres annees, ces facteurs anthropiques ont provoque une erosion acceleree de la cote et la mise en place de phenomenes irreversibles ou, pour le moins, difficiles a corriger. Sur la cote adriatique, le phenomene est particulierement marque et inquietant, resultat d'un important developpement de l'urbanisation qui aujourd'hui couvre plus de la moitie du littoral. Pour contrer cette tendance, de vastes structures defensives ont ete construites, principalement des brise-lames et des epis. Elles ont dramatiquement altere et rigidifie la dynamique hydrosedimentaire de la cote.
Predicting the Use Behavior on Conversational Artificial Intelligence Enabled Online Shopping in Small and Medium Enterprises: A Structural Equation Modeling Approach

Conversational artificial intelligence (CA) for online shopping platforms supports in achieving greater customer satisfaction and experience. However, this emerging phenomenon still needs to be explored in developing countries like the Philippines, given limited empirical studies. Thus, this study aims to fill this gap by providing an empirical understanding of the use behavior of conversational agents for e-commerce by involving customers of small and medium enterprises using CA-enabled online shopping. Through a survey collected among small and medium enterprises, the results show that perceived ease of use and perceived usefulness affect attitude. However, trust, enjoyment, and personal innovativeness did not impact purchase intention. Consequent results show that purchase intention positively affects the use behavior of conversational agents in e-commerce platforms SMEs use. Overall, this study indicates the positive potential role of conversational agents in online shopping for SME customers. This study presents insightful implications and recommendations to further conversational agents for e-commerce in small and medium enterprises.
Evaluating the Quality of Soft Computing in a Fog Computing Environment

The article deals with the problem of assessing the quality of soft computing on the example of solving the problem of processing video images. The factors influencing the accuracy of event detection based on video information analysis and the most popular algorithms used to determine them are indicated. The problem of comparing the results of experiments in quantitative terms (times, percentages) is indicated, which arises due to the heterogeneity of architectures, algorithms and means of implementing soft computing. A solution method is proposed. A system for evaluating the quality of soft computing has been developed and the effectiveness of its application has been proven on the example of processing video images from cameras installed in the subway. The quality of calculations was evaluated based on the accuracy of event detection and the speed of information processing. It was found that the data processing speed indicators for various soft computing methods differ by ±1.2 times, the accuracy values vary in the range of ±11.7%. The proposed method can be applied in other areas where it is required to evaluate the quality of soft computing, regardless of architectures, algorithms, and development tools.
Controls on the distribution of deep‐sea sediments

Deep‐sea sediments represent the largest geological deposit on Earth and provide a record of our planet's response to conditions at the sea surface from where the bulk of material originates. We use a machine learning method to analyze how the distribution of 14,400 deep‐sea sediment sample lithologies is connected to bathymetry and surface oceanographic parameters. Our probabilistic Gaussian process classifier shows that the geographic occurrence of five major lithologies in the world's ocean can be predicted using just three parameters. Sea‐surface salinity and temperature provide a major control for the growth and composition of plankton and specific ranges are also associated with the influx of non‐aerosol terrigenous material into the ocean, while bathymetry is an important parameter for discriminating the occurrence of calcareous sediment, clay and coarse lithogenous sediment from each other. We find that calcareous and siliceous oozes are not linked to high surface productivity. Diatom and radiolarian oozes are associated with low salinities at the surface but with discrete ranges of temperatures, reflecting the diversity of planktonic species in different climatic zones. Biosiliceous sediments cannot be used to infer paleodepth, but are good indicators of paleotemperature and paleosalinity. Our analysis provides a new framework for constraining paleosurface ocean environments from the geological record of deep‐sea sediments. It shows that small shifts in salinity and temperature significantly affect the lithology of seafloor sediment. As deep‐sea sediments represent the largest carbon sink on Earth these shifts need to be considered in the context of global ocean warming.
Optical Imaging Demonstrates Tissue-Specific Metabolic Perturbations in Mblac1 Knockout Mice

Objective: Metabolic changes have been extensively documented in neurodegenerative brain disorders, including Parkinson’s disease and Alzheimer’s disease (AD). Mutations in the C. elegans swip-10 gene result in dopamine (DA) dependent motor dysfunction accompanied by DA neuron degeneration. Recently, the putative human ortholog of swip-10 (MBLAC1) was implicated as a risk factor in AD, a disorder that, like PD, has been associated with mitochondrial dysfunction. Interestingly, the AD risk associated with MBLAC1 arises in subjects with cardiovascular morbidity, suggesting a broader functional insult arising from reduced MBLAC1 protein expression and one possibly linked to metabolic alterations. Methods: Our current studies, utilizing Mblac1 knockout (KO) mice, seek to determine whether mitochondrial respiration is affected in the peripheral tissues of these mice. We quantified the levels of mitochondrial coenzymes, NADH, FAD, and their redox ratio (NADH/FAD, RR) in livers and kidneys of wild-type (WT) mice and their homozygous KO littermates of males and females, using 3D optical cryo-imaging. Results: Compared to WT, the RR of livers from KO mice was significantly reduced, without an apparent sex effect, driven predominantly by significantly lower NADH levels. In contrast, no genotype and sex differences were observed in kidney samples. Serum analyses of WT and KO mice revealed significantly elevated glucose levels in young and aged KO adults and diminished cholesterol levels in the aged KOs, consistent with liver dysfunction. Discussion/Conclusion: As seen with C. elegans swip-10 mutants, loss of MBLAC1 protein results in metabolic changes that are not restricted to neural cells and are consistent with the presence of peripheral comorbidities accompanying neurodegenerative disease in cases where MBLAC1 expression changes impact risk.
Optimized Ensemble Machine Learning Model for Chronic Kidney Disease Prediction

The prevalence of chronic kidney illness has been increasing by a rate of 41.5 over the past few years, posing a significant challenge for healthcare systems worldwide. The disease is characterized by a gradual loss of kidney function over time, resulting in the accumulation of waste products and fluids in the body. Early detection and management of CKD are crucial for improving patient outcomes and reducing healthcare costs. Machine learning (ML) techniques have been increasingly employed for predicting the risk of CKD and developing effective screening tools. In this study, an ensemble ML model for CKD forecast is projected, which attained an accurateness of 96.5% by utilizing the Kaggle-based chronic kidney disease dataset. The model was constructed using two algorithms, random forest, and bagging, which were trained on a large dataset of CKD patients' clinical and demographic information. The ensemble model demonstrated superior performance compared to each algorithm separately, indicating the efficacy of the ensemble approach in enhancing predictive accuracy. The results of this research indicate that the suggested ensemble model could be a valuable tool for early identification and management of CKD, thus reducing the burden of CKD on individuals and healthcare systems.
Adaptive EfficientNet-DeeplabV3+ for Left Ventricular Wall Segmentation

Myocardial infarction (MI) is usually diagnosed by various methods, such as cardiac imaging modalities, by observing the structure and movement of the left ventricular (LV) wall. However, left ventricular ultrasound images often suffer from noise and poor image quality. This challenge can adapt segmentation methods on medical images to help health professionals observe the LV wall more clearly. This research segmented the LV wall using a deep learning architecture that adaptively adjusts the width and depth of the network based on the input resolution, called Adaptive EfficientNet-DeepLabV3+. The modification involves replacing the DeepLabV3+encoder with EfficientNet and incorporating a scaling function to adjust the depth and width of the network based on a given resolution, using scaling coefficients in a scaling function that increases or decreases these parameters equally. To evaluate its performance, it compares the architecture's adaptability with a standard DeepLabV3+model, using publicly available datasets provided by Hamad Medical Corporation, Qatar University, and Tampere University (HMC-QU) with variations in input resolution. The test results show that Adaptive EfficientNet-Deep-LabV3+ can adaptively adjust the depth and width of the network, as indicated by changes in the number of model parameters based on the input resolution while maintaining high accuracy, reaching 0.99225, and achieving an intersection over onion (IOU) of 0.80908 on test data.
Sperm Classification of X and Y Chromosomes Based on Lazy Learning Using K-NN

Sperm cells can be differentiated into 2 based on their sex chromosomes. Sperm cells for male have Y chromosome, while sperm cells for female have an X chromosome. To distinguish between X and Y chromosomes can be seen from the head size of the sperm cell which is X chromosome sperm cells have a larger head size than Y chromosome sperm cells. Therefore, the lazy learning method is used in the k-nearest neighbor algorithm to classify X chromosome sperm cells and Y chromosome sperm cells. So, feature extraction process is needed to obtain the characteristics of the sperm object that can distinguish X chromosome sperm cells and Y chromosome sperm cells. The features are length and width of sperm head in form of an image to determine the head size in each sperm data.
Top-View Person Re-Identification

Although considerable progress has been made in person Re-IDentification (ReID), challenging problems remain. With the growing importance of personal privacy and security, a new task called top-view person ReID is introduced, which contains particularly challenging viewpoint variations and limited discriminative information. Compared to general person ReID, we can obtain the capture angle of a top-view person image through point cloud and head-shoulder detection from deployed binocular cameras. Existing person ReID methods mainly focus on learning view-invariant feature representations, but they are not effective for top-view person ReID. To address this problem, we propose a novel approach that learns angle-related features through angular metric learning. To achieve this goal, we incorporate angular information by encoding the input capture angle in an embedding. In addition, we propose a novel angular metric loss to monitor the effect of viewpoint variations. To facilitate research in top-view person ReID, we contribute a top-view person dataset called TVReID, which contains 9168 top-view images from 148 identities. Extensive experimental results demonstrate the effectiveness of the proposed method, which achieves state-of-the-art performance on TVReID.
Predicting Customer Satisfaction in Brazil E-commerce: A Comparative Study of Machine Learning Techniques

Customers' propensity for brand loyalty, recurrent business, and positive word-of-mouth are directly influenced by the degree to which their needs are met. E-commerce happens to be one of the biggest online industries in the world which till date is not completely understood from a customer's standpoint. Brazil being a developing country is the 12th largest market for e-commerce in the world and accounts for 30 % of e-commerce in Latin America and remains largely unexplored. In this study, we used machine learning (ML) techniques such as LSTM, XGBClassifier, Logistic Regression, Neural Network, and Random Forest to predict customer satisfaction using a retail dataset from Brazil. We contrasted the efficacy of these models using a variety of evaluation criteria, such as precision, recall, and Fl-score. The LSTM model had the highest accuracy with a score of 0.89, followed by the XGBClassifier with 0.83, Naive Bayes with 0.73, Logistic Regression with 0.74, Support Vector Classifier with 0.74, and Random Forest with 0.78. The superior performance of the LSTM model over other models suggests that it has superior predictive abilities.
Cultural Heritage Preservation: A Virtual Reality Tour of the Dato' Onn Gallery

Cultural heritage is one of the most important assets for our country as it helps preserve our identity and prevents ancient treasures from being lost to technological advancements for future generations. However, historical buildings, such as the Dato' Onn Gallery, are rarely visited by many people. Due to this issue, many people remain unaware of historical places. Therefore, to overcome the problem, we have developed a virtual reality (VR) tour of the Dato' Onn Gallery, resembling real environments. A Multimedia Development Life Cycle (MDLC) methodology was implemented to develop this VR tour application. In this study, the evaluation scale was adapted based on a Technology Acceptance Model (TAM) instrument consisting of three elements: Ease of Use, Usefulness and User Satisfaction. The data was collected from 32 participants. The results show that 79.80% of participants accepted of VR concept for Dato' Onn Gallery. For future work, the Virtual Dato' Onn Gallery will be implemented on the mobile platform, providing users with more options.
Dinoflagellate cyst zonation for the middle to upper Eocene in the Austral Basin, southwestern Atlantic Ocean: implications for regional and global correlation

Abstract The well-exposed marine Eocene units from southwestern Patagonia, Argentina, contain useful information for reconstructing regional climate and oceanographic patterns in an area adjacent to the Drake Passage. The aim of this paper is to integrate dinoflagellate cyst data from three sections of the southwestern Austral Basin (Río Turbio Formation) to propose a zonation scheme, which can be applied to other southwestern Atlantic Ocean sites. Assemblages of organic walled dinoflagellate cysts have been analysed in different cropping-out sections and cores, showing the high potential of this fossil group as biostratigraphic markers. Comparison of dinoflagellate cyst events of the upper member of the Río Turbio Formation with calibrated biostratigraphic ranges in the Palaeogene South Pacific Ocean allowed us to date and correlate these sedimentary sections. The resulting zonation consists of four dinoflagellate cyst zones labelled RTF 1 to RTF 4, between the middle Lutetian and late Priabonian. As a final point, we applied dinoflagellate cyst species with importance as palaeoenvironmental markers to assess long-term climatic and oceanographic evolution for the area. This study shows that the endemic–Antarctic dinoflagellate cyst assemblage is dominant during the middle to late Eocene (RTF 1 to RTF 3), while a significant replacement of these taxa by cosmopolitan species characterizes the upper part of the upper member of the Río Turbio Formation (RTF 4). This turnover seems to be a consequence of changes in the ocean circulation patterns forced by deepening of the southern Atlantic gateways (the Drake Passage and the Tasman Gateway).
Development of Security Operation Center (SOC) Governance Blueprint Based on Consideration of Process Maturity Level Parameters

In connection with the development of information technology and its ability to exchange data quickly, precisely and accurately, making the use of information technology today is more than just supporting business activities, but has also entered into the company's core business activities, a company is required to increase the level of governance maturity. information security that covers IT and OT in the company, especially for companies where currently there is no governance that covers all areas of Information Technology (IT) and Operation Technology (OT), so there is a need to develop a Governance Blueprint especially for Supervision through the Security Operation Center. Security Operations Center responsible for monitoring, detecting, and responding to security threats to an organization's computer systems and networks. In addition, the limited competence of human resources in carrying out governance and supporting technology is an indicator that needs to be analyzed. This paper explains our understanding and results of our analysis of the needs of the SOC Governance Blueprint, as well as describes the solutions and approaches we offer to achieve the objectives of these activities. Overall, the solutions and services listed in this proposal will be able to meet the objectives of the SOC Governance Blueprint and are also in line with governance standards such as COBIT or ITIL Process Maturity Framework or similar.
Sonic and Ultrasonic Logging of Hole 504B and Its Implications for the Structure, Porosity, and Stress Regime of the Upper 1 km of the Oceanic Crust

The layered structure of the oceanic crust is characterized by changes in geophysical gradients rather than by discrete layer boundaries. Correlation of geophysical logs and cores recovered from DSDP Hole 504B provides some insight into the physical properties that control these gradient changes. Borehole televiewer logging in Hole 504B provides the deepest continuous image of well-bore reflectivity into the oceanic crust, revealing detailed structures not apparent otherwise, because of the low percentage of core recovery. Physical characteristics of the crustal Layers 2A, 2B, and 2C, such as the detailed sonic velocity and lithostratigraphic structure, are obtained through analysis of the sonic, borehole televiewer, and electrical resistivity logs. A prediction of bulk hydrated mineral content, consistent with comparison to the recovered material, suggests a change in the nature of the alteration with depth. Data from the sonic, borehole televiewer, electrical resistivity, and other porosity-sensitive logs are used to calculate the variation of porosity in the crustal Layers 2A, 2B, and 2C. Several of the well logs that are sensitive to the presence of fractures and open porosity in the formation indicate many zones of intense fracturing. Interpretation of these observations indicates that there may be a fundamental pattern of cooling-induced structure in the oceanic crust.
Beef Image Classification Using the Inception V3 Transfer Learning Model

Economic conditions and increased incomes can impact rising prices for beef and other consumer products. The increase in consumption needs in the community causes the high cost of beef in the market. The high cost of meat is often taken advantage of by irresponsible people by selling beef that is not fresh or mixing fresh and not-fresh meat. The level of freshness of the beef itself can be seen manually by looking directly at the color and texture of the beef. However, not all people can distinguish which meat is fresh and which is not. A model that can classify beef automatically is needed. This research will use machine learning to predict fresh, half-fresh, and spoiled meat images. The machine learning model will use the Inception V3 transfer learning architecture model. The research contribution is using additional datasets to produce different results and obtain the beef image classification model. The results of the model performance evaluation show an accuracy value of 87.14%, a precision of 87.14%, a recall of 87.14%, and an f1 score of 88.28%. These results indicate that the Inception V3 architectural model is the best fit for use in beef classification. The information obtained from the experiments shows that the model evaluation results using Inception are more optimal than the Xception evaluation results from earlier research.