Miracle-dz/UniFAR-FTUs huggingface dataset:
Material Identification in Complex Environments: Neural Network Approaches to Hyperspectral Image Analysis

Hyperspectral imagery is often used in chemometric studies for quality sorting and recycling due to its ability to produce rich spectroscopy data. In this paper, we study plastics detection in a complex environment. In particular, we analyze hyperspectral images of three scenes with spectra in the near-infrared and visible wavelength ranges; our task is to detect plastic within the scenes. The images contain materials with high intraclass variability and significant mixing. Our novel contribution compares various methods for hyperspectral pixel classification in these complicated, real-world environments, specifically deep methods such as contrastive learning and autoencoders, as well as comparing the viability of the hyperspectral cameras’ light spectrum for the application of plastic detection.
Towards Real-Time Polyp Segmentation During Colonoscopy Using an EfficientNet-Based UNet Architecture

In this paper, I present a method for improving polyp diagnosis during endoscopy using image segmentation techniques. The method involves a technique for precisely segmenting polyps from endoscopic images using cutting-edge deep learning algorithms. The deep convolutional neural network (DCNN) is trained using a hand-segmented dataset that includes a range of polyp sizes, shapes, and appearances. Using a different validation dataset, the performance of the suggested technique is assessed by contrasting the outcomes of the automated segmentation with those of the manual annotations. When compared to manual examination, the results show a considerable increase in the rate of polyp discovery while still not sacrificing real-time efficiency as seen in current State-of-the-Art implementations. The automated segmentation technique can reduce the likelihood of missing or incorrectly diagnosed polyps by providing reliable and consistent polyp detection. By integrating this approach into the endoscopy workflow, we can potentially enhance patient outcomes, alleviate the burden on endoscopists, and improve the efficiency and accuracy of colorectal cancer screening.
Driving Cycle-Based Design and Optimization of Reduced Rare-Earth Interior Permanent Magnet Synchronous Motor for Electric Vehicle

The interior permanent magnet synchronous motor (IPMSM) is frequently employed as the propulsion motor for electric vehicles due to their high power density, good field weakening and speed regulation capabilities. Among the different rotor topologies, the double V-shaped configuration is one of the attractive alternatives when the power density needs to be enhanced. The volume of magnets is to be increased in order to achieve higher power density. However, conventional double V-shaped rotor employs only rare-earth magnets, which increases the overall cost of the motor. This work proposes a cost-effective double V-shaped rotor topology considering drive cycle-based design for electric vehicle applications. Variations of double V-shaped topology have been investigated aiming to lower the usage of rare-earth magnet. Multi-objective optimization has been performed to reduce rare earth magnet volume without compromising the power density. Cost estimation and simulation results as obtained from the 2D FEM environment have been presented.
Encoding Block Pixel Compensation With Motion Vector on DNN

Compression efficiency is always being the engine behind video coding. Inter-frame prediction has traditionally been the focus of research as a key technology for removing temporal redundancy. However, the motion vector acquisition of the prediction block in current standards all adopts the block prediction method in order to preset the coding mode, but this does not apply to natural images, as the boundaries of natural images are random, often leading to a decrease in prediction accuracy. This article creatively proposes pixel level motion vector prediction technology, obtaining pixel motion vectors in a simple and efficient way. Firstly, the optimal motion vector of the pixel is calculated by the center template search algorithm, and the dataset is made using the motion vector of current pixel and corner pixel. Finally, a prediction model is obtained by training with a multi-layer neural network, and experimental results verify the effectiveness of the model.
Deep Learning-Based Ocular Disease Classification in Fundus Images

Ocular anomalies, such as glaucoma, cataracts, and age-related macular degeneration (AMD), represent a significant public health burden. In this paper, we propose a deep learning-based multi-class classification model for the accurate detection of these eye conditions using fundus images. The model utilizes a pre-trained ResNet-50 architecture and is trained and tested on a dataset of 1096 fundus images. Our results demonstrate the model’s effectiveness, achieving a classification accuracy of 97%. The precision, recall, and F1-score metrics show the model’s ability to make accurate predictions for each class, highlighting its robustness. Moreover, the model exhibits strong generalization capabilities and avoids overfitting, as evidenced by the training and validation accuracy and loss curves. The proposed model shows promise as a valuable tool for assisting medical professionals in the early and accurate diagnosis of ocular diseases, contributing to improved patient outcomes and ocular health management.
Intelligent Systems-Enabled Decentralized E-Government Framework for Higher Education in Iraq: A Conceptual Data Fusion Approach

The article presents an intelligent systems framework designed to facilitate the implementation of e-government in developing countries, with a specific focus on Iraq. This framework combines the principles of information system success and e-service quality to enhance the adoption of e-government. It encompasses ten influential factors that contribute to the successful implementation of e-government initiatives. By leveraging data from Iraq, the framework underwent thorough evaluation, demonstrating its efficacy in predicting e-government adoption. The versatility of this framework extends beyond Iraq, making it applicable to other developing nations seeking to enhance their e-government adoption rates. By integrating information system success and e-service quality, the framework provides a comprehensive approach to meet user needs and optimize e-government implementation.
Channel Estimation for Reconfigurable Intelligent Surface Aided mmWave MU-MIMO Systems: Hybrid Receiver Architectures

—Channel estimation is one of the key challenges for the deployment of reconfigurable intelligence surface (RIS)-aided communication systems. In this paper, we study the channel estimation problem of RIS-aided mmWave multi-user multiple-input multiple-output (MU-MIMO) systems especially when a hybrid receiver architecture is adopted. For this system, we propose a simple yet efficient channel estimation method using the fact that cascaded channels (to be estimated) have low-dimensional common column space. In the proposed method, the reflection vectors at the RIS and the RF combining matrices at the BS are designed such that the training observations are suitable for estimating the common column space and the user-specific coefficient matrices via a collaborative low-rank approximation. Via simulations, we demonstrate the effectiveness of the proposed channel estimation method compared with the state-of-the-art ones.
Seismic imaging of the lithosphere-asthenosphere boundary with a dense broadband array in central California

OF THESIS Lithospheric attachment to the high-velocity Isabella anomaly (IA) in central California was tested by mapping the lateral extent of interruption of the lithosphere-asthenosphere boundary (LAB). The study area spans the location of two plausible origins for the anomaly, the Monterey microplate (Wang et al., 2013) and Sierra Nevada batholith (Ducea & Saleeby, 1998). Results include 918 binned receiver functions that were made using multi-channel spectral deconvolution and an array-based spectral source estimation on the event data from an 18month deployment of a high density array from the coast to the Sierra Nevada crossing the lateral location of the Isabella anomaly. Common conversion point (CCP) scattered wave imaging shows a strong negative velocity gradient (NVG) west of the San Andreas Fault (SAF) and a gap in a NVG horizon east of the SAF. This is interpreted as prominent arrivals at the base of a partially subducted microplate that become undetectable as the plate dips too steeply east of the SAF for reliable recovery of Sp converted phases. The gap in consistent NVG arrivals would indicate a local disruption of the LAB along the lateral extent of the anomaly. Although the LAB disruption cannot constrain the tectonic origin of the Isabella anomaly, it does indicate the anomaly is
Synoptic Situations of Extreme Hourly Precipitation over China

AbstractIn this study, synoptic situations associated with extreme hourly precipitation over China are investigated using rain gauge data, weather maps, and composite radar reflectivity data. Seasonal variations of hourly precipitation (>0.1 mm h−1) suggest complicated regional features in the occurrence frequency and intensity of rainfall. The 99.9th percentile is thus used as the threshold to define the extreme hourly rainfall for each station. The extreme rainfall is the most intense over the south coastal areas and the North China Plain. About 77% of the extreme rainfall records occur in summer with a peak in July (30.4%) during 1981–2013.Nearly 5800 extreme hourly rainfall records in 2011–15 are classified into four types according to the synoptic situations under which they occur: the tropical cyclone (TC), surface front, vortex/shear line, and weak-synoptic forcing. They contribute 8.0%, 13.9%, 39.1%, and 39.0%, respectively, to the total occurrence and present distinctive characteristics in region.
Risk factors of coronary heart disease patients at Misrata: Awareness and orientation

Many prospective studies have examined the associations between intakes of individual foods and the risk of coronary heart disease (CHD), but few have evaluated the relation of overall dietary patterns to the risk. A descriptive hospital-based study was conducted to assess knowledge of CHD patients about risk factors in Misrata hospitals. The study has consisted of 100 patients with CHD, at different ages, who were selected randomly during 2019. The study data were collected using well-structured questionnaire, patient record, and anthropometric measurements. Pearson correlation was used to examine correlation between risk factors, blood fat profiles, and lifestyle. The results revealed that (52%) of patients fall within the age group 51-70 years, the CHD was widely prevalent among males (55%), most of the patients (41%) were illiterate, majority of patients (92%) were suffering from other chronic diseases such as diabetes, hypertension, and obesity. The majority of patients (63%) were considerably ignorant of the causes and risk factors for CHD (P≤0.05). The majority of patients (54%) have excess weight and engage in sporadic physical activity. When compared to fewer patients (8%) who follow up with a dietician, there is a significant difference (P≤0.05) between patients who have no knowledge of the CHD diet regimen and those who have not received any counseling themselves. The correlation between age groups, chronic illnesses, and blood fat profiles was significant (P≤0.05), according to the results. The majority of patients had no practiced therapeutic lifestyle and little physical activity towards control of CHD. The appropriate nutrition education and patients counseling should be recommended among outpatients of CHD.
Streambed Disturbance over a Long Flood Series

Flooding and channel characteristics control the occurrence, spatial extent, and depth of streambed disturbance. This research quantifies the frequency and depth of streambed disturbance in a small, coastal salmon‐spawning stream resulting from 249 floods. Bed disturbance was documented by monitoring the three‐dimensional positions of gravel tracers and producing cellular maps within a geographic information system (GIS). The most active areas make up about 1% of the streambed. Undisturbed areas constitute between 31 and 53% of the bed. Maximum disturbance depths exceed 20 cm in more than half of the active bed. Logistic regression equations based on two bed characteristics correctly describe disturbance frequency and depth over most of the bed. Results provide insight into the active zone of bedload transport, correspond with expectations of partial sediment transport, and document potential refugia that persist over many life cycles of the stream community. Copyright © 2017 John Wiley & Sons, Ltd.
Crafting a Rigorous, Clinically Relevant Large Animal Model of Chronic Myocardial Ischemia: What Have We Learned in 20 Years?

The past several decades have borne witness to several breakthroughs and paradigm shifts within the field of cardiovascular medicine, but one component that has remained constant throughout this time is the need for accurate animal models for the refinement and elaboration of the hypotheses and therapies crucial to our capacity to combat human disease. Numerous sophisticated and high-throughput molecular strategies have emerged, including rational drug design and the multi-omics approaches that allow extensive characterization of the host response to disease states and their prospective resolutions, but these technologies all require grounding within a faithful representation of their clinical context. Over this period, our lab has exhaustively tested, progressively refined, and extensively contributed to cardiovascular discovery on the basis of one such faithful representation. It is the purpose of this paper to review our porcine model of chronic myocardial ischemia using ameroid constriction and the subsequent myriad of physiological and molecular–biological insights it has allowed our lab to attain and describe. We hope that, by depicting our methods and the insight they have yielded clearly and completely—drawing for this purpose on comprehensive videographic illustration—other research teams will be empowered to carry our work forward, drawing on our experience to refine their own investigations into the pathogenesis and eradication of cardiovascular disease.
Antioxidants in Photoaging: From Molecular Insights to Clinical Applications

Photoaging (PA) is considered a silent disease affecting millions of people globally and is defined as skin damage due to prolonged exposure to ultraviolet radiation (UVR) from the sun. Physiologically, the skin is in a state of renewal and synthesis of components of the extracellular matrix (ECM). However, exposure to UVR affects the production of the ECM, and the functioning and response of skin cells to UVR begins to change, thus expressing clinical and phenotypic characteristics of PA. The primary mechanisms involved in PA are direct damage to the DNA of skin cells, increases in oxidative stress, the activation of cell signaling pathways responsible for the loss of skin integrity, and cytotoxicity. The medical and scientific community has been researching new therapeutic tools that counteract PA, considering that the damage caused by UVR exceeds the antioxidant defense mechanisms of the skin. Thus, in recent years, certain nutraceuticals and phytochemicals have been found to exhibit potential antioxidant and photoprotective effects. Therefore, the main objective of this review is to elucidate the molecular bases of PA and the latest pharmaceutical industry findings on antioxidant treatment against the progression of PA.
Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer

Breast cancer (BC) caused 685,000 deaths globally in 2020, earning the title of the most common type of tumor among females. With a multifactorial genesis, BC is influenced by several factors such as age, genetic and epigenetic predisposition, and an individual’s exposome, and its classification is based on morphological/histological, invasiveness, and molecular futures. Extracellular vesicles (EVs) are cell-derived lipid-bilayer-delimited nanoparticles, which are distinguishable by size, genesis, and the markers expressed in exosomes (40 to 150 nm), microvesicles (40 to 10,000 nm), and apoptotic bodies (100–5000 nm). Produced in physiological and pathological cellular contexts, EVs are shuttles of biological material and are implicated in cell-to-cell communications, thus attracting significant interest in diagnostic and drug delivery research. We report and discuss the latest evidence regarding the important role of EVs in BC, deepening their implication in tumorigenesis and metastatic mechanisms. On the other hand, the use of BC-derived EVs as prognostic biomarkers and therapeutic approaches is undergoing investigation. Hence, EVs have become new weapons in precision medicine; however, only with the support of advanced algorithms such as artificial intelligence (AI) can we develop a wide range of information. Looking ahead, it is possible to see the application of AI in the prognosis and diagnosis of different pathologies.
Development of a Nutritional Model to Predict Digestible Energy Requirements of Broodmares based on Body Condition Changes

Development of a Nutritional Model to Predict Digestible Energy Requirements of Broodmares Based on Body Condition Changes. (May 2011) Viviana Victoria Cordero, B.S., Texas A&M University Chair of Advisory Committee: Dr. Clay A. Cavinder Nutritional models have been developed for beef and dairy cattle to estimate energy balance based on changes in body condition score. These models have not been developed or fully evaluated in horses to date. The objective of this study was to develop a model to predict changes in body weight (BW), rump fat (RF) thickness, and overall % body fat (BF) to maximize profitability and productivity by accurately predicting energy balance of mares. The evaluation of the model was performed using non-lactating Quarter Horse mares (n=20; 4 to 18 yr of age). The initial BW ranged from 376 to 553 kg, with an initial body condition score (BCS) of 3.5 to 7.0 (scale of 1 9; 1 = emaciated, 5 = moderate, and 9 = obese). The BCS, RF thickness, and BW were measured for each mare prior to the commencement of the feeding trial and once/wk thereafter for the duration of a 30 d feeding trial. The pre-trial BCS was used to assign mares to 1 of 4 treatment groups and fed to alter BCS by 1 unit as follows: Group 1, 4 up to 5; Group 2, 5 down to 4; Group 3, 6 up to 7; and Group 4, 7 down to 6. Initial BCS, target BCS, %BF, and BW data was collected from each mare and input into the model. Mares were individually fed according to the digestible energy (DE) suggestions
Promoting or Prohibiting? Investigating How Time Pressure Influences Innovative Behavior under Stress-Mindset Conditions

The existing empirical evidence on the relationship between time pressure and innovative behavior is paradoxical. An intriguing yet unresolved question is “When does time pressure promote or prohibit innovative behavior, and how? ” We theorize that the paradoxical effect of time pressure on innovative behavior can be elucidated by the moderating role of stress mindset, and we also explore the mediating role of thriving at work. Our research involved a field study of 390 research and development personnel from eight enterprises and research institutes in China to test our proposed model. Results indicated that the stress-is-debilitating mindset negatively moderated the association between time pressure and thriving at work, while the stress-is-enhancing mindset positively moderated the link between time pressure and thriving at work. Furthermore, the findings demonstrated that the stress-is-debilitating mindset negatively moderated the indirect impact of time pressure on employees’ innovative behavior through thriving at work, while the stress-is-enhancing mindset positively moderated the indirect effect of time pressure on employees’ innovative behavior through thriving at work. The theoretical and practical implications of these findings are also discussed.
The Effect of Variety Seeking, Dissatisfaction, and Service Quality Toward Brand Switching on Netflix Streaming Service

Research Aims: This study aims to investigate the factors influencing brand switching behavior among streaming service subscribers in Indonesia, specifically focusing on the impact of consumer variety-seeking, dissatisfaction, and service quality on the decline of Netflix subscriptions. Design/Methodology/Approach: Conducted as a quantitative research endeavor, this study employs multiple linear regression analysis through the utilization of SPSS software. The research sample comprises 220 former Netflix subscribers who have transitioned to other streaming services. By examining the relationships between consumer variety-seeking, dissatisfaction, service quality, and brand switching, the study seeks to provide a nuanced understanding of the dynamics influencing subscriber preferences in the competitive streaming landscape. Research Findings: The findings of this study reveal compelling insights into the key drivers behind brand switching among streaming service subscribers in Indonesia. Consumer variety-seeking behavior, dissatisfaction with Netflix services, and perceived service quality emerge as significant factors contributing to the observed decline in Netflix subscriptions. The study underscores the importance of these variables in shaping consumer decisions and preferences in the increasingly competitive streaming market. Theoretical Contribution/Originality: This research contributes to the existing literature by shedding light on the intricate interplay between consumer behavior and brand switching in the context of streaming services. The study's originality lies in its exploration of the unique factors, namely variety-seeking, dissatisfaction, and service quality, influencing the migration of subscribers from one platform to another. By uncovering these dynamics, the research provides valuable theoretical insights that can inform both academia and industry stakeholders, offering a deeper understanding of the challenges and opportunities within the evolving landscape of streaming services in Indonesia.
Novel Complex Intuitionistic Hesitant Fuzzy Distance Measures for Solving Decision-Support Problems

The key objective of this article is to introduce the innovative idea of a complex intuitionistic hesitant fuzzy set (CIHFS), which blends the intuitionistic hesitant fuzzy set with the complex fuzzy set to address the uncertain information in real-life complex problems. In CIHFS, the range of the membership functions is extended from the subset of the real number to the unit disc under the hesitant environment. To determine how well the CIHFSs can be distinguished from one another, we first propose generalized distance measures and weighted generalized distance measures based on the Hamming, Euclidean, and Hausdorff metrics. Some interesting properties and their relationships are thoroughly discussed. Furthermore, a decision-making framework for selecting the optimal option from the feasible set has been proposed, which is grounded in these distance metrics. For the purpose of proving the method’s efficacy, we included examples from pattern recognition and medical diagnostics.