Miracle-dz/UniFAR-FTUs huggingface dataset:
Epicardial Adipose Tissue Thickness in Hypertensive Patients with or without Left Ventricular Hypertrophy

Background: Epicardial adipose tissue (EAT) is a type of visceral fat located between the heart and pericardium, affecting vascular smooth muscle activity through chemical secretion. It is a risk factor for cardiovascular metabolic issues and may relate to hypertension (HT). Objective: To assess the correlation between EAT thickness and HT in individuals with and without left ventricular hypertrophy (LVH). Patients and Methods: This observational study included 105 individuals with HT, defined as having a systolic blood pressure of ≥140 mm Hg, a diastolic pressure of ≥90 mm Hg, or being on antihypertensive medication, and 30 healthy controls. Participants underwent transthoracic echocardiography to measure left ventricle dimensions and EAT thickness. Results: Positive correlations were found between EAT thickness and several cardiac structural and functional parameters (Left ventricular mass index (LVMI), the ratio of early mitral inflow velocity to mitral annular early diastolic velocity (E/'e ratio), etc.), with a significant correlation between EAT thickness and both LVMI and E/'e ratio. EAT thickness above 5.25 mm predicted LVH in HT patients with 85.7% sensitivity, 82.1% specificity, and 83.9% accuracy. Conclusions: Compared to hypertensive patients without hypertrophy and healthy individuals, hypertensive patients with LVH had a much thicker EAT. Based on the findings, EAT may be a useful marker for HT ventricular remodeling and hypertrophy.
Independent and Joint Effect of Brachial-Ankle Pulse Wave Velocity and Blood Pressure Control on Incident Stroke in Hypertensive Adults

Pulse wave velocity (PWV) has been shown to influence the effects of antihypertensive drugs in the prevention of cardiovascular diseases. Data are limited on whether PWV is an independent predictor of stroke above and beyond hypertension control. This longitudinal analysis examined the independent and joint effect of brachial-ankle PWV (baPWV) with hypertension control on the risk of first stroke. This report included 3310 hypertensive adults, a subset of the China Stroke Primary Prevention Trial (CSPPT) with baseline measurements for baPWV. During a median follow-up of 4.5 years, 111 participants developed first stroke. The risk of stroke was higher among participants with baPWV in the highest quartile than among those in the lower quartiles (6.3% versus 2.4%; hazard ratio, 1.66; 95% confidence interval, 1.06–2.60). Similarly, the participants with inadequate hypertension control had a higher risk of stroke than those with adequate control (5.1% versus 1.8%; hazard ratio, 2.32; 95% confidence interval, 1.49–3.61). When baPWV and hypertension control were examined jointly, participants in the highest baPWV quartile and with inadequate hypertension control had the highest risk of stroke compared with their counterparts (7.5% versus 1.3%; hazard ratio, 3.57; 95% confidence interval, 1.88–6.77). There was a significant and independent effect of high baPWV on stroke as shown among participants with adequate hypertension control (4.2% versus 1.3%; hazard ratio, 2.29, 95% confidence interval, 1.09–4.81). In summary, among hypertensive patients, baPWV and hypertension control were found to independently and jointly affect the risk of first stroke. Participants with high baPWV and inadequate hypertension control had the highest risk of stroke compared with other groups.
Association of a lysine-232/alanine polymorphism in a bovine gene encoding acyl-CoA:diacylglycerol acyltransferase (DGAT1) with variation at a quantitative trait locus for milk fat content

DGAT1 encodes diacylglycerol O-acyltransferase (EC 2.3.1.20), a microsomal enzyme that catalyzes the final step of triglyceride synthesis. It became a functional candidate gene for lactation traits after studies indicated that mice lacking both copies of DGAT1 are completely devoid of milk secretion, most likely because of deficient triglyceride synthesis in the mammary gland. Our mapping studies placed DGAT1 close to the region of a quantitative trait locus (QTL) on bovine chromosome 14 for variation in fat content of milk. Sequencing of DGAT1 from pooled DNA revealed significant frequency shifts at several variable positions between groups of animals with high and low breeding values for milk fat content in different breeds (Holstein–Friesian, Fleckvieh, and Braunvieh). Among the variants was a nonconservative substitution of lysine by alanine (K232A), with the lysine-encoding allele being associated with higher milk fat content. Haplotype analysis indicated the lysine variant to be ancestral. Two animals that were typed heterozygous (Qq) at the QTL based on marker-assisted QTL-genotyping were heterozygous for the K232A substitution, whereas 14 animals that are most likely qq at the QTL were homozygous for the alanine-encoding allele. An independent association study in Fleckvieh animals confirmed the positive effect of the lysine variant on milk fat content. We consider the nonconservative K232A substitution to be directly responsible for the QTL variation, although our genetic studies cannot provide formal proof.
A predictive model for narrow passage path planner by using Support Vector Machine in changing environments

Narrow passages in changing environments create huge difficulties, since locations and shapes of narrow passages in Configuration Space(C-space) change frequently. It is very important for a planner to identify narrow passages in real time and boost valid points within them effectively. A novel narrow passage predictive model for designing a path planner in changing environments is proposed in this paper. Firstly, an Expanded Dynamic Bridge Builder is presented to identify narrow passages rapidly with validity-toggle sampling points in C-space. Secondly, the predictive model is adopted to sample possibly free points within these narrow passages without invoking any collision detection in order to avoid intense computational complexity. The predictive model is obtained by the famous classification method of Support Vector Machine (SVM). A new feature, which includes a group of points' distance and validity information, is proposed in SVM training process to capture approximate structure of local narrow passages. Therefore, the predictive model can excavate the hidden similar structure of local narrow passages. Experiments carried out with two 6-DOFs manipulators show that our approach gain higher success rate of planning and time efficiency than other related methods.
Deriving tests from software architectures

Software architectures are intended to describe essential high level structural and behavioral characteristics of a system. Architecture Description Languages (ADLs) describe these characteristics in ways that can be analyzed and manipulated algorithmically. This provides a unique opportunity for deriving tests at the system level. The paper defines formal testing criteria based on architecture relations, which are paths that architectural components use to communicate. The criteria have been applied to a specific ADL. Results from a comparative empirical study on industrial software are presented.
Internal CSR Programs in the Context of a Financial Institution Operating in Nepal

This study investigates the internal corporate social responsibility (CSR) practices of employees in commercial banks and explores the relationship between demographic factors and trust. Adopting a quantitative research design, data were collected through a questionnaire distributed to 40 employees representing various levels and departments within the banks. Purposive sampling was utilized, and ethical considerations were strictly followed. Descriptive and inferential statistical analyses were employed to examine the data. Results reveal gender differences in perceptions of company policies, with females generally showing higher agreement on aspects like career development and work-life balance. Married individuals also tended to perceive higher levels of agreement with company policies compared to unmarried counterparts. Moreover, age and years of service exhibited weak negative correlations with various policy aspects, suggesting limited influence on employee perceptions. Overall, the findings emphasize the importance of tailored CSR practices to address diverse employee needs and preferences, irrespective of demographic factors. These insights can inform strategies to enhance employee engagement and organizational performance in commercial banks.
Identifying Similar Cases in Document Networks Using Cross-Reference Structures

Our objective was to explore the creation of document networks based on different thresholds of shared information and different clustering algorithms on those networks to identify document clusters describing similar clinical cases. We created networks from vaccine adverse event report sets using seven approaches for linking reports. We then applied three clustering algorithms [visualization of similarities (VOS), Louvain, k-means] to these networks and evaluated their ability to identify known clusters. The report sets included one simulated set and three sets from the Vaccine Adverse Event Reporting System; each was split into training and testing subsets. Training subsets were used to estimate parameter values for the clustering algorithms and testing subsets to evaluate clusters. We created the networks by linking reports based on shared information in the form either of individual Medical Dictionary for Regulatory Activities Preferred Terms (PTs) or of dyads, triplets, quadruplets, quintuplets, and sextuplets of PTs; we created another network by weighting the single PT network connections by Lin's information theoretic approach to similarity. We then repeated this entire process using networks based on text mining output rather than structured data. We evaluated report clustering using recall, precision, and f-measure. The VOS algorithm outperformed Louvain and k-means in general. The best weighting scheme appeared to be related to the complexity of the known cluster. For example, singleton weighting performed best for an intussusception cluster driven by a single PT. We observed marginal differences between the code- and textual-based clustering. In conclusion, our approach supported identification of similar nodes in a document network.
From S‐band mobile interactive multimedia to fixed satellite interactive multimedia: making satellite interactivity affordable at Ku‐band and Ka‐band

This paper describes the evolution of the ETSI S‐band mobile interactive multimedia protocol to support fixed satellite interactive multimedia (F‐SIM) services exploiting existing Ku‐band and Ka‐band satellites. The key F‐SIM protocol differences for both physical and upper layers are described and justified. The F‐SIM protocol has been adopted by the recently deployed Eutelsat Broadcast Interactive System whose architecture, key system parameters, link budget examples and key composing elements are also described. Finally, a summary of laboratory and field trials results over the Eutelsat KA‐SAT multibeam satellite are illustrated. Copyright © 2015 John Wiley & Sons, Ltd.
RAN Virtualization: How Hard Is It to Fully Achieve?

Radio Access Network (RAN) virtualization stands as a pivotal concept in the realm of 5G networks and beyond. It emerges as a viable solution not only to address energy cost concerns but also to optimize material and radio resource management. Despite numerous deployment trials worldwide, a comprehensive realization of Virtual RAN (V-RAN) remains elusive. Current platforms tackle various aspects of V-RAN but encounter numerous limitations. In this paper, we conduct a survey on the progress of RAN virtualization to date and discuss the persisting barriers to its full realization in future mobile radio networks. Initially, we revisit the fundamentals of virtualization and its evolution over recent decades, elucidating the distinctions between Cloud RAN (C-RAN), V-RAN, and Open RAN concepts. Subsequently, we offer an in-depth tutorial on the implementation procedures of V-RAN as per different vendors. Lastly, we shed light on persisting issues within vendor platforms and demonstrate, via simulations, the constraints on their performance concerning supported Base Band Units (BBUs) and devices, as well as coordination among various entities.
Integrated Assessment for the Estrogenic Effects of Pyrethroid Compounds: Defining the Molecular Initiating Events and Key Events for the Adverse Outcome Pathway

Pyrethroids, which are derived from natural insecticides found in chrysanthemum flowers, are widely utilized in various sectors, including agriculture, forestry, horticulture, and personal insect protection. Due to their widespread use, concerns have arisen regarding their potential estrogenic effects on female reproductive health. This review aims to address data gaps and inconsistencies in previous studies by defining molecular initiating events and key events within the adverse outcome pathway associated with pyrethroid-induced estrogenic effects. To achieve this, we propose utilizing Integrated Approaches to Testing and Assessment (IATA), which incorporate in vitro assays and in vivo assessments to comprehensively investigate the estrogenic effects of pyrethroids. An initial search was conducted in the PubMed database to identify relevant articles. Subsequently, the findings were classified according to the IATA strategy. This review provides an overview of the current understanding of pyrethroids and their estrogenic effects, identifies data gaps, and highlights the use of IATA in existing studies on the estrogenic effects of various pyrethroids. It emphasizes the urgent need for comprehensive research on the estrogenic effects of pyrethroids and highlights the importance of standardized testing methods like IATA to accurately assess their impact on human and environmental health. By promoting the use of Integrated Testing Strategies (ITSs) and addressing data gaps, researchers and regulators can enhance the accuracy of assessments, ensuring better protection of human and environmental health from the potential estrogenic effects of pyrethroid exposure.
Automatic memory management techniques for the go programming language

Memory management is a complicated task. Many programming languages expose such complexities directly to the programmer. For instance, languages such as C or C++ require the programmer to explicitly allocate and reclaim dynamic memory. This opens the doors for many software bugs (e.g ., memory leaks and null pointer dereferences) which can cause a program to crash. Automated techniques of memory management were introduced to relieve programmers from managing such complicated aspects. Two automated techniques are garbage collection and region-based memory management. The more common technique, garbage collection, is primarily driven by a runtime analysis (e.g ., scanning live memory and reclaiming the bits that are no longer reachable from the program), where as the less common region-based technique performs a static analysis during compilation and determines program points where the compiler can insert memory reclaim operations. Each option has its drawbacks. In the case of garbage collection it can be computationally expensive to scan memory at runtime, often requiring the program to halt execution during this stage. In contrast, region-based methods often require objects to remain resident in memory longer than garbage collection, resulting in a less than optimal use of a system’s resources. This thesis investigates the less common form of automated memory management (region-based) within the context of the relatively new concurrent language Go. We also investigate combining both techniques, in a new way, with hopes of achieving the benefits of a combined system without the drawbacks that each automated technique provides alone. We conclude this work by applying our region-based system to a concurrent processing environment.
Is There Abnormal Return after Seasoned Equity Offerings? * Is There Abnormal Return after Seasoned Equity Offerings?

Previous studies have documented that firms participating in Seasoned Equity Offerings (SEO) show substantial long-run stock underperformance. This finding poses one of the strongest challenges to the Efficient Market Hypothesis (EMH). However, other studies argue that these empirical studies are plagued by the inability of the ma tching techniques to properly control for all risk factors, and thus, the resulting evidence may not be reliable. We show that these arguments are valid because traditional matching methods are not able to balance ex ante variables well due to the " dimens ionality curse ". This study contributes to the literature by implementing the propensity score matching method, which reduces the matching problem from multiple dimensions to one dimension, and is thus able to match multiple firm characteristics simultaneously. We investigate various market and accounting variables and find that size, book-to-market ratios and past returns are the main factors affecting both firms' SEO decision and their long-run stock performance. We estimate propensity scores conditional on these important factors, and apply this matching method to SEO data during the period from 1986 to 1998. We find that even under the equal-weighting and buy-and-hold method, the long-run abnormal returns after SEOs disappear.
The Pd (II) Reduction Mechanisms in Bacillus megaterium Y-4 Revealed by Proteomic Analysis

Many studies have been conducted on the microbial reduction of Pd (II) to palladium nanoparticles (Pd-NPs) due to the environmental friendliness, low cost, and the decreased toxicity of Pd (II) ions. In this study, we investigate the reduction mechanism of Pd (II) by Bacillus megaterium Y-4 through proteomics. The data are available via ProteomeXchange with identifier PXD049711. Our results revealed that B. megaterium Y-4 may use the endogenous electron donor (NAD(P)H) generated by nirB, tdh, and fabG and reductase to reduce Pd (II) to Pd-NPs. The expression levels of fabG, tdh, gudB, and rocG that generate NAD(P)H were further increased, and the number of reduced Pd-NPs was further increased with the exogenous electron donor sodium formate. Endogenous electron mediators such as quinones and flavins in B. megaterium Y-4 can further enhance Pd (II) reduction. The findings provided invaluable information regarding the reduction mechanism of Pd (II) by B. megaterium Y-4 at the proteome level.
Endoscopic grading of adenoid in otitis media with effusion.

INTRODUCTION Adenoid is thought to be one of the causes of otitis media with effusion, though it is controversial. Grading the adenoid by rigid nasal endoscope in patients with otitis media with effusion may justify adenoidectomy in otitis media with effusion in the future. METHODS A prospective study was carried out at GMS Memorial Academy of ENT and head neck studies from 15th December 2005-April 2007. Study group comprised of 32 children with otitis media with effusion and control group of 28 children with clinically normal ear and nose. Rigid nasal endoscope was used for grading of adenoid in study and control group. The severity of otitis media with effusion was assessed by preoperative air-bone gap and thickness of the fluid aspirated from middle ear during ventilation tube insertion. RESULTS In the study group 13 out of 32 had grade 4 adenoid hypertrophy. This grade 4 adenoid hypertrophy was found to be statistically significant in children with otitis media with effusion (P < 0.0002). In control group 15 out of 28 had grade 1 adenoid hypertrophy which was significant in the same group (P < 0.002). Air-bone gap and thickness of fluid did not correlate with the increasing grade of adenoid hypertrophy. CONCLUSIONS Grade 4 adenoid hypertrophy was statistically found to be significant with otitis media with effusion but severity of hypertrophy were not reflected by hearing loss and thickness of fluid.
Architecture independent short vector FFTs

This paper introduces an SIMD vectorization for FFTW-the "fastest Fourier transform in the west" proposed by Frigo and Johnson (see Proceedings of the ACM SIGPLAN '99 , p.169-180, 1999). The new method leads to an architecture independent short vector SIMD FFT vectorization that utilizes the architecture adaptivity of FFTW. It is based on special FFT kernels (up to size 64 and more) that are utilized by FFTW to compute the whole transform. This vectorization supports all features of complex transforms in FFTW (arbitrary size, dimension and stride of the data vector; in-place and out-of-place transforms) and is fully transparent to the user. It is suitable for arbitrary vector sizes of the underlying hardware.
A New Time Series Data Imaging Scheme for Mechanical Fault Diagnosis

Fault diagnosis based on vibration signals is widely used in various industrial production processes to prevent catastrophic accidents and ensure timely repairs. However, traditional fault diagnosis methods provide limited accuracy due to their inability to visually represent fault characteristics and heavy reliance on expert involvement. In this study, we propose a time series image generation scheme that incorporates a convolutional neural network (CNN) to perform data-driven analysis and open-set classification on fault data to represent faults intuitively and diagnose them more accurately. In this study, based on the basic G image, we proposed two series of extended coding methods, gray-transform (G-TF) and transform-gray (TF-G) to convert the 1-D time series signal into a grayscale image from different perspectives for figurative expression, so as to adapt to different fault diagnosis scenarios. Then, a CNN is designed to maximize recognition accuracy based on faster calculation. To demonstrate the effectiveness of the proposed method, three types of faults are tested in this study, i.e., faults with motor bearings, self-priming centrifugal pumps, and hydraulic pumps. The experimental results demonstrate that, when the most suitable method is employed, the proposed approach achieves better recognition performance in diagnosing these three types of faults. The proposed method provides a new solution for future fault diagnosis tasks.
A Generalized Time-Domain Waveform Test-Set

This paper addresses the problem of obtaining affordable and fast voltage and current time domain waveforms of non-linear devices, and describes a generalized testing solution which can be applied by anyone in possession of a Vector Network Analyzer (VNA) and a Sampling Oscilloscope of any kind. Some details on the typical issues arising when calibrating such a measurement system are given. Also, a possible calibration solution is described. Finally, a measurement set-up currently in use at Freescale Semiconductor is shown and some measurement examples obtained with this system are given.
Binary Classification of Skin Cancer using Pretrained Deep Neural Networks

One of the most frequent kinds of cancer in the world is skin cancer. Clinical examination of skin lesions is essential to detect disease characteristics, but it is limited by long timeframes and a broad variety of interpretations. Computer vision is being used to detect diseases, help in diagnosis, and identify patient risks. This is particularly true for skin cancer, which may be lethal if not detected early on. Several computer-aided diagnosis and detection systems have already been developed to do this. Deep learning techniques have been developed to address these issues and assist dermatologists, as early and precise detection of skin cancer is critical to improve patient survival rates. In this paper, some pretrained deep neural networks are utilized for binary classification of skin cancer disease. They are used to classify between benign and malignant cancers in dermoscopic images. AlexNet, ResNet-18, SqueezeNet, and ShuffleNet are the used networks as transfer learning classifiers. In this study, we employed a Kaggle dataset titled "Skin Cancer: Malignant vs. Benign". The networks’ maximum accuracy approaches 89%.