Miracle-dz/UniFAR-FTUs huggingface dataset:
A Machine Learning Model Based on Genetic and Traditional Cardiovascular Risk Factors to Predict Premature Coronary Artery Disease.

BACKGROUND Premature coronary artery disease (PCAD) has a poor prognosis and a high mortality and disability rate. Accurate prediction of the risk of PCAD is very important for the prevention and early diagnosis of this disease. Machine learning (ML) has been proven a reliable method used for disease diagnosis and for building risk prediction models based on complex factors. The aim of the present study was to develop an accurate prediction model of PCAD risk that allows early intervention. METHODS We performed retrospective analysis of single nucleotide polymorphisms (SNPs) and traditional cardiovascular risk factors (TCRFs) for 131 PCAD patients and 187 controls. The data was used to construct classifiers for the prediction of PCAD risk with the machine learning (ML) algorithms LogisticRegression (LRC), RandomForestClassifier (RFC) and GradientBoostingClassifier (GBC) in scikit-learn. Three quarters of the participants were randomly grouped into a training dataset and the rest into a test dataset. The performance of classifiers was evaluated using area under the receiver operating characteristic curve (AUC), sensitivity and concordance index. R packages were used to construct nomograms. RESULTS Three optimized feature combinations (FCs) were identified: RS-DT-FC1 (rs2259816, rs1378577, rs10757274, rs4961, smoking, hyperlipidemia, glucose, triglycerides), RS-DT-FC2 (rs1378577, rs10757274, smoking, diabetes, hyperlipidemia, glucose, triglycerides) and RS-DT-FC3 (rs1169313, rs5082, rs9340799, rs10757274, rs1152002, smoking, hyperlipidemia, high-density lipoprotein cholesterol). These were able to build the classifiers with an AUC >0.90 and sensitivity >0.90. The nomograms built with RS-DT-FC1, RS-DT-FC2 and RS-DT-FC3 had a concordance index of 0.94, 0.94 and 0.90, respectively, when validated with the test dataset, and 0.79, 0.82 and 0.79 when validated with the training dataset. Manual prediction of the test data with the three nomograms resulted in an AUC of 0.89, 0.92 and 0.83, respectively, and a sensitivity of 0.92, 0.96 and 0.86, respectively. CONCLUSIONS The selection of suitable features determines the performance of ML models. RS-DT-FC2 may be a suitable FC for building a high-performance prediction model of PCAD with good sensitivity and accuracy. The nomograms allow practical scoring and interpretation of each predictor and may be useful for clinicians in determining the risk of PCAD.
Determining the Quality and Storage Stability of Pomegranate (Punica granatum L.) Seed Oil with Accelerated Shelf-Life Approach

Pomegranate (Punica granatum L.) is a fruit that grows in most tropical and subtropical regions. It has 52% aril, consisting of 78% juice which is used as; juice, molasses, jam, wine, and dried kernels. Potential health benefits increase the demand for the fruit as well as its products. Pomegranate seeds, which consist of approximately 10% of the whole fruit, are a by-product of the juice and juice using products containing nutraceutical functional components such as sterols and punicic acid. Pomegranate seed oil is considered a healthy alternative source of oils, and its production is a valorization process since it is the by-product that usually goes to waste. In the present study, pomegranate seeds were used for oil extraction using the cold solvent extraction method. Oil samples were then taken to the Schaal oven treatment in order to determine changes due to storage. Oil samples were tested for 14 days of total storage at their 1st,3rd, 7th, and 14th days for the oxidation tests, colour, fatty acid composition, and Fourier transform infrared spectra analysis. Data were tested for significance by using statistical analysis. The results indicated that oxidative stability of pomegranate seed oil was decreased by increasing storage time. The studied techniques used in this paper can be valuable processors to monitor the oxidative stability of oils with storage time and evaluate their acceptance on the market.
Prediction Model of Bubble Formation in Oil-Paper Insulation Based on the ITBE Envelope

Due to rapid temperature rise and insulation moisture, bubbles can generate in the oil-paper insulation of oil-immersed power transformers, which can reduce the dielectric strength of oil-paper insulation and even bring about the risk of insulation breakdown. In order to accurately evaluate the conditions of bubble formation, the physical process of bubble formation in oil-paper insulation is studied in this article. Considering the effects of water vaporization, gas dissolution in oil, and moisture migration on bubble formation, a bubble formation prediction model is constructed. Based on Henry’s law, the dissolution equilibrium of gas in oil is taken into consideration and the upper boundary of the initial temperature of bubble effect (ITBE) envelope is obtained in this model. Based on the moisture equilibrium in oil-paper insulation, the bubble formation process caused by the emergence and rapid evaporation of free water is analyzed, and then, the lower boundary of the ITBE envelope is obtained in this model. The model results show that the moisture content of the pressboard and the presence of free water have a great impact on ITBE. Specifically, the higher the moisture content, the lower the ITBE value. In addition, with the further increase of moisture content, the upper and lower boundaries of the ITBE envelope gradually tend to be the same. However, once free water is present on the surface of cellulose, ITBE decreases rapidly to below 120 °C. The model built in this article is of great significance to study the temperature limit of the power transformer. Hence the ITBE envelope can provide an important theoretical reference for reducing the risk of bubble formation during power transformer operation.
Do the New Data on Second-Generation Drug Eluting Stents Provide Reassurance on Safety , Efficacy , Even for Off-Label Use ?

In in-stent restenosis, drug-eluting stents (DES) are superior compared with bare metal stents (BMS). However, there are concerns about safety because of the reports of increased risk of late and very late stent thrombosis. Stent thrombosis remains a major pitfall in contemporary percutaneous coronary intervention (PCI), leading to high rates of death and nonfatal myocardial infarction. After the US approval of the first two DES, the sirolimus-eluting stent (Cypher) in 2003 and paclitaxel-eluting stent (Taxus) in 2004, concern was raised about the safety of the devices due to the occurrence of late and very late stent thrombosis. Pooled analyses of available randomized trails at the time, however, showed similar rates of death and myocardial infarction in patients treated with one of these DES compared to the BMS counterpart in randomized clinical trials. Registry studies also supported the safety of DES in unselected patients and off-label type lesions and identified predictors of stent thrombosis (Fig. 1). Nonetheless, knowledge of delayed healing in DES and concern for stent thrombosis led to the recommendation to increase the duration of dual anti-platelet therapy with aspirin and clopidogrel in patients treated with a DES to 12 months.The sirolimus-eluting (SES) and paclitaxel-eluting (PES) DES are often referred to as the first-generation DES.
An Integration of Spectrum Analysis and Attention-based Network for Condition Monitoring of Vibration Components

High reliability of some key components in various engineering systems is crucial to ensure stable performance, safety operation and economic life-cycle management. This requires efficient and effective condition monitoring and predictive maintenance. With the availability of massive sensor data, increasing attention has been paid to data-driven methods for automatic condition recognition and fault diagnosis. This study proposes a lightweight framework combining domain knowledge and deep learning technique. A novel network incorporating 1D convolution module and multi-head self-attention mechanism is designed to receive information from frequency domain by spectrum analysis. Experiments on multiple cases indicate that the developed method is able to achieve the state-of-the-art results and has the potential generality to cover vibration scenarios for different engineering applications.
The Multiple Way

This paper aims to describe an epistemological approach which we call "multiple way". It is a way of relating to knowledge that assumes a dual point of view ("double way") or, more generally, various di fferent points of view, from which the name of multiple way. It uses this method, for instance, when dealing with a problem in more diff erent ways, or when using multiple models to describe the same phenomenon, or even when trying to integrate knowledge from diff erent disciplines, and so on.
VALUATION OF EMBEDDED OPTIONS IN NON-MARKETABLE CALLABLE BONDS: A NEW NUMERICAL APPROACH

The issue of how to price options embedded in callable bonds has attracted a lot of interest over the years. The usual bond valuation methods rely on yield curves, risk premium, and other parameters to estimate interest rates used in discounted cash flow calculations. The option to retire the bond is, however, neglected in the standard pricing models, causing a systematic overvaluation of callable bonds. In the event of a decline in interest rates, investors are exposed to the risk of a lower return on investment than indicated by the yield to maturity. We propose a novel approach to valuing the risk that the issuer will use the right to buy back the bond at a specific call price. While prior models are focused on valuing marketable callable bonds, we deliver a unique approach to valuing bonds with an embedded European option (or a multiple option) that are traded solely through private transactions. These can typically be characterized by the lack of historical records on transaction prices. The modular character of calculation we propose allows us to take into account additional information, such as probable behaviour of the issuer, available opportunities for achieving alternative earnings or different estimates in terms of interest rate development.
Automatic Screening of the Eyes in a Deep-Learning–Based Ensemble Model Using Actual Eye Checkup Optical Coherence Tomography Images

Eye checkups have become increasingly important to maintain good vision and quality of life. As the population requiring eye checkups increases, so does the clinical work burden of clinicians. An automatic screening algorithm to reduce the clinicians’ workload is necessary. Machine learning (ML) has recently become one of the chief techniques for automated image recognition and is a helpful tool for identifying ocular diseases. However, the accuracy of ML models is lower in a clinical setting than in the laboratory. The performance of ML models depends on the training dataset. Eye checkups often prioritize speed and minimize image processing. Data distribution differs from the training dataset and, consequently, decreases prediction performance. The study aim was to investigate an ML model to screen for retinal diseases from low-quality optical coherence tomography (OCT) images captured during actual eye chechups to prevent a dataset shift. The ensemble model with convolutional neural networks (CNNs) and random forest models showed high screening performance in the single-shot OCT images captured during the actual eye checkups. Our study indicates the strong potential of the ensemble model combining the CNN and random forest models in accurately predicting abnormalities during eye checkups.
Role of Professional Skills in Promoting Healthcare System: A Performance Perspective

This paper aims to investigate the impact of sustainable practices especially sustainable production and sustainable supplier management on supply chain performance. This empirical study demonstrates the contextual examination of sustainable practices especially with reference to an emerging economy like Pakistan. Survey was employed to collect data from 100 Food Manufacturing Firms. Exploratory Factor Analysis and Structure Equation Modeling were used through AMOS to test hypothesis. The results reveal that sustainable production and sustainable supplier management both significantly impact triple bottom line. However, sustainable production generates stronger impact on social performance, while, sustainable supplier management significantly effects environmental performance. Additionally, the findings provide valuable insights regarding the use of sustainable production and sustainable supplier management and their impact on supply chain performance. Finally, it propagates utility of ecological value chain management mentioning the impact of couple of sustainable practices on tipple bottom line.
Lipid Fractions and Severity of Coronary Artery Lesions in Acute Myocardial Infarction: A Retrospective Single-Center Study

The relationship between different lipid fractions and the occurrence of atherosclerotic coronary artery disease(CAD) is a well-known fact. However, knowledge of the association of different lipid fractions and the severity and extension of coronary lesions has been rarely reported in clinical studies and is often contradictory. This study aimed to evaluate the association between different lipid fractions and the severity and extent of the coronary atherosclerotic lesion in patients with ST elevated acute myocardial infarction (STEMI). We analyzed data from 50 consecutive patients with STEMI who underwent coronary angiography during hospitalization at the Clinical Hospital in Tetovo. The severity and extent of CAD were defined using the Syntax score (SXscore) algorithm stratified according to tertiles. Laboratory analysis for lipid fractions was performed. It was noticed a statistically significant association between SXscore tertiles and the mean of total cholesterol (TC) to high-density lipoprotein cholesterol (HDL) ratio (TC/HDL), (p=0.008). Correlation test showed week but significant positive correlation between SXscore tertiles and TC/HDL ratio (r=0.339, p=0.016) and Non-HDL (r=0.309, p=0.034). In regression analysis, reduced HDL and increased TC values statistically significantly predicted SXscore (p<0.05). Among all lipid variables examined, the TC/HDL ratio appeared as the most powerful indicator of severity and extension of the coronary lesion in patients with STEMI. The TC/HDL ratio should be used as an easy, non-invasive, and inexpensive method to measure the severity of CAD to determine the diagnosis strategy and treatment of patients with atherosclerotic coronary diseases at risk of acute infarction.
The Progress of Research into Flexible Sensors in the Field of Smart Wearables

In modern society, technology associated with smart sensors made from flexible materials is rapidly evolving. As a core component in the field of wearable smart devices (or ‘smart wearables’), flexible sensors have the advantages of excellent flexibility, ductility, free folding properties, and more. When choosing materials for the development of sensors, reduced weight, elasticity, and wearer’s convenience are considered as advantages, and are suitable for electronic skin, monitoring of health-related issues, biomedicine, human–computer interactions, and other fields of biotechnology. The idea behind wearable sensory devices is to enable their easy integration into everyday life. This review discusses the concepts of sensory mechanism, detected object, and contact form of flexible sensors, and expounds the preparation materials and their applicability. This is with the purpose of providing a reference for the further development of flexible sensors suitable for wearable devices.
Pleiad: An Open-Source Modeling Package for Optimizing Residential Flexibility in the Smart Grid

Demand response (DR) has been increasingly growing in significance among the solutions to tackle climate change, by supporting the development of intermittent renewable energy sources in the smart grid. Many models based on mathematical optimization have been developed to address the challenge of supporting residential customers in providing flexibility services to the grid. However, comparing and applying those models is not always straightforward because of particular data handling or specific assumptions. In this work, we take advantage of the common aspects of DR models to build a metamodel, and hence an open source Python library that aims to unify the concepts and the data streaming in and out of the underlying mathematical optimization models. We demonstrate the effectiveness of the metamodel and of the Python library by using it to implement a task scheduler and to optimize the energy consumption for two dwellings.
Effect of immediate reward function on the performance of reinforcement learning-based energy management system

The performance of reinforcement learning-based energy management system for a pure hybrid electric vehicle critically depends on the articulation of immediate reward function. The current brief systematically unveils the fundamental reliance of reinforcement learning-based agent’s performance on the articulation of immediate reward function. Third generation Toyota hybrid system is chosen as the electrified powertrain for formulating the energy management problem. An asynchronous advantage actor-critic-based reinforcement learning framework is chosen as the control strategy for the energy management system of the aforementioned powertrain. The chosen powertrain architecture offers two degrees-of-freedom, i.e., engine speed and engine torque. Since reinforcement learning agent is solely responsible for controlling these two variables over a given drive cycle without any tactical controllers, reinforcement learning-based agent not only has to find the near-optimal trajectory for the control variables, but should also consider the feasibility criteria for practical operation. Since reinforcement learning agent chooses the control variables randomly without any feasibility check, immediate reward function should be articulated in such a way so that the agent is discouraged to choose any control variable resulting in infeasible powertrain operation.
Sustainable production of healthy, affordable food in the UK: The pros and cons of plasticulture

Abstract An evolving green agenda as the UK seeks to achieve ‘net zero’ in greenhouse gas emissions by 2050, coupled with our new trading relationship with the European Union, is resulting in new government policies, which will be disruptive to Britain's traditional food and farming practices. These policies encourage sustainable farming and land‐sparing to restore natural habitats and will provide an opportunity to address issues such as high emissions of GHGs and dwindling biodiversity resulting from many intensive agricultural practices. To address these and other food challenges such as global conflicts and health issues, Britain will need a revolution in its food system. The aim of this paper is to make the case for such a food revolution where additional healthy food for the UK population is produced in‐country in specialised production units for fruits and vegetables developed on sites previously considered unsuitable for crop production. High crop productivity can be achieved in low‐cost controlled environments, making extensive use of novel crop science and modern controlled‐environment technology. Such systems must be operated with very limited environmental impact. In recent years, growth in the application of plasticulture in UK horticulture has driven some increases in crop yield, quality and value. However, the environmental cost of plastic production and plastic pollution is regarded as a generational challenge that faces the earth system complex. The distribution of plastic waste is ubiquitous, with a significant pollution load arising from a range of agricultural practices. The primary receptor of agriplastic pollution is agricultural soil. Impacts of microplastics on crop productivity and quality and also on human health are only now being investigated. This paper explores the possibility that we can mitigate the adverse environmental effects of agriplastics and thereby exploit the potential of plasticulture to enhance the productivity and positive health impact of UK horticulture.
Are behavioral responses to eyespots in sticklebacks influenced by the visual environment? An experimental examination

Abstract Eyespots are taxonomically widespread color patterns consisting of large concentric rings that are commonly assumed to protect prey by influencing the behaviors of predators. Although there is ample experimental evidence supporting an anti‐predator function of eyespots in terrestrial animals, whether eyespots have a similar deterring function in aquatic animals remains unclear. Furthermore, studies in terrestrial systems suggest that the protective function of eyespots depends on ambient light conditions where predators encounter them, but this effect has never been tested in aquatic environments. Here, we examine how eyespots influence behavioral responses in an aquatic environment under different visual environments, using laboratory‐reared three‐spined sticklebacks (Gasterosteus aculeatus) as model predators. Specifically, we experimentally examined behavioral responses of sticklebacks toward artificial prey patterns (control vs. eyespots) under two different light environment treatments (low vs. high). We found that eyespots did not postpone attacks from sticklebacks. However, sticklebacks approaching eyespots stopped more frequently than sticklebacks approaching prey items with a control pattern. Sticklebacks were (marginally) slower to attack prey in the low‐light treatment, but the light level did not influence stickleback behavioral responses toward eyespots. We conclude that eyespots can modulate some behaviors of an aquatic predator, albeit with a different functional role from that previously demonstrated in terrestrial species.
Towards estimating the untapped potential: a global malicious DDoS mean capacity estimate

ABSTRACTWhat is the malicious reflected distributed denial of service (rDDoS) mean potential of the internet? The authors have been using data from the openNTP project which measures the number of reflectors on the internet since 2014 until now, and completed a graph that roughly estimates a lower boundary for global rDDoS mean potential across four internet protocols (IPs); SSDP, NTP, SNMP and open recursive DNS. By summing these values, and adjusting for average uplink capacity from reflectors, we come to a single number: 108.49 Tb/s as an estimate of rDDoS magnitude potential across IPv4. Tracking this number over time can give us insights into global remediation and clean-up efforts and where to invest our resources in when battling of rDDoS attacks. This paper demonstrates that the upstream throughput is the main contributing, measurable limiting factor for a volumetric rDDoS attack. The largest DDoS event reported by a single target (Dyn in 2016) was 1.2 Tb/s. In contrast, our lower estimate for the.