Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Failure Mechanisms of 1200 V Rated Trench SiC MOSFETs Under Repetitive Avalanche Stress

In this article, the repetitive avalanche ruggedness of silicon carbide metal–oxide–semiconductor field-effect transistors (MOSFETs) with asymmetric trench (AT) and double trench (DT) structure is investigated experimentally. The different failure mechanisms, i.e., thermal-induced fatigue or field oxide breakdown for AT-MOSFET and electric field induced gate oxide degradation or breakdown for DT-MOSFET, are verified by device decapsulation and TCAD simulation. Different from the transient failure in single-pulse avalanche test, the degradation and failure under repetitive avalanche stress are related to the accumulation of gate oxide traps or thermal stress. Under high energy ratio condition, DT-MOSFET fails with shorted gate-drain terminal after only 2k unclamped inductive switching (UIS) cycles. Microscopic failure analysis shows an obvious crack through the bottom gate oxide to N-drift layer, whereas the electrical parameters of AT-MOSFET remain stable during 12k UIS cycles until gate leakage current of 10 mA exceeds the failure threshold of devices. The thermal-induced field oxide breakdown is found upon polysilicon gate in AT-MOSFET. Under low energy ratio condition, over 5% reduction of threshold voltage and on-state resistance is observed in DT-MOSFET due to hot holes injection in gate oxide. However, the threshold voltage of AT-MOSFET is almost constant and an approximately 10% increase of on-state resistance caused by thermal fatigue is observed.
Deep Learning-Based Anomaly Traffic Detection Method in Cloud Computing Environment

To address the problem of poor detection performance of existing intrusion detection methods in the environment of high-dimensional massive data with uneven class distribution, a deep learning-based anomaly traffic detection method in cloud computing environment is proposed. First, the fuzzy C -means (FCM) algorithm is introduced and is combined with the general regression neural network (GRNN) to cluster the samples to be classified in the original space by FCM. Then, the GRNN model is trained and the center point is updated using the sample closest to the FCM clustering center until a stable cluster center is obtained. The parameters in FCM-GRNN are optimized using the global optimization feature of the modified fruit fly optimization algorithm (MFOA), and the optimal spread value is found using the three-dimensional search method through an iterative search. Finally, experiments are conducted based on the KDD CUP99 dataset, and the results demonstrate that the detection rate (DR) and false alarm rate (FAR) of the proposed FCM-MFOA-GRNN method are 91% and 1.176%, respectively, which are better than those of the comparison methods. Therefore, the proposed method has good anomaly traffic detection ability.
Personalized Hip Joint Replacement with Large Diameter Head: Current Concepts

Hip arthroplasty is a common procedure in elective orthopaedic surgery that has excellent outcomes. Hip replacement surgery aims to create a “forgotten” joint, i.e., a pain-free joint akin to a native articulation. To achieve such goals, hip arthroplasty must be personalised. This is achieved by restoring: the centre of rotation of the native hip; leg length equality; femoral offset; femoral orientation; soft tissue tension; joint stability with an unrestricted hip range of motion; and having appropriate stress transfer to the bone. In addition, the whole pathway should provide an uneventful and swift postoperative recovery and lifetime implant survivorship with unrestricted activities. At our institution, the preferred option is a personalized total hip arthroplasty (THA) with a large diameter head (LDH) using either monobloc or dual-mobility configuration for the acetabular component. LDH THA offers an impingement-free range of motion and a reduced risk of dislocation. The larger head-neck offset allows for a supraphysiologic range of motion (ROM). This can compensate for a patient’s abnormal spinopelvic mobility and surgical imprecision. Additionally, LDH bearing with a small clearance exerts a high suction force, which provides greater hip micro-stability. With appropriate biomechanical reconstruction, LDH THA can restore normal gait parameters. This results in unrestricted activities and higher patient satisfaction scores. We use LDH ceramic on ceramic for our patients with a life expectancy of more than 20 years and use LDH dual mobility bearings for all others.
Clinical Study of Clopidogrel Combined with Huoxue Tongluo Prescription in Improving Transient Ischemic Attack and the Effect on MMP-9, Hcy, and CRP

Background This study aimed to explore the clinical study of clopidogrel combined with Huoxue Tongluo prescription in improving transient ischemic attack (TIA) and the effect on MMP-9, Hcy, and CRP. Methods A total of 84 patients with TIA admitted to our hospital from December 2019 to February 2021 were selected. The patients were divided into the control group (42 cases: not treated with Huoxue Tongluo prescription) and study group (42 cases: treatment with Huoxue Tongluo prescription). The clinical efficacy, adverse reactions, the levels of blood pressure and lipid, blood rheology and cerebral hemodynamics, neurological function-related indicators, MMP-9, Hcy, and CRP of the two groups were compared. Results The total effective rate in the study group was higher than the control group. Compared with before treatment, the levels of SBP and DBP in both groups decreased memorably after treatment, and those in the study group decreased more particularly than the control group. The levels of LDL, HDL, TC, and TG in the study group were significantly better than those in the control group. The plasma viscosity, whole blood high shear viscosity, whole blood low shear viscosity, and hematocrit of patients in the study group were lower than those in the control group, and the maximum blood flow velocity, minimum blood flow velocity, average blood flow velocity, and average blood flow were higher than those in the control group. The levels of NSE, MBP, and S100β in the study group were more memorably lower than those in the control group. After treatment, the levels of MMP-9, Hcy, and CRP in the study group were significantly lower than those in the control group. There was no obvious difference in the incidence of adverse reactions between the study group and control group. Conclusion Clopidogrel combined with Huoxue Tongluo prescription can significantly improve the therapeutic effect and reduce the levels of MMP-9, Hcy, and CRP in patients with TIA.
Performance analysis in serial-section electron microscopy image registration of neuronal tissue

Serial-section electron microscopy is a widely used technique for neuronal circuit reconstruction. However, the continuity of neuronal structure is destroyed when the tissue block is cut into a series of sections. The neuronal morphology in different sections changes with their locations in the tissue block. These content changes in adjacent sections bring a difficulty to the registration of serial electron microscopy images. As a result, the accuracy of image registration is strongly influenced by neuronal structure variation and section thickness. To evaluate registration performance, we use the spherical deformation model as a simulation of the neuron structure to analyze how registration accuracy is affected by section thickness and neuronal structure size. We mathematically describe the trend that the correlation of neuronal structures in two adjacent sections decreases with section thickness. Furthermore, we demonstrate that registration accuracy is negatively correlated with neuronal structure size and section thickness by analyzing the second-order moment of estimated translation. The experimental results of registration on synthetic data demonstrate that registration accuracy decreases with the neuronal structure size.
Mobile Machine Learning for Real-time Predictive Monitoring of Cardiovascular Disease

Chronic cardiovascular disease (CVD) is increasingly becoming a burden for global healthcare systems. This burden can be attributed in part to traditional methods of managing CVD in an aging population that involves periodic meetings between the patient and their healthcare provider. There is growing interest in developing continuous monitoring systems to assist in the management of CVD. Monitoring systems can utilize advances in wearable devices and health records, which provides minimally invasive methods to monitor a patient’s health. Despite these advances, the algorithms deployed to automatically analyze the wearable sensor and health data is considered too computationally expensive to run on the mobile device. Instead, current mobile devices continuously transmit the collected data to a server for analysis at great computational and data transmission expense. In this thesis a novel mobile system designed for monitoring CVD is presented. Unlike existing systems, the proposed system allows for the continuous monitoring of physiological sensors, data from a patient’s health record and analysis of the data directly on the mobile device using machine learning algorithms (MLA) to predict an individual’s CVD severity level. The system successfully demonstrated that a mobile device can act as a complete monitoring system without requiring constant communication with a server. A comparative analysis between the support vector
Spatiotemporal Trends and Distributions of Malaria Incidence in the Northwest Ethiopia

Understanding and extracting noticeable patterns of malaria surveillance data at the district level are crucial for malaria prevention, control, and elimination progress. This study aimed to analyze spatiotemporal trends and nonparametric dynamics of malaria incidences in northwest Ethiopia, considering spatial and temporal correlations. The data were analyzed using count regression spatiotemporal models under the Bayesian setups, and parameters were estimated using integrated nested Laplace approximations (INLA). The region had a declining linear trend, and the average annual malaria incidence rate was 24.8 per 1,000 persons between 2012 and 2020. The malaria incidence rate was decreased by 0.984 (95% CI: 0.983, 0.986) per unit increase in months between July 2012 and June 2020. Districts found in the western and northwestern parts of the region had a steeper trend, while districts in the eastern and southern parts had a less steep trend than the average trend of the region. Compared to the regional level trend, the decreasing rate of malaria incidence trends was lower in most town administrations. The nonparametric dynamics showed that the monthly malaria incidence had a sinusoidal wave shape that varied throughout study periods. Malaria incidence had a decreasing linear trend changed across districts of the study region, and the steepness of trends of districts might not depend on incidences. Thus, an intervention and controlling mechanism that considers malaria incidences and district-specific differential trends would be indispensable to mitigate malaria transmission in the region.
Combined heat and power system for the potash mining industry and waste heat use for the municipal facilities

This master thesis presents a study which aimed to develop the best-suited and most sustainable Combined Heat and Power (CHP) system so as to provide the heat and electricity necessary in the extraction and processing of the potash deposits for a mining industry. It aimed also to make use of the waste heat (heat losses) resultant from the mineral processing of the current situation (natural gas boiler and electricity from the grid) in the warming of the close municipal facilities by a district heating, to be applied in the context of the potash mining industry in the center of Catalonia (Barcelona). An energy simulation was developed, using the simulation tool DesignBuilder, with the goal of supplying the heat and domestic hot water demands by that energy losses. The results showed that the 10% of heating losses are sufficient to supply the proposed district heating for the eight municipal facilities considered. On the other hand, a state of the art of some CHP technologies was made, and the matrix modeling methodology for cogeneration systems is presented. A comprehensive cogeneration system operation was implemented in Matlab © and using the Excel, and was carried out to develop the best topology design, in a number of eleven proposed systems (cases A to K), on the selection of a CHP system focusing on economic and environmental parameters (costs and carbon dioxide emissions). The final results show that, in terms of environmental analysis, the cases D to J (Case D: PTC and Solar PV system; Case E: LFR and Solar PV system; Case F: Biomass combustion and Solar PV system, Case G: PTC and PTC with steam turbine, Case H: LFR and LFR with Steam turbine, Case I: PTC and Biomass combustion and Steam turbine and Case J: LFR and Biomass combustion with Steam turbine) are all advantageous because they have no CO2 emissions. In terms of economic analysis the case D and G are the most advantageous. The case D has positive validation for energy costs and intermediate validation for the investment costs while case G has positive validation for both the energy and investment costs. An attempt to find the best cogeneration system in economic and environmental terms was made, allowing to suggest the case G, which seems to be the one that has the best results. In conclusion, the final decision should be taken by CTM and, naturally, according to the interests of the mining industry where this project will be applied.
Decision Tree Method to Classify the Electroencephalography-based Emotion Data

Electroencephalography (EEG) data contains recordings of brain signal activity divided into several channels with different impulse responses that can be used to detect human emotions. In classifying emotions, EEG data needs to be parsed or signal processed into values ​​that can help recognize emotions. Research related to electroencephalography has been carried out previously and has experienced success using the Fuzzy C-Means, Multiple Discriminant Analysis, and Deep Neural Network methods. This study was conducted to classify human emotions from electroencephalography data on 10 participants. Each participant carried out 40 trials of testing using the Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT) methods at the initial stage of classification and the Decision Tree method as the final method that can improve the accuracy of the two methods at the initial stage of classification. The results of this study were the finding of 2 participants (3 trials) who were unmatched from a total of 10 participants (400 trials), which were analyzed using the decision tree method. The decision tree method can correct this error and increase the classification result to 100%. The DWT method is used as a reference in the classification of emotions, considering that the DWT method has an output of arousal and valance values ​​. In contrast, the PSD method only has a combined output.
3D coaxial bioprinting: process mechanisms, bioinks and applications

In the last decade, bioprinting has emerged as a facile technique for fabricating tissues constructs mimicking the architectural complexity and compositional heterogeneity of native tissues. Amongst different bioprinting modalities, extrusion-based bioprinting (EBB) is the most widely used technique. Coaxial bioprinting, a type of EBB, enables fabrication of concentric cell-material layers and enlarges the scope of EBB to mimic several key aspects of native tissues. Over the period of development of bioprinting, tissue constructs integrated with vascular networks, have been one of the major achievements made possible largely by coaxial bioprinting. In this review, current advancements in biofabrication of constructs with coaxial bioprinting are discussed with a focus on different bioinks that are particularly suitable for this modality. This review also expounds the properties of different bioinks suitable for coaxial bioprinting and then analyses the key achievements made by the application of coaxial bioprinting in tissue engineering, drug delivery and in-vitro disease modelling. The major limitations and future perspectives on the critical factors that will determine the ultimate clinical translation of the versatile technique are also presented to the reader.
Stereo Vision Method Application to Road Inspection

The paper presents the stereo vision method for the mapping of road pavement. The mapped road is a set of points in three-dimensional space. The proposed method of measurement and its implementation make it possible to generate a precise mapping of a road surface with a resolution of 1 mm in transverse, longitudinal and vertical dimensions. Such accurate mapping of the road is the effect of application of stereo images based on image processing technologies. The use of matching measure CoVar, at the stage of images matching, help eliminate corner detection and filter stereo images, maintaining the effectiveness of the algorithm mapping. The proper analysis of image-based data and application of mathematical transformations enable to determine many types of distresses such as potholes, patches, bleedings, cracks, ruts and roughness. The paper also aims at comparing the results of proposed solution and reference test-bench. The statistical analysis of the differences permits the judgment of error types.
Evaluation of Risk Factors for the Development of Invasive Fungal Infections in Immunosuppressed Patients

Bulgular: Olguların dağılımı; kanıtlanmış İFE (58/84, %69), olası İFE (12/84, %14,2), mümkün İFE (14/84, %16,6) olarak tespit edildi. Kanıtlanmış İFE olgularının %91,3’ü (53/58) invazif kandidiyazis (İK), %5,1’i (3/58) ise invaziv pulmoner aspergillozis (İPA), nazofaringeal invazif fungal tutulum (1/58, %1,7) ve kolonda invazif fungal tutulum (1/58, %1,7) idi. Olası ve mümkün İFE hastalarının hepsi (26/26, %100) İPA olarak saptandı. Bilinen İFE risk faktörleri olan immünsüpresif tedavi kullanımı, renal replasman tedavisi ihtiyacı, mekanik ventilasyon ihtiyacı, santral venöz kateter mevcudiyeti, üriner kateter mevcudiyeti, GİS kateter mevcudiyeti, mukozit/diyare/ileus öyküsü, malnutrisyon, kan ürünü transfüzyonu, bakteriyel enfeksiyon, antibakteriyel tedavi, hastane ve yoğun bakım yatış süreleri, nötropeni süresi tüm hastalarda irdelendi. Hematolojik malignite ve non-hematolojik immünosüpresif ABSTRACT
Bayesian Kernelized Matrix Factorization for Spatiotemporal Traffic Data Imputation and Kriging

Missingness and corruption are common problems for real-world traffic data. How to accurately perform imputation and prediction based on incomplete or even sparse traffic data becomes a critical research question in intelligent transportation systems. Low-rank matrix factorization (MF) is a common solution for the general missing value imputation problem. To better characterize and encode the strong spatial and temporal consistency in traffic data, existing work has introduced flexible spatial/temporal Gaussian process (GP) priors to model the latent factors in MF framework, which also allows us to perform kriging for unseen locations and virtual sensors. However, learning the hyperparameters in GP kernels remains a challenging task. In this paper, we present a Bayesian kernelized matrix factorization (BKMF) model with an efficient Markov chain Monte Carlo (MCMC) sampling algorithm for model inference. By learning the kernel hyperparameters from their marginal posteriors through a slice sampling treatment and updating the latent factors alternatively with Gibbs sampling, we achieve a fully Bayesian model for the spatiotemporally kernelized (i.e., GP prior regularized) MF framework. We apply BKMF on both imputation and kriging tasks, and our results demonstrate the superiority of BKMF compared with state-of-the-art spatiotemporal models. In addition, we also explore the effects of different GP kernels in characterizing networked spatiotemporal traffic state data.
Analysis of the APOB Gene and Apolipoprotein B Serum Levels in a Mexican Population with Acute Coronary Syndrome: Association with the Single Nucleotide Variants rs1469513, rs673548, rs676210, and rs1042034

Apolipoprotein B (APOB) is associated with the development of atherosclerosis and consequently in the acute coronary syndrome (ACS) physiopathology. Single number variants (SNVs) in apolipoprotein B gene (APOB) influence over the susceptibility for this syndrome. The aim of this study was to determine the impact of the rs1469513, rs673548, rs676210, and rs1042034 SNVs and serum levels of APOB in the risk of ACS in a population from western Mexico. We included 300 patients in the group of cases (ACSG) and 300 individuals in the control group (CG). APOB levels were evaluated by immunonephelometry, and SNVs were genotyped with TaqMan probes. We found significant allelic and genotypic differences between groups for rs673548 and rs676210 (OR = 1.33, p=0.030, OR = 2.69, p < 0.001) and rs1042034 (OR = 0.50, p=0.037) SNVs. We found a risk haplotype TAGT (OR: 2.14, IC 1.50–3.04, p < 0.001). Our findings support a significant risk association between rs673548 and rs676210 variants for ACS; meanwhile, rs1042034 could be considered protective factor in a western Mexican population. Also, in this population, haplotype TAGT may confer 2.14 times a higher risk. APOB serum levels were compared by genotype variants in both groups without any significant statistical difference.
Silencing of MsD14 Resulted in Enhanced Forage Biomass through Increasing Shoot Branching in Alfalfa (Medicago sativa L.)

Branching is one of the key determinants of plant architecture that dramatically affects crop yield. As alfalfa is the most important forage crop, understanding the genetic basis of branching in this plant can facilitate breeding for a high biomass yield. In this study, we characterized the strigolactone receptor gene MsD14 in alfalfa and demonstrated that MsD14 was predominantly expressed in flowers, roots, and seedpods. Furthermore, we found that MsD14 expression could significantly respond to strigolactone in alfalfa seedlings, and its protein was located in the nucleus, cytoplasm, and cytomembrane. Most importantly, transformation assays demonstrated that silencing of MsD14 in alfalfa resulted in increased shoot branching and forage biomass. Significantly, MsD14 could physically interact with AtMAX2 and MsMAX2 in the presence of strigolactone, suggesting a similarity between MsD14 and AtD14. Together, our results revealed the conserved D14-MAX2 module in alfalfa branching regulation and provided candidate genes for alfalfa high-yield molecular breeding.