Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Hot-Carrier Reliability of an Ultralow RON,sp SOI-LDMOS by Linearly Doped Technology for Automotive Application

In this work, an ultralow specific ON-resistance (<inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ </tex-math></inline-formula>) silicon-on-insulator lateral double diffusion metal-oxide-semiconductor (SOI-LDMOS) applied in automotive circuits has been fabricated based on the <inline-formula> <tex-math notation="LaTeX">$0.18~\mu \text{m}$ </tex-math></inline-formula> process technology with 16.5 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>mm2 which leads about 30% than that of reported studies. However, the poor hot carrier reliability of the SOI-LDMOS cannot fulfill the automotive circuits. To solve it, a new device has been proposed with linearly doped technology based on the discussions on the inner mechanisms of the <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> degradation. Thanks to the linear doping concentration in lateral and vertical directions near the damage points (poly-gate edge and bird’s beak), the impact ionization and vertical electric field have been weakened evidently. As a result, the <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON}}$ </tex-math></inline-formula> degradation of the proposed device (Device A) has been improved effectively and decreased from 11% to lower 1% when stressing 10000 s under the hot carrier stress. Meanwhile, the static electrical parameters of the Device A are still in an acceptable changes with OFF-state breakdown voltage (BVOFF) about 55 V and <inline-formula> <tex-math notation="LaTeX">${R}_{ \mathrm{\scriptscriptstyle ON},\text {sp}}$ </tex-math></inline-formula> about 17.5 <inline-formula> <tex-math notation="LaTeX">$\text{m}\Omega \cdot $ </tex-math></inline-formula>mm2.
Separating quasi-P-wave in transversely isotropic media with a vertical symmetry axis by synthesized pressure applied to ocean-bottom seismic data elastic reverse time migration

ABSTRACTQuasi-P (qP)-wavefield separation is a crucial step for elastic P-wave imaging in anisotropic media. It is, however, notoriously challenging to quickly and accurately obtain separated qP-wavefields. Based on the concepts of the trace of the stress tensor and the pressure fields defined in isotropic media, we have developed a new method to rapidly separate the qP-wave in a transversely isotropic medium with a vertical symmetry axis (VTI) by synthesized pressure from ocean-bottom seismic (OBS) data as a preprocessing step for elastic reverse time migration (ERTM). Another key aspect of OBS data elastic wave imaging is receiver-side 4C records back extrapolation. Recent studies have revealed that receiver-side tensorial extrapolation in isotropic media with ocean-bottom 4C records can sufficiently suppress nonphysical waves produced during receiver-side reverse time wavefield extrapolation. Similarly, the receiver-side 4C records tensorial extrapolation was extended to ERTM in VTI media in our studie.
Oxygen Vacancies Induced Performance Enhancement of Sensitivity to Acetone Using La-Doped ZnSnO3 Nanosheets: Experimental and DFT-D3 Study

To develop a sensor for acetone gas at low concentrations to protect the environment and human health, La-doped ZnSnO3 (LZSO, La:Sn = 0.25 at%, 0.5 at%, and 1 at%) nanosheets were prepared by a hydrothermal method. The gas-sensing capabilities were extensively examined. According to the results, the optimal doping amount is La: Sn = 0.25 at% (LZSO1), and the LZSO nanosheets demonstrated the best responses of 16.83 and 427.44 at 5 and 100 ppm acetone, respectively, at the optimal working temperature (325 °C). The gas-sensing performance was greatly enhanced compared to that of the ZnSnO3 nanosheets. The concentration of oxygen vacancies (VO) was improved by La doping, which is advantageous for acetone gas-sensing performance. Furthermore, the findings of theoretical calculations suggested that La doping can increase the formation of surface oxygen vacancies, which was consistent with the results of the experiments. The VO can activate the surface, reduce the work function, and promote oxygen adsorption, which is beneficial for electron transfer. Thus, the method of doping La $^{{3}+}$ into ZnSnO3 nanosheets is promising for developing high-performance acetone gas sensors.
Environmental resilience of rangeland ecosystems: assessment drought indices and vegetation trends on arid and semi-arid zones of Central Asia

The Central Asian (CA) rangelands is a part of the arid and semi-arid ecological zones and spatial extent of drylands in CA (Tajikistan, Kazakhstan, Uzbekistan, Kyrgyzstan, and Turkmenistan) is vast. Projections averaged across a suite of climate models, as measured between 1950-2012 by Standardised Precipitation-Evapotranspiration Index (SPEI) estimated a progressively increasing drought risks across rangelands (Turkmenistan, Tajikistan and Uzbekistan) especially during late summer and autumn periods, another index: Potential Evapotranspiration (PET) indicated drought anomalies for Turkmenistan and partly in Uzbekistan (between 1950-2000). On this study, we have combined a several datasets of drought indices ( SPIE, PET, temperature_T°C and precipitation_P) for better estimation of resilience/non-resilience of the ecosystems after warming the temperature in the following five countries, meanwhile, warming of climate causing of increasing rating of degradations and extension of desertification in the lowland and foothill zones of the landscape and consequently surrounding experienced of a raising balance of evapotranspiration (ET0). The study concluded, increasing drought anomalies which is closely related with raising (ET0) in the lowland and foothill zones of CA indicated on decreasing of NDVI indices with occurred sandy and loamy soils it will resulting a loss of vegetation diversity (endangered species) and raising of wind speeds in lowlands of CA, but on regional level especially towards agricultural intensification (without rotation) it indicated no changes of greenness index. It was investigated to better interpret how vegetation feedback modifies the sensitivity of drought indices associated with raising tendency of air temperature and changes of cold and hot year seasons length in the territory of CA.
A modelling study to dissect the potential role of voltage-gated ion channels in activity-dependent conduction velocity changes as identified in small fiber neuropathy patients

Objective Patients with small fiber neuropathy (SFN) suffer from neuropathic pain, which is still a therapeutic problem. Changed activation patterns of mechano-insensitive peripheral nerve fibers (CMi) could cause neuropathic pain. However, there is sparse knowledge about mechanisms leading to CMi dysfunction since it is difficult to dissect specific molecular mechanisms in humans. We used an in-silico model to elucidate molecular causes of CMi dysfunction as observed in single nerve fiber recordings (microneurography) of SFN patients. Approach We analyzed microneurography data from 97 CMi-fibers from healthy individuals and 34 of SFN patients to identify activity-dependent changes in conduction velocity. Using the NEURON environment, we adapted a biophysical realistic preexisting CMi-fiber model with ion channels described by Hodgkin-Huxley dynamics for identifying molecular mechanisms leading to those changes. Via a grid search optimization, we assessed the interplay between different ion channels, Na-K-pump, and resting membrane potential. Main results Changing a single ion channel conductance, Na-K-pump or membrane potential individually is not sufficient to reproduce in-silico CMi-fiber dysfunction of unchanged activity-dependent conduction velocity slowing and quicker normalization of conduction velocity after stimulation as observed in microneurography. We identified the best combination of mechanisms: increased conductance of potassium delayed-rectifier and decreased conductance of Na-K-pump and depolarized membrane potential. When the membrane potential is unchanged, opposite changes in Na-K-pump and ion channels generate the same effect. Significance Our study suggests that not one single mechanism accounts for pain-relevant changes in CMi-fibers, but a combination of mechanisms. A depolarized membrane potential, as previously observed in patients with neuropathic pain, leads to changes in the contribution of ion channels and the Na-K-pump. Thus, when searching for targets for the treatment of neuropathic pain, combinations of several molecules in interplay with the membrane potential should be regarded.
Compensation of Demagnetization Characteristics to Improve the Turn-to-Turn Short-Circuit Insulation Resistance Estimation in PMSG Using Moving Horizon Estimation Method

The severity of turn-to-turn short circuit (TTSC) faults reported in the literature considers the fault progression as number of short circuit turns. However, the deterioration of the winding insulation linked to the insulation/contact resistance is neglected. This article proposes a novel framework to improve the estimation of insulation resistance, and a TTSC fault indicator to capture the degradation of winding insulation. A model-based approach, based on moving horizon estimation with control reference, and current and voltage residuals are used. In addition to the TTSC fault effect on magnetic flux linkage, the demagnetization fault effect overlap resulting in reduced estimation accuracy. Therefore, a novel framework is proposed to compensate the effect, which includes real-time update of the magnet's knee point, and a fault indicator that reflects the initialization of the irreversible demagnetization state. A finite element method-based simulation and an experimental test-rig are used to validate the proposed approach. The proposed estimation method can accurately estimate the TTSC fault parameters for different load conditions under constant frequency generator operation, and can identify a low severity TTSC fault of 5% for any low severity of insulation resistance.
PERKEMBANGAN SENI PERTUNJUKAN LAIS DI GUNUNG GALUNGGUNG KABUPATEN TASIKMALAYA

Penelitian ini bertujuan untuk menelusuri perkembangan seni pertunjukan lais yang masih eksis di Gunung Galunggung, Kabupaten Tasikmalaya. Metode yang digunakan dalam penelitian ini adalah metode penelitian kualitatif. Data penelitian dikumpulkan dengan menggunakan teknik wawancara dan dilanjutkan dengan analisis pemahaman secara interaktif. Subjek penelitian yang dijadikan narasumber adalah pelaku seni pertunjukan yakni seniman Padepokan Jati Diri Nurcahya Putra Galunggung Kabupaten Tasikmalaya. Hasil penelitian ini menunjukan bahwa seni pertunjukan lais masih benar nyata keberadaannya dan mengalami perkembangan secara kontekstual pertunjukan. Simpulan hasil penelitian ini menunjukan bahwa seni pertunjukan lais harus dipahami dan dilestarikan guna melindungi ekosistem seni pertunjukan dan menjaga warisan budaya bangsa.
TARGET-GUIDED LEARNING FOR RARE CLASS SEGMENTATION IN LARGE-SCALE URBAN POINT CLOUDS

Abstract. In large-scale urban areas, the diversity of objects and the complexity of scenes pose challenges to semantic segmentation of point clouds. In particular, the data imbalance problem often results in poor performance for rare classes in large scenes. This paper proposes a rare class segmentation method based on the target-guided transformer network. In the network, all the feature extraction and segmentation procedures are realized by attention mechanisms. The self-attention blocks are embedded in U-Net-like structure to gradually integrate the features from local to global. Then, under the supervision of our target-guided block, the instance features of data-imbalanced rare classes are mapped onto the multi-scale features. At last, a multi-layer perceptron is utilized to convert the fused features to the segmentation logits for generating the semantic labels. Experiments using the Hessigheim High- Resolution 3D Point Cloud Benchmark indicated that our approach considerably outperforms the baseline network by up to 11.66% in terms of mean F1 score. In particular, the rare classes Vehicle and Chimney obtain outstanding F1-scores of 82.40% and 82.51%, respectively. Furthermore, our method achieves an overall accuracy of 87.63%, which increases by 1.09% compared to the baseline model.
Phenotypic and Genotypic Antimicrobial Resistance Profile of Salmonella spp. Isolated from Kampung Chicken Carcasses

Kampung chicken meats have been widely consumed in Indonesia as well as broiler chicken. However, the extensive rearing allowed multidrug-resistant (MDR) bacteria exposure to kampung chicken, including through horizontal gene transfer. This study aimed to observe the correlation between the phenotypic and genotypic antimicrobial resistance profile of Salmonella spp. isolated from kampung chicken carcasses. Phenotypic antimicrobial resistance was evaluated by Kirby-Bauer's disk diffusion method, while the detection of drug resistance genes in seventeen isolates of Salmonella was carried out by PCR. All (17/17) isolates were susceptible to ampicillin and chloramphenicol. Most isolates of Salmonella were resistant to erythromycin (82%; 14/17), while the decreased susceptibility (intermediate category) most occurred in oxytetracycline (82%; 14/17). Salmonella Typhimurium showed a resistance pattern to more antimicrobial groups than S. Newport and S. Weltevreden. Several antimicrobial resistance genes (blaTEM, tetG, cmlA, gyrA) were present in all (17/17) isolates of Salmonella spp. Resistance to antimicrobial agents and the presence of resistance genes were not always related. This study could provide beneficial information regarding the transmission of antimicrobial resistance among Salmonella spp. from kampung chickens.
Performance Analysis of Machine Learning Techniques in Network Intrusion Detection

A lot of sensitive data is being transmitted over the internet nowadays, which leads to increasedrisks of network attacks. To identify suspicious and malicious activities to secure internal networks,intrusion detection systems aim to recognize unusual access or attacks to the network. Machine learningtechnology can play a vital role in a scheme to detect intrusion. It is a technology that is based onclassification and prediction, to deal with security threats. In this work, we focus on significant featureselection and classification using four machine learning algorithms. Adaptive Boost (AdaBoost), GradientBoosting, Random Forest, and Decision Tree classification techniques have been tested on the dataset ofnetwork intrusion detection which is collected from Kaggle. In our analysis, Gradient Boosting outperformsconsidering the F1-score. Therefore, this machine learning technique can be utilized to implement anintelligent intrusion detection system.
Identification of Maturity Stage of Cacao using Visible Near Infrared (Vis-NIR) and Shortwave Near Infrared (SW-NIR) Reflectance Spectroscopy

Choosing the cacao maturity stage is essential for producing high-quality cacao beans. Identifying indicators of the maturity level of cacao is a complex task because these fruits do not exhibit the characteristics of other fruits during the ripening period. Generally, cacao maturity is determined manually based on the estimated daily harvest date using sensory observation, which is marked by changes in the color of the cacao skin. This is certainly inaccurate because visual assessment is only performed subjectively. This is inaccurate because visual assessment is only performed subjectively, which is not in line with the demands of Industrial Revolution 4.0, which is a fast and accurate technology for sorting cacao. In this study, cacao maturity was identified using visible (350-1000 nm) and shortwave near-infrared spectra (SW-NIR) spectroscopy (1000-1600 nm). Chemometric analysis using principal component analysis-linear discriminant analysis (PCA-LDA) was used to classify cacao maturity. The results showed that SW-NIR spectroscopy yielded better performances with calibration and prediction accuracy of 92,50% and 85% using Savitzky–s 1st derivative (SGD1) spectra compared to Vis-NIR spectroscopy had calibration and prediction accuracies of 90% and 86% using raw spectra for PCA-LDA model.
Transfer learning between crop types for semantic segmentation of crops versus weeds in precision agriculture

Agricultural robots rely on semantic segmentation for distinguishing between crops and weeds to perform selective treatments and increase yield and crop health while reducing the amount of chemicals used. Deep‐learning approaches have recently achieved both excellent classification performance and real‐time execution. However, these techniques also rely on a large amount of training data, requiring a substantial labeling effort, both of which are scarce in precision agriculture. Additional design efforts are required to achieve commercially viable performance levels under varying environmental conditions and crop growth stages. In this paper, we explore the role of knowledge transfer between deep‐learning‐based classifiers for different crop types, with the goal of reducing the retraining time and labeling efforts required for a new crop. We examine the classification performance on three datasets with different crop types and containing a variety of weeds and compare the performance and retraining efforts required when using data labeled at pixel level with partially labeled data obtained through a less time‐consuming procedure of annotating the segmentation output. We show that transfer learning between different crop types is possible and reduces training times for up to 80%. Furthermore, we show that even when the data used for retraining are imperfectly annotated, the classification performance is within 2% of that of networks trained with laboriously annotated pixel‐precision data.
The Role of Next Generation Sequencing in the Differential Diagnosis of Caroli’s Syndrome

Abstract We report the case of a 41-year-old man on conservative treatment for more than 20 years for chronic renal insufficiency, chronic hepatitis and recurrent cholangitis. Following lengthy and extensive diagnostics, the differential diagnosis included primary sclerosing cholangitis and Caroli’s disease (CD). To solve the diagnostic challenge, next generation sequencing (NGS) was performed to distinguish between the disorders possibly present in the patient. The diagnosis of CD became evident after two rare known pathogenic mutations were detected in the poly-ductin 1 (PKHD1) gene, c.370C>T (p.Arg124Ter) and c.4870C>T (p.Arg1624Trp). In this case, NGS was instrumental in solving the diagnostic challenge, allowing differentiation among the proposed genetic and non-genetic ethiologies.
Single Traffic Image Deraining via Similarity-Diversity Model

Single traffic image deraining technology based on deep learning is a vital branch of image preprocessing, which is of great help to intelligent monitoring systems and driving navigation system. It is well understood that established deraining methods are derived based on one specific imaging model, neglecting the underlying correlations between different weather models and thereby limiting the applicability of these standard methods in real scenarios. To ameliorate this issue, in this work, we first explore the inherent relationship between a rain model and the haze one established up to date. We discover that these two models experience similar degradations in the low-frequency components (i.e., similarity) but diverse degradations in the high-frequency areas (i.e., diversity). Based on these observations, we develop a Similarity-Diversity model to describe these characteristics. Afterwards, we introduce a novel deep neural network to restore the rain-free background embedding the similarity-diversity model, namely deep similarity-diversity network (DSDNet). Extensive experiments have been conducted to evaluate our proposed method that outperforms the other state of the art deraining techniques. On the other hand, we deploy the proposed algorithm with Google Vision API for object recognition, which also obtains satisfactory results both qualitatively and quantitatively.
Multiscale numerical study of the ELM-induced sputtering on the tungsten castellated divertor gap

Material sputtering and erosion are key issue in fusion science. In the steady state, tungsten sputtering can be maintained at a low level. However, tungsten is seriously sputtered by the hot plasma from edge localized mode (ELM) bursts. The time evolution and spatial distribution of sputtering on the divertor gap are still not clear. This unclearness influences the understanding of ELM-induced sputtering and the explanation of experimental phenomenon. In this study, the evolution of tungsten flux generated by ELM burst is obtained via hybrid multiscale simulation and a double-peak of tungsten flux is found. The first peak is produced by the ELM electrons, which provides great sheath potential, and the second peak is generated by the energetic ions from ELM. The castellated divertor gap (CDG) can influence the tungsten sputtering distribution due to the change of the impacting angle. The sputtering and erosion on CDG are more serious than that on the mono-block top, but the tungsten source from the gap is very insignificant because the gap size is small. The simulation results are compared with EAST experiments and a qualitative consistency is obtained.
Estimation of Left Ventricular Ejection Fraction Based on Swin_Uniform

A new left ventricular ejection index prediction method was proposed by introducing a block attention mechanism, which helps to reduce overfitting problems and improve the power of the model to process new different samples. Swin Transformer is a Transformer architecture that retains the modeling advantages of self attention mechanism. Its hierarchical design has both global and local modeling capabilities, and can output multi resolution feature maps, which is suitable for EchoCoTr's need to extract multi-scale information from time series medical images. Meanwhile, the resources required of Swin Transformer at runtime are linearly related to images to be processed, which reduces computational complexity and is suitable for processing high-resolution sequence images in EchoCoTr[1]. Swin Transformer has better results than Transformer in both classification and detection tasks, which just goes to show that it is somewhat of a great application in the field of vision. This is beneficial for EchoCoTr's understanding of cardiac ultrasound sequences. Therefore, in the original UniFormer model structure of the EchoCoTr[1] model, a new Swin Transformer Block is added. The new model has stronger modeling capabilities. This means that better results can be obtained under the same computing resources.