Miracle-dz/UniFAR-FTUs huggingface dataset:
An Improved Protection Circuit Design for Fast Detection of Short-Circuit in IPM

Short-circuit protection plays a vital role in ensuring the overall reliability of intelligent power module (IPM), where the shorter the duration of a short-circuit fault, the smaller its impact on the module. The conventional $V_{\mathrm {CE}}$ desaturation detection circuit suffers from a blanking time problem. This paper aims to solve this problem by improving the IPM short-circuit fast detection and protection circuit with integrated shunts in order to achieve ultra-fast detection and protection of the IPM short-circuit faults. At the same time, the number of shunts is decreased from three to two, which reduces the impact on the power density of the device. First, the types of IPM short-circuit faults and the experimental principle of a short-circuit are introduced in this paper. Second, the overcurrent fault protection of the improved circuit with the shunts integrated at different positions in the IPM power unit is analyzed and verified using the PSPICE simulations. Third, a method for selecting the component parameters of a compensation link of the improved circuit is proposed, and the functions for the remaining parts in the circuit are introduced. Last, it is verified through experiments that the improved circuit can quickly detect various short-circuit faults, and initiate the corresponding shutting down of the insulated gate bipolar transistor (IGBT) after 490 ns. Compared with the conventional $V_{\mathrm {CE}}$ desaturation detection method, the detection time of the proposed method and the associated short-circuit loss for modules under different short-circuit faults are significantly reduced.
Surface Plasmon Resonance (SPR) Sensor for Cancer Biomarker Detection

A biomarker is a physiological observable marker that acts as a stand-in and, in the best-case scenario, forecasts a clinically significant outcome. Diagnostic biomarkers are more convenient and cost-effective than directly measuring the ultimate clinical outcome. Cancer is among the most prominent global health problems and a major cause of morbidity and death globally. Therefore, cancer biomarker assays that are trustworthy, consistent, precise, and verified are desperately needed. Biomarker-based tumor detection holds a lot of promise for improving disease knowledge at the molecular scale and early detection and surveillance. In contrast to conventional approaches, surface plasmon resonance (SPR) allows for the quick and less invasive screening of a variety of circulating indicators, such as circulating tumor DNA (ctDNA), microRNA (miRNA), circulating tumor cells (CTCs), lipids, and proteins. With several advantages, the SPR technique is a particularly beneficial choice for the point-of-care identification of biomarkers. As a result, it enables the timely detection of tumor markers, which could be used to track cancer development and suppress the relapse of malignant tumors. This review emphasizes advancements in SPR biosensing technologies for cancer detection.
Cooperative Target Detection Based on UAV Jitter Model

Motivated by the improved direction of arrival (DOA) estimation performance by intelligent reflecting surface (IRS) in target detection systems, we propose a system using IRS for the target detection on the unmanned aerial vehicle (UAV) to improve the anti-interference capability and target detection performance. However, the UAV movement degrades the detection performance, so we formulate an UAV jitter model, in which the horizontal and vertical jitters with IRS model is considered. Then, we optimize the beamforming coefficients to maximize signal-to-noise ratio (SNR) of the received signals with UAV movement. Meanwhile, the performance improvement introduced by IRS is shown by the proposed optimization method with UAV. Simulation results illustrate that, When IRS is applied to UAV target detection, with the increased number of IRS units of IRS-aided UAV target detection system, the optimized method has better detection probability and anti-jitter interference capability compared with the existing non-IRS-assisted target detection systems.
Polarized Raman scattering and localized embedded strain in self-organized Si/Ge nanostructures

Using polarized Raman spectroscopy, we examine different vibrational modes (i.e., Si–Si, Si–Ge, and Ge–Ge) in Si/Ge self-organized nanostructures. Here, we present unambiguous proof that multilayers of Ge nanometer-size, “dome-shaped” islands grown on a 〈100〉 Si substrate are nearly fully relaxed and that the built-in strain field is substantially localized in the surrounding Si matrix. In contrast, multilayers with “pyramid-shaped” islands do not show observable relaxation. The large strain in the Si layers of the multilayer dome samples correlates with the greater self-organization in these structures compared to the multilayer pyramid samples.
Study of Viscoelastic Properties of Graphene Foams Using Dynamic Mechanical Analysis and Coarse-Grained Molecular Dynamics Simulations

As a promising nano-porous material for energy dissipation, the viscoelastic properties of three-dimensional (3D) graphene foams (GrFs) are investigated by combining a dynamic mechanical analysis (DMA) and coarse-grained molecular dynamic (CGMD) simulations. The effects of the different factors, such as the density of the GrFs, temperature, loading frequency, oscillatory amplitude, the pre-strain on the storage and loss modulus of the GrFs as well as the micro-mechanical mechanisms are mainly focused upon. Not only the storage modulus but also the loss modulus are found to be independent of the temperature and the frequency. The storage modulus can be weakened slightly by bond-breaking with an increasing loading amplitude. Furthermore, the tensile/compressive pre-strain and density of the GrFs can be used to effectively tune the viscoelastic properties of the GrFs. These results should be helpful not only for understanding the mechanical mechanism of GrFs but also for optimal designs of advanced damping materials.
Development of Online Local History Learning Media Based on Virtual Field Trips to Enhance the Use of Primary Source Evidence

It is important for students who learn history to have the skills to think, read and analyze historical sources because past events can only be reconstructed and understood from these numerous resources. Various media methods are needed to support the development of these skills, especially in online learning. Therefore, this research aims to create a virtual field trip (VFT) based history learning media to help students improve their thinking skills using primary source evidence. It was carried out by using the research and development (R&D) method consisting of four stages, namely analysis, designs, conducting trials, and testing media effectiveness. The sample subjects consisted of six teams as expert validators, seven history teachers, 70 and 280 Class X students who assessed the product's practicality and determined the developed media's effectiveness. Data were collected through expert validation, student and teacher response questionnaire sheets, as well as description exams. The level of validity and practicality of the medium were determined using descriptive analysis, while the N-Gain approach evaluated the media's effectiveness. The expert validation result is very good, with an average score of 3.77. The teacher and student response tests showed an average score of 4.67, indicating that the VFT medium is practical for learning history. The N-Gain value of 72% showed that the students' abilities to use primary source evidence are increasing. Therefore, using VFT based history classes to develop explanation skills using primary source evidence is feasible, practical, and useful.
Research on Risk Situation Awareness and Prediction Method of Industrial Control Systems

In the operation process of the power plant, in addition to the operating state information of the power plant process systems and components is monitored by Industrial Control Systems (ICSs), a large number of the operating state information of ICSs and its components themselves can be obtained. These information helps operators to understand the operation safety state of ICSs themselves, and whether ICSs operate reliably, which plays an important role in maintaining the normal operation of the power plant, controlling abnormal events and mitigating the development of accidents. This paper proposes an integrated methodological framework for real-time risk awareness and prediction of ICS based on the particle filtering model whose features are updating monitoring data and predicting the state transition process, and focus on the measurement of risk states, identification of critical risk states and key components, modeling of the state transition process, prediction of risk levels and estimation of remaining arrival time of high-risk residual. The assumption that the transition probability matrix of system states does not change over time in traditional Markov predictions is discarded in this proposed method. Compared with the traditional ICS safety analysis method, this method can provide global and systemic risk insights for ICS risk situation, with a better real-time performance, and it is a kind of more scientific and objective measurement for ICS risk situation.
Rotational Analysis of the PH and PD A3Πi–X3Σ− Band Systems

PH and PD radicals have been produced in a Schuler type discharge through PH3 and PD3. Besides the already known (0–0) bands of PH and PD, new bands, namely the (0–1) band of PH and the (1–0) and (1–1) bands of PD have been photographed in emission, under high resolution. Rotational analyses have been performed for these six bands and also for the (1–0) band of PH analyzed earlier by Legay in absorption. The 3Π and 3Σ states fine structure has been interpreted by a simple model, which includes spin–spin, and second-order spin–orbit interactions and also centrifugal distortion effects both on rotational and spin–orbit coupling parameters. The ν dependence of the different molecular parameters has been examined and interpreted where possible.
Jerk-Continuous Feedrate Optimization Method for NURBS Interpolation

Feedrate scheduling is one of the most critical technologies in CNC machining, requiring a reasonable balance between efficiency and quality. This paper proposes a jerk-continuous feedrate smoothing (JCFS) method to generate a low-vibration and smooth feedrate profile for non-uniform rational B-spline (NURBS) interpolation. Firstly, the segmentation concept is introduced to subdivide the entire trajectory into segments to accommodate curvature changes of the NURBS curve, accelerating the acceleration/deceleration process. Secondly, a length threshold-based curve segment classification method is proposed to overcome the complexity of the traditional acceleration and deceleration algorithms. The curve segments are divided into long, medium, and short types, and the length threshold calculation model is derived. Next, to avoid computational complexity for engineering applications, a model is established for the first time to calculate the actual maximum feedrate for different types of segments. Finally, the horizontal-8-shaped and butterfly-shaped NURBS curves are simulated and analyzed. The simulation results indicate that the machining quality is steadily improved while several key indicators remain within the given tolerances. Compared with the traditional method, the proposed method reduces the computational and interpolation time by 17.2% and 22.8%, respectively, demonstrating the feasibility and effectiveness of the method.
Comparison of 905 nm and 1550 nm semiconductor laser rangefinders’ performance deterioration due to adverse environmental conditions

Laser rangefinder performance (i.e., maximum range) is strongly affected by environment due to visibility-dependent laser attenuation in the atmosphere and target reflectivity variations induced by surface condition changes (dry vs. wet). Both factors have their unique spectral features which means that rangefinders operating at different wavelengths are affected by specific environmental changes in a different way. Current state of the art TOF (time of flight) semiconductor laser rangefinders are based mainly on two wavelengths: 905 nm and 1550 nm, which results from atmospheric transmission windows and availability of high power pulsed sources. The paper discusses the scope of maximum range degradation of hypothetical 0.9 μm and 1.5 μm rangefinders due to selected water-related environmental effects. Atmospheric extinction spectra were adapted from Standard Atmosphere Model and reflectance fingerprints of various materials have been measured. It is not the aim of the paper to determine in general which wavelength is superior for laser range finding, since a number of criteria could be considered, but to verify their susceptibility to adverse environmental conditions.
Revised Shockley–Read–Hall lifetimes for quantum transport modeling

The inclusion of quantization effects on the carrier densities is now the state of the art in modern semiconductor device simulators and yields, for example, quantum-corrected threshold voltages and quantum-mechanical models of the channel mobility. However, the effect of charge quantization on nonradiative thermal generation–recombination has not received much attention. In this article, Shockley–Read–Hall recombination is examined for situations in which electrons and/or holes are confined in semiconductor devices. For the transitions between band states and a single deep level, a previously developed multiphonon description is adopted. It is found that the lifetimes have to be altered due to the same quantized local density of states that also accounts for the carrier distribution. Numerical evaluation of this model for one-dimensional potentials and small phonon energies results in spatially varying lifetime profiles that exhibit two opposite regimes. The additional nonclassical offset of the subband .
Approximation of Numerical Green’s Function Based on Deep Neural Network with Residual Blocks for Three-Dimensional Dielectric Objects

In this paper, we construct a deep neural network (DNN) model to approximate the three-dimensional numerical Green’s function. A space containing an inhomogeneous cube region is considered. The prediction of the converging DNN we trained is in good agreement with the results obtained by the method of moments (MoM), and it takes less CPU time and memory usage. Numerical examples are shown to illustrate the feasibility of the neural network we proposed.
Development and Characterization of Folic Acid-Conjugated Amodiaquine-Loaded Nanoparticles–Efficacy in Cancer Treatment

The objective of this study was to construct amodiaquine-loaded, folic acid-conjugated polymeric nanoparticles (FA-AQ NPs) to treat cancer that could be scaled to commercial production. In this study, folic acid (FA) was conjugated with a PLGA polymer followed by the formulation of drug-loaded NPs. The results of the conjugation efficiency confirmed the conjugation of FA with PLGA. The developed folic acid-conjugated nanoparticles demonstrated uniform particle size distributions and had visible spherical shapes under transmission electron microscopy. The cellular uptake results suggested that FA modification could enhance the cellular internalization of nanoparticulate systems in non-small cell lung cancer, cervical, and breast cancer cell types. Furthermore, cytotoxicity studies showed the superior efficacy of FA-AQ NPs in different cancer cells such as MDAMB-231 and HeLA. FA-AQ NPs had better anti-tumor abilities demonstrated via 3D spheroid cell culture studies. Therefore, FA-AQ NPs could be a promising drug delivery system for cancer therapy.
Chronic Sleep Reduction and Cyberbullying Among Adolescents: The Roles of Low Self-Control and Family Cohesion

ABSTRACT The serious psychological consequences of cyberbullying urge researchers to explore its antecedents and intervention programs. Based on the I3 theory, which indicates that instigation, impellance, and inhibition are three orthogonal processes of aggressive behavior generation, family cohesion was examined as a moderator of the indirect relationships between chronic sleep reduction and cyberbullying through low self-control. A total of 452 adolescents (M age = 13.91, SD = 1.07; 43.6% female) completed questionnaires regarding their experience with chronic sleep reduction, cyberbullying, low self-control, and family cohesion. Results indicated that individuals with high chronic sleep reduction tend to have more cyberbullying, which was mediated by low self-control. Furthermore, family cohesion moderated the path from low self-control to cyberbullying. Specifically, the relationship between low self-control and cyberbullying became weaker for individuals with high family cohesion. Findings elucidate that chronic sleep reduction and low self-control are risk factors while family cohesion is a protective factor of cyberbullying, which suggest that targeted prevention and intervention programs for cyberbullying should include efforts to ensure adequate sleep and increase family cohesion as well as self-control.
Tuning of cross-linking and mechanical properties of laser-deposited poly (methyl methacrylate) films

The chemical composition, amount of cross-linking and its influence on the mechanical properties of poly(methyl methacrylate) (PMMA) thin films produced by pulsed laser deposition (PLD) at a wavelength of 248nm under ultrahigh vacuum were investigated by infrared spectroscopy, scanning electron microscopy, size-exclusion chromatography, thermogravimetric analysis, and nanoindentation experiments. The films consist of two components, one fraction with a molecular weight well below that of the target material and a second fraction, which is cross-linked. Compared to bulk material, the Young’s modulus of the film is increased. The amount of cross-linking in the film can be tuned by the applied laser fluence leading to changes of the mechanical properties.
An Improved Pose Estimation Method in Dynamic Scene Based on Hierarchical Masking and Point Features Classification

Compared with laser SLAM, visual SLAM solutions have the advantages of low cost and rich collected environmental information, but the accuracy of their estimated pose and building maps can also be seriously disturbed by dynamic objects in the camera scene. For the purpose of eliminating the influence of dynamic objects, this paper proposes an improved pose estimation method based on ORB-SLAM2 and combined with the object detection and instance segmentation network. A special mechanism of mask generation is introduced, which takes the result of detection as a precondition, and adaptively adjusts the intervention of pixel-level segmentation mask, so as to reasonably balance segmentation accuracy and computational overhead. Then geometric constraints are adopted to identify the motion state of point features within the mask above. Finally, the static point features filtered twice are used to estimate the accurate pose of the camera. Experiments on the TUM datasets show that the proposed algorithm has significantly improved the trajectory accuracy and the anti-drift ability compared with some typical existing algorithms in test sequences containing dynamic objects, which proves the effectiveness of the above method.
A time—memory trade‐off frontwidth reduction algorithm for finite element analysis

A frontwidth reduction algorithm is presented with an execution time which may be traded against its primary memory requirement, making it possible to optimize the performance of the algorithm on a particular computer. With an amount of primary memory O(E1/2), where E is the number of elements, the execution time of the algorithm is O(E3/2), in two or three dimensions. The algorithm has two parts: first, new node-based data structures are derived from the conventional element list, then these structures are used to reorder the elements for reduced frontwidth.
A multi-band spectral subtraction-based algorithm for real-time noise cancellation applied to gunshot acoustics

Acoustical sniper positioning is based on the detection and direction-of-arrival estimation of the shockwave and the muzzle blast acoustical signals. In real-life situations, the detection and direction-of-arrival estimation processes is usually performed under the influence of background noise sources, e.g., vehicles noise, and might result in non-negligible inaccuracies than can affect the system performance and reliability negatively, specially when detecting the muzzle sound under long range distance and absorbing terrains. This paper introduces a multi-band spectral subtraction based algorithm for real-time noise reduction, applied to gunshot acoustical signals. The ballistic shockwave and the muzzle blast signals exhibit distinct frequency contents that are affected differently by additive noise. In most real situations, the noise component is colored and a multi-band spectral subtraction approach for noise reduction contributes to reducing the presence of artifacts in denoised signals. The proposed algorithm is tested using a dataset generated by combining signals from real gunshots and real vehicle noise. The noise component was generated using a steel tracked military tank running on asphalt and includes, therefore, the sound from the vehicle engine, which varies slightly in frequency over time according to the engine’s rpm, and the sound from the steel tracks as the vehicle moves.
Deep Learning-based AOI System for Detecting Component Marks

While the automated optical inspection (AOI) was adopted for the surface mounted technology (SMT) on printing circuit board assembly (PCBA), it inspected that the products with miniaturized components and high-density placement of components on boards are becoming important issues. Especially, due to the manufacturing trend in the high-mix and low volume, the detection of the component marks with wide variation are growing into even more challengeable. As for the defect detection of AOI machine by using the similarity technology, it is very sensitive to different marks, including the different brightness of the font, spacing, brand, word shift and direction. Therefore, it implies that the AOI performance which is reduced as the AOI engineer fails to solve the demanding problem, and results in more time on checking the alarmed images by a human operator. Also, it affects the production yield rate and overall equipment effectiveness. Consequently, in this paper, a novel architecture is proposed to solve such a problem. That is, the classification method combined with similarity measurement on basis of the deep learning structure. The technique we used takes the MobileNet lightweight model to modify as the output with double stages to prevent the escape from occurring. The proposed method is approved by the manufacturing line to produce the products due to its no escape occurred, easier model training and faster implement. The proposed method provides a high improved rate of the average 90% with the zero-leakage rate in SMT process according to the experimental results.