Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimized Carrier Phase-Shifted Modulation Strategy for Cuk PV Inverter

There exists the problems of output voltage zero-point drift and high even harmonic content of the three-phase Cuk liftable voltage photovoltaic (PV) inverter when it adopts the conventional carrier modulation method. In view of these, a new optimized Phase-Shifted Pulse Width Modulation (PSPWM) strategy is proposed. Firstly, the optimal zero-sequence component is determined based on the inverter topology and control objectives. Next, the zero-sequence component is injected into the sinusoidal signal and used as the new modulating signal so that the carrier pulse width modulation is equivalent to the space vector pulse width modulation. Finally, the carrier and modulating waveforms are blended in a superior manner, and a new logical operation is applied to generate the required switching drive signals for the control of the inverter switching tubes. In order to verify the effectiveness of the strategy, simulation and experimental platforms were constructed, and numerous simulations and experiments were done. The results show that the modulation strategy can solve the output voltage zero drift problem effectively, and suppress the even harmonics effectively, meanwhile, the harmonics are mainly concentrated near the primary frequency, which significantly reduces the total harmonic content and greatly improves the output voltage waveform. In addition, the modulation can be achieved by reducing one carrier signal, thus simplifying the modulation process and the design of the digital signal system.
Efficacy and safety of topical cyclosporine 0.1% in moderate-to-severe dry eye disease refractory to topical cyclosporine 0.05% regimen

PURPOSE: To evaluate the efficacy and safety of 0.1% cyclosporine A cationic emulsion (CsA CE) following prior treatment with 0.05% cyclosporine A anionic emulsion (CsA AE) in moderate to severe dry eye disease (DED). MATERIALS AND METHODS: We retrospectively identified patients with moderate-to-severe DED who had shown an inadequate response to twice-daily use of topical 0.05% CsA AE but showed a significant improvement after switching to 0.1% CsA CE daily. Dry eye parameters before and after CsA CE were evaluated by tear break-up time (TBUT), corneal fluorescein staining (CFS), cornea sensitivity, Schirmer's test without anesthetics, and Ocular Surface Disease Index questionnaire. RESULTS: Twenty-three patients, including ten patients with Sjogren syndrome and five patients with rheumatoid arthritis, were reviewed. After a 2-month course of treatment with topical 0.1% CsA CE, significant improvements were noted for CFS (P < 0.001), corneal sensitivity (P = 0.008), and TBUT (P = 0.01). Efficacy was similar in the autoimmune versus nonautoimmune group. 39.1% of patients reported treatment-related adverse events, while the majority was transient instillation pain. Visual acuity and intraocular pressure had no significant changes during the study. CONCLUSION: In patients with moderate to severe DED refractory to 0.05% cyclosporine, shifting to 0.1% cyclosporine showed improvement in objective signs but with lower treatment tolerability in the short term.
Numerical approximations of the Ginzburg–Landau models for superconductivity

In this paper, we review various methods for the numerical approximations of the Ginzburg–Landau models of superconductivity. Particular attention is given to the different treatment of gauge invariance in both the finite element, finite difference, and finite volume settings. Representative theoretical results, typical numerical simulations, and computational challenges are presented. Generalizations to other relevant models are also discussed.
Classification of Alzheimer's Disease Stages Through Volumetric Analysis of MRI Data

Neurodegenerative disorders are among the most important problems to be addressed in nations with increasingly aging populations. As a result of these illnesses, neurons in the human brain begin to deteriorate and eventually die, impairing movement or mental function. One of the most talked-about neurological disorders is Alzheimer's disease (AD). Finding the condition as soon as possible is critical as the best chance for survival with Alzheimer's is an early diagnosis. Magnetic resonance imaging (MRI) is one method for diagnosing AD since it may reveal structural abnormalities in the brain. Modern computer vision is highly suited for this image-based categorization challenge. Convolutional neural networks that have been trained expressly for machine learning, are what we suggest can help with early detection. The primary method of diagnosis is the segmentation of the hippocampus region of MRI images. Our approach implements a deep learning model that is trained on an MRI dataset. Using the volume of the hippocampus, this method correctly divides the MRI scans into the four phases of AD i.e. Mildly Demented, Moderately Demented, Non-Demented, and Severely Demented.
Integration of Microfluidic Capabilities into Micromachined Neural Implants

Several approaches for micromachined neural implants have been established worldwide to deliver neuroscientific tools to investigate the function of the brain. Bioelectrical signals of single cells as well as of synaptic background activity have been recorded via multichannel electrode systems and the effect of electrical stimulation has been used to treat diseases or to partially restore sensory functions. However, the brain does not work only electrically but chemical and electrical signals strongly interact. Therefore, chemical analysis of the metabolism of the brain and neurotransmitters is of high interest as well as delivery of chemical substances to modify signal transduction and generation in the brain. This article presents existing micromachining approaches of electrical neural probes for intracortical recording and microfluidic channels, pumps and valves to realize drug delivery and probe sampling. Application scenarios will be discussed with respect to the opportunities and limitations of the .
A Temperature-Dependent $dV_{CE}/dt$ and $dI_{C}/dt$ Model for Field-Stop IGBT at Turn-on Transient

In this article, a complete expression for <inline-formula><tex-math notation="LaTeX">$dV_{CE}/dt$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$dI_{C}/dt$</tex-math></inline-formula> at turn-<sc>on</sc> transient of field-stop (FS) insulated gate bipolar transistor (IGBT) is proposed. With numerical simulation utilized, the critical stray elements and internal physics which have a significant impact on turn-<sc>on</sc> behavior of FS IGBT are identified. Based on the improved understanding on the turn-<sc>on</sc> behavior, the turn-<sc>on</sc> transient is divided into two phases and the equivalent circuits of each phase are obtained. The analytical expressions of <inline-formula><tex-math notation="LaTeX">$dV_{CE}/dt$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$dI_{C}/dt$</tex-math></inline-formula> during the turn-<sc>on</sc> transient are thereby derived based on the equivalent circuits. The temperature dependency on the turn-<sc>on</sc> characteristics of FS IGBT is identified by the experimental data. The temperature-dependent models of various device parameters are proposed to describe the temperature dependency. In the end, the double-pulse test is performed on a 650-V FS IGBT and a 1200-V FS IGBT. The good agreement between the test and analytically derived results validates that the proposed FS IGBT model can accurately predict the <inline-formula><tex-math notation="LaTeX">$dV_{CE}/dt$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$dI_{C}/dt$</tex-math></inline-formula> during the turn-<sc>on</sc> transient.
Intonation precision of choir singers

Results from two experimental investigations on the acoustics of choirs are reported. The first concerns sound‐pressure levels (SPL) inside choirs, and the balance of loudness between a singer’s own voice and the ‘‘reference’’ sound from the rest of the choir. Long‐term SPL averages during rehearsals typically gave 80–100 dB, with little variation between choirs or with position in the choir. The fundamental frequency precision of choir singers was found to deteriorate abruptly when the reference was too loud. The influence of room acoustics and of the spacing between singers is discussed. The second investigation concerns the effect of spectral variations in the reference sound. Experiments were made primarily with individual singers under laboratory conditions. Changes in vowel quality, and absence/presence of certain partials and of vibrato, were all found to affect somewhat the degree of fundamental frequency agreement between singers. Standard deviation in fundamental frequency between section collea.
Boundary and Interior Control for Partial Differential Equations

From the time that the basic existence and regularity problems for partial differential equations have been solved many interesting new variational and control problems could be studied. In general a differential equation or boundary value problem is used to define a class of admissible functions, and then the problem is that of finding the extrema of a given functional defined on that class of functions.
PAN: Part Attention Network Integrating Electromagnetic Characteristics for Interpretable SAR Vehicle Target Recognition

Machine learning methods for synthetic aperture radar (SAR) image automatic target recognition (ATR) can be divided into two main types: traditional methods and deep learning methods. The deep learning methods can learn the high-dimensional features of the target directly and usually obtain high target recognition accuracy. However, they lack full consideration of SAR targets’ inherent characteristics, resulting in poor generalization and interpretation ability. Compared with deep learning methods, traditional methods can get more interpretable and stable results with model-based features. In order to take full advantage of these two kinds of methods, we propose the target part attention network (PAN) based on the attributed scattering center (ASC) model to integrate the electromagnetic characteristics with the deep learning framework. First, considering the importance of scattering structure for SAR ATR, we design a target part model based on the ASC model. Then, a novel part attention module based on the scaled dot-product attention mechanism is proposed, which directly associates the features of target parts with the classification results. Finally, we give the derivation method of the importance of each part, which is of great significance for practical application and the interpretation of SAR ATR. Experiments on the MSTAR dataset demonstrate the effectiveness of the proposed PAN. Compared with existing studies, it can achieve higher and more robust classification accuracy under different complex conditions. Furthermore, combined with the importance of parts, we constructed two effective interpretable analysis methods for deep learning network classification results.
Objective evaluation of tongue diagnosis ability using a tongue diagnosis e-learning/e-assessment system based on a standardized tongue image database

Background In Kampo medicine, tongue examination is used to diagnose the pathological condition “Sho,” but an objective evaluation method for its diagnostic ability has not been established. We constructed a tongue diagnosis electronic learning and evaluation system based on a standardized tongue image database. Purpose This study aims to verify the practicality of this assessment system by evaluating the tongue diagnosis ability of Kampo specialists (KSs), medical professionals, and students. Methods In the first study, we analyzed the answer data of 15 KSs in an 80-question tongue diagnosis test that assesses eight aspects of tongue findings and evaluated the (i) test score, (ii) test difficulty and discrimination index, (iii) diagnostic consistency, and (iv) diagnostic match rate between KSs. In the second study, we administered a 20-question common Kampo test and analyzed the answer data of 107 medical professionals and 56 students that assessed the tongue color discrimination ability and evaluated the (v) correct answer rate, (vi) test difficulty, and (vii) factors related to the correct answer rate. Result In the first study, the average test score was 62.2 ± 10.7 points. Twenty-eight questions were difficult (correct answer rate, <50%), 34 were moderate (50%–85%), and 18 were easy (≥85%). Regarding intrarater reliability, the average diagnostic match rate of five KSs involved in database construction was 0.66 ± 0.08, and as for interrater reliability, the diagnostic match rate between the 15 KSs was 0.52 (95% confidence interval, 0.38–0.65) for Gwet's agreement coefficient 1, and the degree of the match rate was moderate. In the second study, the difficulty level of questions was moderate, with a correct rate of 81.3% for medical professionals and 82.1% for students. The discrimination index was good for medical professionals (0.35) and poor for students (0.06). Among medical professionals, the correct answer group of this question had a significantly higher total score on the Kampo common test than the incorrect answer group (85.3 ± 8.4 points vs. 75.8 ± 11.8 points, p < 0.01). Conclusion This system can objectively evaluate tongue diagnosis ability and has high practicality. Utilizing this system can be expected to contribute to improving learners’ tongue diagnosis ability and standardization of tongue diagnosis.
Infections in DNA Repair Defects

DNA repair defects are heterogenous conditions characterized by a wide spectrum of clinical phenotypes. The common presentations of DNA repair defects include increased risk of cancer, accelerated aging, and defects in the development of various organs and systems. The immune system can be affected in a subset of these disorders leading to susceptibility to infections and autoimmunity. Infections in DNA repair defects may occur due to primary defects in T, B, or NK cells and other factors such as anatomic defects, neurologic disorders, or during chemotherapy. Consequently, the characteristics of the infections may vary from mild upper respiratory tract infections to severe, opportunistic, and even fatal infections with bacteria, viruses, or fungi. Here, infections in 15 rare and sporadic DNA repair defects that are associated with immunodeficiencies are discussed. Because of the rarity of some of these conditions, limited information is available regarding infectious complications.
Improved fundamental parameters for the low-mass pre-main sequence eclipsing system RX J0529.4+0041

We report new photometric observations and a new determination of the fundamental stellar parameters for the low-mass pre-main sequence eclipsing system RX J0529.4+0041A based on high-precision, near-IR ($\it JHK$) differential photometry obtained using adaptive optics at the ESO-La Silla 3.6 m telescope, and $\it UBV(RI)_{\rm C}$ CCD photometry performed with the OIG camera at TNG. The new photometric data, in combination with already published photoelectric photometry and solution of the radial velocity curve, yield a more precise determination of the absolute dimensions and masses as well as of other basic physical properties of the components.
pH stablizing control of a neutral leaching process based on proximal policy optimization

Neutral leaching is the key process of zinc hydrometallurgy. The reasonable flow of waste acid added to make the terminal pH stable in a reasonable range is very important to improve the leaching rate and the removal rate of impurity ions. However, due to the fluctuation of inlet flow rate, feeding amount and composition change, there is great uncertainty in the process. And the stabling control of pH value is very challenging. In this paper, a pH optimization control algorithm based on proximal policy optimization (PPO) for leaching process is proposed. First, a nonlinear autoregressive (NARX) model is established based on process data for interacting with the agent. Then, the state space, action space and reward function are defined for the leaching process, and the strategies of action boundary setting and state normalization are adopted to ensure its learning performance. Finally, the experiments show that PPO controller can realize stabling control of pH value compared with manual experience. The optimal control framework based on deep reinforcement learning brings stability and intelligence to the zinc leaching process.
Dairy Cows Are Limited in Their Ability to Increase Glucose Availability for Immune Function during Disease

Simple Summary Both immune defense and milk synthesis require large amounts of energy, particularly from glucose, resulting in competition between these functions if glucose availability is limited. Based on comprehensive data on cow nutrient intake and on cow health records, the magnitudes and dynamics of glucose and energy balances of dairy cows kept on an experimental farm were evaluated in the weeks before, of, and after a production disease was diagnosed. Because dry matter intake (DMI) decreased during the phase of disease and was not adequately compensated by a reduction in milk yield (MY), glucose availability dropped when it was most needed. Abstract Shortages of energy and glucose have been hypothesized to play a key role in the development of and responses to production diseases in dairy cows during early lactation. Given the importance of glucose for immune functions, we used a recently established method for the estimation of glucose balance (GB) to evaluate glucose availability during disease phases. A dataset comprising ration analyses as well as individual daily milk yields (MY), dry matter intake (DMI), body weights, and health records of 417 lactations (298 cows) was used to calculate individual daily GB and energy balance (EB). The magnitude and dynamics of MY, DMI, GB, and EB were evaluated in the weeks before, at, and after diagnoses of inflammatory diseases in different stages of early lactation from week in milk 1 to 15. Diagnoses were categorized as mastitis, claw and leg diseases, and other inflammatory diseases. Mixed linear models with a random intercept and slope term for each lactation were used to evaluate the effect of diagnosis on MY, DMI, GB, and EB while accounting for the background effects of week in milk, parity, season, and year. When unaffected by disease, in general, the GB of cows was close to zero in the first weeks of lactation and increased as lactation progressed. Weekly means of EB were negative throughout all lactation stages investigated. Disease decreased both the input of glucose precursors due to a reduced DMI as well as the output of glucose via milk due to a reduced MY. On average, the decrease in DMI was −1.5 (−1.9 to −1.1) kg and was proportionally higher than the decrease in MY, which averaged −1.0 (−1.4 to −0.6) kg. Mastitis reduced yield less than claw and leg disease or other diseases. On average, GB and EB were reduced by −3.8 (−5.6 to −2.1) mol C and −7.5 (−10.2 to −4.9) MJ in the week of diagnosis. This indicates the need to investigate strategies to increase the availability of glucogenic carbon for immune function during disease in dairy cows.
Sub PPM Detection of NO2 Using Strontium Doped Bismuth Ferrite Nanostructures

The present work investigates the NO2 sensing properties of acceptor-doped ferrite perovskite nanostructures. The Sr-doped BiFeO3 nanostructures were synthesized by a salt precursor-based modified pechini method and characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), and X-ray photoelectron spectroscopy (XPS). The synthesized materials were drop coated to fabricate chemoresistive gas sensors, delivering a maximum sensitivity of 5.2 towards 2 ppm NO2 at 260 °C. The recorded values of response and recovery time are 95 s and 280 s, respectively. The sensor based on Bi0.8Sr0. 2FeO3–δ (BSFO) that was operated was shown to have a LOD (limit of detection) as low as 200 ppb. The sensor proved to be promising for repeatability and selectivity measurements, indicating that the Sr doping Bismuth ferrite could be a potentially competitive material for sensing applications. A relevant gas-sensing mechanism is also proposed based on the surface adsorption and reaction behavior of the material.
Eccles's Psychons Could be Zero-Energy Tachyons

This paper suggests that mental units called psychons by Eccles could be tachyons defined theoretically by physicists sometime ago. Although experiments to detect faster-than-light particles have not been successful so far, recently, there has been renewed interest in tachyon theories in various branches of physics. We suggest that tachyon theories may be applicable to brain physics. Eccles proposed an association between psychons and what he called dendrons which are dendrite bundles and basic anatomical units of the neocortex for reception. We show that a zero-energy tachyon could act as a trigger for exocytosis (modeled by Friedrich Beck as a quantum tunneling process), not merely at a single presynaptic terminal but at all selected terminals in the interacting dendron by momentarily transferring momentum to vesicles, thereby decreasing the effective barrier potential and increasing the probability of exocytosis at all boutons at the same time. This is consistent with the view of tachyons, which treats them as strictly non-local phenomenon produced and absorbed instantaneously and non-locally by detectors acting in a coherent and cooperative way.
DDIS-based GPR Image Matching Method

Ground Penetrating Radar (GPR) has enabled efficient and high-resolution imaging of subsurface information using electromagnetic radiation. Despite of being cost-effective and non-destructive, GPR imaging process is sensitive to illumination variation, noise and scanning motion, making GPR image matching a challenging problem. In this paper, inspired by the success of Deformable Diversity Similarity (DDIS) in optical image matching, we propose to leverage DDIS to tackle the GPR image matching task. Extensive experiments with GPR images under different conditions are taken against other baseline methods. In addition, we explore different design choices in pre-processing and matching stages. Both quantitative and qualitative results demonstrate the promise of DDIS in the GPR image matching task.
Interdiffusion in Refractory Metal System with a BCC Lattice: Ti/TiZrHfNbTaMo

Interdiffusion of the elements in a diffusion pair consisting of Ti and an equiatomic high-entropy alloy (HEA) TiZrHfNbTaMo in the temperature range of 1473–1673 K has been studied. A calculated results phase diagram of the alloy by Thermo-Calc 2021-B software as used to determine the temperature stability range of the β-phase in the alloy. Ti–HEA diffusion pairs were obtained by low = temperature welding and then diffusion annealing was carried out at temperatures of 1473, 1573, and 1673 K during 12, 9, and 6 h, respectively. The interdiffusion zone was profiled using electron probe microanalysis (EPMA). The diffusion parameters of the HEA’s elements were obtained using Hall’s method. An experimental results discussion is given.