Miracle-dz/UniFAR-FTUs huggingface dataset:
Factors influencing the clinical outcomes of intense pulsed light for meibomian gland dysfunction

Abstract To observe the clinical outcomes of intense pulsed light (IPL) for meibomian gland dysfunction (MGD) and identify its influencing factors. Forty-eight eyes of 48 patients with MGD were included. Subjects were followed up 5 times on day 1, day 15, day 30, day 45, and day 120, and underwent 3 sessions of the IPL treatment on day 1, day 15 and day 30. Gender, age, duration of MGD, time of video display terminal usage, and severity of MGD were recorded at baseline. At every visit, Ocular Surface Disease Index (OSDI), eyelid margin abnormality score, tear film breakup time, Schirmer I test (S ɪ t) and corneal fluorescein staining were recorded. The clinical parameters before and after 3 IPL treatments were compared. Univariate and multivariable logistic regression analyses were performed to explore influencing factors. Compared with baseline, the tear film breakup time was increased and the corneal fluorescein staining score and OSDI were significantly decreased on day 45 and day 120 (all P < .001). In univariate analysis, among the patients with a younger age (18–39 years), moderate MGD, higher baseline S ɪ t and higher baseline OSDI, the IPL treatment had a higher effective rate (P = .032, .004, .024, and .014 respectively). The MGD severity was strongly associated with effective IPL, and patients with moderate MGD had an OR of 22.454 compared with the severe MGD patients (OR = 22.454, 95% CI: 2.890-174.436, P = .003). IPL effectively improves clinical symptoms and some signs in MGD patients. Age, MGD severity, baseline S ɪ t and baseline OSDI are potential factors that may influence the clinical outcomes of IPL. MGD severity is an independent influencing factor.
Optimal active random vibration control for smart truss structures based on reliability

Abstract The current paper presents the reliability-based optimization of the location and feedback gains of active bar in a closed-loop control system for random smart truss structures under stationary random excitation. The mathematical model with reliability constraints on the root mean square value of the structural random dynamic displacement and stress response is developed based on maximization of dissipation energy due to the control action. The randomness of the structural physical materials, geometry, and damping are included in the analysis, and the applied forces are considered as stationary random excitation. The numerical characteristic corresponding to mean values and standard deviations of the stationary random responses of random smart structures is developed by the random factor method. Numerical examples of truss structures are presented to demonstrate the rationality and validity of the active control model.
Analysis of burning probability and molten mark characteristics of wires after short circuit induced by overcurrent

Electrical circuit fault is one of the essential causes of electrical fires. The burning probability of wire after fault is closely related to the fire risks of the conductor itself. Molten marks generated by short circuit fault have important reference value for identifying the cause in fire investigation. The process of short circuit of wires under different overcurrent was simulated. The number of short circuit and combustion was recorded, and the short circuit molten marks were collected and observed. When the current was 16 A, there was no short circuit on the wire. With the increase of current, the probability of wire burning after short circuit was 60 % at 24A, and all wires would short circuit and burn at 48A. The metallographic structure of molten marks on the conductor end was mainly cylindrical crystal, and the spark was mostly branch crystal. However, the primary and secondary short circuit characteristics were observed under the same current condition. Therefore, the type of short circuit cannot be judged only by metallographic structure characteristics in the actual fire evidence identification.
Adaptive neural network based controller for direct torque control of PMSM with minimum torque ripples and EMI noise reduction

Purpose – The paper aims to provide an adaptive neural network controller for permanent magnet synchronous motor (PMSM) under direct torque control (DTC) algorithm to minimize the torque ripple and EMI noise.Design/methodology/approach – The design methodology is based on vector control used for electrical machines. MATLAB simulations supported with experimental study under C++ are used.Findings – The simulated and experimental results show that considerable torque ripple as well as current ripple and EMI noise reduction can be achieved by utilizing adaptive neural switching algorithm to fire the inverter supplying the PMSM.Research limitations/implications – This research is limited to PMSM, however the research can be extended to include other AC motors as well. In addition, the following points can be studied: the effects of harmonics in control signals on the torque ripple can be analyzed; the actual mathematical relation between the torque and flux ripple can be studied to set the flux and torque ban.
Multichannel CPFSK coherent optical communications systems

A detailed theoretical analysis of multichannel coherent CPFSK communications systems is presented. The analysis accounts for the crosstalk between adjacent channels, the intersymbol interference and correlation between noise samples stemming from the limited IF bandwidth the non-Gaussian statistics of the noise at the decision gate, and the impact of the laser phase noise. It is found that the IF bandwidth needed to avoid intersymbol interference is 2.2 bit rates for a modulation index m=1; it is larger for other modulation index values. For m=1, receiver sensitivity is within 1 dB of the shot noise limit, and the electrical domain channel spacing can be as small as 2.05 bit rates with 1-dB sensitivity penalty. The foregoing conclusions are valid for a negligibly narrow linewidth; the degradation due to phase noise is shown to be modest as long as the linewidth does not exceed 1% of the bit rate if m=1. Larger linewidth can be tolerated if the modulation index is larger than unity. >
A 2.17 dB NF, 5 GHz band monolithic CMOS LNA with 10 mW DC power consumption

The state-of-the-art noise figures of 2.17 dB and 3.0 dB at 5 GHz band from monolithic CMOS LNA's with 10 mW dissipation on thin (/spl sim/ 20 /spl mu/m) and normal (750 /spl mu/m) substrates are presented. Excellent Input return loss (S/sub 11/) of -45 dB, high P/sub 1dB/ of -8.3 dBm and large IIP3 of 0.3 dBm were also obtained. The excellent performance of the LNA's is attributed to the methodology we developed.
Paeoniflorin Improved Constipation in the Loperamide-Induced Rat Model via TGR5/TRPA1 Signaling-Mediated 5-Hydroxytryptamine Secretion

Slow transit constipation (STC) is a common type of constipation with a high incidence rate and a large number of patients. We aimed to investigate the therapeutic effects and potential mechanism of paeoniflorin (PAE) on loperamide-induced Sprague Dawley (SD) rat constipation models. Rats with loperamide-induced constipation were orally administered different concentrations of PAE (10, 20, or 40 mg/kg). In vitro, enterochromaffin (EC)-like RIN-14B cells were treated with 20, 40, or 80 μg/ml PAE. We found that PAE treatment significantly improved the symptoms of constipation and increased the intestinal transit rate. Hematoxylin and eosin (H&E) staining showed that PAE alleviated colonic tissue pathological damage. Besides, our results implied that PAE concentration-dependently promoted the content of 5-hydroxytryptamine (5-HT) catalyzed by tryptophan hydroxylase (Tph)-1 in the serum of loperamide-induced rats and in RIN-14B cells. Western blot and immunofluorescence (IF) stain indicated that PAE also promoted the expression of G protein-coupled BA receptor 1 (TGR5), transient receptor potential ankyrin 1 (TRPA1), phospholipase C (PLC)-γ1, and phosphatidylinositol 4,5-bisphosphate (PIP2) in vivo and in vitro. RIN-14B cells were cotreated with a TGR5 inhibitor (SBI-115) to explore the mechanism of PAE in regulating the 5-HT secretion. We observed inhibition of TGR5 reversed the increase of 5-HT secretion induced by PAE in RIN-14B cells. We provided evidence that PAE could promote 5-HT release from EC cells and improve constipation by activating the TRPA1 channel and PLC-γ1/PIP2 signaling. Thus, PAE may provide therapeutic effects for patients with STC.
Pull-in study of an electrostatic torsion microactuator

Pull-in study of an electrostatic microactuator is essential for making the electrostatic actuation more effective. In this paper, pull-in analysis is presented for an electrostatic torsion microactuator. The torsion microactuator can be used as a microtorsion mirror. A polynomial algebraic equation for the pull-in voltage and pull-in angle of a torsion microactuator is derived. Two types of microactuators fabricated using bulk micromachining are presented. Measurements done on the fabricated microactuators are reported, showing deviations within 1% error from the calculations.
Ethnic Diversity of DPD Activity and the DPYD Gene: Review of the Literature

Abstract Pharmacogenomic screening can identify patients with gene variants that predispose them to the development of severe toxicity from fluoropyrimidine (FP) chemotherapy. Deficiency of the critical metabolic enzyme dihydropyrimidine dehydrogenase (DPD) leads to excessive toxicity on exposure to fluoropyrimidine chemotherapy. This can result in hospitalisation, intensive care admissions and even death. Upfront screening of the gene that encodes for DPD (DPYD) has recently been implemented in regions throughout Europe and the United Kingdom. Current screening evaluates DPYD variants that are well described within Caucasian patient populations and provides genotyped-guided dose adjustment recommendations based upon the presence of these variants. This article reviews the differences in DPYD gene variants within non-Caucasian populations compared to Caucasian populations, with regard to the implications for clinical tolerance of fluoropyrimidine chemotherapies and genotype guided dose adjustment guidelines.
Evaluating the large-scale hydrological cycle response within the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2) ensemble

Abstract. The mid-Pliocene (∼3 Ma) is one of the most recent warm periods with high CO2 concentrations in the atmosphere and resulting high temperatures, and it is often cited as an analog for near-term future climate change. Here, we apply a moisture budget analysis to investigate the response of the large-scale hydrological cycle at low latitudes within a 13-model ensemble from the Pliocene Model Intercomparison Project Phase 2 (PlioMIP2). The results show that increased atmospheric moisture content within the mid-Pliocene ensemble (due to the thermodynamic effect) results in wetter conditions over the deep tropics, i.e., the Pacific intertropical convergence zone (ITCZ) and the Maritime Continent, and drier conditions over the subtropics. Note that the dynamic effect plays a more important role than the thermodynamic effect in regional precipitation minus evaporation (PmE) changes (i.e., northward ITCZ shift and wetter northern Indian Ocean). The thermodynamic effect is offset to some extent by a dynamic effect involving a northward shift of the Hadley circulation that dries the deep tropics and moistens the subtropics in the Northern Hemisphere (i.e., the subtropical Pacific). From the perspective of Earth's energy budget, the enhanced southward cross-equatorial atmospheric transport (0.22 PW), induced by the hemispheric asymmetries of the atmospheric energy, favors an approximately 1∘ northward shift of the ITCZ. The shift of the ITCZ reorganizes atmospheric circulation, favoring a northward shift of the Hadley circulation. In addition, the Walker circulation consistently shifts westward within PlioMIP2 models, leading to wetter conditions over the northern Indian Ocean. The PlioMIP2 ensemble highlights that an imbalance of interhemispheric atmospheric energy during the mid-Pliocene could have led to changes in the dynamic effect, offsetting the thermodynamic effect and, hence, altering mid-Pliocene hydroclimate.
Evolution of Surface Drainage Network for Spoil Heaps under Simulated Rainfall

Spoil heaps laid from the infrastructure building sites or the mining sites are confoundedly prone to accelerated soil erosion and inducing debris flows on extreme rainfall occasion, thus threatening water quality and personal safety. In present study, the roughness and drainage network evolution of the loess spoil heap (a 33° slope gradient) were investigated via indoor simulation experiment under three rainfall intensities (60, 90, and 120 mm/h). A detailed scan of the slope using laser scanner, topographic analysis based on ArcGIS software, and statistical analyses were the main methods utilized in the study. The results showed that surface roughness increased with cumulative rainfall. For three rainfall intensity treatments, the proneness of shallow landslide under 90 mm/h intensity resulted in the largest roughness. The drainage density and stream frequency of the spoil heap slope both decreased with cumulative rainfall and negatively correlated with surface roughness, which indicated the convergence of the drainage network. Meanwhile, the individual flow paths presented an increasing sinuosity and a decreasing gradient with cumulative rainfall. However, drainage network features varied in a less marked degree during different rainfall intensities, showing comparable fractal dimensions of 1.350–1.454, 1.305–1.459, and 1.292–1.455 for the three rainfall intensities. Evaluating the response of four hydrodynamic characteristics of runoff to the drainage network evolution, stream power was found to be most sensitive. The linearity of the relationships between stream power and drainage density and that between stream sinuosity and gradient were estimated to have R2 between 0.961 and 0.979.
NOISE MODELLING OF SILICON GERMANIUM HETEROJUNCTION BIPOLAR TRANSISTORS AT MILLIMETRE-WAVE FREQUENCIES

Using 2D device simulations, it is predicted that the cutoff frequencies of SiGe HBTs can be scaled beyond 500GHz. These devices have the potential to enable advanced millimetrewave circuits. However, shot noise correlation, which is captured through noise transit time, becomes increasingly important as circuit designers continue to push the operating frequencies of the circuits. The technique for extracting the SiGe HBT noise parameters only from the measured yparameters is extended to account for the presence of correlation. Unlike earlier publications, this method does not need to fit the noise transit time to measured noise data. The technique was validated using 2D device simulations and measured noise parameter data. It was found that the NFMIN of SiGe HBTs with fT/fMAX of 160GHz is approximately 1.5dB lower at 60GHz when noise correlation is accounted for. However, for these devices, noise correlation proves to be insignificant below 18GHz.
A Broad-Band Dual-Mode Circular Waveguide Transducer

This paper describes a broad-band dual-mode waveguide transducer designed to couple two orthogonal TE/sub 11/ circular waveguide modes in separate rectangular waveguide ports. A compact, rugged, and economical junction has been developed to operate from 8600 mc to 9600 mc with a vswr of less than 1.15 at the rectangular port and a mode isolation of 50 db or greater. Developmental models are described to indicate the evolution from theory to the final model. Some problems encountered in attaining a small physical size are discussed in detail. The new junction has application to mode multiflexing, circular waveguide ferrite devices, circular polarization, and as a circular wave guide magic-T.
A new fault diagnosis model of rolling element bearing based on a recurrent neural network

The rolling element bearing is an important part of mechanical equipment, it has various kinds of malfunctions, the location of the fault may occur in the inner ring, outer ring, or rolling element of the bearing. Therefore, traditional methods of classification are difficult to classify and identify effectively. To improve the accuracy of bearing fault diagnosis, the deep learning method is used to diagnose the fault of the rolling element bearing. In this paper, the long short-term memory and gated recurrent unit are combined to build a bearing fault diagnosis model. On the other hand, this paper adjusts the hidden layer structure and optimizes the network parameters to establish a better long short-term memory–gated recurrent unit–long short-term memory diagnostic model and classify the fault types of bearings with Softmax. The model proposed in this paper can effectively diagnose the bearing fault under the bearing data set of Case Western Reserve University and the University of Cincinnati. Compared with the traditional long short-term memory and the gated recurrent unit, the model proposed in this paper has high accuracy in fault diagnosis as well as certain reliability and generalization ability.
Micromachined pressure sensors: review and recent developments

Since the discovery of piezoresistivity in silicon in the mid 1950s, silicon-based pressure sensors have been widely produced. Micromachining technology has greatly benefited from the success of the integrated circuits industry, borrowing materials, processes, and toolsets. Because of this, microelectromechanical systems (MEMS) are now poised to capture large segments of existing sensor markets and to catalyze the development of new markets. Given the emerging importance of MEMS, it is instructive to review the history of micromachined pressure sensors, and to examine new developments in the field. Pressure sensors will be the focus of this paper, starting from metal diaphragm sensors with bonded silicon strain gauges, and moving to present developments of surface micromachined, optical, resonant, and smart pressure sensors. Considerations for diaphragm design will be discussed in detail, as well as additional considerations for capacitive and piezoresistive devices.
Quantification of Root Biomass in Post-Mining Areas in the Municipality of Capitão Poço – PA, Brazil

The present work aimed to quantify the concentrations and biomass stock of fine andthick roots, in three areas in the municipality of Capitão Poço-PA, Brazil. The areas used were degraded area, recovery area and native forest. For soil sampling, 24 trenches were opened, measuring 70 x 70 x 100 cm. In these trenches, soil samples were taken at depths 0-10, 10-20, 20-30, 30-40, 40-50, 50-60, 60-80 and 80-100 cm and sieving was carried out.All roots and other underground plant structures that remained in the sieve were collected by manual collection. The roots were separated into two diameter classes: fine roots ≤ 5 mm and thick roots > 5 mm, kiln dried and weighed.In the analysis, higherconcentrationsofthickand fine roots were observed in an area of native forest at depths of 0-10 and 10-20 cm. In the areas analyzed in this study, the root density in the topsoil of 0-10 cm was mainly composed of fine roots.In the three areas analyzed in this study, it was observed that from a depth of 10-20 cm there were decreases in theconcentrationsofthick roots. The area under recovery approached the area of native forest in the concentration of fine roots, demonstrating possible improvements in soil quality and recovery is probably actually taking place.
Robust Deep Learning-based Driver Distraction Detection and Classification

Driver distraction is a major cause of road accidents. Distracting activities while driving include text messaging and talking on the phone. In this paper, we propose a robust driver distraction detection system that extracts the driver’s state from the recordings of an onboard camera using Deep Learning. We consider ten driving activities, which consist of one normal driving and nine distracted driving behaviors. Nine drivers were included in the experiments, and each one was asked to perform the ten activities in naturalistic and simulated driving situations. The main feature of the proposed solution is the extraction of the driver’s body parts, using deep learning-based segmentation, before performing the distraction detection and classification task. Experimental results show that the segmentation module significantly improves the classification performance. The average accuracy of the proposed solution exceeds 96% on our dataset and 95% on the public AUC dataset.
Oridonin Attenuates Diabetes‑Induced Renal Fibrosis via Inhibition of the TXNIP/NLRP3 and NF‑κB Pathway in Rats

Background: Oxidative stress and its induced inflammation are important pathological processes of diabetic nephropathy (DN). Oridonin, a component isolated from Rabdosia rubescens, possesses remarkable anti-inflammatory, immunoregulatory properties, it is also a newly reported NLRP3 inhibitor. However, the renoprotective effects of Oridonin and the underlying molecular mechanisms have not been explored in DN. We hypothesized that Oridonin could reduce NLRP3 pathway and ameliorate diabetes‑induced renal fibrosis.Methods: We used STZ-induced diabetic rats combined high-fat diet to establish a T2DM animal model, and then treated with Oridonin (10, 20 mg/kg/day) for two weeks. Kidney function and renal fibrosis were assessed. In addition, the expression of inflammatory factors and fibrotic markers were analyzed by western blot.Results: Oridonin treatment preserved kidney function and markedly limited the renal fibrosis size in diabetic rats. The renal fibrotic markers were inhibited in the 10 mg/kg/day group and 20 mg/kg/day group compared to the T2DM group. Moreover, the expression levels of TXNIP/NLRP3 and NF‑κB pathway were decreased in the Oridonin treatment group compared to non-treated group. Conclusions: The NLRP3-inflammasome inhibitor Oridonin reduces renal fibrosis and preserves kidney function in T2DM rat model, which indicates potential therapeutic effect of Oridonin on DN.
Using Explainable Artificial Intelligence to Improve Process Quality: Evidence from Semiconductor Manufacturing

We develop a data-driven decision model to improve process quality in manufacturing. A challenge for traditional methods in quality management is to handle high-dimensional and nonlinear manufacturing data. We address this challenge by adapting explainable artificial intelligence to the context of quality management. Specifically, we propose the use of nonlinear modeling with Shapley additive explanations to infer how a set of production parameters and the process quality of a manufacturing system are related. Thereby, we contribute a measure of process importance based on which manufacturers can prioritize processes for quality improvement. Grounded in quality management theory, our decision model selects improvement actions that target the sources of quality variation. The decision model is validated in a real-world application at a leading manufacturer of high-power semiconductors. Seeking to improve production yield, we apply our decision model to select improvement actions for a transistor chip product. We then conduct a field experiment to confirm the effectiveness of the improvement actions. Compared with the average yield in our sample, the experiment returns a reduction in yield loss of 21.7%. Furthermore, we report on results from a postexperimental rollout of the decision model, which also resulted in significant yield improvements. We demonstrate the operational value of explainable artificial intelligence by showing that critical drivers of process quality can go undiscovered by the use of traditional methods. This paper was accepted by Charles Corbett, operations management.