Miracle-dz/UniFAR-FTUs huggingface dataset:
Tackling light trapping in organic light-emitting diodes by complete elimination of waveguide modes

Efficiency improvement of organic light-emitting diodes was achieve by complete elimination of waveguide modes. Conventional waveguide mode decoupling methods for organic light-emitting diodes (OLEDs) are typically not scalable and increase fabrication complexity/cost. Indium-tin-oxide–free transparent anode technologies showed efficiency improvement without affecting other device properties. However, previous works lack rigorous analysis to understand the efficiency improvement. Here, we introduced an ultrathin silver (Ag) film as transparent electrode and conducted systematic modal analysis of OLEDs and report that waveguide mode can be completely eliminated by designing an OLED structure that is below the cutoff thickness of waveguide modes. We also experimentally verified the waveguide mode removal in organic waveguides with the help of index-matching fluid and prism. The negative permittivity, extremely thin thickness (~5 nanometers), and highly conductive properties achieved by a uniform copper-seeded Ag film can suppress waveguide mode formation, enhancing external quantum efficiency without compromising any other characteristics of OLEDs, which paves the way for cost-effective high-efficiency OLEDs in current display industry.
Gender Dysphoria in Psychiatric Practice: Understanding the Clinical Ambiguity and Management

There have been several myths and misconceptions about the dichotomous understanding of sex and gender. While sex is biologically determined, gender and gender identity depend on childhood experiences, upbringing, social expectations, beliefs, family environment, and peer interactions and is socially constructed. Gender dysphoria (GD) is the extreme distress experienced by an individual because of a mismatch between their gender identity and the sex assigned at birth. GD has been an ambiguous category in psychiatry, initially termed as “gender identity disorder” till Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) considered removal of the term “disorder” to reduce the stigma associated. The critical element in GD is “clinically significant distress” that differentiates it from gender nonconformity. Individuals with GD identify themselves as transgender and frequently are victims of coercive social norms, discrimination, and stigma. This leads to delay in expression of distress, psychiatric mismanagement, and high comorbidity of depression, anxiety, post-traumatic stress, self-harm, and suicidality. Though management involves a holistic multidisciplinary approach including psychotherapy, social support, and gender-reassignment treatments (medical/surgical), there has been considerable debate and ambiguity related to the same. With this background, the article critics the understanding of GD, focuses on the WPATH SOC-7 treatment guidelines, and highlights the role of mental health professionals for better care.
A Structured Approach for Synchronising the Applications of Failure Mode and Effects Analysis

Abstract Failure Mode and Effects Analysis (FMEA) is a systematic approach for evaluating the potential failure modes in a system, and is mainly employed in three distinct tasks labelled: (1) Functional FMEA – evaluating those failures associated with product functional definition, (2) Design FMEA – analysing those failures associated with design definition and (3) Process FMEA – assessing potential failures in manufacturing and assembly processes. The literature review has shown limited works on the field of synchronising these different tasks into a working model. To address this gap, this research developed a framework for integrating these tasks of FMEAs, and then qualitatively validating the proposed framework. This research adopted a semi-structured questionnaire to collect experts’ feedback and validate the proposed framework. The t-test was then employed to evaluate the collected feedback. The findings highlight that the proposed framework is applicable and could facilitate the synchronisation of the different tasks of FMEA. This research presents a methodological approach for executing and synchronising FMEAs. Therefore, the proposed framework is practically relevant as an aid for the practitioners in catching the cascading failures and reducing the relevant impact.
Research on the System of Pulse Synthesis Pre-pump Rayleigh BOTDA technology

The conventional Rayleigh scattering Brillouin Optical Time Domain Analysis (Rayleigh BOTDA) system adopted the leakage light ahead of the pump pulse to generate Rayleigh back-scattering light. Stimulated Brillouin scattering (SBS) interacts between an optical pulse and a Rayleigh back-scattering light, then the temperature and strain information of the optical fiber are perceived by measuring the Rayleigh scattering light. However, the width of pulse is inversely proportional to the accuracy of spectrum detection, which means the narrower the pulse, the wider the Brillouin spectrum. But the spatial resolution is strictly related to the duration of the optical pulse, when the pulse width is less than the phonon lifetime, the Brillouin spectrum width is much larger than the Brillouin natural gain spectrum width. The spectrum detection accuracy of the system can be effectively improved by adding a pre-pump ahead of the pump pulse to stimulate the acoustic wave field in the Rayleigh BOTDA system. In this paper, we present a new method of synthesizing step pulse. Through the OpiSystem software simulation and Matlab software simulation, the influence of the pre-pump width and power on the performance of the system is analyzed and the feasibility of this method is confirmed by preliminary experiment.
Altered Expression of Ion Channels in White Matter Lesions of Progressive Multiple Sclerosis: What Do We Know About Their Function?

Despite significant advances in our understanding of the pathophysiology of multiple sclerosis (MS), knowledge about contribution of individual ion channels to axonal impairment and remyelination failure in progressive MS remains incomplete. Ion channel families play a fundamental role in maintaining white matter (WM) integrity and in regulating WM activities in axons, interstitial neurons, glia, and vascular cells. Recently, transcriptomic studies have considerably increased insight into the gene expression changes that occur in diverse WM lesions and the gene expression fingerprint of specific WM cells associated with secondary progressive MS. Here, we review the ion channel genes encoding K+, Ca2+, Na+, and Cl− channels; ryanodine receptors; TRP channels; and others that are significantly and uniquely dysregulated in active, chronic active, inactive, remyelinating WM lesions, and normal-appearing WM of secondary progressive MS brain, based on recently published bulk and single-nuclei RNA-sequencing datasets. We discuss the current state of knowledge about the corresponding ion channels and their implication in the MS brain or in experimental models of MS. This comprehensive review suggests that the intense upregulation of voltage-gated Na+ channel genes in WM lesions with ongoing tissue damage may reflect the imbalance of Na+ homeostasis that is observed in progressive MS brain, while the upregulation of a large number of voltage-gated K+ channel genes may be linked to a protective response to limit neuronal excitability. In addition, the altered chloride homeostasis, revealed by the significant downregulation of voltage-gated Cl− channels in MS lesions, may contribute to an altered inhibitory neurotransmission and increased excitability.
Prevalence and spectrum of DNA mismatch repair gene variation in the general Chinese population

Background Identifying genetic disease-susceptible individuals through population screening is considered as a promising approach for disease prevention. DNA mismatch repair (MMR) genes including MLH1, MSH2, MSH6 and PMS2 play essential roles in maintaining microsatellite stability through DNA mismatch repair, and pathogenic variation in MMR genes causes microsatellite instability and is the genetic predisposition for cancer as represented by the Lynch syndrome. While the prevalence and spectrum of MMR variation has been extensively studied in cancer, it remains largely elusive in the general population. Lack of the knowledge prevents effective prevention for MMR variation–caused cancer. In the current study, we addressed the issue by using the Chinese population as a model. Methods We performed extensive data mining to collect MMR variant data from 18 844 ethnic Chinese individuals and comprehensive analyses for the collected MMR variants to determine its prevalence, spectrum and features of the MMR data in the Chinese population. Results We identified 17 687 distinct MMR variants. We observed substantial differences of MMR variation between the general Chinese population and Chinese patients with cancer, identified highly Chinese-specific MMR variation through comparing MMR data between Chinese and non-Chinese populations, predicted the enrichment of deleterious variants in the unclassified Chinese-specific MMR variants, determined MMR pathogenic prevalence of 0.18% in the general Chinese population and determined that MMR variation in the general Chinese population is evolutionarily neutral. Conclusion Our study provides a comprehensive view of MMR variation in the general Chinese population, a resource for biological study of human MMR variation, and a reference for MMR-related cancer applications.
Processing of Heart Rate Signals Using Digital Spectral Analysis on the Plane of Complex Frequencies When Estimating the Reactivity of Regulation Mechanisms of the Human Physiological System

Currently, the high informative value and diagnostic significance of the biomedical signal - heart rate variability (HRV) is noted. The processing of the measurement information obtained based on the observed HRV signal makes it possible to quantitatively assess the reactivity of various mechanisms of regulation of the heart rhythm in response to various functional influences. To analyze the measured physiological signals reflecting transient processes (non-stationary processes), it is proposed to use digital spectral analysis on the plane of complex frequencies (SCF). SCF is a generalization of the usual spectrum. The SCF calculation is based on the Prony procedure. The use of spectral analysis on the plane of complex frequencies for the analysis of transient processes of the heart rate makes it possible to calculate the numerical values of the parameters of the reactions of various regulatory mechanisms of the heart rhythm during the direct action of the functional test or upon exiting it, which increases the number of informative parameters for a more accurate diagnosis of the regulatory mechanisms of the physiological system person.
Impact of Unmanned Aircraft Regulations on Autonomous Navigation Approaches for Indoor Multi-Rotor Applications — Survey

Demand in unmanned aircraft (UA) technologies for real-world applications have increased over the recent years, driving national aviation authorities to implement weight dependent regulations across all UA operations. Introduction of registration for UA weighing 250g and above as well as other regulatory requirements for heavier UA systems have motivated manufacturers to consider weight as a part of design requirement. Although UA weight is not a major concern for most outdoor applications, weight requirements imposed by aviation authorities further emphasizes the importance to develop smaller and lighter UA for safer indoor or urban operations in GPS denied environments. Comparison across various sensors used for autonomous UA navigation methods suggested that benefits of using vision sensors outweighs other methods since most UA are equipped with onboard cameras and thus does not require retrofitting of additional hardware. In addition, vision sensor data can potentially be used for both navigation and non-navigation tasks resulting in a productive and lightweight UA system that is able to avoid or reduce regulatory burdens for GPS denied UA operations.
A Low-Noise mm-Wave Injection-Locked Oscillator designed in 65nm Partially Depleted SOI CMOS Technology

A low-phase-noise injection-locked oscillator (ILO) based on a cross-coupled oscillator topology is presented. The prototype ILO was designed and fabricated in a 65-nm Partially Depleted SOI (PD-SOI) CMOS technology from STMicroelectronics. The 27.47 GHz free-running oscillator exhibits a phase noise of –119.23 dBc/Hz at 10-MHz offset, and its robustness to process variations is less than 4 dB. Using a common mode injection, the proposed ILO generates an output frequency at 27.5-GHz when the oscillator is injection-locked by the 5th harmonic of a 5.5 GHz reference. In injection-locked mode, the phase noise performance is then –116.75 dBc/Hz and – 135 dBc/Hz at 1-MHz and 10-MHz offsets, respectively, while solely consuming 2.06 mW from a 1 V supply. The total active area is 0.031 mm2 (excluding I/O pads).
Operation State Assessment and Prediction of Distribution Transformer Based on Data Driven

With the rapid development of Power Internet of Things, power grid monitoring data and analysis methods are increasing, so real-time dynamic monitoring of power equipment becomes possible. This paper presents a data driven method for evaluation and trend prediction of distribution transformer operation state. The key features reflecting dynamic change of operation state are extracted from voltage and current data of distribution transformer, and characteristic data flow is input into dynamic evaluation model to make real-time portrait description of distribution transformer operation state. According to time order and change trend of characteristic data flow, Long Short-Term Memory network (LSTM) is used to analysis regulation of characteristic data, and Support Vector Regression model (SVR) for its prediction. The future characteristic data flow is obtained, which is input into the dynamic evaluation model to realize the future operation trend prediction of the distribution transformer. Finally, examples are given to illustrate the feasibility, advanced nature and applicability of the method.
Effect of pH on the Kinetics of Crystal Growth by Oriented Aggregation

The kinetics of oriented aggregation are sensitive to solution conditions, including pH. Decreasing the difference between solution pH and the point of zero net proton charge results in an increasing second-order rate constant for nonclassical crystal growth by oriented aggregation. Results are discussed in the context of Derjaguin and Landau, Verwey and Overbeek (DLVO) theory through the use of quantitative statistical comparisons. The magnitude of the increase depends on the primary particle size; however, the magnitude and trend in size dependence deviate from predictions based on DLVO theory. Oriented aggregation occurs in a wide array of important materials often resulting in nanocrystals with unique, anisotropic, and symmetry-defying morphologies. The growth of acicular goethite (α- FeOOH) was used as a model system for this research. Transmission electron microscopy with time-resolved sampling was employed using semiautomated image analysis to quantify the rate constant for growth by oriented aggre.
Advancement Of Deep Learning In Big Data And Distributed Systems

Digital computing space has grown dramatically since the beginning of the 2000s to deal with an increase in data proliferation. These come from a wide variety area. For example, the number of connected devices explodes with the advent of the Internet of Things. These machines generate a growing number of data, which must be analyzed, by their interactions with the outside environment and its various sensors. Social networks are also another field in which various data has been used, interactive data and metadata that provide information on user profiles. All these data require the storage of large capacity and analysis of several data. In effect, if the arrival of this quantities of information demanded storage improvement, significant advances in processing and interpretation were also required and feasible. In this paper, the main contributions are summarized in a comparison table as detailed in Table 1, like the objectives, challenges, and novelty of each paper are clarified. The architecture or model and applications used— finally, the recommendations for each.
Self-Assembled Superstructures of Polymer-Grafted Nanoparticles: Effects of Particle Shape and Matrix Polymer

The incorporation of nanoparticles into polymer matrices increases reinforcement and leads to stronger plastics. However, controlling the spatial distribution of nanoparticles within polymer matrices is a challenge in improving the properties of polymer nanocomposite. Dissipative particle dynamics simulations are employed to investigate the self-assembly of nanoparticles into a variety of anisotropic superstructures. The shape of nanoparticle varies from hexagonal nanoplatelet to nanorod by changing the aspect ratio (S). A hexagonal particle with S ≈ 1 is spherelike. Depending on the grafted chain length and number of grafted chains, the self-assembled structure in the morphology diagram may be spherical aggregate, fillet, ribbon, or string, in addition to dispersed phase. In general, nanoplatelets tend to form string structure while nanorods have a tendency to pack as fillet or ribbon structure. The length of matrix polymer (Lm) changes the phase boundary separating different regions in the morphology di.
Performance Evaluation of Modulation Techniques in Single-Phase Dual Active Bridge Converters

This research presents a thorough comparison of different modulation techniques employed in dual active bridge (DAB) converters. The performance of a DAB converter is essentially affected by the modulation technique and the operating point, including power level, input, and output voltages. A deep understanding of different modulations is required to achieve the highest performance in the entire operating range. Therefore, this paper focuses on comparing different modulation techniques over a DAB converter's operating span. The comparison results provide guidance to apply the correct modulation in certain working areas keeping the converter on top of its performance. Moreover, the paper proposes an optimization solution with distinct objective functions adoptable in DAB converters to reduce power loss. The proposed optimization approach outperforms the existing solutions regarding generality and simplicity. The optimization associates with the modulation techniques that include more than one degree of freedom, such as extended phase shift (EPS) and dual-phase shift (DPS). The proposed optimization and the investigated modulation techniques are evaluated in terms of the converter's efficiency, current stress, and backflow power. The evaluation is realized by the simulation study of a $10{\rm{\,kW}}$ $800{\rm{V}}/500{\rm{V}}$ SiC-based DAB converter in PLECS software.
Equipment Life Prediction Method Based on Multi-source Data

Based on Kalman smoothing to synthesize multi-source data, the degradation model of Weiner process drift coefficient changing with time is established. The analytical expression of model parameter estimation is given by EM algorithm. Combined with the actual test data, the life of marine propulsion system gearbox is predicted. Compared with the traditional method and test results, the method is proved to be more accurate, which is of great significance to the life prediction of ship propulsion system related equipment.
Measurement Precision of Average Frame for Solid-State Time-of-Flight Camera

Three-dimensional (3-D) measurement is extensively used for 3-D modelling, motion detection, robotic navigasyon and information technology. Time-of-flight (ToF) camera is an emerging technology used for 3-D measurement. ToF camera has superior and weak properties compared to the other measurement techniques. In this study error sources of ToF range imaging camera were explained, and measurement precision of average frame was estimated with different image configurations.
Compressing the Scenes of Medical Video by Using Eigenvector

With the technological development, it has become easy to use digital data, and thus medical institutions have turned to analyzing images and video content. Medical video is recorded in order to collect experimental information about the use of technologies and integrated data that consists of a series of doctors with video recording, and this leads to user interaction with computers. Using the coding methodology, the video is analyzed for a medical diagnosis that contains important information about the patient’s condition with the help of computers, as well as performing surgeries in a medical environment to conduct clinical trials and diagnostics. This leads to software development, reduction in medical effort real-time quality measurement and utilization of available treatments for patient medical diagnosis Medical video has become one of the leading and most common fields, in addition to medical images for diagnosing heart and lung diseases, brain and digestive diseases, and this diagnosis is made by using ultrasound and magnetic resonance imaging in addition to a CT scan. All this helped to provide health care through new methods of research and experiments clinical. Lately, Compression technologies developed video and images are widely used, and by using these effective compression techniques, little or no damage is achieved, the file size is reduced and the visual quality is not affected, major target of compression is to enable data to get saved or transmit in the better form types of compression in various manners like the lossless and the lossy. The aim of this study is to compress video film to lower the irrelevance and the redundancy of data, principle component analysis (PCA) is used in this research, the main component analysis of medical video compression based on self-matrix analysis that all readers will be able to understand the PCA method. This paper presents a suitable technique for medical video compression using Principle Component Analysis PCA, compressed the frames of medical video with eigenvalue and eigenvector techniques. Experimental results shown the efficient and quick technique for medical video frames.
Time-based Sampling Methods for Detecting Helpful Reviews

Product reviews describe customer opinions and experiences to products. Better opinions and experiences in the reviews more attract and help people who want to buy the products. The reviews, including such factors, are called helpful reviews. Many studies have been conducted to detect helpful reviews and proposed many useful factors, such as review-related factors, product-related factors, and reviewer-related factors. Meanwhile, the elapsed time of reviews has been used as a factor in detecting helpful reviews but never considered as sampling methods, despite that it is an essential factor to determine the freshness of the reviews, which influence the people being interested in the product. In this paper, we propose time-based sampling methods, which determine the sample size as small as possible in detecting helpful reviews with high accuracy. To investigate the effect of the time-based sampling methods in detecting helpful reviews, we conducted extensive experiments comparing with total sampling and simple random sampling, using two machine learning methods: XGBoost and CNN which involve text and numerical factors. Experimental results illustrate the validity of the proposed methods. Significantly, in large datasets, our proposed sampling methods outperform the other sampling methods.