Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiency Maximization Using SiC Multi-Die Asymmetric Configurations for Composite DC-DC Converters

Electric vehicles (EVs) need high efficiency DC-DC converters to improve drive cycle performance. Asymmetric SiC multi-die configurations in half-bridge modules are shown to improve drive cycle efficiency in composite DC-DC converters for EVs. It is observed that 2:1, 3:1, 4:1 die configurations reduce the average drive cycle losses by 45.38%, 31.93%, 27.65% respectively for the buck converter, and 9.85%, 42.62%, 59.47% respectively for the boost converter compared to the preceding symmetric die configuration 1:1, 2:2 and 3:3 over the US06 drive cycle. Aspects of drive cycle based composite optimization with respect to device and quantity of die per switch position selection are detailed in the paper. All theoretical results are verified by experimental tests over multiple operating points.
Study regarding improving of full immersion for VirtuSphere system

The VirtuSphere system is one of the most advanced immersion systems. The use of this system in military applications, games, education, is due both to its omnidirectional capabilities extremely useful in implementing virtual reality but also to the low price. The authors have proposed to improve the immersive capability of this system by coupling the VirtuSphere to a mechatronic assembly that allows immersive implementation of the soil resistance the user is stepping along with the possibility of simulating situations that may occur relative to the sensation of contact with earth: vibrations with varying intensity and variable (earthquakes), the sensation of falling. In order to develop this system and maintain the characteristics of a relatively low price, the article presents the results of studies conducted by authors on Brushless Direct Current (BLDC) motors. The results of the Matlab / Simulink simulations of the system model in both the open loop and the closed loop are presented, after which these results are compared with the practical results. At the end of the article, as a result of the simulations, a validated hardware-BLDC engine control strategy demonstrates the effectiveness of the system.
Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers

This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain—interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.
Comparative Evaluation of Super Grid Topologies proposed for Europe and Latin America

Super Grids are large continent-wide expansions of existing and planned high voltage power grids which have been proposed for bulk power transfers across international boundaries and for enhanced stability of underlying power infrastructure. This paper focuses on two such schemes in Europe and Latin America, reviews the architectures, analyses the reasons for the differences, and presents a better understanding on how each topology and its characteristics may be leveraged to plan similar Super Grids in other parts of the globe. Predominantly, proposals for such Super Grids nucleate around extensive application of high voltage direct current (HVDC) technology for long distance power transmission with low losses. Ultra high voltage alternating current (UHVAC), with voltages above 800 kV AC, is also a competing alternative for low losses power transmission. This review paper presents a systematic and practical understanding on what could be the most suitable topology for each power transmission requirement.
Backdrivable and Fully-Portable Pneumatic Back Support Exoskeleton for Lifting Assistance

To reduce the possibility of lower back pain (LBP), which is the most frequent injury in manual labor, several back support exoskeletons have been developed and implemented for lifting motion assistance. Although pneumatic power transmission is attractive due to its inherent compliance and backdrivability, the portability of the pneumatic system is highly limited due to the bulky air compressors that provide compressed air to the system. Therefore, we aimed to develop a fully-portable pneumatic back support exoskeleton by integrating all pneumatic components in the system. The compressed air consumption and generation of pneumatic system were modeled to meet design requirements. The developed exoskeleton was completely stand-alone and provides 80 Nm of maximum extension torque for 6 liftings per minute (6 l/m). The upper limit of the resistance torque was estimated to be about 2 Nm, which implies high backdrivability. Finally, lifting experiments were performed and surface electromyography (sEMG) was measured to validate the physical assistance of the developed exoskeleton system for ten subjects. Compared to the case with no exoskeleton, the back muscle activation was significantly reduced with the assistances.
A Reliable Short-Circuit Protection Method with Ultra-Fast Detection for GaN based Gate Injection Transistors

This paper presents a new short-circuit protection method developed for Gallium Nitride (GaN) gate injection transistors (GITs). The proposed protection method is based on ultra-fast detection of the voltage dip on the phase leg of the converter, active current clamping of the gate drive, and fault confirmation with a low pass filter based de-saturation fault detection. Thus, it achieves an ultra-fast reaction and reliable protection for GaN GIT devices. The proposed protection method was experimentally validated with a 600 V rated commercial GaN GIT under single and repetitive short- circuit operations at 400 V. The total short-circuit fault response time was recorded within 224 ns, and the short-circuit energy is reduced with the ultra-fast detection and the active gate current clamping.
Breast cancer prevention: A Unani approach

Breast cancer is an increasing public health problem. In India breast cancer is the 2 nd most common cancer after cancer cervix. Obesity and a sedentary lifestyle are two modifiable risk factors. Advances have been made in the treatment of breast cancer, but the introduction of methods to predict women at elevated risk and prevent the disease has been less successful. Plants have a long history of use in the treatment of cancer. In the recent years, a number of herbs have been found to possess anti-cancer potential. In the Unani system of medicine, breast cancer is described as warme sulabe pistan or sartane pistan. Unani physicians have mentioned in the texts that the warme sulab usually develops in the az'ae ratba. Various herbal formulations in Unani system of medicine have been used for the prevention. A number of herbal drugs such as rehan, mulaithi, methi, alsi etc are being researched and reviewed for their anti-cancerous properties. This review expands the concept of warme sulabe pistan in Unani system of medicine and anticancer effects and related mechanisms of some common natural herbs in the prevention of breast cancer. Details will be presented in full length paper along with scientific research.
Acoustic Scene Classification Using Deep Mixtures of Pre-trained Convolutional Neural Networks

We propose a heterogeneous system of Deep Mixture of Experts (DMoEs) models using different Convolutional Neural Networks (CNNs) for acoustic scene classification (ASC). Each DMoEs module is a mixture of different parallel CNN structures weighted by a gating network. All CNNs use the same input data. The CNN architectures play the role of experts extracting a variety of features. The experts are pre-trained, and kept fixed (frozen) for the DMoEs model. The DMoEs is post-trained by optimizing weights of the gating network, which estimates the contribution of the experts in the mixture. In order to enhance the performance, we use an ensemble of three DMoEs modules each with different pairs of inputs and individual CNN models. The input pairs are spectrogram combinations of binaural audio and mono audio as well as their pre-processed variations using harmonic-percussive source separation (HPSS) and nearest neighbor filters (NNFs). The classification result of the proposed system is 72.1% improving the baseline by around 12% (absolute) on the development data of DCASE 2018 challenge task 1A.
A Layout Generation Algorithm for Unequal-Area Display and Control Console Considering Ergonomics

This study addresses the problem of designing a display and control console layout under ergonomic principles via a mathematical approach. An integer programming model is established with objective functions comprising four ergonomic principles for designing the display and control console. Unlike the traditional design methods that consider equal-area devices, the new method addresses the general ergonomic console layout problem with unequal-area components. The presented approach evaluates the console in its entirety. Due to the NP-hardness of the problem, a modified multiobjective particle swarm algorithm is proposed. Compared with the manually layout console of the electric locomotive HXD3D, our proposed model achieves better results. The layout of most of the devices can embody ergonomic principles. It is convenient for designers that the proposed method can effectively produce many good layout schemes and evaluate the layout from an ergonomic perspective.
Mission Automation for Drone Inspection in Congested Environments

This paper describes work in pursuit of pilot-only operation of a drone for inspection tasks in congested environments. In particular, the work focusses on the automation of mission-planning, monitoring and execution on the ground control station, reducing the need for additional personnel in support of the pilot. Geographic Information Systems are used for planning and behaviour trees for automated execution. Results of initial flight trials at the Clifton Suspension Bridge are reported.
Testing of Neuromorphic Circuits: Structural vs Functional

Deep neural networks have shown great potential in solving difficult cognitive problems such as object recognition and classification. As a result, several neuromorphic circuits are designed and fabricated to perform various cognitive tasks. While these circuits are subjected to manufacturing defects and runtime failures, a proper testing method for neuromorphic circuits should exploit their inherent tolerance to inaccuracies in order to reduce the cost and complexity of post-manufacturing testing. This paper investigates structural and functional testing methodologies for neuromorphic circuits. The proposed test methodologies allow to significantly reduce the number of faults to be tested, and test time accordingly, without sacrificing the output accuracy or fault coverage.
Decentralized Multiple V-Formation Control in Undirected Time-Varying Network Topologies

It is well known that the V-shape formation of fixed-wing aerial vehicles can contribute to saving fuel consumption from its induced drag reduction. Although it has been widely adopted on various flight missions, the conventional formation method of forming a single V-formation has issues on robustness and scalability if large number of agents are involved. In this paper, we propose a decentralized and scalable approach capable of generating separated multiple V-shape formations for the undirected time-varying networked agents. Using a variation of the multi-consensus algorithm, a flock of agents can be divided into the desired number of subgroups regardless of the subgroup network topology if the algebraic connectivity of the entire flock is maintained. Numerical simulation results are provided to show the feasibility of the proposed formation control.
Multimodal Models for Contextual Affect Assessment in Real-Time

Most affect classification schemes rely on near accurate single-cue models resulting in less than required accuracy under certain peculiar conditions. We investigate how the holism of a multimodal solution could be exploited for affect classification. This paper presents the design and implementation of a prototype, stand-alone, real-time multimodal affective state classification system. The presented system utilizes speech and facial muscle movements to create a holistic classifier. The system combines a facial expression classifier and a speech classifier that analyses speech through paralanguage and propositional content. The proposed classification scheme includes a Support Vector Machine (SVM) - paralanguage; a K-Nearest Neighbor (KNN) - propositional content and an InceptionV3 neural network - facial expressions of affective states. The SVM and Inception models boasted respective validation accuracies of 99.2% and 92.78%.
TestDNA: Novel Wafer Defect Signature for Diagnosis and Yield Learning

Wafer defect maps exhibit spatial failure pattern recognition for root cause analysis to improve defect diagnosis resolution and yield learning conventionally. We apply further product-driven test items to propose a wafer-level test methodology to generate a DNA-like wafer defect signature to identify wafer defects. Experience results show that our TestDNA are both effective and efficient, including the tight relation with traditional wafer defect patterns, high prediction accuracy over 90%, and improved lower non-pattern ratio from over 95% to around 11%.
Variation-Aware Small Delay Fault Diagnosis on Compressed Test Responses

With today's tight timing margins, increasing manufacturing variations, and new defect behaviors in FinFETs, effective yield learning requires detailed information on the population of small delay defects in fabricated chips. Small delay fault diagnosis for yield learning faces two main challenges: (1) production test responses are usually highly compressed reducing the amount of available failure data, and (2) failure signatures not only depend on the actual defect but also on omnipresent and unknown delay variations. This work presents the very first diagnosis algorithm specifically designed to diagnose timing issues on compressed test responses and under process variations. An innovative combination of variation-invariant structural analysis, GPU-accelerated time-simulation, and variation-tolerant syndrome matching for compressed test responses allows the proposed algorithm to cope with both challenges. Experiments on large benchmark circuits clearly demonstrate the scalability and superior accuracy of the new diagnosis approach.
A Wavelet Threshold Denoising-Based Imbalance Fault Detection Method for Marine Current Turbines

Blade imbalance fault caused by the marine organisms is considered as the most important fault in marine current turbines. Therefore, it is important to detect the fault accurately and quickly to mitigate its effect, minimize the downtime, and maximize the productivity. Imbalance fault detection methods using generator stator current signals have attracted attentions due to their low cost, operability and stability compared to the ones using vibration analysis. However, it is difficult to extract the fault signature and automatically detect the imbalance fault under different flow velocity conditions. In this paper, a wavelet threshold denoising-based imbalance fault detection method using the stator current is proposed. The signal is analyzed through three consecutive steps: the parameters offline setting based on wavelet threshold denoising, the Hilbert transform method and the Principle Component Analysis-based detection method. With this approach, the imbalance fault can be detected automatically. The imbalance fault detection is assessed under different flow velocity conditions and validated using an experimental platform. The results are promising with false alarm and false negative rates less than 1% and 5% respectively when using Q statistic. Moreover, the experimental results show that the proposed method has good stability under different flow velocity conditions.
Climate warming and trophic mismatches in terrestrial ecosystems: the green–brown imbalance hypothesis

Anthropogenic climate change can give rise to trophic mismatches in food webs owing to differential responses of consumer and resource organisms. However, we know little about the community and ecosystem level consequences of trophic mismatches in food webs. Terrestrial food webs are broadly comprised of two types of food webs: green food webs aboveground and brown food webs belowground between which mass and energy flow mainly via plants. Here, I highlight that the extent of warming-induced trophic mismatches in green and brown food webs differ owing to a greater stasis in brown food webs, which could trigger an imbalance in mass and energy flow between the two food webs. I then discuss the consequences of green–brown imbalance on terrestrial ecosystems and propose research avenues that can help understand the relationships between food webs and ecosystem functions in a warmer world.
A novel workflow for three-dimensional analysis of tumour cell migration

The limitations of two-dimensional analysis in three-dimensional (3D) cellular imaging impair the accuracy of research findings in biological studies. Here, we report a novel 3D approach to acquisition, analysis and interpretation of tumour spheroid images. Our research interest in mesenchymal–amoeboid transition led to the development of a workflow incorporating the generation and analysis of 3D data with instant structured illumination microscopy and a new ImageJ plugin.
Evolving Evidence of Methylglyoxal and Dicarbonyl Stress RelatedDiseases from Diabetic to Non-Diabetic Models

Methylglyoxal (MGO), a byproduct of sugar and lipid metabolic processes, is a major glycating agent. This metabolite reacts with basic residues of proteins and promotes the formation of advanced glycation end products (AGEs). Although MGO and AGEs are widely discussed in the context of diabetes, until recently, MGO was thought to result from insufficient blood sugar control. A new report reveals that plasma MGO, and not blood sugar, distinguishes diabetic patients with no pain from those with pain. This ability brings to MGO a new applicability to disease diagnosis. Diseases with normal sugar conditions, such as hypertension, sepsis, and renal disease, are increasingly recognized as MGO-related. We review the role of MGO in drug-induced nephropathy, induction of hypertension by oral administration, and as a biomarker of sepsis. We also discuss the measurement of MGO and its stable metabolite d-lactate. The metabolism and pathogenic mechanisms of MGO need investigation in diverse disease models. Whether MGO can be considered as an individual pathological factor will be an interesting topic.
Population Change of Red Kites Milvus milvus in Central Southern England between 2011 and 2016 Derived from Line Transect Surveys and Multiple Covariate Distance Sampling

Abstract. Successful reintroduction programmes are usually defined by an increase in size and extent of the new population after a given period of time. Among studies of birds, these population estimates are often focused on the monitoring of nesting attempts and productivity. For many raptors, however, this approach can overlook a large number of non-breeding adults and immature birds leading to underestimation of population size and reproductive potential. A more thorough approach is to generate assessments of total population size. In this study we used a line transect survey methodology and multiple covariate distance sampling to assess the change in population size of a reintroduced raptor species, Red Kite Milvus milvus, across a 2600 km2 area of central southern England. Surveys were performed in spring and autumn between 2011 and 2016 in an area 45 km to the south of the initial English reintroduction project which started 25 years previously. Survey routes avoided using roads where possible to counter the potential attraction that such landscape features may have (e.g. increased food availability, perches etc.). The use of roads was unavoidable in some instances; however, we found no evidence of Red Kite attraction to these landscape features when comparing distances of observations from stretches along roads with 5000 randomly-generated locations. Distance of detection was influenced by bird activity (greatest when the bird was on the ground or interacting with other birds) and extent of woodland but not by time of day, seasonality or when comparing between years. During the five years, estimated population size doubled from approximately 490 to 1100 individuals; a density of one Red Kite per 2.5–3.5 km2. This suggests an increase in the breeding population in the study area from c. 95 to 174 pairs. During the study, rate of population growth was not uniform; rapid growth was recorded in years two and three followed by a slowing over the last two years of the study. While an overall increase in population and availability of suitable nesting habitat across south-eastern England indicates that there is still potential for expansion of the Red Kite breeding population, other factors are potentially limiting this growth.
Using zebrafish (Danio rerio) to study the behavioral impacts of early bisphenol F exposure reveals decreased swim speed, increased distance between fish, and increased freezing behaviors

Abstract. The widespread use of endocrine disrupting chemicals (EDCs) has been a source of concern because of their various effects on the endocrine system. These effects include metabolic disorders, complications in reproductive health, hormone-related cancers, and neurodevelopmental disorders. Of particular concern is bisphenol A (BPA), a synthetic compound commonly found in consumer products such as water bottles, thermal receipt paper, and epoxy resins used in processed food packaging. Previous studies have shown that BPA can mimic estrogen through a variety of mechanisms and thus elicit an endocrine response. Some manufacturers have responded by removing BPA from their products; however, studies using a replacement compound bisphenol S have reported it to be just as, if not more, dangerous. The use of zebrafish (Danio rerio) larvae as a model organism allows for the effects of bisphenol exposure to be rapidly quantified through a simple behavioral assay. In studies involving bisphenol exposure, the use of zebrafish has demonstrated reproductive, developmental, endocrine, and behavioral effects. The study of bisphenol F, yet another endocrine disruptor that has become a replacement for BPA in consumer products, is highly important to public safety.