Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation on the Magnetic Arc Control of Multi-Break VCBs

In order to obtain the effective magnetic arc control method, the synergy effect and the demand of the magnetic arc control in the multi-break VCBs is analyzed. The vital mechanism is the synergy interaction between the dynamic voltage distribution (DVD) and dielectric recovery strength (DRS). Then the test platform of the magnetic arc control in multi-break VCBs is set up and the magnetic arc control system is designed. The influence of the pulse width, control moment and magnetic field on the vacuum arc development and the post arc characteristic is investigated. The different combinations of externally applied AMF are applied to the double-break VCBs to analyze the effect of the AMF arc control on the DVD. The interaction between the magnetic arc control and the post arc characteristic is obtained and the influence of the post arc characteristic on the DVD and DRS is gained. The test results indicate that the mechanism of the magnetic arc control in multi-break VCBs is that the DVD and DRS is affected by the post arc characteristic which is influenced by the arc memory of the magnetic arc control in arcing process. The paper is useful to optimal design of the special serial VCBs and the magnetic arc control of multibreak VCBs.
Memory-Based Extractive Summarization

Recurrent neural networks (RNNs) have been widely used in previous work on extractive text summarization. However, the information stored by RNNs structure is typically limited and is not compartmentalized enough to accurately record facts from the past. That makes it difficult to obtain relationships of the sentences in a document and select salient sentences with important information for a summary. In order to rectify this problem, we propose a memory-based extractive summarization (MES) model which is mainly constructed by memory generalization and sentence extractor. Our model can store more information of features extracted from sentences, relationships between sentences and implications of document, thus giving richer representations for selecting sentences for summary. Our experimental results show that MES model outperforms the baselines. We obtain improvements of up to 3.8%, 8.1%, 8.5%, 3.6% and 5.6% in terms of R-1, R-2, R-3, R-4 and R-L, respectively, over a relevant state-of-the-art baseline.
A Compact Bandpass Filter Using Microstrip Hairpin Resonator for WLAN Applications

This work represents a microstrip hairpin resonator-based bandpass filter for Wireless Local Area Network (WLAN). Two short-circuited microstrip comb-lines have been coupled at the two sides of a rectangular-loop to construct the hairpin resonator. The Taconic TLX-8 material has been used as the substrate to design the filter at a center frequency of 2.45 GHz. The designed filter exhibits a two-pole Chebyshev response with an insertion loss of -0.37 dB and a minimum return loss of -34.03 dB. This compact filter has a fractional bandwidth of 4.37% which sharply selects the entire bandwidth of WLAN frequencies.
Towards An Optimized Smart Traffic for Congestion Avoidance with Multi Layered (ST-CA) Framework

In many cities around the world, traffic congestion is considered a major problem that should be managed properly. Managing traffic flow is not simply applied by constructing new road or maintaining old ones; it is not just a matter of physical infrastructure. For managing traffic, there should be ’’smart’’ ways to be taken by the use of available technologies’ facilities. Countries should think and act smart to manage today’s traffic in their cities. The use of controlled smart traffic systems is gaining a noticeable interest for the past few years and various solutions have been proposed, adopted and even implemented in several countries. In this paper, we propose Smart Traffic for Congestion Avoidance with Multi Layered (ST-CA) Framework, for solving the congestion problem. The proposed framework is constructed in multilayer approach for easy maintenance in the future. The STCA framework should be more flexible, in sending/ receiving messages, than others and depend in its decision making on expert systems.
Combinatorial impact of Sec signal peptides from Bacillus subtilis and bioprocess conditions on heterologous cutinase secretion by Corynebacterium glutamicum

The impact of Sec signal peptides (SPs) from Bacillus subtilis in combination with isopropyl‐β‐ d‐1‐thiogalactopyranoside concentration and feeding profile was investigated for heterologous protein secretion performance by Corynebacterium glutamicum using cutinase as model enzyme. Based on a comprehensive data set of about 150 bench‐scale bioreactor cultivations in fed‐batch mode and choosing the cutinase yield as objective, it was shown that relative secretion performance for bioprocesses remains very similar, irrespective of the applied SP enabling Sec‐mediated cutinase secretion. However, to achieve the maximal absolute cutinase yield, careful adjustment of bioprocess conditions was found to be necessary. A model‐based, two‐step multiple regression approach resembled the collected data in a comprehensive way. The corresponding results suggest that the choice of the heterologous Sec SP and its interaction with the adjusted exponential feeding profile is highly relevant to maximize absolute cutinase yield in this study. For example, the impact of Sec SP is high at low growth rates and low at high growth rates. However, promising Sec SPs could be inferred from less complex batch cultivations. The extensive data were also evaluated in terms of cutinase productivity, highlighting the well‐known trade‐off between yield and productivity in bioprocess development in detail. Conclusively, only the right combination of target protein, Sec SP, and bioprocess conditions is the key to success.
A Beam-Switched Low-Profile Antenna Array with Butler Matrix

A low-profile four-element antenna array for the purpose of monitoring unmanned aerial vehicles (UAV) is proposed in this paper. The operating frequency of this array is from 430 MHz to 440MHz. Each element is a monopole with a circular top-hat and its height is only 34 mm, which is less than 1/20 wavelength. This antenna array is able to cover the four 90° quadrants in the azimuth plane independently through four feeding ports. To enhance the antenna gain in each quadrant, a Butler matrix is introduced in the feeding network so that the four antenna ports are well isolated and the maximum gain is enhanced by about3 dB. This antenna array is fabricated and the measured results show it is suitable for the specified applications.
optimization Technology of Bus System Based On Big Data

With the rapid development of intelligent transportation, the public transport intelligence dispatching system is playing an increasing important role. However, the traditional statistical analysis method can not meet the requirements of current bus passenger flow analysis and prediction. The realization of bus system optimization based on large data plays an important role in solving the shortage of traditional statistical methods, and it is the key to realize the short period prediction of bus traffic. Based on the linear regression forecasting method, this paper uses MATLAB-R2016a as the system development environment, and compares the three forecasting methods of linear regression forecasting model, grey prediction model and time series prediction model, and gives the test to realize the bus system optimization technology. Through the forecast of the passenger flow in the short period of a bus line in Nantong, the optimization of the bus line is perfected and the traffic congestion is alleviated by the dispatch center.
Digitalization of Malayalam Palmleaf Manuscripts Based on Contrast-Based Adaptive Binarization and Convolutional Neural Networks

The palm leaf manuscripts are an abundant source of knowledge, tradition and ancient culture. These scriptures are an unavoidable part of our rich culture and have to be preserved in the best possible way. But the information extraction from palm leaf is a tedious task due to various challenges such as noise enormous character set and the difficulty in reading and understanding the ancient Malayalam script. Handwriting recognition in Malayalam is a challenging and emerging area of pattern recognition. Our proposed system aims at extracting information from old palm leaves (thaaliyola) and translating the ancient Malayalam scripts to their current version based on contrast-based adaptive binarization and convolutional neural networks which simplifies the entire process by avoiding feature extraction. The proposed method is different from the conventional methods which require handcrafted features that are used for classification. Initially, the system is trained with a set of characters. This can be expanded to work with the remaining characters as well. The input will be images of Malayalam palmleaf manuscript and the expected output is their translated script. Our system aims to transform these scripts so as to make it accessible and useful to the current generation. The system will be trained using a number of samples to build a convolutional neural network using which the characters will be recognized.
Anatomical Significance of the Spatial Distribution of the Palatopharyngeus With Regard to Velopharyngeal Closure

Objective: Coordination of the various soft palate and pharyngeal muscles should be considered while evaluating velopharyngeal closure. However, it remains unclear whether different muscle bundles have specific functions during velopharyngeal closure. We macroscopically and microscopically examined these muscles in detail and particularly clarified the morphology of the palatopharyngeus (PP) in velopharyngeal closure. Design: Forty halves of 21 heads from Japanese cadavers (average: 83.9 years) were used for analysis; 37 halves of 19 heads were macroscopically examined and 3 halves of 2 heads were histologically examined. Results: The PP consisted of muscle bundles originating from the superior and inferior surfaces of the palatine aponeurosis. The most superior part of the superior constrictor (SC) and most lateral part of PP on the palatine aponeurosis initially ran in parallel and subsequently, in superoposterior and inferoposterior directions, respectively. The PP appeared as a single continuous sheet that was radially spread as a whole. Its medial margins, located superior and inferior to the aponeurosis, formed a fold that established the palatopharyngeal arch. The stylopharyngeus (StP) adjoined the base of this arch. Conclusions: Since PP consisted of muscle bundles running in various directions, various functions of these bundles should be considered during velopharyngeal closure. The PP can function as a sphincter with SC and as an elevator with StP. In addition, PP forms the medial protrusion in collaboration with StP and SC. Thus, PP plays an important role in velopharyngeal closure with the coordination of various muscles.
Capacitive Electrode Sensor:Design and Testing

This paper proposed an alternative method of measuring water level using a Printed Circuit Board(PCB). The design of the electrode water level sensor went through circuit sketching, printing of sketch on PCB and etching. The signal conditioning circuit board was fabricated using a donut board and other electrical components. Experimentation was carried on the fabricated electrode sensor and the capacitance and current for each electrode finger was measured using digital multimeter and LCR meter. The multiple correlation of the water level, measured current and measured capacitance produced a value of 0.921 withP-values less than 0.05 showing the strength of the data obtained from the test conducted. The electrode water level sensor has proven to be consistent and reliable under normal working condition.
The Remaining Life Prediction of the Fan Bearing Based on Genetic Algorithm and Multi-Parameter Support Vector Machine

In order to meet the needs of the fault prediction and health management of the cooling fan in the industrial field, a new method based on genetic algorithm and multi-parameter support vector machine (GSVM) is proposed to predict the remaining life of the fan bearing. In this method, the fan system is used as the research object to predict the bearing life, which is closer to the actual working condition of the bearing compared with the research on the bearing. The multi-parameter is used as the input value of the support vector machine (SVM), which is more accurate than the single parameter. Genetic algorithm is used to optimize the parameters of SVM model, which improves the accuracy and certainty of parameter selection. The correlation analysis is used to choose the characteristics that are closely related to the remaining life. The unbalanced load is put on the fan blades to accelerate bearing life prediction process. The result shows that the model established by the parameters of the fan current, voltage and speed can accurately predict the residual life of rolling bearing, which not only provides theoretical guidance for the maintenance and replacement of the fan bearing, but also improves the fan predictive maintenance level.
An Enhanced TLD Algorithm Based on Sparse Representation

To build an efficient data processing module in TLD tracking algorithm, the samples of foreground targets and the background were compressed by using a very sparse measurement that can extract the features by a non-adaptive random projections efficiently, and after the sparse representation, the dimensionality reduction data can preserve most of the salient information and allow almost perfect reconstruction of the signal. Building a real-time long-term tracking system based on the sparse representation could improve the efficiency of tracking algorithm, thereby solving the problem of efficiency decline in TLD with the time going. In our algorithm, the sparse representation combines with the three sub-tasks of tracking task: tracking, learning and detection, which can not only guarantee the ability of estimating errors, but also improve the efficiency of data processing.
A response regulator of the OmpR family is part of the regulatory network controlling the oxidative stress response of Rhodobacter sphaeroides

As a free-living bacterium Rhodobacter sphaeroides needs to respond to many environmental stresses. Oxidative stress, membrane stress or heat stress induce the ompR-1 gene encoding a protein of the OmpR family. Overexpression of OmpR-1 results in increased resistance to organic peroxides and diamide. Our data demonstrate that OmpR-1 positively affects expression of several sRNAs with an established role in R. sphaeroides stress defences and negatively affects the promoter of the rpoHI gene. The RpoHI sigma factor has a main role in the activation of many stress responses. Thus OmpR-1 has a balancing effect on the activation of the RpoHI regulon. We present a model with OmpR-1 as part of a regulatory network controlling stress defences in R. sphaeroides.
Autotaxin/lysophosphatidic acid signaling mediates obesity‐related cardiomyopathy in mice and human subjects

Obesity is associated with increased cardiovascular morbidity and mortality, but the direct signals to initiate or exaggerate cardiomyopathy remain largely unknown. Present study aims to explore the pathophysiological role of autotaxin/lysophosphatidic acid (LPA) in the process of cardiomyopathy during obesity. Through utilizing mouse model and clinical samples, present study investigates the therapeutic benefits of autotaxin inhibitor and clinical correlation to obesity‐related cardiomyopathy. The elevated circulating levels of autotaxin are closely associated with cardiac parameters in mice. Administration with autotaxin inhibitor, PF‐8380 effectively attenuates high fat diet‐induced cardiac hypertrophy, dysfunction and inflammatory response. Consistently, autotaxin inhibition also decreases circulating LPA levels in obese mice. In in vitro study, LPA directly initiates cell size enlargement and inflammation in neonatal cardiomyocytes. More importantly, circulating levels of autotaxin are positively correlated with cardiac dysfunction and hypertrophy in 55 patients. In conclusion, present study uncovers the correlation between circulating autotaxin and cardiac parameters in mice and human patient, and provided solid evidence of the therapeutic application of autotaxin inhibitor in combating obesity‐related cardiomyopathy.
Speech Facilitates the Categorization of Motions in 9-Month-Old Infants

Two experiments were used to investigate the influence of both native and non-native speech on the categorization of a set of an object’s motions by 9-month-olds. In Experiment 1, infants were habituated to a set of three object motions and tested with familiar and novel motions. Results of Experiment 1 show that infants were more likely to categorize the motion stimuli if they listened to either the native or non-native speech during the categorization process than if they listened to music or heard nothing at all. Results of Experiment 2 show that discrimination of the motions was not impaired by the presence of the labeling phrases. These results are consistent with a number of findings that report a unique influence of labels on categorization of static objects in infancy and extend those findings to categorization of motions.
Fusion Methods for Hyperspectral Image and LIDAR Data at Pixel-Level

Hyperspectral image data and LIDAR data have found to be complimentary modailities in case of remotely sensed images, which can be fused if both are geo-referenced. Hyperspectral images provide the spectral response of each object in the area and can be used to identify the material composition of the image which can be used for the object classification. LIDAR data provides the elevation and geometrical information of the objects in the scene. Pixel-level fusion ensures no loss of information because there is no dimensionality reduction. This paper assesses the different methods of pixel fusion like wavelet transform, IHS transform and linear pixel fusion.
Neutropenia in Barth syndrome: characteristics, risks, and management

Purpose of review Barth syndrome (BTHS) is an X-linked disease characterized by defective remodeling of phospholipid side chains in mitochondrial membranes. Major features include neutropenia, dilated cardiomyopathy, motor delay and proximal myopathy, feeding problems, and constitutional growth delay. We conducted this review of neutropenia in BTHS to aid in the diagnosis of this disease, and to improve understanding of both the consequences of neutropenia and the benefits of treatment with granulocyte colony-stimulating factor (G-CSF). Recent findings In 88 patients with BTHS, neutropenia, that is, at least one count below 1.5 × 109/l, was detected in 74 (84%) and 44% had severe chronic neutropenia, with multiple counts below 0.5 × 109/l. The pattern of neutropenia varied between intermittent and unpredictable, chronic and severe, or cyclical with mathematically regular oscillations. Monocytosis, that is, monocytes more than 1.0 × 109/l, was observed at least once in 64 of 85 (75%) patients. G-CSF was administered to 39 of 88 patients (44%). Weekly average G-CSF doses ranged from 0.12 to 10.92 &mgr;g/kg/day (mean 1.16 &mgr;g/kg/day, median 1.16 &mgr;g/kg/day). Antibiotic prophylaxis was additionally employed in 21 of 26 neutropenic patients. Pretreatment bone marrow evaluations predominantly showed reduced myeloid maturation which normalized on G-CSF therapy in seven of 13 examined. Consistent clinical improvement, with reduced signs and symptoms of infections, was observed in response to prophylactic G-CSF ± prophylactic antibiotics. However, despite G-CSF and antibiotics, one adult patient died with multiple infections related to indwelling medical devices and gastrostomy site infection after 15.5 years on G-CSF and a pediatric patient required gastrostomy removal for recurrent abdominal wall cellulitis. Summary BTHS should be considered in any men with neutropenia accompanied by any of the characteristic features of this syndrome. Prophylaxis with G-CSF ± antibiotics prevents serious bacterial infections in the more severe neutropenic patients although infections remain a threat even in patients who are very compliant with therapy, especially in those with indwelling devices.
Machine Learning Solutions to Vehicular Traffic Congestion

Traffic management of metropolitan cities in India is becoming a challenging factor day by day. Traffic congestion and improper management leads to rise in accident cases in the city. Efficiency of existing traffic management solutions is decreasing, as the number of private vehicles is on the rise. In the context of increasing complexity of urban traffic and to reduce the accident rise, a machine learning solution is proposed. It provides predictive analysis of traffic in a given area using Supervised Learning techniques such as Back Propagation Neural Network (BPN). The work discusses about an android application that makes use of real-time traffic data and predicts the traffic densities of entire map area in an offline mode. It also specifically suggests best routes from source to destination based on the traffic data. The bigger picture here is the reduction of congested roads all over the city. This mechanism will also help to minimize the battery consumption of mobile devices.
Efficient Synapse Memory Structure for Reconfigurable Digital Neuromorphic Hardware

Spiking Neural Networks (SNNs) have high potential to process information efficiently with binary spikes and time delay information. Recently, dedicated SNN hardware accelerators with on-chip synapse memory array are gaining interest in overcoming the limitations of running software-based SNN in conventional Von Neumann machines. In this paper, we proposed an efficient synapse memory structure to reduce the amount of hardware resource usage while maintaining performance and network size. In the proposed design, synapse memory size can be reduced by applying presynaptic weight scaling. In addition, axonal/neuronal offsets are applied to implement multiple layers on a single memory array. Finally, a transposable memory addressing scheme is presented for faster operation of spike-timing-dependent plasticity (STDP) learning. We implemented a SNN ASIC chip based on the proposed scheme with 65 nm CMOS technology. Chip measurement results showed that the proposed design provided up to 200X speedup over CPU while consuming 53 mW at 100 MHz with the energy efficiency of 15.2 pJ/SOP.