Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhanced data-driven fault diagnosis for machines with small and unbalanced data based on variational auto-encoder

Deep learning (DL) has become a popular option for data-driven fault diagnosis, because it can avert the influence of subjective factors in an artificial feature extraction process. However, it also suffers from the adverse effects accompanied with small fault sample and unbalanced data, resulting in limited accuracy improvement. For the aforementioned problem, this paper introduces a variational auto-encoder (VAE) into a fault diagnosis framework to realize data amplification by vibration signal generation, then an enhanced fault diagnosis approach is proposed combining with a convolution neural network. The well-trained VAE can realize the infinite generation of vibration signal by using the hidden variables sampled from Gaussian distribution, and then the generated artificial signals are mixed with the real original signals to form an enhanced training set, which can be utilized for classifier training to realize fault identification. Experimental results show that the generated artificial signals have similar time-frequency characteristics compared with the original real ones, and the enhanced fault diagnosis method holds a higher and more stable recognition accuracy than the unenhanced version and other typical methods.
A Simple Multilevel Space Vector Modulation Technique and MATLAB System Generator Built FPGA Implementation for Three-Level Neutral-Point Clamped Inverter

The pulse width modulation (PWM) is an important segment in power electronic inverters and multilevel inverters (MLIs) design. The space vector modulation (SVM) methods own distinct advantages over other PWM methods. However, MLI SVM has involved more mathematics in their executions. Hence, the digital signal processors (DSPs) or field programmable gate arrays (FPGAs) based digital implementations are highly preferred for MLI SVM realizations, which require exceptional properties. The conventional MLI SVMs use complex mathematical functions to solve their internal functions to identify the space vector diagram (SVD) sub-triangle and over modulation boundary switching on-times. Particularly these are the changes in the position of reference vector with respect to their sub-triangle positions involving higher mathematical functions. This paper proposes a simplified three-level MLI SVM that reduces the sub-triangle and over modulation switching on-time calculations with reduced mathematical functions. The proposed MLI SVM is derived based on two-level SVM without changing the reference vector position, unlike the traditional approaches. This helps in extending the SVM for any n-level inverter with additional LUTs. The detailed theoretical study, MATLAB-Simulink system generator simulations and Xilinx FPGA family SPARTAN-III-3A based experimental implementations are done with three-level neutral point MLI fed induction motor drive. The theoretical design, analysis, and experimentation results validate the advantages of the proposed PWM design and its implementation. In addition, the proposed implementation is executed from the MATLAB Xilinx system generator directly into target FPGA, which makes it faithful, efficient and minimizes the time spent.
Functionalized PdAu Alloy on Nanocones Fabricated on Optical Fibers for Hydrogen Sensing

This paper reports the development of fiber optical hydrogen sensors using palladium and gold alloy nanostructures as sensor materials for hydrogen measurements using a D-shaped fiber as a platform. Using a maskless reactive ion etching technique, silica nanocone structures were formed on the surface of the D-shaped fiber. Palladium and gold alloys were deposited on the surface of nanostructured D-shaped fiber to form a nano-alloy sensor film. Evanescent interaction between guided light propagated in the fiber core and nano-alloy enabled highly sensitive hydrogen detection from concentrations that ranged from 0.25% to 10% in atmosphere pressure. The formation of nanostructured alloy enabled by the nanocone surface led to more than 3 times faster in sensor response time and significant improvements in sensor sensitivity and reversibility. The work presented here demonstrates that highly controllable VLSI microfabrication schemes can be applied to produce nanostructured sensor films on optical fibers for high-sensitivity chemical sensing.
IMPACT OF LONG‐TERM FRESHWATER IRRIGATION ON SOIL FERTILITY

Irrigation increases agricultural productivity and inevitably affects soil properties. When irrigation water is of a good quality and does not cause soil salinity or sodicity or contamination, and when there is no soil erosion, a gradual increase in soil fertility under irrigation conditions is, as a rule, expected. However, studies in various countries present examples of a gradual decline in soil fertility under long‐term freshwater irrigation due to leaching of organic matter and exchangeable cations. It is known that ploughing and agricultural activities modify natural soil formation processes and soil fertility. Irrigation enhances soil formation processes but also causes soil leaching. This paper presents data on the impact of long‐term freshwater irrigation on soil fertility for different climatic zones of Mexico, assessed by comparing some agrochemical properties of irrigated and non‐irrigated soils. This comparison shows that irrigation generally has a positive effect on soil fertility in arid conditions and a negative effect in semi‐arid, semi‐humid and humid tropical zones. Therefore, when planning and applying irrigation to crops, it is necessary to take into account not only crop water requirements, but also the need to preserve and improve soil fertility. © 2019 John Wiley & Sons, Ltd.
Forecasting Oil Price Using Web-based Sentiment Analysis

International oil price forecasting is a complex and important issue in the research area of energy economy. In this paper, a new model based on web-based sentiment analysis is proposed. For the oil market, sentiment analysis is used to extract key information from web texts from the four perspectives of: compound, negative, neutral, and positive sentiment. These are constructed as feature and input into oil price forecasting models with oil price itself. Finally, we analyze the effect in various views and get some interesting discoveries. The results show that the root mean squared error can be reduced by about 0.2 and the error variance by 0.2, which means that the accuracy and stability are thereby improved. Furthermore, we find that different types of sentiments can all improve performance but by similar amounts. Last but not least, text with strong intensity can better support oil price forecasting than weaker text, for which the root mean squared error can be reduced by up to 0.5, and the number of the bad cases is reduced by 20%, indicating that text with strong intensity can correct the original oil price forecast. We believe that our research will play a strong supporting role in future research on using web information for oil price forecasting.
Prediction of ultra-short-term wind power based on BBO-KELM method

For ultrashort-term wind power prediction, an optimized extreme learning machine method based on biogeography-based optimization (BBO-KELM) is proposed. The kernel extreme learning machine (KELM) method only uses the kernel function to represent the unknown nonlinear feature map of the hidden layer and does not need to select the number of nodes of the hidden layer. Meanwhile, the output weight of the network is calculated by the regularized least squares algorithm. The BBO algorithm, which is a new evolutionary algorithm (EA) motivated by biogeography, which is the study of the distribution of biological species through time and space, is efficient in solving high dimensional, multiobjective optimization problems. In this paper, the KELM method is optimized using the BBO algorithm to optimize the selection of input variable sets, the parameters of the kernel function, and the Tikhonov regularization coefficient, so as to further improve the learning performance of the KELM method. To verify the effectiveness of the BBO-KELM method proposed in this paper, the BBO-KELM method is applied to ultrashort-term wind power prediction research in different regions and is compared with benchmark methods such as persistence, neural networks, support vector machine, extreme learning machine (ELM), and other optimized ELM (O-ELM) or KELM (O-KELM) methods such as BBO-ELM, particle swarm optimization (PSO)-ELM, differential evolution-KELM, simulated annealing-KELM, and PSO-KELM, under the same conditions. Experimental results show that the BBO-KELM methods with cosine migration can give better prediction accuracy; in addition, in the proposed method, the parameters of the kernel function do not need to be selected by trial-and-error and the relevant input variables can be automatically selected, improving the generalization capability.
Solid–Liquid Interdiffusion Bonding of Cu–Sn–Cu Interconnection and Sealing for High-Temperature Pressure Sensor Based on Graphene

Cu–Sn–Cu solid–liquid interdiffusion (SLID) bonding for high-temperature pressure sensors based on graphene has been investigated. Square bumps with a length of <inline-formula> <tex-math notation="LaTeX">$160~\mu \text{m}$ </tex-math></inline-formula> and an 80-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-wide sealing ring were simultaneously fabricated by the evaporation process. The die surface was treated with Ar (5% H<sub>2</sub>) plasma before bonding. Chip-level bonding was performed at 260 °C for 15 min with a pressure of 9.13 MPa under N<sub>2</sub> atmosphere. Pure Sn and Cu<sub>6</sub>Sn<sub>5</sub> have been exhausted completely, and the as-bonded interface only contains three layers of Cu–Cu<sub>3</sub>Sn–Cu, with no serious Sn overflow. The average shear strength of 50.6 MPa and an excellent leak rate of around 2.64 <inline-formula> <tex-math notation="LaTeX">$\times 10^{-4}$ </tex-math></inline-formula> Pa<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>cm<sup>3</sup>/s are achieved. The resistance measurements are similar to the theoretical estimation. After high-temperature storage (HTS) at 300 °C for 10 h, the average shear strength slightly decreases to 46.7 MPa and the leak rate increases to 1.01 <inline-formula> <tex-math notation="LaTeX">$\times 10^{-3}$ </tex-math></inline-formula> Pa<inline-formula> <tex-math notation="LaTeX">$\cdot $ </tex-math></inline-formula>cm<sup>3</sup>/s. More importantly, there is no degeneration of the graphene nanofilm during electrical measurement in the bonding process and HTS test. It is concluded that Cu–Sn–Cu SLID bonding is appropriate for high-temperature pressure sensors based on graphene.
Contactless Monitoring of Microcirculation Reaction on Local Temperature Changes

Assessment of skin blood flow is an important clinical task which is required to study mechanisms of microcirculation regulation including thermoregulation. Contactless assessment of vasomotor reactivity in response to thermal exposure is currently not available. The aim of this study is to show the applicability of the imaging photoplethysmography (IPPG) method to measure quantitatively the vasomotor response to local thermal exposure. Seventeen healthy subjects aged 23 ± 7 years participated in the study. A warm transparent compress applied to subject’s forehead served as a thermal impact. A custom-made IPPG system operating at green polarized light was used to monitor the subject’s face continuously and simultaneously with skin temperature and electrocardiogram (ECG) recordings. We found that the thermal impact leads to an increase in the amplitude of blood pulsations (BPA) simultaneously with the skin temperature increase. However, a multiple increase in BPA remained after the compress was removed, whereas the skin temperature returned to the baseline. Moreover, the BPA increase and duration of the vasomotor response was associated with the degree of external heating. Therefore, the IPPG method allows us to quantify the parameters of capillary blood flow during local thermal exposure to the skin. This proposed technique of assessing the thermal reactivity of microcirculation can be applied for both clinical use and for biomedical research.
An Evolutionary Computation Approach for the Online/On-Board Identification of PEM Fuel Cell Impedance Parameters with A Diagnostic Perspective

Online/on-board diagnosis would help to improve fuel cell system durability and output power. Therefore, it is a feature the manufacturers may wish to provide for final users to increase the attractiveness of their product. This add-on requires suitable stack models, parametric identification tools and diagnostic algorithms to be run on low-cost embedded systems, ensuring a good trade-off between accuracy and computation time. In this paper, a computational approach for the impedance parameter identification of polymer electrolyte membrane fuel cell stack is proposed. The method is based on an evolutionary algorithm including sub-population and migration features, which improves the exploration capability of the search space. The goal of the evolutionary algorithm is to find the set of parameters that minimizes an objective function, representing the mismatch between two impedance plots in a normalized plane. The first plot is associated with experimental impedance and the second is computed on the basis of the identified parameters using a circuit model. Three kinds of impedance models, characterized by increasing computational complexity, are used, depending on the experimental data—a linear model made of resistors and capacitors, the Fouquet model and the Dhirde model. Preliminary analysis of the experimental impedance data may evidence correlations among parameters, which can be exploited to reduce the search space of an evolutionary algorithm. The computational approach is validated with literature data in a simulated environment and with experimental data. The results show good accuracy and a computational performance that fits well with the commercial embedded system hardware resources. The implementation of the approach on a low-cost off-the-shelf device achieves small computation times, confirming the suitability of such an approach to online/on-board applications. From a diagnostic perspective, the paper outlines a diagnostic approach based on the identified impedance parameters, on the basis of a small set of experimental data including fuel cell stack faulty conditions.
A case-control study of the impact of CYP2E1 RsaI polymorphism on the risk of lung cancer

Objective: To evaluate the correlation of polymorphism of CYP2E1 RsaI and environmental factors such as smoking,alcohol drinking,independently and in combination with the risk of lung cancer. Methods: A case-control study was conducted which included 91 cases of lung cancer collected from the First Affiliated Hospital of Sun Yat-sen University of Medical Sciences,Guangzhou Tumour Hospital and The Red Cross Hospital of Guangzhou,and 138 controls. All subjects were investigated with a uniform questionnaire. The contents of survey included general characteristics such as age,sex,education,etc;habit of smoking,alcohol drinking. Blood samples were collected from all cases and controls for detecting CYP2E1 RsaI polymorphisms by PCR based RELP. Results: The frequency of C 1C 1 genotypes of CYP2E1 in the case group (67.03%) was higher than that of the control group(59.42%),but the frequency of C 1C 2 genotypes of CYP2E1 in the case group (25.28%) was lower than that of the control group(38.41%);and the OR of C 1C 1 genotypes of CYP2E1 was 1.71(comparing with C 1C 2 genotypes of CYP2E1),but there was no significant difference;comparing with non-smoker with C 1C 2 genotype,the odds ratios of smoker with C 1C 1 genotype,with C 1C 2 genotype and nonsmoker with C 1C 1 genotype of CYP2E1 RsaI were 3.63(1.37～34.97),3.27(1.09～9.84),2.58(0.96～6.91) respectively;Alcohol drinking was strongly associated with the risk of lung cancer (OR3.33,95%CI 1.74-6.38);the OR of alcohol drinker with C 1C 1 genotype, with C 1C 2 genotype of CYP2E1 and non-alcohol drinker with C 1C 1 genotype of CYP2E1 were 6.27(2.41～16.29),4.23(1.43～12.48),2.03(0.99～4.15) respectively. Conclusion: Polymorphism of a single gene of CYP2E1 was not associated with the risk of lung cancer. Risk of lung cancer was obviously elevated when smoking and alcohol drinking was combined with C 1C 1 genotype CYP2E1. Our data do suggest there was a combined effect among smoking and alcohol drinking and C 1C 1 genotype of CYP2E1.
A Novel Plus Type DVCC-Based Current-Mode Biquad

This paper presents a novel current-mode biquad using only plus type DVCCs (differential voltage current conveyors). The circuit enables LP (low-pass), BP (band-pass), HP (high-pass), BS (band-stop) and AP (all-pass) responses by the selection and/or addition of the circuit currents with no component matching constraints. Moreover the circuit parameters ω0 and Q can be set orthogonally adjusting the circuit components. An achievement example is given together with simulation results by PSPICE.
Understanding the Video Codec Jungle: A Comparison of TCO and Compression Efficiency

The codec landscape has rapidly accelerated, especially with the recent announcement by Apple to fully support High-Efficiency Video Coding (HEVC) and join the Alliance for Open Media (AOM) consortium. At the 2018 National Association of Broadcasters (NAB) Show, the AOM froze its specification of the AV1 codec. In addition, the Joint Video Exploration Team (JVET), a collaborative initiative between the Motion Picture Experts Group (MPEG) and International Telecommunication Union - Telecommunication (ITU-T), is making progress with the development of the next generation of MPEG compression, which will address future applications such as 8K and virtual reality (VR) six degrees of freedom (6DoF). This paper provides a thorough comparison of different codec technologies, including Advanced Video Coding (AVC), HEVC, AV1, and Versatile Video Coding (VVC). The total cost of ownership (TCO) and compression efficiency of each codec will be evaluated based on objective testing methods [i.e., peak signal-to-noise ratio (PSNR) and Video Multimethod Assessment Fusion (VMAF)] and subjective testing. Based on the factual data presented, readers will be able to make a better decision about which codec to use.
Clinical analysis of humeral shaft fracture fixation: intramedullary nails versus plates

Objective To compare the clinical outcome of interlocking intramedullary nail with that of plate in treating of humeral shaft fractures. Methods Eighty-eight patients with humeral shaft fractures were treated surgically. Thirty-nine cases were treated with interlocking intramedullary nail(IIN) fixation,49cases were treated with plate fixation(PLT). Clinical outcome measurements included fracture healing, radial nerve injury, infection, shoulder function according to Neer's score and soft tissue engorgement. Results The average follow-up time was 13 months(4 months to 44 months). Forty-nine cases those were treated with PLT fixation ,the average time of healing was 13.4 weeks; 39 cases those were treated with IIN fixation ,the average time of healing was 12.3 weeks . 100% of patients had good to excellent postoperative shoulder function,. 6 cases displayed postoperative radial never palsy in PLT group, but no case has iatrogenic injury in IIN group. Five cases displayed secondary postoperative radial never palsy in PLT group, and no case in IIN group. Four cases with swollen limbs persisted over 2 weeks in PLT group while 11 cases with swollen limbs persisted over 2 weeks in IIN group. Conclusions For patients who need surgical treatment of a humeral shaft fracture, both interlocking intramedullary nailing and compression plating provide predictable results for achieving fracture stabilization and ultimate healing, but IIN has fewer complications.
The Immunomodulatory and the Protection of Experimental Liver Injury of Lonicera macranthoides Hand.-Mazz on Mice

OBJECTIVE To study the immunomodulatory and the protection of experimental liver injury of Lonicera macranthoides Hand. -Mazz(LHM) which is the main source of Lonicera confusa DC.METHODS Immunocompromised mice model induced by intraperitoneal injection of cyclophosphamide and in vitro proliferation experiment of its peritoneal macrophages and spleen lymphocytes were used to evaluate the immunomdulatory effect of LHM.The inhibition of LHM on hepatic injury induced by CCl4 or alcohol on mice was measured.RESULTS The doses of 1.6,3.2 and 6.4 g·kg-1 of LHM could significantly increased the thymus index,the spleen index,the carbon dissection index and the macrophage phagocytic index of mice(P0.01),and obviously raised the proliferation ability of peritoneal macrophages and transformation ability of splenic lymphocytes(P0.01). The doses of 3.2 and 6.4 g·kg-1 of LHM could significantly lower the incremental ALT,AST and MDA level(P0.01),raise the low SOD level in liver(P0.01) of hepatic injury mice induced by CCl4 or alcohol,and obviously lighten the pathological damage of hepatic tissue.CONCLUSION The LHM could significantly promote the proliferation and phagocytosis function of immunocytes on immunocompromised mice.Moreover,it has good protection effect on hepatic injury of mice which is induced by CCl4 or alcohol.
Design of a novel CMOS/MTJ-based multibit SRAM cell with low store energy for IoT applications

ABSTRACT With rapid growth of Internet of Things, more and more devices are getting connected, which results in generation of large amount of data. To convert this collected raw data into useful information, it needs to be processed. However, during processing a significant amount of energy is spent in bringing data from off-chip non-volatile memory to on-chip memory. In addition, these devices are generally operated in ‘Normally OFF’ mode, which requires energy intensive boot process for waking up. Addressing these issues, Magnetic Tunnel Junction offers features like non-volatility and high integration density, which allows storage of data and instruction closer to the core. Therefore, this work proposes a hybrid multibit SRAM cell, which integrates MTJ with conventional 6T SRAM cell. The proposed SRAM cell supports multiple bit storage within a single cell. However, one of the critical issues with the MTJ is high-energy consumption while storing data. Hence, we also propose a store assist circuit for multibit SRAM cell, which asynchronously terminates the store operation after its completion to reduce the power consumption. Our simulation results show that the store assist circuit results in reduction of store energy by 35% and 72% when compared with existing multibit non-volatile SRAM.
A High-Efficiency Wideband Frequency-Reconfigurable Water Antenna With a Liquid Control System: Usage for VHF and UHF Applications

A distilled water dielectric resonator antenna (DRA) with a wide frequency-tuning range and high efficiency is presented in this article. It consists of a water resonator and a ground plane with a wideband bowtie feeding slot. The water resonator, having tunable dimensions, is employed to achieve frequency-reconfigurable characteristics. Mechanical switches and a microfluid pump are integrated to control the tuning process. The water DRA, with and without the liquid control system, is designed with a simulation tool and then fabricated and measured to validate the antenna's performance. It is demonstrated that the antenna exhibits high total efficiency (>80%) across a wide frequency-tuning range from 168 to 474 MHz (for S11, -10 dB) with a very compact volume of 278 × 278 × 131 mm3 (0.155λ × 0.155λ × 0.073λ at 168 MHz). Compared with traditional reconfigurable antennas, this design provides a wider frequency-tuning range, a more compact size, and a higher degree of design freedom. It is therefore a promising candidate for very high-frequency (VHF) and ultrahigh-frequency (UHF) applications.