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Video question answering (VideoQA) is a task that involves answering questions related to videos. The main idea is to understand the content of the video and to combine it with the relevant semantic context to answer various types of questions. Existing methods typically analyze the spatiotemporal correlations of the entire video to answer questions. However, for some simple questions, the answer is related to only a specific frame of the video, and analyzing the entire video undoubtedly increases the learning cost. For some complex questions, the information contained in the video is limited, and these methods are not sufficient to fully answer such questions. Therefore, we proposes a VideoQA model based on question classification and a traffic knowledge database. The model starts from the perspective of the question and classifies the questions into general scene questions and causal questions using different methods to process these two types of questions. For general scene questions, we first extract the key frames of the video to convert it into a simpler image question-answering task and then we use top–down and bottom–up attention mechanisms to process it. For causal questions, we design a lightweight traffic knowledge database that provides relevant traffic knowledge not originally present in VideoQA datasets, to help model reasoning. Then, we use a question and knowledge-guided aggregation graph attention network to process causal questions. The experimental results show that while greatly reducing resource costs, our model performs better on the TrafficQA dataset than do models utilizing millions of external data for pretraining. [ABSTRACT FROM AUTHOR]

Purpose. To analyse the clinical efficacy of biofeedback electrical stimulation combined with doxycycline in the treatment of type IIIA chronic prostatitis. Methods. Eighty patients who met the diagnostic criteria of type IIIA chronic prostatitis in our hospital between February 2020 and February 2022 were selected and equally divided into the drug group and electrical stimulation group according to the random number table method. The drug group was treated with medication alone for 4 weeks; the electrostimulation group was treated with biofeedback electrostimulation on top of medication for 12 weeks. The expressed prostatic secretious (EPS) routine (lecithin bodies, white blood cells) and the maximum urinary flow rate (Qmax) and mean urinary flow rate (Qave) were measured before and after treatment in both groups, and the National Institutes of Health chronic prostatitis symptom index (NIH-CPSI) was used to score the urinary symptom, pain or discomfort, and quality of life and determine the efficacy of the treatment in both groups. Results. After treatment, the number of lecithin bodies and white blood cells in EPS improved significantly in both groups compared to before, and both the electrical stimulation group was better than the drug group (P < 0.05). After treatment, the Qmax and Qave were significantly higher in both groups than before, and both the electrical stimulation groups were higher than the drug group (P < 0.05). After treatment, the urinary symptom scores, pain or discomfort scores, quality of life scores, and total NIH-CPSI scores were significantly lower in both groups than before, and all were lower in the electrical stimulation group than in the drug group (P < 0.05). After treatment, the overall efficiency of patients in the electrical stimulation group was significantly higher than that of the drug group (P < 0.05). Conclusion. Biofeedback electrical stimulation combined with doxycycline in the treatment of type IIIA chronic prostatitis can synergistically improve the patient's inflammation level, urinary dysfunction, relieve pelvic floor tension myalgia, and improve their quality of life, opening up new avenues for the rehabilitation of patients with type IIIA chronic prostatitis. [ABSTRACT FROM AUTHOR]

The induced voltage ripple in DC field windings can cause many problems in hybrid excitation machines (HEMs). It can generate ripple current in field winding, thus causing unsteady magnetic field excitation and hence additional torque ripple and losses. Besides, the torque and power density of hybrid excitation machines may be deteriorated as a result of the influence of the induced voltage ripple on field winding's excitation circuit, particularly under high-speed conditions. Based on a hybrid excitation switched flux machine (HESFM), the influence of stator slot and rotor pole number combination on no-load and on-load field winding induced voltage ripples are studied and compared by the finite element method (FEM). It reveals that the harmonic content and thus the peak-to-peak value of the field winding induced voltage ripple are closely related to the stator slot and rotor pole number combination at both no-load and on-load operating conditions. The FEM calculations are verified by experiments on a prototype HESFM. [ABSTRACT FROM AUTHOR]

To find out the bioelectric signal discriminative features of different acupoints at the same time and at the same acupoint, and then analyze these differences to determine the type of acupuncture points and assist in judging the patient's condition, a hardware system was designed that can collect subcutaneous potential information of acupoints. Then, the changes of ultrasonic images were analyzed after acupuncture at Zu San Li, as well as the influence of acupuncture at Zu San Li on vagal nerve activity. The hardware system is composed of 4 parts: an analog circuit module, a digital circuit module, a power module, and a host personal computer. The power module adopted dual-module power supply mode, which was composed of MC78M05CT and LM2576 and their related peripheral circuits to ensure the stability of the circuit. The analog circuit module collected the electric signals from the 2 acupoints of Zu San Li and Shang Ju Xu of the volunteers' calves through a multistage electrode probe. In the amplifier module, Butterworth filter and 50 HZ notch are used to reduce the noise of electric signals. The filtered and amplified electric signals are converted into A/D converter in the digital circuit module. Serial communication is used to transmit data to the upper computer of the personal computer to compare the noise reduction effect of acupoint information, multiple sequences of different acupoints, and multiple sequences of the same acupoint. The influence of acupuncture at the Zu San Li acupoint on the muscle tension around the acupoint was investigated by shear wave acoustic elastic imaging technology. The influence of acupuncture at this acupoint was explored on vagal nerve activity. The results show that the wavelet noise reduction method can greatly filter out the interference signals. In the case of artificial stimulation, the signal waveform of each twist shows a big increase, and the comparison shows that there is no significant change in the frequency of other bands. Through ultrasonic image evaluation, it was found that acupuncture at Zu San Li can significantly affect the mean value of Young modulus. After acupuncture at Zu San Li, the vagal activity was increased and the balance ratio of sympathetic vagal activity was reduced. Part of the potential frequency of the acupoint is extremely active, which is the response potential of the subcutaneous induction tissue at the acupoint, which proved the feasibility of collecting electric signals around the acupoints in this kind of hardware system. It is of great significance to diagnose the disease according to the characteristics of acupoint electric signals. [ABSTRACT FROM AUTHOR]

The dc winding induced voltage in hybrid-excited machines can lead to dc winding current ripple and hence, fluctuating field excitation. It may also challenge the dc power source and increase the difficulty of machine control, especially at high speed. The dc winding induced voltage in a hybrid-excited switched-flux permanent magnet (HESFPM) machine is investigated in this article. The phenomenon and mechanism of the dc winding induced voltages under both open-circuit and on-load conditions are analyzed, with particular emphasis on on-load operation which consists of open-circuit induced voltage and armature current induced voltage, respectively. Two techniques, i.e., rotor step skewing and unequal rotor teeth, are proposed and comparatively investigated by the finite-element (FE) method to suppress the dc winding induced voltage under on-load condition. FE results show that the peak-to-peak value of on-load dc winding induced voltage can be effectively suppressed by 96.15% or 89.05% by rotor step skewing or unequal rotor teeth, respectively, while the average on-load electromagnetic torque can be maintained at 89.4% or 87.8%. A prototype HESFPM machine is built and tested to verify the FE results. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a method of blind modulation format identification using decision tree twin support vector machine classifier trained with the features extracted from the high-order cumulant and cyclic spectrum after compressed sensing in order to solve the problem of identification efficiency and computing speed and reduce the performance requirements of the sampling system in low optical signal to noise ratio. By reconstructing the feature parameters of fourth-order, eighth-order cumulants and cyclic spectrum under the theory of compressed sensing, and introducing the decision tree twin support vector machine classifier to achieve high-precision classification, the different modulation formats of amplitude shift keying, multiple phase-shift keying and multiple quadrature amplitude modulation are effectively identified. Simulation analysis of the influence of identification accuracy and identification time improves the identification performance and achieves the purpose of identifying more signals with fewer feature parameters. The results indicate that the average identification accuracy of the optical modulation format signals can be achieved over 94% when the OSNR is -5dB, and the identification time is 4.3 times higher than the standard SVM. Owing to its excellent performance, this method can be employed in the next generation optical transport network for auto-adaption real-time modulation format identification. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction is critical for enhancing traffic network operational efficiency. With the continuous expansion of traffic networks, providing reliable and efficient multi-step traffic flow prediction for large-scale traffic networks with a large number of sensors deployed has become a challenging issue. In this paper, we propose a multi-step many-to-many traffic prediction model for large-scale traffic networks, called spatio-temporal Shared GRU (STSGRU), which receive inputs from multiple sensors and provides predictions for all sensors simultaneously. First, we model the weekly pattern of traffic flow, using periodicity to explore long-term temporal features and provide smooth traffic flow to reduce the impact of data volatility. Second, different from existing models, we propose a shared weight mechanism to achieve many-to-many prediction without mapping traffic networks to images or graph structures. The proposed model strikes a delicate balance between complexity and accuracy. We validate the effectiveness of the proposed method on the Caltrans Performance Measurement System (PeMS) dataset. The results show that our model achieves similar prediction performance with advanced graph neural networks and has higher flexibility. • Modeling traffic flow weekly patterns to infer future trends of traffic flow. • Provides smooth traffic flow to help the network identify traffic flow patterns. • Many-to-many prediction without additional structure to represent the sensor network. • The proposed model strikes a delicate balance between complexity and accuracy. [ABSTRACT FROM AUTHOR]

When dealing with safety‐critical applications that involve multiphase permanent magnet synchronous motors (PMSMs), the current source inverter (CSI) is a superior choice compared with the voltage source inverter. The conventional technique of pulse width modulation (PWM) presents a challenge for CSI, particularly during faults. The fault‐tolerant control of multiphase PMSMs fed by CSIs has not been well investigated, particularly in situations involving double‐phase open‐circuit faults. Furthermore, fault‐tolerant control becomes more complex when the multiphase PMSM includes harmonic back electromotive forces (EMFs). To deal with these issues, an ampere‐second (AS) vector PWM technique is developed for a five‐phase PMSM powered by CSI11. The modulation ratio of the proposed PWM is identical to that of space vector PWM. The proposed method, however, is easier to implement. A fault‐tolerant control method using AS‐vector is proposed for single‐phase, adjacent double‐phase, and non‐adjacent double‐phase open‐circuit faults. Additionally, a method is developed to suppress the torque ripple that results from harmonic back‐EMFs during fault conditions. Compared without the torque ripple suppression method, during the speed rising, the proposed method can reduce torque ripple to 16.7%, 54.5%, and 37.5%, respectively. The experiments have confirmed the effectiveness of the proposed PWM technique and fault‐tolerant control method. [ABSTRACT FROM AUTHOR]

A new regulatory class of small endogenous RNAs called circular RNAs (circRNAs) has been described as miRNA sponges in animals. Using 16 Arabidopsis thaliana RNA-Seq data sets, we identified 803 circRNAs in RNase R-/non-RNase R-treated samples. The results revealed the following features: Canonical and noncanonical splicing can generate circRNAs; chloroplasts are a hotspot for circRNA generation; furthermore, limited complementary sequences exist not only in introns, but also in the sequences flanking splice sites. The latter finding suggests that multiple combinations between complementary sequences may facilitate the formation of the circular structure. Our results contribute to a better understanding of this novel class of plant circRNAs. [ABSTRACT FROM AUTHOR]

Alternative splicing plays a key role in the regulation of the central dogma. Four major types of alternative splicing have been classified as intron retention, exon skipping, alternative 5 splice sites or alternative donor sites, and alternative 3 splice sites or alternative acceptor sites. A few algorithms have been developed to detect splice junctions from RNA-Seq reads. However, there are few tools targeting at the major alternative splicing types at the exon/intron level. This type of analysis may reveal subtle, yet important events of alternative splicing, and thus help gain deeper understanding of the mechanism of alternative splicing. This paper describes a user-friendly R package, extracting, annotating and analyzing alternative splicing types for sequence alignment files from RNA-Seq. SplicingTypesAnno can: (1) provide annotation for major alternative splicing at exon/intron level. By comparing the annotation from GTF/GFF file, it identifies the novel alternative splicing sites; (2) offer a convenient two-level analysis: genome-scale annotation for users with high performance computing environment, and gene-scale annotation for users with personal computers; (3) generate a user-friendly web report and additional BED files for IGV visualization. SplicingTypesAnno is a user-friendly R package for extracting, annotating and analyzing alternative splicing types at exon/intron level for sequence alignment files from RNA-Seq. It is publically available at https://sourceforge.net/projects/splicingtypes/files/ or http://genome.sdau.edu.cn/research/software/SplicingTypesAnno.html . [ABSTRACT FROM AUTHOR]

In interior permanent magnet synchronous motor (IPMSM) position‐sensorless drives, the high‐frequency (HF) square‐wave voltage injection method is often used to estimate the rotor position and speed in low‐speed range by tracking the salient polarity of the motor. In order to reduce the torque ripple caused by HF signal injection, a strategy to update the magnitude of the injected signal online by back propagation neural network is proposed in this paper. With the proposed method, the neural network can update the magnitude of the injected signal online according to the d‐axis current and the position error information. It can not only ensure the accuracy of position extraction but also effectively reduce the current harmonics caused by the injected signal, and then the torque ripple can be reduced. In addition, the proposed method is easy to implement, resulting in low computation burden. Finally, the experiments are implemented on a 1‐kW IPMSM drive. The experimental results show that compared with the conventional fixed magnitude injection, the peak‐to‐peak value of the torque ripple is reduced by nearly half along with the decrease of the injected magnitude. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a new visualization software called pairheatmap, which is able to generate and compare two heatmaps so as to compare expression patterns of gene groups. It adds a conditioning variable such as time to the heatmap, and provides separate clustering for row groups in the first heatmap in order to visualize pattern changes between two heatmaps. pairheatmap is developed in R statistical environment. It provides: (1) the flexible framework for comparing two heatmaps; and (2) high-quality figures based on R package grid. The general architecture can be efficiently incorporated into bioinformatics pipeline. The package and user documentation are free to download at http://cran.r-project.org/web/packages/pairheatmap/index.html. [Copyright &y& Elsevier]

The inherent interfacial bonding formation of layers determines the quality of complex 3D parts fabricated using fused deposition modeling (FDM). The inferior and anisotropic mechanical properties of FDM parts prevent their industrial application. This research is aimed at enhancing the mechanical properties and reducing the mechanical anisotropy of FDM parts through an innovative auxiliary heating treatment. Polylactide (PLA), which is a distinct semicrystalline polymer commonly used in 3D printing, was selected to examine the enhancement of mechanical properties by implementing auxiliary heating treatment. When the auxiliary heating treatment is applied, the tensile strength of the FDM-printed PLA part increases from 38.4 MPa to 63.6 MPa, and the degree of mechanical anisotropy decreases from 0.51 to 0.22. The auxiliary heating treatment yields superior mechanical properties compared to those attained through post-thermal annealing. Auxiliary heating represents a kind of "in-situ" annealing strategy during FDM printing. Moreover, the auxiliary heating temperature is similar to the cold crystallization temperature (Tcc) of PLA and is the optimal processing condition to obtain superior mechanical properties. As indicated by the SEM and DSC methods, the interfacial bonding of adjacent strands and crystallinity considerably influence the mechanical properties of FDM-printed PLA parts. Therefore, the auxiliary heating approach has the potential to be applied in the fabrication of large FDM parts. • An enhanced interface of bond zone between adjacent strands can be obtained through auxiliary heating treatment. • Auxiliary heating treatment increased tensile property and mechanical isotropy of FDM-printed PLA parts. • Both the interfacial bonding and crystallinity contribute to the mechanical properties of FDM-printed PLA parts. [ABSTRACT FROM AUTHOR]

Abstract: Population pharmacokinetic (PopPK) modeling has become increasing important in drug development because it handles unbalanced design, sparse data and the study of individual variation. However, the increased complexity of the model makes it more of a challenge to diagnose the fit. Graphics can play an important and unique role in PopPK model diagnostics. The software described in this paper, PKgraph, provides a graphical user interface for PopPK model diagnosis. It also provides an integrated and comprehensive platform for the analysis of pharmacokinetic data including exploratory data analysis, goodness of model fit, model validation and model comparison. Results from a variety of modeling fitting software, including NONMEM, Monolix, SAS and R, can be used. PKgraph is programmed in R, and uses the R packages lattice, ggplot2 for static graphics, and rggobi for interactive graphics. [Copyright &y& Elsevier]

Male immune infertility is a kind of disease that damages family life and happiness. The development of novel methods treating male immune infertility is of great significance. This study aimed to investigate the therapeutic effects of Chinese medicine Xiaokang Liuwei Dihuang decoction on immune infertility of male rats and explored the involved mechanisms. Model rats were established by lipopolysaccharide (LPS) injection. Anti-sperm antibody (AsAb) was detected by ELISA assay and testicular cell apoptosis was evaluated by TUNEL staining to verify the successful model establishment and screen suitable doses of Xiaokang Liuwei Dihuang decoction. Thirty rats were then divided into five groups (n = 6 per group): Control, LPS, Xiaokang Liuwei Dihuang decoction (15.12 g/kg, 30.24 g/kg and 45.36 g/kg). Results of HE staining showed that compared with LPS group, Xiaokang Liuwei Dihuang decoction treatments gradually improved the morphology of seminiferous tubules and elevated the number of spermatogenic cells as the doses increased. The sperm number and the levels of testosterone, luteinizing hormone (LH) and follicle stimulating hormone (FSH) in the serum of 15.12 g/kg, 30.24 g/kg and 45.36 g/kg Xiaokang Liuwei Dihuang decoction groups were much higher than those in LPS group. Results of TUNEL staining, ELISA assay and western blot showed that compared with LPS group, the testicular cell apoptosis and the levels of interleukin 1β (IL-1β), tumor necrosis factor α (TNF-α), AsAb, malondialdehyde (MDA) and toll-like receptor 2 (TLR2) in the testicular tissue significantly decreased in three Xiaokang Liuwei Dihuang decoction groups. Compared with LPS group, Bax expression in the 30.24 g/kg and 45.36 g/kg Xiaokang Liuwei Dihuang decoction groups was significantly down-regulated as well. In conclusion, Xiaokang Liuwei Dihuang decoction might ameliorate the immune infertility of male rats induced by LPS through regulating the levels of sex hormones, reactive oxygen species, pro-apoptotic and immune factors. [Display omitted] • Xiaokang Liuwei Dihuang decoction ameliorates the immune infertility of male rats. • Xiaokang Liuwei Dihuang decoction regulates sex hormones and ROS. • Xiaokang Liuwei Dihuang decoction regulates pro-apoptotic and immune factors. [ABSTRACT FROM AUTHOR]

This article proposes and investigates two asymmetric consequent-pole hybrid excited flux reversal machine (AS-CP-HEFRM) topologies, which have same numbers of iron poles and permanent magnets (PMs) with concentrated ac and dc windings on each stator pole and a salient pole rotor. If PM number on one stator pole is larger than 1, PMs are magnetized radially in the same direction on one stator pole, while opposite on adjacent stator poles. The phenomenon of the phase shift between the flux linkages of dc and PM excitations in the AS-CP-HEFRMs is revealed for the first time, which will weaken the regulation of dc excitation field to PM produced field. The elimination condition of the phase shift is derived and verified by finite-element analysis in this article, which can be considered as design guidelines in AS-CP-HEFRMs. Airgap magnetic field space harmonics of AS-CP-HEFRM are also investigated and verified by finite-element analyses. Meanwhile, the relationship between the rotor pole number and the stator pole arc (PM ratio) is derived and verified by global optimization based on genetic algorithm for maximum average torque. The influence of stator/rotor pole number combinations is also studied and compared in terms of torque, torque ripple, and flux regulation capability. The torque components produced by airgap magnetic field space harmonics are calculated and compared. A prototype has been built and tested to validate the finite element calculations. [ABSTRACT FROM AUTHOR]

Fused Deposition Modeling (FDM) technology offers opportunities for high-efficiency design and low-cost manufacturing of 3D complex parts in additive manufacturing (AM). The warpage remains a big problem which prevents the industrial application of FDM parts, especially parts fabricated by fiber-reinforced composite. In this study, an auxiliary heating plate was developed and mounted on the printing head of an FDM 3D printer to suppress the warpage of the parts made with short carbon fiber (CF)-reinforced acrylonitrile-butadiene-styrene copolymers (ABS) composites. It is found that auxiliary heating and raster angle of FDM process are the two dominating parameters on the tensile properties and the warpage of FDM parts. For the best parameter combination, 160 oC for auxiliary heating temperature and 0 degree for raster angle, the warpage was completely suppressed and the tensile strength got 31% enhancement, ductility got 439% enhancement compared to the FDM part without auxiliary heating and 0 degree in raster angle. Cyclic fatigue behavior and anisotropy of CF/ABS specimen was also significantly improved by auxiliary heating treatment. The reduced stain caused by the annealing ability of auxiliary heating during the 3D printing, is responsible for this enhancement. Compared with auxiliary heating and post thermal annealing treatment, the tensile property and cyclic fatigue behavior of CF/ABS specimen was equivalent. Notably, auxiliary heating is the "in-situ" annealing during the process of 3D printing, which can break through the existing dimension constraints of post thermal annealing treatment. Therefore, the approach broadens the design for CF/ABS composites through temperature-specific optimization and makes it possible to fabricate huge-size FDM parts. Unlabelled Image • An innovative FDM 3D printer with an auxiliary heating plate was developed to fabricate CF/ABS composite parts; • Auxiliary heating process can reduce the warpage of CF/ABS composite parts; • Auxiliary heating process can increase the mechanical properties of CF/ABS composite parts. [ABSTRACT FROM AUTHOR]

In this paper, a blind modulation classification method based on compressed sensing using a high-order cumulant and cyclic spectrum combined with the decision tree–support vector machine classifier is proposed to solve the problem of low identification accuracy under single-feature parameters and reduce the performance requirements of the sampling system. Through calculating the fourth-order, eighth-order cumulant and cyclic spectrum feature parameters by breaking through the traditional Nyquist sampling law in the compressed sensing framework, six different cognitive radio signals are effectively classified. Moreover, the influences of symbol length and compression ratio on the classification accuracy are simulated and the classification performance is improved, which achieves the purpose of identifying more signals when fewer feature parameters are used. The results indicate that accurate and effective modulation classification can be achieved, which provides the theoretical basis and technical accumulation for the field of optical-fiber signal detection. [ABSTRACT FROM AUTHOR]

• The proposed model is adaptively adjusted. • The average accuracy of the proposed model exceeds 85% on several datasets. • The model ensures better accuracy for each subject. • The proposed data augmentation method is applicable to many types of data. • The proposed model captures both intra- and inter-channel features of EEG. Background and objective: The development of deep learning has led to significant improvements in the decoding accuracy of Motor Imagery (MI) EEG signal classification. However, current models are inadequate in ensuring high levels of classification accuracy for an individual. Since MI EEG data is primarily used in medical rehabilitation and intelligent control, it is crucial to ensure that each individual's EEG signal is recognized with precision. Methods: We propose a multi-branch graph adaptive network (MBGA-Net), which matches each individual EEG signal with a suitable time-frequency domain processing method based on spatio-temporal domain features. We then feed the signal into the relevant model branch using an adaptive technique. Through an enhanced attention mechanism and deep convolutional method with residual connectivity, each model branch more effectively harvests the features of the related format data. Results: We validate the proposed model using the BCI Competition IV dataset 2a and dataset 2b. On dataset 2a, the average accuracy and kappa values are 87.49 % and 0.83, respectively. The standard deviation of individual kappa values is only 0.08. For dataset 2b, the average classification accuracies obtained by feeding the data into the three branches of MBGA-Net are 85.71 % , 85.83 % , and 86.99 % , respectively. Conclusions: The experimental results demonstrate that MBGA-Net could effectively perform the classification task of motor imagery EEG signals, and it exhibits strong generalization performance. The proposed adaptive matching technique enhances the classification accuracy of each individual, which is beneficial for the practical application of EEG classification. [ABSTRACT FROM AUTHOR]

Intronic RNAs have been overlooked for a long time: They are functional, but treated as "junk." In this work, we designed a new sequencing strategy to investigate intronic RNAs. By using intron-capture RNA-seq, we systematically analyzed the intronic RNAs in Arabidopsis by zooming into the intronic regions an order of magnitude deeper than in previous work. Our key findings include: (1) Intron-capture RNA-seq is a much more efficient approach to analyze intronic RNAs than total RNA-seq and mRNA-seq. (2) We identified three types of intronic RNAs, and found that the GC pattern differs significantly between the introns with and without intronic RNAs. (3) We detected many hidden elements in introns, including circular RNAs, splice junctions, and transcripts that have previously been overlooked. (4) The expression of these intronic RNAs varies during the time course of pathogen infection, which indicates that an unknown mechanism may exist for these RNAs. (5) We also demonstrated that most of intronic RNAs are detectable in both Arabidopsis and rice, suggesting that these non-coding molecules are conserved. Taken together, this work proposes an efficient strategy to analyze intronic RNAs, and provides an unprecedented view of this essential component in biological pathways. • Intron-capture RNA-seq is a much more efficient approach to analyze intronic RNAs than total RNA-seq and mRNA-seq. • Three types of intronic RNAs are identified, and GC pattern of all types differs significantly between the introns with and without intronic RNAs. • Intronic RNAs are detectable in both Arabidopsis and rice, suggesting that these non-coding molecules are conserved. [ABSTRACT FROM AUTHOR]

Background: Graphics play an important and unique role in population pharmacokinetic (PopPK) model building by exploring hidden structure among data before modeling, evaluating model fit, and validating results after modeling.Results: The work described in this paper is about a new R package called PKreport, which is able to generate a collection of plots and statistics for testing model assumptions, visualizing data and diagnosing models. The metric system is utilized as the currency for communicating between data sets and the package to generate special-purpose plots. It provides ways to match output from diverse software such as NONMEM, Monolix, R nlme package, etc. The package is implemented with S4 class hierarchy, and offers an efficient way to access the output from NONMEM 7. The final reports take advantage of the web browser as user interface to manage and visualize plots.Conclusions: PKreport provides 1) a flexible and efficient R class to store and retrieve NONMEM 7 output, 2) automate plots for users to visualize data and models, 3) automatically generated R scripts that are used to create the plots; 4) an archive-oriented management tool for users to store, retrieve and modify figures, 5) high-quality graphs based on the R packages, lattice and ggplot2. The general architecture, running environment and statistical methods can be readily extended with R class hierarchy. PKreport is free to download at http://cran.r-project.org/web/packages/PKreport/index.html. [ABSTRACT FROM AUTHOR]

Lodging is one of the primary factors that reduce wheat yield; therefore, rapid and accurate monitoring of wheat lodging helps to provide data support for crop loss and damage response and the subsequent settlement of agricultural insurance claims. In this study, we aimed to address two problems: (1) calculating the wheat lodging area. Through comparative experiments, the SegFormer-B1 model can achieve a better segmentation effect of wheat lodging plots with a higher prediction rate and a stronger generalization ability. This model has an accuracy of 96.56%, which realizes the accurate extraction of wheat lodging plots and the relatively precise calculation of the wheat lodging area. (2) Analyzing wheat lodging areas from various growth stages. The model established, based on the mixed-stage dataset, generally outperforms those set up based on the single-stage datasets in terms of the segmentation effect. The SegFormer-B1 model established based on the mixed-stage dataset, with its mIoU reaching 89.64%, was applicable to wheat lodging monitoring throughout the whole growth cycle of wheat. [ABSTRACT FROM AUTHOR]

The photovoltaic properties of nanoporous TiO2 films treated by different hydrochloric acid (HCl) concentrations were investigated in dye-sensitized solar cells (DSSCs). The nanostructure of the films was reformed, and the thickness decreased after the HCl hydrothermal treatment, which was observed by SEM analysis. It was found that the electron lifetime, band gap, recombination rate and TiO2 film electron injection efficiency had been changed by the processing when different analysis methods were employed to analyze the experimental results, including OCVD, UV-Vis and Mott–Schottky plots. In addition, the schematic for electron transfer and recombination was shown to discuss the process in clear detail. The performance of the DSSCs based on the different TiO2 films was measured by J–V curves and IPCE. When the TiO2 film was treated by 1 mol L−1 HCl at 180 °C for 3 h, the device had the optimum 7.77% photo-electric conversion performance and 16.04 mA cm−2Jsc. The results suggested that the reformed film nanostructure led to an optimized electron transportation pathway, and the decreased electron recombination and increased injection efficiency resulted in improved DSSCs. [ABSTRACT FROM AUTHOR]

Among the existing harmonic current suppression methods, it is difficult and complicated to suppress any nth harmonic current accurately for multi-phase permanent magnet synchronous motors (PMSMs). To solve this problem, this paper takes a five-phase dual-rotor PMSM fed with a current source inverter (CSI) as an example, and proposes an nth harmonic current suppression method based on the impulse current PWM algorithm. Firstly, the analysis is conducted and presented for the nth harmonic current in the mth harmonic space. Then, based on the Sliding Discrete Fourier Transformation (SDFT), a low-pass filter (LPF) named SDFT-LPF is designed. Additionally, the impulse current PWM technique for the five-phase CSI is realized. In this paper, the experiments have confirmed that the SDFT-LPF has good filter performance. Compared with the SVPWM, the impulse current PWM technique has the same DC-link current utilization rate, but it is easier to implement. Moreover, the proposed harmonic current control method can accurately control any nth harmonic current without changing the PWM technique, which has significantly reduced the complexity of the harmonic current control. Additionally, the proposed scheme is easy to implement and can be directly extended to the multiple harmonic current's control. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the article on the transcriptional regulation beyond miRNAs in plants.

In this work, nitrogen doped carbon fiber loaded copper (Cu/CF-N) sensing electrode and Nafion proton conductor are used to fabricate fuel cell type gas sensors that realize fast response/recovery for SO 2 detection at room temperature. This is the first time that this type of sensor has been used to detect SO 2. We investigated the effect of Cu content on sensors' performance, and found that the Cu (20 wt%)/CF based sensor exhibited the highest response. This can be attributed to the optimal Cu content, which exposes the most active sites. Moreover, nitrogen was doped into electrode material to further improve the performance. Nitrogen atoms enhance the conductivity of the material and its ability to adsorb SO 2. These are confirmed by the complex impedance measurement and TPD analysis. The upgraded Cu/CF-N fuel cell type gas sensor showed a response of − 435.6 nA to 50 ppm SO 2 and a low detection limit of 500 ppb. Significantly, the Cu/CF-N sensor demonstrated short response time (44 s) and recovery time (87 s) at room temperature. Furthermore, with the advantages of no power consumption, excellent selectivity and good repeatability, the fuel cell type gas sensors have broad prospects in the field of room temperature gas detection. ● The fuel cell type gas sensor realizes the detection of SO 2 for the first time. ● This work provides guideline to extend the applications of fuel cell type gas sensor. ● The Cu/CF-N sensor shows short response/recovery time of 44 s/87 s at room temperature. ● The device can detect SO 2 from 0.5 to 50 ppm. [ABSTRACT FROM AUTHOR]

Circular RNAs are widely existing in eukaryotes. However, there is as yet no tissue-specific Arabidopsis circular RNA database, which hinders the study of circular RNA in plants. Here, we used 622 Arabidopsis RNA sequencing data sets from 87 independent studies hosted at NCBI SRA and developed AtCircDB to systematically identify, store and retrieve circular RNAs. By analyzing back-splicing sites, we characterized 84 685 circular RNAs, 30 648 tissue-specific circular RNAs and 3486 microRNA–circular RNA interactions. In addition, we used a metric (detection score) to measure the detection ability of the circular RNAs using a big-data approach. By experimental validation, we demonstrate that this metric improves the accuracy of the detection algorithm. We also defined the regions hosting enriched circular RNAs as super circular RNA regions. The results suggest that these regions are highly related to alternative splicing and chloroplast. Finally, we developed a comprehensive tissue-specific database (AtCircDB) to help the community store, retrieve, visualize and download Arabidopsis circular RNAs. This database will greatly expand our understanding of circular RNAs and their related regulatory networks. AtCircDB is freely available at http://genome.sdau.edu.cn/circRNA. [ABSTRACT FROM AUTHOR]

Orthonormal basis function (OBF) decomposition method and minimum entropy (ME) detector are two typical methods of magnetic anomaly detection. The OBF detector works effectively only when the assumptions of the target signal and noise are appropriate, and the ME detector is limited by low signal-to-noise ratio (SNR). In order to improve magnetic anomaly detection performance in the case of low SNR and no prior information of the target signal, we proposed a novel detector by using stochastic resonance (SR) method and named it SR detector in the paper. The SR detector consists of a bistable SR system and a corresponding receiver. Firstly, the noise is used to enhance the magnetic anomaly signal with the help of SR system, instead of being suppressed in a traditional method; then the anomaly signal can be detected more effectively by the receiver. Experimental results show that the SR detector did work well, its detection probability approximated to 70% even if the input SNR was −3 dB, and was about 100% if the input SNR was 0dB, when the false alarm rate (FAR) was 1.5%. Furthermore, the SR detector provided higher detection probability than the ME detector under the same basis. In a word, due to the good detection performance and simple implementation, the SR detector would be more attractive in practice. [ABSTRACT FROM AUTHOR]

Owing to the ferromagnetism and stray fields of inertial navigation system, component compensation of magnetic field distortion is significant for geomagnetic field vector measurement system. However, traditional scalar compensation methods cannot be used for component compensation of magnetic field distortion, so some improvement works have been researched for component compensation in the study, the major of which is estimating compensation parameters using the Lagrange multiplier method. Experiment results show that the performance of the component compensation method is much better than traditional scalar compensation method. After compensation, the maximal measurement errors of north, vertical, east components and total intensity caused by magnetic field distortion are reduced to 10.4 nT (0.91% of the raw error), 32.5 nT (5.20%), 12.9 nT (1.27%), and 18.4 nT (2.02%), respectively. In addition, compared with previous component compensation method, the proposed method has three advantages: (i) simplified equipment, (ii) easier operation process, and (iii) better generality. [ABSTRACT FROM AUTHOR]

• A parallel NLOS identification method is proposed using the multiple input neural network model. • The raw CIR and its time-frequency diagram are effectively employed. • Five different typical obstacles are tested to validate the identification method within 1–4 m. • The UWB-based positioning results of LS and WLS algorithms are compared using the proposed NLOS identification method. Ultra wide band (UWB) radio positioning technology is widely used in indoor high-precision positioning scenes, while obstacles in the wireless signal propagation path will cause the non-line of sight (NLOS) propagation of UWB signal and the reduction of positioning reliability. In this paper, an efficient NLOS identification scheme based on multiple input learning (MIL) neural network model with channel impulse response (CIR) and time-frequency diagram of CIR (TFDOCIR) is proposed by direct detection in UWB positioning system. It is experimentally demonstrated that the average NLOS identification accuracies reach 86.82%, 92.53%, 91.61%, 92.91%, 92.02% corresponding to five different obstacles including wooden door, concrete wall, metal plate, human body and glass window, respectively. Additionally, the overall NLOS identification accuracy achieves 91.74%. Through the proposed NLOS identification scheme with weight least squares (WLS), the indoor UWB-based positioning tests are performed with the average error 7.35 cm, thereby proving its ability of ranging error preventation. [ABSTRACT FROM AUTHOR]

Regulation of proteome diversity by alternative splicing has been widely demonstrated in plants and animals. NAGNAG splicing, which was recently defined as a tissue specific event, results in the production of two distinct isoforms that are distinguished by three nucleotides (NAG) as a consequence of the intron proximal or distal to the splice site. Since the NAGNAG mechanism is not well characterized, tools for the identification and quantification of NAGNAG splicing events remain under-developed. Here we report nagnag, an R-based NAGNAG splicing detection tool, which accurately identifies and quantifies NAGNAG splicing events using RNA-Seq. Overall, nagnag produces user-friendly visualization reports and highlights differences between the DNA/RNA/protein across the identified isoforms of the reported gene. The package is available on https://sourceforge.net/projects/nagnag/files/ ; or http://genome.sdau.edu.cn/research/software/nagnag.html . [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks. [ABSTRACT FROM AUTHOR]

Circular RNA (circRNAs) may mediate mRNA expression as miRNA sponge. Since the community has paid more attention on circRNAs, a lot of circRNA databases have been developed for plant. However, a comprehensive collection of circRNAs in crop response to abiotic stress is still lacking. In this work, we applied a big-data approach to take full advantage of large-scale sequencing data, and developed a rich circRNA resource: CropCircDB for maize and rice, later extending to incorporate more crop species. We also designed a metric: stress detections score, which is specifically for detecting circRNAs under stress condition. In summary, we systematically investigated 244 and 288 RNA-Seq samples for maize and rice, respectively, and found 38 785 circRNAs in maize, and 63 048 circRNAs in rice. This resource not only supports user-friendly JBrowser to visualize genome easily, but also provides elegant view of circRNA structures and dynamic profiles of circRNA expression in all samples. Together, this database will host all predicted and validated crop circRNAs response to abiotic stress. [ABSTRACT FROM AUTHOR]

With the wide application of wireless sensor networks in military and environmental monitoring, security issues have become increasingly prominent. Data exchanged over wireless sensor networks is vulnerable to malicious attacks due to the lack of physical defense equipment. Therefore, corresponding schemes of intrusion detection are urgently needed to defend against such attacks. Considering the serious class imbalance of the intrusion dataset, this paper proposes a method of using the synthetic minority oversampling technique (SMOTE) to balance the dataset and then uses the random forest algorithm to train the classifier for intrusion detection. The simulations are conducted on a benchmark intrusion dataset, and the accuracy of the random forest algorithm has reached 92.39%, which is higher than other comparison algorithms. After oversampling the minority samples, the accuracy of the random forest combined with the SMOTE has increased to 92.57%. This shows that the proposed algorithm provides an effective solution to solve the problem of class imbalance and improves the performance of intrusion detection. [ABSTRACT FROM AUTHOR]