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The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet's two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model's superior recognition precision and rapid convergence capabilities in complex fault environments.

This study presents a novel method for the nighttime detection of waterborne individuals using an enhanced YOLOv5s algorithm tailored for infrared thermal imaging. To address the unique challenges of nighttime water rescue operations, we have constructed a specialized dataset comprising 5736 thermal images collected from diverse aquatic environments. This dataset was further expanded through synthetic image generation using CycleGAN and a newly developed color gamut transformation technique, which significantly improves the data variance and model training effectiveness. Furthermore, we integrated the Convolutional Block Attention Module (CBAM) at the end of the last encoder's feedforward network. This integration maximizes the utilization of channel and spatial information to capture more intricate details in the feature maps. To decrease the computational demands of the network while maintaining model accuracy, Ghost convolution was employed, thereby boosting the inference speed as much as possible. Additionally, we applied hyperparameter evolution to refine the training parameters. The improved algorithm achieved an average detection accuracy of 85.49% on our proprietary dataset, significantly outperforming its predecessor, with a prediction speed of 23.51 FPS. The experimental outcomes demonstrate the proposed solution's high recognition capabilities and robustness, fulfilling the demands of intelligent lifesaving missions.

Significant strides have been made in the field of WiFi-based human activity recognition, yet recent wireless sensing methodologies still grapple with the reliance on copious amounts of data. When assessed in unfamiliar domains, the majority of models experience a decline in accuracy. To address this challenge, this study introduces Wi-CHAR, a novel few-shot learning-based cross-domain activity recognition system. Wi-CHAR is meticulously designed to tackle both the intricacies of specific sensing environments and pertinent data-related issues. Initially, Wi-CHAR employs a dynamic selection methodology for sensing devices, tailored to mitigate the diminished sensing capabilities observed in specific regions within a multi-WiFi sensor device ecosystem, thereby augmenting the fidelity of sensing data. Subsequent refinement involves the utilization of the MF-DBSCAN clustering algorithm iteratively, enabling the rectification of anomalies and enhancing the quality of subsequent behavior recognition processes. Furthermore, the Re-PN module is consistently engaged, dynamically adjusting feature prototype weights to facilitate cross-domain activity sensing in scenarios with limited sample data, effectively distinguishing between accurate and noisy data samples, thus streamlining the identification of new users and environments. The experimental results show that the average accuracy is more than 93% (five-shot) in various scenarios. Even in cases where the target domain has fewer data samples, better cross-domain results can be achieved. Notably, evaluation on publicly available datasets, WiAR and Widar 3.0, corroborates Wi-CHAR's robust performance, boasting accuracy rates of 89.7% and 92.5%, respectively. In summary, Wi-CHAR delivers recognition outcomes on par with state-of-the-art methodologies, meticulously tailored to accommodate specific sensing environments and data constraints.

Lodging poses a significant challenge to rice yield, prompting the need to identify elite alleles for lodging resistance traits to improve cultivated rice varieties. In this study, a natural population of 518 rice accessions was examined to identify elite alleles associated with plant height (PH), stem diameter (SD), stem anti-thrust (AT/S), and various internode lengths (first (FirINL), second (SecINL), third (ThirINL), fourth (ForINL), and fifth (FifINL) internode lengths). A total of 262 SSR markers linked to these traits were uncovered through association mapping in two environmental conditions. Phenotypic evaluations revealed striking differences among cultivars, and genetic diversity assessments showed polymorphisms across the accessions. Favorable alleles were identified for PH, SD, AT/S, and one to five internode lengths, with specific alleles displaying considerable effects. Noteworthy alleles include RM6811-160 bp on chromosome 6 (which reduces PH) and RM161-145 bp on chromosome 5 (which increases SD). The study identified a total of 42 novel QTLs. Specifically, seven QTLs were identified for PH, four for SD, five for AT/S, five for FirINL, six for SecINL, five for ThirINL, six for ForINL, and four for FifINL. QTLs qAT/S-2 , qPH2.1 , qForINL2.1 , and qFifINL exhibited the most significant phenotypic variance (PVE) of 3.99% for the stem lodging trait. AT/S, PH, ForINL, and FifINL had additive effects of 5.31 kPa, 5.42 cm, 4.27 cm, and 4.27 cm, respectively, offering insights into eight distinct cross-combinations for enhancing each trait. This research suggests the potential for crossbreeding superior parents based on stacked alleles, promising improved rice cultivars with enhanced lodging resistance to meet market demands.

The eating and cooking quality (ECQ) directly affects the taste of rice, being closely related to factors such as gelatinization temperature (GT), gel consistency (GC) and amylose content (AC). Mining the quantitative trait loci (QTLs), and gene loci controlling ECQ-related traits is vital. A genome-wide association study on ECQ-related traits was conducted, combining 1.2 million single nucleotide polymorphisms (SNPs) with the phenotypic data of 173 rice accessions. Two QTLs for GT, one for GC and five for AC were identified, of which two were found in previously reported genes, and six were newly found. There were 28 positional candidate genes in the region of qAC11 . Based on a linkage disequilibrium (LD) analysis, three candidate genes were screened within the LD region associated with AC. There were significant differences between the haplotypes of LOC_Os11g10170 , but no significant differences were found for the other two genes. The qRT-PCR results showed that the gene expression levels in the accessions with high ACs were significantly larger than those in the accessions with low ACs at 35d and 42d after flowering. Hap 2 and Hap 3 of LOC_Os11g10170 reduced the AC by 13.09% and 10.77%, respectively. These results provide a theoretical and material basis for improving the ECQ of rice.

Gait recognition, crucial in biometrics and behavioral analytics, has applications in human-computer interaction, identity verification, and health monitoring. Traditional sensors face limitations in complex or poorly lit settings. RF-based approaches, particularly millimeter-wave technology, are gaining traction for their privacy, insensitivity to light conditions, and high resolution in wireless sensing applications. In this paper, we propose a gait recognition system called Multidimensional Point Cloud Gait Recognition (PGGait). The system uses commercial millimeter-wave radar to extract high-quality point clouds through a specially designed preprocessing pipeline. This is followed by spatial clustering algorithms to separate users and perform target tracking. Simultaneously, we enhance the original point cloud data by increasing velocity and signal-to-noise ratio, forming the input of multidimensional point clouds. Finally, the system inputs the point cloud data into a neural network to extract spatial and temporal features for user identification. We implemented the PGGait system using a commercially available 77 GHz millimeter-wave radar and conducted comprehensive testing to validate its performance. Experimental results demonstrate that PGGait achieves up to 96.75% accuracy in recognizing single-user radial paths and exceeds 94.30% recognition accuracy in the two-person case. This research provides an efficient and feasible solution for user gait recognition with various applications.

Human movement recognition is the use of perceptual technology to collect some of the limb or body movements presented. This practice involves the use of wireless signals, processing, and classification to identify some of the regular movements of the human body. It has a wide range of application prospects, including in intelligent pensions, remote health monitoring, and child supervision. Among the traditional human movement recognition methods, the widely used ones are video image-based recognition technology and Wi-Fi-based recognition technology. However, in some dim and imperfect weather environments, it is not easy to maintain a high performance and recognition rate for human movement recognition using video images. There is the problem of a low recognition degree for Wi-Fi recognition of human movement in the case of a complex environment. Most of the previous research on human movement recognition is based on LiDAR perception technology. LiDAR scanning using a three-dimensional static point cloud can only present the point cloud characteristics of static objects; it struggles to reflect all the characteristics of moving objects. In addition, due to its consideration of privacy and security issues, the dynamic millimeter-wave radar point cloud used in the previous study on the existing problems of human body movement recognition performance is better, with the recognition of human movement characteristics in non-line-of-sight situations as well as better protection of people's privacy. In this paper, we propose a human motion feature recognition system (PNHM) based on spatiotemporal information of the 3D point cloud of millimeter-wave radar, design a neural network based on the network PointNet++ in order to effectively recognize human motion features, and study four human motions based on the threshold method. The data set of the four movements of the human body at two angles in two experimental environments was constructed. This paper compares four standard mainstream 3D point cloud human action recognition models for the system. The experimental results show that the recognition accuracy of the human body's when walking upright can reach 94%, the recognition accuracy when moving from squatting to standing can reach 84%, that when moving from standing to sitting can reach 87%, and the recognition accuracy of falling can reach 93%.

Automatic driving technology refers to equipment such as vehicle-mounted sensors and computers that are used to navigate and control vehicles autonomously by acquiring external environmental information. To achieve automatic driving, vehicles must be able to perceive the surrounding environment and recognize and understand traffic signs, traffic signals, pedestrians, and other traffic participants, as well as accurately plan and control their path. Recognition of traffic signs and signals is an essential part of automatic driving technology, and gesture recognition is a crucial aspect of traffic-signal recognition. This article introduces mm-TPG, a traffic-police gesture recognition system based on a millimeter-wave point cloud. The system uses a 60 GHz frequency-modulated continuous-wave (FMCW) millimeter-wave radar as a sensor to achieve high-precision recognition of traffic-police gestures. Initially, a double-threshold filtering algorithm is used to denoise the millimeter-wave raw data, followed by multi-frame synthesis processing of the generated point cloud data and feature extraction using a ResNet18 network. Finally, gated recurrent units are used for classification to enable the recognition of different traffic-police gestures. Experimental results demonstrate that the mm-TPG system has high accuracy and robustness and can effectively recognize traffic-police gestures in complex environments such as varying lighting and weather conditions, providing strong support for traffic safety.

There have been outbreaks of SARS-CoV-2 around the world for over three years, and its variants continue to evolve. This has become a major global health threat. The main protease (M pro , also called 3CL pro ) plays a key role in viral replication and proliferation, making it an attractive drug target. Here, we have identified a novel potential inhibitor of M pro , by applying the virtual screening of hundreds of nilotinib-structure-like compounds that we designed and synthesized. The screened compounds were assessed using SP docking, XP docking, MM-GBSA analysis, IFD docking, MD simulation, ADME/T prediction, and then an enzymatic assay in vitro. We finally identified the compound V291 as a potential SARS-CoV-2 M pro inhibitor, with a high docking affinity and enzyme inhibitory activity. Moreover, the docking results indicate that His41 is a favorable amino acid for pi-pi interactions, while Glu166 can participate in salt-bridge formation with the protonated primary or secondary amines in the screened molecules. Thus, the compounds reported here are capable of engaging the key amino acids His41 and Glu166 in ligand-receptor interactions. A pharmacophore analysis further validates this assertion.

Secondary cell wall (SCW) thickening has a significant effect on the growth and development of plants, as well as in the resistance to various biotic and abiotic stresses. Lignin accounts for the strength of SCW. It is synthesized through the phenylpropanoid pathway that also leads to flavonoid synthesis. The coupling strategies for lignin and flavonoid syntheses are diverse in plants. How their syntheses are balanced by transcriptional regulation in fleshy fruits is still unclear. The diploid strawberry ( Fragaria vesca ) is a model for fleshy fruits research due to its small genome and wide scope of genetic transformation. SCW thickening is regulated by a multilevel transcriptional regulatory network wherein vascular-related NAC domains (VNDs) act as key regulators. In this study, we systematically characterized VNDs in Fragaria vesca and explored their functions. The overexpression of FvVND4c in diploid strawberry fruits resulted in SCW thickening and fruit color changes accompanied with the accumulation of lignin and flavonoids. Genes related to these phenotypes were also induced upon FvVND4c overexpression. Among the induced genes, we found FvMYB46 to be a direct downstream regulator of FvVND4c . The overexpression of FvMYB46 resulted in similar phenotypes as FvVND4c , except for the color change. Transcriptomic analyses suggest that both FvVND4c and FvMYB46 act on phenylpropanoid and flavonoid biosynthesis pathways, and induce lignin synthesis for SCW. These results suggest that FvVND4c and FvMYB46 cooperatively regulate SCW thickening and flavonoid accumulation in Fragaria vesca .

As a key area in the Yellow River basin for sand control and management, the land change process in the Hobq Desert plays a crucial role in keeping the river and desert ecosystems and promoting the construction of ecological civilization in human systems. Based on multi-temporal remote sensing from 1991 to 2019 in the Hobq Desert along the Yellow River section, this study selected spatial statistical methods (land-use monitoring and landscape metrics) to examine land-use change dynamics. Then, we evaluated habitat quality using the InVEST model and quantitatively analyzed the factors causing spatial changes in habitat quality using geographic detectors. Finally, this paper predicted the pattern of land use and habitat quality in 2030 using the PLUS model. The results reveal that (1) from 1991 to 2019, the total area of forest grassland increased by 3572.5 km 2 , providing the most vegetation cover, and the sandy land and water area decreased continuously, while the cultivated land and construction land increased. There were 38.01% conversions of land types, with the land-use dynamic decreasing the greatest in sandy land (-12.66%) and increasing the greatest in construction land (9.26%); the comprehensive land-use dynamics were the highest in 2010-2019 (1.68%), which was the most active stage during our study period. (2) Both of the landscape indices NP and PD showed "N" type fluctuations during 1991-2019, and CONTAG and LSI rose from 69.19% to 70.29% and 36.01% to 38.89%, respectively, indicating that the land-use degree of landscape fragmentation increased, landscape connectivity turned better, and landscape dominance was enhanced, balanced, and developed evenly in overall landscape type. (3) From the overall region analysis, the average habitat quality in 1991, 2000, 2010, and 2019 was 0.3565, 0.5108, 0.5879, and 0.6482, respectively, with the overall habitat value showing a gradually increasing trend. Spatially, the habitat quality along the Yellow River section of the Hobq Desert has a certain regularity, and the overall pattern there is high in the south and low in the north, high in the east and west, and low in the middle. (4) The change in land use between 2019 and 2030 is similar to the previous period, but the change rate is generally lower. The habitat quality improved significantly, with the growth of high and medium habitat quality.

With the global spread of the novel coronavirus, avoiding human-to-human contact has become an effective way to cut off the spread of the virus. Therefore, contactless gesture recognition becomes an effective means to reduce the risk of contact infection in outbreak prevention and control. However, the recognition of everyday behavioral sign language of a certain population of deaf people presents a challenge to sensing technology. Ubiquitous acoustics offer new ideas on how to perceive everyday behavior. The advantages of a low sampling rate, slow propagation speed, and easy access to the equipment have led to the widespread use of acoustic signal-based gesture recognition sensing technology. Therefore, this paper proposed a contactless gesture and sign language behavior sensing method based on ultrasonic signals-UltrasonicGS. The method used Generative Adversarial Network (GAN)-based data augmentation techniques to expand the dataset without human intervention and improve the performance of the behavior recognition model. In addition, to solve the problem of inconsistent length and difficult alignment of input and output sequences of continuous gestures and sign language gestures, we added the Connectionist Temporal Classification (CTC) algorithm after the CRNN network. Additionally, the architecture can achieve better recognition of sign language behaviors of certain people, filling the gap of acoustic-based perception of Chinese sign language. We have conducted extensive experiments and evaluations of UltrasonicGS in a variety of real scenarios. The experimental results showed that UltrasonicGS achieved a combined recognition rate of 98.8% for 15 single gestures and an average correct recognition rate of 92.4% and 86.3% for six sets of continuous gestures and sign language gestures, respectively. As a result, our proposed method provided a low-cost and highly robust solution for avoiding human-to-human contact.

Fly ash-slag-based geopolymer is a grouting material with good fluidity and excellent mechanical and eco-friendly properties. The geopolymer can react chemically with the inert minerals of road subgrade under alkali excitation to form a good interfacial bond between road subgrade; therefore, it is suitable for the repair of weak road sections. In order to solve the problems such as the difficulty to store and transport the liquid activator of existing geopolymer grouting materials and to study the unclear mechanism of the influence factors on the fluidity and mechanical properties of geopolymer; the research on the mechanical properties of fly ash-slag based geopolymer was carried out in this paper. Experiments on the preparation of geopolymer and research on different ash-slag ratios under solid alkali excitation were studied. The influence of slag content and solid alkali content (NaOH, Na 2 SiO 3 ) on the fluidity, compressive and flexural strength of fly ash-slag-based grouting materials was also researched on the basis of single-factor gradient tests. The results showed that the slurry fluidity decreased but the compressive strength gradually increased when the content of slag was increased from 20% to 50%. With the increase in alkali content (NaOH: 2-5%; sodium silicate: 0-6%), the slurry fluidity decreased and the compressive strength increased and then decreased. Combined with the analysis of the test results of Scanning Electron Microscopy (SEM), the microscopic structures of mechanical properties of geopolymer were investigated. Lastly based on ridge regression theory, a regression model was established to predict the mechanical properties of fly ash-slag-based geopolymer. The results indicate that fly ash-slag-based geopolymer has good mechanical properties and fluidity with proper contents of slag and alkali activator, which provide a reference for experiment research and engineering application.

Emotions significantly impact human physical and mental health, and, therefore, emotion recognition has been a popular research area in neuroscience, psychology, and medicine. In this paper, we preprocess the raw signals acquired by millimeter-wave radar to obtain high-quality heartbeat and respiration signals. Then, we propose a deep learning model incorporating a convolutional neural network and gated recurrent unit neural network in combination with human face expression images. The model achieves a recognition accuracy of 84.5% in person-dependent experiments and 74.25% in person-independent experiments. The experiments show that it outperforms a single deep learning model compared to traditional machine learning algorithms.

Currently, there are still many challenges in prenatal diagnosis, such as limited or uncertain fetal phenotyping, variant interpretation, and rapid turnaround times. The aim of this study was to illustrate the value of a comprehensive genomic evaluation in prenatal diagnosis. We retrospectively reviewed 20 fetuses with clinically significant copy number variants (CNVs) detected by chromosomal microarray analysis (CMA) and no further exome sequencing testing in our tertiary center between 2019 and 2020. The residual DNA from the prenatal cases was used for the parallel implementation of CNV sequencing (CNV-seq) and trio-based clinical exome sequencing (trio-CES). CMA revealed 26 clinically significant CNVs (18 deletions and eight duplications) in 20 fetuses, in which five fetuses had two or more CNVs. There were eight fetuses with pathogenic CNVs (e.g., del 1p36), nine fetuses with likely pathogenic CNVs (e.g., dup 22q11.21), and three fetuses with variants of unknown significance (VOUS, e.g., dup 1q21.1q21.2). Trio-CES identified four fetuses with likely pathogenic mutations (SNV/InDels). Of note, a fetus was detected with a maternally inherited hemizygous variant in the SLX4 gene due to a 16p13.3 deletion on the paternal chromosome. The sizes of CNVs detected by CNV-seq were slightly larger than that of the SNP array, and four cases with mosaic CNVs were all identified by CNV-seq. In conclusion, microdeletion/duplication syndromes and monogenic disorders may co-exist in a subject, and CNV deletion may contribute to uncovering additional recessive disease alleles. The application of a comprehensive genomic evaluation (CNVs and SNV/InDels) has great value in the prenatal diagnosis arena. CNV-seq based on NGS technology is a reliable and a cost-effective technique for identifying CNVs.

With the increasing popularity of smart devices, users can control their mobile phones, TVs, cars, and smart furniture by using voice assistants, but voice assistants are susceptible to intrusion by outsider speakers or playback attacks. In order to address this security issue, a millimeter-wave radar-based voice security authentication system is proposed in this paper. First, the speaker's fine-grained vocal cord vibration signal is extracted by eliminating static object clutter and motion effects; second, the weighted Mel Frequency Cepstrum Coefficients (MFCCs) are obtained as biometric features; and finally, text-independent security authentication is performed by the WMHS (Weighted MFCCs and Hog-based SVM) method. This system is highly adaptable and can authenticate designated speakers, resist intrusion by other unspecified speakers as well as playback attacks, and is secure for smart devices. Extensive experiments have verified that the system achieves a 93.4% speaker verification accuracy and a 5.8% miss detection rate for playback attacks.

In this article, we reported on a Ga 2 O 3 -based Schottky barrier diode and heterojunction diode from MOCVD. The Si-doped n-type Ga 2 O 3 drift layer, grown by MOCVD, exhibited high crystal quality, flat surfaces, and uniform doping. The distribution of unintentional impurities in the films was studied. Then nickel Schottky barrier diode and p-NiO/n-Ga 2 O 3 heterojunction diode were fabricated and measured. Without any electric field management structure, the Schottky barrier diode and heterojunction diode have specific resistances of 3.0 mΩ·cm 2 and 6.2 mΩ·cm 2 , breakdown voltages of 380 V and 740 V, thus yielding power figures of merit of 48 MW·cm -2 and 88 MW·cm -2 , respectively. Besides, both devices exhibit a current on/off ratio of more than 10 10 . This shows the prospect of MOCVD in power device manufacture.

To address the dangerous driving behaviors prevalent among current car drivers, it is necessary to provide real-time, accurate warning and correction of driver's driving behaviors in a small, movable, and enclosed space. In this paper, we propose a method for detecting dangerous behaviors based on frequency-modulated continuous-wave radar (mm-DSF). The highly packaged millimeter-wave radar chip has good in-vehicle emotion recognition capability. The acquired millimeter-wave differential frequency signal is Fourier-transformed to obtain the intermediate frequency signal. The physiological decomposition of the local micro-Doppler feature spectrum of the target action is then used as the eigenvalue. Matrix signal intensity and clutter filtering are performed by analyzing the signal echo model of the input channel. The signal classification is based on the estimation and variety of the feature vectors of the target key actions using a modified and optimized level fusion method of the SlowFast dual-channel network. Nine typical risky driving behaviors were set up by the Dula Hazard Questionnaire and TEIQue-SF, and the accuracy of the classification results of the self-built dataset was analyzed to verify the high robustness of the method. The recognition accuracy of this method increased by 1.97% compared with the traditional method.

Towns serve as the basic unit of implementation for comprehensive land consolidation and rehabilitation. The utilization of scaling law can provide a new perspective for construction land consolidation. From two perspectives of the town hierarchic system and the growth of a single town, this research applies the Rank-Size Rule and Allometric Scaling Law to analyze the scale structure, hierarchy and allometric scaling evolution relationship of population and construction land in the Loess Plateau at the town scale in 2000, 2010, and 2017. Furthermore, the consolidation potential of construction land is divided into five zones and puts forward recommendations for the comprehensive consolidation of the construction land. The results indicate: (1) The majority of towns have small or medium populations and 62% of the towns in the study show negative population growth. Geographically, the northern part has a smaller population size compared with the southern part. 96% of the towns show an increasing trend in the quantity of construction land, and the south and north parts of the study area have more construction land compared with the center part. The zone of the Valley Plain has the largest population size, and the zone of the Sandy and Desert Area has the largest quantity of construction land. (2) The rank-size distributions of both population and construction land comply with the power-law relation. The population hierarchy has changed from equilibrium to concentration, while the hierarchy of construction land shows an opposite pattern. So, the whole town hierarchic system of the Loess Plateau is gradually tending to the optimal distribution, which is the town hierarchic system gradually forming an ideal sequence structure. (3) The population-construction land relationship obeys the allometric scaling law, and the major allometric type is positive allometry. The human-land relationship tends to be coordinated, and the town system tends to be reasonable. The allometric scaling coefficient is not robust in different geographical areas, especially in Irrigated Agricultural Areas. Furthermore, 90% of the towns have weak coordination of human-land relationships, and 60% of the towns have a relatively faster growth rate of construction land than the relative growth (decline) rate of population. (4) The consolidation potential of construction land is divided into five types. High Consolidation Potential Area concentrates in the Eastern Loess Plateau, while Medium and Low Consolidation Potential Area concentrically distribute in the Western Loess Plateau. The Human-land Coordination Area has a small number and scattered spatial distribution. The land use of towns that are concentrated around prefecture-level cities or with unique resources is not intensive enough. The zoning of construction land consolidation potential based on the results of the allometric scale is in line with reality, and local governments should make use of the characteristics and trends of the town system to formulate planning schemes to promote the integrated development of urban and rural areas.

Secondary cell wall thickening plays a crucial role in plant growth and development. Diploid woodland strawberry ( Fragaria vesca ) is an excellent model for studying fruit development, but its molecular control of secondary wall thickening is largely unknown. Previous studies have shown that Arabidopsis NAC secondary wall thickening promoting factor1 (AtNST1) and related proteins are master regulators of xylem fiber cell differentiation in multiple plant species. In this study, a NST1-like gene, FvNST1b , was isolated and characterized from strawberry. Sequence alignment and phylogenetic analysis showed that the FvNST1b protein contains a highly conserved NAC domain, and it belongs to the same family as AtNST1. Overexpression of FvNST1b in wild-type Arabidopsis caused extreme dwarfism, induced ectopic thickening of secondary walls in various tissues, and upregulated the expression of genes related to secondary cell wall synthesis. In addition, transient overexpression of FvNST1b in wild-type Fragaria vesca fruit produced cells resembling tracheary elements. These results suggest that FvNST1b positively regulates secondary cell wall formation as orthologous genes from other species.

Background: Spinal cord injury (SCI) is a central nervous system (CNS) trauma involving inflammation and oxidative stress, which play important roles in this trauma's pathogenesis. Therefore, controlling inflammation is an effective strategy for SCI treatment. As a hormone, melatonin is capable of producing antioxidation and anti-inflammation effects. In the meantime, it also causes a neuroprotective effect in various neurological diseases. Nrf2/ARE/NLRP3 is a well-known pathway in anti-inflammation and antioxidation, and Nrf2 can be positively regulated by melatonin. However, how melatonin regulates inflammation during SCI is poorly explored. Therefore, it was investigated in this study whether melatonin can inhibit the NLRP3 inflammasome through the Nrf2/ARE signaling pathway in a mouse SCI model., Methods: A model of SCI was established in C57BL/6 mice and PC12 cells. The motor function of mice was detected by performing an open field test, and Nissl staining and terminal deoxynucleotidyl transferase dUTP nick end labeling were carried out to evaluate the survival of neurons. Mitochondrial dysfunction was detected by transmission electron microscopy (TEM) and by assessing the mitochondrial membrane potential. In addition, the expression of NLRP3 inflammasome and oxidative-stress-related proteins were detected through Western blot and immunofluorescence double staining., Results: By inhibiting neuroinflammation and reducing neuronal death, melatonin promotes the recovery of neuromotor function. Besides this, melatonin is able to reduce the damage that causes neuronal mitochondrial dysfunction, reduce the level of reactive oxygen species (ROS) and malondialdehyde, and enhance the activity of superoxide dismutase and the production of glutathione peroxidase. Mechanically, melatonin inhibits the activation of NLRP3 inflammasomes and reduces the secretion of pro-inflammatory factors through the Nrf2/ARE signaling., Conclusions: In conclusion, melatonin inhibits the NLRP3 inflammasome through stimulation of the Nrf2/ARE pathway, thereby suppressing neuroinflammation, reducing mitochondrial dysfunction, and improving the recovery of nerve function after SCI.

The current antiretroviral therapy (ART) for human immunodeficiency virus (HIV) can halt viral replication but cannot eradicate HIV infection because proviral DNA integrated into the host genome remains genetically silent in reservoir cells and is replication-competent upon interruption or cessation of ART. CRISPR/Cas9-based technology is widely used to edit target genes via mutagenesis (i.e., nucleotide insertion/deletion and/or substitution) and thus can inactivate integrated proviral DNA. However, CRISPR/Cas9 delivery systems often require viral vectors, which pose safety concerns for therapeutic applications in humans. In this study, we used synthetic guide RNA (gRNA)/Cas9-ribonucleoprotein (RNP) as a non-viral formulation to develop a novel HIV gene therapy. We designed a series of gRNAs targeting different HIV genes crucial for HIV replication and tested their antiviral efficacy and cellular cytotoxicity in lymphoid and monocytic latent HIV cell lines. Compared with the scramble gRNA control, HIV-gRNA/Cas9 RNP-treated cells exhibited efficient viral suppression with no apparent cytotoxicity, as evidenced by the significant inhibition of latent HIV DNA reactivation and RNA replication. Moreover, HIV-gRNA/Cas9 RNP inhibited p24 antigen expression, suppressed infectious viral particle production, and generated specific DNA cleavages in the targeted HIV genes that are confirmed by DNA sequencing. Because of its rapid DNA cleavage, low off-target effects, low risk of insertional mutagenesis, easy production, and readiness for use in clinical application, this study provides a proof-of-concept that synthetic gRNA/Cas9 RNP drugs can be utilized as a novel therapeutic approach for HIV eradication.

With the vigorous development of ubiquitous sensing technology, an increasing number of scholars pay attention to non-contact vital signs (e.g., Respiration Rate (RR) and Heart Rate (HR)) detection for physical health. Since Impulse Radio Ultra-Wide Band (IR-UWB) technology has good characteristics, such as non-invasive, high penetration, accurate ranging, low power, and low cost, it makes the technology more suitable for non-contact vital signs detection. Therefore, a non-contact multi-human vital signs detection method based on IR-UWB radar is proposed in this paper. By using this technique, the realm of multi-target detection is opened up to even more targets for subjects than the more conventional single target. We used an optimized algorithm CIR-SS based on the channel impulse response (CIR) smoothing spline method to solve the problem that existing algorithms cannot effectively separate and extract respiratory and heartbeat signals. Also in our study, the effectiveness of the algorithm was analyzed using the Bland-Altman consistency analysis statistical method with the algorithm's respiratory and heart rate estimation errors of 5.14% and 4.87%, respectively, indicating a high accuracy and precision. The experimental results showed that our proposed method provides a highly accurate, easy-to-implement, and highly robust solution in the field of non-contact multi-person vital signs detection.

Amide proton transfer (APT)-weighted MRI is a promising molecular imaging technique that has been employed in clinic for detection and grading of brain tumors. MTRasym, the quantification method of APT, is easily influenced by B0 inhomogeneity and causes artifacts. Current model-free interpolation methods have enabled moderate B0 correction for middle offsets, but have performed poorly at limbic offsets. To address this shortcoming, we proposed a practical B0 correction approach that is suitable under time-limited sparse acquisition scenarios and for B1 ≥ 1 μT under 3T. In this study, this approach employed a simplified Lorentzian model containing only two pools of symmetric water and asymmetric solutes, to describe the Z-spectral shape with wide and ‘invisible’ CEST peaks. The B0 correction was then performed on the basis of the fitted two-pool Lorentzian lines, instead of using conventional model-free interpolation. The approach was firstly evaluated on densely sampled Z-spectra data by using the spline interpolation of all acquired 16 offsets as the gold standard. When only six offsets were available for B0 correction, our method outperformed conventional methods. In particular, the errors at limbic offsets were significantly reduced (n = 8, p < 0.01). Secondly, our method was assessed on the six-offset APT data of nine brain tumor patients. Our MTRasym (3.5 ppm), using the two-pool model, displayed a similar contrast to the vendor-provided B0-orrected MTRasym (3.5 ppm). While the vendor failed in correcting B0 at 4.3 and 2.7 ppm for a large portion of voxels, our method enabled well differentiation of B0 artifacts from tumors. In conclusion, the proposed approach could alleviate analysis errors caused by B0 inhomogeneity, which is useful for facilitating the comprehensive metabolic analysis of brain tumors.

The positioning of indoor electronic devices is an essential part of human-computer interaction, and the accuracy of positioning affects the level of user experience. Most existing methods for RF-based device localization choose to ignore or remove the impact of multipath effects. However, exploiting the multipath effect caused by the complex indoor environment helps to improve the model's localization accuracy. In response to this question, this paper proposes a multipath-assisted localization (MAL) model based on millimeter-wave radar to achieve the localization of indoor electronic devices. The model fully considers the help of the multipath effect when describing the characteristics of the reflected signal and precisely locates the target position by using the MAL area formed by the reflected signal. At the same time, for the situation where the radar in the traditional Single-Input Single-Output (SISO) mode cannot obtain the 3D spatial position information of the target, the advantage of the MAL model is that the 3D information of the target can be obtained after the mining process of the multipath effect. Furthermore, based on the original hardware, it can achieve a breakthrough in angular resolution. Experiments show that our proposed MAL model enables the millimeter-wave multipath positioning model to achieve a 3D positioning error within 15 cm.

Mitochondrial fusion is essential to mitochondrial fitness and cellular health. Neurons of patients with genetic neurodegenerative diseases often exhibit mitochondrial fragmentation, reflecting an imbalance in mitochondrial fusion and fission (mitochondrial dysdynamism). Charcot-Marie-Tooth (CMT) disease type 2A is the prototypical disorder of impaired mitochondrial fusion caused by mutations in the fusion protein mitofusin (MFN)2. Yet, cultured CMT2A patient fibroblast mitochondria are often reported as morphologically normal. Metabolic stress might evoke pathological mitochondrial phenotypes in cultured patient fibroblasts, providing a platform for the pre-clinical individualized evaluation of investigational therapeutics. Here, substitution of galactose for glucose in culture media was used to redirect CMT2A patient fibroblasts (MFN2 T105M, R274W, H361Y, R364W) from glycolytic metabolism to mitochondrial oxidative phosphorylation, which provoked characteristic mitochondrial fragmentation and depolarization and induced a distinct transcriptional signature. Pharmacological MFN activation of metabolically reprogrammed fibroblasts partially reversed the mitochondrial abnormalities in CMT2A and CMT1 and a subset of Parkinson's and Alzheimer's disease patients, implicating addressable mitochondrial dysdynamism in these illnesses.

Mitochondrial dynamics encompass mitochondrial fusion, fission, and movement. Mitochondrial fission and fusion are seemingly ubiquitous, whereas mitochondrial movement is especially important for organelle transport through neuronal axons. Here, we review the roles of different mitochondrial dynamic processes in mitochondrial quantity and quality control, emphasizing their impact on the neurological system in Charcot-Marie-Tooth disease type 2A, amyotrophic lateral sclerosis, Friedrich's ataxia, dominant optic atrophy, and Alzheimer's, Huntington's, and Parkinson's diseases. In addition to mechanisms and concepts, we explore in detail different technical approaches for measuring mitochondrial dynamic dysfunction in vitro, describe how results from tissue culture studies may be applied to a better understanding of mitochondrial dysdynamism in human neurodegenerative diseases, and suggest how this experimental platform can be used to evaluate candidate therapeutics in different diseases or in individual patients sharing the same clinical diagnosis.

Motion recognition has a wide range of applications at present. Recently, motion recognition by analyzing the channel state information (CSI) in Wi-Fi packets has been favored by more and more scholars. Because CSI collected in the wireless signal environment of human activity usually carries a large amount of human-related information, the motion-recognition model trained for a specific person usually does not work well in predicting another person's motion. To deal with the difference, we propose a personnel-independent action-recognition model called WiPg, which is built by convolutional neural network (CNN) and generative adversarial network (GAN). According to CSI data of 14 yoga movements of 10 experimenters with different body types, model training and testing were carried out, and the recognition results, independent of bod type, were obtained. The experimental results show that the average correct rate of WiPg can reach 92.7% for recognition of the 14 yoga poses, and WiPg realizes "cross-personnel" movement recognition with excellent recognition performance.

With the new coronavirus raging around the world, home isolation has become an effective way to interrupt the spread of the virus. Effective monitoring of people in home isolation has also become a pressing issue. However, the large number of isolated people and the privatized isolated spaces pose challenges for traditional sensing techniques. Ubiquitous Wi-Fi offers new ideas for sensing people indoors. Advantages such as low cost, wide deployment, and high privacy make indoor human activity sensing technology based on Wi-Fi signals increasingly used. Therefore, this paper proposes a contactless indoor person continuous activity sensing method based on Wi-Fi signal Wi-CAS. The method allows for the sensing of continuous movements of home isolated persons. Wi-CAS designs an ensemble classification method based on Hierarchical Clustering (HEC) for the classification of different actions, which effectively improves the action classification accuracy while reducing the processing time. We have conducted extensive experimental evaluations in real home environments. By recording the activities of different people throughout the day, Wi-CAS is very sensitive to unusual activities of people and also has a combined activity recognition rate of 94.3%. The experimental results show that our proposed method provides a low-cost and highly robust solution for supervising the activities of home isolates.

The deployment of the innate immune system in humans is essential to protect us from infection. Human cathelicidin LL-37 is a linear host defense peptide with both antimicrobial and immune modulatory properties. Despite years of studies of numerous peptides, SK-24, corresponding to the long hydrophobic domain (residues 9-32) in the anionic lipid-bound NMR structure of LL-37, has not been investigated. This study reports the structure and activity of SK-24. Interestingly, SK-24 is entirely helical (~100%) in phosphate buffer (PBS), more than LL-37 (84%), GI-20 (75%), and GF-17 (33%), while RI-10 and 17BIPHE2 are essentially randomly coiled (helix%: 7-10%). These results imply an important role for the additional N-terminal amino acids (likely E16) of SK-24 in stabilizing the helical conformation in PBS. It is proposed herein that SK-24 contains the minimal sequence for effective oligomerization of LL-37. Superior to LL-37 and RI-10, SK-24 shows an antimicrobial activity spectrum comparable to the major antimicrobial peptides GF-17 and GI-20 by targeting bacterial membranes and forming a helical conformation. Like the engineered peptide 17BIPHE2, SK-24 has a stronger antibiofilm activity than LL-37, GI-20, and GF-17. Nevertheless, SK-24 is least hemolytic at 200 µM compared with LL-37 and its other peptides investigated herein. Combined, these results enabled us to appreciate the elegance of the long amphipathic helix SK-24 nature deploys within LL-37 for human antimicrobial defense. SK-24 may be a useful template of therapeutic potential.

Several classes of immunomodulators are used for treating relapsing-remitting multiple sclerosis (RRMS). Most of these disease-modifying therapies, except teriflunomide, carry the risk of progressive multifocal leukoencephalopathy (PML), a severely debilitating, often fatal virus-induced demyelinating disease. Because teriflunomide has been shown to have antiviral activity against DNA viruses, we investigated whether treatment of cells with teriflunomide inhibits infection and spread of JC polyomavirus (JCPyV), the causative agent of PML. Treatment of choroid plexus epithelial cells and astrocytes with teriflunomide reduced JCPyV infection and spread. We also used droplet digital PCR to quantify JCPyV DNA associated with extracellular vesicles isolated from RRMS patients. We detected JCPyV DNA in all patients with confirmed PML diagnosis ( n = 2), and in six natalizumab-treated ( n = 12), two teriflunomide-treated ( n = 7), and two nonimmunomodulated ( n = 2) patients. Of the 21 patients, 12 (57%) had detectable JCPyV in either plasma or serum. CSF was uniformly negative for JCPyV. Isolation of extracellular vesicles did not increase the level of detection of JCPyV DNA versus bulk unprocessed biofluid. Overall, our study demonstrated an effect of teriflunomide inhibiting JCPyV infection and spread in glial and choroid plexus epithelial cells. Larger studies using patient samples are needed to correlate these in vitro findings with patient data.

In this study, the Ti-Al-Si + x TiC ( x = 0, 2, 6, 10 wt.%) composite coatings, each with a different content of TiC were fabricated on a Ti-6Al-4V alloy by laser surface cladding. The microstructure of the prepared coatings was analyzed by the scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction (XRD). The microhardness and the wear resistance of these coatings were also evaluated. The results show that α-Ti, Ti 3 Al, Ti 5 Si 3 , TiAl 3 , TiAl, Ti 3 AlC 2 and TiC particles can be found in the composites. The microstructure can obviously be refined by increasing the content of TiC particles, while the microhardness increases and the coefficient of friction decreases. The Ti-Al-Si-6TiC composite shows the best wear resistance, owing to its relatively fine microstructure and high content of TiC particles. The microhardness of this coating is 5.3 times that of the substrate, while the wear rate is only 0.43 times. However, when the content of TiC was up to 10 wt.%, the original TiC could not be dissolved completely during the laser cladding process, leading to formation of cracks on the coatings.

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot's state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

The contradiction between the supply and demand of public medical resources in China is serious. On the basis of the "graded diagnosis and treatment" model, the Chinese government divides the medical grade and adjusts the allocation of medical facilities so as to alleviate the adverse impact of these issues on residents' health. Although the government tries to guide residents' medical treatment according to the level of medical facilities, there are differences between residents' medical treatment mode and policy rules in reality. Therefore, it is of great significance to explore spatial differences in accessibility to medical services for residents on the basis of the actual medical behavior. This article takes Shaanxi province as the research area, and uses the improved node cost network analysis method with the space-time distance model and the two-step floating catchment area method, respectively, to analyze the spatial differences of accessibility to three-level medical services and evaluate the equity of accessibility in different areas and groups in Shaanxi. Results showed that the overall level of accessibility to primary medical services in the province is good, and spatial distribution is balanced; the polarization of accessibility to secondary and tertiary medical services is a serious issue, and within the research area, a band-shaped multicore spatial structure was formed with the built-up areas of various cities as high-level centers of accessibility. Provincial residents have poor equity to access three-level medical services, and the equity of accessibility to primary medical services is better than that to highly specialized medical services. There is no obvious gap between accessibility to three-level medical services for the aging and the nonaging populations in Shaanxi, but the unfair phenomenon between agricultural and the nonagricultural populations is prominent. In addition, this article found that the improvement in traffic conditions can produce space-time convergence and effectively weaken spatial deprivation. Therefore, developing public transportation is an effective approach to improve the equity of accessibility to medical services.

RUNX1 overlapping RNA (RUNXOR) is a long non-coding RNA and plays a pivotal role in the differentiation of myeloid cells via targeting runt-related transcription factor 1 (RUNX1). We and others have previously reported that myeloid-derived suppressor cells (MDSCs) expand and inhibit host immune responses during chronic viral infections; however, the mechanisms responsible for MDSC differentiation and suppressive functions, in particular the role of RUNXOR-RUNX1, remain unclear. Here, we demonstrated that RUNXOR and RUNX1 expressions are significantly upregulated and associated with elevated levels of immunosuppressive molecules, such as arginase 1 (Arg1), inducible nitric oxide synthase (iNOS), signal transducer and activator of transcription 3 (STAT3), and reactive oxygen species (ROS) in MDSCs during chronic hepatitis C virus (HCV) infection. Mechanistically, we discovered that HCV-associated exosomes (HCV-Exo) can induce the expressions of RUNXOR and RUNX1, which in turn regulates miR-124 expression via STAT3 signaling, thereby promoting MDSC differentiation and suppressive functions. Importantly, overexpression of RUNXOR in healthy CD33 + myeloid cells promoted differentiation and suppressive functions of MDSCs. Conversely, silencing RUNXOR or RUNX1 expression in HCV-derived CD33 + myeloid cells significantly inhibited their differentiation and expressions of suppressive molecules and improved the function of co-cultured autologous CD4 T cells. Taken together, these results indicate that the RUNXOR-RUNX1-STAT3-miR124 axis enhances the differentiation and suppressive functions of MDSCs and could be a potential target for immunomodulation in conjunction with antiviral therapy during chronic HCV infection.

Laser beam drift greatly influences the accuracy of a four degrees of freedom (4-DOF) measurement system during the detection of machine tool errors, especially for long-distance measurement. A novel method was proposed using bellows to serve as a laser beam shield and air pumps to stabilize the refractive index of air. The inner diameter of the bellows and the control mode of the pumps were optimized through theoretical analysis and simulation. An experimental setup was established to verify the feasibility of the method under the temperature interference condition. The results indicated that the position stability of the laser beam spot can be improved by more than 79% under the action of pumping and inflating. The proposed scheme provides a cost-effective method to reduce the laser beam drift, which can be applied to improve the detection accuracy of a 4-DOF measurement system.

Human identity recognition has a wide range of application scenarios and a large number of application requirements. In recent years, the technology of collecting human biometrics through sensors for identification has become mature, but this kind of method needs additional equipment as assistance, which cannot be well applied to some scenarios. Using Wi-Fi for identity recognition has many advantages, such as no additional equipment as assistance, not affected by temperature, humidity, weather, light, and so on, so it has become a hot topic of research. The methods of individual identity recognition have been more mature; for example, gait information can be extracted as features. However, it is difficult to identify small-scale (2-5) group personnel at one time, and the tasks of fingerprint storage and classification are complex. In order to solve this problem, this paper proposed a method of using the random forest as a fingerprint database classifier. The method is divided into two stages: the offline stage trains the random forest classifier through the collected training data set. In the online phase, the real-time data collected are input into the classifier to get the results. When extracting channel state information (CSI) features, multiple people are regarded as a whole to reduce the difficulty of feature selection. The use of random forest classifier in classification can give full play to the advantages of random forest, which can deal with a large number of multi-dimensional data and is easy to generalize. Experiments showed that WiGId has good recognition performance in both LOS (line of sight) and N LOS (None line of sight) environments.

In recent years, with the development of wireless sensing technology and the widespread popularity of WiFi devices, human perception based on WiFi has become possible, and gesture recognition has become an active topic in the field of human-computer interaction. As a kind of gesture, sign language is widely used in life. The establishment of an effective sign language recognition system can help people with aphasia and hearing impairment to better interact with the computer and facilitate their daily life. For this reason, this paper proposes a contactless fine-grained gesture recognition method using Channel State Information (CSI), namely Wi-SL. This method uses a commercial WiFi device to establish the correlation mapping between the amplitude and phase difference information of the subcarrier level in the wireless signal and the sign language action, without requiring the user to wear any device. We combine an efficient denoising method to filter environmental interference with an effective selection of optimal subcarriers to reduce the computational cost of the system. We also use K-means combined with a Bagging algorithm to optimize the Support Vector Machine (SVM) classification (KSB) model to enhance the classification of sign language action data. We implemented the algorithms and evaluated them for three different scenarios. The experimental results show that the average accuracy of Wi-SL gesture recognition can reach 95.8%, which realizes device-free, non-invasive, high-precision sign language gesture recognition.

Precision spherical joint is a spherical motion pair that can realize rotation with three degrees of freedom. This joint is widely used in robots, parallel mechanisms, and high-end medical equipment, as well as in aerospace and other fields. However, the rotation orientation and angle cannot be determined when the joint is in passive motion. The real-time determination of the rotation orientation and angle is crucial to the improvement of the motion control accuracy of the equipment where the joint is installed in. In this study, a new measurement method that utilizes eddy current sensors is proposed to identify the special features of the joint ball and realize angle measurements indirectly. The basic idea is to manufacture the specific shape features on the ball without affecting its movement accuracy and mechanical performance. An eddy current sensor array is distributed in the ball socket. When the ball head rotates, the features on the ball opposite to the sensor, as well as the output signal of every eddy current sensor, change. The measurement model that establishes the relationship between the output signal of the eddy current sensor array and the rotation direction and angle of the ball head is constructed by learning and training an artificial neural network. A prototype is developed using the proposed scheme, and the model simulation and feasibility experiment are subsequently performed. Results show that the root mean square angular error of a single axis within a range of ±14° is approximately 20 min, which suggests the feasibility of the proposed method.

In this work, Ti 3 SiC 2 -based ceramics were fabricated by the infiltration of liquid silicon into TiC preform by incorporating a small amount of Al. Al can play a catalytic role to promote the formation of TiC twins before liquid silicon infiltration (LSI), which leads to the increase of transformation efficiency from TiC to Ti 3 SiC 2 in the LSI process. When the Al content in the TiC preform increases to 9 wt.%, the volume content of Ti 3 SiC 2 reaches 85 vol.%, revealing the high electromagnetic interference shielding effectiveness of 39 dB in the frequency range of 8.2-12.4 GHz. The results indicate that it is an effective way to synthesize Ti 3 SiC 2 -based ceramics with excellent electromagnetic shielding performance.

In order to remove zearalenone (ZEA) detriment- Bacillus subtilis , Candida utilis, and cell-free extracts from Aspergillus oryzae were used to degrade ZEA in this study. The orthogonal experiment in vitro showed that the ZEA degradation rate was 92.27% ( p < 0.05) under the conditions that Candida utilis , Bacillus subtilis SP1, and Bacillus subtilis SP2 were mixed together at 0.5%, 1.0%, and 1.0%. When cell-free extracts from Aspergillus oryzae were combined with the above probiotics at a ratio of 2:1 to make mycotoxin-biodegradation preparation (MBP), the ZEA degradation rate reached 95.15% ( p < 0.05). In order to further investigate the MBP effect on relieving the negative impact of ZEA for pig production performance, 120 young pigs were randomly divided into 5 groups, with 3 replicates in each group and 8 pigs for each replicate. Group A was given the basal diet with 86.19 μg/kg ZEA; group B contained 300 μg/kg ZEA without MBP addition; and groups C, D, and E contained 300 μg/kg ZEA added with 0.05%, 0.10%, and 0.15% MBP, respectively. The results showed that MBP addition was able to keep gut microbiota stable. ZEA concentrations in jejunal contents in groups A and D were 89.47% and 80.07% lower than that in group B ( p < 0.05), indicating that MBP was effective in ZEA biodegradation. In addition, MBP had no significant effect on pig growth, nutrient digestibility, and the relative mRNA abundance of estrogen receptor alpha ( ERα ) genes in ovaries and the uterus ( p > 0.05)., Competing Interests: The authors declare that there are no conflicts of interest associated with this research.

With the wide application of Channel State Information (CSI) in the field of sensing, the accuracy of positioning accuracy of indoor fingerprint positioning is increasingly necessary. The flexibility of the CSI signals may lead to an increase in fingerprint noise and inaccurate data classification. This paper presents an indoor localization algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Endpoints-Clipping (EC) CSI amplitude, and Support Vector Machine (EC-SVM). In the offline phase, the CSI amplitude information collected through the three channels is combined and clipped using the EC, and then a fingerprint database is obtained. In the online phase, the SVM is used to train the data in the fingerprint database, and the corresponding relationship is found with the CSI data collected in real time to perform matching and positioning. The experimental results show that the positioning accuracy of the EC-SVM algorithm is superior to the state-of-art indoor CSI-based localization technique.

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.

Information sharing plays a significant role in improving investment performance in the venture capital network community, which serves as an important requirement for the venture capital network to expand to the meso-level and promote its healthy development. Given the important role of Environmental-Social-Governance (ESG) start-ups in promoting sustainable development, this paper selects a sample of ESG start-ups in China to explore the relationship between venture capital network community information sharing and investment performance. We also examine the intermediary role of investment capability in this relationship. Empirical results show that venture capital network community information sharing, from both the prospective of breadth and depth, has a significant positive impact on investment performance of ESG start-ups. We also find that the investment capability, such as scouting and coaching, plays a partial intermediary role in affecting investment performance by community information sharing. This research helps to reveal the relationship between venture capital network community information sharing and investment performance. The results provide a theoretical basis and managerial insights for improving investment performance of ESG start-ups.

With the rapid development of wireless network technology, wireless passive indoor localization has become an increasingly important technique that is widely used in indoor location-based services. Channel state information (CSI) can provide more detailed and specific subcarrier information, which has gained the attention of researchers and has become an emphasis in indoor localization technology. However, existing research has generally adopted amplitude information for eigenvalue calculations. There are few research studies that have used phase information from CSI signals for localization purposes. To eliminate the signal interference existing in indoor environments, we present a passive human indoor localization method named FapFi, which fuses CSI amplitude and phase information to fully utilize richer signal characteristics to find location. In the offline stage, we filter out redundant values and outliers in the CSI amplitude information and then process the CSI phase information. A fusion method is utilized to store the processed amplitude and phase information as a fingerprint database. The experimental data from two typical laboratory and conference room environments were gathered and analyzed. The extensive experimental results demonstrate that the proposed algorithm is more efficient than other algorithms in data processing and achieves decimeter-level localization accuracy.

Zearalenone (ZEA) and aflatoxin B₁ (AFB₁) are two main kinds of mycotoxins widely existing in grain and animal feed that cause a lot of economic loss and health problems for animals and humans. In order to alleviate the cytotoxic effects of AFB₁ and ZEA on swine jejunal epithelial cells (IPEC-J2), the combination of a cell-free supernatant of compound probiotics (CFSCP) with mycotoxin degradation enzymes (MDEs) from Aspergillus oryzae was tested. The results demonstrated that coexistence of AFB₁ and ZEA had synergetic toxic effects on cell viability. The cell viability was decreased with mycotoxin concentrations increasing, but increased with incubation time extension. The necrotic cell rates were increased when 40 µg/L AFB₁ and/or 500 µg/L ZEA were added, but the addition of CFSCP + MDE suppressed the necrotic effects of AFB₁ + ZEA. The viable cell rates were decreased when AFB₁ and/or ZEA were added: However, the addition of CFSCP + MDE recovered them. The relative mRNA abundances of Bcl-2 , occludin , and ZO-1 genes were significantly upregulated, while Bax , caspase-3 , GLUT2 , ASCT2 , PepT1 , and IL6 genes were significantly downregulated by CFSCP + MDE addition, compared to the groups containing 40 µg/L AFB₁ and 500 µg/L ZEA. This research provided an effective strategy in alleviating mycotoxin cytotoxicity and keeping normal intestinal cell structure and animal health.

Plasticized spinning polyacrylonitrile (PAN) fibers with different diameters were chemically modified by potassium permanganate (KMnO₄). The modification effects of different diameter fibers were studied for the first time. Differential scanning calorimetry (DSC) results show that, compared with the large diameter ones, small diameter modified fibers show lower cyclization starting temperature ( T i ) and activation energy (E). Both kinds of fibers exhibit better modification effects compared with solution-spun fibers. For the small diameter fibers, chemical modification can occur at low treatment temperature, even at 70 °C. X-ray diffraction analysis (XRD) results show that modification not only occurs in the amorphous region of the fibers but also in the crystalline region.

The effect of arc modes on the microstructure and tensile properties of 5183 aluminium alloy fabricated by cold metal transfer (CMT) processes has been thoroughly investigated. Heat inputs of CMT processes with three arc modes, i.e., CMT, CMT advance (CMT+A), and CMT pulse (CMT+P), were quantified, and their influence on the formation of pores were investigated. The highest tensile strength was found from samples built by the CMT+A process. This agrees well with their smallest average pore sizes. Average tensile strengths of CMT+A arc mode-built samples were 296.9 MPa and 291.8 MPa along the horizontal and vertical directions, respectively. The difference of tensile strength along the horizontal and vertical directions of the CMT+P and CMT samples was mainly caused by the pores at the interfaces between each deposited layer. The successfully built large 5183 aluminium parts by the CMT+A arc mode further proves that this arc mode is a suitable mode for manufacturing of 5183 aluminium alloy.

Hydrogen peroxide (H2O2) and nitric oxide (NO) are key signaling molecules in cells whose levels are increased in response to various stimuli and are involved in plant secondary metabolite synthesis. In this paper, the roles of H2O2 and NO on salvianolic acid B (Sal B) production in salicylic acid (SA)-induced Salvia miltiorrhiza cell cultures were investigated. The results showed that H2O2 could be significantly elicited by SA, even though IMD (an inhibitor of NADPH oxidase) or DMTU (a quencher of H2O2) were employed to inhibit or quench intracellular H2O2. These elicited H2O2 levels significantly increased NO production by 1.6- and 1.46 fold in IMD+SA and DMTU+SA treatments, respectively, and induced 4.58- and 4.85-fold Sal B accumulation, respectively. NO was also markedly elicited by SA, in which L-NNA (an inhibitor of NO synthase) and cPTIO (a quencher of NO) were used to inhibit or quench NO within cells, and the induced NO could significantly enhance H2O2 production by 1.92- and 1.37-fold in L-NNA+SA and cPTIO+SA treatments, respectively, and 3.27- and 1.50-fold for Sal B accumulation, respectively. These results indicate that elicitation of SA for either H2O2 or NO was independent, and the elicited H2O2 or NO could act independently or synergistically to induce Sal B accumulation in SA-elicited cells.