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This paper presents a dual-band fully integrated high linearity CMOS power amplifier (PA). The PA employs a reconfigurable transformer in the input matching network to achieve low reflection coefficient across both bands, demonstrating significant flexibility in the design of dual-band power amplifiers with high output powers. Additionally, a detailed design methodology for the dual-band matching network is introduced. By utilizing this methodology, the PA has been designed using 55 nm CMOS technology. For continuous-wave operation, the PA achieves a saturated power ( P s a t ) of 28.03 dBm and 27.5–28.2 dBm, with power-added efficiency (PAE) of 33.2% and 24.6–31.1%, in the 2.4 GHz and 5 GHz WLAN bands, respectively. Concurrently, the PA power cells, which employ multi-gate transistor (MGTR) technology, achieve an intermodulation distortion (IMD3) of below 30 dBc at an output power of 15 dBm in both the 2.4 GHz and 5 GHz WLAN bands. The proposed PA outperforms other dual-band or multi-band PAs in terms of output power and exhibits great potential for WLAN applications. [ABSTRACT FROM AUTHOR]

Postharvest grapes exhibit a limited shelf life due to susceptibility to rot and deterioration, significantly reducing their nutritional and economic value. Sulfur dioxide (SO2) is a widely recognized preservative for extending grape storage life. This study performed a detailed analysis of 'Munage' table grapes treated with SO2 fumigation, employing transcriptomic and metabolomic approaches. Results indicate that SO2 fumigation significantly extends the shelf life of grapes, as demonstrated by improved visual quality, reduced decay rates, and increased fruit firmness. We identified 309 differentially accumulated metabolites (DAMs) and 1906 differentially expressed genes (DEGs), including 135 transcription factors (TFs). Both DEGs and DAMs showed significant enrichment of flavonoid-related metabolism compared with the control, and the relative content of four flavonoid metabolites (Wogonin-7-O-glucuronide, Acacetin-7-O-glucuronide, Apigenin-7-O-glucuronide, and Baicalein 7-O-glucuronide) were significantly increased in grapes upon SO2 treatment, suggesting that SO2 treatment had a substantial regulatory effect on grape flavonoid metabolism. Importantly, we constructed complex regulatory networks by screening key enzyme genes (e.g., PAL, 4CLs, CHS, CHI2, and UGT88F3) related to the metabolism of target flavonoid, as well as potential regulatory transcription factors (TFs). Overall, our findings offer new insights into the regulatory mechanisms by which SO2 maintains the postharvest quality of table grapes. [ABSTRACT FROM AUTHOR]

Tetratoluene has the following three isomers: durene (DR), prehnitene (PR), and isodurene (IR). DR and PR often coexist during the separation of C10 heavy aromatics at different levels. They are both important organic chemical raw materials and their separation is the key to the high-efficiency industrial utilization of C10 heavy aromatics. In this paper, six metal–organic frameworks (MOFs), including ZU-61, MIL-101, UIO-66, UIO-66-NH2, Mg-MOF-74, and MIL-53(Al), were used as the adsorbents of DR and PR. Their skeletons were structurally optimized using VASP software (latest v. 6.4.3). The adsorption capacity and isosteric heats of both pure components and mixtures (the molar ratio was 1:1) in gas were calculated using Grand Canonical Monte Carlo simulation from 10 kPa to 300 kPa at 298 K. The results indicated that all adsorption processes were physical. ZU-61, UIO-66, UIO-66-NH2, and Mg-MOF-74 presented suitable capacity differences for DR and PR at 300 kPa. The selectivity values of these frameworks were all above 1.5. Thus, the four MOFs were prepared using the solvothermal method and characterized by SEM and XRD. Then, the competitive adsorption of DR and PR in liquid on the four MOFs was carried out as well. The results showed good agreement with the simulation in general, with a lower adsorption attained capacity due to the different phase states of both DR and PR. This study can guide the separation of tetratoluene isomers in C10 heavy aromatics. [ABSTRACT FROM AUTHOR]

Water quality and aquatic ecosystems along lakeshores are vital for ecological balance and human well-being. However, research has primarily focused on plankton, with benthic niches being largely overlooked. To enhance understanding of benthic microbial communities, we utilized 16S and 18S rRNA sequencing alongside multivariate statistical methods to analyze samples from the shoreline of Lake Taihu in Huzhou City, Zhejiang Province. Our results reveal a marked difference in species composition between benthic and planktonic microorganisms, with benthic cyanobacteria predominantly comprising filamentous genera like Tychonema, while 95% of planktonic cyanobacteria were Cyanobium. The β-diversity of benthic microorganisms was notably higher than that of planktonic counterparts. The neutral community model indicated that stochastic processes dominated planktonic microbial assembly, while deterministic processes prevailed in benthic communities. Null models showed that homogeneous selection influenced benthic community assembly, whereas planktonic communities were affected by undominated processes and dispersal limitation. Network analysis indicated that planktonic networks were more stable than benthic networks. Importantly, dominant benthic cyanobacterial genera posed potential toxin risks, highlighting the need for enhanced monitoring and ecological risk assessment. Overall, these findings enhance our understanding of benthic and planktonic microbial communities in lakeshores and offer valuable insights for aquatic assessment and management in eutrophicated environments. [ABSTRACT FROM AUTHOR]

Biological composites such as bone, nacre, and teeth show excellent mechanical efficiency because of the incorporation of biominerals into the organic matrix at the nanoscale, leading to hierarchical composite structures. Adding a large volume of ceramic nanoparticles into an organic molecular network uniformly has been a challenge in engineering applications. However, in natural organisms, biominerals grow inside organic fibers, such as chitin and collagen, forming perfect ceramic/polymer composites spontaneously via biomineralization processes. Inspired from these processes, the in situ growth of calcium carbonate nanoparticles inside the chitosan network to form ceramic composites was proposed in the current work. The crystal growth of CaCO3 nanoparticles in the chitosan matrix as a function of time was investigated. A weight percentage of ~35 wt% CaCO3 composite was realized, resembling the high weight percentage of mineral phase in bones. Scanning and transmission electron microscopy indicated the integration of CaCO3 nanocrystals with chitosan macromolecules. By growing CaCO3 minerals inside the chitosan matrix, the elastic modulus and tensile strength increases by ~110% and ~90%, respectively. The in situ crystal growth strategy was also demonstrated in organic frameworks prepared via 3D printing, indicating the potential of fabricating ceramic/polymer composites with complicated structures, and further applications in tissue engineering. [ABSTRACT FROM AUTHOR]

Soft soil sites can amplify the peak acceleration by a factor of 1.5 to 3.5 and exhibit the filtering effect on seismic waves. This effect results in the attenuation of high frequencies, amplification of low frequencies, and extension of the predominant period of ground motion. Consequently, soft soil sites have a more pronounced impact on isolation buildings constructed on them. The seismic isolation structure design typically involves assuming rigid foundation for calculations. However, the soil properties can significantly impact the dynamic response of the structure, affecting factors such as input ground motion, changes in vibration characteristics, radiation energy dissipation, and material damping energy dissipation. Therefore, neglecting these influences and relying solely on the rigid foundation assumption for calculations can lead to significant errors in the final seismic response analysis of the structure. Currently, there are numerous LNG storage tanks, museums, and other isolation buildings constructed on soft soil sites. Therefore, research on seismic isolation measures for soft soil sites holds significant practical importance. In light of this, this paper, firstly, provides a systematic summary of seismic isolation strategies and engineering applications for soft soil sites. Secondly, it further discusses advancements in research on the dynamic interactions of soil–isolated structures, covering analytical methods, numerical investigations, and experimental studies on soft soil sites. Lastly, the paper concludes with insights on current research progress and prospects for further studies. [ABSTRACT FROM AUTHOR]

Simple Summary: Progress in passion fruit virus research has identified infections caused by Potyvirus, Begomovirus and other viruses, as well as genome sequences of major viruses such as PaVY, TeMV and BYMV, using molecular biology techniques. The transmission, infection cycle, and pathogenesis of these viruses have been elucidated. Virus control strategies include the use of resistant species, RNA interference, physical barriers, and chemotherapy. The focus of this review is on the impact of viruses on fruit quality, the importance of control measures for yield and quality, and the development of more effective prevention and control strategies. Passiflora edulis, also known as passion fruit, is celebrated for its rich nutritional content, distinctive flavour, and significant medicinal benefits. At present, viral diseases pose a major challenge to the passion fruit industry, affecting both the production and quality of the fruit. These diseases impede the sustainable and healthy growth of the passion fruit sector. In recent years, with the expansion of P. edulis cultivation areas, virus mutations, and advances in virus detection technology, an increasing number of virus species infecting P. edulis have been discovered. To date, more than 40 different virus species have been identified; however, there are different strains within the same virus. This poses a challenge for the control and prevention of P. edulis virus disease. Therefore, this review discusses the different types of viruses and their characteristics, modes of transmission, and effects on the growth of the passion fruit plant, as well as the mechanisms of virus generation and preventive measures, with the hope that these discussions will provide a comprehensive understanding of and countermeasures for viruses in passion fruit. [ABSTRACT FROM AUTHOR]

Lightning strike is one of the main factors affecting power transmission lines and may lead to broken strands and damage of transmission lines, resulting in abnormal power supply. To improve the accuracy of lightning location, this paper presents a polarization optical time-domain reflectometer (POTDR) lightning location method based on orthogonal coding technology. The lightning strike point can be located by detecting the state of polarization (SOP) mutation of Rayleigh backscattering in the POTDR system. The orthogonal coding technology is used to improve the distance and accuracy of POTDR lightning location. Through calculation and simulation analysis, the location accuracy is significantly improved at the same sensing distance compared with the ordinary POTDR lightning location method. The lightning strike location error can be limited to 200 m in 20 km sensing distance which meets the actual engineering needs. The results of this paper are important to improve the operation and maintenance efficiency of transmission lines and reduce maintenance costs in the power industry. [ABSTRACT FROM AUTHOR]

Constipation, a widespread gastrointestinal disorder, often leads to the exploration of natural remedies. This study examines the efficacy of Golden Flower Tibetan Tea Polysaccharides (GFTTPs) in alleviating constipation in mice. Chemical analyses reveal that GFTTPs possess O-H, carboxyl, carboxylic acid (-COOH), and C-O-C groups, alongside a porous crystal structure with thermal stability. In animal experiments, GFTTPs significantly upregulated aquaporin 3 (AQP3) and aquaporin 8 (AQP8) expressions in the colon, enhancing water absorption and reducing fecal water content. At a 400 mg/kg dosage, GFTTPs notably improved colonic tissue alterations and serum levels of excitatory neurotransmitters caused by loperamide hydrochloride. They also beneficially altered gut microbiota, increasing Coprococcus, Lactobacillus, and Pediococcus populations. These changes correlated with improved stool frequency, consistency, and weight in constipated mice. Importantly, GFTTPs at 200 and 400 mg/kg doses exhibited comparable effects to the normal control group in key parameters, such as gastrointestinal transit rate and fecal moisture. These findings suggest that GFTTPs may serve as a potent natural remedy for constipation, offering significant therapeutic potential within the context of gut health and with promising implications for human applications. [ABSTRACT FROM AUTHOR]

Accurately classifying the intra-pulse modulations of radar emitter signals is important for radar systems and can provide necessary information for relevant military command strategy and decision making. As strong additional white Gaussian noise (AWGN) leads to a lower signal-to-noise ratio (SNR) of received signals, which results in a poor classification accuracy on the classification models based on deep neural networks (DNNs), in this paper, we propose an effective denoising method based on a denoising diffusion probabilistic model (DDPM) for increasing the quality of signals. Trained with denoised signals, classification models can classify samples denoised by our method with better accuracy. The experiments based on three DNN classification models using different modal input, with undenoised data, data denoised by the convolutional denoising auto-encoder (CDAE), and our method's denoised data, are conducted with three different conditions. The extensive experimental results indicate that our proposed method could denoise samples with lower values of the SNR, and that it is more effective for increasing the accuracy of DNN classification models for radar emitter signal intra-pulse modulations, where the average accuracy is increased from around 3 to 22 percentage points based on three different conditions. [ABSTRACT FROM AUTHOR]

Respiratory burst oxidase homologs (Rbohs) are the primary producers of reactive oxygen species (ROS), which have been demonstrated to play critical roles in plant responses to abiotic stress. Here, we explored the function of OsRbohH in heat and drought stress tolerance by generating overexpression lines (OsRbohH-OE). OsRbohH was highly induced by various abiotic stress and hormone treatments. Compared to wild-type (WT) controls, OsRbohH-OE plants exhibited enhanced tolerance to heat and drought, as determined by survival rate analyses and total chlorophyll content. Histochemical staining revealed that OsRbohH-OE accumulated less ROS. This is consistent with the observed increase in catalase (CAT) and peroxidase (POD) activities, as well as a reduced electrolyte leakage rate and malondialdehyde (MDA) content. Moreover, OsRbohH-OE exhibited enhanced sensitivity to exogenous abscisic acid (ABA), accompanied by altered expression levels of ABA synthesis and catabolic genes. Further analysis indicated that transgenic lines had lower transcripts of ABA signaling-related genes (OsDREB2A, OsLEA3, OsbZIP66, and OsbZIP72) under heat but higher levels under drought than WT. In conclusion, these results suggest that OsRbohH is a positive regulator of heat and drought tolerance in rice, which is probably performed through OsRbohH-mediated ROS homeostasis and ABA signaling. [ABSTRACT FROM AUTHOR]

Deep learning has brought great development to radar emitter identification technology. In addition, specific emitter identification (SEI), as a branch of radar emitter identification, has also benefited from it. However, the complexity of most deep learning algorithms makes it difficult to adapt to the requirements of the low power consumption and high-performance processing of SEI on embedded devices, so this article proposes solutions from the aspects of software and hardware. From the software side, we design a Transformer variant network, lightweight convolutional Transformer (LW-CT) that supports parameter sharing. Then, we cascade convolutional neural networks (CNNs) and the LW-CT to construct a one-dimensional-CNN-Transformer(1D-CNN-Transformer) lightweight neural network model that can capture the long-range dependencies of radar emitter signals and extract signal spatial domain features meanwhile. In terms of hardware, we design a low-power neural network accelerator based on an FPGA to complete the real-time recognition of radar emitter signals. The accelerator not only designs high-efficiency computing engines for the network, but also devises a reconfigurable buffer called "Ping-pong CBUF" and two-level pipeline architecture for the convolution layer for alleviating the bottleneck caused by the off-chip storage access bandwidth. Experimental results show that the algorithm can achieve a high recognition performance of SEI with a low calculation overhead. In addition, the hardware acceleration platform not only perfectly meets the requirements of the radar emitter recognition system for low power consumption and high-performance processing, but also outperforms the accelerators in other papers in terms of the energy efficiency ratio of Transformer layer processing. [ABSTRACT FROM AUTHOR]

Industrial anomaly detection is constrained by the scarcity of anomaly samples, limiting the applicability of supervised learning methods. Many studies have focused on anomaly detection by generating anomaly images and adopting self-supervised learning approaches. Leveraging pre-trained networks on ImageNet has been explored to assist in this training process. However, achieving accurate anomaly detection remains time-consuming due to the network's depth and parameter count not being reduced. In this paper, we propose a self-supervised learning method based on Feature Enhancement Patch Distribution Modeling (FEPDM), which generates simulated anomalies. Unlike direct training on the original feature extraction network, our approach utilizes a pre-trained network to extract multi-scale features. By aggregating these multi-scale features, we are able to train at the feature level, thereby adapting more efficiently to various network structures and reducing domain bias with respect to natural image classification. Additionally, it significantly reduces the number of parameters in the training process. Introducing this approach not only enhances the model's generalization ability but also significantly improves the efficiency of anomaly detection. The method was evaluated on MVTec AD and BTAD datasets, and (image-level, pixel-level) AUROC scores of (95.7%, 96.2%), (93.4%, 97.6%) were obtained, respectively. The experimental results have convincingly demonstrated the efficacy of our method in tackling the scarcity of abnormal samples in industrial scenarios, while simultaneously highlighting its broad generalizability. [ABSTRACT FROM AUTHOR]

Wireless Local-Area Networks (WLANs), as a popular internet access solution, are widely used in numerous places, including enterprises, campuses, and public venues. As the number of devices increases, large-scale deployments will cause the problem of dense wireless networks, including a lot of energy consumption. Thus, the optimization of energy-efficient wireless AP devices has become a focal point of attention. To reduce energy consumption, we have proposed the active access-point (AP) configuration algorithm for WLANs using APs with a dual interface. This uses the greedy algorithm combined with the local search optimization method to find the minimum number of activated APs while satisfying the minimum throughput constraint. However, the previous algorithm basically satisfies only the average throughput among the multiple hosts associated with one AP, wherein some hosts may not reach the required one. In this paper, to overcome this limitation, we propose an enhanced active AP configuration algorithm by incorporating the throughput request satisfaction method that controls the actual throughput at the target value (target throughput) for every host by applying traffic shaping. The target throughput is calculated from the single and concurrent communicating throughput of each host based on channel occupancy time. The minimum throughput constraint will be iteratively adjusted to obtain the required target throughput and achieve the fair throughput allocation. For evaluations, we conducted simulations using the WIMNET simulator and experiments using the testbed system with a Raspberry Pi 4B for APs in four topology cases with five APs and ten hosts. The results show that the proposed method always achieved the required minimum throughput in simulations as well as in experiments, while minimizing the number of active APs. Thus, the validity and effectiveness of our proposal were confirmed. [ABSTRACT FROM AUTHOR]

The flow characteristics of methane and water in sedimentary layers are important factors that affect the beneficial exploitation of marine hydrates. To study the influencing factors of methane drive-off water processes in porous media, we constructed nonhomogeneous geometric models using MATLAB 2020a random distribution functions. We developed a mathematical model of gas–water two-phase flow based on the Navier–Stokes equation. The gas-driven water processes in porous media were described using the level-set method and solved through the finite element method. We investigated the effects of the nonhomogeneous structure of pore media, wettability, and repulsion rate on gas-driven water channeling. The nonhomogeneity of the pore medium is the most critical factor influencing the flow. The size of the throat within the hydrophilic environment determines the level of difficulty of gas-driven water flow. In regions with a high concentration of narrow passages, the formation of extensive air-locked areas is more likely, leading to a decrease in the efficiency of the flow channel. In the gas–water drive process, water saturation changes over time according to a negative exponential function relationship. The more hydrophilic the pore medium, the more difficult the gas-phase drive becomes, and this correlation is particularly noticeable at higher drive rates. The significant pressure differentials caused by the high drive-off velocities lead to quicker methane breakthroughs. Instantaneous flow rates at narrow throats can be up to two orders of magnitude higher than average. Additionally, there is a susceptibility to vortex flow in the area where the throat connects to the orifice. The results of this study can enhance our understanding of gas–water two-phase flow in porous media and help commercialize the exploitation of clean energy in the deep ocean. [ABSTRACT FROM AUTHOR]

In the present paper, an affordable innovative physical experimental equipment consisting of an upper computer, an ultrasonic sensor module, and an Arduino microcontroller has been designed. The relationship between the position of the slider fixed on two springs and time is measured by using the ultrasonic sensor module. A system for slider motion data and image acquisition is constructed by using the LabVIEW interface of Arduino UNO R3. The purpose of this experiment is to demonstrate and interpret the propagation of waves represented by harmonic motion. The spring oscillator system including a slider and two springs is measured and recorded, and the motion can be realized using curve fitting to the wave equation in Sigmaplot. The vibration periods obtained from experimental measurements and curve fitting of the wave equation are 1.130 s and 1.165 s, respectively. The experimental data are in good agreement with the theoretical model. The experimental measurement results show that the maximum kinetic energy is 0.0792 J, the maximum potential energy is 0.0795 J, and the total energy at the position of half the amplitude is 0.0791 J. The results verify the mechanical energy conservation of spring oscillator system in a short time. This self-made instrument has improved the visualization and the automation level of the corresponding experiments. [ABSTRACT FROM AUTHOR]

In order to scientifically and rationally evaluate the susceptibility to landslide hazards in Tekes County, Yili State. This paper takes Tekes County in Xinjiang as an example, on the basis of a comprehensive analysis of the regional geological environment conditions and the distribution pattern and formation conditions of geological disasters, using the data of geological disaster points (landslide center points), and through the correlation matrix calculation of the evaluation factors, the nine evaluation factors with larger absolute values of correlation coefficients were determined to construct the evaluation system of the susceptibility to landslide geological hazards in Tekesi County. Combining the information quantity method and the entropy value method, using the weights determined by the entropy value method, the information quantity method is used to calculate the information quantity value of each factor within the factor, calculate the susceptibility index of landslide geological disasters within the territory of Tekes County, and then carry out the landslide susceptibility evaluation. The susceptibility of landslide disasters was evaluated by ArcGIS. The results show that the landslide disaster susceptibility level in Tekes County can be divided into four levels: high susceptibility, medium susceptibility, low susceptibility, and not susceptible, with areas of 491.3276 km2, 1181.5171 km2, 1674.7609 km2 and 5295.2976 km2 accounting for 5.68%, 13.67%, 19.38% and 61.27% of the total area of Tex County, respectively. The AUC number obtained by the success curve method (ROC) is 0.8736, reflecting the evaluation accuracy of 87.36%, indicating that the model method used in this paper is effective. The results are expected to provide practical data support for landslide disaster control in Tekes County and provide a reference for geological disaster monitoring, early warning and engineering prevention and control deployment in Yili Valley. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) have an extremely diverse application nowadays as an environmentally friendly and renewable new energy storage technology. The porous structure of the separator, one essential component of LIBs, provides an ion transport channel for the migration of ions and directly affects the overall performance of the battery. In this work, we fabricated a composite separator (GOP-PH-ATP) via simply laminating an electrospun polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) nanofibrous membrane coated with attapulgite (ATP) nanoparticles onto a PP nonwoven microfibrous fabric, which exhibits a unique porous structure with a pore-size gradient along the thickness direction that ranges from tens of microns to hundreds of nanometers. As a result, besides the enhanced thermal stability given by the chosen materials, the GOP-PH-ATP separator was endowed with a superhigh porosity of ~95%, strong affinity with electrolyte, and great electrolyte uptake of ~760%, thus effectively enabling an ionic conductivity of 2.38 mS cm−1 and a lithium-ion transference number of 0.62. Furthermore, the cell with the GOP-PH-ATP separator shows an excellent cycling performance with a capacity retention of 91.2% after 150 cycles at 1 C, suggesting that the composite separator with a pore-size gradient structure has great potential to be applied in LIBs. [ABSTRACT FROM AUTHOR]

Implant surface nanofiber (NF) coatings represent an alternative way to prevent/treat periprosthetic joint infection (PJI) via local drug release. We developed and characterized a coaxial erythromycin (EM)-doped PLGA/PCL-PVA NF coating. The purpose of this study was to determine the efficacy of EM-NF coatings (EM0, no EM, EM100 (100 mg/mL), and EM1000 (1000 mg/mL) wt/wt) in a rat PJI model. A strong bond of the EM-NF coating to the surface of titanium (Ti) pins was confirmed by in vitro mechanical testing. Micro-computed tomography (mCT) analysis showed that both EM100 and EM1000 NF effectively reduced periprosthetic osteolysis compared to EM0 at 8 and 16 weeks after implantation. Histology showed that EM100 and EM1000 coatings effectively controlled infection and enhanced periprosthetic new bone formation. The bone implant contact (BIC) of EM100 (35.08%) was higher than negative controls and EM0 (3.43% and 0%, respectively). The bone area fraction occupancy (BAFO) of EM100 (0.63 mm2) was greater than controls and EM0 (0.390 mm2 and 0.0 mm2, respectively). The BAFO of EM100 was higher than that of EM1000 (0.3 mm2). These findings may provide a basis for a new implant surface fabrication strategy aimed at reducing the risks of defective osseointegration and PJI. [ABSTRACT FROM AUTHOR]

Urban built-up areas are the main space carrier of population and urban activities. It is of great significance to accurately identify urban built-up area for monitoring urbanization dynamics and their impact on Sustainable Development Goals. Using only nighttime light (NTL) remote sensing data will lead to omission phenomena in urban built-up area extraction, especially for SDGSAT-1 glimmer imager (GLI) data with high spatial resolution. Therefore, this study proposed a novel nighttime Lights integrate Building Volume (LitBV) index by integrating NTL intensity information from SDGSAT-1 GLI data and building volume information from Digital Surface Model (DSM) data to extract built-up areas more accurately. The results indicated that the LitBV index achieved remarkable results in the extraction of built-up areas, with the overall accuracy of 81.25%. The accuracy of the built-up area extraction based on the LitBV index is better than the results based on only NTL data and only building volume. Moreover, experiments at different spatial resolutions (10 m, 100 m, and 500 m) and different types of NTL data (SDGSAT-1 GLI data, Luojia-1 data, and NASA's Black Marble data) showed that the LitBV index can significantly improve the extraction accuracy of built-up areas. The LitBV index has a good application ability and prospect for extracting built-up areas with high-resolution SDGSAT-1 GLI data. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disease for which there is a lack of effective pharmacological treatments. Hirudin, a natural peptide extracted from leeches, has been used for broad pharmacological purposes. In this study, we investigated the therapeutic effects of hirudin on IPF and its related mechanism of action. By constructing a mouse model of pulmonary fibrosis and treating it with hirudin in vivo, we found that hirudin exerted anti-fibrotic, anti-oxidative, and anti-fibroblast senescence effects. Moreover, using an in vitro model of stress-induced premature senescence in primary mouse lung fibroblasts and treating with hirudin, we observed inhibition of fibroblast senescence and upregulation of PGC1-alpha and Sirt3 expression. However, specific silencing of PGC1-alpha or Sirt3 suppressed the anti-fibroblast senescence effect of hirudin. Thus, the PGC1-alpha/Sirt3 pathway mediates the anti-fibroblast senescence effect of hirudin, potentially serving as a molecular mechanism underlying its anti-fibrosis and anti-oxidative stress effects exerted on the lungs. [ABSTRACT FROM AUTHOR]

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35–0.48 m, while the R2 of the DBH fit was increased to a range of 0.97–0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001–0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data. [ABSTRACT FROM AUTHOR]

In recent years, the substantial growth in satellite data transmission tasks and volume, coupled with the limited availability of ground station hardware resources, has exacerbated conflicts among missions and rendered traditional scheduling algorithms inadequate. To address this challenge, this paper introduces an improved tabu genetic hybrid algorithm (ITGA) integrated with heuristic rules for the first time. Firstly, a constraint satisfaction model for satellite data transmission tasks is established, considering multiple factors such as task execution windows, satellite–ground visibility, and ground station capabilities. Leveraging heuristic rules, an initial population of high-fitness chromosomes is selected for iterative refinement. Secondly, the proposed hybrid algorithm iteratively evolves this population towards optimal solutions. Finally, the scheduling plan with the highest fitness value is selected as the best strategy. Comparative simulation experimental results demonstrate that, across four distinct scenarios, our algorithm achieves improvements in the average task success rate ranging from 1.5% to 19.8% compared to alternative methods. Moreover, it reduces the average algorithm execution time by 0.5 s to 28.46 s and enhances algorithm stability by 0.8% to 27.7%. This research contributes a novel approach to the efficient scheduling of satellite data transmission tasks. [ABSTRACT FROM AUTHOR]

Recycled manure solids (RMSs) are widely utilised as beddings due to their economic and environmentally friendly features. Internal change in RMSs plays a vital role in the stable operation and management of beddings. However, the internal microenvironment of various manure beddings has not been fully reported. Therefore, we evaluated the physicochemical properties, internal gases and changes in the microbial community of the in situ fermentation beds, which were prefermented by cow manure with sawdust (FSD), straw (FST) and sawdust–straw mixture (FM), at a farm in Jiangsu, China, from June to September 2022. The results indicated that the FSD and FM beds were more capable of degrading organic matter (OM), accumulating total nitrogen and processing a more stable pH environment. FSD bed promoted the conversion of nitrate–nitrogen and ammonium–nitrogen (NH4+-N). Different treatments and times had significant effects on bacterial and fungal communities. FSD enriched Chloroflexi, and FST enriched Actinobacteriota in the early stage, while FM enriched Proteobacteria in the late stage. Bacterial communities were more sensitive to NH4+-N and OM, while fungal communities were more sensitive to temperature and pH. FSD had potential advantages concerning N conversion and C emission reduction. The results of the study revealed the microenvironmental dynamics during bedding use, providing a theoretical basis for the use of a compost bedding system for managing recycled dairy manure. [ABSTRACT FROM AUTHOR]

Milk is a kind of dairy product with high nutritive value. Tracing the origin of milk can uphold the interests of consumers as well as the stability of the dairy market. In this study, a fuzzy direct linear discriminant analysis (FDLDA) is proposed to extract the near-infrared spectral information of milk by combining fuzzy set theory with direct linear discriminant analysis (DLDA). First, spectral data of the milk samples were collected by a portable NIR spectrometer. Then, the data were preprocessed by Savitzky–Golay (SG) and standard normal variables (SNV) to reduce noise, and the dimensionality of the spectral data was decreased by principal component analysis (PCA). Furthermore, linear discriminant analysis (LDA), DLDA, and FDLDA were employed to transform the spectral data into feature space. Finally, the k-nearest neighbor (KNN) classifier, extreme learning machine (ELM) and naïve Bayes classifier were used for classification. The results of the study showed that the classification accuracy of FDLDA was higher than DLDA when the KNN classifier was used. The highest recognition accuracy of FDLDA, DLDA, and LDA could reach 97.33%, 94.67%, and 94.67%. The classification accuracy of FDLDA was also higher than DLDA when using ELM and naïve Bayes classifiers, but the highest recognition accuracy was 88.24% and 92.00%, respectively. Therefore, the KNN classifier outperformed the ELM and naïve Bayes classifiers. This study demonstrated that combining FDLDA, DLDA, and LDA with NIR spectroscopy as an effective method for determining the origin of milk. [ABSTRACT FROM AUTHOR]

Scanning ion conductance microscopy (SICM) enables the non-invasive three-dimensional imaging of live cells and other structures in physiological environments. However, when imaging complex samples, SICM faces challenges such as having a low temporal resolution during slow scanning and a reduced signal-to-noise ratio during fast scanning, making it difficult to simultaneously improve both temporal and spatial resolution. To address these issues, this paper proposes an algorithm for enhancing image resolution under high-speed scanning. Firstly, scanning images are preprocessed using a median filtering algorithm to remove the salt-and-pepper noise generated during high-speed scanning. Next, the Canny edge detection algorithm is employed to extract the edges of the image targets. To avoid blurring the edges, the new edge-directed interpolation (NEDI) algorithm is then used to fill the edges, while non-edge areas are filled using bilinear interpolation, thereby enhancing the image resolution. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to analyze the imaging of articular chondrocytes. The results show that under a scanning speed of 480 nm/ms, the proposed algorithm improves the temporal resolution of imaging by 60% compared to traditional 2× resolution imaging, increases the peak signal-to-noise ratio of the scanning images by 7 dB, and achieves a structural similarity of 0.97. Therefore, the proposed algorithm effectively removes noise during high-speed scanning and improves the SICM scanning imaging resolution, thereby avoiding the reduction in temporal resolution when scanning larger resolution samples and effectively enhancing the performance of SICM scanning imaging. [ABSTRACT FROM AUTHOR]

The quality of chrysanthemum tea has a great connection with its variety. Different types of chrysanthemum tea have very different efficacies and functions. Moreover, the discrimination of chrysanthemum tea varieties is a significant issue in the tea industry. Therefore, to correctly and non-destructively categorize chrysanthemum tea samples, this study attempted to design a novel feature extraction method based on the fuzzy set theory and improved direct linear discriminant analysis (IDLDA), called fuzzy IDLDA (FIDLDA), for extracting the discriminant features from the near-infrared (NIR) spectral data of chrysanthemum tea. To start with, a portable NIR spectrometer was used to collect NIR data for five varieties of chrysanthemum tea, totaling 400 samples. Secondly, the raw NIR spectra were processed by four different pretreatment methods to reduce noise and redundant data. Thirdly, NIR data dimensionality reduction was performed by principal component analysis (PCA). Fourthly, feature extraction from the NIR spectra was performed by linear discriminant analysis (LDA), IDLDA, and FIDLDA. Finally, the K-nearest neighbor (KNN) algorithm was applied to evaluate the classification accuracy of the discrimination system. The experimental results show that the discrimination accuracies of LDA, IDLDA, and FIDLDA could reach 87.2%, 94.4%, and 99.2%, respectively. Therefore, the combination of near-infrared spectroscopy and FIDLDA has great application potential and prospects in the field of nondestructive discrimination of chrysanthemum tea varieties. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of mitigating low-frequency flow noise signals in autonomous underwater vehicles through the optimization of a placoid-scale skin. Drawing inspiration from the bio-inspired surface features of cylindrical shell structures, an enhanced design of placoid-scale skin is developed using 3D printing technology. This improved structure effectively reduced boundary layer vortices and wake intensity, thereby contributing to the suppression of low-frequency flow noise signals. Experimental results demonstrate that the notable reduction in low-frequency flow noise within the frequency range of 0–500 Hz, with average noise reduction of approximately 5 dB observed at 150 Hz. This reduction is validated by a combination of numerical simulations and experimental testing, confirming the efficacy of the optimized placoid-scale skin in attenuating the low-frequency flow noise associated with uniformly advancing turbulent boundary layers underwater. [ABSTRACT FROM AUTHOR]

Multispectral remote sensing data with abundant spectral information can be used to compute vegetation indices to improve the accuracy of Ginkgo biloba yield prediction. The limited spatial resolution of multispectral cameras restricts the detail capture over wide farmland, but super-resolution (SR) reconstruction methods can enhance image quality. However, most existing SR models have been trained on images processed from downsampled high-resolution (HR) images, making them less effective in reconstructing real low-resolution (LR) images. This study proposes a GAN-based super-resolution reconstruction method (RMSRGAN) for multispectral remote sensing images of Ginkgo biloba trees in real scenes. A U-Net-based network is employed instead of the traditional discriminator. Convolutional block attention modules (CBAMs) are incorporated into the Residual-in-Residual Dense Blocks (RRDBs) of the generator and the U-Net of the discriminator to preserve image details and texture features. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was employed to capture field multispectral remote sensing images of Ginkgo biloba trees at different spatial resolutions. Four matching HR and LR datasets were created from these images to train RMSRGAN. The proposed model outperforms the traditional models by achieving superior results in both quantitative evaluation metrics (peak signal-to-noise ratio (PSNR) is 32.490, 31.085, 27.084, 26.819, and structural similarity index (SSIM) is 0.894, 0.881, 0.832, 0.818, respectively) and qualitative evaluation visualization. Furthermore, the efficiency of our proposed method was tested by generating individual vegetation indices (VIs) from images taken before and after reconstruction to predict the yield of Ginkgo biloba. The results show that the SR images exhibit better R 2 and R M S E values than LR images. These findings show that RMSRGAN can improve the spatial resolution of real multispectral images, increasing the accuracy of Ginkgo biloba yield prediction and providing more effective and accurate data support for crop management. [ABSTRACT FROM AUTHOR]

Simple Summary: Insect-specific viruses (ISVs) are increasingly recognised for their role in causing severe illnesses and mortality in both humans and animals. In this study, the full genome of ISV (Rice thrips ollusvirus 1, RTOV1) was revealed in rice thrips using metatranscriptome sequencing, RT-PCR, and RACE, respectively. RTOV1 has a typical linear G-N-L genome structure of the order Jingchuvirales. Phylogenetic analysis categorised this virus as an Ollusvirus. The infection of host insects with RTOV1 triggered antiviral RNA interference (RNAi), resulting in a significant accumulation of 22-nt virus-derived small interfering RNAs (vsiRNAs), with a notable bias towards the A/U content. Our study provides valuable information on ISVs in thrips that may be useful for pest management. Insects constitute the largest proportion of animals on Earth and act as significant reservoirs and vectors in disease transmission. Rice thrips (Haplothrips aculeatus, family Phlaeothripidae) are one of the most common pests in agriculture. In this study, the full genome sequence of a novel Ollusvirus, provisionally named "Rice thrips ollusvirus 1" (RTOV1), was elucidated using transcriptome sequencing and the rapid amplification of cDNA ends (RACE). A homology search and phylogenetic tree analysis revealed that the newly identified virus is a member of the family Aliusviridae (order Jingchuvirales). The genome of RTOV1 contains four predicted open reading frames (ORFs), including a polymerase protein (L, 7590 nt), a glycoprotein (G, 4206 nt), a nucleocapsid protein (N, 2415 nt) and a small protein of unknown function (291 nt). All of the ORFs are encoded by the complementary genome, suggesting that the virus is a negative-stranded RNA virus. Phylogenetic analysis using polymerase sequences suggested that RTOV1 was closely related to ollusvirus 1. Deep small RNA sequencing analysis reveals a significant accumulation of small RNAs derived from RTOV1, indicating that the virus replicated in the insect. According to our understanding, this is the first report of an Ollusvirus identified in a member of the insect family Phlaeothripidae. The characterisation and discovery of RTOV1 is a significant contribution to the understanding of Ollusvirus diversity in insects. [ABSTRACT FROM AUTHOR]

This study explores low-intensity extracorporeal shock wave therapy (LiESWT)'s efficacy in alleviating detrusor hyperactivity with impaired contractility (DHIC) induced by ovarian hormone deficiency (OHD) in ovariectomized rats. The rats were categorized into the following four groups: sham group; OVX group, subjected to bilateral ovariectomy (OVX) for 12 months to induce OHD; OVX + SW4 group, underwent OHD for 12 months followed by 4 weeks of weekly LiESWT; and OVX + SW8 group, underwent OHD for 12 months followed by 8 weeks of weekly LiESWT. Cystometrogram studies and voiding behavior tracing were used to identify the symptoms of DHIC. Muscle strip contractility was evaluated through electrical-field, carbachol, ATP, and KCl stimulations. Western blot and immunofluorescence analyses were performed to assess the expressions of various markers related to bladder dysfunction. The OVX rats exhibited significant bladder deterioration and overactivity, alleviated by LiESWT. LiESWT modified transient receptor potential vanilloid (TRPV) channel expression, regulating calcium concentration and enhancing bladder capacity. It also elevated endoplasmic reticulum (ER) stress proteins, influencing ER-related Ca2+ channels and receptors to modulate detrusor muscle contractility. OHD after 12 months led to neuronal degeneration and reduced TRPV1 and TRPV4 channel activation. LiESWT demonstrated potential in enhancing angiogenic remodeling, neurogenesis, and receptor response, ameliorating DHIC via TRPV channels and cellular signaling in the OHD-induced DHIC rat model. [ABSTRACT FROM AUTHOR]

To address the issue associated with poor accuracy rates for specific emitter identification (SEI) under low signal-to-noise ratio (SNR) conditions, where the single-dimension radar signal characteristics are severely affected by noise, we propose an attention-enhanced dual-branch residual network structure based on the adaptive large-margin Softmax (ALS). Initially, we designed a dual-branch network structure to extract features from one-dimensional intermediate frequency data and two-dimensional time–frequency images, respectively. By assigning different attention weights according to their importance, these features are fused into an enhanced joint feature for further training. This approach enables the model to extract distinctive features across multiple dimensions and achieve good recognition performance even when the signal is affected by noise. In addition, we have introduced L-Softmax to replace the original Softmax and propose the ALS. This approach adaptively calculates the classification margin decision parameter based on the angle between samples and the classification boundary and adjusts the margin values of the sample classification boundaries; it reduces the intra-class distance for the same class while increasing the inter-class distance between different classes without the need for cumbersome experiments to determine the optimal value of decision parameters. Our experimental findings revealed that, in comparison to alternative methods, our proposed approach markedly enhances the model's capability to extract features from signals and classify them in low-SNR environments, thereby effectively diminishing the influence of noise. Notably, it achieves the highest recognition rate across a range of low-SNR conditions, registering an average increase in recognition rate of 4.8%. [ABSTRACT FROM AUTHOR]

With the wide applications of the Internet of Things (IoT) in smart home systems, IEEE 802.11n Wireless Local Area Networks (WLANs) have become a frequently chosen communication technology due to their adaptability and affordability. In a high-density network of devices such as the smart home scenerio, a host often meets interferences from other devices and unequal Received Signal Strength (RSS) from Access Points (APs). This results in throughput unfairness/insufficiency problems between hosts communicating concurrently in WLAN. Previously, we have studied the throughput request satisfaction method to address this problem. It calculates the target throughput from measured single and concurrent throughputs of hosts and controls the actual throughput at this target one by applying traffic shaping at the AP. However, the insufficiency problem of maximizing the throughput is not solved due to interferences from other hosts. In this paper, we present an extension of the throughput request satisfaction method to maximize the throughput of a high-priority host under concurrent communications. It recalculates the target throughput to increase the actual throughput as much as possible while the other hosts satisfy the least throughput. For evaluations, we conduct experiments using the test-bed system with Raspberry Pi as the AP devices in several topologies in indoor environments. The results confirm the effectiveness of our proposal. [ABSTRACT FROM AUTHOR]

Dielectric elastomers have attracted considerable attention both from academia and industry alike over the last two decades due to their superior mechanical properties. In parallel, research on the mechanical properties of dielectrics has been steadily advancing, including the theoretical, experimental, and numerical aspects. It has been recognized that the electromechanical coupling property of dielectric materials can be utilized to drive deformations in functional devices in a more controllable and intelligent manner. This paper reviews recent advances in the theory of dielectrics, with specific attention focused on the theory proposed by Dorfmann and Ogden. Additionally, we provide examples illustrating the application of this theory to analyze the electromechanical deformations and the associated bifurcations in soft dielectrics. We compared the bifurcations in elastic and dielectric materials and found that only compressive bifurcation modes exist in elastic structures, whereas both compressive and tensile modes coexist in dielectric structures. We summarize two proposed ways to suppress and prevent the tensile bifurcations in dielectric materials. We hope that this literature survey will foster further advancements in the field of the electroelastic theory of soft dielectrics. [ABSTRACT FROM AUTHOR]

Exposure to hypoxia results in the development of pulmonary arterial hypertension (PAH). An increase in the intracellular Ca2+ concentration ([Ca2+]i) in pulmonary artery smooth muscle cells (PASMCs) is a major trigger for pulmonary vasoconstriction and proliferation. This study investigated the mechanism by which KMUP-1, a xanthine derivative with phosphodiesterase inhibitory activity, inhibits hypoxia-induced canonical transient receptor potential channel 1 (TRPC1) protein overexpression and regulates [Ca2+]i through store-operated calcium channels (SOCs). Ex vivo PASMCs were cultured from Sprague-Dawley rats in a modular incubator chamber under 1% O2/5% CO2 for 24 h to elucidate TRPC1 overexpression and observe the Ca2+ release and entry. KMUP-1 (1 μM) inhibited hypoxia-induced TRPC family protein encoded for SOC overexpression, particularly TRPC1. KMUP-1 inhibition of TRPC1 protein was restored by the protein kinase G (PKG) inhibitor KT5823 (1 μM) and the protein kinase A (PKA) inhibitor KT5720 (1 μM). KMUP-1 attenuated protein kinase C (PKC) activator phorbol 12-myristate 13-acetate (PMA, 1 μM)-upregulated TRPC1. We suggest that the effects of KMUP-1 on TRPC1 might involve activating the cyclic guanosine monophosphate (cGMP)/PKG and cyclic adenosine monophosphate (cAMP)/PKA pathways and inhibiting the PKC pathway. We also used Fura 2-acetoxymethyl ester (Fura 2-AM, 5 μM) to measure the stored calcium release from the sarcoplasmic reticulum (SR) and calcium entry through SOCs in hypoxic PASMCs under treatment with thapsigargin (1 μM) and nifedipine (5 μM). In hypoxic conditions, store-operated calcium entry (SOCE) activity was enhanced in PASMCs, and KMUP-1 diminished this activity. In conclusion, KMUP-1 inhibited the expression of TRPC1 protein and the activity of SOC-mediated Ca2+ entry upon SR Ca2+ depletion in hypoxic PASMCs. [ABSTRACT FROM AUTHOR]

The plant-specific NAC gene family is one of the largest transcription factor families, participating in plant growth regulation and stress response. Despite extensive characterization in various plants, our knowledge of the NAC family in oat is lacking. Herein, we identified 333 NAC genes from the latest release of the common oat genome. We provide a comprehensive overview of the oat NAC gene family, covering gene structure, chromosomal localization, phylogenetic characteristics, conserved motif compositions, and gene duplications. AsNAC gene expression in different tissues and the response to various abiotic stresses were characterized using RT-qPCR. The main driver of oat NAC gene family expansion was identified as segmental duplication using collinearity analysis. In addition, the functions of AsNAC109 in regulating abiotic stress tolerance in Arabidopsis were clarified. This is the first genome-wide investigation of the NAC gene family in cultivated oat, which provided a unique resource for subsequent research to elucidate the mechanisms responsible for oat stress tolerance and provides valuable clues for the improvement of stress resistance in cultivated oat. [ABSTRACT FROM AUTHOR]

Cytotoxic T-lymphocyte antigen 4 (CTLA-4) is a pivotal immune checkpoint receptor, playing a crucial role in modulating T-cell activation. In this study, we delved into the underlying mechanism by which a common mutation, G199R, in the cytoplasmic domain of CTLA-4 impacts its inhibitory function. Utilizing nuclear magnetic resonance (NMR) spectroscopy and biochemical techniques, we mapped the conformational changes induced by this mutation and investigated its role in CTLA-4 activity. Our findings reveal that this mutation leads to a distinct conformational alteration, enhancing protein–membrane interactions. Moreover, functional assays demonstrated an improved capacity of the G199R mutant to downregulate T-cell activation, underscoring its potential role in immune-related disorders. These results not only enhance our understanding of CTLA-4 regulatory mechanisms but also provide insights for targeted therapeutic strategies addressing immune dysregulation linked to CTLA-4 mutations. [ABSTRACT FROM AUTHOR]

The Inverse Synthetic Aperture Radar (ISAR) has been proven to be an effective tool for space target sensing due to its capability of performing high-resolution imaging. Since the component information of the spacecraft is key to the identification of the target and diagnosis of its status, ISAR images with a clear and complete representation of the typical components are much desired. This requires a selection of the imaging time, during which a certain spacecraft component has a good projection on the ISAR image plane with the shape feature well conserved and a high resolution. In addition, a fully automated implementation with a high computational efficiency is also highly preferred for on-orbit operations so as to improve the intelligence level of the space-borne system. We propose a bicriterion-based automated optimal imaging time-selection method for the space-borne ISAR, which is seeking the slow time section of the data that result in the best image. A good image means a high azimuth resolution and the best presentation of the solar panels. One criterion is the Maximum Doppler Spread (MDS), which indicates the maximum Effective Rotational Velocity (ERV) leading to high image resolution, but it is influenced by the satellite attitude. of the spacecraft. The other is the Maximum Component Area (MCA), which is defined to indicate the completeness of the component considered. The radar echoes are processed sequentially by way of a sliding window. The interval with the co-maximization of the DS and CA is selected, and fine processing is performed further to obtain the best images. The results of the simulation experiments show that the proposed method can achieve spacecraft images with the solar panels presented the best. The computational complexity is low. [ABSTRACT FROM AUTHOR]

This research delves into the characterization of the ultrasonic transmission coefficient pertaining to various types of bonding defects in Fiber-Reinforced Polymer (FRP)-bonded structures. Initially, an ultrasonic transmission coefficient calculation model for FRP-bonded structures in a water immersion environment is established. This model is used to analyze the variation in the ultrasonic transmission coefficient under different defect types, namely intact bonding, interfacial slip, and debonding defects. Subsequently, a frequency domain finite element analysis model of FRP-bonded structures with different defect types is constructed. The simulation validates the accuracy of the theoretical analysis results and concurrently analyzes the variation in the transmission signal when the defects alter. Lastly, an experimental platform for water immersion ultrasonic transmission measurement is set up. The transmission signals under different defect types are extracted through experiments and evaluated in conjunction with theoretical calculations to assess the types of bonding defects. [ABSTRACT FROM AUTHOR]

An ordinary double steel plate–concrete composite wall (ODSC wall) is composed of core concrete, the faceplates, and shear connectors such as studs, etc. Based on an ODSC wall, a new type of stiffened double steel plate–concrete composite wall (SDSC wall) is conceived by incorporating additional stiffeners and tie plates on the internal surface, which aims to improve the local stability of the faceplates. In the authors' previous study, a series of axial compression tests were conducted on the SDSC walls. The SDSC walls in the test showed better mechanical performance, as the presence of stiffeners changed the buckling deformation mode and significantly improved the corresponding local buckling stress and ultimate strength. In this paper, a comprehensive summary of the prior research on SDSC walls is provided, and the effect of the constructive parameters on the local stability is discussed. The results reveal that the modified formula of the critical stress can degrade to the Euler formula when the stiffener-to-stud spacing ratio (i.e., a/B ratio) approaches infinity. What is more, the analysis model is also applicable for DSC walls with enclosed side plates, and the proposed formula can predict the buckling stress of the SDSC walls with different a/B ratios. In addition, according to the analysis of the numerical simulation, a design approach for SDSC walls to prevent local buckling is provided, which is applicable in practical engineering applications. [ABSTRACT FROM AUTHOR]

Respiratory illnesses present a significant threat to porcine health, with co-infections involving Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Streptococcus suis (SS), Porcine Circovirus Type 2 (PCV2), and Porcine Circovirus Type 3 (PCV3) acting as the primary causative agents. As a result, the precise diagnosis of PRRSV, PCV2, PCV3 and SS is of paramount importance in the prevention and control of respiratory diseases in swine. Therefore, we conducted a molecular bioinformatical analysis to concurrently detect and differentiate PRRSV, PCV2, PCV3 and SS. We selected the ORF6 gene of PRRSV, the ORF2 gene of PCV2 and PCV3, and the glutamate dehydrogenase (GDH) gene of SS as targets. Specific primers and probes were designed for each pathogen, and following meticulous optimization of reaction conditions, we established a multiple TaqMan fluorescence quantitative PCR detection method. Subsequently, we subjected this method to a comprehensive assessment, evaluating its specificity, sensitivity, and repeatability. The research results demonstrated that the established multiple TaqMan fluorescence quantitative PCR detection method displays displayed exemplary specificity, with no instances of cross-reactivity with other pathogens. The method's minimum detection concentrations for PRRSV, PCV2, PCV3, and SS were 2.80 × 101 copies/µL, 1.96 × 102 copies/µL, 2.30 × 102 copies/µL, and 1.75 × 103 copies/µL, respectively. When applied to the analysis of 30 clinical samples, the results closely mirrored those obtained through Chinese standard uniplex real-time qPCR detection method for PRRSV, as well as the general PCR methods for SS, PCV2, and PCV3. This study underscores the robust specificity, high sensitivity, and consistent stability of the multiple TaqMan fluorescence quantitative PCR detection method that we have developed. It is ideally suited to the clinical monitoring of PRRSV, PCV2, PCV3, and SS, and it carries significant importance in ongoing efforts to prevent and manage respiratory diseases in porcine populations. [ABSTRACT FROM AUTHOR]

The multibeam high-throughput satellites (HTS) are regarded as a crucial component in the forthcoming space-based Internet of Things (S-IoT) network. The multi-band frequency conversion capability of HTS is essential for achieving high-capacity information interconnection in the S-IoT network. To enhance the frequency conversion capability of the on-board payload, a reconfigurable local oscillator (LO) harmonic mixer with simultaneous phase shifting and image-rejection is proposed and demonstrated based on a polarization division multiplexing dual-parallel Mach–Zehnder modulator (PDM-DPMZM). By adjusting the input radio frequency (RF) signal and direct current (DC) bias voltage of the modulator, four different LO frequency-multiplication mixing functions can be achieved. The phase of the generated signal can be flexibly tuned over a full 360° range by controlling the angle α between the polarization direction of the polarizer and one axis of the modulator, and it has a flat amplitude response. When combined with an optical frequency comb, the scheme can be extended to a multi-channel multi-band frequency conversion system with an independent phase tuning capability. Additionally, by adjusting the phase difference between dual channel output signals, it can be reconfigured to implement in-phase/quadrature (I/Q) mixing, double-balanced mixing and image-reject mixing. [ABSTRACT FROM AUTHOR]

In this study, a scheme for generating tunable microwave frequency combs (MFCs) based on optical mutual injection is proposed and experimentally investigated. The scheme is based on the optical injection of lasers to generate MFCs, and constitutes a feedback loop by using dual-laser mutual injection to obtain MFCs with a large continuous bandwidth and tunable comb spacing. The experimental setup analyzes the effects of injected optical power, modulation frequency and amplitude, and wavelength detuning on the generated MFC signals. The experimental results indicate that when the single-frequency electrical signal is set to 2 GHz, flat MFCs with amplitude variations within 10 dB can be obtained by optimizing the injected power and the frequency detuning between the two semiconductor lasers. Furthermore, the comb spacing of the MFCs can be made tunable by varying the modulation frequency and selecting the matched operating parameters to adapt to different application scenarios. [ABSTRACT FROM AUTHOR]

Exploring an appropriate immobilization approach to enhance catalytic activity and reusability of cellulase is of great importance to reduce the price of enzymes and promote the industrialization of cellulose-derived biochemicals. In this study, Fe3O4 magnetic nanoparticles (MNPs) were functionalized with meso-2,3-dimercaptosuccinic acid to introduce carboxyl groups on the surface (DMNPs). Then, melamine–glutaraldehyde dendrimer-like polymers were grafted on DMNPs to increase protein binding sites for the immobilization of processive endoglucanase EG5C-1. Moreover, this dendrimer-like structure was beneficial to protect the conformation of EG5C-1 and facilitate the interaction between substrate and active center. The loading capacity of the functionalized copolymers (MG-DMNPs) for EG5C-1 was about 195 mg/g, where more than 90% of the activity was recovered. Immobilized EG5C-1 exhibited improved thermal stability and increased tolerability over a broad pH range compared with the free one. Additionally, MG-DMNP/EG5C-1 biocomposite maintained approximately 80% of its initial hydrolysis productivity after five cycles of usage using filter paper as the substrate. Our results provided a promising approach for the functionalization of MNPs, enabling the immobilization of cellulases with a high loading capacity and excellent activity recovery. [ABSTRACT FROM AUTHOR]

In recent years, radar emitter signal recognition has enjoyed a wide range of applications in electronic support measure systems and communication security. More and more deep learning algorithms have been used to improve the recognition accuracy of radar emitter signals. However, complex deep learning algorithms and data preprocessing operations have a huge demand for computing power, which cannot meet the requirements of low power consumption and high real-time processing scenarios. Therefore, many research works have remained in the experimental stage and cannot be actually implemented. To tackle this problem, this paper proposes a resource reuse computing acceleration platform based on field programmable gate arrays (FPGA), and implements a one-dimensional (1D) convolutional neural network (CNN) and long short-term memory (LSTM) neural network (NN) model for radar emitter signal recognition, directly targeting the intermediate frequency (IF) data of radar emitter signal for classification and recognition. The implementation of the 1D-CNN-LSTM neural network on FPGA is realized by multiplexing the same systolic array to accomplish the parallel acceleration of 1D convolution and matrix vector multiplication operations. We implemented our network on Xilinx XCKU040 to evaluate the effectiveness of our proposed solution. Our experiments show that the system can achieve 7.34 giga operations per second (GOPS) data throughput with only 5.022 W power consumption when the radar emitter signal recognition rate is 96.53%, which greatly improves the energy efficiency ratio and real-time performance of the radar emitter recognition system. [ABSTRACT FROM AUTHOR]

To investigate the pipeline deformation pattern caused by the excavation of deep foundation pits in composite soil–rock strata, a comprehensive study integrating on-site monitoring and numerical simulation was conducted. This study centered on a deep foundation excavation project in the soft soil in Nanjing's floodplain region. The analyses of pipeline settlement and deformation were performed based on field-measured data. This study investigated the impact of excavation on the mechanical properties of the surrounding soil that resulted in the progressive deformation of adjacent pipelines. Furthermore, numerical simulations were conducted using Plaxis 3D CONNECT Edition v22 finite element analysis software. This study elucidated the influence of factors such as pipeline–pit distance and burial depth on pipeline deformation, conducting a quantitative analysis of their effects. The results indicated that deformation primarily occurs unevenly near pit corners and is less pronounced in soil–rock strata than in single-type soil layers. This study established correlations between pipeline displacements and various factors, offering valuable insights for future excavation projects conducted under similar conditions. [ABSTRACT FROM AUTHOR]

This paper presents an amplifier with low noise, high gain, low power consumption, and high linearity for electrocardiogram (ECG) recording. The core of this design is a chopper-stabilized capacitive-feedback operational transconductance amplifier (OTA). The proposed OTA has a two-stage structure, with the first stage using a combination of current reuse and cascode techniques to obtain a large gain at low power and the second stage operating in Class A state for better linearity. The amplifier additionally uses a DC servo loop (DSL) to improve the rejection of DC offsets. The amplifier is implemented in a standard 0.13 μm CMOS process, consuming 1.647 μA current from the supply voltage of 1.5 V and occupying an area of 0.97 mm2. The amplifier has a 0.5 Hz to 6.1 kHz bandwidth and 59.7 dB gain while having no less than a 65 dB common-mode rejection ratio (CMRR). The amplifier's total harmonic distortion (THD) is less than 0.1% at 800 mVpp output. The amplifier can provide a noise level of 1.18 μVrms in the 0.5 Hz to 500 Hz bandwidth that the ECG signal is interested in and has 3.38 μVrms input-referred noise (IRN) over the entire bandwidth, so its noise efficiency factor (NEF) is 2.13. [ABSTRACT FROM AUTHOR]

Heavy-metal ions (HMIs) as a pollutant, if not properly processed, used, and disposed of, will not only have an influence on the ecological environment but also pose significant health hazards to humans, making them a primary factor that endangers human health and harms the environment. Heavy metals come from a variety of sources, the most common of which are agriculture, industry, and sewerage. As a result, there is an urgent demand for portable, low-cost, and effective analytical tools. Bionanosensors have been rapidly developed in recent years due to their advantages of speed, mobility, and high sensitivity. To accomplish effective HMI pollution control, it is important not only to precisely pinpoint the source and content of pollution but also to perform real-time and speedy in situ detection of its composition. This study summarizes heavy-metal-ion (HMI) sensing research advances over the last five years (2019–2023), describing and analyzing major examples of electrochemical and optical bionanosensors for Hg2+, Cu2+, Pb2+, Cd2+, Cr6+, and Zn2+. [ABSTRACT FROM AUTHOR]

TC4ELI+TC21 titanium gradient composite structures with direct transition (TD1) and cross transition (TD2) were prepared using laser deposition manufacturing technology. The microstructure of the gradient interface was observed, and the distribution of alloying elements was detected. The tensile properties of the two alloys at room temperature were tested, and the effects of different heat treatment regimens on the microstructure and mechanical properties were investigated. The results show that there is no obvious defect at the gradient interface of the two alloys. Compared with direct transition alloys, the alloying elements of TD2 alloys change less at the interface, the structure of the transition zone is more closely bound, and the elongation is higher. After heat treatment, the α phase in the alloy is coarsened, and the alloy elements at the interface are fully diffused, so that the gradient interface of the alloy is eliminated to a certain extent, the tensile strength of the alloy decreases, and elongation increases. The strength and plasticity of the alloy reached their best match at a solution temperature of 930 °C. [ABSTRACT FROM AUTHOR]

Radar emitter signal intra-pulse modulation recognition is important for modern electronic reconnaissance systems to analyze target radar systems. In the actual environment, the intra-pulse modulations of the sampled radar emitter signals contain not only the known types in the library but also the unknown types. Therefore, the existing recognition methods, which are based on a closed set, cannot recognize the unknown samples. In order to solve this problem, in this paper, we proposed a method for radar emitter signal intra-pulse modulation open set recognition. The proposed method could classify the known modulations and identify the unknown modulation by using an original deep neural network-based recognition model trained on a closed set, estimating the signal-to-noise ratio, and calculating the reconstruction loss by an encoder–decoder model. For a given sample, the original deep neural network-based recognition model will label it as a certain known class temporarily. By estimating the SNR of the sample and calculating the reconstruction loss by inputting the sample to the corresponding encoder–decoder model related to the temporary predicted known class, whether the sample belongs to the predicted temporary known class or the unknown class will be confirmed. Experiments were conducted with five different openness conditions. The experimental results indicate that the proposed method has good performance on radar emitter signal intra-pulse modulation open set recognition. [ABSTRACT FROM AUTHOR]