The effects of exogenous inorganic [i.e., nitrate (NO3-)/ammonium (NH4+)] and organic [i.e., glutamate (Glu)/argine (Arg)] N alone and in combination on the accumulation of proline (Pro) in rice plant under trivalent chromium [Cr(III)] stress were studied through the mass balance matrix model (MBMM). ‘NH4+’ application showed the largest contribution to the Pro content in rice shoots under different concentrations of Cr(III), followed by ‘NO3-’, ‘Arg’, and ‘Glu’ application respectively. While, ‘Arg’ application displayed the largest contribution to the Pro content in roots under Cr(III) stress, followed by ‘NH4+’, ‘Glu’, and ‘NO3-’ application. Among the combination cases of N sources used, the application of ‘NH4++Arg’ showed the greatest contribution to the Pro content in both roots and shoots of Cr(III)-treated rice seedlings, while the application of ‘NO3-+Glu’ showed the least contribution to the Pro content in rice seedlings under Cr(III) stress. Results from this study indicated that the level of indigenous Pro in rice seedlings is quite sensitive to Cr(III) stress under different N sources, and mathematical modeling showed a reliable result to estimate the relationship between Pro content and N source application in rice plants in response to Cr(III) exposure .

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

Agronomic traits such as biomass yield and abiotic stress tolerance are genetically complex and challenging to improve by conventional breeding strategies. Genomic selection (GS) is an alternative approach in which genome-wide markers are used to determine the genomic estimated breeding value (GEBV) of individuals in a population. In alfalfa, previous results indicated that low to moderate prediction accuracy values (<70%) were obtained in complex traits such as yield and abiotic stress resistance. There is a need to increase the prediction value in order to employ GS in breeding programs. In this paper we reviewed different statistic models and their applications in polyploid crops including alfalfa. Specifically, we used empirical data affiliated with alfalfa yield under salt stress to investigate approaches which use DNA marker importance values derived from machine learning models, and genome-wide association studies (GWAS) of marker-trait association scores based on different GWASpoly models, in weighted GBLUP analyses. This approach increased prediction accuracies from 50% to more than 80% for alfalfa yield under salt stress. This is the first report in alfalfa to use variable importance and GWAS-assisted approaches to increase the prediction accuracy of GS, thus helping to select superior alfalfa lines based on their GEBVs.

Objectives: Stress urinary incontinence (SUI) negatively affects the quality of life and sexual function in women. This study aimed to evaluate the efficacy of radiofrequency (RF) therapy in reducing SUI symptoms and its impact on sexual function. Methods: Thirty-four women with SUI were enrolled and underwent a single RF treatment session using the Viveve® System (Viveve Medical Inc., USA) with parameters of 90 J/cm² and 220 pulses per hour. Assessments at baseline and 6 months post-treatment included perineal ultrasound and personal interviews to evaluate lower urinary tract symptoms and sexual function. Urodynamic studies, voiding diaries, and questionnaires such as the Female Sexual Function Index (FSFI), Overactive Bladder Symp-tom Score (OABSS), Urogenital Distress Inventory-6 (UDI-6), Incontinence Impact Question-naire-7 (IIQ-7), and International Consultation on Incontinence Questionnaire-Short Form (ICIQ-SF) measured outcomes. Results: RF therapy significantly improved sexual function, with higher FSFI scores in all domains except pain at 6 months. SUI symptoms were significantly re-duced, as indicated by improved scores on OABSS, UDI-6, IIQ-7, and ICIQ-SF, alongside better voiding diary results. Anatomical changes included reduced bladder neck mobility, decreased vaginal width, and a reduced rotation angle of the proximal urethra. Conclusions: RF therapy is effective and safe for treating mild to moderate SUI and enhances sexual function, potentially due to changes in vaginal topography. These results suggest RF therapy as a viable non-surgical op-tion for managing SUI and improving sexual health.

The bilateral trade relationship between PRC and ASEAN is close. As early as 2012, China has been the largest trading partner of ASEAN, and ASEAN has also taken the place of the United States as PRC’s second largest trading partner in 2019.This paper uses the theory of ecological population evolution, selects the total import and export volume of PRC and the ten ASEAN countries from 2000 to 2019 as the data sample, studies the bilateral trade relations between PRC and the ten ASEAN countries, analyzes the competition and symbiotic relationship of bilateral trade between PRC and the ten ASEAN countries and the possibility of trade friction according to the results, and proposes corresponding improvement strategies. According to the results of the population evolution competition model, China has a competitive relationship with Indonesia, Malaysia, the Philippines, Singapore and Thailand, which is most likely to cause trade friction. China has a biased relationship with Brunei, Cambodia, Laos, Myanmar and Vietnam, which is more likely to cause trade friction. On this basis, in order to further strengthen the trade relations between PRC and the ten ASEAN countries, this paper puts forward policy suggestions on improving infrastructure, strengthening independent innovation, seeking cooperation fields and actively investing abroad.

Conclusions: Molecular classification could provide reliable supplementary information for evaluating prognostic and contribute to treatment option decision-making in EEC patients. Fertility-sparing treatment is not recommended for EEC patients with CNH and MSI-H. Furthermore, fertility-sparing treatment can be attempted in EEC patients with CHL, but regular follow-up should be carried out to early detection of EC relapse and prevention of disease progression.

Multi-Object Tracking (MOT) is a key technology for Unmanned Aerial Vehicles (UAVs). Traditional tracking-by-detection methods firstly employ an object detector to retrieve targets in each image and then track them based on a matching algorithm. Recently, the popular multi-task learning methods has been dominating this area since they can detect targets and extract Re-Identification (Re-ID) features in a computationally efficient way. However, the detection task and the tracking task have conflicting requirements on image features, leading to the poor performance of the joint learning model compared to separate detection and tracking methods. The problem is more severe when it comes to UAV images due to the presence of irregular motion of large number of small targets. In this paper, we propose a balanced Joint Detection and Re-ID learning (JDR) network to address the MOT problem in UAV vision. To better handle the non-uniform motion of objects in UAV videos, the Set-Member Filter is applied which describes object state as a bounded set. An appearance matching cascade is then proposed based on target state set. Furthermore, a Motion-Mutation module is designed to address the challenges posed by the abrupt motion of UAV. Extensive experiments on the VisDrone-MOT2019 dataset demonstrate that our proposed model, termed as SMFMOT, outperforms state-of-the-arts by a large margin and achieves superior performance on the MOT tasks in UAV videos.

Mulberry is an economically significant crop for the sericulture industry worldwide. Stresses such as drought exposure have a significant influence on plant survival. Metabolome directly reflects plant physiological status; thus, a way to assess this impact is to perform a global metabolomic analysis. This study investigated the effect of drought stress on mulberry Yu-711 metabolic balance using a liquid chromatography-mass spectrometry (LC-MS) based on an untargeted metabolomic approach. For this objective, Yu-711 leaves were subjected to two weeks of drought stress treatment and control without drought stress. Multivariate and univariate statistical analyses highlighted numerous differentially-accumulated metabolic elements as a function of time and treatment. Drought stress led to a more differentiated metabolites response than the control. We found that the levels of total lipids and galactolipids, and phospholipids (PC, PA, PE) were significantly altered, producing 48% of the total differentially expressed metabolites. Fatty acyls were the most abundant lipids expressed and decreased considerably by 73.6%. Prenol lipids class of lipids increased in drought leaves. Other classes of metabolites, including polyphenols( flavonoids and cinnamic acid), organic acid (amino acids), carbohydrates, benzenoids, and organoheterocyclic, all had a dynamic trend in response to the drought stress. However, their levels under drought stress generally decreased significantly compared to the control. These results provide an overview of the metabolic profile of the mulberry plant through differentially-accumulated compounds and provide a better understanding of global plant metabolic changes in defense mechanisms.

Determining the general-name-derived semantic relationships between geographic names often requires consulting many geographic name documents for verification. In geographic name translation and geographic name knowledge graphs, this method cannot meet the needs of large-scale extraction of general-name-derived semantic relationships between geographic entities. To solve this problem, this paper proposes a general-name derivation relation recognition method based on Prompt learning and Semantic Spatiotemporal Distribution Attention Feature Fusion (SSD-AFF). This method adopts a pipe-based relationship recognition strategy. First, the Prompt learning method is adopted to form a general-name derivation pattern with the general-name-derived geographical name category and the original geographical name category as the prompt word, and then the derivation pattern is combined with the geographical name as the input of the model so that the recognition problem of the general-name derivation of the geographical name can be transformed into a classification problem using the bidirectional language model. Then, by analyzing the characteristics of the semantic association and geographical spatio-temporal distribution between the general-name-derived geographical name and the original geographical name, the semantic spatio-temporal distribution characteristics of the general-name-derived geographical name are constructed. Finally, the network model based on the semantic spatio-temporal distribution Attention Feature Fusion (SSD-AFF) is used to distinguish the general-name-derived semantic relationships. The experimental results show that the proposed method has good performance. It can effectively mine the general-name-derived semantic relations between the vector data of geographical entities and has important application value in geographical name translation and geographical name knowledge maps.

A rapid and efficient method using an alkyl functionalized magnetic nanoparticles-based extraction technique combined with Ultra-High Performance Liquid Chromatography was developed for the detection of trace amounts of polycystic aromatic hydrocarbons in tea leaves. As a popular coating for chromatographic column packing materials, C18-alkyl has been demonstrated to be effective in separating polycystic aromatic hydrocarbons. And the magnetism of the nanomaterials accelerates the extraction process while their high surface ratio enables desirable dispersity in the sample matrix. Meanwhile, the adsorbents can be washed and reused 30 times without compromise on recovery, which greatly reduced the budget. The effects of various parameters were investigated and optimized, and the recoveries for five analytes were in the range of 84.8–103.7%. The reproducibility rate of intra-day and inter-day were below 7.1% and 11.3% respectively. The limits of detection and limits of quantification ranged from 1.69–9.97 ng g-1 and 5.12–30.21 ng g-1, indicating satisfactory sensitivity. Thus, the proposed methodology is rapid, highly efficient, and economical, and it expands the application of magnetic cleanup approaches in complex food matrices.

A full connected world is expected to gain in the 6th generation mobile network (6G). As a typi-cal fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. In the future of vehicular net-works, wide variety of services need powerful computing resources and higher quality of ser-vice (QoS). Existing resources are insufficient to match these requirements. Aim to this problem, An intelligent service offloading framework is provided. Based on the framework, an Algorithm of Improved Gradient Descent (AIGD) is created to accelerate the speed of iteration. So, the con-vergence of convolutional neural network (CNN) based on AIGD is able to be accelerated too. Then, an Algorithm of convolutional long short-term memory (CN_LSTM) Based Traffic Predic-tion (ACLBTP) is designed to gain the predicted number of vehicles belonged to the edge node. At last, an Algorithm of Service Offloading Based on CN_LSTM (ASOBCL) is conducted to of-fload these services to the vehicles belonged to the edge node. In ASOBCL, sorting technique is adopted to speed up the offloading work. Simulation results demonstrate the fact that the pre-diction strategy designed in this paper has high accuracy. The low offloading time and main-taining stable load balance is gained via running ASOBCL. Low offloading time means short response time. And, the QoS is guaranteed. So, these strategies designed in this paper are effec-tive and valuable.

Arc sensors have been used in seam tracking and widely studied since the 80s; commercial arc sensing products for T and V shaped grooves have been developed. However, it is difficult to use these arc sensors in narrow gap welding because arc stability and sensing accuracy are not satisfactory. Pulse gas melting arc welding (P-GMAW) has been successfully applied in narrow gap welding and all position welding processes, so it is worthwhile to research P-GMAW arc sensing technology. In this paper, we derived a linear mathematical P-GMAW model for arc sensing, and the assumptions for the model are verified through experiments and finite element methods. Finally, the linear characteristics of the mathematical model were investigated. In torch height changing experiments, uphill experiments, and groove angle changing experiments the P-GMAW arc signals all satisfied the linear rules. In addition, the faster the welding speed, the higher the arc signal sensitivities; the smaller the groove angle, the greater the arc sensitivities. The arc signal variation rate needs to be modified according to the welding power, groove angles, and swing or rotate speed.

The lactic acid bacteria Streptococcus thermophilus and Lactobacillus helveticus are commonly used as starter cultures in dairy product production. This study aimed to investigate the characteristics of fermented milk using different ratios of these strains and analyze the changes in volatile compounds during fermentation and storage. The 10:1 ratio of Streptococcus thermophilus CICC 6063 to Lactobacillus helveticus CICC 6064 showed optimal fermentation time (4.2 h), viable cell count (9.64 log10 colony-forming units/mL), and sensory evaluation score (79.1 points). In total, 56 volatile compounds were identified and quantified by solid-phase microextraction and gas chromatography–mass spectrometry (SPME-GC-MS), including aldehydes, ketones, acids, alcohols, esters, and others. Among these, acetaldehyde, 2,3-butanedione, 2,3-pentanedione, 2-heptanone, acetoin, and 1-heptanol had the highest odor activity values and contributed to the characteristic flavor of fermented milk. The concentrations and types of key flavor compounds reached their peak at the end of fermentation. Throughout the fermentation and storage process, the levels of 2,3-pentanedione and acetoin exhibited synergistic dynamics. These findings enhance our understanding of the chemical and molecular characteristics of milk fermented with Streptococcus thermophilus and Lactobacillus helveticus, providing a basis for improving the flavor and odor of dairy products during fermentation and storage.

Mobility as a Service (MaaS) is an innovative transport concept, anticipated to provide travelers with different kinds of travel services, more sustainable than a private car, in a simpler, packaged way. It combines different transport modes to offer a tailored mobility package, like a monthly mobile phone contract. The rapid development of intelligent transportation system and the shared economy has speeded up the development of MaaS in these years. In this paper, we aim at classifying the existing research on MaaS and the characteristics of MaaS into different categories, in order to answer the following questions after reviewing the existing literature: What is MaaS? Who are the main actors in MaaS? How can MaaS be implemented? Why should it be implemented? Where will MaaS end up in this wave of disruption? When we talk about MaaS, what are we focusing on? What is the future leading frequency of MaaS? Finally, based on the existing literature, we envision the leading future of MaaS.

Fusarium species are one of the filamentous fungal taxa with the most pronounced impact on agri-cultural production and human health. The mycotoxins produced by pathogenic Fusarium not on-ly attack various plants including crops, causing various plant diseases that lead to reduced yields and even death, but also penetrate into the food chain of humans and animals to cause food poi-soning and consequent health hazards. Although sporadic studies have revealed some of the bio-synthetic pathways of Fusarium toxins, they are insufficient to satisfy the need for a comprehen-sive understanding of Fusarium toxin production. In this study, we focused on 35 serious patho-genic Fusarium species with available genomes and systematically analyzed the ubiquity of the dis-tribution of identified Fusarium and non-Fusarium derived fungal toxin biosynthesis gene clusters (BGCs) in these species through the mining of core genes and comparative analysis of correspond-ing BGCs. Additionally, novel sesterterpene synthases and PKS_NRPS clusters were discovered and analysed. This work is the first to systematically analyze the distribution of related mycotoxin biosynthesis in pathogenic Fusarium species. These findings enhance the knowledge of mycotoxin production and provide a theoretical grounding for the prevention of fungal toxin production us-ing biotechnological approaches.

Light-colored and transparent polyimide (PI) films with good high-temperature dimensional stability are highly desired for advanced optoelectronic applications. However, in practice, the simultaneous achievement of good optical and thermal properties in one PI film is usually difficult due to the inter-conflicting molecular design for the polymers. In the present work, a series of PI-SiO2 nanocomposite films (ABTFCPI) were developed based on the PI matrix derived from hydrogenated pyromellitic anhydride (HPMDA) and an aromatic diamine containing benzanilide and trifluoromethyl substituents in the structure, 2,2'-bis(trifluoromethyl)-4,4'-bis[4-(4-aminobenzamide)]biphenyl (ABTFMB). The inorganic SiO2 fillers were incorporated into the nanocomposite films with the form of colloidal nanoparticles dispersed in the good solvent of N,N-dimethylacetamide (DMAc) for the PI matrix. The derived ABTFCPI nanocomposite films showed good film-forming ability, flexible and tough nature, good optical transparency, and good thermal properties with the loading amounts of SiO2 up to 30 wt% in the system. The ABTFCPI-30 film with the SiO2 content of 30 wt% in the film showed the optical transmittance of 79.6% at the wavelength of 400 nm (T400) with a thickness of 25 μm, the yellow index (b*) of 2.15, and the 5% weight loss temperatures (T5%) of 491 oC, which are all comparable to those the pristine ABTFCPI-0 matrix without filler (T400=81.8%; b*=1.77; T5%=492 oC). Meanwhile, the ABTFCPI-30 film exhibited obviously enhanced high-temperature dimensional stability with the linear coefficients of thermal expansion (CTE) of 25.4×10-6/K in the temperature range of 50 to 250 oC, which is much lower than that of the AMTFCPI-0 film (CTE=32.7×10-6/K).

The interfacial polymerization (IP) of piperazine (PIP) and trimesoyl chloride (TMC) has been extensively utilized to synthesize nanofiltration (NF) membranes. However, it is still a huge challenge to monitor the IP reaction, because of the fast reaction rate and the formed ultra-thin film. Herein, two effective strategies were applied to reduce the IP reaction rate: (1) the introduction of hydrophilic interlayers between the porous substrate and the formed polyamide layer, and (2) the addition of macromolecular additives in the aqueous solution of PIP. As a result, in situ Fourier transform infrared (FT-IR) spectroscopy was firstly used to monitor the IP reaction of PIP/TMC with hydrophilic interlayers or macromolecular additives in the aqueous solution of PIP. Moreover, the formed polyamide layer growth on the substrate was studied in a real-time manner. The in situ FTIR experimental results confirmed that the IP reaction rates were effectively suppressed and that the formed polyamide thickness was reduced from 138 ± 24 nm to 46 ± 2 nm according to TEM observation. Furthermore, an optimized NF membrane with excellent performance was consequently obtained, which included boosted water permeation of about 141–238 (L/m²·h·MPa) and superior salt rejection of Na₂SO₄ > 98.4%.

The pollution caused by nitrogen dioxide is a major environmental problem in China. This study introduces a new type of atmospheric trace gas remote-sensing instrument, an airborne fiber imaging spectrometer. This spectrometer has a spectral range of 300–410 nm and works in push-broom mode with a 30° field of view on a flight path. Flight experiments were conducted on December 30, 2022 and January 5, 2023, covering heavily polluted areas of east of Beijing and Tangshan. This equipment obtained the density distribution of NO2 over the flight area. The results showed that pollution was mainly concentrated in the Caofeidian area and at the power station in the north, and the main source of pollution was anthropogenic. Satellite and airborne data near the pollution points were compared, and the two datasets showed a positive correlation with a correlation coefficient of 0.78 and 0.7, on the two days, respectively. This study demonstrates the capability of an airborne fiber imaging spectrometer for NO2 regional emission remote sensing and identifying the pollution points.

Steam Generator Level Control System (SGLCS) is one of the key instrumentations and control subsystems to ensure the safe operation of nuclear power plants. It is highly recommended to perform reliability analysis for SGLCS for making better maintenance strategy. SGLCS is a digital control system with complex redundant configuration and failure\self-diagnosis\repair action, traditional fault tree is not applicable due to its static property, Markov model is not a good choice too because of heavy burden of creating state transition graph for even medium-size system. Boolean Logic Driven Markov Process (BDMP) can describe complex failure-repair state transition via ‘trigger link’ and three kinds of leaf nodes, as well as keep compact and easy-read struct like fault tree. So BDMP is adopted to analysis reliability of SGLCS. Graphical BDMP modeling environment KB3 and Monte Carlo simulator YAMS is used for BDMP modeling and quantitative analysis respectively, and unavailability of SGLCS is obtained. The contribution of three parts of SGLCS to the unavailability of the system is also evaluated, and it is found that the water supply flow control part contributes the most to the failure of SGLCS.

Dissolved organic matter (DOM) derived from organic fertilizers may increase soil phosphorus (P) availability. However, the frequently observed correlation between soil P availability and dissolved organic carbon (DOC) content has led to an excessive focus on DOC content at the expense of DOM properties. The present study investigated the influence of DOM characteristics on soil P availability by using a 6-year field experiment and microcosms of P sorption in paddy soil. Our results showed that partial replacement of chemical P fertilizer with manure or crop straw increased P fertilizer-use efficiency, even when decreasing chemical P input by 34 %, compared to normal chemical fertilization. The microcosm experiment demonstrated that DOM properties, rather than total DOC content, determine soil P sorption capacity, despite the significant correlation between DOC content and P availability observed in the field experiment. Manure-DOM exerted stronger inhibitory effects on P sorption than straw-DOM, and high molecular weight (HMW)-fractionated DOM exerted stronger inhibitory effects on P sorption than low-molecular-weight-fractionated DOM by 16-20%. The mechanism was rooted in the HMW DOM with unique characteristics (e.g., strong aromaticity, hydrophobicity, abundant humic-like components, carboxyl groups, and benzene ring structures) competing for soil P sorption sites (e.g., reduction in specific surface area and micropore volume), decreasing the soil surface charge (e.g., zeta potential), and thereby suppressing P sorption in paddy soil. Our study points out a promising avenue for regulating organic matter properties with organic fertilization to improve P use efficiency in agricultural soils.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

Introduction: Novel robotic surgery systems (da Vinci Xi) are superior to classical open and laparoscopic techniques with its clear and three-dimensional view. We aimed to present the first case low anterior resection of rectal cancer and vaginal specimen extraction with Da Vinci Xi.Case: A 75-year-old female patient with rectum adenocarcinoma was undergone robotic-assisted low anterior resection (LAR) of the rectum, vaginal removal of the specimen, colorectal anastomosis and loop ileostomy. The operation time was 190 minutes. There were no postoperative complications. Pathological tumor stage was stage pT1N0 with negative proximal, distal and radial resection margins. The patient was discharged on the third postoperative day.Conclusion: Robot-assisted LAR, total mesorectal excision, vaginal removal of the specimen, colorectal anastomosis, and loop ileostomy can be performed easily and safely with Da Vinci Xi at early stage rectal cancer. And the vaginal extraction of the specimen avoids us from a traditional abdominal incision.

Promoting the sustainable development of resource-based cities is of great significance to the ecological protection and high-quality development of the Yellow River Basin. Combining the environmental status and economic development, we objectively evaluate the ecological safety level of resource-based cities in the Yellow River Basin, compare and analysis the main factors affecting the ecological safety of different resource-based cities, and formulate precise and differentiated ecological safety protection measures in river basins to achieve high-quality development. Taking the Yellow River Basin as an example, this paper analyses the ecological level of the Yellow River Basin based on the theory of sustainable development, calculates the ecological safety level of 30 resource-based cities in the Yellow River Basin from 2006 to 2020 by TOPSIS model, and makes convergence analysis by classification. Through the DID empirical test of sustainable development planning policy on ecological safety factors, distinguish the development characteristics of different resource-based cities. The results show that: (1) the ecological safety level of resource-based cities in the Yellow River Basin is generally developing well, and there are differences among different types of resource-based cities; (2) the kinetic energy of sustainable development of resource-based cities in the Yellow River Basin is mainly industrial transformation, and the power of science and technology is insufficient; (3) local governments pay limited attention to environmental protection, and the sustainable development planning of resource-based cities in 2013 mainly promotes the adjustment of industrial structure of resource-based cities.

The research of object classification and part segmentation is a hot topic in computer vision. A considerable number of studies have been carried out about deep learning on 3D point clouds. However,it is challenging to achieve effective feature learning due to sparsity of point clouds. Recently, a variety of Transformers have been adopted to improve point cloud processing and display great potential. Nevertheless, large numbers of Transformer layers tend to incur huge computational and memory costs. PointNet++ is one of the most influential neural architectures for point cloud understanding. Although the accuracy of PointNet++ has been largely surpassed by recent networks, this does not mean that PointNet++ has no potential.Thus, this paper offer two major contributions that significantly improve PointNet++ performance. Firstly, we introduce a novel contextual feature extraction (CFE) block that significantly enhances the feature extraction capabilities of PointNet++ networks. Secondly, to further enhance feature fusion, we seamlessly integrate a mutual learning (ML) block into the network architecture. By embedding these two innovative blocks within each layer of the network, we not only enrich the network's functionality but also impart it with greater robustness and adaptability. The specific experiments were conducted on the S3DIS (6-fold cross-validation) and Modelnet40 datasets with 86.5% and 92.7% accuracy, respectively, which proved that our model is comparable or even better than most existing methods for classification and segmentation tasks.

Influenced by the quasi-stationary Mei-yu front and the development of mesoscale low vortices on the Mei-yu front, a torrential rain event occurred in Suzhou on July 6, 2020. The 24-hour rainfall recorded by the automatic station exceeded 200 millimeters, with the highest hourly intensity surpassing 70 millimeters, accompanied by thunderstorms and strong winds. This heavy precipitation process was characterized by suddenness, extremity, long duration, high precipitation efficiency, and backward propagation. Using various data sources such as the European ERA5 reanalysis data, ground-based automatic stations, sounding, Doppler weather radar, and wind profile radar, this paper analyzed in detail the circulation characteristics and radar echo structures of this torrential rain event, and further studied the triggering, feature evolution, and maintenance mechanisms of the heavy precipitation.The results show: (1) The continuously strengthening low-level jet stream propagates downward forming an ultra-low-level jet stream. Heavy precipitation is located on the left front side of the jet exit area, where strong convergence and lifting in the boundary layer overlap with the ascending branch of the secondary circulation above the shear line, resulting in heavy precipitation. (2) A supercell storm with intense precipitation produced this heavy rainfall event, characterized by a low centroid of strong echo; echo-free vault, bounded weak echo region, and other supercell structural features. During this event, the high reflectivity factor areas corresponded to high KDP value areas, indicating a large amount of liquid water content. Vertically, the KDP column can track the rainwater mixing ratio well, and its location corresponds to the vicinity of the short-term heavy precipitation occurrence. The high KDP value area mainly consists of numerous precipitation particles, and the vertical vortex wind field structure causes rapid growth of precipitation particles through continuous rolling. Furthermore, it can be found through correlation coefficients (CC) that there are always high-value areas for CC since they correspond to areas of intense precipitation. (3) Continuous triggering of mesoscale convective cloud clusters on the east-west ground convergence line leads to new development and forms a "train effect." Strong frontogenesis in both horizontal and vertical directions triggers and sustains this heavy precipitation event. The significant increase in liquid water and water vapor density has indicative significance for the occurrence of heavy precipitation.

The research aims to resolve water pollution generated by dye wastewater. In this paper, the aminated sodium lignosulfonate (AELS) was prepared through the Mannich reaction, and the synthesis conditions were optimized using orthogonal experiments to determine the best amination conditions. The successful preparation of AELS was demonstrated through the use of FT-IR, XRD, TG, SEM and XPS characterization techniques. Then, the adsorption capacity of Methyl blue (MB) by AELS was compared in a range of different circumstances, like pH, adsorbent dose, initial concentration, time, and temperature. Additionally, the kinetics, isotherms, and thermodynamic properties of the AELS adsorption for MB were studied and discussed. The results indicated that the fitting results were consistent with the pseudo-second-order kinetic model and Langmuir isotherm model, in accordance with the mechanism of single-molecular-layer chemisorption. And the adsorption of MB by AELS was a spontaneous and endothermic process. Upon completion of five adsorption experiments, the adsorption capacity of MB by AELS was determined to be 107.95 mg g-1, which was 5.3% lower than the first experiment. This research promotes the effective use of natural resources and allows for the sustainable use of lignin and the advancement of new applications for it.

Currently, urban renewal activities in China are booming. And promoting the renovation of public buildings is the key due to its large scale, high cost and significant impact to the nature and social environment. To reduce the ambiguity and uncertainty in evaluating the potential for renewal of existing public buildings, a renewal potential evaluation model combining game theory combination weighting method and cloud model theory is proposed. The paper constructs a comprehensive evaluation index system based on relevant standards and literature. Game theory is used to optimize the weights obtained by AHP and entropy weight methods to obtain a combined weight. MATLAB programming is used to calculate the comprehensive cloud parameters of the evaluation index for the potential renewal of existing public buildings and therefore generate cloud Graphs. Through case study in Nanjing of China, it has been demonstrated that the combination empowerment cloud model can objectively reflect the relationship between the fuzziness and randomness of evaluation indicators for public building renewal potential. The expression of cloud Graphs can intuitively reflect the magnitude of renewal and renovation potential and the degree of uncertainty in evaluation results. The research result provides useful references for the sustainable utilization of building resources in the era of building.

Cellulose-based carbon (CBC) has been widely concerned with its porous structure, high specific surface area, and is liable to adsorb gas molecules and macromolecular pollutants. However, the application of CBC in gas sensing has been little studied. In this paper, ZnO/CBC heterojunction was formed by means of simple co-precipitation and high temperature carbonization. As a new ammonia sensor, the prepared ZnO/CBC sensor can detect ammonia compared with that of the previous pure ZnO ammonia sensor can not at room temperature. It has great gas sensing response, stability and selectivity to ammonia concentration at 200 ppm. This study provides a new idea for the design and synthesis of biomass carbon-metal oxide composites.

Flavin-dependent monooxygenases (FMOs) have made substantial influence as catalysts for monooxygenation reactions, impacting diverse field such as drug metabolism, environmental studies, and natural product synthesis. Their application in biocatalysis boasts several advantages over conventional chemical catalysis, such as heightened selectivity, safety, sustainability, and eco-friendliness. In the realm of biomedicine, FMOs are pivotal in antibiotic research, modulating natural products, antimicrobial agents, and pathways involved in drug synthesis. They're also underscored as potential pharmaceutical targets, pivotal in disease progression and viable for therapeutic intervention. Additionally, FMOs play a substantial role in environmental science, especially in pesticide processing and plant vitality. Their involvement in biosynthesizing compounds like polyethers, tropolones, and ω-hydroxy fatty acids, with remarkable regio- and stereoselectivity, renders them indispensable in drug discovery and development. As our comprehension of FMOs' catalytic mechanisms and structures advances, coupled with cutting-edge biotechnologies like computational design and directed evolution, FMOs are poised to occupy an increasingly significant role in both scientific exploration and industrial applications.

Decades of research identified numerous gene biomarkers of cardiac diseases whose restored sequence or/and expression level was hoped to recover the normal cardiac function. However, each human has unique and dynamic pathophysiological characteristics resulting from the unrepeatable combination of favoring factors such are: race, sex, age, medical history, diet, stress, exposure to toxins, habits etc. As such, no treatment fits everybody and finding personalized solutions is a top priority for medicine of 21st century. The Genomic Fabric Paradigm (GFP) provides the most theoretically possible comprehensive characterization of the transcriptome, its alterations in disease and recovery following a treatment. By attaching to each gene the independent average expression level, expression variation and expression coordination with each other gene, GFP delivers thousands times more information than the traditional analysis. This report presents the theoretical bases of the GFP and some applications to our microarray data from mouse models of post ischemic, and constant and intermittent hypoxia-induced heart failure. The GFP analyses revealed novel transcriptomic aspects of the gene expression control and networking under ischemic conditions. Through all-inclusive characterization of the transcriptome and the unrepeatable gene hierarchy in each condition, GFP is an essential avenue towards development of a truly personalized cardiogenomic therapy.

Previous studies have revealed the microbial metabolism of dietary choline in the gut, leading to its conversion into trimethylamine (TMA), a precursor of the cardiovascular inflammatory marker trimethylamine N-oxide (TMAO). Polymethoxyflavones (PMFs), exemplified by tangeretin, demonstrate efficacy in mitigating TMAO-induced cardiovascular inflammation. However, the specific mechanism by which these compounds exert their effects, particularly in modulating the gut microbiota, remains uncertain. This investigation focused on tangeretin, a representative PMFs, exploring its influence on the gut microbiota and the choline-TMA conversion process. Experimental results showed that tangeretin treatment attenuated the population of CutC-active bacteria, specifically Clostridiaceae and Lactobacillus, induced by choline chloride in rat models. This inhibition resulted in reduced efficiency in choline conversion to TMA, thereby ameliorating cardiovascular inflammation resulting from prolonged choline consumption. In conclusion, tangeretin's preventive action against cardiovascular inflammation is intricately linked to its targeted modulation of TMA-producing bacterial activity.

Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships. The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

This study primarily investigates the effect of fly ash (FA) content on the mechanical properties and hydration performance of alkali-activated ground granulated blast furnace slag cements (AAGC), and compares the related properties with ordinary Portland cement (OPC). Additionally, the study examined the hydration products, thermal analysis, MIP, SEM, chemically bound water, and pH values of AAGC. The compressive strength of AAGC showed a retrogression phenomenon from 3 to 28 days, with the 14-day and 28-day compressive strengths of AAGC being higher than those of OPC. The AAGC with 20% FA content exhibits the highest 28-day compressive strength (75 MPa). The hydration heat release rate curved of OPC and AAGC can be divided into the initial induction period, induction period, acceleration period, deceleration period and steady period. As FA content increases, the 28-day pore volume of AAGC increased while pH values and chemically bound water decrease. SEM images of AAGC with low FA content showed more microcracks.

The AREB/ABF (ABA response element binding) proteins in plants are vital for plant stress responses, but the understanding of AREB/ABFs in orchid species, an important traditional medicinal and ornamental plants, is limited. Here, twelve AREB/ABF genes were identified within three orchids’ complete genome and classified into three groups (groups I, II, and III) via a phylogenetic analysis, which was further supported by the analysis of their conserved motifs and gene structures. The cis-elements analysis showed that hormone response elements followed by light and stress responses were widely rich in AREB/ABFs. A prediction analysis of orchid ABRE/ABF-mediated regulatory network was further constructed through cis-regulatory elements (CREs) analysis of their promoter regions and revealed that several transcriptional factor (TF) gene family were abundant as the potential regulators for orchid AREB/ABFs. Expression profile analysis based on public transcriptomic data showed that most AREB/ABF genes have distinct tissue-specific expression patterns in orchid plants. Additionally, DcaABI5 as the homolog of ABA INSENSITIVE 5 (ABI5) from Arabidopsis was selected for further analysis. The results showed that Arabidopsis transgenic overexpressing DcaABI5 could rescue the ABA insensitive phenotype in the mutant abi5. Collectively, these findings will provide valuable information on AREB/ABF genes in orchids.

The basic idea is to expand the completed zeta function $\xi(s)$ in MacLaurin series (infinite polynomial), which can be further expressed as infinite product (Hadamard product) by conjugate complex roots. Finally, the functional equation $\xi(s)=\xi(1-s)$ leads to $(s-\alpha_i)^2 = (1-s-\alpha_i)^2, i \in \mathbb{N}$ with solution $\alpha_i= \frac{1}{2}, i \in \mathbb{N}$, where $\alpha_i$ are the real parts of the zeros of $\xi(s)$, i.e., $s_i =\alpha_i\pm j\beta_i, i\in \mathbb{N}$. Therefore, a proof of the Riemann Hypothesis is achieved.

The expansion of concrete subjected to extreme elevated temperature is linked with intricate micro-structural variations, such as the transformation of the constituent phases. This study proposes a model to predict the thermal expansion of cement paste and concrete considering micro-structural changes under elevated temperatures ranging from 20°C to 800°C. The model presented can consider characteristics of various aggregates in the calculation of thermal expansion for concrete. The model is a combination of a multi-scale stoichiometric model and a multi-scale composite model. At the cement paste level, the model satisfactorily predicted a test result. At concrete level, upper bounds from the model were matched relatively well with test results by previous researcher. If the mechanical properties, such as elastic modulus (E), Poisson’s ratio (ν), and thermal deformation, of the aggregates used in concrete are given, it is likely that the model will reasonably predict experimental results.

From the mid-19th century, Collegiate Gothic architecture gained prominence among American universities. Subsequently, through the efforts of American missionaries, the Collegiate Gothic style spread to Christian universities in Asia. Specifically, this study concentrates on the nuanced relationship between the transnational missionary routes and adopting the Collegiate Gothic style, primarily focusing on Anderson Hall at Soochow University, China. The investigation meticulously dissects the multifaceted factors influencing the choice of architectural styles, considering the perspectives of missionaries, architects, and other key stakeholders. Through an intricate analysis of Anderson Hall, this research illuminates the unique characteristics of Collegiate Gothic architecture. By unraveling the complex layers of East Asian campus heritage, this study enriches our comprehension of its origins. It establishes a robust foundation for guiding the preservation and revitalization of architectural styles within university campuses. This scholarly inquiry holds particular significance for experts in academic heritage conservation.

Factory safety inspections are crucial for ensuring safe production. However, manual inspections present issues such as low efficiency and high workload. Inspection robots provide an efficient and reliable solution for completing patrol tasks. The development of robot localization and path planning technologies provides guarantees for factory inspection robots to autonomously complete inspection tasks in complex environments. This paper studies mapping and localization, as well as path planning methods for robots in order to meet the application requirements of factory inspections. Two SLAM application systems based on multiple-line laser radar and vision are designed for different scenarios in consideration of the limitations of cameras and laser sensors in terms of their own characteristics and applicability in different environments. To address the issue of low efficiency in inspection tasks, a hybrid path planning algorithm that integrates the A-star algorithm and time elasticity band algorithm is proposed. This algorithm effectively solves the problem of path planning in complex environments that is prone to falling into local optimal solutions, thereby improving the inspection efficiency of robots. Experimental tests show that the designed SLAM and path planning methods can meet the needs of robot inspection in complex scenes and have good reliability and stability. The code used in this article is open source and can be accessed at https://github.com/Mxiii99/RSPP_CS.git.

To address three important issues related to extraction of water features from Landsat imagery, i.e., selection of water indexes and classification algorithms for image classification, collection of ground truth data for accuracy assessment, this study applied four sets (ultra-blue, blue, green, and red light based) of water indexes (NWDI, MNDWI, MNDWI2, AWEI_ns, and AWEI_s) combined with three types of image classification methods (zero-water index threshold, Otsu, and kNN) to 24 selected lakes across the globe to extract water features from Landsat-8 OLI imagery. 1440 (4x5x3x24) image classification results were compared with the extracted water features from high resolution Google Earth images with the same (or ±1 day) acquisition dates through computing the Kappa coefficients. Results show the kNN method is better than the Otsu method, and the Otsu method is better than the zero-water index threshold method. If the computational cost is not an issue, the kNN method combined with the ultra-blue light based AWEI_ns is the best method for extracting water features from Landsat imagery because it produced the highest Kappa coefficients. If the computational cost is taken into account, the Otsu method is a good choice. AWEI_ns and AWEI_s are better than NDWI, MNDWI and MNDWI2. AWEI_ns works better than AWEI_s under the Otsu method, and the average rank of the image classification accuracy from high to low is the ultra-blue, blue, green, and red light-based AWEI_ns.

Tourism activities generally have a ∩-type lock on the level of tourism ecological security in an area, but when applied to the border areas of China, there are certain specificities in the spatial evolution of tourism ecological security (TES) compared to traditional findings. This paper measures tourism ecological security in China’s border areas from 2009 to 2020 by using the DPSIR model with the superefficient SBM-DEA and analyzes the spatial differences, evolutionary characteristics, and driving factors of tourism ecological security in border areas by using Pearson’s correlation coefficients, center of gravity models, and geographic probes: (1) The overall tourism ecological security index of China’s border provinces is relatively good. Tourism activities do not completely affect the traditional “∩ lock” of the border provinces. The tourism ecological security level of the border provinces presents three spatial-temporal changes (“∩” type, “U” type, “\” type) and four evolution trends (“high–high–high”, “middle–middle–medium”, “medium–low–low”, and “low–low–low”). (2) The overall tourism ecological security level in border areas is polarized between high and low levels, and the ecological security efficiency of the three large areas is spatially characterized as “Southwest Area &gt; Northeast Area &gt; Northwest Area”, and the center of gravity of ecological security is mostly concentrated in Xinjiang, Tibet, and Neimenggu, where the ecological security level is higher. (3) Social and environmental factors are the main factors that influence tourism ecological security in border areas, while economic factors account for a smaller proportion. Accordingly, this thesis also proposes the driving mechanism of the ecological security of tourism sites in border areas in China with a view to providing theoretical support for policy formulation.

This paper uses the $\exp(-\Phi(\xi))$-Expansion method to investigate solitons to the M-fractional nonlinear Schrödingers equation with cubic nonlinearity. The results obtained are dark solitons, trigonometric function solutions, hyperbolic solutions and rational solutions. Thus, the constraint relations between the model coefficients and the traveling wave frequency coefficient for the existence of solitons solutions are also derived.

The temperature-dependent energy storage properties of four tungsten bronze phase compounds are studied together with an investigation of their structure and temperature-dependent permittivity response, i.e., Ba6Ti2Nb8O30 (BTN), Ba6Zr2Nb8O30 (BZN), Sr3TiNb4O15 (STN) and Sr3ZrNb4O15 (SZN) ceramics. It was found that BZN has smaller grains and a more porous structure than BTN. SZN shows no clear grain boundaries with the most porous structure among all samples, exhibiting a much lower permittivity response than other samples with no signs of phase transitions from room temperature to 400 °C. Though the energy storage response of those samples is generally quite low, it exhibits rather good temperature stability. It was suggested that by obtaining a denser structure through chemical modification or other methods, those tungsten bronze ceramics with good temperature stability could be promising as energy storage devices when improved energy storage properties are achieved.

Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Brain-stimulation reward, also known as intracranial self-stimulation (ICSS), is a commonly used procedure for studying brain reward function and drug reward. In electrical ICSS, an electrode is surgically implanted into the medial forebrain bundle in the lateral hypothalamus or the ventral tegmental area in the midbrain. Operant lever responding leads to the delivery of electrical pulse stimulation. The alteration in the stimulation frequency-lever response curve is used to evaluate the impact of pharmacological agents on brain reward function. If a test drug induces a leftward or upward shift in the ICSS response curve, it implies a reward-enhancing or abuse-like effect. Conversely, if a drug causes a rightward or downward shift in the functional response curve, it suggests a reward-attenuating or aversive effect. A significant drawback of electrical ICSS is the lack of cellular selectivity in understanding the neural substrates underlying drug reward versus aversion. Excitingly, recent advancements in optical ICSS (oICSS) have facilitated the development of at least three cell type-specific oICSS models – dopamine-, glutamate-, and GABA-dependent oICSS. In these new models, a comparable frequency-rate response curve has been established and employed to study the substrate-specific mechanisms underlying brain reward function and a drug's rewarding versus aversive effects. In this review article, we summarize recent progress in this exciting research area. The findings in these new behavioral models have not only increased our understanding of the neural mechanisms underlying drug reward and addiction but have also introduced a novel experimental approach in preclinical medication development for treating substance use disorders.

As of not long ago, understanding the administrative conduct of cells has been sought after through autonomous investigation of the transcriptome or the proteome. In view of the focal creed, it was commonly accepted that there exist an immediate correspondence between mRNA records and produced protein articulations. In any case, late examinations have demonstrated that the relationship among's mRNA and Protein articulations can be low because of different factors, for example, unique half lives and post record apparatus. In this manner, a joint investigation of the transcriptomic and proteomic information can give valuable experiences that may not be translated from singular examination of mRNA or protein articulations. This article audits the current significant methodologies for joint investigation of transcriptomic and proteomic information. We order the various methodologies into eight primary classes dependent on the underlying calculation and last investigation objective. We further present analogies with different spaces and talk about the current exploration issues around there.

Numerous researchers acknowledge that occupational pension protects employees. However, the total occupational pension is shared by employees and employers in China, which is a cost. This study aimed to explore the occupational pension on corporate social responsibility (CSR) and organizational resilience. We drew upon insights from cost-stickiness and resource-based theories to develop a model that illuminates how occupational pension affected firms’ stance toward CSR in the context of the COVID-19 pandemic and how this, in turn, has result organizational resilience. This study categorized CSR into strategic and responsive activities based on cost stickiness. We selected a total of 34,145 observations of Chinese listed companies over the period 2010-2023 as a sample to investigate whether occupational pension changes CSR strategies. The results showed that the cost pressure to contribute to occupational pension drove firms to reduce their responsive CSR and boost their strategic CSR. Moreover, strategic CSR improved organizational resilience, but responsive CSR did not. This relationship between occupational pension and CSR was significantly and negatively moderated by the minimum wage and population aging. Whereas, the relationship between CSR and organizational resilience was significantly and positively moderated by digital transformation and marketing capabilities. This study has implications for decision-making regarding high-quality firm development.

A fluorescent probe, N'-((3-methyl-5-oxo-1-phenyl-4, 5-dihydro-1H-pyrazol-4-yl) methylene)-2-oxo-2H-chromene-3-carbohydrazide (MPMC), was synthesized and characterized. Characterizations of the synthetic MPMC were conducted by proton nuclear magnetic resonance (1HNMR) spectroscopy and carbon-13 nuclear magnetic resonance spectroscopy (13C NMR). The fluorescence emission behaviors of probe MPMC towards diverse metal ions were detected and the probe exhibited high sensitivity and selectivity towards Cu2+ over other metal ions via quenching of its fluorescence. Furthermore, the existence of other metal actions made no difference on the fluorescence intensity of the MPMC-Cu2+ system apparently, that is, MPMC displayed a good anti-interference ability. Job’s plot of MPMC and copper ions indicated the detection limit was 15.16 nM (R2=0.9612) for the assayed actions, with a stoichiometric ratio of 1:1 for MPMC and Cu2+. Additionally, the color of MPMC probe solution changed from nearly colorless to yellow in the presence of Cu2+ in visible light, which the color change could be observed by the naked eye. Similarly, the color resolved bright yellow into blue in ultraviolet light. Also reusability studies indicated that MPMC probe was reusable. The pH effect of probe MPMC to Cu2+ had a broad range of pH detection 4.0 to 11.0. The response time of MPMC probe for determining Cu2+ was within 1 min. The recognition of Cu2+ by MPMC performed on pre-treated paper under sunlight and UV light both had a distinct colour change. Thus, the solid-state method for detecting Cu2+ with the naked-eye, was both economical and convenient.

In this study, cement is used as the component that provides the stabilizing effect in order to evaluate the hardness and stability of loess soil. To assess the strength characteristics of loess soil reinforced with cement, samples were created with four different cement contents and three different curing times. The materials were put through a series of tests to determine their flexural strength, direct shear strength, indirect tensile strength, and unconfined compressive strength. An appropriate cement dosage was found, in addition to a durability index that could be used to quantify the effect of water absorption investigations on cement-stabilized loess. Both of these discoveries were made simultaneously. Analyses of investigations such as scanning electron microscopy (SEM) and energy dispersive X-ray fluorescence spectrometry (XRF) examinations were carried out so that the fundamental mechanics of the materials could be comprehended. According to the findings, the cohesion of cement-stabilized loess is significantly more sensitive to structure than the friction angle of the material, and the cohesion is responsible for the increase in shear strength after remolding. To get the desired level of strength, it is necessary to adjust the cement's proportions. In addition, as the curing period progresses, we see an increase in the cement-stabilized loess's resistance and stiffness. This is because of the interactions that take place between the structure and the mineral composition. It is believed that this event was caused by the cementation that occurs naturally. As a consequence of this reaction, the production of new cementitious materials takes place. The cation exchange that causes the hydration and pozzolanic reaction that leads to the creation of aggregates and interparticle flocculation is responsible for their production. These findings suggest that cement may be utilized as a simple and effective method of loess stabilization, which will ultimately result in improved performance of the loess.

The robust control of high precision electromechanical systems, such as micropositioners, is challenging in terms of the inherent high nonlinearity, the sensitivity to external interference, and the complexity of accurate identification of the model parameters. To cope with these problems, this work investigates a disturbance observer-based deep reinforcement learning control strategy to realize high robustness and precise tracking performance. Reinforcement learning has shown great potential as optimal control scheme, however, its application in micropositioning systems is still rare. Therefore, embedded with the integral differential compensator (ID), deep deterministic policy gradient (DDPG) is utilized in this work with the ability to not only decrease the state error but also improves the transient response speed. In addition, an adaptive sliding mode disturbance observer (ASMDO) is proposed to further eliminate the collective effect caused by the lumped disturbances. The sterling performance is revealed with intensive tracking simulation experiments and demonstrates the improvement in the accuracy and response time of the controller.

In order to make full use of the complementarity of existing navigation systems to improve the navigation and positioning accuracy, this paper investigates the visual/INS integrated navigation system based on the factor graph optimization (FGO) algorithm. In the traditional visual/INS integrated navigation system, there is often the problem of image blurring due to the rapid change of light and shadow captured by the vision sensor and the attitude change of the carrier, which leads to the decrease of the positioning accuracy of the combined navigation system. Therefore, this paper firstly introduces Retinex algorithm and BID algorithm to carry out the design of image de-blurring method; then implements the visual/INS integrated navigation algorithm based on the factor graph optimization method; finally, the experimental scheme is designed, comparative experiments are carried out, and the researched method is verified by using the public dataset. The experimental results show that the combined navigation algorithm realized in this paper has high accuracy, and its positioning accuracy is improved by 40% compared with visual positioning.

A large body of research has examined people’s attitudes toward mandatory vaccination to cope with the COVID-19 pandemic. However, studies have yet to explore how Chinese people view the COVID-19 vaccine mandates, let alone placing such views in the country’s highly politicized context. On the other hand, facing the high pressure of loosening its zero-COVID policies amidst the worldwide practices of co-existing with the COVID-19 virus, the country has to improve its vaccination coverage quickly. In this situation, it is more than necessary to systematically investigate the Chinese public’s attitude to mandatory vaccination against the virus and the underlying determinants. The current study utilizes data from a national survey adopting quota sampling to analyze the Chinese public’s medical and non-medical considerations when judging compulsory COVID-19 vaccination. The study reveals that thanks to China’s successful lockdown policies, personal risk, and benefit perceptions did not dominate their views regarding vaccination mandates. Instead, conspiracy beliefs regarding the pandemic outbreaks, conspiratorial thinking, and nationalism more strongly predicted their willingness to accept mandatory vaccination. Given the potentially massive impacts of the COVID-19 infection, if China opens its door and loosens its strict quarantine restrictions, the Chinese public needs to be educated with more medically valuable and relevant information to improve their vaccination protection against COVID-19.

Biological neural network (BNN) is the core brain for creature to accomplish its intelligent behaviors through unique network structure and mechanisms. It also inspires controlling human-designed autonomous agents including robots to generate more advanced intelligent behaviors by mimicking the neural control mechanism of creatures at a deeper and more interactive level. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of cellular synapse. The neural network simulation integrates computational programming and visualization of neurons and synapse connections of C. elegans, containing the specific controllable circuits summarization with their dynamic characteristics within the whole network. To illustrate its particular intelligent control capability in terms of robotics, we introduce the first innovative methodology for applying the BNN model to a 12-legged robot’s movement control based on the established numerical simulation platforms. We accomplished and designed two methods and corresponding encoding processes, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of BNN. Both simulation and experiment results indicate that the robot exhibits stronger autonomy and more intelligent moving performance under the control of BNN. We then summarized the contributions for digitalizing the C. elegans’ whole-brain neural network in real-time and utilizing it to control robot in a closed loop to validate its advanced intelligence control ability in terms of a scientific robot.

Negative corona discharges occur widely in high voltage transmission lines and charged aircraft, which can cause strong electromagnetic interference. Negative corona discharge is typically performed simultaneously at multiple discharge points. In this study, the current and its EM radiation characteristics of single-needle and multi-needle negative corona discharge in different conditions were tested. The current and electromagnetic radiation characteristics of the two discharge structures were compared. The dipole radiation model was established to analyze the EM radiation characteristics of the negative corona discharge.The results show that, It is only when the voltage reaches a certain threshold that the current and electromagnetic radiation fields of the multi-needle discharge structure will be superimposed and their amplitudes will increase significantly. The frequency of electromagnetic radiation signal does not change with the number of needles, cathode geometry and applied voltage, but only depends on ambient pressure. It provides a basis for detecting corona discharge sources under different conditions.

The role of fractional calculus in circuit systems has received increased attention in recent years. In order to evaluate the effectiveness of time-domain calculation methods in the analysis of fractional-order piecewise smooth circuit systems, an experimental prototype is developed and the effects of three typical calculation methods in different test scenarios are compared and studied in this paper. It is proved that Oustaloup’s rational approximation method usually overestimates the peak-to-peak current and brings in pulse-voltage phenomenon in piecewise smooth test scenarios, while the results of two iterative recurrence-form numerical methods are in good agreement with the experimental results. The study results are dedicated to provide a reference for efficiently deploying calculation methods in fractional-order piecewise smooth circuit systems. Some quantitative analysis results are concluded in this paper.

This study aims to put forth a new method to study the socio-spatial boundaries by using georeferenced community-authored reviews for restaurants. In this study, we show that food choice, drink choice, and restaurant ambience can be good indicators of socio-economic status of the ambient population in different neighborhoods. To this end, we use Yelp user reviews to distinguish different neighborhoods in terms of their food purchases and identify resultant boundaries in 10 North American metropolitan areas. This data-set includes restaurant reviews as well as a limited number of user check-ins and rating in those cities. We use Natural Language Processing (NLP) techniques to select a set of potential features pertaining to food, drink and ambience from Yelp user comments for each geolocated restaurant. We then select those features which determine one’s choice of restaurant and the rating that he/she provides for that restaurant. After identifying these features, we identify neighborhoods where similar taste is practiced. We show that neighborhoods identified through our method show statistically significant differences based on demographic factors such as income, racial composition, and education. We suggest that this method helps urban planners to understand the social dynamics of contemporary cities in absence of information on service-oriented cultural characteristics of urban communities.

Focused microwave breast hyperthermia (FMBH) employs a phased antenna array to perform beamforming that can focus microwave energy at targeted breast tumors. Selective heating of the tumor endows the hyperthermia treatment with high accuracy and low side effects. The effect of FMBH is highly dependent on the applied phased antenna array. This work investigates the effect of polarizations of antenna elements on the microwave focusing results by simulations. We explore two kinds of antenna arrays with the same number of elements using different digital realistic human breast phantoms. The first array has all the elements’ polarization in the vertical plane of the breast while the second array has half elements’ polarization in the vertical plane and the other half in the transverse plane, i.e., cross polarization. In total 96 sets of different simulations are performed, and the results show that the second array leads to a better focusing effect in dense breasts than the first array. This work is very meaningful for the potential improvement of the antenna array for FMBH, which is of great significance for the future clinical applications of FMBH. The antenna array with cross polarization can also be applied in micro-wave imaging and sensing for biomedical applications.

In this paper, the fixed-time stabilization problem for a class of uncertain chained system is addressed by using a novel nonsingular recursive terminal sliding mode control approach. A fixed-time controller and an adaptive law are designed to guarantee the uncertain chained form system both Lyapunov stable and fixed-time convergent within the settling time. The advantage of the controller based on the sliding mode is that the settling time does not depend on the system initial state. Furthermore, we use RBF neural network to estimate the uncertainty of the system. Finally, the simulation results demonstrate the performance of the control laws.

The paper focuses on the admissibility problem of descriptor fractional-order systems (DFOSs). The alternate admissibility criteria are addressed for DFOSs with order in (0,2) which involve a non-strict linear matrix inequality (LMI) method and a strict LMI method, respectively. The forms of non-strict and strict LMIs are brand-new and distinguished with the existing literature, which fill the gaps of studies for admissibility. These approaches are available to the order in (0,2) without separating the order ranges into (0,1) and [1,2). In addition, a method involved least real decision variables in terms of strict LMIs is derived which is more convenient to process the practical solution. Three numerical examples are given to illustrate the validity of proposed results.

Copper-catalyzed and organocopper-involved reactions are of great significance in organic synthesis. To have a deep understanding of the reaction mechanisms, the structural characterizations of organocopper intermediates become indispensable. Meanwhile, the structure-function relationship of organocopper compounds would advance rational design and development of new Cu-based reactions and organocopper reagents. Compared to the mono-carbonic ligand, the C,N- and C,C-bidentate ligands better stabilize the unstable organocopper compounds. The bidentate ligands can chelate to the same copper atom via ?2-mode, forming a mono-cupra-cyclic compounds with at least one acute C-Cu-C angle. When the bidentate ligands bind to two copper atoms via ?1-mode at each coordinating site, the bimetallic macrocyclic compounds will form nearly linear C-Cu-C angles. The anionic coordinating sites of the bidentate ligand can also bridge two metals via ?2-mode, forming organocopper aggregates with Cu-Cu interactions and organocuprates with contact ion pair structures. The reaction chemistry of some selected organocopper compounds is highlighted, showing their unique structure-reactivity relationships.

This study reports rapid hydrolysis, acylation, and lipolysis with lipase B from Candida antarctica (CalB) and nanofructosome encapsulated CalB (CalB@NF) enzymes immobilized on silica-coated magnetic nanoparticles (Si-MNPs). Enzymatic hydrolysis was confirmed for conversion to p-nitrophenol (p-NP, λex=400 nm) from p-nitrophenyl butyrate (p-NPB, λex=270 nm), and various catalytic parameters such as Km, Vmax, and Kcat were calculated from Michaelis-Menten and Lineweaver-Burk plot. Efficiency of enzymatic hydrolysis was in the order of CalB > Si-MNPs@CalB > CalB@NF > Si-MNPs@CalB@NF, and reusability was much better with the enzyme immobilized on Si-MNPs than with the native enzyme. Enzymatic acylation was confirmed by the formation of benzyl benzoate (λex=229 nm) from benzoic anhydride (λex=240 nm), and efficiency exceeded 99% in all samples. Finally, enzymatic lipolysis was confirmed by the decomposition of olive oil to fatty acid such as oleic acid (λex=230 nm) using thin-layer chromatography (TLC), column chromatography, and proton nuclear magnetic resonance (1H -NMR). The lipases immobilized on Si-MNPs have the advantage of good thermal stability and reusability and are thus applicable to a wide range of industries.

This paper presents a new semi-explicit algorithm for parameters estimation in a time-fractional generalization of dual-phase-lag heat conduction model with the Caputo fractional derivatives. It is shown that this model can be derived from a general linear constitutive relation for the heat transfer by conduction when the heat conduction relaxation kernel contains the Mittag-Leffler function. The model can be used to describe heat conduction phenomena in a material with power-law memory. The proposed algorithm of parameters estimation is based on the time integral characteristics method. The explicit representations of the thermal diffusivity and the fractional analogues of the thermal relaxation time and the thermal retardation are obtained via a Laplace transform of the temperature field and utilized in the algorithm. An implicit relation is derived for the order of fractional differentiation. In the algorithm, this relation is resolved numerically. An example illustrates the proposed technique.

Zearalenone is one of the most prevalent estrogenic mycotoxins produced mainly by Fusarium family fungi, and harmed the heath of animals. Zearalenone hydrolase is an important enzyme capable of degrading zearalenone into a non-toxic compound. Although previous research has investigated the catalytic mechanism of zearalenone hydrolase, information on its dynamic interaction with zearalenone remains unknown. This study aimed to develop a pipeline for identifying the allosteric pathway of zearalenone hydrolase. Using an identity analysis, we identified hub genes whose sequences can generalize a set of sequences in a protein family. We then utilized a neural relational inference (NRI) model to identify the allosteric pathway of the protein throughout the entire molecular dynamics simulation. The production run lasted 1 microsecond, and we analyzed residues 139-222 for the allosteric pathway using the NRI model. We found that the cap domain of the protein opened up during catalysis, resembling a hemostatic tape. We used umbrella sampling to simulate the dynamic docking phase of the ligand-protein complex and found that the protein took on a square sandwich shape. Our energy analysis using both MMPBSA and PMF analysis showed discrepancies, with scores of -8.45 kcal/mol and -1.95 kcal/mol, respectively. MMPBSA, however, obtained a similar score to that of a previous report.

Rapid urbanization has dramatically spurred the economic development over the past three decades, especially in China, but has nevertheless had negative impacts on natural resources since it is an irreversible process. Thus, it is essential to timely monitor and quantitatively analysis the changes in land use over time and to identify the landscape pattern variation related to growth mode in different period. This study aims at inspecting spatiotemporal characteristics of landscape pattern respond to land use changes in Xuzhou city during the period from 1985 to 2015. In this connection, we proposed a new spectral index, named the Normalized Difference Enhanced Urban Index (NDEUI), which combines data from NTL (Nighttime light) from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) with annual maximum Enhanced Vegetation Index (EVI) to reduce the detection confusion between urban areas and barren land, as well as follows. NDEUI-assisted Random Forests algorithm was implemented to obtain the land use/land cover (LULC) maps of Xuzhou in 1985, 1995, 2005 and 2015, respectively. Here, four different periods viz. 1985–1995, 1995–2005, 2005–2015 and 1985–2015 are chosen for the change analysis of land use and landscape pattern. The results indicated that the urban area has increased by about 30.65%, 10.54%, 68.77%, and 143.75% during the four periods mentioned above at the main expense of agricultural land, respectively. The spatial trend maps revealed that continuous transition from other land use types into urban land has appeared a dual-core development mode throughout the urbanization process, located at the new city region and the Jiawang district, mainly affected by the construction of new city region, freeway and the high railway station. Furthermore, we quantified the patch complexity, aggregation, connectivity and diversity of landscape employing a number of landscape metrics to represent the changes of landscape pattern at both class and landscape level, affected by urbanization during the study period. The results showed that with regard to the four aspects of landscape pattern, there were considerable differences among the four years, mainly owing to the increasing dominance of urbanized land. Spatiotemporal variation of landscape pattern was also conducted on the basis of subgrids in 900 m × 900 m. Combined with the land use changes and spatiotemporal variation of landscape pattern, it can be concluded that different urbanization modes and intensity result in variously the spatiotemporal evolution of landscape patterns. For Xuzhou city, the urban growth mainly appeared a leapfrog mode alone both sides of the roads during the period of 1985 to 1995, and then shifted into edge-expansion mode during the period from 1995 to 2005, whereas the edge-expansion and leapfrog modes coexisted for the period from 2005 to 2015. The high valuable spatiotemporal information generated utilizing RS and GIS in this study may give assistance to urban planners and policymakers to well understand urban dynamics and evaluate their spatiotemporal and environmental impacts at a local level for the sake of sustainable urban planning in the future.

Although mask-wearing is now widespread, knowledge of how to quantify or improve its performance remains surprisingly limited and largely based on empirical evidence. The objective of this study was to visualize the expiratory airflows from facemasks and evaluate aerosol transmission between two persons. Different visualization methods were explored, including the Schlieren optical system, laser/LED-particle imaging system, thermal camera, and vapor-SarGel system. The leakage flows and escaped aerosols were quantified using a hotwire anemometer and a particle counter, respectively. The results show that mask-wearing reduces the exhaled flow velocity from 2~4 m/s (with no facemask) to around 0.1 m/s, thus decreasing droplet transmission speeds. Cloth, surgical, and KN95 masks showed varying leakage flows at the nose top, sides, and chin. The leakage rate also differed between inhalation and exhalation. The neck gaiter has low filtration efficiency and high leakage fractions, providing low protection efficiency. There was considerable deposition in the mouth-nose area, as well as the neck, chin, and jaw, which heightened the risk of self-inoculation through spontaneous face-touching. A face shield plus surgical mask greatly reduced droplets on the head, neck, and face, indicating that double face coverings can be highly effective when a single mask is insufficient. The vapor-SarGel system provided a practical approach to study interpersonal transmission under varying close contact scenarios or with different face coverings.

Our results demonstrated that SWT stimulates osteoblastogenesis and increase the cell number of osteoblasts, suggesting positive regulatory effects on osteoblast differentiation. However, the effect of SWT on bone formation need further examination, since the expression of alp gene is downregulated. On the other hand, SWT appears to negatively regulate osteoclast differentiation and bone resorption, as number of osteoclasts and the expression of critical bone resorptive genes are all downregulated. Collectively, SWT might change the equilibrium of bone remodeling and increase the bone mass. Our data provided supportive evidences that this medicine formulation can be applied to treat osteoporosis or bone destructive diseases.

With prosperous development and successful application of deep learning technologies in the field of remote sensing, numerous deep-learning based methods have emerged for synthetic aperture radar (SAR) automatic target recognition (ATR) tasks over the past few years. Generally, most deep-learning based methods can achieve outstanding recognition performance in condition that an abundant of labeled samples are available to train the model. However, in real application scenarios, it is difficult and costly to acquire and to annotate abundant SAR images due to the imaging mechanism of SAR, which poses a big challenge to existing SAR ATR methods. Thereby, SAR target recognition under the situation of few-shot, where only a scarcely few of labeled samples are available, is a fundamental problem that needs to be solved. In this paper, we put forward a new method named enhanced prototypical network with customized region-aware convolution (CRCEPN) to specially tackle the few-shot SAR ATR tasks. To be specific, We first develop a feature extraction network based on a customized and region-aware convolution, which can adaptively adjust convolutional kernels and their receptive fields according to each SAR image’s own characteristics as well as the semantical similarity among spatial regions, thus augmenting its capability to extract more informative and discriminative features. To achieve accurate and robust target identity prediction under the few-shot condition, we then propose an enhanced prototypical network which can improve the representation ability of the class prototype by properly making use of training and test samples together, thus effectively raising the classification accuracy. Meanwhile, a new hybrid loss is designed to learn a feature space with both inter-class separability and intra-class tightness as much as possible, which can further upgrade the recognition performance of the proposed method. Experiments performed on the moving and stationary target acquisition and recognition (MSTAR) dataset and the OpenSARShip dataset demonstrate that the proposed method is competitive with some state-of-the-art methods for few-shot SAR ATR tasks.

Smart agriculture is a new way of farming that uses advanced technology. It makes a significant contribution to improving the efficiency and productivity of farming practices. Systems that use drones to monitor pests and spray pesticides greatly reduce the burden on farmers. In addition, remote sensing technology facilitates the monitoring of crop growth conditions and the early detection of diseases and pests. However, the high cost of equipment to communicate sensor data is preventing farmers from adopting sensing systems. As a result, many studies have been conducted to make the sensing system available at a low cost. Nevertheless, many farmers have not yet adopted them. Therefore, this paper proposes an agricultural sensor network system using drones and optical camera communication (OCC). The proposed system aims to achieve high-density farm sensing at low cost. The proposed system uses OCC as the communication method for sensor data. Low cost light sources such as LED panels can be used as transmitters. In addition, existing agricultural drones equipped with cameras can be used as receivers. We also propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. The proposed system is suitable for deploying sensor networks in areas with underdeveloped infrastructure and radio silence. In this paper, we conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama to demonstrate the effectiveness of the proposed system using a Mavic 2 Pro drone, launched by DJI Co., Ltd.

In this study, the degradable superhydrophobic Mg/P/Z/F/H (magnesi-um/poly(-caprolactone)/zinc oxide/1H,1H,2H,2H-perfluorodecyltriethoxysilane (PFDTES)/ heating process) composite materials were prepared through one-step method for enhancing the corrosion resistance of AZ91D magnesium alloys. Electrochemical measurements showed that Mg/P/Z/F/H materials significantly improved the corrosion resistance of magnesium alloys in 3.5 wt.% NaCl. The self-cleaning, adhesion and stability tests suggested that Mg/P/Z/F/H composite materials had well self-cleaning properties, good adhesion strength and stability in wet atmosphere.

The valley pseudospin properties of electrons in two–dimensional hexagonal materials result in lots of fascinating physical phenomena, which opens up the new field of valleytronics. The valley-contrasting physics aims at distinguishing the valley degree of freedom based on valley–dependent effects. Here, we theoretically demonstrate that both of the valley–selective high harmonic generation and valley–selective electronic excitation can be achieved by using an orthogonal two–color (OTC) laser field in gapped graphene. It is shown that the asymmetry degrees of harmonic yields in the plateaus, cutoff energies of generated harmonics and electron populations from two different valleys can be precisely controlled by the relative phase of the OTC laser field. Thus the selectivity of the dominant valley for the harmonic radiation and electronic polarization can be switched by adjusting the relative phase of the OTC laser field. Our work offers an all–optical route to produce the valley–resolved high harmonic emissions and manipulate the ultrafast valley polarization on a femtosecond timescale in condensed matter.

Abstract In order to solve the problem that it is difficult to obtain the accurate trigger signal moment when using the time difference of UAV flight acoustic signals collected by acoustic sensors arranged in advance to locate the position of UAVs when they cannot be tracked by radar and are difficult to be observed by the human eye, an acoustic source localisation method with improved empirical modal decomposition (REMD) under an adaptive frequency window is proposed. Firstly, the collected UAV flight signals are smoothed and filtered, then the robust empirical modal decomposition (REMD) is used for IMF modal decomposition and spectral analysis of the collected signals, and a panning frequency window with variable bandwidth is introduced for corresponding frequency locking and extracting the IMFs; the GWO optimisation is used and the sliding metrics are set to determine the position of the panning frequency window automatically; finally, the extracted Finally, the extracted IMF components are reconstructed according to the criterion, and the trigger signal moments in the reconstructed IMF components are resolved and recorded, and the algorithm is used to calculate the difference between the reception moments of different sensors; for the traditional sound source localisation algorithms with a small detection range, which are prone to fall into the no-solution or false-solution, i.e., unable to achieve the localisation, a weighted least-squares-based Chan-Taylor algorithm is proposed for the localisation computation, and a weighted least-squares algorithm is proposed for the localisation computation, through the Inputting the sensor delay parameter and solving the linear equation system to get the target position information; finally, this paper verifies the robustness and performance of the design method with simulated and actual signals. The results show that the maximum positioning error is no more than 5% in an area with a side length of 15m in the measurement area. With the improved accuracy of time delay estimation, the positioning simulation results meet the positioning requirements even though the positioning error is further expanded when the measurement area is further expanded.

Cartilage development is a tightly regulated process that involves multiple molecules and signaling pathways. The loss of expression of a single gene can alter cell behavior and disrupt the development of the cartilage. Col11a1 encodes the alpha one chain of the minor fibrillar collagen type XI that is essential in skeletal development. We used an RNAi mediated knockdown approach to investigate the role of Col11a1 expression during chondrogenesis in ATDC5 cells. Col11a1 expression promotes the transition of mesenchymal cells to the chondrogenic phenotype, and reduction of Col11a1 expression interferes with cellular differentiation. Our results indicate that collagen α1(XI) protein is required for chondrocyte cell shape, matrix production, mineralization and gene expression through a mechanism that involves AKT/GSK3β/β-catenin and TCF/LEF activity in ATDC5 cells.

Even though inhalation dosimetry is determined by three factors (i.e., breathing, aerosols, and the respiratory tract), the first two categories have been more widely studied than the last. Both breathing and aerosols are quantitative variables that can be easily changed, while respiratory airway morphologies are difficult to reconstruct, modify, and quantify. Despite several methods are available for model reconstruction and modification, developing an anatomically accurate airway model and morphing it to various physiological conditions remains labor-intensive and technically challenging. The objective of this study is to explore the feasibility of using an adjoint-CFD model to understand airway shape effects on vapor deposition and control vapor flux into the lung. A mouth-throat model was used, with the shape of the mouth and tongue being automatically varied via adjoint morphing, and the vapor transport being simulated using ANSYS Fluent coupled with a wall absorption model. Two chemicals with varying adsorption rates, Acetaldehyde and Benzene, were considered, which exhibited large differences in dosimetry sensitivity to airway shapes. For both chemicals, large shape deformations were explored to find the maximal possible morphing; both the maximal and intermediate deformations were simulated for morphology parametric studies. Results show that changing the mouth-tongue shape can alter the oral filtration by 3.2% for Acetaldehyde and 0.27% for Benzene under a given inhalation condition. This study demonstrates that the hybrid adjoint-CFD approach can be a practical and efficient method to investigate morphology-associated variability in inhalation dosimetry of vapors and nanomedicines.

An effective approach to fabricate large-scale of conducting polyaniline (PANI) by an in situ anodic electrochemical polymerization on the nickel foam, which coated by a novel aryl diazonium salt (ADS)-functionalized graphene (ADS-G). In present work, ADS-G was used as a high surface area support material for electrochemical polymerization of PANI. Electrochemical performances of the ADS-G/PANI composites exhibited better suitability as supercapacitor electrode materials than that of the PANI. The ADS-G/PANI composites achieved the specific capacitance of 528 F g-1, which was higher than that of PANI (266 F g-1) due to the excellent electrode-electrolyte interaction and synergistic effect of electrical conductivity between ADS-G and PANI in the composites. These findings suggested that the ADS-G/PANI composites were a suitable composite for the potential supercapacitor applications.

The service conditions of underground coal mine equipment are poor, and it is difficult to accurately extract the fault characteristics of rolling bearings. In order to better improve the accuracy of fault identification of rolling bearings, a fault detection method based on multiscale permutation entropy and SOA-SVM is proposed. First, the whale optimization algorithm is used to select the modal analysis number K and the penalty factor α of the variational mode decomposition algorithm. Then, the vibration signal of rolling bearings is dissolved according to the optimized variational mode decomposition algorithm, and the multi-scale permutation entropy of the main intrinsic mode function is calculated. Finally, the feature values of the matrix are entered into the SVM algorithm optimized by the seagull optimization algorithm to obtain the classification result. The experimental results based on the published rolling bearing datasets of Western Reserve University show that the identification success rate of the proposed method can reach 98.75%. The fault detection of the rolling bearings can be completed accurately and efficiently.

The structure characterization of two new polysaccharides from Agrocybe aegerita (AA-P) and Hygrophorus olivaceoalbus (HO-P) by HPGPC, GC-MS, NMR and FT-IR indicated that AA-P was composed of Galactose, Glucose and Arabinose, in the ratio of 3:2:1. Its skeleton structure was consisted of (1→4)-Arap, (1→4,6)-Glup and (1→6)-Galp with one branched chain. The HO-P was consisted of mannose, galactose and glucose in a ratio of 1:1:2. Its skeleton structure was consisted of (1→6)-Galactose residues, (→1)-glucose residues, (1→4)-glucose residues and (1→4,6)-D-mannose residues. There were two branched chains connected to the main chain. AA-P and HO-P had the best stimulation effect on B cells and RAW264.7 cells, respectively, and could both mainly by impacting and reducing G0/G1 phase which lead to a significant proliferation of B cells, T cells and RAW264.7 cells. In addition, AA-P and HO-P could significantly promote the secretion of TNF-α from T cells, the secretion of IgA, IgD, IgE, IgG and IgM from B cells, and the secretion of TNF-α from RAW264.7 cells, but neither of them could impact the secretion of IL-1β from RAW264.7 cells.

An analytical method was established by HPLC-DAD-ELSD with C18-NH2 column tandem for the simultaneous determination of hydrophobic components atractylenolide I, II, III, atractylone and hydrophilic compounds glucose, fructose and sucrose in the dried rhizome of Atractylodes macrocephala Koidz (a natural raw material for health foods, Bai-Zhu aka. in Chinese). The method combines different separation capabilities of reversed-phase liquid chromatography and hydrophilic interaction liquid chromatography, can provide a new choice for the simultaneous determination of hydrophilic and hydrophobic compounds in traditional Chinese medicines and health foods. It provided a reference method for the quality control of Bai-Zhu. The results showed that the linear correlation coefficients of the established column tandem chromatographic method were all greater than 0.9990, the relative standard deviation was 0.1%-2.8%, and the average recovery was 96.7%-103.1%. The contents of atractylenolide I, II, III, atractylone, fructose, glucose, and sucrose in 17 batches of Baizhu were 172.3-759.8 μg/g, 201.4-612.8 μg/g, 160.3-534.2 μg/g, 541.4-8723.1 μg/g, 6.9-89.7 mg/g, 0.7-7.9 mg/g, and 1.2-21.0 mg/g, respectively.

A dynamic impact test was performed on as-rolled AZ31 alloy with gradient microstructure under various strains. The microstructural evolution and mechanical properties were systematically investigated. As the strain rate gradually increases, an increasing number of twins are formed, facilitating dynamic recrystallization (DRX), and the mechanical properties are also gradually improved as well. The microstructure becomes heterogeneous at higher strain rates, but the peak stress decreases. The impact process results in significantly higher performance due to microstructural refinement, work hardening by dislocations and precipitates. In addition, both the adiabatic shear band and the adjacent crack experience a temperature rise that exceeds the recrystallization temperature of the alloy. This observation also explains the presence of ultrafine recrystallized grains within the adiabatic shear band and the appearance of molten metal around the crack.

This paper delves into the concept of information within the context of conscious systems, emphasizing the pivotal role of entropy as a fundamental aspect in comprehending consciousness. Drawing on insights from Integrated Information Theory (IIT) and information theory, the argument posits that consciousness entails dynamic information processing, distinct from the static storage and retrieval model employed by modern computers. The intrinsic dynamics of neural systems are explored, illuminating how they integrate incoming stimuli into a unified state of subjective experience, thereby shaping conscious awareness. By considering entropy as a temporal and qualitative value, its significance in reducing uncertainty and facilitating adaptability in conscious systems is underscored. Through a series of principles, the intricate relationship between entropy, neural dynamics, and consciousness is elucidated. This analysis suggests the necessity of rethinking traditional notions of information when applied to conscious systems, highlighting the dynamic nature of consciousness and its interaction with the environment.

Aiming at the problems that the parameters of YOLOv5s model are too large and the computing resources of development board memory are limited, a new target detection method based on deep learning YOLOv5 algorithm model is proposed. First, the light-weight module Gz-ShffleNetv2 is used to construct cotton top bud feature extraction unit, which reduces the number of parameters and improves the running speed. It can be better applied in image classification, speed up detection and meet the requirements of mobile end of development board. Secondly, in order to solve the problem of decreasing detection accuracy caused by lightweight, BotNet and C3SE attention mechanism are added to focus on specific areas of cotton terminal bud. Combined with YYG loss function XIOU boundary frame regression loss, more feature information and rich feature MAP were obtained to further improve the accuracy of target detection. Through analysis of research and experimental results, the average accuracy map reached 91.3% under the Windos system NVIDIA Geforce RTX 2060 SUPER detection. While maintaining high precision identification, the number of parameters of YOLOv6 and YOLOv7-tiny network model is reduced by 83% and 53%, respectively, and the detection accuracy is increased by 1.2% and 7.7%, respectively. Compared with YOLOv5 reasoning image, the speed of image is increased by 0.035s, and the detection accuracy of MAP_0.5:0.95 is increased by 1%. At the same time, PyQt5 and YOLOv5 target detection algorithms are used to design a cotton top bud identification system, which makes cotton top bud detection more intuitive and convenient for subsequent hardware development and use.

By the study of the pattern book Ying Zao Fa Shi (building regulations of Song Dynasty, 1103 AD), while analyzing the combining and dimensioning rule of timber framework and tile work, a model self-generating program has been compiled for the first time. The operating framework has been firstly defined, while solving the issues of clustering principle, connecting method, output classification, etc. with the detailed description of algorithm theory. Taking the corner bracket set and nine-ridge roof for example, after the compilation and debug by Grasshopper, according to various input parameters, various models have been generated automatically by the plugin, proving the velocity and the veracity of the algorithm.

There is a confusion in the original design concept for the tensioning of longitudinally connected reinforcement of CRTSII slab ballastless track. In order to clarify the effect of tension value of longitudinal reinforcement on mechanical characteristics of ballastless track, a three dimensional finite element model considering the nonlinear interaction between the track slab and CA mortar of CRTSII slab ballastless track was established. The mechanical characteristics of the track structure under longitudinal tension load and temperature gradient load of the longitudinal joint were calculated. A method of applying pre-stress to post-pouring concrete was proposed according to the concept of pre-stress loss of pretensioning pre-stressed concrete, reasonable tensile force value was proposed after the crack width and the reinforcement stress of the ballastless track in the operation stage were checked and calculated according to the concrete design principle. When the tension force is greater than 300 kN, it’s harmful to the bonding between the slab and mortar layer, which is prone to interlayer damage. In order to adding pre-stress to concrete of wide joints to ensure the longitudinal stability of ballastless track and the reinforcement stress and crack width to meet the design requirements. It is suggested that the tension force value should be 230 kN, and the temperature difference between reinforcement and concrete should be 30 °C before the initial curdle of wide joint concrete.

Psoriasis is a chronic, multisystem, inflammatory disease. The typical clinical cutaneous manifestation of psoriasis is scaly erythema or plaque, limited or widely distributed. However, psoriasis is far beyond the skin involvement and provides many challenges, including associated comorbidities. In this review, we classify psoriasis as cutaneous and systemic psoriasis, according to the clinical diversity and associated comorbid diseases, and recommend classification criteria for psoriasis. The key objective of this novel classification is to raise awareness of the complexity of this multifaceted disease and help to better understand and manage this complex disease comprehensively.

Background: The Hengduan Mountains are one of the most species–rich mountainous areas in the world. The origin and evolution of such a remarkable biodiversity are likely to be associated with geological or climatic dynamics, as well as taxon-specific biotic processes (e.g., hybridization, polyploidization, etc.). Here, we investigate the mechanisms fostering the diversification of the endemic Himalopsyche martynovi complex, a poorly known group of aquatic insects. Methods: We used multiple allelic datasets generated from 691 AHE loci to reconstruct species and RaxML phylogenetic trees. We selected the most reliable phylogenetic tree to perform network and gene flow analyses. Results: Phylogenetic reconstructions and network analysis identified three clades, including H. epikur, H. martynovi sensu stricto and H. cf. martynovi. Himalopsyche martynovi sensu stricto and H. cf. martynovi present an intermediate morphology between H. epikur and H. viteceki, the closest known relative to the H. martynovi–complex. The gene flow analysis revealed extensive gene flow among these lineages. Conclusion: Our results suggest that H. viteceki and H. epikur are likely to have contributed to the evolution of H. martynovi sensu stricto and H. cf. martynovi via gene flow, and thus, our study provides insights in the diversification process of a lesser–known ecological group, and hints at the potential role of gene flow in the emergence of biological novelty in the Hengduan Mountains.

In order to enhance the comfort of cycling, it is imperative to investigate the effects of vibration on non-motorized bicycle riding from the perspectives of road characteristics and traffic features. Through an analysis of the mechanisms by which road and traffic conditions influence cycling vibrations, 13 influencing factors were identified. Subsequently, the non-motorized bicycle lanes in Wuhan city were selected as the subject of empirical research, where three-axis accelerometers attached to the rider's torso were employed to measure and categorize vibration comfort levels. The experimental road segments were found to exhibit comfort levels falling between slightly uncomfortable and relatively uncomfortable. Further analysis of the influencing factors was conducted using the Random Forest algorithm and Logistic Regression. The results revealed that six factors significantly impact the comfort of cycling: the presence of dedicated non-motorized bicycle lanes, the absence of physical separation between non-motorized and motorized traffic, cycling speed, the number of road surface irregularities, the presence of parking areas within the non-motorized bicycle lane, and the type of non-motorized bicycle. This study provides valuable insights into the factors affecting non-motorized bicycle lane usage and contributes to the refined design of urban non-motorized bicycle infrastructure, thereby facilitating better support for sustainable urban transportation.

In this paper, we present a relapse based demonstrating way to deal with investigate various arrangement MTC information. A commonplace use of this displaying approach incorporates three stages: first, define a model that approximates the connection between quality articulation and trial factors, with boundaries consolidated to address the exploration premium; second, utilize least-squares and assessing condition methods to gauge boundaries and their relating standard blunders; third, register test insights, P-qualities and NFD as proportions of factual criticalness. The benefits of this methodology are as per the following. To begin with, it tends to the exploration interest in a particular, precise way, and maximally uses all the information and other important data. Second, it represents both orderly and irregular varieties related with the information, and the consequences of such examination give not just quality explicit data applicable to the exploration objective, yet additionally its dependability, in this way helping agents to settle on better choices for subsequent investigations. Third, this methodology is truly adaptable, and can undoubtedly be stretched out to different sorts of MTC considers or other microarray explores by detailing various models dependent on the test plan of the investigations.

In the contemporary era, youthful and healthy skin is a pivotal determinant of beauty. Choices pertaining to one's dietary and lifestyle practices wield substantial influence over skin health. Currently, the focal point of attention lies in strategies to delay skin aging and maintain skin quality. Remarkably, the skin, the body's largest organ, serves as the primary defense barrier against external elements. Skin aging encompasses intrinsic and extrinsic categories, both susceptible to genetic, lifestyle, and environmental factors. Given the strides in science and technology, the pursuit of effective and safe interventions for skin aging assumes paramount importance. Thus, this review delves into the intricate relationship between diet, lifestyle, and skin aging, culminating in an exploration of the crucial role played by excess iron in this intricate nexus. Understanding these dynamics holds promise for advancing our knowledge of skincare and the quest for timeless vitality.

The Sry-related HMG-box (Sox) family is a group of transcriptional regulators that play a critical role in many important developmental processes in both vertebrates and invertebrates. In aquatic animals, the function of Sox genes on sexual development has been gained particular attentions. The present study reported the molecular characterization of a Sox member (PtSoxE) in the swimming crab, Portunus trituberculatus, and tissue distribution analysis showed it was male-specific. Since the most highly expression of PtSoxE was found in the androgenic gland (AG), its relation to the insulin-like androgenic gland hormone (IAG) was further investigated. The PtSoxE siRNA caused a significant decrease in IAG expression in both AG and testis, whereas PtSoxE expression could be induced by treating with AG homogenate and rIAG. The result suggested a transcriptional interaction between PtSoxE and IAG. In addition, PtSoxE expression showed a closely positive correlation with several reported spermatogenesis-related genes, suggesting its involvement in testicular development of P. trituberculatus.

Manufacturing enterprises are increasingly compelled to pursue innovative solutions to environmental issues. Addressing such issues requires mindful leadership to support employees and organizations in maintaining awareness during complex situations, thereby promoting environmental sustainability. Drawing on social information processing theory, this study theorizes and tests the relationships mindfulness leadership, employee moral reflectiveness, environmental passion, and employee green creativity. To test the model, we utilize a two-wave multi-source dataset comprising 215 workers from manufacturing firms in China. The findings demonstrate that mindful leadership is positively related to employee green creativity. Additionally, our research reveals that both moral reflectiveness and environmental passion serve as mediators in linking mindful leadership to employee green creativity. Crucially, our empirical analysis suggests a serial mediation model, examining the sequential role of moral reflectiveness and environmental passion in the relationship between mindful leadership and employee green creativity. The findings provide a new viewpoint on employees' eco-friendly behaviors and have practical implications for improving environmental sustainability within organizations.

Based on acoustic fluid elements, dynamic analysis was performed on liquid sloshing modes and liquid-filled containers considering the fluid-structure interaction (FSI) effect. The liquid sloshing modes in two-dimensional (2D) and three-dimensional (3D) containers were analyzed, and the results were compared with liquid sloshing modes measured in tests and theoretically calculated modes. This thus verifies correctness of the simulation method based on acoustic fluid elements. Cylindrical liquid-filled containers with different water levels were subjected to modal analysis and dynamic and time-historical analysis. Results show that the finite element analysis (FEA) based on acoustic fluid elements can accurately simulate liquid sloshing modes in liquid-filled containers and vibration characteristics of these containers with different liquid levels. The vibration frequency of liquid-filled containers declines obviously with rising liquid level. The liquid level significantly affects distributions of the maximum displacement, maximum acceleration, and maximum von Mises stress on the sidewall of liquid-filled containers. Numerical simulations based on acoustic fluid elements provides an effective and reliable method for dynamic analysis of liquid-filled containers considering the FSI effect.

Generative models for images, audio, text and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The object of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using Tree Structure Skeleton Image format. We evaluate our approach on the 3D-skeleton data provided in the large NTU RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of its 60 action classes. We also quantitatively evaluate the performance of our model by computing Frechet Inception Distances, which shows strong correlation to human judgement. Up to our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

It is meaningful to monitor and identify the concrete cracks as they will seriously reduce the fatigue life of pavement. In this paper a new method based on distributed long-gauge optic fiber sensing is proposed to implement the crack identification. Firstly, the damage indicator, namely strain curve envelope area ratio (SCEAR), is proposed and calculated with the measured strain in time series when the plane goes across the pavement where the sensors are installed. Then the indicators with different sensors are normalization by the value of the indicator from one referenced sensor where damage normally will not happen. If the crack happens within the sensing gauge of the sensor, the value of damage indicator SCEAR from the sensor will increase. Therefore, the crack will be identified with the change of the damage indicator. The effect of the proposed method is first verified by some simulations of prefabricated pavement as the cracks can be accurately identified by the proposed method. Moreover, the influence from the aircraft, such as taxiing position, aircraft load and aircraft type, is not obvious enough to mislead the identification results. Lastly the static loading tests were implemented with one small-scale model of prefabricated concrete pavement in the lab. From the experimental results the prefabricated cracks inside the pavement slab can be identified accurately with the proposes method as the parameter SCEAR is increased by about 100% at the crack position, while it is only changed by about 3% at the undamaged position.

The impact of antecedent soil moisture content on soil erosion is a complicated phenomenon, which is still under argument with contradictions in research results. Hence, the objective of this study is to investigate the impact of antecedent soil moisture content to soil loss on clay soil by conducting two-year field experiments under natural rainfall on runoff plots with length of 10 meters, width of 3 meters, and uniform slope of 9%. Volumetric soil moisture sensors were used to log soil moisture changes, and soil moisture readings were used along with rainfall records to quantify the antecedent soil moisture conditions for each rainfall event. Results of this study show duration of the effective erosion event outranks the antecedent volumetric soil moisture content. Field study also suggests that accumulative rain falling within 48 hours (Pp48) prior to an effective erosion event strongly correlated with soil loss per Rainfall-Runoff Erosivity Index (Soil Loss / EI30), particularly when the duration of an effective erosion event is either between 3 ~ 7 hours or 10~ 30 hours. Hence, Pp48 can be considered as an alternative to replace antecedent soil moisture content in RUSLE 2’s cover and management factor.

We propose an innovative approach called SMPTC3, designed specifically to enhance security and privacy in cross-chain transaction verification. This approach addresses multi-chain, multi-participant information exchange and large-scale cross-chain transfers, resisting various types of malicious attacks. We leverage the verifiability of cross-chain transactions based on smart contracts and innovatively transform transaction information into confidential sets, organizing them into quadratic secret polynomials. By utilizing secret sharing and random distribution techniques, we construct a secure multiparty computation method, tailored for cross-chain transactions. To enhance the efficiency of cross-chain transactions, we introduce cross-chain batch processing technology, grouping inter-chain transactions into cross-chain transaction sets. Unlike traditional distributed notary technologies, SMPTC3 designates honest participants as a cross-chain notary group, reducing the time required for redundant signature confirmations and significantly lowering the possibility of malicious notaries. Theoretical analysis and empirical experiments demonstrate that SMPTC3 is highly efficient in addressing cross-chain transaction security issues.

Papillary thyroid carcinoma (PTC) accounts for approximately 85%-90% of all thyroid cancers. BRAFV600E mutation is a highly specific target for papillary thyroid carcinoma (PTC) and may have a reciprocal causative relationship with iodide-metabolizing genes. Here, we performed a review of studies published in the past 10 years to determine the relationship between iodine intake and BRAFV600E mutation in patients with PTC. We searched the MEDLINE, PubMed, and EMBASE databases for studies published from 2009 to 2019; seven partially matched the selection criteria and were suitable for review, and five passed all selection criteria. We divided the patients into three groups by iodine intake: low (urinary iodine concentration [UIC] &lt;100 μg/L), adequate (UIC 100–200 μg/L), and high iodine intake groups (UIC ≥200 μg/L). Between-group analysis revealed no significant differences in the odds ratio of the prevalence of BRAFV600E mutation between the high and adequate/low iodine intake groups and between the adequate and low iodine intake groups. To further analyzed the results of studies, they exhibited U-shaped curves in the relation of deficient and excessive dietary iodine intake in BRAFV600E mutation. The results might suggest that iodine intake slightly influences the prevalence of BRAFV600E mutation in patients with PTC despite the heterogeneity of studies. Further research should explore potential mechanisms underlying the associations between iodine intake and BRAF mutation in PTC. The systematic review was registered in PROSPERO (CRD42021279462).

In this paper, a semi-empirical model of air waves induced by falling rock is described. The model is composed of a uniform motion phase (velocity close to 0 m·s^-1) and an acceleration movement phase. The uniform motion phase was determined based on experimentally and the acceleration movement phase was derived by theoretical analysis. A series of experiments were performed to verify the semi-empirical model and elucidated the law of the uniform motion phase. The acceleration movement phase accounted for a larger portion with a greater height of the falling rock. Experimental results of different falling heights of the goaf showed close agreement with theoretical analysis values. The semi-empirical model could accurately and conveniently estimate the velocity of air wave induced by falling rock. Thus, the semi-empirical model can provide a reference and basis for estimating the speed of air waves and designing protective measures in mines.

The heavy metal lead (Pb) can irreversibly damage the human nervous system. To help understand Pb-induced damage, we applied a genetically encoded Förster resonance energy transfer (FRET)-based Pb biosensor Met-lead 1.44 M1 to two living systems to monitor the concentration of Pb: induced pluripotent stem cell (iPSC)-derived cardiomyocytes as a semi-tissue platform, and Drosophila melanogaster fruit flies as an in vivo animal model. Different FRET imaging modalities were used to obtain FRET signals, which represented the presence of Pb in the tested samples in different spatial dimensions. Using iPSC-derived cardiomyocytes, the relationship between beating activity (20–24 beats per minute, bpm) determined from the fluctuation of fluorescent signals and the concentrations of Pb represented by the FRET emission ratio values of Met-lead 1.44 M1 was revealed from simultaneous measurements. Pb (50 μM) affected the beating activity of cardiomyocytes, whereas two drugs that stop the entry of Pb differentially affected this beating activity: verapamil (2 μM) did not reverse the cessation of beating, whereas 2-APB (50 μM) partially restored this activity (16 bpm). The results clearly demonstrate a potential of this biosensor system as an anti-Pb drug screening application. In the Drosophila model, Pb was detected within the adult brain or larval central nervous system (Cha-gal4>UAS-Met-lead 1.44 M1) using fast epifluorescence and high-resolution two-photon 3D FRET ratio image systems. The tissue-specific expression of Pb biosensors provides an excellent opportunity to explore the possible Pb-specific populations within living organisms. We believe that this integrated Pb biosensor system can be applied to the prevention of Pb poisoning and advanced research on Pb neurotoxicology.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

The thoracic surgery has increased drastically in recent years, especially in the light of the severe outbreak of 2019 novel coronavirus disease (COVID-19). Routine “passive” chest computed tomography (CT) screening of inpatients detects some pulmonary diseases requiring thoracic surgeries timely. As an essential device for thoracic anesthesia, the double-lumen tube (DLT) is particularly important for anesthesia and surgery. With the continuous upgrading of the DLTs and the widespread use of the fiberoptic bronchoscopy (FOB), the position of DLT in thoracic surgery is gradually becoming more stable and easier to observe or adjust. However, the DLT malposition still occurs during transferring patients from supine to lateral position in thoracic surgery, which leads to lung isolation failure and hypoxemia during one-lung ventilation (OLV). Recently some innovative DLTs or improved intervention methods have shown good results in reducing the incidence of DLT malposition. This review aims to summarize the recent studies of the incidence of left-sided DLT malposition, the reasons and effects of malposition, and summarize current methods for reducing DLT malposition and prospects for possible approaches. Meanwhile, we use bibliometric analysis to summarize the research trends and hot spots of the DLT research.

Objectives: Robotic bilateral axillary breast approach (BABA) thyroid surgery began in 2008 and is now one of the most widely used remote-access thyroid surgeries worldwide. This study aimed to analyze the results of 500 robotic BABA thyroid surgeries performed in a single institution in Korea compared with open thyroid surgery. Methods: From December 2018 to March 2020, 502 robotic BABA thyroidectomies (RTs) and 531 conventional open thyroidectomies (OTs) were performed in our institution by a single endocrine surgeon. We retrospectively reviewed patient medical records and performed a comparative analysis of OT and RT. Results: The RT group was younger (43.41±11.41 versus 54.28±13.41 years, p<0.001) and had a higher proportion of females (84.3% versus 69.3%, p<0.001), a lower BMI (24.66±3.97 versus 25.83±4.07 kg/m2), a higher proportion of lobectomies (52.6% versus 45.2%) and a lower proportion of lateral neck dissections (3.4% versus 10.0%, p<0.001). The RT group had a lnger operation time (145.33±40.80 versus 93.39±43.55 minutes, p<0.001) and higher surgical cost. Although the OT group had a larger tumor size and higher proportion of extrathyroidal extension, the numbers of retrieved lymph nodes were not significantly different between the two groups. Additionally, there was no difference in the stimulated thyroglobulin level before radioactive iodine therapy (7.01±35.73 versus 8.39±58.77, p=0.782). The rates of transient vocal cord palsy and transient hypoparathyroidism were significantly lower in the RT group, and those of scar-related complications were higher in the OT group. Conclusion: Robotic BABA thyroid surgery has advantages not only in better cosmetic outcomes but also in lower rates of vocal cord palsy and hypoparathyroidism, with comparable lymph node retrieval and serum thyroglobulin levels.