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[Objective] In order to solve the issue of excessive redundant information in near-infrared spectroscopy, enhance the accuracy of wine quality evaluation models, a rapid and non-destructive method was established for wine quality evaluation. [Methods] A wine quality evaluation model was proposed based on competitive adaptive reweighting sampling method for feature wavelength screening and extreme learning machine improved by whale optimization algorithm. Various feature wavelength screening methods such as competitive adaptive reweighting sampling was used, and the most suitable method for wine spectral feature wavelength screening was determined. In response to the problem of initial value and hidden layer bias in ELM, the whale optimization method was used to optimize the initial value and hidden layer bias of ELM, and an wine quality evaluation model based on extreme learning machine improved by whale optimization algorithm was constructed. [Results] Compared with GA-ELM, PSO-ELM, and the traditional ELM model, the accuracy of WOA-ELM was the highest, reaching 0.944 5, which was better than GA-ELM (0.929 0), PSO-ELM (0.906 1) and traditional ELM (0.817 7). [Conclusion] The parameters of the ELM model optimized by intelligent algorithms can effectively improve the accuracy of wine quality evaluation.

In the production process of electronic grade ultrapure water, trace H2O2 is mainly compounded by hydroxyl radicals generated by vacuum UV degradation devices, which can corrode certain specific processes of semiconductor materials surface cleaning. The application of multiphase nanocatalysation technology in advanced process semiconductor ultrapure water preparation system can effectively remove the micropollution H2O2 produced by vacuum ultraviolet irradiation, which is a green cleaning technology to promote the rapid dissociation of active oxygen species containing O—O bonds. The generation of micropollution H2O2 and its influence on the cleaning process of semiconductor materials were briefly discussed. The reaction pathway and mechanism of platinum series metal nanoparticles in group Ⅷ transition elements to catalyze H2O2 dissociation were analyzed. The effects of metal surface adsorption configuration, adsorption energy, electronic structure, carrier chemical properties, halogen anions in water and other factors on the enhanced removal characteristics of nanoparticles were also discussed. The application and research progress of group Ⅷ transition metals in ultrapure water production, detection and semiconductor assisted process systems were reviewed. The research provided reference for the construction of a new reaction system in ultrapure water process and the development and application of water treatment materials.

Abstract Repeated and widespread use of single chemical pesticides raises concerns about efficiency and safety, developing multi-component synergistic pesticides provides a new route for efficient control of diseases. Most commercial compound formulations are open systems with non-adjustable released rates, resulting in a high frequency of applications. Meanwhile, although nano pesticide delivery systems constructed with different carrier materials have been extensively studied, realizing their actual scale-up production still has important practical significance due to the large-scale field application. In this study, a boscalid and pyraclostrobin dual-loaded nano pesticide system (BPDN) was constructed with industrial-grade carrier materials to facilitate the realization of large-scale production. The optimal industrial-scale preparation mechanism of BPDN was studied with surfactants as key factors. When agricultural emulsifier No.600 and polycarboxylate are used as the ratio of 1:2 in the preparation process, the BPDN has a spherical structure with an average size of 270 nm and exhibits superior physical stability. Compared with commercial formulation, BPDN maintains rate-stabilized release up to 5 times longer, exhibits better dispersion and spreading performance on foliar, has more than 20% higher deposition amounts, and reduces loss. A single application of BPDN could efficiently control tomato gray mold during the growing period of tomatoes due to extended duration and combinatory effectiveness, reducing two application times and labor costs. Toxicology tests on various objects systematically demonstrated that BPDN has improved safety for HepG2 cells, and nontarget organism earthworms. This research provides insight into creating safe, efficient, and environmentally friendly pesticide production to reduce manual operation times and labor costs. Accompanied by production strategies that can be easily scaled up industrially, this contributes to the efficient use of resources for sustainable agriculture.

Summary: Generative design of promoters has enhanced the efficiency of de novo creation of functional sequences. Though several deep generative models have been employed in biological sequence generation, including variational autoencoder (VAE) or Wasserstein generative adversarial network (WGAN), these models might struggle with mode collapse and low sample diversity. In this study, we introduce the multinomial diffusion model (MDM) for promoter sequence design and propose a structured set of criteria for effectively comparing the performance of generative models. In silico experiments demonstrate that MDM outperforms existing generative AI approaches. MDM demonstrates superior performance in various computational evaluations, remains robust during the training process, and exhibits a strong ability in capturing weak signals. In addition, we experimentally validated that the majority of our model designed promoters have expression activities in vivo, indicating the practicality and potential of MDM for bioengineering.

China’s rapid urbanization has significantly impacted carbon emissions in rural areas, driving the need for region-specific carbon management strategies to achieve the country’s dual carbon goals. However, previous research has primarily focused on large urban centers, leaving a gap in understanding the spatial and temporal patterns of carbon emissions at the county level in rural regions. This study focuses on the Guanzhong region of Shaanxi Province and develops a county-level carbon emission accounting system based on geographic, socio-economic, and land-use data. The carbon emissions are categorized into energy, industrial processes, agriculture, and waste management. Key findings indicate that industrial and residential sectors are the primary contributors to carbon emissions, with Xi’an being the largest emitter. Carbon emission intensity follows a ranking of Xi’an < Weinan < Baoji < Xianyang < Tongchuan. Spatial patterns show a “core-edge” distribution, with higher emissions in urban centers and lower emissions in rural areas. The study also highlights the carbon sink potential in the southern Qinling National Park. This research provides a valuable framework for rural low-carbon development and offers critical insights for policymakers aiming to balance carbon reduction and economic growth in rural China.

BackgroundThe bulbs of Allium sativum are widely used as food or seasoning (garlic), while they have also been utilized as a famous traditional medicine since ancient eras for the treatment of scabies, tuberculosis, pertussis, diarrhea and dysentery, etc. However, very few studies focus on their abundant aerial parts, which are normally discarded during the harvest season.MethodsThe hyperlipidemic mice model has been used to study the lipid-lowering effect of the aerial parts in this article. 180 mice were randomly divided into 18 groups, including blank control (BC), model (Mod), positive control (PC), and low-, medium-, and high-dose groups of the crude extract, petroleum ether, ethyl acetate, n-butanol, and residual water extracts (corresponding to CE, PEE, EAE, NBE, WE), with 10 mice in each group. The preventive effects of the extracts on hyperlipidemic mice lasted for four weeks. Ultra performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) and gas chromatography tandem mass spectrometry (GC-MS/MS) were used to analyze the chemical components of NBE and PEE respectively.ResultsThe results of the mice experiment showed that n-butanol extract (NBE) and petroleum ether extract (PEE) from the aerial parts could significantly reduce the contents of total cholesterol (TC), triglycerides (TG), low density lipoprotein cholesterol (LDL-C), alanine transaminase (ALT) and aspartate transaminase (AST) in serum of hyperlipidemic mice, and increase the contents of high density lipoprotein cholesterol (HDL-C). They could enhance the activity of superoxide dismutase (SOD) in liver and reduce the level of malondialdehyde (MDA). At the same time, they could improve steatosis and inflammation of liver cells. The results of phytochemical components analysis showed that NBE was rich in organic acids, flavonoids and nitrogen-containing constituents, while PEE contained organic sulfur compounds, aliphatic acids and derivatives, alkaloids, phytosterols, etc.ConclusionThese results support that the aerial parts of A. sativum are an interesting source of bioactive ingredients that may be useful in the prevention and treatment of hyperlipidemia.

BackgroundIcanbelimod (formerly CBP-307) is a next-generation S1PR modulator, targeting S1PR1. In this first-in-human study, icanbelimod was investigated in healthy men in Australia.MethodsParticipants were randomized 3:1, double-blind, to icanbelimod or placebo in four single-dose cohorts (0.1 mg, 0.25 mg, 0.5 mg [n=8 per cohort], 2.5 mg [n=4]) or for 28-days once-daily treatment in two cohorts (0.15 mg, 0.25 mg [n=8 per cohort]). Participants in the 0.25-mg cohort received 0.1 mg on Day 1. Treatments were administered orally after fasting; following one-week washout, icanbelimod was administered after breakfast in the 0.5-mg cohort.ResultsIcanbelimod exposure increased rapidly and dose-dependently with single and multiple dosing (Tmax 4–7 hours). Lymphocyte counts decreased rapidly after single (-11%, 0.1 mg; -40%, 0.25 mg; -71%, 0.5 mg; -77%, 2.5 mg) and multiple doses (-49%, 0.15 mg; -75%, 0.25 mg), and recovered quickly, 7 days after dosing. After single-dose 0.5 mg, although a high-fat breakfast versus fasting did not affect maximal decrease, lymphocyte counts tended to be lower after breakfast across most timepoints up to 72 hours. Twenty-eight participants (63.6%) experienced mainly mild treatment-emergent adverse events (TEAEs). After single-dose icanbelimod, the most common TEAEs were headache (28.6%, n=6) and dizziness (19.0%, n=4). Three participants experienced transient bradycardia, with one serious, following single-dose 2.5 mg icanbelimod. After multiple-dose icanbelimod, the most common TEAEs were headache (50.0%, n=6) and lymphopenia (41.7%, n=5), and two participants withdrew due to non-serious TEAEs. Up-titration attenuated heart rate reductions.ConclusionIcanbelimod was well-tolerated up to 0.5 mg and effectively reduced lymphocyte counts.Clinical trial registrationClinicalTrials.gov, identifier NCT02280434.b

BackgroundKaposiform hemangioendothelioma (KHE) is a rare vascular tumor with a high risk of mortality. Few studies with large samples of KHE have been reported. KHE may develop into the Kasabach–Merritt phenomenon (KMP), which is characterized by thrombocytopenia and consumptive coagulopathy. The features of severe symptomatic anemia and life-threatening low platelets make the management of KHE associated with KMP challenging.ObjectiveThe aim of this study was to examine the clinical characteristics of patients with KHE and discuss the treatment experience for different risk groups of KHE.MethodsThrough a retrospective review of 70 patients diagnosed with KHE between 2017 and 2022 in our center, we classify lesions into three clinicopathological stages based on the tumor involving depth, and divided the severity of KHE into three levels by estimating clinicopathological stages and severity of thrombocytopenia. Treatments of different severity groups were estimated with sufficient data.ResultsIn our cohort, 27% were neonates, and KHE lesion occurred at birth in 84% of patients. There was a slight male predominance (32 girls and 38 boys). Common clinical characteristics included associated coagulation disorder (100%), locally aggressive cutaneous blue–purple mass (89%), thrombocytopenia (78%), and local pain or joint dysfunction (20%). The lower extremities were the dominant location (35%), followed by the trunk (29%), the maxillofacial region and neck (24%), and the upper extremities (10%). Of the total cohort, 78% developed KMP; the median age at which thrombocytopenia occurred was 27.8 days. The median platelet count of patients who were associated with KMP was 24,000/µL in our cohort. Ninety-two percent of patients were given surgery treatment and 89% of these patients were given high-dose methylprednisolone (5-6 mg/kg daily) before surgery. In 55 patients with KMP, 36% were sensitive to high-dose corticosteroid therapy. Patients from the low-risk group (eight cases) underwent operation, all of whom recovered without recurrence after a maximum follow-up of 5 years. Out of 26 patients from the high-risk group, 25 underwent surgery treatment, with 1 case undergoing secondary surgery after recurrence and 1 case taking sirolimus. Out of 36 cases from the extremely high-risk group, 32 underwent surgery (including 2 cases who underwent external carotid artery ligation and catheterization), 3 of whom underwent secondary operation after recurrence, and the remaining 4 cases took medicine. The mean length of having sirolimus was 21 months; two cases stopped taking sirolimus due to severe pneumonia. Two cases died at 1 and 3 months after discharge.ConclusionsOur study describes the largest assessment of high-risk patients with KHE who have undergone an operation to date, with 5 years of follow-up to track recovery, which provides invaluable knowledge for the future treatment of patients with KHE and KMP from different risk groups: Early surgical intervention may be the most definitive treatment option for most patients with KHE; multimodality treatment is the best choice for the extremely high-risk group.

In embedded feature selection methods, one criterion for evaluating features is the Feature Contribution to Each Class (FCEC). Unlike traditional feature contribution, it focuses more on the ability of features to represent each class, aiding in understanding the dependency of recognition tasks on different features. However, current feature selection methods often cannot fully utilize the FCEC when evaluating features, resulting in insufficient accuracy in feature evaluation. To address this problem, this paper proposes a novel supervised Feature Selection with Orthogonal Regression and Mountain Range Area (FORMRA). Specifically, orthogonal regression can retain more discriminative information, thereby obtaining more accurate FCEC. We employ a Projection-based Non-monotonic Line Search (PNMLS) method to solve the objective function of orthogonal regression and propose a multi-vector linear fusion search direction on this basis to improve convergence speed. After the objective function converges, we map the FCEC onto mountain ranges in a two-dimensional coordinate system, employing area calculation that is more sensitive to data distribution and shape for feature assessment, thus achieving the full utilization of FCEC. We conducted experiments on eight publicly available datasets, and the results indicate that compared to currently advanced feature selection methods, this approach can provide a more accurate evaluation of features, thereby selecting superior feature subsets and achieving higher model recognition rates.

MoS2 is a promising alternative to Pt in hydrogen evolution reaction (HER) due to its low cost. To enhance the catalytic properties of the 2H MoS2 inert basal plane, we propose an approach of employing oxygen incorporated MoS2 as a catalyst for HER. Different density of oxygen substitution doped monolayer MoS2 samples (MoS1.51O0.49, MoS1.55O0.45, MoS1.67O0.33) were achieved by chemical vapor deposition method and their catalytic performance were tested. Experimentally, we demonstrate that oxygen substitution can activate the inert basal plane and the catalytic performance is dependent on the oxygen substitution percentage. Also, combining with DFT calculations, we confirm that oxygen substitution act as catalytic sites. Our work provides a strategy for enhancing of monolayer MoS2 HER activities through in situ substitution doping.

IntroductionIn recent years, the use of EEG signals for seizure detection has gained widespread academic attention. Aiming at the problem of overfitting deep learning models due to the small number of EEG signal data during epilepsy detection, this paper proposes an epilepsy detection method that combines data augmentation and deep learning.MethodsFirst, the Adversarial and Mixup Data Augmentation (AMDA) method is used to realize the data augmentation, which effectively enriches the number of training samples. To further improve the classification accuracy and robustness of epilepsy detection, this paper proposes a one-dimensional convolutional neural network and gated recurrent unit (AM-1D CNN-GRU) network model based on attention mechanism for epilepsy detection.Results and discussionThe experimental results show that the performance of epilepsy detection achieved by using augmented data is significantly improved, and the accuracy, sensitivity, and area under the subject’s working characteristic curve are up to 96.06, 95.48%, and 0.9637, respectively. Compared with the non-augmented data, all indicators are increased by more than 6.2%. Meanwhile, the detection performance was significantly improved compared with other epilepsy detection methods. The results of this research can provide a reference for the clinical application of epilepsy detection.

Background: Anorectal malformations (ARMs) are among the most common congenital digestive tract malformations worldwide. Although there have been extensive efforts for elucidating their pathogenesis, the specific molecular mechanisms underlying ARMs remain unclear. Methods: We generated ARM Wistar rat models using ethylenethiourea. Nine pregnant rats were allocated to the ARM and control groups, respectively. Hindgut tissue was isolated from embryos collected on gestational days 14, 15, and 16, representing the key timepoints of anorectal development. High-throughput sequencing was used to identify differentially expressed genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the obtained genes. Module analysis was performed via Search Tool for the Retrieval of Interacting Genes (STRING), and a protein-protein interaction (PPI) network was constructed using Cytoscape software. Gene Set Enrichment Analysis (GSEA) was conducted to verify the enrichment of development/apoptosis/epithelium/morphogenesis/Wnt pathway-related core gene sets and identify ARM hub genes. Reverse transcription-quantitative polymerase chain reaction assay was employed to verify mRNA levels of hub genes. Results: 313 GSEA core genes form ten core gene sets were selected. Based on the 281 differentially expressed circRNAs (|Fc| > 2, FDR < 0.05), 42 PPI core genes were obtained. According to the integration of GSEA analysis and PPI network, three hub genes (Cul1, Gli3, and Osr2) highly associated with ETU-induced ARMs were identified. The qRT-PCR validation data were consistent with the sequencing results. Conclusions: These results provide a theoretical basis for the further identification of potential diagnostic and therapeutic targets in ARMs.

The extraction and stripping of uranium(VI) using membrane dispersion microextractor were investigated in high phase ratio. The effects of residence time, aqueous phase flowrates and organic phase flowrates, as well as nitric acid and the extractant concentration on the stage efficiency were evaluated. With the increase of aqueous phase flowrate and organic phase, the efficiency firstly decreased and then increased. Both the extraction efficiency and stripping efficiency can reach >90 % at high phase ratio within 10 s. The efficiency decreased with the increase of nitric acid in aqueous phase, whereas the efficiency increased with the increase of tributyl phosphate (TBP) concentration. Finally, mass transfer characteristic in extraction process in membrane dispersion microextractor was discussed. The overall mass transfer coefficient in membrane dispersion microextractor could reach 0.13 s−1 at A:O = 5:1 and 0.058 s−1at high A:O ratio of 40:1.

Reasonable dense planting is an important measure to increase crop yield per unit area and save resources. However, there is no unified view of the competition for photosynthetic radiation in different stratification structures of maize plants due to different planting densities, as well as the internal mechanism of yield increase. In order to investigate these issues, field experiments were conducted from 2021 to 2022 in Daxing, Beijing, China (39°37′ N, 116°25′ E, altitude 31.3 m a.s.l.). Field plots were arranged in a randomized block design, with the main plot factor representing plant density. In each replicate, four densities were set, i.e., 33,000 (D1), 42,000 (D2), 55,000 (D3), and 83,000 (D4) plants·ha−1. Canopy stratification structure characteristics, including leaf area index, biomass, and photosynthetically active radiation (PAR), were measured in each stratification structure, and transmitted PAR, radiation use efficiency (RUE), and light extinction coefficient (K) were calculated. We found that increasing plant density significantly increased biomass, leaf area index (LAI), and precipitation use efficiency (PUE), but the light extinction coefficient (K) and harvest index (HI) showed opposite trends. Compared to the D1 treatment, the grain yield, precipitation use efficiency (PUE), radiation use efficiency (RUE), and LAI increased by 22.6–88.2%, 9.2–50.5%, 26.7–116.9%, and 27.7 to 150.6% in the D2, D3, and D4 treatments, and K and HI decreased by 19.7–50.3% and 4.2–11.5%, respectively. These showed that a density of 83,000 plants·ha−1 was effective in promoting maize growth in the Daxing area of Beijing, China.

Electrochemical biosensors have emerged as powerful tools for the ultrasensitive detection of lung cancer biomarkers like carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and alpha fetoprotein (AFP). This review comprehensively discusses the progress and potential of nanocomposite-based electrochemical biosensors for early lung cancer diagnosis and prognosis. By integrating nanomaterials like graphene, metal nanoparticles, and conducting polymers, these sensors have achieved clinically relevant detection limits in the fg/mL to pg/mL range. We highlight the key role of nanomaterial functionalization in enhancing sensitivity, specificity, and antifouling properties. This review also examines challenges related to reproducibility and clinical translation, emphasizing the need for standardization of fabrication protocols and robust validation studies. With the rapid growth in understanding lung cancer biomarkers and innovations in sensor design, nanocomposite electrochemical biosensors hold immense potential for point-of-care lung cancer screening and personalized therapy guidance. Realizing this goal will require strategic collaboration among material scientists, engineers, and clinicians to address technical and practical hurdles. Overall, this work provides valuable insight for developing next-generation smart diagnostic devices to combat the high mortality of lung cancer.

Land surface temperature (LST) serves as a pivotal component within the surface energy cycle, offering fundamental insights for the investigation of agricultural water environment, urban thermal environment, and land planning. However, LST monitoring at a point scale entails substantial costs and poses implementation challenges. Moreover, the existing LST products are constrained by their low spatiotemporal resolution, limiting their broader applicability. The fusion of multi-source remote sensing data offers a viable solution to enhance spatiotemporal resolution. In this study, the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) was used to estimate time series LST utilizing multi-temporal Landsat 8 (L8) and MOD21A2 within the Haihe basin in 2021. Validation of ESTARFM LST was conducted against L8 LST and in situ LST. The results can be summarized as follows: (1) ESTARFM was found to be effective in heterogeneous regions within the Haihe basin, yielding LST with a spatiotemporal resolution of 30 m and 8 d while retaining clear texture information; (2) the comparison between ESTARFM LST and L8 LST shows a coefficient determination (R2) exceeding 0.59, a mean absolute error (MAE) lower than 2.43 K, and a root mean square error (RMSE) lower than 2.63 K for most dates; (3) comparison between ESTARFM LST and in situ LST showcased high validation accuracy, revealing a R2 of 0.87, a MAE of 2.27 K, and a RMSE of 4.12 K. The estimated time series LST exhibited notable reliability and robustness. This study introduced ESTARFM for LST estimation, achieving satisfactory outcomes. The findings offer a valuable reference for other regions to generate LST data with a spatiotemporal resolution of 8 d and 30 m, thereby enhancing the application of data products in agriculture and hydrology contexts.

MXene (Ti3C2Tx) is a promising electrode material for supercapacitors. However, the current MXene material preparation process often requires hydrofluoric acid, and the etched MXene is easily oxidized and stacked, which is not conducive to the transfer of electrons and ions. In this study, we proposed a high concentration of sodium hydroxide solution mixed with sodium hypochlorite solution as an etchant for the preparation of fluorine-free MXene (Ti3C2Tx). Based on the alkali-assisted hydrothermal method, Ti3AlC2 was used as the raw material, and 10 % NaClO aqueous solution was added as the oxidant during the etching process to accelerate the etching and in-situ generate TiO2 with excellent conductivity. When the oxidation time is 12 h and the amount of NaClO is 0.2 mL, the TiO2 particles in-situ generated by the oxidation of Ti element in MXene are evenly distributed on the surface and interlayer of the material, which effectively curbs the stacking problem between the Ti3C2Tx layers and improves the specific surface area of the material. The prepared MXene/TiO2 electrode in 1 M H2SO4 electrolyte, − 0.5–0.2 V voltage window, the highest mass specific capacity can reach 321F/g, and after 10,000 charge–discharge cycles, the capacitance retention rate can still reach 86.4 %, which is higher than the capacitance retention rate of Ti3C2Tx material (81.7 %).

In this paper, we propose a centralized SDN network architecture under a smart factory for the problems under the traditional industrial IoT architecture and carry out the security analysis of industrial IoT to improve the performance of the smart factory network. Briefly describing the industrial SDN network architecture, combined with the design of a blockchain system for smart factories, the hash algorithm and asymmetric encryption algorithm are introduced to anonymize and protect the data. For traditional SDN, propose a blockchain-based SDN and analyze the probability of state transfer between SDNs. In order to solve the security problem at the SDN control level, the blockchain-based distributed SDN control model is proposed, and the PBFT consensus algorithm is introduced to realize the blockchain consensus-based multi-controller decision-making model. Add the MD5 algorithm and compare its performance with the SHA256 algorithm. Set up Adder modules with different bit widths and CSA Tree modules with different bases to calculate the encryption time of the RSA algorithm for various combinations. Set up simulation experiments to compare different malicious nodes and the average trust value of the consensus mechanism proposed in this paper for abnormal nodes in an SDN multi-controller. From the results, it can be seen that the computational speed of the RSA system is the fastest for the system implemented by Montgomery’s algorithm in base 16 and bit width 32 bits, and the time to complete the encryption process of 1024 bits once is only 0.97 ms. However, when the number of nodes is controlled in [0~5000], the rejection rate of abnormal nodes by the PBFT consensus algorithm in this paper basically maintains at about 92%. The consensus node designed and proposed in this paper is able to reject error nodes effectively and effectively secure the SDN smart factory network architecture.

Blockchain technology is increasingly pervasive in social production and life, and its influence on the accounting and auditing industry is undeniable. The article extensively researches blockchain technology and smart contracts, and it constructs a blockchain-based cloud data auditing model. Combining it with the evolutionary game model completes the construction of the blockchain-based cloud data auditing system. The running cost of the auditing model is analyzed in terms of communication and computation overhead, smart contract cost, time cost, etc., and how it affects the strategies of auditing clients and auditors is studied from the aspects of cost and benefit, respectively. In this paper, the audit model’s average communication overhead and computation overhead are 9.86 KB and 0.82 seconds, respectively. The cost of the smart contract model is relatively low and shows a steady trend. The average query time of this paper’s auditing model is 0.78ms, and none of the validation times on the validator exceeds 5ms, while the average validation times on the blockchain and cloud servers are 11.01ms and 534.35ms, respectively. The auditing client and the auditor tend to adopt the blockchain technology strategy when the cost reduces, or the benefit increases, with a probability of adoption approaching 1 after numerous games.

BackgroundDepression is a common public health problem, characterized by persistent low mood, lack of pleasure and exhaustion. Conventional treatments such as antidepressants and psychotherapy have some limitations, including variable efficacy, adverse side effects and high costs. Acupoint catgut embedding (ACE) therapy, as a subtype of acupuncture, has gained increasing clinical application due to its long-term effects, higher patient compliance, and cost-effectiveness. This study aims to conduct a meta-analysis to evaluate the efficacy and safety of ACE for depression.MethodsElectronic searches will be conducted in 12 databases (both in English and Chinese databases), encompassing from inception to April 2022, without language restrictions. Randomized controlled trials (RCTs) that involve ACE for treating depression will be included. The primary outcome measures will include the response rate, Hamilton Depression Rating Scale (HAMD), Beck Depression Inventory (BDI), and Traditional Chinese Medicine Symptom Scale (TCMSSS). The secondary outcome measure will include Quality of Life scale score (QoL) and the incidence of adverse events. Results will be presented as risk ratios for dichotomous data and mean differences for continuous data. Two reviewers will independently conduct study selection, data extraction, and quality assessment. The methodological quality of eligible studies will be evaluated according to the criteria specified by the Cochrane Handbook for Systematic Reviews of Interventions (Version 5.1.0). Meta-analysis will be performed by RevMan 5.3 software.DiscussionDue to the limitations, a safer, high-efficacy and non-pharmacological intervention with minimal side effects is required for treating depression. ACE has the advantages of longer-lasting effects, improved patient compliance, and reduced treatment costs. This protocol represents a meta-analysis and systematic review, aiming to present the current evidence regarding the efficacy and safety of ACE for depression. It seeks to provide clinicians with a theoretical basis and valuable references for complementary and alternative medicine therapies in their treatment approaches.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/#recordDetails, Identifier CRD42022325966.

In Chinese piano teaching practice, the importance of auditory perception of music has been neglected, so that in piano performance and teaching, many people play music that lacks personality and emotional experience. This paper proposes an auditory perception model based on wavelet packet domain based on the acoustic properties of the human ear. The model analyzes the acoustical perception characteristics of piano audio signals through wavelet transform, and introduces adaptive wavelet packet decomposition into the calculation of the auditory perception model to simplify the calculation of the model, so that the obtained auditory masking threshold is closer to the actual value. Finally, the acoustic perception model is practically applied to piano teaching to verify its effectiveness. And the traditional Chinese music elements of Gong, Zheng and Shang were infiltrated in piano teaching. The results showed that the model detected the 3rd harmonic g1 with root tone C, whose frequency peak was most prominent at 397.25 Hz, and the sound pressure value reached 39.50 dB, indicating that the auditory perception ability directly affects the musical expression and creativity. This study cultivates students’ musical auditory perception ability, which significantly improves piano playing ability.

Improving the quality of preschool education management is the basic guarantee of national talent training. In this paper, the data envelopment analysis of resource allocation efficiency of preschool education is conducted by analyzing the characteristics of preschool education and the allocation process of educational resources. Based on the original input and original output, the productivity of preschool education is analyzed by combining different environmental variables in the CCR model. The asymptotic approximation method was used to improve the fit of the initial data and to speed up the convergence of the fit by weighting. Based on the study of preschool education in County M, kindergartens with resource allocation at a high level ( 0.8≤ θ

This paper first analyzes the innovation and entrepreneurial ability demands and development influencing factors of university students in economics and management and proposes the “one body and four wings” cultivation model of innovation and entrepreneurship based on this model. Secondly, the evaluation system of innovation and entrepreneurship education indexes in colleges and universities is constructed according to this model, and the similarity degree is calculated using K-means clustering, and the Kruskal algorithm optimizes the K value. Finally, the quantitative data analysis was carried out by examining the index weights and index evaluation. The results showed that the evaluation scores of innovation and entrepreneurship education objectives increased by 35.51%, 40.31%, 19.01% and 26.40%, respectively, compared with other indicators, indicating that the use of the “one body and four wings” cultivation mode can promote the development of innovation and entrepreneurship education.

Surface soil moisture (SSM) reflects the dry and wet states of soil. Microwave remote sensing technology can accurately obtain regional SSM in real time and effectively improve the level of agricultural drought monitoring, and it is of great significance for agricultural precision irrigation and smart agriculture construction. Based on Sentinel-1, Sentinel-2, and Landsat-8 images, the effect of vegetation was removed by the water cloud model (WCM), and SSM was retrieved and validated by a radial basis function (RBF) neural network model in bare soil and vegetated areas, respectively. The normalized difference vegetation index (NDVI) calculated by Landsat-8 (NDVI_Landsat-8) had a better effect on removing the influence the of vegetation layer than that of NDVI_Sentinel-2. The RBF network model, established in a bare area (R = 0.796; RMSE = 0.029 cm3/cm3), and the RBF neural network model, established in vegetated areas (R = 0.855; RMSE = 0.024 cm3/cm3), have better simulation effects on SSM than a linear SSM inversion model with single polarization. The introduction of surface parameters to the RBF neural network model can improve the accuracy of the model and realize the high-accuracy inversion of SSM in the study area.

With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios.

Blasting technology is widely applied in various engineering applications due to its cost-effectiveness and high efficiency, such as in mining, transport infrastructure construction, and building demolition. However, the occurrence of cracking in the rear row has always been a major problem that disrupts mining bench blasting. To address this issue, a three-hole simultaneous blasting technology is proposed in this study. Both numerical simulations and onsite blasting experimental testing were conducted. To aid this endeavor, the three-hole simultaneous blasting and the hole-by-hole blasting methods were adopted to comparatively analyze the severity of the damage caused to the original rock and the effect of rock fragmentation in the rear row. The obtained results highlighted that the outcome of the blast produced by the three-hole simultaneous blasting method is satisfactory, with fewer flying stones and concentrated blasting piles required. Additionally, the original rock in the rear row showed no obvious sign of tensile damage and had uniform fragmentation. It was also found that a block size of less than 60 cm accounts for 100%, while a block size of less than 50 cm accounts for 98.7% of the whole blocks, with no large blocks reported. Moreover, a penetrating horizontal crack occurred in the direction of the connection of the blast hole center when the three-hole simultaneous blasting method was adopted. This resulted in a smooth and flat rear part of the rocks at the interface. Compared to the hole-by-hole blasting method, the three-hole simultaneous blasting method improved the effective stress and displacement at each measurement point. At the measurement point directly at the front of the borehole, the maximum effective stress attained 67.9 GPa, and the maximum displacement reported was 31.9 cm. Overall, it was shown that the three-hole simultaneous blasting technology is applicable in similar applications of mine bench blasting, which is conducive to addressing the rear row original rock strain for onsite bench blasting.

The interplay between non-Hermitian effects and topological insulators has become a frontier of research in non-Hermitian physics. However, the existence of a non-Hermitian skin effect for topological-protected edge states remains controversial. In this paper, we discover a new type of the non-Hermitian skin effect called the non-Hermitian chiral skin effect. Non-Hermitian chiral skin effect is induced by inhomogeneous edge dissipation under closed boundary conditions. In particular, the chiral modes of the non-Hermitian chiral skin effect localize around “topological defects” characterized by global dissipation rather than being confined to the system boundaries. We show its detailed physical properties by taking the non-Hermitian Haldane model as an example. As a result, the intrinsic mechanism of the hybrid skin-topological effect in Chern insulators is fully understood via the non-Hermitian chiral skin effect. Therefore this work will be helpful for the research on both non-Hermitian physics and topological quantum states.

Aiming at the far-fetched connection of the current combination of science and engineering professional courses teaching and morality cultivation, ideological and political education of professional curriculum has been reformed in the “Sensor Technology and Application” course to explored the curriculum ideological and political education mode of the effective combination of professional education and morality cultivation under the background of Chinese modernization. Various ways of curriculum ideological and political reform have been tried in the course teaching process, curriculum ideological and political elements have been designed with regional, college-based and specialty group characteristics which have been also implemented and evaluated in the teaching process. The research results showed that the construction and implementation of the curriculum ideological and political education in the course of “Sensor Technology and Application” have improved students’ learning interests and professional qualities in multiple dimensions, and have received unanimous praise from teachers and students.

Ensuring the safety of tunnel structures in unique underwater environments is of paramount importance. This study focuses on the entrance section of a river-crossing tunnel, characterized by shallow burial depths with significant variations. The tunnel structure faces challenges related to structural weakening, and its deformation response exhibits specific characteristics under seismic loads. Numerical simulation software was employed to model and calculate the deformation response of the shallowly buried entrance section under seismic load conditions, assessing the impact of changes in tunnel structural strength and burial depth. The results reveal the following key findings: (1) As the tunnel structure weakens, structures of varying strengths primarily experience vertical deformation under seismic loads, followed by horizontal deformation. Furthermore, as the tunnel structure’s strength decreases, it exhibits an increasing trend in deformation. Routine maintenance should prioritize locations where the tunnel structure weakens, implementing timely reinforcement measures to ensure adequate load-bearing capacity. (2) Considering the influence of tunnel structural burial depth, lateral deformation values rank in descending order from largest to smallest, including the tunnel bottom, middle carriageway, middle of the tunnel, and tunnel vault, while the overall lateral deformation of the tunnel is not significant, it decreases as the structural burial depth increases. (3) In relation to tunnel structural burial depth, vertical deformation values generally exceed horizontal deformation values. Sections with the highest to lowest deformation values are the tunnel bottom, middle lane of the tunnel, middle of the tunnel, and position of the tunnel vault. With increasing burial depth, the vertical deformation values decrease at a slower rate than horizontal deformation. The tunnel structure demonstrates a higher sensitivity of lateral deformation to changes in burial depth factors.