Transdermal drug delivery (TDD) is one of the key approaches for treating diseases, avoiding first-pass effects, reducing systemic adverse drug reactions and improving patient compliance. Microneedling, iontophoresis, electroporation, laser ablation and ultrasound facilitation are often used to improve the efficiency of TDD. Among them, microneedling is a relatively simple and efficient means of drug delivery. Microneedles usually consist of micron-sized needles (50-900 μm in length) in arrays that can successfully penetrate the stratum corneum and deliver drugs in a minimally invasive manner below the stratum corneum without touching the blood vessels and nerves in the dermis, improving patient compliance. Hydrogel-forming microneedles (HFMNs) are safe, non-toxic, no residual matrix material, high drug loading capacity, controlled drug release and are suitable for long-term, multiple drug delivery. This work reviewed the characteristics of skin structure and transdermal drug delivery; introduced TDD strategies based on hydrogel microneedles; summarised the characteristics of HFMNs TDD systems and the evaluation methods of HFMNs; and the application of HFMNs drug delivery systems in disease treatment. The HFMNs drug delivery system has a wide scope for development, but the translation to clinical application still has more challenges.

The Imbalance Cost Pass-Through (ICPT) is a flexible component of the Incentive Regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity generation. However, the cost of electricity generation is significantly influenced by the volatility of commodity prices, including coal, gas and LNG. In Malaysia, the Energy Commission has conducted biannual reviews of fuel and other generation costs. Any cost savings or increases identified during these reviews will be passed on to customers in the form of rebates or surcharges. Due to that reason, this study proposes ICPT price forecasting for the electricity market in Peninsular Malaysia. The study aims to construct an ICPT-related baseline model for the peninsular generation data by employing three forecasting methods. The forecasting performance is analysed by a Mean Absolute Percentage Error (MAPE). In findings, the ARIMA method is one of the most accurate forecasting methods for fuel prices compared to the Moving Average (MA) and LSSVM methods. The observed price differences between the ARIMA and LSSVM models for ICPT are minimal. The ICPT price for July-December 2022 and January-June 2023 is MYR 0.21/ kWh for ARIMA and MYR 0.18/kWh for LSSVM, which are close to the actual TNB’s ICPT tariff. As for the forecasting, the ICPT price is expected to drop for the next announcement. The findings of this study may contribute to a positive impact on the sustainability of the energy sector in Malaysia.

Metformin（Met） is a commonly used drug in the treatment of type 2 diabetes. Currently, it has been found that Met can effectively reduce the incidence of stroke and exert anti-inflammatory effects. However, its role in ischemia-reperfusion (I/R) induced nerve injury remains unclear. This study aims to investigate the neuroprotective effects of Met in I/R-induced neuron injury as well as the underlying mechanism.A middle cerebral artery occlusion (MCAO) model was established in SD rats, which were then treated with different doses of Met.Neurological deficits of rats were measured at different times post-surgery. TTC staining to observe the volume of cerebral infarction.HE staining was performed to observe pathological changes of brain tissues .Immunohistochemistry was performed to observe the expression of inflammatory factors in the cerebral tissues .qRT-PCR method was used to detect the relative expression of PI3K、Akt mRNA in cells after 24 h of drug action.Western blot method was used to detect the expression of PI3K、p-PI3K、Akt and p-Akt in hippocampus.What’s more, in vitro experiments were performed on BV2 microglia to verify the role of Met against oxygen-glucose deprivation (OGD). As a result, Met dose-dependently attenuated neurological deficits and neuronal apoptosis. Besides, Met administration also significantly reduced BV2 cells apoptosis and inflammatory response. Mechanistically, Met inactivated PI3K/Akt pathway induced by I/R and OGD, while upregulated PI3K. In conclusion, Met protected rats from cerebral I/R injury via reducing neuronal apoptosis and microglial inflammation through PI3K/Akt pathway.

An investigation of pair partial radial distribution functions and molecular pair potentials within a system has established that the existing potential functions are rooted in the assumption of a static arrangement of atoms, overlooking their distribution and vibration. In this study, Hill’s proposed radial distribution function polynomials are applied for the pure gaseous state to a binary liquid alloy system to derive the pair potential energy. The partial radial distribution functions of 36 binary liquid alloy systems reported in literature were examined and then validated using four thermodynamic models. Results show that the molecular interaction volume model (MIVM) and regular solution model (RSM) outperform other models when an asymmetric method is used to calculate the partial radial distribution function. The MIVM exhibits a mean standard deviation (SD) of 0.095 and a mean absolute relative difference (ARD) of 32.2%. Similarly, the RSM demonstrates a mean SD of 0.078 and an ARD of 32.2%. The Wilson model yields a mean SD of 0.124 and a mean ARD of 226%. The nonrandom two-liquid (NRTL) model exhibits a mean SD of 0.225 and a mean ARD of 911%. On applying the partial radial distribution function symmetry method, the MIVM and RSM outperform the other approaches, with a mean SD of 0.143 and a mean ARD of 165.9% for the MIVM. The RSM yields a mean SD of 0.117 and an ARD of 208.3%. The Wilson model exhibits the mean values of 0.133 and 305.6% for SD and ARD, respectively. The NRTL model shows an average SD of 0.200 and an average ARD of 771.8%. Based on this result, the influence of the symmetry degree on the thermodynamic model is explored by examining the symmetry degree as defined by the experimental activity curves of the two components.

The Schwarzschild metric describes a non-rotating and charge-free celestial body, and it results in things stopping at the event horizon of a black hole and spending infinite time across the event horizon by the observer far away from the black hole. The analysis of the particle’s behavior at the event horizon tells us that this solution predicts an un-expanded black hole which violates the astronomical observations and our knowledge about the black hole. Although some alternative metrics have been proposed, the singularity problem is still unsolved. In this research, the degenerate Fermi electron gas is used to reveal that the Fermi electron gas cannot shrink to a point no matter how large energy it obtains, so the singularity exists at the center very unreasonably. In order to avoid these problems, a finite-size nucleus of the black hole is proposed and reasonably explained by the behaviors of the Fermi electron gas and the Fermi neutron gas there. On the other hand, the Kerr-Newman metric is the one describing the rotating and charged black hole and the equation of the light velocity at each space point can be obtained. It tells us that there are two real and non-imaginary solutions for the radial speed of light at the position larger than the Schwarzschild radius.

Both SARS-CoV-2 and SARS-CoV initially appeared in China and spread to other parts of the world. SARS-CoV-2 has generated a COVID-19 pandemic causing more than 6 million human deaths worldwide while the SARS outbreak quickly ended in six months with a global total of 774 reported deaths. One of the factors contributing to this stunning difference in the outcome between these two outbreaks is the inaccuracy of the RT-qPCR tests for SARS-CoV-2, which generated a large number of false-negative and false-positive test results that have misled patient management and public health policy-makers. This article presented Sanger sequencing evidence to show that the RT-PCR diagnostic protocol established in 2003 for SARS-CoV can in fact detect SARS-CoV-2 accurately due to the well-known nonspecific PCR amplification of DNAs with similar sequences. Using nested RT-PCR followed by Sanger sequencing to retest 50 patient samples collected in January, 2022 and sold as RT-qPCR positive reference confirmed 21 (42%) were false-positive. Although the other 29 positive isolates were categorized as Omicron variant by partial sequencing of the N gene, and the RBD and the NTD of the S gene, 9 (31%) showed focal to complete sequencing failure in the S gene segments due to multi-allelic SNPs. During the course of the study, an Omicron variant isolate containing a BA.1 NTD and a BA.2 RBD in its S gene was also detected. Routine partial S gene sequencing of all PCR-positive samples can timely discover multi-allelic SNPs and viral recombination in the circulating variants for investigation of their impacts on vaccine efficacies, therapeutics and diagnostics.

As SARS-CoV-2 continues to spread among human populations, genetic changes occur and accumulate in the circulating virus. Some of these genetic changes have caused amino acid mutations, including deletions, which may have potential impact on critical SARS-CoV-2 countermeasures, including vaccines, therapeutics, and diagnostics. Considerable efforts have been made to categorize the amino acid mutations of the angiotensin-converting enzyme 2 (ACE2) receptor binding domain (RBD) of the spike (S) protein along with certain mutations in other regions within the S protein as specific variants in an attempt to study the relationship between these mutations and the biological behavior of the virus. However, the currently used whole genome sequencing surveillance technologies can test only a small fraction of the positive specimens with high viral loads and often generate uncertainties in nucleic acid sequencing that needs additional verification for precision determination of mutations. This article introduces a generic protocol to routinely sequence a 437-bp nested RT-PCR cDNA amplicon of the ACE2 RBD and a 490-bp nested RT-PCR cDNA amplicon of the N-terminal domain (NTD) of the S gene for detection of the amino acid mutations needed for accurate determination of all variants of concern and variants of interest according to the definitions published by the U.S. Centers for Disease Control and Prevention. This protocol was able to amplify both nucleic acid targets into cDNA amplicons to be used as templates for Sanger sequencing on all 16 clinical specimens that were positive for SARS-CoV-2.

Large population passages of the SARS-CoV-2 in the past two and a half years have allowed the circulating virus to accumulate an increasing number of mutations in its genome. The most recently emerging Omicron subvariants have the highest number of mutations in the Spike (S) protein gene and these mutations mainly occur in the receptor-binding domain (RBD) and the N-terminal domain (NTD) of the S gene. The eCDC and the WHO recommend partial Sanger sequencing of the SARS-CoV-2 S gene RBD and NTD on the PCR-positive samples in diagnostic laboratories as a practical means of determining the variants of concern to monitor a possible increased transmissibility, increased virulence, or reduced effectiveness of vaccines against them. The author’s diagnostic laboratory has implemented the eCDC/WHO recommendation by sequencing a 398-base segment of the N gene for the definitive detection of SARS-CoV-2 in clinical samples, and sequencing a 445-base segment of the RBD and a 490-509-base segment of the NTD for variant determination. This paper presents 5 selective cases to illustrate the challenges of using Sanger sequencing to diagnose Omicron subvariant when the samples harbor a high level of co-existing minor subvariant sequences with multi-allelic single nucleotide polymorphisms (SNPs) or possible recombinant Omicron subvariants containing a BA.1 NTD and a BA.2 RBD, which can only be detected by using specially designed PCR primers. The current large-scale surveillance programs using next-generation sequencing (NGS) do not face similar problems because NGS focuses on deriving consensus sequence.

In this research, the other reasonable explanations for the cosmic microwave background radiation is revealed. Due to the microwave resolution, it very roughly shows the image of galaxies in the universe. Moreover, the intensity measurement on each pixel of the image is the sum of the incident microwaves from different directions, so the microwave image cannot represent the microwave sources clearly far away from the Earth. Hence, we propose a simulation after removing several strongest microwave sources, the remaining microwave radiation sources can establish a very uniform intensity distribution over a range of several ten light years. On the other hand, Sloan Digital Sky Survey reveals 200 million galaxies in the universe and, in fact, only to eliminate the contributions of all galaxies from the microwave image is impossible. The way to further obtain the fine-scale structure by only removing the few strongest microwave sources as the foreground effect will keep the other contributions from all the rest galaxies and stars. Therefore, the Cosmic Microwave Background cannot be uniquely explained the radiation which was left after the initial formation of the universe. Moreover, it is the mainly residual radiation from the un-calculated galaxies and inaccurate estimation of the microwave source strength.

In the context of "dual carbon", with the continuous deepening of the power system reform, the application of high proportion renewable energy is becoming increasingly widespread. All sectors of society have put forward higher requirements for electricity sales packages, which can guide the behavior of power users, promote energy conservation and emission reduction, and finally promote low-carbon operation of the power market economy.Therefore, the requirements of power users and power selling companies for the power selling package are increasing, which puts forward higher requirements to the power selling package recommended by the power selling company . However, the existing recommendation methods are unable to make appropriate recommendations for the users with lack of preference information. Therefore, this paper proposes a two-sided matching decision-making method of electricity sales package based on disappointment theory. First of all, according to the incomplete fuzzy preference relationship provided by the power user and the electricity sales package, the respective priority weight vector is obtained, and then the subjective satisfaction matrix of the power user and the electricity sales package to each other is obtained; Next, the adjusted satisfaction matrix is calculated by adding the influence of the theory of joy and disappointment; Then, based on the adjusted satisfaction matrix, an optimization model aiming at maximizing the total satisfaction of power users and electricity sales packages is established, and the optimal stable matching model of power users and electricity sales packages is obtained; Finally, taking an industrial park in Zhejiang Province as an example, by using the bilateral matching method proposed in this article, the optimal matching schemes for 5 power users and 6 electricity sales packages are obtained, which shows the effectiveness and rationality of the two-sided matching decision-making method of electricity sales package based on the disappointment theory proposed in this paper.

Objective: The purpose of this study was to investigate the effect of aerobic exercise on myocardial injury induced by I/ R in rats by regulating SIRT3/SOD2/NF-κB signaling pathway, and to provide theoretical guidance for clinical treatment of myocardial I/R injury.Methods:SPF Male Sprague-Dawley(SD) rats were randomly assigned to 4 groups: Sham operation group(n=10), I/R group(n=10), Aerobic exercise group(n=10)and Aerobic exercise+κ-receptor antagonist group(Pro DTC group，n=10). The left anterior descending coronary artery(LAD) of rats was ligated and re-canalized to establish I/R rat model. Hematoxylin-eosin(HE) staining was performed to examine histological morphology in myocardial tissues of each group. The biological analysis was performed to measure cTnI、CK-MB、BNP levels in blood samples of each group. The expression levels of SOD2, TLR4, and p65 in myocardial tissues were measured by immunohistochemical assay. The influence of aerobic exercise on Beclin-1 、LC3II/I、SIRT3, TLR4, and phosphorylated p65 was measured by Western blotting.Results: The result of histological morphology examination revealed that Aerobic exercise group exhibited integrated cardiac myofilament, less inflammatory cell infiltration, as much as significantly decreased cellular edema. Measurement of cTnI、CK-MB、BNP revealed that oxycodone post-treatment reduces the injury of myocardial tissues(P<0.05). Immunohistochemical staining results revealed that aerobic exercise clearly decreased the expression of TLR4 and p65, and increased the expression of SOD2(P<0.05). Besides, Western blotting revealed that aerobic exercise down-regulated the expression of Beclin-1 、LC3II/I、TLR4 and phosphorylated p65, up-regulated the expression of SIRT3(P<0.05).Conclusions: Aerobic exercise significantly improved myocardial I/R injury. The mechanisms may be associated with activating κ-receptor to regulate SIRT3/SOD2/NF-κB pathway.

A lack of trust in Energy Service Company (ESCo) is the most critical factor affecting the development of Energy Performance Contracting (EPC) in China compared with other constraints. One cannot easily estimate the energy-saving performance of an EPC project. Under that condition, lack of trust may cause the Energy-Consuming Unit (ECU) to suspect the energy-saving performance promised by the ESCo, thus leaving potentially profitable projects without necessary funding. Currently, specific studies taking an across-projects viewpoint on annual energy-saving performance of EPC projects in multiple subsectors objectively and quantitatively are lacking. This paper studies the regression relationships of annual energy-saving quantity in terms of revamping cost and the regression relationships of annual cost saving in terms of revamping cost. The regression results show that there are statistically significant correlations in the above relationships in the nine subsectors investigated. This is significant for ESCos and ECUs because knowledge on energy-saving performance could contribute to EPC investment decisions and trust relationships between ESCos and ECUs. Then a multiple linear regression model of revamping cost is set up to analyze its influencing factors. The model indicates that the subsector the sample belongs to, financing, registered capital of the ESCo, and contract period have significant effects on revamping cost. Thus, policy implications regarding innovation of EE promotion technology, clarifying ESCos’ exit mechanism, innovation of financing mechanism, and improving the market credit environment for promoting investment in EPC projects are provided.

Hamacher operation which is generalization of the Algebraic and Einstein operation, can widely provide a large number of arithmetical operation with respect to uncertainty information, and Heronian mean can deal with correlations of the input arguments or different criteria and don’t make calculation redundancy, meanwhile, the normal intuitionistic fuzzy numbers (NIFNs) can depict distinctively normal distribution information in practical decision making. In this paper, a multi-criteria group decision-making (MCGDM) problem is researched under the NIFNs environment, and a new MCGDM approach is introduced on the basis of the Hamacher operation. Firstly, according to Hamacher t-conorm and t-norm, some operational laws of NIFNs are presented. Secondly, it is noticed that Heronian mean can’t only once take into account mutual relation between attribute values once, but also consider the correlation between input argument and itself. Therefore, we develop some operators and study their properties in order to aggregate normal intuitionistic fuzzy numbers information, these operators include Hamacher Heronian mean (NIFHHM), Hamacher weighted Heronian mean (NIFHWHM), Hamacher geometric Heronian mean (NIFHGHM) and Hamacher weighted geometric Heronian mean (NIFHWGHM). Furthermore, we apply the proposed operators to the MCGDM problem and present a new method. The main characteristics of this new method involve that: (1) it is suitable to make decision under the normal intuitionistic fuzzy numbers environment and more reliable and reasonable to aggregate the normal distribution information. (2) it utilizes Hamacher operation which can provide more reliable and flexible decision-making results and offer an effective and powerful mathematic tool for the MAGDM under uncertainty. (3) it uses the Heronian mean operator which can considers relationships between the input arguments or the attributes and don’t brings subsequently about redundancy. Lastly, an application is given for showing the feasibility and effectiveness of the presented method in this paper.

The study examines the return performance and resilience to market volatility of the recently introduced Environment, Social/Sustainable, and Governance (ESG) weight-tilted Hang Seng Index (HSIESG) compared to its parent, the Hang Seng Index (HSI). The ESG-infused index has a higher mean return and lower return volatility than the parent index, although the differences are statistically and economically insignificant, a result consistent with the high correlation between the two index returns. Most important, the ESG weighted-tilted index is more resilient to volatility spikes than the parent index and therefore has lower downside risks. The overall results show that stocks with high ESG ratings are less susceptible to trading pressures triggered by volatility-induced turnovers. The paper contributes to the literature by providing significant incremental information on the emerging market for ESG-related equity products in Hong Kong.

Adaptive mechanisms, such as channel hopping and packet replication, are used in low-power wireless networks to deal with the spatial and temporal variations in the link quality, and meet the reliability requirements of industrial applications (i.e., PDR>99%). However, the benefits of such mechanisms are limited and may have a large impact on end-to-end latency and energy consumption. Hence, in this paper we propose using adaptive modulation diversity, which allows to dynamically select different modulations, to improve link reliability. We present three adaptive modulation diversity selection strategies and validate them using the data derived from a real-world deployment using the IEEE 802.15.4g SUN modulations (i.e., SUN-FSK, SUN-OQPSK and SUN-ODFM) in an industrial environment. The results show that by using adaptive modulation diversity it is possible to improve link reliability regardless of node conditions.

Hang-ju was one of five officinal varieties of Flos chrysanthemum for its edible and potable usage. Besides Flos Chrysanthemum (FL), there were also Bud Chrysanthemum (BC) and Fetal Chrysanthemum (FC) at the early and late stage of buds, respectively, in the consumption market of Hang-ju with higher prices. Whether the quality and efficiency of BC and FC was superior to FL or merely consumption misunderstandings? Three commercial products of Hongxinju, a representive cultivar of Hang-ju were studied with a GC-MS based metabolomics approach, complemented with morphology, contents of moisture and protein and the anti-oxidant activity, to reveal the metabolic alterations of violate components in Hongxinju in different flowering stage and at different processing periods. It revealed that most of the violate components were increased from fresh FC to FL, and the low-boiling fractions, inflammatory methyl arachidonate and air-polluting component of ethylbenzene were declined while the representative components with pungent flavor and cool nature of a-curcumene and (Z,Z,Z)-9,12,15-octadecatrienoic acid, vision improving carotenol of rhodopin and high-boiling fractions were elevated after processed in final FL compared with that in BC and/or FC. Though the content of protein and anti-oxidative capacity of final BC and FC were nearly equal to those of FL, in comprehensive consideration of the representative components related with the efficiency in heat cooling and vision improving, as well as the representative components related with inflammation and air-pollution, final FL was recommended other than BC and FC in the practice of medicine with the yield and quality integrated into account.

In this paper, a new axial-type magnetic gear with auxiliary flux-enhancing structure (AFS-AMG) is proposed. Compared to conventional AFS-AMG, it has the higher torque density and higher permanent magnet (PM) utilization factor. Firstly, the design rules and operating principles of proposed AFS-AMG are elaborated. Then, the mapping relation between the radial-type magnetic gears (RMGs) and AMGs are elucidated. Compared to its counterpart in RMGs, AFS-AMGs achieve small size. Then, the geometrical parameters of AFS-AMGs are optimized to obtain the better electromagnetic performances, where the torque density per volume and per PM volume is adopted as the evaluation standard. Finally, three different AMG topologies are constructed in finite element analysis (FEA) software for comparison. It is proven that the AFS-AMG has the largest torque density per volume and per PM volume.

Background: SARS-CoV-2 Omicron JN.1 has the highest number of amino acid mutations in its receptor binding domain (RBD) and has acquired a hallmark L455S mutation. The immune evasion capability of JN.1 is a subject of scientific investigation. The US CDC used RT-qPCR SGTF as the proxy indicator of JN.1 infections for evaluation of the effectiveness of updated monovalent XBB.1.5 COVID-19 vaccines against JN.1 and recommended that all persons aged ≥6 months should receive an updated COVID-19 vaccine dose. Objective: Recommend Sanger sequencing for diagnosis of JN.1 infections to generate the data based on which guidelines are made to direct vaccination policies. Methods: The RNA in nasopharyngeal swab specimens from patients with clinical respiratory infection was subjected to nested RT-PCR, targeting a 398-base segment of the N-gene and a 445-base segment of the RBD of SARS-CoV-2 for amplification. The DNA sequences were analyzed for amino acid mutations. Results: The N-gene sequence showed Q229K, usually associated with Omicron BA.2.86 (+JN.1). The RBD sequence showed 24 amino acid mutations, including the hallmark L455S mutation for JN.1 and the V483del for BA.2.86 lineage. Conclusions: The CDC should consider Sanger sequencing of the RBD to diagnose JN.1 infections for statistical analysis in making vaccination policies.

Fetal weight estimation before delivery is important in obstetrics, which assists doctors diagnose abnormal or diseased cases. Linear regression based on ultrasound measures such as bi-parietal diameter (bpd), head circumference (hc), abdominal circumference (ac), and fetal length (fl) is common statistical method for weight estimation but the regression model requires that time points of collecting such measures must not be too far from last ultrasound scans. Therefore this research proposes a method of early weight estimation based on expectation maximization (EM) algorithm so that ultrasound measures can be taken at any time points in gestational period. In other words, gestational sample can lack some or many fetus weights, which gives facilities to practitioners because practitioners need not concern fetus weights when taking ultrasound examinations. The proposed method is called dual regression expectation maximization (DREM) algorithm. Experimental results indicate that accuracy of DREM decreases insignificantly when completion of ultrasound sample decreases significantly. So it is proved that DREM withstands missing values in incomplete sample or sparse sample.

The Mara River basin (MRB) has a world-famous ecosystem, but the vegetation has been damaged due to economic development in recent years, and there is little known about the area that will experience severe vegetation damage in the future. Based on the vegetation vulnerability system, principal component analysis, and three CMIP6 scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), vegetation vulnerability was calculated in the base period (2010-2019), near-term period (2020-2059) and long-term period (2060-2099) in the MRB. The spatial cluster of vegetation vulnerability was revealed by spatial correlation analysis, and the transition of vegetation vulnerability of different periods was analyzed by stochastic matrix. The results showed that in all periods, the vulnerability showed a high-high cluster in the east, and a low-low cluster midstream and downstream. From the stochastic matrix, the area of high vulnerability increased the least under the SSP1-2.6, while it increased the most under the SSP5-8.5. The vegetation vulnerability upstream increased the most from the base period to the near-term period and long-term period in the MRB. By comparing the vegetation vulnerability under different scenarios, and pointing out the areas with the highest vulnerability increase, this study can better provide comprehensive decision-making for vegetation protection in the MRB.

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR -CDR) provides a nearly 40-year global dataset that has a potential to fill a gap in long term snow cover fractional monitoring. Our study selects the Qinghai-Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface reflectance at 0.64μm (SR1) and Surface reflectance at 0.86μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm & improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1、 ground snow-depth measurements and ERA5. Results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. Improved algorithm results in better consistency with MOD10A1 than vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2,000 meters or above 6,000 meters, and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. Results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land cover type and topographic factors are important to the algorithm. Our study lays the foundation for the global snow cover percentage product based on AVHRR-CDR，furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset.

The contribution of genetic and environmental factors to the pathophysiology of cerebral aneurysms in monozygotic twins is under-reported and presents ambiguous arguments. The morphology and hemodynamics of neurovascular arteries in a pair of monozygotic twins (MTs) were investigated to reveal the underlying mechanisms. Four arterial models were reconstructed for the twin A-right brain and left brain, twin B-left brain, and B-left brain without anterior cerebral arteries based on preclinical scanned information. Subsequently, the dimensions, configurations and outlined curves of the three-perspective geometries were compared between the MTs. Adopting an in-vitro validated numerical cerebral aneurysm model, hemodynamic patterns were investigated and compared in the MT models, respectively. Morphological comparisons of the MTs show the size and shape of cerebral arteries exist significant differences, despite of the expected genetic similarities. These differences can be attributed to variations during embryological development and external environmental influences. Qualitatively and generally, numerical results indicate the MTs have some hemodynamic similarities (e.g., time-averaged pressure (TAP) distributions (~13400 Pa), and oscillatory shear index (0~0.49), but present significant differences in specific local arteries due to morphological variances. Specifically, the difference in the volumetric blood flow rate in corresponding arteries between the MTs is from 16% (smallest) in anterior choroidal artery (AChA) to 221% (largest) in the ophthalmic artery (OphA), varying with specific compared arteries. Also, the registered hemodynamic indicators, such as the maximum time-averaged wall shear stress (TWSS) (53.6 Pa vs. 37.8 Pa), and different local OSI distributions were observed between the MTs. The findings revealed that morphological variations in MTs could be generated by embryological and environmental factors, thus assuming they share the identical morphology in cardiovascular and neurovascular systems may lead to significant misevaluations in hemodynamics quantifications and further lesions.

This study provides a novel method to prepare metal-ceramic composites from magnetically selected iron ore using microwave heating. By introducing three different microwave susceptors (Activated Carbon, SiC, and a mixture of Activated Carbon and SiC) during the microwave process, effective control of the ratio of metallic and ceramic phases has been achieved easily. The effects of the three susceptors on the microstructure of the metal-ceramics and the related reaction mechanisms were also investigated in detail. The results show that the metal phase (Fe) and ceramic phase (Fe2SiO4, FeAl2O4) can be maintained, but the metal phase to ceramic phase changed significantly. In particular, the microstructures appeared as well-distributed nanosheet structures with diameters of ~400 nm and thicknesses of ~20 nm when SiC was used as the microwave susceptor.

Microwave absorbers with lightweight, good stability and high efficiency have attracted much attention for their applications in many contemporary fields. In this work, the 3D porous (Ni@NO-C)n/NO-C composite absorber was prepared by a wet chemistry method with Ni chains and melamine as precursors, in which NO-C (N,O-doped carbon) encapsulated Ni particles are homogenously dispersed in the 3D porous networks of NO-C in the form of (Ni@NO-C)n chains. The special microstructure of the as-prepared material is proved to be benefit for the improvement of its microwave adsorption performance. The as-synthesized (Ni@NO-C)n/NO-C composite ab-sorber exhibited an effective absorption bandwidth of 4.1 GHz and an extremely large reflection loss of -72.3 dB. The excellent microwave absorbing performances can be ascribed to the coopera-tive consequence of dielectric loss and magnetic loss along with the balance between attenuation capability and impedance match.

This paper presents an OSA patient interactive monitoring system based on the Internet of Things (IoT) framework. This system allows OSA patients to get timely rescue when they are sleepy outside. Because the Beidou position marker has an interactive function, it can reduce the anxiety of the patient while waiting for the rescue. At the same time, if a friend helps the OSA patients to call the doctor, the friend can also report the patient's condition in time. This system uses the popular IoT framework. At the bottom is the data acquisition layer, which uses wearable sensors to collect vital signs from patients, with a focus on ECG and SpO2 signals. The middle layer is the network layer that transmits the collected physiological signals to the Beidou indicator using the Bluetooth Low Energy (BLE) protocol. The top layer is the application layer, and the application layer uses the mature rescue interactive platform of Beidou. Since the GPS indicator has not included the communication satellite, So it has no SMS function. OSA patients can only passively wait for a rescue. Moreover, due to the lack of satellites in Asia and the insufficient density of the ground-enhanced system, the positioning error of OSA patients is large. The Beidou system developed by China itself, the main coverage of the satellite is in Asia, and is equipped with a high-density ground-based augmentation system. Therefore, the Beidou model improves the positioning accuracy and is equipped with a special communication satellite, which increases the short message interaction function. Therefore, patients can report disease progression in time while waiting for a rescue. After our simulation test, the effectiveness of the OSA patient rescue monitoring system based on the Internet of Things framework and the positioning accuracy of OSA patients have been greatly improved. Especially when OSA patients work outdoors, the cell phone base station signal coverage is relatively weak. The satellite signal is well covered, plus the SMS function of the Beidou indicator. Therefore, the system can be used to provide timely patient progress and provide data support for the medical rescue team to provide a more accurate rescue plan. After a comparative trial, the rescue rate of OSA patients using the detection device of this system was increased by 15 percentage points compared with the rescue rate using only GPS satellite phones.

Long-read single-molecule sequencing has revolutionized de novo genome assembly and enabled the automated reconstruction of reference-quality genomes. It also has been widely used to study structural variants, phase haplotypes and more. Here, we introduce the assembler— SMARTdenovo, which is an SMS assembler that follows the overlap-layout-consensus (OLC) paradigm. SMARTdenovo (RRID: SCR_017622) was designed to be a fast assembler that did not require highly accurate raw reads for error correction, unlike other, contemporaneous SMS assemblers. It has performed well for evaluating congeneric assemblers and has been successful for a variety of assembly projects. It is compatible with Canu for assembling high-quality genomes, and several of the assembly strategies in this program have been incorporated into subsequent popular assemblers. The assembler has been in use since 2015, and here we provide information on the development of SMARTdenovo and how to implement its algorithms into current projects.

In order to study the movement characteristics of fuel in the fuel tank during aircraft maneuvering, this paper conducts numerical simulations of a typical wing fuel tank using the finite point set method based on certain boundary conditions and excitation. The results show that the finite point set method can capture the complex phenomena of fuel free surface fluctuations, surging, rolling and breaking, as well as the change in center of gravity caused by fuel movement. Under aircraft acceleration and pitch angle excitation, fuel will generate sustained impact pressure on the fuel tank wall. The velocity attenuation of fuel flowing through the baffle communication hole is fast, and the flow resistance and energy dissipation of the communication hole buffer the impact of fuel on the fuel tank wall.

Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. Accurate risk assessment is critical to an efficient flash flood management. There are more than 530,000 small watersheds in 2058 counties in China where flash flood should be prevented. In practice, with limited fund and different risk levels, the priorities of each small watershed for flash flood prevention and control are also needed for an efficient flash flood management. This paper, take Licheng county in China as an example, aims to give out these priorities for management. First, sensitive indexes are identified among index system, which includes 9 indexes based on underlying surface characteristics of small watershed in hilly region. Second, the range of each index and the rank division of each index for evaluation are determined. Based on the rank divisions for evaluation, the flash flood risk grade eigenvalue (H) is calculated by Variable Fuzzy Method (VFM ) using 1000 samplings generated by Latin hypercube sampling method. Third, the key sensitivity factors that affect flash flood risk grade eigenvalue (H) are assessed by two different global sensitivity analysis methods -- stepwise regression analysis and mutual entropy. Both results indicate that watershed slope (S) is the most sensitive factor; the second is antecedent precipitation index (CN); while other factors are slightly different sensitive in sequence. This study shows that stepwise regression analysis and mutual information analysis are appropriate for the sensitivity analysis of mountain flash flood risk. Finally, based on watershed slope (S), the priorities of flash flood prevention and control of 119 small watersheds in Licheng county are given out.

The IEEE 802.15.4-2015 standard includes the SUN (Smart Utility Networks) modulations, i.e., SUN-FSK, SUN-OQPSK and SUN-OFDM, which provide long range communications and allow to trade data rate, occupied bandwidth and reliability. However, given the constraints of low-power devices and the challenges of the wireless channel, communication reliability cannot still meet the PDR (Packet Delivery Ratio) requirements of industrial applications, i.e., PDR>99%. Hence, in this paper we evaluate the benefits of improving communication reliability by combining packet transmissions with modulation diversity using multiple IEEE 802.15.4g SUN modulations. The results derived from a real-world deployment show that going from 1 to 3 packet transmissions with the same SUN modulation can increase PDR from 85.0/84.6/71.3% to 94.2/94.1/86.0% using SUN-FSK, SUN-OQPSK and SUN-OFDM, respectively. Combining the same number of packet transmissions with modulation diversity allows to further increase the average PDR to 97.1%, indicating its potential as a tool to help meeting the reliability requirements of industrial applications.

In this article we present a deployment of 11 nodes using the three different SUN (Smart Utility Network) modulation schemes, as defined in the IEEE 802.15.4-2015 standard. The nodes were deployed in a 110.044 m2 warehouse for 99 days, and the resulting dataset contains a total of 10.710.868 measurements with RSSI (Received Signal Strength Indicator), CCA (Clear Channel Assessment) and PDR (Packet Delivery Ratio) values. The analyzed results show a high variability in average RSSI (i.e., between -82.1 dBm and -101.7 dBm) and CCA (i.e., between -111.2 dBm and -119.9 dBm) values, which are caused by the effects of multi-path propagation and external interference. Despite being above the sensitivity limit for each modulation, this values result in poor average PDR values (i.e., from 65.9% to 87.4%), indicating that additional schemes are required for low-power wireless communications to meet the dependability requirements of industrial applications. For that purpose, we also introduce the concept of modulation diversity, which can be combined with packet repetition to meet such requirements (i.e., PDR>99%) while minimizing the energy expenditure of nodes and meeting regulatory constraints.

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.

Salivarian trypanosomes are extracellular parasites causing anthroponotic and zoonotic infections. Anti-parasite vaccination in considered the only sustainable method for global trypanosomosis control. Unfortunately, not a single field applicable vaccine solution has been successful so far. Active destruction of the host’s adaptive immune system by trypanosomes is believed to contribute to this problem. Here we show Trypanosome brucei brucei infection results in the lasting obliteration of immunological memory, including vaccine-induced memory against non-related pathogens. Using the well-established DTPa vaccine model in combination with a T. b. brucei infection and diminazene diaceturate anti-parasite treatment scheme, our result demonstrate that while the latter ensured the full recovery from the T. b. brucei infection, it failed to restore an efficacious anti-pertussis vaccine recall response. DTPa vaccine failure coincided with a shift in the IgG1/IgG2a anti-pertussis antibody ratio in favor of the latter, and a striking impact on all spleen immune cell populations. Interestingly, an increased plasma IFNy level in DTPa vaccinated trypanosome infected mice did result in a temporary antibody-independent improvement of early-stage trypanosomosis control. In conclusion, our results are the first to show that trypanosome inflicted immune damage is permanent, and is not restored by successful anti-parasite treatment.

This study aimed to evaluate the effect of incorporating linseed oil and fish oil in the diet on intake, ruminal fermentation milk yield and milk fatty acid profile in dairy goats. Four crossbred Saanen lactating goats in mid-lactation and milking 1.30±0.28 g/day were used in a 4 × 4 Latin square design. The basal diet consisted of concentrate and Para grass (C:F 40:60). Treatments included the basal diet without oil supplement (Ctrl) or with 2.5% linseed oil (LO2.5), 2.5% linseed oil and fish oil (3:2, w/w, LFO2.5), and 4.16% linseed oil and fish oil (3:2, w/w, LFO4.16). Diets had no effect on intake, milk yield, milk composition and ruminal fermentation patterns (P > 0.05). The lower (P < 0.05) proportions of C10:0-C14:0 in milk fat were observed with LFO4.16 compared with Ctrl. Compared with the Ctrl and linseed oil added alone, feeding LFO4.16 resulted in a 589% and 303% increase in C18:1 t11, respectively. Milk c9,t11 CLA was markedly increased with the LFO4.16 diet, accounting for 4.53 and 2.94 times greater concentrations than those in Ctrl and LO2.5 diets, respectively (P < 0.01). Goats fed LFO2.5 and LFO4.16 had greater levels of C22:6n-3 (0.63% and 0.87%; P < 0.001) compared with those fed Ctrl and LO2.5 diets (0.06% and 0.08%). Overall, the combined data suggested that including 4.16% linseed oil and fish oil in the diet of dairy goats is effective for improving the concentrations of healthy fatty acids in milk, without affecting milk production.

News on the stock market contains positive or negative sentiments depending on whether the information provided is favorable or unfavorable to the stock market. This study aims to discover the news sentiment and classify news according to its sentiments with the application of PhoBERT, a Natural Language Processing model designed for the Vietnamese language. A collection of nearly 40,000 articles on financial and economic websites is used to train the model. After training, the model succeeds in assigning news to different classes of sentiments with an accuracy level of over 81%. The research also aims to investigate how investors concern about the daily news by testing the movements of the market before and after the news is released. The analysis results show that there is an insignificant difference in the stock price as a response to the news. However, investors tend to overreact to negative and positive news in the Vietnam stock market.

Marketing is one of the important stages, the decisive factor affecting the success of production and business activities of every business. Therefore, marketing is considered to be the first and most important stage in the process of introducing, bringing products to market and branding of businesses, especially in the period when the disease situation is taking place very complicated worldwide. This study provides both qualitative and quantitative results for the following research objectives. Firstly, the study will assess the situation and analyze the appropriateness of marketing models in the current market situation trend. Secondly, the study proposes an approach in building a new type of marketing model, which is 3D web-based marketing applied in e-commerce to support the modeling of enterprise products in the form of Interactive 3D products are similar to real products. Thirdly, testing and evaluating 3D e-commerce web marketing model at Truong Hai Auto Showroom, Thai Nguyen City branch. This study also proposes a number of solutions for the research and deployment of 3D web marketing model application for businesses in the current market situation.

Stemming from the urgency of the actual situation, commercial banks need an effective credit risk management tool to limit risks. The authors went to survey, study and propose a set of factors affecting the ability of debt repayment of individual customers and conducting surveys. The topic uses data sets including 240 observation samples. Using the SPSS software to clean data and run the model based on Maddala's Binary logistics regression published in 1984 to find out the impact of each individual element of customers affecting their ability to repay such debts. Come on. The authors also specify the order of influence of each factor determining the ability to repay individual customers, thereby helping bank managers have a better visual view to make decisions for borrowing accurately, limiting risks.

The research objectives of this study are to analyse the volatile compositions of different basil types available in Thai markets and to descriptively determine their aromatic qualities. Essential oils were hydro-distillated from fresh leaves of 2 Holy basil (Ocimum sanctum) varieties namely, white and red and other basil species, including Tree basil (O. gratissimum), Sweet basil (O. basilicum var. thyrsiflorum) and Lemon basil (O. citriodorum). Oil physicochemical characteristics and volatile chromatograms from Gas Chromatography-Mass Spectrometry (GC-MS) were used to qualitatively and quantitatively describe the chemical compositions. Methyl eugenol, estragole and eugenol were among the major chemicals found in the essential oils of these basil types. Classification by Principal Component Analysis (PCA) advised that these Ocimum spp. samples are grouped based on either the distinctive anise, citrus aroma (estragole, geranial and neral) or spice-like aroma (β-methyl eugenol, caryophyllene and α-cubebene). The essential oil was also used for descriptive sensorial determination by five trained panelists, using the following developed terms: anisic, citrus, herb, spice, sweet and woody. The panelists were able to differentiate essential oil of white Holy basil from red Holy basil based on the intensity of the anisic attribute, while the anise and citrus scents were detected as dominant in the Lemon basil, Tree basil and Sweet basil essential oils. The overall benefit from this research was the elucidation of aromatic qualities from Thai common Ocimum species in order to assess their potential as the raw materials for future food research and development.

: In this paper, we examine potential methods for autonomously tracking and landing multi-rotor unmanned aerial vehicles (UAVs), a complex yet essential problem. Autonomous tracking and landing control technology utilizes visual navigation, relying solely on vision and landmarks to track targets and achieve autonomous landing. This technology improves the UAV's environment perception and autonomous flight capabilities in GPS-free scenarios. In particular, we are researching tracking and landing as a cohesive unit, devising a switching plan for various UAV tracking and landing modes, and creating a flight controller that has an inner and outer loop structure based on relative position estimation. The inner and outer nested markers aid in the independent monitoring and touchdown of UAV. Optimal parameters are determined via optimized experiments on the measurements of the inner and outer markers. An indoor experimental platform for tracking and landing UAV was established. Tracking performance was verified by tracking three trajectories of unmanned ground vehicle (UGV) at varying speeds, and landing accuracy was confirmed through static and dynamic landing experiments. The experimental results show that the proposed scheme has good dynamic tracking and landing performance.

In this paper, we propose and evaluate two mechanisms aimed at improving the communication reliability of IEEE 802.15g SUN (Smart Utility Networks) in industrial scenarios: RTS (Re-Transmission Shaping), which uses acknowledgements to track channel conditions and dynamically adapt the number of re-transmissions per packet, and AMS (Adaptive Modulation Selection), which makes use of reinforcement learning based on MAB (Multi-Armed Bandits) to choose the modulation that provides the best reliability for each packet re-transmission. The evaluation of both mechanisms is performed through computer simulations using a dataset obtained from a real-world deployment and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packet transmissions). The PDR measures the ratio between received and transmitted packets, whereas the RNP is the number of packet repetitions before a successful transmission. The results show that both mechanisms allow to increase the communication reliability while not jeopardizing the battery life-time constraints of end devices. For example, when three re-transmissions per packet are allowed, the PDR reaches 98/96\% with a RNP of 2.03/1.32 using RTS and AMS, respectively. Additionally, the combination of both proposed mechanisms allows to reach a 99% PDR with a RNP of 1.7, making IEEE 802.15.4g SUN compliant with the stringent data delivery requirements of industrial applications.

To obtain a better estimation frequency band for source bearing estimation, source bearing estimation experiments using an ice-mounted geophone were performed at three sites on the Arctic ice cap. By transforming to the frequency domain, errors of the bearing estimation in a number of frequency bands within 1000 Hz and the horizontal peak displacements received by the geophone in the corresponding frequency bands were obtained. After comparing the variation of errors to the peak displacements in the same frequency band, it is found that the errors are related to the peak displacements, and higher peak displacement values mean smaller errors. When the incident angle of the acoustic source and the ice thickness meet certain conditions, some modes of the Lamb waves of the ice plate are excited, the peak displacements at the same frequency band are increased, the errors of bearing estimation are reduced, and the effective frequency band for bearing estimation is broadened. These results provide some new ideas for the bearing estimation of acoustic sources beneath Arctic ice.

The global demands for various grains including durum wheat (Triticum durum Desf.) are expected to increase substantially in the coming years due to ever-growing human population’s needs for food, feed and fuel. Thus, providing consistent or increased durum grain to the world market is one of the priorities for policy-makers, researchers, and farmers. What are the major achievements in agronomic advancement for durum wheat cultivation in recent decades? How might the current cropping systems be improved to increase crop yield and quality and improve resource use efficiencies while minimizing input costs and decreasing negative impact on the environment? Canada is one of the major durum wheat producers in the world, as Canada contributes about 50% to global trade of durum grain. Canada’s research achievements in durum wheat might serve as a guide for advancing the cultivation of the crop in other regions/countries on the planet. This review summarizes the major Canadian research findings in the aspects of durum wheat agronomics during the period 2001 to 2017 years. It highlights the main advancements in seeding and tillage, crop rotation and diversification, and use of pulse-induced microbiomes to improve soil health and feedback mechanism. The genetic gain and breeding for resistance against abiotic and biotic stresses are discussed. Finally, we identified main constraints and suggested some near-term research priorities. The research findings highlighted in this review will be of use for other areas on the planet to increase durum wheat productivity, improve soil fertility and health, and enhance long-term sustainability.

In this study, we reported that novel single-chain fusion-proteins linked thromboxane A2 (TXA2) receptor (TP) to a selected G-protein α-subunit q (SC-TP-Gαq), or s (SC-TP-Gαs) could be stably expressed in megakaryocytes (MK). We tested the MK-released platelet-linked particles (PLPs) to be used as a vehicle to deliver the over-expressed SC-TP-Gαq), or s (SC-TP-Gαs) to regulate human platelet function. To understand how the single-chain TP-Gα fusion proteins could regulate opposite platelet activities by an identical ligand TXA2, we used dual-binding domains linked to different signaling pathways within a single polypeptide chain using a 3D structural model. The immature MKs were cultured and transfected with cDNAs constructed from structural models of the individual SC-TP-Gαq and SC-TP-Gαs, respectively. After transient expression was identified, the immature MKs stably expressing SC-TP-Gαq or SC-TP-Gαs (stable cell lines) were selected. The stable cell lines were induced into mature MKs which released PLPs. Western blot analysis confirmed that the released PLPs were carrying the recombinant SC-TP-Gαq or SC-TP-Gαs. Flow cytometry analysis showed the PLPs carrying SC-TP-Gαq were able to perform the activity by promoting platelet aggregation. In contrast, PLPs carrying SC-TP-Gαs reversed Gq to Gs signaling to inhibit platelet aggregation. This is the first time demonstrating that SC-TP-Gαq and SC-TP-Gαs were successfully overexpressed in MK cells and released as PLPs with proper folding. This bio-engineering led to the formation of two sets of biologically active PLP forms, which mediate calcium and cAMP signaling, respectively. As a result, these PLPs are able to bind to identical endogenous TXA2 with opposite activities, inhibiting and promoting platelet aggregation as required for therapeutic process.The results also demonstrated the nucleus-free PLPs could be used to deliver recombinant membrane-bound GPCRs to regulate cellular activity in general.

Although the relations between sleep and academic performance have been extensively examined, how sleep predicts future academic performance (e.g., 2 -3 years) remains to be further investigated. Using wearable smartwatches and a self-report questionnaire, we tracked sleep activities of 45 college students over a period of approximate half a month to see whether their sleep activities predicted their academic performance, which was estimated by grade point average (GPA). Results showed that both nighttime sleep awakening frequency and its consistency in the tracking period were not significantly correlated with the GPA for the courses taken in the semester during sleep tracking (current GPA). However, both nighttime sleep awakening frequency and its consistency inversely predicted the GPA for the rest of the courses taken after that semester (future GPA). Moreover, students with more difficulty staying awake throughout the day obtained lower current and future GPAs, and students with lower inconsistency of sleep quality obtained lower future GPA. Together, these findings highlight the importance of nighttime sleep awakening frequency and consistency in predicting future academic performance and emphasize the necessity of assessing the consistency of sleep measures in future studies.

In recent years, it has been found in engineering practice that the service life of cemented carbide shield machine tools used in uneven soft and hard strata is substantially reduced. The study found that thermal stress is the main reason for the failure of cemented carbide shield tunneling tools when shield tunneling is carried out in uneven soft and hard soil. To maintain the hardness of cemented carbide, improving the thermal conductivity of the shield machine tool is of great importance for prolonging its service life and reducing engineering costs. In this paper, graphene and carbon nanotubes were mixed with WC-Co powder and sintered by SPS (Spark Plasma Sintering). The morphology was observed by using scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS). A Rockwell hardness tester and bending strength tester were used to test hardness, bending strength and thermal conductivity. The results show that adding trace graphene or carbon nanotubes can increase the bending strength of the cemented carbide by approximately 50% while keeping the hardness of the cemented carbide unchanged. The thermal conductivity of the cemented carbide can be increased by 10% with the addition of 0.12% graphene alone.

The moisture content of maize seed is an important indicator for evaluating seed quality and a fundamental item in grain testing. The experiment used direct drying method to measure the moisture content of 80 different varieties of maize samples, and captured their images using a hyperspectral imaging (HSI) system in the spectral range of 1100~2498nm. By comparing seven spectral preprocessing methods, the PLSR model established after Normalize pretreatment had the best effect. The characteristic wavelengths were selected by Successive Projections Algorithm (SPA), Competitive Adaptive Reweighted Sampling (CARS) and Uninformative Variable Elimination (UVE). Twelve prediction models were established for full band spectra and characteristic wavelengths based on PLSR, PCR and SVM. By comparing the performance of the prediction models, it was found the Normalize-SPA-PLSR algorithm was optimized. The values of RC2 and RP2 in the model were higher, which were 0.9936 and 0.9933, respectively, while the values of RMSEP and RMSECV were lower, which were 0.0357 and 0.0380, respectively. The Normalize-SPA-PLSR model was used as a visual prediction model for moisture content of maize seed, the 20 maize varieties in the prediction set were visualized to obtain visualized color images of moisture content. The color differences between different moisture content images were significant. The result indicated that hyperspectral imaging could accurately, rapidly, and non-destructive predict moisture content of maize seed, which provided technical support for moisture content detection in the process of maize harvest, storage and processing.

Blind deconvolution (BD) is one of the effective methods that help pre-process vibration signals and assist in bearing fault diagnosis. Currently, most BD methods design an optimization criterion and use frequency or time domain information independently to optimize a deconvolution filter. It recovers weak periodic impulses related to incipient faults. However, the random noise interference may cause the optimizer to overfit. The time-domain-based BD methods tend to extract fault-unrelated single peak impulse, and the frequency-domain-based BD methods tend to retain the maximum energy frequency component, which will lose the fault-related harmonics frequency components. To solve the above issue, we propose a hybrid criterion that combines the kurtosis for time domain optimization and the $G-l_1/l_2$ norm for the frequency domain. These two criteria are monotonically increasing and decreasing, so they mutually constrain to avoid overfitting. After that, we design a multi-task one-dimensional convolutional neural network with time and frequency branches to achieve an optimal solution for this hybrid criterion. The multi-task neural network realizes the simultaneous optimization of two domains. Experimental results show that our proposed method outperforms other state-of-the-art methods.

Compared with the traditional slow charging loads, random integration of large scale fast charging loads will exert more serious impacts on the security of power network operation. Besides, to maximize social benefits, effective scheduling strategy guiding fast charging behaviors should be formulated rather than simply increasing infrastructure construction investments on the power grid. This paper has analyzed the charging users’ various responses to the elastic charging service fee, introduced the index of charging balance degree to a target region by considering the influence of fast charging loads on power grid. Then, a multi-objective optimization model of the fast charging service fee is constructed, whose service fee can be further optimized by employing fuzzy programming method. Therefore, both users’ satisfaction degree and the equilibrium of charging loads can be maintained simultaneously by guiding EVs to different fast charging stations reasonably. The simulation results demonstrate the effectiveness of proposed dynamic charging service pricing and the proposed fast charging load guidance strategy.

Two-dimensional (2D) materials show advantages as surface enhanced Raman scattering (SERS) substrate over traditional noble metals, such as chemically inert flat surface and uniform SERS signals. However, due to the low density of free electrons of 2D materials, the enhancement mechanism is mainly restricted to chemical enhancement instead of electromagnetic enhancement, resulting a relatively low enhancement factor. In this work, we reported a sensitive 2D SERS substrate based on chemical vapor deposited MoN nanosheets with electromagnetic enhanced mechanism. The maximum enhancement factor can be 3×105 with a limit of detected concentration of 4×10-8 M, which is comparable to those noble metal SERS substrates without hot spots. The MoN nanosheets show strong surface plasmon resonance in the visible range as evidenced by the UV-vis absorption spectroscopy and first principle calculation. The MoN nanosheets SERS substrates exhibit excellent Raman signal uniformity within the nanosheets. Meanwhile, Distinct thickness dependent sensitivity was observed in the MoN nanosheets, higher sensitivity can be achieved by decreasing the thickness of the nanosheets. Furthermore, the MoN nanosheets SERS substrate also show high thermal stability, which can be annealed in air for more than 300℃ and maintain its SERS activity. The high stability of MoN nanosheets allows it to be reused for 20 cycles without obvious signal decay. The high sensitivity, stability, reusability and Raman signal uniformity, makes MoN nanosheets an ideal 2D SERS substrate for practical applications.

The pulsatile flow rate (PFR) in the cerebral artery system and shunt ratios in bifurcated arteries are two patient-specific parameters which may affect the hemodynamic characteristics on the pathobiology of cerebral aneurysms (CAs). Accordingly, a systematic study was employed to investigate the effects of the two parameters on hemodynamic characteristics in two internal carotid artery sidewall aneurysms (i.e., ICASA-1 and ICASA-2) models. Numerical results indicate larger PFRs can cause higher WSS in some local regions of the aneurysmal dome that may increase the probability of small/secondary aneurysms generation than under smaller PFRs. The low WSS and relatively high oscillatory shear index (OSI) could appear under a smaller PFR, which has the potential to cause aneurysmal sac growth and rupture. However, the variances in PFRs and bifurcated shunt ratios have rare impacts on the time-average pressure distributions on the aneurysmal sac, although a higher PFR can contribute more to the pressure increase in ICASA-1 dome due to the relatively stronger impingement by the redirected blood stream than in ICASA-2. Simulation results also present the variances of shunt ratios have rare impacts on the hemodynamic characteristics in sacs, mainly because the bifurcated location is not close enough to the sac in present models. Furthermore, it has been found that the vortex location plays a major role in the temporal and spatial distribution of the WSS on the luminal wall, varying significantly with the cardiac period.

Viscoelastic testing (VET) by both TEG and ROTEM has demonstrated hypercoagulability early in Covid Associated Coagulopathy (CAC). Additional VET studies demonstrated fibrinolytic shutdown late in a majority of severely ill Covid-19 patients with associated elevation of D-dimer. Elevated d-dimer confirms that coagulation, followed by fibrinolysis, has occurred. These findings imply that, during CAC, three enzymes—thrombin, Factor XIIIa and plasmin—must have acted in sequence. However, limitations in standard VET analyses preclude exploration of the earliest phases of clot induction, as well as clot formation and clot dissolution in flowing blood. Herein we describe a novel method illuminating aspects of this unexplored area. In addition, we created an ex vivo blood flow model in which the interactions of thrombin, Factor XIII and plasmin with fibrinogen can be studied, allowing determination of Soluble Fibrin (SF), the highly unstable form of fibrin that precedes the appearance of a visible clot. This model allows determination of the SF level at which fibrin microclots begin to form and the length of time the resulting microclots persist under normal fibrinolytic attack. In addition, the flow model allows exploration of the late fibrinolytic shutdown that VET has previously demonstrated in CAC through standard methodology.

Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 liters per minute (LPM), respectively, compared to 4.05 and 3.82 LPM of the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability, particularly in high-motion environments.

Chemical safety is related to public health, safety and environmental concerns, and the dangerous chemicals safety problem is becoming the one that people commonly pay attention to. Strengthening chemical safety supervision not only involves safe production, but also is an important link to maintain social safety. Most related studies focus on chemical safety under normal regulation, this paper addresses the perspective of ‘special rectification’ and ‘normal regulation’, and establishes an evolutionary game model between chemical enterprises and government supervision departments under different supervision modes. Based on the evolutionary game theory, this paper studies the evolution process of the two game players' strategy choices, and compares and analyzes the evolution, stability and equilibrium between the chemical safety and the behavior strategies of government regulatory departments. The model is effectively analyzed based on numerical simulation, and the results show that: Under the ‘special rectification’ mode, the strategic choice of chemical enterprises engaging in safety without investment depends on the difference between the benefits and costs of safety without investment; In the ‘normal regulation’ mode, the choice of its safety non-investment strategy depends on the difference between the cost of engaging in safety investment and the cost of safety non-investment; Increasing the government's punishment will encourage chemical enterprises to take safety investment behavior under the two supervision modes. Increasing the punishment has a significant impact on the safety investment behavior of chemical enterprises under the ‘normal regulation’ mode, but it has no significant impact on the behavior of chemical enterprises under the ‘special rectification’ mode. At the same time, increasing the punishment will inhibit the strict supervision behavior of the government. The research conclusion provides important decision support for government departments to effectively supervise the safety production of chemical enterprises.

Over the last ten years, the integrated pond aquaculture (IPA) has been developing rapidly as a new aquaculture mode, and mechanical power oxygenator is a common oxygenation device in this aquaculture mode. However, with the gradual expansion of the application range of this aquaculture mode, the problems of low efficiency, poor oxygenation uniformity, and low applicability of mechanical power oxygenators, which can produce water reflux and suction pollution accumulation, have gradually emerged. Therefore, we designed a new type of push-water aerator device (NPWAD) applicable to the integrated recirculating water aquaculture mode of ponds. Mainly, the specific parameters of the key components were clarified through the selection calculation and structural design, and the experiments were carried out in the self-constructed pond integrated aquaculture system at the experimental base. The experiments were carried out on the water in the tank for grass carp culture, and the oxygenation and water pushing effects of the type of push-water aerator device under 15 groups of different operating parameters were carried out by using multiple types of high-precision water quality monitoring sensors, with the rotational speed of the device, the operating time (0-30 min) and the height of the water (0.5-1.5 m) as the independent variables, and the oxygenation amount R, the oxygenation power efficiency E_s and the surface layer of the water as the evaluation indexes, average flow velocity V_f were used as its evaluation indexes of oxygenation and water pushing. The results show that the optimal parameters of the NPWAD are impeller water lifting component speed n_1=1000r/min, small-hole aeration component speed n_2=2280r/min. At this time, the average oxygen increase of this device is R=1.96mg/L, the average oxygenation power efficiency is E_s=3.8 kg·(h·kW)-1, the DO uniformity of the water M_1=95.3%, and the average flow velocity V_f=190.8mm/s, which can effectively promote the water directional flow and achieve the water circulation and layer exchange, and it is in line with the preferred swimming speed of grass carp at various growth stages. The new type of push-water aerator device is able to enhance the DO concentration of the water faster, but also to achieve the purpose of promoting the directional flow of the water and renewing the water in the tank, as well as in a short period of time to improve the uniformity of the DO of the water faster, for the improvement of the pond aquaculture production efficiency has practical significance.

Micro-arc oxidation (MAO) is an emerging technology employed to produce high hardness or anti-corrosion ceramic coatings on lightweight metals such Al, Ti, Mg and their respective alloys. However, under non-lubrication conditions, such coatings exhibit relatively high friction coeffi-cients. To enhance the friction reduction properties of the MAO coatings, we employed a one-step MAO process with varying concentrations of graphene oxide (GO) in an aluminate sys-tem electrolyte to create composite coatings on the surfaces of Ti-6Al-4V alloy. The effect of concentrations of GO particles on microstructure, composition, and wear behavior of the result-ing composite coatings was investigated. Measurements of the coating's thickness, hardness, and roughness have also been conducted. Ball-on-disk friction test under dry condition were carried out to reveal tribological behavior of the MAO coating. The addition of graphene oxide in the electrolyte reduces the friction coefficient, with the composite coating containing 5 g/L of GO particles displaying the most effective friction reduction. Moreover, coating thickness increases with higher GO concentration. This friction reduction can be attributed to the particles in the coating acting as lubricants during friction, while GO also reduces adhesive damage, shifting the wear mechanism from adhesive to abrasive.

Knee Osteoarthritis (KOA) is a chronic disease characterized by progressive disability and joint pain. Meniscus chondrocytes apoptosis is the main cause of reduced chondrocyte number and self-repair function. The purpose of this study was to investigate the role of miR-27b-3p in KOA.In this study, we found that the expression of miR-27b-3p was downregulated in cultured IL-1β treated chondrocyte and cartilage tissues in KOA. KOA overexpression evidently reduced IL-1β induced chondrocyte apoptosis and caspase-3 and caspase-9 expression.The upregulated iNOS and COX-2 mRNA and proteins expression was also inhibited by miR-27b-3p mimics. The expression of nitric oxide, PGE2, TNF-α and IL-6 was also inhibited by miR-27b-3p mimics. The target gene of miR-27b-3p was confirmed to be BDNF. TrkB/CREB pathway was proved to be the downstream pathway of miR-27b-3p/BDNF axis.The apoptotic cell percentage and nitric oxide, PGE2, TNF-α and IL-6 expression was induced by BDNF+IL-1β. This induction was inhibited by miR-27b-3p mimics. The cartilage tissues stained with safranin O results showed miR-27b-3p greatly decreased KOA induced cartilage degradation. The expression of BDNF、TrkB and p-CREB was inhibited by len-miR-27b-3p. MiR-27b-3p also reduced the expression of TNF-α、IL-6 and Bax, and increased Bcl-2 expression. These results indicated miR-27b-3p could applied to inhibit the development of KOA and miR-27b-3p/BDNF/TrkB/CREB pathway could serve as novel treatment target to handle KOA.

Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to use a single type of task, and the exploration on its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifac-eted flow-induction tasks. Six tasks spanning mindfulness, artistic tasks, free recall, and varying levels of Tetris complexity (easy, flow, and hard conditions) were employed to have a relatively complete coverage of the known flow-induction tasks for a better induction of individualized flow experience. Twenty-eight participants were recruited to perform these six tasks, with a sin-gle-channel prefrontal electroencephalography (EEG) recording. Significant positive correlations were observed between the subjective flow scores of individual’s best-flow-experience task and the EEG activities at delta, gamma, and theta bands, peaking at latencies around 2 minutes after task onset. The outcomes of our multiple regression analysis yield a maximum coefficient of de-termination (R²) of 0.279. Our findings report the EEG correlates of flow experience in naturalistic settings and highlight the potential of portable and unobtrusive EEG technology for an objective measurement of flow experience.

Background: The SARS-CoV-2 laboratory PCR tests were generally reported only as binary positive or negative outcomes. Instead test positivity, these results contain a great deal of epidemiological information related to viral transmission patterns in populations. These transmission patterns during India’s SARS-CoV-2 viral waves remain largely undocumented. Methods: We analysed 2.7 million real-time polymerase chain reaction (PCR) testing records collected in Mumbai, a bellwether for other Indian cities. We used the inverse of cycle threshold (Ct) values to determine community-level viral load. We quantified wave-specific differences by age, sex and slum population density. Results: Overall PCR positivity was 3.4% during non-outbreak periods, rising to 23.2% and 42.8% during the Aleph (June-November 2020) and Omicron waves (January 2022), respectively, but only 9.9% during the Delta wave (March-June 2021). The community-level median Ct values fell and rose ~7-14 days prior to PCR positivity rates. Viral loads were 4-fold higher during the Delta and Omicron waves than during non-outbreak months. The Delta wave had high viral loads at older ages, in women and in areas of higher slum density. During the Omicron wave, differences in viral load by sex and for slum density had disappeared, but older adults continued to show higher viral load. Conclusions: Mumbai’s viral waves had markedly high viral loads representing an early signal of pandemic trajectory. Continue the vaccination in elderly Indians could reduce viral load in subsequent waves. Ct values are practicable monitoring tools.

Three new compounds (1, 11 and 12), together with 32 known ones, were isolated from the root bark of Morus alba L. using various chromatographic methods. The structures of the undescribed compounds were elucidated based on 1D, 2D NMR and HRESIMS data analysis, while the known ones were identified by comparison of their spectroscopic data with those reported in the literature. All the isolates were evaluated for their cytotoxic activities against human gastric cancer HGC27 cells by CCK-8 assay. Among them, compounds 5, 8, 10 and 30 exhibited cytotoxic activities on HGC27 cells with IC50 values of 10.24 ± 0.89 μM, 28.94 ± 0.72 μM, 6.08 ± 0.34 μM and 33.76 ± 2.64 μM, respectively. Furthermore, compound 10 was confirmed to reduce proliferation ability, increase apoptosis rate and inhibit cell migration pathway by annexin V/PI double staining experiment, transwell experiment and western blot analysis.

Increased mitotic activity is associated with the genesis and aggressiveness of many cancers. To assess the clinical value of mitotic activity as prognostic biomarker, we performed a pan-cancer study on the mitotic network activity index (MNAI) constructed based on 54-gene mitotic apparatus network. Our pan-cancer assessment on TCGA (33 tumor types, 10,061 patients) and validation on other publicly available cohorts (23 tumor types, 9,209 patients) confirmed the significant association of MNAI with overall survival, progression-free survival and other prognostic endpoints in multiple cancer types, including Lower-Grade Gliomas (LGG), Breast Invasive Carcinoma (BRCA) and many others. We also showed significant association of MNAI with genetic instability, which provides a biological explanation of its prognostic impact at pan-cancer landscape. Futhermore, we found that patients with high MNAI benefit more from anti-PD-1 and Anti-CTLA-4 treatment. In addition, we demonstrated on LGG and BRCA that the multimodal integration of MNAI and the AI-empowerd Cellular Morphometric Subtypes (CMS) significantly improved the predictive power of prognosis compared to MNAI and CMS alone. Our results suggest that MNAI can be used as a potential prognostic biomarker for different tumor types toward different clinical endpoints, and multimodal integration of MNAI and CMS exceeds individual biomarker for precision prognosis.

The soil-structure interaction represents an important parameter when analyzing the dynamic behavior of bridges, taking into account the integration between soil, foundation, and structure to ensure an adequate structural representation. In this paper, a computational modeling strategy using soil-structure interaction (SSI) methodology was applied to represent the dynamic behavior of a pedestrian bridge (LOP). The computational models were validated using monitoring results from an extensive structural health monitoring campaign. The bridge monitoring program was characterized using embedded sensors and cutting-edge technology for remote monitoring: ground-based radar interferometry and video motion amplification. The instrumentation results were compared to establish the capabilities and advantages of this novel remote monitoring technique. The experimental results obtained from free and forced vibrations in the asset served as a basis for validating the developed computational model and allowed evaluating the asset be-havior in service for different loading conditions. This paper emphasizes the SSI approach to represent the pedestrian bridge dynamic behavior more accurately, allowing computational models to represent the LOP structural responses over the course of its life-cycle.

This article explored how some state policies on rehabilitation, protection and utilization of mangroves are implemented at two locals; namely, Ca Mau provice and its sub-unit Ngoc Hien District as well as Ho Chi Minh City and its sub-unit Can Gio District and what are considered as emgerging issues in the implementation process. The study employed mixed research methods including document and thematic analysis, in-depth interviews, and group discussion. The findings showed that state policies would be adapted through the ways in which these two locals manage to silmultaneously orient their work toward accommodating the madates placed upon them and cope with the reality of socio-economic difficulties at their levels. The findings also showed some issues that need to consider addressing; namely, locals’ struggles to fill the gaps between written and implemented policies in mangrove rehabilitation, conflict among sectors and stakeholders in mangrove utilization and protection, problems in guaranteeing adequate budget for in-need-locals, inconsistence in policies on "forest: water surface” ratio in mangrove-shrimp farm, and unintended consequences in forest thinning and exploitation regimes. Based on the finding analysis, the articles discussed lessons learned from the policy implementation process on mangrove rehabilitation, management and forest allocation to household in the south of Vietnam.

The achievement of reproductive competence in male mammals is dependent on the testis. Goat testis’ development and spermatogenesis involve physiological events with high complexity. In the current work, 6 testes were respectively collected from immature, sexually mature and physically mature Qianbei Ma goats (1, 6 and 12 months old, respectively). RNA-Seq was carried out to reveal changes in testis mRNA expression levels in Qianbei Ma goats at various developmental stages, and gene expression profiling at different ages was established. Totally 18 libraries were established for screening genes and pathways associated with testis development and spermatogenic processes. Totally 9,724 upregulated and 4,153 downregulated genes were identified between immature (I) and sexually mature (S) testes; 7 upregulated and 3 downregulated genes were detected between sexually mature (S) and physically mature (P) testes, and approximately 4% of genes were alternately spliced between the I and S groups. Selected genes were verified by qRT-PCR, in agreement with sequencing data indicating their reliability. Those genes have critical functions in various developmental stages of goat testicular development and spermatogenesis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were carried out to evaluate differentially expressed genes (DEGs). GO analysis suggested DEGs were involved in “reproduction process”, “channel activity” and “cell periphery part” between I and S, and in “ion transport process”, “channel activity” and “transporter complex part” between S and P. KEGG analysis indicated that pathways such as “glycerolipid metabolism”, “steroid hormone biosynthesis” and “MAPK signaling pathway” may be involved in testis development and spermatogenesis. Genes including IGF1, TGFB1, TGFBR1 and EGFR may regulate the development of the testis from immature to sexually mature, which may be key candidate genes for the development of goat testis. These findings provide novel insights into goat testicular development and spermatogenesis.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Lipid nanoparticles have an interesting part of drug delivery system. In this study, the modification of the convention nano-lipid based soybean lecithin was demonstrated. Ginger oil derived Zingiber officinale was used along with lecithin, cholesterol and span 80 to fabricate nano-lipid (GL nano-lipid) using thin-film method. Through TEM and confocal microscope, GL nano-lipid exposes the liposome- like morphology. The average size of the resultant nanoparticles was 249.1nm with monodistribution (PDI= 0.021). The ζ-potential of GL nano-lipid was negative as similar to as prepared nano-lipid based lecithin. GL nano-lipid express the highly stable over 60 days of storage at room temperature in term of size, ζ-potential. A shift of pH value from alkaline to acid was detected in lecithin nano-lipid, while with the incorporation of ginger oil, pH value of nano-lipid dispersion was around 7.0. Furthermore, due to the rich of shogaol-6 and other active compounds in ginger oil, the GL nano-lipid is endowed with intrinsic antibacterial feature. In addition, the sulforhodamine B (SRB) assay and live/dead imaging revealed the excellent biocompatibility of GL nano-lipid. Notably, GL nano-lipid was capable to carry the hydrophobic agents as curcumin and perform a pH-dependent release profile. A subsequent characterization are a suitable potential for drug delivery system.

Detailed flow distributions in vascular systems are the key to identifying hemodynamic risk factors for the development and progression of vascular diseases. Although computational fluid dynamics (CFD) has been widely used in bioengineering research on hemodynamics predictions, not only are high-fidelity CFD simulations time-consuming and computing-expensive, but also not friendly to clinical applications due to the difficulty of comprehensive numerical calculations. Machine learning (ML) algorithums to estimate the flow field in vascular systems based on the angiographic images of the blood flow using existed diagnostic tools are emerging as a new pathway to facilitate the mapping of hemodynamics. In present work, the dye injection in a water flow was simulated as an analogy of the contrast perfusion in blood flow using CFD. In the simulation, the light passes through the flow field and generates projective images, as an analogy of X-ray imaging. The simulations provide both the ground truth velocity field and the projective images of the flow with dye patterns. A rough velocity field was estimated using the optical flow method (OFM) based on projective images. ML algorithums are then trained using the ground truth CFD data and the OFM velocity estimation as the input. Finally, the interpretable (logistic regression) and deep (neural networks, convolutional neural networks, long short term memory) machine learning models are validated by using parallel in vitro experiments on the same flow setup. The validation results showed that the employed ML model significantly reduced the error rate from 53.5% to 2.5% in average for the v-velocity estimation.

: Leveraging the sustainability of the power system market, researchers have developed various ML models for forecasting electricity demand. The LSSVM is well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a hybrid model based on the LSSVM optimized by the IBFOA for forecasting the daily electricity load in Peninsular Malaysia. The IBFOA for parameter optimization of LSSVM is introduced. The IBFOA based on the sine cosine equation is proposed to adjust the constant step size in the BFOA, which creates an imbalance between the exploration and exploitation during optimization. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using MAPE as the objective function. Comparative analysis demonstrates the model, achieving the highest R2 (0.9880) and significantly reducing error metrics (MAPE, MAE, RMSE, MSE, NRMSE) compared to the baseline LSSVM (average reduction of 27.72% to 47.72%). Additionally, IBFOA exhibits faster convergence and higher accuracy compared to BFOA, highlighting the accuracy of LSSVM-IBFOA for short-term load forecasting.

Chromoblastomycosis (CBM) and phaeohyphomycosis (FEO) are infections caused by melanized filamentous fungal agents, primarily found in tropical and subtropical regions. Both infections pose significant challenges for the correct identification of the causative agent due to their morphological similarity, making conventional methods of morphological analysis highly subjective. Therefore, molecular techniques are necessary for the precise determination of these species. In this regard, this study aimed to contribute to a new methodology based on PCR-RFLP for the identification of agents causing CBM and FEO. Sequences from the ITS region were used to identify potential restriction enzyme sites in silico, followed by in vitro validation using the selected restriction enzymes. The obtained results were compared with species identification through morphological analyses and sequencing. The results demonstrated that the PCR-RFLP applied in this study accurately identified two major agents of chromoblastomycosis, Fonsecaea pedrosoi and Fonsecaea monophora, as well as Cladophialophora bantiana and Exophiala dermatitidis, both causative agents of phaeohyphomycosis. In this context, the proposed assay can complement current methods for identifying these species, aiding in diagnosis, and contributing to the proper management of these infections.

Enamel matrix derivative (EMD) is used for periodontal tissue regeneration therapy. We designed a synthetic amelogenin peptide (SP) derived from EMD, and have previously investigated the biological function of SP. However, it is unknown whether SP affects odontoblastic differentiation. In the present study, we investigated the effects of SP in odontoblast-like cells, KN-3 cells. KN-3 cells were treated with SP (0, 1, 10, 100, or 1000 ng/mL) and then cultured for 3, 8, 24, or 48 hours, in order to determine the effects of SP on cell proliferation and detect its optimum concentration. To investigate the effect of SP on odontogenic differentiation, KN-3 cells were treated with SP in odontogenic differentiation medium cultured for 3 or 7 days. Odontogenic differentiation was performed by measuring alkaline phosphatase (ALP) activity, the mRNA expression of dentin sialophosphoprotein (DSPP), the formation of calcified nodules, and calcium deposition into the extracellular matrix. The addition of SP significantly promoted KN-3 cell proliferation; a concentration of 100 ng/ml generated the greatest change in cell proliferation. SP also showed increased expression of markers of odontogenic differentiation and mineralization. These results suggest that SP, derived from EMD, could be a potential for applicate to the dental pulp capping.

Enamel matrix derivative (EMD) is used for periodontal tissue regeneration therapy, and can induce mineralization in dental pulp cells (DPCs). We designed a synthetic peptide (SP) derived from the response of cells to EMD, and investigated the effect of the SP on potentiating osteogenesis in DPCs, which have a critical role of dental pulp homeostasis. DPCs were treated with 0, 10, 100, or 1000 ng/mL SP to determine its effect on cell proliferation, cell migration, cell differentiation, and mineralization. We then examined the molecular effects of the SP, focusing on changes in the mitogen-activated protein kinases (MAPK) signaling pathway in these cells. The SP significantly promoted DPC proliferation and migration. Cultures treated with the SP also showed an enhanced expression of markers of osteogenic differentiation and mineralization. The SP also induced the activation of MAPK signaling pathway components. These results suggest that our SP could promote the dental pulp tissue repair by hard tissue formation and the mineralization through activating MAPK signaling pathway. This study provides the first evidence that SP might be a new material for dental pulp tissue treatment.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

A novel scheme that can simultaneously measure the Doppler frequency shift (DFS) and angle of arrival (AOA) of microwave signals is proposed. At the signal receiving unit (SRU), two echo signals and the reference signal are modulated by a Sagnac loop structure and sent to the central station (CS) for processing. At the CS, two low-frequency electrical signals are generated after polarization control and photoelectric conversion. The DFS without direction ambiguity and wide AOA measurement can be real-time acquired by monitoring the frequency and power of the two low-frequency electrical signals. In the simulation, an unambiguous DFS measurement with errors of ±3×10-3 Hz and a -90° to 90° AOA measurement range with errors of less than ±0.5° are realized. The safety and robustness of the system to environmental disturbance are improved, and it is more suitable for the modern electronic warfare system.

To digital grade the color fastness of fabrics after rubbing, an automatic grading method based on spectral reconstruction technology and BP neural network was proposed. Firstly, the modeling samples are prepared by rubbing the fabrics according to the ISO standard of 105-X12. Then, to comply with visual rating standards for color fastness, the modeling samples are professionally graded to obtain the visual rating result. After that, a digital camera is used to capture digital images of the modeling samples inside a closed and uniform lighting box, and the color data values of the modeling samples are obtained through spectral reconstruction technology. Finally, the color fastness prediction model for rubbing was constructed using the modeling samples data and BP neural network. The color fastness level of the testing samples was predicted using the prediction model, and the prediction results were compared with the existing color difference conversion method and curve fitting method. Experimental show that the prediction model of fabric color fastness can be better constructed using the BP neural network, where the root-mean-square error of the prediction for the training sample is 0.30, and the root-mean-square error of the prediction for the test sample is 0.25. The overall performance of the method is slightly better than the color difference conversion method, and it is significantly outperforming the curve fitting method. It can be seen that the digital rating method of fabric color fastness to rubbing based on spectral reconstruction and BP neural network has a high consistency with the visual evaluation, which will help for the automatic color fastness grading.

A novel coronavirus (SARS-CoV-2) that is initially found to trigger human severe respiratory illness in Wuhan City of China in 2019, has killed 2,718 people in China by February 26, 2020, and which has been recognized as a public health emergency of international concern as well. And the virus has spread to more than 38 countries around the world. However, the drug has not yet been officially licensed or approved to treat SARS-Cov-2 infection. NSP12-NSP7-NSP8 complex of SARS-CoV-2, essential for viral replication and transcription, is generally regarded as a potential target to fight against the virus. According to the NSP12-NSP7-NSP8 complex (PDB ID: 6NUR) structure of SARS, two homologous models were established for virtual screening in the present study, namely NSP12-NSP7 interface model and NSP12-NSP8 interface model. Seven compounds (Saquinavir, Tipranavir, Lonafarnib, Tegobuvir, Olysio, Filibuvir, and Cepharanthine) were selected for binding free energy calculations based on virtual screening and docking scores. All seven compounds can combine well with NSP12-NSP7-NSP8 in the homologous model, providing drug candidates for the treatment and prevention of SARS-CoV-2.

In scenarios such as natural disasters and military strike, it is common for unmanned aerial vehicles (UAVs) to form groups to execute reconnaissance and surveillance. To ensure the effectiveness of UAV communications, repeated resource acquisition issues and transmission mechanism design need to be addressed urgently. In this paper, we build an information interaction scenario in a Flying Ad-hoc network (FANET). The data transmission problem with the goal of throughput maximization is modeled as a coalition game framework. Then, a novel mechanism of coalition selection and data transmission based on group-buying is investigated. Since large-scale UAVs will generate high transmission overhead due to the overlapping resource requirements, we propose a resource allocation optimization method based on distributed data content. Comparing existing works, a data transmission and coalition formation mechanism is designed. Then the system model is classified into graph game and coalition formation game. Through the design of the utility function, we prove that both games have stable solutions. We also prove the convergence of the proposed approach with coalition order and Pareto order. Binary log-linear learning based coalition selection algorithm (BLL-CSA) is proposed to explore the stable coalition partition of system model. Simulation results show that the proposed data transmission and coalition formation mechanism can achieve higher data throughput than the other contrast algorithms.

UAV cooperative control has been an important issue in UAV-assisted sensor network, thanks to the considerable benefit obtained from cooperative mechanism of UAVs being applied as a flying base station. In coverage scenario, the tradeoff between coverage performance and transmission performance often makes deployment of UAVs fall into a dilemma, since both indexes are related to the distance between UAVs. To address this issue, UAV coverage and data transmission mechanism is analyzed in this paper, then an efficient multi-UAV cooperative deployment model is proposed. The problem is also modeled as a coalition formation game (CFG). The CFG with Pareto order is proved to have a stable partition. Then, an effective approach consisting of coverage deployment and coalition selection is designed, wherein UAVs can decide strategies cooperatively to achieve better coverage performance. Combining analysis of game approach, a coalition selection and position deployment algorithm based on Pareto order (CSPDA-PO) is designed to execute coverage deployment and coalition selection. Finally, simulation results are shown to validate the proposed approach based on efficient multi-UAV cooperative deployment model.

This paper proposes an analytical model of the double-stator spoke-type permanent-magnet vernier machine (DSSTVM) using the subdomain model (SDM), which can be used to calculate the mag-netic field distribution and corresponding electromagnetic parameters of the DSSTVM. The whole field domain is divided into several subdomains according to the magnetic characteristics of each region, within which Laplace's and Poisson's equations are solved accordingly in terms of magnetic vector potential (MVP). Then, the magnetic flux density distribution, back electromotive force (EMF), and electromagnetic torque of the DSSTVM can be obtained correspondingly. Ultimately, finite element analysis (FEA) is adopted to validate the proposed analytical model's effectiveness for quickly predicting the no-load and on-load performances of the DSSTVM.

Effective biomarkers are essential to the early diagnosis of non-small cell lung cancer (NSCLC). Herein, a retrospective study of 49 newly diagnosed and recurrent NSCLC patients, 31 patients with benign pulmonary disease and 24 healthy volunteers was conducted, to evaluate the diagnostic value of circulating rare cells for NSCLC. The expression of circulating tumor cells (CTCs) and circulating tumor-derived endothelial cells (CTECs) in peripheral blood were measured by subtraction enrichment-immunostaining-fluorescence in situ hybridization (SE-iFISH). The level of CTCs (P＜0.001) and CTECs (P＜0.001) was significantly higher in NSCLC group than that in benign pulmonary disease group. The proportion of small CTCs (P＜0.001) and CTECs (P＜0.0001) significantly increased from benign lung disease individuals to NSCLC patients. The AUC of ROC curves of total CTCs and CTECs were 0.815 (95%CI: 0.722~0.907), 0.739 (95%CI: 0.618~0.860), respectively. The cut-off values for discriminating NSCLC with benign lung disease patients were total CTCs 11.5 units/6ml and total CTECs 10.5 units/6ml, with sensitivity and specificity being 67.3% and 83.9%, 77.6% and 77.4%, respectively. When CTCs and CTECs were combined, predictive value significantly increased to 82.6% as measured by the area under the curve. Small CTCs and triploid CTCs had high positive predictive value (PPV) and positive likelihood ratio (LR+) of the diagnosis of NSCLC in early stage. CTCs and CTECs can not only be used as new biomarkers for the diagnosis of NSCLC, but can also improve diagnostic performance of the early stage NSCLC. Moreover, the combined examination of CTCs and CTECs is be superior to the single.

Magnesium ions play an important immune-regulatory role during bone repair. For this study, we prepared micro-nano bioactive glass containing magnesium, which can release magnesium, silicon, and calcium ions and has a positive impact on osteogenic differentiation and vascular regeneration. In this study, MgMNBG was compounded and combined with PLGA and PCL for 3D printing. Afterwards, the physicochemical properties and bone repair performance of the scaffolds were evaluated through in vitro and in vivo experiments. We also investigated the effects of MgMNBG on osteogenic differentiation, immune regulation, and vascular regeneration. The results showed that MgMNBG can inhibit inflammation and promote osteogenesis and angiogenesis by regulating macrophages. PLGA/PCL/MgMNBG scaffolds have good osteogenic and angiogenic effects, and the composite scaffolds have excellent bone repair performance and potential application value.

The meniscus is a meniscus-shaped fibrocartilage tissue located between the femur and tibia，it is mainly composed of meniscus cells and related extracellular matrix.The synovial limbus area of the knee joint capsule near the meniscus is divided into red areas with rich blood vessels and white areas with less blood supply according to the distribution of blood vessels,there is a transition zone called the red and white zone between the two;Red zone has better self-repair ability,The injury in this area can be treated by conservative treatment or surgical suture;Once the white area of the meniscus is torn and involves the free edge area,It is often necessary to partially remove the damaged meniscus.When most of the entire meniscus is severely torn and involved,not only the course of the disease is very long, but it cannot be repaired by sutures,Often a subtotal or total meniscus resection is required,whether it is a partial meniscus resection, a subtotal meniscus resection or a complete resection.In the later period, it may cause quadriceps atrophy and osteoarthritis (OA）.OA is a refractory multi-system disease,involve the patient’s peripheral joints,it has high disability and teratogenicity, and is very harmful to human health.Chondrocyte pyrolysis, degradation, and inflammation play a vital role in the destruction of OA articular cartilage and chondrocyte apoptosis.Meniscus stem cells have strong proliferation and differentiation ability,has become one of the hot spots in the field of meniscus repair,this article studies the role of meniscal stem cells in the development of OA.

The effect of Pr6O11 on the microstructure and mechanical properties of high-strength steel weld metal was investigated by optical microscopy, scanning electron microscopy and mechanical testing. Three different contents of Pr6O11 were added to the flux-cored wires. The results demonstrate that the addition of 1% Pr6O11 can promote the refinement and spheroidization of inclusions, refine the grains, form acicular ferrites in the weld metal, and significantly improve the toughness. The addition of Pr6O11 promoted the formation of rare earth composite inclusions and acicular ferrites in the weld metal, refined the lath microstructure, inhibited the formation of martensite and bainite. The crack formation mode changed from the boundary cracking of the bainite clusters caused by the surface shear stress to the surface shear stress-induced decohesion of inclusion. Excessive addition of Pr6O11 will reduce the number of inclusion nucleation and deteriorate the mechanical properties. The wire No.2 with 1% Pr6O11 had the good comprehensive mechanical properties.

With the global outbreak of COVID-19, the panic buying incidents triggered by the variants of the Omicron strain have severely affected the normal social order. This paper considers the complex interest game and interactive relationship among multiple subjects in the mass panic buying event caused by rumors and constructs a three-party evolution game model of local government, rumor-monger, and public. Based on the theory of evolution game, the evolutionary process of the strategy selection of game subjects is studied, and the strategy selection of the three-game subjects under different scenarios is compared and analyzed. Taking the example of the montmorillonite powder panic buying caused by the XBB virus strain rumor in China, the evolutionary game model constructed in this study is simulated and analyzed. The study shows that: the evolution process of the mass panic buying event is characterized by six stages: the initial stage E1(0,0,0); the outbreak stage E3(0,1,0);the spread stage E7(0,1,1);the climax stage E8(1,1,1);the relief stage E5(1,1,0);and the recovery stage E4(1,0,0); there are four stable points in the evolutionary game of the three game subjects, namely (no intervention, no rumor, no panic buying), (no intervention, rumor, no panic buying), (intervention, no rumor, no panic buying), and (intervention, rumor, no panic buying), and the strategy of government intervention will be adjusted according to the strategy selection of the public and the rumor-monger; under the mechanism of reward and punishment of the higher-level government, increasing the punishment and reward intensity of the higher-level government will promote the local government to intervene in the rumor-mongering event faster, but increasing the reward intensity has a more significant impact on the intervention behavior of the local government than punishment, and increasing punishment intensity has a more significant impact on the non-rumor-mongering behavior of the rumor-monger than reward; the parameters of social risk-bearing cost, risk transmission coefficient, rumor-mongering income and cost, and public drug purchase cost have different degrees of influence on the evolutionary behavior of game subjects. This study provides new ideas for effectively responding to mass panic buying events in the context of public emergencies.

Market crises pose significant challenges for businesses, emphasizing the importance of effective crisis management strategies. Central to these strategies is the ability to identify and retain loyal customers, who often serve as the bedrock of stability during tumultuous times. This paper investigates the application of deep meta-learning analysis to predict loyal customers as a cornerstone of market crisis management. Drawing upon an extensive literature review, the study explores previous research on market crises, customer loyalty, and the evolution of deep learning and meta-learning in crisis management contexts. Methodologically, the paper outlines data collection, model development, and evaluation procedures tailored for deep meta-learning-based customer prediction. In results, the overall accuracy of model 85%, a precision 0.88, recall 0.82, and F1-score 0.85 were obtained. The analysis demonstrates the effectiveness of deep meta-learning models in accurately identifying loyal customers during market crises, offering insights into their performance and applicability compared to traditional methods. Practical implications include potential applications in crisis management for businesses and considerations for real-world implementation.

Recently, Indium Tin Oxide, a highly transparent, well conductive, and CMOS-compatible material, has been paying strong attention to the thermo-optic controlled silicon photonics industry because it allows a miniature of the gap between the core silicon and the heater, thus enabling reducing the electric power consumption and enhancing the switching speed. In this article, we propose an ultralow loss and small-size ITO microheater for the phase shift tuning. The designated microheater is manipulated in realizing a numerical co-design of compact and high bandwidth three-mode converter and three-mode selective router. Simulation results illustrate the 3-dB bandwidth for the three-mode converter and three-mode selective router as much as 100-nm and 40-nm during crosstalk under -25 dB, respectively. Besides, co-designed devices attain relatively large fabrication tolerances corresponding to width and height tolerances of ±50 nm and ±5 nm. In addition, the proposed devices consumed less than 90 mW total power consumption and took a fast switching time below 8 μs. Moreover, both two co-designs can be integrated into an estimated compact footprint of 8 μm2160 μm. Such excellent performances demonstrate the attractive potential of ITO as low-loss thermo-optic phase shifters and open an alternative way for enabling ultrafast and high-speed mode division multiplexing systems and very large-scale photonic integrated circuits.

In this paper, a method for the preparation of ultrasound-responsive microgels was developed based on RAFT-HDA dynamic covalent bonding. First, a styrenic cross-linked network was suc-cessfully prepared by DA reaction between phosphoryl dithioester and furan using double-ended BDEPDF as RAFT reagent mediated St polymerization as double-ended dienophile linker and co-polymer of FMA and St as dienophile. Subsequently, the microgel system was constructed by the HDA reaction between phosphoryl disulfide and furan groups using the copolymer of polyethylene glycol monomethyl ether acrylate (OEGMA) and FMA as the dienophore building block and hy-drophilic segment, and the polystyrene pro-dienophile linker as the cross-linker and hydrophobic segment. The number of furans in the dienophile chain and the length of the dienophile linker were regulated by RAFT polymerization to investigate the effects of single-molecule chain functional group degree (f), furan/dithioester ratio (r), and hydrophobic linker length (D) on the microgel system. These microgels can achieve the reversible transformation of materials under force re-sponsiveness, and the steps are simple and adaptive, which are expected to show important po-tential applications in the fields of biomedical materials and adaptive electrical materials.

Fabry-Pérto (FP) cavity is the essential component of ultra-stable laser (USL) for gravitational wave detection, which couples multi-physic (Optical/Thermal/Mechanics) fields and requires ultra-high precision. To satisfy the requirements of precise and efficient design, a multi-physic coupling optimization method for fixing cubic FP cavity based on data learning is proposed. A multi-physic model for the cubic FP cavity is established and the performance is obtained by finite element analysis. The key performance indices (V, wF, wF) and key design variables (d, l, F) are determined considering the features of the FP cavity. The 49 sets of data by orthogonal experiment are acquired for the establishment and comparison of different data learning models (NN, RSF, KRG). The result turns out that the neural network has the best performance. Based on NSGA-II, the Pareto optimal front is obtained and the optimal combination of design variables is finally determined as {5,32,250}. The performance after optimization has proven to be a great improvement, of which the displacement under the fixing force and vibration test are decreased by more than 60%. The optimization strategy can not only help the design of the FP cavity but also enlighten other optimization fields.

This work considers the task of representation learning on the attributed relational graph (ARG). Both the nodes and edges in an ARG are associated with attributes/features allowing ARGs to encode rich structural information widely observed in real applications. Existing graph neural networks offer limited ability to capture complex interactions within local structural contexts, which hinders them from taking advantage of the expression power of ARGs. We propose Motif Convolution Module (MCM), a new motif-based graph representation learning technique to better utilize local structural information. The ability to handle continuous edge and node features is one of MCM’s advantages over existing motif-based models. MCM builds a motif vocabulary in an unsupervised way and deploys a novel motif convolution operation to extract the local structural context of individual nodes, which is then used to learn higher-level node representations via multilayer perceptron and/or message passing in graph neural networks. When compared with other graph learning approaches to classifying synthetic graphs, our approach is substantially better in capturing structural context. We also demonstrate the performance and explainability advantages of our approach by applying it to several molecular benchmarks.

The effect of rare earth Pr6O11 on the microstructure and mechanical properties of high-strength steel weld metal was investigated by optical microscopy, scanning electron microscopy and mechanical testing. Three different contents of Pr6O11 were added to the flux-cored wires. The results showed that the addition of 1% Pr6O11 can promote the refinement and spheroidization of inclusions, refine the grains, form acicular ferrites, and significantly improve the toughness of weld metal. The addition of Pr6O11 promoted the formation of rare earth composite inclusions and acicular ferrites in the weld metal, refined the lath microstructure, inhibited the formation of martensite and bainite. The crack formation mode changed from the boundary cracking of the bainite clusters caused by the surface shear stress to the surface shear stress-induced decohesion of inclusion. However, excessive addition of Pr6O11 will reduce the number of inclusion nucleation and deteriorate the mechanical properties. The wire No.2 with 1% Pr6O11 had the good comprehensive mechanical properties, and the corresponding values were 835MPa of tensile strength and 72 J of impact toughness. These findings suggest that the control of Pr6O11 can be an effective way to improve the impact toughness of weld metal.

This paper investigates the impact of sediment deposition and inflow conditions on horizontal impact pressure and frequency analysis of bridge deck vibrations during flooding. Flooding-induced pressure and vibrations contribute to bridge collapse, and sediment deposition influences water flow and impact pressure. The study explores the relationship between sediment deposition height and impact pressure, revealing a significant increase as sediment approaches 50% of bridge deck clearance. Sediment amplifies impact pressure response to flow velocity changes. The dimensionless sediment deposition height has a greater influence on impact pressure compared to the inflow Froude number. Two distinct frequencies, dominant and secondary, are identified for impact pressure and water level fluctuations. Dominant frequencies positively correlate with sediment deposition height and Froude number, indicating an increasing trend. Secondary frequencies remain stable (0.31-0.58 Hz). These findings enhance understanding of flow dynamics and bridge-flow interaction in sediment-deposited channels, providing theoretical support for evaluating and managing disasters related to bridges in such environments. Overall, this research contributes to the field of bridge engineering and supports improved design and maintenance practices for bridges exposed to sediment-deposited channels.

An experimental ultra wideband ground penetrating radar based on Golay complementary sequences is proposed to locate underground pipes. Golay complementary sequences with the code length of 1024 and frequency of 1 GHz are used as the probe signals. Two-dimensional image of the buried pipes is achieved by correlation method and back projection algorithm. The experimental results show that both the plastic pipe and metallic pipe can be located with a range resolution of 10 cm. Furthermore, as the Golay complementary sequences are a pair of complementary sequences, the sum of their correlation function yields twice the value of the peak at the target position and zero elsewhere. Thus, compared with the stepped frequency signal radar or chaotic signal radar, the Golay-based radar can significantly improve the signal noise ratio and has capability of deep detection.

Broadband reverse saturable absorption is systematically investigated via Z-scan, transient absorption spectrum (TAS). The excited state absorption and negative refraction of Orange IV are observed in the Z-scan experiment at 532 nm. Meanwhile, two-photon induced excited-state absorption and pure two-photon absorption are observed at 600 nm and 700 nm with the pulse width of 190 fs, respectively. An ultrafast broadband absorption in visible wavelength region is observed via TAS. The different nonlinear absorption mechanism at multiple wavelengths is discussed and interpreted from the results of TAS. In addition, the ultrafast dynamics of negative refraction in the excited state of Orange IV is investigated via degenerate phase object pump probe, from which the weak long-lived excited state is extracted. All studies indicate that Orange IV has the potential to be further optimized into a superior broadband reverse saturable absorption material and also has certain reference significance for the study of optical nonlinearity in organic molecules containing azobenzene groups.

Female infertility is a global medical condition that can be caused by various disorders of the reproductive system, including premature ovarian failure (POF), polycystic ovary syndrome (PCOS), endometriosis, Asherman syndrome, and preeclampsia. It affects the quality of life of both patients and couples. Mesenchymal stem cells (MSCs) have received increasing attention as a potential cell-based therapy with several advantages over other cell sources, including greater abundance, fewer ethical considerations, and high capacity for self-renewal and differentiation. Clinical researchers have examined the therapeutic use of MSCs in female infertility. In this review, we discuss recent studies on the use of MSCs in various reproductive disorders that lead to infertility. We also describe the role of microRNAs (miRNAs) and exosomal miRNAs in controlling MSC gene expression and driving MSC therapeutic outcomes. The clinical application of MSCs holds great promise for the treatment of infertility or ovarian insufficiency and to improve reproductive health for a significant number of women worldwide.

Background: The link between nocturia and aging male symptoms (AMS) has not been scientifically established. This study aimed to measure the degree of severity of AMS that impact on the health-related quality-of-life (HRQoL) in adult males living with nocturia, and to determine the predictive values of nocturnal factors on AMS. Methods: It is an extended analysis of new data collected by using the Hong Kong Traditional AMS (HK-AMS) scale and Cantonese version of the Pittsburgh Sleep Quality Index (PSQI) in a recently published cross-sectional population-based survey. Results: Of the 781 respondents that have completed the set of questionnaires, 68% and 61% of men living with nocturia reported clinically significant (at moderate-to-severe levels) somato-vegetative and sexual AMS, whereas the prevalence and severity were increased with advancing nighttime voiding frequency. The nocturia-specific QoL (NQoL) score and nocturnal frequency were found to be significant predictive factors for composite, somato-vegetative and sexual AMS, in addition to age, global OSQI score, and certain metabolic diseases. Conclusions: Current findings suggested the inclusion of nocturia when measuring the male-specific HRQoL related to aging.

Inequality of health service for different specialty categories not only occurs in different areas inequality of health service for different specialty categories in the world, but also happens in the online service platform. In the online health community (OHC), health service was often of inequality for different specialty categories, including both online views and medical consultation for offline registered service. Moreover, how the factor city-level impacts the inequality of health service in OHC is still unknown. We designed a causal inference study with data on distributions of serviced patients and online views in over 100 distinct specialty categories on one largest OHC in China. To derive the causal effect of the city-levels (two levels inducing 1 and 0) on the Gini coefficient, we matched the focus cases in cities of rich healthcare resources with the potential control cities. For the Gini coefficient of serviced patients in over 100 specialty categories, the average treatment effect of level-1 cities is 0.470, which is 0.029 higher than that of the matched group. Similarly, for the Gini coefficient of online views, the average treatment effect of Level-1 cities is 0.573, which is 0.016 higher than that of the matched group. For each of the specialty categories, we also estimated the average treatment effect the specialty category’s Gini coefficient ( ) with the balanced covariates. The results support the argument that the total Gini coefficient of all the doctors in OHC shows that the inequality of health service is still very serious. This study contributes to the development of the theoretically grounded understanding of the causal effect of city-level on the inequality of health service in an online to offline healthcare service setting.

Oxygen extraction fraction (OEF), the fraction of oxygen that tissue extracts from blood, is an es-sential biomarker for directly assessing tissue viability and function in neurologic disorders. For quantitative mapping of OEF, an integrative model of quantitative susceptibility mapping and quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or QQ) was recently pro-posed. However, QQ assumes Gaussian noise in both susceptibility and multi-echo gradient echo (mGRE) magnitude signals for OEF estimation. This assumption is unreliable in low sig-nal-to-noise ratio (SNR) regions like disease-related lesions, risking inaccurate OEF estimation and potentially impacting clinical decisions. Addressing this, our study presents a novel multi-echo complex QQ (mcQQ) that models realistic noise in mGRE complex signals. The proposed mcQQ was implemented using a deep learning framework (mcQQ-NET) and compared with the existing deep learning-based QQ (QQ-NET) in simulations, ischemic stroke patients, and healthy subjects. Both mcQQ-NET and QQ-NET used identical training and testing datasets and schemes for a fair comparison. In simulations, mcQQ-NET provided more accurate OEF than QQ-NET. In the sub-acute stroke patients, mcQQ-NET showed a lower average OEF ratio in lesions relative to unaf-fected contralateral normal tissue than QQ-NET. In the healthy subjects, mcQQ-NET provided uniform OEF maps, similar to QQ-NET, but without unrealistically high OEF outliers in areas of low SNR. Therefore, mcQQ-NET improves OEF accuracy by better reflecting realistic data noise characteristics.

Simultaneous Localization and Mapping (SLAM), as one of the core technologies in intelligent robotics, has gained substantial attention in recent years. Addressing the limitations of SLAM systems in dynamic environments, this research proposes a system specifically designed for plant factory transportation environments, named GY-SLAM. GY-SLAM incorporates a lightweight target detection network GY based on YOLOv5, which utilizes GhostNet as the backbone network. This integration is further enhanced with CoordConv coordinate convolution, CARAFE up-sampling operators, and SE attention mechanism, leading to simultaneous improvements in detection accuracy and model complexity reduction. While improving [email protected] by 0.514%, the model simultaneously reduces the number of parameters by 43.976%, computational cost by 46.488%, and model size by 41.752%. Additionally, the system constructs pure static octree maps and grid maps. Tests conducted on the TUM dataset and a proprietary dataset demonstrate that GY-SLAM significantly outperforms ORB-SLAM3 in dynamic scenarios in terms of system local-ization accuracy and robustness. It shows a remarkable 92.58% improvement in RMSE for Absolute Trajectory Error (ATE). Compared to YOLOv5s, the GY model brings a 41.5944% improvement in detection speed and a 17.7975% increase in SLAM operation speed to the system, indicating strong competitiveness and real-time capabilities. These results validate the effectiveness of GY-SLAM in dynamic environments and provide substantial support for the automation of logistics tasks by robots in specific contexts.

With the rapid development of Internet of Things (IoT)-based near-earth remote sensing technology, the problem of network intrusion for near-earth remote sensing systems has become more complex and large-scale. Therefore, it is essential to seek an intelligent, automated, and robust network intrusion detection method. In recent years, network intrusion detection methods based on graph neural networks (GNNs) have been proposed. However, there are still some practical issues with these methods. For example, they have not taken into consideration the characteristics of near-earth remote sensing systems, the state of the nodes, and the temporal features. Therefore, this article analyzes the characteristics of existing near-earth remote sensing systems and proposes a spatio-temporal graph attention network (N-STGAT) that considers the state of nodes. The proposed network applies spatiotemporal graph neural networks to the network intrusion detection of near-earth remote sensing systems and validates the effectiveness of the proposed method on the latest flow-based dataset.

SARS-CoV-2 causes severe pneumonia epidemics and probably originated in horseshoe bats, but the intermediate host is unknown. The interaction of SARS-CoV-2 spike protein and its acceptor protein ACE2 is an important issue in determining viral host range and cross-species infection, while the binding capacity of Spike protein to ACE2 of different species is unknown. Here, we used the atomic structure model of SARS-CoV-2 and human ACE2 to assess the receptor utilization capacity of ACE2s from different species including cats, chimpanzees, dogs, cattles. Our results show, domestic cats (Felis catusc) and dogs (Canis lupus familiaris) are more susceptible to infection by SARS-CoV-2 and that they can efficiently transmit the virus to previously uninfected animals that are housed with them. Especially, cats could be a choice of animal model for screening antiviral drugs or vaccine candidates against SARS-CoV-2.

This study aimed to conduct importance-performance analyses (IPAs) based on the perception of Korean middle-school students toward health management awareness during the post-coronavirus disease 2019 (COVID-19) era. Data were collected from 867 Korean middle-school students via online and offline surveys between May and June 2023. A frequency analysis, a reliability analysis, an IPA based on the entire target students, and an IPA depending on sex were carried out with the collected data, and they revealed the following. First, the IPA results regardless of sex indicated that four factors of the mental health were located in the third quadrant with one factor of the same variable in the fourth quadrant. The three factors of the disease management were located in the third quadrant. As for physical activity, two factors were located in the first quadrant, one in the second quadrant, and one in the third quadrant. As for sleep management, two factors were located in the second quadrant, one in the third quadrant, and one in the first quadrant. As for the eating management, two factors were located in the third quadrant, and one in the fourth quadrant. As for the social distancing variable, all four factors were located in the third quadrant. As for the hygiene management, two factors were located in the first quadrant, one in the third quadrant, and one in the fourth quadrant. Furthermore, the IPA results indicated gender differences in regular sports and vigorous movement activities associated with the physical activity. Additionally, a gender difference was observed in regular diet associated with the eating management. This study proposed possible measures for encouraging adolescents to recognize the importance of health and increase their health-related performance during the COVID-19 endemic phase.

In this paper, a modular wire-actuated robotic arm was designed to improve the safety and adaptability of service robots during human-robot interaction. To raise the stiffness adjustment range of the robotic arm, a symmetric variable-stiffness unit was designed based on flexure, which had compact and simple structure, and lowly nonlinear stiffness-force relationship. In this paper, we focused on the research of the symmetric 1-DOF joint module. Based on the kinematics and stiffness analysis, the pose of the joint module could be adjusted by controlling the length of the wires, and the stiffness of the joint module could be adjusted by controlling the tension of the wires. Because of the actuation redundancy, the pose and stiffness of the joint module could be controlled synchronously. Furthermore, a directly method was proposed for the stiffness-oriented wire tension distribution problem of the 1-DOF joint module. A simulation was carried out to verify the proposed method.