Hydrogen is considered as an ideal substitute to the traditional fossil fuels because of its widespread sources, high calorific value of combustion, and zero carbon emissions. The electrocatalytic water-splitting to produce hydrogen is also deemed to be the best approach, however, it is a challenge to make high-efficient and low-cost electrocatalysts. Single-atom catalysts (SACs) are considered the most promising candidate to replace the traditional noble-metal catalysts. Compared with SACs, the dual-atom catalysts (DACs) are of greater attraction including higher metal loading, more versatile active sites, and excellent catalytic activity. In the review, several general synthetic strategies and structural characterization methods of DACs are introduced, and the mechanisms of hydrogen evolution reaction (HER) and oxygen evolution reactions (OER) are discussed. The authors hope that the review has given some insights and inspiration to the researchers about DACs in electrocatalytic water-splitting.

While considering the sensitivity over the parameter optimization, it is essential to determine which parameters have the most significant implications on model performance. This study focuses on the baseflow recession constant as one of the independent basin parameters to forecast low flows, perform hydrograph analysis, and calibrate rainfall-runoff models for significant improvement. Prior studies examined that the optimization of data adjustment parameters can improve the hydrological model performance and determine the minimum acceptable data length for data scarce regions using the Xinanjaing model. However, it is essential to pay special attention to the sensitivity of the recession constant, which can also impact the model performance during the data scarcity. Therefore, this study extends the research to comprehend the recession constant sensitivity over data adjustment parameters in the shorter datasets leading to more reliable parameter estimation. In terms of that, this study explores how recession constant affects hydrological parameter estimation on annual scale while keeping data adjustment parameters constant in continuous hydrological modeling, employing the Xinanjiang (XAJ) model as a case study. This study considered two approaches of recession constant (cg); (i) assessing the relationship between cg and the data adjustment parameter (Cep), for the 28-year datasets, (ii) investigating the significant impacts of the sensitivity of cg over Cep in shorter datasets which can affect the estimation of the acceptable minimum data length in the data scarce basins. The study underscores the importance of the recession constant sensitivity for reliable continuous hydrological model predictions, especially in data-scared areas. The study’s outcomes enhance the understanding of the importance of parameter sensitivity and its relationship in conceptual hydrological modeling during the data limitations.

In the recent decade, Building Information Modelling (BIM) has widely been adopted in the Architecture, Engineering and Construction (AEC) industry and completely upended the way we build. While BIM continues gain momentum in the industry, it has also attracted increasing attentions from researchers. However, most of the current study focuses on reviewing BIM for management, BIM for green building, BIM for infrastructure and BIM for Facilities Management (FM). There are few studies about Global BIM review and to discuss their complex inter-connections. In this study, we adopted a scientometric analysis method to review global BIM research from 2004-2019. A total of 1455 scholarly bibliographic records obtained from Web of Science Core Collection databased were established for the analysis. This study has identified the top productive and influential researchers, research institutes, regions/countries, subject categories and journals in the BIM field. In addition, 11 clusters of Global BIM research were also identified including construction project, green BIM, construction safety planning, automated IFC-based workflow and so on. Authors distinguished 11 clusters of global BIM research into 3 stages, namely formulating stage, accelerating stage and transforming stage. Furthermore, authors reviewed the BIM policy of Singapore and observed there is a co-production relationship between evolution of BIM policy and global BIM research. These findings provide valuable information for researchers, practitioners and policy makers by visualizing the current progress in the research field of BIM and highlighting future research needs.

Liquid-liquid phase separation (LLPS) of proteins has been found ubiquitously in eukaryotic cells, critical in the controlling of many biological processes through forming a temporary condensed phase with different bimolecular components. TDP-43 is recruited to stress granules in cells and is the main component of TDP-43 granules and proteinaceous amyloid inclusions in patients with amyotrophic lateral sclerosis (ALS). TDP-43 low complexity domain (LCD) is able to demix in solution forming the protein condensed droplets. The molecular interactions regulating its LLPS were investigated at the protein fusion equilibrium stage, where the droplets stopped growing. We found the molecules in the droplet were still liquid-like but with enhanced intermolecular helix-helix interaction in the LCD. The protein would start to aggregate after about 200 minutes of lag time and aggregate slower than at the condition when the protein does not phase separate or the molecules have a reduced intermolecular helical interaction. A structural transition intermediate towards protein aggregation was also discovered involving a decrease of the intermolecular helix-helix interaction and a reduction in the helicity. Therefore, LLPS and the intermolecular helical interaction could help maintain the stability of TDP-43 LCD.

Background: Previous in vitro and in vivo studies have demonstrated that storage of cooked rice at 4 °C for 24 h and reheating to 65 °C significantly reduced starch digestibility and postprandial glycaemic responses. Moreover, the effect was greater for parboiled rice compared to other rice varieties commonly consumed in New Zealand. This study aimed to evaluate consumer preferences of related sensory attributes and consumer acceptability of several rice varieties freshly cooked or reheated. Method: Sixty-four consumers volunteered and recorded on Visual Analoge Scales their preference and acceptability of freshly prepared or cold-stored and reheated medium grain white, medium grain brown and parboiled rice. Results: All six rice samples were accepted by participants (average 54%). Reheated parboiled rice and reheated medium grain brown rice were both accepted by participants as a preferred staple meal compared to other rice samples. Among all rice samples, the sweetness and the flavour of freshly cooked warm medium-grain white rice were less preferred (scored 42.1% and 45.0% respectively) compared with other samples (P = 0.05). Participants who prepared and consumed brown rice at home regularly (more than 10 times per month), preferred the reheated brown rice (73.8% (67.4, 80.2)) and reheated parboiled rice (74.3% (67.9, 80.7)) (P < 0.001). Conclusions: It is suggested that reheated parboiled rice, with the lowest starch digestibility and glycaemic impact (both in vitro glucose release and in vivo glucose response) could be accepted as a healthier alternative for the daily staple meal.

Due to the major impact of climate change on the world's environment, political and economic systems, climate change mitigation has become a pressing priority for the international community and requires rapid action from the whole society. With the continuous advancement of artificial intelligence research, the integration of AI and other technologies makes it more and more used in the whole society. It has become a promising and innovative avenue in the field of climate change mitigation. This paper comprehensively considers the key role of AI technology in the field of climate change mitigation, such as climate modeling, the optimization of renewable energy systems, the development of intelligent solutions for sustainable practices and CSS technology, and affirms its future prospects. It also describes the challenges of AI in the field of climate change mitigation. As researchers, policymakers, and industries collaborate to refine AI methodologies and integrate them into practical applications, a concerted effort is required to establish ethical guidelines, transparency standards, and inclusive governance frameworks.

We study the Gaudin models with gl(1|1) symmetry that are twisted by a diagonal matrix and defined on tensor products of polynomial evaluation gl(1|1)[t]-modules, extending all the results of to the twisted case. Namely, we give an explicit description of the algebra of Hamiltonians (Gaudin Hamiltonians) acting on tensor products of polynomial evaluation gl(1|1)[t]-modules and show that there exists a bijection between common eigenvectors (up to proportionality) of the algebra of Hamiltonians and monic divisors of an explicit polynomial written in terms of the highest weights and evaluation parameters. In particular, our result implies that each common eigenspace of the algebra of Hamiltonians has dimension one. We also give dimensions of the generalized eigenspaces.

We present a class of efficient parametric closure models for 1D stochastic Burgers equations. Casting it as statistical learning of the flow map, we derive the parametric form by representing the unresolved high wavenumber Fourier modes as functionals of the resolved variables’ trajectory. The reduced models are nonlinear autoregression (NAR) time series models, with coefficients estimated from data by least squares. The NAR models can accurately reproduce the energy spectrum, the invariant densities, and the autocorrelations. Taking advantage of the simplicity of the NAR models, we investigate maximal and optimal space-time reduction. Reduction in space dimension is unlimited, and NAR models with two Fourier modes can perform well. The NAR model’s stability limits time reduction, with a maximal time step smaller than that of the K-mode Galerkin system. We report a potential criterion for optimal space-time reduction: the NAR models achieve minimal relative error in the energy spectrum at the time step where the K-mode Galerkin system’s mean CFL number agrees with the full model’s.

Due to the periodic movement of the piston in the reciprocating pump, the fluid will cause pressure pulsation, and the vibration of the pipeline will lead to instrument distortion, pipe failure and equipment damage. Therefore, it is necessary to study the vibration phenomena of the reciprocating pump pipeline based on the pressure pulsation theory. This paper starts from the reciprocating pump pipe pressure pulsation caused by fluid, pressure pulsation in the pipeline and the excited force is calculated under the action of the reciprocating pump. Then, the numerical simulation model is established based on the pipe beam model, and the rationality of the numerical simulation method is verified by the indoor experiment. Finally, a case study is taken as an example to analyze the vibration law of the pipeline system, and proposed the stress reduction and vibration reduction measures. The main conclusions are drawn from the analysis: (1) Excited force is produced in the bend or tee joint, and it can also influence the straight pipe in different levels; (2) In this pipeline system, the pump discharge pipe has a larger vibration amplitude and lower natural frequency; (3) The vibration amplitude increases with the pipe thermal stress, and when the oil temperature is higher than 85°C, it had a greater influence on the vertical vibration amplitude of the pipe.

Accumulating literature demonstrates that omega-3 polyunsaturated fatty acid (N3PUFA) can be incorporated into the phospholipid bilayer of cell membrane in the human body to positively affect the cardiovascular system, including improving epithelial function, decreasing coagulopathy, and attenuating uncontrolled inflammatory responses and oxidative stress. Moreover, it has been proven that the N3PUFA, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are precursors of some potent endogenous bioactive lipid mediators that mediate some favorable effects attributed to their parent substances. A linear dose-response relationship between increased EPA and DHA intake and reduced thrombotic outcomes have been reported. The excellent safety profile of dietary N3PUFA makes them a prospective adjuvant treatment for people exposed to a higher risk of cardiovascular problems associated with COVID-19. This review presented the potential mechanisms that might contribute to the beneficial effect of N3PUFA and the optimal form and dose applied.

Epitaxial lithium ferrite (LiFe5O8) films with different thicknesses have been successfully fabricated on SrTiO3 (001) substrates by magnetron sputtering deposition technique. The microstructural and magnetic properties are characterized by advanced transmission electron microscope and magnetic measurement device. It was found that the formation of structural defects can be influenced by the thickness of the film. Apart from the misfit dislocations, the orientation domains form in thinner film and twin boundaries appear in thicker film, respectively, contributing to the misfit strain relaxation in the heterosystem. The magnetic measurement shows that the thinner films have enhanced magnetization and a relatively lower coercive field compared with the thicker films containing the antiferromagnetic twin boundaries. Our results provide a way for tuning the microstructure and magnetic properties of lithium ferrite films by changing the film thickness.

In order to investigate the relationship between government policy, the service and design of rural crowdsourcing spaces, the self-efficacy of returning youth, and the innovation performance of businesses, this study focuses on gathering data by distributing questionnaires to returning youth in rural crowdsourcing spaces and developing a structural model. The results demonstrate how important government policies are in encouraging youth entrepreneurship in their local communities. It has the potential to enhance both the development of rural crowdsourcing spaces and the self-efficacy of young entrepreneurs, thereby enhancing the innovative capabilities of local entrepreneurial businesses. Therefore, in order to encourage economic development in rural areas, the government should improve pertinent support measures.

On the basis of the force and safety factor analysis of traditional retaining wall, a new analytical method of force and displacement of slope is suggested, the numerical theoretical solution of the stress distribution of the sliding body can be obtained under the conditions, which the stresses distribution are satisfied with the differential equilibrium equations, the boundary conditions, the compatibility equation and the macroscopic equilibrium equations. The interface stresses between the sliding body and the retaining wall are continuous, the theoretical solution of retaining wall stress distributions can be gotten, while the stress differential equilibrium equation, the compatibility equation, macroscopic force and moment balance equations are satisfied. The strain and displacement solutions can be obtained by using Duncan Chang and Hooke constitutive equations for the slope and the retaining wall respectively. The transfer station of landfill in the Guandukou Town of Badong County is taken as an example, the results of the sliding body and retaining wall analysis show: The stress and strain solutions of a slope and a retaining wall can be obtained by the proposed method, the forces between the retaining wall and the sliding body include shear stress and normal stress, and the stress distributions change with the deformation of the sliding body, the shape and material of retaining wall can be optimized according to the calculation results，it is feasible for the proposed analysis method of slope with retaining wall design to be run many years.

Background: Globally, hot cooked refined rice is consumed in large quantities and is a major contributor to dietary glycaemic load. This study aimed to compare the glycaemic potency of hot and cold stored parboiled rice to widely available medium grain white rice. Method: Twenty-eight healthy volunteers participated in a three treatment experiment where postprandial blood glucose was measured over 120 minutes after consumption of 140g of rice. Three rice samples were freshly cooked medium-grain white rice, freshly cooked parboiled rice, and parboiled rice stored overnight at 4 ºC. All rice was served warm at 65 ºC. Chewing time was recorded. Results: The 24-hours cold-stored and reheated parboiled rice resulted in a 42% and 12% lower blood glucose concentration trajectory than freshly cooked medium-grain white rice and freshly cooked parboiled rice. Chewing time for 10g cold stored parboiled rice was 6 seconds (25%) longer and was considered more palatable, visually appealing and better tasting than freshly cooked medium grain (all P<0.05). Conclusions:. For regular consumers of rice reheating cooked rice after cold storage would lower the dietary glycaemic load and long term may reduce risk for type 2 and gestational diabetes. More trials are needed to identify the significance.

Abstract: Bacillus subtilis fmb60, which has broad-spectrum antimicrobial activities was isolated from plant straw compost. A hybrid NRPS/PKS cluster was screened from the genome. Sixteen secondary metabolites produced by the gene cluster were isolated and identified using LC-HRMS and NMR. Three lipoamides D–F (1-3) and two amicoumacin derivatives, amicoumacins D, E (4, 5), were identified, and are reported here for the first time. Lipoamides D–F exhibited strong antibacterial activities against harmful foodborne bacteria, with the MIC ranging from 6.25 to 25 µg/mL. Amicoumacin E scavenged 38.8% of ABTS+ radicals at 1 mg/mL. Direct cloning and heterologous expression of the NRPS/PKS and ace gene cluster identified its importance for the biosynthesis of amicoumacins. This study demonstrated that there is a high potential for biocontrol utilization of B. subtilis fmb60, and genome mining for clusters of secondary metabolites of B. subtilis fmb60 has revealed a greater biosynthetic potential for the production of novel natural products than previously anticipated.

Coptisine (Cop) exerts a neuroprotective effect in central nervous system disease, particularly ischemic stroke. However, its protective mechanism is still unclear. This study aimed to investigate the protective effect of Cop on cerebral ischemia-reperfusion (IR) rats with a middle cerebral artery occlusion model by integrating a gas chromatography-mass spectrometry (GC-MS) based metabolomics approach with biochemical assessment. Our results showed that Cop could improve neurobehavioral function and decrease the ischemia size in IR rats. In addition, Cop was found to decrease inflammatory mediators (e.g., prostaglandin D2 (PGD2) and tumor necrosis factor-α (TNF-α) and attenuate oxidative stress response (e.g., increase the superoxide dismutase (SOD) expression and decrease 8-iso-PGF2α level). Furthermore, GC-MS based cerebrospinal fluid (CSF) metabolomics analysis indicated that Cop influenced the level of glycine, 2,3,4-trihydroxybutyric acid, oleic acid, glycerol, and ribose during IR injury. Cop exhibited a good neuroprotective effect against cerebral IR injury and metabolic alterations which might be mediated through its antioxidant and anti-inflammatory property.

Primary ovarian ependymoma is a rare neuroectodermal neoplasm that can arise from immature ovarian teratoma. Due to the paucity of this entity, a complete molecular analysis of these tumors has not been done, thus creating a challenge for finding an effective and safe therapeutic treatment. In limited literature, patient with primary ovarian ependymoma showed various responses to an array of individualized therapies ranging from surgeries and chemotherapies. Here, we present a 38-year-old female with persistent ovarian ependymoma with molecular profile similar to traditional central nervous system ependymoma that is irresponsive to multiple cytoreduction and clinical experimental therapies. Therefore, a prompt recognition and reporting of this entity can greatly aid in expanding the understanding and standardization of therapies for this neoplasm.

The rapid development of multicopters has led to many security problems. In order to prevent multicopters from invading restricted areas or famous buildings, an Anti-UAV Defense System (AUDS) has been developed and become a research topic of interest. Topics under research in relation to this include electromagnetic interference guns for unmanned aerial vehicles (UAVs), high-energy laser guns, US military net warheads, and AUDSs with net guns. However, these AUDSs use either manual aiming or expensive radar to track UAVs. This paper proposes a dual-axis rotary platform with UAV automatic tracking. The tracking platform uses visual image processing technology to track and lock the dynamic displacement of a UAV. When a target UAV is locked, the system uses a nine-axis attitude meter and laser rangefinders to measure its flight altitude and calculates its longitude and latitude coordinates through sphere coordinates to provide UAV monitoring for further defense or attack missions. Tracking tests of UAV flights in the air were carried out using a DJI MAVIC UAV at a height of 30 meters to 100 meters. It was set up for UAV image capture and visual recognition for tracking under various weather conditions by a thermal imager and a full-color camera respectively. When there was no cloud during the daytime, the images captured by the thermal imager and full-color camera provide a high-quality image recognition result. However, under dark weather, black clouds will emit radiant energy and seriously affect the capture of images by a thermal imager. When there is no cloud at night, the thermal imager performs well in UAV image capture. When the UAV is tracked and locked, the system can effectively obtain the flight altitude and longitude and latitude coordinate values.

IMPORTANCE: On December 10, 2021, the FDA published a Determination Letter, along with a Statistical Review and Evaluation Report, and concluded that under the non-informative prior, the local Bayesian optimal interval design (BOIN) design, in its revised form, can be designated fit-for-purpose for identifying the maximum tolerated dose of a new drug, assuming that dose-toxicity relationship is monotonically increasing. Although setting the BOIN design parameter p.tox = 1.4 * target.DLT.rate is recommended in almost all BOIN methodology articles and is the default value in the R package BOIN, it's unclear if the choice of p.tox should only depend on the target DLT rate and if certain range of p.tox could produce the same BOIN boundary table. DESIGN: In this simulation study, following parameters were varied one at a time, using R package BOIN, to explore their effects on the equivalence intervals of p.saf and p.tox: 1) target DLT rate, 2) n.earlystop, 3) cutoff.eli, 4) cohortsize, and 5) ncohort. And a simple 3+3 design was used as an example to explore equivalent sets of BOIN design parameters that can generate the same boundary table. RESULTS: When the early stopping parameter n.earlystop is relatively small or the cohortsize value is not optimized via simulation, it might be better to use p.tox < 1.4 * target.DLT.rate, or try out different cohort sizes, or increase n.earlystop, whichever is both feasible and provides better operating characteristics. This is because if the cohortsize was not optimized via simulation, even when n.earlystop = 12 and cohortsize > 3, the BOIN escalation/de-escalation rules generated using p.tox = 1.4 * target.DLT.rate could be exactly the same as those calculated using p.tox > 3 * target.DLT.rate, which might not be acceptable for some pediatric trials targeting 10% DLT rate. The traditional 3+3 design stops the dose finding process when 3 patients have been treated at the current dose level, 0 DLT has been observed, and the next higher dose has already been eliminated. If additional 3 patients were required to be treated at the current dose in the situation described above, the decision rules of this commonly used 3+3 design could be generated using BOIN design with target DLT rates ranging from 18% to 29%, p.saf ranging from 8% to 26%, and different p.tox values ranging from 39% to 99%. These BOIN parameters also need to satisfy a set of conditions. Meaning: This study demonstrates the importance of interpreting BOIN design parameter p.tox as an interval of toxicity rates that are considered too toxic, rather than one prespecified value that corresponds to the lowest toxicity probability that is deemed overly toxic. When designing a dose-finding trial using BOIN, it is important to perform simulation studies to identify equivalent sets of BOIN design parameters that can generate the same boundary table so that we can better compare the safety properties of different boundary tables.

Wound healing is a complex, dynamic process involving the coordinated interaction of diverse cell types, growth factors, cytokines, and extracellular matrix components. Despite emerging evidence highlighting their importance, dermal sheath cells remain a largely overlooked aspect of wound healing research. This review explores the multifunctional roles of dermal sheath cells in various phases of wound healing, including modulating inflammation, aiding in proliferation, and contributing to extracellular matrix remodeling. Special attention is devoted to the paracrine effects of dermal sheath cells and their role in fibrosis, highlighting their potential in improving healing outcomes, especially in differentiating between hairy and non-hairy skin sites. By drawing connections between dermal sheath cells activity and wound healing outcomes, this work proposes new insights into the mechanisms of tissue regeneration and repair, marking a step forward in our understanding of wound healing processes.

Despite UAV multi-target detection exhibits considerable developmental potential worldwide, it suffers distinct challenges compared with traditional tasks in this field. These challenges include insufficient feature extraction capabilities for small targets, limited capabilities for multi-dimensional feature fusion, as well as constraints on hardware computation parameters. Especially in mission scenarios such as disaster detection, these challenges will be further amplified. Consequently, this paper introduces SSE-YOLO, an innovative YOLO framework algorithm specially designed to address these challenges. To enhance the model's feature extraction capability, we employ the SPDConv module to replace the original Conv in the backbone section, utilizing depth-separable convolution instead of traditional convolution pooling. Concurrently, we eliminate the SPPF module at the bottom and address a new Separate Kernel Attention Pyramid Pooling (SKAPP) module, substantially enhancing the feature fusion capability at the model's core. Moreover, to address the challenge of multi-dimensional feature fusion, we replace the Concat module of the neck and head with E-BiFPN, transmitting feature information from the backbone to the lower network through four CBS blocks, which effectively resolves the issue of lost contextual information in the model. Meanwhile, SSE-YOLO undergoes ablation experiments on the VisDrone2019 dataset to evaluate its effectiveness against alternative methods, and experimental results illustrate the model's exceptional precision in detecting UAV targets. In comparison to models with comparable experimental accuracy, SSE-YOLO requires remarkably fewer parameters. On the VisDrone2019-DET-test-dev dataset, SSE-YOLO enhances mAP by 17.3%, with a 42.5% reduction in the parameter amounts. Therefore, the proposed method effectively tackles the challenge of reconciling low parameters and high accuracy, presenting a novel pathway for deep learning-based UAV multi-target detection.

Wendan (a type of pomelo), as a popular fruit in China, is less known in other countries. Its peel is widely used in food and traditional medicine. Four different origins of Wendan pomelo were selected, and crude extracts were obtained by the Soxhlet extraction method. The composition of Wendan peel was analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS) in positive ion mode. The compounds were identified by searching the Metabolite Link (METLIN), Spectral Database for Organic Compounds (SDBS) and referring to literature reports. A total of 20 compounds were identified among the samples from the four origins, of which 8 compounds were common. The majority of the compounds belonged to the flavonoid and coumarin classes, Meranzin hydrate was identified for the first time in pomelo peel. In vitro antioxidant activity experiments showed that samples from Taizhou, Zhejiang exhibited the highest antioxidant activity in the DPPH, ABTS, and FRAP assays, with values of 0.59 mg/mL, 97.06 μmol TE/g, and 60.62 μmol Fe2+/g, respectively. Samples from Zhangzhou, Fujian showed antioxidant activity second only to the samples from Taizhou, Zhejiang. The sample from Zhangzhou, Fujian Province, showed excellent inhibitory activity in the α-glucosidase inhibition assay (IC50=7.99 mg/mL).

Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality into the research of seismic velocity inversion and yielded a wealth of research outcomes. Typically, seismic velocity inversion based on machine learning lacks control over physical processes and interpretability. Starting from wave theory and the physical processes of seismic data acquisition, this paper proposes a method for seismic velocity model inversion based on Physical Embedding Recurrent Neural Networks. Firstly, the wave equation is a mathematical representation of the physical process of acoustic waves propagating through a medium, and the finite difference method is an effective approach to solving the wave equation. With this in mind, we introduce the architecture of recurrent neural networks to describe the finite difference solution of the wave equation, realizing the embedding of physical processes into machine learning. Secondly, in seismic data acquisition, the propagation of acoustic waves from multiple sources through the medium represents a high-dimensional causal time series (wavefield snapshots), where the influential variable is the velocity model, and the received signals are the observations of the wavefield. This forms a forward modeling process as the forward simulation of the wavefield equation, and the use of error back-propagation between observations and calculations as the velocity inversion process. Through time-lapse inversion and incorporating the causal information of wavefield propagation, the non-uniqueness issue in velocity inversion is mitigated. Through mathematical derivations and theoretical model analyses, the effectiveness and rationality of the method are demonstrated. In conjunction with simulation results for complex models, the method proposed in this paper can achieve velocity inversion in complex geological structures.

Demethyleneberberine is an active component extracted from the Chinese herbal drug Cortex Phellodendri. It is also a metabolite of the berberine in animals and humans. However, the pharmacokinetics, tissue distribution and excretion of demethyleneberberine have not been reported. The present study aimed to investigate the pharmacokinetic parameters of demethyleneberberine by developing a method of high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS). The detection was performed by using positive ion electrospray ionization in multiple reaction monitoring mode. The MS/MS ion transitions were monitored at m/z 324.4→308.3 for demethyleneberberine and 465.5→350.3 for IS. After oral administration of demethyleneberberine in rats and mice, the pharmacokinetics, tissue distribution, and excretion of desmethylberberine in rats and mice were comparatively studied for the first time. The plasma concentration of desmethylberberine reached its peak within 5 min after oral administration in both rats and mice. The bioavailability of rats and mice was comparable, ranging from 4.47% to 5.94%, higher than that of berberine. The total excretion of desmethylberberine in urine, feces and bile is 7.28~9.77%. These findings provide valuable insights into the pharmacological and clinical research on desmethylberberine.

To estimate the degree of quantum entanglement, it is important to understand the statistical behavior of functions of spectrum of density matrices such as von Neumann entropy, quantum purity, and entanglement capacity. These entangle- ment metrics over different generic state ensembles have been studied intensively in the literature. As an alternative metric, in this work we study sum of square root spectrum of density matrices, which is relevant to negativity and fidelity in quantum information processing. In particular, we derive the exact mean and vari- ance of sum of square root spectrum over the Bures-Hall generic state ensemble extending known results obtained recently over the Hilbert-Schmidt ensemble.

Incremental stability analysis for time-delay systems has attracted more and more attentions for its contemporary applications in transportation processes, population dynamics, economics, satellite positions, etc. This paper researches criteria on v(t)-incremental stability, which is defined to demonstrate the convergence rate for incremental stability, for time-delay systems. Firstly, the sufficient conditions for v(t)-incremental stability for time-delay systems with continuous right-hands are studied, and several corollaries for specific cases are provided. As for time-delay systems with discontinuous right-hands, after expounding the relevant conditions for the existence and uniqueness of the Filippov solution, by using approximation methods, sufficient conditions for v(t)-incremental stability are obtained. The conclusions are applied to linear switched time-delay systems and Hopfield neural network system with composite right-hand afterwards.

Hessenberg differential algebraic equations (Hessenberg-DAEs) with high index play a critical role in the modeling of mechanical systems and multibody dynamics. Motivated by the widely used Lie Group Differential Algebraic Equation (LGDAE) method which only handles index 2 systems, we propose a Modified Extended Lie Group Differential Algebraic Equation (MELGDAE) method for solving index 3 Hessenberg-DAEs, and provide theoretical analysis to deepen the foundation of the MELGDAE method.The performance of the MELGDAE method is compared with the standard methods RADAU and MEBDF on index 2 and 3 DAE systems, and it is demonstrated that the MELGDAE integrator exhibits competitive performance in terms of high accuracy and the preservation of algebraic constraints. In particular, all differential variables in index 3 Hessenberg DAEs achieve second-order convergence using the MELGDAE method, which suggests potential for extension to Hessenberg-DAEs with an index of 4 or higher.

As an important component of modern service industry, tourism is regarded as a "green" driver to regional economic growth and industrial upgrading. Applying the "quasi-natural experiment" advocated by the “The Belt and Road” Initiative, this paper, based on the method of DID, as-sessed the influence of this initiative on regional tourism development by analyzing panel data collected from 286 prefecture-level cities in China over the period of 2007-2018. It is found that the "The Belt and Road" initiative has stimulated market dynamics and promoted tourism de-velopment in the regions along the route, and has significantly improved indicators such as per capita domestic and inbound tourism revenue and per capita domestic and inbound tourist re-ceipt. Also, the degree of contribution of domestic tourists to the tourism economy of the re-gions where the policy has been implemented is stronger than that of inbound tourists. In sum, the results of this paper not only confirm that the “The Belt and Road” Initiative has significantly contributed to the high-quality tourism development of Chinese provinces along the routes, but also provide important policy enlightenment for the upgrading of regional tourism industry and regional economic balance.

The Ras family of proteins is tethered to the inner leaflet of the cell membranes which play an essential role in signal transduction pathways that promote cellular proliferation, survival, growth, and differentiation. KRas-4B, the most mutated Ras isoform in different cancers, has been under extensive study for more than two decades. Here we have focused our interest on the influence of cholesterol on the orientations that KRas-4B adopts with respect to the plane of the anionic model membranes. How cholesterol in the bilayer might modulate preferences for specific orientation states is far from clear. Herein, after analyzing data from in total 4000 ns-long MD simulations for four KRas-4B systems, properties such as the area per lipid and thickness of the membrane as well as selected radial distribution functions, penetration of different moieties of KRas-4B, and internal conformational fluctuations of flexible moieties in KRas-4B have been calculated. It has been shown that high cholesterol content in the PM favors OS1, exposing the effector-binding loop for signal transduction in the cell from the atomic level. We confirm that high cholesterol in the PM helps KRas-4B mutant stay in its constitutively active state, which suggests that high cholesterol intake can increase mortality and may promote cancer progression for cancer patients. We propose that during the treatment of KRas-4B-related cancers, reducing the cholesterol level in the PM and sustaining cancer progression by controlling the plasma cholesterol intake might be taken into account in anti-cancer therapies.

Innovative interventions in the planning and design of stair and escalator facilities call for understanding their effects on stair use. This study considers five planning variables for the twinned stair and escalator facilities–stair pitch and width, angle of deviation, height and separating distance. Public sites were identified in 8 commercial districts in Beijing. Facilities (n=21) presenting heterogeneously across variables were sampled for simultaneous up and down pedestrian counts in 18 5-minute video segments middays, for a total of 1464 counts. Stair width accounts for 20% of the variance in ascending rate and 10% in descending. Plan angle accounts for 2% in ascending and 5% in descending, while pitch accounts for 1% in ascending and 5% in descending. The study confirms the effects of layout and design of stairway and escalator facilities on facility choice. The results point directly to interventions in support of higher stair-climbing rates.

We introduce and investigate a simple and explicitly mechanical model of Maxwell's demon -- a device that interacts with a memory register (a stream of bits), a thermal reservoir (an ideal gas) and a work reservoir (a mass that can be lifted or lowered). Our device is similar to one that we have briefly described elsewhere [1], but it has the additional feature that it can be programmed to recognize a chosen reference sequence, for instance, the binary representation of $\pi$. If the bits in the memory register match those of the reference sequence, then the device extracts heat from the thermal reservoir and converts it into work to lift a small mass. Conversely, the device can operate as a generalized Landauer's eraser (or copier), harnessing the energy of a dropping mass to write the chosen reference sequence onto the memory register, replacing whatever information may previously have been stored there. Our model can be interpreted either as a machine that autonomously performs a conversion between information and energy, or else as a feedback-controlled device that is operated by an external agent. We derive generalized second laws of thermodynamics for both pictures. We illustrate our model with numerical simulations, as well as analytical calculations in a particular, exactly solvable limit.

In this paper, a new approach is proposed for designing ultra-low-power FFT (Fast Fourier Transform) system suitable for use in energy harvesting powered sensors. Bit-serial architecture is adopted to reduce the power consumption of butterfly operation. Simulation results show that, compared with state-of-the-art bit-serial and conventional parallel processors, the proposed technique is superior in terms of silicon area, power consumption, dynamic energy use due to variable precision arithmetic. A sample design of a 64-point FFT shows that the implementation can save about 40% area and 36% leakage power compared with a conventional parallel counterpart, accordingly achieving significant power benefits at a low sample rate and low voltage domain. The dynamic variation of the arithmetic precision can be achieved through a simple modification of the controller with hardware area overhead of 10% gate count.

LC-SPIK is a liver cancer-specific isoform of Serine Protease Inhibitor Kazal and has been proposed as a new biomarker for the detection of HCC given its unique 3-D structure which differs from normal pancreatic SPIK. An ELISA technology based on its unique structure was developed to use LC-SPIK as an effective biomarker for the clinical diagnosis of HCC. AFP, the most widely used biomarker for HCC surveillance currently, suffers from poor clinical performance, especially in the detection of early-stage HCC. In one case-control study, which included 164 HCC patients and 324 controls, LC-SPIK had an AUC of 0.87 compared to only 0.70 for AFP in distinguishing HCC from liver disease controls (cirrhosis, HBV/HCV). LC-SPIK also performed significantly better than AFP for the 81 patients with early-stage HCC (BCLC stage 0 and A), with an AUC of 0.85 compared to only 0.61 for AFP. Cirrhosis is the major risk factor for HCC, about 80% of patients with newly diagnosed HCC have preexisting cirrhosis. LC-SPIK’s clinical performance was also studied in HCC patients with viral and non-viral cirrhosis including cirrhosis caused by nonalcoholic fatty liver disease (NAFLD) and alcoholic liver disease (ALD). In a total of 163 viral cirrhosis patients with 93 HCC patients (50 early-stage), LC-SPIK had an AUC of 0.85, while AFP had an AUC of 0.70. For patients with early-stage HCC, LC-SPIK had a similar AUC of 0.83, while AFP had an AUC of only 0.60. For 120 patients with nonviral cirrhosis, including 62 HCC (23 early-stage) patients, LC-SPIK had an AUC of 0.84 while AFP had an AUC of only 0.72. For the 23 patients with early-stage HCC, LC-SPIK had a similar AUC of 0.83, while the AUC for AFP decreased to 0.65. All these results suggest that LC-SPIK has significantly better performance in the detection of HCC than AFP in all etiologies of liver diseases. In addition, LC-SPIK accurately detected the presence of HCC in 71%-91% of HCC patients with false-negative AFP test results in viral associated HCC and non-viral associated HCC.

The water resources carrying capacity (WRCC) system comprises multiple complex and nonlinear interactions related to society, economy, water resources and the water environment. A full understanding of its internal mechanism is conducive to the further improvement of the regional WRCC. This article focuses on the temporal and spatial variability of WRCC to explore a method of dynamic successive assessment. Firstly, the Pressure-State-Response (PSR)framework is used to develop a systematic and causal indicator system. Then, The Variable Fuzzy Pattern Recognition (VFPR) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resources carrying capacity in Hebei Province is mainly due to the basic attribute of the decision of the water resources shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which makes the province's water resources carrying capacity have a tendency to become better gradually. At the same time, the system dynamics model (SD) of water resources carrying capacity based on the analysis of the model structure was established. Moreover, three scenarios were designed, including existing continuation, high-efficiency water-saving, cross-regional water transfer and coordinated development, each scenario divided into high and low-speed economic development and population growth schemes, then total 6 schemes between 2023 and 2030 were simulated and predicted respectively. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of economy and population, the efficient water conservation and inter-basin transfer scenario (scenario 2 with high-speed) is the best choice for sustainable development of water resources and social economy in Hebei.

Purpose The objective of this study was to eliminate any ambiguity by examining the correlation between gut microbiota and both AMD and glaucoma. Methods Mendelian randomization studies were conducted utilizing the data sourced from the GWAS database for the gut microbiome, AMD, and glaucoma. SNP estimates were summarized through five MR methods. We utilized Cochran's Q statistic to evaluate the heterogeneity of the instrumental variables. Additionally, we employed a "leave-one-out" approach to verify the stability of our findings. Results IVW suggests that Eubacterium (oxidoreducens group) and Parabacteroides had a protective effect on AMD. Both weighted median and IVW suggests that Lachnospiraceae (NK4A136 group) and Ruminococcaceae (UCG009) had a protective effect on AMD. However, both weighted median and IVW suggests that Dorea had a risk effect on AMD. Similarly, The IVW of Eubacterium (ventriosum group) showed a risk effect on AMD. The weighted median of Eubacterium (nodatum group), Lachnospiraceae (NC2004 group), and Roseburia had a risk effect on glaucoma. IVW suggested that Ruminococcaceae (UCG004) had a risk effect on glaucoma. Reverse MR analysis found a causal link between Eubacterium (nodatum group) and glaucoma. No causal relationships were found between AMD or glaucoma and the other mentioned bacterial groups. No significant heterogeneity or evidence of horizontal pleiotropy was detected. Conclusions This study found that certain gut bacteria had protective effects on AMD, while others may be risk factors for AMD or glaucoma. Besides, reverse MR found that glaucoma led to increased abundance of certain gut bacteria. Further trials are needed to clarify the specific mechanisms involved.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

Interferon regulatory factor 5 (IRF5) is known to be involved in the innate immune response and pro-inflammatory cytokines. However, the roles of IRF5 in immune responses in Malabar grouper (Epinephelus malabaricus) have not been extensively explored. In this study, IRF5 gene was identified and characterized from M. grouper. The full-length IRF5 cDNA consisted of a 5’ terminal untranslated region (5’-UTR) of 289 bp and a 3’-UTR of 542 bp, an open reading frame (ORF) of 1500 bp encoding a polypeptide of 499 amino acids with a predicted molecular mass of 56.28 kDa and isoelectric point (pI) of 5.2. The putative MgIRF5 protein consists of four important conserved domains: a helix DNA-binding domain (DBD) at the N-terminus, a middle region, an IRF association domain (IAD) and a virus activated domain (VAD) at the C-terminus. Sequence alignment and phylogenetic analysis showed that highest sequence similarity of IRF5 was observed between the IRF5 genes from Oplegnathus fasciatus and Miichthys miiuy. The mRNA transcripts of IRF5 were detected in a wide range of tissues types from healthy M. grouper with highest expression in muscle, liver and skin. After treatment with poly (I: C), it was significantly up-regulated in spleen and liver tissues. When infected with NNV, the expression level of MgIRF5 was up-regulated in spleen and head kidney and their transcriptional responses to IRF5 increased in the grouper kidney cells. This approach suggests that MgIRF5 is important in the underlying mechanism of the innate immune responses against antiviral response.

JIS SUJ2 is most widely used in bearing steels. The advantages are good hardenability, excellent fatigue, wear resistance and comprehensive mechanical properties. For wear resistance and fatigue resistance of the steel are getting more and more attention, the residual stress state and its distribution on the surface of the heat affect zone are critical factors affecting the fatigue life and wear resistance of the parts. In this study, using the SUJ2 as the material to talk about the surface residual stress and retained austenite distribution. Doing the quenching and tempering treatment to obtain different microstructures, using the induction method to re-quench the case region. After the heat treatment, measuring the residual stress and retained austenite volume on the surface by X-ray diffraction, observing the microstructure and comparing the hardness. The results show that the microstructure after heat treatment contains unsolidified carbides, tempered martensite and retained austenite. The residual stress on the surface is the tensile state. In the re-hardened area after induction quenching, the residual stress is all compressive, and the values are more than -800 MPa. In conclusion, in the same output power condition can be observed that the microstructure of the specimens before induction hardening has a significant impact on the effective case depth. The surface residual stress has changed from a tensile to a compressive state. In the re-hardened area, the maximum of the residual compressive stress increased with the austenitized temperature increase.

Shut-in after fracturing is generally adopted for wells in shale oil reservoirs, and imbibition occurring in matrix nanopores has been proven as an effective way to improve recovery. In this research, the molecular dynamics (MD) simulation was used to investigate the effects of wettability and pressure on nanopores imbibition during shut-in for a typical shale reservoir Jimsar. The results indicate that the microscopic advancement mechanism of the imbibition front is the competitive adsorption between “interfacial water molecules” at the imbibition front and “adsorbed oil molecules” on the pore wall. The essence of spontaneous imbibition involves the adsorption and aggregation of water molecules onto the hydroxyl groups on the pore wall. The flow characteristics of shale oil suggest that the overall push of the injected water to the oil phase is the main reason for the displacement of adsorbed oil molecules. Thus, shale oil especially the heavy hydrocarbon component in the adsorbed layer tends to slip on the walls. However, the weak slip ability of heavy components on the wall surface is an important reason that restricts the displacement efficiency of shale oil during spontaneous imbibition. The effectiveness of spontaneous imbibition is strongly dependent on the hydrophilicity of the matrix pore's wall. The better hydrophilicity of the matrix pore's wall facilitates higher levels of adsorption and accumulation of water molecules on the pore wall and requires less time for “interfacial water molecules” to compete with adsorbed oil molecules. During the forced imbibition process, the pressure difference acts on both the bulk oil and the boundary adsorption oil, but mainly on the bulk oil, which leads to the occurrence of wetting hysteresis. Meanwhile, shale oil still existing in the pore always maintains a good stratified adsorption structure. Because of the wetting hysteresis phenomenon, as the pressure difference increases, the imbibition effect gradually increases, but the actual capillary pressure gradually decreases and there is a loss in the imbibition velocity relative to the theoretical value. Simultaneously, the decline in hydrophilicity further weakens the synergistic effect of pressure difference because of the more pronounced wetting hysteresis. Thus, selecting an appropriate well pressure enables cost savings and maximizes the utilization of the formation's natural power for EOR.

With the rise of the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), an abundant amount of trajectory data has made research on location prediction more popular. The check-in data shared through LBSN hides information related to life patterns, obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices is different from check-in data that has semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose A Self-Attention for Next Location Prediction based on Semantic Mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k nearest POI, then use the reciprocal of the distance from the stay point to the k nearest POI and the number of categories as weights. Finally, we use the probability to express semantic without losing other important semantic information. Furthermore, this research combined with sequential pattern mining can bring richer semantic features. In order to better perceive the trajectory, temporal features learn periodicity of time series by sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.

Power spectra always play an important role in the theory of inflation. In particular, the ability to reproduce the galaxy matter power spectrum $ P(k) $ and the CMB temperature angular power spectrum $ C_l $’s to high accuracy is often considered a triumph of inflation. In our previous work, we presented an alternative explanation for the matter power spectrum based on nonperturbative quantum field-theoretical methods applied to Einstein’s gravity, instead of inflation models based on scalar fields. In this work, we review the basic concepts and provide further in-depth investigations. We first update the analysis with more recent data sets and error analysis, and then extend our predictions to the CMB angular spectrum coefficients $ C_l $, which we did not consider previously. Then we investigate further the potential freedoms and uncertainties associated with the fundamental parameters that are part of this picture, and show how recent cosmological data provides significant constraints on these quantities. Overall, we find good general consistency between theory and data, even potentially favoring the gravitationally-motivated picture at the largest scales. We summarize our results by outlining how this picture can be tested in the near future with increasingly accurate astrophysical measurements.

Anthracnose is one of primary diseases in tea plants that affects tea yield and quality. The geographical distribution, occurrence regularity, and agronomic measures of tea plants with anthracnose have been researched for decades. However, the pathogenic cause of anthracnose in tea plants is diverse in different regions of the world. Identifying the specific pathogenic fungi causing tea anthracnose is an essential control measure to mitigate this disease. In this study, 88 Diaporthales and 45 Colletotrichum isolates were obtained from 3 different types of diseased tea leaves. Based on multilocus phylogenetic and morphological analysis, 3 known species, including Discula theae-sinensis, Diaporthe ueckerae, Diaporthe lithocarpus, and 3 indistinguishable species of Diaporthales were identified. In adddition, 8 known species of Colletotrichum, C. fructicola, C. camelliae, C. aenigma, C. siamense, C. henanense, C. karstii, C. tropicicola, C. gigasporum, were identified. This study is the first to report C. tropicicola and C. gigasporum in tea plant in China. Discula theae-sinensis was the most common species in this study and caused disease lesions around wounded areas of tea leaves. The dual trials in vitro indicated Discula theae-sinensis and Colletotrichum were slight inhibition. Co-inoculating Discula theae-sinensis and C. fructicola was superior to single inoculation at low concentration.

Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is that how to integrate highly sparse genetic genomics data with a mass of minor effects into prediction model for improving prediction power. We find that deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower dimensional continuous data in a non-linear way. This idea may provide benefits in risk prediction based on genome-wide data associated e.g. integrating most of the information in the genotype data. Hence, we developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for risk prediction. Specifically, we first reduced the number of biomarkers via a univariable regression model to a moderate size. Then a trainable auto-encoder was used to extract compact representations from the reduced data. Next, we performed a LASSO problem process over a grid of tuning parameter values to select the optimal combination of extracted features. Finally, we applied such feature combination to two prognostic models, and evaluated predictive effect of the models. The results of simulation studies and real data applying indicated that these highly compressed transformation features could better improve predictive performance and did not easily lead to over-fitting.

The spread of 2019 novel coronavirus disease (COVID-19) throughout the world has been a severe challenge for public health. The human angiotensin-converting enzyme 2 (ACE2) has a remarkably high affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). By the search for network database and re-analysis of pubic data, we found the level of ACE2 expression in adipose tissue was higher than that in lung tissue, which indicated the adipose tissue might be vulnerable to SARS-CoV-2 as well; the levels of ACE2 expressed by adipocytes and adipose progenitor cells were similar between non-obese individuals and obese individuals, but obese individuals have more adiposes so as to increase the number of ACE2-expressing cells; the expression of ACE2 in tumor tissues posed by five different types of cancers increased significantly compared with that in adjacent tissues. Thus, we suggest that more attentions might be given to obese individuals and the five types of cancer patients during the outbreak of COVID-19.

We present the generation and nonlinear frequency conversion of nanosecond cylindrical vector beams (CVBs) in two-mode fiber (TMF). Based on the polarized dependence vector mode coupling characteristic and the precise wavelength tunability of an acoustically-induced fiber grating (AIFG), the nanosecond cylindrical vector beams (CVBs, 1064 nm, 10 ns) is directly generated in a TMF with convenient switching characteristics between the radial and azimuthal vector beams. Furthermore, the stimulated Raman scattering (SRS) is produced based on the transmission of the nanosecond CVBs in the 100-meters long TMF, and the spatial intensity and polarization distributions characteristics the 1st-order Stokes shifted component is consistent with the nanosecond CVBs pump pulse. This work provides a method for achieving wavelength conversion of the CVBs in optical fiber.

Language bias stands as a noteworthy concern in Visual Question Answering (VQA), wherein models tend to rely on spurious correlations between questions and answers for prediction. This prevents the models from effectively generalizing, leading to a decrease in performance. To address this bias, we propose a novel modality fusion collaborative de-biasing algorithm (CoD). In our approach, bias is considered as the model’s neglect of information from a particular modality during prediction. We employ a collaborative training approach to facilitate mutual modeling between different modalities, achieving efficient feature fusion and enabling the model to fully leverage multi-modal knowledge for prediction. Our experiments on various datasets, including VQA-CP v2, VQA v2, and VQA-VS, using different validation strategies, demonstrate the effectiveness of our approach. Notably, employing a basic baseline model resulted in an accuracy of 60.14% on VQA-CP v2.

Vulnerabilities in smart contracts may trigger serious security events, and the detection of smart contract vulnerabilities has become a significant problem. In this paper, by using the multi-scale cascade encoder architecture as the backbone, we propose a novel Multi-scale Encoder Vulnerability Detection (MEVD) approach to detect well-known high-risk vulnerabilities in smart contracts. Firstly, we use the gating mechanism to design a unique Surface Feature Encoder (SFE) to enrich the semantic information of code features. Then, by combining a Base Transformer Encoder (BTE) and a Detail CNN Encoder (DCE), we introduce a dual-branch encoder to capture the global structure and local detail features of the smart contract code, respectively. Finally, to focus the model's attention on vulnerability-related characteristics, we employ the Deep Residual Shrinkage Network (DRSN). Experimental results on three types of high-risk vulnerability datasets demonstrate performance compared to state-of-the-art methods, and our method achieves an average detection accuracy of 90%.

ABSTRACT: Waterlogging is a common abiotic stress in global maize production. Maize flowering stage (from tasseling to silking) is more fragile to environmental stresses among different growth stages. The effects of waterlogging at flowering stage on the grain yield and starch quality of waxy maize were studied using Suyunuo5 (SYN5) and Yunuo7 (YN7). The grain yield and starch concentration of both hybrids were reduced by waterlogging. The size of starch granules and proportion of small-molecule amylopectin increased in SYN5 and decreased in YN7 when plants suffered waterlogging at flowering, in contrast to the change trend of relative crystallinity. The pasting viscosities and gelatinization temperatures were reduced, whereas the gelatinization enthalpy was unaffected by waterlogging in both hybrids. Our study indicated that waterlogging at flowering reduced the grain yield, restricted starch deposition, and deteriorated the pasting property of waxy maize. Between the two hybrids, YN7 and high pasting viscosities and low retrogradation percentage than SYN5, indicated its advantages on produce starch for viscous and less retrograde food. Results provide information for utilization of waxy maize grain in food production.

As the COVID-19 pandemic has caused enormous damage to society, economy and public health security, it is crucial to combat the epidemic and allocate social and health resources effectively. Global optimization algorithms are extensively used in many domains to find optimal solutions to complex problems. Thus, a survey of three typical global optimizations applied on the COVID-19 pandemic is timely. This article is structured into three typical global optimization algorithms - Genetic Algorithm, Particle Swarm Optimization, and Cat Swarm Optimization - and summarizes the framework models and practical applications, as well as other optimization methods that may be applied to COVID-19. Finally, current unresolved challenges and future research opportunities to be explored are summarized.

As one of the common techniques for measuring coseismic deformations, optical image correlation techniques are capable of overcoming the drawbacks of inadequate coherence and phase blurring which can occur in radar interferometry, as well as the problem of low spatial resolution in radar pixel offset tracking. However, the scales of the correlation window in optical image correlation techniques typically influence the results, the conventional SAR POT method faces a fundamental trade-off between the accuracy of matching and the preservation of details in the matching window size. This study regards co-seismic deformation as a two-dimensional vector, and develops a new post-processing workflow called VACI-OIC to reduce the dependence of shift estimation on the size of the matching window. This paper takes the coseismic deformations in both the east-west and north-south directions into account at the same time, treating them as vectors, while also considering the similarity of displacement between adjacent points on the surface. Herein, the angular continuity index of the coseismic deformation vector was proposed as a more reasonable constraint condition to fuse the deformation field results obtained by optical image correlation across different matching window. Taking the earthquake of 2021 in Maduo, China, as the study area, the deformation with the highest spatial resolution in the violent surface rupture area was determined (which could not be provided by SAR data). Compared to the results of single-scale optical correlation, the presented results were more uniform (i.e., more consistent with published results). At the same time, the proposed index also detected the strip fracture zone of the earthquake with impressive clarity.

Link prediction is to complete the missing links in the network or to predict the generation of new links according to the current network structure information, which is very important for mining and analyzing the evolution of the network such for construction and analysis of logical architecture in 6G network. Link prediction algorithms based on node similarity need predefined similarity functions, which is highly hypothetical and only applies to specific network structures without generality. To solve this problem, this paper proposes a link prediction algorithm based on the subgraph of the target node pair. In order to automatically learn the graph structure characteristics, the algorithm firstly extracts the h-hop subgraph of the target node pair, and then predicts whether the target node pair will be linked according to the subgraph. Experiments on seven real data sets show that the link prediction algorithm based on target node pair subgraph is suitable for various network structures and superior to other link prediction algorithms.

Galangin(Gal) is a natural active flavonoid compound separated from the roots and rhizomes of Alpinia ofcinarum Hance. Modern pharmacological studies have shown that Gal has a variety of biological activities such as anti-tumor, anti-fungal, anti-bacterial, anti-inflammatory, anti-ischemic stroke, suppressing vitiligo and Alzheimer’s disease, etc. The purpose of this research was to prepare a galangin self-microemulsion drug delivery system (Gal-SMEDDS) and compare its anti-oxidant activity and pharmacokinetics with free Gal.The average particle size of the prepared Gal-SMEDDS was approximately 21.33 nm, the polydispersity index was 0.096, the zeta potential was -4.09 mV, and the entrapment efficiency was 96.74%. Compared with free Gal, the release of Gal-SMEDDS was improved in vitro release experiment. Cell experiments showed that Gal had obvious anti-oxidation effect, and the effect of Gal-SMEDDS was better than that of free Gal. In vivo pharmacokinetic experiments showed that the pharmacokinetic parameters of Gal-SMEDDS were better than that of free Gal, which indicated that the self-microemulsion drug delivery system(SMEDDS) effectively increases the oral bioavailability of Gal and alters its pharmacokinetic parameters, such that it may be effective in the treatment of anti-oxidant.

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.

The majority of cellular responses to external stimuli are mediated by receptors such as G protein-coupled receptors (GPCRs) and systems including endoplasmic reticulum stress (ER stress). Since GPCR signalling is pivotal in numerous malignancies, they are widely targeted by a number of clinical drugs. Cancer cells often negatively modulate GPCRs in order to survive, proliferate and to disseminate. Similarly, numerous branches of the unfolded protein response (UPR) act as pro-survival mediators and are involved in promoting cancer progression via mechanisms such as epithelial to mesenchymal transition (EMT). However, there are a few proteins among these groups which impede deleterious effects by orchestrating the pro-apoptotic phenomenon and paving a therapeutic pathway. The present review exposes and discusses such critical mechanisms and some of the key processes involved in carcinogenesis.

In present work, the effects of bioflavonoids (ginkgetin and sciadopitysin) on stimulus-induced superoxide generation, tyrosyl and serine/threonine phosphorylation of proteins in human neutrophils, and the translocation of cytosolic compounds (p47phox, p67phox and Rac) to cell membrane were studied, which were isolated from the needles of Taxus media var. Hicksii. Meanwhile, three normal flavonoids (apigenin, quercetin and isoquercetin) were involved as contrasts. The results indicated that ginkgetin and sciadopitysin were capable of concentration-dependently inhibitory effects on the superoxide generation induced by N-formyl-methionyl-leucyl-phenylalanine (fMLP), arachidonic acid (AA) and phorbol-12-myristate 13-acetate (PMA). And they also suppressed fMLP- and AA- induced tyrosyl or PMA-induced serine/threonine phosphorylation and the translocation of cytosolic compounds (p47phox, p67phox and Rac) to cell membrane, which were in parallel with the suppression of the stimulus-induced superoxide generation. The effect of these compounds on the radical-scavenging was also investigated. Ginkgetin and sciadopitysin did not show remarkable effect on DPPH radical-scavenging activity, and they didn’t display the radical-scavenging activity on superoxide anion generated by phenagine methoxysulfate (PMS)-NADH system. Apparently, ginkgetin and sciadopitysin had great performance in pharmacological value and they are worthy of in-depth study.

Achieving sustainable management of forests in China is becoming increasingly important with more awareness and realization of the importance of forests in environmental protection. Forest certification, a market-based instrument to promote sustainable forest management, has been recognized by many countries including China. While landowners’ perception and perspective regarding this voluntary program have been well-documented in literature, how to motivate and incentivize landowners to participate in forest certification remained under-studied questions. With the face-to-face survey of landowners in Shandong, China, this study analyzed landowners’ willingness to accept compensation for participating in forest certification. Results indicated that landowners average accepted compensation, in terms of increased timber price, was about RMB120 (~$17.2)/m3 to have their forests certified. Results from multiple regression showed that the level of such compensation required for certification adoption was influenced by ownership size, owners’ demographics, certification program requirements, as well as importance placed on timber sale. These findings would inform policy-makers in designing compensation-related polices and establish incentive-based mechanisms to motivate landowners participate in forest certification programs in China.

Eddy current based approaches have been investigated for a wide range of inspection applications. Dodd-Deeds model and the truncated region eigenfunction expansion (TREE) method are widely applied in various occasions, mostly for the cases that the sample is relatively larger than the radius of the sensor coil. The TREE method converts the integral expressions to the summation of many terms in the truncated region. In a recent work, the impedance of the co-axial air-cored sensor due to a plate of finite radius was calculated by the modified Dodd-Deeds analytical approach proposed by authors. In this paper, combining the modified analytical solution and the TREE method, a new finite region eigenfunction expansion (FREE) method is proposed. This method involves modifying its initial summation point from the first zero of the Bessel function to a value related to the radius of the plate, therefore makes it suitable for plate with finite dimensions. Experiments and simulations have been carried out and compared for the verification of the proposed method. Further, the planar size measurements of the metallic circular plate can be achieved by utilising the measured peak frequency feature.

This article investigates the information matching efficiency of the sponsored auction mechanism of search engine platform by implementing consumer purchase strategy. Then we examine the incentive mechanism for sellers to report product quality truthfully under information asymmetry and the reason how product quality signal could improve information matching efficiency of the sponsored auction mechanism. Using the data collected from Baidu search engine, we empirically test the theoretical research results. Our main research finding: under information asymmetry and without product quality signal mechanism, the equilibrium of bidding rank cannot match the consumer purchase strategy at all, causing the consumer welfare to suffer double losses due to the adverse selection of sellers. In the contrast, the search engine could improve partially the information matching efficiency of the sponsored search auction mechanism by identifying the product quality signal brought by the sellers with the highest product quality. However, the search engine has no incentive to design product quality signal for sellers.

With the development of Internet technology, teachers are constantly seeking innovative teaching methods to match the potential of enhanced technology. Although many studies have been performed before , they aren’t enough in this field. The purpose of this paper is to explore innovative teaching designs and examine the impact of Internet-assisted English teaching of reading based on constructivism. The case is carried out in NO.9 middle school of Bengbu, a underdeveloped area of China, most of students aren’t interested in learning English .We compared Internet-assisted with traditional textbook literature methods to improve the reading proficiency of students, using a questionnaire survey,pre-test and post-tests comparisons. The result showed Internet-assisted English reading teaching is better than textbook. It can arouse students' interest and motivation,reading proficiency and exam result of students have been improved significantly, created a positive learning situation.

Abstract: As the massive deployment of the heterogeneous IoT devices in the coexisting environment such as smart homes，Traditional channel-based spectrum sharing algorithms such as CSMA has great limitations to further optimize spectrum utilization. Therefore, exploring more efficient spectrum sensing algorithm becomes hot topic these years. This paper proposes Subcarrier-Sniffer, which utilizes Channel State Information (CSI) to sense the subcarrier-level detailed status of the spectrum. In order to evaluate the performance of Subcarrier-Sniffer, we implemented Subcarrier-Sniffer by USRP B200min, and the experimental results show that when the distance between Subcarrier-Sniffer and the monitored devices is not great than 7 m, the accuracy of subcarrier-level spectrum sensing could achieve 100% in our settings.

We investigated fabrication of neodymium doped thin film optical waveguide-based devices as potential active sources for planar integrated optics. Liquid-phase epitaxial growth was used to fabricate neodymium-doped yttrium aluminum borate films on compatible lattice-matched un-doped yttrium aluminum borate substrates. We observed the refractive index contrast of the doped and un-doped crystal layers by differential interference contrast microscopy. In addition, characterization by X-ray powder diffraction, optical absorption and luminescence spectra demonstrated the crystal quality and uniformity and optical guiding of the resulting thin films.

In this study, a parameter of volume stability was proposed for the first time to design polymorph zirconia ceramics used for special components. A series of heterogeneous polymorph zirconia ceramics with various amounts of monoclinic (M) phase were fabricated by two-step sintering. Samples with about 27%, 31%, and 51% M phase content were selected to study the properties. The thermal shock durability was found to be associated with thermal expansion behavior and noncritical micro cracks, which both based on M phase initial content. In good agreement with experimental results, the correlation of normalized K versus M phase initial content was established. It could provide a repeatable reference to prepare special zirconia components without loss of density.

The phase composition design principle is introduced to obtain balanced properties of ionic conductivity and thermo-tolerant for zirconia solid electrolytes used in solid oxide fuel cells (SOFCs). The zirconia ceramic solid electrolytes are fabricated by two-step free sintering. With increasing Y/Mg ionic ratio from 1.78:1 to 1.88:1, the content of monoclinic phase fluctuates little (±3%). The ionic conductivity, including the total electrical resistance; grain electrical resistance and grain boundary electrical resistance at 1223K, are all gradually declining with the increasing of Y/Mg ionic ratio. Furthermore, the enrichment of Mg ion in grain boundary acts as a disincentive to grain boundary ionic conductivity. In addition, the maximum total equivalent conductivity at 1223K in this study reaches to 0.143 Scm-1 which can compare with that of certain YSZ. It will be beneficial to SOFCs application profited from increasing ionic conductivity of ceramic solid electrolytes.

Abstract: This work for the first time investigated the densification of multi-doping zirconia ceramic body with organic coating powders for solid electrolyte of solid oxide fuel cells via online imaging technology. The densification results show the initial stage plays a key role in the sintering. It can be found the covered organic PVA (polyvinyl acetate) supplies a potential kinetics to the initial densification during sintering. As a result, a kinetic function of densification in the initial stage was suggested. Furthermore, a novel sintering model with six sub-stages is developed for polycrystalline zirconia ceramics. The findings would be a valuable reference for predicting final temperature of sintering, the equivalent strain during the sintering process, as well as optimizing the densification behavior.

Partially stabilized zirconia (PSZ) is a promising material with superior combination properties in a wide range of industries. In this study, the effect of grain size on the mechanical properties such as hardness, bending strength and fracture toughness (named as ‘indentation toughness’ in this study) was investigated. The multi-doped zirconia ceramics were prepared by powder processing and pressure-less sintered following four sintering schedules. The measured mechanical properties results show that the polymorph structure sintered at a higher temperature has a lower bending strength and hardness. But 300% more indentation toughness (30.54 Kg/mm) of samples sintered at 1600 °C +1150 °C relative to others was estimated due to the grain size effect by our suggested equation. By contrast, the fracture toughness was calculated with an existing method. The results showed that similar trends were obtained. It appeared to be a novel method for evaluating the toughness of partially stabilized zirconia ceramics with micron grains based on the size effect.

There have been several cases of large-scale wind generators (WGs) tripping off caused by untimely fault removing in recent years. Currently, the discoordination between the box-type transformer fuse protection (BTFP) and two-section collecting line current protection (CLCP) brings a security risk to wind farm. In order to ensure the selectivity, the first section (Sec-I) CLCP should be set a enough interval that is longer than the fuse melting time, and another interval is set for the Sec-II CLCP, which weakens the speed of the CLCP. When a fault occurs on the collecting line, there is no doubt that WGs cannot work too long in abnormal operation, which may cause WGs to be placed off the grid. For a power system with high penetration of wind power, large-scale WGs tripping off will cause a great power shortage, and affect the stability of the power system. The selectivity and sensitivity of the CLCP is analyzed in detail to make the CLCP speed better. Considering the fault ride-through ability of WGs, the fault clear time is an important factor to lead to large-scale WGs tripping off. Two main works are done in this paper. The first is to accelerate the speed of the Sec-I CLCP though reducing the protection zone. Another one is introduce the risk assessment module into the CLCP, which not only improve the speed of the CLCP but also ensure the safety of the wind farm during faults. According to the deference in trip-off causes of WGs, the matching functions are created to assess the trip-off risk of WGs on the spot. In the case of fault, the trip-off risk indicators of WGs are timely updated to data sharing center and open to the CLCPs. The set of risk indicators is divided into several subsets according to the risk range. The dynamic changes of the subsets during fault help to improved CLCP scheme. This scheme can accelerate protection speed based on the increasing risk of large-scale WGs tripping off in wind farms. Compared with traditional CLCP, this approach can make the CLCP combines selectivity and speed better based on the analysis of the ride- through ability of WGs.

Stimuli-responsive polymeric materials have attracted significant attentions in a variety of high-value-added and industrial applications during the past decade. Among various stimuli, light is of particular interest as a stimulus due to its unique advantages such as precisely spatiotemporal control, mild conditions, ease of use, and tunability. In recent years, a lot of effort toward synthesis of biocompatible and biodegradable polypeptide has resulted in many examples of photo-responsive nanoparticles. Depending on the specific photochemistry, those polypeptide derived nano-assemblies are capable of crosslinking, disassembling, or morphing into other shapes upon light irradiation. In this mini-review, we aim to assess the current state of photo-responsive polypeptide based nanomaterials. First, those “smart” nanomaterials will be categorized by their photo-triggered events (i.e., crosslinking, degradation, and isomerization) which are inherently governed by photo-sensitive functionalities including o-nitrobenzyl, coumarin, azobenzene, cinnamyl, and spiropyran. In addition, the properties and applications of those polypeptide nanomaterials will be highlighted as well. Finally, the current challenges and future directions of this subject will be evaluated.

Under the guidance of our previous achievements, and in order to extend this small molecular library. In current work, other 21 novel structures of thieno[2,3-d]pyrimidines containing isoxazole-moiety (1a-1u) were firstly synthesized and the cytotoxicity to A549, HCT116 and MCF-7 cell lines was evaluated using the MTT method. The results showed that most target compounds exhibited good to excellent cytotoxicity to A549, HCT116 and MCF-7 cell lines, especially 6-Methyl-4-{[3-(4-chlorophenyl)-isoxazol-5-yl-]-methoxy-}-thieno[2,3-d]-pyrimidine (1e) exhibited a broad-spectrum and the most potent cytotoxicity to A549, HCT116 and MCF-7 cell lines (IC50s: 2.79, 6.69 and 4.21×10^-3 μM, respectively) as compared with the reference drug gefitinib (IC50s: 17.90, 21.55 and 20.68 μM, respectively). 1e can be seen as the best drug candidates for development of anticancer drugs.

Small molecule modulators of neurotensin receptor 1 (NTSR1), a class A G protein-coupled receptor (GPCR), has been emerging as promising therapeutics for psychiatric disorders and cancer. Interestingly, a chemical group substitution of NTSR1 modulators can launch different downstream regulation, highlighting the significance of deciphering the internal fine-tuning mechanism. Here, we conducted synergistic application of Gaussian accelerated molecular dynamics simulation, conventional molecular dynamics simulation, and Markov state models (MSM) to investigate the underpinning mechanism of ‘driver chemical groups’ of modulators triggering inverse signaling. The result indicated that the flexibility of leucine moiety in NTSR1 agonists contributes to the inward displacement of TM7 through a loosely coupled allosteric pathway, while the rigidity of adamantane moiety in NTSR1 antagonists leads to unfavorable downward transduction of agonistic signaling. Furthermore, we found that R3226.54, Y3196.51, F3537.42, R1483.32, S3567.45 and S3577.46 may play a key role in inducing the activation of NTSR1. Together, our findings not only highlight the ingenious signal transduction within class A GPCRs, but also lay a foundation for the development of targeted drugs harboring different regulatory function of NTSR1.

Pinus squamata is one of the most threatened conifer endemic to northeastern Yunnan Province, China, and it is prioritized as one of the Plant Species with Extremely Small Populations (PSESP) for urgent protection in China. However, we lack a comprehensive consideration of the protection effect and the endangered mechanism, especially based on the underground growth status of endangered plants. The integrated study of soil properties and rhizosphere bacteria can assist conservation to understand therequired conditions for the protection and survival of rare and endangered species. In our study, bacterial communities in wild, ex-situ, and reintroduced P. squamata rhizosphere were compared using Illumina sequencing of the V3-V4 region of the 16S rRNA genes. We determined the soil physicochemical properties, analyzed the relationships between the bacterial communities and soil physicochemical factors, and predicted the potential bacterial ecological functions. The reintroduced site Qiaojia (RQ) had the highest richness and diversity. Samples were scattered (R = 0.93, P = 0.001), indicating significant difference between the different conservation sites. Soil total potassium (TK) and available nitrogen (AN) were the main factors driving bacterial community (0.01 &lt; P ≤ 0.05). The low abundance of Plant growth-promoting rhizobacteria (PGPR) may be a biotic factor contributing to the endangerment of P. squamata. This study provides a foundation to assess the effect of conservation based on bacterial diversity and plant growth-promoting rhizobacteria (PGPR) to guide future research into the conservation of P. squamata.

Abstract: Objectives: To establish the expression profile of N6-methyladenosine(m6A) modifications in the hippocampus of mice with diabetic cognitive impairment (DCI) and elucidate the potential regulatory mechanism. Methods: A DCI model was established by feeding a high-fat diet to C57BL/6J mice. m6A and RNA sequencing was conducted to profile the m6A-tagged transcripts in the hippocampus. Results: Methylated RNA immunoprecipitation with next-generation sequencing and RNA sequencing analyses yielded differentially m6A-modified and expressed genes in the hippocampus of DCI mice, which were enriched in pathways involving synaptic transmission and axonal guidance. Mechanistic analyses revealed a remarkable change in m6A modification levels through alteration of the mRNA expression of m6A methyltransferases (METTL3 and METTL14) and demethylase (FTO) in the hippocampus of DCI mice. Conclusions: We identified a co-mediated specific RNA regulatory strategy that broadens the epigenetic regulatory mechanism of RNA-induced neurodegenerative disorders associated with metabolic and endocrine diseases.

Due to the highly nonlinear, multi-stage, and strongly time-varying marine lysozyme fermentation process, it is difficult to assure the stability and prediction accuracy of the traditional single global soft sensor model on a global scale. This study innovatively proposed a soft sensor model based on an improved seagull optimization algorithm (ISOA) combined with Gaussian process regression (GPR) weighted ensemble learning. First, the sample data set is divided into multiple local sample subsets by the improved density peak clustering algorithm (ADPC). Second, the Gaussian process regression model is optimally altered with an improved seagull optimization algorithm for the purpose of establishing the corresponding sub-prediction model. Finally, the prediction model's fusion strategy is ultimately determined depending on the degree of connection between the test samples and a subset of local pieces. Simulation results show that the proposed soft sensor model, which searches GPR based on ISOA and combines various sub-models, can predict the key biochemical parameters of the marine lysozyme fermentation process better with less prediction error under the condition of fewer training data, and it can be expanded to the soft sensor models of general nonlinear systems, according to simulation results.

Four new unusual pentacyclic triterpenoids (1-4) were isolated from the roots of Jasminum sambac (L.) Ait. Their structures were elucidated by 1D and 2D NMR analysis, single crystal X-ray diffraction and HRESIMS.

Hydro generator sets serve as the peak and frequency regulation of the power system,and play a very important role in maintaining system stability and improving power quality.Based on the internal characteristics model of a specific type of hydro generator,this paper constructs a hydroelectric coupling model for hydropower units,studies the influence of the regulation system of hydropower units on low-frequency oscillations under different load conditions,and adopts variable speed reaching law and fuzzy sliding mode control strategy to improve the regulation quality of the system and ensure the stable operation of the units.Results of simulation and water turbine set under different load conditions show that varible speed reaching law can effectively improve the power-angle oscillation behavior of the hydropower set and suppress the occurrence of low-frequency oscillations of the system when the system is subject to power disturbance,which provide a new theoretical basis and solution for the vibration analysis and control optimization of hydraulic turbine units.

Business processes are continuously evolving in order to adapt to changes due to various factors. One important process drift perspective yet to be investigated is the detection of branching condition changes in the process model. None of the existing process drift detection methods focus on detecting changes of branching conditions in process models. Existing branching condition detection methods do not take changes within the process into account, hence results are inadequate to represent the changes of decision criteria of the process. In this paper, we present a method which can detect branching condition changes in process models. The method takes both process models and event logs as input, and translates event logs into decision sequences for change points detection. The proposed method is evaluated by simulated event logs.

Glucocorticoids (GCs) have been the mainstay of immunosuppressive therapy in solid organ transplantation (SOT) for decades due to their potent effects on the innate immunity and tissue protective effects. But, some SOT centers are reluctant to administer GCs for long-time due to the various side effects. This review summarizes advantages and disadvantages of GCs in SOT. PubMed and Scopus databases were searched from 2011 to April 2021 using search syntaxes cover “transplantation” and “glucocorticoids”.GCs are used in transplant recipients, transplant donors, and organ perfusate solution to improve transplant outcomes. In SOT recipients GCs are administered as induction and maintenance immunosuppressive therapy. GCs are also the cornerstone to treat acute anti-body- and T-cell-mediated rejections. Addition of GCs to organ perfusate solution and pretreatment of transplant donors with GCs are recommended by some guidelines and protocols to reduce ischemia-reperfusion injury peri-transplant. GCs with low bioavailability and high potency for GC receptors such as budesonide, nanoparticle-mediated targeted delivery of GCs to specific organs, and combination use of dexamethasone with inducers of immune-regulatory cells are new methods of GC usage in SOT patients to reduce side effects or induce immune-tolerance instead of immunosuppression. Various side effects on different non-targeted organs/tissues such as bone, cardiovascular, neuromuscular, skin, and gastrointestinal tract have been noted for GCs. There are also potential drug-drug interactions for GCs in SOT patients.

During the last 20 years, with the development of Information and Communication Technologies (ICTs), an emerging interest has appeared in Digital Community Engagement (DCE) in the process of cultural heritage management. Due to a growing need to involve a broader community in the Historic Urban Landscape approach, social media are considered one of the most important platforms to promote the public participation process of urban heritage conservation in the context of rapid urbanization. Despite the growing literature on DCE, which has delivered a general overview of different digital technologies and platforms to enhance heritage conservation, little research has been done on taking stock of the utilization of social media in this process. This study aims to fill the research gap by providing a more comprehensive picture of the functionalities of social media platforms and impacts on sustainable urban development through a systematic literature review. As a result, 19 out of 248 DCE relevant articles are selected as objects to illustrate the contribution of social media. The study identified the characteristics of these applied social media tools, explores their roles and influences in cases. The article concludes with directions for further research.

With the development of computer science and software engineering, software becomes more and more complex. Traditional software reliability assurance techniques including software testing and evaluation can't ensure software reliable execution after being deployed. Software failure prediction techniques based on failure indicators can predict software failures according to abnormal indicator values. The latter can be collected using runtime monitoring techniques. An essential part of this method is finding proper indicators which have strong correlation with software failures. We propose a novel type of indicators in this work named software runtime entropy, which takes both software module execution time and call times into consideration. Three common open source software, grep, flex and gzip are used as study cases for finding the relationships between the indicators and software failures. Firstly, a series of fault injection experiments are conducted on those three software respectively. The decision tree algorithm is used to train those data to build the correlation models between software runtime entropy and software failures. Several common measures in machine learning domains such as accuracy, recall rates, and F-measure are used to evaluate the models. The decision tree models can be used as failure mechanisms to assist the failure prediction work. One can examine the value of runtime entropy and make a warning report when it ranges from the normal interval to abnormal one.

Based on the principle of Pancharatnam-Berry phase, we propose an approach to construct dual-functional dielectric metasurface doublets. This type of doublet, which is composed of a substrate and two metasurface layers, performs two different functionalities at two different operating wavelengths. The simulated results of the examples show that the designed dual-functional doublet works well as expected. The proposed approach can be used to design dielectric meta-structures with more layers of meta-surface and thereby with more functionalities, and would be of interest for miniaturization and integration.

Reconstruction of fine-scale information from sparse data is often needed in practical fluid dynamics where the sensors are typically sparse and yet, one may need to learn the underlying flow structures or inform predictions through assimilation into data-driven models. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches encode the physics into an underlying sparse basis space that spans the manifold to generate well-posedness. To achieve this, one commonly uses generic orthogonal Fourier basis or data specific proper orthogonal decomposition (POD) basis to reconstruct from sparse sensor information at chosen locations. Such a reconstruction problem is well-posed as long as the sensor locations are incoherent and can sample the key physical mechanisms. The resulting inverse problem is easily solved using $l_2$ minimization or if necessary, sparsity promoting $l_1$ minimization. Given the proliferation of machine learning and the need for robust reconstruction frameworks in the face of dynamically evolving flows, we explore in this study the suitability of non-orthogonal basis obtained from Extreme Learning Machine (ELM) auto-encoders for sparse reconstruction. In particular, we assess the interplay between sensor quantity and sensor placement for a given system dimension for accurate reconstruction of canonical fluid flows in comparison to POD-based reconstruction.

Colorectal cancer (CRC) is the third most common cancer, with rising incidence due to lifestyle and diet. 40% of CRC cases are found to have KRAS mutations. In this study, we investigate the survival outcome of metastatic Colorectal cancer mCRC) patients in Brunei Darussalam restrospectively. Chi-squared test was used to compare the survival outcomes of mCRC patients, and Mann-Whitney U test was used to compare the median ages of both groups. Kaplan-Meier survival curves were drawn and logrank test was used to compare the survival outcome between two groups. There was a total of 105 patients with stage IV CRC being treated during the study period. 81.6% (n=62) of mCRC patients were found to have the primary tumours on the left side of the colon. 19 of these 26 (73.1%) mutant KRAS mCRC patients died, while 23 of 50 (46.0%) wild-type KRAS mCRC patients died at the end of the study period, contributing to death rates of 45.2% and 54.8%, correspondingly. 30.3% (n=23) of the study population had a single metastatic site detected (either liver, or lung or any other organs), while 69.7% (n=53) of the 76 mCRC patients had two (double) or more metastatic sites. 69.2% (n=18) and 30.8% (n=8) of the mutant KRAS mCRC patients had mutations within codons 12 and 13, respectively. To our knowledge, this is the first study in Brunei Darussalam to analyse both the survival outcomes of metastatic CRC patients and those of mutant KRAS mCRC patients. Chi-squared analysis showed a significant difference between the survival outcomes of wild-type KRAS and mutant KRAS mCRC patients (p-value = 0.024). There was a significant difference in the survival outcome between the mutant KRAS mCRC patients with RCC and mutant KRAS mCRC with LCC patients. There was no significant difference between the survival outcomes of mutant KRAS patients with mutations in either codon 12 or 13 of the KRAS gene (Table 3). However, there is a significant difference in the median survival periods between the mutant KRAS mCRC patients with mutations in codon 12 and those with mutation in codon 13 of the KRAS gene (p-value = 0.003). In conclusion, we found that mutant KRAS mCRC patients had a significantly poorer OS, which was shown to be worse when the primary tumours were found at the left side of the colon. Mutant KRAS mCRC patients with mutations in codon 12 were found to have shorter survival median periods than those with mutations within codon 13.

The most recent National Research Council Nutrient Requirements for Sheep and Goats was published in 2007, one of the most consequential nutrient requirement recommendations for sheep and goats in the world. It is pertinent to continue the refinement and discussion of nutrient utilization and requirements for the purposes of enhancing the efficiency of production systems, maximizing resource economy, and minimizing the carbon footprint, among others. Progress has been made in the estimation of energy and protein requirements in sheep and goats, mainly utilizing empirical feeding experimentation, comparative slaughter technique and minimum endogenous loss method. In sheep, newer estimates of energy and protein requirements for maintenance and growth and partial efficiencies had been reported since 2007. There were suggestions that energy and protein requirements could have been affected by breed, wool growth, gender and size; with these reported values being similar or lower than the recommended values in international feeding systems such as NRC, ARC, INRA and AFRC. In goats, energy and protein requirements for growing goats were reported to be either higher or lower than the established recommendations, depending upon meat or dairy breeds. Effect of gender on energy requirement appeared to be related to the stage of growth or degree of maturity. Newer data also suggested that the guidelines of the major feeding systems for the entire growth phase may not be adequate for non-pregnant and non-lactating pubertal females. In multiparous pregnant goats, energy and protein requirements for maintenance did not appear to be affected by days of pregnancy, but efficiencies of metabolizable energy and metabolizable protein utilization for pregnancy were. There were suggestions that that metabolizable protein synthesis in the rumen can be predicted from energy intake using combined equations that encompass both sheep and goats, but more data on goats were called for to account for specific differences in nutrition. In addition to sulfur, there has been progress made on the estimation of maintenance and growth requirements of calcium, phosphorus, potassium and magnesium in goats, with suggestions on the consideration of gender and breed differences. Species, level of Intake, genotype, mature size, age, gender, body composition, use of tissue energy, activity, season, and acclimatization continue to be important considerations for the estimation of nutrient requirements in sheep and goats, with emerging factors such as climate change, heat stress, parasitism and secondary plant compounds. Model equations and partial efficiencies used by NRC to predict energy and protein requirements for maintenance, growth, lactation, and fiber have been highlighted and discussed for the purpose of a more focus discussion and refinement for the future. Potential and limitation of both traditional and emerging methodologies in determining the nutrient requirements in sheep and goats were discussed. To justify the research investment, emerging methodologies such as nutrigenomics will have to be linked more directly to the improvement of production efficiency via more precise prediction of nutrient requirement.

Vitamin D has important anti-inflammatory, anti-microbial properties and plays a central role in host immune response. Due to the crucial role of the kidneys in the metabolism of vitamin D, patients with chronic kidney disease (CKD), are prone to vitamin D deficiency. The resultant reduction in the production of calcitriol, the activated form of vitamin D in patients with CKD is responsible for exacerbating the existing renal impairment and periodontal inflammation. Recent evidence suggests a bidirectional, causal relationship between periodontitis and renal functional status. Both conditions have shared pathophysiologic mechanisms including oxidative stress, increases in the systemic inflammatory burden and impaired host response. This review explores the association between vitamin D, CKD and periodontitis. The review summarises the current evidence base for the classical and non-classical vitamin D metabolic pathways, the biological mechanisms linking vitamin D deficiency, CKD and periodontitis, as well as the bidirectional relationship between the two chronic inflammatory conditions. Finally, the paper explores the impact of vitamin D deficiency on CKD, periodontitis, and related co-morbidities.

In order to further investigate the study of the effect of grouped stud effect on the force properties of stud connector, based on the premise that the correctness of the finite element simulation method in this paper, finite element modeling of grouped stud connectors was developed, the grouped stud effect and its sensitivity factors were analyzed, to validate the recommended formula for calculating the shear capacity of grouped stud connectors. Results show that the number of grouped stud rows and row spacing have a significant effect on the grouped stud effect, and the unevenness coefficient of grouped stud force Ⅰ is negatively correlated with the number of grouped stud rows as well as the grouped stud row spacing. For grouped stud connectors with commonly used concrete grades greater than C50 and height-to-diameter ratios greater than 4 in steel-concrete composite structural bridges, which is insensitive to changes in the concrete strength grades and the length of the studs. The direction of force transmission for the grouped stud changes with the change of loading angle, and the unevenness coefficient of force for the grouped stud will therefore be reduced. By comparing the results of the 62 existing groups of grouped stud connectors push-out tests, the mean value of the ratio of tested to calculated values is found to be 1.12, the variance is 0.023, the dispersion is small, it is shown that the recommended formula has a high degree of accuracy. The results of this paper can be used as a theoretical basis for the study of the shear stress performance of grouped stud connectors.

Symbolic semantic understanding of staff images is an important part in music information retrieval. Due to the complex composition of staff symbols and the strong semantic correlation between symbol spaces, it is difficult to understand the pitch and duration of each note during performances. In this paper, we design a semantic understanding system for optical staff symbols. The system uses the YOLOv5 to implement optical staff’s low-level semantic understanding stage, which understands the pitch and duration in natural scales and other symbols that affect the pitch and duration. The proposed note encoding reconstruction algorithm is used to implement high-level semantic understanding stage. Such algorithm understands the logical, spatial, and temporal relationships between natural scales and other symbols based on music theory, and outputs digital codes for the pitch and duration of main notes during performances. The model is trained with a self-constructed SUSN dataset. Experimental results of YOLOv5 show that the precision is 0.989 and the recall is 0.972. For the system, the error rate is 0.031 and the omission rate is 0.021. The paper concludes by analysing the causes of semantic understanding errors and offers recommendations for further research. The results of this paper provide a method for multimodal music artificial intelligence applications such as notation recognition through listening, intelligent score flipping and automatic performance.

To determine the metamorphic ages of the accretionary complexes in the Northern Chichibu Belt in SW Japan, K–Ar dating was conducted using weakly metamorphosed sedimentary rocks collected from the Kanto Mountains, Central Japan. Whole-rock ages were obtained for chert and red shale samples, and the mineral ages of fine-grained illite with the grain size less than 4 μm were obtained for chert, red shale, mudstone, acidic tuff, and basic tuff. The K–Ar ages of chert and red shale presented large variations, with systematically older ages compared to those of mudstone and tuff in the same strata. The influence of submarine hydrothermal activities on chert and red shale before subduction is a possible cause of this deviation. The illite samples, which were fractionated into four grain-size classes using a suspension method, yielded older ages and higher illite crystallinity (i.e., smaller values of Kübler’s crystallinity index) for larger grain-size classes. The peak metamorphic ages were determined from the K–Ar ages of the 3–4 μm class illite in mudstone and tuff. The Late Jurassic to the Earliest Cretaceous accretionary complex of the lowest structural unit (Kashiwagi Unit) was dated within a small range between 117–110 Ma, which is distinctly older than the K–Ar ages of white mica reported from the Sanbagawa Belt. The peak metamorphic age of acidic tuff (113 Ma) at the type locality of the Mikabu Greenstones indicates that the subducted Mikabu seamount is a constituent of the Kashiwagi Unit. The peak metamorphic ages of the Manba and Kamiyoshida Units were obtained as 132–107 Ma and 163–144 Ma, respectively. Major structural discontinuity is suggested within the Middle Jurassic accretionary complexes.

Aims: To assess the occurrence of Chemotherapy-induced nausea and vomiting (CINV) after standard antiemetic therapy in the acute (24 h post-chemotherapy) and delayed (2–5 days post-chemotherapy) phases, as well as to identify risk factors for CINV in the acute and delayed phases. Methods: This prospective longitudinal and observational study analyzed the data of 400 breast cancer patients scheduled for chemotherapy over two cycles in two hospitals. The self-report survey was developed to assess the occurrence of CINV and their associated factors. CINV was evaluated with a Multinational Association of Supportive Care in Cancer Antiemetic Tool (MAT) on days 2 and 6 of chemotherapy. The incidence of acute and delayed CINV were presented by frequency and percentage. Generalized equation estimates (GEE) was used to identify risk factors of acute and delayed CINV. Results: There were 400 evaluable patients with complete Round 1 data, 334 for Round 2 data. Among 400 patients, 29.8% and 23.5% experienced acute and delayed CINV, respectively. Risk factors associated with for acute CINV were pain/insomnia, history of CINV, history of motion sickness (MS), and highly emetogenic chemotherapy regimen, while history of MS, CINV history, number of completed chemotherapy cycle number < 3, and the incidence of acute CINV were risk factors of delayed CINV (all p < 0.05). Conclusions: The findings may help nurses working for Chinese population in identifying patients at risk for CINV and in planning effective program to reduce the occurrence of CINV.

There are some controversies about 2G bioethanol and it is in need of a reliable test data in order to make a further analysis and proof. In collecting basic data from three classes of typical raw materials, such as wheat, maize stalk juice, and straw, it was found that 4.32 tons straw or 3.358 tons wheat is needed to produce 1-ton bioethanol. Using the coefficient of variation calculation and analysis method, it is able to obtain the C.V. value of straw fermentation efficiency 0.08932<0.15, indicating the fermentation technology and process of 2G bioethanol is already quite mature and reliable, without risk, and also with a raw material cost advantage. There is a problem in that the C.V. value of straw volume is 1.2648>0.95, indicating 2G bioethanol is in an extremely high-risk area. Using the straw volume calculation method, building and its costs – capital expenditure of 2G bioethanol plant is approximately seven times that of a wheat ethanol plant. This should be some of the important reasons why it is noncompetitive, extremely high price, and requiring government subsidies. Straw volume data study has caused the 2G bioethanol plant to require more cement, steel, equipment, and other materials. Thus, there are ample reasons to question the CO2 emissions and its carbon footprint of 2G bioethanol plant. Actually, the volume is not the only existing problem, it is necessary to re-evaluate and discuss the definition of 2G bioethanol and its future.

This paper introduces a surrogate model to reduce the huge computational load in the process of simulation-optimization and uncertainty analysis. First, the groundwater numerical simulation model was established, calibrated and verified in the northeast of Hetao Plain. Second, two surrogate models of simulation model were established using support vector regression (SVR) method, one (surrogate model A, SMA) was used to describe the corresponding relationship between the pumping rate and average groundwater table drawdown, and another (surrogate model B, SMB) was used to express the corresponding relationship between the hydrogeological parameter values and average groundwater table drawdown. Third, an optimization model was established to search an optimal groundwater exploitation scheme using the maximum total pumping rate as objective function and the limitative average groundwater table drawdown as constraint condition, the SMA was invoked by the optimization model for obtaining the optimal groundwater exploitation scheme. Finally, the SMB was invoked in the process of uncertainty analysis for assessing the reliability of optimal groundwater exploitation scheme. Results show that the relative error and root mean square error between simulation model and the two surrogate models are both less than 5%, which is a high approximation accuracy. The SVR surrogate model developed in this study could not only considerably reduce the computational load, but also maintain high computational accuracy. The optimal total pumping rate is 7947 m3/d and the reliability of optimal scheme is 40.21%. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme and assessing the reliability of scheme quickly and accurately.

This study compared two object-oriented land use change detection methods—detection after classification (DAC) and classification after detection (CAD) —based on a digital elevation model, slope data, and multi-temporal Landsat images (TM image for 2000 and ETM image for 2010). We noted that the overall accuracy of the DAC (86.42%) was much higher than that of the CAD (71.71%). However, a slight difference between the accuracies of the two methods exists for deciduous broadleaf forest, evergreen coniferous forest, mixed wood, upland, paddy, reserved land, and settlement. Owing to substantial spectrum differences, these land use types can be extracted using spectral indexes. The accuracy of DAC was much higher than that of CAD for industrial land, traffic land, green shrub, reservoir, lake, river, and channel, all of which share similar spectrums. The discrepancy was mainly because DAC can completely utilize various forms of information apart from spectrum information during a two-stage classification. In addition, the change-area boundary was not limited at first, but was adjustable in the process of classification. DAC can overcome smoothing effects to a great extent using multi-scale segmentations and multi-characters in detection. Although DAC yielded better results, it was more time-consuming (28 days) because it uses a two-stage classification approach. Conversely, CAD consumed less time (15 days). Thus, a hybrid of the two methods is recommended for application in land use change detection.

Unmanned aerial vehicle (UAV), or drone is recognized for its potential to improve efficiency and address last-mile delivery issues. As a result, there has been a lot of activity in recent years in the field of drone scheduling and routing. Unlike the vehicle routing problem, drone route design is difficult due to several operational characteristics, such as speed optimization, multitrip operation, and energy consumption estimation. On the one hand, drone energy consumption is a complex nonlinear function of both speed and payload in practice. On the other hand, the high operating speed of drones can significantly curtail the drone range, thereby limiting the efficiency of drone delivery systems. Most of the existing drone delivery models either assume constant drone speed or do not consider the effect of drone speed and parcel weight on energy consumption, leading to costly or energy-infeasible routes. This paper addresses the trade-off between speed and flight range in a multi-trip drone routing problem with variable flight speeds (DRP-VFS), in which a team of homogeneous drones is employed for delivery services. We propose a new model to particularly consider energy constraints using a nonlinear energy consumption model and treat drone speeds as decision variables so that various drone speeds can be adopted in applications. The DRP-VFS is initially formulated as mixed-integer linear programming (MILP) to minimize total energy consumption. To solve large-scale instances, we propose a three-phase adaptive large neighborhood search (ALNS) algorithm. The experimental results demonstrate that suboptimal solutions can be found effectively in practical scenarios using the proposed method. Furthermore, results indicate that operating drones at variable speeds leads to about 21% of energy savings compared to fixed speeds, boasting advantages in cost-savings and range extensions.

Force reduction is one of the most important benefit of applying ultrasonic vibration on milling. However, most of studies so far are limited to experimental investigation. In the current study, an analytical predictive model on cutting forces in ultrasonic vibration-assisted milling is proposed. The three types of tool-workpiece criteria are considered based on the instantaneous position and velocity of tool center. Type I criterion indicates that there is no contact if the instantaneous velocity is opposite to tool rotation direction. Type II criterion checks whether the vibration displacement is larger than the instantaneous uncut chip thickness. Type III criterion considers the overlaps between current and previous tool paths due to vibration. If none of these criteria is satisfied, milling forces are nonzero. Then the calculation is performed by transforming milling and tool geometry configuration to orthogonal cutting at each instant. The orthogonal cutting forces are predicted through the exhaustive search of shear angle and calculation of shear flow stress on tool-chip interface. The axial force is then calculated based on tool geometry, and the milling forces in feed, cutting, and axial directions are calculated after coordinate transformation. The proposed predictive force model in ultrasonic vibration-assisted milling is validated through comparison to experimental measurements on Aluminum alloy 2A12. The predicted values are able to match the measured milling forces with high accuracy of average difference of 13.6% in feed direction and 13.8% in cutting direction.

In laser-assisted milling, higher temperature in shear zone softens the material potentially resulting in a shift of mean residual stress, which significantly affects the damage tolerance and fatigue performance of product. In order to guide the selection of laser and cutting parameters based on the preferred mean residual stress, inverse analysis is conducted by predicting residual stress based on guessed process parameters, which is defined as the forward problem, and applying iterative gradient search to find process parameters for next iteration, which is defined as the inverse problem. An analytical inverse analysis is therefore proposed for the mean residual stress in laser-assisted milling. The forward problem is solved by analytical prediction of mean residual stress after laser-assisted milling. The residual stress profile is predicted through the calculation of thermal stress, by treating laser beam as heat source, and plastic stress by first assuming pure elastic stress in loading process, then obtaining true stress with kinematic hardening followed by the stress relaxation. The variance-based recursive method is applied to solve inverse problem by updating process parameters to match the measured mean residual stress. Three cutting parameters including depth of cut, feed per tooth, and cutting speed, and two laser parameters including laser-tool distance and laser power, are updated with respected to the minimization of resulting residual stress and measurement in each iteration. Experimental measurements are referred on the laser-assisted milling of Ti-6Al-4V grade 5 and ELI. The percentage difference between experiments and predictions is less than 5% for both materials, and the selection is completed within 50 loops.

Transcranial direct current stimulation (tDCS) is acknowledged for its non-invasive modulation of neuronal activity in psychiatric disorders. However, its application in insomnia research yields varied outcomes depending on different tDCS types and patient conditions. Our primary objective is to elucidate its efficiency and uncover the underlying mechanisms in insomnia treatment. We hypothesized that anodal prefrontal cortex stimulation activates glutaminergic projections from the infralimbic cortex (IL) to ventrolateral preoptic area (VLPO) to promote sleep. After administering 0.06 mA of electrical currents for 8 minutes, our results indicate significant non-rapid eye movement (NREM) enhancement in naïve mice within the initial 3 hours post-stimulation, persisting up to 16-24 hours. In the insomnia group, tDCS enhanced NREM sleep bout numbers during acute stress response and improved NREM and REM sleep duration in subsequent acute insomnia. Sleep quality, assessed through NREM delta powers, remains unaffected. Interference of the IL-VLPO pathway, utilizing designer receptors exclusively activated by designer drugs (DREADDs) with the cre-DIO system, partially blocked tDCS's sleep improvement in stress-induced insomnia. This study elucidated that the activation of the IL-VLPO pathway mediates tDCS's effect on stress-induced insomnia. These findings support the understanding of tDCS effects on sleep disturbances, providing valuable insights for future research and clinical applications in sleep therapy.

Sign language recognition is essential in hearing-impaired people’s communication. Sign language recognition is an important concern in computer vision and has been developed with rapid progress in image recognition technology. However, sign language recognition using a general monocular camera has problems with occlusion and recognition accuracy in sign language recognition. In this research, we aim to improve accuracy by using a 2-axis bending sensor as an aid in addition to image recognition. We aim to achieve higher recognition accuracy by acquiring hand keypoint information of sign language actions captured by a monocular RGB camera and adding sensor assist. To improve sign language recognition, we need to propose new AI models. In addition, the amount of dataset is small because it uses the original data set of our laboratory. To learn using sensor data and image data, we used MediaPipe, CNN, and BiLSTM to perform sign language recognition. MediaPipe is a method for estimating the skeleton of the hand and face provided by Google. In addition, CNN is a method that can learn spatial information, and BiLSTM can learn time series data. Combining the CNN and BiLSTM methods yields higher recognition accuracy. We will use these techniques to learn hand skeletal information and sensor data. Additionally, the 2-axis Bending sensor glove data support training AI model. Using these methods, we aim to improve the recognition accuracy of sign language recognition by combining sensor data and hand skeleton data. Our method performed better than using skeletal information, achieving 96.5% accuracy in Top-1.

Using the Super-SBM method, this study calculates the carbon emission efficiency (CEE) of 30 provinces in mainland China from 2011 to 2019. Then, using the systematic GMM model, spatial Durbin model, and mediating effect model, it examines the direct effect, spatial effect, and influence mechanism of the digital economy (DE) on CEE. It was found that (1) the DE significantly promoted regional CEE, but had a negative effect on CEE in provinces with high economic correlation; (2) mechanism studies showed that the DE improved CEE by reducing energy intensity, promoting industrial upgrading and green technology innovation; (3) regional heterogeneity analysis found that the DE significantly improved CEE in eastern provinces, but not in central and western provinces. DE improves CEE in provinces with high level of economic development, but decreases CEE in provinces with low level of economic development.This paper provides some empirical and theoretical references for the development of DE to improve CEE.

Commercial banks usually classify customers according to their credit reports when making loans. In this study, we put our focus on classifying customers based on their credit reports from the People's Bank of China (PBC). Since there are no target labels of users in the credit report of the People's Bank of China, we put forward the fuzzy clustering method for the initial label, and then Construct ant colony search to optimize intelligent recognition. Finally, this study uses SVM, BP neural network, and random forest to classify users and compare their results. The research re-sults indicate that using ant colony clustering algorithm and random forest for classification is the most effective method with the PBC credit reports.

The fluorine-less noble-gas containing anions OBONgO- and OCNNgO-have been studied by correlated electronic structure calculation and density functional theory. The obtained energetics indicates that for Ng = Kr and Xe, these anions should be kinetically stable at low temperature. The molecular structures and electron density distribution suggests that these anions are stabilized by ion-induced dipole interactions with charges concentrated on the electronegative OBO and OCN groups. The current study shows that in additional to the fluoride ion, polyatomic groups with strong electronic affinities can also form stable noble-gas containing anions of the type X-...NgO.

Sparse recovery of fluid flows using data-driven proper orthogonal decomposition (POD) basis is systematically explored in this work. Fluid flows are manifestations of nonlinear multiscale PDE dynamical systems with inherent scale separation that impact the system dimensionality. Given that sparse reconstruction is inherently an ill-posed problem, the most successful approaches require the knowledge of the underlying basis space spanning the manifold in which the system resides. In this study, we employ an approach that learns basis from singular value decomposition (SVD) of training data to reconstruct sparsely sensed information. This results in a set of four control parameters for sparse recovery, namely, the choice of basis, system dimension required for sufficiently accurate reconstruction, sensor budget and their placement. The choice of control parameters implicitly determines the choice of algorithm as either $l_2$ minimization reconstruction or sparsity promoting $l_1$ norm minimization reconstruction. In this work, we systematically explore the implications of these control parameters on reconstruction accuracy so that practical recommendations can be identified. We observe that greedy-smart sensor placement provides the best balance of computational complexity and robust reconstruction for marginally oversampled cases which happens to be the most challenging regime in the explored parameter design space.

Oriental lacquer, a natural and renewable polymeric coating, comes from the sap produced by lacquer trees. For practical application, oriental lacquer must be refined to reduce excess water and enhance its quality. In this study, drying oils were blended with oriental lacquer during the refining process to prepare an oil-modified refined lacquer (OMRL). The type and adding amount (0, 10, and 20% by wt.) of drying oils for wood coatings utilization were evaluated. Rhus succedanea oriental lacquer is composed of 54.1% urushiols, 34.3% water, 7.2% plant gum, and 4.4% nitrogenous compounds, and drying oils, including tung oil (TO), linseed oil (LO), and dehydrated castor oil (DCO) were used as materials in this study. The results show that the drying oil acts as a diluent, which reduces the viscosity and enhances the workability and could shorten the touch-free drying time and speed up the hardened drying of the OMRL. The results also indicate that the hardness, mass retention, Tg, tensile strength, abrasion resistance, and lightfastness of OMRL films decrease as more drying oils are blended. Conversely, the bending resistance, elongation at break, impact resistance increase, and particularly, the gloss, is greatly improved through the blending of more drying oils. In conclusion, the LO-modified refined lacquer (RL) has the highest film gloss and the DCO-modified RL has the shortest drying time for coating; otherwise, the film properties are similar among the three types of drying oil.