基于Valued ERGMs模型的核心技术网络成长机制研究——以量子计算领域为例

对核心技术网络及其成长机制的研究有助于梳理核心技术之间的依赖和促进关系,为研究核心技术演化提供新的理论视角和方法。以技术演化相关理论为基础,归纳核心技术网络成长的影响因素,基于量子计算领域专利数据,识别以技术概念和技术关系为基础的量子计算领域核心技术网络,并基于Valued ERGMs模型构建核心技术网络成长机制模型。结果表明,技术要素中心性、专利技术研发能力、与TRIZ进化法则匹配程度以及技术要素同配性对核心技术网络成长具有正向促进作用;在技术路径方面,核心技术网络成长受到技术要素路径依赖性和技术突破的双重影响。最后,根据量子计算领域核心技术网络成长机制的实证研究结果,从技术研发层面、企业层面、政府层面为相关领域技术发展提出策略建议。

The Economics of Competing Water Uses under a FERC Licensing Agreement: Estimation of Property Value, Recreation, and Hydroelectric Impacts

Reservoirs provide a variety of services with economic values across multiple sectors. As demands for reservoir services continue to grow and precipitation patterns evolve, it becomes ever more important to consider the integrated suite of values and tradeoffs that attend changes in water uses and availability. Section 316 (b) of the Clean Water Act requires that owners of certain water cooled power plants evaluate technologies and operational measures that can reduce their impacts to aquatic organisms. The studies must discuss the social costs and benefits of alternative technologies including cooling towers (79 Fed. Reg. 158, 48300 - 48439). Cooling towers achieve their effect through evaporation. This manuscript estimates the property value, recreation, and hydroelectric generation impacts that could result from the evaporative water loss associated with installing cooling towers at the McGuire Nuclear Generating Station (McGuire) located on Lake Norman, North Carolina. Although this study specifically evaluates the effects of evaporative water loss from cooling towers, its methods are applicable to estimating the economic benefits and costs of a new water user or reduced water input in any complex reservoir system that supports steam electric generation, hydroelectric generation, residential properties, recreation, irrigation, and municipal water use.

A Bitcoin Address Multi-Classification Mechanism Based on Bipartite Graph-Based Maximization Consensus

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges,gambling,marketplaces,and also scams such as high-yield investment projects.Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly.Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way.In this paper,we address the problem of identifying multiple classes of Bitcoin services,and for the poor classification of individual addresses that do not have significant features,we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities.The innovation of the method is to(1)Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task.(2)Unlike the general supervised model approach,this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships.Specifically,after obtaining the overall features,the address classification and entity clustering tasks are performed separately,and the results are subjected to graph-basedmaximization consensus.The final result ismade to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible.By testing and evaluating over 26,000 Bitcoin addresses,our feature extraction method captures more useful features.In addition,the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Decision Making Based on Valued Fuzzy Superhypergraphs

This paper explores the defects in fuzzy (hyper) graphs (as complex (hyper) networks) and extends the fuzzy(hyper) graphs to fuzzy (quasi) superhypergraphs as a new concept.We have modeled the fuzzy superhypergraphsas complex superhypernetworks in order to make a relation between labeled objects in the form of details andgeneralities. Indeed, the structure of fuzzy (quasi) superhypergraphs collects groups of labeled objects and analyzesthem in the form of the part to part of objects, the part of objects to the whole group of objects, and the whole tothe whole group of objects at the same time.We have investigated the properties of fuzzy (quasi) superhypergraphsbased on any positive real number as valued fuzzy (quasi) superhypergraphs, considering the complement of valuedfuzzy (quasi) superhypergraphs, the notation of isomorphism of valued fuzzy (quasi) superhypergraphs based onthe permutations, and we have presented the isomorphic conditions of (self complemented) valued fuzzy (quasi)superhypergraphs. The concept of impact membership value of fuzzy (quasi) superhypergraphs is introducedin this study and it is applied in designing the real problem in the real world. Finally, the problem of businesssuperhypernetworks is presented as an application of fuzzy valued quasi superhypergraphs in the real world.

Effect of Biostimulants Based on Natural Products on the Growth and Nutritional Value of Bean (Phaseolus vulgaris L.)

Beans (Phaseolus vulgaris L.) are widely grown in Cameroon and play a key role in the fight against food insecurity, malnutrition and poverty. However, its cultivation encounters problems due to abiotic and biotic stresses, which leads to the use of synthetic fertilizers and pesticides, which cause significant damage to the environment and human health due to the presence of synthetics residues in the seeds, pods and in the leaves that are eaten. Promoting the use of natural products is becoming a necessity for organic and eco-responsible agriculture that limits contamination problems and improves people’s purchasing power. This study aims to assess the effect of biostimulants based on natural products on the growth and nutritional value of common bean (Phaseolus vulgaris L.). Bean seedlings from white variety (MEX-142) and red variety (DOR-701) were treated every seven days in the field from their pre-emergence, emergence and growth to their maturation under a randomized block experimental design. Six treatments and three repetitions with the biostimulants based on natural products and controls were thus performed and the agromorphological parameters were measured. After 120 days, the contents of growth biomarkers and defense-related enzymes were evaluated in leaves, while the contents of macromolecules, minerals and antinutrients were evaluated in seeds. These biostimulants significantly increased (P P < 0.0001) of antinutrients including oxalates, phytates, tannins and saponins in seeds compared to controls (T+ and T−). Treatment with biostimulants, in particular BS4, improves the performance of bean plants in the field as well as the biofortification of seeds regardless of the variety.

Research on Data Theory of Value

This paper explores the data theory of value along the line of reasoning epochal characteristics of data-theoretical innovation-paradigmatic transformation and,through a comparison of hard and soft factors and observation of data peculiar features,it draws the conclusion that data have the epochal characteristics of non-competitiveness and non-exclusivity,decreasing marginal cost and increasing marginal return,non-physical and intangible form,and non-finiteness and non-scarcity.It is the epochal characteristics of data that undermine the traditional theory of value and innovate the“production-exchange”theory,including data value generation,data value realization,data value rights determination and data value pricing.From the perspective of data value generation,the levels of data quality,processing,use and connectivity,data application scenarios and data openness will influence data value.From the perspective of data value realization,data,as independent factors of production,show value creation effect,create a value multiplier effect by empowering other factors of production,and substitute other factors of production to create a zero-price effect.From the perspective of data value rights determination,based on the theory of property,the tragedy of the private outweighs the comedy of the private with respect to data,and based on the theory of sharing economy,the comedy of the commons outweighs the tragedy of the commons with respect to data.From the perspective of data pricing,standardized data products can be priced according to the physical product attributes,and non-standardized data products can be priced according to the virtual product attributes.Based on the epochal characteristics of data and theoretical innovation,the“production-exchange”paradigm has undergone a transformation from“using tangible factors to produce tangible products and exchanging tangible products for tangible products”to“using intangible factors to produce tangible products and exchanging intangible products for tangible products”and ultimately to“using intangible factors to produce intangible products and exchanging intangible products for intangible products”.

A Value for Games Defined on Graphs

Given a graph g=( V,A ) , we define a space of subgraphs M with the binary operation of union and the unique decomposition property into blocks. This space allows us to discuss a notion of minimal subgraphs (minimal coalitions) that are of interest for the game. Additionally, a partition of the game is defined in terms of the gain of each block, and subsequently, a solution to the game is defined based on distributing to each player (node and edge) present in each block a payment proportional to their contribution to the coalition.

Evaluation of the value of combined detection of tumor markers CA724,carcinoembryonic antigen,CA242,and CA19-9 in gastric cancer

BACKGROUND Gastric cancer is a global health concern that poses a significant threat to human well-being.AIM To detecting serum changes in carcinoembryonic antigen(CEA),carbohydrate antigens(CA)724,CA242,and CA19-9 expression among patients with gastric cancer.METHODS Eighty patients diagnosed with gastric cancer between January 2020 and January 2023 were included in the observation group,while 80 patients with benign gastric diseases were included in the control group.Both groups were tested for tumor markers(CA724,CEA,CA242,and CA19-9].Tumor marker indicators(CA724,CEA,CA242,and CA19-9)were compared between the two groups,assessing positive rates of tumor markers across various stages in the observation group.Additionally,single and combined detection of various tumor markers were examined.RESULTS The sensitivity,specificity,accuracy,positive predictive value,and negative predictive value observed for the combined detection of CA724,CEA,CA242,and CA19-9 were higher than those of CA724,CEA,CA242,and CA19-9 individually.Therefore,the combined detection of CA724,CEA,CA242,and CA19-9 has a high diagnostic accuracy and could reduce the occurrence of missed or misdiagnosed cases,facilitating the early diagnosis and treatment of patients.CONCLUSION CA724,CEA,CA242,and CA19-9 serum levels in gastric cancer patients significantly surpassed those in non-gastric cancer patients(P<0.05).Their combined detection can improve the diagnostic accuracy for gastric cancer,warranting clinical promotion.

Sum-Rate Maximization in Active RIS-Assisted Multi-Antenna WPCN

In this paper,we propose an active reconfigurable intelligent surface(RIS)enabled hybrid relaying scheme for a multi-antenna wireless powered communication network(WPCN),where the active RIS is employed to assist both wireless energy transfer(WET)from the power station(PS)to energyconstrained users and wireless information transmission(WIT)from users to the receiving station(RS).For further performance enhancement,we propose to employ both transmit beamforming at the PS and receive beamforming at the RS.We formulate a sumrate maximization problem by jointly optimizing the RIS phase shifts and amplitude reflection coefficients for both the WET and the WIT,transmit and receive beamforming vectors,and network resource allocation.To solve this non-convex problem,we propose an efficient alternating optimization algorithm with the linear minimum mean squared error criterion,semidefinite relaxation(SDR)and successive convex approximation techniques.Specifically,the tightness of applying the SDR is proved.Simulation results demonstrate that our proposed scheme with 10 reflecting elements(REs)and 4 antennas can achieve 17.78%and 415.48%performance gains compared to the single-antenna scheme with 10 REs and passive RIS scheme with 100 REs,respectively.

Improving the nutritional values of yellow mealworm Tenebrio molitor(Coleoptera: Tenebrionidae) larvae as an animal feed ingredient: a review

Yellow mealworm larvae(YML;Tenebrio molitor) are considered as a valuable insect species for animal feed due to their high nutritional values and ability to grow under different substrates and rearing conditions. Advances in the understanding of entomophagy and animal nutrition over the past decades have propelled research areas toward testing multiple aspects of YML to exploit them better as animal feed sources. This review aims to summarize various approaches that could be exploited to maximize the nutritional values of YML as an animal feed ingredient. In addition, YML has the potential to be used as an antimicrobial or bioactive agent to improve animal health and immune function in production animals. The dynamics of the nutritional profile of YML can be influenced by multiple factors and should be taken into account when attempting to optimize the nutrient contents of YML as an animal feed ingredient. Specifically, the use of novel land-based and aquatic feeding resources, probiotics, and the exploitation of larval gut microbiomes as novel strategies can assist to maximize the nutritional potential of YML. Selection of relevant feed supplies, optimization of ambient conditions, the introduction of novel genetic selection procedures, and implementation of effective post-harvest processing may be required in the future to commercialize mealworm production. Furthermore, the use of appropriate agricultural practices and technological improvements within the mealworm production sector should be aimed at achieving both economic and environmental sustainability. The issues highlighted in this review could pave the way for future approaches to improve the nutritional value of YML.

Kinematic calibration under the expectation maximization framework for exoskeletal inertial motion capture system

This study presents a kinematic calibration method for exoskeletal inertial motion capture (EI-MoCap) system with considering the random colored noise such as gyroscopic drift.In this method, the geometric parameters are calibrated by the traditional calibration method at first. Then, in order to calibrate the parameters affected by the random colored noise, the expectation maximization (EM) algorithm is introduced. Through the use of geometric parameters calibrated by the traditional calibration method, the iterations under the EM framework are decreased and the efficiency of the proposed method on embedded system is improved. The performance of the proposed kinematic calibration method is compared to the traditional calibration method. Furthermore, the feasibility of the proposed method is verified on the EI-MoCap system. The simulation and experiment demonstrate that the motion capture precision is significantly improved by 16.79%and 7.16%respectively in comparison to the traditional calibration method.

Extreme value theory applied to the auroral electrojet indices

The study of extreme weather and space events has gained paramount importance in modern society owing to rapid advances in high technology.Understanding and describing exceptional occurrences plays a crucial role in making decisive assessments of their potential impact on technical,economic,and social aspects in various fields.This research focuses on analyzing the hourly values of the auroral electrojet(AE)geomagnetic index from 1957 to 2019 by using the peak over threshold method in extreme value theory.By fitting the generalized Pareto distribution to extreme AE values,shape parameter indices were derived,revealing negative values that establish an upper bound for this time series.Consequently,it became evident that the AE values had reached a plateau,suggesting that extreme events exceeding the established upper limit are rare.As a result,although the need for diligent precautions to mitigate the consequences of such extreme events persists,surpassing the upper limit of AE values becomes increasingly challenging.It is also possible to observe an aurora in the middle-and low-latitude regions during the maximum period of the AE index.

Effect of Light Emitting Diodes (LEDs) on Growth, Mineral Composition, and Nutritional Value of Wheat & Lentil Sprouts

Sprouts are ready-to-eat and are recognized worldwide as functional components of the human diet.Recent advances in innovative agricultural techniques could enable an increase in the production of healthy food.The use of light-emitting diode(LED)in indoor agricultural production could alter the biological feedback loop,increasing the functional benefits of plant foods such as wheat and lentil sprouts and promoting the bioavailability of nutrients.The effects of white(W),red(R),and blue(B)light were investigated on the growth parameters and nutritional value of wheat and lentil sprouts.In the laboratory,seeds were sown under three different LED treat-ments:white,red,and blue light,while normal incandescent light served as a control.Percentage seed germina-tion improved by 18.34%and 12.67%for wheat and 18.34%and 12.67%for lentil sprouts under LED treatments R and B,respectively.An increase in total soluble protein and sugar by 33.4%and 9.23%in wheat and by 31.5%and 5.87%in lentils was observed under the R LED treatment.Vitamin C concentrations in wheat and lentils were significantly increased by R LED compared to all other treatments.Other parameters,including potassium and sodium concentrations,were significantly increased under red and blue light compared to the control;white light,on the other hand,significantly decreased all these parameters.According to the experimental data,red and blue LED light could be beneficial in the production of functional wheat and lentil sprouts with high nutrient concentrations.

Dirac method for nonlinear and non-homogenous boundary value problems of plates

The boundary value problem plays a crucial role in the analytical investigation of continuum dynamics. In this paper, an analytical method based on the Dirac operator to solve the nonlinear and non-homogeneous boundary value problem of rectangular plates is proposed. The key concept behind this method is to transform the nonlinear or non-homogeneous part on the boundary into a lateral force within the governing function by the Dirac operator, which linearizes and homogenizes the original boundary, allowing one to employ the modal superposition method for obtaining solutions to reconstructive governing equations. Once projected into the modal space, the harmonic balance method(HBM) is utilized to solve coupled ordinary differential equations(ODEs)of truncated systems with nonlinearity. To validate the convergence and accuracy of the proposed Dirac method, the results of typical examples, involving nonlinearly restricted boundaries, moment excitation, and displacement excitation, are compared with those of the differential quadrature element method(DQEM). The results demonstrate that when dealing with nonlinear boundaries, the Dirac method exhibits more excellent accuracy and convergence compared with the DQEM. However, when facing displacement excitation, there exist some discrepancies between the proposed approach and simulations;nevertheless, the proposed method still accurately predicts resonant frequencies while being uniquely capable of handling nonuniform displacement excitations. Overall, this methodology offers a convenient way for addressing nonlinear and non-homogenous plate boundaries.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

CAUCHY TYPE INTEGRALS AND A BOUNDARY VALUE PROBLEM IN A COMPLEX CLIFFORD ANALYSIS

Clifford analysis is an important branch of modern analysis;it has a very important theoretical significance and application value,and its conclusions can be applied to the Maxwell equation,Yang-Mill field theory,quantum mechanics and value problems.In this paper,we first give the definition of a quasi-Cauchy type integral in complex Clifford analysis,and get the Plemelj formula for it.Second,we discuss the H?lder continuity for the Cauchy-type integral operators with values in a complex Clifford algebra.Finally,we prove the existence of solutions for a class of linear boundary value problems and give the integral representation for the solution.

Missing Value Imputation for Radar-Derived Time-Series Tracks of Aerial Targets Based on Improved Self-Attention-Based Network

The frequent missing values in radar-derived time-series tracks of aerial targets(RTT-AT)lead to significant challenges in subsequent data-driven tasks.However,the majority of imputation research focuses on random missing(RM)that differs significantly from common missing patterns of RTT-AT.The method for solving the RM may experience performance degradation or failure when applied to RTT-AT imputation.Conventional autoregressive deep learning methods are prone to error accumulation and long-term dependency loss.In this paper,a non-autoregressive imputation model that addresses the issue of missing value imputation for two common missing patterns in RTT-AT is proposed.Our model consists of two probabilistic sparse diagonal masking self-attention(PSDMSA)units and a weight fusion unit.It learns missing values by combining the representations outputted by the two units,aiming to minimize the difference between the missing values and their actual values.The PSDMSA units effectively capture temporal dependencies and attribute correlations between time steps,improving imputation quality.The weight fusion unit automatically updates the weights of the output representations from the two units to obtain a more accurate final representation.The experimental results indicate that,despite varying missing rates in the two missing patterns,our model consistently outperforms other methods in imputation performance and exhibits a low frequency of deviations in estimates for specific missing entries.Compared to the state-of-the-art autoregressive deep learning imputation model Bidirectional Recurrent Imputation for Time Series(BRITS),our proposed model reduces mean absolute error(MAE)by 31%~50%.Additionally,the model attains a training speed that is 4 to 8 times faster when compared to both BRITS and a standard Transformer model when trained on the same dataset.Finally,the findings from the ablation experiments demonstrate that the PSDMSA,the weight fusion unit,cascade network design,and imputation loss enhance imputation performance and confirm the efficacy of our design.

Effect of FeSi additive in dual-chamber sample cup on thermal analysis characteristic values and vermiculating rate of compacted graphite iron

Thermal analysis plays a key role in the online inspection of molten iron quality.Different solidification process of molten iron can be reflected by thermal analysis curves,and silicon is one of important elements affecting the solidification of molten iron.In this study,FeSi75 was added in one chamber of the dual-chamber sample cup,and the influences of FeSi75 additive on the characteristic values of thermal analysis curves and vermiculating rate were investigated.The results show that with the increase of FeSi75,the start temperature of austenite formation TALfirstly decreases and then increases,but the start temperature of eutectic growth TSEF,the lowest eutectic temperature TEU,temperature at maximum eutectic reaction rate TEM,and highest eutectic temperature TERkeep always an increase.The temperature at final solidification point TEShas little change.The FeSi75 additive has different influences on the vermiculating rate of molten iron with different vermiculation,and the vermiculating rate increases for lower vermiculation molten iron while decreases for higher one.According to the thermal analysis curves obtained by a dual-chamber sample cup with 0.30wt.%FeSi75 additive in one chamber,the vermiculating rate of molten iron can be evaluated by comparing the characteristic values of these curves.The time differenceΔtERcorresponding to the highest eutectic temperature TERhas a closer relationship with the vermiculating rate,and a parabolic regression curve between the time differenceΔtERand vermiculating rateηhas been obtained within the range of 65%to 95%,which is suitable for the qualified melt.

Bivariate Analysis of Pollutants Monthly Maxima in Mexico City Using Extreme Value Distributions and Copula

In the present work, we are interested in studying the joint distributions of pairs of the monthly maxima of the pollutants used by the environmental authorities in Mexico City to classify the air quality in the metropolitan area. In order to obtain the joint distributions a copula will be considered. Since we are analyzing the monthly maxima, the extreme value distributions of Weibull and Fréchet are taken into account. Using these two distributions as marginal distributions in the copula a Bayesian inference was made in order to estimate the parameters of both distributions and also the association parameters appearing in the copula model. The pollutants taken into account are ozone, nitrogen dioxide, sulphur dioxide, carbon monoxide, and particulate matter with diameters smaller than 10 and 2.5 microns obtained from the Mexico City monitoring network. The estimation was performed by taking samples of the parameters generated through a Markov chain Monte Carlo algorithm implemented using the software OpenBugs. Once the algorithm is implemented it is applied to the pairs of pollutants where one of the coordinates of the pair is ozone and the other varies on the set of the remaining pollutants. Depending on the pollutant and the region where they were collected, different results were obtained. Hence, in some cases we have that the best model is that where we have a Fréchet distribution as the marginal distribution for the measurements of both pollutants and in others the most suitable model is the one assuming a Fréchet for ozone and a Weibull for the other pollutant. Results show that, in the present case, the estimated association parameter is a good representation to the correlation parameters between the pair of pollutants analyzed. Additionally, it is a straightforward task to obtain these correlation parameters from the corresponding association parameters.

Hepatocellular carcinoma:An analysis of the expression status of stress granules and their prognostic value

BACKGROUND Hepatocellular carcinoma(HCC)is a global popular malignant tumor,which is difficult to cure,and the current treatment is limited.AIM To analyze the impacts of stress granule(SG)genes on overall survival(OS),survival time,and prognosis in HCC.METHODS The combined The Cancer Genome Atlas-Liver Hepatocellular Carcinoma(TCGA-LIHC),GSE25097,and GSE36376 datasets were utilized to obtain genetic and clinical information.Optimal hub gene numbers and corresponding coefficients were determined using the Least absolute shrinkage and selection operator model approach,and genes for constructing risk scores and corresponding correlation coefficients were calculated according to multivariate Cox regression,respectively.The prognostic model’s receiver operating characteristic(ROC)curve was produced and plotted utilizing the time ROC software package.Nomogram models were constructed to predict the outcomes at 1,3,and 5-year OS prognostications with good prediction accuracy.RESULTS We identified seven SG genes(DDX1,DKC1,BICC1,HNRNPUL1,CNOT6,DYRK3,CCDC124)having a prognostic significance and developed a risk score model.The findings of Kaplan-Meier analysis indicated that the group with a high risk exhibited significantly reduced OS in comparison with those of the low-risk group(P<0.001).The nomogram model’s findings indicate a significant enhancement in the accuracy of OS prediction for individuals with HCC in the TCGA-HCC cohort.Gene Ontology and Gene Set Enrichment Analysis suggested that these SGs might be involved in the cell cycle,RNA editing,and other biological processes.CONCLUSION Based on the impact of SG genes on HCC prognosis,in the future,it will be used as a biomarker as well as a unique therapeutic target for the identification and treatment of HCC.