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。

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.

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.

Diagnostic value of serum vascular endothelial growth factor and interleukin-17 in primary hepatocellular carcinoma

BACKGROUND Despite significant advancements in the medical treatment of primary hepato-cellular carcinoma(PHC)in recent years,enhancing therapeutic effects and im-proving prognosis remain substantial challenges worldwide.AIM To investigate the expression levels of serum vascular endothelial growth factor(VEGF)and interleukin(IL)-17 in patients with PHC and evaluate their diagnostic value while exploring their relationship with patients’clinical characteristics.METHODS The study included 50 patients with confirmed PHC who visited Wuhan Han-yang Hospital from January 2021 to January 2022,and 50 healthy individuals from the same period served as the control group.Serum VEGF and IL-17 levels in both groups were measured by Enzyme-Linked Immunosorbent Assay,and their diagnostic value was assessed using receiver operating characteristic(ROC)curves.Pearson correlation analysis was performed to examine the relationship between serum VEGF and IL-17 levels.Pathological data of the PHC patients were analyzed to determine the relationship between serum VEGF and IL-17 levels and pathological characteristics.RESULTS Serum VEGF and IL-17 levels were significantly higher in the study group com-pared to the control group(P<0.05).No significant association was observed between serum VEGF and IL-17 levels and gender,age,combined cirrhosis,tumor diameter,or degree of differentiation(P>0.05).However,there was a significant relationship between clinical TNM stage,tumor metastasis,and serum VEGF and IL-17 levels(P<0.05).Correlation analysis revealed a positive correlation between serum VEGF and IL-17(P<0.05).ROC analysis demonstrated that both serum VEGF and IL-17 had good diagnostic efficacy for PHC.CONCLUSION Serum VEGF and IL-17 levels were significantly higher in PHC patients compared to healthy individuals.Their levels were closely related to pathological features such as tumor metastasis and clinical TNM stage,and there was a significant positive correlation between VEGF and IL-17.These biomarkers may serve as valuable reference in-dicators for the early diagnosis and treatment guidance of PHC.

Predictive value of diaphragm ultrasound for mechanical ventilation outcome in patients with acute exacerbation of chronic obstructive pulmonary disease

BACKGROUND Acute exacerbation of chronic obstructive pulmonary disease(AECOPD)is often combined with respiratory failure,which increases the patient's morbidity and mortality.Diaphragm ultrasound(DUS)has developed rapidly in the field of critical care in recent years.Studies with DUS monitoring diaphragm-related rapid shallow breathing index have demonstrated important results in guiding intensive care unit patients out of the ventilator.Early prediction of the indications for withdrawal of non-invasive ventilator and early evaluation of patients to avoid or reduce disease progression are very important.AIM To explore the predictive value of DUS indexes for non-invasive ventilation outcome in patients with AECOPD.METHODS Ninety-four patients with AECOPD who received mechanical ventilation in our hospital from January 2022 to December 2023 were retrospectively analyzed,and they were divided into a successful ventilation group(68 cases)and a failed ventilation group(26 cases)according to the outcome of ventilation.The clinical data of patients with successful and failed noninvasive ventilation were compared,and the independent predictors of noninvasive ventilation outcomes in AECOPD patients were identified by multivariate logistic regression analysis.RESULTS There were no significant differences in gender,age,body mass index,complications,systolic pressure,heart rate,mean arterial pressure,respiratory rate,oxygen saturation,partial pressure of oxygen,oxygenation index,or time of inspiration between patients with successful and failed mechanical ventilation(P>0.05).The patients with successful noninvasive ventilation had shorter hospital stays and lower partial pressure of carbon dioxide(PaCO_(2))than those with failed treatment,while potential of hydrogen(pH),diaphragm thickening fraction(DTF),diaphragm activity,and diaphragm movement time were significantly higher than those with failed treatment(P<0.05).pH[odds ratio(OR)=0.005,P<0.05],PaCO_(2)(OR=0.430,P<0.05),and DTF(OR=0.570,P<0.05)were identified to be independent factors influencing the outcome of mechanical ventilation in AECOPD patients.CONCLUSION The DUS index DTF can better predict the outcome of non-invasive ventilation in AECOPD patients.

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.

Yield and Nutritive Values of Semi- and Non-Fall Dormant Alfalfa Cultivars under Late-Cutting Schedule in California’s Central Valley

California is one of the major alfalfa (Medicago sativa L) forage-producing states in the U.S, but its production area has decreased significantly in the last couple of decades. Selection of cultivars with high yield and nutritive value under late-cutting schedule strategy may help identify cultivars that growers can use to maximize yield while maintaining area for sustainable alfalfa production, but there is little information on this strategy. A field study was conducted to determine cumulative dry matter (DM) and nutritive values of 20 semi- and non-fall dormant (FD) ratings (FD 7 and FD 8 - 10, respectively) cultivars under 35-day cut in California’s Central Valley in 2020-2022. Seasonal cumulative DM yields ranged from 6.8 in 2020 to 37.0 Mg·ha−1 in 2021. Four FD 8 - 9 cultivars were the highest yielding with 3-yrs avg. DM greater than the lowest yielding lines by 46%. FD 7 cultivar “715RR” produced the highest crude protein (CP: 240 g·Kg−1) while FD 8 cultivar “HVX840RR” resulted in the highest neutral detergent fiber digestibility (NDFD: 484 g·Kg−1, 7% greater than the top yielding cultivars) but with DM yield intermediate. Yields and NDFD correlated positively but weakly indicating some semi- and non-FD cultivars performing similarly. These results suggest that selecting high yielding cultivars under 35-day cutting schedule strategy can be used as a tool to help growers to maximize yield while achieving good quality forages for sustainable alfalfa production in California’s Central Valley.

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.

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.

Analysis of System Value Evolution Trends of Energy Storage in Decarbonization Process

With the continuous expansion of the scale of renewable energy installation,the demand for energy storage has increased significantly.However,there are significant differences in the value of energy storage in different scenarios,and the phenomenon of diminishing marginal benefits of energy storage is becoming more apparent.Therefore,themulti-dimensional value evolution trend of energy storage has become a key issue.This study selects indicators from three dimensions of energy storage:low-carbon emission reduction,smoothing wind and solar power fluctuations,and saving generation costs,quantifying the economic,environmental,and technical values of energy storage.This forms a quantitative evaluation system for energy storage value.By comparing the calculated system values under different energy storage capacities,the marginal value evolution trend of energy storage is obtained.Meanwhile,considering factors such as the utilization rate of renewable energy,the change in energy storage value under different scenarios is analyzed.The results show that the value of long-duration energy storage is significantly affected by the energy storage capacity.Specifically,when the charge-discharge efficiency of longduration energy storage reaches 0.6 or above,the system value increases significantly.Additionally,appropriately reducing the cost of energy storage capacity also helps to improve its system value.

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.

Dynamical distribution of continuous service time model involving non-Maxwellian collision kernel and value functions

The distribution of continuous service time in call centers is investigated.A non-Maxwellian collision kernel combining two different value functions in the interaction rule are used to describe the evolution of continuous service time,respectively.Using the statistical mechanical and asymptotic limit methods,Fokker–Planck equations are derived from the corresponding Boltzmann-type equations with non-Maxwellian collision kernels.The steady-state solutions of the Fokker–Planck equation are obtained in exact form.Numerical experiments are provided to support our results under different parameters.

The Clinical Value of Accelerated Recovery Care in Pulmonary Rehabilitation of Perioperative Lung Cancer Patients

Aim: To study the clinical value of accelerated recovery care in pulmonary rehabilitation of perioperative lung cancer patients. Methods: 98 lung cancer patients undergoing surgery were admitted to our hospital from March 2020 to November 2021 and randomly divided into an observation group and an ACBT (Active Cycle of Breathing Technique) training group. Accelerated recovery care and routine care were respectively used in the perioperative period to compare the nursing effects of the two groups. Results: All perioperative indicators in the observation group were shorter than those in the ACBT training group. The pain scores at different time periods after surgery were lower in the observation group than in the ACBT training group (P 0.05). The improvement in the above indicators was higher in the observation group than in the ACBT training group after nursing care (P Conclusion: Accelerated recovery care during the perioperative period for lung cancer surgery patients showed significant effectiveness.

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.

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.

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.

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.

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.

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.