Theoretical study on the effective thermal conductivity of silica aerogels based on a cross-aligned and cubic pore model

Aerogel nanoporous materials possess high porosity, high specific surface area, and extremely low density due to their unique nanoscale network structure. Moreover, their effective thermal conductivity is very low, making them a new type of lightweight and highly efficient nanoscale super-insulating material. However, prediction of their effective thermal conductivity is challenging due to their uneven pore size distribution. To investigate the internal heat transfer mechanism of aerogel nanoporous materials, this study constructed a cross-aligned and cubic pore model(CACPM) based on the actual pore arrangement of SiO_(2) aerogel. Based on the established CACPM, the effective thermal conductivity expression for the aerogel was derived by simultaneously considering gas-phase heat conduction, solid-phase heat conduction, and radiative heat transfer. The derived expression was then compared with available experimental data and the Wei structure model. The results indicate that, according to the model established in this study for the derived thermal conductivity formula of silica aerogel, for powdery silica aerogel under the conditions of T = 298 K, a_(2)= 0.85, D_(1)= 90 μm, ρ = 128 kg/m^(3), within the pressure range of 0–10^(5)Pa, the average deviation between the calculated values and experimental values is 10.51%. In the pressure range of 10^(3)–10^(4)Pa, the deviation between calculated values and experimental values is within 4%. Under these conditions, the model has certain reference value in engineering verification. This study also makes a certain contribution to the research of aerogel thermal conductivity heat transfer models and calculation formulae.

A Blind Spot in the Reframing of a Universe of Possibles: Towards a Suitable Model for Decision-Making Theory and A.I.

Bayesian inference model is an optimal processing of incomplete information that, more than other models, better captures the way in which any decision-maker learns and updates his degree of rational beliefs about possible states of nature, in order to make a better judgment while taking new evidence into account. Such a scientific model proposed for the general theory of decision-making, like all others in general, whether in statistics, economics, operations research, A.I., data science or applied mathematics, regardless of whether they are time-dependent, have in common a theoretical basis that is axiomatized by relying on related concepts of a universe of possibles, especially the so-called universe (or the world), the state of nature (or the state of the world), when formulated explicitly. The issue of where to stand as an observer or a decision-maker to reframe such a universe of possibles together with a partition structure of knowledge (i.e. semantic formalisms), including a copy of itself as it was initially while generalizing it, is not addressed. Memory being the substratum, whether human or artificial, wherein everything stands, to date, even the theoretical possibility of such an operation of self-inclusion is prohibited by pure mathematics. We make this blind spot come to light through a counter-example (namely Archimedes’ Eureka experiment) and explore novel theoretical foundations, fitting better with a quantum form than with fuzzy modeling, to deal with more than a reference universe of possibles. This could open up a new path of investigation for the general theory of decision-making, as well as for Artificial Intelligence, often considered as the science of the imitation of human abilities, while being also the science of knowledge representation and the science of concept formation and reasoning.

Contribution of the MERISE-Type Conceptual Data Model to the Construction of Monitoring and Evaluation Indicators of the Effectiveness of Training in Relation to the Needs of the Labor Market in the Republic of Congo

This study proposes the use of the MERISE conceptual data model to create indicators for monitoring and evaluating the effectiveness of vocational training in the Republic of Congo. The importance of MERISE for structuring and analyzing data is underlined, as it enables the measurement of the adequacy between training and the needs of the labor market. The innovation of the study lies in the adaptation of the MERISE model to the local context, the development of innovative indicators, and the integration of a participatory approach including all relevant stakeholders. Contextual adaptation and local innovation: The study suggests adapting MERISE to the specific context of the Republic of Congo, considering the local particularities of the labor market. Development of innovative indicators and new measurement tools: It proposes creating indicators to assess skills matching and employer satisfaction, which are crucial for evaluating the effectiveness of vocational training. Participatory approach and inclusion of stakeholders: The study emphasizes actively involving training centers, employers, and recruitment agencies in the evaluation process. This participatory approach ensures that the perspectives of all stakeholders are considered, leading to more relevant and practical outcomes. Using the MERISE model allows for: • Rigorous data structuring, organization, and standardization: Clearly defining entities and relationships facilitates data organization and standardization, crucial for effective data analysis. • Facilitation of monitoring, analysis, and relevant indicators: Developing both quantitative and qualitative indicators helps measure the effectiveness of training in relation to the labor market, allowing for a comprehensive evaluation. • Improved communication and common language: By providing a common language for different stakeholders, MERISE enhances communication and collaboration, ensuring that all parties have a shared understanding. The study’s approach and contribution to existing research lie in: • Structured theoretical and practical framework and holistic approach: The study offers a structured framework for data collection and analysis, covering both quantitative and qualitative aspects, thus providing a comprehensive view of the training system. • Reproducible methodology and international comparison: The proposed methodology can be replicated in other contexts, facilitating international comparison and the adoption of best practices. • Extension of knowledge and new perspective: By integrating a participatory approach and developing indicators adapted to local needs, the study extends existing research and offers new perspectives on vocational training evaluation.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Phosphorus limitation on CO_(2)fertilization effect in tropical forests informed by a coupled biogeochemical model

Tropical forests store more than half of the world's terrestrial carbon(C)pool and account for one-third of global net primary productivity(NPP).Many terrestrial biosphere models(TBMs)estimate increased productivity in tropical forests throughout the 21st century due to CO_(2)fertilization.However,phosphorus(P)liaitations on vegetation photosynthesis and productivity could significantly reduce the CO_(2)fertilization effect.Here,we used a carbon-nitrogen-phosphorus coupled model(Dynamic Land Ecosystem Model;DLEM-CNP)with heterogeneous maximum carboxylation rates to examine how P limitation has affected C fluxes in tropical forests during1860-2018.Our model results showed that the inclusion of the P processes enhanced model performance in simulating ecosystem productivity.We further compared the simulations from DLEM-CNP,DLEM-CN,and DLEMC and the results showed that the inclusion of P processes reduced the CO_(2)fertilization effect on gross primary production(GPP)by 25%and 45%,and net ecosystem production(NEP)by 28%and 41%,respectively,relative to CN-only and C-on ly models.From the 1860s to the 2010s,the DLEM-CNP estimated that in tropical forests GPP increased by 17%,plant respiration(Ra)increased by 18%,ecosystem respiration(Rh)increased by 13%,NEP increased by 121%per unit area,respectively.Additionally,factorial experiments with DLEM-CNP showed that the enhanced NPP benefiting from the CO_(2) fertilization effect had been offset by 135%due to deforestation from the 1860s to the 2010s.Our study highlights the importance of P limitation on the C cycle and the weakened CO_(2)fertilization effect resulting from P limitation in tropical forests.

An improved strain-softening constitutive model of granite considering the effect of crack deformation

This paper presents an improved strain-softening constitutive model considering the effect of crack deformation based on the triaxial cyclic loading and unloading test results.The improved model assumes that total strain is a combination of plastic,elastic,and crack strains.The constitutive relationship between the crack strain and the stress was further derived.The evolutions of mechanical parameters,i.e.strength parameters,dilation angle,unloading elastic modulus,and deformation parameters of crack,with the plastic strain and confining pressure were studied.With the increase in plastic strain,the cohesion,friction angle,dilation angle,and crack Poisson's ratio initially increase and subsequently decrease,and the unloading elastic modulus and the crack elastic modulus nonlinearly decrease.The increasing confining pressure enhances the strength and unloading elastic modulus,and decreases the dilation angle and Poisson's ratio of the crack.The theoretical triaxial compressive stress-strain curves were compared with the experimental results,and they present a good agreement with each other.The improved constitutive model can well reflect the nonlinear mechanical behavior of granite.

Drivers for Inter-city Innovation Networks Across Chinese Cities:Modelling Physical Versus Intangible Effects

Cross-region innovation is widely recognized as an important source of the long-term regional innovation capacity.In the recent past,a growing number of studies has investigated the network structure and mechanisms of cross-region innovation collaboration in various contexts.However,existing research mainly focuses on physical effects,such as geographical distance and high-speed railway connections.These studies ignore the intangible drivers in a changing environment,the more digitalized economy and the increasingly solidified innovation network structure.Thus,the focus of this study is on estimating determinants of innovation networks,especially on intangible drivers,which have been largely neglected so far.Using city-level data of Chinese patents(excluding Hong Kong,Macao,and Taiwan Province of China),we trace innovation networks across Chinese cities over a long period of time.By integrating a measure on Information and Communications Technology(ICT)development gap and network structural effects into the general proximity framework,this paper explores the changing mechanisms of Chinese innovation networks from a new perspective.The results show that the structure of cross-region innovation networks has changed in China.As mechanisms behind this development,the results confirm the increasingly important role of intangible drivers in Chinese inter-city innovation collaboration when controlling for effects of physical proximity,such as geographical distance.Since digitalization and coordinated development are the mainstream trends in China and other developing countries,these countries'inter-city innovation collaboration patterns will witness dramatic changes under the influence of intangible drivers.

Study on the effect of four kinds of raw materials in hypertonic dehydration cell model

It aims to investigate the protective effects of sodium hyaluronate,panthenol,Portulaca oleracea L.and Calendula officinalis L.on hyperosmotic dehydration-induced injury of human immortalized keratinocytes(HaCaT).The safety mass concentrations of four raw materials were screened by detecting cell viability,and the secretion of hyaluronic acid(HA)was determined using the ELISA method.The expression of HaCaT barrier function related genes(OVOL1,EREG,TGM1,TGM2,IVL,IRF6,THBS1,CASP14)was detected at the mRNA level to explore the regulatory effect of four raw materials on these genes.The results demonstrate that pretreatment with the four kinds of raw materials could increase the cell viability after hyperosmotic dehydration,promote the secretion of HA,and improve the expression of barrier function related genes after hyperosmotic dehydration,among which panthenol and Calendula officinalis L.are better.The results show that the four raw materials have a certain protective effect on the hyperosmotic dehydration cell model,which provides data support for its application in cosmetics.

Adaptive Random Effects/Coefficients Modeling

Adaptive fractional polynomial modeling of general correlated outcomes is formulated to address nonlinearity in means, variances/dispersions, and correlations. Means and variances/dispersions are modeled using generalized linear models in fixed effects/coefficients. Correlations are modeled using random effects/coefficients. Nonlinearity is addressed using power transforms of primary (untransformed) predictors. Parameter estimation is based on extended linear mixed modeling generalizing both generalized estimating equations and linear mixed modeling. Models are evaluated using likelihood cross-validation (LCV) scores and are generated adaptively using a heuristic search controlled by LCV scores. Cases covered include linear, Poisson, logistic, exponential, and discrete regression of correlated continuous, count/rate, dichotomous, positive continuous, and discrete numeric outcomes treated as normally, Poisson, Bernoulli, exponentially, and discrete numerically distributed, respectively. Example analyses are also generated for these five cases to compare adaptive random effects/coefficients modeling of correlated outcomes to previously developed adaptive modeling based on directly specified covariance structures. Adaptive random effects/coefficients modeling substantially outperforms direct covariance modeling in the linear, exponential, and discrete regression example analyses. It generates equivalent results in the logistic regression example analyses and it is substantially outperformed in the Poisson regression case. Random effects/coefficients modeling of correlated outcomes can provide substantial improvements in model selection compared to directly specified covariance modeling. However, directly specified covariance modeling can generate competitive or substantially better results in some cases while usually requiring less computation time.

Sterilization Effect of Cooking Process for Guilin Rice Noodles Based on Heat Conduction Model

Guilin rice noodles, a unique cuisine from Guilin, Guangxi, is renowned both domestically and internationally as one of the top ten “Guilin Classics”. Utilizing a heat conduction model, this study explores the effectiveness of the cooking process in sterilizing Guilin rice noodles before consumption. The model assumes that a large pot is filled with boiling water which is maintained at a constant high temperature heat resource through continuous gentle heating. And the room temperature is set as the initial temperature for the preheating process and the final temperature for the cooling process. The objective is to assess whether the cooking process achieves satisfactory sterilization results. The temperature distribution function of rice noodle with time is analytically obtained using the separation of variables method in the three-dimensional cylindrical coordinate system. Meanwhile, the thermal diffusion coefficient of Guilin rice noodles is obtained in terms of Riedel’ theory. By analyzing the elimination characteristics of Pseudomonas cocovenenans subsp. farinofermentans, this study obtains the optimal time required for effective sterilization at the core of Guilin rice noodles. The results show that the potential Pseudomonas cocovenenans subsp. farinofermentans will be completely eliminated through continuously preheating more than 31 seconds during the cooking process before consumption. This study provides a valuable reference of food safety standards in the cooking process of Guilin rice noodles, particularly in ensuring the complete inactivation of potentially harmful strains such as Pseudomonas cocovenenans subsp. farinofermentans.

Analysis of the Effect of the Comprehensive Nursing Model on Patients with Moyamoya Disease Undergoing Intracranial and Extracranial Revascularization Surgery

Objective:To explore the effect of a comprehensive nursing model on patients with Moyamoya disease who underwent intracranial and extracranial revascularization surgery.Methods:110 cases were divided into control and observation groups with 55 cases each.The control group received routine perioperative care,and the observation group received perioperative care along with comprehensive nursing care.The two groups’disease cognition levels,anxiety,symptoms,daily living ability scores,and postoperative complication rates were compared.Results:The anxiety score and total postoperative complications of the observation group upon discharge were lower than that of the control group,and the disease cognition level and daily living ability upon discharge were higher than that of the control group(P<0.05).Conclusion:Applying the comprehensive nursing model in conjunction with perioperative care for patients undergoing surgery can effectively improve their anxiety,strengthen activities of daily living,and reduce the risk of postoperative complications.

A Prediction Model of Effective Thermal Conductivity for Metal Powder Bed in Additive Manufacturing

In current research,many researchers propose analytical expressions for calculating the packing structure of spherical particles such as DN Model,Compact Model and NLS criterion et al.However,there is still a question that has not been well explained yet.That is:What is the core factors affecting the thermal conductivity of particles?In this paper,based on the coupled discrete element-finite difference(DE-FD)method and spherical aluminum powder,the relationship between the parameters and the thermal conductivity of the powder(ETC_(p))is studied.It is found that the key factor that can described the change trend of ETC_(p) more accurately is not the materials of the powder but the average contact area between particles(a_(ave))which also have a close nonlinear relationship with the average particle size d_(50).Based on this results,the expression for calculating the ETC_(p) of the sphere metal powder is successfully reduced to only one main parameter d_(50)and an efficient calculation model is proposed which can applicate both in room and high temperature and the corresponding error is less than 20.9%in room temperature.Therefore,in this study,based on the core factors analyzation,a fast calculation model of ETC_(p) is proposed,which has a certain guiding significance in the field of thermal field simulation.

Consensus model of social network group decision-making based on trust relationship among experts and expert reliability

Due to people’s increasing dependence on social networks,it is essential to develop a consensus model considering not only their own factors but also the interaction between people.Both external trust relationship among experts and the internal reliability of experts are important factors in decision-making.This paper focuses on improving the scientificity and effectiveness of decision-making and presents a consensus model combining trust relationship among experts and expert reliability in social network group decision-making(SN-GDM).A concept named matching degree is proposed to measure expert reliability.Meanwhile,linguistic information is applied to manage the imprecise and vague information.Matching degree is expressed by a 2-tuple linguistic model,and experts’preferences are measured by a probabilistic linguistic term set(PLTS).Subsequently,a hybrid weight is explored to weigh experts’importance in a group.Then a consensus measure is introduced and a feedback mechanism is developed to produce some personalized recommendations with higher group consensus.Finally,a comparative example is provided to prove the scientificity and effectiveness of the proposed consensus model.

Research on the Evaluation of VBSE Practical Training Teaching Effectiveness Based on CIPP Model at X University

Based on the CIPP(context,input,process,and product)model,this paper constructs an index system suitable for evaluating the teaching effect of VBSE(virtual business social environment)practical training from four aspects:context evaluation,input evaluation,process evaluation,and product evaluation.Taking the students participating in VBSE practical training in X university as the survey population,381 valid sample data were obtained through an online questionnaire survey,and the index weights were determined by factor analysis method.The score value of the VBSE practical training teaching effect was calculated based on the evaluation mean value of three indexes.The results showed that the context evaluation score was 1.56,the input evaluation score was 1.54,the process evaluation score was 1.51,and the product evaluation score was 1.48.Subsequently,this paper put forward some countermeasures from the aspects of optimizing course arrangement,improving hardware facilities,and enhancing team cooperation to provide a guideline for improving the effect of VBSE practical training.

AIRCRAFT CONCEPT EVALUATION AND EFFECTIVENESS-BASED DECISION-MAKING

Based on effectiveness analysis , a novel method is presented for combat aircraft top-hierarchy concept evaluation and decision-making. Applying multi-criterion decision-making ( MCDM ) and analytic hierarchy process , the new method can help to overcome the limitations of existing evaluation systems and decision-make methods.The proposed method includes the following process :( 1 ) Establish a multi-criterion and multi-hierarchy evaluation attribute system by introducing combat effectiveness ;( 2 ) Assign weight to the attributes and normalize them ;( 3 ) Evaluate and decision-make top-hierarchy aircraft concept based on effectiveness to reach a satisfactory design by comprehensively applying four multi-criterion decision-making methodologies , i.e.grey correlation projection method , weighted summation method , weighted quadrature method and ideal solution decision-making method , while considering the attribute hierarchy system and the logical relations among the attributes.Finally , an example is given to indicate the validity and feasibility of the proposed method.

Bi-level model for shared parking decision-making based on parking lot assignment simulation

In order to carry out comprehensive decision-making of multi-class shared parking measures within a region, a bilevel model assisting decision-making is proposed. The upper level selects parkers＇ average satisfaction and the violation rate during peak hours as indices in object function, and sets probability distribution models describing dynamic parking demand of each site, the feasibility of shared parking scenarios and occupancy requirements during peak hours of each parking lot as restrictions. The simulation model in the lower level sets up rules to assign each parker in the random parking demand series to the proper parking lot. An iterative method is proposed to confirm the state of each parking lot at the start of formal simulations. Besides, two patterns linking initialization and formal simulation are presented to acquire multiple solutions. The results of the numerical examples indicate the effectiveness of the model and solution methods.

DECISION-MAKING OF SLICING SCHEME IN FUSED DEPOSITION MODELING PROCESS BASED ON ANALYTICAL HIERARCHICAL PROCESS

Based on analyzing the influences of a slicing scheme on stair-stepping effect, supporting structure, efficiency and deformation, etc. , analytical hierarchical process （AHP） combining with fuzzy synthetic evaluation is introduced to make decision in slicing schemes for a processing part. The application in determining the slicing scheme for a computer mouse during prototyping shows that the method increases the rationality during decision- making and improves quality and efficiency for the prototyping part.

Cost-effectiveness in Clostridium difficile treatment decision-making

AIM: To develop a framework for the clinical and health economic assessment for management of Clostridium difficile infection(CDI).METHODS: CDI has vast economic consequences emphasizing the need for innovative and cost effective solutions, which were aim of this study. A guidance model was developed for coverage decisions and guideline development in CDI. The model included pharmacotherapy with oral metronidazole or oral vancomycin, which is the mainstay for pharmacological treatment of CDI and is recommended by most treatment guidelines.RESULTS: A design for a patient-based cost-effectiveness model was developed, which can be used to estimate the cost-effectiveness of current and future treatment strategies in CDI. Patient-based outcomes were extrapolated to the population by including factors like, e.g., person-to-person transmission, isolation precautions and closing and cleaning wards of hospitals. CONCLUSION: The proposed framework for a population-based CDI model may be used for clinical and health economic assessments of CDI guidelines and coverage decisions for emerging treatments for CDI.

Experimental and theoretical study on the dynamic effective stress of loaded gassy coal during gas release

In the process of mining coalbed methane(CBM),an unsteady state often arises due to the rapid extraction,release and pressure relief of CBM.In this case,the effective stress of coal changes dynamically,affecting the stability of the gassy coal seam.In this paper,gas release tests of gassy coal under conventional triaxial compression were performed,and the dynamic effective stress(DES)during gas release was obtained indirectly based on a constitutive equation and deformation of coal.The results show that the maximum increases in DES caused by the release of free gas and adsorbed gas under the stress of 1.1 MPa were 0.811 and 5.418 MPa,respectively,which seriously affected the stress state of the coal.During the gas release,the free gas pressure and the adsorbed gas volume were the parameters that directly affected the DES and showed a positive linear relationship with the DES with an intercept of zero.The DES of the coal sample increased exponentially with time,which was determined by the contents of free and adsorbed gas.Based on the experimental results and theoretical analysis,an effective stress model was obtained for loaded gassy coal during gas release.The results of verification indicated accuracy greater than 99%.

Improving maize growth and development simulation by integrating temperature compensatory effect under plastic film mulching into the AquaCrop model

Temperature compensatory effect, which quantifies the increase in cumulative air temperature from soil temperature increase caused by mulching, provides an effective method for incorporating soil temperature into crop models. In this study, compensated temperature was integrated into the AquaCrop model to investigate the capability of the compensatory effect to improve assessment of the promotion of maize growth and development by plastic film mulching(PM). A three-year experiment was conducted from2014 to 2016 with two maize varieties(spring and summer) and two mulching conditions(PM and non-mulching(NM)), and the AquaCrop model was employed to reproduce crop growth and yield responses to changes in NM, PM, and compensated PM. A marked difference in soil temperature between NM and PM was observed before 50 days after sowing(DAS) during three growing seasons. During sowing–emergence and emergence–tasseling, the increase in air temperature was proportional to the compensatory coefficient, with spring maize showing a higher compensatory temperature than summer maize. Simulation results for canopy cover(CC) were generally in good agreement with the measurements, whereas predictions of aboveground biomass and grain yield under PM indicated large underestimates from 60 DAS to the end of maturity. Simulations of spring maize biomass and yield showed general increase based on temperature compensation, accompanied by improvement in modeling accuracy, with RMSEs decreasing from 2.5 to 1.6 t ha^(-1)and from 4.1 t to 3.4 t ha^(-1). Improvement in biomass and yield simulation was less pronounced for summer than for spring maize, implying that crops grown during low-temperature periods would benefit more from the compensatory effect. This study demonstrated the effectiveness of the temperature compensatory effect to improve the performance of the AquaCrop model in simulating maize growth under PM practices.