Based on the insight gained from single jet analysis performed earlier [1], CFD analysis on turbulent jets impinging on one another at an angle was performed. Multiple impingement angles were considered for this study to gain better understanding of the parameters affecting resultant jet growth and velocity distribution. From the study of single jet, it was concluded that the SST k-ω turbulence model was the ideal turbulence model capable of accurately predicting the flow physics of the jets exiting a fully developed pipe at low Reynolds number. Hence, for the study of impinging jets, SST k-ω turbulence model was used to study the velocity and jet growth characteristics. It became evident that the mesh alignment with the velocity vector at exit of the pipe domain plays a crucial role in the accuracy of the results. The parameter used to evaluate this condition was identified as False Diffusion and was observed to affect the TKE parameter significantly. Methods to reduce false diffusion are also discussed in this article. Based on the mesh obtained from the grid sensitivity study, jets impinging at 30, 45 and 60 degrees at Reynolds number of 7500 were numerically analyzed. It was observed that the profile of the resultant jet closely matched with the prediction of elliptical profile predicted by past researchers, [2] and [3]. Also, it was seen that higher jet growth was predicted in case of jets impinging at a higher impingement angle.

Colleges and universities play a crucial role in fostering innovation, making it essential to explore effective strategies for promoting innovation at the institutional policy level. This paper focuses on the establishment of intellectual property (IP) model cities as a starting point and conducts an empirical analysis using innovation data from 234 cities and 942 colleges and universities between 2007 and 2017. By constructing a multi-temporal double-difference model, this study reveals that the establishment of IP model cities effectively fosters innovation in colleges and universities. Further analysis demonstrates that this promotion effect is particularly significant in the western region, key cities, key colleges and universities, as well as in the fields of invention and utility model patents. These conclusions withstand a series of robustness tests, confirming their validity. In terms of the underlying mechanisms, it is found that the national IP pilot city policy has a substantial impact on university innovation by promoting research and development (R&D) investment and strengthening innovation cooperation. The insights provided by this study offer valuable policy recommendations for leveraging the innovation potential of the IP model city policy, thereby driving economic transformation, upgrading, and fostering innovation development in China.

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 this paper, a novel strategy is adopted in a degradation model to affect the implied lifetime distribution. The multiplicative degradation model is utilized as a postulate in the model. It will be established that the implied lifetime distribution forms a classical mixture model. In this mixture model, time-to-failure is lying with some probabilities between two first passage times of the degradation process to reach two specified levels. Stochastic comparisons in the model under a change in the probabilities are studied. Several examples are provided to highlight the applicability of the results in the cases when typical degradation models are candidate.

This study aimed to determine the feasibility of the development of an over-the-counter (OTC) screening model using machine learning for breast cancer screening in the Asian women population. Data were retrospectively collected from women who came to the Hospital Universiti Sains Malaysia, Malaysia. Five screening models were developed based on machine learning methods; random forest, artificial neural network (ANN), support vector machine (SVM), elastic-net logistic regression and extreme gradient boosting (XGBoost). Features used for the development of the screening models were limited to information from the patients’ registration form. The model performance was assessed across the dense and non-dense groups. SVM had the best sensitivity while elastic-net logistic regression had the best specificity. In terms of precision, both random forest elastic-net logistic regression had the best performance, while, in terms of PR-AUC, XGBoost had the best performance. Additionally, SVM had a more balanced performance in terms of sensitivity and specificity across the mammographic density groups. The three most important features were age at examination, weight and number of children. In conclusion, OTC models developed from machine learning methods can improve the prognostic process of breast cancer in Asian women.

A micromechanical model is implemented to indicate the progressive fatigue problem of a laminated composite with a central circular hole under fatigue loading based on a finite element model. The mechanical properties of the composite lamina are represented based on the characteristics of the fiber and the matrix through a micromechanics model. An appropriate algorithm is then adopted to simulate fatigue damage development in the composite lamina. According to this algorithm, the stress field of the composite subjected to fatigue load is initially obtained using the finite element method. Finally, the predicted results of the stresses in the constituents i.e. fiber and matrix are determined according to the micromechanical bridging model. Finally applying proper damage driving relations leads to damage degree in each element. The proposed model is proven to be successful in the observation of the fatigue behavior with stiffness degradation in each element of the composite in each cycle. Results are reported and validated using those micromechanical models and experimental data available in the literature.

Model selection and model averaging have been the popular approaches in handling modelling uncertainties. Fan and Li(2006) laid out a unified frame work for variable selection via penalized likelihood. The tuning parameter selection is vital in the optimization problem for the penalized estimators in achieving consistent selection and optimal estimation. Since the OLSpost-LASSO estimator by Belloni and Chernozhukov (2013), few studies have focused on the finite sample performances of the class of OLS post-penalty estimators with the tuning parameter choice determined by different tuning parameter selection approaches. We aim to supplement the existing model selection literature by studying such a class of OLS post-selection estimators. Inspired by the Shrinkage Averaging Estimator (SAE) by Schomaker(2012) and the Mallows Model Averaging (MMA) criterion by Hansen (2007), we further propose a Shrinkage Mallows Model Averaging (SMMA) estimator for averaging high dimensional sparse models. Based on the Monte Carlo design by Wang et al. (2009) which features an expanding sparse parameter space as the sample size increases, our Monte Carlo design further considers the effect of the effective sample size and the degree of model sparsity on the finite sample performances of model selection and model averaging estimators. From our data examples, we find that the OLS post-SCAD(BIC) estimator in finite sample outperforms most of the current penalized least squares estimators as long as the number of parameters does not exceed the sample size. In addition, the SMMA performs better given sparser models. This supports the use of the SMMA estimator when averaging high dimensional sparse models.

A unified critical state model has been developed for both clean sand and silty sand using the modified Cam-clay model (MCC). The main feature of the proposed model is a new critical state line equation in the e-ln(p) plane that is capable of handling both straight and curved test results. With this feature, the error in calculating plastic volumetric strain is eliminated in theory. Another crucial feature of the model is the transformed stress tensor based on the SMP (spatially mobilized plane) criterion, which takes into account the proper shear yield and failure of soil under three-dimensional stresses. Additionally, the proposed model applies the intergranular void ratio with the fines influence factor for silty sand. Only eight soil parameters are required for clean sand, and a total number of twelve soil parameters are needed for silty sand.

Dyadic data contains co-occurrences of objects, which is often modeled by finite mixture model which in turn is learned by expectation maximization (EM) algorithm. Objects in traditional dyadic data are identified by names, causing the drawback which is that it is impossible to extract implicit valuable knowledge under objects. In this research, I propose the so-called attributed dyadic data (ADD) in which each object has an informative attribute and each co-occurrence of two objects is associated with a value. ADD is flexible and covers most of structures / forms of dyadic data. Conditional mixture model (CMM), which is a variant of finite mixture model, is applied into learning ADD. Moreover, a significant feature of CMM is that any co-occurrence of two objects is based on some conditional variable. As a result, CMM can predict or estimate co-occurrent values based on regression model, which extends applications of ADD and CMM.

Green innovation has become an important combination of high-quality economic growth and sustainable development of ecological environment. In this paper, the super-efficiency network SBM model is used to measure the two-stage green innovation efficiency of industrial science and technology R&D and achievement transformation in 30 provinces and cities from 2009 to 2019, and exploratory Data Analysis (ESDA) and spatial econometric model are used to investigate the spatial-temporal evolution characteristics and influencing factors of green innovation efficiency. The results show that: firstly, the overall efficiency of industrial green innovation is low, and the efficiency of scientific research and development and achievement transformation has experienced three stages of "upward-declining-revitalized period". The low efficiency of achievement transformation is an important factor hiding the improvement of the efficiency of industrial green innovation. Secondly, The industrial green innovation efficiency gradually increases from northwest to southeast, forming a centralized "line" and "block" distribution. The high efficiency area is still concentrated in the eastern coastal region, and the balanced development trend is obvious in the central and western regions. Finally, openness has a positive impact on the two-stage green innovation efficiency; Industrial structure and government investment in science and technology have a positive impact on the efficiency of science and technology research and development, but have no significant effect on the efficiency of achievement transformation. Enterprise size has a positive effect on achievement transformation efficiency, but has no significant effect on R&D efficiency. Environmental regulation has a positive impact on R&D efficiency and a negative impact on achievement transformation efficiency.

Background: Nursing services provided by nurses are based on knowledge and competence in the field of nursing science developed in accordance with Client needs, scientific developments, and the demands of globalization. These health services include nursing services that are carried out responsibly, accountably, with quality, and safely by nurses who have obtained registration and permission to practice. Nursepreneur is innovation and courage to take risks and is ready to work hard to achieve goals with optimism. Which has important components namely: 1) quality, 2) role, 3) option and success. The post-pandemic new world, especially in the health care system, continues to grapple with the demands of costs to provide patient care and safety. By using the application of the nursing theory model. Purpose :. Assessing scientific studies on the application of evidence-based intervention in wound care using nursing theory, identifying the important role of telenursing in improving nursing services in Nerspreneurship, Method: literature study Conclusion: Telenursing in nursing focuses on providing nursing care through information technology, such as telephone, computers, and the Internet that can be used to improve treatment outcomes among older people, especially those with low literacy skills and low financial status (Esmaeilpour-BandBoni et al., 2020). The statement from the results of the study refutes that Telenursing is intended for patients who have higher education and middle and upper economic states.

This paper utilizes a life cycle assessment to evaluate the energy resources and environmental impacts of three heating systems. In the first step, life cycle assessments (LCAs) are prepared for the renewable, conventional, and combined systems. The system boundary of the LCA method is "cradle to gate," which means it includes the entire life cycle of a product or process. This includes energy supply, transport, energy generation, and operational energy use. The system scenarios were compared based on ecological and energy loads in the second step. Primary energy, resources, emissions, and environmental potentials are calculated for the system scenarios using the life cycle assessment methods CML 2016 and ReCiPe 2016. Finally, models for environmental reliability and complex decision-support tools have been developed. This work addresses two main questions: how to characterize the environmental and energy impacts of the systems under study, and what is the optimal scenario for the system? The research results show that the gas boiler system has a greater environmental impact. The most significant differences between the systems relate to the degrada-tion of abiotic fossil fuels when using a gas boiler and the process of acidification when using an electric heat pump. The results can be used to develop sustainable heating systems with reduced environmental impacts and enhanced energy efficiency.

This study is dedicated to understanding and simulating the mechanical properties of concrete, with a specific focus on the mesoscopic scale. Investigating concrete at this level involves examining its composition as a heterogeneous amalgamation comprising mortar, aggregates, and the interfacial transition zone (ITZ). Such an approach provides a detailed examination of the material's behavior and attributes. Numerical models are utilized, leveraging the finite element method (FEM), to examine the behavior of concrete. The study employs MATLAB programming to develop three-dimensional models, subsequently subjected to FEM analysis. Various mesoscopic Representative Volume Elements (RVEs) are formulated, encompassing varied shapes and dimensions. The MATLAB framework generates input files for numerical FEM simulations, replicating compression strength tests. As complexity increases with the inclusion of the ITZ, prismatic RVEs are developed. The proposed mesoscopic model establishes a foundational framework for a numerical simulation methodology tailored to laboratory compression tests. It provides detailed insights into concrete behavior, elucidating deformation, and fracture mechanisms. Although not a complete substitute for experimental methods, these models offer a cost-effective and expeditious alternative, pinpointing vulnerable areas and exploring the implications of additional materials on concrete behavior. Experimental data and virtual tests pave the way for mitigating carbon footprint and improving concrete sustainability.

The state estimation for lithium-ion battery cells has been the topic of many publications concerning the different states of a battery cell. They often focus on a battery cell’s state of charge (SOC) or state of health (SOH). Therefore this paper introduces a, on one hand, a new lithium-ion battery data set with dynamic validation data over degradation and on the other hand a model-based SOC and SOH estimation based on this dataset as a reference. An unscented Kalman filter-based approach was used for SOC estimation and extended with a holistic ageing model to handle the SOH estimation. The paper describes the dataset, the models, the parameterisation, the implementation of the state estimations, and their validation using parts of the dataset resulting in a SOC and SOH estimation over battery life. The results show that the dataset can be used to extract parameters, design models based on it and validate with dynamically degraded battery cells.

This study aims to develop an elderly care system for improving the interpersonal relationship of the elderly with mild cognitive impairment (MCI) by employing the speaker recognition technique and association functionality of social network platforms. Firstly, the speaker recognition units based on the Gaussian Mixture Model (GMM) and Gaussian Mixture Model-Universal Background Model (GMM-UBM) are implemented to identify the visitor via individual input utterance. After the visitor is identified, the proposed system will be linked to the private database and social network platforms to extract the associated message of two parties. Experimental results indicate that the speaker recognition unit based on GMM-UBM achieves the best performance. Finally, five elderly persons are invited to measure the usability of the proposed system. A questionnaire is used to survey the five elderly persons, and the result indicates that the proposed system is highly potentially applicable in improving the interpersonal relationship of the elderly with MCI.

This work investigates the utility of the National Oceanic and Atmospheric Administration’s National Water Model (NWM) for water management operations by assessing the total inflow into a select number of reservoirs across the Central and Western U.S. Total inflow is generally an unmeasured quantity, though critically important for anticipating both floods and shortages in supply over a short-term (hourly) to sub-seasonal (monthly) time horizon. The NWM offers such information at over 5,000 reservoirs across the U.S., however, its skill at representing inflow processes is largely unknown. The goal of this work is to understand the drivers for both well performing and poor performing NWM inflows such that managers can get a sense of the capability of NWM to capture natural hydrologic processes and in some cases, the effects of upstream management. We analyzed the inflows for a subset of Bureau of Reclamation (BoR) reservoirs within the NWM over the long-term simulations (retrospectively, seven years) and for short, medium and long-range operational forecast cycles over a one-year period. We utilize ancillary reservoir characteristics (e.g. physical and operational) to explain variation in inflow performance across the selected reservoirs. In general, we find that NWM inflows in snow-driven basins outperform those in rain-driven, and that assimilated basin area, upstream management, and calibrated basin area all influence the NWM’s ability to reproduce daily reservoir inflows. The final outcome of this work proposes a framework for how the NWM reservoir inflows can be useful for reservoir management, linking reservoir purposes with the forecast cycles and retrospective simulations.

The shape of the knee is a chain-like structure of an ellipsoid. This suggests that, in the latest study, in addition to rolling, there is slippage in the motion of the knee. This paper selects a mechanical braking model for cattle based on two mechanical structures in different directions at the joints. The experimental results show that the modified dynamical system has strong chaotic properties. This can be one of the basis for judging various health states.

Knowledge-intensive business services (KIBS) are professional services that create new added value by creating, accumulating, and disseminating new knowledge. When it comes to conducting business involving new technology, KIBS play the roles of innovator, user, and producer of new technology that has led to technological innovation. This study aimed to determine the role of KIBS as a tool for innovation in a country’s economic system. Specifically, the degree and role of their impact on the Korean economy were analyzed and compared for the entire KIBS sector, T-KIBS (a new technology-based professional service), P-KIBS (a traditional professional service), and every subsector. For this purpose, the demand-inducement model, supply inducement model, and interlinkage effects method were applied using the 2019 input-output table published in 2022. The analysis showed that the indirect production inducement effect of the entire KIBS industry on other industries was 0.800 KRW, the indirect added value inducement effect was 0.330 KRW, and the supply disruption effect was 1.144 KRW. For T-KIBS, the indirect production inducement effect was 0.687 KRW, the indirect added value inducement effect was 0.272 KRW, and the supply disruption effect was 0.730 KRW. For P-KIBS, the indirect production inducement effect was 1.472 KRW, the indirect added value inducement effect was 0.646 KRW, and the supply disruption effect was 2.657 KRW. Finally, regarding the economic ripple effect of the KIBS subsector, legal and management support services and advertisements corresponding to P-KIBS showed higher figures than the T-KIBS subsectors in all sectors, including production inducement, the added value inducement effect, and the supply disruption effect. These results revealed that in the South Korean economic system, KIBS contribute to production and value addition across all industrial sectors. It is apparent that the absence of supply significantly disrupts other industries. Furthermore, production inducement effects are evenly distributed among all the KIBS subsectors in the secondary and tertiary sectors, while the value-added effects have a greater impact on the tertiary sector. In terms of the supply shortage effects, the secondary sector experiences a more significant impact. This underscores the crucial role of KIBS in sustaining and enhancing overall economic activity in South Korea. This study is significant in that it not only investigated KIBS as an industry group using the advantages provided by industry linkage analysis but also examined and compared detailed subsectors, thereby elaborately evaluating the influence relationship between KIBS and other industries. Therefore, the results presented in this study are expected to be useful for fostering the KIBS industrial sector and establishing economic innovation policies using KIBS.

Effectively avoiding gas accident is vital to the security of mineral manufacture, and the coal mine gas accident is often caused by gas concentration overrun. The prediction accuracy of gas emission quantity in coal mine is the key to solve this problem. To maintain concentration of gas in a secure range，grey theory and neural network model increasingly diffusely used in forecasting gas emission quantity in coal mine critically. Nevertheless, the limitation of the grey neural network model is that researchers merely bonded the conventional neural network and grey theory. To enhance accuracy of prediction, a modified grey GM(1,1) and RBF neural network model is proposed combined amended grey GM(1,1) model and RBF neural network model. Then the proposed model was put into simulation experiment which is built based on Matlab software. Ultimately, conclusion of the simulation experiment verified that the modified grey GM(1,1) and RBF neural network model not only boosts the precision of prediction, but also restricts relative error in a minimum range. This showed that the modified grey GM(1,1) and RBF neural network model achieves effectiveness in precision of prediction much better than grey GM(1,1) model and RBF neural network model.

The limitedness of the nonrenewable local energy resources in Israel, even in background of the later gas fields’ findings, continues to force the state to devote various efforts for the ‘green’ energy development. These efforts include installations both in the solar and in the wind energy, with a purpose to improve the diversity of energy sources. While the standard discounted cash flow (DCF) method using the net present value (NPV) criterion is extensively adopted to evaluate investments, the standard DCF method is inappropriate for the rapidly changing investment climate and for the managerial flexibility in investment decisions. In recent years, the real options analysis (ROA) technique is widely applied in many studies for valuation of renewable energy investment projects. Hence, we apply in this study the real options analysis approach for the valuation of wind energy turbines and apply it to the analysis of wind energy economic potential in Israel.

The Societal Patterns Evolution Model (SPEM) (Gakh, 2023a) has been developed to apply to socio-technical systems. It contains patterns representing stages of societal development. Mapping of these patterns to the development of Information and Communication Technologies (ICTs) and Information Systems was presented at FedCSIS 2022 conference (Gakh, 2022c). This paper discusses the application of SPEM to model the development of three pillars of sustainability: economic, social, and environmental (Purvis, et al., 2019; United Nations, 2023).

Infrared sensors are applied more and more widely in industrial production applications. Based on the theory of thermal radiation, this paper discusses the system design principle, temperature calibration method and thermal image analysis method in detail. The system has passed the measurement unit certification, showing that the field of view is 180°, the number of scanning points is 2048, the linear velocity is 10-100Hz, the spatial resolution is 2.5mrad, and the precision is ±1℃. On-line monitoring test has been done in the steelmaking plant of Bao Steel. The results show that the system has strong anti-interference ability, stability and reliability, and meets the application requirements of online monitoring.

Flood Management remains a major problem in many urban environments. Commonly, catchment models are used to generate the data needed for estimation of flood risk; event-based and continuous-based models have been used for this purpose. Use of catchment models requires calibration and validation with a calibration metric used to assess the predicted catchment response against the recorded catchment response. In this study, a continuous model based on SWMM using the Powells Creek catchment as a case study is investigated. Calibration of the model was obtained using 25 selected events from the monitored data for the catchment. Assessment of the calibration used a normalised peak flow error. Using alternative sets of parameter values to obtain estimates of the peak flow for each of the selected events and different accuracy criteria, the best datasets for each of the accuracy criteria were identified. These datasets were used with SWMM in a continuous simulation mode to predict flow sequences for extraction of Annual Maxima Series for an At-Site Flood Frequency Analysis. From analysis of these At-Site Flood Frequency Analyses, it was concluded that the normalised peak flow error needed to be less than 10% if reliable design flood quantile estimates were to be obtained.

Since the outbreak of 2019 novel coronavirus (2019-nCoV) at the hardest-hit city of Wuhan, the fast-moving spread has killed over three hundred people and infected more than ten thousands in China¹. There are more than one hundred cases outside of China, affecting a dozen of countries globally². The genome sequence of 2019-nCoV has been reported and fast diagnostic kits, effective treatment as well as preventive vaccines are rapidly being developed³. Initial fast-growing confirmed cases triggered lock-down of Wuhan as well as nearby cities in Hubei Province. Mathematical models have been proposed by scientists around the world to project the numbers of infected cases in the coming days ^4,5. However, major factors such as transportation and cultural customs have not been weighed enough. Our model is not set out for precise prediction of the number of infected cases, rather, it is meant for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors. We hope that our model and simulation would provide more insights and perspective information to public health authorities around the globe for better informed prevention and containment solution.

Over the years influenza B virus (IBV) contribute annual disease and can lead to serious respiratory disease among humans. More attention should be paid to the mammalian adaptive processes of B viruses and development of vaccines against current influenza. Because of preclinical trials of anti-influenza drugs are conducted mainly on mice, we developed adequate animal model using antigenically-relevant IBV strain for testing anti-influenza drugs and protective efficacy of flu vaccines. We serially passaged Victoria lineage (clade 1A) IBV 17 times in BALB/c mice. The adaptive amino acid substitutions were found in HA (T214I) and NA (D432N). By the electron microscopic examination, we showed spherical and elliptical shapes of IBV. Light microscopy showed that mouse-adapted B virus caused influenza pneumonia on day 6 post inoculation. We evaluated the illness pathogenicity, viral load and histopathological features of mouse-adapted IBV and estimated anti-influenza drugs and vaccine efficiency in vitro and in vivo. Assessment of investigational anti-influenza drug oseltamivir ethoxisuccinate and flu vaccine Ultrix® revealed effectivity against our mouse-adapted influenza B virus.

We quantitatively investigated the statistical behaviors of semiflexible polymer chains, which are simultaneously subjected to force stretching and rectangular tube confinement. Based on the wormlike chain model and Odijk deflection theory, we derived a new deflection length, by which new compact formulas are obtained for the confinement free energy and force-confinement-extension relation. These newly derived formulas have been justified by numerical solutions of an eigenvalue problem associated with the Fokker-Planck governing equation and extensive Brownian dynamics simulations based on the so-called Generalized Bead-Rod (GBR) model. We found that, comparing to the classical deflection theory, these new formulas are valid for a much extended range of the confinement-size /persistence-length ratio, and have no adjustable fitting parameters for sufficient long semiflexible chains in the whole deflection regime.

Stagnation is the transient state of a solar thermal system under high solar irradiation where the useful solar gain is zero. Both flat-plate collectors with selective absorber coatings and vacuum-tube collectors exhibit stagnation temperatures far above the saturation temperature of the glycol-based heat carriers within the range of typical system pressures. Therefore, stagnation is always associated with vaporization and propagation of vapor into the pipes of the solar circuit. It is therefore essential to design the system in such a way that vapor never reaches components that cannot withstand high temperatures. In this article, a thermal-hydraulic model based on the integral form of a two-phase mixture model and a drift-flux correlation is presented. The model is applicable to solar thermal flat-plate collectors with meander-shaped absorber tubes and selective absorber coatings. Experimental data from stagnation experiments on two systems, which are identical except for the optical properties of the absorber coating, allowed comparison with simulations carried out under the same boundary conditions. The absorber of one system features a conventional highly selective coating, while the absorber of the other system features a thermochromic coating, which exhibits a significantly lower stagnation temperature. Comparison of simulation results and experimental data show good conformity. This model is implemented into an open-source software tool called “THD” for the thermal-hydraulic dimensioning of solar systems. The latest version of THD, updated by the results of this article, enables planners to achieve cost-optimal design of solar thermal systems and to ensure failsafe operation by predicting the steam range under the initial and boundary conditions of worst-case scenarios.

Genome-scale metabolic model (GEM) is a powerful tool for interpreting and predicting cellular phenotypes under various environmental and genetic perturbations. However, GEM only consid-ers stoichiometric constraints, and the simulated growth and product yield values will show a monotonic linear increase with increasing substrate uptake rate, which deviates from the experi-mentally measured values. Recently, the integration of enzymatic constraints into stoichiometry-based GEMs was proven to be effective in making novel discoveries and predicting new engineer-ing targets. Here we present the first genome-scale enzyme-constrained model (eciCW773) for Corynebacterium glutamicum reconstructed by integrating enzyme kinetic data from various sources using ECMpy workflow based on the high-quality GEM of C. glutamicum (obtained by modifying the iCW773 model). The enzyme-constrained model improved the prediction of pheno-types and simulated overflow metabolism, while also recapitulating the trade-off between biomass yield and enzyme usage efficiency. Finally, we used eciCW773 to identify several gene modifica-tion targets for L-lysine production, most of which agree with previously reported genes. This study shows that incorporating enzyme kinetic information into the GEM enhances the cellular phenotypes prediction of C. glutamicum, which can help identify key enzymes and thus provide reliable guidance for metabolic engineering.

Background: Electrical conductivity of trabecular bone at 100 kHz was recently reported as a good predictor of bone volume fraction. However, to quantify its relationship with the free water (or physiological solution) content and between the conductivities of its constituents, is still unclear. Methods: In this contribution, in silico models inspired by microCT images of trabecular bovine samples were used to build realistic geometries. Finite Element Method was applied to solve the electrical problem and to robustly fit the conductivity of the constituents with literature data. The obtained effective electrical conductivity was compared to Bruggeman three media mixture model: physiological solution, bone marrow and bone matrix. Results: The values for physiological solution plus bone marrow and bone matrix that captured better the bone volume fraction in the two media Finite Element model were: σps+bm = 298.4 mS/m and σb = 21.0 mS/m, respectively. Additionally, relative good results were obtained by a three media Bruggeman mixture model with σbm= 103 mS/m, σb= 21.0 mS/m, and σps= 1200 mS/m. Simple linear relationships between the proportions of constituents depending on bone volume fraction were tested. Degree of anisotropy and fractal dimension do not show detectable changes in effective conductivity. Conclusions: These results provided some useful findings for simulation purposes: first, a higher value of electrical conductivity of bone marrow has to be used in order to obtain similar values to those experimental published data. Second, anisotropy is not detectable by conductivity measurements for small trabecular samples (cube 5 mm). Finally, the simulations presented here showed a relatively good fitting of the mixture Bruggeman model which potentially would allow both, accounting for the free water content and re-scaling the model for whole bone electrical simulations.

The pile foundation in the permafrost region is in a negative temperature environment, so the concrete is affected by the negative temperature of the surrounding soil.It not only affects the formation of concrete strength, but also leads to engineering quality accidents in serious cases.Based on the actual measurement of temperature at different strata depths and the comprehensive consideration of surface temperature, terrestrial heat flux and other parameters, the law curve of temperature change along depth in Greater Khingan is established.The calculated results of the curve are consistent with the measured results of ground temperature.The results show that the variation trend of ground temperature along the strata depth at different monitoring sites is basically the same. From June to November, the ground temperature at different depths tends to be constant.From December to May, the ground temperature at any depth within the depth range of 0 to 5.5m follows the law of the cosine function.Below 5.5m, the earth temperature no longer varies with depth.The research results can be used as reference for pile foundation construction under negative temperature environment.

Active front-end (AFE) rectifiers have been becoming widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines named the detailed model and the time average model. The former approach deals with the switching element model (SEM) leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents’ harmonics as well as provide fast responses. However, even the AVM baseline has contained problems during the starting stage (lack of control signals) and over the dead-time periods which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion.

An aneurysm is a vascular malformation, it can be classified according to its location (cerebral, aortic); and according its shape (saccular, fusiform, and mycotic). The study of blood flow interaction with the aneurysms has gained attention from physicists an engineers in recent years. Shear stresses, oscillatory shear index (OSI), gradient oscillatory number (GON), and residence time had been used as potential variables to describe the hemodynamics as well as the origin and evolution of aneurysm. However, the causes of aneurysms and the hemodynamics conditions that promote the growth of these structures are still under debate. The present work presents numerical simulation on three aneurysms: 2 aortic and 1 cerebral. The results shows that the blood rheology is not relevant for aortic aneurysms, however, for cerebral aneurysm could play a relevant role in the hemodynamics. The turbulence models evaluated show equivalent results in both cases. Lastly, a simulation considering the fluid-structure interaction (FSI) shows that this phenomenon is the dominant factor for aneurysms simulation.

In the field of computational fluid dynamics (CFD) for rotorcraft, two significant challenges are resolving the complex vortex structures in rotor wakes, and representing the moving rotor blades in the ambient airflow. This study addresses the first challenge by using a third-order unstructured finite volume solver, which, compared to its second-order counterpart, has substantially lower dissipation. Consequently, even relatively coarse meshes are capable of resolving small vortices. With this background flow field solver, the second challenge is addressed by modeling each rotor as an Actuator Disk Model (ADM), or describing individual rotor blades as actuator lines, designated as the Actuator Line Model (ALM). Both of these models are equipped with an improved correction for aerodynamic losses at blade tips, which is thoroughly presented in the methodology section. The numerical experiments section centers on analyzing errors linked to various sampling approaches. Additionally, the article discusses comparisons between vortex theory and ALM, specifically regarding calculations for fixed-wing aircraft. Furthermore, high-order precision and parallel efficiency are exemplified in scenarios encompassing rotors engaged in both hovering and forward flight rotors. The results in this paper demonstrate that the combination of the ALM/ADM with the improved tip loss correction and the third-order finite volume solver presents a new way of developing efficient tools for the aerodynamic analysis of helicopter rotors.

To solve two key problems in the identification of people who are infected with COVID-19: the first problem is that the identification accuracy is not high enough. The second problem is that present identification method such as nucleic acid testing is expensive in many countries. Methods: So, I decided to design a fast identification method for COVID-19 patients which is based on deep learning. After the model (CoughNet) learns more than 6,000 cough spectrograms of both COVID-19 patients and normal people, the accuracy rate of identification of COVID-19 patients and normal people is higher than 99% in the test set. Structure: This paper is mainly divided into three parts: the first part introduces the main background and research status of the research; The second part introduces the research methods; The third part introduces the specific process of the experiment.

In recent years, the utilization of machine learning algorithms and advancements in Unmanned Aerial Vehicle (UAV) technology have marked significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV-satellite data fusion have emerged as two prominent approaches for estimating vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for mapping crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV-satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV-satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV-RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root-mean-square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian processes regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is closely observed for the UAV-Landsat and UAV-PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, maximum performance is attained with Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, RapidEye data yielded the most stable performance, with R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data which enables effective monitoring of canopy chlorophyll in small agricultural lands.

Abstract—In this paper, decidability of the structures of real, rational, integer and natural numbers will be studied in different languages. Decidability or undecidability of mathematical structures is one of the fundamental and sometimes very difficult problems of mathematical logic, where several examples of problems in this field are still open and unresolved even after decades. One of the goals of Mathematical Logic is the axiomatization of mathematical theoriesTarski has proved the decidability of the theory of real and complex numbers in the language of addition and multiplication, and it is proved that theories of natural, integer, and rational numbers, in the language of addition and multiplication, are undecidable (Theorems of Gödel and Robinson). We will review The following problemes: The Main Problem 1: ⟨Q; ⊑⟩ is decidable? Problem 2: an explicit axiomatization for ⟨Z; \times⟩? and we will study boolean algebras. Boolean algebras are famous mathematical structures.Tarski showed the decidability of the elementary theory of Boolean algebras.In this paper, we study the different kinds of Boolean algebras and their properties. And we present for the first-order theory of atomic Boolean algebras a quantifier elimination algorithm. The subset relation is a partial order and indeed a lattice order,And I will prove that the theory of atomic Boolean lattice orders is decidable, and furthermore admits elimination of quantifiers. So the theory of the subset relation is decidable. And we will study decidability of atomlss boolean algebra.

The aim of this work was to evaluate the influence of various food and beverages on the viability of probiotic bacteria during passing through artificial digestion. As a model food, solutions with various concentrations of alcohol, sugar, salt, protein and acid were prepared. Different types of real foods and beverages were used as well. Viability in presence of food matrices was tested on monocultures of Lactobacillus acidophilus CCM4833 and Bifidobacterium breve CCM7825T and on mixed commercial culture with 9 different strains of probiotic microorganisms( Lactobacillus, Bifidobacterium, Streptococcus). The concentration and viability of probiotic bacteria was tested by cultivation assay and flow cytometry. In model foods, the best growth of was determined in the presence of 10% albumin and 10% ethanol. Survival of the probiotics delivered in different food matrices through a simulated gastrointestinal tract was quantitatively different. As the best food environment for probiotics complex food matrices such as pasta with cream sauce, chocolate spread and homemade beef broth were selected, followed by mixed vegetables, potato salad, salted chips, fruits and yoghurt. Among beverages the best option was milk, followed by black tea, coffee and Coca Cola. Probiotic microorganisms are more viable when consumed with meals than with beverages only. In general, the highest viability of probiotic cells has been observed in presence of foods containing high concentration of sugar and fat or their suitable combination. The increase of cell viability observed in such foods during model digestion may further contribute to the positive effect of probiotics on human health.

Transportation authorities aim to boost road safety by identifying risky locations and applying suitable safety measures. The Highway Safety Manual (HSM) is a vital resource for US transportation professionals, aiding in the creation of Safety Performance Functions (SPFs), which are predictive models for crashes. These models rely on Negative Binomial distribution-based regression and misinterpreting them due to unmet statistical assumptions can lead to erroneous conclusions, including inaccurately assessing crash rates or missing high-risk sites. The Florida Department of Transportation (FDOT) has introduced context classifications to HSM SPFs, complicating assumption violation identification. This study, part of an FDOT-sponsored project, investigates established statistical diagnostic tests to identify model violations and proposes a novel approach to determine optimal spatial regions for Empirical Bayes adjustment. This adjustment aligns HSM-SPFs with regression assumptions. The study employs a case study involving Florida roads. Results indicate that a 20-mile radius offers an optimal spatial sample size for modeling crashes of all injury levels, ensuring accurate assumptions. For severe injury crashes, which are less frequent and harder to predict, a 60-mile radius is suggested to fulfill statistical modeling assumptions. This methodology guides FDOT practitioners in assessing the conformity of HSM-SPFs with intended assumptions and determining appropriate region sizes.

The pervasiveness of cardiovascular disease and physician misdiagnosis creates the urgent need for artificial intelligence models to improve diagnosis accuracy. The first objective of this study was to train machine learning models on publicly available data sets containing simple medical information of patients to diagnose cardiovascular disease. The Multilayer Perceptron (MLP) assembled for this task performed optimally with an F1 score of 0.8968. This prompted the creation of an open-source, automated cardiovascular disease diagnosis tool, powered by the MLP. The second objective of this study was to employ a meta-learning methodology called Local Interpretable Model-Agnostic Explanations (LIME) to understand the impact of different features on the model's diagnosis in the form of marginal probabilities. K-Means Clustering was employed to segment the data into ten clusters, after which each data example was passed through LIME. The resulting histograms depict the complex relationship between feature, cluster, and impact on diagnosis. A series of P-values with contrasting orders of magnitude shows the nuances in the MLP's understanding of patients from different clusters. The results of meta-learning analysis reveal that the most important features for cardiovascular disease diagnosis are fasting blood sugar, type of chest pain, and slope of the ST segment on an electrocardiogram. Future experiments should replicate the novel methodology introduced in this study on data sets containing more specialized medical features in order to gain practical medical insights about different types of cardiovascular disease represented by each cluster. Finally, feature engineering pathways should be explored with consideration of these results to create versatile diagnosis models not only for cardiovascular disease, but adaptable to other diseases as well.

Uganda’s agriculture depends mainly on rainwater. As farmers are trying to increase on the food output to match the demands of a fast growing population, they are susceptible to make losses due to fluctuating weather patterns which are being caused by the global climate change. Therefore, it is necessary to explore ways of improving water use efficiency in rainfed agricultural systems to save farmers labour and input costs in situations where the grain harvest would be zero due to crop failure. The water driven FAO AquaCrop model is used as a support tool for making informed decisions during planning and situation analysis. In this study, AquaCrop model was evaluated for prediction of maize growth and yields at MUARIK in Uganda, for rainfed agriculture in three growing seasons. The model efficiency (E) and root mean square value (RMSE) for the maize canopy simulation during the September–December 2015 season was 0.945 and 7.24 respectively. The deviation of the simulated final biomass from measured data ranged from −15.4 to 11.6%, while the deviation of the final yield ranged from −2.8 to 2.0. The results suggest that the model can be used in the prediction of rainfed agricultural outputs, hence helping in guiding on management practices to increase food production.

The research develops and validates new decomposition models for DNI estimations from Southern African data. The results demonstrated improved DNI estimation accuracy compared to the baseline models across all testing and validation datasets. These outcomes suggest that utilising a localised model can significantly enhance DNI estimations for Southern Africa and potentially for developing similar models in diverse geographic regions worldwide. Furthermore, clustered models highlighted the potential advantages of grouping data based on shared geographical and climatic attributes. This clustering approach could enhance decomposition model performance, particularly when local data is limited or data is available from multiple nearby stations. The Southern African decomposition model, which encompasses a wide spectrum of climatic regions and geographic locations, exhibited notable improvements over the baseline models despite occasional overestimation or underestimation. The overall metrics affirm the substantial advancement achieved with the Southern African model. This study focused on validating the model for hourly DNI in Southern Africa within a range of clearness index-intervals from 0.175 to 0.875. Implementing accurate decomposition models in developing countries can accelerate the adoption of renewable energy sources, diminishing reliance on coal and fossil fuels.

Industrial process control systems commonly exhibit features of time-varying, strong coupling, and strong nonlinearity. Obtaining accurate mathematical models of these nonlinear systems and achieving satisfactory control performance is still a challenging task. In this paper, data-driven modeling techniques and deep learning methods are used to accurately capture a category of smooth nonlinear system’s spatiotemporal features. The operating point of these systems may change over time, and their nonlinear characteristics can be locally linearized. We established the LSTM-CNN-ARX model by utilizing a fusion of long short-term memory (LSTM) network and convolutional neural network (CNN) to fit the coefficients of the state-dependent exogenous variable autoregressive (SD-ARX) model. Compared to other models, the hybrid LSTM-CNN-ARX model is more effective in capturing the nonlinear system’s spatiotemporal characteristics due to its incorporating the strengths of LSTM for learning temporal characteristics and CNN for capturing spatial characteristics. The model-based predictive control (MPC) strategy, namely LSTM-CNN-ARX-MPC, is developed by utilizing the model's local linear and global nonlinear features. The control comparison experiments conducted on a water tank system (WTS) show the effectiveness of the developed models and MPC methods.

The cellular model we teach and have theorized assumes that the cell is the basic unit of multicellular living beings. This fundamental element has been the subject of many theories concerning its properties and the exchanges that exist with its environment. In this article, we demonstrate that certain functional aspects, in particular the electrical aspects related to diffusion, have not been correctly assumed or that certain initial conditions have been purely ignored and are in contradiction with physics, chemistry and thermodynamics.

This paper examines the potential of large language models, specifically ChatGPT, BingChat, and Bard, in enhancing the work of teachers in various educational settings. These models, powered by cutting-edge artificial intelligence, offer a wide range of applications that can aid educators in curriculum development, personalized instruction, student engagement, and administrative tasks. By harnessing the capabilities of these language models, teachers can not only streamline their workload but also improve the overall quality of education. This paper explores the ways in which these models can empower educators and revolutionize teaching practices.

With the branding development of the international exhibition industry and the increase in the number of mergers and acquisitions of exhibitions, the assessment of the brand value of exhibitions has gradually come into the view of governments, investment and financing institutions and researchers. Compared with the brand value assessment of consumer goods and industrial sectors, the brand value assessment of exhibitions, especially B2B exhibitions, has special characteristics. A model for brand value assessment of B2B exhibitions is proposed, which can not only provide a reference for decision-making on M&A behaviour of exhibitions, but also provide a decision-making tool for government exhibition brand promotion. The aim of this paper is to explore the feasibility of constructing an AHP-based conceptual model for brand value assessment of exhibitions in Shanghai through a hybrid study. The applicability of the conceptual model for exhibition brand value assessment is empirically demonstrated by analysing six steps, including establishing pairwise comparison matrices, calculating the maximum eigenvalues and normalised eigenvectors of each comparison matrix, conducting consistency checks, group decision matrix synthesis and weight allocation of exhibition brand value assessment indicators. On this basis, the three values of the model in practice and theory are explored separately. The research results can provide better brand value assessment and development support for exhibition stakeholders and improve the sustainability of exhibition brands.

HPV vaccine induces a herd immunity effect in genital warts when a large number of the population is vaccinated. That aspect should be taken into account when devising new vaccine strategies, like vaccination at older ages or male vaccination. Therefore it is important to develop mathematical models with good predictive capacities. We devised a sexual contact network that was calibrated to simulate the Spanish epidemiology of different HPV genotypes. Through this model we simulated the scenario that occurred in Australia in 2007, where 12-13 year-old girls where vaccinated with a three-dose schedule of a vaccine containing genotypes 6 and 11, that protect against genital warts, and also a catch-up program in women up to 26 years of age. Vaccine coverage were 73 % in girls with three doses and with coverage rates decreasing with age until 52 % for 20-26 year-olds. A fast 59 % reduction in the genital warts diagnoses occurred in the model in the first years after the start of the program, similar to what was described in the literature.

We continue in this paper to illustrate the implications of the Dual Model of Liquids (DML) by deriving the expression for the isochoric specific heat as function of the collective degrees of freedom available at a given temperature and analyzing its dependence on temperature. Two main tasks will be accomplished. First, we show that the expression obtained for the isochoric specific heat in the DML is in line with the experimental results. Second, the expression will be compared with the analogous one obtained in another theoretical dual model of the liquid state, the Phonon Theory of Liquid Thermodynamics. This comparison will allow to get interesting insights about the number of collective degrees of freedom available in a liquid and on the value of the isobaric thermal expansion coefficient, two quantities that are related to each other in this framework.

Staphylococcus aureus(S. aureus) is associated with mastitis in dairy cows, this study investigates the therapeutic effect of maggot antimicrobial peptides on S. aureus induced mastitis in mice. The maggot AMP was isolated and purified, tested for antimicrobial activity, and used to treat a pathological model of rat mastitis. The concentration of 1.5mg/（Kg. BW）AMP was selected as intramammary treatment in S. aureus-induced mastitis in mice. The mice were divided into four groups of 30 mice each: group I (healthy control), group II (disease control), group III (AMP), and group IV (ciprofloxacin). To evaluate the therapeutic efficacy of maggot AMP in mastitis induced by S. aureus based on clinical presentation, mammary gland structure, bacterial load, histopathology, and cure. During the 7-day treatment period, all the mice in Group II died in the afternoon of the fifth day, and the clinical signs of the mice in Group III began to improve on the third day, as evidenced by the gradual recovery of the mice's mental status, dietary intake, and water intake. During the treatment period, the bacterial load count of Group III was significantly lower than that of Group II but higher than that of Group IV, while the bacterial load count of Group III gradually decreased and approached that of Group IV from the fifth to the seventh day. The levels of TNF-α in groups III and IV were significantly lower than those in group II, suggesting that maggot AMP has a certain inhibitory effect on TNF-α. Treatment with AMP@ 1.5 mg/(Kg. BW) showed restoration of clinical signs, reduced bacterial load, and lower TNF-α levels in mice, leading to significant improvement in histopathological changes as compared to disease controls. The final cure rate was 40% for mice in Group III compared to 70% for mice in Group IV, compared to all mice in Group II that died. From this study, it can be concluded that maggot AMP can be used as a potential alternative treatment option to antibiotics for the treatment of bovine mastitis.

Hepatitis delta virus (HDV) infection causes the most severe form of viral hepatitis but little is known about the molecular mechanisms involved. The recently developed HDV mouse model based on the delivery of HDV replication-competent genomes using adeno-associated vectors (AAV) develop a liver pathology very similar to the human disease, and allowed us to perform mechanistic studies. We have generated different AAV-HDV mutants to eliminate the expression HDV antigens (HDAgs), characterized them both in vitro and in vivo. We confirmed that S-HDAg is essential for HDV replication and cannot be replaced by L-HDAg or host cellular proteins, and the L-HDAg is essential for HDV infectious particle production. We have also found that the lack of L-HDAg resulted in the increase of of S-HDAg expression levels and the exacerbation of liver damage which is T cell independent but is associated with an increment in liver inflammation. Interestingly, early expression of L-HDAg significantly ameliorated the liver damage induced by the mutant expressing only the S-HDAg. In summary, the use of AAV-HDV represents a very attractive platform to interrogate in vivo the role of viral components in the HDV life cycle and to better understand the mechanism of HDV-induced liver pathology.

The aim of this study was to investigate the effect of discoursal approach on Iranian intermediate EFL learners’ reading comprehension ability. Quick Placement Test (QPT) was used to select 60 intermediate EFL learners as the participants of this study. Then, they were randomly divided into experimental and control groups. Each consisted of 30 learners. Prior to the treatment, the participants of both groups were given a pretest to ensure their reading comprehension. The experimental group was exposed to the Hymes’ model. In the control group the researcher used placebo for teaching reading skill. Then a posttest was administered to both groups. An Independent samples t-test between posttests of the study and a paired-samples t-test between the pretest and posttest of the groups of the study were run. The results of the study revealed and emphasized that the Hymes’ model improved the learners’ reading comprehension.

Bayesian model calibration techniques are commonly employed in the characterization of nonlinear dynamic systems, as they provide a conceptual and effective framework to deal with model uncertainties, experimental errors and procedure assumptions. This understanding has resulted in the need to introduce a model discrepancy term to account for the differences between model-based predictions and real observations. Indeed, the goal of this work is to enhance model-driven Structural Health Monitoring procedures by incorporating the posterior uncertainty linked to updated model discrepancy, and thus make relevant considerations for its use in the Structural Health Monitoring. Specifically, the Bayesian inference has been applied to the calibration of nonlinear hysteretic systems to both provide: (i) most probable values (MPV) of the parameters following the calibration, and; (ii) estimates of the model discrepancy posterior distribution. The effect of the model discrepancy in the calibration is first illustrated recurring to a single degree of freedom Bouc-Wen type oscillator, and then applied for calibrating a reference nonlinear Bouc-Wen model, deriving from real data acquired on a monitored masonry building.

Background: Little is known about the decisional background of choosing an appropriate disease model. Therefore, this paper aims, (1) to identify a spectrum of reasons for choosing between animal and non-animal disease models and (2) provides an ethical analysis of these reasons. Methods: 13 researchers from basic and preclinical research were interviewed, the interviews were analyzed qualitatively and the results were categorized. The ethical analysis was based on the principlism approach and a value judgement model. Results: This paper presents 66 reasons why researchers use animal (41 %) or alternative, non-animal disease models (59 %). Most of the reasons were be assigned to the work environment (29 reasons) and scientific standards (22 reasons). Other reasons were assigned to personal attitudes (11 reasons) and animal welfare (4 reasons). Qualitative relevant normative differences are presented in the ethical analysis. Even if few reasons can be rejected outright from an ethical point of view, there are good reasons to give some more weight than others. Conclusions: The spectrum of reasons and its ethical assessment provide a framework for reflection for researchers who may have to choose between animal models and (investing in) alternatives. This can help to reflect on and justify decisions.

The Circular Economy of plastics is a promising concept that has the potential to reduce pollution and close the loop on plastic waste. However, further research is needed to develop more efficient and environmentally friendly methods of recycling plastic. This review article discusses the Circular Economy of plastics, its potential benefits and drawbacks, and the challenges that need to be addressed to make it a reality. Some case studies are also examined to explore how the Circular Economy of plastics has been implemented across the globe.

Electricity generation from renewable energy sources is emerging as a result of global carbon emission reduction policies. However, most renewable energy sources are non-dispatchable and cannot be adjusted to meet the fluctuating electricity demands of society. A flexible operation process has been proposed as an effective solution to compensate for the unstable nature of renewable energy sources. Thermal load fluctuations during flexible operation may cause creep-fatigue damage to the high-temperature components of thermal power plants, as they are designed with a focus on creep damage under a constant power level. This study investigated the residual life of high-temperature components, such as a superheater tube and a reheater header, to failure under flexible operation conditions using finite element analysis and empirical models. First, we determine an analytical solution for the straightened superheater tube under thermal conditions and compare it with the numerical solution to verify the numerical models. Through the verified finite element model, the creep-fatigue life of the reheater header is estimated by considering flexible operation factors and employing the Coffin-Manson and Larson-Miller models. Although fatigue damage increases with decreasing minimum load and ramp rate, we confirmed that creep damage significantly affects the residual life during flexible operation. In addition, a surrogate model was proposed to evaluate the residual life of the reheater as a function of the flexible operation factors using the machine learning methodology, based on the results of finite element methods. It can be used to predict its residual life without performing complex thermal-structure analysis and relying on empirical models for fatigue and creep life. We expect our findings to contribute to the efficient operation of thermal power plants by optimizing the flexible operation factors.

We introduce the Concept-Model-Graph-View Cycle (CMGVC). The CMGVC facilitates coherent architecture analysis, reasoning, insight, and decision-making based on conceptual models that are transformed into a generic, robust graph data structure (GDS). The GDS is then transformed into multiple views of the model, which inform stakeholders in various ways. This GDS-based approach decouples the view from the model and constitutes a powerful enhancement of model-based systems engineering (MBSE). The CMGVC applies the rigorous foundations of Category Theory, a mathematical framework of representations and transformations. We show that modeling languages are categories, drawing an analogy to programming languages. The CMGVC architecture is superior to direct transformations and language-coupled common representations. We demonstrate the CMGVC to transform a conceptual system architecture model built with the Object Process Modeling Language (OPM) into dual graphs and a stakeholder-informing matrix that stimulates system architecture insight.

The non-existence of a land ownership database in most of the developing countries moves the inhabitants to the occupation of public lands. Some of this situation are the origin to areas of informal housing, commerce and agriculture and in the end into new ​​informal settlements. Informal settlements become a serious problem in developing countries. The most common typology of informal settlements is that they are the population settled in public lands without any infrastructures and against the administrator's will. Thought this action the result in an uncontrolled land occupation process that promotes new informal areas without any proper built-up utilities, located in risk areas on the territory, barely ensuring the minimum requirements for a heaty living of the population and in various cases incentives to an informal economy. The process of build a cadastral map in informal settlement areas is a fundamental base to support the future transformation of illegal areas and to regulate the occupation of new subdivision planning and into the creation of new expansion areas. In this paper, it is presented a methodology developed to be applied to support a new register of land and to management. The transformation of informal settlement areas. The model to register the land tenure has been associated with allows the process application to multiple typology of informal settlements. The model to register land tenure has developed on a series of qualitative and quantitative data that determine the identification and classification of the buildings and its physical and functional description. The model was developed using Geographic Information System and with an initial survey of existing land titles of possession and public proposals to develop new expansion areas. A case study of the method is presented, where the land management model was implemented - Chã da Caldeiras in Ilha do Fogo an informal settlement in Cape Verde. The results are a great acceptance of the proposal by the population and local authorities and the starting of the implementation phase.

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.

Ongoing outbreak of pneumonia caused by novel coronavirus (2019-nCoV) began in December 2019 in Wuhan, China, and the number of new patients continues to increase. On the contrary to ongoing outbreak in China, however, there are limited secondary outbreaks caused by exported case outside the country. We here conducted simulations to estimate the impact of potential secondary outbreaks at a community outside China. Simulations using stochastic SEIR model was conducted, assuming one patient was imported to a community. Among 45 possible scenarios we prepared, the worst scenario resulted in total number of persons recovered or removed to be 997 (95% CrI 990-1,000) at day 100 and maximum number of symptomatic infectious patients per day of 335 (95% CrI 232-478). Calculated mean basic reproductive number (R0) was 6.5 (Interquartile range, IQR 5.6-7.2). However, with good case scenarios with different parameter led to no secondary case. Altering parameters, especially time to hospital visit could change the impact of secondary outbreak. With this multiple scenarios with different parameters, healthcare professionals might be able to prepare for this viral infection better.

Recent advancements in Natural Language Processing (NLP), particularly in Large Language Models (LLMs), associated with deep learning-based computer vision techniques, have shown substantial potential for automating a variety of tasks. One notable model is Visual ChatGPT, which combines ChatGPT’s LLM capabilities with visual computation to enable effective image analysis. The model’s ability to process images based on textual inputs can revolutionize diverse fields. However, its application in the remote sensing domain remains unexplored. This is the first paper to examine the potential of Visual ChatGPT, a cutting-edge LLM founded on the GPT architecture, to tackle the aspects of image processing related to the remote sensing domain. Among its current capabilities, Visual ChatGPT can generate textual descriptions of images, perform canny edge and straight line detection, and conduct image segmentation. These offer valuable insights into image content and facilitate the interpretation and extraction of information. By exploring the applicability of these techniques within publicly available datasets of satellite images, we demonstrate the current model’s limitations in dealing with remote sensing images, highlighting its challenges and future prospects. Although still in early development, we believe that the combination of LLMs and visual models holds a significant potential to transform remote sensing image processing, creating accessible and practical application opportunities in the field.

Spirit of science is one of the important components of science education theory. It not only directly affects the level of science education, but also indirectly affects the selection, education and evaluation of scientific researchers, and the development of science and technology in a country or region. Although more than a century ago, the researchers began to discuss the topic of spirit of science. However, to date, the research of the definition, attribute, structural model and its characteristics of the spirit of science has not yet made a breakthrough. Based on the detailed literature review, the related theoretical analysis and the research of the structural model of the spirit of science, this paper puts forward the new definition, the attribute and the establishment of the structural model of the spirit of science, and analyzes the match the situation from new structural model of the spirit of science and the scientific nature published by the American Society for the Advancement of Science. The results of this study are of great significance for raising the level of scientific education and cultivating future scientific researchers, enhancing their motivation and skills in innovation in scientific research and promoting the development of future scientific undertakings.

Natural disasters are showing an increase in the magnitude, frequency, and geographic distribution. Studies have shown that individuals’ self-sufficiency, which largely depends on household preparedness, is very important for hazard mitigation in at least the first 72 hours following a disaster. However, for factors that influence a household’s disaster preparedness, though there are many studies trying to identify from different aspects, we still lack an integrative analysis on how these factors contribute to a household’s preparation. This paper aims to build a classification model to predict whether a household has prepared for a potential disaster based on their personal characteristics and the environment they located. We collect data from the Federal Emergency Management Agency’s National Household Survey in 2018 and train four classification models - logistic regression, decision trees, support vector machines, and multi-layer perceptron classifier models- to predict the impact of personal characteristics and the environment they located on household prepare for the potential natural disaster. Results show that the multi-layer perceptron classifier model outperforms others with the highest scoring on both recall (0.8531) and f1 measure (0.7386). In addition, feature selection results also show that among other factors, a household’s accessibility to disaster-related information is the most critical factor that impacts household disaster preparation. Though there is still room for further parameter optimization, the model gives a clue that we could support disaster management by gathering publicly accessible data.

The Earth's water resources, totaling 1.386 billion cubic kilometers, predominantly consist of saltwater in oceans. Groundwater, plays a pivotal role, with 99% of usable freshwater supporting 1.5–3 billion people as drinking water source and 60–70% for irrigation. Climate change, with temperature increase and altered precipitation patterns, directly impacts groundwater systems, affecting recharge, discharge, and temperature. Hydrological models are crucial for assessing climate change effects on groundwater, aiding in management decisions. Advanced hydrological models, incorporating data assimilation and improved process representation, contribute to understanding complex systems. Recent studies, employ numerical models to assess climate change impacts on groundwater recharge that could help in management of Groundwater. Groundwater vulnerability assessments vary with the spatial and temporal considerations, as well as assumptions in modelling groundwater susceptibility. The review assesses the vulnerability of groundwater to climate change and stresses the importance of accurate assessments for sustainable water resource management. It highlights challenges in assumptions related to soil and aquifer properties, multiple stressors, adaptive capacity, topography, aquifer properties, and groundwater contamination processes and gradual sea level rise scenarios and realistic representations of the region of study. The advancements in hydrological modelling, including the integration of uncertainty quantification and remote sensing data, artificial intelligence, could assist in the efforts to improve models for assessing the impacts of climate change on hydrological.

Social insects, such as honey bees exhibit complex behavioral patterns and their inconspicuous coordination enables decision-making on the colony level. It is thus proposed, that a high-level description of their collective behavior might share commonalities with neural processes in the brains. At the same time, recent research concerning overarching features of neural activity implies that brains are poised at the edge of the critical phase transition and that such a state is enabling maximal computational power and adaptability. In our research, we applied some tools developed in the computational neuroscience field to the dataset of bee trajectories recorded within the hive, during the course of many days. Our results imply that certain characteristics of the system are remarkably similar to the Ising model when it operates at critical temperature and also shares some of the features with the human brain at the resting state

Stabilization of planar premixed flame front on a lattice (porous) burner is considered. It is rigorously mathematically proved that there exist two different stabilization regimes: one with flame front located nearby the surface of the burner, and another with flame front located inside the lattice. These two regimes result in qualitatively different gas temperature profiles along the flow, that is monotonic and non-monotonic, respectively. Boundary between the two regimes is described in terms of dependence of the lattice solid material temperature on flow Peclet number. Likewise temperature profiles, such dependencies may be both monotonic and non-monotonic. Transition between the two types of dependencies is controlled by Arrhenius number. Conclusions of the study are supported by numerical analysis. They also compare favourably with available experimental data. Novelty of the present approach is fundamentally rigorous analytical analysis of the problem.

Molecular motors play a vital role in the transport of material within the cell. A family of motors of growing interest are burnt bridge ratchets (BBRs). BBRs rectify spatial fluctuations into directed motion by creating and destroying motor-substrate bonds. It has been shown that the motility of a BBR can be optimized as a function of the system parameters. However, the amount of energy input required to generate such motion and the resulting efficiency has been less well characterized. Here, using a deterministic model, we calculate the efficiency of a particular type of BBR, namely a polyvalent hub interacting with a surface of substrate. We find that there is an optimal burn rate and substrate concentration that leads to optimal efficiency. Additionally, the substrate turnover rate has important implications on motor efficiency. We also consider the effects of force-dependent unbinding on the efficiency and find that under certain conditions the motor works more efficiently when bond breaking is included. Our results provide guidance for how to optimize the efficiency of BBRs.

Globally, seagrass meadows provide critical ecosystem services. However, seagrasses are globally degraded at an accelerated rate. The lack of information on seagrass spatial distribution and seagrass health status seriously hinders seagrass conservation and management. Therefore, this study proposes to combine remote sensing big data with a new hybrid machine learning model (RF-SWOA) to predict potential seagrass habitats. The multivariate remote sensing data is used to train the machine learning model, which can improve the prediction accuracy of the model. This study shows that a hybrid machine learning model (RF-SWOA) can predict potential seagrass habitats more accurately and effectively than traditional models. At the same time, it has been shown that the most important factors influencing the potential habitat of seagrass in the Hainan region were the distance from land (38.2%) and the depth of the ocean (25.9%). This paper provides a more accurate machine learning model approach for predicting the distribution of marine species, which can help develop seagrass conservation strategies to restore healthy seagrass ecosystems.

The shallow water equations are widely applied for the simulation of flow routing in rivers and floodplains, as well as for flood inundation mapping. From a mathematical point of view, they are a hyperbolic system of nonlinear partial differential equations, whose numerical integration is sometimes computationally burdensome. For this reason, the interest of many researchers has been focused on the study of simplified forms of the original set of equations, which requires less computational effort. One of the most commonly applied simplifications consists in neglecting the inertial terms, which changes the hyperbolic model to a parabolic one. The effects of such a choice on the outputs of the simulations of flooding events are controversial and an important topic of debate. In the present paper, two numerical models, recently proposed for the solution of the complete and zero-inertia forms of the shallow waters equations, are applied to several unsteady flow routing scenarios. We simulate synthetic and laboratory studies, starting from very simple geometries and moving towards complex topographies. Analyzing the role of the terms in the momentum equations, we try to understand the effect, on the computed results, of neglecting the inertial terms in the zero-inertia formulation. We analyze the computational costs.

Historical and Endowment Properties are different from Heritage and cultural Properties, as Historical and Endowment properties are governed by a unique set of laws that Waqf recipients must abide by. Property that is entrusted is usually in the form of buildings, land or valuables which in preservation is not limited to time as long as the property can be utilized. Reliable information technology is needed to ensure data security both digitally and physically, while the rapid development of information technology demands information openness and this will be a challenge in itself. The objectives of this study include examining the collection of historical databases and endowments, the relationship between digital data and physical data and management organizations. The method of how to design a query model to display data is then analyzed whether the data conforms to the rules in waqf management. The results are expected to bring up accurate data between digital data and physical data and if there are differences into findings for the next analysis.

A modified Nambu{Jona-Lasinio Model with lattice structure is very instructive. It shows several similar problems and their solutions as the Lattice QCD. We study the limits of the large box size, small cell size and realistic pion mass. In particular, we study the relation of the discrete (bound state) solutions to the physical scattering states, for example the pion-pion scattering.

Several empirical studies indicate that the lack of financing is one of the main barriers that affects the economic growth of small and medium enterprises (SMEs). The main objective of this investigation was to determine to what extent the economic sector; enterprise size; the characteristics inherent to the enterprise; legal status; the variables linked to the performance of the enterprise; and the attributes of the owner influence the access to bank financing of SMEs in Mexico. Using a discrete response probit regression model, the impact of enterprises characteristics on the probability of obtaining a bank loan was determined. The data collected is from the Enterprise Surveys of Mexico, carried out by the World Bank. The sample of 1,480 enterprises is representative by enterprise size, by economic sector, and by region. The research has a quantitative approach with a correlational scope and a non-experimental and transectional design. One of the main results highlights that the determinants with the greatest influence on access to bank financing are: age, the small size, foreign participation, and the manufacturing sector. These results are consistent with other empirical studies, as well as with the pecking order theory and the financial life cycle theory.

Worldwide the steel corrosion is one of the greatest deterioration problems for reinforced concrete structures. Comparing some literature experimental results with a complex FEM model, the present paper points out the principal aspects those characterize the static behavior of RC elements damaged by corrosion. Moreover, the non-dimensional abaci defined for some specific case studies finalized to the evaluation of the residual flexural strength of corroded elements highlight the dangerousness of the corrosion degradation if the failure of the element is governed by the steel

Hemoconcentration membranes used in cardiopulmonary bypass require a pore structure design with high pure water permeability, and which does not allow protein adsorption and useful protein loss. However, studies on hemoconcentration membranes have not been conducted yet. The purpose of this study was to analyze three-dimensional pore structures and protein fouling before and after blood contact with capillary membranes using the tortuous pore diffusion model and a scanning probe microscope system. We examined two commercially available capillary membranes of similar polymer composition that are successfully used in hemoconcentration clinically. Assuming the conditions of actual use in cardiopulmonary bypass, we perfused these membranes with bovine blood. Pure water permeability before and after bovine blood perfusion was measured using the dead-end filtration. The scanning probe microscopy system was used for analysis. High-resolution three-dimensional pore structures on the inner surface of the membranes were observed before blood contact. On the other hand, pore structures after blood contact could not be observed due to protein fouling. The pore diameters calculated by the tortuous pore diffusion model and scanning probe microscopy were mostly similar and could be validated reciprocally. Achievable pure water permeabilities showed no difference despite protein fouling, leading to low values of albumin SC. This is due to the mechanism that protein fouling occurs on the membrane surface, while there is little internal pore blocking. Therefore, controlling the fouling is essential for membranes in medical use. These characteristics of the hemoconcentration membranes examined in this study are suitable for clinical use.

The coal mine underground reservoir is an appropriate solution between coal mining and groundwater resource protection and utilization. By calculating the storage capacity of a groundwater reservoir, the storage coefficient has been proved to be always an empirical value. Based on the mathematical derivation of the vertical fracture area ratio and the horizontal fracture area rate of the collapse zone and the fissure zone in the goaf area of the coal seam, the mathematical models of tem are derived, and the model for calculating the water storage coefficient is derived. The water storage coefficient derived from the theoretical model had more basis and more advanced than the traditional empirical value. By using this method, the practical calculation of No.1 underground reservoir of the DaLliuta Coalmine in Shenhua Shendong, has got a perfect matching with the actual groundwater storage capacity.

This article proposes the multilayer and multilevel cosmological model, on average, balanced with respect to the Einsteinian vacuum. This hierarchical model is based on 10 possible Kottler solutions of the extended Einstein's field equations. The extension of the Einstein field equations is associated with the addition of an infinite number of lambda_j-terms to these equations with the condition that the series (i.e., the total sum) of these terms converges to zero. A simplified ten-layer and ten-level case is considered, in which ten spherical formations are sequentially nested into each other like "nesting dolls", while the radii of these spherical formations correspond to the characteristic sizes of a discrete sequence of observed spherical objects: metagalaxy core, galactic cores, stellar (planet) core, biological cells and nuclei of atoms and elementary particles, etc. As an example, from the general ten-level (hierarchical) solution of extended Einstein's field equations, two antipodal spherical formations with a core radius commensurate with the classical electron radius are singled out. Therefore, the metric-dynamic models of these formations obtained in this way are called «electron» and «positron». The article is aimed at the development of differential geometry and the program for the complete geometrization of physics by Clifford-Einstein-Wheeler.

In this paper, we consider an epidemiological Susceptible-Infected-Susceptible (SIS) model in which the individuals cured from the infection return back into the group of people endangered by the infection. Delay differential equations were introduced to create more realistic models since many processes depend on past history. Many different mathematical methods, including the Susceptible-Infected-Recovered (SIR) model as well as derivatives thereof, can be used to prognose the spread of many various infectious diseases, including Covid-19.We use a nonlinear differential system with time delay as a mathematical formulation of this model. It is examined by the existence of a positive solution which is bounded by two functions $k_1, k_2\in C([t_0,\infty),(0,\infty))$ with constants $\lambda_1 > 0,\,\lambda_2 > 0$ where $0<\lambda_1\leq \lambda_2$. The conditions are put in place for existence of a positive solution that approaches zero in infinity, if $t\to\infty$. Infectious diseases accompany a person throughout their entire life, and in the history of mankind often appeared in frequent epidemics caused high mortality. The occurrence of infectious diseases in the population represents a serious health, social and economic problem. There is a lot of ambiguity concerning the time span and final spread of the epidemic thus making it very demanding for the governing bodies, healthcare institutions, and economic sector to precisely judge the future development.Several examples are shown at the end of this paper.

Fires are a significant threat to people, property, and environment. One of the requirements for the buildings during fires is ensuring the safe evacuation of persons. The evacuation assessment has usually been based on a comparison of the Required Safe Egress Time (RSET) and the Available Safe Egress Time (ASET). One of the important factors influencing the safe evacuation of persons is smoke, which is an important accompanying phenomenon in the burning of polymers. The smoke layer interface is one basic barrier for effective evacuation. The current calculation technique for assessing the smoke layer interface in the Czech Republic is unsatisfactory. For this reason, a calculation technique CSN has been derived, which has been compared with the selected simple calculation techniques, the zone fire model Consolidated Model of Fire and Smoke Transport (CFAST) and the field type model Fire Dynamics Simulator (FDS). The CSN technique was also compared with a real large-scale experiment. The data were also subjected to statistical evaluation using the root mean square error method (RMSE). The deviations between the NFPA, ISO and CSN calculation techniques, the CFAST and FDS fire models and the implemented experiment ranged from 0.05 to 2.41. The established deviations indicate that the calculation technique CSN will be practically usable.

People are drawn to woodcut-style designs due to their striking visual impact and strong contrast. However, traditional woodcut prints and previous computer-aided methods have not addressed the issues of dwindling design inspiration, lengthy production times, and complex adjustment procedures. We propose a novel network framework, the Woodcut-style Design Assistant Network (WDANet), to tackle these challenges. Notably, our research is the first to utilize diffusion models to streamline the woodcut-style design process. We've curated the Woodcut-62 dataset, featuring works from 62 renowned historical artists, to train WDANet in absorbing and learning the aesthetic nuances of woodcut prints, offering users a wealth of design references. Based on a noise reduction network, our dual cross-attention mechanism effectively integrates text and woodcut-style image features. This allows users to input or slightly modify a text description to quickly generate accurate, high-quality woodcut-style designs, saving time and offering flexibility. As confirmed by user studies, quantitative and qualitative analyses show that WDANet outperforms the current state-of-the-art in generating woodcut-style images and proves its value as a design aid.

This study evaluates the agricultural carbon emission efficiency of Shandong Province from 2011 to 2020 using the DEA-SBM model. The nexus between endogenous and exogenous variables is estimated using the Tobit model. The findings reveal an overall increasing trend in agricultural carbon emissions, with significant variations in efficiency values among different cities, leading to a severe polarization. Spatial evaluation shows a high distribution trend in the central region and low in the eastern and western regions of Shandong Province. The empirical tests conducted for Shandong Province and its three regions indicate that urbanization plays a major role in sup-porting the growth of agricultural carbon emission efficiency, while the education level of the la-bor force has a suppressive impact. Economic development and crop cultivation structure, how-ever, have no significant influence. The impact of these variables varies across the eastern, central, and western regions. The proposed countermeasures include improving planting structure and reducing brain drain in the eastern region, strengthening agricultural and rural inputs and in-creasing the added value of agricultural products in the central region, and intensifying the link-age between urbanization and industrial layout in the western region to reduce agricultural car-bon emissions efficiency in Shandong Province.

Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. Key terms of model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes in model detail. Additional research in this area can advance the modeling and simulation body of knowledge toward the practical application of measuring simulation model complexity. Examples show that KC and NCD measurements of simulation models can detect changes in scope and detail.

Guessing effects frequently occur in testing data in educational or psychological applications. Different item response models have been proposed to handle guessing effects in dichotomous test items. However, it has been pointed out in the literature that the oftenemployed three-parameter logistic model poses implausible assumptions regarding the guessing process. The four-parameter guessing model has been proposed as an alternative to circumvent these conceptual issues. In this article, the four-parameter guessing model is compared with alternative item response models for handling guessing effects through a simulation study and an empirical example. It turned out that model selection for item response models should be rather based on the AIC than the BIC. However, the RMSD item fit statistic used with typical cutoff values was found to be ineffective in detecting misspecified item response models. Furthermore, sufficiently large sample sizes are required for sufficiently precise item parameter estimation. Moreover, it is argued that the criterion of statistical model fit should not be the sole criterion of model choice. The item response model used in operational practice should be valid with respect to the meaning of the ability variable and the underlying model assumptions. In this sense, the four-parameter guessing model could be the model of choice in educational large-scale assessment studies.

Background: According to the National Highway Traffic Safety Administration, 116 pedestrians were killed in motor vehicle crashes in Ohio in 2015. However, no study to date has analyzed crashes in Ohio exploring the factors contributing to the pedestrian injury severity resulting from motor vehicle crashes. This study fills this gap by investigating the crashes involving pedestrians exclusively in Ohio. Materials and Methods: This study uses the crash data from the Highway Safety Information System, from 2009 to 2013. The explanatory factors include the pedestrian, driver, vehicle, crash, and roadway characteristics. Both fixed- and random-parameters ordered probit models of injury severity (where possible outcomes are major, minor, and possible/no injury) were estimated. Results: The model results indicate that being older pedestrian (65 and over), younger driver (less than 24), driving under influence (DUI), being struck by truck, dark-unlighted roadways, six-lane roadways, and speed limit of 40 mph and 50 mph were associated with more severe injuries to the pedestrians. Conversely, older driver (65 and over), passenger car, crash occurring in urban locations, daytime traffic off-peak (10 AM to 3:59 PM), weekdays, and daylight condition were associated with less severe injuries. Conclusion: This study provides specific safety recommendations so that effective countermeasures could be developed and implemented by the policy makers, which in turn will improve overall highway safety.

By reinterpreting the calibration of structural models, a reassessment of the importance of the input variables is undertaken. The analysis shows that volatility is the key parameter to any calibration exercise by several orders of magnitude. In order to maximize the sensitivity to volatility, a simple formulation of Merton’s model is proposed that employs deep out-of-the-money option implied volatilities. The methodology also eliminates the use of historic data to specify the default barrier thereby leading to a full risk neutral calibration. Subsequently, a new technique for identifying and hedging capital structure arbitrage opportunities is illustrated. The approach seeks to hedge the volatility risk, or vega, as opposed to the exposure from the underlying equity itself, or delta. The results question the efficacy of the common arbitrage strategy of only executing the delta hedge.

Large-scale volumetric fracturing is generally used during shale gas development. The return rate of fracturing fluid is low, and a large amount of slickwater will be retained in the reservoir. The adsorption and desorption of partially hydrolyzed polyacrylamide (HPAM), an additive commonly used in slickwater, on the surface of shale was studied using Longmaxi Shale from the Sichuan Basin. The experimental results showed that the mass ratio of the HPAM solution to shale reached saturation adsorption at 20:1 when the concentration of HPAM solution was 1000 mg/L and 25:1 when the concentration of HPAM solution was 500 mg/L. The mass ratio of the HPAM solution to shale was fixed at 25:1, and the adsorption equilibrium was reached at a HPAM concentration of 1000 mg/L when the aqueous solution temperature was 30°C and 800 mg/L when the aqueous solution temperature was 60°C. The Langmuir adsorption model yielded a better fit than the Freundlich adsorption model. The adsorption equilibrium time at 30°C was at 60 min for a HPAM concentration of 500 mg/L, while for a concentration of 1000 mg/L, it was at 90 min. The adsorption equilibrium time at 60°C was 40 min for a HPAM concentration of 500 mg/L, whereas it was 60 min for a HPAM concentration at 1000 mg/L. The pseudo-second order kinetics model yielded better fits than the pseudo-first order kinetics model. The adsorption of HPAM on shale was strong, and the adsorbed HPAM resembled cobwebs adhering to the shale surface. HPAM on the surface of shale after adsorption could resist the desorption capacity of water. However, when the amount of adsorbed HPAM on shale increased significantly, the amount of residual HPAM on the surface of the shale decreased rapidly during desorption in deionized water. The desorption of HPAM on the shale surface followed a modified desorption model. The higher the concentration of HPAM adsorbed on the shale surface was, the easier it was to desorb, the easier it was to be removed from the shale.

Violence is a complex and multifaceted problem requiring a holistic and individualized response. The Good Lives Model (GLM) suggests violence occurs when an individual experiences internal and external obstacles in the pursuit of universal human needs (termed primary goods). With a twin focus, GLM-consistent interventions aim to promote attainment of primary goods, whilst simultaneously reducing risk of reoffending. This is achieved by improving an individuals’ internal (i.e., skills and abilities) and external capacities (i.e., opportunities, environments, and resources). This paper proposes that collaborations between different agencies (e.g., psychological services, criminal justice systems, social services, education, community organizations and healthcare) can support the attainment of primary goods through the provision of specialized skills and resources. Recommendations for ensuring interagency collaborations are effective are outlined, including embedding a project lead, regular interagency meetings and training, establishing information sharing procedures, and defining the role each agency plays in client care.

Traditionally, studies on freeze–thaw erosion have used the analytic hierarchy process (AHP) to calculate the weight of evaluation factors, however, this method cannot accurately depict the fuzziness and randomness of the problem. To overcome this disadvantage, the present study has proposed an improved AHP method based on the cloud model to evaluate the impact factors in freeze–thaw erosion. To establish an improved evaluation method for freeze–thaw erosion in Tibet, the following six factors were selected: annual temperature range, average annual precipitation, slope, aspect, vegetation coverage, and topographic relief. The traditional AHP and the cloud model were combined to determine the weight of the impact factors, and a consistency check was performed. The comprehensive evaluation index model was used to evaluate the intensity of freeze–thaw erosion in Tibet. The results show that freeze–thaw erosion is extensive, stretching over approximately 66.1% of Tibet. The problem is the most serious in Ngari Prefecture and Nagqu. However, mild erosion and moderate erosion, accounting for 37.1% and 25.0%, respectively, of the total freeze–thaw erosion are the most widely distributed. The evaluation results for the freeze–thaw erosion was confirmed to be consistent with the actual situation. In brief, this study provided a new approach to evaluate the conditions of freeze–thaw erosion quantitively in Tibet.

Otodus megalodon is known as the biggest shark ever alive and recent records show this species was only existed in America, Africa and Europe continents in the last Miocene period 20 million years ago (Ma). Recently, megalodon teeth have been discovered in South coast of West Java. Here this study aims to present and model the presence of O. megalodon. The length of the excavated tooth was ranging from 13 to 19 cm. The lithological analysis shows that the tooth was found in Miocene rock layers. Paleogeographic model shows that during Miocene southern parts of Java island were submerged including the recent locations where the megalodon tooth have been found. Half parts of the modeled West Java were an ocean with depth ranging from 0 to 200 m. High habitat use preferences by juvenile megalodon were estimated in the shelf (depth 0-40 m) with the size of 1365 km² . Whereas habitat use preference by adult megalodon was low at this depth. Both juvenile and adult habitat use frequencies were low at a depth of between 80 m and 160 m. Habitat use preferences were increasing at a depth of > 200 m. After Miocene, the paleoclimate records show a decline in temperature and lead to the decline of whale population in ocean. The declining of megalodon’s prey explains the declining of this giant shark especially in post Miocene and early Pliocene periods.

Preeclampsia is a multifactorial cardiovascular disorder diagnosed after 20 weeks of gestation that is the leading cause of death for both mothers and babies in pregnancy. The pathophysiology remains poorly understood due to variability and unpredictability of disease manifestation when studied in animal models. After preeclampsia, both mothers and offspring have a higher risk of cardiovascular disease (CVD) including myocardial infarction or heart attack and heart failure (HF). Myocardial infarction is an acute myocardial damage that can be treated through reperfusion, however, that therapeutic approach leads to ischemic/reperfusion injury (IRI) often leading to HF. In this review, we compared the current in vivo, in vitro and ex vivo model systems used to study preeclampsia, IRI and HF. Future studies aiming at evaluating CVD in preeclampsia patients could benefit from novel models that better mimic the complex scenario described in this article.

AbstractTo overcome the negative impacts on the environment and other problems associated with fossil fuels have forced many countries to inquire into and change to environmentally friendly alternatives that are renewable to sustain the increasing energy demand. Solar energy is one of the best renewable energy sources with the least negative impacts on the environment. Different countries have formulated solar energy policies to reduce dependence on fossil fuel and increasing domestic energy production by solar energy. According to the 2010 BP Statistical Energy Survey, the world cumulative installed solar energy capacity was 22928.9 MW in 2009, a change of 46.9% compared to 2008. In this study, a PV generation system has been modeled and installed considering uncertain whether based on the hourly wind speed data of New York City (NYC) of year 2014. Regression models has been used to forecast the hourly, weekly, and monthly wind speed of NYC year 2014. Design of experiment (DOE) has been used to determine the optimal panel size (area), the battery capacity size, and other levels of factors.

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

The effect of divergence of chute sidewalls with three different baffle block geometries namely USBR, trihedral and semicircular blocks, as well as the depth and dimensions of the scour hole downstream of the chute were studied using a physical model. For this purpose, 9 models of baffled chutes were designed and constructed with divergence ratios of 1.45, 1.75, and 2.45 and without divergence (with a divergence ratio of 1). Comparing the results on the effect of block geometry at different divergence ratios revealed that the use of blocks proposed in this study instead of standard USBR blocks reduced the mean and maximum scour hole by 50%. For a given block geometry, the mean depth, maximum depth, and length of scour hole were reduced by 75%, 58%, and 50%, respectively.

A diagnostic method is presented for analysing the large-scale behaviour of the Met Office Unified Model, which is a comprehensive atmospheric model used for weather and climate prediction. Outside the boundary layer, on scales larger than the radius of deformation, semigeostrophic theory will give an accurate approximation to the model evolution. In particular, the ageostrophic circulation required to maintain geostrophic and hydrostatic balance against prescribed forcing and a rate of change of the geostrophic pressure can be calculated. In the tropics the balance condition degenerates to the weak temperature gradient approximation. Within the boundary layer the semigeostriptic approximation has to be used because friction and rotation are equally important. Assuming the calculated pressure tendency and ageotriptic circulation match the observed model behaviour, the influence of the large-scale state and the nature of the forcing on the model response can be deduced in a straightforward way. This process is illustrated by comparing predictions of the ageotriptic circulation from the theory and the model. It is then used to show that the effects of latent heat release can be included by modifying the static stability, and to show the effect of an idealised tropical heat source on the subtropical jet. Finally the response of the ageotriptic flow to boundary layer heating in the tropics is demonstrated. These illustrations show that the model behaviour on large scales conforms with theoretical expectations, so that the results of the diagnostic can be used to aid the development of further improvements to the model.

The move to a low carbon economy is very important for enhancing international competitiveness. The eco-innovation is the critical factor of the green paradigm. This study is designed to investigate deeply the determinants of eco-innovation of manufacturing firms in Korea by suggesting anticipated regulations, self-regulations, and industry-specific characteristics as external factors and open information sources as internal factors. The data used in the analysis is 1946 sample firms from Korean Innovation Survey 2010 based on the Oslo Manual. Using the multi-variate probit analysis and the zero-inflated negative binomial (ZINB) regression analysis, we have found out that the anticipated regulations and self-regulations have significant influences both on eco-process innovation and eco-product innovation, while industrial characteristics have no effects. The empirical results also show that the breadth of information sources has a positive effect on businesses in implementing eco-innovations. Our findings show that the Korean government should provide a good platform where firms can better understand the future trends of environmental policies, particularly policies on anticipated and self-regulations. At the same time, Korean firms should establish a voluntary system to control environmental activities so that they can improve eco-innovations through integrating external information.

This study explores the application of numerical analysis and material models to predict ice impact loads on ships and offshore structures operating in polar regions. An explicit finite element analysis (FEA) approach was employed to simulate an ice and steel plate collision experiment conducted in a cold chamber. The pressure and strain history during the ice collision were calculated and compared with the experimental results. Various material model configurations were applied to the FEA to account for the versatile behavior of ice—whether ductile or brittle—its elastic-plastic yield criteria, and its dynamic strain rate dependency. In addition to the standard linear elastic perfectly plastic and linear elastic-plastic relationships, this study incorporated the crushable foam and Drucker-Prager models, based on the specific ice yield criteria. Considering the ice’s strain rate dependency, collision simulations were conducted for each yield criteria model to compute the strain and reaction force of the plate specimens. By comparing the predicted pressures for each material model combination with the pressures from ice collision experiments, our study proposes material models that consider the yielding, damage, and behavioral characteristics of ice. Lastly, our study proposes a combination of ice material properties that can accurately predict collision force.

I deduce an exact and analytic Bianchi type I solution of Einstein’s field equations which generalizes the isotropic ΛCDM universe model to a corresponding model with anisotropic expansion. The main point of the article is to present the anisotropic generalization of the ΛCDM universe model in a way suitable for investigating how anisotropic expansion modifies observable properties of the ΛCDM universe model. Although such generalizations of the isotropic ΛCDM universe model have been considered earlier, they have never been presented in this form before. Several physical properties of the model are pointed out and compared with properties of special cases such as the isotropic ΛCDM universe model. The solution is then used to investigate the Hubble tension. It has recently been suggested that that the cosmic large scale anisotropy may solve the Hubble tension. I consider those earlier suggestions and find that the formulae of these papers lead to the result that the anisotropy of the cosmic expansion is too small to solve the Hubble tension. Then I investigate the problem in a new way, using the exact solution of the field equations. This gives the result that the cosmic expansion anisotropy is still too small to solve the Hubble tension in the general Bianchi type I universe with dust and LIVE (Lorentz Invariant Vacuum Energy with a constant energy density which is represented by the cosmological constant) and anisotropic expansion in all three directions – even if one neglects the constraints coming from the requirement that the anisotropy should be sufficiently small, so that it does not have any significant effect upon the results coming from the calculations of the comic nucleosynthesis during the first ten minutes of the universe. If this constraint is taken into account, the cosmic expansion anisotropy is much too small to solve the Hubble tension.

In order to master the structure state of subway shield tunnel, reduce the diseases and avoid the potential safety hazards, the lateral convergence deformation of subway shield tunnels should be predicted. For the non-stationarity small sample size of subway shield tunnel lateral convergence deformation value, the existing prediction models have poor performance in terms of accuracy and stability. In this paper, a lateral convergence model of subway shield tunnel based on Kalman algorithm is constructed based on Kalman filtering theory. The model is efficient, adaptive and robust, and can accurately predict the lateral convergence deformation of subway shield tunnel. Taking the horizontal diameter of 200 rings shield segment in the interval section of a subway tunnel as an example, it is proved that the residuals of the Kalman prediction model are small, the residual distribution conforms to the normal distribution, and the prediction effect is great. The model is suitable for the prediction of more than 5 periods of data, and the prediction accuracy of the model is improved with the increase of the number of data periods. In addition, the Kalman model is compared with GM(1,1) model and GM-Markov model, and RMSE and MAPE are introduced as evaluation indexes. The results show that the Kalman model has higher prediction accuracy and is more suitable for predicting the lateral convergence deformation of subway shield tunnel.

As a representative of Exponential Organizations, MI went from obscurity to the Fortune 500 in just ten years. Mi's success is inextricably linked to its outstanding business model. This paper summarizes the elements of MI's business model from four perspectives: value proposition, value creation, value delivery, and value capture, as well as its measures for developing into an exponential organization. Expecting to provide theoretical references for the transformation and upgrading of manufacturing organizations.

Eliashberg theory provides a theoretical framework for understanding the phenomenon of superconductivity when pairing between two electrons is mediated by phonons, and retardation effects are fully accounted for. However, when a direct Coulomb interaction between two electrons is also present, this interaction is only partially accounted for. In this work we use a well-defined Hamiltonian, the Hubbard-Holstein model, to examine this competition more rigorously, using exact diagonalization on a two-site system. We find that the direct electron-electron repulsion between two electrons has a significantly more harmful effect on pairing than suggested through the standard treatment of this interaction.

Surface activity of humic acid (HA) and its six sub-fractions isolated from forest soil were characterized by surface tension measurements, dynamic light scattering, and laser doppler electrophoresis. The surface tension of HA sub-fractions decreased from 72.4 mN·m-1 to 36.8 mN·m-1 with the increasing concentration from 0 to 20000 mg·L-1. The critical micelle concentration (CMC) of HA and its sub-fractions ranged from 216 to 1024 mg·L-1 with the multiple linear regression equation of CMC = 18896–6.9×C–296×alkyl-C–331×aromatic-C–17019×H/C+4054×HB/HI (p &lt; 0.05). The Z-average particle size ranges from 108.2 nm to 186.9 nm. The maximum particle size was 5000 nm after filtered with a membrane of 0.45 μm, indicating HA and its sub-fractions progressed self-assembly during 2000 mg·L-1, pH = 6.86. The number-base particle size distribution of HA sub-fractions was identified as four peaks by Gaussian model, and the aggregate sizes were mainly in six clusters including 2 ± 1 nm, 5 ± 2 nm, 10 ± 3 nm, 21 ± 8 nm, 40 ± 10 nm, and &gt; 50 nm indicating multiple aggregations of different particle sizes in the HA fractions solution.

Missing item responses are prevalent in educational large-scale assessment studies like the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians advocated a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. This article uses the PISA 2018 mathematics dataset to investigate the consequences of different missing data treatments on country means. Obtained country means can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.

The study considers building a dynamic model of profit maximization for the car-sharing system and its verification on the example of implementing such a system in Astana (Republic of Kazakhstan). The region, bounded by the administrative boundaries of Astana, was divided into subregions by covering the region with regular hexagons placed side by side. A dataset was built with information on 1168 trips to Astana from January to March 2023. The Kepler visualization service constructed maps of the beginning and end of trips in the region and a map of trips binding to the hexagonal grid cells. Each cell of the grid corresponds to a specific sub-region, for which the quantitative parameters necessary for solving the profit maximization problem in the car-sharing system are calculated. Stations with cars will be placed in the cells of the grid, which will be available to car-sharing service customers. Based on the collected data, a dynamic (four periods per day) and static profit maximization model in the car-sharing system was built. Modeling was carried out based on the built models in the example of Astana. It was established that using a dynamic model of profit maximization in the car-sharing system increases the profit by 3.7%.