Process model discovery covers different methodologies to mine a process model from traces of process executions, and is gaining an important role in Artificial Intelligence research. Current approaches in the area, with few exceptions, focus on determining a model of the flow of actions only. However, in several contexts, (i) restricting the attention to actions is quite limitative, since the effects of such actions have to be analysed, too, and (ii) traces provide additional pieces of information, in the form of states (i.e., values of parameters possibly affected by the actions): for instance, in several medical domains traces include both actions and measurements of patients’ parameters. In this paper, we propose AS-SIM (Action-State SIM), the first approach able to mine a process model which comprehends two distinct classes of nodes, to capture both actions and states.

With the rapid development of network science, Turing pattern on complex networks has attracted extensive attention from researchers. In this paper, we focus on spatial patterns in multiplex ER (Erdös-Rényi) random networks, taking the predator-prey model with Allee effect and hyperbolic mortality as an example. In theory, the threshold condition for generating Turing pattern is given using the Turing instability theory of multiplex networks. Numerically, we design relevant experiments to explore the impact of network topology on Turing pattern. The factors considered include model parameters, diffusion rate, average degree of the network, and differences in the average degree of different layers. The results indicate that the importance of diffusion rate and network average degree for Turing pattern is affirmed on the single-layer network. For multiplex networks, the differentiation of average degrees in different layers controls the generation of Turing pattern, which is not affected by the diffusion rates of the two populations. More interestingly, we observe the switching of Turing pattern and spatiotemporal pattern. We believe that these findings contribute to a better understanding of self-organization on complex networks.

We investigate the impact of incoming nuclear data on the predictions of the neutron star (NS) stripping model for short gamma-ray bursts. The main area to which we pay attention is the NS crust. We show that the uncertain properties of the NS equation of state can significantly influence the stripping time tstr, the main dynamical parameter of the model. Based on the known time delay (tstr≈1.7s) between the peak of the gravitational wave signal GW170817 and the detection of gamma photons from GRB170817A, we obtain new restrictions on the nuclear matter parameters, in particular, the symmetry energy slope parameter: L<114.5MeV. In addition, we study the process of nucleosynthesis in the outer and inner crusts of a low-mass NS. We show that the nucleosynthesis is strongly influenced by both the incoming nuclear data and the equation of state of the NS matter.

To solve the problem of smooth switching between car-following model and lane-changing model, the Intelligent Driver Model (IDM) for a single lane was used to study the driver's behavior switching mechanism of normally following, generating intentions to change lanes, creating space and speed gains, and performing lane change. In the case of sufficient lane-changing space and speed gains, the ego vehicle's intention to change lane was considered to solve the switching boundary between car-following behavior and lane-changing behavior, which is also the IDM failure point. In the event that there is no lane-changing gains, the IDM was optimized by incorporating the constraint components of the target lane vehicles in conjunction with the actual motion state of the ego vehicle, and the Stepless Switching Intelligent Driver Model (SSIDM) was constructed. Drivers' natural driving information was collected and scenario mining was performed on structured roads. On the basis of the collected data, an elliptic equation was used to fit the behavior switching boundary, and the two component balance coefficients of the front and rear vehicles on the target lane were identified. According to the test set verification results, the Mean Square Error (MSE) of the SSIDM is 2.172, which is 57.98% less than that of the conventional single-lane IDM. The SSIDM can accomplish stepless switching comparable to driver's behavior between the car-following behavior and the lane-changing behavior, with greater precision than IDM.

This article examines the wavelet transforms and the machine learning methods in accelerating large eddy simulations of turbulent flows. An overarching challenge of large eddy simulations is to accurately represent the cascade of kinetic energy across the cut-off length scale. Taylor hypothesis suggests that the energy cascade occurs through the process of vortex stretching. However, Richardson hypothesis assumes the self-amplification of the strain field and a hierarchical break down of large eddies lead the energy cascade. The large eddy simulations typically employs the self-amplification of the strain in formulating subgrid models. However, several studies also proposed subgrid models based on vortex stretching. The wavelet-based coherent vortex simulation of turbulence directly accounts for vortex stretching in overall forward scatter of energy, while allowing local backscatter. The wavelet-based large eddy simulation adapts the grid to capture the creation of small-scale eddies, while adopting subgrid models based on the self-amplification of strain. The advancement of artificial intelligence in turbulence modelling is currently evolving around accelerating the numerical simulations of turbulent flow. However, there is a clear connection between the application of wavelet transfroms and neural networks for directly solving the Navier-Stokes equation, indicating some potential benefits of wavelet methods over the neural networks.

he Grassmann variables are used to formally transform a system with constraints into an unconstraint system. As a result, the Schr\"{o}dinger equation arises instead of the Wheeler-DeWitt one. The Schrodinger equation describes a system's evolution, but a definition of the scalar product is needed to calculate the mean values of the operators. We suggest an explicit formula for the scalar product related to the Klein-Gordon scalar product. The calculation of the mean values is compared with an etalon method, in which a redundant degree of freedom is excluded. Nevertheless, we could note that a complete correspondence with the etalon picture is not found. Apparently, the picture with the Grassmann variables requires a further understanding of the underlying Hilbert space.

This paper proposes an imperfectly vaccinated SVEIR model for latent age. We calculate the equilibrium points and basic reproduction number of the model. The asymptotic smoothness, uniform persistence and the existence of the attractor of the semi-flow generated by the solutions of the system are addressed. Moreover, using LaSalle’s invariance set principle and constructing Volterra-type Lyapunov functions, we can prove the global asymptotic stability of both the disease-free equilibrium and the endemic equilibrium of the model. The conclusion is that if the basic reproduction number R0 is less than one, the disease will gradually disappear. On the other hand, if the number is greater than one, the disease will become endemic and persist. Finally, measures that can effectively control the ongoing transmission of the disease have been obtained.

Abstract. Background: COVID-19's first victim was announced by Chinese health authorities on the 11th of January 2020. On January 13, the first official case was reported outside China, in Thailand. On January 25, the same occurred in São Paulo and on March 8, the first case was recorded in Minas Gerais. From that point onwards until the 3rd of October 2020, a total of 370,911 cases and 9,204 deaths were recorded in the state. This study aims to investigate spatiotemporal patterns of COVID-19 with incidence from March 22 to October 3 of 2020. Methods: The database itself was obtained from Health Division of Minas Gerais state. The vulnerability index was calculated using a principal component analysis. Moran's I autocorrelation was tested, z-score and P-value < 0.05. Results: From March 22 to October 3 of 2020 the incidence level varied from 45.680/100,000 to 312.130/100,000. The most influential variables were: illiteracy, gross domestic product and breath apparel per municipality. The clusters were concentrated in the metropolitan area of Belo Horizonte, Zona da Mata and Triangulo Mineiro. Conclusion: The spatial distribution of COVID-19 from week 13 until week 40 showed that different levels of endemicity and mesoregional vulnerabilities were represented in these maps.

By using various skill scores and spatial characteristics of spatial verification methods and tradi-tional techniques of the model evaluation tool (MET, V10.0.0), the gridded precipitation obser-vation CMPAV (V2.0) and three datasets that derived from local (LOC), Shanghai (SHA), and Grapes (GRA) model respectively are conducted to assess the 3 lead day rainfall forecast with 0.5-day intervals during summer of 2020 over central east China. Results have shown that LOC generally outperforms the other two for most skill scores but usually with relatively larger un-certainties than SHA, and it has the least displacement errors for moderate rainfall among the three datasets. However, the rainfall of GRA has been heavily underestimated and accompanied with large displacement error. Both LOC and SHA have shown almost equitable abilities in forecasting convection and rainstorms of the large area but with a slightly over-forecast of local convection, while LOC likely over-forecasts the local rainstorms. In addition, SHA slightly favors over-forecast on a broad scale range and a broad threshold range, and LOC slightly misses the rainfall exceeding 100 mm. Generally, for a broadly comparative evaluation on rainfall, the popular dichotomous methods should be recommended under considering reasonable classifi-cation of thresholds if the accuracy is highly demanded. And most spatial methods should be suggested to conduct with proper pre-handling of non-rainfall event cases. Especially, the veri-fications including spatial characteristic difference information could be recommended in a computationally sufficient environment.

In today's world, news events are emerging incessantly. Classifying the same event reported by different countries is of significant importance for public opinion control and intelligence gathering. Due to the diverse types of news, relying solely on translators would be costly and inefficient, while depending solely on translation systems would incur considerable performance overheads in invoking translation interfaces and storing translated texts. To address this issue, we mainly focus on the clustering problem of cross-lingual news. To be specific, we use a combination of sentence vector representations of news headlines in a mixed semantic space and the topic probability distributions of news content to represent a news article. In the training of cross-lingual models, we employ knowledge distillation techniques to fit two semantic spaces into a mixed semantic space. We abandon traditional static clustering methods like K-Means and AGNES in favor of the incremental clustering algorithm Single-Pass, which we further modify to better suit cross-lingual news clustering scenarios. Our main contributions are as follows: (1) We adopt the English standard BERT as the teacher model and XLM-Roberta as the student model, training a cross-lingual model through knowledge distillation that can represent sentence-level bilingual texts in both Chinese and English. (2) We use the LDA topic model to represent news as a combination of cross-lingual vectors for headlines and topic probability distributions for content, introducing concepts such as topic similarity to address the cross-lingual issue in news content representation. (3) We adapt the Single-Pass clustering algorithm for the news context to make it more applicable. Our optimizations of Single-Pass include adjusting the distance algorithm between samples and clusters, adding cluster merging operations, and incorporating a news time parameter.

Organizational diagnosis (OD) is a comprehensive review of an organization's objectives, re-source allocation, and utilization to achieve those objectives. This study employed OD to en-hance the effectiveness of the Department of Special Education within the Government of Punjab. A questionnaire based on Weisbord's six-box organizational model called the Organ-izational Diagnostic Questionnaire (ODQ), was administered to N=1200 participants, consist-ing of heads of top, middle, and bottom tiers at district and tehsil levels of special education schools, centers, and the main office. Out of these, 506 participants voluntarily completed the ODQ survey. The study findings indicated that the employees of the department possess a good understanding of the organization's objectives. However, there is a lack of opportunities for employee participation in departmental discussions, centralized decision-making, irra-tional task distribution, and unsupportive allocation of human resources. Additionally, there is poor communication, unsatisfactory promotions, incentives, and a lack of knowledge shar-ing among employees, as well as poor inter-relationships, conflicts, and ineffective help-seeking mechanisms. The study also revealed low readiness for change in the organization. Respondents' opinions differed significantly based on their qualifications, job experience, years served, and gender. To improve the department's capacity to absorb change, the study recommends enhancing collaboration and inter-departmental communication, soliciting comprehensive input from all tiers, and promoting employee participation, supportive supervision, and communication. The study contributes to the body of knowledge on OD and highlights the need for improved communication, collaboration, and employee participation, as well as a supportive and supervisory support system to enhance the effectiveness of organizations.

Biogeochemical models estimate soil organic carbon (SOC) sequestration, crop growth, and yield. The DeNitrification and DeComposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice-wheat rotation under organic/inorganic fertilizations with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC under-predicted harvestable grain C-biomass of rice by 29.22% to 42.14% and over-simulated grain C-biomass of wheat by 55.01% with equal amounts of NPK and animal manure applied under CT. However, after calibration by adjusting default values of soil/crop parameters, DNDC simulated harvestable grain C-biomass of both crops very close to observed values (only -2.81% to -6.17% less). DNDC also predicted effects of nutrient management practices on grain C-biomass of rice/wheat under CT/RT using d-index (0.76 to 0.96) and the calculated root mean squared error (RMSE of 165.36 to 494.18 kg C ha-1). DNDC simulated SOC trends for rice-wheat using measured values of several statistical indices. Regression analysis between modeled and observed SOC dynamics was significant with R2 ranging from 0.35 to 0.46 (p < 0.01), and intercept ranging from 0.30 to 1.34 (p < 0.65). DNDC demonstrated that combined inorganic and organic fertilization may result in higher C-biomass and more SOC sequestration in rice-wheat systems.

In eutrophic waters, such as Mikawa Bay, Japan, anoxic bottom water develops in summer. This causes sulfide release into seawater by sulfate reduction in the sediment, leading to environmental problems. The addition of iron to sediments is a method used to improve the sediment environment, which was devised from a natural phenomenon. However, this method has not yet been quantitatively evaluated. In this study, we aim to quantitatively evaluate the suppressive effect of iron on sulfide release. First, we develop a sediment model that focuses on S and Fe. We then attempted to reproduce the observations and experiments on sulfide dynamics using the model. Consequently, the proposed model was able to reproduce field sulfur and Fe dynamics in sediments. Additionally, the model described the characteristics of sulfide release considering the effects of additive iron materials. Finally, we conducted predictive calculations and quantitatively evaluated the effects of adding iron materials to the sediments in terms of sulfur and iron cycles.

The propagation of electromagnetic waves beyond the line of sight can be caused by atmospheric ducts, which are a significant concerns in the fields of radar and communication. This paper utilizes data from seven automatic stations and five radio-sounding stations to statistically analyze the characteristics of the atmospheric ducts in the northwest region of the South China Sea (SCS). After verifying the practicality of numerical analysis data from NCEP CFSv2 and ERA5 in studying atmospheric ducts using measured data, we analyzed the space-time distribution characteristics of the height of the regional evaporation duct and the bottom height of the elevated duct. Using the parabolic equation model, we simulated electromagnetic propagation loss under different frequencies and radiation elevation angles in both uniform and non-uniform duct environments within a typical atmospheric duct structure. The study found that the NCEP CFSv2 data accurately captures the evaporation duct height and duct occurrence rate in the study area, and the elevated duct bottom height obtained from the inversion of ERA5 and the measured data has a good consistency. The occurrence rate and height of evaporation duct in coastal stations in the northwest of the SCS vary significantly by month, demonstrating clear monthly distribution patterns. Conversely, changes in the Xisha station are minimal, indicating good temporal uniformity. For lower atmospheric ducts, the difference in occurrence rates between 00:00 and 12:00 (UTC) is negligible. The occurrence probability of elevated ducts in the Beibu Gulf area is relatively high, mainly concentrated from January to April, and the Xisha area is dominated by surface ducts without foundation layer, mainly concentrated from June to August. The monsoon plays a critical role in the generation and evolution of atmospheric ducts in the northwest of the SCS, with the height of the evaporation duct increasing and the bottom height of the elevated duct decreasing after the onset of the summer monsoon. Electromagnetic propagation simulations demonstrate that higher frequency and lower elevation angles of radiation sources in the trapping layer of the evaporation duct make it easier to be trapped. As the evaporation duct height decreases, the amplitude of the "sinusoidal fluctuation" of the propagation loss also decreases. Frequency changes of the radiation source in the surface duct environment have minimal impact on electromagnetic propagation loss, but the elevation angle of the radiation source is a critical factor. The frequency of the "sinusoidal fluctuation" in propagation loss is higher in a hybrid duct environment compared to a uniform surface duct. Additionally, the propagation loss increases faster with distance at the height of the evaporation duct, resulting in greater electromagnetic propagation loss.

Physics of high-Tc superconducting cuprates is obscured by the effect of strong electronic correlations. One way to overcome the problem is to seek for an exact solution at least within the small cluster and expand it to the whole crystal. Such an approach is in the heart of the cluster perturbation theory (CPT). Here we develop CPT for the dynamic charge and spin susceptibilities (spin-CPT), within which the correlation effects are explicitly taken into account by the exact diagonalization. We apply spin-CPT to the effective two-band Hubbard model for the cuprates obtained from the three-band Emery model and calculate one- and two-particle correlation functions, namely, spectral function and spin and charge susceptibilities. Doping dependence of the spin susceptibility was studied within spin-CPT and CPT-RPA that is the CPT generalization of the random phase approximation (RPA). Both methods produce the low energy response at four incommensurate wave vectors in qualitative agreement to the results of the inelastic neutron scattering on overdoped cuprates.

The study aims to investigate Gen Z's intentions to utilize the Waze mobile application as a solution for sustainable traffic management in Meto Manila. The extended Technology Acceptance Model (TAM) was used to define user behavior and technology usage to determine the factors influencing Filipino drivers' application acceptance of Waze. The study’s data results have revealed that the System Quality, Perceived Location Accuracy, Perceived Usefulness, and Perceived Ease of Use affect Filipinos' intentions to use traffic navigation applications, particularly Waze. The study utilized a survey and was given to 300 Filipino drivers traveling within the National Capital Region to accurately measure the factors and their connections to usage intention and actual use. The analytical approach used for this investigation was partial least squares structural equation modeling (PLS-SEM). The study's results can be applied as a theoretical framework for future researchers of Waze mobile applications, Waze route accuracy, and Waze overall application performance, as well as the features of the Waze application, to evaluate how the use of the Waze mobile application could help in the effective management of traffic situations in Metro Manila. The study was able to assess the effectiveness of the Waze navigation app as a platform for efficiently managing traffic situations in Metro Manila. Moreover, this study also highlighted the application performance of Waze and its acceptance by Filipino drivers in Metro Manila. Furthermore, the study implores a critical review of Waze's application performance. The study recommends that similar future studies must consider samples from a more diverse geographic background to obtain a more accurate representation of Filipinos traveling. Future researchers are also advised to adopt a qualitative research design to gain more detailed insights of Filipinos regarding their experience with the Waze application.

Fracture of viscoplastic materials is a complex process due to its time-dependent and plastic responses. Numerical simulation for fracture plays a significant role in crack prediction and failure analysis. In recent years, the phase field model is becoming a competitive approach to predict crack growth and has been extended to inelastic materials such as elasto-plastic, viscoelastic and viscoplastic materials, etc. However, the contribution of inelastic energy to crack growth is seldom studied. For this reason, we implement the phase field model coupled with a viscoplastic constitutive in finite element framework, in which the elastic energy and inelastic energy are used as crack driving energy. The implicit algorithm for a viscoplastic constitutive is presented, this procedure is suitable for other viscoplastic constitutive relations. The strain rate effect, creep effect, stress relaxation effect and cyclic loading responses are tested using a single element model with different inelastic energy contributions; A titanium alloy plate specimen and a stainless-steel plate specimen under tension are studied compared with the experimental observations in existing literature. The results show that the above typical damage phenomenon and fracture process can be well reproduced; The inelastic energy significantly affects the force-displacement responses but has slight effect on the crack paths.

Threshing device is the core component of grain combine harvester, and its working performance determines the working quality and production efficiency of the harvester to a great extent. The straw guide plate plays an important role in the threshing device to dredge the threshing matter and control the threshing time of the combine harvester matter in the threshing chamber. In the past, the guiding structure of threshing device could not optimize the working performance of the machine by adjusting the spiral angle, that is, most cereal combine harvester may not be fed smoothly or even clogged when operating under different crop types, yield differences and operating environments, a kind of threshing device with adjustable guide straw board was studied. In this study, the movement model of the straw on the straw guide plate was analyzed, and it was found that the spiral angle “α”of the straw guide was 0<α<58°.Three-factor and three-level response surface method was used to carry out field experiment on drum speed, working speed and guide grass plate spiral angle, and the experimental data were analyzed by quadratic polynomial regression.The results show that the drum speed, working speed and the spiral angle of the guide grass plate have significant effects on the loss rate (PLR) , impurity content (PIR) and breakage rate (PBR) , the drum speed and working speed have an interactive effect on the impurity ratio (PIR) , and the working speed and the helical angle of the guide grass plate have an interactive effect on the loss ratio (PLR) and breakage ratio (PBR) , there is an interaction between the rotary speed of the roller and the helical angle of the guide grass plate on the crushing rate (PBR) .Further optimization analysis showed that the predicted value was PLR = 1.18% , PIR = 0.72% , PBR = 0.53% , the experimental verification value was PLR = 1.26% , PIR = 0.73% , PBR = 0.61% . The absolute error between the experimental value and the predicted value was very small, however, the optimized field test verified value decreased by 8.31% , 50.04% and 60.30% respectively, which indicated that the optimized harvester had better operation quality, in particular, PIR, PBR and other two indicators have a greater increase in the effect.

Many clinical trials end uninformatively. Informativeness, in the context of clinical trials, defines whether a study’s results definitively answer its research questions with meaningful next steps. One subset of these trials are those focused on global health set in low-resource settings. Global health clinical trials benefitting people in low-resource settings are funded primarily by a limited number of large foundations, pharmaceutical firms (“industry”), and national governments. While clinical trial protocols are required to go through reviews in regulatory and ethical domains, outside of industry-funded trials, funders rarely require focused scientific design reviews. There are no documented standards and processes, or even best practices, for funders to perform scientific reviews after the funding commitment. Considering the investment in and standardization of ethical and regulatory reviews, and the prevalence of studies ending without clarity or never finishing, it may be that scientific reviews of trial designs with a focus on informativeness offer the best chance for improved outcomes and return on investments in clinical trials. A maturity model is a helpful tool for knowledge transfer to help grow capabilities in a new area, or for those looking to perform a self-assessment in an existing area. Such a model is offered for scientific design reviews of clinical trial protocols: a valuable and often-neglected governance step for funders or sponsors, among others. This maturity model includes 11 process areas and 5 maturity levels. Each of the 55 process area levels is populated with descriptions on a continuum toward an optimal state to improve trial protocols in the area of risk of failure. This tool allows for prescriptive guidance on next investments to improve attributes of post-funding reviews of trials, with a focus on informativeness.

Diethyldithiocarbamate (DDTC) has been employed in the sulfide ore flotation process, due to its excellent collection performance. Herein, we investigated the interfacial adsorption behavior of DDTC on four mineral phases of high-sulfur residue — sulfur, pyrite, sphalerite and lead sulfate. The adsorption behavior — adsorption structure and energy and electron localization function cross section — of DDTC and H2O were explored using density function theory calculation. The result was helpful to construct a co-adsorption model of DDTC and H2O, which was validated by the pure minerals flotation operation and the characterization of fourier transform infrared spectra. The co-adsorption model indicated that the DDTC adsorption on the sulfur, sphalerite and lead sulfate was weak with physical bonding, while its adsorption on the pyrite was strong with chemical bonding. Practical bench-scale operation of high-sulfur residue flotation was performed, whose result was different from that obtained from the pure mineral flotation. To explain the difference, our developed model predictions and the mineral fugacity pattern analysis on the high-sulfur residue flotation were synergistically used. Overall, this work first proposed the co-adsorption model of DDTC and H2O and provided important insights into the interfacial adsorption in the high-sulfur residue flotation.

Considering the dynamic stall effects in engineering calculations is essential for correcting the aerodynamic loads acting on wind turbines, both during power production and stand-still cases, and impacts significantly the turbine aeroelastic stability. The employed dynamic stall model needs to be accurate and robust for a wide range of airfoils and range of angle of attack. The present studies are intended to demonstrate the performance of a recently implemented "IAG dynamic stall" model in a wind turbine design tool Bladed. The model is transformed from the indicial type of formulation into a state-space representation. The new model is validated against measurement data and other dynamic stall models in Bladed for various flow conditions and airfoils. It is demonstrated that the new model is able to reproduce the measured dynamic polar accurately without airfoil specific parameter calibration and has a superior performance compared to other models in Bladed.

This study investigates the possible mechanisms related to the wide range of relative humidity in cirrus clouds (RHi). Under the closed adiabatic assumption, the impacts of vertical motion and ice crystal deposition/sublimation on RHi are investigated through in situ observations and parcel model simulations. Vertical motion is an active external force that changes the RHi, and ice crystal deposition/sublimation plays a role in mitigating the change in RHi. They are the two most important mechanisms involved in controlling the RHi fluctuation during cirrus evolution and could well explain the wide range of RHi in wave-related cirrus clouds. Furthermore, a comparison of cloud statistical characteristics from both observations and simulations shows that a very low value (e.g., 0.001) for the water vapor ice deposition coefficient is highly unlikely.

Liver fibrosis is based on complex interactions between extracellular matrix-producing hepatic stellate cells and is defined as excessive matrix deposition and an abundance of infiltrating cells in the liver. Studying these processes requires in vitro and in vivo experimental work on animals. In fibrosis research, experimental work in rodents is currently the gold standard for confirming a proposed disease-associated mechanism. Bile duct ligation (BDL) induces obstructive jaundice resulting in cholestasis. BDL is useful for preclinical research studies of liver injury due to extrahepatic cholestasis such as apoptosis and fibrosis. Here we provide a stepwise surgical approach to resect the common bile duct and monitor in situ hepatic metabolism by microdialysis.

Based on orbital angular momentum (OAM) properties of Laguerre Gaussian beams LG(p,ℓ), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and Machine-learning detection method. In the encoding process, the intensity profile for data encoding is generated based on selection of p and ℓ indices, while the decoding process is performed using support vector machine (SVM) algorithm. Two different decoding models based on SVM algorithm are tested to verify the robustness of optical encoding model, finding a BER =10−9 for 10.2dB of signal-to-noise ratio in one of the SVM models.

As the rate of discovery of new antibacterial compounds towards multidrug resistant bacteria is declining, there is an urge for the search of molecules that could revert this tendency. Acinetobacter baumannii has emerged as a highly virulent Gram-negative bacterium that has acquired multiple mechanisms against antibiotics and is considered of critical priority. In this work we developed a quantitative structure-property relationship (QSPR) model with 592 compounds for the identification of structural parameters related to their property as antibacterial agents against A. baumannii. QSPR mathematical validation (R2 = 70.27, RN = -0.008, aR2 = 0.014 and δK = 0.021) and its prediction ability (Q2LMO= 67.89, Q2EXT = 67.75, a(Q2)= -0.068, δQ = 0.0, rm2 = 0.229, and ∆rm2 = 0.522) were obtained with different statistical parameters; additional validation jobs were done using three sets of external molecules (R2 = 72.89, 71.64 and 71.56). We used the QSPR model to perform a virtual screening on the BIOFACQUIM natural product database. From this screening our model showed that molecules 32 to 35 and 54 to 68, isolated from different extracts of plants of the Ipomoea sp., are potential antibacterial against A. baumannii. Furthermore, biological assays showed that molecules 56 and 60 to 64 have a wide antibacterial activity against clinical isolated strains of A. baumannii, as well as other multidrug resistant bacteria including Staphylococcus aureus, Escherichia coli, Klebsiella pneumonia, and Pseudomonas aeruginosa. Finally, we propose 60 as a potential lead compound due to its broad-spectrum activity and its structural simplicity. Therefore, our QSPR model can be used as a tool for the investigation and search of new antibacterial compounds against A. baumannii.

This paper presents the experimental and numerical studies of the vertical stress distribution beneath the centerline of circular footings resting on the sand. Vertical stress values due to circular load in medium-dense sand were measured using soil stress transducers. Reduced-scale laboratory model tests were carried out in a square-shaped test box and vertical stresses were measured at the centerline of the circular footing at different depths of the sand. In order to determine the effect of footing width on vertical stress distribution, laboratory experiments were performed on circular footings of different diameters. In addition, a series of finite element analyses was carried out on the test model to validate the findings of the experimental study. In the numerical analysis, the sand was modeled as a non-linear elastoplastic material. The analyses were performed using the finite element method with two-dimensional axisymmetric and three-dimensional conditions. The results obtained from laboratory model tests and numerical analysis were compared with the vertical stress values obtained from the theoretical solution in the literature. Vertical stress distribution in horizontal planes at any depth from the footing base shows the same characteristic form in all methods. However, some differences were observed between experimental, numerical, and theoretical results and discussed.

The main objective of this work is the design and implementation of self-adaptive capabilities in wireless sensors by applying control engineering and model-based design methodologies. It has been addressed the problem related to the changes in the flow of data packets through the network connection and the excess energy consumption that this causes in these devices. To design the solution, a systemic characterization of the scheduling and execution process of embedded tasks on the device has been carried out. This means defining cause-effect relationships in the system and its modelling theoretically and/or experimentally. In turn, these models facilitate the design of control strategies to improve the dynamic behavior of the system. As a solution, a self-adaptation strategy based on feedforward control algorithm has been designed and developed, which has been applied to improve the dynamic behavior and resource consumption. The developed solution has been satisfactorily evaluated experimentally.

Background: The first few minutes after birth are the most dangerous for the survival of an infant. Babies in neonatal intensive care units are either under heated or overheated, and hypothermic infants remain hypothermic or develop a fever. As a result, special attention must be paid to monitoring and maintaining the time of recovery from hypothermia states. Despite numerous studies, only a few have examined the transition from neonatal hypothermia and associated risk factors in depth. Method: A retrospective observational study was conducted to track axillary temperatures taken at the time of neonatal intensive care unit admission, which were then tracked every 30 minutes until the newborn's temperature stabilized. All hypothermic neonates admitted to the neonatal intensive care unit between January 2018 and December 2020 was included in the study. Temperature data were available at birth and within the first three hours of admission for 391 eligible hypothermic neonates. The effect of factors on the transition rate in different states of hypothermia was estimated using a multi-state Markov model. Result: The likelihood of progressing from mild to severe hypothermia was 5%, while the likelihood of progressing to normal was 34%. The average time spent in a severe hypothermia state was 48, 35, and 24 minutes for three different levels of birth weight, and 53, 41, and 31 minutes for low, moderate, and normal Apgar scores, respectively. Furthermore, the mean sojourn time in a severe hypothermia state was 48, 39, and 31 minutes for three different levels of high, normal, and low pulse rate, respectively. Conclusion: For hypothermic survivors within the first three hours of life, very low birth weight, low Apgar, and high pulse rate had the strongest association with hypothermia and took the longest time to improve/recover. As a result, there is an urgent need to train all levels of staff dealing with maintaining the time of recovery from neonatal hypothermia.

Poso regency, Central Sulawesi, Indonesia, has a coastal area that has marine tourism potential to be developed. It is expected that marine tourism can bring socio-economic impact to the com-munity. This research was conducted with the objective of assessing the suitability of the area to be developed as a marine and coastal tourism site to provide benefits to the coastal community. Hydrodynamic model will be used in this research as coastal area mapping. As an approach, Analysis Hierarchy Process (AHP) is utilized whose parameters consist of depth, coast type, coast width, brightness, current speed, water base materials, observation of dangerous biota and availability of fresh water. Based on the overall mapping area of 98,644 ha, the research results show that the area that can be utilized is 7,979 ha with a very suitable category, while there is an area of 1,045 ha which can still be classified in the appropriate category.

The objective of this study is to design an optimal model predictive control (MPC) strategy using manipulated variables to control the production of a sufficient amount of hydrogen through low temperature autothermal reforming of dimethyl ether (DME), a reforming reaction performed using PdO/ZnO/γ-Al2O3 catalysts coated on honeycomb cordierite ceramics. Experiments and simulation have verified that the optimal activity temperature of the catalyst is 400 °C, and the hydrogen volume fraction in syngas is over 43%. In the implementation of the hydrogen production system, the MPC controller can precisely determine the feed rates of DME, high-purity air, and water based on the space state equation of the reformer, to achieve the anti-disturbance of the reformer temperature. Thus, the reduction of hydrogen yield and sintering of the catalyst as a result of overheating are prevented. As the static and dynamic performance of hydrogen production exhibits excellent tracking of the setpoints, an autonomous, automated, and reliable continuous system was designed to meet the desired hydrogen demand situation. This study shows that an autonomous, automated, and reliable continuous hydrogen production reforming system can be designed to actively respond to the on-board hydrogen usage situation.

Purpose: To construct a hidden Markov model (HMM) for vigilance assessment to improve the real-time performance and accuracy of current vigilance measurement. Methods: ECG signal was collected by sensors, while the noise and baseline drift was eliminated from the original ECG signal. 10 volunteers were randomly selected. Their heart rate variability (HRV) were measured and trained parameters of the modified Hidden Markov model for vigilance assessment. Then, these data were collected to optimize using the Baum-Welch algorithm and obtained the state transition probability matrix A ̂ and the observation probability matrix B ̂. Finally, the data of three volunteers with different transition patterns of mental state were selected randomly and used the Viterbi algorithm to find the optimal state, which compared with the actual state. Results: The constructed vigilance assessment model had a high accuracy rate the accuracy rate of data prediction for these three volunteers exceeded 80%. Conclusion: The Hidden Markov model for vigilance assessment can accurately predict the vigilance level and indicate broad application prospects.

According to known laws of physics, chronic positive mass balance is the actual etiology of obesity, not positive energy balance. The relevant physical law in terms of body mass regulation is the Law of Conservation of Mass, not the Law of Conservation of Energy. A recently proposed mass balance model (MBM) describes the temporal evolution of body weight and body composition under a wide variety of feeding experiments, and it seems to provide a highly accurate description of the very best experimental human feeding data. By shifting to a mass balance paradigm of obesity, a deeper understanding of this disease may follow in the near future. The purpose of this living review is to present the core issues of the upcoming paradigm shift as well as some practical applications related to the topic.

Interprofessional collaboration between general practitioners (GPs) and community pharmacists (CPs) is central to implement antimicrobial stewardship (AMS) programs in primary care. This study aimed to design a GP-pharmacist antimicrobial stewardship (GPPAS) model in Australian primary care. A seven-component exploratory study was conducted since 2017 to 2021 to inform a GPPAS model. We generated both secondary and primary evidence through a systematic review, a scoping review, a rapid review, nationwide surveys of Australian GPs and CPs including qualitative components and a pilot study of a GPPAS model. All study evidence was synthesised, reviewed, merged and triangulated to design a prototype GPPAS model using a Systems Engineering Initiative for Patient Safety theoretical framework. Secondary evidence informed effective GPPAS interventions, and primary evidence captured interprofessional issues, challenges and future needs to implement GPPAS interventions by GPs and CPs. A GPPAS model framework involving GP-pharmacist team-based five GPPAS sub-models were successfully designed to foster AMS education, antimicrobial audits, diagnostic stewardship, delayed prescribing, and routine review of antimicrobial prescription by improved GP-CP collaboration. A GPPAS model could be used as a guide to collaboratively optimise antimicrobial use by GPs and CPs. Implementation studies on GPPAS model and sub-models are required to integrate GPPAS model into GP-pharmacist interprofessional care models in Australia.

The dynamics of many complex systems can be classified as ordered, chaotic, or critical. Order offers stability and robustness, while chaos allows for change and adaptability. Criticality, then, is often seen as a balance required by living systems at different scales. In classical models, however, criticality is only found near phase transitions, restricting the parameter space (and thus the likelihood) of critical dynamics, as most parameters yield ``undesirable'' solutions. Here we show that this limitation is due to the homogeneity built-in these models, i.e., all elements sharing parameter values. By exploring heterogeneous versions of archetypal models in physics and computer science, we observe critical dynamics in a broader range of parameters, and thus could be more common than previously thought.

Mental health and social resilience play a significant role in refugees’ adaptation during the resettlement process in the host country. Maintaining good mental wellbeing helps the refugees to respond to stressful experiences with healthy life choices. This study aimed to explore the mental wellbeing and social resilience of Eritrean refugees living in Germany and to identify social conditions and enablers to foster adaptation. This study employs a qualitative approach with a semi-structured, in-depth interview data collection method. Informants were identified among mostly young adult refugees living in Heidelberg, Germany, with a migration history of 3-6 years. In total, 15 informants were recruited through snowball sampling. Data were sorted and analyzed using the five pillars of the Adaptation and Development after Persecution and Trauma (ADAPT) model. The findings suggest that Eritrean refugees experience psychological distress after resettlement in Germany, however with time, their mental health has improved. The study revealed conditions that were experienced as hindrances, as well as ones that were considered to be resources of positive mental wellbeing and social resilience for resettled refugees. Challenges described were the language barrier, discrimination, unemployment, insecure residence status, loss of family and friends, conflict within the diaspora community, and isolation. The main sources of mental wellbeing and social resilience include the feeling of being welcomed by local communities, access to social services, adopting new relationships, and educational opportunities. These experiences encouraged refugees to have a favorable view of their lives and futures as well as also found to facilitate better integration and adaptation. Understanding refugee mental wellbeing and social resilience require a multidimensional perspective. Eritrean refugees living in Germany have experienced and still are experiencing resettlement challenges, as for example loss of family and friends, negative perception of the German system, loss of past achievements, or unemployment. But they have developed adaptive and resilience mechanisms, too, such as seeing an opportunity for a better life, adopting new roles, and accepting Germany as a “second home”. In addressing those by the refugees as hindrances reported issues, these could be turned into sources of mental well-being and resilience.

The higher-order interactions in complex systems are gaining attention. Extending the classic bounded confidence model where an agent’s opinion update is the average opinion of its peers, this paper proposes a higher-order version of the bounded confidence model. Each agent organizes a group opinion discussion among its peers. Then, the discussion’s result influences all participants’ opinions. Since an agent is also the peer of its peers, the agent actually participates in multiple group discussions. We assume the agent’s opinion update is the average over multiple group discussions. The opinion dynamics rules can be arbitrary in each discussion. In this work, we experiment with two discussion rules: centralized and decentralized. We show that the centralized rule is equivalent to the classic bounded confidence model. The decentralized rule, however, can promote opinion consensus. In need of modeling specific real-life scenarios, the higher-order bounded confidence is convenient to combine with other higher-order dynamics, from the contagion process to evolutionary dynamics.

The fault diagnosis of the gearbox is primarily based on the analysis of the harmonic response. However, the generation mechanism of the harmonic response of the gear pair is still not distinctly explained in a healthy gearbox. Therefore, this paper is dedicated to establishing a dynamic model of the gearbox for this issue. In this model, the contact force of the bearing and the meshing stiffness of the gear pair are determined by the Hertz theory and the potential energy method, then the shaft motion is used as the connection to establish the coupling relationship between the gear pair and the bearing. After this dynamic model is confirmed by experiments and existing models, the coupling relationship inside the gearbox and the harmonic response of the gear pair are studied using this model. The result demonstrates that a closed control loop is formed due to the vibration transmission between the gear pair, the bearing and the shaft, and there is a strong coupled vibration between the gear pair and the bearing in the gearbox. Due to the generation of the center distance error, misalignment angle and disorganized meshing process of the gear pair, the meshing stiffness is altered, which excites the harmonic response of the gear pair. When the speed of the gearbox is increased, although the vibration amplitude of the gearbox is increased, the system predominantly vibrates at the fundamental frequency, and the harmonic of the gear pair is also shifted to the low frequency band.

Ankylosing spondylitis (AS) is a chronic inflammatory disorder with unknown etiology and hard to early diagnose. It’s imperative to investigate the changes in AS patients’ peripheral blood, which may contribute to the diagnosis and further understanding of AS. Common differential expressed genes between normal and AS patients in GSE73754 and GSE25101 were screened by machine learning algorithms. IL2RB and ZDHHC18 were hubgenes screened and a diagnostic model was established. C-indexes and calibration analyses suggested high prediction accuracy of the model in training and validation cohorts. The AUC values of the model in GSE73754, GSE25101, GSE18781 and GSE11886 were 0.86, 0.84, 0.85 and 0.89 respectively. Decision curve analyses suggested high net benefit by the model. Functional analysis of the differential expressed genes indicated that they were mainly clustered in processes related to immune response. Immune microenvironment analysis revealed that neutrophils were expanded and activated in AS, while some T cells were decreased. IL2RB and ZDHHC18 were potential blood biomarkers for AS and might be used for early diagnosis and a supplementary diagnostic tool to the existing methods. Our study deepened the insight into the pathogenesis of AS.

In this note, we provide some effective treatments of a general linear model with adding-up restrictions via algebraic operations of given vectors and matrices in the model, including analytic expressions of the well-known ordinary least-squares estimator (OLSE) and the best linear unbiased estimator (BLUE) of the unknown parameters in the model.

Solar assisted hybrid cooling systems are promising for the energy saving of refrigeration systems. In most cases, the solar thermal gain is only able to power the heat-driven process of facilities in part of the working period. Therefore, the reduction of compressor power strongly depends upon the duration of heat-driven processes, which has not been addressed properly. Motivated by such knowledge gap, the thermodynamic understanding of solar assisted hybrid cooling systems is deepened through considering the duration in heat-driven processes. Three absorption-compression integrated cooling cycles were taken as examples. It is found that optimal parameters, e.g., inter-stage pressure and temperature, corresponding to various performance indicators trend to be identical, as the duration of heat-driven processes is taken into account. Furthermore, the optimal parameter for different working conditions was obtained. It is displayed that the dimensionless optimal intermediate temperature of layout with the cascade condensation process varies slightly, e.g., 4%, for different conditions. Moreover, the fall of compressor power in entire working periods is nearly independent upon the intermediate temperature. The paper is favorable for the efficient design and operation of solar assisted hybrid cooling systems.

The ABCDEFG Protocol provides an evolving tool for imposing structure on the flow of Covid infection information obtained from community testing, collective policy and individual compliance. ABCDEFG Protocol could not assume soundness, invariance, symmetry and completeness of the available information and relied on signaling game theory to design solutions that could evolve with the variable narratives, theories, individual utilities and pathogen variants. Thus, ABCDEFG Protocol suggests a novel and a very flexible pool-testing and badging protocol in the context of controlling contagious epidemics and tackling the far-reaching associated challenges, including understanding and evaluating individual and collective risks of returning prior infected individuals to normal society and other economic and social arrangements and interventions to protect against disease. AbCDEFG Proto uses both control theoretic and game theoretic mathematical models that may be centralized (an optimizing policy maker mandates behavior based on estimated models) or decentralized (a strategizing individual selects their behavior based on available asymmetric information). ABCDEFG protocol demonstrates how society can continue to carry out plausible economic activities in addition to controlling the prevalence of a contagious disease by keeping the number of infected people below a desired limit without compromising an individuals' privacy despite the presence of deception and selfishness among people, and limitations of available resources. Different types of badges would come with different restrictions. Badges would be reissued periodically by third-party testing centers via suitably frequent pool testing of samples of the participants. The size of the pools, frequency of tests, and allowable activities for people with a given type of badge would depend on the available resources, the prevalence of the disease, and the efficacy of the equipment used in the tests.

Water resources are under pressure because of human activities. Its management faces the challenge of enhancing long-term water security while minimizing undesirable economic, social, and environmental impacts along with its production chain. Since water and wastewater treatment plants are designed to maintain and conserve freshwater provisioning services, it is paramount to understand how it operates before proposing options for sustainability. At this point, the diagnosis phase claims for methods scientifically-based, systemic, and more objective to provide information for decision-makers towards strategic management of water resources. This work applies the five-sector sustainability model (5SenSu) to assess Brazil's twenty major water and wastewater treatment companies (WWTC) to quantify their sustainability levels that allow ranking procedures and the establishment of benchmarks for improvements. Under comparative basis, results identified the top-three sustainable companies, CORSAN, CASAN, and SANEPAR, which should be considered examples of best practices. Specifically, the following best-ranked companies in each sector within 5SenSu should be used as benchmark patterns for more oriented best practices: SANEAGO, sector 1; AGESISA, sector 2; CORSAN, sector 3; CASAL, sector 4; MA, sector 5. This work contributes to the advancement in modeling sustainability assessment of human-managed systems (applied in WWTCs in this present study) from a systemic and epistemologically rooted approach, avoiding shortcomings and misleading discussions on the sustainability issue. Quantifying sustainability of WWTCS from 5SenSu allows the identification of those sectors/indicators that requires immediate cleaner production practices by decision-makers to improve overall sustainability, besides identifying those companies more aligned with the requirements of UN SDGs.

Glioblastoma (GB) is the most frequent malignant brain tumor among adults and currently there is no effective treatment. It is a very aggressive tumor that grows fast and spreads through the brain causing the death of patients in 15 months. GB cells mutate frequently and generate a heterogeneous population of tumoral cells genetically distinct. Thus, the contribution of genes and signaling pathways relevant for GB progression is of great relevance. We use a Drosophila model of GB that reproduces the features of human GB, and describe the upregulation of the circadian gene cry in GB patients and in a Drosophila GB model. We study the contribution of cry to the expansion of GB cells, to the neurodegeneration caused by GB, and to premature death and determine that cry is required for GB progression. Moreover, we analyze the mechanisms that regulate cry expression by the PI3K pathway. Finally, we conclude that cry is necessary and sufficient to regulate myc expression in GB. These results contribute to the understanding of the signals that impulse GB malignancy and lethality and open novel opportunities for the treatment of GB patients.

Abstract: Urban regeneration decision-making is a complex process, as it involves a wide range of decision-makers, public-private partnerships in finance and implementation, including the inevitable considerable amount of risk on a long-term basis. There are a multitude of stakeholders, the citizens being the key stakeholders. It is necessary to involve the citizens in the planning process. Such involvement allows the communities to express their needs and aspirations, which is useful in the policymaking, delivery of planning programs, and in the monitoring process. In such a context, Factor analysis was the statistical technique used (1) Carry out factor analysis based on the principal component analysis method using the software XLSTAT 2021.4.1.1205 - (2) Construct a factor model of Urban Regeneration. (3) Interpret and label the factor dimensions. The results of the analysis indicated that the first two principal components accounted for 60.04% of the total variance of the original dataset. All variables seemed to be, positively correlated to each other and contributed similarly to principal components PC1 &PC2. The observations were well clustered; except for very few outliers. The limitation of the work was that the perceptions of the citizens were limited to the variables derived by the researcher.

The increase in the number of firms manipulating financial reports has misled shareholders' investment decisions and resulted in an indelible blot on foreign investors’ trust. Due to earnings management (EM) practice, managers' inefficiency, and lack of transparency in Iraq companies. This study tested the influence of the corporate governance mechanisms (CG), (board independence, audit committee, meeting frequency) on EM based on agency theory, as well, to link between EM and firm's performance (FP) in Iraqi listed companies and the impact of moderating role of corporate social responsibility (CSR) based on the Stakeholder Theory. The study's sample consists of 65 companies for the 2013-2018 financial years. Data were collected mainly from the annual reports (secondary data) of the Iraqi listed firms. This study uses the M-score model to detect EM practices as practical techniques in detecting earnings manipulation practices. The panel static model estimators. Hence, this paper adds to the CG literature from the perspective of stakeholder theory using Iraq's unique industrial environment. Based on the research results, policy-makers might use the study‘s findings to recognize the essential roles of several CG mechanisms in alleviating the opportunistic practices in Iraq. Further, companies should also be encouraged to enhance the CSR disclosure quality.

Amyloid-beta plays an important role in the pathogenesis of Alzheimer’s disease. Aberrant amyloid-beta and tau accumulation induce neuroinflammation, cerebrovascular alterations, synaptic deficits, functional deficits, and neurodegeneration, leading to cognitive impairment. Animal models recapitulating the amyloid-beta pathology such as transgenic, knock-in mouse and rat models have facilitated the understanding of disease mechanisms and development of therapeutics targeting at amyloid-beta. There is a rapid advance in high-field MR in small animals. Versatile high-field magnetic resonance imaging (MRI) sequences such as diffusion tensor imaging, arterial spin labelling, resting-state functional MRI, anatomical MRI, MR spectroscopy as well as contrast agents have been developed for the applications in animal models. These tools have enabled high-resolution in vivo structural, functional, and molecular readouts with a whole brain field-of-view. MRI have been utilized to visualize non-invasively the amyloid-beta deposits, synaptic deficits, regional brain atrophy, impairment in white matter integrity, functional connectivity, cerebrovascular and glymphatic system in animal models of amyloidosis. Many of the readouts are translational in clinical MRI in the brain of patients with Alzheimer’s disease. In this review, we summarize the recent advance of using MRI for visualizing the pathophysiology in amyloidosis animal model. We discuss the outstanding challenges in brain imaging using MRI in small animal and propose future outlook in visualizing amyloid-beta-related alterations in brain of animal models.

This study is the first to examine whether the performance of credit rating, one of the most important data-based decision-making of banks, can be improved by using banking system log data that is extensively accumulated inside the bank for system operation. This study uses the log data recorded for the mobile app system of Kakaobank, a leading internet bank used by more than 14 million people in Korea. After generating candidate variables from Kakaobank's vast log data, we develop a credit scoring model by utilizing variables with high information values. Consequently, the discrimination power of the new model compared to the credit bureau grades was significantly improved by 1.84% points based on the Kolmogorov–Smirnov statistics. Therefore, the results of this study imply that if a bank utilizes its log data that have already been extensively accumulated inside the bank, decision-making systems, including credit scoring, can be efficiently improved at a low cost.

Many models with structures of matter associated with a structure of extra spatial dimensions have been proposed in recent decades. On employing a further generalisation from the local 4-dimensional spacetime form to a general form for proper time, we describe how matter fields resembling the Standard Model of particle physics can be accommodated far more directly than with a higher-dimensional spacetime theory. The successful identification of key features of visible matter in this non-spatial sector of extra dimensions in turn motivates seeking a candidate for dark matter residing in the original extra spatial dimension sector, and provides a close guide for the explicit form this invisible matter might take. We describe how such Standard Model and dark matter sectors in different extra-dimensional branches of generalised proper time are gravitationally connected through their common root in the local 4-dimensional spacetime and consider further possible mutual interactions and implications in comparison with existing dark matter models. A yet further possible branch of generalised proper time can be connected with dark energy models, hence in principle accounting for all three major components of cosmological structure within this framework.

In large eddy simulation (LES) of turbulent flows, the most critical dynamical processes to be considered by dynamic subgrid models to account for an average cascade of kinetic energy from the largest to the smallest scales of the flow is not fully clear. Furthermore, evidence of vortex stretching being the primary mechanism of the cascade is not out of the question. In this article, we study some essential statistical characteristics of vortex stretching and its role in dynamic approaches of modeling subgrid-scale turbulence. We have compared the interaction of subgrid stresses with the filtered quantities among four models using invariants of the velocity gradient tensor. This technique is a single unified approach to studying a wide range of length scales in the turbulent flow. In addition, it also provides a rational basis for the statistical characteristics a subgrid model must serve in physical space to ensure an appropriate cascade of kinetic energy. Results indicate that the stretching mechanism extracts energy from the large-scale straining motion and passes it onto small-scale stretched vortices.

Virtual human body models contribute to designing safe and user-friendly products through virtual prototyping. Anthropometric biomechanical models address different physiques using average dimensions. In designing personal protective equipment, biomechanical models with the correct geometry and shape shall play a role. The presented study shows the variations of subject-specific anthropometric dimensions from the average for the different population groups in the Czech Republic and China as a background for the need for personalized human body models. The study measures a set of clothing industry dimensions of Czech children, Czech teens, Czech adults and Chinese adults and compares them to the corresponding age average, which is represented by a scaled anthropometric human body model. The cumulative variation of clothing industry dimensions increases the farer is the population group from the average. It is smallest for the Czech adults 7.54% ± 6.63%, Czech teens report 7.93% ± 6.25% and Czech children differ 9.52% ± 6.08%. Chinese adults report 10.86% ± 11.11%. As the variations of the particular clothing industry dimensions from the average prove the necessity of having personalized subject-specific models, the personalization of particular body segments using the measured clothing industry dimensions leading to a subject-specific virtual model is addressed. The developed personalization algorithm results in the continuous body surface desired for contact applications for assessing body behavior and injury risk under impact loading.

This paper presents the results of quantitative research regarding the predictive model of citizens' attitudes about the risks of introducing the death penalty in the Republic of Serbia legal system. The research was conducted with the use of a questionnaire that was requested and then collected online from 427 people in June 2021. A multivariate regression analysis was used, identifying the extent to total scores of the main dependent variables (introducing the death penalty; trust in the legal system; advantages of introduction; disadvantages of introduction scores) were associated with five demographic and socio-economic variables: gender, marital, education, income, and age. We tested the central hypothesis of which gender is predicting variables citizens' attitudes about the risks of introducing the death penalty in the legal system of Serbia. The findings revealed that gender and educational level were the most effective predictors of the research variables under question. The majority of respondents support the introduction of the death penalty and the most important predictor of disadvantages of introducing the death penalty in the legal system is age. Based on the findings that there are major differences in the citizens' attitudes about the risks of introducing the death penalty in the legal system, policies, strategies, and regulations must take into account these very important findings.

The main obstacle affecting the natural and herbal products industry is the infringement of trade patents. This resulted in losing the opportunity to compete with others in this business. Creating additional value to this business is, therefore, a way of leading to patenting for trade protection for entrepreneurs. This study was conducted to develop the structural equation modeling of guidelines for creating additional value for Thai herbs to support trade patent. The study comprises a mixed method with three parts. In the first part, data comes from interviews with selected 9 specialists. The second part was supported by interviews to create the questionnaire and distributed it to 500 entrepreneurs, the next step was the data analysis using structural equation modeling (SEM). The third part used data from the SEM model to discussion focused on developing the conceptual model by 7 experts. The analysis of the developed model passed the criteria for evaluating consistency and empirical data. All hypotheses proposed in this study were supported, the findings revealed that all factors had a direct effect at a statistically significant level of 0.001. Furthermore, this investigation allows knowing the Brand Identity factor were practically important for creating value of Thai herbs leading to patent protection.

Self-organized criticality (SOC) is a celebrated paradigm from the 90’s for understanding dynamical systems naturally driven to its critical point, where the power-law dynamics taking place make predictions practically impossible, such as in stock prices, earthquakes, pandemics and many other problems in science related to phase transitions. Shortly thereafter, it was realized that traffic flow might be in the SOC category, implying that conventional traffic management strategies seeking to maximize the local flows can become detrimental. This paper shows that the Kinematic Wave model with triangular fundamental diagram, and many other related traffic models, indeed exhibit SOC, thanks in part to the fractal nature of traffic exposed here on the one hand, and our need to get to our destinations as soon as possible, on the other hand. Important implications for congestion management of traffic near the critical region are discussed, such as: (i) Jam sizes obey a power-law distribution with exponent 1/2, implying that both its mean and variance become ill-defined and therefore impossible to estimate. (ii) Traffic in the critical region is chaotic in the sense that predictions becomes extremely sensitive to initial conditions. (iii) However, aggregate measures of performance such as delays and average speeds are not heavy tailed, and can be characterized exactly by different scalings of the Airy distribution, (iv) Traffic state time-space “heat maps” are self-affine fractals where the basic unit is a triangle, in the shape of the fundamental diagram, containing 3 traffic states: voids, capacity and jams. This fractal nature of traffic flow calls for analysis methods currently not used in our field.

The aim was to evaluate the influence of model (alcohol, sugar, salt, protein and acid) and real foods and beverages on the viability of probiotics during incubation and artificial digestion. Viability of monocultures Lactobacillus acidophilus CCM4833 and Bifidobacterium breve CCM7825T and commercial mixture of 9 probiotic bacterial strains were tested by cultivation assay and flow cytometry. In model foods, the best viability was determined in the presence of 0.2 g/L glucose, 10% albumin and 10% ethanol. As the most suitable real food for probiotic survival complex protein and carbohydrate substrates were found, such as beef broth, potato salad with pork, chicken with rice, chocolate spread, porridge and yoghurt. The best liquid was milk and meat broth, followed by coca-cola, beer and coffee. Viability of probiotics was higher when consumed with meals than with beverages only. Addition of prebiotics increased the viability of probiotics especially in presence of instant and fast foods. Generally, the highest viability of probiotics during artificial digestion was observed in mixed culture in presence of protein, sugar and fat or their 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.

Exploring the effect of the sample size on the estimation accuracy of airborne LiDAR forest attributes in a large-scale area can help in optimizing the technical application scheme of operational ALS-based large-scale forest stand inventories. In our study, sample datasets composed of different sample plots were constructed by repeated sampling from 1003 sample plots in a subtropical study area covering 2376 × 103 km2. Sixteen multiplicative power models were built in each forest type consisting of four forest attributes. Through these models, the variations of standard deviation (SD) and coefficient of variation (CV) of R2 and rRMSE of forest attribute estimation models for different quantity levels of sample plots were also analyzed. The results showed that, first, when the sample size increased from 30 to the top limit, the SD of the forest attributes and LiDAR variables showed a decreasing trend. Second, as the sample size increased, the rRMSE of the 16 forest attribute estimation models gradually decreased, while the R2 gradually increased. Third, when the sample size was small, both the SD of R2 and rRMSE of the models were large, and the SD of R2 and rRMSE gradually decreased as the sample size increased. In 50 models conducted for each attribute at the same sample size, for the mean standard deviations of forest attributes, the ten best performing models were lower than those of the total 50 models, and the worst ten models were the opposite. When the sample size increased, the accuracy of each forest attribute estimation model for each forest type gradually improved. The variation of forest attributes and the LiDAR variable of the construction model are critical factors that affect the model’s accuracy. To efficiently apply airborne LiDAR in order to survey large-scale subtropical forest resources, the sample size of the Chinese fir forest, pine forest, eucalyptus forest, and broad-leaved forest should be 110, 80, 85, and 70, respectively.

Melanoma is considered the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in prognosticating this type of cancer. With the emergence of new therapeutic strategies for metastatic melanoma that have shown improvement in patient survival, we developed a transfer learning-based biomarker discovery model that could help in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results reveal that the genes we found show consistency with other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set, and our methods found novel biomarker genes as well. Our ensemble model achieved Area Under the Receiver Operating Characteristic (AUC) of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB). We also assessed the potential sources of bias for our model and confirmed some of them by the model's performance.

In the book “Everyhing Is A Circle: A New Model For Orbits Of Bodies In The Universe”, and further in the Article “Distance Between Two Circles in Any Number of Dimensions is a Vector Ellipse”, it has been mathematically demonstrated that “distance between points on any two different circles in any number of multiple dimensions” is equivalent to “distance of points on a vector ellipse from another fixed or moving point”. Using this mathematical methodology, a method is provided in this Article as a measure for the amount of interaction between two international economies, which are two countries or economic zones, by modelling each economy in terms of a “circle in four dimensions”. Based on this method, the proximity of the two economies at the end of each fiscal period, which is generally a fiscal year, is then measured by the distance between points at the end of the given period on the two circles in four dimensions, associated with these two economies in our model.

The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable software according to the goals and needs of the organization makes measuring the quality of software products an important issue for most organizations and institutions. To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study, experts and skilled in this field were used and the impact of each factor and quality characteristics. It was applied at different levels according to their opinion to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. These processes link geological processes at depth to surface features. Consequently, the study of landscapes can reveal essential information about the geochemical footprint of ore deposits at depth. Advances in satellite imaging and computing power have enabled the creation of large geospatial datasets, the sheer size of which necessitates automated processing. We describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available Digital Elevation Model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve about 98\% classification accuracy with reasonable runtime of 48 minutes on a single core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, while smaller tiles result in a more detailed, and at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region.

The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

Fitts' law predicts the human movement response time for a specific task by a simple linear formulation, in which the intercept and the slope are estimated from the task's empirical data. This research was motivated by our pilot study, which found that the linear regression's essential assumptions are not satisfied in the literature. Furthermore, the keystone hypothesis in Fitts' law, that the movement time per response will be directly proportional to the minimum average amount of information per response demanded by the particular amplitude and target width, has never been formally tested. Therefore, this study developed an optional formulation derived from fusing the findings in psychology, physics, and physiology for fulfilling the statistical assumptions. An experiment was designed to test the hypothesis in Fitts' law and validate the proposed model. To conclude, our results indicated that movement time could be related to the index of difficulty underlying the same constant amplitude. The optional formulation accompanies the index of difficulty in Shannon form robustly performs the prediction better than the traditional model across studies. Finally, a new approach to modeling movement time prediction is deduced from our research results

To increase rice production, fertilizer plays a crucial role in rice yield. In this research, we applied the coupled atmospheric and crop model, which is based on the WRF and CERES-Rice models, to find the appropriate nitrogen fertilizer level for increasing rice yield production in northern Thailand. The model was conducted from October to December in 2011 to 2015. To evaluate the model capability, the output from the model, including meteorological data, i.e., precipitation and temperature, and rice production, as compared to actual observation data. The modeling system shows an acceptable level of output for statistical examination; for example, the R² values were 0.93, 0.76, and 0.97 for precipitation, temperature, and rice production, respectively. To assess the optimization of the nitrogen fertilizer level, we designed 9 experiments: control cases and other cases that were multiplied by a factor of 2 – 10 times the nitrogen fertilizer levels. The model suggested that we can produce worthwhile rice yield production by approximately 4830 kg/ha if we increase the nitrogen fertilizer levels by 36 kg/ha.

Commercial classification is essential to spatially describe commerce. Although most of the proposed classifications have been classic functional analyses, rooted on qualitative studies, some recent classifications efforts are found to rely heavily on more quantitative methods, due eventually to increasing data availability and technological advancements. In this paper, a classification is proposed, using k-means clustering and a minimal set of variables (density, diversity and clustering) to derive the commercial structure of Lisbon. The classification is implemented for 1995, 2002 and 2010, using a 150m-sided square grid. The cross-sectional analysis of the results shows the rise of shopping-malls against city centre decline and gentrification, along with changes in cluster composition considering 9 different commercial categories (6 categories of retail, and restaurants, cafes and bars). These findings are in line with literature, thus supporting the obtained classification. Since the classification can be used to accurately describe the commercial structure of the city in different time periods, it is implied that it may also be generalized to different cities. Furthermore, the potential use of cluster membership in retail location models, which is an advantage of the proposed classification, could help strengthen the relationship between location modelling and commercial classification, thus reinforcing the role of commercial studies in urban planning and policymaking.

Abstract Aim: To determine the efficacy of health beliefs model –based intervention in changing the belief related to substance use among university student in Mosul city-Iraq. Design: A randomized controlled trial. Methods: A probability (simple random sample) of (N=80) undergraduate student in different specialties would be selected. The study sample will be recruited from (4) colleges in the University of Mosul's Engineering, Sciences, Medicine and Education Colleges. The sample will be randomly assigned into experimental and control groups of (40) undergraduate student for each group. Such chosen is employed of pool of topics that have the criteria contain students who have using on (Smoking, Hookah, Drug abuse and Alcohol).For during from 25of October / 2019 till 1 of February/2021. Data is analyzed using the "Statistical Package for Social Science" (SPSS) software for Windows (V:26). Results: This finding indicated that before the intervention, mean scores for all concepts of HBM, add to Motivation, Control, and behaviors intensions of students they were almost equal. However, after the intervention were significantly different in the study group, while it was not significant in the control group. Conclusion:This study concluded that designing an HBM-based study could affect students' understanding and their behaviors in the field of substance abuse. Considering the positive correlation between construct of HBM, particularly in "perceived benefits and perceived severity" related to students’ beliefs. These beliefs implied a significant correlation with each other and with the attention to the prevention of addiction.

This review describes the subjective experience of visual aura in migraine, outlines theoretical models of this phenomenon, and explores how these may be linked to neurochemical, electrophysiological and psychophysical differences in sensory processing that have been reported in migraine with aura. Reaction-diffusion models have been used to model the hallucinations thought to arise from cortical spreading depolarisation and depression in migraine aura. One aim of this review is to make the underlying principles of these models accessible to a general readership. Cortical spreading depolarisation and depression in these models depends on the balance of the diffusion rate between excitation and inhibition, and the occurrence of a large spike in activity to initiate spontaneous pattern formation. We review experimental evidence, including recordings of brain activity made during the aura and attack phase, self-reported triggers of migraine, and psychophysical studies of visual processing in migraine with aura, and how these might relate to mechanisms of excitability that make some people susceptible to aura. Increased cortical excitability, increased neural noise, and fluctuations in oscillatory activity across the migraine cycle are all factors likely to contribute to the occurrence of migraine aura. There remain many outstanding questions relating to the current limitations of both models and experimental evidence. Nevertheless, reaction-diffusion models, by providing an integrative theoretical framework, support the generation of testable experimental hypotheses to guide future research.

The center vortex model of quantum-chromodynamics can explain confinement and chiral symmetry breaking. We present a possible resolution for problems of the vortex detection in smooth configurations and discuss improvements for the detection of center vortices.

The widely used logistic model for epidemic case reporting data may be either restrictive or unrealistic in presence of containment measures when implemented after an epidemic outbreak. For flexibility in epidemic case reporting data modelling, we combined an exponential growth curve for the early epidemic phase with a flexible growth curve to account for the potential change in growth pattern after implementation of containment measures. We also fitted logistic regression models to recoveries and deaths from the confirmed positive cases. In addition, the growth curves were integrated into a SIQR (Susceptible, Infective, Quarantined, Recovered) model framework to provide an overview on the modelled epidemic wave. We focused on the estimation of: (1) the delay between the appearance of the first infectious case in the population and the outbreak (“epidemic latency period"); (2) the duration of the exponential growth phase; (3) the basic and the time-varying reproduction numbers; and (4) the peaks (time and size) in confirmed positive cases, active cases and new infections. The application of this approach to COVID-19 data from West Africa allowed to discuss the effectiveness of some containment measures implemented across the region.

Background: Renewed measles outbreaks in recent years indicate that despite the routine availability of vaccines for a disease that is considered contagious, dangerous and deadly, many anti-vaccinationists do not vaccinate their children, which consequently endangers public health. This study aimed to investigate the factors that influence mothers to vaccinate their children, and whether the Health Belief Model (HBM) could predict compliance or non-compliance. Methods: This was a quantitative correlational research, utilizing a 40-item questionnaire administered to 181 mothers in Israel. Results: The findings indicated two main factors that affected mothers' intention to vaccinate their children against measles: first, their perception of the vaccine's advantages, and second, their perception of the severity of the disease. It was also found that the HBM variables significantly affected the intention to administer vaccines. Conclusion: Consequently, raising public awareness of the vaccine's advantages and importance to preventing mass infection, as well as attempts by the health system and practitioners to understand the motivations of anti-vaccinationists (including health beliefs and cultural sensitivities) could significantly increase the percentage of vaccinated children, and eradicate the measles epidemic.

In a large variety of organisms, antimicrobial peptides (AMPs) are primary defences against pathogens. BP100 (KKLFKKILKYL-NH2), a short, synthetic, and cationic AMP, is active against bacteria and displays low toxicity towards eukaryotic cells. BP100 acquires an α-helical conformation upon interaction with membranes and increases membrane permeability. Despite the volume of information available, the mechanism of action of BP100, the selectivity of its biological effects, and its applications are far from consensual. In this work, we synthesized a fluorescent BP100 analog containing naphthalimide linked to its N-terminal end, Napht-BP100 (Napht-AAKKLFKKILKYL-NH2). The fluorescence properties of naphthalimides, especially their spectral sensitivity to microenvironment changes, are well established, and their biological activities against different types of cells are known. A wide variety of techniques were used to demonstrate that a-helical Napht-BP100 was bound and permeabilized POPC and POPG LUV. Napht-BP100, different from that observed for BP100, was bound to, and permeabilized POPC LUV. With zwitterionic (POPC) and negatively charged (POPG) containing LUVs, membrane surface high peptide/lipid ratios triggered complete disruption of the liposomes in a detergent-like manner. This disruption was driven by charge neutralization, lipid aggregation, and membrane destabilization. Napht-BP100 also interacted with double-stranded DNA, indicating that this peptide could also affect other cellular processes in addition to membrane destabilization. Napht-BP100 showed superior antibacterial activity, increased hemolytic activity compared to BP100, and may constitute an efficient antimicrobial agent for dermatological use. By conjugating BP100 and naphthalimide antimicrobial properties, Napht-BP100 was bound more efficiently to the bacterial membrane and could destabilize the membrane and enter the cell by interacting with its cytoplasm- exposed DNA.

The aim was to implement a value-promoting program (Teaching Personal and Social Responsibility, TPSR) and to assess its impact on students. Method: The program was applied for 8 months with 257 students from three secondary school centres (151 boys and 106 girls) with a mean of 15.97 years old (SD = 2.31). They were in three groups, 67 students (control group), 90 students receiving at least 60% of the total teaching time the value-promoting program (experimental group for global education, EG-GE) and 100 students (experimental group only for physical education, EG-PE). As main results improvements were found in the EG-GE for responsibility, psychological mediator index, self-determination index, resilience, climate and prosocial behaviours. In the EG-PE improved were observed in the self-determination index, classroom climate and prosocial behaviours. Female students and from EG-GE improve much more than males and from EG-PE. The outcomes in psychological variables can be higher if TPSR is applied to the whole subjects apart from physical education. These results are even more pronounced for female students in personal and social responsibility. It is worth highlighting the importance of coordinating educational institutions to facilitate the involvement of the greatest number of teachers.

Accurate monthly runoff estimation is fundamental in water resources management, planning, and development, resulting in preventing and reducing water-related problems, such as flooding and drought. This article evaluates the performance of the monthly hydrological rainfall-runoff model, GR2M model, in Thailand's southern basins. The GR2M model requires only two parameters, and no prior research has been reported on its application in this region. The 37 runoff stations, which are distributively located in three sub-watersheds of Thailand's southern region, namely; Thale Sap Songkhla, Peninsular-East Coast, and Peninsular-West Coast, were selected as study cases. The available monthly hydrological data of runoff, rainfall, air temperature from the Royal Irrigation Department (RID) and the Thai Meteorological Department (TMD) were collected and analyzed. Thornthwaite method was utilized for the determination of evapotranspiration. The model's performance was conducted using three statistical indices: Nash-Sutcliffe Efficiency (NSE), Correlation Coefficient (r), and Overall Index (OPI). The model's calibration results for 37 runoff stations gave the average of NSE, r, and OPI of 0.637, 0.825, and 0.757, and those values for verification of 0.465, 0.750, and 0.639, respectively. It indicated a model's acceptable performance and could apply the GR2M model for determining monthly runoff variation in this region. The spatial distribution of X1 and X2 values was conducted by using IDW method. It was susceptible to the X1 value and X2 value of approximately more than 0.90 gave the higher model's performance.

Research professors develop scientific products that impact and benefit society, but their competencies in doing so are rarely evaluated. Therefore, employing a mixed two-stage sequential design, this study developed a self-assessment model of research professors’ competencies with four domains, seven competencies, and 30 competency elements. Next, we conducted descriptive statistical analysis of those elements. In the first year, 320 respondents rated themselves on four levels: initial, basic, autonomous, and consolidated. In the assessment model’s second year, we compared 30 respondents’ results with those of their initial self-assessment. The main developmental challenge was Originality and Innovation, which remained at the initial level. Both Training of Researchers and Transformation of Society were at the basic level, and Digital Competency was at the autonomous level. Both Teaching Competence and Ethics and Citizenship attained the consolidated level. This information helps establish priorities for accelerating researchers’ training and the quality of their research.

In the present study, we compare energy transition scenarios from a new set of Integrated Assessment Models, the suite of MEDEAS models, based on a systems dynamic modelling approach, with scenarios from two already well know structurally and conceptually different Integrated Assessment Models, the Integrated MARKAL-EFOM System (TIMES) and the Long-range Energy Alternatives Planning system (LEAP). The investigation was carried out to cross-compare and benchmark the response of MEDEAS models with TIMES and LEAP in depicting the energy transition in two different countries, Austria and Bulgaria. The preliminary results show a good agreement across all the models in representing scenarios projecting historical trends, while a major discrepancy is detectable when the rate of implementation of renewable energy is forced to increase to achieve energy system decarbonization. The discrepancy is mainly traceable to the differences in the models’ conception and structures rather than in a real mismatch in representing the same scenarios. The present study is put forward as a guideline for validating new modelling approaches that link energy policy decision tools to the global biophysical and socioeconomic constraints.

This paper presents a method to determine material constants of a standard basketball shell together with the development of a virtual numerical model of a basketball. Material constants were used as the basis for the hyperelastic material model in the strain energy function (SEF). Material properties were determined experimentally by strength testing in uniaxial tensile tests. Additionally, the digital image correlation technique (DIC) was applied to measure strain in axial planar specimens, thus providing input stress-strain data for the Autodesk software. Analysis of testing results facilitated the construction of a hyperelastic material model. The optimal Ogden material model was selected. Two computer programmes by Autodesk were used to construct the geometry of the virtual basketball model and to conduct simulation experiments. Geometry was designed using Autodesk Inventor Professional 2017, while Autodesk Simulation Mechanical 2017 was applied in simulation experiments. Simulation calculations of the model basketball bounce values were verified according to the FIBA recommendations. These recommendations refer to the conditions and parameters which should be met by an actual basketball. A comparison of experimental testing and digital calculation results provided insight into the application of numerical calculations, designing of structural and material solutions for sports floors.

Species have shown their habital variations in responding to climate change, especially during the spring and summer spawning seasons. The species distribution model (SDM) is considered the most favorable tool to study the potential effects of climate change on species distribution. Therefore, we developed the ensemble SDM to predict the changes in species distribution of Portunus trituberculatus among different seasons in 2050 and 2100 under the climate scenarios RCP4.5 and RCP8.5. The results of SDM indicate that the distribution of this species will move northward and have obviouse seasonal variations. Meanwhile, the suitable habitat for the species will be significantly reduced in summer, with loses rates ranging from 45.23% (RCP4.5) to 88.26% (RCP.8.5) by 2100s. Habitat reduction will mainly occur in the East China Sea and southern part of the Yellow Sea, while there will be a small increase in the northern Bohai Sea. These findings will be important to manage the ecosystem and fishery, provide an information forecast of this species in the future, and maintain species diversity if the seawater temperature rises.

In order to maintain the best conditions of the building from the first day of opening, explorations of office buildings in the world investigated the approaches of optimizing the building operation and property facilities, whereas building managers often have trouble prioritizing improvements toward their sustainable objectives. The challenge is due to numerous equipment types in the building, which has various durability in years and represents users’ different feelings of importance and satisfaction. When faced with multiple improvement projects, building managers are often unable to determine the order of implementation.Hence, this research applies the Analytic Hierarchy Process (AHP) using TAIPEI 101 Tower as a case study to explore the key factors affecting tenants’ willingness to lease office buildings by distributing questionnaires to real estate experts. By filtering out key indicators related to property facilities that users care about, this study used Importance and Satisfaction Analysis (IS Analysis) to identify items that need urgent improvement. The research revealed that air conditioning is the top priority for improvement through empirical evaluation and weight ranking. This study hopes to establish an evaluation model for facility improvements to help clarify the prioritization and defeat limited resources to projects effectively, which is helpful for sustainable property management.

Aim: This research is aimed at examining the cost-effectiveness of olanzapine versus risperidone in dementia patients with behavioural and psychological symptoms in Thailand. Methods: An existing Markov model based on a critical review through the comprehensive literature search and a justification for the most appropriate model for a Thai setting was adapted to simulate the disease progression of patients with dementia with behavioural disturbances until their need for full-time care (FTC). The time to the FTC state was estimated by a predictive equation developed by Rive et al. (2010). The model was conducted to assess the expected costs and outcomes associated with olanzapine compared with risperidone for Thai patients with BPSD aged 60 years and above. This model performed over a five-year time horizon with a one-month cycle length based on a societal perspective. The incremental cost-effectiveness ratio was used as the estimated outcome. Sensitivity analyses were also conducted to demonstrate the robustness of the results. Results: Over 5 years, olanzapine was found to be a cost-effective therapeutic option for the treatment of behaviourally disturbed patients with dementia compared with risperidone, in Thailand from a societal perspective (ICER < THB 160,000). The model underwent extensive sensitivity analyses, which also confirmed that olanzapine was the dominant strategy following the base-case findings. Conclusions: By comparison with risperidone, the model suggests that olanzapine can be regarded as a cost-effective therapeutic strategy for the management of patients with behavioural and psychological symptoms in Thailand.

Research Highlights: Hagenia abyssinica is geographically localized, poor regenerated and endangered species in Ethiopia. Ethiopia has been experiencing variability of rainfall and rise in temperature due to the climate change. This study has hypothesized that the suitable areas for the species will be narrowed by the year 2070. Background and Objective The prediction of species distribution models help to implement appropriate conservation actions. The aim of this research was to identify the current and likely future distribution range and suitable areas for the species, and to determine the presence of H. absyssinica in risk in a short-term future. Material and method: To this end, occurrence data, bioclim variables, soil, elevation, and land cover map of Ethiopia were used. MaxEnt was used to predict distribution. Climate change impacts on the distribution of the species was performed using bioclimatic variables of the future climate data, 2070 (average for 2061-2080) was obtained from IPPC5 (CMIP5) at 30 seconds (1km) spatial resolution. The climate data was projected from GCMs, downscaled and calibrated using rcp4.5. Results: Both current and likely future distribution models were excellent and significantly better than random performance. This study has computed 59987 km2 to be the low impact area for the species under current conditions and will remain habitat under future climates and 39025 km2 area has been identified as the possible high impact areas or declining habitat. The model has also determined that 1238724 km2 of the areas are unsuitable at present and for future climates. The current study found that 15751 km2 of the area will be modified as a new suitable area for H. abyssinica due to climate change. Conclusion: Species distribution modeling is essential for the implementation of conservation actions that are compatible with the inevitable changing climatic conditions of the country.

The discovery of a growing number of exoplanets and even extrasolar systems supports the scientific consensus that it is possible to find other signs of life in the universe. The present work proposes for the first time, an explicit mechanism inspired by the dynamics of biological dispersion, widely used in ecology and epidemiology, to study the dispersion of biogenic units, interpreted as complex organic molecules, between rocky or water exoplanets (habitats) located inside star clusters. The results of the dynamic simulation suggest that for clusters with populations lower than 4 M+/ly³ it is not possible to obtain biogenic worlds after 5 Gyr. Above this population size, biogenic dispersion seems to follow a power law, the larger the density of worlds lesser will be the impact rate (β ) value to obtain at least one viable biogenic Carrier habitat after 5 Gyr. Finally, when we investigate scenarios by varying β, a well-defined set of density intervals can be defined in accordance to its characteristic β value, suggesting that biogenic dispersion has a behavior of “minimal infective dose” of “minimal biogenic effective” events by interval i.e. once this dose has been achieved, doesn’t matter if additional biogenic impact events occur on the habitat.

Environmental decentralization (ED), or the allocation of environmental protection affairs and responsibilities among various administrative authorities, affects the effectiveness of environmental regulation in promoting green total factor productivity (GTFP). Based on panel data of 30 Chinese provinces from 2001 to 2015, this paper employs dynamic panel models to test the effects of environmental regulations (environmental protection investment, ENV; pollutant discharge fees, PDF) on GTFP, with or without being influenced by ED. Without the impact of ED, GTFP is significantly inhibited by ENV while significantly promoted by PDF. Considering the impact of ED, with the strengthening of ED, the negative effects of ENV on GTFP is significant; contrarily, the positive effects of PDF on GTFP is significant; improving provincial ED adds negative effects of ENV, while reduces the positive effects of PDF; increasing prefectural ED reduces negative effects of ENV; expanding county-level ED adds the positive effects of PDF. Therefore, to boost GTFP growth, prefectural environmental protection authorities should have more autonomy in ENV, while the county-level should have more autonomy in PDF.

In this project, we use a statistical multiple regression to study the impact of eight various predictors (relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution) to estimate the cooling load energy efficiency of residential buildings. We try to analyze and visualize the effect of each predictor with each of the response variable using different classical statistical analysis tools used in describing linear models, in such a way so that we can find out the most strongly related predictor variables. Before starting all of this, we use the idea of model selection by stepwise regression technique and compare the AIC of these models and identified a better model between all of them. Then, we compare a classical linear regression approach by simulations on 768 diverse residential buildings show that we can predict CL with low mean absolute error. By using ANOVA we determine variation in the different residuals. Also, we use non constant variance test to verify it. Furthermore, we check leverage and influence points as well as outliers as well as determined cook distance for influential points. By taking box cox transformation and weights, we also introduce WLS technique to fit the model for better results and did all type of important analysis to understand the energy efficiency. Finally, we show 5-fold cross validation to verify our model.

Indoor Positioning Systems (IPSs) are designed to provide solutions for location-based services. Wireless local area network (WLAN)-based positioning systems are the most widespread around the globe and are commonly found to have a ready-to-use infrastructure composed mostly of access points (APs). They provide useful information on signal strength to be processed by adequate location algorithms, which are not always capable of achieving the desired localization error only by themselves. In this sense, this paper proposes a new method to improve the accuracy of IPSs by optimizing some of their most relevant infrastructure components. Included are the arrangement of APs over the environment, the number of reference points (RPs), and the number of samples per location estimation test. A simulation environment is also proposed, in which the impact of key influencing factors on system accuracy is analyzed. Finally, a case study is simulated to validate an optimal combination of design parameters and its compliance with the requirements of localization error and the limited number of access points. Our simulation results clearly show that the desired localization accuracy, which is set as a goal, can be achieved while maintaining the factors already mentioned at minimal levels, which decreases both system deployment costs and computational effort.

We investigate, whereas the SU(4)/SU(2) model, is viable for describing strong interactions. The existing problems of confinement and asymptotic freedom, two phenomena that can not be described by the SU(3) model, might indicate that we need something "larger" than the SU(3) model. The considered coset SU(4)/SU(2) or the Stiefel manifold V^2(C_4) contains an su(3) algebra, plus additional degrees of freedom that resembes the Feddev-Poppov concept. The richer structure of this coset, give us enough room, to seek for new phenomena, as its dimensionality is 12. The consideration of the SU(4)/SU(2) model flavors firstly the unification of nuclear fields (strong and weak) in an SU(4) model.

Parallel lives is a model which provides an interpretation of quantum theory that is both local and realistic. This model assumes that all quantum fields are composed of point beings called ”lives”. Lives interact locally and have a memory of their previous interactions. The reduction of the state vector is not included in this model: lives can be divided into different worlds. This feature resembles many worlds interpretation. However in the parallel lives model, the division of lives into different worlds takes place locally. The parallel lives model is expected to be compatible with special relativity, as the lives propagate at a speed that does not exceed the speed of light and interact locally. On the other hand, it is open to paradoxes based on counterfactual propositions, as it provides a realistic interpretation of quantum theory. In this paper, we confront the parallel lives model with the paradox proposed by Hardy. We show that the parallel lives model cannot overcome the dilemma in Hardy’s paradox. We discuss implications of this confrontation on special theory of relativity, and speculate a solution that we believe, fits the spirit of the parallel lives model.

Occasionally, officials of the world’s regulatory agencies embark upon attempts at bringing previously unregulated physical systems under regulation by them. Each such attempt raises an epistemological issue. At issue is whether enough information about the outcomes of the events of the future, given the outcomes of the events of the present, will be in the hands of a would-be regulator for this regulator to regulate effectually. If present, this information is provided by runs of a model of the physical system that is slated for regulation. Ideally, this model makes an argument that draws its conclusion from the evidence presented to it. If so, this argument is of the form of a predictive inference. However, the process by which an argument draws its conclusion from the evidence may go awry. This happens, for example, when the axiom of probability theory called unit measure is falsified by a conclusion that is drawn from the evidence by this argument.A method is derived from first principles for determination of whether unit measure is satisfied or falsified by an argument made by a model, given that this argument may attach unusual meanings to statistical terms. This method is used in a study of whether unit measure is satisfied or falsified by the arguments that are made by a pair of models. Both models are in active use by regulatory agencies around the world. Under neither argument do runs of the model provide an official of a regulatory agency with the information gain aka mutual information that he or she would need to regulate effectually. Under both arguments, attachment of unusual meanings to statistical terms creates the illusion that such an official can and does regulate effectually.

The dynamic increase in non-cash transactions is a global trend in payments. In rich countries, cashless transactions have been dynamically replacing cash. The fact that non-cash transactions are pro-ecological is worth noting. This means that it is conducive to sustainable development of a country's economy. The purpose of this study is to analyze the process of changes in non-cash payments in Poland, against the background of payment card use for ATM cash withdrawals. The tool used to analyze the potential process of cash transaction erosion caused by non-cash payments is a two-equation econometric model with feedback. For this purpose, statistical data, obtained from the National Bank of Poland was used. Accordingly, clarification is expected regarding the question of whether the Polish payment system is heading towards a dominance of cashless transactions or not. The reaction, in the form of changes in the value of non-cash payment card transactions in Poland, affected by the increase in the wealth of citizens, measured by an increase in the average pay, was examined. The modeling results were used to estimate forecasts of the share of non-cash transactions in the total value of market transactions and of the share of ATM cash withdrawals in the total value of transactions.

Agricultural land fires have been linked to various and adverse impacts on ecosystems, food security and the agriculture sector. Understanding the patterns and drivers of agricultural land fires is essential for effective agricultural land fire management. The key objectives of this study were to (1) analyze the temporal and spatial patterns of agricultural land fires using satellite remote sensed data, (2) assess a range of environmental conditions that could drive the occurrence of agricultural land fires, (3) determine the best model for predicting agricultural land fires and (4) determine the relative contribution of each environmental condition variable on the best predictive model. We used both univariate and multivariate regressions for the fire prediction capability of four independent environmental conditions (fuel, weather, topographic and anthropogenic). Analysis of historical satellite data revealed that agricultural land fires were more frequent than forested land fires. Our analyses also revealed that fuel condition was the most important variable for predicting agricultural land fires followed by weather, topographic and anthropogenic conditions. This study provides a novel multivariate model for predicting agricultural land fires that harbors the potential to improve agricultural land fire management and reduce fire risk within the agricultural sector.

Tissues of the brain, especially white matter, are extremely heterogeneous - with constitutive response varying spatially. In this paper, we implement a high-resolution Finite Element (FE) head model where heterogeneities of white matter structures are introduced through Magnetic Resonance Elastography (MRE) experiments. Displacement of white matter under shear wave excitation is captured and the material properties determined though an inversion algorithm are directly used in the FE model. This approach is found to improve model predictions when compared to experimental results. In the first place, responses in the cerebrum near stiff structures such as the corpus callosum and corona radiata are markedly different compared with a homogenized material model. Additionally, the heterogeneities introduce additional attenuation of the shear wave due to wave scattering within the cerebrum.

The application of naturally ventilated pig buildings (NVPBs) with outdoor exercise yards is on the rise mainly due to animal welfare considerations, while the issue of emissions from the buildings to the surrounding environment is important. Since air pollutants are mainly transported by airflow, the knowledge on the airflow characteristics downwind the building is required. The objective of this research was to investigate airflow properties downwind of a NVPB with a roofed outdoor exercise yard for roof slopes of 5°, 15°, and 25°. Air velocities downwind a 1:50 scaled NVPB model were measured using a Laser Doppler Anemometer in a large boundary layer wind tunnel. A region with reduced mean air velocities was found along the downwind side of the building with a distance up to 0.5 m (i.e. 3.8 times building height), in which the emission concentration might be high. It was found that a smaller roof slope (i.e. 5° slope) resulted in a higher and shorter wake zone and thus a shorter air pollutant dispersion distance. It was concluded that a smaller roof slope could contribute to the dilution of air pollutants and a lower air pollutant concentration near the ground.

Rapidly spreading disease, COVID-19 is classified as the human-to-human transmission-able disease and currently is a pandemic in the Globe. In this paper, we propose conceptual mathematical models for COVID-19 outbreak and it's control measurement; quarantine, hospitalization and the effect of panic and anxiety. In this situation, mathematical models are a important tool to employ an effective strategy in order to fight against this pandemic. We establish the positivity and boundedness of solutions, local and global stability analysis of equilibria to examine its epidemiological relevance. To validate the model and estimating the important model parameters and prediction about the disease, we consider the real cases of Italy from $15^{th}$ Feb to $13^{th} $ April 2020. In a series of graphical map, we have presented the comparative study to estimate the current scenarios and to predict the control measurement time boundary of the outbreak.

Background: The world, now in an emergency of preventing the drastic spread of COVID-19. After the infection was first reported in December 2019, almost every country did not pay attention to this highly contaminated disease and failed to react swiftly. Now the whole universe is in an vulnerable state, loosing a great loss of lives and facing difficulties in all socio-economic aspects. That is why we have the urge to develop an efficient mathematical model (quarantine) based on social consciousness to control the epidemic. Methods: This is a quarantine mathematical model. The outcome of the system is dependent on social consciousness. We have calculated the awareness level by considering various socio-economic factor of each country. In our model, the parameters are Education Index, GDP per capita, population density, high literacy and stable economy. To maximize the efficiency of the model, it has to be implemented in initial stage. However, strict application of the method in vigorous stage of epidemic will also bring a satisfactory outcome. Results: Higher social consciousness will decrease the number of infected population dramatically while minimal or lower awareness will do a outburst. Conclusion: Outbreak will be in control of health care system, lower the death rate and will ensure social and economic stability.

The ongoing outbreak of the novel coronavirus pneumonia (also known as COVID-19) has triggered a series of stringent control measures in China, such as city closure, traffic restrictions, contact tracing and household quarantine. These containment efforts often lead to changes in the contact pattern among individuals of the population. Many existing compartmental epidemic models fail to account for the effects of contact structure. In this paper, we devised a pairwise epidemic model to analyze the COVID-19 outbreak in China based on confirmed cases reported during the period February 3rd--17th, 2020. By explicitly incorporating the effects of family clusters and contact tracing followed by household quarantine and isolation, our model provides a good fit to the trajectory of COVID-19 infections and is useful to predict the epidemic trend. We obtained the average of the reproduction number $R=1.494$ ($95\%$ CI: $1.483-1.507$) for Hubei province and $R=1.178$ ($95\%$ CI: $1.145-1.158$) for China (except Hubei), suggesting that some existing studies may have overestimated the reproduction number by neglecting the dynamical correlations and clustering effects. We forecasted that the COVID-19 epidemic would peak on February 13th ($95\%$ CI: February $9-17$th) in Hubei and 6 days eariler in the regions outside Hubei. Moreover the epidemic was expected to last until the middle of March in China (except Hubei) and late April in Hubei. The sensitivity analysis shows that ongoing exposure for the susceptible and population clustering play an important role in the disease propagation. With the enforcement of household quarantine measures, the reproduction number $R$ effectively reduces and epidemic quantities decrease accordingly. Furthermore, we gave an answer to the public concern on how long the stringent containment strategies should maintain. Through numerical analysis, we suggested that the time for the resumption of work and production in China (except Hubei) and Hubei would be the middle of March and the end of April, 2020, respectively. These constructive suggestions may bring some immeasurable social-economic benefits in the long run.

The paper describes an algorithm for cognitive representation of triples of related behavioral contexts two of which correspond to mutually exclusive states of some binary situational factor while uncertainty of this factor is the third context. The contexts are mapped to vector states in the two-dimensional quantum Hilbert space describing a dichotomic decision alternative in relation to which the contexts are subjectively recognized. The obtained triad of quantum cognitive representations functions as a minimal carrier of semantic relations between the contexts, which are quantified by phase relations between the corresponding quantum representation states. The described quantum model of subjective semantics supports interpretable vector calculus which is geometrically visualized in the Bloch sphere view of quantum cognitive states.

Legacy mining industry has left a large number of tailings ponds exposed to water and wind erosion that causes serious environmental and health problems. Prior to rehabilitation actions a deep sampling of the materials infilling the pond used to be necessary. Thus, the primary objective of this study is to demonstrate the usefulness of the Electrical Resistivity Tomography (ERT) method as a non-invasive tool to determine the physicochemical composition of mine tailings ponds, enabling more efficient and low-cost surveys. To achieve this objective, three ERT profiles and three boreholes in each profile were carried out, from each borehole three waste samples from differents depths were collected and a geochemical characterization of the samples was carried. In order to estimate the composition of the infilling wastes in tailing ponds from electrical resistivity measures, several regression models were calculated for different physicochemical properties and metal concentrations. As a result, a high resistivity area was depicted in profiles G2 and G3 while a non-resistive area (profile G1) was also found. Relationships among low resistivity values and high salinity, clay content and high metal concentrations and mobility were established. Specifically, calibrated models were obtained for electrical conductivity, particles sizes of 0.02-50 µm and 50-2000 µm, total Zn and Cd concentration, and bioavailable Ni, Cd and Fe. Therefore, the ERT technique could be considered as a useful tool for mine tailings ponds characterization, and it can be used to estmate some physicochemical properties and metal concentrations of this mine waste.

Most human genetic disease arises from point mutations. These genetic diseases can theoretically be corrected by gene therapy but clinic practice is still far from mature. Nearly half of the pathogenic single-nucleotide polymorphisms (SNPs) are caused by G:C>A:T or T:A>C:G base changes. The best current methods to repair these changes are by base editing without footprint using recently developed CRISPR-Cas9 technology by David Liu’s lab. These base editing methods have been confirmed with precision and efficiency in cultured mammalian cells, but it is barely confirmed and the efficiency is still very low. Animal models are important in dissecting pathogenic mechanism for human genetic diseases and efficacy testing of base correction in vivo. Cytidine base editor BE4 is a newly developed version of cytidine base editing system that converts cytidine (C) to uridine (U) in cultured mammalian cells but has not been proven in vivo. In this study, we have tested this system in cells to inactivate GFP gene and in mice by introducing single-base substitution that leads to a stop codon in tyrosinase gene. High percentage albino coat-colored mice were obtained from black coat-colored donor zygotes after pronuclei microinjection. Sequencing results showed that expected base changes were obtained with high precision and efficiency (56.25%). There are no off-targeting events identified in predicted off-target sites. Results confirm BE4 system can work in vivo with high precision and efficacy, and has great potentials in clinic to repair human genetic mutations.

The potential of Thai industrial product design is still inferior to those of leading competitors in world market that give more importance on the design during their product development to increase their competitive edges on commercial scale. The product design is very important part for sustainable growth in this industry. Thus, this research aims at investigating footwear design strategies for Thai footwear industry to be excellence in world market. The research has been designed with the mixed method of both qualitative and quantitative study. The quantitative data were collected through semi-structure interview from 500 designers who presented their designs to join the award competition. The results revealed that the footwear design strategies consisted of 4 factors, i.e. 1) design, 2) market analysis, 3) innovation, and 4) information technology. This paper utilizes the method of Structural Equation Modeling (SEM) to establish a strategies model for competitive advantage in Thai footwear industry. The analysis results indicated that the footwear design strategies model could help make more effective policies and organization strategies for enterprises and designers to develop themselves to be excellence in world market.

In the present paper, a measure of the influence of irregularities of the underlying surface (for instance, the real terrain profile) on the dynamics of air flow is estimated. It is shown that these irregularities significantly perturbing the pressure, density, velocity and temperature of air flow; for perturbed pressure, density, velocity and temperature of air flow the closed formulas in the Fourier integral form are obtained. In addition, in the present paper, it is proved that due to these perturbations, at a certain altitude of the atmosphere some surface of the "sturdy wall" form appears, on which the vertical and horizontal components of the air flow velocity become infinitesimal and infinitely large, respectively.

Cardiac hypertrophy is an important and independent risk factor for the development of heart failure. To better understand the mechanisms and regulatory pathways involved in cardiac hypertrophy, there is a need for improved in vitro models. In this study, we investigated how hypertrophic stimulation affected human induced pluripotent stem cell (iPSC)-derived cardiomyocytes (CMs). The cells were stimulated with endothelin-1 (ET-1) for 8, 24, 48, 72, or 96h. Parameters including cell size, ANP-, proBNP-, and lactate concentration were analyzed. Moreover, transcriptional profiling using RNA-sequencing was performed to identify differentially expressed genes following ET-1 stimulation. The results show that the CMs increase in size by approximately 13% when exposed to ET-1 in parallel to increases in ANP and proBNP protein and mRNA levels. Furthermore, the lactate concentration in the media was significantly increased indicating that the CMs consume more glucose, a hallmark of cardiac hypertrophy. Using RNA-seq, a hypertrophic gene expression pattern was also observed in the stimulated CMs. Taken together, these results show that hiPSC-derived CMs stimulated with ET-1 display a hypertrophic response. The results from this study also provide new molecular insights about the underlying mechanisms of cardiac hypertrophy and may help accelerate the development of new drugs against this condition.

Gauge fields control the dynamics of fermions. On the other hand a back reaction of fermions on the gauge field is expected. This back reaction is investigated within the vortex picture of the QCD vacuum.

We use a random gap model to describe a metal-insulator transition in three-dimensional semiconductors due to doping and find a conventional phase transition, where the effective scattering rate is the order parameter. Spontaneous symmetry breaking results in metallic behavior, whereas the insulating regime is characterized by the absence of spontaneous symmetry breaking. The transition is continuous for the average conductivity with critical exponent equal to 1. Away from the critical point the exponent is roughly 0.6, which may explain experimental observations of a crossover of the exponent from 1 to 0.5 by going away from the critical point.