In India the first case of coronavirus disease 2019 (COVID-19) reported on 30 January 2020, and thereafter cases were increasing daily after the last week of Feb. 2020. COVID-19 identified as family member of coronaviridae where previously Middle East Respiratory Syndrome MERS and Severe Acute Respiratory Syndrome SARS belongs to same family. The COVID-19 attacks on respiratory system signing fever, cough and breath shortness, in severe cases may cause pneumonia, SARS or some time death. The aim of this study work is to develop model which predicts the epidemic peak for COVID-19 in India by using the real-time data from 30 Jan to 10 May 2020. There are uncertainties while identifying the population information due to the incomplete and inaccurate data, we initiate the most popular model for epidemic prediction i.e Susceptible, Exposed, Infectious, & Recovered SEIR initially the compartmental model for the prediction. Based on the solution of the state estimation problem for polynomial system with Poisson noise, we estimate that the epidemic peak may reach the early-middle July 2020, initializing recovered R0 to 0 and Infected I0 to 1. The outcomes of the model will help epidemiologist to isolate the source of the disease geospatially and analyze the death. Also government authorities will be able to target their interventions for rapidly checking the spread of the epidemic.

Estimates of extreme water level return periods in river systems are crucial for hydraulic engineering design and planning. Recorded historical water level data of Taiwan’s rivers are not long enough for traditional frequency analyses when predicting extreme water levels for different return periods. In this study, the integration of a one-dimensional flash flood routing hydrodynamic model with the Monte Carlo simulation was developed to predict extreme water levels in the Danshuei River system of northern Taiwan. The numerical model was calibrated and verified with observed water levels using four typhoon events. The results indicated a reasonable agreement between the model simulation and observation data. Seven parameters, including the astronomical tide and surge height at the mouth of the Danshuei River and the river discharge at five gauge stations, were adopted to calculate the joint probability and generate stochastic scenarios via the Monte Carlo simulation. The validated hydrodynamic model driven by the stochastic scenarios was then used to simulate extreme water levels for further frequency analysis. The design water level was estimated using different probability distributions in the frequency analysis at five stations. The design high-water levels for a 200-year return period at Guandu Bridge, Taipei Bridge, Hsin-Hai Bridge, Da-Zhi Bridge, and Chung-Cheng Bridge were 2.90 m, 5.13 m, 6.38 m, 6.05 m, and 9.94 m, respectively. The estimated design water levels plus the freeboard are proposed and recommended for further engineering design and planning.

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

This paper proposes a novel model for a PV cell with parameters variance dependency on temperature and irradiance included. The model relies on commercial available data, calculates the cell parameters for standard conditions and then extrapolates them for the whole operating range. An up-to-date review of the PV modeling is also included with series and parallel parasitic resistance values and dependencies discussed. The parameters variance is analyzed and included in the proposed PV model, where the self-heating phenomenon is also considered. Each parameter variance is compared to the results from different authors. The model includes only standard components and can be run on any SPICE-based simulator. Unlike other approaches that consider the internal temperature as a parameter, our proposal relies on air temperature as an input and computes the actual internal temperature accordingly. Finally, the model is validated via experiments and comparisons to similar approaches are provided.

The COVID-19 pandemic situation, disease intensity, weak healthcare facilities, unawareness, and misinformation led people to fear and anxiety in Bangladesh. This study intended to get peoples’ perception on psychosocial, socio-economic and environmental crisis amidst the pandemi. An online questionnaire was surveyed nationwide (respondents no.1066). Datasets were analyzed through the Principal Component Analysis (PCA), hierarchical Cluster Analysis (CA), Pearson’s correlation matrix (PCM), Linear regression analysis (LRA), and psychometric characteristics were included in the Classical Test Theory (CTT) analysis. There were good associations among the psychosocial, socio-economic and environmental parameters. A significant association between fear of COVID-19 with struggling healthcare system (p<0.05) was found. Also, negative association between fragile health system and government’s ability to deal with the pandemic (p<0.05) revealing poor governance. Again, a positive association of shutdown and social distancing with fear of losing life, and due to lack of health treatment (p<0.05) reveals that shut down hampers normal activities which may lead to mental and economic stress. However, a positive association of socio-economic impact of the shutdown with poor people’s suffering, the price hike of basic need, hamper of formal education (p<0.05) may lead to severe socio-economic and health crisis. There is a possibility of climate-induced disaster during/after the pandemic, which will create severe food insecurity (p<0.01). Daily wage earners and poors will suffer most by food and nutritional deficiency, and the country may face huge economic burden. Proper risk assessment and communications is needed to alleviate fear and anxiety. Thus, financial support and mental boosting is required.

Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high-fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Experiments with the wind turbine simulator and the Monte–Carlo tools represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model-reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics.

To enable purposeful design and implementation of automated concrete technologies, precise assessment and prediction of the complex material flow at various stages of the process chain are necessary. This paper investigates the intermediate stage of the extrusion and deposition processes in extrusion-based 3D-concrete-printing, using a numerical model based on the Particle Finite Element Method (PFEM). In PFEM, due to the Lagrangian description of motion, remeshing algorithms and the alpha shape method are used to track the free surface during large deformation scenarios. The Bingham constitutive model was used for describing the rheological behaviour of fresh concrete. This model is validated by comparing the numerically predicted layer geometries with those obtained from laboratory 3D printing experiments. Extensive parametric studies were then conducted using the numerical simulation, delineating the influence of process and material parameters on the layer geometries, the dynamic surface forces generated under the extrusion nozzle and the inter-layer interactions.

The quantified residence time distribution (RTD) provides a numerical characterization of mixing in the continue casting tundish, thus allowing the engineer to better understand the metallurgical performance of the reactor. This paper describes a computational fluid dynamic (CFD) modelling study for analyzing the flow pattern and the residence time distribution in a five-strand tundish. Two passive scalar transport equations are applied to separately calculate the E-curve and F-curve in the tundish. The numerical modelling results are compared to the water modelling results for the validation of the mathematical model. The volume fraction of different flow regions (plug, mixed and dead) and the intermixing time during the ladle changeover are calculated to study the effects of the flow control device (FCD) on the tundish performance. The result shows that a combination of the U-baffle with deflector holes and the turbulence inhibitor has three major effects on the flow characteristics in the tundish: i) reduce the extent of the dead volume; ii) evenly distribute the liquid streams to each strand and iii) shorten the intermixing time during the ladle changeover operation.

As a high fatal disease, fat embolism syndrome is complication, which brought great pain to patient and their family served as a serious social burden. The mechanism of FES remains unclear. Autophagy controls the cell survival and homeostasis by removing the mis-folded proteins and damaged organelles as well as intracellular pathogens through a lysosomal degradation pathway. Increasing research documented that autophagy was wildly involved in variety of human diseases and had huge therapeutic potential. In our study, we first established the rat model of FES with the liquid fat by tail vein injection. We observed the up-regulated MPO expression and activity, increased Wet-to-Dry (W/D) lung weight, promoted quantity of fat granules, and the dominant disorder in the lung rat model of FES, compared to the control group. These observations demonstrated that we successfully build the rat model of FES. Then, we sought to dissect the role of the autophagy in the rat model of FES. The western blots analysis showed that the autophagy was inhibited in the rat model of FES constructed with liquid fat. Furthermore, Rapamycin could restore the repression of autophagy in rat model of FES. These investigations illustrated that autophagy was involved in FES. In addition, our experiments showed that Rapamycin could alleviate the symptoms of FES. Taken together, our study demonstrated the participation of autophagy in FES and further, as a potential therapeutic target, the modulation of autophagy could affect the symptom of rat model of FES.

Strata and surface movement induced by mining under open-pit final slope is a huge threat to mine safety. Physical model test is an important method to study mining-induced strata and surface movement laws. Because of rock joints predominantly control rock mass deformation and failure, thus physical model test leaving out of consideration of rock joints is difficult to reflect the influence of rock joints on rock mass deformation. Therefore, this paper presents a three-dimensional physical model test considering simplified dominant rock joints. This test process includes the design of testing equipment, the construction of physical model with dominant rock joint sets, conduction of mining and deformation monitoring. And mining under eastern final slope of Yanqianshan iron mine was selected as a case to study the behavior of mining-induced strata and surface movement.

We present a brief history of the construction of models of the universe, followed by calculations of quantitative characteristics of basic geometric and kinematic properties of the Standard Cosmological Model ($\Lambda$CDM). Using the Friedmann equations of uniform space, we derive equations characterizing a $\Lambda$CDM model that describes a universe corresponding to current observational data. The equations take into account the effects of radiation and ultra-relativistic neutrinos. It is shown that the universe at very early and late stages can be described to sufficient accuracy by simple formulas. Certain important moments of cosmic evolution are determined: the times when densities of the gravitational components of the universe become equal, when they contribute equally to the gravitational force, when the accelerating expansion of space begins, and several others. The dependences of different distances on redshift and the scale factor of space are derived. The distance to the sphere that expands with the speed of light (the Hubble distance), and its current and future acceleration, are found. Concepts of a horizon, second inflation, and second horizon are discussed. We consider the remote future of the universe and the opportunity, in principle, of connection with extraterrestrial civilizations.

The peroxiredoxin (PRDX) family, a new family of proteins with a pivotal antioxidative function, is ubiquitously synthesized and abundantly identified in various organisms. In contrast to the intracellular localization of other family members (PRDX1/2/3/5/6), PRDX4 is the only known secretory form and protects against oxidative damage by scavenging reactive oxygen species in both the intracellular (especially the endoplasmic reticulum) compartments and the extracellular space. Recently, we generated unique human PRDX4 (hPRDX4) transgenic (Tg) mice on a C57BL/6J background and investigated the critical and diverse protective roles of PRDX4 against diabetes mellitus, atherosclerosis, insulin resistance, and nonalcoholic fatty liver disease (NAFLD) as well as evaluated its role in the intestinal function in various animal models. Our published data have shown that PRDX4 helps prevent the progression of metabolic syndrome by reducing local and systemic oxidative stress and synergistically suppressing steatosis, inflammatory reactions, and/or apoptotic activity. These observations suggest that Tg mice may be a useful animal model for studying the relevance of oxidative stress on inflammation and the dysregulation of lipid/bile acid/glucose metabolism upon the progression of human metabolic syndrome, and that specific accelerators of PRDX4 may be useful as therapeutic agents for ameliorating various chronic inflammatory diseases.

Publicly-funded scientists have a responsibility to engage with the public on scientific information, but are lacking a standardized framework and assessment strategy to do it well. The PEPS (Public Engagement Practices for Scientists) Method is an outcomes-centered framework employing standardized pedagogical methods with quantifiable outcomes. This approach reveals that scientists often have unrealistic expectations for achieving affective learning outcomes (i.e. changing views from anti- to pro-vaccine) by solely cognitive learning strategies (i.e. supplying data). The PEPS Method can serve as a roadmap for standardized science communication assessments, complementing existing science communication training programs for the next generation of scientists.

Both climate change and anthropogenic activities contribute to the deterioration of terrestrial water resources and ecosystems worldwide. Central Asian endorheic basins are among the most affected regions through both climate and human impacts. Here, we used a digital elevation model, digitized bathymetry maps and Landsat images to estimate the areal water cover extent and volumetric storage changes in small terminal lakes in Burabay National Nature Park (BNNP), located in Northern Central Asia (CA), for the period of 1986 to 2016. Based on the analysis of long-term climatic data from meteorological stations, short-term hydrometeorological network observations, gridded climate datasets (CRU) and global atmospheric reanalysis (ERA Interim), we have evaluated the impacts of historical climatic conditions on the water balance of BNNP lake catchments. We also discuss the future based on regional climate model projections. We attribute the overall decline of BNNP lakes to long-term deficit of water balance with lake evaporation loss exceeding precipitation inputs. Direct anthropogenic water abstraction has a minor importance in water balance. However, the changes in watersheds caused by the expansion of human settlements and roads disrupting water drainage may play a more significant role in lake water storage decline. More precise water resources assessment at the local scale will be facilitated by further development of freely available higher spatial resolution remote sensing products. In addition, the results of this work can be used for the development of lake/reservoir evaporation models driven by remote sensing and atmospheric reanalysis data without the direct use of ground observations.

Access to reliable, clean, modern cooking enhances life chances. One option is photovoltaic cooking systems. Accurate solar data is needed to ascertain to what extent these can satisfy the needs of local people. This paper investigates how to choose the most accurate satellite derived solar irradiance database for use in Africa. This is necessary because there is a general shortage of ground measurements for Africa. The solar data is needed to model the output of solar cooking systems, for instance, a solar panel, battery and electric pressure cooker. Four easily accessible satellite databases are validated against ground measurements using a range of statistical tests. Results demonstrate the impact of the mathematical measure used and the phenomenon of balancing errors. Fitting of the satellite model to appropriate climate zone and/or nearby measurements improves accuracy, as does higher spatial and temporal resolution of input parameters. That said, all the four databases reviewed were found to be suitable for simulating PV yield in East Africa.

This paper deals with a non-contact method to identify the aerodynamic propeller parameters of the Parrot AR.Drone quadrotor. The experimental set consists in a camera recording the vehicle flights, the audio signal is extracted and is used a spectrogram analysis to estimates the propeller velocity. First, the aerial vehicle takes off and starts a hovering maneuver. The experiment is repeated with different additional masses attached to its rigid body. If the weight over the UAV increases/decreases, then the propeller must rotates faster/slower to produce a higher/lower thrust, and consequently, the sound frequency increases/decreases. Finally, this proposition is validated experimentally, and the estimated velocity is used to identify the quadrotor thrust parameters.

A B S T R A C T Genital herpes disease is caused mainly by herpes simplex virus type 2 (HSV-2), which is sexually transmitted with a high prevalence in both developed and developing countries. No vaccine is currently available against genital herpes. In this study, we introduced and expressed two genes encoding HSV-2 glycoprotein D (gD) and VP16 protein and a GFP marker gene (control) in tobacco seedlings. Positive plant infection was assessed through characteristic tobacco mosaic virus disease symptoms on leaves and by monitoring the fluorescence emitted by the expressed GFP protein. Expression of the HSV-gD2 and VP16 antigens was verified by RT-PCR, ELISA and Western blot. As a proof of concept, the immunogenicity and the protection ability of the plant produced gD antigen was tested in a mouse model of genital herpes and compared to gD antigen produced in a mammalian expression system. This showed that the plant-gD preparation, when used in combination with a CpG oligodeoxynucleotide as adjuvant, was highly immunogenic and capable of inducing complete immunity to lethal vaginal HSV-2 challenge in mice. Thus, the data presented here may have implications for the development of a production system for highly immunogenic plant-based HSV-2 vaccine antigens.

Analysis of Fatigue Strain, Fatigue Modulus and Fatigue Damage for the modeling of concrete plays a vital role in the evolution material behaviour which is heterogeneous and anisotropic in nature. In this paper, the Level-S nonlinear fatigue strain curve, fatigue modulus curve, residual strain curve of concrete in compression, tension, flexure and torsional fatigue loading were proposed using strain life approach. The parameters such as physical meaning, the ranges, and the impact on the shape of the curve were discussed. Then, the evolution model of fatigue modulus was established based on the fatigue strain evolution model, fatigue modulus evaluation model, residual strain evaluation model, secondary strain evaluation model. The hypothesis of fatigue modulus is inversely related with the fatigue strain amplitude. The fatigue evolution of concrete damages the bond between material grains, changed the orientation of structure of molecules and affects the elastic properties resulting in the reduction of material stiffness and modulus by utilizing strain life analysis, regarding stiffness degradation and inelastic deformation by formation of microcracking, macro cracking, cracking which is heterogeneous and anisotropic in nature . This paper presents the Fatigue Strain Life Model and analyses of fatigue strain, fatigue modulus and damage parameters of concrete which is capable of predicting stiffness degradation, inelastic deformation, strength reduction under fatigue loading. Hence, the obtainable results were compared with experimental results for the validation of the proposed model.

Here, we have discovered a X-ray excited long afterglow phosphor β-NaYF₄: Tb³⁺. After the irradiation of X-ray, the green emission can persist for more than 240 h. After 36 h, the afterglow intensity arrived at 0.69 mcd•m^-2, which can clearly be observed by naked eyes. Even after 84 h, the afterglow emission brightness still reached 0.087 mcd•m^-2. Also, combined with the results of thermoluminescence and photoluminescence, the super long afterglow emission of β-NaYF₄: Tb³⁺ can be ascribed to the tunneling model associated with F centers. More importantly, the super long green afterglow emission of β-NaYF₄: Tb³⁺ has been successfully used as in vivo light source to activate g-C₃N₄ for photodynamic therapy（PDT）and bacteria destruction. Furthermore, super long persistent luminescence of β-NaYF₄: Tb³⁺ could be repeatedly charged by X-ray for many circulations, which indicates that the phosphors have high photo stability under repeated cycles of alternating X-ray irradiation.

The aim of this work is to introduce new ways to model the I-V characteristic of a PV cell or PV module using straight lines and Bézier curves. This is a complete novel approach, Bézier curves being previously used mainly for computer graphics. The I-V characteristic is divided in three sections, modeled with lines and a quadratic Bézier curve in the first case and with three cubic Bézier curves in the second case. The result proves to be accurate and relies on the fundamental points usually present in the PV cell datasheets: Voc (the open circuit voltage), Isc (the short circuit current), Vmp (the maximum power corresponding voltage) and Imp (the maximum power corresponding current) and the parasitic resistances Rsh0 (shunt resistance at Isc) and Rs0 (series resistance at Voc). The proposed algorithm completely defines all the implied control points and the error is analyzed. The temperature and irradiance influence is also analyzed. The model is also compared using the least squares fitting method. The final validation shows how to use Bézier cubic curves to accurately represent the I-V curves of an extensive range of PV cells and arrays.

Major marine construction projects, resulting in the release of sediments, are subject to environmental assessment and other regulatory approval processes. An important tool used for this is the development of specialized numerical methods for these marine activities. An integrated set of numerical methods addresses four distinct topics: the near-field release and mixing of suspended sediments into the water column (i.e. the initial dilution zone); the transport of the suspended sediments under the influence of complex ocean currents in the far-field; the settling of the transported suspended sediments onto the seabed; and the potential for resuspension of the deposited sediments due to sporadic occurrences of unusually large near-bottom currents. A review of projects subjected to environmental assessment in the coastal waters of British Columbia from the year 2006 to 2017, is presented to illustrate the numerical models being used and their ongoing development. Improvements include higher resolution model grids to better represent the near-field, the depiction of particle size dependent vertical settling rates and the computation of resuspension of initially deposited sediments, especially in relation to temporary subsea piles of sediments arising from trenching for marine pipelines. The ongoing challenges for this numerical modeling application area are also identified.

Historical forest management practices in the southwestern US have left forests prone to high intensity, stand-replacement fires. Effective management to reduce the cost and impact of forest-fire management and allow fires to burn freely without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to reduce costs and increase ability of managers to monitor AGB and other forest metrics. Using Bayesian Model Averaging (BMA), we develop a regionally applicable lidar-based statistical model for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. The selected regional model includes a mid and low canopy height metric, a canopy cover, and height distribution term. It explains 72% of the variability in field estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model developed is structured in accordance with previously described models fit to local data, and performs equivalently to models designed for smaller scale application. Developing regional models for broad scale application provides a cost-effective, robust approach for managers to monitor and plan adaptively at the landscape scale.

Using hierarchical regression analysis within a mediation model framework, the present study explores direct and indirect (through life satisfaction) causal impacts of commuting stress on turnover intention of employees from 29 business organizations in six populous cities of Turkey. A semi-random heterogeneous sample of 214 employees with different demographics was surveyed in winter and summer times for also capturing seasonal variations of variables. The results supporting the partial mediating role of life satisfaction in the positive relationship between commuting stress and turnover intention infer that commuting stress induces turnover intention directly and indirectly (by reducing life satisfaction). The analysis of variance reveals that demographic characteristics of employees such as gender, marital status, age, and family size together with commuting type and commuting duration matter for their perceived commuting stress, life satisfaction, and turnover intention levels. Commuting stress perception is relatively higher in summer time whereas the other magnitudes are consistently and significantly invariant between two survey implementations. The study concludes with a call for the consideration of commuting stress and life satisfaction together with environmental and demographic factors when analyzing the antecedents and consequences of employee turnover intention.

Background: Metabolic flux analysis (MFA) is an indispensable tool in metabolic engineering. The simplest variant of MFA relies on an overdetermined stoichiometric model of the cell’s metabolism under the pseudo-steady state assumption, to evaluate the intracellular flux distribution. Despite its long history, the issue of model error in the overdetermined MFA, particularly misspecifications of the stoichiometric matrix, has not received much attention. Method: We evaluated the performance of statistical tests from linear least square regressions, namely Ramsey RESET test, F-test and Lagrange multiplier test, in detecting model misspecifications in the overdetermined MFA, particularly missing reactions. We further proposed an iterative procedure using the F-test to correct such an issue. Result: Using Chinese hamster ovary and random metabolic networks, we demonstrated that: (1) a statistically significant regression does not guarantee high accuracy of the flux estimates, (2) the removal of a reaction with a low flux magnitude can cause disproportionately large biases in the flux estimates, (3) the F-test could efficiently detect missing reactions, and (4) the proposed iterative procedure could robustly resolve the omission of reactions. Conclusion: Our work demonstrated that statistical analysis and tests could be used to systematically assess, detect and resolve model misspecifications in the overdetermined MFA.

XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims vs. no claims can be used to identify the determinants of traffic accidents. We compare the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contains information from an insurance company about individuals’ driving patterns – including total annual distance driven and percentage of total distance driven in urban areas. Our findings show that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards interpretation.

The Rasch model is one of the most prominent item response models. In this article, different item parameter estimation methods for the Rasch model are compared through a simulation study. The type of ability distribution, the number of items, and sample sizes were varied. It is shown that variants of joint maximum likelihood estimation and conditional likelihood estimation are competitive to marginal maximum likelihood estimation. However, efficiency losses of limited-information estimation methods are only modest. It can be concluded that in empirical studies using the Rasch model, the impact of the choice of an estimation method with respect to item parameters is almost negligible for most estimation methods. Interestingly, this sheds a somewhat more positive light on old-fashioned joint maximum likelihood and limited information estimation methods.

Background: Flattening the curve refers to community isolation measures that keep the daily number of disease cases at a manageable level for medical providers. Efforts to completely contain the COVID – 19 have failed. As there is currently no vaccine or specific medication to treat COVID-19, the only way to flatten the curve is through collective action of mitigation efforts. Objectives: The study was carried out with aim to study the effect of mitigation efforts in reducing the overall COVID -19 attack rate and flattening the curve of COVID-19 in Egypt. Methods: Secondary data in the situation reports of WHO, Worldometer and Egyptian MoHP Report about the COVID-19 epidemic in Egypt were analyzed till June 27, 2020. Survival analysis was carried out to determine the cumulative proportional survival of COVID-19. The Susceptible Infected Recovered (SIR) model was used to study the epidemic dynamics of COVID-19 pandemic. Results: In Egypt in the fourth week after exposure β was 0.17 and γ = 0.07. The fraction of infectious individuals will grow exponentially as the exponential growth rate was 0.10, with an epidemic time of 7 days. R in 22 ^nd June was 0.80 (less than one person infected) where β=0.21 and γ =0.26. There was reduction in the actual overall attack rate at 22^nd June of 96 % of its value without intervention with a cumulative proportional survival of 0.89. There was flattening of the curve. Conclusion and Recommendation: Mitigation efforts of Egypt had succeeded in flattening the curve of COVID-19 Pandemic. Even though, strengthening all the mitigation efforts to keep the exposure probability, as well as the transmission rate as low as possible is mandatory for containment of the epidemic.

Introduction. The use of probiotics demonstrate efficacy against obesity and metabolic syndrome (MetS). Detection effective probiotic strains for hyperglycemia and immunity associated with is important task. The aim of the study was to evaluate an influence of Lactobacillus casei IMV B-7280 separately and composition L. casei IMV B-7280 / Bifidobacterium animalis VKB / B. animalis VKL on the levels of blood glucose and immunity in obese mice. Meterials and methods. Obesity was induced by fat-enriched diet (FED) in male BALB/c mice. Obese mice were transferred to standard diet and received per os probiotic strains daily during 10 days. We measured tumor necrosis factor-a (TNF-a) in blood serum using enzyme-linked immunosorbent assay and functional activity of peritoneal exudate macrophages (PEMs). Glucose levels in blood was defined with glucometer. Results. We ascertained that all probiotic strains induced reducing mice weight and visceral fat, normalization of TNF-a production and functional activity of PEMs. Treatment with L. casei IMV B-7280 was associated with decreasing blood glucose levels. No normalization of glucose and TNF-a levels in obese mice, transferred to standard diet without probiotic treatment, although we revealed decreasing their weight and visceral fat and partial recover of functional activity of PEMs. Conclusions. Probiotic strain L. casei IMV B-7280 (separately) and composition L. casei IMV B-7280 / B. animalis VKB / B. animalis VKL can re-equilibrate metabolic and inflammation indices in mouse obesity model. L. casei IMV B-7280 alone was more efficient in decreasing glucose levels than composition of strains.

Strategic researches on green mine construction are of great theoretical and practical significance to the sustainable development of China's mining industry as well as the great-leap-forward development strategies of China. Strategies of green mine construction in China are methods summarized to solve all potential problems from mine production to ecological restoration. At present, strategies of green mine construction in China are not fully evaluated and studied yet. Therefore, on the basis of green mine construction related literatures carried out by researchers in China and abroad, this study took the green mine of Yongcheng City in China as the research object to evaluate the current situation of green mine construction in Yongcheng City and put forward corresponding countermeasures. First of all, driving force-pressure-state-impact-response (DPSIR) model was introduced for the construction of evaluation index system; construction principles and selection methods of indexes and the index system based on driving force, pressure, state, impact and response were constructed. Secondly, principal component analysis (PCA) was adopted to calculate and evaluate data of green mine of Yongcheng City in recent years, and construction state of green mine in Yongcheng City was analyzed concretely according to the evaluation results. Empirical results showed that, the evaluation system constructed in this study was feasible, which could be applied to evaluate construction of green mine effectively.

With the proliferation of locating devices, more and more raw spatial trajectories are formed, and many works enrich these raw trajectories with semantics, and mine patterns from both raw and semantic trajectories, but access control of spatial trajectories is not considered yet. We present a multi-policy secure model for semantic spatial trajectories. In our model, Mandatory Access Control, Role Based Access Control and Discretionary Access control are all enforced, separately and combined, and we represent the model semi-formally in Ontology Web Language.

This study examined the link between tax revenue components and economic growth in South Africa, utilizing time series data for the period of 22 years. The stationarity of the variables was established using the Phillips-Perron (PP) unit root test, and the existence of long-run and short-run equilibrium conditions was tested using the Autoregressive Distributed Lag (ARDL) model. As a proxy for economic growth, the study used the real GDP growth rate as the dependent variable, with company income tax, personal income tax, taxes on international trade and transactions, taxes on income, profits, and capital gains tax, foreign direct investment, inflation, and gross savings as the independent variables. According to the PP findings, none of the variables are integrated at a higher order than one, i.e. (1). All variables are found to be cointegrated, and all explanatory variables have a long-run link with economic growth. According to the ARDL findings, company income tax, personal income tax, and taxes on international trade and transactions all have a positive long-run and short-run link with economic growth, whereas capital gain tax, foreign direct investment, and gross savings all have a negative long-run and short-run link with economic growth. The long-run coefficient is negatively related to RGDP, while the short-run coefficient revealed a positive link between inflation and economic growth, among other findings. Heteroskedasticity and autocorrelation are not present in our model, according to diagnostic tests. The CUSUM and CUSUMSQ values indicate that the model is structurally sound.

Several epidemiological models are being used around the world to project the number of infected individuals and the mortality rates of the COVID-19 outbreak. Advancing accurate prediction models is of utmost importance to take proper actions. Due to a high level of uncertainty or even lack of essential data, the standard epidemiological models have been challenged regarding the delivery of higher accuracy for long-term prediction. As an alternative to the susceptible-infected-resistant (SIR)-based models, this study proposes a hybrid machine learning approach to predict the COVID-19 and we exemplify its potential using data from Hungary. The hybrid machine learning methods of adaptive network-based fuzzy inference system (ANFIS) and multi-layered perceptron-imperialist competitive algorithm (MLP-ICA) are used to predict time series of infected individuals and mortality rate. The models predict that by late May, the outbreak and the total morality will drop substantially. The validation is performed for nine days with promising results, which confirms the model accuracy. It is expected that the model maintains its accuracy as long as no significant interruption occurs. Based on the results reported here, and due to the complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research.

Doxorubicin (DOX) is a frequently used chemotherapeutic drug for breast cancer, but its site specificity and local internalization into tumor cells is rather low. In this paper we conjugated magnetic nanoparticles (MNPs) with DOX and/or a pseudopeptide NucAnt (N6L) as modality to enhance DOX-induced antitumor effects in breast cancer cells (BT474). In this context, we determined cellular uptake of MNP formulations, analyzed cell viability and expression of apoptotic and cell cycle proteins after magnetic hyperthermia (43°C, 1 h) in vivo and in vitro. We have shown that i) the presence of N6L on the surface of DOX-functionalized MNPs increases their internalization into a target cells and potentiates the cytotoxic potential of the anticancer drug, ii) in combination with hyperthermia, DOX functionalized MNPs influence the expression of apoptotic and cell cycle proteins, and also favors tumor regression in vivo. Our data show that intratumoral application of DOX coupled MNPs is able to overcome biological barriers to chemotherapeutic drugs, enabling them to penetrate into the target cells. Combined with hyperthermia these MNPs can be an effective method in enhancing the localised delivery and penetration of DOX into breast cancer cells.

In this study we discuss critical issues in modelling the structure and function of lymph nodes (LNs), with emphasis on how LN physiology is related to its multi-scale structural organization. In addition to macroscopic domains such as B-cell follicles and the T cell zone, there are vascular networks which play a key role in the delivery of information to the inner parts of the LN, i.e., the conduit and blood microvascular networks. We propose object-oriented computational algorithms to model the 3D geometry of the fibroblastic reticular cell (FRC) network and the microvasculature. Assuming that a conduit cylinder is densely packed with collagen fibers, the computational flow study predicted that the diffusion should be a dominating process in mass transport than convective flow. The geometry models are used to analyze the lymph flow properties through the conduit network in unperturbed- and damaged states of the LN. The analysis predicts that elimination of up to 60–90 % of edges is required to stop the lymph flux. This result suggests a high degree of functional robustness of the network.

Economic development and advancements in information technology contributed to the shift on accounting objective from commission responsibility view to decision-making useful view. The decision-making useful view claims that the accounting measurement methods should be changed to improve the usefulness of accounting information, to enable information users to make correct decisions. In addition, the development of financial instruments leads to the birth of fair value accounting. In recent years, fair value has been one of the most important measurement methods in the International Accounting Standard and US Generally Accepted Accounting Principles. However, China has been quite cautious when it comes to applying fair value full time, especially after the global financial crisis happened in 2008, wherein fair value accounting was exposed to strong suspicion. Hence, studying the value relevance of fair value is of great significance to exploring the accounting profession and reforming accounting standards. This paper attempts to investigate the value-relevance of fair value based on the data of listed financial companies and manufacturing companies in China. The results indicate that the fair value information in the financial industry has a strong explanatory influence to corporate share price. In contrast, the fair value information shows limited relevance in the manufacturing industry. Finally, consulting with the results of normative analysis and empirical study, this paper suggests several rational advices for the application of fair value in China.

In this study, the values of the interfacial energies of seven different polymer-water systems obtained by Sessile Drop Accelerometry (SDACC) are compared with the values obtained by the Young’s-equation-based Owens-Wendt method. The SDACC laboratory instrument –a combination of a drop shape analyzer with high-speed camera and a microgravity tower- and the evaluation algorithms, are designed to measure the interfacial energies as a function of the geometrical changes of a sessile droplet shape due to the effect of “switching off” gravity during the experiment. The method bases on Thermodynamics of Interfaces and differs from the conventional aproach of the two hundred-years-old Young’s equation in that it assumes a thermodynamic equilibrium between interfaces, rather than a balance of forces on a point of the solid-liquid-gas contour line. A comparison of the mathematical model that supports the SDACC method with the widely accepted Young`s equation is discussed in detail in this study.

Here, applying the theory of the generalization of the Boltzmann constant in curved space-time, we will model the mechanism by which elementary particles are formed. We will determine the relationship between gravity and each of the elementary particles that form the standard model following analogies to that of stellar bodies. In order to achieve our goal, we will propose new models for photons, quarks and gluons; with this, we will demonstrate why there are stable and unstable elementary particles; why the first family of elementary particles forms hadrons and why the second and third families cannot form hadrons; why the fermions comply with the Pauli exclusion principle and why the bosons do not comply with the Pauli exclusion principle, etc. Finally, we will analyse the generalization of the ADS/CFT correspondence and propose a theory of everything (T.O.E.).

The aim of this study was to develop a basic model to predict enteric methane emission from dairy cows and to update operational calculations for the national inventory in Norway. Basic models were developed using a database with 63 treatment means from 19 studies. The database included records for enteric CH4 production (MJ/day), dry matter intake (DMI), and dietary nutrient composition. The basic models were evaluated against an external database (n=36, from ten studies) along with other extant models. When evaluated by low root mean square prediction errors and high concordance correlation coefficients, the developed basic models that included DMI, dietary concentrations of fatty acids and neutral detergent fiber performed slightly better in predicting CH4 emissions than extant models. In order to propose country-specific values for the CH4 conversion factor Ym (% of gross energy intake partitioned into CH4) and thus to carry out the national inventory for Norway, the existing operational model was updated for the prediction of Ym over a wide range of feeding situations using energy corrected milk and dietary concentrate share as predictor variables. Input values of Ym were updated based on the results from the basic models. The predicted Ym ranged from 6.22 to 6.72%. In conclusion, the prediction of CH4 production from dairy cows was improved with the help of newly published data, which enabled an update of the operational model for calculating the national inventory of CH4 in Norway.

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.

Many agonists for the estrogen receptor are known to disrupt endocrine functioning. We have developed a computational model that predicts agonists for the estrogen receptor ligand-binding domain in an assay system. Our model was entered into the Tox21 Data Challenge 2014, a computational toxicology competition organized by the National Center for Advancing Translational Sciences. This competition aims to find high-performance predictive models for various adverse-outcome pathways, including the estrogen receptor. Our predictive model, which is based on the random forest method, delivered the best performance in its competition category. In the current study, the predictive performance of the random forest models was improved by strictly adjusting the hyperparameters to avoid overfitting. The random forest models were optimized from 4,000 descriptors simultaneously applied to 10,000 activity assay results for the estrogen receptor ligand-binding domain, which have been measured and compiled by Tox21. At this time, our model delivers the highest predictive power on estrogen receptor agonists in the world. Furthermore, analysis of the optimized model revealed some important features of the agonists, such as the number of hydroxyl groups in the molecules.

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.

There is no simple method to measure glomerular filtration rate (GFR) in swine, an established model to study renal disease. We developed a protocol to measure GFR in conscious swine with the plasma clearance of iohexol. We used two groups: testing and validation, of 8 animals each. Ten milliliters of iohexol (6.47 g) were injected by the marginal auricular vein and blood samples (3 ml) were collected from the orbital sinus at different points after injection. GFR was determined considering two models: two-compartments (CL2: all samples) and one-compartment (CL1: the last six samples). In the testing group, CL1 overestimated CL2 by ~30%: CL2=245±93 and CL1=308±123 ml/mn. This error was corrected by a first order polynomial quadratic equation to CL1, which was considered the simplified method: SM=-47.909+(1.176xCL1)–(0.00063968xCL1²). SM showed narrow limits of agreement with CL2, and a concordance correlation of 0.97 and a total deviation index of 14.73%. Similar results were obtained for the validation group. This protocol is reliable, reproducible, can be performed in conscious animals, uses a single dose of the marker, and requires a reduced number of samples avoiding urine collection. Finally, it portends a significant improvement in animal-welfare conditions and handling necessities in experimental trials.

Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology. Hence, for the sake of reinforcing the trust between Cloud Clients (CC) and Cloud Service Providers (CSP), as well as safeguarding the CC’s data in the cloud, several security paradigms of cloud computing based on a Third-Party Auditor (TPA) have been introduced. The TPA, as a trusted party, is responsible for checking the integrity of the CC’s data and all the critical information associated with it. However, the TPA could become an adversary and could aim to deteriorate the privacy of the CC’s data by playing a malicious role. In this paper, we present the state-of-art of cloud computing’s privacy-preserving models (PPM) based on a TPA. Three TPA factors of paramount significance have been discussed: TPA involvement, security requirements, and security threats caused by vulnerabilities. Moreover, TPA’s privacy preserving models have been comprehensively analyzed and categorized into different classes with an emphasis on their dynamicity. Finally, we discuss the limitations of the models and present our recommendations for their improvement.

The purpose of this paper was to analyze the development trend of hazardous chemical packaging towards low carbon economy from both qualitative and quantitative perspectives. Four types of relatively small volume packaging with volume/weight less than 450L/400kg, respectively, and three intermediate bulk containers (IBCs), which are widely used for hazardous chemicals were studied to calculate the carbon footprint (CF) from cradle to grave using life cycle assessment (LCA) method and to predict the future carbon emission of hazardous chemical packaging in the next five years (2016-2020), based on the export data of Tianjin Port in China. Grey model (GM) was adopted in the prediction. The results showed that majority of IBCs have lower carbon footprint than other types when the packaging contained same amount of same hazardous chemical. With the development of international trading, the demand of hazardous chemicals will increase as well. As the result, carbon emission generated by hazardous chemical packaging will increase accordingly. However, based on GM simulation result, increasing the amount of IBC use will effectively reduce the relative amount of carbon emission.

Geometric Akaike Information Criteria (G-AICs) for generalized noise-level dependent crystallographic symmetry classifications of two-dimensional (2D) images that are more or less periodic in either two or one dimensions as well as Akaike weights for multi-model inferences and predictions are reviewed. Such novel classifications do not refer to a single crystallographic symmetry class exclusively in a qualitative and definitive way. Instead, they are quantitative, spread over a range of crystallographic symmetry classes, and provide opportunities for inferences from all classes (within the range) simultaneously. The novel classifications are based on information theory and depend only on information that has been extracted from the images themselves by means of maximal likelihood approaches so that these classifications are objective. This is in stark contrast to the common practice whereby arbitrarily set thresholds or null hypothesis tests are employed to force crystallographic symmetry classifications into apparently definitive/exclusive states, while the geometric feature extraction results on which they depend are never definitive in the presence of generalized noise, i.e. in all real world applications. Thus, there is unnecessary subjectivity in the currently practiced ways of making crystallographic symmetry classifications, which can be overcome by the approach outlined in this review.

Experimentally tractable organisms like C. elegans, Drosophila, zebrafish, and mouse are popular models for addressing diverse questions in biology. In 1997, two of the most valuable invertebrate model organisms to date – C. elegans and Drosophila – were found to be much more closely related to each other than expected. C. elegans and Drosophila belong to the nematodes and arthropods respectively, and these two phyla and six other phyla make up a clade of molting animals referred to as the Ecdysozoa. The other ecdysozoan phyla could be valuable models for comparative biology, taking advantage of the rich and continual sources of research findings as well as tools from both C. elegans and Drosophila. But when the Ecdysozoa was first recognized, few tools were available for laboratory studies in any of these six other ecdysozoan phyla. In 1999 I began an effort to develop tools for studying one such phylum, the tardigrades. Here, I describe how the tardigrade species Hypsibius exemplaris and tardigrades more generally have emerged over the past two decades as valuable new models for answering diverse questions. To date, these questions have included how animal body plans evolve and how biological materials can survive some remarkably extreme conditions.

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

Starting with a hypothetical geostrophic zonal current in an unbounded ocean, the investigation points out the response of this simple system to a thermal forcing, applied to the free surface and consistent with the maintenance of the geostrophic balance. The main result is the formation of a meridional component of the current, according to the Sverdrup relation, such that the full velocity vector rotates clockwise for heating and anticlockwise for cooling to adjust eventually in the initial zonal direction for large depths.

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

This paper is an attempt to present a COVID-19 prediction model for India. Lockdown plays an important role in the arrest of community spread of the disease. This was evident from the study of other countries such as Russia, Belgium and Germany, where peak cases were recorded within a month of the imposition of lockdown, that it showed an immediate positive effect. However, in India, even after 65 days of lockdown, there is no decrease in the number of daily new cases reported. There were many models prepared for India and almost all of them were proven wrong by the increase in the number of cases. The model in this paper is prepared using the COVID-19 trend in other countries, population density and the pandemic bell curve. Based on the available data until 24^th May 2020, two scenarios have been presented. In one, the peak shall be obtained when the number of daily new cases per million reaches 190 and in the second when the daily new cases per million reach 724. One model predicts the number of cases to reach 1 million by mid-July 2020. The other model predicts the number of cases to peak by mid-July with the total cases reaching 20 million. The predicted cases were compared with the actual cases recorded for the period 25^th May to 11^th June 2020. It was observed that the actual values matched quite reasonably with the predicted values.

The SARS-CoV-2 polybasic furin cleavage site is still a missing link. Remarkably, the two arginine residues of this protease recognition site are encoded by the CGG codon, which is rare in Betacoronavirus. However, the arginine pair is common at viral furin cleavage sites, but are not CGG-CGG encoded. The question is: Is this genetic footprint unique to the SARS-CoV-2? To address the issue, using Perl scripts, here I dissect in detail the NCBI Virus database in order to report the arginine dimers of the Betacoronavirus proteins. The main result reveals that a group of Middle East respiratory syndrome-related coronavirus (MERS-CoV) (isolates: camel/Nigeria/NVx/2016, host: Camelus dromedarius) also have the CGG-CGG arginine pair in the spike protein polybasic furin cleavage region. In addition, CGG-CGG encoded arginine pairs were found in the orf1ab polyprotein from HKU9 and HKU14 Betacoronavirus, as well as, in the nucleocapsid phosphoprotein from few SARS-CoV-2 isolates. To quantify the probability of finding the arginine CGG-CGG codon pair in Betacoronavirus, the likelihood ratio (LR) and a Markov model were defined. In conclusion, it is highly unlikely to find this genetic marker in betacoronaviruses wildlife, but they are there. Collectively, results shed light on recombination as origin of the virus CGG-CGG arginine pair in the S1/S2 cleavage site.

Software engineers from all over the world solve independently a lot of similar problems. In this condition the problem of code or even better architecture reusing becomes an issue of the day. In this paper two phase approach to determining the functional and structural likenesses of software projects is proposed. This approach combines two methods of artificial intelligence: natural language processing techniques with a novel method for comparing software projects based on ontological representation of their architecture automatically obtained from the projects source code. Additionally several similarity metrics are proposed to estimate similarity between projects.

Fractional differential mathematical model unfolding the dynamics of the COVID-19 pandemic in India is presented and explored in this paper. The purpose of this study is to estimate the future outbreak of disease and potential control strategies using mathematical models in India as a whole country as well as in some of the states of the country. This model is calibrated based on reported cases of infections over the month of April 2020 in India. We have used iterative fractional complex transform method to find approximate solutions of the model having modified Riemann Liouville fractional differential operator. We have also carried out a comparative analysis between actual and estimated cumulative cases graphically, moreover, most sensitive parameters for basic reproduction number$(R_0)$ are computed and their effect on transmission dynamics of COVID-19 pandemic is investigated in detail.

This paper describes a new approach that can be used to determine the mechanical properties of unknown materials and complex material systems. The approach uses inverse finite element modelling (FEM) accompanied with a designed algorithm to obtain the modulus of elasticity, yield stress and strain hardening material constants of an isotropic hardening material model, as well as the material constants of the Drucker-Prager material model (modulus of elasticity, cap yield stress and angle of friction). The algorithm automatically feeds the input material properties data to finite element software and automatically runs simulations to establish a convergence between the numerical loading-unloading curve and the target data obtained from continuous indentation tests using common indenter geometries. A further module was developed to optimise convergence using an inverse FEM analysis interfaced with a non-linear MATLAB algorithm. A sensitivity analysis determined that the dual Spherical and Berkovich (S&B) approach delivered better results than other dual indentation methods such as Berkovich and Vickers (B&V) and Vickers and Spherical (V&S). It was found that better convergence values can be achieved despite a large variation in the starting parameter values and / or material constitutive model and such behaviour reflects the uniqueness of the dual S&B indentation in predicting complex material systems. The study has shown that a robust optimization method based on a non-linear least-squares curve fitting function (LSQNONLIN) within MATLAB and ABAQUS can be used to accurately predict a unique set of elastic plastic material properties and Drucker-Prager material properties. This is of benefit to the scientific investigation of properties of new materials or obtaining the material properties at different location of a part which might be not be similar due to manufacturing processes (e.g. different heating and cooling rates at different locations).

In era of ever-expanding data and knowledge, we lack a centralized system that maps all the faculties to their research works. This problem has not been addressed in the past and it becomes challenging for students to connect with the right faculty of their domain. Since we have so many colleges and faculties this lies in the category of big data problem. In this paper, we present a model which works on the distributed computing environment to tackle big data. The proposed model uses apache spark as an execution engine and hive as database. The results are visualized with the help of Tableau that is connected to Apache Hive to achieve distributed computing.

Background: Superspreading events are important drivers of the SARS-CoV-2 pandemic. By analyzing two outbreaks associated with choir rehearsals in March 2020, we demonstrate the risk of indoor, long-range (LR) transmission and singing, to help prevent similar outbreaks. Methods: We conducted two retrospective cohort studies and obtained demographic, clinical, laboratory and contact data, performed SARS-CoV-2 serology, whole genome sequencing (WGS), calculated LR transmission probabilities, measured particle emissions of selected choir members, and calculated particle air concentrations and inhalation doses.Results: We included 65 (84%) and 42 (100%) members of choirs 1 and 2, respectively. WGS confirmed strain identity in both choirs and the primary case of choir 1 (transmitting presymptomatically). Particle emission rate when singing was 7 times higher compared to talking. In choir 1, the median concentration of primary cases’ emitted particles was 8 times higher, exposure at least 30 minutes longer and room volume smaller than in choir 2, resulting in markedly different estimated probabilities for LR transmission (median: 89% vs. 18%, 95%CI: 80-95% vs. 6-36%). Observed AR in choir 1 (89%) was significantly higher than in choir 2 (24%). According to a risk model, first transmission in choir 1 occurred likely after 7 minutes of singing. The number of inhaled particles emitted by an infectious case, sufficient to infect 50% of exposed, was calculated to 1039-2883 particles (95%CI).Conclusions: Even in large rooms, singing of an infectious person may lead to secondary infections through LR exposure within minutes. Given the potential for presymptomatic infectiousness, greatest caution is required wherever aerosols can accumulate.

The study aims to analyze the performance criteria of the GR4J model to reproduce high water flows in the Ouémé watershed at Bonou's outlet which has been vulnerable to climate change in recent decades. The methodology focused on the use of daily climatological and hydrometric data extracted from files of National Directorate of Meteorology, and General Directorate of Water; they were supplemented by those of SIEREM/HSM dataset over the period 1961-2015. The rainfall was regionalized using Thiessen method. The performance of the GR4J model was assessed with NSE, RMSE and KGE criteria. The results indicate that the study area is marked by rainfall variabilities and detection of two breakpoints (1968 and 1987) which divide the series into three sub-periods; these discontinuities have repercussions on the streamflow. It's found that GR4J model overestimates the streamflow during the low water period and underestimates them in high water. However, the efficiency and performance criteria NSE, RMSE and KGE calculated on high water flow rates are better in calibration than in validation. The KGE values are range between 83-85% in calibration and 56-68% during validation, which gives to GR4J model the efficiency and performance to reproduce high flow rates in the study area

In the promptness of the COVID-19 outbreak, it would be very important to observe and estimate the pattern of diseases to reduce the contagious infection. To study this effect, we developed a COVID-19 epidemic model that incorporates five various groups of individuals. Then we analyze the model by evaluating the equilibrium points and analyzing their stability as well as determining the basic reproduction number. Also, numerical simulations show the dynamics of a different group of the population over time. Thus, our findings based on the sensitivity analysis and the reproduction number highlight the role of outbreak of the virus that can be useful to avoid a massive collapse in Bangladesh and rest of the world. The outcome of this study concludes that outbreak will be in control which ensure the social and economic stability.

: The cyber-physical gap (CPG) is the difference between the 'real' state of the world and the way the system perceives it. This discrepancy often stems from the limitations of sensing and data collection technologies and capabilities, and is an inevitable issue in any cyber-physical system (CPS). Ignoring or misrepresenting such limitations during system modeling, specification, design, and analysis can potentially result in systemic misconceptions, disrupted functionality and performance, system failure, severe damage, and potential detrimental impacts on the system and its environment. We propose CPG-Aware Modeling & Engineering (CPGAME), a conceptual model-based approach for capturing, explaining, and mitigating the CPG, on top of and in sync with the conventional system model, and as an inherent systems engineering activity. This approach enhances the systems engineer’s ability to cope with CPGs, mitigate them by design, and prevent erroneous decisions, actions, and hazardous implications. CPGAME is a generic, conceptual approach, specified and demonstrated with Object Process Methodology (OPM). OPM is a holistic conceptual modeling paradigm for multidisciplinary, complex, dynamic systems, which is also ISO-19450. We analyze the 1979 Three Miles Island 2 nuclear accident as a prime example of the disastrous consequences of unmitigated CPGs in complex systems.

In this paper, the computational fluid dynamics (CFD) method has been used to simulate the gas flow and the catalytic hydrogenation process of the C-2 hydrogenation adiabatic reactor in a practical ethylene production unit of a chemical plant. The laminar finite-rate model was used for the chemical reaction model. The standard two-equation model was used for the turbulence model. The exponent-function kinetic model was used for the reaction dynamics model. The simulation results were in good agreement with the actual industrial data obtained from the C-2 hydrogenation reactor, and the distributions of reaction parameters such as temperature and component concentrations in the reactor were further analyzed. Based on the validated CFD models, the structure and operating conditions of the reactor, as well as hydrogen load distribution ratios, are simulated for comparing the comprehensive performance metrics of ethylene selectivity and acetylene conversion rate; as a result, we can obtain the optimal temperature of 312 K, the optimal hydrogen/acetylene ratio of 0.10 as well as the optimal hydrogen load distribution ratio of 4:4:2, and the diameter/height ratio exerts a marginal effect on the selectivity, conversion rate and harmonic mean of the reactor.

During speciation-with-gene-flow, a transition from single-locus to multi-locus processes can occur, as strong coupling of multiple loci creates a barrier to gene flow. Testing predictions about such transitions with empirical data requires building upon past theoretical work and the continued development of quantitative approaches. We simulated genomes under different evolutionary scenarios of gene flow and divergent selection, extending previous work with the additions of neutral sites and coupling statistics, allowing us to investigate if and how selected and neutral sites differ in the conditions they require for transitions during speciation. As the per-locus strength of selection grew and/or migration decreased, it became easier for selected sites to show divergence – and thus to rise in linkage disequilibrium (LD) with each other as a statistical consequence – farther in advance of the conditions under which neutral sites could diverge. Indeed, even very low rates of gene flow were sufficient to prevent differentiation at neutral sites. However, once strong enough, coupling among selected sites eventually reduced gene flow at neutral sites as well. To explore whether similar transitions might be detectable in empirical data, we used published genome resequencing data from three taxa of Heliconius butterflies. We found that allele-frequency outliers and F ST outliers exhibited stronger patterns of LD than the genomic background, as expected. The statistical characteristics of LD – likely indicative of the strength of coupling of barrier loci – varied between chromosomes and taxonomic comparisons. Broad qualitative agreement between the patterns we observed in the empirical data and our simulations suggests that selection drives rapid genome-wide transitions to multi-locus coupling, illustrating how divergence and gene flow interact along the speciation continuum.

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

(1) Background: This study examines the intention to behave actively prevent COVID-19 among local tourism practitioners by adopting an empirically validated norm activation model (NAM) of Schwartz and merging it with the Expectancy theory of Vroom; (2) Methods: The study aims to develops a theoretical framework for understanding the formation and predicting the change of personal protective intention to prevent COVID-19. Based on 514 valid responses from the field surveys; (3) Results: The author develops the refined model including 7 constructs and 26 observational items, and the results showed that the refined model has enjoys a better predictive accuracy of protective intention than the original NAM; and (4) Conclusions: The intention of preventing COVID-19 should needs wider public support and advocacy, and recognizing the change rule of improving behavioral intentions of preventing COVID-19 to maintain the safe tourism image of tourist attractions in Zhangjiajie is also benefits for local tourism practitioners.

Postgraduate research is a massive stumbling block to students who had never had an opportunity of exposure to research methods. A study conducted by the researcher (Costa, 2018) revealed challenges in postgraduate supervision—one of these challenges was lack of exposure to research language by students. Qualitative research and its complexities due to a variety of approaches including rigour determination pose a plethora of challenges to novice researchers. This document presents C.O.S.T.A model as a tool suitable for use by academics and students, with a panoramic view of steps to be taken first to understand foundational concepts and the language of research and secondly to make informed choices on the research methods and design strategy options available for the prospective researcher. Conclusions drawn on the reliability of the model are based on empirical evidence of application of the model observed over a period of 12 months, with some students testifying to have passed their research projects satisfactorily at their respective leading universities in South Africa. Secondary research also provides voluminous sources of success of similar research supervision programmes both in South Africa and abroad.

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.

Decision tables have been used for many years in data processing and business applications to simulate complex rule sets. Several computer languages have been developed based on rule systems and they are easily programmed in several current languages. Land management and river-reservoir models simulate complex land management operations and reservoir management in highly regulated river systems. Decision tables are a precise yet compact way to model the rule sets and corresponding actions found in these models. In this study, we discuss the suitability of decision tables to simulate management in the river basin scale Soil and Water Assessment Tool (SWAT+) model. Decision tables are developed to simulate automated irrigation and reservoir releases. A simple auto irrigation application of decision tables was developed using plant water stress as a condition for irrigating corn in Texas. Sensitivity of the water stress trigger and irrigation application amounts were shown on soil moisture and corn yields. In addition, the Grapevine Reservoir near Dallas, Texas was used to illustrate the use of decision tables to simulate reservoir releases. The releases were conditioned on reservoir volumes and flood season. The release rules as implemented by the decision table realistically simulated flood releases as evidenced by a daily NSE (Nash-Sutcliffe Efficiency) of 0.52 and a percent bias of -1.1%. Using decision tables to simulate management in land, river and reservoir models was shown to have several advantages over current approaches including: 1) mature technology with considerable literature and applications, 2) ability to accurately represent complex, real world decision making, 3) code that is efficient, modular and easy to maintain, and 4) tables that are easy to maintain, support, and modify.

The first case of Corona Virus Disease 2019 (COVID-19) was reported in Wuhan, China in December 2019. Since then, COVID-19 has quickly spread out to all provinces in China and over 150 countries or territories in the world. With the first level response to public health emergencies (FLRPHE) launched over the country, the outbreak of COVID-19 in China is achieving under control in China. We develop a mathematical model based on epidemiology of COVID-19, incorporating the isolation of healthy people, confirmed cases and close contacts. We calculate the basic reproduction numbers 2.5 in China (excluding Hubei province) and 2.9 in Hubei province with the initial time on January 30 which show the severe infectivity of COVID-19, and verify that the current isolation method effectively contains the transmission of COVID-19. Under the isolation of healthy people, confirmed cases and close contacts, we find a noteworthy phenomenon that is the potential second epidemic of COVID-19, and estimate the peak time and value and the cumulative number of cases. Simulations show that the isolation of close contacts tracked measure can efficiently contain the transmission of the potential second epidemic of COVID-19. With isolation of all susceptible people or all infected people or both, there is no potential second epidemic of COVID-19. Furthermore, resumption of work and study can increase the transmission risk of the potential second epidemic of COVID-19.

The system of non-interacting electrically charged core-massless spring-shell mechanical units, demonstrating negative effective mass, is considered, seen as a Drude-Lorentz gas. When such an ideal gas is exposed to the external harmonic field, it demonstrates as the certain conditions the negative frequency-dependent electrical conductivity. The negative value of the electrical conductivity implies that the electrical current will flow against the direction of the electric field and correspondingly the direction of the electrical force. Low- and high-frequency asymptotic behavior of the electrical conductivity is addressed. The same system demonstrates at low frequencies the negative asymptotic refraction. Experimental realization of the introduced model system, based on the exploitation of plasma oscillations of the free electron gas is suggested.

Cloud computing is an emerging area which provide on demand computing resources and services through internet. It is faster and efficient technique but prone to severe security attacks. In this paper author have proposed a Network Intrusion Detection System (NIDS) to detect attacks at front end and backend when bulky flow of data packets flowing in a cloud environment. In our framework we used Signature based detection system for identifying the intruder and the Anomaly based detection system for detecting network attacks. The NIDS sensors were placed in a collaborative manner to prevent the attacks and to update the knowledge bases. Author have used supervised learning model to detect abnormal behavior of packets from network traffic. The dataset were trained and tested in terms of precision, recall, accuracy and model build time to select the best machine-learning model for detection of intruder and to improve the computational time and performance.

This paper introduces a method for tunnel point cloud and BIM model integration and cross-section monitoring, providing information to analyse tunnel cross-sections and surrounding rock deformation, and support for tunnel maintenance and reconstruction. Three types of data are processed for the integration: laser scanning point cloud, BIM tunnel model and terrain model from oblique photogrammetry. An adaptive BIM modelling scheme is proposed for tunnels with alien structures. Precise spatial registration of the data sets is conducted by applying singular value decomposition (SVD) algorithm to calculate transformation parameters from the point cloud model to the BIM model. Since the tunnel central line has high-order derivability, a cross-section calculation method based on tangent vector is proposed to obtain the cross-sectional profile of tunnels at any mileage. The proposed method has been verified by applying it to a tunnel reconstruction project. The experiment results show that the tunnel point cloud and the BIM model were highly coincident after the integration. The developed program can effectively get the cross-section of the tunnel at any mileage, and correctly express the spatial relationship between the BIM tunnel, the point cloud of tunnel and the external mountainous terrain.

Recently, the awareness of energy conservation, carbon emission reduction, and environmental protection in Taiwan has begun to gradually improve, and bicycles have become an important vehicle for residents. In 2009, Kaohsiung began promoting public bicycle sharing. The number of users in that year amounted to 140,000. By 2015, the accumulated number of users reached nine million. This study adopted the Theory of Planned Behavior (TPB) to explore the factors influencing the behavioral intentions of public bicycle users, including the three dimensions of attitude, subjective norm, and perceived behavioral control, and probed into the relationship between the factors influencing the behavioral intentions of public bicycle users in Kaohsiung City and the dimensions. This study used a questionnaire to collect data and SPSS & AMOS for statistical analysis. The empirical results demonstrated that the most prominent factors influencing the behavioral intentions of public bicycle users in Kaohsiung City included perceived behavioral control, attitude, and subjective norm, in order. Among the influencing factors in the three dimensions, personal interests, main groups, and self-competence had a positive and significant correlation. Administrators should provide more incentives related to the three influencing factors to encourage the public in Kaohsiung City to use public bicycles more frequently as their short-distance travel mode, so as to achieve the purposes of saving energy, reducing carbon emissions, and protecting the urban environment.

Based on the fractional order of nonlinear system for love model with a periodic function as an external force, analyzed the characteristics of the chaotic dynamic in this study. The relationship between the chaotic dynamic of the fractional-love model with the external force and the fractional-order system was analyzed when the parameters are fixed. Further, we also studied the relationship between the chaotic systemic dynamic and the parameters when the fractional-order system is fixed. The results show that when the parameters are fixed, the fractional-order system exhibited segmented chaotic states for the different fractional orders of the system. When fixed the fractional-order system, the system exhibited the periodic and chaotic states as parameter changes.

Flash flooding, a hazard which is triggered by heavy rainfall is a major concern in many regions of the world often with devastating results in mountainous elevated regions. We adapted remote sensing modelling methods to analyse one flood in July 2015, and believe the process can be applicable to other regions in the world. The isolated thunderstorm rainfall occurred in the Chitral River Basin (CRB), which is fed by melting glaciers and snow from the highly elevated Hindu Kush Mountains (Tirick Mir peak’s elevation is 7708 m). The devastating cascade, or domino effect, resulted in a flash flood which destroyed many houses, roads, and bridges and washed out agricultural land. CRB had experienced devastating flood events in the past, but there was no hydraulic modelling and mapping zones available for the entire CRB region. That is why modelling analyses and predictions are important for disaster mitigation activities. For this flash flood event, we developed an integrated methodology for a regional scale flood model that integrates the Tropical Rainfall Measuring Mission (TRMM) satellite, Geographic Information System (GIS), hydrological (HEC-HMS) and hydraulic (HEC-RAS) modelling tools. We collected and use driver discharge and flood depth observation data for five river sub-stream areas, which were acquired in cooperation with the Aga Khan Rural Support Program (AKRSP) organization. This data was used for the model’s calibration and verification. This modelling methodology is applicable for other regional studies especially for rough mountainous areas which lack local observations and river discharge gauges. The results of flood modelling are useful for the development of a regional early flood warning system and flood mitigation in hazardous flood risk areas. The flood simulations and prepared connected video visualization can be used for local communities. This approach is applicable for flood mitigation strategies in other regions.

The fractional telegraph partial differential equation with fractional Atangana-Baleanu-Caputo (ABC) derivative is studied. Laplace method is used to find the exact solution of this equation. Stability inequalities are proved for the exact solution. Difference schemes for the implicit finite method are constructed. The implicit finite method is used to deal with modelling the fractional telegraph differential equation defined by Caputo fractional of Atangana-Baleanu (AB) derivative for different interval. Stability of difference schemes for this problem is proved by the matrix method. Numerical results with respect to the exact solution confirm the accuracy and effectiveness of the proposed method.

In cotton cultivars, an insect that causes irreversible damage is the Spodoptera frugiperda, known as the fall armyworm. Since the visual detection of plants is a burdensome task for human inspection, the spectral information related to plant damage, registered on a spectral scale, can be useful. These measurements, associated with machine learning techniques, produce useful information for a rapid and non-invasive inspection method development. To contribute to this gap fulfillment, this paper proposes a machine learning framework to model the spectral response of cotton plants under the attack of the fall armyworm. Additionally, a theoretical model is presented, built from the results of the machine learning analysis, to infer this damage with up-to-date orbital sensors. The data was composed of the reflectance measurements collected at a cotton field with control plants and plants submitted to Spodoptera frugiperda damage. Their spectral response was recorded with a hand-held spectroradiometer ranging from 350 to 2,500 nm, for eight consecutive days. Different machine learning models were evaluated and the overall best model was defined by accuracies comparisons on a testing-set. A ranking approach was adopted based on the model accuracy, returning the most contributive wavelengths for the classification. Sequentially, an unsupervised neural network (Self-Organizing Map - SOM) was implemented to reduce data-dimensionality and assist in the definition of important spectral regions. The regions were associated with the spectral bands of the two sensors (OLI and MSI) and a theoretical model using a band simulation process with the overall best machine learning model was proposed. The results indicated that the Random Forest (RF) algorithm is the most suitable to predict cotton-plants damaged by insects and that the last day of analysis (8th day) was better to separate it, with F-measure equals 0.912. The ranking approach combined with the SOM method indicated the spectral regions at the red to near-infrared (650 to 1,350 nm) and shortwave infrared (1,570 to 1,640 nm) as the most important regions to the analysis. The proposed theoretical model simulated with the OLI and MSI sensor-bands returned an F-Measure of 0.865 and 0.886, respectively. In conclusion, this framework can be used to map cotton-plants under insect-attack. The theoretical model presents high accuracy to infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

In this study, the model proposed by Yang et al. to generalize the stress–strain model for unconfined concrete with consideration of the size effect is expanded. Sim et al.’s compressive strength model that is based on the function of specimen width and aspect ratio was used for the maximum stress. In addition, a strain at the maximum stress was formulated as a function of compressive strength by considering the size effect using the regression analysis of datasets compiled from a wide variety of specimens. The descending branch after the peak stress was formulated with consideration of less dissipated area of fracture energy with the increase in specimen width and aspect ratio in the compression damage zone (CDZ) model. The key parameter for the slope of the descending branch was formulated as a function of specimen width and aspect ratio, concrete density, and compressive strength of concrete considering the size effect. Consequently, a rational stress–strain model for unconfined concrete was proposed. This model explains the trends of the peak stress and strain at the peak stress to decrease and the slope of the descending branch to increase, as the specimen width and aspect ratio increase. The proposed model agrees well with the test results, irrespective of the compressive strength of concrete, concrete type, specimen width and aspect ratio. In particular, the proposed model for the stress–strain curve rationally considered the effect of decreasing peak stress and increasing the descending branch slope, with the increase in specimen width and aspect ratio.

The aim of this study has been to develop a novel two-level multi-objective genetic algorithm (GA) to optimize time series forecasting data for fans used in road tunnels by the Swedish Transport Administration (Trafikverket). Level 1 is for the process of forecasting time series cost data, while level 2 evaluates the forecasting. Level 1 implements either a multi-objective GA based on the ARIMA model or a multi-objective GA based on the dynamic regression model. Level 2 utilises a multi-objective GA based on different forecasting error rates to identify a proper forecasting. Our method is compared with using the ARIMA model only. The results show the drawbacks of time series forecasting using only the ARIMA model. In addition, the results of the two-level model show the drawbacks of forecasting using a multi-objective GA based on the dynamic regression model. A multi-objective GA based on the ARIMA model produces better forecasting results. In level 2, five forecasting accuracy functions help in selecting the best forecasting. Selecting a proper methodology for forecasting is based on the averages of the forecasted data, the historical data, the actual data and the polynomial trends. The forecasted data can be used for life cycle cost (LCC) analysis.

The diversity and inclusion discussion permeates many sectors of society. Within this dialogue, science and scientists are acutely aware of the value of diversity and the need for inclusion. While demographic diversity in science has received considerable recent attention, very little research and understanding exists on inclusion. Our study presents empirical data on the meaning of inclusion using a crowdsourcing approach that sought responses to the question “What does inclusion mean to you?”. The most prominent concepts were those of empathy, warmth, support, love, acceptance and curiosity; diverse perspectives; and participation. We clustered conceptual elements of inclusion into four themes: access and participation, embracing diverse perspectives, a welcome environment, and team belonging. On the basis of these data, we theorize a conceptual framework model from which inclusion may be put into practice. Our model suggests a dynamic process of inclusion operating from principal structural elements of 1) a foundation that involves place, access and participation, and space; 2) reciprocal engagement as an engine for inclusion; and 3) expression of inclusion as culture. The framework model demonstrates a means by which the practice of diversity can be more than shifts in demographic statistics, and instead promote the full expression of benefits derived when the many dimensions of diversity are truly included.

Looking at different operating climatic conditions, the electrical behavior predicting photovoltaic modules gets very important. For the estimation of output power from photovoltaic (PV) plants this is a very essential and basic aspect. In this paper, the relationship between the I-V curve and the irradiation spectrum is discussed by combining the single diode model. An explicit elementary analytical model with two defined shape parameters is discussed and improved with three approximations and second order Taylor expansion. Then, the explicit elementary analytical model is investigated under varying conditions leveraging the four parameters I_ph, I₀, R_s and R_sh from the single diode model. The relationship between the physical parameters and the condition parameters are discussed and applied to extract the shape parameters at different scenarios. Considering the aging effect, the process of calculation to predict the I-V curve under different splitting spectra is simplified as follow: (1) two shape parameters are gotten from the I-V data at measurement reference conditions (MRC); (2) the short circuit current, open circuit voltage and shape parameters under any splitting spectrum can be calculated based on the relationship provided in article; (3) the performance of PV panel can be predicted with parameters. The validation of this model was experimentally proven leveraging monocrystalline silicon photovoltaic module with different splitting films. Results showed that the model accurately predicts the I-V characteristics for the examined PV modules at different irradiance spectra and cell temperatures. Moreover, the presented model performs superior compared to other investigated models when looking at accuracy and simplicity.

The lack of reliable rainfall projection records remains a major challenge to Uganda. In the advent of extreme wetness or drought events, reliable rainfall estimates for local planning and adaptation are essential. The present study used two main datasets to conduct a historical analysis from 1981 to 2019, coupled with future projections under representative concentration pathway (RCP 8.5) for the period 2020-2050. Historical analysis revealed bimodal annual rainfall pattern for March-May (MAM) and September-November (SON) gradients representing heavier to lighter rainfall events respectively over the study area. Investigation of recent trends in rainfall patterns revealed an upward trend from 2010 onwards in annual and seasonal rainfall. Moreover, results for future projections show wet conditions are projected to occur over the study area between the months of April/May and October. Contrarily, March is likely to experience a reduction in wet conditions. Mann-Kendall test employed to make future projections of rainfall depicted decreasing patterns during MAM season whilst increasing tendencies with strong shift was highlighted for SON season over the study region. Meanwhile, annual projections indicate huge variations with linear trends showing a marginal increase as compared to historical trends. Findings would serve as baseline print to propel further studies that could delve into impact analysis of drought extreme events which pose significant threats to the agricultural sector which is heavily reliant on rainfall.

Aiming at the industrial scale development of a Scandium (Sc)-selective leaching process of Bauxite Residue (BR), a sufficiently numerous set of process design aspects has been investigated, by appropriate exploitation of available experimental data. The interpretation of experimental data for Sc leaching yield, with sulfuric acid as the leaching solvent, has shown significant impact from acid feed concentration, mixing residence time, liquid to solids ratio, and times of leachate re-usage onto fresh BR. The thin film diffusion model, as the fundamental theory for leaching, either with constant particle size for selective leaching, or with shrinking particle size for less-, or non-, selective leaching, interprets sufficiently well the relevant experimental data. In both cases, a concept for an unyielding core supplements the basic model. Especially for the selective leaching mild conditions, the simplest model version keeps step with the experiments, since both prove 1^st order kinetics, while especially for the extreme conditions including very low solvent excess, it is proposed a combined conversion rate model with diffusion and chemical reaction inside particles. The maximization of Sc recovery per unit of consumed solvent (i.e., specific recovery) emerged as highly critical for the process economics.

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.

Investigation into geological storage of CO2 is underway at the Technology Development Plant (TDP) at Hontomín (Burgos, Spain), the only current onshore injection site in the European Union. The storage reservoir is a deep saline aquifer located within Low Jurassic Formations (Lias and Dogger), formed by fractured carbonates with low matrix permeability. Understanding the processes involved in CO2 migration within this kind of low-primary permeability carbonates influenced by fractures and faults is key to ensure safe operation and reliable plume prediction. During the hydraulic characterization tests, 2300 tons of liquid CO2 and 14000 m3 of synthetic brine were co-injected on site in various sequences to characterize the pressure response of the seal-storage pair [de Dios et al, 2017] The injection tests were analyzed with a compositional dual media model which accounts for both temperature effects (as the CO2 is liquid at the bottom of the wellbore) and multiphase flow hysteresis (to effectively simulate the alternating brine and CO2 injection tests that were performed). The pressure and temperature responses of the storage formation were history-matched mainly through the petrophysical characteristics of the fracture network [Le Gallo et al, 2017]. The dynamic characterization of the fracture network dominates the CO2 migration while the matrix does not appear to significantly contribute to the storage capacity. This initial modeling approach was improved using the characterization workflow developed within the European FP7 CO2ReMove project for sandstone fractured reservoirs [Ringrose et al., 2011; Deflandre et al., 2011]. Fractured reservoirs are challenging to handle because of their high level of heterogeneity that conditions the reservoir behaviour during the injection. In particular, natural fractures have a significant impact on well performance [Ray et al, 2012]. Furthermore, the understanding of the processes involved in CO2 migration within relatively low-permeability storage influenced by fractures and faults is essential for enabling safe storage operation [Iding and Ringrose, 2010]. As part of the European H2020 ENOS project, the site geological model is updated by integration of the recently acquired data such as the image log interpretations from injection and observation wells. The geological model is generated through the analysis and integration of data including borehole images and well test data. Following a methodology developed for naturally fractured hydrocarbon reservoirs [Ray et al., 2012], the image log analysis identified two sets of diffuse fractures. A Discrete Fracture Network [Bourbiaux et al., 2005] was built around both wells which encompass the caprock, storage and underburden formations. The fracture characteristics of the two sets of diffuse fractures, such as orientations, densities and conductivities, are calibrated upon the interpretation of the injection tests [Le Gallo et al, 2017]. For each facies, the DFN characteristics were then upscaled and propagated to the full-field reservoir simulation model as 3D fracture properties (fracture porosity, fracture permeability and equivalent block size).

Climate change has become the greatest threat to the survival of world and its ecosystem. With the irreversible impact on the ecosystem, problems like rise in sea level, food-insecurity, natural resources scarcity, seasonal disorders have increased over the past few years. Among these problems, the issue of water scarcity due to the lack of water resources and global warming has plagued several nations. Owing to the rising concerns over water scarcity United Nations (UN) has acknowledged water as a primary resource to the development of societies under the ‘Water Goal’ of the sustainable development goals. As the changing climate and intermittent availability of water resources pose major challenges to forecast demand, especially in countries like the United Arab Emirates (UAE) which has one of the highest per capita residential water consumption rates in the world. Therefore, the aim of this study is to propose an accurate water demand forecasting technique that incorporates all significant factors to predict the future water demands of the UAE. The forecasting model used is the Long Short Term Memory (LSTM), with the factors considered are mean temperature, mean rainfall, relative humidity, Gross Domestic Product (GDP), Consumer Price Index (CPI) and population growth. The LSTM model predicts the water demand forecasting in the UAE showing that the future demand will decrease from 1821 million m3 in 2018 to 1809.9 million m3 in 2027.

In this paper, using the discrete time model, we consider the average age of all files for a cached-files-updating system where a server generates N files and transmits them to a local cache. In order that the cached files are fresh, in each time slot the server updates files with certain probabilities. The age of one file or its age of information (AoI) is defined as the time the file stays in cache since it was last time sent to cache. Assume that each file in cache has corresponding request popularity. In this paper, we obtain the distribution function of the popularity-weighted average age over all files, which gives a complete description of this average age. For the random age of single file, both the mean and its distribution have been derived before by establishing a simple Markov chain. Using the same idea, we show that an N dimensional stochastic process can be constituted to characterize the changes of N file ages simultaneously. By solving the steady-state of the resulting process, we obtain the explicit expression of stationary probability for an arbitrary state-vector. Then, the distribution function of the popularity-weighted average age can be derived by mergering a proper set of stationary probabilities. For the possible applications, the distribution function can be utilized to calculate the probability that the average age violates certain statistical guarantee.

Degradation of environment quality is currently the prime cause of the recent occurrence of natural disasters; it also contributes in the increase of the area that is prone to natural disasters. This research is aimed to map the potential of areas around Pesanggrahan river in DKI Jakarta by segmenting the Digital Elevation Model derived from LIDAR data. The objective of this segmentation is to find the watershed lines of the DEM image. Data processing in this research is using LIDAR data which take the ground surface data, which is overlaid with Jakarta river map and subsequently, the data is then segmented the image. The expected result of the research is the flood potential area information, especially along the Pesanggrahan river in South Jakarta.

Corona loss generated from conductors is one of the important design factors on ac transmission lines. Based on helical charge simulation method, the 3-D corona loss calculation model considering the conductors’ outer strands in the corona cage is established. With regard to 2-D corona-loss calculation method, the calculation model proposed in this paper can take the electric field strength’s nonuniformity along the axial direction of stranded conductor into consideration, furthermore, it can also calculate the corona loss when the conductor has sag. The calculation results of the LGJ-300/40 and LGJ-400/35 in the small corona cage well coincide with the measurement results, which indicates that the corona-loss calculation model is appropriate. In addition, the corona loss of the bundle conductors 4 × LGJ720 with sag in UHV corona cage is also analyzed.

Users on the social networking platform have the freedom to express themselves freely. Towards the same time, this has created a forum for disagreement and hate directed at someone, society, racism, sexual orientation, and so on. Identifying hate online is a challenging task. Researchers from all around the world have contributed major methods for detecting hate speech, but owing to the issue's complexity, there are still many unresolved issues. In this research, we offer a multi-model learning strategy for detecting hate speech on Twitter. We utilised the Kaggle TwitterHate dataset, which had 31962 tweets categorised as binary hate or non-hate, to evaluate our technique. The suggested method is tested using commonly used machine learning classifiers with multi-model technique. Using TF-IDF features, we acquired detection results of 96.29 %, precision of 96%, recall of 96%, and f1-score of 96%.

This paper considers a non-mixture cure model for right censored data. It utilizes the maximum likelihood method to estimate model parameters in the non-mixture cure model. The simulation study is based on Fréchet susceptible distribution to evaluate the performance of the method. Comparing with Weibull and exponentiated exponential distributions, the non-mixture Fréchet distribution is shown to be the best in modeling a real data on allogeneic marrow HLA-matched donors and ECOG phase III clinical trial e1684 data.

The value of an NBA basketball player varies at each crucial point in time, depending on the player’s career and performance. This study constructs a player’s value assessment model. The model comprises two parts. First, from an objective perspective, entropy is employed to measure each player’s achievement in five categories—rebounds, assists, steals, blocked shots, and scores. The total entropy assessment value is calculated and then combined with the players’ scores to develop a player's value matrix to assess the relative value model among players of the same type.

Carbonic anhydrases (CAs) are metalloenzymes that are omnipresent in nature. The CAs catalyze the basic reaction of reversible hydration of CO₂ to HCO₃⁻ and H⁺ in all living organisms. Photosynthetic organisms contain six evolutionarily different classes of CAs, namely, α-CAs, β-CAs, γ-CAs, δ-CAs, ζ-CAs, and θ-CAs. Many of the photosynthetic organisms contain multiple isoforms of each CA family. Model alga, Chlamydomonas reinhardtii contains fifteen CAs belonging to three different CA gene families. Out of the fifteen CAs, three belong to α-CA gene family, nine to β-CA gene family, and three are γ-CAs. The multiple copies of the CAs in each gene family may be due to gene duplications within the particular CA gene family. The CAs of Chlamydomonas reinhardtii are localized in different subcellular compartments of this unicellular alga. The presence of a large number of CAs and their diverse subcellular localization within a single cell suggests the importance of these enzymes in metabolic and biochemical roles they perform in this unicellular alga. In the present review, we update the information on molecular biology of all the fifteen CAs and their metabolic and biochemical roles in Chlamydomonas reinhardtii. We also present a hypothetical model showing the known functions of CAs and predicting the functions of CAs for which precise metabolic roles are yet to be discovered.

Agile methods promise to achieve high productivity and provide high-quality software. Agile software development is the most important approach that has spread through the world of software development over the past decade. Software team productivity measurement is essential in agile teams to increase the performance of software development. Due to the prevalence of agile methodologies and increasing competition of software development companies, software team productivity has become one of the crucial challenges for agile software companies and teams. Awareness of the level of team productivity can help them to achieve better estimation results on the time and cost of the projects. However, to measure software productivity, there is no definitive solution or approach whether in traditional and agile software development teams that lead to the occurrence of many problems in achieving a reliable definition of software productivity. Hence, this study aims to propose a statistical model to assess the team’s productivity in agile teams. A survey was conducted with forty software companies and measured the impact of six factors of the team on productivity in these companies. The results show that team effectiveness factors including inter-team relationship, quality conformance by the team, team vision, team leader, and requirements handled by the team had a significant impact on the team’s productivity. Moreover, the results also state that inter-team relations affect the most on software teams’ productivity. Finally, the model fit test showed that 80% of productivity depends on team effectiveness factors.

We consider partition functions, in the form of state sums, and associated probabilistic measures for aperiodic substrates described by model sets and their associated tiling spaces. We propose model set tiling spaces as microscopic models for small scales in the context of quantum gravity. Model sets possess special self-similarity properties that allow us to consider implications on large and observable scales from the underlying (non-ergodic) dynamics. In particular we consider the implication of the underlying aperiodic substrate for the well known problem of time in quantum gravity, and propose a correspondence between small and large scales, the so-called ergodic correspondence, that addresses the emergence of matter properties and spacetime structure. In the process we find a possible bound in the mass spectrum of fundamental particles.

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.

Balanced and coordinated economic development across regions is a critical goal of regional economic development and new-type urbanization in China. However, few studies have examined economic growth convergence clubs at the county level. To extend the research on convergence clubs, this research applies a log t convergence test and a dynamic spatial ordered Probit model (DSOP) to endogenously identify economic growth convergence clubs in counties and to examine the influence of initial states and structures on club convergence probability. The study sample covers 2286 counties of China from 1992 to 2010. The results show significant convergence club patterns at the county levels, resulting in the gradual formation of six convergence clubs. The DSOP estimation results show that per capita fixed assets, population density and industrialization have promoted convergence club formation to varying degrees.

We present a generalized state-space model formulation particularly motivated by an online scheduling perspective. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings.

Rainfall is a major climate parameter whose variation in space and time influences activities in different weather sensitive sectors such as agriculture, transport, and energy among others. Therefore, accurately forecasting rainfall is of paramount importance to the development of these sectors. In this regard, this study sought to contribute to quantitative forecasting of rainfall over Eastern Uganda through assessing the Weather Research and Forecasting model’s ability to simulate the intra–seasonal characteristics of the September to December rain season. These were: onset and cessation dates; wet days and lengths of the wet spells. The data used in the study included daily ground rainfall observations and lateral and boundary conditions data from the National Centers for Environmental Prediction (NCEP) final analysis at 1 0 horizontal resolution and at a temporal resolution of 6 hours for the entire study period were used to initialize the Weather Research and Forecasting (WRF) model. The study considered four weather synoptic weather stations namely; Jinja, Serere, Soroti and Tororo. The results show that the WRF model generally simulated fewer wet days at each station except for Tororo. Also, the WRF model simulated earlier onset and cessation dates of the rainfall season and overestimated the length of the wet spells.

The high dimensions and governing non linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non linearities. The work here presented applies the ideas motivated by non linear dynamical systems theory - the Koopman Operator - to an innovative algorithm in the context of wind farm systems - Input Output Dynamic Mode Decomposition - to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm.

Ti64 alloy plays a significant role in the biomedical applications such as bioimplants for its excellent biocompatibility. Its usage can be further extended by improving the surface hardness and wear resistance. In this respect, laser surface glazing (LSG), an advanced surface modification technique, is very useful which can produce thin hardened surface layer and strong metallurgical bonding. Investigation of temporal and spatial temperature distributions of laser glazed surface of materials are essential because temperature plays significant role in achieving required surface properties. Therefore, in this study, a 3D Finite element analysis has been developed to perform transient thermal analysis of LSG for Ti64 alloy. The model investigated temperature distribution, depth of modified zone and heating and cooling. The results show that the peak temperature is attained 2095 K for 300 W laser power, 0.2 mm beam width and 0.15 ms residence time. Since this temperature is above the melting point (1933 K) of Ti64 alloy, the melt depth is calculated 22.5 μm. Furthermore, from the simulation results, the average heating and cooling rates are estimated 1.19×10⁷ Ks^-1 and 2.71×10⁶ Ks^-1 respectively which indicate the presence of hard phases in the modified zone.

Meloidogyne javanica causing root-knot nematode in soybean is an important problem in soybean areas, leading to several yield losses. Some accessions have been identified carrying resistance loci to this nematode specie. In this study, a set of 317 soybean accessions were characterized for resistance to M. javanica. Genome-wide association study (GWAS) was performed using SNPs from genotyping-by-sequencing (GBS), and a region of 29.2 Kbp on chromosome 13 was identified. The haplotype analysis showed that SNPs were able to discriminate susceptible and resistant accessions, leading to 25 accessions sharing the resistance locus. Furthermore, 5 accessions may be new M. javanica resistance sources. The screening of the SNPs in the USDA soybean germplasm showed that several accessions previous reported as resistance to other nematodes also showed the resistance haplotype on chromosome 13. High levels of concordance among the phenotypes of Brazilian cultivars and the SNPs in chromosome 13 were observed. A in silico analysis of the mapped region on soybean genome revealed a presence of 5 genes with structural similarity with major resistance genes. The expression levels of the candidate genes in the interval demonstrated a potential pseudogene, and other two model genes up-regulated in the resistance source after pathogen infection. The SNPs associated to the region conferring resistance is a important tool for introgression of the resistance by marker-assisted selection in soybean breeding programs.