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Study region: Lower Mekong River Basin (LMRB), Southeast Asia Study focus: The links between meteorological drought (MD) and hydrological drought (HD) from a 3D perspective have not been fully investigated. A 3D identification method was employed to identify MD and HD events in the LMRB. Response of HD to MD were evaluated using linear and nonlinear models. Furthermore, the occurrence probabilities and propagation thresholds (PTs) from MD to HD were examined by applying a copula joint distribution and conditional probability based on a Bayesian network (CPBN). New hydrological insights for the region: A total of 81 MDs and 87 HDs were identified during 1950–2021. HDs had longer durations but affected a smaller area and were less severe than MDs, with their centroids mainly located in eastern Thailand. 46 pairs of MD-HD events were matched spatiotemporally, revealing that a HD event in typical drought years could be driven by multiple MD events. The duration, area, and severity of these drought pairs were optimally fitted using linear, 3P-power, and polynomial function models, respectively. Additionally, the occurrence probabilities of moderate, severe, and extreme HDs successively decreased under the same MD conditions. The PT from MD to HD increased with increasing severity levels of HDs. The study of the links between MD and HD from a 3D perspective provides a more comprehensive understanding of drought in the LMRB.

Summary: Response models used in marketing are not always constructed for later marketing optimisation, which often results in unsatisfactory results in target selection for future marketing activities. To solve this problem, we develop a new binary response model and a new marketing target selection method. The proposed model can predict multiple propensity scores per customer through customer‐specific propensity score distributions, which is not possible with existing response models, filling a gap in the literature. The target selection method can determine the best propensity scores from those predicted by the proposed model and use them to select customers for further marketing activities. Our simulation results and application to real marketing data confirm that the performance of the proposed model in target selection is significantly better than that of the existing models, including some popular machine learning methods, which indicate that our method can be very useful in practice. [ABSTRACT FROM AUTHOR]

Nonprobability samples, especially web survey data, have been available in many different fields. However, nonprobability samples suffer from selection bias, which will yield biased estimates. Moreover, missingness, especially nonignorable missingness, may also be encountered in nonprobability samples. Thus, it is a challenging task to make inference from nonprobability samples with nonignorable missingness. In this article, we propose a Bayesian approach to infer the population based on nonprobability samples with nonignorable missingness. In our method, different Logistic regression models are employed to estimate the selection probabilities and the response probabilities; the superpopulation model is used to explain the relationship between the study variable and covariates. Further, Bayesian and approximate Bayesian methods are proposed to estimate the response model parameters and the superpopulation model parameters, respectively. Specifically, the estimating functions for the response model parameters and superpopulation model parameters are utilized to derive the approximate posterior distribution in superpopulation model estimation. Simulation studies are conducted to investigate the finite sample performance of the proposed method. The data from the Pew Research Center and the Behavioral Risk Factor Surveillance System are used to show better performance of our proposed method over the other approaches. [ABSTRACT FROM AUTHOR]

Hengqin, located at the southern end of Zhuhai city and connected to Macao by a bridge and a river, serves as a key platform for promoting Macao's moderately diversified economic development, leveraging the inherent advantages of Guangdong-Macao cooperation. To enhance the capability of responding to public health emergency is very important for the development of cultural tourism and exhibition industries in the Guangdong-Macao In-Depth Cooperation Zone in Hengqin due to high population mobility in the region. In this article, we briefly discussed the role, strength and future development of community engagement-based public health emergency response in the in-depth cooperation zone.

Fractures are crucial as main natural gas transport channels in tight sandstone reservoirs. In order to reveal the correlation between the combination of fractures in Block Keshen 2 and sandstone, we have collected drilling core data, logging curve data, imaging logging data, and rock thin-section data from the Bashjiqike Formation in the Keshen 2 area, and by classifying and statistically analyzing the different influencing factors of fractures, we have established a correlation between the development of fractures and sandstone thickness, lithology, and sedimentary microfacies. The results reveal the following: (1) frequent vertical superposition and lateral migration occur in the sedimentary sand bodies of the Bashijiqike Formation. Three types of patterns of sand bodies have been identified according to the changes in microfacies. Type I refers to the patterns of sand bodies developed in main subaqueous distributary channels, type II refers to the patterns of sand bodies developed in secondary subaqueous distributary channels or mouth bars, and type III refers to the patterns of sand bodies developed in isolated subaqueous distributary channels; (2) three types of fracture patterns have been described in the various sand bodies of the Bashijiqike Formation in the Keshen area, including high–medium-angle branch-like fracture patterns, medium-angle reticular fracture patterns, and isolated fractures; (3) the coupling relationship among sedimentary microfacies, sand body patterns, and fracture patterns has been established. The high–medium-angle branch-like fracture patterns mainly develop in the main underwater distributary channel and the type I sand body patterns. The medium-angle reticular fracture patterns mainly develop in the secondary underwater distributary channel and mouth bars, as well as the type II sand body patterns. Isolated fractures can occur in all sedimentary microfacies but are sporadically distributed within the three types of sand body patterns. The research results present the regularity of fracture development in fractured reservoirs, which can be applied to oil and gas fields with the same background, providing certain geological evidence for exploration and development. [ABSTRACT FROM AUTHOR]

This article proposes a nonlinear mental health evaluation model after combining the nonlinear finite element equation with the mental health education of teachers in colleges and universities. We take the “self-concept” in psychology as an order parameter to judge mental health. At the same time, logical thinking is used to guide college teachers to rethink the process of their psychological activities. Research has found that the nature of human psychology is nonlinear. It is feasible to use nonlinear analysis methods to improve the status quo of original data analysis in psychological and behavioral control research.

The mixture design of cement-based materials can be complex due to the increasing number of constituent raw materials and multiple requirements in terms of engineering performance and economic and environmental efficiency. Designing experiments based on factorial plans has shown to be a powerful tool for predicting and optimising advanced cement-based materials, such as self-compacting high-early-strength cement-based mortars. Nevertheless, the number of factor interactions required for factor scheduling increases considerably with the number of factors. Consequently, the probability that the interactions do not significantly affect the answer also increases. As such, fractioned factorial plans may be an exciting option. For the first time, the current work compares the regression models and the predicting capacity of full, fractionated (A and B fractions) and small factorial designs to describe self-compacting high-early-strength cement-based mortars' properties, namely, the funnel time, flexure and compressive strength at 24 h for the function of the mixture parameters Vw/Vc, Sp/p, Vw/Vp, Vs/Vm and Vfs/Vs for the different factorial designs. We combine statistical methods and regression analysis. Response models were obtained from the full, fractionated, and small plans. The full and fractionated models seem appropriate for describing the properties of self-compacting high-early-strength cement-based mortars in the experimental region. Moreover, the predicting ability of the full and fractionated factorial designs is very similar; however, the small design predictions reveal some concerns. Our results confirm the potentiality of fractioned plans to reduce the number of experiments and consequently reduce the cost and time of experimentation when designing self-compacting high-early-strength cement-based mortars. [ABSTRACT FROM AUTHOR]

The use of SCC in Europe began in the 1990s and was mainly promoted by the precast industry. Precast companies generally prefer high early-strength concrete mixtures to accelerate their production rate, reducing the demoulding time. From a materials science point of view, self-compacting and high early-strength concrete mixes may be challenging because they present contradicting mixture design requirements. For example, a low water/binder ratio (w/b) is key to achieving high early strength. However, it may impact the self-compacting ability, which is very sensitive to Vw/Vp. As such, the mixture design can be complex. The design of the experimental approach is a powerful tool for designing, predicting, and optimising advanced cement-based materials when several constituent materials are employed and multi-performance requirements are targeted. The current work aimed at fitting models to mathematically describe the flow ability, viscosity, and mechanical strength properties of high-performance self-compacting cement-based mortars based on a central composite design. The statistical fitted models revealed that Vs/Vm exhibited the strongest (negative) effect on the slump-flow diameter and T-funnel time. Vw/Vp showed the most significant effect on mechanical strength. Models were then used for mortar optimisation. The proposed optimal mixture represents the best compromise between self-compacting ability—a flow diameter of 250 mm and funnel time equal to 10 s—and compressive strength higher than 50 MPa at 24 h without any special curing treatment. [ABSTRACT FROM AUTHOR]

Climate impact of anthropogenic activities is more and more of public concern. But while CO2 emissions are accounted in emissions trading and mitigation plans, emissions of non-CO2 components contributing to climate change receive much less attention. One of the anthropogenic emission sectors, where non-CO2 effects play an important part, is aviation. Hence, for a quantitative estimate of total aviation climate impact, assessments need to comprise both CO2 and non-CO2 effects (e.g., water vapor, nitrogen dioxide, and contrails), instead of calculating and providing only CO2 impacts. However, while a calculation of CO2 effects relies directly on fuel consumption, for non-CO2 effects detailed information on aircraft trajectory, engine emissions, and ambient atmospheric conditions are required. As often such comprehensive information is not available for all aircraft movements, a simplified calculation method is required to calculate non-CO2 impacts. In our study, we introduce a simple calculation method which allows quantifying climate assessment relying on mission parameters, involving distance and geographic flight region. We present a systematic analysis of simulated climate impact from more than 1000 city pairs with an Airbus A330-200 aircraft depending on the flight distance and flight region to derive simplified but still realistic representation of the non-CO2 climate effects. These new formulas much better represent the climate impact of non-CO2 effects compared to a constant CO2 multiplier. The mean square error decrease from 1.18 for a constant factor down to 0.24 for distance dependent factors and can be reduced even further to 0.19 for a distance and latitude dependent factor. [ABSTRACT FROM AUTHOR]

This article proposes a mental health education model for college students based on non-linear finite element equations and explores the concepts, content, methods and approaches for mental health education. After analysing college students’ psychological behaviours, the study is based on stressors and stress response models. It specifically explores the psychological and behavioural manifestations of college students with four different attachment types. The research provides a preliminary theoretical basis for the mechanism of the psychological and physical health of college students.

Background: Scoring and analysis of Forced Choice(FC) scale has caused a great deal of controversy among researchers. Most of the discussions are related to the superficiality of the obtained results due to the ipsative nature of FC scale datas. Aims: The aim of this study was to use the Thurstonian Item Response (T -IRT) approach in scoring and examining the factor structure of FC scales. Methods: The statistical population of this study was students of Allameh Tabataba'i University of Tehran. The T -IRT model was used to score and analyze the data obtained from the Kolb Learning Styles Questionnaire (LSI), which is a Forced Choice approach. Results: The results of the T -IRT model in examining the factor structure of the LSI showed that the four -factor model fit well with the data and also, although the correlation structure between the factors in the orthogonal bipolar bifactor model was consistent with the proposed Kolb structure; but this model did not fit well with the data. The results of Multinomial logistic regression model in examining the relationship between learning styles of gender and field of study of students showed that the gender variable has no Share in predicting learning styles (CH:4.87, P>0.05); but the model related to the field of study was significant (CH:305.26, P<0.001) . Also, the results of beta coefficients and significance of the model showed that students of psychology (Sig=0.01, β = -1.69) and law (Sig=0.01, β = 1.48) have more Divergent and Assimilator learning style. Conclusion: These findings indicated that the T -IRT approach works well in solving problems related to scoring and analyzing FC scales, and this could expand the use of these scales. Also, the results of studying learning styles indicate that in teaching and education, more attention should be paid to students' learning styles and teaching methods appropriate to their learning styles should be used. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the mathematical model of ship motion in the maritime simulator is too complicated and the effectiveness of simulation is difficult to verify, a new ship motion parameters generator is designed based on the characteristics of the ship's maneuvering, which can generate ship motion parameters required by the maritime simulator. Firstly, on the basis of the response model, a model of ship motion is established based on linear acceleration and angular velocity (a&w). Secondly, the motion parameters such as ship's position, speed, attitude and heading are solved by ship motion differential equation. Finally, the similarity evaluation index is proposed and applied to the verification of the simulation with the experimental data. The simulation and verification results show that the ship motion parameter generator can simply and effectively simulate the motion of the ship in various maneuver conditions, and the similarity of each ship motion parameter is above 80%, which is an effective research method of ship motion simulation of maritime simulators. [ABSTRACT FROM AUTHOR]

The mixture design of cement-based materials can be complex due to the increasing number of constituent raw materials and multiple requirements in terms of engineering performance and economic and environmental efficiency. Designing experiments based on factorial plans has shown to be a powerful tool for predicting and optimising advanced cement-based materials, such as self-compacting high-early-strength cement-based mortars. Nevertheless, the number of factor interactions required for factor scheduling increases considerably with the number of factors. Consequently, the probability that the interactions do not significantly affect the answer also increases. As such, fractioned factorial plans may be an exciting option. For the first time, the current work compares the regression models and the predicting capacity of full, fractionated (A and B fractions) and small factorial designs to describe self-compacting high-early-strength cement-based mortars’ properties, namely, the funnel time, flexure and compressive strength at 24 h for the function of the mixture parameters Vw/Vc, Sp/p, Vw/Vp, Vs/Vm and Vfs/Vs for the different factorial designs. We combine statistical methods and regression analysis. Response models were obtained from the full, fractionated, and small plans. The full and fractionated models seem appropriate for describing the properties of self-compacting high-early-strength cement-based mortars in the experimental region. Moreover, the predicting ability of the full and fractionated factorial designs is very similar; however, the small design predictions reveal some concerns. Our results confirm the potentiality of fractioned plans to reduce the number of experiments and consequently reduce the cost and time of experimentation when designing self-compacting high-early-strength cement-based mortars.

Copyright of China Rural Water & Hydropower is the property of China Rural Water & Hydropower Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fresh and hardening properties of cement-based materials are key factors for correctly choosing the constituent materials and their mix proportions. To optimize design-based mortar compositions for specific applications, response models are frequently applied to data collected from scientific approaches. Here, experimental dataset regarding to a design of experiments carried out in mortars through a central composite design with five independent variables is presented. Among the five independent variables, four were quantitative ones: Waterv/Cementv, Superplasticyzerm/Powderv, Waterv/Powderv, Sandv/Mortarv. The other independent variable was a qualitative one: Superplasticiser A or Superplasticiser B. In total 60 mortar compositions were done: for each qualitative variable a 24 factorial design comprising of 16 treatment combinations enlarged by 8 axial runs plus 6 central runs, resulting in a central composite design with 30 mortar trial mix compositions. The following dependent variables were tested: the D-flow and the t-funnel to evaluate the fresh properties and the compressive at the age of 24 h and at the age of 28 days to evaluate the hardened properties. Based on this dataset, response models can be applied to find optimized mix compositions, with the effect of the two qualitative variables being determined.

The use of SCC in Europe began in the 1990s and was mainly promoted by the precast industry. Precast companies generally prefer high early-strength concrete mixtures to accelerate their production rate, reducing the demoulding time. From a materials science point of view, self-compacting and high early-strength concrete mixes may be challenging because they present contradicting mixture design requirements. For example, a low water/binder ratio (w/b) is key to achieving high early strength. However, it may impact the self-compacting ability, which is very sensitive to Vw/Vp. As such, the mixture design can be complex. The design of the experimental approach is a powerful tool for designing, predicting, and optimising advanced cement-based materials when several constituent materials are employed and multi-performance requirements are targeted. The current work aimed at fitting models to mathematically describe the flow ability, viscosity, and mechanical strength properties of high-performance self-compacting cement-based mortars based on a central composite design. The statistical fitted models revealed that Vs/Vm exhibited the strongest (negative) effect on the slump-flow diameter and T-funnel time. Vw/Vp showed the most significant effect on mechanical strength. Models were then used for mortar optimisation. The proposed optimal mixture represents the best compromise between self-compacting ability—a flow diameter of 250 mm and funnel time equal to 10 s—and compressive strength higher than 50 MPa at 24 h without any special curing treatment.

Copyright of Xinjiang Agricultural Sciences is the property of Xinjiang Agricultural Sciences Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The concrete workability and the compressive strength are the principal properties of the fresh and hardened concrete, respectively. When self-compacting properties are required, scientific knowledge is important and appropriate models applied to achieve optimized compositions. Here, experimental data regarding to the mortars is presented. The dataset regards to a design of experiments carried out in mortars with commercial materials through a central composite design with five independent variables: Waterv/Cementv, Superplasticyzerm/Powderv, Waterv/Powderv, Sandv/Mortarv, FineSandv/Sandv. In total 64 mortar composition were done: 25 factorial design consisting on 32 treatment combinations augmented by 10 axial runs plus 8 central runs, resulting in a central composite design with 50 mortar trial mix composition. Beside 14 extra mixes were done to allow comparing and validating results for the response models to be applied. Four dependent variables were measured: the D-flow and the t-funnel to measure the workability and the tensile strength and the compressive at the age of 24 h to assess the mechanical properties. Since the experiments were run based in a central composite design and extra mixes were prepared, response models can be applied to the dataset in order to find optimized mix compositions.

Rice threshing drum of combine harvester is difficult to monitor and repair the abnormal state in time. When slippage or blockage occurs in the threshing drum, the working efficiency of the threshing drum will reduce seriously, such as the threshing efficiency will decline seriously, and then cleaning performance of combine harvester will decline. In order to reveal the vibration characteristics of rice threshing drum under different working conditions and the vibration response state of the drum bearing undergoing different faults occur, the vibration system model of the threshing drum is established in this paper. With the help of the vibration test bench of the multi-stage threshing drum, the vibration signal test of the slippage and blockage of the threshing drum under the belt drive mode is carried out. The results show that the Z direction signal has a vibration peak at the low frequency (25 Hz) when the threshing drum is blocked, the maximum vibration displacement is about 0.55 mm; the Z direction signal signal peak is concentrated at 50 Hz when a slip fault occurs, and the maximum vibration displacement is about 0.184 mm, the fitting effect between frequency and mathematical model is good. The results of this paper reveal the vibration characteristics of the threshing drum under different abnormal working conditions, at the same time provide a theoretical basis for the fault diagnosis of the threshing drum of the combine harvester.

At present, the concentration of PM2.5 in the Fenwei Plain has become the second highest in China, ranking second to the Beijing–Tianjin–Hebei region. At the same time, China began to monitor and control the PM2.5 concentration in the Fenwei Plain, but the data indicated that the concentration of PM2.5 in the Fenwei Plain rose instead of falling. PM2.5 concentrations in 6 cities from the Fenwei Plain ranked highest among the 20 cities in 2018 in China, and PM2.5 pollution can cause some health economic loss. Based on this background, the exposure–response model is used to estimate the impact of PM2.5 pollution on the health economic loss in the Fenwei Plain, and PM2.5 concentration in 2020, 2025, and 2030 is also predicted based on the setting of three scenarios: baseline scenario, emission reduction scenario, and enhanced emission reduction scenario. Then, according to the estimated results, the paper provides suggestions for reducing public health loss in the Fenwei Plain in the future. [ABSTRACT FROM AUTHOR]

In order to obtain accurate contact parameters of a particulate material in residual film mixture collected by cotton field machine in Xinjiang, the angle of repose test and inclined plane test were carried out. In the tests, the angles of repose of the particulate material with the water content of (6.26±1.5)% and (14.1±2.1)% were measured respectively, as well as the static sliding friction angle between the particulate material and the residual film. At the same time, the EDEM software was used to calibrate the coefficient of restitution, static friction coefficient and dynamic friction coefficient between the material and the film. Then, the second-order response model between contact parameters and the angle of repose and static sliding friction angle was constructed. In addition, the optimal contact parameters between the granular materials and the mulch were obtained by fitting the physical test data. The results indicated that the errors between the physical test results and the numerical simulation results are small. It was proved that the second-order response model could predict the repose angle of granular materials and the static sliding friction angle between granular materials and farmland film. This study could provide theoretical support for the subsequent model construction of the residual film mixture collected by the cotton field machine. [ABSTRACT FROM AUTHOR]

Quantification of emission changes is a prerequisite for the assessment of control effectiveness in improving air quality. However, the traditional bottom-up method for characterizing emissions requires detailed investigation of emissions data (e.g., activity and other emission parameters) that usually takes months to perform and limits timely assessments. Here we propose a novel method to address this issue by using a response model that provides real-time estimation of emission changes based on air quality observations in combination with emission-concentration response functions derived from chemical transport modeling. We applied the new method to quantify the emission changes in the North China Plain (NCP) due to the COVID-19 pandemic shutdown, which overlapped the Spring Festival holiday. Results suggest that the anthropogenic emissions of NO2, SO2, VOC, and primary PM2.5 in NCP were reduced by 51%, 28%, 67% and 63%, respectively, due to the COVID-19 shutdown, indicating longer and stronger shutdown effects in 2020 compared to the previous Spring Festival holiday. The reductions of VOC and primary PM2.5 emissions are generally effective in reducing O3 and PM2.5 concentrations. However, such air quality improvements are largely offset by reductions in NOx emissions. NOx emission reductions lead to increases in O3 and PM2.5 concentrations in NCP due to the strongly VOC-limited conditions in winter. A strong NH3-rich condition is also suggested from the air quality response to the substantial NOx emission reduction. Well-designed control strategies are recommended based on the air quality response associated with the unexpected emission changes during the COVID-19 period. In addition, our results demonstrate that the new response-based inversion model can well capture emission changes based on variations in ambient concentrations, and thereby illustrate the great potential for improving the accuracy and efficiency of bottom-up emission inventory methods. [ABSTRACT FROM AUTHOR]

Copyright of Larq Journal for Philosophy, Linguistics & Social Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Reducing PM2.5 and ozone concentrations is important to protect human health and the environment. Chemical transport models, such as the Community Multiscale Air Quality (CMAQ) model, are valuable tools for exploring policy options for improving air quality but are computationally expensive. Here, we statistically fit an efficient polynomial function in a response surface model (pf-RSM) to CMAQ simulations over the eastern U.S. for January and July 2016. The pf-RSM predictions were evaluated using out-of-sample CMAQ simulations and used to examine the nonlinear response of air quality to emission changes. Predictions of the pf-RSM are in good agreement with the out-of-sample CMAQ simulations, with some exceptions for cases with anthropogenic emission reductions approaching 100%. NOx emission reductions were more effective for reducing PM2.5 and ozone concentrations than SO2, NH3, or traditional VOC emission reductions. NH3 emission reductions effectively reduced nitrate concentrations in January but increased secondary organic aerosol (SOA) concentrations in July. More work is needed on SOA formation under conditions of low NH3 emissions to verify the responses of SOA to NH3 emission changes predicted here. Overall, the pf-RSM performs well in the eastern U.S., but next-generation RSMs based on deep learning may be needed to meet the computational requirements of typical regulatory applications.

This article presents a parametrized response model that enhances the limit of detection (LOD) of piezoelectrically driven microcantilever (PD-MC) based gas sensors by accounting for the adsorption-induced variations in elastic properties of the functionalization layer (binder) and the nonlinear motional dynamics of the PD-MC. The developed model is demonstrated for quantifying cadaverine, a volatile biogenic diamine whose concentration is used to assess the freshness of meat. At low concentrations of cadaverine, an increase in the resonance frequency is observed, contrary to the expected reduction due to mass added by adsorption. The study explores the variations in the elastic modulus vis-à-vis the adsorbed mass of cadaverine and derives the resonance frequency to the adsorbed mass response function. We advance a blended technique involving the analysis of atomic force microscopy (AFM) force-distance ( f - d ) curves and fitting of the quartz crystal microbalance (QCM) impedance response spectrum to deduce the adsorption-induced changes in the viscoelastic properties of the functionalization layer. The findings obtained are subsequently employed in modeling the response function for a structurally nonhomogenous PD-MC, highlighting the significance of the functionalization layer to the global elastic properties. The structural composition of the PD-MC beam adopted herein features a trapezoidal base hosting the actuating piezoelectric stratum and a rectangular free end with a functionalization layer. The Euler-Bernoulli beam theory coupled with Hamilton's principle is used to develop the equation of motion, which is subsequently discretized into a set of nonlinear ordinary differential equations via Galerkin expansion, and the solutions to the first fundamental mode of vibration are determined using the method of multiple scales. The obtained solutions provide a basis for deducing the nonlinear response function model to the adsorbed mass. The derived model is validated by recorded resonance frequency changes resulting from exposure to known concentrations of cadaverine. We demonstrate that the increase in resonance frequency for low concentrations of cadaverine is due to the dominance of the variation of the elastic modulus of the functionalization layer originating from the initial binder-analyte interactions over damping due to added mass. It is concluded that the developed nonlinear response function model can reliably be used to quantify the cadaverine concentration at low concentrations with an elevated Limit of Detection.

The 'Fear of Missing Out' ('FOMO') is a well-known concept in popular culture. Consequently, it has been co-opted and successfully utilised in commercial advertising appeals to initiate sales. However, academic research to date has focussed exclusively on FOMO as an individual trait leading to self-initiated FOMO-driven behaviours. By contrast, the success of FOMO sales appeals relies upon consumers' responses; therefore, it is necessary to understand these response mechanisms. This is the first known academic research to investigate consumer response mechanisms in relation to externally initiated FOMO appeals. In doing so, this research develops an original taxonomy of FOMO appeals; establishes a thematic map of response elements; identifies theory relevant to individuals' responses; formulates an operational response model; and proposes a future FOMO research agenda. [ABSTRACT FROM AUTHOR]

Data assimilation for multiple air pollutant concentrations has become an important need for modeling air quality attainment, human exposure, and related health impacts, especially in China, which experiences both PM2.5 and O3 pollution. Traditional data assimilation or fusion methods are mainly focused on individual pollutants and thus cannot support simultaneous assimilation for both PM2.5 and O3. To fill the gap, this study proposed a novel multipollutant assimilation method by using an emission-concentration response model (noted as RSM-assimilation). The new method was successfully applied to assimilate precursors for PM2.5 and O3 in the 28 cities of the North China Plain (NCP). By adjusting emissions of five pollutants (i.e., NOx, sulfur dioxide = SO2, ammonia = NH3, VOC, and primary PM2.5) in the 28 cities through RSM-assimilation, the RMSEs (root mean square errors) of O3 and PM2.5 were reduced by about 35% and 58% from the original simulations. The RSM-assimilation results in small sensitivity to the number of observation sites due to the use of prior knowledge of the spatial distribution of emissions; however, the ability to assimilate concentrations at the edge of the control region is limited. The emission ratios of five pollutants were simultaneously adjusted during the RSM-assimilation, indicating that the emission inventory may underestimate NO2 in January, April, and October, and SO2 in April, but overestimate NH3 in April, and VOC in January and October. Primary PM2.5 emissions were also significantly underestimated, particularly in April (dust season in NCP). Future work should focus on expanding the control area and including NH3 observations to improve the RSM-assimilation performance and emission inventories.

Highlights • An infrared HDR image fusion method based on an improved response model is proposed. • Two influencing factors are introduced to derive the improved response model. • Experimental results show the applicability of the model to cooled thermal cameras. • The fused HDR images retain more details, and the nonuniformity could be corrected. Abstract The multi-exposure fusion method is an effective way to extend the dynamic range of the infrared focal plane array (IRFPA), but the traditional method doesn't take into account the impact of the integration time on every pixel's response function, thereby introducing nonuniform noises and affecting the fusion quality. Based on the traditional response model of an infrared detector, this article derives the relationship between the response function and the integration time by introducing new influence factors, and conducts verification experiments with MW and LW thermal cameras. The experimental results are consistent with the proposed model, which shows that, within the linear response range of the detector, the gain parameters of the pixels are independent of the integration time, and the offset parameters are approximately inversely proportional to it when the ambient temperature is determined. Meanwhile, based on the results, an infrared HDR image fusion method under a variable integration time is studied. The resulting images retain more details of the bright and dark areas of the scene, and the nonuniformity can be corrected to some extent at the same time. This proves that the model proposed in this paper is effective for extending the dynamic range of the IRFPA and has theoretical significance and practical value for further HDR thermal imaging research. [ABSTRACT FROM AUTHOR]

We present the development and validation of a numerical modeling suite for bubble and droplet dynamics of multiphase plumes in the environment. This modeling suite includes real-fluid equations of state, Lagrangian particle tracking, and two different integral plume models: an Eulerian model for a double-plume integral model in quiescent stratification and a Lagrangian integral model for multiphase plumes in stratified crossflows. Here, we report a particle tracking algorithm for dispersed-phase particles within the Lagrangian integral plume model and a comprehensive validation of the Lagrangian plume model for single- and multiphase buoyant jets. The model utilizes literature values for all entrainment and spreading coefficients and has one remaining calibration parameter κ, which reduces the buoyant force of dispersed phase particles as they approach the edge of a Lagrangian plume element, eventually separating from the plume as it bends over in a crossflow. We report the calibrated form κ=[(b-r)/b]4, where b is the plume half-width, and r is the distance of a particle from the plume centerline. We apply the validated modeling suite to simulate two test cases of a subsea oil well blowout in a stratification-dominated crossflow. These tests confirm that errors from overlapping plume elements in the Lagrangian integral model during intrusion formation for a weak crossflow are negligible for predicting intrusion depth and the fate of oil droplets in the plume. The Lagrangian integral model has the added advantages of being able to account for entrainment from an arbitrary crossflow, predict the intrusion of small gas bubbles and oil droplets when appropriate, and track the pathways of individual bubbles and droplets after they separate from the main plume or intrusion layer. [ABSTRACT FROM AUTHOR]

Innovative test methods for circuit breakers are constantly sought after to reduce maintenance time and costs, yet still provide accurate assessment of this critical substation equipment. This paper proposes a novel method for response modelling of high voltage SF6 circuit breakers, based on artificial neural networks, to provide a means of assessing its condition. The proposed method enables a timing response model of the circuit breaker to be developed using trip command parameters. In this paper, an experimental setup is used to perform trip response testing of a three-phase 75 kV circuit breaker. The obtained data is then used to train, validate and test a Bayesian regularised artificial neural network that can predict response times of the breaker for a given set of trip command parameters. [ABSTRACT FROM AUTHOR]