Heterogeneous Parameters Research Articles

Transcranial magnetic stimulation as a new therapeutical approach in autism spectrum disorders and attention deficit hyperactivity disorder – a review

Introduction and aim. Autism spectrum disorders (ASD) and attention deficit hyperactivity disorder (ADHD) neurobiology reveals a complex picture of altered excitation-inhibition balance, aberrant neuronal and neurotransmitter activity, and network disorganization that could be addressed through repetitive transcranial magnetic stimulation (TMS). In this paper, we provide a narrative review of the most recent literature on the use of TMS to treat patients with ASD and ADHD. Material and methods. Literature search from 2018 up to November 2022 has been conducted on PubMed database. Keywords reflected diagnoses and treatment modalities of interest. Analysis of the literature. Eleven clinical trials regarding the use of TMS as a therapeutic tool in ASD, and seven studies (of which 3 are case reports) for ADHD have been reported. The dorsolateral prefrontal cortex (DLPFC) is the most frequent area stimulated. Heterogeneity in stimulation parameters, patient age, and outcome measures limited the interpretation of findings. Conclusion. TMS as a therapeutic tool for neurodevelopmental disorders is still in its infancy. To define the real efficacy of TMS, future studies must be randomized, sham-controlled, and double-blind, and should include a larger sample with adequate inclusion/exclusion criteria, and longitudinal follow-up.

Self-construal, face concerns and conflict management strategies: a meta-analysis

PurposeIn this meta-analysis, the authors investigate the relationship between self-construal and conflict management strategies and shed light on the inconsistent findings in the literature. Moreover, they examine the mediating role of face concerns in this relationship. Importantly, the present meta-analysis is the first to test the assumptions of face-negotiation theory with the meta-analytic structural equation modeling (MASEM), which enabled the authors to test the hypothesized relationships in one single model.Design/methodology/approachThe authors used the method of MASEM to test the relationship between self-construal and conflict management and assess the mediating role of face concerns. In this regard, the authors employed one-stage meta-analytic structural equation modeling to perform MASEM and its moderators.FindingsTwo hundred fifty-four effect sizes based on thirty-three studies were pooled in this meta-analysis. The authors found that individuals with stronger independent self-construal and stronger self-face concerns were more likely to use forcing. Moreover, the relationship between independent self-construal and forcing was mediated by self-face concerns. Individuals with a stronger interdependent self-construal and individuals with stronger other-face concerns were more likely to use problem-solving and yielding. The relationship between interdependent self-construal and problem-solving and yielding was mediated by other-face concerns. Finally, interdependent self-construal also had an indirect effect, via other-face concern, on avoiding and compromising.Originality/valueThe present meta-analysis is the first effort that the authors are aware of to test the assumptions of face-negotiation theory (FNT) using MASEM method. The authors used one-stage meta-analytic structural equation modeling viewed as the state of the methods to perform MASEM and its moderators. They employed full information meta-analytic structural equation modeling to show the generalizability and heterogeneity of structural equation modeling parameters. They applied studentized deleted residuals to assess outlier analysis and also conducted different methods to perform MASEM to check the robustness of the findings resulted. Finally, the current study adds multiple methods of assessing for publication bias.

How does fiscal decentralization lead to renewable energy transition and a sustainable environment? Evidence from highly decentralized economies

Effective fiscal management ensures the coordination of taxes and spending authority for attaining inclusive and sustainable growth. Therefore, this study intends to estimate the direct and indirect channels of expenditure, revenue, and composite fiscal decentralization on carbon emissions through renewable energy transition in highly decentralized countries. It employs a cross-sectional augmented auto-regressive distributed Lag (CS-ARDL) method, allowing dependencies across countries and heterogeneity in the slope parameters. In the long run, the direct effects of expenditure (revenue) decentralization on carbon emissions are significantly positive (negative) with coefficient magnitudes of 0.16% and 0.04% (0.09% and 0.08%). The indirect effects of expenditure and revenue decentralization are substantially negative through renewable energy consumption with the coefficient magnitude of 0.135% and 0.138%, respectively. In contrast, composite decentralization and its interactive terms with renewable energy show an emissions-mitigating effect of 0.179% and 0.192%, respectively. These outcomes exhibit that the down-delegation of financial authority (revenue or composite) at local government increases resource efficiency and leads to higher renewable energy consumption. Similar results are echoed in the short run; however, the magnitude of coefficients is significantly higher in the long run. The overall findings provide fascinating policy recommendations.

Deep prostate-specific antigen response and overall survival in patients with metastatic castration-sensitive prostate cancer: A systematic review and meta-analysis.

195 Background: Preliminary data suggests that patients with metastatic castration-sensitive prostate cancer (mCSPC) who achieve deep prostate-specific antigen (PSA) response may have improved survival. This finding may have implications for developing treatment de-escalation strategies. Thus, we assessed the overall survival by deep PSA response in mCSPC patients receiving intensified treatments. Methods: MEDLINE and EMBASE were systematically searched from each database’s inception through 1st October 2022. Trials assessing androgen deprivation therapy intensification (doublets, triplets) and reporting overall survival (OS) by deep PSA response were considered eligible for inclusion. Deep PSA response was defined as the PSA level of less than 0.1 or 0.2 ng/ml within eight months after initiation of intensified treatment. Precomputed effect estimates of OS (deep PSA response vs no deep PSA response) were pooled using an inverse-variance approach after logarithmic transformation; a random-effects meta-analysis was conducted within the Bayesian framework using empirical informative priors for heterogeneity parameter as specified by Turner et al. Summary effect was expressed as hazard ratios (HR) with the corresponding 95% credible intervals (CrI). A sensitivity analysis was conducted using the Der Simonian-Lairds random-effects meta-analysis with Hartung-Knapp (HK) adjustment. Results: Five RCTs (PEACE-1, ARASENS, CHAARTED, LATITUDE, TITAN) with 2533 patients were included in this systematic review. A total of 1335 patients experienced a deep PSA response, while 1218 did not achieve a deep PSA response after the initiation of intensified treatment. The pooled incidence of deep PSA response was 49.45% (95% confidence interval: 37.75-61.18). Bayesian meta-analysis showed significantly improved OS in overall mCSPC patient population who achieved a deep PSA response after intensified treatment with either triplet or doublet therapy as compared to those who did not achieve a deep PSA response (HR: 0.39; 95% CI: 0.30-0.50) as shown. The results were consistent with HK adjustment. Conclusions: Excellent overall survival in patients with deep PSA response may offer an opportunity to guide treatment de-escalation trials in carefully selected mCSPC patients. [Table: see text]

Predictive and prognostic potential of pretreatment 68Ga-PSMA PET tumor heterogeneity index in patients with metastatic castration-resistant prostate cancer (mCRPC).

200 Background: 177Lu-PSMA therapy has been introduced as an effective therapeutic option for metastatic castration resistant prostate cancer (mCRPC). Since about one third of the treated patients show no biochemical response (BCR) after therapy, prediction of treatment outcome is important for patient selection. Therefore, this study was conducted to evaluate to the predictive values of volumetric parameters and radiomic features (RFs) extracted form pretreatment 68Ga-PSMA PET and baseline clinical parameters in response to 177Lu-PSMA therapy. Methods: In this multicenter study, mCRPC patients undergoing 177Lu-PSMA therapy were enrolled from September 2017 to January 2022. The available clinical data of all patients was collected and documented from three different centers. The patients underwent several cycles of therapy. According to the outcome of therapy, the patients were classified into two groups including positive BCR (≥ 50% reduction in the serum PSA value) and negative BCR (< 50%). Sixty-five RFs, eight volumetric parameters and also seventeen clinical parameters were evaluated for prediction of BCR. In addition, the impact of such parameters on overall survival (OS) was evaluated. Results: In this retrospective study, 33 prostate cancer patients with a median age of 69 years (range: 49-89) were enrolled. BCR was observed in 22 cases (66%), and 16 cases (48.5%) died during the follow-up time. The results of Spearman correlation test indicated a significant relationship between BCR and treatment cycle, administered dose, HISTO energy, GLCM entropy, and GLZLM LZLGE (p<0.05). In addition, according to the Mann-Whitney U test, age, cycle, dose, GLCM entropy, and GLZLM LZLGE were significantly different between BCR and non BCR patients (p<0.05). According to the ROC curve analysis for feature selection for prediction of BCR, GLCM entropy, age, treatment cycle, and administered dose showed acceptable results (p<0.05). According to SVM for the assessing of the best model for predication of response to therapy, GLCM entropy alone showed the highest predictive performance in treatment planning. For the entire cohort, the Kaplan-Meier test revealed a median OS of 21 months (95% CI: 12.12-29.88). The median OS was estimated at 26 months (95% CI: 17.43-34.56) for BCR patients and 13 months (95% CI: 9.18-16.81) for non BCR patients. Among all variables included in the Kaplan Meier, only response to therapy was statistically significant (p=0.01) and none of the RFs and other clinical factors achieved a p-value of less than 0.05. Conclusions: This exploratory study showed that heterogeneity parameter of pretreatment 68Ga-PSMA PET images might be a potential predictive value for response to 177Lu-PSMA therapy in mCRPC; however, further prospective studies need to be carried out to verify these findings.

SNAPenrollment cycles: New insights from heterogeneous panel models withcross‐sectionaldependence

AbstractThe Supplemental Nutrition Assistance Program (SNAP) has grown rapidly over the past 2 decades. A large literature relies on state‐level panel data on SNAP enrollment and implements traditional two‐way fixed effects estimators to identify the impact of economic conditions on SNAP enrollment. This empirical strategy implicitly assumes slope parameter homogeneity and ignores the possibility of cross‐sectional dependence in the regression error terms. The latter could feasibly arise in state‐level panel data if the time‐varying unobserved common shocks, such as national financial crises, have differential effects on SNAP participation across states in the United States. This study empirically evaluates the appropriateness of these two assumptions by adopting a more general common factor model, allowing for slope parameter heterogeneity and error term cross‐sectional dependence both separately and jointly. We find that although assuming a common slope parameter across states does not seem problematic for identification, allowing for the error term cross‐sectional dependence leads to a roughly 40% reduction in the estimated long‐run impact of the unemployment rate on SNAP enrollment. This finding has important implications for policymaking decisions—even small biases could lead to suboptimal policy responses considering the program's size. Our counterfactual simulations support our main results, implying the importance of carefully accounting for time‐varying unobserved heterogeneity when studying the cyclicality of SNAP enrollment using state‐level panel data.

The influence of facemasks on communication in healthcare settings: a systematic review

Purpose Although a well-established aspect of healthcare practice, the impact of facemasks on verbal communication is surprisingly ambiguous. Materials and Methods A systematic search was conducted in APA PSYCHinfo, CINAHL, NHS Knowledge Network, Medline and SPORTDiscus databases from inception to November 2022 according to the PRISMA guidelines. Studies reporting an objective measure of speech understanding in adults, where information was transmitted or received whilst wearing a facemask were included. Risk of bias of included studies was assessed with the Newcastle-Ottawa score. Results Four hundred and thirty-three studies were identified, of which fifteen were suitable for inclusion, incorporating 350 participants with a median age of 49 (range 19 to 74) years. Wide heterogeneity of test parameters and outcome measurement prohibited pooling of data. 93% (14 of 15) studies reported a deleterious effect of facemasks on speech understanding, and 100% (5 of 5) of the included studies reported attenuation of sound with facemask usage. Background noise added further deleterious effects on speech understanding which was particularly problematic within hearing-impaired populations. Risk of bias in included studies varied but overall was modest. Conclusions Despite considerable complexity and heterogeneity in outcome measure, 93% (14 of 15) articles suggest respiratory protective equipment negatively affects speech understanding in normal hearing and hearing-impaired adults.

Comparison of travel-time and geostatistical inversion approaches for hydraulic tomography: Synthetic modeling study on data density and well configuration issues

Hydraulic tomography (HT) has been proven to be an effective approach in mapping the heterogeneity of hydraulic parameters. The travel-time based inversion (TTI) and geostatistical inversion (GI) approaches are two of several interpretation methods for HT data. In particular, GI is used to estimate heterogeneous hydraulic conductivity (K) and specific storage (Ss) tomograms, while TTI yields the diffusivity (D = K/Ss) tomogram. The main objective of this paper is to compare the performances of these two methods through a synthetic study. Two cases are designed based on different monitoring configurations. In each case, two independent scenarios are designed as: (1) providing the same dataset, and (2) providing datasets based on recommended strategies for each inversion approach. Then, the estimated tomograms are evaluated by direct comparison of estimated parameter distributions and assessment of model validation results. Comparison results show that the advantages of TTI are: (1) imaging of structural features representing high-D zones; and (2) requirement of less data for inverse modeling. In contrast, the advantages of GI are: (1) estimation of parameters throughout the simulation domain; (2) better characterization of low-D zones; and (3) generating the best estimated tomogram leading to best drawdown predictions. Collectively, our study suggests that TTI is suitable for rapid, coarse characterization of heterogeneity that could be utilized for providing hydrogeological structures for GI as an initial model. GI is more robust and preferable to applications that require higher accuracy in parameter estimation over a larger volume.

Distributed and Asynchronous Population-Based Optimization Applied to the Optimal Design of Fuzzy Controllers

Designing a controller is typically an iterative process during which engineers must assess the performance of a design through time-consuming simulations; this becomes even more burdensome when using a population-based metaheuristic that evaluates every member of the population. Distributed algorithms can mitigate this issue, but these come with their own challenges. This is why, in this work, we propose a distributed and asynchronous bio-inspired algorithm to execute the simulations in parallel, using a multi-population multi-algorithmic approach. Following a cloud-native pattern, isolated populations interact asynchronously using a distributed message queue, which avoids idle cycles when waiting for other nodes to synchronize. The proposed algorithm can mix different metaheuristics, one for each population, first because it is possible and second because it can help keep total diversity high. To validate the speedup benefit of our proposal, we optimize the membership functions of a fuzzy controller for the trajectory tracking of a mobile autonomous robot using distributed versions of genetic algorithms, particle swarm optimization, and a mixed-metaheuristic configuration. We compare sequential versus distributed implementations and demonstrate the benefits of mixing the populations with distinct metaheuristics. We also propose a simple migration strategy that delivers satisfactory results. Moreover, we compare homogeneous and heterogenous configurations for the populations’ parameters. The results show that even when we use random heterogeneous parameter configuration in the distributed populations, we obtain an error similar to that in other work while significantly reducing the execution time.

Quantitative Evaluation of the Effects of Input Parameter Heterogeneity on Model Behavior for Bonded Block Models of Laboratory Rock Specimens

Quantitative Evaluation of the Effects of Input Parameter Heterogeneity on Model Behavior for Bonded Block Models of Laboratory Rock Specimens

Implications of lower indoor temperatures – Not cool for cold susceptible individuals across both sexes

Wider temperature ranges in buildings can reduce building energy use and prevent shortage of energy availability. However, humans do not perceive temperature equally and a general lowering of indoor temperature may in particular impact susceptible individuals. The discrepancy between individuals has been ascribed to sex differences, but is not well understood and could relate to heterogeneity in endogenous heat production or other personal parameters. We, therefore, evaluated individual thermal responses including physiological measurements of metabolic heat production in both men and women, identified, and via experiments, verified as cold sensitive or cold resilient. On average, the cold sensitive group had an 18 % lower resting metabolic rate compared to the cold resilient group when controlling for clothing and other important parameters for heat exchange. We observed a 0.9 °C difference in neutral temperature between sexes, but no difference in thermal perception or skin temperature. We concluded that cold susceptibility is not simply a matter of perception, but relates to a measurable difference in endogenous heat production. Currently mandated temperature setpoints at workplaces or recommended household temperatures do therefore not seem to discriminate between sexes as a result of sex-related differences in physiology, but they might have negative implications for cold sensitive individuals.

Heat transfer analysis in hydromagnetic flow of couple stress fluid in presence of homogeneous and heterogeneous chemical reactions over a porous oscillatory stretchable sheet

The prime objective of this theoretical study is to investigate the flow, mass, and heat transfer in the time-dependent couple stress fluid over a permeable stretching oscillatory surface. The effect of applied magnetic field is also taken in the momentum equation. In addition, the energy equation is modeled by incorporating thermal radiation and heat generation. Furthermore, the concentration equation is designed in presence of homogenous and heterogeneous chemical reactions. The flow mechanism occurs because of the to and fro motion of the surface about a fixed point. For similar solution of the developed flow problem suitable similarity transformations is adopted. The analytical solution of the obtained flow equations in the form of partial differential equations (PDEs) is attained by utilizing a valuable method called Homotopy analysis method (HAM). An extensive assessment is performed to study the influence of various involved parameters namely the couple stress parameter, the relation of the parameter of the surface oscillatory frequency to its stretched rate constant, the Prandtl number, the strength of heterogeneous and homogeneous chemical reactions parameters, Schmidt number, heat production parameter and magneto-porosity parameter on the flow, concentration and temperature distributions, skin friction coefficient, and rate of heat transfer via tables and graphs and are discussed in detail. It is observed that the amplitude of velocity and skin friction coefficient shows a declining response with varying values of the couple stress parameter and it increases for higher values of the magneto-porous parameter.

Comparing the Conditional Logit Estimates and True Parameters under Preference Heterogeneity: A Simulated Discrete Choice Experiment

Health preference research (HPR) is the subfield of health economics dedicated to understanding the value of health and health-related objects using observational or experimental methods. In a discrete choice experiment (DCE), the utility of objects in a choice set may differ systematically between persons due to interpersonal heterogeneity (e.g., brand-name medication, generic medication, no medication). To allow for interpersonal heterogeneity, choice probabilities may be described using logit functions with fixed individual-specific parameters. However, in practice, a study team may ignore heterogeneity in health preferences and estimate a conditional logit (CL) model. In this simulation study, we examine the effects of omitted variance and correlations (i.e., omitted heterogeneity) in logit parameters on the estimation of the coefficients, willingness to pay (WTP), and choice predictions. The simulated DCE results show that CL estimates may have been biased depending on the structure of the heterogeneity that we used in the data generation process. We also found that these biases in the coefficients led to a substantial difference in the true and estimated WTP (i.e., up to 20%). We further found that CL and true choice probabilities were similar to each other (i.e., difference was less than 0.08) regardless of the underlying structure. The results imply that, under preference heterogeneity, CL estimates may differ from their true means, and these differences can have substantive effects on the WTP estimates. More specifically, CL WTP estimates may be underestimated due to interpersonal heterogeneity, and a failure to recognize this bias in HPR indirectly underestimates the value of treatment, substantially reducing quality of care. These findings have important implications in health economics because CL remains widely used in practice.

Influence of renewable energy infrastructure, Chinese outward FDI, and technical efficiency on ecological sustainability in belt and road node economies

Renewable energy infrastructure is highly interrelated to environmental sustainability and the accomplishment of sustainable development goals. A significant portion of China's outward foreign direct investment (FDI) has been allocated to energy production and transmission across the Belt and Road Initiative (BRI) region. Therefore, we examine the influence of renewable energy infrastructure, China's outward FDI, and the host country's technical efficiency on ecological sustainability. This research uses the cross-sectional autoregressive distributive lag (CS-ARDL) model to address the issues of parameter and cross-sectional heterogeneity. The findings show that renewable energy infrastructure and technical efficiency significantly reduce carbon emissions in the long run. Likewise, FDI inflows and economic growth increase emissions in the BRI region due to higher energy consumption. Moreover, the direct effect of FDI on environmental quality is negative, while the indirect impact through moderation of FDI and renewable energy infrastructure leads to lower emissions. These results implied that the reallocation of inward FDI in energy infrastructure neutralizes the negative consequences, improves energy efficiency, and resultantly lower emissions. Therefore, it opens new policy implications that we focus more on investing FDI in the energy infrastructure sector.

Renewable Energy, Knowledge Spillover and Innovation: Capacity of Environmental Regulation

The European Union (EU) countries have declared the ambitious goal of providing carbon-free economic development. Considering this, the EU countries are going to pursue relevant policies for a step-by-step refusal of mining and coal energy, consequently reducing greenhouse gas emissions. The analysis of the theoretical background showed that renewable energy is the core dimension of reducing greenhouse gas emissions. In this case, the paper aims to justify the impact of core dimensions (knowledge spillover, innovation, and environmental regulation) that could boost renewable energy penetration into all sectors and levels. The following methods are applied to test the hypotheses: stationarity testing in panels; cross-section dependence testing; cointegration testing; and estimation in heterogeneous parameter models. The data are obtained from Eurostat, the OECD, and the World Data Bank. The object of research is the EU country in the period 2010–2020. The findings confirm the hypothesis on the statistically significant impact of innovation and knowledge spillover on renewable energy. In addition, environmental regulation has a mediating positive effect on interconnections among knowledge spillover, innovations, and renewable energy. In this case, countries should boost the development of appropriate environmental regulations, which should be effective and transparent for all stakeholders.

Heterogeneity estimation in meta-analysis of standardized mean differences when the distribution of random effects departs from normal: A Monte Carlo simulation study

BackgroundAdvantages of meta-analysis depend on the assumptions underlying the statistical procedures used being met. One of the main assumptions that is usually taken for granted is the normality underlying the population of true effects in a random-effects model, even though the available evidence suggests that this assumption is often not met. This paper examines how 21 frequentist and 24 Bayesian methods, including several novel procedures, for computing a point estimate of the heterogeneity parameter ({tau }^{2}) perform when the distribution of random effects departs from normality compared to normal scenarios in meta-analysis of standardized mean differences.MethodsA Monte Carlo simulation was carried out using the R software, generating data for meta-analyses using the standardized mean difference. The simulation factors were the number and average sample size of primary studies, the amount of heterogeneity, as well as the shape of the random-effects distribution. The point estimators were compared in terms of absolute bias and variance, although results regarding mean squared error were also discussed.ResultsAlthough not all the estimators were affected to the same extent, there was a general tendency to obtain lower and more variable {tau }^{2} estimates as the random-effects distribution departed from normality. However, the estimators ranking in terms of their absolute bias and variance did not change: Those estimators that obtained lower bias also showed greater variance. Finally, a large number and sample size of primary studies acted as a bias-protective factor against a lack of normality for several procedures, whereas only a high number of studies was a variance-protective factor for most of the estimators analyzed.ConclusionsAlthough the estimation and inference of the combined effect have proven to be sufficiently robust, our work highlights the role that the deviation from normality may be playing in the meta-analytic conclusions from the simulation results and the numerical examples included in this work. With the aim to exercise caution in the interpretation of the results obtained from random-effects models, the tau2() R function is made available for obtaining the range of {tau }^{2} values computed from the 45 estimators analyzed in this work, as well as to assess how the pooled effect, its confidence and prediction intervals vary according to the estimator chosen.

Neuro-evolution heuristics for Prandtl-Eyring nanofluid flow with homogenous/heterogeneous reaction across a linearly heated stretched sheet

The present research-oriented study provides the application of a renowned numerical-based neuro-evolution heuristic technique by the utilization of feed-forward neural networks (FFNNs) trained with a hybrid composition of genetic algorithms (GAs) and sequential quadratic programming (SQP) i.e. FFNNs-GAs-SQP to provide a descriptive analysis on the dynamics of Prandtl-Eyring nanofluid flow over the stretchable sheet under the influence of heat and viscous dissipation. FFNNs-GAs-SQP approach is used to calculate numerical results based on the variation of temperature-difference parameter, material-parameter, stretching-parameter, heterogeneous-reactionparameter along with Biot-number, Prandtlnumber as well as the Schmidt-number. The effect of these parameters on velocity profile, temperature and nanofluid concentration is expressed in form of tables/graphs. Furthermore, uplifting the values of the temperature difference parameter and the stretching parameter enhances the nanofluid velocity. The larger values of the material parameter and Prandtl number diminish the temperature of nanofluid but this effect is reversed in the case of the Biot number and temperature difference parameter. It is also observed that a boost in the values of heterogeneous reaction parameter, Schmidt number and temperature difference parameter minimize the nanofluid concentration however this effect is reversed in Biot number and Prandtl number cases.

Impact of sensor placement in soil water estimation: a real-case study

One important problem in implementing a closed-loop irrigation system is the determination of the optimal locations to install the sensors, both vertically and horizontally, such that improved pressure head estimation can be obtained. In this work, we find the best locations of the sensors in terms of degree of observability in an actual field and investigate whether the optimal sensor placement can significantly improve the pressure head estimation performance in actual applications. We investigate the impact of sensor placement in pressure head estimation for an actual agricultural field in Lethbridge, Alberta, Canada. In an experiment on the studied field, 42 watermark sensors were installed at different depths to collect the soil water tension measurements. A three-dimensional agro-hydrological model with heterogeneous soil parameters of the studied field is set up. The modal degree of observability is applied to the three-dimensional system to determine the optimal sensor locations. The extended Kalman filter (EKF) is chosen as the data assimilation tool to estimate the pressure head of the studied field. Pressure head estimation results for different scenarios are obtained and analyzed to investigate the effects of sensor placement on the performance of pressure head estimation in the actual applications. Estimation results indicate that optimally placed sensors reduce the average root mean square error by about 30% compared to the case the sensor positions are selected randomly.

Influence of heterogeneity and elevated temperatures on the seismic translational stability of engineered landfills

This paper proposes a new generalised closed-form solution to estimate the safety factors for translational failure of landfills considering the effects of heterogeneity and temperature variation with the depth of the landfill. The influence of heterogeneity was considered in the form of a shear wave velocity profile, while the influence of temperature was accounted for by considering the temperature-dependent shear strength properties. The proposed method also considered the influence of material damping and the mode change behaviour of the landfill. The safety factor for translational failure of the landfill was estimated using the limit equilibrium-based two-part wedge method. The proposed method was validated by comparing the safety factor obtained from this method with comparable analytical solutions. An extensive parametric study was conducted to demonstrate the influences of the landfill geometry, shear strength properties of municipal solid waste and liner components, parameters of strong ground motion, heterogeneity, and landfill temperature on the calculation of safety factors. The interfacial shear strength properties of liner components played a vital role in the translational stability of landfills compared to the shear strength properties of municipal solid waste. The frequency of base excitation showed a strong influence on landfill stability, while the material damping displayed a nominal effect. For the cases considered in this study, when the frequency of base excitation was close to the fundamental frequency of the landfill, the base acceleration was amplified by 6.7 times which reduced the safety factor by approximately 1.6 times. Heterogeneity and temperature have a significant influence on landfill stability. The increased degree of heterogeneity caused an approximately 27% reduction in the safety factor. For the given set of input parameters, the elevated temperature in the landfill alone decreased the safety factor by a maximum of 18%, while it reduced by approximately 60% under the combined influence of seismic, heterogeneity, and elevated temperature.

Application of All-Ages Lead Model Based on Monte Carlo Simulation of Preschool Children’s Exposure to Lead in Guangdong Province, China

Introduction: Lead (Pb) poisoning in children is a major public health issue worldwide. The physiologically based pharmacokinetic model (PBPK model) has been extensively utilized in Pb exposure risk assessment and can connect external exposure with biological monitoring data. This study aimed to combine a Monte Carlo simulation with the all-ages lead model (ALLM) to quantify the heterogeneity and uncertainty of certain parameters in the population. The parameters of the all-ages lead model based on Monte Carlo simulation (ALLM + MC) were localized in Guangdong Province. Our study discusses the practicability of the application of the localized ALLM + MC in Guangdong Province. Methods: A local sensitivity analysis was used to assess the impact of pharmacokinetic parameters on the prediction of blood lead level (BLL). Environmental Pb concentration, exposure parameters, and sensitive parameters were included in the ALLM + MC, and the differences between the ALLM- and the ALLM + MC-predicted values were compared. Additionally, we localized the exposure parameters in the ALLM + MC and used them to evaluate BLL in preschool children from Guangdong Province. Finally, we compared the predictive values to those observed in the literature. Results: The predictive values of ALLM and ALLM + MC had a significant correlation (r = 0.969, p < 0.001). The predictive value of ALLM was included in the ALLM + MC prediction range. Moreover, there were no significant differences between the predictive and the observed values of preschool children from Guangdong Province (z = −0.319, p = 0.749). Except for children aged 5–6, the difference between the predictive and the observed values was less than 1 μg/dL. The root mean square error (RMSE) and the mean deviation (RMD) of ALLM and ALLM + MC were reduced by 24.73% and 32.83%, respectively. Conclusions: The localized ALLM + MC is more suitable for predicting the BLL of preschool children in Guangdong Province, which can be used to explain the heterogeneity and uncertainty of parameters in the population. The ALLM + MC has fewer time, space, and financial restrictions, making it more appropriate for determining the BLLs in large populations. The use of ALLM + MC would improve the feasibility of regular and long-term blood Pb detection.