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ABSTRACTRenewable generation plays an important part in today's power system. With the inclusion of the inverter interfaced renewable energy sources (RESs) into a microgrid, the total system inertia decreases and it leads to increased frequency deviation in presence of a disturbance. This paper proposes a cascaded Integral Minus Tilt Integral Derivative with Filter (I–TDN)‐Proportional‐Integral (PI), [(I‐TDN)‐PI] controller for frequency stabilization of a hybrid microgrid in presence of electric vehicles (EV). The microgrid model includes reheated thermal power plant with high degree of non‐linear system such as inverter based RESs like photovoltaic and wind generation systems. A diesel engine generator is incorporated for load frequency control during perturbation in the system frequency. Virtual inertia controller (VIC) with inverter based energy storage system (ESS) is commonly used to improve system inertia and frequency stability of the microgrid. In addition to the ESS, this paper proposes inclusion of the EVs in this VIC. The optimal gains of the proposed cascaded I‐TDN‐PI controller are determined using Mountain Gazelle Optimizer (MGO), a modern metaheuristic optimization algorithm. Sensitivity of the proposed controller is investigated in presence of system nonlinearities, load perturbations, time delay, system parameter variation and RES power fluctuations. The simulation results justify the robustness of the proposed control structure for frequency stabilization of the microgrid. [ABSTRACT FROM AUTHOR]

There is something particularly depressing about the current moment—when the possibility of thriving as a geography academic in British academia seems incompatible with anything else, be that a life outside work, a family, activism, or even sleep. What connects these multiple intensifications of demands on academics, and many of the external crises we are living through (the climate emergency, wage deflation, cost of living, the COVID pandemic etc.) is how we value, use and understand time. The non‐linearity of time is a useful way to understand how we work in academia. In this paper, I explore how can we change how we use our time. I propose three responses, which each play with time as non‐linear, multiple rhythms, and as having a lack of balance or stability. First, the possibility of working with the delight of shifting pace in helping us to enjoy our working time more. Second, in acting in collaboration, solidarity, and being alongside each other in ways where shared time is more expansive than individual time. Finally, in how we can collectively and structurally re‐order our time together. [ABSTRACT FROM AUTHOR]

Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage. [ABSTRACT FROM AUTHOR]

Background: The triglyceride–glucose (TyG) index has garnered recognition as a surrogate marker for insulin resistance, a pivotal factor in the pathogenesis of various metabolic disorders. Despite its emerging role, the empirical evidence delineating its association with prediabetes mellitus (Pre-DM) remains scant. This research aims to clarify the link between the TyG index and the likelihood of Pre-DM development within a Chinese demographic. Methods: This investigation was structured as a retrospective cohort analysis, encompassing a sample of 179,177 Chinese adults. These individuals underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. To ascertain the relationship between the TyG index and the incidence of Pre-DM, this study employed Cox regression analysis complemented by sensitivity and subgroup assessments. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore the existence of any non-linear connection within this association. Results: Upon adjusting for a comprehensive array of confounding variables, a statistically significant positive correlation between the TyG index and the risk of Pre-DM was identified (HR: 1.60, 95%CI 1.56–1.65, P < 0.001). The analysis illuminated a non-linear relationship, with an inflection point at a TyG index value of 8.78. For TyG index values below and above this inflection point, the HR was calculated to be 1.94 (95%CI 1.86–2.03) and 1.26 (95%CI 1.20–1.33), respectively. Sensitivity analyses further fortified the reliability of these findings. Conclusions: This comprehensive examination delineated a significantly positive, non-linear correlation between the TyG index and the risk of Pre-DM within a Chinese population. Individuals with TyG index values below 8.78 have a significantly increased risk of developing prediabetes. These findings underscore the TyG index's potential efficacy as a predictive tool for assessing Pre-DM risk in clinical practice. [ABSTRACT FROM AUTHOR]

Thermophysical properties have wide applications in various fields like chemical industries, mass and heat transfer processes, design and engineering calculations of industrial plants and their evaluation in liquid mixtures gives us a better understanding of intermolecular interactions taking place therein. The binary liquid mixtures under the current study comprise a range of alkanones viz. 2-Propanone, 2-Butanone, 2-Pentanone and 2-Heptanone with aromatic amines like Aniline, N-methylaniline and Pyridine at temperatures 293.15 K to 303.15 K which have multifarious industrial applications. In the present investigation, ultrasonic velocity (u) studies have been carried out using five approaches at three temperatures. Furthermore, surface tension (σ) , acoustical impedance (z) and thermoacoustical parameters along with non-linearity parameters (B/A) have been evaluated. The variations in molecular interactions are well understood through interaction parameters (χ) . The obtained results indicate that the order of interactions is Pyridine < N-methylaniline < Aniline for a specific alkanone and 2-Propanone > 2-Butanone > 2-Pentanone > 2-Heptanone for a given amine at all three temperatures. The study of thermophysical properties have been elaborated to understand the order of the interactions between various amines and alkanones. [ABSTRACT FROM AUTHOR]

Background: The triglyceride-glucose (TyG) index, recognized for its cost)efficiency and simplicity, serves as an accessible indicator of insulin resistance. Yet, its correlation with the risk of prediabetes and diabetes (Pre-DM/DM) in the Chinese demographic remains uncertain. Consequently, our study explored the association between the TyG index and the development of Pre-DM/DM within the Chinese population. Methods: The retrospective cohort study was carried out utilizing data from a health screening initiative. The study included 179541 adults over 20 who underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. The correlation between the TyG index and Pre-DM/DM risk was investigated using Cox regression analysis. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore their non-linear connection. Results: The mean age of study participants was 41.18 ± 12.20 years old, and 95255 (53.05%) were male. During a median follow-up of 3.01 years, 21281 (11.85%) participants were diagnosed with Pre-DM/DM. After adjusting the potential confounding factors, the results showed that the TyG index was positively correlated with incident Pre-DM/DM (HR: 1.67, 95%CI: 1.62-1.71, P< 0.001). Additionally, a non-linear association was observed between the TyG index and the onset of Pre-DM/DM, with an inflection point identified at 8.73. Hazard ratios (HR) to the left and right of this inflection point were 1.95 (95%CI: 1.86-2.04) and 1.34 (95%CI: 1.27-1.42), respectively. Furthermore, sensitivity analyses confirmed the stability of these findings. Conclusion: The TyG index exhibited a non-linear positive relationship with the risk of Pre-DM/DM. These findings imply that maintaining the TyG index at a lower, specified threshold may be beneficial in mitigating the onset of Pre-DM/DM. [ABSTRACT FROM AUTHOR]

ABSTRACT Aim Methods Results Main Conclusions Population dynamics are usually assessed through linear trend analysis, quantifying their general direction. However, linear trends may hide substantial variations in population dynamics that could reconcile apparent discrepancies when quantifying the extent of the biodiversity crisis. We seek to determine whether the use of non‐linear methods and the quantification of temporal variability can offer a more complete representation of changes in global population dynamics than commonly‐used linear approaches.We analysed 6437 population time series from 1257 vertebrate species from the Living Planet Database over the period 1950–2020. We modelled populations through the use of second‐order polynomials and classified trajectories according to their direction and acceleration. We modelled and classified these same populations using a more classical linear trend analysis. We quantified temporal variability using the mean squared error of the fitted polynomials. We then used generalised linear mixed models to test potential sources of heterogeneity in non‐linear trajectories and temporal variability.In all, 44.8% of the analysed population time series were non‐linear. Across all populations, 30% were declining, 30% were increasing, and 40% were with no linear trend. Among the population showing no linear trend, half were concave or convex. Non‐linearity was expressed differently between taxonomic groups, with mammals showing higher prevalence of non‐linearity. Marine and freshwater populations were more variable than terrestrial populations, and fish were more variable than other vertebrates. Differences between geographical regions were detected in both non‐linearity and temporal variability, but no straightforward pattern emerged. There were no differences in both components between IUCN categories.Non‐linearity and temporal variability reveal usually overlooked dramatic declines or recovery signals in global population dynamics. Thus, moving beyond linearity can improve our understanding of complex population dynamics and better inform conservation decisions. In particular, populations usually classified as ‘stable’ can hide informative changes in non‐linear and variability patterns that need to be considered in global biodiversity assessments. [ABSTRACT FROM AUTHOR]

Scholars increasingly raise concerns about the alleged detrimental impact of affective polarization on citizens’ democratic attitudes. Yet, prior studies on the relationship between affective polarization and democratic support have yielded mixed results: Whereas some scholars report evidence that affective polarization erodes citizens’ support for democracy, others report null findings. In this research note, we posit that one relevant explanation for these mixed results is that the relationship between affective polarization and democratic support is not linear, but rather negatively curvilinear (i.e., an inverted U‐shape). Though extreme levels of affective polarization may harm citizens’ democratic commitments, a moderate amount of affective polarization can strengthen democratic support by heightening the political stakes and stimulating democratic involvement. Employing generalized additive modelling on data from the CNEP collected in Germany, the United Kingdom, and the United States, we show strong and robust support for this negatively curvilinear pattern. These findings have important implications for our understanding of the dynamics between affective polarization and democratic support, as well as for the recommended estimation strategies of future studies that aim to explore this relationship. [ABSTRACT FROM AUTHOR]

The mechanical behaviour of polymer adhesives is influenced by the environmental conditions leading to ageing and affecting the integrity of the material. The polymer adhesives have hygroscopic behaviour and tend to absorb moisture from the environment, causing the material to swell without applying external load. The focus of the work is to investigate the viscoelastic material behaviour under ageing conditions. The constitutive equations and the governing equations to numerically investigate the fracture in swollen viscoelastic material are discussed to describe the numerical implementation. Phase-field damage modelling has been used in numerical studies of ductile and brittle materials for a long time. The finite-strain phase-field damage model is used to investigate the fracture behaviour in aged viscoelastic polymer adhesives. The finite-strain viscoelastic model is formulated based on the continuum rheological model by combining spring and Maxwell elements in parallel. Commercially available post-cured crosslinked polyurethane adhesives are used in the current investigation. Post-cured samples of crosslinked polyurethane adhesives are prepared for different humidity conditions under isothermal conditions. These aged samples are used to perform tensile and tear tests and the test data are used to identify the material parameters from the curve fitting process. The experiment and simulation are compared to relate the findings and are the first step forward to improve the method to model crosslinked polymers. [ABSTRACT FROM AUTHOR]

The hemodynamics in Fontan patients with single ventricles rely on favorable flow and energetics, especially in the absence of a subpulmonary ventricle. Age-related changes in energetics for extracardiac and lateral tunnel Fontan procedures are not well understood. Vorticity (VOR) and viscous dissipation rate (VDR) are two descriptors that can provide insights into flow dynamics and dissipative areas in Fontan pathways, potentially contributing to power loss. This study examined power loss and its correlation with spatio-temporal flow descriptors (vorticity and VDR). Data from 414 Fontan patients were used to establish a relationship between the superior vena cava (SVC) to inferior vena cava (IVC) flow ratio and age. Computational flow modeling was conducted for both extracardiac conduits (ECC, n = 16) and lateral tunnels (LT, n = 25) at different caval inflow ratios of 2, 1, and 0.5 that corresponded with ages 3, 8, and 15+. In both cohorts, vorticity and VDR correlated well with PL, but ECC cohort exhibited a slightly stronger correlation for PL-VOR (>0.83) and PL-VDR (>0.89) than that for LT cohort (>0.76 and > 0.77, respectively) at all ages. Our data also suggested that absolute and indexed PL increase (p < 0.02) non-linearly as caval inflow changes with age and are highly patient-specific. Comparison of indexed power loss between our ECC and LT cohort showed that while ECC had a slightly higher median PL for all 3 caval inflow ratio examined (3.3, 8.3, 15.3) as opposed to (2.7, 7.6, 14.8), these differences were statistically non-significant. Lastly, there was a consistent rise in pressure gradient across the TCPC with age-related increase in IVC flows for both ECC and LT Fontan patient cohort. Our study provided hemodynamic insights into Fontan energetics and how they are impacted by age-dependent change in caval inflow. This workflow may help assess the long-term sustainability of the Fontan circulation and inform the design of more efficient Fontan conduits. [ABSTRACT FROM AUTHOR]

The sustainability of the current account balance for five fragile economies—Brazil, Argentina, South Africa, India, and Türkiye (namely, BASIT)—is investigated. These countries' economies operate under a time current account deficit almost all the time, a condition that causes fragility to external shocks; the following fallout from these shocks may risk not only the domestic economy but also the international economy, such as by clogging trade and income distribution. In this study, the sustainability of the current account in BASIT countries is examined via wavelet-based Kapetanios, Shin and Snell (WKSS) and Fourier wavelet-based KSS (FWKSS) unit root tests, in conjunction with linear unit root tests. Even though traditional unit root tests generally support the sustainability of a current account deficit for all countries, a non-linear unit roots test confirms the traditional tests for only India and South Africa. Results from the wavelet transform of non-linear unit root tests indicate the unsustainability of the current account balance, except in the case Türkiye. Moreover, the FWKSS test confirms WKSS. [ABSTRACT FROM AUTHOR]

Abstract Background The triglyceride–glucose (TyG) index has garnered recognition as a surrogate marker for insulin resistance, a pivotal factor in the pathogenesis of various metabolic disorders. Despite its emerging role, the empirical evidence delineating its association with prediabetes mellitus (Pre-DM) remains scant. This research aims to clarify the link between the TyG index and the likelihood of Pre-DM development within a Chinese demographic. Methods This investigation was structured as a retrospective cohort analysis, encompassing a sample of 179,177 Chinese adults. These individuals underwent medical examinations at the Rich Healthcare Group over a period spanning from 2010 to 2016. To ascertain the relationship between the TyG index and the incidence of Pre-DM, this study employed Cox regression analysis complemented by sensitivity and subgroup assessments. Furthermore, Cox proportional hazards regression with cubic spline functions and smooth curve fitting was incorporated to explore the existence of any non-linear connection within this association. Results Upon adjusting for a comprehensive array of confounding variables, a statistically significant positive correlation between the TyG index and the risk of Pre-DM was identified (HR: 1.60, 95%CI 1.56–1.65, P

Abstract Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

Existing methods for handling uncertainty and imprecision often fall short in addressing complex real-world problems. To overcome these limitations, this paper introduces a novel Generalized Non-Linear Triangular Neutrosophic Number (GNLTNN) that effectively captures uncertainty, indeterminacy, and falsity. By analysing GNLTNN through (α,β,γ)-cuts and defining arithmetic operations using the max-min principle, we provide a robust framework for handling neutrosophic information. The proposed neutrosophic Laplace transform method enables efficient solutions to integral equations involving non-linear neutrosophic numbers. The efficacy of our approach is demonstrated through graphical representations. [ABSTRACT FROM AUTHOR]

In recent years, the growing publication of interactive picturebooks and the use of electronic tablets by children have prompted us to revise and reconsider how children read in different media. As a material object, a reading device can trigger various actions of the reader, such as tapping, swiping, and shaking. This paper investigates a type of interactive picturebooks that prompts rotational behavior from the reader through composition and layout. The paper presents a typological survey of such books arguing that graphic design invites rotational reading by combining different orientations of texts and images on the page and the connection of back and front covers. The rotation of books takes three paths through space: planar, vertical and horizontal flipping. Rotation, as a kinetic practice during reading, unveils visual wonder, achieves a circular narrative, extends storytelling space, displays parallel narrative perspectives, and reinforces crucial plots. This article opens questions the non-linearity of reading and about how to account for the body's participation in the process. [ABSTRACT FROM AUTHOR]

Some critical investigations on the phase non-linearity in Delay Line Discriminator (DLD) are reported in this article to increase phase noise measurement sensitivity. A critical issue of phase non-linearity in wide-band phase noise measurement is studied during practical system realization. Substrate attachment process for longer delay line printed circuit board (PCB) over metallic housing has been proposed for DLD based phase noise measurement system development in details. Conventional lead or lead free reflow methods for PBC attachment over metal base have void formation issue for large surface area. Phase nonlinearity effect has been analyzed for its importance in measurement sensitivity. Vapour Phase Assembly (VPA) at 250 °C is suggested in case of long track PCBs for avoidance of any phase mismatch. Finally, non-linearity result of conventional reflow are compared with VPA substrate attachment. Examination of non-linearity reduction within ± 20° for improving measurement sensitivity is applicable for narrow and wideband phase noise analyzers. [ABSTRACT FROM AUTHOR]

Abstract Prediabetes and related complications constitute significant public health burdens globally. As an indicator closely associated with abnormal glucose metabolism and atherosclerosis, the utility of Pulse Pressure Index (PPI) as a prediabetes risk marker has not been explored. We performed a retrospective cohort analysis to investigate this putative association between PPI and prediabetes hazard. Our analysis encompassed 183,517 Chinese adults ≥ 20 years registered within the Rich Healthcare Group 2010–2016. PPI was defined as (systolic blood pressure − diastolic blood pressure)/systolic blood pressure. The relationship between PPI and prediabetes risk was assessed via Cox proportional hazards regression modeling. Non-linearity evaluations applied cubic spline fitting approaches alongside smooth curve analysis. Inflection points of PPI concerning prediabetes hazard were determined using two-piecewise Cox models. During a median follow-up of 3 years (2.17–3.96 years), new-onset prediabetes was documented in 20,607 patients (11.23%). Multivariate regression analysis showed that PPI was an independent risk factor for prediabetes, and the risk of prediabetes increased by 0.6% for every 1% increase in PPI (Hazard Ratio [HR]: 1.006, 95% Confidence Interval [CI] 1.004–1.008, P

Abstract Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein–protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects.

Prediabetes and related complications constitute significant public health burdens globally. As an indicator closely associated with abnormal glucose metabolism and atherosclerosis, the utility of Pulse Pressure Index (PPI) as a prediabetes risk marker has not been explored. We performed a retrospective cohort analysis to investigate this putative association between PPI and prediabetes hazard. Our analysis encompassed 183,517 Chinese adults ≥ 20 years registered within the Rich Healthcare Group 2010–2016. PPI was defined as (systolic blood pressure − diastolic blood pressure)/systolic blood pressure. The relationship between PPI and prediabetes risk was assessed via Cox proportional hazards regression modeling. Non-linearity evaluations applied cubic spline fitting approaches alongside smooth curve analysis. Inflection points of PPI concerning prediabetes hazard were determined using two-piecewise Cox models. During a median follow-up of 3 years (2.17–3.96 years), new-onset prediabetes was documented in 20,607 patients (11.23%). Multivariate regression analysis showed that PPI was an independent risk factor for prediabetes, and the risk of prediabetes increased by 0.6% for every 1% increase in PPI (Hazard Ratio [HR]: 1.006, 95% Confidence Interval [CI] 1.004–1.008, P < 0.001). This association was non-significant for PPI ≤ 37.41% yet exhibited a sharp upsurge when PPI surpassed 37.41% (HR: 1.013, 95% CI 1.005–1.021, P = 0.0029). Our analysis unveils a positive, non-linear association between PPI and future prediabetes risk. Within defined PPI ranges, this relationship is negligible but dramatically elevates beyond identified thresholds. [ABSTRACT FROM AUTHOR]

This study investigates the intricate relationship between external debt, debt service, and economic growth by using the panel data of 32 Asian Developing Economies (ADE) spanning 1995 to 2020. Employing a two-step system generalized method of moments (GMM) and a dynamic common correlated estimate (DCCE) model, the research explores key macroeconomic channels through which debt influences growth and rigorously tests for debt overhang and crowding-out effects. Findings reveal that public and private investment, total factor productivity, and national savings are pivotal channels transmitting the non-linear effect of external debt on economic growth. Notably, only private and public investment convey the non-linear effects of debt service to economic growth, while productivity and savings convey the linear effect. Evidence of debt overhang and crowding-out effects is identified in the sampled economies. The study suggests strategic measures for developing countries, emphasizing the productive use of accumulated debt, enhanced debt management, and timely project completion. Furthermore, it advocates for fostering economic growth through increased productivity, domestic savings, and private sector expansion to reduce dependence on foreign debt, facilitating both debt repayment and economic self-sufficiency. Plain language summary: This study explores the intricate links among external debt, debt service, and economic growth in 32 Asian Developing Economies from 1995 to 2020. Using advanced statistical methods like the two-step system generalized method of moments (GMM) and a dynamic common correlated estimate (DCCE) model, the research investigates various macroeconomic pathways, specifically testing for non-linear effects such as debt overhang and crowding-out. Key findings emphasize the significance of public and private investment, total factor productivity, and national savings as pivotal channels for the non-linear impact of external debt on economic growth. Notably, the study reveals that private and public investment exhibit non-linear effects in response to debt service, while productivity and savings show linear effects. The research recommends strategic approaches for developing countries, focusing on judicious debt management, timely project completion, and initiatives to boost productivity, domestic savings, and private sector growth, thereby reducing reliance on foreign debt. Acknowledging valuable insights, the study recognizes limitations in the available data from 32 countries and emphasizes the need for further investigation into mediating and moderating variables in the relationship between external debt and economic growth. Particularly in the context of foreign debt financing policies, the study underscores the importance of exploring threshold values for negative impacts on transmission channels, suggesting avenues for future research to provide a more nuanced understanding of the dynamics involved. In essence, the study offers valuable insights into the nuanced relationship between external debt and economic growth, along with strategic recommendations for sustainable development in developing economies. [ABSTRACT FROM AUTHOR]

Increased risks of economic policy uncertainty and overexploitation of natural resources exist in China. At the same time, the growth rate of urban residents' consumption has generally declined. The paper analyses the role of economic policy uncertainty (EPU) and natural resource exploitation on the urban residents' consumption in China. Based on the data from the first quarter of 2002 to the third quarter of 2021, the paper uses a nonlinear autoregressive distributed lag model to verify the asymmetric effects. Then the paper constructs a time-varying parameter vector autoregressive model with stochastic volatility term to analyze the nonlinear responses. Impulse response analysis was used to further explain the relationship between the three. The negative impact of rising EPU on urban residents' consumption is larger than its reduction. Negative shocks to natural resource development increase the urban residents' consumption. Positive shocks reduce the urban residents' consumption. There is a time-varying non-linear effect of EPU and natural resource development on urban residents' consumption in China. The negative impact of EPU on urban consumption has been further exacerbated by major crises such as the financial crisis, COVID-19 and the post-crisis period. The negative impact of natural resource development diminished after the government introduced industrial upgrading policies and environmental regulations. This study provides constructive suggestions for the optimization of economic policies and the improvement of urban consumption. This study also enriches consumer theory and provides new evidence for the resource curse hypothesis. [ABSTRACT FROM AUTHOR]

Rainfall serves as a lifeline for crop cultivation in many agriculture-dependent countries including India. Being spatio-temporal data, the forecasting of rainfall becomes a more complex and tedious process. Application of conventional time series models and machine learning techniques will not be a suitable choice as they may not adequately account for the complex spatial and temporal dependencies integrated within the data. This demands some data-driven techniques that can handle the intrinsic patterns such as non-linearity, non-stationarity, and non-normality. Space–Time Autoregressive Moving Average (STARMA) models were highly known for its ability to capture both spatial and temporal dependencies, offering a comprehensive framework for analyzing complex datasets. Spatial Weight Matrix (SWM) developed by the STARMA model helps in integrating the spatial effects of the neighboring sites. The study employed a novel dataset consisting of annual rainfall measurements spanning over 50 (1970–2019) years from 119 different locations (grid of 0.25 × 0.25 degree resolution) of West Bengal, a state of India. These extensive datasets were split into testing and training groups that enable the better understanding of the rainfall patterns at a granular level. The study findings demonstrated a notable improvement in forecasting accuracy by the STARMA model that can exhibit promising implications for agricultural management and planning, particularly in regions vulnerable to climate variability. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) and abnormal / anomaly detection have significant applications for health monitoring in a smart environment. Abnormal / anomaly prediction during daily activities helps to indicate whether the person is healthy or having behavior issues that need assistance. HAR has been accomplished using deep learning approaches. The activation functions employed in deep learning models have a significant impact on the training model and the reliability of the model. Several activation functions are developed for deep learning models. Nevertheless, most existing activation functions suffer from the dying gradient problem and lack of utilization of large negative input values. This work proposes a novel activation function called Optimized Parametric Hyperbolic Tangent Swish (OP-Tanish), which is non-monotonicity, unbounded in both negative and positive directions, smooth variations, and introduces a higher degree of non-linearity than Relu and other state-of-art activation functions. We test this activation function by training on customized shallow 1D- Convolutional Neural Networks (CS1DCNN) for adequate recognition of human activities and anomaly detection. The contributions are compared to the state-of-the-art activation functions on benchmark datasets (UCI-HAR, PAMPA2, Opportunity, and Daphnet Gait HAR datasets; UP-FALL and Simulated Activities of Daily Living (SIMADL) anomaly datasets), namely: ReLu and its variants, SWISH, MISH, and LiSHT. The proposed OP-Tanish activation function outperforms other state-of-art activation functions with an accuracy of 99.58%, 99.58%, 95.14%, and 97.79%over UCI, Opportunity, PAMPA2, and Daphnet Gait datasets, respectively, and achieved an accuracy of 97.28% and 98% on UP-Fall and SIMADL dataset. [ABSTRACT FROM AUTHOR]

Deep Mutational Scanning (DMS) assays are powerful tools to study sequence-function relationships by measuring the effects of thousands of sequence variants on protein function. During a DMS experiment, several technical artefacts might distort non-linearly the functional score obtained, potentially biasing the interpretation of the results. We therefore tested several technical parameters in the deepPCA workflow, a DMS assay for protein–protein interactions, in order to identify technical sources of non-linearities. We found that parameters common to many DMS assays such as amount of transformed DNA, timepoint of harvest and library composition can cause non-linearities in the data. Designing experiments in a way to minimize these non-linear effects will improve the quantification and interpretation of mutation effects. [ABSTRACT FROM AUTHOR]

Understanding and acting upon risk is notably challenging, and navigating complexity with understandings developed for stable environments may inadvertently build a false sense of safety. Neglecting the potential for non-linear change or "black swan" events - highly impactful but uncommon occurrences - may lead to naive optimisation under assumed stability, exposing systems to extreme risks. For instance, loss aversion is seen as a cognitive bias in stable environments, but it can be an evolutionarily advantageous heuristic when complete destruction is possible. This paper advocates for better accounting of non-linear change in decision-making by leveraging insights from complex systems and psychological sciences, which help to identify blindspots in conventional decision-making and to develop risk mitigation plans that are interpreted contextually. In particular, we propose a framework using attractor landscapes to visualize and interpret complex system dynamics. In this context, attractors are states toward which systems naturally evolve, while tipping points - critical thresholds between attractors - can lead to profound, unexpected changes impacting a system's resilience and well-being. We present four generic attractor landscape types that provide a novel lens for viewing risks and opportunities, and serve as decision-making contexts. The main practical contribution is clarifying when to emphasize particular strategies - optimisation, risk mitigation, exploration, or stabilization - within this framework. Contextappropriate decision making should enhance system resilience and mitigate extreme risks. [ABSTRACT FROM AUTHOR]

This perspective article reviews how the chemometrics community approached non-linear methods in the early years. In addition to the basic chemometric methods, some methods that fall under the term "machine learning" are also mentioned. Thereafter, types of non-linearity are briefly presented, followed by discussions on important aspects of modeling related to non-linear data. Lastly, a simulated data set with non-linear properties is analyzed for quantitative prediction and batch monitoring. The conclusion is that the latent variable methods to a large extent handle non-linearities by adding more linear combinations of the original variables. Nevertheless, with strong nonlinearities between the X and Y space, non-linear methods such as Support Vector Machines might improve prediction performance at the cost of interpretability into both the sample and variable space. Applying multiple local models can improve performance compared to a single global model, of both linear and non-linear nature. When non-linear methods are applied, the need for conservative model validation is even more important. Another approach is pre-processing of the data which can make the data more linear before the actual modeling and prediction phase. [ABSTRACT FROM AUTHOR]

The stability and dynamic characteristics of the modified Spar-type offshore wind power floating foundation under the action of wind and wave coupling, based on the establishment of the dynamics model and the non-linear Mathieu equation, the boundary equations of the stable and unstable domains are solved based on Floquet theory, the Mathieu stability map is obtained, and the numerical simulation verifies the displacement between the stable and unstable domains with time. The results show that: in the same sea state background, When the ratio of heave motion frequency to pitch motion natural frequency is close to 2:1, even if the excitation amplitude is small, it will cause the pitch parameter excitation motion, increasing the system damping can reduce the unstable area, so as to effectively suppress that pitch parametrically-excited vibration of the float foundation. Under level 6 sea conditions, the maximum heave displacement of the floating foundation reaches 2.007 m and the maximum pitch angular displacement reaches 3.9534°, but it is still within the permissible range. [ABSTRACT FROM AUTHOR]

This paper examines the nonlinear effects of public debt on economic growth in Asian developing economies through the application of panel Generalized Method of Moments (GMM) regressions and panel vector autoregression (VAR) models. Our analysis reveals a statistically significant nonlinear (i.e., an inverted U-shape) relationship between public debt (as a percentage of GDP) and GDP per capita growth. The turning points are identified at 52 percent for all Asian developing economies and 50 percent for Asian coastal developing economies. Asymmetric mutual feedback effects are observed between economic growth and public debt, contingent on the level of public debt. These two-way effects are both statistically significant and more pronounced when public debt surpasses its threshold level. Additionally, our results unveil geographical (cross-country) heterogeneity in the mutual feedback effects. These findings carry significant policy implications, emphasizing the necessity of employing region-specific debt threshold levels and asymmetric response coefficients in the analysis of public debt policies. [ABSTRACT FROM AUTHOR]

Government expenditures may tend to defray to increasing social needs with economic growth and development. This study focused on the effects of government expenditure on unemployment and conducted empirical research on the possible positive or negative effects of government expenditure on unemployment. This study employed the asymmetric causality and Kernel-based Regulated least squares (KRLS) method with machine learning to examine the effect of government expenditure on the unemployment rate for two Baltic countries (Estonia and Latvia) for annual time frequency data from 1995 to 2019. Asymmetric causality findings show that increase government final government consumption has a decreasing impact on unemployment in the Estonia. In addition to this, no enough evidence for Abrams Curve from Estonia and Latvia. Additionally, the government's final consumption and expenditure effects have different signs at different quantiles. KRLS quantile coefficient findings point out that V-shape and inverse V-shape curve instead of the Abrams Curve for our sample. [ABSTRACT FROM AUTHOR]

IntroductionWith the rapid optimization and evolution of various neural networks, the control problem of robotic arms in the area of automation control has gradually received more attention.MethodsTo improve the control performance of robotic arms under complex dynamic models, this study proposes an adaptive affective radial basis function network control strategy. Firstly, the kinematic and dynamic mathematical models of the tendon driven robotic arm are constructed. Then, by integrating the affective computing model and the radial basis function network, an adaptive affective radial basis function network control algorithm is constructed.Results and DiscussionThe research results indicate that the designed algorithm significantly outperforms the other two compared algorithms in terms of control accuracy and stability. In benchmark performance testing, the designed algorithm has a error accuracy of up to 0.97 and a steady state of up to 0.95. In the simulation results, the maximum torque change of the designed algorithm is only 3.8 Nm, which is much lower than other algorithms. In addition, the control error fluctuation range of this algorithm is between −0.001 and 0.001, almost close to zero error. This study provides a new optimization strategy for precise control of tendon driven robotic arms, and also opens up new avenues for the application of artificial intelligence technology in complex nonlinear system control.

We are witnessing an explosion in the concept of the Internet of Things (IoT) as smart objects become more important in our daily lives to make it easier to achieve our goals. It is obvious that the application areas of IoT are extremely diverse. Indeed, several sectors will be deeply impacted by IoT applications. Of which, security in the Internet of Things (IoT) has become a major concern due to increasing vulnerabilities and threats. Connected devices, often with limited security capabilities, have become attractive targets for attackers. Vulnerabilities in IoT devices, whether resulting from unmodified default configurations, lack of regular updates, or insecure communication protocols, expose these systems to potential risks. Cryptography, as a discipline dedicated to securing communications, uses advanced mathematical techniques to guarantee the confidentiality of electronic exchanges by making the data incomprehensible to unauthorized persons.In this paper, we show an in-depth exploration of the generation of new S-boxes using the genetic algorithm. These new S-boxes are dynamically generated with the new genetic algorithm-based approach. This new method offers adaptive optimization, iteratively exploring the solution space to identify robust S-boxes, thereby strengthening the security of the encryption system. The study continues with the evaluation of these S-boxes through dedicated metrics, allowing an in-depth analysis of their performance in terms of resistance to cryptographic attacks and preservation of substitution properties. Finally, this evaluation extends to the incorporation of these newly generated S-boxes into the AES encryption algorithm, where different dimensions, such as cryptanalysis resistance, scene visibility, and other performance levels, will be examined.

The need to prioritize sustainable development is expanding in light of the world's environmental concerns. To address these concerns, entrepreneurship is essential as a catalyst for inventions, economic expansion, and social change. Entrepreneurship activities have direct and indirect effects in the face of environmental risks and uncertainties. This study addresses gaps in understanding entrepreneurship's non-linear and complementarity effects, particularly opportunity-driven entrepreneurship and information and communication technologies (ICT), on the quality of the environment in Saudi Arabia. The dynamic ordinary least squares (DOLS) method is used to estimate long-run relationships. Saudi Arabia will be the perfect context for sustainability because the country has prioritized sustainability through its Vision 2030, and the Saudi government has substantially supported entrepreneurship. The main contribution of this paper to the existing literature is evident in its examination of the quadratic relationships between both opportunity-driven entrepreneurship and ICT diffusion, including ICT access, use, and skills, on environmental quality. In addition, the study delves into the ICT diffusion's modulating effects on the nexus of opportunity entrepreneurship with environmental quality, providing insights into how these factors can effectively improve environmental quality. The findings show that opportunity entrepreneurship and ICT diffusion initially deteriorate environmental quality before leading to improvements as their levels mature in the economy. Moreover, interactions between ICT proxies and opportunity entrepreneurship yield mixed effects, with negative net effects on CO2 emissions and ecological footprint countered by positive net effects on the environmental performance index. These findings highlight the dual role of ICT diffusion as a contributor to environmental challenges and a potential solution, depending on the level of its diffusion and interaction with entrepreneurial activities. Therefore, policymakers should create plans that encourage and direct business activity toward more environmentally friendly methods. They should also consider the short- and long-term effects of growing digital technologies on environmental sustainability and how they might revolutionize how entrepreneurship and sustainability goals are aligned.

Generalized frequency division multiplexing (GFDM) and non-orthogonal multiple access (NOMA) schemes are currently regarded as promising techniques for meeting the requirements of sixth-generation (6G) communication systems. While significant attention has been devoted to studying these techniques, specific scenarios still need to be explored. Considering that one essential aspect of any communication system is the utilization of high-power amplifiers, which are typically modeled as non-linear systems, in this paper, we propose a novel methodology to analyze the bit error probability (BEP) of NOMA-GFDM systems operating on memoryless non-linear system. To account for the non-linear distortions induced by high-power amplifiers, a polynomial model is employed, and new theoretical expressions are developed to compute the BEP of the system. Specifically, exact BEP expressions for a two-user downlink NOMA-GFDM system are provided. The numerical results obtained demonstrate that non-linear distortion significantly degrades system performance.

The purpose of the written study is to study the relationship between the qualifications of the CFO and the nonlinear system of the firm’s internal control system. We used three indicators of the competence of the CFO: financial education, accounting, seniority, and education. Using the model of Kazakhstan companies registered on the stock exchange in 2023. In the methodology of the internal control system, in which stimuli cause a reverse response, the internal control system should be recognized as a nonlinear approach that can have a high impact if it has little impact. The results of this study show a negative relationship between the financial, accounting experience of a CFO and work experience under a nonlinear system of internal control, which indicates that firms with high-quality CFO are facing a nonlinear internal control system in business. A business management team with a large number of employees is associated with an effective internal control system. In addition, the political preferences of senior management directly and indirectly affect the quality of financial statements through the internal control system. Several reliability tests confirm our main results. The results show that a capable CFO can effectively implement a suitable subsystem. The results of the study of a nonlinear system of internal control in business, in terms of improving individual elements of internal control in companies, can be used by members of audit firms.

‎We present an enhanced approach to solving the combined non-linear time-dependent Burgers-Fisher equation‎, ‎which is widely used in mathematical biology and has a broad range of applications‎. ‎Our proposed method employs a modified version of the finite element method‎, ‎specifically the virtual element method‎, ‎which is a robust numerical approach‎. ‎We introduce a virtual process and an Euler-backward scheme for discretization in the spatial and time directions‎, ‎respectively‎. ‎Our numerical scheme achieves optimal error rates based on the degree of our virtual space‎, ‎ensuring high accuracy‎. ‎We evaluate the efficiency and flexibility of our approach by providing numerical results on both convex and non-convex polygonal meshes‎. ‎Our findings indicate that the proposed method is a promising tool for solving non-linear time-dependent equations in mathematical biology‎.

В статье представлено исследование вычислительной эффективности параллельной версии численного алгоритма для решения уравнения Риккати с производной дробного перменного порядка типа Герасимова-Капуто. Численный алгоритм представляет собой нелокальную неявную конечно-разностную схему, которая сводится к системе нелинейных алгебраических уравнений и решается с помощью модифицированного метода Ньютона. Нелокальность численной схемы создает высокую вычислительную нагрузку на вычислительные ресурсы, из-за чего возникает необходимость в реализации эффективных параллельных алгоритмов их решения. Исследуемый на эффективность численный алгоритм реализован на языке C из-за его универсальности при работе с памятью. Распаралеливание проводилось с помощью технологии OpenMP. Проводится серия вычислительных экспериментов на вычислительном сервере NVIDIA DGX STATION (Институт математики имени В.И. Романовского, г. Ташкент, Узбекистан) и ноутбуке HP Pavilion Gaming Laptop Z270X, где решалась задача Коши для дробного уравнения Риккати с непостоянными коэффициентами. На основе среднего времени вычисления вычисляются: ускорение, эффективность и стоимость алгоритма. Из анализа данных видно, что OpenMP параллельная программная реализация нелокальной неявной конечно-разностной схемы показывает ускорение работы от 9-12 раз в зависимости от количества задействованных ядер CPU.

AbstractSocioeconomic hardships are often advocated as the drivers of radicalization, but the existing research shows mixed evidence for this relationship. This study argues that socioeconomic hardships should increase the likelihood of radicalization only for sufficiently religious people, thereby implying a non-linear relationship. The study suggests that as a result of the lower opportunity costs of extreme acts during socioeconomic hardships, the likelihood of committing or supporting such acts for obtaining religiously inspired mental rewards should be the highest for the economically disadvantaged, religious individuals. The study tests this hypothesis through the non-linear threshold regression method developed by Hansen (2000), using survey data from the Khyber Pakhtunkhwa province of Pakistan. Results indicate that radicalization has a statistically significant positive relationship with indicators of the individual level socioeconomic conditions such as the perceptions of poor economic prospects, individual relative deprivation, injustice, and inequality only above the religiosity threshold. These findings support the hypothesis that socioeconomic hardships (or the perception of these) drive radicalization only in sufficiently religious people. This implies that apart from security-centric approaches, radicalization can also be deterred by increasing the opportunity costs of political violence through socioeconomic improvements. Moreover, promoting balanced/diverse religious views may also help check radicalization.

The global movement for ecosystem restoration has gained momentum in response to the Bonn Challenge (2010) and the UN Decade on Ecosystem Restoration (UNDER, 2021–2030). While several science‐based guidelines exist to aid in achieving successful restoration outcomes, significant variation remains in the outcomes of restoration projects. Some of this disparity can be attributed to unexpected responses of ecosystem components to planned interventions.Given the complex nature of ecosystems, we propose that concepts from Complex Systems Science (CSS) that are linked to non‐linearity, such as regime shifts, ecological resilience and ecological feedbacks, should be employed to help explain this variation in restoration outcomes from an ecological perspective.Our framework, Explore Before You Restore, illustrates how these concepts impact restoration outcomes by influencing degradation and recovery trajectories. Additionally, we propose incorporating CSS concepts into the typical restoration project cycle through a CSS assessment phase and suggest that the need for such assessment is explicitly included in the guidelines to improve restoration outcomes.To facilitate this inclusion and make it workable by practitioners, we describe indicators and methods available for restoration teams to answer key questions that should make up such CSS assessment. In doing so, we identify key outstanding science and policy tasks that are needed to further operationalize CSS assessment in restoration.Synthesis and applications. By illustrating how key Complex Systems Science (CSS) concepts linked to non‐linear threshold behaviour can impact restoration outcomes through influencing recovery trajectories, our framework Explore Before You Restore demonstrates the need to incorporate Complex Systems thinking in ecosystem restoration. We argue that inclusion of CSS assessment into restoration project cycles, and more broadly, into international restoration guidelines, may significantly improve restoration outcomes. [ABSTRACT FROM AUTHOR]

Never has the domain of sports coaching been so inundated with secondary information. In high-performance contexts, for example, coaches are routinely presented with detailed reports specifying features about an athlete's or team's performance. Here, we question whether such detailed secondary information has led us to know too much, obscuring what the world has to share directly with us. To over-rely on secondary information is to narrow in on certainty, on cause-effects that are oft-espoused through de-contextualised 'performance' tests and metrics. This indirect approach eschews opening up to uncertainty, to ongoing inquiry embedded in primary experience. For where certainty risks closures, uncertainty opens to the possibility of carrying on with and alongside others. We explore this thesis through the reflections of an Olympic Canoe Slalom coach, meandering through three sections: (i) on paying attention; (ii) on knowing better; (iii) on guidance without specification. In presenting this thesis, we hope to encourage others - in sports coaching and beyond - to embrace an ethos of not-knowing, opening up to the 'goings on' of what interests them, actively attending and directly responding with genuine care and curiosity. Indeed, while embracing an ethos of not-knowing can be unsettling, vulnerably exposing oneself to changing power relations in a world perpetually on the move, it can facilitate primary experience of the surrounding ecology. The accompanying growth of responsiveness to one's surroundings emerges from listening to what it has share, joining in conversation to find ways of carrying on. It is in this responsiveness, we contend, that a wisdom can be found; a wisdom of not-knowing. [ABSTRACT FROM AUTHOR]

In this study, the cumulative buckling deformation process is evaluated for a stiffened panel subjected to two different uniaxial cyclic loading conditions, namely compressive loading only and alternating tensile-compressive loading. The impact of the cyclic load on the ultimate strength of the panel is then estimated. It should be noted that the occurrence of permanent deformations could be simulated by finite element analysis using a suitable model capable of correctly capturing the deformation components. The results show that the structure accumulates a deformation that appears to grow almost linearly therefore, consequently, it is realistic to expect that if a large number of load cycles of this magnitude are imposed, the accumulated plastic deformation can be quite severe. Furthermore, it is found that these residual deformations, caused by an entity of the cyclic load lower than the design limit, affect the tensile strength of the stiffened panel. Finally, the effects on the numerical evaluation of the ultimate strength of the structure are shown. [ABSTRACT FROM AUTHOR]

Linear amplitude sweep tests have been demonstrated to have good potential in being used for the evaluation of self-healing properties of neat and polymer-modified bituminous binders. Past research works, however, have neglected the effects of material non-linearity and thixotropy. This implies that the whole material integrity loss is attributed to damage and all restoration to self-healing, thus resulting in the possible overestimate of both damage and self-healing when evaluating the fatigue performance of materials. In the study described in this paper, specific experimental and analytical methods were adopted with the purpose of separating non-linearity and thixotropy in LAS healing tests. Non-linearity was assessed by means of multiple strain sweep tests carried out to determine the material non-linear viscoelastic moduli at specific testing temperatures. Thixotropy was considered by coupling self-healing LAS testing with a purposely defined test in which loading was applied to the undamaged material after a rest period equal to that applied in self-healing tests. Obtained results were processed by means of an analytical approach based on the simplified viscoelastic continuum damage model. Quantification of self-healing included the determination of material integrity and damage parameters recovered after the rest period. Such parameters, calculated by excluding time-dependent and non-linear biasing effects, can be used as straightforward indicators of the self-healing potential of neat and polymer-modified bituminous binders. Moreover, obtained results substantiate the concept that time–temperature superposition is still applicable when non-linearity is incorporated into the simplified viscoelastic continuum damage model. [ABSTRACT FROM AUTHOR]

This article integrates the exposure and adaptation hypotheses concerning gambling engagement by quantitatively modeling, at the community level, the non-linear relationship between gamblers' engagement and their years of gambling experience and identifies the years of experience corresponding to the maximum/minimum engagement. Gambling engagement indicators were based on the odds of three metrics concerning casino visits, gambling budgets, and gambling affordability being beyond predefined thresholds. Empirical data were collected offline and online (n = 206 and 481) in Macao. Curve fitting of these indicators against gamblers' years of experience found that the odds of casino visit frequency being less than daily and the gambling budget being greater than both the first and second quartiles followed up-maximum-down quadratic curves over gamblers' experience. Their maxima were at around 13.3, 22.0, and 22.4 years, respectively. In conclusion, these indicators are non-linearly and bi-directionally related to gamblers' years of experience as per the aforesaid curves, i.e. the two hypotheses take turns to prevail over gamblers' lives and are thus integrated temporally. Unprecedentedly, such curves quantitatively model and quantify the timing, magnitude, maximization, and minimization of these indicators' variations over gamblers' lives, which matter to governments, the public, therapeutic/rehabilitative professionals, and gambling operators. [ABSTRACT FROM AUTHOR]

The methodology of the correct identification of the aerodynamic coefficients of the aircraft is considered, namely the determination of the partial derivatives of the aerodynamic forces and moments of the aircraft according to its state variables. The method of determining static (balancing) dependencies from short-term dynamic modes of a natural experiment is also presented. The effectiveness of the proposed approach is confirmed by the practice of determining the aerodynamic coefficients and aerodynamic characteristics of various aircraft. [ABSTRACT FROM AUTHOR]

The finite number of pixels in a silicon photomultiplier (SiPM) limits its dynamic range to light pulses up to typically 80% of the total number of pixels in a device. Correcting the non-linear response is essential to extend the SiPM's dynamic range. One challenge in determining the non-linear response correction is providing a reference linear light source. Instead, the single-step method used to calibrate PMTs is applied, based on the difference in responses to two light sources. With this method, the response of an HPK SiPM (S14160-1315PS) is corrected to linearity within 5% while extending the linear dynamic range by a factor larger than ten. The study shows that the response function does not vary by more than 5% for a variation in the operating voltage between 2 and 5 V overvoltage in the gate length between 20 and 100 ns and for a time delay between the primary and secondary light of up to 40 ns. [ABSTRACT FROM AUTHOR]

For the calibration of linear scales, comparators are generally used. Comparators are devices that enable the movement of an evaluation apparatus over a calibrated scale along a linear base with high precision. The construction of a comparator includes a movable carriage that carries the device for the evaluation of the position of the given edge of the line scale relative to the beginning of the scale. In principle, it involves a camera capturing the scale of the measurer, where the position of the camera's projection center is measured using an interferometer. This article addresses the development of a comparator assembled from low-cost components, as well as the description of systematic influences related to the movement of individual parts of the system, such as the inclination and rotation of the camera and directional and height deviations during the carriage's movement. This article also includes an evaluation of the edge of the given scale with subpixel accuracy, addressing distortion elimination and excluding the influences of impurities or imperfections on the scale. The proposed solution was applied to linear-scale measurers, such as leveling rods with coded and conventional scales and measuring tapes. The entire process of measurement and evaluation was automated. [ABSTRACT FROM AUTHOR]

We present an enhanced approach to solving the combined non-linear time-dependent Burgers-Fisher equation, which is widely used in mathematical biology and has a broad range of applications. Our proposed method employs a modified version of the finite element method, specifically the virtual element method, which is a robust numerical approach. We introduce a virtual process and an Euler-backward scheme for discretization in the spatial and time directions, respectively. Our numerical scheme achieves optimal error rates based on the degree of our virtual space, ensuring high accuracy. We evaluate the efficiency and flexibility of our approach by providing numerical results on both convex and non-convex polygonal meshes. Our findings indicate that the proposed method is a promising tool for solving non-linear time-dependent equations in mathematical biology. [ABSTRACT FROM AUTHOR]

Nowadays, epidemiological modeling is applied to a wide range of diseases, communicable and non-communicable, namely AIDS, Ebola, influenza, Dengue, Malaria, Zika. More recently, in the context of the last pandemic declared by the World Health Organization (WHO), several studies applied these models to SARS-CoV-2. Despite the increasing number of researches using spatial analysis, some constraints persist that prevent more complex modeling such as capturing local epidemiological dynamics or capturing the real patterns and dynamics. For example, the unavailability of: (i) epidemiological information such as the frequency with which it is made available; (ii) sociodemographic and environmental factors (e.g., population density and population mobility) at a finer scale which influence the evolution patterns of infectious diseases; or (iii) the number of cases information that is also very dependent on the degree of testing performed, often with severe territorial disparities and influenced by context factors. Moreover, the delay in case reporting and the lack of quality control in epidemiological information is responsible for biases in the data that lead to many results obtained being subject to the ecological fallacy, making it difficult to identify causal relationships. Other important methodological limitations are the control of spatiotemporal dependence, management of non-linearity, ergodicy, among others, which can impute inconsistencies to the results. In addition to these issues, social contact, is still difficult to quantify in order to be incorporated into modeling processes. This study aims to explore a modeling framework that can overcome some of these modeling methodological limitations to allow more accurate modeling of epidemiological diseases. Based on Geographic Information Systems (GIS) and spatial analysis, our model is developed to identify group of municipalities where population density (vulnerability) has a stronger relationship with incidence (hazard) and commuting movements (exposure). Specifically, our framework shows how to operate a model over data with no clear trend or seasonal pattern which is suitable for a short-term predicting (i.e., forecasting) of cases based on few determinants. Our tested models provide a good alternative for when explanatory data is few and the time component is not available, once they have shown a good fit and good short-term forecast ability.

Abstract The on-site P-wave earthquake early warning (EEW) based on the site-specific ratios of S-waves to P-waves has been applied to large-sized offshore earthquakes, and the efficiency of the method has been validated. However, the method requires the P-waves including earthquake ground motions radiated from a large slip area while avoiding the inclusion of S-waves. In this study, we investigated the applicability of the on-site P-wave EEW method for ground motions near an earthquake source fault region, using strong-motion data observed during the 2016 Kumamoto earthquake sequence in Japan. At first, we examined the appropriate time-window length following the arrival of the P-waves. As a result, P-waves with a time-window length of 2.56 s after the arrival at most strong-motion stations were required at least to predict appropriately S-waves for the 2016 Kumamoto earthquake sequence, including the large-sized earthquakes. On the other hand, in the case of the large-sized earthquake as the mainshock (Mj 7.3), the method can predict within a brief time of 0.5 to 2 s in the operational use that strong ground motions exceeding a certain threshold (e.g., acceleration of 150 cm/s2) will come. Moreover, we found that the method was not strongly affected by the non-linearity of soil deposits due to strong ground motions during the 2016 Kumamoto earthquake sequence. The variability of the relationship between P- and S-waves at the seismic bedrock influenced by the source and path effects is larger than the variability of the relationships between P-/S-waves at the seismic bedrock and at the ground surface by the site effects, and therefore, it hides the effect of the non-linearity of soil deposits. Graphical Abstract

In this study, this paper presents a new method for linearizing thermocouple data using Python and compares the performance of higher-order polynomial models in achieving linearization. It involves fitting a non-linear model to the thermocouple data using the curve fit function from Python and then calculating the linearized temperature values using the optimized parameters. The paper also presents a comparative analysis of different polynomial models, ranging from 3rd to 12th order, and evaluates their performance in achieving linearization. The results show that higher-order polynomial models generally perform better than lower-order models in achieving linearization, but also have a higher risk of overfitting. The paper concludes that the presented method provides an effective way of linearizing thermocouple data using Python and that the choice of polynomial model should be carefully considered based on the data characteristics and the desired level of accuracy.