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Traditionally, CMOS is not considered a light emitting device. Similar to laser or LED, and microwave devices, Photonic Millimeter Wave CMOS transistors are both light as well as microwave emitting devices. Ever since AMD releases the 64-bits Athlon processor in around 2005–2006, the clock speed race for ASIC and processors has been stopped. Wireless tools always replace wired electronic tools. The very large number (trillions) and multiple layers of metal wires seen in ASICs and ULSI significantly slow down the processors. Using microwave photonic CMOS and wireless ULSI, clock speed races may resume, reaching far beyond 6G wireless generations. [ABSTRACT FROM AUTHOR]

Spacecraft, launch vehicles, and hypersonic craft utilize thermal protection systems (TPS) with tiles to prevent thermal hazards at high speeds. Traditionally, manufacturers attach tiles using silicone adhesives. This paper proposes a bistable compliant clamp for secure, rapid tile exchange on engineered surfaces without chemical adhesives. Computational design optimization implemented a hyperelastic solid model with yield constraint, and applied sequential sweeps of geometric parameters. The optimized clamp was prototyped in polylactic acid (PLA, σ Y = 43.5 MPa, E = 1.014 GPa), with retention force measured at 53.41 N for a device with a mass of 47.3 g and footprint of 789 mm2. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of electric vehicles (EVs) in China, the landscape of transportation carbon emissions has undergone significant changes. However, research on the impact of the built environment on the carbon emissions of mixed traffic from gasoline and electric vehicles remains sparse. This paper focuses on urban traffic scenarios with a mix of gasoline and electric vehicles, analyzing the spatiotemporal distribution of carbon emissions from both types of vehicles and their nonlinear association with the built environment. Utilizing trajectory data from gasoline-powered and electric taxis in Chengdu, China, we establish segment-level carbon emission estimation models based on the vehicle-specific power of gasoline vehicles and the equivalent energy consumption of electric vehicles. Subsequently, we employ the XGBoost algorithm and SHapley Additive ExPlanation (SHAP) to analyze the nonlinear relationships between 13 built environment variables and vehicle carbon emissions. This paper reveals that most built environment variables exhibit nonlinear relationships with traffic carbon emissions, with five factors—population density, road density, residential density, metro accessibility, and the number of parking lots—having a significant impact on road carbon emissions. Finally, we discuss the carbon reduction benefits of EV adoption and propose policy recommendations for low-carbon initiatives in the transportation field. [ABSTRACT FROM AUTHOR]

The double-blinded randomized trial is considered the gold standard to estimate the average causal effect (ACE). The naive estimator without adjusting any covariate is consistent. However, incorporating the covariates that are strong predictors of the outcome could reduce the issue of unbalanced covariate distribution between the treated and controlled groups and can improve efficiency. Recent work has shown that thanks to randomization, for linear regression, an estimator under risk consistency (e.g. Random Forest) for the regression coefficients could maintain the convergence rate even when a nonparametric model is assumed for the effect of covariates. Also, such an adjusted estimator will always lead to efficiency gain compared to the naive unadjusted estimator. In this paper, we extend this result to the competing risk data setting and show that under similar assumptions, the augmented inverse probability censoring weighting (AIPCW) based adjusted estimator has the same convergence rate and efficiency gain. Extensive simulations were performed to show the efficiency gain in the finite sample setting. To illustrate our proposed method, we apply it to the Women's Health Initiative (WHI) dietary modification trial studying the effect of a low-fat diet on cardiovascular disease (CVD) related mortality among those who have prior CVD. [ABSTRACT FROM AUTHOR]

An analytical method is introduced to assess the susceptibility of radio altimeter (RA) receivers to adjacent-band fifth-generation (5G) signal interference and to quantify its impact on RA performance. The power-series method is employed to analyze the intermediate frequency (IF) signal gain compression effect of 5G signal interference on RA receivers. A behavioral-level simulation model of the RA receiver's radio frequency (RF) front-end is constructed based on the advanced design system (ADS), and a 5G signal injection simulation is performed. The simulation results indicate that 5G signals can induce nonlinear effects in the RF front-end circuit of the RA, leading to IF signal gain compression, thereby affecting the subsequent signal processing of RA receivers. The interference effect on the RA receiver is influenced by factors such as the power and frequency of the 5G interference signal. To investigate this, an interference injection test was conducted on a specific RA receiver to validate the aforementioned interference mechanisms. The test results indicate that when the average power of the injected 5G signal at a frequency of 4000 MHz reaches −16 dBm, the IF signal power is significantly reduced. As the power of the 5G signal increases, this nonlinear effect becomes more pronounced. Furthermore, the height error ratio significantly increases, with consistent trends observed across different test frequencies. The interference threshold for the RA is lower when the signal frequency is closer to the RA operational signal frequency. Our research results demonstrate the efficacy of this method, providing a reference basis for studies on interference mechanisms and the evaluation of interference effects related to RA receivers within the electromagnetic environment of 5G signals. [ABSTRACT FROM AUTHOR]

System nonlinearity due to hardware impairments has always been a challenging issue. Distortion cancellation and iterative detection based receivers such as the Bussgang Noise Cancelling (BNC) receiver are used to detect the original data in the presence of strong nonlinear (NL) effects. However, these receivers require knowledge of the system nonlinearity which is usually unknown in practical systems. Bussgang decomposition and its general form denoted Generalized Bussgang decomposition (GBD), have been commonly used to model system nonlinearity. In GBD the nonlinearity output is decomposed as the sum of uncorrelated terms of increased orders and provides spectral characteristics of the useful and distortion terms. In this paper we consider nonlinearity at the transmitter side and model it with GBD. We aim to estimate the scalar weights in the GBD to later use them at the BNC receiver. However, knowledge of the channel is required to make a reliable estimate of the NL parameters. On the other hand the pilots for channel estimation are affected by the system nonlinearity, which can preclude reliable channel estimation. Therefore, in this paper we propose a joint channel and NL parameter estimation technique by designing appropriate training signals for each estimation phase (i.e. channel estimation and NL parameter estimation). We also derive a closed form expression for the average power of residual distortion in GBD with estimated parameters to see how well this model can characterize the nonlinearity. The results show that the proposed estimation technique has good accuracy and enables quasi-ideal performance for a BNC receiver.

Multiple-Input Multiple-Output (MIMO) architectures are now widely adopted in wireless systems, providing substantial capacity benefits by harnessing spatial diversity and spatial multiplexing. Nonetheless, the large Peak-to-Average Power Ratio (PAPR) associated with common pre-processing techniques, like Singular Value Decomposition (SVD), increases the system’s susceptibility to nonlinear distortion. Conventional receiver designs that mitigate this distortion often neglect the fact that it has useful information on the transmitted data. Maximum Likelihood (ML) detection offers the capability to take advantage of the nonlinear distortion, but its inherent complexity is prohibitively high. This paper introduces a new MIMO receiver design aimed at exploiting the diversity introduced by the transmitter nonlinearities. It also provides an approximate bound on the achievable ML Bit Error Rate (BER) performance. Our results indicate that the proposed receiver can have a performance close to the ML receiver with just a few iterations.

The logistic additive regression model has been a standard method in modeling nonlinear effects for multivariate analyses of binary outcomes in the generalized additive model (GAM) framework. However, the resultant nonlinear estimate of the smooth function is interpreted as a nonproportional increment of the odds ratio in the increment of the explanatory variable. The odds ratio cannot be interpreted as an effect measure by itself; it is only interpretable as an approximation of the risk ratio when the frequency of events is low. In this article, we propose alternative nonlinear regression methods to estimate the risk ratio and risk difference directly. We propose extending Zou’s modified Poisson regression (Am J Epidemiol 159: 702–6) and Cheung’s modified least squares (Gaussian) regression (Am J Epidemiol 166: 1337–44) to the GAM framework and fitting the Poisson and Gaussian additive regression models to binary outcome data. We show that valid nonlinear effects estimates are obtained using these approaches and that they can be easily implemented using existing GAM statistical packages. We also provide valid computational methods for obtaining the standard errors and confidence intervals using a bootstrap method. We illustrate these proposed methods through applications to a breast cancer clinical study.

This paper is devoted to the memory of Volodymyr Methodyevich Yakovenko, an outstanding scientist in the field of solid-state radio physics, Honored Worker of Science and Technology of Ukraine, Full Member of the National Academy of Sciences (NAS) of Ukraine, Honorary Director of the O.Ya. Usykov Institute for Radiophysics and Electronics (IRE) within the NAS. At the IRE, Dr. Yakovenko founded a scientific school that is well-known for its studies of charged particles’ interaction with electromagnetic fields in inhomogeneous conducting media. In addition, he initiated research in a new branch of today’s solid state physics, specifically investigation of plasma instabilities in semiconductors that occur because of heating of conduction electrons by the existing elecromagnetic fields.

Abstract Polycentric urban development is promoted by urban planners and policy makers for its perceived benefits of alleviating urban issues. Theoretically, a polycentric urban structure is formed when different demographic groups present a collection of diverse and unique housing location preferences. Yet, only limited empirical studies have fully captured the complex effects of demographic characteristics on urban polycentric structures. Our study utilizes detailed demographic data and employs interpretable machine learning models to elucidate the nonlinear and synergistic relationship between demographic characteristics and the urban polycentric structure in the context of a city of 10 million population in China. When characterizing urban centers, the three most important demographic groups are young females, senior married couples, and middle-aged single individuals with basic education. Each urban center presents distinctive demographic compositions; for instance, middle-aged married individuals exerting a stronger influence in certain contexts, whereas in senior resident groups, married individuals have a more pronounced impact. Spatial heterogeneity is observed in the demographic profiles of urban centers; for instance, primary central cores are predominantly young, single females, whereas peripheral and secondary centers have stronger presence of highly educated residents.

Abstract Background Childbearing under the age of 20 is referred to as teenage childbearing. Compared to high-income countries, it is significantly higher in low-income countries. Adolescent childbearing is influenced by a number of variables, including economic, demographic, and social factors, and these vary geographically. Thus, this study aimed to determine the predictors of adolescent childbearing among Ethiopian women with spatial effect adjustment. Methods A total weighted sample of 4712 women aged 15 to 49 were included. The data were obtained from the 2019 Ethiopia Demographic and Health Survey. A generalized Geoadditive model which accounts for spatial effect and the non-linear effect of continuous variables was adopted to determine the associated factors of adolescent childbearing among Ethiopian women. Results The spatial pattern of adolescent childbearing was non-random in Ethiopia with Moran’s index statistics 1.731999 (P-value

This paper proposes a parametric hierarchical model for functional data with an elliptical shape, using a Gaussian process prior to capturing the data dependencies that reflect systematic errors while modeling the underlying curved shape through a von Mises–Fisher distribution. The model definition, Bayesian inference, and MCMC algorithm are discussed. The effectiveness of the model is demonstrated through the reconstruction of curved trajectories using both simulated and real-world examples. The discussion in this paper focuses on two-dimensional problems, but the framework can be extended to higher-dimensional spaces, making it adaptable to a wide range of applications. [ABSTRACT FROM AUTHOR]

Polycentric urban development is promoted by urban planners and policy makers for its perceived benefits of alleviating urban issues. Theoretically, a polycentric urban structure is formed when different demographic groups present a collection of diverse and unique housing location preferences. Yet, only limited empirical studies have fully captured the complex effects of demographic characteristics on urban polycentric structures. Our study utilizes detailed demographic data and employs interpretable machine learning models to elucidate the nonlinear and synergistic relationship between demographic characteristics and the urban polycentric structure in the context of a city of 10 million population in China. When characterizing urban centers, the three most important demographic groups are young females, senior married couples, and middle-aged single individuals with basic education. Each urban center presents distinctive demographic compositions; for instance, middle-aged married individuals exerting a stronger influence in certain contexts, whereas in senior resident groups, married individuals have a more pronounced impact. Spatial heterogeneity is observed in the demographic profiles of urban centers; for instance, primary central cores are predominantly young, single females, whereas peripheral and secondary centers have stronger presence of highly educated residents. [ABSTRACT FROM AUTHOR]

The logistic additive regression model has been a standard method in modeling nonlinear effects for multivariate analyses of binary outcomes in the generalized additive model (GAM) framework. However, the resultant nonlinear estimate of the smooth function is interpreted as a nonproportional increment of the odds ratio in the increment of the explanatory variable. The odds ratio cannot be interpreted as an effect measure by itself; it is only interpretable as an approximation of the risk ratio when the frequency of events is low. In this article, we propose alternative nonlinear regression methods to estimate the risk ratio and risk difference directly. We propose extending Zou's modified Poisson regression (Am J Epidemiol 159: 702–6) and Cheung's modified least squares (Gaussian) regression (Am J Epidemiol 166: 1337–44) to the GAM framework and fitting the Poisson and Gaussian additive regression models to binary outcome data. We show that valid nonlinear effects estimates are obtained using these approaches and that they can be easily implemented using existing GAM statistical packages. We also provide valid computational methods for obtaining the standard errors and confidence intervals using a bootstrap method. We illustrate these proposed methods through applications to a breast cancer clinical study. [ABSTRACT FROM AUTHOR]

Dengue fever (DF) is one of the most rapidly spreading mosquito-borne viral diseases in the world. It can impose an enormous socioeconomic and disease burden on the world's population. The spatial and temporal distribution patterns of DF cases in China are heterogeneous. It is evident that further research is necessary to identify the high-risk areas of dengue occurrence and associated risk factors at a fine spatiotemporal scale. This will facilitate the prevention and control of DF transmission. With the rapid development of remote sensing (RS) and geographic information system (GIS) technology, RS and GIS technology have played an important role in monitoring, forecasting, and identifying influencing factors and formulating prevention and control strategies for DF. In China, the majority of dengue cases were clustered in Guangdong and Yunnan Provinces. In this study, Bayesian spatiotemporal models were fitted at the county level in order to quantify the relationships between environmental and socioeconomic factors and DF. Our findings indicate that both environmental and socioeconomic factors can affect the transmission of DF in Guangdong and Yunnan Provinces. However, the underlying drivers and the spatially clustered patterns observed in the two provinces appear to be different. The results indicate that elevated temperatures facilitate the transmission of DF, but it was found that the risk of DF begins to decrease when the temperature exceeds 27.6 oC in Guangdong Province. There was a positive correlation between temperature and the incidence of DF, although no statistical significance was found in Yunnan Province. In Guangdong Province, the amount of precipitation did not significantly affect the incidence of DF. In Yunnan Province, the relationship between precipitation and DF is nonlinear. As the amount of precipitation increases, the risk of DF epidemics increases. In addition, vegetation cover can significantly affect the DF transmission, but the nonlinear relationships between vegetation cover and DF are diverse and complex in these two provinces. The incidence of DF was found to be positively correlated with the level of urbanization at the county level. The findings may prove beneficial to the governments of both provinces in the development of targeted strategies for the control of DF outbreaks at the county level. [ABSTRACT FROM AUTHOR]

A new family of IAN‐type amine N‐oxides is presented as catalysts for the allylation and crotylation of aromatic aldehydes with allyltrichlorosilanes. These reaction exhibit a remarkably positive nonlinear effect which enables utilization of the catalysts in subenantiopure form. As enantiopure catalysts are not required under this regime, the synthesis of these N‐oxides is straightforward through catalytic asymmetric synthesis, avoiding lenghty synthesis from the chiral pool or resolution of diastereoisomers. Studies of the corresponding crotylation with Z‐ and E‐crotylsilanes suggest that the reaction proceeds through a chair‐like transition state. [ABSTRACT FROM AUTHOR]

Ultrafast vortex beams have significant scientific and practical value because of their unique phase properties in both the longitudinal and transverse modes, enabling multi‐dimensional quantum control of light fields. Directly generating watt‐level ultrafast vortex beams with large angular momentum has remained a major challenge due to the limitations of mode‐locked materials and existing spatiotemporal mode‐locking generation methods. In this study, quasi‐2D PEA2(CsPbBr3)n‐1PbBr4 perovskite films are prepared by an anti‐solvent method and employed for the first time in a mode‐locked resonator operating in free space. Utilizing the angle‐based non‐collinear pumping and frequency doubling techniques, the second‐order ultrafast green vortex beams with a power of up to 1.05 W and a duration of 373 ps are generated. Experimental findings demonstrate the strong nonlinear saturable absorption properties of quasi‐2D PEA2(CsPbBr3)n‐1PbBr4 perovskite films at high power levels, highlighting their considerable potential in ultrafast laser technology and nonlinear optics. [ABSTRACT FROM AUTHOR]

Evaporative fraction (EF) is a useful measure for quantifying land surface energy partitioning processes and determining evaporative regimes; however, its influencing factors remain highly uncertain. Here, global data sets were compiled to disentangle the effects of environmental variables on EF variations along climate and land surface gradients. We found that (a) at annual timescales, ecosystem‐level EF could be expressed as a power law function of aridity index. The relationships of mean annual soil water content (SWC) and leaf area index (LAI) with mean annual EF resembled the traditional evaporative regime theory; (b) at daily timescales, the boosted regression tree method quantitatively revealed that the impacts of environmental variables (including meteorological variables) on EF showed equal importance, especially at humid sites, primarily due to the different response direction and magnitude of latent heat (LE) and sensible heat (H) fluxes to environmental changes. Particularly, the contrasting responses of LE (positive) and H (negative) to SWC, LAI, and relative humidity enhanced the positive effects of those influencing variables on EF; whereas, the correlations between EF and energy‐related factors (i.e., net radiation‐Rn and air temperature‐Ta) deteriorated as both LE and H showed positive response patterns to those variables; (c) meteorological factors were also found to have nonlinear effects on daily EF, further modified by climatic conditions. Rn near 150 W/m2 and Ta near 15°C appeared to be important energy‐partitioning thresholds at drier and humid sites, respectively. Moreover, changing interactions among environmental variables with climates were demonstrated to be important for better explaining EF variations. Plain Language Summary: Evaporative fraction (EF; defined as latent heat flux‐LE divided by the sum of LE and sensible heat flux‐H) can vary under different climatic and land surface conditions, but its influencing factors remain poorly understood. To this end, we explored the effects of environmental variables on annual and daily EF variations at different sites around the globe. We found that mean annual EF decreased with increasing aridity index and increased with mean annual soil water content and leaf area index. By comparison, the boosted regression tree method quantitatively showed that environmental variables exerted equally important roles in regulating daily EF, especially at humid sites. The complex interplays of daily EF with environmental variables were mainly due to the different responses of LE and H to surrounding environments and the strong nonlinear and interactive effects of environmental variables. These results are important for understanding the driving mechanisms of EF and land surface energy partitioning processes along climate and land surface gradients. Key Points: Environmental variables exerted equally important roles in regulating daily evaporative fraction, especially at humid sitesSynchronous responses of latent and sensible heat to surroundings determined how environmental variables affected evaporative fractionEnvironmental factors had strong nonlinear and interactive effects on daily evaporative fraction, which was further modified by climates [ABSTRACT FROM AUTHOR]

Background: Childbearing under the age of 20 is referred to as teenage childbearing. Compared to high-income countries, it is significantly higher in low-income countries. Adolescent childbearing is influenced by a number of variables, including economic, demographic, and social factors, and these vary geographically. Thus, this study aimed to determine the predictors of adolescent childbearing among Ethiopian women with spatial effect adjustment. Methods: A total weighted sample of 4712 women aged 15 to 49 were included. The data were obtained from the 2019 Ethiopia Demographic and Health Survey. A generalized Geoadditive model which accounts for spatial effect and the non-linear effect of continuous variables was adopted to determine the associated factors of adolescent childbearing among Ethiopian women. Results: The spatial pattern of adolescent childbearing was non-random in Ethiopia with Moran's index statistics 1.731999 (P-value < 0.001). Based on the evidence of spatial variation in a model, the highest risk of adolescent childbearing was observed in Jijiga, Shinilie, Welwel and Walder, Afar (Zone1 and Zone 5), Assosa, Metekel, and Gambela (Zone1). We also noted that women not intending to use a contraceptive method, Muslim religion, living in a rural area, and large household family size were significantly associated with a high risk of adolescent childbearing. Furthermore, our model results also confirmed that higher educational levels, older household age, and good economic status significantly reduced the risk of adolescent childbearing. Conclusions: This study revealed that adolescent childbearing distribution was significantly clustered in the Eastern and Southwestern parts of Ethiopia. Intervention programs aimed at the prevention of early marriage and raising awareness of sexual activity are essential to reducing adolescent childbearing. [ABSTRACT FROM AUTHOR]

Station-level ridership is an important indicator for understanding the relationship between land use and rail transit, which is crucial for building more sustainable urban mobility systems. However, the nonlinear effects of the built environment on metro ridership, particularly concerning temporal heterogeneity, have not been adequately explained. To address this gap, this study proposes a versatile methodology that employs the eXtreme gradient boosting (XGBoost) tree to analyze the effects of factors on station-level ridership variations and compares these results with those of a multiple regression model. In contrast to conventional feature interpretation methods, this study utilized Shapley additive explanations (SHAP) to detail the nonlinear effects of each factor on station-level ridership across temporal dimensions (weekdays and weekends). Using Shanghai as a case study, the findings confirmed the presence of complex nonlinear and threshold effects of land-use, transportation, and station-type factors on station-level ridership in the association. The factor "Commercial POI" represents the most significant influence on ridership changes in both the weekday and weekend models; "Public Facility Station" plays a role in increasing passenger flow in the weekend model, but it shows the opposite effect on the change in ridership in the weekday model. This study highlights the importance of explainable machine learning methods for comprehending the nonlinear influences of various factors on station-level ridership. [ABSTRACT FROM AUTHOR]

Background: Focused ultrasound therapy has been widely used for the treatment of various diseases, employing different types of transducers. The focused ultrasound pressure fields inevitably exhibit nonlinear effects, which can influence the ablation region. However, the nonlinear effects exhibit noticeable variations across different applications. The characterization of the nonlinear pressure fields of ultrasound is important for the effective implementation of focused ultrasound therapy. Purpose: The traditional angular spectrum method (ASM) was extended to accurately and efficiently simulate the propagation of weakly nonlinear ultrasound in heterogeneous mediums of clinical model. The nonlinear effects were further analyzed in relationship to the transducer parameters that are different in various applications. Methods: The pressure fields were simulated using the extended ASM, incorporating calculations for phase aberration in the frequency domain and magnitude compensation in the spatial domain to account for heterogeneous acoustic impedance mismatch. Validation was performed by comparison to k‐Wave simulation results using two simplified clinical models, an abdominal soft tissue and a transcranial skull model. The nonlinear effects were then analyzed in relation to the transducer parameters of f‐number and effective source area based on the same acoustic output power. The analysis of nonlinear effects was conducted under both homogeneous medium and the clinical models. Results: The simulation results demonstrated a maximum error of 3.93% in the calculated harmonic pressure of the abdominal model, and a maximum error of 4.89% within the transcranial model when comparing the extended ASM simulation results to those obtained from k‐Wave simulations. The characterization of the nonlinear effects reveals a strong correlation with the transducer parameters. Specifically, the results indicate that the nonlinear effects intensify with an increase in the effective source area and f‐number, under the same acoustic output power of the transducer. However, the clinical model also showed an influence on the nonlinear effects in relation to the f‐number. Conclusion: The extended ASM was demonstrated as an accurate and efficient simulation tool, and the simulation results provide a reference for evaluating the intensity of nonlinear effects in various transducer designs. [ABSTRACT FROM AUTHOR]

Wave-induced loads on the ship hull are of significant interest for many reasons. These loads, however, are highly nonlinear in realistic wave conditions and consequently difficult to predict. In this paper, we analysed and compared wave-induced loads acting on a container vessel in regular head waves computed with a newly developed weakly nonlinear potential theory-based code and a flow solver based on the Reynolds-averaged Navier-Stokes Equations. We investigated both long and short waves with moderate steepness. Initially, the vessel was completely fixed and the ship motions were suppressed, then the vessel was free to heave and pitch motion. We compared time-histories of the computed wave-induced longitudinal and vertical global forces as well as the pitch moment and we deeply analysed nonlinear effects by applying a Fourier transformation and comparing the corresponding harmonic amplitude up to the fourth order. Our results demonstrated satisfactory agreement between the two numerical methods, especially when the ship was free to heave and pitch. Additionally, the results provided detailed information about the capabilities and limitations of the weakly nonlinear potential theory-based approach. [ABSTRACT FROM AUTHOR]

Background and Objectives: Progress in the development of pulsed radiation sources with high energy density makes it possible to study the nonlinear response of condensed matter to the disturbing influence of high-intensity electromagnetic fields. To understand the processes occurring in this case, adequate models are needed that qualitatively and quantitatively reproduce the characteristics of the materials under study. In this area, graphene is considered one of the most promising materials due to the specificity of its band structure. The purpose of the work is to present and test a new model based on the quantum kinetic equation, free from restrictions on such parameters as the frequency and strength of the electric field of the disturbing influence. Materials and Method: The approach used in the work is based on the quantum kinetic equation for the distribution function of charge carriers in the state space. It makes it possible, in the one-electron approximation, to nonperturbatively reproduce the ultrafast dynamics of carriers in an external classical electric field. The system under consideration is specified by the electron dispersion law. The approach was developed and implemented for the pseudo-relativistic approximation of massless fermions, successfully used in describing the features of graphene. However, by its definition, this approximation quite accurately reproduces the real dispersion law only in the low-energy region in the vicinity of the Dirac points. Therefore, the direct use of this version of the model to describe processes in which electronic states with high excitation energies are known to participate raises questions about the accuracy of the results obtained. The problem can be resolved by moving to an exact definition of the dispersion law through the parameters of the tight-binding model of nearest neighbors in the crystal lattice of the graphene. The presented work proposes an implementation option for such a procedure and verifies the results obtained. A generalization of the formalism for a two-level system with a massless Hamiltonian of general form is used, which universally defines the explicit form of the quantum kinetic equation and expressions for macroscopic observable parameters. Results: A computational model based on the exact tight-binding model Hamiltonian has been determined, which strictly takes into account the real law of graphene dispersion in reciprocal space. The new model has been verified. For this purpose, the results of its use are compared with the results of a similar model based on the massless fermion approximation. Under conditions of limiting the parameters of the perturbing influence, ensuring the generation of excited states with only low energies in the immediate vicinity of the Dirac points, an exact coincidence has been demonstrated both at the stage of determining the values of the distribution function and for the observed parameters. It has been shown that going beyond the applicability limits of the massless fermion approximation is accompanied by the appearance of qualitative and quantitative differences in the results obtained. Conclusion: The results of the work provide new opportunities for studying the behavior of graphene under extreme conditions of strong high-frequency fields, modeling and searching for new nonlinear effects, and accurately reproducing the ultrafast quantum dynamics of its electrons for states with high energy values.

Abstract With the rapid development of the digital economy, its environmental impact, particularly on carbon dioxide emissions in resource-based cities, has emerged as a vital research topic. Resource-based cities, often central to traditional industries, are confronted with the dual challenges of environmental pollution and economic transformation. This study employs empirical analysis to examine the influence of the digital economy on carbon dioxide emissions in these cities. The findings reveal that the digital economy significantly reduces carbon dioxide emissions, with this impact being more pronounced in the early stages of digital economic development and gradually diminishing thereafter. In the mechanism analysis, we found that the digital economy can reduce carbon dioxide emissions in resource-based cities by raising public concern about the environment. Moreover, the study highlights significant variations in carbon reduction effects among different types of resource-based cities, noting that stronger environmental regulations further enhance these effects. These insights not only provide a new theoretical perspective but also offer practical guidance for policymakers in promoting sustainable development within the digital economy.

With the rapid development of the digital economy, its environmental impact, particularly on carbon dioxide emissions in resource-based cities, has emerged as a vital research topic. Resource-based cities, often central to traditional industries, are confronted with the dual challenges of environmental pollution and economic transformation. This study employs empirical analysis to examine the influence of the digital economy on carbon dioxide emissions in these cities. The findings reveal that the digital economy significantly reduces carbon dioxide emissions, with this impact being more pronounced in the early stages of digital economic development and gradually diminishing thereafter. In the mechanism analysis, we found that the digital economy can reduce carbon dioxide emissions in resource-based cities by raising public concern about the environment. Moreover, the study highlights significant variations in carbon reduction effects among different types of resource-based cities, noting that stronger environmental regulations further enhance these effects. These insights not only provide a new theoretical perspective but also offer practical guidance for policymakers in promoting sustainable development within the digital economy. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of electric vehicles (EVs) in China, the landscape of transportation carbon emissions has undergone significant changes. However, research on the impact of the built environment on the carbon emissions of mixed traffic from gasoline and electric vehicles remains sparse. This paper focuses on urban traffic scenarios with a mix of gasoline and electric vehicles, analyzing the spatiotemporal distribution of carbon emissions from both types of vehicles and their nonlinear association with the built environment. Utilizing trajectory data from gasoline-powered and electric taxis in Chengdu, China, we establish segment-level carbon emission estimation models based on the vehicle-specific power of gasoline vehicles and the equivalent energy consumption of electric vehicles. Subsequently, we employ the XGBoost algorithm and SHapley Additive ExPlanation (SHAP) to analyze the nonlinear relationships between 13 built environment variables and vehicle carbon emissions. This paper reveals that most built environment variables exhibit nonlinear relationships with traffic carbon emissions, with five factors—population density, road density, residential density, metro accessibility, and the number of parking lots—having a significant impact on road carbon emissions. Finally, we discuss the carbon reduction benefits of EV adoption and propose policy recommendations for low-carbon initiatives in the transportation field.

As one of the important driving forces of sustainable development in coastal areas, foreign direct investment (FDI) has provided new ideas for exploring optimal strategies. This analysis explores the linear impact of FDI on sustainable development in coastal areas with 53 cities in China from 2012 to 2020. Accordingly, a dynamic panel smoothed transition regression (PSTR) model is used to analyze the non-linear impact of FDI on sustainable development, with transition mechanisms of industrial structure and technological innovation level. The findings reveal that the non-linear effect of FDI on the sustainable development of coastal areas is obvious. When the coastal area's industrial structure is more optimized, and the level of technological innovation is higher, the promotion effect of FDI on sustainable development is more obvious. Further, the threshold effect of industrial structure and technological innovation is different. The threshold conversion rate of industrial structure is faster, but the threshold effect of technological innovation is stronger. Regionally, the impact of FDI on the sustainable development of coastal adjacent areas is significant, but not on the sustainable development of inland areas due to the few FDI inflows. This analysis offers guidance for policymakers to further develop the tertiary industry, increase financial investment in innovation in coastal areas and encourage enterprises to improve their independent innovation capacity. [ABSTRACT FROM AUTHOR]

New protocols have been developed of biological tissue volumes with shock-wave bursts using trajectories uniformly filled with discrete foci within a given volume. Each focus was sonicated with a millisecond-long pulse, which immediately generated a single thermal lesion. In developing the most advantageous irradiation protocols, the effect of the source peak power at a constant time-averaged value, the distance between single foci, and geometry of the outer contour of the trajectory on the shape, volume, and thermal ablation rate was analyzed. It is shown that for an arbitrary geometry of the outer contour of a single-layer trajectory, the most advantageous is the saturation mode of the shock front amplitude at the array focus using a trajectory with a spatial step 1.5 times greater than the transverse size of the single lesion. To obtain thermal ablation volumes on the order of cubic centimeters, protocols have been suggested with layer-by-layer irradiation of tissue, which make it possible to accelerate the thermal ablation process by 2.5 times compared to protocols used in clinical practice. The advantage of the proposed protocols that use the shock-wave exposure is the ability to generate localized and predictable thermal lesion without using MRI temperature monitoring. [ABSTRACT FROM AUTHOR]

The two most important wave modes responsible for energetic electron scattering to the Earth's ionosphere are electromagnetic ion cyclotron (EMIC) waves and whistler‐mode waves. These wave modes operate in different energy ranges: whistler‐mode waves are mostly effective in scattering sub‐relativistic electrons, whereas EMIC waves predominately scatter relativistic electrons. In this study, we report the direct observations of energetic electron (from 50 keV to 2.5 MeV) scattering driven by the combined effect of whistler‐mode and EMIC waves using ELFIN measurements. We analyze five events showing EMIC‐driven relativistic electron precipitation accompanied by bursts of whistler‐driven precipitation over a wide energy range. These events reveal an enhancement of relativistic electron precipitation by EMIC waves during intervals of whistler‐mode precipitation compared to intervals of EMIC‐only precipitation. We discuss a possible mechanism responsible for such precipitation. We suggest that below the minimum resonance energy (Emin) of EMIC waves, the whistler‐mode wave may both scatter electrons into the loss‐cone and accelerate them to higher energy (1–3 MeV). Electrons accelerated above Emin resonate with EMIC waves that, in turn, quickly scatter those electrons into the loss‐cone. This enhances relativistic electron precipitation beyond what EMIC waves alone could achieve. We present theoretical support for this mechanism, along with observational evidence from the ELFIN mission. We discuss methodologies for further observational investigations of this combined whistler‐mode and EMIC precipitation. Plain Language Summary: Energetic electron precipitation into the upper atmosphere is an important loss process of outer radiation belt fluxes. Whistler‐mode and electromagnetic ion cyclotron (EMIC) waves are two of the most important wave modes responsible for energetic electron scattering to the Earth's ionosphere through wave‐particle interaction. These wave modes typically drive losses of electrons in different energy ranges (above 1 MeV for EMIC waves and tens to hundreds of keV for whistler‐mode waves), occurring in different spatial regions. We report the first observations of energetic electron scattering driven by the combined effect of whistler‐mode and EMIC waves. Our results from equatorial and low‐altitude observations, and a data‐driven test particle simulation explain the wide energy range of electron precipitation from tens of keVs to a few MeVs due to the combined whistler‐mode and EMIC waves effect and explain the unusually high intensity of relativistic electron precipitation at such times. Key Points: We report observations of energetic electron precipitation likely driven by concurrent whistle‐mode and electromagnetic ion cyclotron (EMIC) wavesThe combined scattering of whistler‐mode and EMIC waves leads to electron precipitation over a wide energy range of 50 keVs to a few MeVsThis study highlights the potential nonlinear effects for explaining the observed energetic electron fluxes in the inner magnetosphere [ABSTRACT FROM AUTHOR]

Green development (GD) has emerged as a key area of focus for the sustainable development in the new era. Discussing spatial correlation networks and their multidimensional location impact on GD is of paramount significance for facilitating spillover benefits & narrowing regional disparities in GD, and optimizing development pathways. This study follows the basic steps of quantitative research, we first constructed the spatial correlation network of GD (NGD), then further clarified the structural characteristics from the perspective of social network analysis (SNA), finally in order to explore whether different network locations (NL) have different mechanisms on GD of individuals, and whether this mechanism is non-linear, we introduced the Tobit model and the Threshold model to conduct empirical tests respectively. The quantitative research results show that: (1) While the NGD in China exhibits a significant hierarchical structure, it is gradually being disrupted (dropped sharply to around 0.325 since 2014); (2) The center of the NGD is mainly concentrated in the eastern region and “city clusters” such as the “Yangtze River Delta”, which have both internal and external connections; meanwhile, the “peripheral” regions are located in the western part which only exist inter-regional connections leading their GD tend to take advantage of “neighborhood” to form regional GD scale effects; (3) The results of the influence of multidimensional NL on GD showed that closeness holds greater stability than centrality (showing positive values in different threshold intervals), and the GD sharing mechanism between regions in China has not yet formed. Then we address that elevating the value of GD can be achieved through three pathways: attaining a higher core status, establishing short-distance network relationships, or securing strategic positions among ’partners.

It is crucial to clarify the nonlinear effects of urban multidimensional characteristics on land surface temperature (LST). However, the combined consideration of the urban green space (UGS), water bodies, buildings, and socio-economic factors is limited. And the diurnal differences in their thermal effects have been less considered. In this study, central Beijing was taken as study area. Local climate zones (LCZ) were firstly applied to reveal spatiotemporal heterogeneity of LST. Then, the interpretable machine learning methods were utilized to quantitatively reveal nonlinear thermal effects of urban multidimensional characteristics, i.e., the UGS, water bodies, and building landscape features, and socio-economic features. The results indicated that built type LCZs have a higher average LST compared to natural type LCZs. And the LST of built type LCZs is simultaneously influenced by buildings’ density and height characteristics. Daytime LST is mainly affected by the landscape proportions of UGS, buildings, and trees, while nighttime LST is more influenced by socio-economic and building characteristics. The thermal effects of key factors exhibit nonlinear characteristics. Whether during the day or night, the impact of building coverage on LST is greater than that of building height, consistently exhibiting a warming effect. While, the building height and water body edge density factors both exhibited a reversal trend in their thermal impact between day and night. Our study also emphasized the importance of trees type in UGS and provided recommendations for UGS planning based on sensitivity and contribution considerations. These findings can help to regulate urban LST and promote sustainable urban development.

Is is widely known that the power consumption of Analog-to-Digital Converters (ADCs) is strongly related with the number of bits of resolution. Using smaller resolutions can greatly reduce the power consumption of the RX frontend. However, the use of low-resolution ADCs introduces significant nonlinear distortion, especially when Orthogonal Frequency Division Multiplexing (OFDM) signals are employed, which might severely degrade performance. In this work, we consider nonlinear effects at the receiver side, associated with quantization and saturation of low-resolution ADCs, and we propose a Generalized Approximate Message Passing (GAMP) receiver that is specially designed to cope with nonlinear effects at the OFDM receiver. We show that our receiver can significantly mitigate the distortion that arises from low-resolution ADCs, allowing larger M-ary Quadrature Amplitude Modulation (M-QAM) constellations to be employed. The proposed receiver is shown to be robust to strong channel fading effects, as well as errors during the channel estimation process.

An analytical method is introduced to assess the susceptibility of radio altimeter (RA) receivers to adjacent-band fifth-generation (5G) signal interference and to quantify its impact on RA performance. The power-series method is employed to analyze the intermediate frequency (IF) signal gain compression effect of 5G signal interference on RA receivers. A behavioral-level simulation model of the RA receiver’s radio frequency (RF) front-end is constructed based on the advanced design system (ADS), and a 5G signal injection simulation is performed. The simulation results indicate that 5G signals can induce nonlinear effects in the RF front-end circuit of the RA, leading to IF signal gain compression, thereby affecting the subsequent signal processing of RA receivers. The interference effect on the RA receiver is influenced by factors such as the power and frequency of the 5G interference signal. To investigate this, an interference injection test was conducted on a specific RA receiver to validate the aforementioned interference mechanisms. The test results indicate that when the average power of the injected 5G signal at a frequency of 4000 MHz reaches −16 dBm, the IF signal power is significantly reduced. As the power of the 5G signal increases, this nonlinear effect becomes more pronounced. Furthermore, the height error ratio significantly increases, with consistent trends observed across different test frequencies. The interference threshold for the RA is lower when the signal frequency is closer to the RA operational signal frequency. Our research results demonstrate the efficacy of this method, providing a reference basis for studies on interference mechanisms and the evaluation of interference effects related to RA receivers within the electromagnetic environment of 5G signals.