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The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications. [ABSTRACT FROM AUTHOR]

Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in recent years by advances in applied technology. This paper presents the design and development of a novel device composed of several sensors. Specifically, we propose a proximity sensor integrated by three devices: a Bluetooth sensor, a global positioning system (GPS) unit and an accelerometer. By means of this sensor it is possible to detect the presence of neighboring sensors in various configurations and operating conditions. Profiles based on the Received Signal Strength Indicator (RSSI) exhibit behavior consistent with that reported by empirical relationships. The present sensor is functional in detecting the proximity of other sensors and is thus useful for the identification of interactions between people in relevant contexts such as schools. [ABSTRACT FROM AUTHOR]

The post-deployment challenges in developing and upgrading blockchain smart contracts necessitate a high level of accuracy in their development and business logic. However, current methodologies for verifying the business logic of smart contracts frequently fail to address their alignment with end-user business requirements. This paper introduces a two-step language transformation process to bridge this gap. Initially, we establish a transformation rule from the Business Process Model and Notation (BPMN) to Prolog, enabling the translation of business processes into a Prolog representation. This step not only validates the business process logic but also ensures it meets user specifications. Subsequently, we introduce a transformation rule from the BPMN to Go, which facilitates the transformation of the BPMN model, once validated, into a Go language smart contract. To enhance usability, we have engineered a dedicated tool that streamlines this transformation process. We present a case study involving a banking loan process to exemplify the utility of our tool in creating BPMN diagrams, conducting requirement and syntax validations, and effecting the transformation to Go smart contracts. The case study and empirical results suggest that our methodology and the accompanying tool mitigate the complexities inherent in smart contract development. They also ensure the fidelity of business logic to user demands, thereby promoting the broader adoption of blockchain smart contract technology. [ABSTRACT FROM AUTHOR]

With the growing integration of the Internet of Things in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of WSNs in a commercial multi-story building, specifically addressing the difficulty of dealing with the variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights into the potential to significantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system's performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. We provide an in-depth analysis for scrutinizing the disparities and commonalities between the datasets obtained from real-world deployments and simulation. The results obtained show the significance of accurate simulation models for reliable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. This research's findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental parameters. This includes insights into temperature, humidity, and light intensity, offering opportunities for enhanced comfort and efficiency in intelligent environments. [ABSTRACT FROM AUTHOR]

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as "sensors", which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. [ABSTRACT FROM AUTHOR]

In this paper, we consider the strong convergence of L p -norms ( p ≥ 1 ) of a kernel estimator of a cumulative distribution function (CDF). Under some mild conditions, the law of the iterated logarithm (LIL) for the L p -norms of empirical processes is extended to the kernel estimator of the CDF. [ABSTRACT FROM AUTHOR]

Accurate estimation of software effort and time in the software development process is a key activity to achieve the necessary product quality. However, underestimation or overestimation of effort has become a key challenge for software development. One of the main problems is the estimation with metrics from late stages, because the product must already be finished to make estimates. In this paper, the use of statistical models and machine learning approaches for software estimation are used in early stages such as software design, and a data set is presented with metric values of design artifacts with 37 software projects. As results, models for the estimation of development time and effort are proposed and validated through leave-one-out cross-validation. Further, machine learning techniques were employed in order to compare software projects estimations. Through the statistical tests, it was proven that the errors were not statistically different with the regression models for effort estimation. However, with Random Forest the best statistical results were obtained for estimating development time. [ABSTRACT FROM AUTHOR]

It is recognized that most real systems and networks exhibit a much higher clustering with comparison to a random null model, which can be explained by a higher probability of the triad formation—a pair of nodes with a mutual neighbor have a greater possibility of having a link between them. To catch the more substantial clustering of real-world networks, the model based on the triadic closure mechanism was introduced by P. Holme and B. J. Kim in 2002. It includes a "triad formation step" in which a newly added node links both to a preferentially chosen node and to its randomly chosen neighbor, therefore forming a triad. In this study, we propose a new model of network evolution in which the triad formation mechanism is essentially changed in comparison to the model of P. Holme and B. J. Kim. In our proposed model, the second node is also chosen preferentially, i.e., the probability of its selection is proportional to its degree with respect to the sum of the degrees of the neighbors of the first selected node. The main goal of this paper is to study the properties of networks generated by this model. Using both analytical and empirical methods, we show that the networks are scale-free with power-law degree distributions, but their exponent γ is tunable which is distinguishable from the networks generated by the model of P. Holme and B. J. Kim. Moreover, we show that the degree dynamics of individual nodes are described by a power law. [ABSTRACT FROM AUTHOR]

This work examines the asymptotic characteristics of a conditional set-indexed empirical process composed of functional ergodic random variables with missing at random (MAR). This paper's findings enlarge the previous advancements in functional data analysis through the use of empirical process methodologies. These results are shown under specific structural hypotheses regarding entropy and under appealing situations regarding the model. The regression operator's asymptotic (1 − α) -confidence interval is provided for 0 < α < 1 as an application. Additionally, we offer a classification example to demonstrate the practical importance of the methodology. [ABSTRACT FROM AUTHOR]

A decade after the publication of seminal papers on personal carbon trading (PCT), few empirical studies on its implementation exist. Investigating how to design, set up and implement a PCT scheme for a community or country raises several difficulties. For instance, it is unclear how to introduce a reduction rate of CO2 allowances to ensure a steady decrease in CO2 emissions from households. Computational approaches have been introduced to address these challenges of PCT by providing an opportunity to test counterfactual scenarios. Among the benefits of an agent-based modeling approach (ABM) is the potential to directly address dynamic developments and introduce counterfactual situations. In this paper, we review existing modeling approaches and present an ABM for PCT. With simulations of an artificial population of 1000 and 30,000 agents, we address questions on the price and reduction rate of allowances. A key contribution of our model is the inclusion of an adaptive reduction rate, which reduces the yearly allocated amount of allowances depending on a set CO2 abatement target. The results confirm that increased emissions targets are related to higher allowance prices and a higher proportion of buying households. Our analysis also suggests a significant path dependence in the dynamics of allowance prices and availability, but that adaptive reduction rates have little impact on outcomes other than the price. We discuss data availability and computational challenges to modeling a PCT scheme with an ABM. Ideal data to populate an ABM on PCT are not available due to the lack of real-world implementations of a PCT. Nonetheless, meaningful insights about the dynamics and the focal variables in a PCT scheme can be generated by the exploratory use of an ABM. [ABSTRACT FROM AUTHOR]

An effective dead-time control strategy for the three-phase dual active bridge (3P-DAB) converter of a distribution system is studied to reduce the switching losses of power switches and improve the under-light-load power conversion efficiency. Because of the advantages of a dual-active bridge converter, such as an inherent zero-voltage switching (ZVS) capability without any additional resonant tank and a seamless bi-directional power transition, this is an attractive topology for bi-directional application. The 3P-DAB converter is apt for high-power applications such as aircraft due to an interleaved structure, which can reduce conduction losses. However, the design of the dead time depends on engineering experience and empirical methods. In order to overcome the conventional practicality of the dead-time design method, the effective control of dead time is proposed based on the theoretical analysis. In this paper, the overall explanation of the 3P-DAB converter is shown with operation principles. In addition, the dead-time effect of the 3P-DAB converter is examined and the practical variable dead-time control strategy is studied. Finally, experimental results validate the proposed variable dead-time control strategy using a 25 kW prototype 3P-DAB converter. [ABSTRACT FROM AUTHOR]

Shunt defects are often detected in solar panels intended for photovoltaic applications. However, existing nondestructive detection technologies have certain inherent drawbacks depending on the application scenario. In this context, this paper reports a comprehensive empirical investigation into lock-in thermography (LIT) and its applicability to diagnosing shunt defects in copper indium gallium selenide (CIGS) solar modules. LIT was compared with biased thermography, and its distinctive attributes were elucidated. The comparison results demonstrate the superior capabilities of LIT at enhancing the signal-to-noise ratio, improving the visibility, resolution, and quantification of defects, and highlighting the usefulness of LIT for advanced defect analysis. We explored scenarios in which biased thermography could be appropriate despite its inherent limitations and identified conditions under which it might be preferred. The complex thermal behavior of different types of defects under various voltage conditions was analyzed, contributing to a more nuanced understanding of their behavior. Thus, integrating experimental results and theoretical understanding, we provide valuable insights and scientific guidelines for photovoltaic research. Our findings could help enhance the efficiency of defect detection in CIGS modules, highlighting the critical role of optimized thermographic techniques in developing photovoltaic technologies. [ABSTRACT FROM AUTHOR]

The relationship between secularity or modernity and religious belief choices is a hot topic of academic concern. However, there has been scarce attention from the academic community regarding the religious belief choices and driving factors of China's Generation Z, who have grown up in a secular or modern society. In this paper, we use the Centrality of Religiosity Scale (CRS) and a modified Cost and Rewards Scale to measure this group's tendency towards "religious centrality", and investigate the inherent driving factors using logistic regression. The results show that the religiousness of China's Generation Z is higher than the overall level of religious beliefs in China. Their choice of religious beliefs is the result of a rational consideration and balance of "religious rewards", but most of them are not driven by the consideration of "afterlife rewards" as the supernatural core factor of religion. Instead, they are driven by the actual benefits brought by the choice of religious beliefs and the factors that affect these benefits, such as religious socialization. This conclusion validates the opinions of Stark and Finke, as well as Hartmut Rosa, and breaks the status quo of single research model and method in previous relevant studies. [ABSTRACT FROM AUTHOR]

Signalling protocols are responsible for fundamental tasks such as initiating and terminating communication and identifying the state of the communication in telecommunication core networks. Signalling System No. 7 (SS7), Diameter, and GPRS Tunneling Protocol (GTP) are the main protocols used in 2G to 4G, while 5G uses standard Internet protocols for its signalling. Despite their distinct features, and especially their security guarantees, they are most vulnerable to attacks in roaming scenarios: the attacks that target the location update function call for subscribers who are located in a visiting network. The literature tells us that rule-based detection mechanisms are ineffective against such attacks, while the hope lies in deep learning (DL)-based solutions. In this paper, we provide a large-scale empirical study of state-of-the-art DL models, including eight supervised and five semi-supervised, to detect attacks in the roaming scenario. Our experiments use a real-world dataset and a simulated dataset for SS7, and they can be straightforwardly carried out for other signalling protocols upon the availability of corresponding datasets. The results show that semi-supervised DL models generally outperform supervised ones since they leverage both labeled and unlabeled data for training. Nevertheless, the ensemble-based supervised model NODE outperforms others in its category and some in the semi-supervised category. Among all, the semi-supervised model PReNet performs the best regarding the Recall and F1 metrics when all unlabeled data are used for training, and it is also the most stable one. Our experiment also shows that the performances of different semi-supervised models could differ a lot regarding the size of used unlabeled data in training. [ABSTRACT FROM AUTHOR]

The Median-of-Mean (MoM) estimation is an efficient statistical method for handling data with contamination. In this paper, we propose a variance-dependent MoM estimation method using the tail probability of a binomial distribution. The bound of this method is better than the classical Hoeffding method under mild conditions. This method is then used to study the concentration of variance-dependent MoM empirical processes and sub-Gaussian intrinsic moment norm. Finally, we give the bound of the variance-dependent MoM estimator with distribution-free contaminated data. [ABSTRACT FROM AUTHOR]

In recent decades, several countries have implemented paid parental leave for parents. This paper discusses the correlation between paid parental leave—which in Austria corresponds with receiving childcare benefits—and changing gender role attitudes in different-gender parent couples. It draws on evidence from 42 semi-structured couple interviews (n = 42, 2021–2022) in which couples were asked how they arranged childcare, employment, and household chores and how they reached agreements with employers. The empirical data were first analyzed based on content and then by applying constructivist-informed grounded theory to investigate whether and how gender affected couples' efforts to reconcile childcare, employment, and household chores and how employers responded to parental arrangements that challenged traditional gender role attitudes. Compared to other countries, Austria offers rather generous childcare benefits, including long leave periods. In addition, Austria has introduced a partnership bonus for parents who share childcare benefits 50:50 or 60:40. However, the number of couples who take advantage of these benefits is still low. In the presented research, parents who succeeded in sharing these childcare benefits were interviewed. All interviewees had taken at least five months of paid parental leave. The data were analyzed using a content-oriented approach and by applying informed grounded theory. The results show that a substantial number of fathers continued working in minor employment while receiving childcare benefits, in line with the prevailing assumption among employers that the man is the main family breadwinner. By contrast, mothers had to strongly insist that they could continue working during the early transition to parenthood. However, on an individual level and in specific domains, Austrian parents were able to successfully overcome traditional gender inequalities in reconciling work and family with the support of the newly implemented childcare benefits. The observation that in Austria, the share of fathers who take the income-related form of parental leave is higher than that of fathers who claim other forms of childcare benefits points to the importance of all parents having access to well-compensated parental leave, regardless of their income level before the birth of a child. [ABSTRACT FROM AUTHOR]

This paper takes the China Health and Retirement Longitudinal Survey (CHARLS) as a sample to assess the long-term impacts of domestic violence experienced in childhood on individuals. First, from the four dimensions of injury from violence, negligent care, emotional abuse and witness to domestic violence, an indicator system for quantifying domestic violence is constructed. Second, the simultaneous equation of self-evaluation health and life satisfaction is estimated by the seemingly unrelated regression model. Starting with education, health and life satisfaction, the long-term impact of domestic violence experiences on individuals is quantitatively assessed, providing empirical evidence for preventing and curing domestic violence and healing trauma. The empirical research shows the following: (1) An experience of domestic violence significantly reduces educational achievements. Compared with the three dimensions of injury from violence, negligent care and witnessing domestic violence, emotional abuse has the greatest negative impact on educational achievements. (2) Domestic violence significantly reduces the self-assessed health level and life satisfaction and increases the subjective mental health risk. Based on the complexity and concealment of domestic violence, combined with empirical research conclusions, this paper proposes countermeasures to prevent and control domestic violence. [ABSTRACT FROM AUTHOR]

We explore an exchangeably weighted bootstrap of the general function-indexed empirical U-processes in the Markov setting, which is a natural higher-order generalization of the weighted bootstrap empirical processes. As a result of our findings, a considerable variety of bootstrap resampling strategies arise. This paper aims to provide theoretical justifications for the exchangeably weighted bootstrap consistency in the Markov setup. General structural conditions on the classes of functions (possibly unbounded) and the underlying distributions are required to establish our results. This paper provides the first general theoretical study of the bootstrap of the empirical U-processes in the Markov setting. Potential applications include the symmetry test, Kendall's tau and the test of independence. [ABSTRACT FROM AUTHOR]

Corrosion-induced deterioration of the in-service marine reinforced concrete (RC) structures may result in unsatisfactory serviceability or insufficient safety. Surface deterioration analysis based on random fields can provide information regarding the future development of the surface damage of the in-service RC members, but its accuracy needs to be verified in order to broaden its applications in durability assessment. This paper performs an empirical study to verify the accuracy of the surface deterioration analysis based on random fields. The batch-casting effect is considered to establish the "step-shaped" random fields for stochastic parameters in order to better coordinate their actual spatial distributions. Inspection data from a 23-year-old high-pile wharf is obtained and analyzed in this study. The simulation results of the RC panel members' surface deterioration are compared with the in-situ inspection results with respect to the steel cross-section loss, cracking proportion, maximum crack width, and surface damage grades. It shows that the simulation results coordinate well with the inspection results. On this basis, four maintenance options are established and compared in terms of the total amounts of RC panel members needing restoration and the total economic costs. It provides a comparative tool to aid the owners in selecting the optimal maintenance action given the inspection results, to minimize the lifecycle cost and guarantee the sufficient serviceability and safety of the structures. [ABSTRACT FROM AUTHOR]

Case, agreement, and A-movement dependencies across finite clause boundaries, such as Hyperraising (to subject or object) or Long-Distance Case or Agreement [LDA], are available in many typologically diverse languages. The research on such dependencies typically distinguishes between cross-linguistically restricted true A-dependencies across finite clauses, and generally available binding-like A ′ -dependencies as found in Prolepsis. In this paper, we investigate both types of configurations in parallel and refer to this as the A -domain. Since the diagnostics to distinguish A -configurations vary across languages and often cannot be compared directly, we define four characteristic properties: (A) whether the construction is restricted by matrix predicate selection, (B) whether movement in the embedded clause is involved, (C) whether the dependency shows locality restrictions (in particular, A-Minimality), and (D) whether there are semantic restrictions on the relevant DP. By combining different values of the characteristic properties, we show, differently from previous approaches, that the A -domain does not simply consist of two types of configurations, but that the empirical landscape represents a continuum of five A -constructions. We suggest a theoretical implementation of our empirical findings, which is built on a predicational relator phrase above the embedded CP and propose that, in some of the constructions, these two projections fuse into one. We employ a minimalist probing approach which relies on differences in the base-generated position of the relevant DP (matrix clause, high in the embedded clause, argument position in the embedded clause), differences in the feature composition of the embedded C (a plain A ′ -head, or a bundled predicational C head involving composite A/A ′ probes), a composite probe hierarchy yielding three types of feature-dependencies of composite probes, and, resulting from that, different probing mechanisms (conjunctive satisfaction, dependent satisfaction, and independent satisfaction). Lastly, this paper also contributes methodological tools for disentangling constructions of the A -domain. [ABSTRACT FROM AUTHOR]

The question of God is one of the central themes of religious education. Therefore, research in the field of concepts of knowledge and faith can provide a substantial basis for the work of educators and teachers and, thus, for the foundation of Islamic religious education. The following paper presents the results of a qualitative study on Muslim children's relationships with God. The results provide impulses for rethinking Islamic religious education in Europe. [ABSTRACT FROM AUTHOR]

Existing research has insufficiently explored the nexus between the new energy industry and CO2 emissions from the standpoint of export sophistication. This study analyses the implications of the new energy industry's export sophistication on CO2 emissions, regional heterogeneity, and its influencing mechanism by gathering data from 31 major economies throughout the world between 1996 and 2021. The study found that the new energy industry's export sophistication helps reduce carbon dioxide emissions, and this conclusion still holds after robustness testing; the carbon emission reduction effect of the export sophistication of the new energy industry is more significant in developed countries than in developing countries; the new energy industry's export sophistication possesses a crowding-out effect on domestic technological progress, which to a certain extent impedes carbon reduction effect. This paper's findings provide theoretical guidance for the global low-carbon energy transition. [ABSTRACT FROM AUTHOR]

The SAT problem is maybe one of the most famous NP-complete problems. This paper deals with the 3-SAT problem. We follow a sort of incremental strategy to save computational costs with respect to the classical quantum computing approach. We present an heuristics that leads this strategy, improving the performance of the purely random incremental scheme. We finally validate our approach by means of a thorough empirical study. [ABSTRACT FROM AUTHOR]

This paper introduces a novel distributionally robust mean-variance portfolio estimator based on the projection robust Wasserstein (PRW) distance. This approach addresses the issue of increasing conservatism of portfolio allocation strategies due to high-dimensional data. Our simulation results show the robustness of the PRW-based estimator in the presence of noisy data and its ability to achieve a higher Sharpe ratio than regular Wasserstein distances when dealing with a large number of assets. Our empirical study also demonstrates that the proposed portfolio estimator outperforms classic "plug-in" methods using various covariance estimators in terms of risk when evaluated out of sample. [ABSTRACT FROM AUTHOR]

This paper aims to explore the potential offered by emotion recognition systems to provide a feasible response to the growing need for audience understanding and development in the field of arts organizations. Through an empirical study, it was investigated whether the emotional valence measured on the audience through an emotion recognition system based on facial expression analysis can be used with an experience audit to: (1) support the understanding of the emotional responses of customers toward any clue that characterizes a staged performance; and (2) systematically investigate the customer's overall experience in terms of their overall satisfaction. The study was carried out in the context of opera live shows in the open-air neoclassical theater Arena Sferisterio in Macerata, during 11 opera performances. A total of 132 spectators were involved. Both the emotional valence provided by the considered emotion recognition system and the quantitative data related to customers' satisfaction, collected through a survey, were considered. Results suggest how collected data can be useful for the artistic director to estimate the audience's overall level of satisfaction and make choices about the specific characteristics of the performance, and that emotional valence measured on the audience during the show can be useful to predict overall customer satisfaction, as measured using traditional self-report methods. [ABSTRACT FROM AUTHOR]

As high-resolution global coverage cannot easily be achieved by direct bathymetry, the use of gravity data is an alternative method to predict seafloor topography. Currently, the commonly used algorithms for predicting seafloor topography are mainly based on the approximate linear relationship between topography and gravity anomaly. In actual application, it is also necessary to process the corresponding data according to some empirical methods, which can cause uncertainty in predicting topography. In this paper, we established analytical observation equations between the gravity anomaly and topography, and obtained the corresponding iterative solving method based on the least square method after linearizing the equations. Furthermore, the regularization method and piecewise bilinear interpolation function are introduced into the observation equations to effectively suppress the high-frequency effect of the boundary sea region and the low-frequency effect of the far sea region. Finally, the seafloor topography beneath a sea region (117.25°–118.25°E, 13.85°–14.85°N) in the South China Sea is predicted as an actual application, where gravity anomaly data of the study area with a resolution of 1′ × 1′ are from the DTU17 model. Comparing the prediction results with the data of ship soundings from the National Geophysical Data Center (NGDC), the root-mean-square (RMS) error and relative error can be up to 127.4 m and approximately 3.4%, respectively. [ABSTRACT FROM AUTHOR]

This paper presents the uniform concentration inequality for the stochastic integral of marked point processes. We developed a new chaining method to obtain the results. Our main result is presented under an entropy condition for partitioning the index set of the integrands. Our result is an improvement of the work of van de Geer on exponential inequalities for martingales in 1995. As applications of the main result, we also obtained the uniform concentration inequality of functional indexed empirical processes and the Kakutani–Hellinger distance of the maximum likelihood estimator. [ABSTRACT FROM AUTHOR]

Technical efficiency (TE) and total factor productivity (TFP) are important criteria to ensure the enhancement of the quality and efficiency of state-owned enterprises (SOEs) and function as important indicators to assess the quality of their accomplishments. The purpose of this study is to explore whether the efficiency of SOEs is higher or lower than that of private enterprises. Transcendental logarithmic production function and stochastic frontier analysis (SFA) are used to assess the TE and TFP of listed central SOEs, local SOEs, and private enterprises, the data of which were taken from 2006–2020. The results show that the sampled private enterprises had the highest average TE during the study period, followed by the central and local SOEs. The private enterprises also had the highest average TFP growth rate, followed by the local and central SOEs. The TFP decompositions show that the TE change (TEC) and technical change (TC) indices of the SOEs were lower than those of the private enterprises. The TC, TEC, and scale change (SC) are limiting the TFP growth rates of the SOEs in labor-intensive industries. The SC of the SOEs has changed less than that of private enterprises in the sampled capital-intensive industries. Northern and southern China had the highest rates of TE and TFP growth. Indeed, this paper measures and decomposes TFP, and analyzes the efficiency of SOEs and private enterprises in different industries and regions in an international context. [ABSTRACT FROM AUTHOR]

In China, blue sky defense is a crucial part of ecological environment governance. Objective environmental governance performance needs to be perceived by the public to more truly affect the public's evaluation of the government's environmental governance. This paper focuses on the public's subjective perception of air pollution and evaluation of the local government's environmental governance. Based on the Chinese General Social Survey data, the matched economic indicators, and air pollution data, we conduct a diachronic study on the public's evaluation of local governments' environmental protection work, and we analyze the relationship between the subjective perception of air pollution, the objective air pollution data, and the evaluation of local governments' environmental protection work. The results showed the following: (1) People's evaluation of local governments' environmental protection work significantly improved from 2013 to 2021. The objective indicator improved, while the subjective perception declined. (2) The subjective perception of air pollution has a significant negative impact on the evaluation of local governments' environmental protection work and needs to be better considered to improve air quality. At the same time, the effect of the objective indicator is insignificant. [ABSTRACT FROM AUTHOR]

The modeling and forecasting of dynamically varying covariances has received a great deal of attention in the literature. The two most widely used conditional covariances and correlations models are BEKK and the DCC. In this paper, we advance a new method based on network analysis and a new targeting approach for both the above models with the aim of better estimating covariance matrices associated with financial time series. Our approach is based on specific groups of highly correlated assets in a financial market and assuming that those relationships remain unaltered at least in the long run. Based on the estimated parameters, we evaluate our targeting method on simulated series by referring to two well-known loss functions introduced in the literature. Furthermore, we find and analyze all the maximal cliques in correlation graphs to evaluate the effectiveness of our method. Results from an empirical case study are encouraging, mainly when the number of assets is not large. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between property return and seller behavior and aims to test the disposition effect in China's real estate market. Using transaction data in Beijing, we find that loss properties have a lower sell propensity relative to gain properties, confirming the existence of the disposition effect. We also find that the disposition effect is more pronounced in samples with shorter holding periods. Sellers with financial constraints and popular projects are more likely to show the disposition effect. Furthermore, we find that sellers exhibit loss aversion; specifically, sellers with loss properties are likely to set a higher listing price, which provides indirect evidence for the disposition effect. [ABSTRACT FROM AUTHOR]

This paper presents a study on the influence of the construction of an ultralarge-diameter shield tunnel undercrossing the existing high-speed railway using the empirical method, numerical analysis method, and geotechnical centrifuge model experiment based on the Wuhan Lianghu Highway Tunnel project. The comparison of the results obtained from the three methods shows first, that the results obtained from the centrifuge model experiment and numerical simulation match well with the results obtained from the empirical method for the worst-case scenario and the most likely scenario, which are consistent with the unfavorable geological and construction conditions modeled in the centrifuge test and the possible geological and construction conditions modeled in numerical simulation. Second, both the results obtained from the numerical method and the centrifuge model experiment show that the asymmetry of the settlement of railway subgrade was induced by a shield tunnel, while the asymmetry of railway subgrade settlement curve is gradually weakening with tunneling. Third, the maximum settlement of the railway subgrade could vary between 20 mm (in the most likely scenario) and 65 mm (in the worst scenario). Both the results from the centrifuge test and the numerical simulation show that the allowed value of maximum differential settlement along the railway subgrade (5 mm/10 m) would be exceeded when the tunnel excavation passes the first track at 10 m. It indicates that some mitigation measures should be taken for controlling the influence of the construction of a shield tunnel, especially when the shield tunnel machine is about underneath the pass railway subgrade. It is suggested that the shield machine should underpass the railway subgrade during the skylight period of railway operation. [ABSTRACT FROM AUTHOR]

A direct application of autoregressive (AR) models with independent and identically distributed (iid) errors is sometimes inadequate to fit the time series data well. A natural alternative is further to assume the model errors following an AR process, whose structure however has essential impacts on the statistical inferences related to the autoregressive models. In this paper, we construct a new unified test for checking the AR error structure based on the empirical likelihood method. The proposed test is desirable because its limit distribution is always chi-squared regardless of whether the autoregressive model is stationary or non-stationary, with or without an intercept term. Some simulations are also provided to illustrate the finite sample performance of this test. Finally, we apply the proposed test to a financial real data set. [ABSTRACT FROM AUTHOR]

High-resolution snow water equivalent studies are important for obtaining a clear picture of the potential of water resources in arid areas, and SAR-based sensors can achieve meter-level snow water equivalent inversion. The advanced C-band SAR satellite Gaofen-3 (GF-3) can now achieve meter-level observations of the same area within one day and has great potential for the inversion of the snow water equivalent. The EQeau model is an empirical method for snow water equivalent inversion using C-band SAR satellites, but the model has major accuracy problems. In this paper, the EQeau model is improved by using classification of underlying surface types and polarization decomposition, and the inversion of the snow water equivalent was also completed using the new data source GF-3 input model. The results found that: (1) the classification of underlying surface types can significantly improve the fit between the snow thermal resistance and the backscattering coefficient ratio; (2) the accuracy of the snow density extracted by the GF-3 satellite using the Singh–Cloude Three-Component Hybrid (S3H) decomposition is better than IDW spatial interpolation, and the overall RMSE can reach 0.005 g/cm3; (3) the accuracy of the optimized EQeau model is significantly improved, and the overall MRE is reduced from 27.4% to 10.3%. Compared with the original model, the optimized model is superior both in terms of verification accuracy and image detail. In the future, with the combination of advanced technologies such as the Internet of Things (IoT), long, gapless, all-weather, and high-resolution snow water equivalent inversion can be achieved, which is conducive to the realization of all-weather monitoring of the regional snow water equivalent. [ABSTRACT FROM AUTHOR]

According to the natural language perspective, UGC has been significantly used for the screening of key nodes in knowledge discovery and strategic investment. This article presents a new research framework that is proposed for the decomposition of UGC knowledge feature extraction into topic recognition and language analysis, mainly. For visual analysis of associated topics, the LDAvis approach is utilized. Then, risk features of UGC knowledge are assigned according to language attribution. Based on previous studies, the risk attribute lexicon is further updated by judging semantic distance through word vectors. This research uses platform data and individual stock data as samples for subject recognition and knowledge feature extraction. A regression model is constructed based on the panel data after natural language processing to verify the feedback effect of the market at strategic risk measurement. It can be found from the conclusion that the change in market behavior is regular and correlates with the change in the UGC risk degree of individual stocks. The purpose of this paper is to examine the value of UGC in investment decision-making from the perspective of knowledge discovery. The research content can provide a reference for data mining, fintech, strategic risk monitoring, and other related works. [ABSTRACT FROM AUTHOR]

Contemporary philosophers have paid increasing attention to the empirical research on emotions that has blossomed in many areas of the social sciences. In this paper, I first sketch the common roots of science and philosophy in Ancient Greek thought. I illustrate the way that specific empirical sciences can be regarded as branching out from a central trunk of philosophical speculation. On the basis of seven informal characterizations of what is distinctive about philosophical thinking, I then draw attention to the fact that scientific progress frequently requires one to make adjustments to the way its basic terms are conceptualized, and thus cannot avoid philosophical thought. The character of emotions requires attention from many disciplines, and the links among those disciplines inevitably require a broader philosophical perspective to be understood. Thus, emotion science, and indeed all of science, is inextricably committed to philosophical assumptions that demand scrutiny. [ABSTRACT FROM AUTHOR]

Masonry material characteristics, such as compressive strength, and the relationship between brick, mortar, and masonry compressive strengths are required for the analysis and assessment of masonry structures. This paper aimed to investigate the compressive strength relationship of Cambodian masonry. A total number of 42 prisms were constructed in the laboratory using six different brick/mortar combinations. Two brick types, solid and hollow, and three—1:3, 1:4, and 1:6—cement–sand mortar combinations were used. The effects of brick and mortar strengths were analysed. Using regression analysis, a simple empirical relationship was derived for masonry compressive strength as a function of brick and mortar strength in the Cambodian context. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a vital parameter in the surface energy budget and water cycle. One of the most important foundations for LST studies is a theory to understand how to model LST with various influencing factors, such as canopy structure, solar radiation, and atmospheric conditions. Both physical-based and empirical methods have been widely applied. However, few studies have compared these two categories of methods. In this paper, a physical-based method, soil canopy observation of photochemistry and energy fluxes (SCOPE), and two empirical methods, random forest (RF) and long short-term memory (LSTM), were selected as representatives for comparison. Based on a series of measurements from meteorological stations in the Heihe River Basin, these methods were evaluated in different dimensions, i.e., the difference within the same surface type, between different years, and between different climate types. The comparison results indicate a relatively stable performance of SCOPE with a root mean square error (RMSE) of approximately 2.0 K regardless of surface types and years but requires many inputs and a high computational cost. The empirical methods performed relatively well in dealing with cases either within the same surface type or changes in temporal scales individually, with an RMSE of approximately 1.50 K, yet became less compatible in regard to different climate types. Although the overall accuracy is not as stable as that of the physical method, it has the advantages of fast calculation speed and little consideration of the internal structure of the model. [ABSTRACT FROM AUTHOR]

The purpose of the article is to define the risk factors in rail transport and to show that the lack of sufficient identification of risks in individual phases affects the implementation of this type of transport. A literature study has been conducted to identify key risk factors and their impact on rail transport in Poland. For this purpose, a list of railway accidents in 2010–2020 in which people were injured, or there were significant losses in terms of the environment was presented. The realization of the objective focused on research proceedings covering the theoretical and cognitive sphere. This study included an analysis of the existing theoretical heritage in the area of risk management processes in rail transport, as well as a survey of the empirical research, which concentrated on the identification and assessment of key factors that influence the realization of rail transport in Poland. The work is of a utilitarian nature, as the need for conscious risk management has been demonstrated in rail transport, and guidelines for risk management in rail transport have been developed. In addition, the paper presents the possibilities offered by modeling a problem situation with the help of network thinking methodology for solving complex problems, including supporting creativity on the example of railroad transport in Poland. To define problem situations in the studied area, a complex process of a multifaceted approach to the analyzed issue is required. When assessing the effectiveness of risk management in rail transport, one should take into account the adverse factors influencing the studied phenomenon. The main sources used to write this article were the available literature in the field of emergency management and publications on railroad transport. The conclusions have been based on the results of logical analysis, verified empirically with the use of statistical methods. [ABSTRACT FROM AUTHOR]

Predictive Process Monitoring (PPM) has been integrated into process mining use cases as a value-adding task. PPM provides useful predictions on the future of the running business processes with respect to different perspectives, such as the upcoming activities to be executed next, the final execution outcome, and performance indicators. In the context of PPM, Machine Learning (ML) techniques are widely employed. In order to gain trust of stakeholders regarding the reliability of PPM predictions, eXplainable Artificial Intelligence (XAI) methods have been increasingly used to compensate for the lack of transparency of most of predictive models. Multiple XAI methods exist providing explanations for almost all types of ML models. However, for the same data, as well as, under the same preprocessing settings or same ML models, generated explanations often vary significantly. Corresponding variations might jeopardize the consistency and robustness of the explanations and, subsequently, the utility of the corresponding model and pipeline settings. This paper introduces a framework that enables the analysis of the impact PPM-related settings and ML-model-related choices may have on the characteristics and expressiveness of the generated explanations. Our framework provides a means to examine explanations generated either for the whole reasoning process of an ML model, or for the predictions made on the future of a certain business process instance. Using well-defined experiments with different settings, we uncover how choices made through a PPM workflow affect and can be reflected through explanations. This framework further provides the means to compare how different characteristics of explainability methods can shape the resulting explanations and reflect on the underlying model reasoning process. [ABSTRACT FROM AUTHOR]

Recently, the high-dimensional negative binomial regression (NBR) for count data has been widely used in many scientific fields. However, most studies assumed the dispersion parameter as a constant, which may not be satisfied in practice. This paper studies the variable selection and dispersion estimation for the heterogeneous NBR models, which model the dispersion parameter as a function. Specifically, we proposed a double regression and applied a double ℓ 1 -penalty to both regressions. Under the restricted eigenvalue conditions, we prove the oracle inequalities for the lasso estimators of two partial regression coefficients for the first time, using concentration inequalities of empirical processes. Furthermore, derived from the oracle inequalities, the consistency and convergence rate for the estimators are the theoretical guarantees for further statistical inference. Finally, both simulations and a real data analysis demonstrate that the new methods are effective. [ABSTRACT FROM AUTHOR]

Contemporary information-sharing environments such as Facebook offer a wide range of social and practical benefits. These environments, however, may also lead to privacy and security violations. Moreover, there is usually a trade-off between the benefits gained and the accompanying costs. Due to the uncertain nature of the information-sharing environment and the lack of technological literacy, the layperson user often fails miserably in balancing this trade-off. In this paper, we use game theory concepts to formally model this problem as a "game", in which the players are the users and the pay-off function is a combination of the benefits and costs of the information-sharing process. We introduce a novel theoretical framework called Online Information-Sharing Assistance (OISA) to evaluate the interactive nature of the information-sharing trade-off problem. Using these theoretical foundations, we develop a set of AI agents that attempt to calculate a strategy for balancing this trade-off. Finally, as a proof of concept, we conduct an empirical study in a simulated Facebook environment in which human participants compete against OISA-based AI agents, showing that significantly higher utility can be achieved using OISA. [ABSTRACT FROM AUTHOR]

This paper presents a plantar pressure sensor system (P2S2) integrated in the insoles of shoes to detect thirteen commonly used human movements including walking, stooping left and right, pulling a cart backward, squatting, descending, ascending stairs, running, and falling (front, back, right, left). Six force sensitive resistors (FSR) sensors were positioned on critical pressure points on the insoles to capture the electrical signature of pressure change in the various movements. A total of 34 adult participants were tested with the P2S2. The pressure data were collected and processed using a Principal Component Analysis (PCA) for input to the multiple machine learning (ML) algorithms, including k-NN, neural network and Support-Vector Machine (SVM) algorithms. The ML models were trained using four-fold cross-validation. Each fold kept subject data independent from other folds. The model proved effective with an accuracy of 86%, showing a promising result in predicting human movements using the P2S2 integrated in shoes. [ABSTRACT FROM AUTHOR]

For computationally intensive problems, data-driven evolutionary algorithms (DDEAs) are advantageous for low computational budgets because they build surrogate models based on historical data to approximate the expensive evaluation. Real-world optimization problems are highly susceptible to noisy data, but most of the existing DDEAs are developed and tested on ideal and clean environments; hence, their performance is uncertain in practice. In order to discover how DDEAs are affected by noisy data, this paper empirically studied the performance of DDEAs in different noisy environments. To fulfill the research purpose, we implemented four representative DDEAs and tested them on common benchmark problems with noise simulations in a systematic manner. Specifically, the simulation of noisy environments considered different levels of noise intensity and probability. The experimental analysis revealed the association relationships among noisy environments, benchmark problems and the performance of DDEAs. The analysis showed that noise will generally cause deterioration of the DDEA's performance in most cases, but the effects could vary with different types of problem landscapes and different designs of DDEAs. [ABSTRACT FROM AUTHOR]

Working with art is considered to have a special potential for heterogeneity-sensitive learning processes. It should pick up on elementary experiences, open up emotional access and diverse learning paths, and serve different learning types, strategies, and levels. In the heterogeneity-sensitive didactics of religion and art, however, these assumptions have not yet been empirically proven, and, in general, theoretical designs dominate in the heterogeneity-sensitive didactics of religion. In our contribution, we explore the question of how art processes enable heterogeneity-sensitive learning processes in religious education and help to reduce social inequality. For this purpose, we evaluate the first results of our qualitative empirical study on heterogeneity-sensitive learning processes with art in religious education (hekuru, which is based on subject didactic development research. There are initial indications that, contrary to widespread opinions, the thesis of a generally easy accessibility of art proves that needs have to be differentiated, and that special milieu sensitivity is advisable. [ABSTRACT FROM AUTHOR]

In recent years, as people's awareness of energy conservation, environmental protection, and sustainable development has increased, discussions related to electric vehicles (EVs) have aroused public debate on social media. At some point, most consumers face the possible risks of EVs—a critical psychological perception that invariably affects sales of EVs in the consumption market. This paper chooses to deconstruct customers' perceived risk from third-party comment data in social media, which has better coverage and objectivity than questionnaire surveys. In order to analyze a large amount of unstructured text comment data, the natural language processing (NLP) method based on machine learning was applied in this paper. The measurement results show 15 abstracts in five consumer perceived risks to EVs. Among them, the largest number of comments is that of "Technology Maturity" (A13) which reached 25,329, and which belongs to the "Performance Risk" (PR1) dimension, indicating that customers are most concerned about the performance risk of EVs. Then, in the "Social Risk" (PR5) dimension, the abstract "Social Needs" (A51) received only 3224 comments and "Preference and Trust Rank" (A52) reached 22,324 comments; this noticeable gap indicated the changes in how consumers perceived EVs social risks. Moreover, each dimension's emotion analysis results showed that negative emotions are more than 40%, exceeding neutral or positive emotions. Importantly, customers have the strongest negative emotions about the "Time Risk" (PR4), accounting for 54%. On a finer scale, the top three negative emotions are "Charging Time" (A42), "EV Charging Facilities" (A41), and "Maintenance of Value" (A33). Another interesting result is that "Social Needs" (A51)'s positive emotional comments were larger than negative emotional comments. The paper provides substantial evidence for perceived risk theory research by new data and methods. It can provide a novel tool for multi-dimensional and fine-granular capture customers' perceived risks and negative emotions. Thus, it has the potential to help government and enterprises to adjust promotional strategies in a timely manner to reduce higher perceived risks and emotions, accelerating the sustainable development of EVs' consumption market in China. [ABSTRACT FROM AUTHOR]

Many visual representations, such as volume-rendered images and metro maps, feature a noticeable amount of information loss due to a variety of many-to-one mappings. At a glance, there seem to be numerous opportunities for viewers to misinterpret the data being visualized, hence, undermining the benefits of these visual representations. In practice, there is little doubt that these visual representations are useful. The recently-proposed information-theoretic measure for analyzing the cost–benefit ratio of visualization processes can explain such usefulness experienced in practice and postulate that the viewers' knowledge can reduce the potential distortion (e.g., misinterpretation) due to information loss. This suggests that viewers' knowledge can be estimated by comparing the potential distortion without any knowledge and the actual distortion with some knowledge. However, the existing cost–benefit measure consists of an unbounded divergence term, making the numerical measurements difficult to interpret. This is the second part of a two-part paper, which aims to improve the existing cost–benefit measure. Part I of the paper provided a theoretical discourse about the problem of unboundedness, reported a conceptual analysis of nine candidate divergence measures for resolving the problem, and eliminated three from further consideration. In this Part II, we describe two groups of case studies for evaluating the remaining six candidate measures empirically. In particular, we obtained instance data for (i) supporting the evaluation of the remaining candidate measures and (ii) demonstrating their applicability in practical scenarios for estimating the cost–benefit of visualization processes as well as the impact of human knowledge in the processes. The real world data about visualization provides practical evidence for evaluating the usability and intuitiveness of the candidate measures. The combination of the conceptual analysis in Part I and the empirical evaluation in this part allows us to select the most appropriate bounded divergence measure for improving the existing cost–benefit measure. [ABSTRACT FROM AUTHOR]

Contemporary literature continues to foster discussion whether financial development is important for economic growth. In the clash of theoretical arguments, the prevailing idea is that finance exerts a direct positive influence on GDP growth. However, the presence of theoretical counterarguments and contradictory results of empirical studies suggest that scientists, in search of an answer about the direction and power of the net effect, should develop methods of empirical analysis, and the very mystery of the relationship between finance and growth will finally be solved exclusively empirically. In this paper, the authors contribute to the development of the 'finance-growth' literature by answering some existing questions concerning the transmission channels from finance to growth, relying on more recent data compared to already conducted studies. We use panel data covering the period 1995 to 2019 for 168 countries. In addition, the paper touches on the problem of studying the exogenous conditions of such channels, considering the assumption that among these conditions there may be those that hinder the impact of financial deepening on economic growth. Our focus is on monetary conditions, and in the empirical part of the study, we touch upon the problem of the influence of price stability on the operation of these transmission channels. The methods of the conducted study are based on the dynamic panel data analysis techniques (System GMM). The novelty of this paper lies in the development of the modern theory of the financial sector transmission mechanism in the economic growth context. The main result of the study is that productivity channel is the most reliable transmission channel of financial deepening to economic growth. Furthermore, the effectiveness of this channel remains virtually unaffected by inflation. The channel of capital accumulation should be considered less reliable (in terms of statistical reliability of estimates obtained), but it has turned out to be a more economically significant transmission channel. This channel is sensitive to the inflation factor in certain categories of countries. Finally, as follows from the estimates gained, the non-linearity of the "finance-growth" relationship can be explained by the non-linearity of the variable responsible for the capital accumulation channel. [ABSTRACT FROM AUTHOR]

This paper mainly focuses on the relationship between the subjective evaluation of air quality and the quality of life (QOL) of middle-aged and elderly residents in China. The 2018 China Health and Retirement Longitudinal Study (CHARLS) project database is the key sources of data, from which 16,736 valid samples were used in our research. Multivariate linear regression analysis and binomial logistic regression model were applied to detect the impact of the subjective evaluation of air quality on QOL, which was evaluated in two dimensions, which are health utility and experienced utility, using the health utility EQ-5D score and the experienced utility of life satisfaction score. Our results show that there is a significant positive correlation between the subjective evaluation of air quality and the two dimensions of QOL. Age, education, marital status and sleep status also have a relatively great impact on the QOL of residents. This worked studied the overall QOL of middle-aged and elderly residents in China, while policy suggestions regarding high-quality air public goods are also given in the paper. [ABSTRACT FROM AUTHOR]

Web-based participatory mapping technologies are being increasingly harnessed by local governments to crowdsource local knowledge and engage the public in urban planning policies as a means of increasing the transparency and legitimacy of planning processes and decisions. We refer to these technologies as "geoparticipation". Current innovations are outpacing research into the use of geoparticipation in participatory planning practices. To address this knowledge gap, this paper investigates the objectives of web-based geoparticipation and uses empirical evidence from online survey responses related to 25 urban planning projects in nine countries across three continents (Europe, North America, and Australia). The survey adopts the objectives of the Spectrum for Public Participation that range from information empowerment, with each category specifying promises about how public input is expected to influence decision-making (IAP2, 2018). Our findings show that geoparticipation can leverage a 'middle-ground' of citizen participation by facilitating involvement alongside consultation and/or collaboration. This paper constitutes a pilot study as a step toward more robust and replicable empirical studies for cross-country comparisons. Empowerment (or citizen control) is not yet a normative goal or outcome for web-based geoparticipation. Our evidence also suggests that information is pursued alongside other objectives for citizen participation, and therefore functions not as a "low-hanging fruit" as portrayed in the literature, but rather as a core component of higher intensities of participation. [ABSTRACT FROM AUTHOR]