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The choice of bandwidth is crucial in circular kernel density estimation. Various bandwidth selection techniques have been proposed in the literature. New bandwidth selectors based on the measure β -divergence for kernel density estimation with circular data are presented in this work. These selectors are obtained by minimizing the mean of the measure β -divergence between the density to be estimated and its estimator. The idea is based on the generalization of the standard method which selects the bandwidth by minimizing the mean integrated squared error (MISE). The performance of the proposed selectors is evaluated through a simulation study and compared with other existing selectors. These selectors are illustrated with some real datasets. [ABSTRACT FROM AUTHOR]

Kernel density estimation is a prevalent technique employed for nonparametric density estimation, enabling direct estimation from the data itself. This estimation involves two crucial elements: selection of the kernel function and the determination of the appropriate bandwidth. The selection of the bandwidth plays an important role in kernel density estimation, which has been developed over the past decade. A range of methods is available for selecting the bandwidth, including the plug-in bandwidth. In this article, the proposed plug-in bandwidth is introduced, which leverages shifted Chebyshev series-based approximation to determine the optimal bandwidth. Through a simulation study, the performance of the suggested bandwidth is analyzed to reveal its favorable performance across a wide range of distributions and sample sizes compared to alternative bandwidths. The proposed bandwidth is also applied for kernel density estimation on real dataset. The outcomes obtained from the proposed bandwidth indicate a favorable selection. Hence, this article serves as motivation to explore additional plug-in bandwidths that rely on function approximations utilizing alternative series expansions. [ABSTRACT FROM AUTHOR]

Mastering, on the other hand, is the process of refining a recording made after the mixing stage using various techniques before the album is pressed and distributed. The mastering process includes adjusting the dynamics of signal frequencies, regulating tonal balances with equalizers, and determining and downsizing audio file formats. Mastering creates coherence among the tracks within an album and provides listeners with a higherquality listening experience. The entire process of recording and mixing, encompassing mastering, involves converting signals from analog equipment and software into digital values using an Analog-to-Digital (A/D) converter within a Digital Audio Workstation (DAW). These software programs have become essential in music productions. Due to the decreasing cost of technology and the opportunities it provides, music production software has shifted towards home users. People can now, without the need for highbudget studios, complete many recording, mixing, and mastering processes entirely with computer-based systems at lower costs in their own homes. The term "in the box" refers to all these production stages taking place within a computer. With the advancement of technology, individuals have been able to produce albums in home studios, and digital processors, which are cheaper and more practical than analog equipment due to the digitization of analog devices, have started to be preferred over analog equipment. This study explores the extent of changes in technical and technological approaches towards the use of digital signal processors (DSP), which emerged in the 1960s, in place of analog processors. The research aims to identify and examine the differences between digitalbased signal processors and analog devices in terms of their usage, implementation, and processing in music technologies. This research investigates how digital-based signal processors differ from analog processors in terms of processing method, processing flexibility, processing speed, processing quality, and sensitivity on the audio signal. Based on these findings, it is concluded that digital-based signal processors, which are replacing analog processors in the music industry, do not possess the same qualities as analog devices. [ABSTRACT FROM AUTHOR]

Software components such as application programming interfaces (APIs) provided by external developers are vital to online digital platforms. Although APIs generally increase the variety of products according to anecdote, the precise relationship between the categories of APIs and product variety is not yet known. We find that APIs, regarding their use frequency, are categorized into three groups. The core is a group of frequently used APIs, whereas the periphery is a group of sparsely used APIs. In a large and mature platform ecosystem, an additional group of APIs, the regular core, mainly provided by third-party developers, emerges. APIs in the regular core are the main driver of product variety. However, we also find that the strength of this effect diminishes in a newly created product category when most of the new products are built by duplicating the usage of APIs from other products. A platform owner can stimulate developers' creativity by acting as a bridge between digital product providers and third-party developers. It can collect functional needs from third-party developers and then share them with product providers. Therefore, the latter can build APIs that developers need. Software components, such as application programming interfaces (APIs), provided by both the platform owner and external developers in a platform ecosystem are vital to online digital platforms. These APIs are associated with functionality provided by the platform. A larger number of APIs used is generally associated with an increase in the platform ecosystem's variety of products. However, the precise nature of the relationship between the use of APIs and product variety in a platform ecosystem is not yet known because of the lack of appropriate methodologies to analyze the complex data generated in such platform ecosystems. Drawing on a recombinatorial design perspective, we use a dynamic network model to create metrics for product variety in a digital ecosystem and study how the use of different types of APIs affects product variety. In particular, we theorize that, contrary to existing models, the structure of APIs in a large platform ecosystem is not limited to a core–periphery structure, but includes an additional third layer that we refer to as the regular core. We further hypothesize that external APIs in the regular core play a crucial role in increasing product variety in the ecosystem. To test our hypothesis, we conduct an empirical study using longitudinal data of all available digital product (i.e., plug-in) source codes on WordPress.org, the world's largest content management system and one of the most extensive digital platform ecosystems. By analyzing all of the plug-ins on WordPress over 10 years from its inception, we find the support of our three-layer structure of APIs in a platform ecosystem: complete core, regular core, and periphery. Our empirical analysis further supports the role of external APIs in the regular core in increasing product variety. We, however, find that the strength of this effect diminishes in a newly created product category. History: Param Singh, Senior Editor; Pallab Sanyal, Associate Editor. Funding: This work was supported by the Ministry of Education Singapore [Grants R-253-000-122-133 and T1-251RES1916], the National Science Foundation [Grants 1261977 and 1120966], and Club Informatique des Grandes Entreprises Françaises. Supplemental Material: The e-companion is available at https://doi.org/10.1287/isre.2022.1172. [ABSTRACT FROM AUTHOR]

This paper presents an ABAQUS™ plug-in for predicting the stiffness of a composite plate with periodic microstructure. The plug-in supports the 3D finite element models of unit cells with in-plane symmetry from a periodic plate. With the help of the plug-in, a periodic plate can be considered a representative of the homogeneous plate. In this article, a plug-in is developed for determining the composite plate stiffness by using the principle of homogenization of plates with periodic microstructure. The plug-in is verified through numerical examples and the results closely corroborate with the published data.

Multi-state models are frequently used when data come from subjects observed over time and where focus is on the occurrence of events that the subjects may experience. A convenient modeling assumption is that the multi-state stochastic process is Markovian, in which case a number of methods are available when doing inference for both transition intensities and transition probabilities. The Markov assumption, however, is quite strict and may not fit actual data in a satisfactory way. Therefore, inference methods for non-Markov models are needed. In this paper, we review methods for estimating transition probabilities in such models and suggest ways of doing regression analysis based on pseudo observations. In particular, we will compare methods using land-marking with methods using plug-in. The methods are illustrated using simulations and practical examples from medical research. [ABSTRACT FROM AUTHOR]

Weakly supervised semantic segmentation (WSSS) generally utilizes the Class Activation Map (CAM) to synthesize pseudo-labels. However, the current methods of obtaining CAM focus on salient features of a specific layer, resulting in highlighting the most discriminative regions and further leading to rough segmentation results for WSSS. In this paper, we rethink the potential of the ordinary classifier and find that if features of all the layers are applied, the classifier will obtain CAM with complete discriminative regions. Inspired by this, we propose Fully-CAM for WSSS, which can fully exploit the potential of the ordinary classifier and yield more accurate segmentation results. Precisely, Fully-CAM firstly weights feature with their corresponding gradients to yield CAMs of each layer, then fusing these layers’ CAMs could generate an ultimate CAM with complete discriminative regions. Furthermore, Fully-CAM is encapsulated into a plug-in, which can be mounted on any trained ordinary classifier with convolution layer, and it exceeds its previous performance without extra training.

Ground-penetrating radar (GPR) is a precious and reliable research tool broadly used in archaeology because of its capacity to produce three-dimensional data about features preserved underground, such as buildings, infrastructures, and burials, as well as building rubble. GPR data (time-slices) management and exploitation in Geographic Information Systems (GIS) is mostly limited to the visualization and the manual interpretation and mapping of separate single time-slices. This study presents a newly developed plug-in designed to automatically post-process GPR time-slices in a GIS environment, to identify anomalies, and to produce a synchronic view of them. This map product, when combined with a DTM, results in a 2D map of subsurface anomalies which shows the absolute height of features above sea level, thus offering a comprehensive view of the three-dimensional configuration of the subsurface features identified. The paper illustrates the pixel-based processing chain of the plug-in and the results of the tests carried out in the case study of the Roman town of Falerii Novi (Italy), on the basis of high-resolution open access GPR data recently collected by the University of Cambridge and Ghent. [ABSTRACT FROM AUTHOR]

Digital learning environments in education are still dominated by traditional learning management system (LMS). LMS Moodle is one of the leading open source learning management systems. The system is supported by a strong active community that contributes to the development and extends the capabilities of Moodle. One way to enhance the functionality of Moodle is to add plugins to the default Moodle install. Various plugins are available to install in Moodle, one of them is CodeRunner. One of the key trends in educational technology is growing focus on measuring learning. LMS Moodle offers tools to assess, measure, and document learning, and CodeRunner is one of them. First of all, CodeRunner is a Moodle question-type plug-ing which allows setting questions, where the answer is program code, and runs the program in order to grade a student's answer. But the scope of the plug-in can be expanded by the ability to add new types of questions that combine different user interfaces of the answer, programming languages, templates and methods for grading and displaying the result. This allows using CodeRunner in other courses of computer science and engineering to test assignment for which a program should be used to evaluate correctness, in other words, for the questions with computer assisted assessment. Capabilities of plug-in CodeRunner of LMS Moodle are demonstrated by the example of creating the e-course “Applied Automata Theory”.

Ground-penetrating radar (GPR) is a precious and reliable research tool broadly used in archaeology because of its capacity to produce three-dimensional data about features preserved underground, such as buildings, infrastructures, and burials, as well as building rubble. GPR data (time-slices) management and exploitation in Geographic Information Systems (GIS) is mostly limited to the visualization and the manual interpretation and mapping of separate single time-slices. This study presents a newly developed plug-in designed to automatically post-process GPR time-slices in a GIS environment, to identify anomalies, and to produce a synchronic view of them. This map product, when combined with a DTM, results in a 2D map of subsurface anomalies which shows the absolute height of features above sea level, thus offering a comprehensive view of the three-dimensional configuration of the subsurface features identified. The paper illustrates the pixel-based processing chain of the plug-in and the results of the tests carried out in the case study of the Roman town of Falerii Novi (Italy), on the basis of high-resolution open access GPR data recently collected by the University of Cambridge and Ghent.

Despite—or because of—California’s vaunted love affair with the automobile, the state could become a leader in the development of new, climate change–friendly transport. Electric cars, hybrid vehicles, plug-in electric hybrids, robotic cars, vehicles powered by natural gas or other fuels, car sharing, bike sharing, and the matching of unused vehicle space with potential passengers are just some of the ideas on the near horizon in the Golden State. [ABSTRACT FROM PUBLISHER]

The kernel distribution function estimator method is the most popular nonparametric method to estimate the cumulative distribution function F(x). In this investigation, we propose a new estimator for F(x) based on a linear combination of kernels. The mean integrated squared error, asymptotic mean integrated squared error and the asymptotically optimal bandwidth for the new estimator are derived. Also, based on the plug-in technique in density estimation, we propose a data based method to select the bandwidth for the new estimator. In addition, we evaluate the new estimator using simulations and real life data. [ABSTRACT FROM AUTHOR]

In recent years, the body of literature on frequentist model averaging in econometrics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this article, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimization of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favorable properties compared with some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function mis-specification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two data sets on housing and economic growth. [ABSTRACT FROM AUTHOR]

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With the introduction of plug-in vehicles (PEVs) into the light-duty vehicle fleet, the tail-pipe emissions of GHGs and criteria pollutants will be partly transferred to electricity generating units. To study the impact of PEVs on well-to-wheels emissions, the U.S. Western electrical grid serving the South Coast Air Basin (SoCAB) of California is modeled with both spatial and temporal resolution at the level of individual power plants. Electricity load is calculated and projected for future years, and the temporal electricity generation of each power plant within the SoCAB is modeled based on historical data and knowledge of electricity generation and dispatch. Due to the efficiency and pollutant controls governing the performance of the Western grid, the deployment of PEVs results in a daily reduction of greenhouse gases (GHGs) and tail-pipe emissions, especially in the critical morning and afternoon commute hours. The extent of improvement depends on charging scenarios, future grid mix, and the number and type of plug-in vehicles. In addition, charging PEVs using wind energy that would otherwise be curtailed can result in a substantial emissions reduction. Smart control will be required to manage PEV charging in order to mitigate renewable intermittencies and decrease emissions associated with peaking power production. © 2011 Elsevier B.V. All rights reserved.

A Bayesian procedure for bandwidth selection in kernel circular density estimation is investigated, when the Markov chain Monte Carlo (MCMC) sampling algorithm is utilized for Bayes estimates. Under the quadratic and entropy loss functions, the proposed method is evaluated through a simulation study and real data sets, which were already discussed in the literature. The proposed Bayesian approach is very competitive in comparison with the existing classical global methods, namely plug-in and cross-validation techniques. [ABSTRACT FROM AUTHOR]

• Below 200 g CO 2 eq./kWh, chargeable bus emissions lower than HVO-biodiesel hybrids. • Above 750 g CO 2 eq./kWh, diesel hybrids preferable to plug-in/all-electric buses. • Excluding operation, chassis, frame and body dominate load in all buses. • All-electric buses best for reducing local air pollution and regional impact. This study explores life cycle environmental impacts of city buses, depending on the: (1) degree of electrification; (2) electricity supply mix, for chargeable options; and (3) choice of diesel or hydrogenated vegetable oil (HVO), a biodiesel, for options with combustion engine. It is a case study, which uses industry data to investigate the impact on climate change, a key driver for electrification, and a wider set of impacts, for average operation in Sweden, the European Union and the United States of America. The results show that non-chargeable hybrid electric vehicles provide clear climate change mitigation potential compared to conventional buses, regardless of the available fuel being diesel or HVO. When fueling with HVO, plug-in hybrid and all-electric buses provide further benefits for grid intensities below 200 g CO 2 eq./kWh. For diesel, the all-electric option is preferable up to 750 g CO 2 eq./kWh. This is the case despite batteries and other electric powertrain parts causing an increase of CO 2 emissions from vehicle production. However, material processing to make common parts, i.e. chassis, frame and body, dominates the production load for all models. Consequently, city buses differ from passenger cars, where the battery packs play a larger role. In regard to other airborne pollutants, the all-electric bus has the best potential to reduce impacts overall, but the results depend on the amount of fossil fuels and combustion processes in the electricity production. For toxic emissions and resource use, the extraction of metals and fossil fuels calls for attention. [ABSTRACT FROM AUTHOR]

Ontologies are spreading more and more in the field of information technologies as a privileged solution allowing the formalization of knowledge. The theoretical model of ontologies is most promising. They are increasingly ubiquitous given the benefits they present. Despite the proliferation of research proposing approaches dedicated to the design of a database from an ontology, the tools to design a database from an ontology are rare or inaccessible. Thus, in this contribution, we present our approach for the development of an Eclipse Plug-in, in order to automatically generate a conceptual model of a relational database from an ontology. To evaluate the usefulness of our approach, we used our resulting Eclipse Plug-in to automatically generate a conceptual model of a relational database from an ontology, customize it, and automatically generate the corresponding SQL script for Data Definition. The results of this experiment showed that our Plug-in constitutes a concretization of our approach and a means of automatic translation from the ontological model to the relational model. [ABSTRACT FROM AUTHOR]