Aluminum Material Recycling Efficiency Study Based on Multi-Country Comparison

Aluminum is one of the most abundant metals in the world and plays an important role in various industries, but the synthesis of bauxite into aluminum is accompanied by serious environmental pollution, such as electricity consumption, carbon dioxide, and toxic by-products. This paper analyzes the development of aluminum materials, specifically discusses how aluminum metal is made and recycled, and analyzes ways to add value to aluminum from the perspective of circular economics, then proposes ways to popularize the standardization of automotive parts so as to improve the efficiency of aluminum recycling in the automotive industry

On the asymptotic properties of a bagging estimator with a massive dataset

Bagging is a useful method for large-scale statistical analysis, especially when the computing resources are very limited. We study here the asymptotic properties of bagging estimators for $M$-estimation problems but with massive datasets. We theoretically prove that the resulting estimator is consistent and asymptotically normal under appropriate conditions. The results show that the bagging estimator can achieve the optimal statistical efficiency, provided that the bagging subsample size and the number of subsamples are sufficiently large. Moreover, we derive a variance estimator for valid asymptotic inference. All theoretical findings are further verified by extensive simulation studies. Finally, we apply the bagging method to the US Airline Dataset to demonstrate its practical usefulness

Research on acceptance analysis of application programming learning platform for industrial robots

Objective To investigate the college students’ acceptance of solid model teaching and virtual model teaching. Methods Several factors (behavioral intention, effort expectation and performance expectation) in UTAUT (Integrated Technology Acceptance Model) were used for data analysis using T-test. Results The experimental results showed that students had higher behavioral intention to the entity model, higher eff ort expectation and performance expectation to the entity model, and the diff erence was signifi cant. Compared with the virtual 3D model, students prefer the physical device that can be held in their hands and operated. Conclusion In the design of robot application programming teaching platform, we should appropriately introduce the teaching link of solid model, and combine the advantages of virtual model and solid model

Large-scale Multi-layer Academic Networks Derived from Statistical Publications

The utilization of multi-layer network structures now enables the explanation of complex systems in nature from multiple perspectives. Multi-layer academic networks capture diverse relationships among academic entities, facilitating the study of academic development and the prediction of future directions. However, there are currently few academic network datasets that simultaneously consider multi-layer academic networks; often, they only include a single layer. In this study, we provide a large-scale multi-layer academic network dataset, namely, LMANStat, which includes collaboration, co-institution, citation, co-citation, journal citation, author citation, author-paper and keyword co-occurrence networks. Furthermore, each layer of the multi-layer academic network is dynamic. Additionally, we expand the attributes of nodes, such as authors' research interests, productivity, region and institution. Supported by this dataset, it is possible to study the development and evolution of statistical disciplines from multiple perspectives. This dataset also provides fertile ground for studying complex systems with multi-layer structures

Least squares estimation of spatial autoregressive models for large-scale social networks

Due to the rapid development of various social networks, the spatial autoregressive (SAR) model is becoming an important tool in social network analysis. However, major bottlenecks remain in analyzing largescale networks (e.g., Facebook has over 700 million active users), including computational scalability, estimation consistency, and proper network sampling. To address these challenges, we propose a novel least squares estimator (LSE) for analyzing large sparse networks based on the SAR model. Computationally, the LSE is linear in the network size, making it scalable to analysis of huge networks. In theory, the LSE is root n-consistent and asymptotically normal under certain regularity conditions. A new LSE-based network sampling technique is further developed, which can automatically adjust autocorrelation between sampled and unsampled units and hence guarantee valid statistical inferences. Moreover, we generalize the LSE approach for the classical SAR model to more complex networks associated with multiple sources of social interaction effect. Numerical results for simulated and real data are presented to illustrate performance of the LSE.National Natural Science Foundation of China [71532001, 11525101, 71332006, 11701560, 11401482]; Beijing Municipal Social Science Foundation [17GLC051]; Center for Applied Statistics, School of Statistics, Renmin University of China; Center of Statistical Research, Southwestern University of Finance and Economics; China's National Key Research Special Program [2016YFC0207700]; NSF [DMS-1309507, DMS-1418172]; NSFC [11571009]Open Access JournalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

An Accelerated Hierarchical Approach for Object Shape Extraction and Recognition

We present a novel automatic supervised object recognition algorithm based on a scale and rotation invariant Fourier descriptors algorithm. The algorithm is hierarchical in nature to capture the inherent intra-contour spatial relationships between the parent and child contours of an object. A set of distance metrics are introduced to go along with the hierarchical model. To test the algorithm, a diverse database of shapes is created and used to train standard classification algorithms, for shape-labeling. The implemented algorithm takes advantage of the multi-threaded architecture and GPU efficient image-processing functions present in OpenCV wherever possible, speeding up the running time and making it efficient for use in real-time applications. The technique is successfully tested on common traffic and road signs of real-world images, with excellent overall performance that is robust to moderate noise levels

A Holistic Approach for Enhancing Distributed Education with Multi-Campus Course Delivery Methods

To create an emerging research institution, a regional university was created that spans multiple campuses within a radius of more than one hundred miles by merging at least three current institutions. The merge allowed the university to pool its human and technical resources. Students can now pursue new degrees that were not available before at one campus or another, take a newly available technical or specialty courses, and even select their own preferred professor when a course is offered by many faculty. In order to serve students at multiple campuses that are geographically far a part, the university instituted policies to facilitate accessibility of courses to all students while meeting prerequisites and minimum enrollment requirements. This paper chronicles the policies, procedures, and faculty efforts in creating a sustainable framework for implementing a distributed campus course delivery that is acceptable by the university/college administration, the department, the faculty, and most importantly the student. Our experience shows that a successful framework should address many issues, including: - Logistics o Where to offer the courses; one campus, all campuses. o Is transportation provided for student at a convenient time o Etc. - Scheduling o Schedule classes so that student can attend all their classes on-time without conflicts o Coordinate scheduling among campuses - Faculty incentives o Maintain good faculty-to-student ratio o Provide formula for workload computation o Provide teaching/grading assistance o Home campus course Attribution - IT support o Provide Interactive TV with high bandwidth o Allow for faculty-to-student interaction o Provide state-of-the-art class podium o Allow for class recording o Allow for in-office tutorials or Q/A session through collaboration - Course Management System Delivery Methods o Enable many productive tools in the course management system o Allow proper notification for the student - Assessment and student participation o Maintain interaction with student on daily and weekly basis o Compare results from both campuses to avoid any emerging issues. The paper will present our efforts in each of the above areas, showing that despite the challenges faced, a distributed delivery system can be successful when the above issues/factors are adequately addressed. The results from our courses at the graduate and undergraduate levels show that students assessments don’t show any significant difference across campuses or based on where the home campus of the faculty is. By presenting our study, we hope that other institutions who are considering distributed education can benefit from our experience by adopting best practices while avoiding pitfalls

Modeling Social Media User Content Generation Using Interpretable Point Process Models

In this article, we study the activity patterns of modern social media users on platforms such as Twitter and Facebook. To characterize the complex patterns we observe in users' interactions with social media, we describe a new class of point process models. The components in the model have straightforward interpretations and can thus provide meaningful insights into user activity patterns. A composite likelihood approach and a composite EM estimation procedure are developed to overcome the challenges that arise in parameter estimation. Using the proposed method, we analyze Donald Trump's Twitter data and study if and how his tweeting behavior evolved before, during and after the presidential campaign. Additionally, we analyze a large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors; in this analysis, we also discuss the effect of social ties on a user's online content generating behavior

An Asymptotic Analysis of Minibatch-Based Momentum Methods for Linear Regression Models

Momentum methods have been shown to accelerate the convergence of the standard gradient descent algorithm in practice and theory. In particular, the minibatch-based gradient descent methods with momentum (MGDM) are widely used to solve large-scale optimization problems with massive datasets. Despite the success of the MGDM methods in practice, their theoretical properties are still underexplored. To this end, we investigate the theoretical properties of MGDM methods based on the linear regression models. We first study the numerical convergence properties of the MGDM algorithm and further provide the theoretically optimal tuning parameters specification to achieve faster convergence rate. In addition, we explore the relationship between the statistical properties of the resulting MGDM estimator and the tuning parameters. Based on these theoretical findings, we give the conditions for the resulting estimator to achieve the optimal statistical efficiency. Finally, extensive numerical experiments are conducted to verify our theoretical results.Comment: 45 pages, 5 figure