Classification Perspective Research Articles

Spatial distribution of soil bulk density and its relationship with slope and vegetation allocation model in rehabilitation of dumping site in loess open-pit mine area.

Studies of soil bulk density (BD) spatial variations of land reclaimed after mining have become a focus of land reclamation and ecological restoration research. However, there have been few studies on the relationship among the reconstructed BD, terrain conditions, and vegetation growth. We examined the southern dumping site of the Pingshuo Antaibao open-pit coal mine located in a loess area in China. Field sampling data, digital elevation models (DEMs), and high-definition images were obtained, and indoor testing, geostatistics, and inverse distance weighting (IDW) were applied. This paper aims to analyze the spatial distribution law of the reconstructed BD and focus on its relationship with slope and vegetation allocation models. We demonstrated that (1) BD increased with soil depth and varied moderately within each layer. (2) The BD variation amplitude of the top 0-20-cm soil layer in both the east-west and south-north directions was small and more similar in the east-west direction than in the south-north direction, which was affected by herbaceous root systems. In the next four layers from 20 to 60 cm, the variation in BD in the east-west direction was far larger than that in the south-north direction, which was affected by vegetation classification. (3) On the whole, BD decreased with increasing slope, but when the slope was between 0° and 21°, BD exhibited a specific change law. (4) From the perspective of vegetation classification, the orders of magnitude of BD in the 0-20-cm and 20-60-cm layers differed. Overall, BD in areas vegetated with Korshinsk Peashrub was the lowest, and BD was moderate in areas with mixed vegetation, while BD was the highest in areas without vegetation or only vegetated with Black Locust. The mixed grass-bush-tree or bush-tree mode attained the best effect in regulating BD. These results can improve the basic principles of land reclamation in mining areas and provide a basis for further optimizing land reclamation technology in practice.

A Systematic Literature Review for Personnel Scheduling Problems

Organizations need to focus on many parameters to reach their goals such as personnel satisfaction at the top level, profit maximization, increasing system efficiency and minimizing costs. By carefully examining the significant effect of personnel scheduling on the production of goods and services, achieving a fair distribution of work among the employees paves the way for higher motivation and performance of the employees, particularly, in production and service industries. In this paper, a systematic literature review (SLR) approach is used to demonstrate the necessity of scheduling studies in order to overcome the deficiencies in auxiliary activity groups. It sheds light on a new and very important areas such as examining the model structures of sector differences, and guiding researchers. New trends and approaches are presented for the personnel scheduling problems. Different classification perspectives are displayed.

ІНТЕГРАЛЬНЕ ОЦІНЮВАННЯ РИЗИКІВ УКРАЇНСЬКОГО РИНКУ ІПОТЕЧНОГО КРЕДИТУВАННЯ

Стаття присвячена дослідженню підходів до класифікації ризиків на ринку іпотечного кредитування України. Встановлено наявність взаємодоповнюючих поглядів на систематизацію ризиків, що дозволило запропонувати новий ракурс класифікації – розподіл ризиків іпотечного кредитування на раціональні та ірраціональні. Виокремлено найбільш вагомі ризики українського ринку іпотечного кредитування: кредитний ризик, інвестиційний ризик, інституційний ризик, інфляційний ризик, ризик незавершеного будівництва. Запропоновано авторська методика розрахунку інтегрального показника оцінки ризиків на ринку іпотечних кредитів через множення відносних коефіцієнтів (відношення показників національного ринку до відповідних показників європейського ринку).

Thermal Metamaterial: Fundamental, Application, and Outlook.

SummaryThermal metamaterials have amazing properties in heat transfer beyond naturally occurring materials owing to their well-designed artificial structures. The idea of thermal metamaterial has completely subverted the design of thermal functional devices and makes it possible to manipulate heat flow at will. In this perspective, we review the up-to-date progress of thermal metamaterials starting from 2008. We focus on both the key theoretical fundamentals and techniques for applications and give a perspective of scale-based classification on thermal metamaterials' theories and applications. We also discuss the junction between macroscale and microscale design methods and propose some prospects for the future trend of this field.

Automatic catalog of RR Lyrae from ∼14 million VVV light curves: How far can we go with traditional machine-learning?

Context. The creation of a 3D map of the bulge using RR Lyrae (RRL) is one of the main goals of the VISTA Variables in the Via Lactea Survey (VVV) and VVV(X) surveys. The overwhelming number of sources undergoing analysis undoubtedly requires the use of automatic procedures. In this context, previous studies have introduced the use of machine learning (ML) methods for the task of variable star classification. Aims. Our goal is to develop and test an entirely automatic ML-based procedure for the identification of RRLs in the VVV Survey. This automatic procedure is meant to be used to generate reliable catalogs integrated over several tiles in the survey. Methods. Following the reconstruction of light curves, we extracted a set of period- and intensity-based features, which were already defined in previous works. Also, for the first time, we put a new subset of useful color features to use. We discuss in considerable detail all the appropriate steps needed to define our fully automatic pipeline, namely: the selection of quality measurements; sampling procedures; classifier setup, and model selection. Results. As a result, we were able to construct an ensemble classifier with an average recall of 0.48 and average precision of 0.86 over 15 tiles. We also made all our processed datasets available and we published a catalog of candidate RRLs. Conclusions. Perhaps most interestingly, from a classification perspective based on photometric broad-band data, our results indicate that color is an informative feature type of the RRL objective class that should always be considered in automatic classification methods via ML. We also argue that recall and precision in both tables and curves are high-quality metrics with regard to this highly imbalanced problem. Furthermore, we show for our VVV data-set that to have good estimates, it is important to use the original distribution more abundantly than reduced samples with an artificial balance. Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step and that most errors in the identification of RRLs are related to low-quality observations of some sources or to the increased difficulty in resolving the RRL-C type given the data.

Promoções em ação: classificando as ações promocionais na percepção dos consumidores

Objetivo: Investigar a percepção e os critérios de classificação dos diferentes formatos de promoções de vendas pelo olhar dos consumidores.Método: Após análise da literatura e levantamento de dados de mercado, dados primários foram coletados via questionário on-line e analisados de forma sistemática por meio de interpretação e busca de padrões de respostas. Este processo permitiu identificar os temas centrais ou formas de classificação das promoções de venda pelos consumidores.Originalidade/Relevância: Este artigo traz uma nova perspectiva na classificação dos diferentes tipos de promoções, revelando uma análise pela lente do consumidor, enquanto estudos anteriores apresentam classificações pontuais e binárias, e em geral com uma visão das consequências da promoção para a empresa.Resultados: Com base nos dois critérios de classificação mais citados pelos consumidores - tipo de benefício em termos de prazo de execução (imediato ou futuro) e relação direta ou não com o produto em si - chegou-se a uma matriz com quatro grupos de promoções de vendas.Contribuições teóricas/metodológicas: esta pesquisa compila em uma única análise diversos estudos anteriores sobre tipos, classificações e efeitos das promoções de vendas, e unifica essas diferentes reflexões na literatura trazendo o entendimento sobre o tema pela perspectiva empírica do consumidor.Contribuições sociais / para a gestão: Contribui para orientar os gestores de marcas no momento da construção de seu plano de marketing, mais especificamente da sua estratégia de promoção.

The effect of different classification of hospitals on medical expenditure from perspective of classification of hospitals framework: evidence from China

BackgroundDifferent classification of hospitals (COH) have an important impact on medical expenditures in China. The objective of this study is to examine the impact of COH on medical expenditures with the hope of providing insights into appropriate care and resource allocation.MethodsFrom the perspective of COH framework, using the Urban Employee Basic Medical Insurance (UEBMI) data of Chengdu City from 2011 to 2015, with sample size of 488,623 hospitalized patients, our study empirically analyzed the effect of COH on medical expenditure by multivariate regression modeling.ResultsThe average medical expenditure was 5468.86 Yuan (CNY), the average expenditure of drug, diagnostic testing, medical consumables, nursing care, bed, surgery and blood expenditures were 1980.06 Yuan (CNY), 1536.27 Yuan (CNY), 500.01 Yuan (CNY), 166.23 Yuan (CNY), 221.98 Yuan (CNY), 983.18 Yuan (CNY) and 1733.21 Yuan (CNY) respectively. Patients included in the analysis were mainly elderly, with an average age of 86.65 years old. Female and male gender were split evenly. The influence of COH on total medical expenditures was significantly negative (p < 0.001). The reimbursement ratio of UEBMI had a significantly positive (p < 0.001) effect on various types of medical expenditures, indicating that the higher the reimbursement ratio was, the higher the medical expenditures would be.ConclusionsCOH influenced medical expenditures significantly. In consideration of reducing medical expenditures, the government should not only start from the supply side of healthcare services, but also focus on addressing the demand side.

Survey on JPEG compatible joint image compression and encryption algorithms

In recent years, image encryption has been broadly researched. Since a large proportion of images on Internet are compressed, and JPEG is the most widely adopted standards for image compression, a variety of joint image compression and encryption algorithms have been proposed. JPEG image encryption uses signal processing techniques as well as cryptographic techniques in different stages of the JPEG compression process. Based on the location where the encryption is taken place, these algorithms can be generally classified into three major categories: pre-compression encryption algorithms, in-compression encryption algorithms, and post-compression encryption algorithms, which correspond to conduct encryption operations before, during, and after the compression process. From this perspective of classification, the authors give a comprehensive survey on representative image encryption algorithms of each type, and show their properties and limitations. Some of the most recent encryption schemes that achieve protection at various positions of the compression process are selected for comparison, which are two pre-compression encryption algorithms, four in-compression encryption algorithms, and two post-compression encryption algorithms. Possible future research directions on designing joint JPEG compression and encryption schemes are provided in the end, which may facilitate solving application scenario-oriented JPEG security problems with new technologies.

语言学理论视野下的汉韩动词分类体系对比研究综述

Since the 21st century, with the broadening of research region and the emergence of new linguistic theories, the classification of Chinese-Korean verbs in academic circle is no longer limited to the perspective of classification under traditional grammar rules. There has been research on the classification of verbs from new perspectives such as light verb theory, typology linguistics, cognitive linguistics, and the research results show that new subcategories such as light verbs, unaccusativeverbs and unergative verbs, absolutive verbs and ergative verbs have been applied. The Chinese-Korean internal structure of the verb systems have changed as well, showing the trend that the subcategories become more differentiated, but the internal structure is more concise and has stronger explanatory power. After comparing the new and the old classification systems of Chinese-Korean verbs and summarizing the classification and subcategories of verbs in Chinese and Korean, it can be seen that the Chinese-Korean linguistic circle attach great importance to the application of new theories when studying the subcategories of verbs; but the Chinese academic circle seem to be one step ahead in the application of the new theory to constructing the macroscopic study of verb system; and the research on the classification system of verbs in Korean still needs improvement.

InterSentiment: combining deep neural models on interaction and sentiment for review rating prediction

Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and product-level features, and fails to capture sufficient interactions between them represented typically in a sparse matrix as CF can do. In this paper, we propose a novel, extensible review rating prediction model called InterSentiment by bridging the user-product interaction model and the sentiment model based on deep learning. InterSentiment is a specific instance of our proposed Deep Learning based Collaborative Filtering framework. The proposed model aims to learn the high-level representations combining user-product interaction and review sentiment, and jointly project them into the rating scores. Results of experiments conducted on IMDB and two Yelp datasets demonstrate clear advantage of our proposed approach over strong baseline methods.

Panel estimation for the impact factors on carbon dioxide emissions: A new regional classification perspective in China

Global warming is an urgent issue facing humanity. Excessive emissions of CO2 are the leading cause of global climate change. As economy grows, China’s CO2 emissions grows rapidly correspondingly. Since 2006, China has ranked first in terms of total CO2 emissions, results of different contributions of different provinces. In view of the shortcomings in the previous regional classification, this work adopted a new regional classification framework which combines factor analysis and Ward clustering method, and classified 30 provinces into four regions by using panel data from 2002 to 2016. Then, this work investigated the regional differences in terms of the effect of population size, GDP per capita, energy structure, energy intensity, urbanization level and industrialization level on CO2 emissions by STIRPAT model. The outcomes indicated that population size, GDP per capita and energy intensity were the top three factors affecting CO2 emissions for all regions. The impact of population size on carbon emissions is not only determined by the size of the population. Along with the process of urbanization, the role of urbanization on CO2 emissions gradually changes from positive to negative. The factor urbanization had positive impacts in region (Ⅲ and Ⅳ) but negative impacts in region (Ⅰ and Ⅱ). The greatest positive impacts of energy structure can be seen in region IV. Yet, statistically, energy structure shows no significant impact in region Ⅱ. As for industrialization level, it demonstrated negative effect in region Ⅱ but positive effect in the other three regions. From these results, implications for CO2 emission-reduction policies in each region were discussed.

The impact of RMB swap agreements on foreign exchange reserve demand

ABSTRACT This paper constructs panel data of 150 economies from 2001 to 2017 to study the impact of RMB (Renminbi) swap agreements on foreign exchange reserve demand. The empirical results show that the swap agreements can significantly reduce the foreign reserve demand of non-OECD economies that have relatively insufficient liquidity. From the perspective of specific country classification, economies neighbouring China have higher RMB demand, Latin American and African economies hold less reserve, and commodity exporters are stuck in liquidity-nervous situations, in which RMB swap agreements are able to improve liquidity so that their reserve demand decreases. Regarding the channels of influence, swap agreements impose different impacts on the reserve demand mainly through bilateral trade and more impacts on economies that import more from China. On the contrary, the RMB swap agreements will not affect the OECD economies’ foreign exchange reserve demand.

An examination of associations between business process management capabilities and the benefits of digitalization: all capabilities are not equal

PurposeWhile organizations have learned to understand the importance of developing business process management (BPM) capabilities, digitalization now transforms business processes, and introduces new challenges. Extending prior research examining the value of BPM capabilities in organizations, this study examines the associations of BPM capabilities across direct and indirect digitalization benefits.Design/methodology/approachDrawing from the capabilities perspective of strategic management, the dynamic versus ordinary classification was used to classify eight BPM capabilities. An empirical investigation of associations between the eight BPM capabilities and 20 digitalization benefits is presented based on data collected from 165 BPM professionals across four continents. Factor analyses were performed to verify the framework measures for BPM capabilities and digitalization benefits. The Kendall's tau-b (τb) correlation coefficient was used to measure the strength and direction of associations.FindingsOverall results confirm positive associations between BPM capabilities and digitalization benefits, but the relationship was less dominant for ordinary BPM capabilities and indirect benefits. Furthermore, relationships between individual BPM capabilities and specific digitalization benefits vary both across and within the categories.Practical implicationsThese findings support the moderate capability-based view that puts ordinary and dynamic capabilities on equal footing in dynamic environments, while also providing insight for managers focused on specific outcomes with digitalization efforts.Originality/valueThis study reveals that the strength of associations between BPM capabilities and digitalization benefits varies. This highlights the relevance of ordinary-dynamic and direct-indirect distinctions, and the value of a more fine-grained understanding to better inform practice.

Abstract PR16: Comprehensive transcriptomic characterization of 1,400 sarcomas for diagnosis and immune contexture

Abstract Introduction: Sarcomas represent about 15 % of all childhood cancers and are still lethal in a large proportion of cases. They comprise a heterogeneous group of bone and soft tissue tumors with more than 100 distinct histologic entities, often presenting a diagnostic challenge to pathologists. Methods: To address this pathological complexity and better characterize the molecular biology of sarcomas, we performed whole-transcriptome RNA-seq on frozen tumor tissues as part of the diagnostic workflow for more than 1,400 patients, mainly children and young adults, addressed for sarcoma molecular diagnosis throughout France. Results: From a clinical perspective, RNA-seq proves to be a powerful complementary test to pathology for diagnosis of sarcomas: it allows agnostic detection of subtype-specific fusion transcripts, as well as diagnostic indication by gene-expression profile clustering. In addition to its diagnostic utility, RNA-seq provides a rich source of information for studying the molecular biology of sarcomas. Indeed, as shown recently in our group, it allows detection of previously uncharacterized sarcoma entities associated with novel fusion genes. Using dimensionality reduction techniques, either linear such as PCA, or nonlinear such as t-SNE or UMAP, we perform unsupervised clustering of our cohort, revealing insights into the global structure of the transcriptomic landscape of sarcomas. We show that many histologic entities have a distinct gene-expression profile, emphasizing the utility of RNA-seq for diagnosis. To explore the immune microenvironment of sarcomas and potential correlates of response to immunotherapy, we deconvolute bulk RNA-seq data to estimate the presence of immune and stromal cell types, allowing a comprehensive characterization of the immune landscape of sarcomas. Finally, we apply state-of-the-art deep learning tools to perform unsupervised clustering and nonlinear dimensional reduction of our RNA-seq samples, allowing a finer and more insightful representation of the structure of the sarcoma transcriptomic landscape. Conclusion: Besides improving the diagnostic workflow for patients, our comprehensive analysis of sarcoma RNA-seq allows characterization of distinct molecular subtypes and of the immune landscape, opening up perspectives for better diagnosis, classification, and biologic discoveries in sarcoma. This abstract is also being presented as Poster B75. Citation Format: Julien Vibert, Sarah Watson, Camille Benoist, Joshua Waterfall, Gaëlle Pierron, Olivier Delattre. Comprehensive transcriptomic characterization of 1,400 sarcomas for diagnosis and immune contexture [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr PR16.

Multi-spectral remote sensing land-cover classification based on deep learning methods

It is of great significance and practical application value to extract land-cover type accurately. However, the input data usually used in classification such as reflectance data or vegetation index are very simple and quantitative remote sensing products are rarely used. In this paper, a multi-spectral land-cover classification method based on deep learning is proposed. Using the excellent detail capture ability of contourlet transform to obtain the potential information to supplement the spectral feature space, combined with deep learning for feature selection and feature extraction, a spectral–texture classification model is constructed. The multi-spectral sensing remote data and field measurement data in Dadukou District of Chongqing, northern Negev, and Changping region of Beijing were used for evaluation. Experiment results show the proposed method can outperform principal component analysis, linear discriminant analysis and neural network, and effectively improve the classification accuracy of multi-spectral images; this method provides a new perspective for land-use classification.

A unified spatial multigraph analysis for public transport performance

Public transport performance not only directly depicts the convenience of a city’s public transport, but also indirectly reflects urban dwellers’ life quality and urban attractiveness. Understanding why some regions are easier to get around by public transport helps to build a green transport friendly city. This paper initiates a new perspective and method to investigate how public transport network’s morphology contributes significantly to its performance. We investigate the significance of morphology from the perspective of graph classification – by extracting the typical local structures, called “motifs”, from the multi-modal public transport multigraph. A motif is seen as a certain connectivity pattern of different transport modes at a local scale. Combinations of various motifs decide the output of graph classification, particularly, public transport performance. We invent an innovative method to extract motifs on complex spatial multigraphs. The proposed method is adaptable to unify complex factors into one simple form for swift coding, and depends less on observational data to handle data unavailability. In the study area of Beijing, we validate that the measure can infer varied public transport efficiencies and access equalities of different regions. Some typical areas with undeveloped or unevenly distributed public transport are further discussed.

Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending

Online peer-to-peer (P2P) lending is a new form of loans. Different from traditional banks, lenders provide loans to borrowers directly through P2P platforms. Since many P2P loans are unsecured personal loans, credit rating of loans is vital to control default risk and improve profit for lenders and platforms. Standard binary classifiers are inappropriate in P2P lending because there are multiple credit classes and misclassification costs vary largely across classes in P2P lending. Though there are a few works that studied cost-sensitive classifiers in P2P lending, none of them have analyzed this issue from the perspective of multi-class classifications and measured misclassification costs of different credit grades using real losses and opportunity costs. The objective of this paper is to model credit rating in P2P lending as a cost-sensitive multi-class classification problem. We proposed a misclassification cost matrix for P2P credit grading with a set of equations and models to calculate the costs. An experiment using publicly available data from Lending Club was conducted to validate the usefulness of the proposed misclassification cost matrix. The results showed that the cost-sensitive classifiers can significantly reduce the total cost, which is essential for the survival and profitability of P2P platforms.

Design of Remote Monitoring System for Limb Rehabilitation Training Based on Action Recognition

Aimed at the high cost of domestic rehabilitation medical care, the limited number of doctors, the shortage of training venues, and the lack of follow-up tracking for patients who recovered better after rehabilitation training, and a remote monitoring system to understand the patient’s rehabilitation situation, a kind of motion recognition-based Remote monitoring system for physical rehabilitation training based on motion recognition was proposed. From the perspective of machine learning and intelligent classification, the system uses the wavelet transform principle and Support Vector Machine (SVM) algorithm to inject intelligence into the remote monitoring system for limb rehabilitation training, so that doctors can receive patients walking and running energy characteristic and their movement distance data in the rehabilitation center, and based on this data to determine the patient’s recovery and rehabilitation training plan, the doctor can make a diagnosis for dozens or even hundreds of patients even if they never leave home, which greatly improves the efficiency of treatment, saves the corresponding manpower and material resources for the country and society t, and benefits the people.

Natural skin-whitening compounds for the treatment of melanogenesis (Review).

Melanogenesis is the process for the production of melanin, which is the primary cause of human skin pigmentation. Skin-whitening agents are commercially available for those who wish to have a lighter skin complexions. To date, although numerous natural compounds have been proposed to alleviate hyperpigmentation, insufficient attention has been focused on potential natural skin-whitening agents and their mechanism of action from the perspective of compound classification. In the present article, the synthetic process of melanogenesis and associated core signaling pathways are summarized. An overview of the list of natural skin-lightening agents, along with their compound classifications, is also presented, where their efficacy based on their respective mechanisms of action on melanogenesis is discussed.

Crosslink‐Enhanced Emission Effect on Luminescence in Polymers: Advances and Perspectives

AbstractThe crosslink‐enhanced emission effect was first proposed to explore the strong luminescence of nonconjugated polymer dots possessing only either non‐emissive or weakly emissive sub‐luminophores. Interesting phenomena in recent research indicate such enhancement caused by extensive crosslinking appears in diverse luminescent polymers with sub‐luminophores (electron‐rich heteroatomic moieties) or luminophores (conjugated π domains). This enhancement can promote the emission from nonluminous to luminous, from weakly luminous to strongly luminous, and even convert the pathway of radiative transitions. The concept of the crosslink‐enhanced emission effect should be updated and extended to an in‐depth spatial effect, such as electron overlap and energy splitting in confined domains by effective crosslinking, more than initial immobilization. This Minireview outlines the development of the crosslink‐enhanced emission effect from the perspective of the detailed classification, inherent mechanism and applicable systems. An outlook on the further exploration and application of this theory are also proposed.