Mapping Bauxite Mining Residues Using Remote Sensing Techniques

Bauxite residuals from abandoned mining sites are both an environmental challenge and a possible source of secondary raw materials. Processing of multispectral and hyperspectral images with the best available techniques can help to produce multiscale spatial maps of elements inside and around the mining sites. The authors propose a procedure for mapping elements concentration using multiple data sets at different scales and resolutions. A comparison between multispectral Sentinel-2 images and hyperspectral PRISMA processing is performed over some case studies of bauxite residues in the Mediterranean area. Specifically, a case study from Italy is composed regarding artificial canyons created by past artisanal mining activities and by stockpiles of extracted bauxite. Hyperspectral punctual measurements (spectroradiometer surveys) were taken in various zones of the bauxite site, where infield topsoil samples were also taken for X-ray fluorescence chemical analysis. Final concentration maps were estimated by performing geostatistical techniques.

Achieving robust somatic mutation detection with deep learning models derived from reference data sets of a cancer sample

Background Accurate detection of somatic mutations is challenging but critical in understanding cancer formation, progression, and treatment. We recently proposed NeuSomatic, the first deep convolutional neural network-based somatic mutation detection approach, and demonstrated performance advantages on in silico data. Results In this study, we use the first comprehensive and well-characterized somatic reference data sets from the SEQC2 consortium to investigate best practices for using a deep learning framework in cancer mutation detection. Using the high-confidence somatic mutations established for a cancer cell line by the consortium, we identify the best strategy for building robust models on multiple data sets derived from samples representing real scenarios, for example, a model trained on a combination of real and spike-in mutations had the highest average performance. Conclusions The strategy identified in our study achieved high robustness across multiple sequencing technologies for fresh and FFPE DNA input, varying tumor/normal purities, and different coverages, with significant superiority over conventional detection approaches in general, as well as in challenging situations such as low coverage, low variant allele frequency, DNA damage, and difficult genomic regions

Can earning much lead to doing good? Firm performance implications for corporate social responsibility

Purpose The purpose of this study is to examine the influence of firm performance on corporate social responsibility (CSR) and its possible moderating effect. Despite the significance of CSR, there remains an extensive debate about how it is affected by firm performance. Design/methodology/approach The conceptual model is mainly built on goal-setting theory. Based on archival data from multiple data sets on 1,650 companies, collected from 2010 to 2017, the hypotheses are tested using the two-stage instrumental variable regression method. Findings There is an inverted U-shaped relationship between firm performance and CSR that first increases and then decreases. In addition, considering the boundary conditions, state ownership makes the inverted U-shaped curve steeper, while high executive wage concentration makes the inverted U-shaped curve flatter. Research limitations/implications This study harmonizes the traditional contradictory findings of the influence of firm performance on CSR, that is, it supports a positive, negative or neutral relationship between the two. Originality/value This research provides a necessary structure for the CSR literature. By delving deeply into the relationship between firm performance and CSR, it enables scholars to better address the critical management question of whether earning more will lead to doing good.

Integration of Neural Networks and Wellbore Stability, a Modern Approach to Recognize Drilling Problems Through Computer Vision

Cavings are a valuable source of information when drilling operations are being performed, and multiple parameters can contribute to producing cavings which indicate that failure has occurred or is about to occur downhole. This study will describe a project which is an integrated study of Machine Learning, Computer Vision, Geology, and Photography so that the recognition of cavings in the shaker is possible and how to link the cavings morphology with causal mechanisms related to wellbore instability problems. This study aims to develop a model which can extract caving features such as Shape, Edge Definition, Color, and Size. One of the core aspects of this study was to develop a structured image database of cavings from the Norwegian Continental Shelf which contains important feature information and the application of different algorithms used for automation enabled several opportunities to analyze and identify causal mechanism related to wellbore instability problems in real-time. As a result of that, the drilling operations would experience an improvement in terms of a faster decision-making process to solve operative problems related to wellbore stability which will lead to optimization not only in time and resources but also in safer drilling operations. Different algorithms and artificial intelligence tools were used to investigate the best approach to correctly detect and derive meaningful information about the shape, color size and edge from cavings like supervised learning, unsupervised learning, neural networks and computer vision. A key part of this study was image augmentation which plays a significant role for the detection of the cavings and their features. Multiple data sets can be created, and by using data augmentation, this will enable recognition of more complex patterns that will have on-rig applicability. Also, this new approach can deliver multiple outcomes besides failure mechanism identification such as volume of rocks being drilled, transport of cutting, type of formation being drilled.

Research on Methods of English Text Detection and Recognition Based on Neural Network Detection Model

With the rapid development of computer science, a large number of images and an explosive amount of information make it difficult to filter and effectively extract information. This article focuses on the inability of effective detection and recognition of English text content to conduct research, which is useful for improving the application of intelligent analysis significance. This paper studies how to improve the neural network model to improve the efficiency of image text detection and recognition under complex background. The main research work is as follows: (1) An improved CTPN multidirectional text detection algorithm is proposed, and the algorithm is applied to the multidirectional text detection and recognition system. It uses the multiangle rotation of the image to be detected, then fuses the candidate text boxes detected by the CTPN network, and uses the fusion strategy to find the best area of the text. This algorithm solves the problem that the CTPN network can only detect the text in the approximate horizontal direction. (2) An improved CRNN text recognition algorithm is proposed. The algorithm is based on CRNN and combines traditional text features and depth features at the same time, making it possible to recognize occluded text. The algorithm was tested on the IC13 and SVT data sets. Compared with the CRNN algorithm, the recognition accuracy has been improved, and the detection and recognition accuracy has increased by 0.065. This paper verifies the effectiveness of the improved algorithm model on multiple data sets, which can effectively detect various English texts, and greatly improves the detection and recognition performance of the original algorithm.

AHP-Based Analysis Factors Influencing the Construction of a Smart City with Three-Dimensional Regional Color

Urban color is becoming more and more important in the construction of smart cities. This research mainly discusses the influencing factors of AHP-based 3D regional color smart city construction. From the perspective of city managers, the factors selected in the article that affect the choice of urban color planning are economic development, urban culture, designer’s ideas, customs, and natural environment. We use GIS to comprehensively collect basic geographic information data of the city. We use CityEngine and GIS data to quickly create virtual cities. When constructing the AHP model, once the weight value of each factor in the color-influencing factors of the entire smart city is obtained, the total ranking of the levels needs to be checked once. After that, the study uses the MATLAB program platform to realize the parametric design of the main color of the city, including the control of the changes in the spatial structure of the main color of the city, followed by the control of the transition and coordination mode between the key elements of the city color. Explorer provides comprehensive and rich 3D digital city roaming browsing, query positioning, analysis, and other functions; supports the loading of multiple data sets from the Internet and local; and can dynamically load massive 3D city simulation models above and below ground. Finally, the design of the urban architectural color or improvement plan is further carried out, and the urban color main color design plan is finally implemented in the implementation of the urban architectural color, which effectively realizes the quantitative control of the urban color. When evaluating smart city color resources, the color weight value of public facilities is 0.147, which accounts for 14.7% of the total weight value. This research helps to enhance the regional and local cultural characteristics.

The impact of chlorine chemistry combined with heterogeneous N₂O₅ reactions on air quality in China

The heterogeneous reaction of N2O5 on Cl-containing aerosols (N2O5 − ClNO2 chemistry) plays a key role in chlorine activation, NOx recycling and consequently O3 formation. In this study, we use the GEOS-Chem model with additional anthropogenic and biomass burning chlorine emissions combined with updated parameterizations for N2O5 − ClNO2 chemistry (i.e. the uptake coefficient of N2O5 (γN2O5) and the ClNO2 yield (φClNO2)) to investigate the impacts of chlorine chemistry on air quality in China, the role of N2O5 − ClNO2 chemistry, as well as their sensitivities to chlorine emissions and parameterizations for γN2O5 and φClNO2. The model evaluation with multiple data sets observed across China demonstrated significant improvement especially regarding the simulation of Cl−, N2O5 and ClNO2 with the updates in chlorine emissions and N2O5 − ClNO2 chemistry. Total tropospheric chlorine chemistry could increase annual mean MDA8 O3 by up to 4.5 ppbv but decrease PM2.5 by up to 7.9 μg m−3 in China, 83 % and 90 % of which could be attributed to the effect of N2O5 – ClNO2 chemistry. The heterogeneous uptake of N2O5 on chloride-containing aerosol surfaces is an important loss pathway of N2O5 as well as a important source of O3, and hence is particularly useful in elucidating the commonly seen ozone underestimations. The importance of chlorine chemistry largely depends on both chlorine emissions and the parameterizations for N2O5 – ClNO2 chemistry. With the additional chlorine emissions annual mean maximum daily 8-hour average (MDA8) O3 in China could be increased by up to 3.5 ppbv. The corresponding effect on PM2.5 concentrations varies largely with regions, with an increase of up to 4.5 μg m−3 in the North China Plain but a decrease of up to 3.7 μg m−3 in the Sichuan Basin. On the other hand, even with the same chlorine emissions, the effects on MDA8 O3 and PM2.5 in China could differ by 48 % and 27 %, respectively between different parameterizations.

Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas

Importance: Lower-resource areas in Africa and Asia face a unique set of healthcare challenges: the dual high burden of communicable and non-communicable diseases; a paucity of highly trained primary healthcare providers in both rural and densely populated urban areas; and a lack of reliable, inexpensive internet connections. Objective: To address these challenges, we designed an artificial intelligence assistant to help primary healthcare providers in lower-resource areas document demographic and medical sign/symptom data and to record and share diagnostic data in real-time with a centralized database. Design: We trained our system using multiple data sets, including US-based electronic medical records (EMRs) and open-source medical literature and developed an adaptive, general medical assistant system based on machine learning algorithms. Main outcomes and Measure: The application collects basic information from patients and provides primary care providers with diagnoses and prescriptions suggestions. The application is unique from existing systems in that it covers a wide range of common diseases, signs, and medication typical in lower-resource countries; the application works with or without an active internet connection. Results: We have built and implemented an adaptive learning system that assists trained primary care professionals by means of an Android smartphone application, which interacts with a central database and collects real-time data. The application has been tested by dozens of primary care providers. Conclusions and Relevance: Our application would provide primary healthcare providers in lower-resource areas with a tool that enables faster and more accurate documentation of medical encounters. This application could be leveraged to automatically populate local or national EMR systems.

An Enhanced Cascading Model for E-Commerce Consumer Credit Default Prediction

As an important global policy guide to promote economic transformation and upgrading, the upsurge of E-Commerce has been continuously upgraded with continuous breakthroughs in information technology. In recent years, China’s e-commerce consumer credit has developed well, but due to its short time of production and insufficient experience for reference, credit risk, fraud risk, and regulatory risk continue to emerge. Aiming at the problem of E-Commerce Consumer Credit default analysis, this paper proposes a Fusion Enhanced Cascade Model (FECM). This model learns feature data of credit data by fusing multi-granularity modules, and incorporates random forest and GBDT trade-off variance and bias methods. The paper compares FECM and gcForest on multiple data sets, to prove the applicability of FECM in the field of E-commerce credit default prediction. The research results of this paper are helpful to the risk control of financial development, and to construct a relatively stable financial space for promoting the construction and development of E-Commerce.

An Enhanced Cascading Model for E-Commerce Consumer Credit Default Prediction

As an important global policy guide to promote economic transformation and upgrading, the upsurge of E-Commerce has been continuously upgraded with continuous breakthroughs in information technology. In recent years, China’s e-commerce consumer credit has developed well, but due to its short time of production and insufficient experience for reference, credit risk, fraud risk, and regulatory risk continue to emerge. Aiming at the problem of E-Commerce Consumer Credit default analysis, this paper proposes a Fusion Enhanced Cascade Model (FECM). This model learns feature data of credit data by fusing multi-granularity modules, and incorporates random forest and GBDT trade-off variance and bias methods. The paper compares FECM and gcForest on multiple data sets, to prove the applicability of FECM in the field of E-commerce credit default prediction. The research results of this paper are helpful to the risk control of financial development, and to construct a relatively stable financial space for promoting the construction and development of E-Commerce.