The Effect of Good Corporate Governance, Profitability, and Corporate Social Responsibility on Market Reaction and Company Value in the Registered Mining Industry on the Indonesian Stock Exchange

Purpose: The objective of this analysis was to analyze the direct impact of good corporate governance, profitability and corporate social duty on market response and company value.   Theoretical framework: The data analysis process used in this research is a descriptive, namely, analysis that aims to determine the extent to which the variables of moral corporate governance, profitability, and corporate social responsibility influence market reactions and firm value in mining companies listed on the Indonesia Stock Exchange which are based on empirical and theoretical facts.   Design/methodology/approach: This research uses secondary data from 27 mining companies listed on the Indonesian Stock Exchange between 2017 and 2019. The data analysis methods used in this study are quantitative and qualitative descriptive research methods, as well as relational research methods. The analysis used to test the hypothesis using SEM (Structural Equation Model Analysis).   Findings:  The consequences of this report show that good corporate governance (1) does not directly affect market reactions with a positive correlation. (2) profitability does not directly affect market response with a negative correlation; (3) corporate social responsibility does not directly influence the market response in a positive direction; (4) good corporate governance does not directly affect company value, with a positive relationship;   Research, Practical & Social implications: Since there are no evaluation standards or standard form for determining the rate of diffusion of corporate social responsibility, the assessment of the spread of corporate social responsibility remains subjective, so there will be differences in each researcher. The announcement of the dissemination of corporate social responsibility is made by the company, together with the announcement of the Annual Report, so there are other aspects made by investors in investment decisions.   Originality/value: corporate social responsibility does not directly disturb the company's values with a positive relationship; market response does not directly affect company value with negative correlation; good corporate governance does not directly affect company value through a negative correlation market response; profitability does not directly affect company value through market feedback with a positive correlation

Hydrologic Response and Erosion Modeling of Geomorphic Landform Reclamation in Mountainous Terrain

Surface mining and valley-fill practices often lead to environmental impacts including headwater stream loss, increased flooding risk, and degraded downstream water quality. Geomorphic landform design (GLD) is an innovative reclamation technique proposed to lessen the impacts associated with surface mining and valley-fill activities. GLD incorporates mature landform shapes and created stream channels on site, imitating the function of the undisturbed landscape. The purpose of this research was to model GLDs in mountainous terrain and evaluate the hydrologic response and erosion potential of GLD in surface mining application. Computer modeling of valley-fill designs using geomorphic landform principles of a study site in southern West Virginia was performed. Four enhanced GLDs were created for application on new and previously constructed valley fills: 1) regional data GLD for new valley fill, 2) retrofit GLD for existing conventional valley fill, 3) regional data GLD enhanced with bench pond retention structures, and 4) regional data GLD enhanced with valley pond retention structures. Soil erosion was evaluated using the Revised Universal Soil Loss Equation (RUSLE) for the regional data GLD, conventional valley fill, and the undisturbed site during different stages of the reclamation process. Soil loss rates were highest (conventional: 123.2 t ha-1 yr-1; GLD: 204.3 t ha-1 yr-1) during the post-mining, pre-vegetation condition along the stream channels and steep slopes (slope \u3e50%). Erosion rates were lowest for the post-reclamation, long term condition (conventional: 35.6 t ha-1 yr-1 ; GLD: 41.8 t ha-1 yr-1) along the ridges. Model predictions of soil erosion rates and spatial distributions illustrated areas of increased erosion potential for future minimization and reclamation method/management practices improvement. Hydrologic response modeling was performed for a watershed in southern West Virginia disturbed by surface mining and valley-fill activities to predict impacts on stream flows at the landscape scale. Incorporation of GLD reclamation methods did not result in substantial changes in current (2011-2020) or future (36500-2050) stream flowrates (≤3.3% difference) or stormflow volumes (≤2.1% difference). The differences in flows and volumes could be used for mitigation plans in watersheds disturbed by surface mining and valley-fill activities

A Photogrammetry Program for Physical Modeling of Subsurface Subsidence Process

Safe and economic longwall coal extraction requires a good knowledge of overburden strata characteristics and understanding of overburden strata movement in response to the full extraction mining. Subsidence induced overburden movement will affect overlying mining, surface and subsurface water bodies, and methane emission and migration in the overburden strata. Therefore, an understanding of subsurface strata movement is essential for optimal layout of multiple seam mining, protecting water resources, and designing of gob hole patterns for longwall degasification.;Physical simulation experiments play an important role in mining engineering research by allowing visual observations of strata movement in the mining process. In recent years, the advantage physical simulations has supplemented to weakness of computer simulation in some aspects. Some advanced research results in ground control have been obtained through physical simulation, and the findings include key hinged-beam theory, the formation of fractured zones with different permeability in overlying strata.;To improve the method of obtaining and processing measured data during a simulation experiment, a photogrammetry program for capturing and processing the data from physical modeling of mine subsurface subsidence has been developed. The program can capture the observation or measurement points accurately by simply clicking a mouse on an image of the experiment. Four point geometric affine transformation was used in the program to make corrections to images with relation to the camera\u27s change of position over the experiment process. Finally, the program can generate the subsurface subsidence database and export to an spreadsheet file for further data analysis. Subsurface displacement, horizontal strain, and void ratio can then obtained from the data generated by the program. Overall, the program provides an alternative, rapid approach to capturing and processing subsurface subsidence data from a physical simulation model, and introduces a new way to help minimize the error brought by both human and measurement devices

VO: Vaccine Ontology

Vaccine research, as well as the development, testing, clinical trials, and commercial uses of vaccines involve complex processes with various biological data that include gene and protein expression, analysis of molecular and cellular interactions, study of tissue and whole body responses, and extensive epidemiological modeling. Although many data resources are available to meet different aspects of vaccine needs, it remains a challenge how we are to standardize vaccine annotation, integrate data about varied vaccine types and resources, and support advanced vaccine data analysis and inference. To address these problems, the community-based Vaccine Ontology (VO, "http://www.violinet.org/vaccineontology":http://www.violinet.org/vaccineontology) has been developed through collaboration with vaccine researchers and many national and international centers and programs, including the National Center for Biomedical Ontology (NCBO), the Infectious Disease Ontology (IDO) Initiative, and the Ontology for Biomedical Investigations (OBI). VO utilizes the Basic Formal Ontology (BFO) as the top ontology and the Relation Ontology (RO) for definition of term relationships. VO is represented in the Web Ontology Language (OWL) and edited using the Protégé-OWL. Currently VO contains more than 2000 terms and relationships. VO emphasizes on classification of vaccines and vaccine components, vaccine quality and phenotypes, and host immune response to vaccines. These reflect different aspects of vaccine composition and biology and can thus be used to model individual vaccines. More than 200 licensed vaccines and many vaccine candidates in research or clinical trials have been modeled in VO. VO is being used for vaccine literature mining through collaboration with the National Center for Integrative Biomedical Informatics (NCIBI). Multiple VO applications will be presented.
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An investigation into gas emission and outburst control in thick seam coal mining

Nowadays, coal mining is extending to deeper and deeper levels, facing ever increasing coal seam gas contents, much higher gas emissions and outburst risks. Capturing coal seam gas before it migrates into atmosphere has been seen as an effective approach to simultaneously improve mining safety, reduce greenhouse gas emissions, and produce clean energy. Thick seams account for a considerable share of global coal reserve. The application of longwall top coal caving (LTCC) method to extract thick seams generally yields a much higher productivity and is more efficient in comparison to a mechanised single-slice longwall panel. However, the greater productivity achieved by LTCC may further exacerbate the gas emission problems often faced in longwall mining. Geomechanical response of the strata and associated gas emission patterns around thick seam layouts are significantly different from coal mining under thinner multi-seam mining conditions, which is not well understood. This thesis focuses on establishing an understanding of the stresses, pressure regimes, and gas emission patterns around advancing LTCC faces. During the PhD research, gas pressure and gas concentration were measured in a large number of boreholes in and around an advancing LTCC face at a coal mine. These data are complemented with ventilation and seismic monitoring programmes at the same LTCC district. An integrated analysis of the monitoring data has been carried out and conceptual models for gas emission and drainage for LTCC faces have been developed. These were later used as the basis for numerical modelling research. A two-way sequential coupling of a geomechanical simulator with a reservoir simulator has been achieved, whereby mining induced stresses and pressures are linked by two coupling parameters: permeability and pore pressure. By applying this approach, gas emission during coal extraction at a LTCC panel in the study coal mine has been successfully modelled and history matched with field data. Recognising that coal and gas outbursts are the most serious and violent gas emissions in both thick and thin seam mining, the application of the coupled modelling approach has been further extended to model two common types of outbursts experienced in an outburst-prone coalfield. Gas drainage before mining is a standard gas emission control technique, however, its application is largely limited to high permeability coal seams and roof/floor source seams undermined/overmined by single level longwall mining. The feasibility of utilising mining induced permeability enhancement zones to drain gas at thick and tight seams mined by multi-level LTCC method was studied via field trials and numerical models. Building upon the gas emission model developed earlier, a parametric study was carried out to assess different borehole layouts in order to optimise gas drainage designs. It is believed that the findings of this research and gas drainage methods developed for thick seam mining will create a safer underground environment for miners at high productivity LTCC panels.Open Acces

Prediction for Resolution Time of Software Defect

In practical software development projects, solving test issues efficiently during Software Development Life Cycle is critical to release software products on time. Different test environments, test resources and test requirements could result in different outcomes. Therefore, getting accurate prediction of the software defects\u27 resolution time could be beneficial to the practical projects.;In our study, data mining techniques offer great promise in prediction of software defects\u27 resolution time. Our research is conducted based on the NASA Metrics Data Program (MDP). We first calculate the resolution time for available projects. Using unsupervised discretization methods, we split resolution time into certain interval as response variable. Then, investigating the relationship between metric properties and time intervals, we fit a model that attempts to produce prediction on resolution time. Experiments and analysis successfully demonstrate the feasibility of our approach

Predictive Modelling of Student Academic Performance – the Case of Higher Education in Middle East

One of the main issues in higher education is student retention. Predicting students' performance is an important task for higher education institutions in reducing students' dropout rate and increasing students' success. Educational Data mining is an emerging field that focuses on dealing with data related to educational settings. It includes reading the data, extracting the information and acquiring hidden knowledge. This research used data from one of the Gulf Cooperation Council (GCC) universities, as a case study of Higher Education in the Middle East. The concerned University has an enrolment of about 20,000 students of many different nationalities. The primary goal of this research is to investigate the ability of building predictive models to predict students' academic performance and identify the main factors that influence their performance and grade point average. The development of a generalized model (a model that could be applied on any institution that adopt the same grading system either on the Foundation level (that use binary response variable (Pass/ Fail) or count response variable which is the Grade Average Point for students enrol in the undergraduate academic programs) to identify students in jeopardy of dismissal will help to reduce the dropout rate by early identification of needed academic advising, and ultimately improve students' success. This research showed that data science algorithms could play a significant role in predicting students' Grade Point Average by adopting different regression algorithms. Different algorithms were carried out to investigate the ability of building predictive models to predict students' Grade Point Average after either 2, 4 or 6 terms. These methods are Linear/ Logistic Regression, Regression Trees and Random Forest. These predictive models are used to predict specific students' Grade Point Average based on other values in the dataset. In this type of model, explicit instruction is given about what the model needs to learn. An optimization function (the model) is formed to find the target output based on specific input values. This research opens the door for future comprehensive studies that apply a data science approach to higher-education systems and identifying the main factors that influence student performance

Applications of Surface and Subsurface Subsidence Theories to Solve Ground Control Problems

The stability of the underground mine openings largely depends on the surrounding ground conditions, such as stress concentrations, geological conditions and support intensities etc. In particular, the ground control stability associated with large movements and deformations of the strata is much more complicated and could induce much more severe safety problems. A ground control failure could endanger the coal miner\u27s safety not only directly by roof, pillar, floor and/or rib failure, but also by ground cracks induced methane and water inundations indirectly. This study is aimed to develop comprehensive models to simulate the ground response to mining and solve the ground control problems associated with it.;During the last four decades, many research works have been conducted on the ground control study, and numerous models, including analytical, empirical, numerical and hybrid models, were developed to facilitate ground control and support design. If a model is to be used as a common mine design tool, the simplicity of the model itself and the consistency between actual in-mine and modeled ground response to mining are essential. For the study of the ground control stability associated with large movements and deformations, the key is to know the movements and deformations of the subsurface strata. The subsidence prediction models can determine the movements and deformations very accurately as proven by plenty of surface subsidence survey data. In this study, the subsidence prediction models are employed to analyze the stability of some subsidence related ground control problems based on the subsurface strata movements and deformations.;In this dissertation, an innovative approach, employing the influence function method while considering the hard rock layers, is applied in the development of an enhanced subsurface subsidence prediction model. This improved model is then applied in analyzing three specific subsidence related ground control problems. An analytical model, employing dynamic subsurface subsidence theory and considering the roof support interaction, is developed to analyze the stability of pre-driven longwall recovery room. The mechanism of the ground control stability problems as well as the potential safety problems associated with multi-seam mining interactions is discussed. Multi-seam mining subsidence prediction methods are re-examined based on the multi-seam mining interaction analysis. The redistribution of the stresses and strains in overburden is also able to affect the surface and subsurface water bodies in various degrees. Mathematical models are developed to link longwall induced overburden strata permeability change and subsurface deformations. A ground water flow model is used to assess the longwall mining impacts on surface and subsurface hydrological systems.;This study provides a greater understanding of the mechanism of the subsidence-related ground control problems. Innovative methods are developed to derive stress, strain and permeability change, and quantify the subsidence effects on mine structure stability and the hydrological system sustainability. The developed models are coded and incorporated into a software suite to provide an easy-to-use tool for the mine planning and designing of all subsidence related issues