Search results for: panel data models

Authors: Wanda Luen-Wun Siu, Xiaowen Zhang

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This paper offered the primary methodical proof on how CSR reporting related to enterprise earnings in listed firms in China in light of most evidence focusing on cross-sectional data or data in a short span of time. Using full economic and business panel data on China’s publicly listed enterprise from 2006 to 2020 over two decades in the China Stock Market and Accounting Research database, we found initial evidence of significant direct relations between CSR reporting and firm corporate performance in both state-owned and privately owned firms over this period, supporting the stakeholder theory. Results also revealed that state-owned enterprises performed as well as private enterprises in the current period. But private enterprises performed better than state-owned enterprises in the subsequent years. Moreover, the release of social responsibility reports had a more significant impact on the financial performance of state-owned and private enterprises in the current period than in the subsequent periods. Specifically, CSR release was not significantly associated with the financial performance of state-owned enterprises on the lag of the first, second, and third periods. But it had an impact on the lag of the first, second, and third periods among private enterprises. Such findings suggested that CSR reporting helped improve the corporate financial performance of state-owned and private enterprises in the current period, but this kind of effect was more significant among private enterprises in the lag periods.

Keywords: China’s listed firms, CSR reporting, financial performance, panel analysis

Procedia PDF Downloads 143

Authors: Min Kyung An

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In this paper, we study the data collection problem in Wireless Sensor Networks (WSNs) adopting the two interference models: The graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR). The main issue of the problem is to compute schedules with the minimum number of timeslots, that is, to compute the minimum latency schedules, such that data from every node can be collected without any collision or interference to a sink node. While existing works studied the problem with unit-sized and unbounded-sized message models, we investigate the problem with the bounded-sized message model, and introduce a constant factor approximation algorithm. To the best known of our knowledge, our result is the first result of the data collection problem with bounded-sized model in both interference models.

Keywords: data collection, collision-free, interference-free, physical interference model, SINR, approximation, bounded-sized message model, wireless sensor networks

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Authors: Bahram Fathi, Seyedhossein Sajadifar, Naser Khiabani

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Data envelopment analysis (DEA) has recently gained popularity in energy efficiency analysis. A common feature of the previously proposed DEA models for measuring energy efficiency performance is that they treat energy consumption as an input within a production framework without considering undesirable outputs. However, energy use results in the generation of undesirable outputs as byproducts of producing desirable outputs. Within a joint production framework of both desirable and undesirable outputs, this paper presents several DEA-type linear programming models for measuring energy efficiency performance. In addition to considering undesirable outputs, our models treat different energy sources as different inputs so that changes in energy mix could be accounted for in evaluating energy efficiency. The proposed models are applied to measure the energy efficiency performances of 12 OPEC countries and the results obtained are presented.

Keywords: energy efficiency, undesirable outputs, data envelopment analysis

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Authors: Ali Hesari, Arezoo Esmaeeli

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Regarding to dramatic changes of fertility and higher order births during recent decades in Iran, access to knowledge about affecting factors on different birth orders has crucial importance. In this study, According to hierarchical structure of many of social sciences data and the effect of variables of different levels of social phenomena that determine different birth orders in 365 days ending to 1390 census have been explored by multilevel approach. In this paper, 2% individual row data for 1390 census is analyzed by HLM software. Three different hierarchical linear regression models are estimated for data analysis of the first and second, third, fourth and more birth order. Research results displays different outcomes for three models. Individual level variables entered in equation are; region of residence (rural/urban), age, educational level and labor participation status and province level variable is GDP per capita. Results show that individual level variables have different effects in these three models and in second level we have different random and fixed effects in these models.

Keywords: fertility, birth order, hierarchical approach, fixe effects, random effects

Procedia PDF Downloads 318

Authors: Alexandra McKinzie, Lucy Brown, Sarah Komanapalli, Sarah Swiezy, Caitlin Bernard

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Background: While 25% of US women will seek an abortion before age 45, targeted laws have led to a decline in abortion clinics, subsequently leaving 96% of Indiana counties and the 70% of Hoosier women residing in these counties without access to services they desperately need.1,2 Despite the need for a physician workforce that is educated and able to provide full-spectrum reproductive health care, few medical institutions have a standardized family planning and abortion pre-clinical curriculum. Methods: A Qualtrics survey was disseminated to students from Indiana University School of Medicine (IUSM) to evaluate (1) student interest in curriculum reform, (2) self-assessed preparedness to counsel on contraceptive and pregnancy options, and (3) preferred modality of instruction for family planning and abortion topics. Based on the pre-panel survey feedback, a case-based pregnancy options counseling panel will be implemented in the students’ pre-clinical, didactic course Endocrine, Reproductive, Musculoskeletal, Dermatologic Systems (ERMD) in February 2022. A Qualtrics post-panel survey will be disseminated to evaluate students’ perceived efficacy and quality of the panel, as well as their self-assessed preparedness to counsel on pregnancy options. Results: Participants in the pre-panel survey (n=303) were primarily female (61.72%) and White (74.43%). Across all class levels, many (60.80%) students expected to learn about family planning and abortion in their pre-clinical education. While most (84-88%) participants felt prepared to counsel about common, non-controversial pharmacotherapies (e.g. beta-blockers and diuretics), only 20% of students felt prepared to counsel on abortion options. Overall, 85.67% of students believed that IUSM should enhance its reproductive health coverage in pre-clinical, didactic courses. Traditional lectures, panels, and direct clinical exposure were the most popular instructional modalities. Expected Results: The authors predict that following the panel, students will indicate improved confidence in providing pregnancy options counseling. Additionally, students will provide constructive feedback on the structure and content of the panel for incorporation into future years’ curriculum. Conclusions: IUSM students overwhelmingly expressed interest in expanding their pre-clinical curriculum’s coverage of family planning and abortion topics. To specifically improve students’ self-assessed preparedness to provide pregnancy options counseling and address students’ self-cited learning gaps, a case-based provider panel session will be implemented in response to students’ preferred modality feedback.

Keywords: options counseling, family planning, abortion, curriculum reform, case-based panel

Procedia PDF Downloads 124

Authors: Salahaeddine Sbai

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Air quality analysis using machine learning models is a method employed to assess and predict air pollution levels. This approach leverages the capabilities of machine learning algorithms to analyze vast amounts of air quality data and extract valuable insights. By training these models on historical air quality data, they can learn patterns and relationships between various factors such as weather conditions, pollutant emissions, and geographical features. The trained models can then be used to predict air quality levels in real-time or forecast future pollution levels. This application of machine learning in air quality analysis enables policymakers, environmental agencies, and the general public to make informed decisions regarding health, environmental impact, and mitigation strategies. By understanding the factors influencing air quality, interventions can be implemented to reduce pollution levels, mitigate health risks, and enhance overall air quality management. Climate change is having significant impacts on Morocco, affecting various aspects of the country's environment, economy, and society. In this study, we use some machine learning models under python environment to predict and analysis air quality change over North of Morocco to evaluate the climate change impact on agriculture.

Keywords: air quality, machine learning models, pollution, pollutant emissions

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Authors: Anthony Usoro

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In this paper, two classes of bilinear time series model are obtained under certain conditions from the general bilinear autoregressive moving average model. Bilinear Autoregressive (BAR) and Bilinear Moving Average (BMA) Models have been identified. From the general bilinear model, BAR and BMA models have been proved to exist for q = Q = 0, => j = 0, and p = P = 0, => i = 0 respectively. These models are found useful in modelling most of the economic and financial data.

Keywords: autoregressive model, bilinear autoregressive model, bilinear moving average model, moving average model

Procedia PDF Downloads 375

Authors: H. Taylan Selamlar, I. Yavuz, G. Yapar

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It is difficult to make predictions especially about the future and making accurate predictions is not always easy. However, better predictions remain the foundation of all science therefore the development of accurate, robust and reliable forecasting methods is very important. Numerous number of forecasting methods have been proposed and studied in the literature. There are still two dominant major forecasting methods: Box-Jenkins ARIMA and Exponential Smoothing (ES), and still new methods are derived or inspired from them. After more than 50 years of widespread use, exponential smoothing is still one of the most practically relevant forecasting methods available due to their simplicity, robustness and accuracy as automatic forecasting procedures especially in the famous M-Competitions. Despite its success and widespread use in many areas, ES models have some shortcomings that negatively affect the accuracy of forecasts. Therefore, a new forecasting method in this study will be proposed to cope with these shortcomings and it will be called ATA method. This new method is obtained from traditional ES models by modifying the smoothing parameters therefore both methods have similar structural forms and ATA can be easily adapted to all of the individual ES models however ATA has many advantages due to its innovative new weighting scheme. In this paper, the focus is on modeling the trend component and handling seasonality patterns by utilizing classical decomposition. Therefore, ATA method is expanded to higher order ES methods for additive, multiplicative, additive damped and multiplicative damped trend components. The proposed models are called ATA trended models and their predictive performances are compared to their counter ES models on the M3 competition data set since it is still the most recent and comprehensive time-series data collection available. It is shown that the models outperform their counters on almost all settings and when a model selection is carried out amongst these trended models ATA outperforms all of the competitors in the M3- competition for both short term and long term forecasting horizons when the models’ forecasting accuracies are compared based on popular error metrics.

Keywords: accuracy, exponential smoothing, forecasting, initial value

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Authors: Yazhi Huang, Jing Yang, Dingge Ying, Yan Zhang, Vorasuk Shotelersuk, Nattiya Hirankarn, Pak Chung Sham, Yu Lung Lau, Wanling Yang

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Human leukocyte antigen (HLA) typing from next generation sequencing (NGS) data has the potential for applications in clinical laboratories and population genetic studies. Here we introduce a novel technique for HLA typing from NGS data based on read-mapping using a comprehensive reference panel containing all known HLA alleles and de novo assembly of the gene-specific short reads. An accurate HLA typing at high-digit resolution was achieved when it was tested on publicly available NGS data, outperforming other newly-developed tools such as HLAminer and PHLAT.

Keywords: human leukocyte antigens, next generation sequencing, whole exome sequencing, HLA typing

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Authors: Adeleke Omolade

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The study investigated the impacts of capital goods' import and human capital on the economic growth of the Sub Sahahra Africa (SSA). 30 countries were used in the Panel- ARDL analysis where economic growth is the dependent variables and capital goods' import, human capital, primary export, investment exchange rate, among others were used as the independent variables. The result from the panel analysis indicates that capital goods' import will significantly and positively influence economic growth but human capital fails to have significant positive impact on economic growth of the SSA. Earlier the trend analysis and the correlation results have shown that there is a weak association between capital goods' import and human capital in the SSA. The results offer an expository analysis that reveals that the quality of the human capital is very germane to the effective utilization of capital goods' import for the purpose of growth in a primary goods' export dominated region like the SSA.

Keywords: capital goods import, economic growth, human capital, Sub-Sahara Africa

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Authors: Jianzhou Ni, Hua Fan

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It has been determined that supervisor support has a major influence on postgraduate students' academic emotions and is considered a method of successfully anticipating postgraduates' good psychological well-being levels. As a result, by assessing the mediating influence upon academic emotions for contemporary postgraduates in China, this study investigated the tight reciprocal relationship between psychological empowerment and positive mental well-being among postgraduates. To that end, a help enables a theoretical analysis of role clarity, academic emotion, and positive psychological health was developed, and its validity and reliability were demonstrated for the first time using the normalized postgrad relationship with supervisor scale, academic emotion scale, and positive mental scale, as well as questionnaire data from Chinese postgraduate students. This study used the cross-lagged (ARCL) panel model data to longitudinally measure 798 valid data from two survey questions polls taken in 2019 (T1) and 2021 (T2) to investigate the link between supervisor support and positive graduate student mental well-being in a bidirectional relationship of influence. The study discovered that mentor assistance could have a considerable beneficial impact on graduate students' academic emotions and, as a result, indirectly help learners attain positive mental health development. This verifies the theoretical premise that academic emotions partially mediate the effect of mentor support on positive mental health development and argues for the coexistence of the two. The outcomes of this study can help researchers gain a better knowledge of the dynamic interplay among three different research variables: supervisor support, academic emotions, and positive mental health, as well as fill gaps in previous research. In this regard, the study indicated that mentor assistance directly stimulates students' academic drive and assists graduate students in developing good academic emotions, which contributes to the development of positive mental health. However, given the restricted measurement time in this study's cross-lagged panel data and the potential effect of moderating effects other than academic mood on graduate students' good mental health, the results of this study need to be more fully understood and validated.

Keywords: supervisor support, academic emotions, positive mental health, interaction effects, longitudinal cross-lagged measurements

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Authors: Abdol-Hassan Doulah

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In this research, some of the inorganic complexes of uranyl with N- donor ligands were synthesized. Complexes were characteriezed by FT-IR and UV spectra, ¹HNMR, ¹³CNMR and some physical properties. The uranyl unit (UO2) is composed of a center of uranium atom with the charge (+6) and two oxygen atom by forming two U=O double bonds. The structure is linear (O=U=O, 180) and usually stable. So other ligands often coordinate to the U atom in the plane perpendicularly to the O=U=O axis. The antitumor activity of some of ligand and their complexes against a panel of human tumor cell lines (HT29: Haman colon adenocarcinoma cell line T47D: human breast adenocarcinoma cell line) were determined by MTT(3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyl-tetrazolium bromide) assay. These data suggest that some of these compounds provide good models for the further design of potent antitumor compounds.

Keywords: inorganic, uranyl complex-donor ligands, Schiff bases, anticancer activity

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Authors: Zainab Almukhtar, Adel Merabet

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In this paper, a method for maximum power point tracking of a photovoltaic energy conversion system is presented. This method is based on using the difference between the power from the solar panel and an estimated power value to control the DC-DC converter of the photovoltaic system. The difference is continuously compared with a preset error permitted value. If the power difference is more than the error, the estimated power is multiplied by a factor and the operation is repeated until the difference is less or equal to the threshold error. The difference in power will be used to trigger a DC-DC boost converter in order to raise the voltage to where the maximum power point is achieved. The proposed method was experimentally verified through a PV energy conversion system driven by the OPAL-RT real time controller. The method was tested on varying radiation conditions and load requirements, and the Photovoltaic Panel was operated at its maximum power in different conditions of irradiation.

Keywords: control system, error, solar panel, MPPT tracking

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Authors: Yina F. Muñoz, Alexander Paz, Hanns De La Fuente-Mella, Joaquin V. Fariña, Guilherme M. Sales

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The primary approach for estimating bridge deterioration uses Markov-chain models and regression analysis. Traditional Markov models have problems in estimating the required transition probabilities when a small sample size is used. Often, reliable bridge data have not been taken over large periods, thus large data sets may not be available. This study presents an important change to the traditional approach by using the Small Data Method to estimate transition probabilities. The results illustrate that the Small Data Method and traditional approach both provide similar estimates; however, the former method provides results that are more conservative. That is, Small Data Method provided slightly lower than expected bridge condition ratings compared with the traditional approach. Considering that bridges are critical infrastructures, the Small Data Method, which uses more information and provides more conservative estimates, may be more appropriate when the available sample size is small. In addition, regression analysis was used to calculate bridge deterioration. Condition ratings were determined for bridge groups, and the best regression model was selected for each group. The results obtained were very similar to those obtained when using Markov chains; however, it is desirable to use more data for better results.

Keywords: concrete bridges, deterioration, Markov chains, probability matrix

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Authors: Ramon Santana

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The use of biometric identifiers in the field of information security, access control to resources, authentication in ATMs and banking among others, are of great concern because of the safety of biometric data. In the general architecture of a biometric system have been detected eight vulnerabilities, six of them allow obtaining minutiae template in plain text. The main consequence of obtaining minutia templates is the loss of biometric identifier for life. To mitigate these vulnerabilities several models to protect minutiae templates have been proposed. Several vulnerabilities in the cryptographic security of these models allow to obtain biometric data in plain text. In order to increase the cryptographic security and ease of reversibility, a minutiae templates protection model is proposed. The model aims to make the cryptographic protection and facilitate the reversibility of data using two levels of security. The first level of security is the data transformation level. In this level generates invariant data to rotation and translation, further transformation is irreversible. The second level of security is the evaluation level, where the encryption key is generated and data is evaluated using a defined evaluation function. The model is aimed at mitigating known vulnerabilities of the proposed models, basing its security on the impossibility of the polynomial reconstruction.

Keywords: fingerprint, template protection, bio-cryptography, minutiae protection

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Authors: John P. O’Sullivan, Thomas M. P. Ratouis, Michael J. O’Sullivan

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As the use of geothermal energy grows internationally more effort is required to monitor and protect areas with rare and important geothermal surface features. A number of approaches are presented for developing and calibrating numerical geothermal reservoir models that are capable of accurately representing geothermal surface features. The approaches are discussed in the context of cases studies of the Rotorua geothermal system and the Orakei-korako geothermal system, both of which contain important surface features. The results show that models are able to match the available field data accurately and hence can be used as valuable tools for predicting the future response of the systems to changes in use.

Keywords: geothermal reservoir models, surface features, monitoring, TOUGH2

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Jeongmin Kim, Eunju Lee, Kwang Ryel Ryu

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This paper addresses the problem of predictive control for energy management of large-scaled exhibition halls, where a lot of energy is consumed to maintain internal atmosphere under certain required conditions. Predictive control achieves better energy efficiency by optimizing the operation of air-conditioning facilities with not only the current but also some future status taken into account. In this paper, we propose to use predictive models learned from past sensor data of hall environment, for use in optimizing the operating plan for the air-conditioning facilities by simulating future environmental change. We have implemented an emulator of an exhibition hall by using EnergyPlus, a widely used building energy emulation tool, to collect data for learning environment-change models. Experimental results show that the learned models predict future change highly accurately on a short-term basis.

Keywords: predictive control, energy management, machine learning, optimization

Procedia PDF Downloads 246

Authors: Gonzalez Carlos, Martinez Fransisco

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There are various types of analysis that allow us to involve seismic phenomena that cause strong requirements for structures that are designed by society; one of them is a probabilistic analysis which works from prediction equations that have been created based on metadata seismic compiled in different regions. These equations form models that are used to describe the 5% damped pseudo spectra response for the various zones considering some easily known input parameters. The biggest problem for the creation of these models requires data with great robust statistics that support the results, and there are several places where this type of information is not available, for which the use of alternative methodologies helps to achieve adjustments to different models of seismic prediction.

Keywords: GMPM, 5% damped pseudo-response spectra, models of seismic prediction, PSHA

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Authors: Sarma Binti Aralas

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The relationship between property rights and trade specialization is examined for developing and developed countries using panel data analysis. Property rights is measured using the international property rights index while trade specialization is measured using the comparative advantage index. Cross country differences in property rights are hypothesized to lead to differences in trade specialization. Based on the argument that a weak protection of natural resources implies greater trade in resource-intensive goods, developing countries with less defined property rights are hypothesized to have a comparative advantage in resource-based exports while countries with more defined property rights will not have an advantage in resource-intensive goods. Evidence suggests that developing countries with weaker environmental protection index but are rich in natural resources do specialize in the trade of resource-intensive goods. The finding suggests that institutional frameworks to increase the stringency of environmental protection of resources may be needed to diversify exports away from the trade of resource-intensive goods.

Keywords: environmental protection, panel data, renewable resources, trade specialization

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Authors: Heba Abdelmotaal

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This paper explores the impact of the extent of a company's environmental, social, and governance (ESG) disclosure on credit rating and capital structure. The analysis is based on a sample of 202 firms from the 350 FTSE firms over the period of 2008-2013. ESG disclosure score is measured using Proprietary Bloomberg score based on the extent of a company's Environmental, Social, and Governance (ESG) disclosure. The credit rating is measured by The QuiScore, which is a measure of the likelihood that a company will become bankrupt in the twelve months following the date of calculation. The Capital Structure is measured by long term debt ratio. Two hypotheses are test using panel data regression. The results suggested that the higher degree of ESG disclosure leads to better credit rating. There is significant negative relationship between ESG disclosure and the long term debit percentage. The paper includes implications for the transparency which is resulting of the ESG disclosure could support the Monitoring Function. The monitoring role of disclosure is the increasing in the transparency of the credit rating agencies, also it could affect on managers’ actions. This study provides empirical evidence on the material of ESG disclosure on credit ratings changes and the firms’ capital decision making.

Keywords: capital structure, credit rating agencies, ESG disclosure, panel data regression

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Authors: Nuwan Kuruwitaarachchi, M. K. M. Peiris, C. N. Madawala, K. M. A. R. Perera, V. U. N Perera

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Dealing with businesses with the foreign market always took a special place in a country’s economy. Political and social factors came into play making currency rate changes fluctuate rapidly. Currency rate prediction has become an important factor for larger international businesses since large amounts of money exchanged between countries. This research focuses on comparing the accuracy of mainly three models; Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Networks(ANN) and Support Vector Machines(SVM). series of data import, export, USD currency exchange rate respect to LKR has been selected for training using above mentioned algorithms. After training the data set and comparing each algorithm, it was able to see that prediction in SVM performed better than other models. It was improved more by combining SVM and SVR models together.

Keywords: ARIMA, ANN, FFNN, RMSE, SVM, SVR

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Authors: Elfatih M. Abdel-Rahman, Tobias Landmann, Richard Kyalo, George Ong’amo, Bruno Le Ru

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Maize (Zea mays L.) is a major staple food crop in Africa, particularly in the eastern region of the continent. The maize growing area in Africa spans over 25 million ha and 84% of rural households in Africa cultivate maize mainly as a means to generate food and income. Average maize yields in Sub Saharan Africa are 1.4 t/ha as compared to global average of 2.5–3.9 t/ha due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In East Africa, yield losses due to stem borers are currently estimated between 12% to 40% of the total production. The objective of the present study was therefore to predict stem borer larvae density in maize fields using RapidEye reflectance data and generalized linear models (GLMs). RapidEye images were captured for a test site in Kenya (Machakos) in January and in February 2015. Stem borer larva numbers were modeled using GLMs assuming Poisson (Po) and negative binomial (NB) distributions with error with log arithmetic link. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were employed to assess the models performance using a leave one-out cross-validation approach. Results showed that NB models outperformed Po ones in all study sites. RMSE and RPD ranged between 0.95 and 2.70, and between 2.39 and 6.81, respectively. Overall, all models performed similar when used the January and the February image data. We conclude that reflectance data from RapidEye data can be used to estimate stem borer larvae density. The developed models could to improve decision making regarding controlling maize stem borers using various integrated pest management (IPM) protocols.

Keywords: maize, stem borers, density, RapidEye, GLM

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Authors: Rehab Abdulmajed, Amr Hamada, Ahmed Elsaid, Hisashi Hayakawa, Ayman Mahrous

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Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of solar wind using mathematical models, MHD models, and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulates the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar cycles (sc) 21, 22, 23, and most of 24.

Keywords: artificial neural network, coronal hole area, feed-forward neural network models, solar high speed streams

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Ramona Zharfpeykan, Paul Rouse

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Organizational sustainability is important to both organizations themselves and their stakeholders. Despite its increasing popularity and increasing numbers of organizations reporting sustainability, research on evaluating and comparing the sustainability performance of companies is limited. The aim of this study was to develop models to measure sustainability performance for both cross-sectional and longitudinal comparisons across companies in the same or different industries. A secondary aim was to see if sustainability reports can be used to evaluate sustainability performance. The study used both a content analysis of Australian sustainability reports in mining and metals and financial services for 2011-2014 and a survey of Australian and New Zealand organizations. Two methods ranging from a composite index using uniform weights to data envelopment analysis (DEA) were employed to analyze the data and develop the models. The results show strong statistically significant relationships between the developed models, which suggests that each model provides a consistent, systematic and reasonably robust analysis. The results of the models show that for both industries, companies that had sustainability scores above or below the industry average stayed almost the same during the study period. These indices and models can be used by companies to evaluate their sustainability performance and compare it with previous years, or with other companies in the same or different industries. These methods can also be used by various stakeholders and sustainability ranking companies such as the Global Reporting Initiative (GRI).

Keywords: data envelopment analysis, sustainability, sustainability performance measurement system, sustainability performance index, global reporting initiative

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Authors: Joaquim Rubens Fontes-Filho, Felipe Buchbinder, Marcelo Desterro

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The study aims to identify the effects of an economic recession on the compensation of executives of listed companies. Market-based and labor environment explanations have received particular attention, both to explain the reasons for a growth in this compensation and to indicate that they may increase agency problems rather than mitigate them. In this sense, labor forces, especially related to the market for executives, contribute to defining the terms of compensation packages and represent a significant external control mechanism to moderate agency problems, but may be of little effect if the executives are entrenched and concentrate enough power to have a say in his/her compensation. Based on a five-year data panel related to executive compensation in 250 listed companies in Brazil, we examine whether the economic recession in the last two years produced any impact in this compensation, controlling for the sector and level of governance of the company.

Keywords: agency problems, executive compensation, control mechanisms, corporate governance

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Authors: K. C. R. Perera, B. M. Hapuwatte

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This paper presents an effective method to divide panels for mesh-less methods of source, vortex and doublet panel methods. In this research study the physical domain of air-foils were transformed into computational domain of a circle using conformal mapping technique of Joukowsky transformation. Then the circle is divided into panels of equal length and the co-ordinates were remapped into physical domain of the air-foil. With this method the leading edge and the trailing edge of the air-foil is panelled with a high density of panels and the rest of the body is panelled with low density of panels. The high density of panels in the leading edge and the trailing edge will increase the accuracy of the solutions obtained from panel methods where the fluid flow at the leading and trailing edges are complex.

Keywords: conformal mapping, Joukowsky transformation, physical domain, computational domain

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Authors: Minttu Laukkanen, Janne Huiskonen

Abstract:

This study investigates the sharing economy business models as examples of the sustainable business models. The aim is to contribute to the limited literature on sharing economy in connection with sustainable business models by explaining sharing economy business models value capturing. Specifically, this research answers the following question: How business model choices affect captured sustainable value? A multiple case study approach is applied in this study. Twenty different successful sharing economy business models focusing on consumer business and covering four main areas, accommodation, mobility, food, and consumer goods, are selected for analysis. The secondary data available on companies’ websites, previous research, reports, and other public documents are used. All twenty cases are analyzed through the sharing economy business model framework and sustainable value analysis framework using qualitative data analysis. This study represents general sharing economy business model value attributes and their specifications, i.e. sustainable value propositions for different stakeholders, and further explains the sustainability impacts of different sharing economy business models through captured and uncaptured value. In conclusion, this study represents how business model choices affect sustainable value capturing through eight business model attributes identified in this study. This paper contributes to the research on sustainable business models and sharing economy by examining how business model choices affect captured sustainable value. This study highlights the importance of careful business model and sustainability impacts analyses including the triple bottom line, multiple stakeholders and value captured and uncaptured perspectives as well as sustainability trade-offs. It is not self-evident that sharing economy business models advance sustainability, and business model choices does matter.

Keywords: sharing economy, sustainable business model innovation, sustainable value, value capturing

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Authors: Khalid Sekkat, Fredj Fhima, Ridha Nouira

Abstract:

This paper focuses on three main limitations of the literature regarding the impact of corruption on democracy. These limitations relate to the distinction between causality and correlation, the components of democracy underlying the impact and the shape of the relationship between corruption and democracy. The study uses recent developments in panel data causality econometrics, breaks democracy down into different components, and examines the types of the relationship. The results show that Control of Corruption leads to a higher quality of democracy. Regarding the estimated coefficients of the components of democracy, they are significant at the 1% level, and their signs and levels are in accordance with expectations except in a few cases. Overall, the results add to the literature in three respects: i). corruption has a causal effect on democracy and, hence, single equation estimation may pose a problem, ii) the assumption of the linearity of the relationships between control of corruption and democracy is also possibly problematic, and iii) the channels of transmission of the effects of corruption on democracy can be diverse. Disentangling them is useful from a policy perspective.

Keywords: corruption, governance, causality, threshold models

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