Search results for: circular business models

Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Etienne Provencal, David L. St-Pierre

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A facility exploiting only electronic gambling machines (EGMs) opened in 2007 in Quebec City, Canada under the name of Salons de Jeux du Québec (SdjQ). This facility is one of the first worldwide to rely on that business model. This paper models the performance of such EGMs. The interest from a managerial point of view is to identify the variables that can be controlled or influenced so that a comprehensive model can help improve the overall performance of the business. The EGM individual performance model contains eight different variables under study (Game Title, Progressive jackpot, Bonus Round, Minimum Coin-in, Maximum Coin-in, Denomination, Slant Top and Position). Using data from Quebec City’s SdjQ, a linear regression analysis explains 90.80% of the EGM performance. Moreover, results show a behavior slightly different than that of a casino. The addition of GameTitle as a factor to predict the EGM performance is one of the main contributions of this paper. The choice of the game (GameTitle) is very important. Games having better position do not have significantly better performance than games located elsewhere on the gaming floor. Progressive jackpots have a positive and significant effect on the individual performance of EGMs. The impact of BonusRound on the dependent variable is significant but negative. The effect of Denomination is significant but weakly negative. As expected, the Language of an EGMS does not impact its individual performance. This paper highlights some possible improvements by indicating which features are performing well. Recommendations are given to increase the performance of the EGMs performance.

Keywords: EGM, linear regression, model prediction, slot operations

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Authors: Imene Skhakhfa, Lahbaci Ouerdachi

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To ensure the overall coherence of simulated results, it is necessary to develop a robust validation process. In many applications, it is no longer content to calibrate and validate the model only in relation to the hydro graph measured at the outlet, but we try to better simulate the functioning of the watershed in space. Therefore the timing also performs compared to other variables such as water level measurements in intermediate stations or groundwater levels. As part of this work, we limit ourselves to modeling flood of short duration for which the process of evapotranspiration is negligible. The main parameters to identify the models are related to the method of unit hydro graph (HU). Three different models were tested: SNYDER, CLARK and SCS. These models differ in their mathematical structure and parameters to be calibrated while hydrological data are the same, the initial water content and precipitation. The models are compared on the basis of their performance in terms six objective criteria, three global criteria and three criteria representing volume, peak flow, and the mean square error. The first type of criteria gives more weight to strong events whereas the second considers all events to be of equal weight. The results show that the calibrated parameter values are dependent and also highlight the problems associated with the simulation of low flow events and intermittent precipitation.

Keywords: model calibration, intensity, runoff, hydrograph

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Authors: Abdunaser Abduelmula, Maria Luisa M. Bastos, José A. Gonçalves

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The modeling of the earth's surface and evaluation of urban environment, with 3D models, is an important research topic. New stereo capabilities of high-resolution optical satellites images, such as the tri-stereo mode of Pleiades, combined with new image matching algorithms, are now available and can be applied in urban area analysis. In addition, photogrammetry software packages gained new, more efficient matching algorithms, such as SGM, as well as improved filters to deal with shadow areas, can achieve denser and more precise results. This paper describes a comparison between 3D data extracted from tri-stereo and dual stereo satellite images, combined with pixel based matching and Wallis filter. The aim was to improve the accuracy of 3D models especially in urban areas, in order to assess if satellite images are appropriate for a rapid evaluation of urban environments. The results showed that 3D models achieved by Pleiades tri-stereo outperformed, both in terms of accuracy and detail, the result obtained from a Geo-eye pair. The assessment was made with reference digital surface models derived from high-resolution aerial photography. This could mean that tri-stereo images can be successfully used for the proposed urban change analyses.

Keywords: 3D models, environment, matching, pleiades

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Authors: James R. Ogden, Denise T. Ogden

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The National Football League (USA) is the largest sporting business in the United States. In order to generate revenue, it is important that NFL teams win. Coaches, owners and general managers of the NFL teams want to create powerful teams with reliable players and they are willing to spend large amounts of money in order to do so. This research looks at one of the National Football League’s key players, the kicker. It would be intuitively obvious to suggest that those kickers who perform the best get paid the most. In this paper the researchers performed a correlation and regression analysis to determine if there is a correlation between an NFL kicker’s field goal percentage and salary. The research proposition was that higher performing kickers receive higher salaries. The data suggest that there is no correlation between salary and on-field performance.

Keywords: business management, sports marketing, tourism, American football

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Authors: Shailaja Thakur

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Managerial literature abounds with discussions on business strategies, success stories as well as cases of failure, which provide an indication of the parameters that should be considered in gauging the dynamism of an entrepreneur. Neoclassical economics has reduced entrepreneurship to a mere factor of production, driven solely by the profit motive, thus stripping him of all creativity and restricting his decision making to mechanical calculations. His ‘dynamism’ is gauged simply by the amount of profits he earns, marginalizing any discussion on the means that he employs to attain this objective. With theoretical backing, we have developed an Index of Entrepreneurial Dynamism (IED) giving weights to the different moves that the entrepreneur makes during his business journey. Strategies such as changes in product lines, markets and technology are gauged as very important (weighting of 4); while adaptations in terms of technology, raw materials used, upgradations in skill set are given a slightly lesser weight of 3. Use of formal market analysis, diversification in related products are considered moderately important (weight of 2) and being a first generation entrepreneur, employing managers and having plans to diversify are taken to be only slightly important business strategies (weight of 1). The maximum that an entrepreneur can score on this index is 53. A semi-structured questionnaire is employed to solicit the responses from the entrepreneurs on the various strategies that have been employed by them during the course of their business. Binary as well as graded responses are obtained, weighted and summed up to give the IED. This index was tested on about 150 tribal entrepreneurs in Mizoram, a state of India and was found to be highly effective in gauging their dynamism. This index has universal acceptability but is devoid of the socio-cultural context, which is very central to the success and performance of the entrepreneurs. We hypothesize that a society that respects risk taking takes failures in its stride, glorifies entrepreneurial role models, promotes merit and achievement is one that has a conducive socio- cultural environment for entrepreneurship. For obtaining an idea about the social acceptability, we are putting forth questions related to the social acceptability of business to another set of respondents from different walks of life- bureaucracy, academia, and other professional fields. Similar weighting technique is employed, and index is generated. This index is used for discounting the IED of the respondent entrepreneurs from that region/ society. This methodology is being tested for a sample of entrepreneurs from two very different socio- cultural milieus- a tribal society and a ‘mainstream’ society- with the hypothesis that the entrepreneurs in the tribal milieu might be showing a higher level of dynamism than their counterparts in other regions. An entrepreneur who scores high on IED and belongs to society and culture that holds entrepreneurship in high esteem, might not be in reality as dynamic as a person who shows similar dynamism in a relatively discouraging or even an outright hostile environment.

Keywords: index of entrepreneurial dynamism, India, social acceptability, tribal entrepreneurs

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Authors: Rafal Morawczynski

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The paper presents results of a study of technology angels in Poland, who are important for the development of the high technology industries. For entrepreneurs, they offer not only capital but also expertise, engagement, and networking. A technology angel is a relatively new type of investor who invests in high-tech start-ups and supports their founders (entrepreneurs) in the development process of a new venture. Conclusions are drawn from a comparison between 8 technology angels and 7 'classical' business angels. Results present features and behaviors of technology angels that distinguish them from traditional (typical, classic) business angels. As this type of investor actively cooperates with entrepreneurs, the study focuses mainly on their perception of venture founders and several aspects of this cooperation: perception of entrepreneurs’ characteristics by angels, correction of expectations toward corporate governance, and 'value adding' activities.

Keywords: business angels, entrepreneurs, Poland, start-up, technology entrepreneurship, venture capital

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Authors: Prakash Prajapati

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E commerce is certainly one of the business alternatives that individual will have to analyze in the forthcoming years. E-commerce is said to bring about arched type conversion in the world for exchange market. Prognosis E-commerce is presenting dreadful business advancement in our country. Endorsed by ascending online user base & mobile phone presentation, Indian e-commerce has been splendid development in the last few years. Conceding India’s analytical dividend and spiraling internet admittance, the sector is contracted to scale higher heights. Although, India’s overall peddle opportunity is consequential, the sector is beset with some deliberate challenges. The current study has been proceeded to explore the present scenario, status & future advancement of e-commerce in India and review the challenges and opportunities of e-commerce in India.

Keywords: online selling, retail selling online, product process, business opportunity

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Authors: Subhan Ahmad, Pradeep Bhargava, Ajay Chourasia

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Shear failure in reinforced concrete beams without shear reinforcement leads to loss of property and life since a very little or no warning occurs before failure as in case of flexural failure. Shear strength of reinforced concrete beams decreases as its depth increases. This phenomenon is generally called as the size effect. In this paper, a comparative analysis is performed to estimate the performance of shear strength models in capturing the size effect of reinforced concrete beams made with conventional concrete, self-compacting concrete, and recycled aggregate concrete. Four shear strength models that account for the size effect in shear are selected from the literature and applied on the datasets of slender reinforced concrete beams. Beams prepared with conventional concrete, self-compacting concrete, and recycled aggregate concrete are considered for the analysis. Results showed that all the four models captured the size effect in shear effectively and produced conservative estimates of the shear strength for beams made with normal strength conventional concrete. These models yielded unconservative estimates for high strength conventional concrete beams with larger effective depths ( > 450 mm). Model of Bazant and Kim (1984) captured the size effect precisely and produced conservative estimates of shear strength of self-compacting concrete beams at all the effective depths. Also, shear strength models considered in this study produced unconservative estimates of shear strength for recycled aggregate concrete beams at all effective depths.

Keywords: reinforced concrete beams; shear strength; prediction models; size effect

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Authors: Joana Carvalho, Margarida Soares, André Ribeiro, Lucas Nascimento, Nádia Valério, Zlatina Genisheva

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Following the exponential growth of human population, a remarkable increase in the amount of fish waste has been produced worldwide. The fish processing industry generates a considerable amount of by-products which represents a considerable environmental problem. Accordingly, the reuse and valorisation of these by-products is a key process for marine resource preservation. The significant volume of fish waste produced worldwide, along with its environmental impact, underscores the urgent need for the adoption of sustainable practices. The transformative potential of utilizing fish processing waste to create industrial value is gaining recognition. The substantial amounts of waste generated by the fish processing industry present both environmental challenges and economic inefficiencies. Different added-value products can be recovered by the valorisation industries, whereas fishing companies can save costs associated with the management of those wastes, with associated advantages, not only in terms of economic income but also considering the environmental impacts. Fish processing by-products have numerous applications; the target portfolio of products will be fish oil, fish protein hydrolysates, bacteriocins, pigments, vitamins, collagen, and calcium-rich powder, targeting food products, additives, supplements, and nutraceuticals. This literature review focuses on the main valorisation ways of fish wastes and different compounds with a high commercial value obtained by fish by-products and their possible applications in different fields. Highlighting its potential in sustainable resource management strategies can play and important role in reshaping the fish processing industry, driving it towards circular economy and consequently more sustainable future.

Keywords: fish process industry, fish wastes, by-products, circular economy, sustainability

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Authors: K. Matušínská, Z. Bednarčík, M. Klepek

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This paper examines international marketing in business practice of Czech exporting small and medium-sized enterprises (SMEs) with regard to the strategic perspectives. Research was focused on Czech exporting SMEs from Moravian-Silesia region and their behaviour on international markets. For purpose of collecting data, a questionnaire was given to 262 SMEs involved in international business. Statistics utilized in this research included frequency, mean, percentage, and chi-square test. Data were analysed by Statistical Package for the Social Sciences software. The research analysis disclosed that there is certain space for improvement in strategic marketing especially in marketing research, perception of cultural and social differences, product adaptation and usage of marketing communication tools.

Keywords: international marketing, marketing mix, marketing research, small and medium-sized enterprises, strategic marketing

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Authors: Hamidreza Ghazvinian, Khosro Ghazvinian, Touba Khodaiean

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The importance of evaporation estimation in water resources and agricultural studies is undeniable. Pan evaporation are used as an indicator to determine the evaporation of lakes and reservoirs around the world due to the ease of interpreting its data. In this research, intelligent models were investigated in estimating pan evaporation on a daily basis. Shahroud and Mayamey were considered as the studied cities. These two cities are located in Semnan province in Iran. The mentioned cities have dry weather conditions that are susceptible to high evaporation potential. Meteorological data of 11 years of synoptic stations of Shahrood and Mayamey cities were used. The intelligent models used in this study are Artificial Neural Network (ANN), Least Squares Support Vector Machine (LSSVM), and M5 tree models. Meteorological parameters of minimum and maximum air temperature (Tmax, Tmin), wind speed (WS), sunshine hours (SH), air pressure (PA), relative humidity (RH) as selected input data and evaporation data from pan (EP) to The output data was considered. 70% of data is used at the education level, and 30 % of the data is used at the test level. Models used with explanation coefficient evaluation (R2) Root of Mean Squares Error (RMSE) and Mean Absolute Error (MAE). The results for the two Shahroud and Mayamey stations showed that the above three models' operations are rather appropriate.

Keywords: pan evaporation, intelligent methods, shahroud, mayamey

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Authors: Awol Seid Ebrie

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HIV results as an incurable disease, AIDS. After a person is infected with virus, the virus gradually destroys all the infection fighting cells called CD4 cells and makes the individual susceptible to opportunistic infections which cause severe or fatal health problems. Several studies show that the CD4 cells count is the most determinant indicator of the effectiveness of the treatment or progression of the disease. The objective of this paper is to investigate the progression of the disease over time among patient under HAART treatment. Two main approaches of the generalized multilevel ordinal models; namely the proportional odds model and the nonproportional odds model have been applied to the HAART data. Also, the multilevel part of both models includes random intercepts and random coefficients. In general, four models are explored in the analysis and then the models are compared using the deviance information criteria. Of these models, the random coefficients nonproportional odds model is selected as the best model for the HAART data used as it has the smallest DIC value. The selected model shows that the progression of the disease increases as the time under the treatment increases. In addition, it reveals that gender, baseline clinical stage and functional status of the patient have a significant association with the progression of the disease.

Keywords: nonproportional odds model, proportional odds model, random coefficients model, random intercepts model

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Authors: Abhishek Saha, Serene Tay, Gerard Pijcke

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The availability of high-resolution topography and rainfall information in urban areas has made it necessary to revise modeling approaches used for simulating flood risk assessments. Lidar derived elevation models that have 1m or lower resolutions are becoming widely accessible. The classical approaches of 1D-2D flow models where channel flow is simulated and coupled with a coarse resolution 2D overland flow models may not fully utilize the information provided by high-resolution data. In this context, a study was undertaken to compare three different modeling approaches to simulate flooding in an urban area. The first model used is the base model used is Sobek, which uses 1D model formulation together with hydrologic boundary conditions and couples with an overland flow model in 2D. The second model uses a full 2D model for the entire area with shallow water equations at the resolution of the digital elevation model (DEM). These models are compared against another shallow water equation solver in 2D, which uses a subgrid method for grid refinement. These models are simulated for different horizontal resolutions of DEM varying between 1m to 5m. The results show a significant difference in inundation extents and water levels for different DEMs. They are also sensitive to the different numerical models with the same physical parameters, such as friction. The study shows the importance of having reliable field observations of inundation extents and levels before a choice of model and data can be made for spatial flood risk assessments.

Keywords: flooding, DEM, shallow water equations, subgrid

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Authors: Samuel Faboyede, Obiamaka Nwobu, Samuel Fakile, Dickson Mukoro

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In the present dynamic business environment of corporate governance and regulations, financial reporting is an inevitable and extremely significant process for every business enterprise. Several financial elements such as Annual Reports, Quarterly Reports, ad-hoc filing, and other statutory/regulatory reports provide vital information to the investors and regulators, and establish trust and rapport between the internal and external stakeholders of an organization. Investors today are very demanding, and emphasize greatly on authenticity, accuracy, and reliability of financial data. For many companies, the Internet plays a key role in communicating business information, internally to management and externally to stakeholders. Despite high prominence being attached to external reporting, it is disconnected in most companies, who generate their external financial documents manually, resulting in high degree of errors and prolonged cycle times. Chief Executive Officers and Chief Financial Officers are increasingly susceptible to endorsing error-laden reports, late filing of reports, and non-compliance with regulatory acts. There is a lack of common platform to manage the sensitive information – internally and externally – in financial reports. The Internet financial reporting language known as eXtensible Business Reporting Language (XBRL) continues to develop in the face of challenges and has now reached the point where much of its promised benefits are available. This paper looks at the emergence of this revolutionary twenty-first century language of digital reporting. It posits that today, the world is on the brink of an Internet revolution that will redefine the ‘business reporting’ paradigm. The new Internet technology, eXtensible Business Reporting Language (XBRL), is already being deployed and used across the world. It finds that XBRL is an eXtensible Markup Language (XML) based information format that places self-describing tags around discrete pieces of business information. Once tags are assigned, it is possible to extract only desired information, rather than having to download or print an entire document. XBRL is platform-independent and it will work on any current or recent-year operating system, or any computer and interface with virtually any software. The paper concludes that corporate stakeholders and the government cannot afford to ignore the XBRL. It therefore recommends that all must act locally and think globally now via the adoption of XBRL that is changing the face of worldwide business reporting.

Keywords: XBRL, financial reporting, internet, internal and external reports

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Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

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To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

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Authors: Sandeep Kaur, Jenny Vuong, Marcel Julliard, Sean O'Donoghue

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Time-series data are useful for modelling as they can enable model-evaluation. However, when reconstructing models from phosphoproteomic data, often non-exact methods are utilised, as the knowledge regarding the network structure, such as, which kinases and phosphatases lead to the observed phosphorylation state, is incomplete. Thus, such reactions are often hypothesised, which gives rise to uncertainty. Here, we propose a framework, implemented via a web-based tool (as an extension to Minardo), which given time-series phosphoproteomic datasets, can generate κ models. The incompleteness and uncertainty in the generated model and reactions are clearly presented to the user via the visual method. Furthermore, we demonstrate, via a toy EGF signalling model, the use of algorithmic verification to verify κ models. Manually formulated requirements were evaluated with regards to the model, leading to the highlighting of the nodes causing unsatisfiability (i.e. error causing nodes). We aim to integrate such methods into our web-based tool and demonstrate how the identified erroneous nodes can be presented to the user via the visual method. Thus, in this research we present a framework, to enable a user to explore phosphorylation proteomic time-series data in the context of models. The observer can visualise which reactions in the model are highly uncertain, and which nodes cause incorrect simulation outputs. A tool such as this enables an end-user to determine the empirical analysis to perform, to reduce uncertainty in the presented model - thus enabling a better understanding of the underlying system.

Keywords: κ-models, model verification, time-series phosphoproteomic datasets, uncertainty and error visualisation

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Authors: Baikunthanath Sahoo, Santosh Kumar Sahu, Krishna Malakar

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With the essence of global environmental sustainability and green business management, the wind of business research moved towards Corporate Social Responsibility. In addition to international and national treaties, businesses have also started realising environmental protection and energy efficiency through CSR as part of business strategy in response to climate change. Considering the ambitious emission reduction target and rapid economic development of India, this study is an attempt to explore the effect of CSR on the energy efficiency management of manufacturing firms in India. By using firm-level data, the panel fixed effect model shows that the CSR dummy variable is negatively influencing the energy intensity or technically, they are energy efficient. The result demonstrates that in the presence of CSR, all the production economic variables are significant. The result also shows that doing environmental expenditure does not improve energy efficiency might be because very few firms are motivated to do such expenditure and also not common to all sectors. The interactive effect model result conforms that without considering CSR dummy as an intervening variable only Manufacturers of Chemical and Chemical products, Manufacturers of Pharmaceutical, medical chemical, and botanical products firms energy intensity low but after considering CSR in their business practices all six sub-sector firms become energy efficient. The empirical result also validate that firms are continuously engaged in CSR activities they are highly energy efficient. It is an important motivational factor for firms to become economically and environmentally sustainable in the corporate world. This analysis would help business practitioners to know how to manage today’s profitability and tomorrow’s sustainability to achieve a comparative advantage in the emerging market economy. The paper concludes that reducing energy consumption as part of their social responsibility to care for the environment, will need collaborative efforts of business society and policy bodies.

Keywords: CSR, Energy Efficiency, Indian manufacturing Sector, Business strategy

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Authors: Abd Elrahman Elgamala, N. Darwish, I. Bondouk, Sh. Hamada

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Available experimental angular distributions for ⁶Li elastically scattered from ¹⁶O nucleus in the energy range 13.0–50.0 MeV are investigated and reanalyzed using optical model of the conventional phenomenological potential and also using double folding optical model of different interaction models: DDM3Y1, CDM3Y1, CDM3Y2, and CDM3Y3. All the involved models of interaction are of M3Y Paris except DDM3Y1 which is of M3Y Reid and the main difference between them lies in the different values for the parameters of the incorporated density distribution function F(ρ). We have extracted the renormalization factor N_R for ⁶Li+¹⁶O nuclear system in the energy range 13.0–50.0 MeV using the aforementioned interaction models.

Keywords: elastic scattering, optical model, folding potential, density distribution

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Authors: D.Mendes, J. Henriques, P. Carvalho, T. Rocha, S. Paredes, R. Cabiddu, R. Trimer, R. Mendes, A. Borghi-Silva, L. Kaminsky, E. Ashley, R. Arena, J. Myers

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The continued exploration of clinically relevant predictive models continues to be an important pursuit. Cardiorespiratory fitness (CRF) portends clinical vital information and as such its accurate prediction is of high importance. Therefore, the aim of the current study was to develop a data-driven model, based on computational intelligence techniques and, in particular, clustering approaches, to predict CRF. Two prediction models were implemented and compared: 1) the traditional Wasserman/Hansen Equations; and 2) an interpretable clustering approach. Data used for this analysis were from the 'FRIEND - Fitness Registry and the Importance of Exercise: The National Data Base'; in the present study a subset of 10690 apparently healthy individuals were utilized. The accuracy of the models was performed through the computation of sensitivity, specificity, and geometric mean values. The results show the superiority of the clustering approach in the accurate estimation of CRF (i.e., maximal oxygen consumption).

Keywords: cardiorespiratory fitness, data-driven models, knowledge extraction, machine learning

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Authors: Shaheena Erkiah, Adjnu Damar Ladkoo

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Worldwide, improving tourism competitiveness has been on the agendas of many stakeholders of the hotel sector, and they seem to have agreed that one of the best ways to compete is via the implementation of electronic customer relationship management (e-CRM). In so doing, the organizations enjoy strategic positioning on the competitive market by managing better not only the customers but, other business components including knowledge and employee management. Over the recent years, the tourism industry in Mauritius has witnessed a drastic economic boom at international and national levels; providing a new outlook to boost business performance through existing and potential customers. E-CRM has been one of the management tools used to achieving this position. Thus, this insightful context- Mauritius- was opted for the study. The aim was to assess the effectiveness of e-CRM as a strategic tool in the hotel sector in Mauritius through the implementation of business strategy to create competitive advantage and impact on the business performance. To achieve the objectives of the study, a quantitative research methodology was adopted and the research revealed that e-CRM is indeed an effective strategic tool in the hotel industry in Mauritius that can provide a competitive advantage and impact positively on the organization’s performance.

Keywords: customer, electronic, management, relationship, strategic

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Authors: Chonlada Choovanichchanon

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This research paper was aimed to investigate a relationship between the customer service marketing mix and the level of customers’ satisfaction from purchasing goods and service from small business around campus, Suan Sunandha Rajabhat University, Bangkok, Thailand. Based on the survey of 200 customers who frequently purchased goods and service around campus, the level of satisfaction for each factor of marketing mix was reached. An accidental random sampling was applied by using questionnaire in collecting the data. The findings revealed that the means values can help to rank these variables from high to low mean as follows: 1) forms and system of service, 2) physical environment of service center, 3) service from staff and employee, 4) product quality and service, 5) market channel and distribution, 6) market price, and 7) market promotion and distribution.

Keywords: service marketing mix, satisfaction, small business, survey

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Authors: M. Keršič, M. Lužnik, J. Lužnik

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Cervical dilatation and membrane herniation before 26th week of gestation poses very high risk for extremely and very premature childbirth. Cerclage with mesh cap (mesh cerclage, MC) can greatly diminish this risk and provide additional positive effects. Between 2005 and 2014, MC has been performed in 9 patients with singleton pregnancies who had prolapsed membranes beyond external cervical/uterine os before 25th week of pregnancy (one in 29th). With patients in general anaesthesia, lithotomy and Trendelenburg position (about 25°) prolapsed membranes were repositioned in the uterine cavity, using tampon soaked in antiseptic solution (Skinsept mucosa). A circular, a type of purse-string suture (main band) with double string Ethilon 1 was applied at about 1 to 1.5 cm from the border of the external uterine os - 6 to 8 stitches were placed, so the whole external uterine os was encircled (modified McDonald). In the next step additional Ethilon 0 sutures were placed around all exposed parts of the main double circular suture and loosely tightened. On those sutures, round tailored (diameter around 6 cm) mesh (Prolene® or Gynemesh* PS) was attached. In all 9 cases, gestation was prolonged on average for 9 weeks and 4 days (67 days). In four cases maturity was achieved. Mesh was removed in 37th–38th week of pregnancy or if spontaneous labour began. In two cases, a caesarean section was performed because of breech presentation. In the first week after birth in 22nd week one new born died because of immaturity (premature birth was threatening in 18th week and then MC was placed). Ten years after first MC, 8 of 9 women with singleton pregnancy and MC performed have 8 healthy children from these pregnancies. Mesh cerclage successfully closed the opened cervical canal or uterine orifice and prevented further membrane herniation and membrane rupture. MC also provides a similar effect as with occluding the external os with suturing but without interrupting the way for excretion of abundant cervical mucus. The mesh also pulls the main circular band outwards and thus lowers the chance of suture cutting through the remaining cervix. MC prolonged gestation very successfully (mean for 9 weeks and 4 days) and thus increased possibility for survival and diminished the risk for complications in very early preterm delivered survivors in cases with cervical dilatation and membrane herniation before 26th week of gestation. Without action possibility to achieve at least 28th or 32nd week of gestation would be poor.

Keywords: cervical insufficiency, mesh cerclage, membrane protrusion, premature birth prevention, physical exam-indicated cerclage, rescue cerclage

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Authors: Elias K. Maragos, Petros E. Maravelakis

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In Dynamic Data Envelopment Analysis (DDEA), which is a subfield of Data Envelopment Analysis (DEA), the productivity of Decision Making Units (DMUs) is considered in relation to time. In this case, as it is accepted by the most of the researchers, there are outputs, which are produced by a DMU to be used as inputs in a future time. Those outputs are known as intermediates. The common models, in DDEA, do not take into account the shape of the distribution of those inputs, outputs or intermediates data, assuming that the distribution of the virtual value of them does not deviate from linearity. This weakness causes the limitation of the accuracy of the analytical power of the traditional DDEA models. In this paper, the authors, using the concept of piecewise linear inputs and outputs, propose an extended DDEA model. The proposed model increases the flexibility of the traditional DDEA models and improves the measurement of the dynamic performance of DMUs.

Keywords: Dynamic Data Envelopment Analysis, DDEA, piecewise linear inputs, piecewise linear outputs

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Authors: A. G. Matveev

Abstract:

The copyrights system is a combination of different elements. The number, content and the correlation of these elements are different for different legal orders. The models of copyrights systems display this system in terms of the interaction of economic and author's moral rights. Monistic and dualistic models are the most popular ones. The article deals with different points of view on the monism and dualism in copyright system. A specific model of the copyright in Switzerland in the XXth century is analyzed. The evolution of a French dualistic model of copyright is shown. The author believes that one should talk not about one, but rather about a number of dualism forms of copyright system.

Keywords: copyright, exclusive copyright, economic rights, author's moral rights, rights of personality, monistic model, dualistic model

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Authors: Yogendra Sisodia

Abstract:

Researchers are becoming more interested in extracting useful information from legal documents thanks to the development of large-scale language models in natural language processing (NLP), and deep learning has accelerated the creation of powerful text mining models. Legal fields like contracts benefit greatly from semantic text search since it makes it quick and easy to find related clauses. After collecting sentence embeddings, it is relatively simple to locate sentences with a comparable meaning throughout the entire legal corpus. The author of this research investigated two pre-trained language models for this task: MiniLM and Roberta, and further fine-tuned them on Legal Contracts. The author used Multiple Negative Ranking Loss for the creation of sentence transformers. The fine-tuned language models and sentence transformers showed promising results.

Keywords: legal contracts, multiple negative ranking loss, natural language inference, sentence transformers, semantic textual similarity

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Authors: Herlandson C. Moura, Jorge H. Bidinotto, Eduardo M. Belo

Abstract:

The present work proposes the development of an adaptive control system which enables the suppression of Pilot Induced Oscillations (PIO) in Digital Fly-By-Wire (DFBW) aircrafts. The proposed system consists of a Modified Model Reference Adaptive Control (M-MRAC) integrated with the Gain Scheduling technique. The PIO oscillations are detected using a Real Time Oscillation Verifier (ROVER) algorithm, which then enables the system to switch between two reference models; one in PIO condition, with low proneness to the phenomenon and another one in normal condition, with high (or medium) proneness. The reference models are defined in a closed loop condition using the Linear Quadratic Regulator (LQR) control methodology for Multiple-Input-Multiple-Output (MIMO) systems. The implemented algorithms are simulated in software implementations with state space models and commercial flight simulators as the controlled elements and with pilot dynamics models. A sequence of pitch angles is considered as the reference signal, named as Synthetic Task (Syntask), which must be tracked by the pilot models. The initial outcomes show that the proposed system can detect and suppress (or mitigate) the PIO oscillations in real time before it reaches high amplitudes.

Keywords: adaptive control, digital Fly-By-Wire, oscillations suppression, PIO

Procedia PDF Downloads 134

Authors: Riona Dighe

Abstract:

This research attempts to identify an alternative approach to the measurement of Gross National Happiness (GNH). It uses artificial intelligence (AI), incorporating natural language processing (NLP) and sentiment analysis to measure GNH. We use ‘off the shelf’ NLP models responsible for the sentiment analysis of a sentence as a building block for this research. We constructed an algorithm using NLP models to derive a sentiment analysis score against sentences. This was then tested against a sample of 20 respondents to derive a sentiment analysis score. The scores generated resembled human responses. By utilising the MLP classifier, decision tree, linear model, and K-nearest neighbors, we were able to obtain a test accuracy of 89.97%, 54.63%, 52.13%, and 47.9%, respectively. This gave us the confidence to use the NLP models against sentences in websites to measure the GNH of a country.

Keywords: artificial intelligence, NLP, sentiment analysis, gross national happiness

Procedia PDF Downloads 119

Authors: Fazıl Gökgöz, Fahrettin Filiz

Abstract:

Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

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Authors: Mingook Lee, Sungjoo Lee

Abstract:

As technology-based service industries grow drastically worldwide; companies are recognizing the importance of market preoccupancy and have made an effort to capture a large market to gain the upper hand. To this end, a focus on patents can be used to determine the properties of a technology, as well as to capture advantages in technical skills, in comparison with the firm’s competitors. However, technology-based services largely depend not only on their technological value but also their economic value, due to the recognized worth that is passed to a plurality of users. Thus, it is important to determine whether there are any competitors in the target areas and what services they provide in any field. Despite this importance, little effort has been made to systematically benchmark competitors in order to identify business opportunities. Thus, this study aims to not only identify each position of technology-centered service companies in complex market dynamics, but also to discover new business opportunities. For this, we try to consider both technology and market environments simultaneously by utilizing patent data as a representative proxy for technology and trademark dates as an index for a firm’s target goods and services. Theoretically, this is one of the earliest attempts to combine patent data and trademark data to analyze corporate strategies. In practice, the research results are expected to be used as a decision criterion to diagnose the economic value that companies can obtain by entering the market, as well as the technological value to be passed onto their customers. Thus, the proposed approach can be useful to support effective technology and business strategies in a firm.

Keywords: business opportunity, patent, Portfolio analysis, trademark

Procedia PDF Downloads 294