Search results for: market prediction

Authors: Christopher Deekia Nwimae, Nigel Simms, Liyun Lao

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Erosion in pipe bends caused by particles is a major obstacle in the oil and gas fields and might cause the breakdown of production equipment. This work studied the effects imposed by flow velocity and impact of solid particles diameter in an elbow; erosion rate was verified with experimental data using the computational fluid dynamics (CFD) approach. Two-way coupled Euler-Lagrange and discrete phase model was employed to calculate the air/solid particle flow in an elbow. One erosion model and three-particle rebound models were used to predict the erosion rate on the 90° elbows. The generic erosion model was used in the CFD-based erosion model, and after comparing it with experimental data, results showed agreement with the CFD-based predictions as observed.

Keywords: erosion, prediction, elbow, computational fluid dynamics

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Authors: Taisier A. Zoubi, Feras Salama, Mahmud Hossain, Yass A. Alkafaji

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The purpose of this study is to examine the equity pricing of other comprehensive income when earnings are disaggregated into several components. Our findings indicate that other comprehensive income can better explain variation in stock returns when net income is reported in a disaggregated form. Additionally, we found that disaggregating both net income and other comprehensive income can explain more of the variation in the stock returns than the two summary components of comprehensive income. Our results survive a series of robustness checks.

Keywords: market valuation, other comprehensive income, value-relevance, incremental information content

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Authors: Firas M. Tuaimah

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This paper presents an Artificial Neural Network based approach for short-term load forecasting and exactly for two days ahead. Two seasons have been discussed for Iraqi power system, namely summer and winter; the hourly load demand is the most important input variables for ANN based load forecasting. The recorded daily load profile with a lead time of 1-48 hours for July and December of the year 2012 was obtained from the operation and control center that belongs to the Ministry of Iraqi electricity. The results of the comparison show that the neural network gives a good prediction for the load forecasting and for two days ahead.

Keywords: short-term load forecasting, artificial neural networks, back propagation learning, hourly load demand

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Authors: Seyran Naghdi

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Background: Orphan drug development is limited by very high costs attributed to the research and development and small size market. How health policymakers address this challenge to consider both supply and demand sides need to be explored for directing the policies and plans in the right way. The price is an important signal for pharmaceutical companies’ profitability and the patients’ accessibility as well. Objective: This study aims to find out the orphan drugs' price-setting patterns and approaches in health systems through a systematic review of the available evidence. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) approach was used. MedLine, Embase, and Web of Sciences were searched via appropriate search strategies. Through Medical Subject Headings (MeSH), the appropriate terms for pricing were 'cost and cost analysis', and it was 'orphan drug production', and 'orphan drug', for orphan drugs. The critical appraisal was performed by the Joanna-Briggs tool. A Cochrane data extraction form was used to obtain the data about the studies' characteristics, results, and conclusions. Results: Totally, 1,197 records were found. It included 640 hits from Embase, 327 from Web of Sciences, and 230 MedLine. After removing the duplicates, 1,056 studies remained. Of them, 924 studies were removed in the primary screening phase. Of them, 26 studies were included for data extraction. The majority of the studies (>75%) are from developed countries, among them, approximately 80% of the studies are from European countries. Approximately 85% of evidence has been produced in the recent decade. Conclusions: There is a huge variation of price-setting among countries, and this is related to the specific pharmacological market structure and the thresholds that governments want to intervene in the process of pricing. On the other hand, there is some evidence on the availability of spaces to reduce the very high costs of orphan drugs development through an early agreement between pharmacological firms and governments. Further studies need to focus on how the governments could incentivize the companies to agree on providing the drugs at lower prices.

Keywords: orphan drugs, orphan drug production, pricing, costs, cost analysis

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Authors: Andy H. Clark

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This research looks into the relationship between language comprehension and production with a specific focus on the role of salience in shaping these processes. Salience, our most immediate perception of what is most probable out of all possible situations and outcomes strongly affects our perception and action in language production and comprehension. This study investigates the impact of geographic and emotional attachments to the target language on the differences in the learners’ comprehension and production abilities. Using quantitative research methods (Qualtrics, SPSS), this study examines preferential choices of two groups of Japanese English language learners: those residing in the United States and those in Japan. By comparing and contrasting these two groups, we hope to gain a better understanding of how salience of linguistics cues influences language processing.

Keywords: intercultural pragmatics, salience, production, comprehension, pragmatics, action, perception, cognition

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Authors: Michal Pachocki

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This study investigates the effects of mobility in the context of a different environment and work culture through analysing the learners perception of their international work experience. Since this kind of professional training abroad is becoming more popular in Europe, mainly due to the EU funding opportunities, it is of paramount importance to assess its long-term impact on educational and career paths of former students. Moreover, the tracer study aimed at defining what professional, social and intercultural competencies were gained or developed by the interns and to which extent those competences proved to be useful meeting the labor market requirements. Being a populous EU member state which actively modernizes its vocational education system (also with European funds), Poland can serve as an illustrative case study to investigate the above described research problems. However, the examined processes are most certainly universal, wherever mobility is included in the learning process. The target group of this research was the former mobility participants and the study was conducted using quantitative and qualitative methods, such as the online survey with over 2 600 questionnaires completed by the former mobility participants; -individual in-depth interviews (IDIs) with 20 Polish graduates already present in the labour market; - 5 focus group interviews (FGIs) with 60 current students of the Polish vocational schools, who have recently returned from the training abroad. As the adopted methodology included a data triangulation, the collected findings have also been supplemented with data obtained by the desk research (mainly contextual information and statistical summary of mobility implementation). The results of this research – to be presented in full scope within the conference presentation – include the participants’ perception of their work mobility. The vast majority of graduates agrees that such an experience has had a significant impact on their professional careers and claims that they would recommend training abroad to persons who are about to enter the labor market. Moreover, in their view, such form of practical training going beyond formal education provided them with an opportunity to try their hand in the world of work. This allowed them – as they accounted for them – to get acquainted with a work system and context different from the ones experienced in Poland. Although the work mobility becomes an important element of the learning process in the growing number of Polish schools, this study reveals that many sending institutions suffer from a lack of the coherent strategy for planning domestic and foreign training programmes. Nevertheless, the significant number of graduates claims that such a synergy improves the quality of provided training. Despite that, the research proved that the transnational mobilities exert an impact on their future careers and personal development. However, such impact is, in their opinion, dependant on other factors, such as length of the training period, the nature and extent of work, recruitment criteria and the quality of organizational arrangement and mentoring provided to learners. This may indicate the salience of the sending and receiving institutions organizational capacity to deal with mobility.

Keywords: learning mobility, transnational training, vocational education and training graduates, tracer study

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Authors: Bhaumik Patel, Amit Gotecha

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Commercial centres of different hierarchy and sizes play a vital role in the growth and development of the city. Economic uncertainty and demand for space leads to more urban sprawl and emerging more commercial spaces. The study was focused on the understanding of various indicators affecting the commercial development that can help to solve many issues related to commercial urban development and can guide for future employment growth centre development, Accessibility, Infrastructure, Planning and development regulations and Market forces. The aim of the study was to review characteristics and identifying employment density of Commercial Employment Sub-centres by achieving objectives Understanding various employment sub-centres, Identifying characteristics and deriving behaviour of employment densities and Evaluating and comparing employment sub-centres for the Ahmedabad city. Commercial employment sub-centres one in old city (Kalupur), second in highly developed commercial (C.G.road-Ashram road) and third in the latest developing commercial area (Prahladnagar) were identified by distance from city centre, Land use diversity, Access to Major roads and Public transport, Population density in proximity, Complimentary land uses in proximity and Land price. Commercial activities were categorised into retail, wholesale and service sector and sub categorised into various activities. From the study, Time period of establishment of the unit is a critical parameter for commercial activity, building height, and land-use diversity. Employment diversity is also one parameter for the commercial centre. The old city has retail, wholesale and trading and higher commercial density concerning units and employment both. Prahladnagar area functioned as commercial due to market pressure and developed as more units rather than a requirement. Employment density is higher in the centre of the city, as far as distance increases from city centre employment density and unit density decreases. Characters of influencing employment density and unit density are distance from city centre, development type, establishment time period, building density, unit density, public transport accessibility and road connectivity.

Keywords: commercial employment sub-centres, employment density, employment diversity, unit density

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Authors: Paolo Magnata

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The energy market in the Philippines remains to have one of the highest electricity rates in the region averaging at US$0.16/kWh (PHP6.89/kWh), excluding VAT, as opposed to the overall energy market average of US$0.13/kWh. The movement towards renewable energy, specifically solar energy, will pose as an expensive one with the country’s energy sector providing Feed-in-Tariff rates as high as US$0.17/kWh (PHP8.69/kWh) for solar energy power plants. Increasing the share of renewables at the current state of the energy regulatory background would yield a three-fold increase in residential electricity bills. The issue lies in the uniform charge that consumers bear regardless of where the electricity is sourced resulting in rates that only consider costs and not the consumers. But if they are given the option to choose where their electricity comes from, a number of consumers may potentially choose economically costlier sources of electricity due to higher levels of utility coupled with the willingness to pay of consuming environmentally-friendly sourced electricity. A contingent valuation survey was conducted to determine their willingness-to-pay for solar energy on a sample that was representative of Metro Manila to elicit their willingness-to-pay and a Single Bounded Dichotomous Choice and Double Bounded Dichotomous Choice analysis was used to estimate the amount they were willing to pay. The results showed that Metro Manila residents are willing to pay a premium on top of their current electricity bill amounting to US$5.71 (PHP268.42) – US$9.26 (PHP435.37) per month which is approximately 0.97% - 1.29% of their monthly household income. It was also discovered that besides higher income of households, a higher level of self-perceived knowledge on environmental awareness significantly affected the likelihood of a consumer to pay the premium. Shifting towards renewable energy is an expensive move not only for the government because of high capital investment but also to consumers; however, the Green Energy Option (a policy mechanism which gives consumers the option to decide where their electricity comes from) can potentially balance the shift of the economic burden by transitioning from a uniformly charged electricity rate to equitably charging consumers based on their willingness to pay for renewably sourced energy.

Keywords: contingent valuation, dichotomous choice, Philippines, solar energy

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Authors: Angelo Corallo, Maria Elena Latino, Marta Menegoli

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Agri-food value chain involves various stakeholders with different roles. All of them abide by national and international rules and leverage marketing strategies to advance their products. Food products and related processing phases carry with it a big mole of data that are often not used to inform final customer. Some data, if fittingly identified and used, can enhance the single company, and/or the all supply chain creates a math between marketing techniques and voluntary traceability strategies. Moreover, as of late, the world has seen buying-models’ modification: customer is careful on wellbeing and food quality. Food citizenship and food democracy was born, leveraging on transparency, sustainability and food information needs. Internet of Things (IoT) and Analytics, some of the innovative technologies of Industry 4.0, have a significant impact on market and will act as a main thrust towards a genuine ‘4.0 change’ for agriculture. But, realizing a traceability system is not simple because of the complexity of agri-food supply chain, a lot of actors involved, different business models, environmental variations impacting products and/or processes, and extraordinary climate changes. In order to give support to the company involved in a traceability path, starting from business model analysis and related business process a Framework to Manage Product Data in Agri-Food Supply Chain for Voluntary Traceability was conceived. Studying each process task and leveraging on modeling techniques lead to individuate information held by different actors during agri-food supply chain. IoT technologies for data collection and Analytics techniques for data processing supply information useful to increase the efficiency intra-company and competitiveness in the market. The whole information recovered can be shown through IT solutions and mobile application to made accessible to the company, the entire supply chain and the consumer with the view to guaranteeing transparency and quality.

Keywords: agriculture 4.0, agri-food suppy chain, industry 4.0, voluntary traceability

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: B. Gepts, E. Meex, E. Nuyts, E. Knaepen, G. Verbeeck

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The importance of resource efficiency and environmental impact assessment has raised the interest in knowing the amount of materials used in buildings. If no BIM model or energy performance certificate is available, material quantities can be obtained through an estimation or time-consuming calculation. For the interior wall area, no validated estimation method exists. However, in the case of environmental impact assessment or evaluating the existing building stock as future material banks, knowledge of the material quantities used in interior walls is indispensable. This paper presents a validated method for the estimation of the interior wall area for dwellings based on easy-to-collect building characteristics. A database of 4963 residential buildings spread all over Belgium is used. The data are collected through onsite measurements of the buildings during the construction phase (between mid-2010 and mid-2017). The interior wall area refers to the area of all interior walls in the building, including the inner leaf of exterior (party) walls, minus the area of windows and doors, unless mentioned otherwise. The two predictive modelling techniques used are 1) a (stepwise) linear regression and 2) a decision tree. The best estimation method is selected based on the best R² k-fold (5) fit. The research shows that the building volume is by far the most important variable to estimate the interior wall area. A stepwise regression based on building volume per building, building typology, and type of house provides the best fit, with R² k-fold (5) = 0.88. Although the best R² k-fold value is obtained when the other parameters ‘building typology’ and ‘type of house’ are included, the contribution of these variables can be seen as statistically significant but practically irrelevant. Thus, if these parameters are not available, a simplified estimation method based on only the volume of the building can also be applied (R² k-fold = 0.87). The robustness and precision of the method (output) are validated three times. Firstly, the prediction of the interior wall area is checked by means of alternative calculations of the building volume and of the interior wall area; thus, other definitions are applied to the same data. Secondly, the output is tested on an extension of the database, so it has the same definitions but on other data. Thirdly, the output is checked on an unrelated database with other definitions and other data. The validation of the estimation methods demonstrates that the methods remain accurate when underlying data are changed. The method can support environmental as well as economic dimensions of impact assessment, as it can be used in early design. As it allows the prediction of the amount of interior wall materials to be produced in the future or that might become available after demolition, the presented estimation method can be part of material flow analyses on input and on output.

Keywords: buildings as material banks, building stock, estimation method, interior wall area

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Authors: Angga S. Fajar, A. Aminullah, J. Kiyono, R. A. Safitri

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This paper discusses about the application of ANN for optimizing of bridge structure design. ANN has been applied in various field of science concerning prediction and optimization. The structural optimization has several benefit including accelerate structural design process, saving the structural material, and minimize self-weight and mass of structure. In this paper, there are three types of bridge structure that being optimized including PSC I-girder superstructure, composite steel-concrete girder superstructure, and RC bridge pier. The different optimization strategy on each bridge structure implement back propagation method of ANN is conducted in this research. The optimal weight and easier design process of bridge structure with satisfied error are achieved.

Keywords: bridge structures, ANN, optimization, back propagation

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Authors: Tusar Kanti Dash, Ganapati Panda

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The evaluation of speech quality and intelligence is critical to the overall effectiveness of the Speech Enhancement Algorithms. Several intrusive and non-intrusive measures are employed to calculate these parameters. Non-Intrusive Evaluation is most challenging as, very often, the reference clean speech data is not available. In this paper, a novel non-intrusive speech evaluation measure is proposed using audio features derived from the Stockwell transform. These features are used with the Light Gradient Boosting Machine for the effective prediction of speech quality and intelligibility. The proposed model is analyzed using noisy and reverberant speech from four databases, and the results are compared with the standard Intrusive Evaluation Measures. It is observed from the comparative analysis that the proposed model is performing better than the standard Non-Intrusive models.

Keywords: non-Intrusive speech evaluation, S-transform, light GBM, speech quality, and intelligibility

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Authors: M. A. Ghebouli, A. Bouhemadou, H. Choutri, L. Louaila

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Some physical properties of the cubic perovskites CsXF3 (X = Sr, Ca, and Hg) have been investigated using pseudopotential plane–wave (PP-PW) method based on the density functional theory (DFT). The calculated lattice constants within GGA (PBE) and LDA (CA-PZ) agree reasonably with the available experiment data. The elastic constants and their pressure derivatives are predicted using the static finite strain technique. We derived the bulk and shear moduli, Young’s modulus, Poisson’s ratio and Lamé’s constants for ideal polycrystalline aggregates. The analysis of B/G ratio indicates that CsXF3 (X = Ca, Sr, and Hg) are ductile materials. The thermal effect on the volume, bulk modulus, heat capacities CV, CP, and Debye temperature was predicted.

Keywords: perovskite, PP-PW method, elastic constants, electronic band structure

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Authors: A. M. Byrne, E. Bonfiglio, C. Rigby, N. Edelstyn

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Introduction: Neuromarketing employs numerous methodologies when investigating products and advertisement effectiveness. Electroencephalography (EEG), a non-invasive measure of electrical activity from the brain, is commonly used in neuromarketing. EEG data can be considered using time-frequency (TF) analysis, where changes in the frequency of brainwaves are calculated to infer participant’s mental states, or event-related potential (ERP) analysis, where changes in amplitude are observed in direct response to a stimulus. This presentation discusses the findings of a systematic review of EEG measures in neuromarketing. A systematic review summarises evidence on a research question, using explicit measures to identify, select, and critically appraise relevant research papers. Thissystematic review identifies which EEG measures are the most robust predictor of customer preference and purchase intention. Methods: Search terms identified174 papers that used EEG in combination with marketing-related stimuli. Publications were excluded if they were written in a language other than English or were not published as journal articles (e.g., book chapters). The review investigated which TF effect (e.g., theta-band power) and ERP component (e.g., N400) most consistently reflected preference and purchase intention. Machine-learning prediction was also investigated, along with the use of EEG combined with physiological measures such as eye-tracking. Results: Frontal alpha asymmetry was the most reliable TF signal, where an increase in activity over the left side of the frontal lobe indexed a positive response to marketing stimuli, while an increase in activity over the right side indexed a negative response. The late positive potential, a positive amplitude increase around 600 ms after stimulus presentation, was the most reliable ERP component, reflecting the conscious emotional evaluation of marketing stimuli. However, each measure showed mixed results when related to preference and purchase behaviour. Predictive accuracy was greatly improved through machine-learning algorithms such as deep neural networks, especially when combined with eye-tracking or facial expression analyses. Discussion: This systematic review provides a novel catalogue of the most effective use of each EEG measure commonly used in neuromarketing. Exciting findings to emerge are the identification of the frontal alpha asymmetry and late positive potential as markers of preferential responses to marketing stimuli. Predictive accuracy using machine-learning algorithms achieved predictive accuracies as high as 97%, and future research should therefore focus on machine-learning prediction when using EEG measures in neuromarketing.

Keywords: EEG, ERP, neuromarketing, machine-learning, systematic review, time-frequency

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Authors: Jiachen Wang, Dongxu Ji

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Geothermal power generation is a mature technology with zero carbon emission and stable power output, which could play a vital role as an optimum substitution of base load technology in China’s future decarbonization society. However, the development of geothermal power plants in China is stagnated for a decade due to the underestimation of geothermal energy and insufficient favoring policy. Lack of understanding of the potential value of base-load technology and environmental benefits is the critical reason for disappointed policy support. This paper proposed a different energy-economic model to uncover the potential benefit of developing a geothermal power plant in Puer, including the value of base-load power generation, and environmental and economic benefits. Optimization of the Organic Rankine Cycle (ORC) for maximum power output and minimum Levelized cost of electricity was first conducted. This process aimed at finding the optimum working fluid, turbine inlet pressure, pinch point temperature difference and superheat degrees. Then the optimal ORC model was sent to the energy-economic model to simulate the potential economic and environmental benefits. Impact of geothermal power plants based on the scenarios of implementing carbon trade market, the direct subsidy per electricity generation and nothing was tested. In addition, a requirement of geothermal reservoirs, including geothermal temperature and mass flow rate for a competitive power generation technology with other renewables, was listed. The result indicated that the ORC power plant has a significant economic and environmental benefit over other renewable power generation technologies when implementing carbon trading market and subsidy support. At the same time, developers must locate the geothermal reservoirs with minimum temperature and mass flow rate of 130 degrees and 50 m/s to guarantee a profitable project under nothing scenarios.

Keywords: geothermal power generation, optimization, energy model, thermodynamics

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Authors: Emmanuel Dele Balogun

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This paper analyzes, using descriptive statistics and econometrics data which span the period 1981 to 2014 to gauge the effects of trade policy incentives on economic growth in Nigeria. It argues that the provided incentives penalize economic growth during pre-trade liberalization eras, but stimulated a rapid increase in total factor productivity during the post-liberalization period of 2000 to 2014. The trend analysis shows that Nigeria maintained high tariff walls in economic regulation eras which became low in post liberalization era. The protections were in favor of infant industries, which were mainly appendages of multinationals but against imports of competing food and finished consumer products. The trade openness index confirms the undue exposure of Nigeria’s economy to the vagaries of international market shocks; while banking sector recapitalization and new listing of telecommunications companies deepened the financial markets in post-liberalization era. The structure of economic incentives was biased in favor of construction, trade and services, but against the real sector despite protectionist policies. Total Factor Productivity (TFP) estimates show that the Nigerian economy suffered stagnation in pre-liberalization eras, but experienced rapid growth rates in post-liberalization eras. The regression results relating trade policy incentives to TFP growth rate yielded a significant but negative intercept suggesting that a non-interventionist policy could be detrimental to economic progress, while protective tariff which limits imports of competing products could spur productivity gains in domestic import substitutes beyond factor growth with market liberalization. The main constraint to the effectiveness of trade policy incentives is the failure of benefiting industries to leverage on the domestic factor endowments of the nation. This paper concludes that there is the need to review the current economic transformation strategies urgently with a view to provide policymakers with a better understanding of the most viable options that could make for rapid success.

Keywords: economic growth, macroeconomic incentives, total factor productivity, trade policies

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Authors: Jialin Wu, Zhiwei Lian, Jieyu Tang, Jingyun Shen

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Tornado is a serious and unpredictable natural disaster, which has an important impact on people's production and life. The probability of being hit by tornadoes in China was analyzed considering the principles of tornado formation. Then some suggestions on layout and shapes for newly-built buildings were provided combined with the characteristics of tornado wind fields. Fuzzy clustering and inverse closeness methods were used to evaluate the probability levels of tornado risks in various provinces based on classification and ranking. GIS was adopted to display the results. Finally, wind field single-vortex tornado was studied to discuss the optimized design of rural low-rise houses in Yancheng, Jiangsu as an example. This paper may provide enough data to support building and urban design in some specific regions.

Keywords: tornado probability, computational fluid dynamics, fuzzy mathematics, optimal design

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Authors: Melanie Girdharilall, Anjli Himraj, Shivan Bhagwandin, Marike Venter De Villiers

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The allure of luxury has long been attractive, fashionable, mystifying, and complex. As globalisation and the popularity of social media continue to evolve, consumers are seeking status products. However, in emerging economies like South Africa, where 60% of the country lives in poverty, this desire is often far-fetched and out of reach to most of the consumers. As a result, luxury brands are introducing masstige products: products that are associated with luxury and status but within financial reach to the middle-class consumer. The biggest challenge that this industry faces is the lack of knowledge and expertise on black female’s hair composition and offering products that meet their intricate requirements. African consumers have unique hair types, and global brands often do not accommodate for the complex nature of their hair and their product needs. By gaining insight into this phenomenon, global cosmetic brands can benefit from brand expansion, product extensions, increased brand awareness, brand knowledge, and brand equity. The purpose of this study is to determine how cosmetic brands can leverage the concept of masstige products to cater to the needs of middle-income black African woman. This study explores the 18- to 35-year-old black female cohort, which comprises approximately 17% of the South African population. The black hair care industry in Africa is expected a 6% growth rate over the next 5 years. The study is grounded in Paul’s (2019) 3-phase model for masstige marketing. This model demonstrates that product, promotion, and place strategies play a significant role in masstige value creation and the impact of these strategies on the branding dimensions (brand trust, brand association, brand positioning, brand preference, etc.).More specifically, this theoretical framework encompasses nine stages, or dimensions, that are of critical importance to companies who plan to infiltrate the masstige market. In short, the most critical components to consider are the positioning of the product and its competitive advantage in comparison to competitors. Secondly, advertising appeals and use of celebrities, and lastly, distribution channels such as online or in-store while maintain the exclusivity of the brand. By means of an exploratory study, a qualitative approach was undertaken, and focus groups were conducted among black African woman. The focus groups were voice recorded, transcribed, and analysed using Atlas software. The main themes were identified and used to provide brands with insight and direction for developing a comprehensive marketing mix for effectively entering the masstige market. The findings of this study will provide marketing practitioners with in-depth insight into how to effectively position masstige brands in line with consumer needs. It will give direction to both existing and new brands aiming to enter this market, by giving a comprehensive marketing mix for targeting the growing black hair care industry in Africa.

Keywords: africa, masstige, cosmetics, hard care, black females

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Authors: Serhat Tüzün, Tufan Demirel

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Decision Support Systems (DSS) have been investigated by researchers and technologists for more than 35 years. This paper analyses the developments in the architecture and software of these systems, provides a systematic analysis for different Web-based DSS approaches and Intelligent Decision-making Technologies (IDT), with the suggestion for future studies. Decision Support Systems literature begins with building model-oriented DSS in the late 1960s, theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-80s. Then it documents the origins of Executive Information Systems, online analytic processing (OLAP) and Business Intelligence. The implementation of Web-based DSS occurred in the mid-1990s. With the beginning of the new millennia, intelligence is the main focus on DSS studies. Web-based technologies are having a major impact on design, development and implementation processes for all types of DSS. Web technologies are being utilized for the development of DSS tools by leading developers of decision support technologies. Major companies are encouraging its customers to port their DSS applications, such as data mining, customer relationship management (CRM) and OLAP systems, to a web-based environment. Similarly, real-time data fed from manufacturing plants are now helping floor managers make decisions regarding production adjustment to ensure that high-quality products are produced and delivered. Web-based DSS are being employed by organizations as decision aids for employees as well as customers. A common usage of Web-based DSS has been to assist customers configure product and service according to their needs. These systems allow individual customers to design their own products by choosing from a menu of attributes, components, prices and delivery options. The Intelligent Decision-making Technologies (IDT) domain is a fast growing area of research that integrates various aspects of computer science and information systems. This includes intelligent systems, intelligent technology, intelligent agents, artificial intelligence, fuzzy logic, neural networks, machine learning, knowledge discovery, computational intelligence, data science, big data analytics, inference engines, recommender systems or engines, and a variety of related disciplines. Innovative applications that emerge using IDT often have a significant impact on decision-making processes in government, industry, business, and academia in general. This is particularly pronounced in finance, accounting, healthcare, computer networks, real-time safety monitoring and crisis response systems. Similarly, IDT is commonly used in military decision-making systems, security, marketing, stock market prediction, and robotics. Even though lots of research studies have been conducted on Decision Support Systems, a systematic analysis on the subject is still missing. Because of this necessity, this paper has been prepared to search recent articles about the DSS. The literature has been deeply reviewed and by classifying previous studies according to their preferences, taxonomy for DSS has been prepared. With the aid of the taxonomic review and the recent developments over the subject, this study aims to analyze the future trends in decision support systems.

Keywords: decision support systems, intelligent decision-making, systematic analysis, taxonomic review

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Authors: Mohammad Asadpour, Maryam Sadeghi, Mahmoud Jafari

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The transformation of carbon dioxide into fuels and commodity chemicals is considered one of the most attractive methods to meet energy demands and reduce atmospheric CO₂ levels. Cobalt complexes have previously shown high faradaic efficiency in the reduction of CO₂ to CO. In this study, a nanostructure, referred to as a porphyrin-like buckyball, is simulated and analyzed for its electrical properties. The investigation aims to understand the unique characteristics of this material and its potential applications in electronic devices. Through computational simulations and analysis, the electrocatalytic reduction of CO₂ to CO by Cobalt-porphyrin-like buckyball is explored. The findings of this study offer valuable insights into the electrocatalytic properties of this predicted structure, paving the way for further research and development in the field of nanotechnology.

Keywords: porphyrin-like buckyball, DFT, nanomaterials, CO₂ to CO

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Authors: Parth Shah

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The author examines the recent trend where business organizations are significantly reducing their developmental cycle times to stay competitive in today’s global marketspace. The author proposes a rapid systems engineering framework to address late design changes and allow for flexibility (i.e. to react to unexpected or late changes and its impacts) during the product development cycle using a Systems Engineering approach. A System Engineering approach is crucial in today’s product development to deliver complex products into the marketplace. Design changes can occur due to shortened timelines and also based on initial consumer feedback once a product or service is in the marketplace. The ability to react to change and address customer expectations in a responsive and cost-efficient manner is crucial for any organization to succeed. Past literature, research, and methods such as concurrent development, simultaneous engineering, knowledge management, component sharing, rapid product integration, tailored systems engineering processes, and studies on reducing product development cycles all suggest a research gap exist in specifically addressing late design changes due to the shortening of life cycle environments in increasingly competitive markets. The author’s research suggests that 1) product development cycles time scales are now measured in months instead of years, 2) more and more products have interdepended systems and environments that are fast-paced and resource critical, 3) product obsolesce is higher and more organizations are releasing products and services frequently, and 4) increasingly competitive markets are leading to customization based on consumer feedback. The author will quantify effectiveness with respect to success factors such as time-to-market, return-of-investment, life cycle time and flexibility in late design changes by complexity of product or service, number of late changes and ability to react and reduce late design changes.

Keywords: product development, rapid systems engineering, scalability, systems engineering, systems integration, systems life cycle

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Authors: Pacharaporn Lekyan

Abstract:

The purpose of this study is to explore the conflict management affecting the workplace and analyze the ability of the prediction of leadership of the headman and the methods to handle the conflict in an organization. The quantitative research and developed the questionnaire in order to collect information from the respondents from 200 samples from leader or manager who worked in frozen food factories in Thailand. The result analysis shows about the problem of the relationship between conflict management factors, leadership, and the confliction in organization. The emotion of the leader in the organization is not the only factor that can affect conflict management but also the emotion of surrounding people which this factor can happen all the time and shows that four out of five factors of interpersonal conflict management have affected on emotion intelligence and also shows that the behaviors of leadership have an influence on conflict management.

Keywords: conflict management, emotional intelligence, leadership, factories' employee

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Authors: Yeliz Ekinci, Melis Koyuncu

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Successful planning of solid waste management systems requires successful prediction of the amount of solid waste generated in an area. Waste management planning can protect the environment and human health, hence it is tremendously important for countries. The lack of information in waste generation can cause many environmental and health problems. Turkey is a country that plans to join European Union, hence, solid waste management is one of the most significant criteria that should be handled in order to be a part of this community. Solid waste management system requires a good forecast of solid waste generation. Thus, this study aims to forecast solid waste generation in Turkey. Artificial Neural Network and Linear Regression models will be used for this aim. Many models will be run and the best one will be selected based on some predetermined performance measures.

Keywords: forecast, solid waste generation, solid waste management, Turkey

Procedia PDF Downloads 507

Authors: Ruixia Liu

Abstract:

Qinghai-Tibet Plateau has an important influence on the precipitation of its lower reaches. Data from remote sensing has itself advantage and numerical prediction model which assimilates RS data will be better than other. We got the assimilation data of MHS and terrestrial and sounding from GSI, and introduced the result into WRF, then got the result of RH and precipitation forecast. We found that assimilating MHS and terrestrial and sounding made the forecast on precipitation, area and the center of the precipitation more accurate by comparing the result of 1h,6h,12h, and 24h. Analyzing the difference of the initial field, we knew that the data assimilating about Qinghai-Tibet Plateau influence its lower reaches forecast by affecting on initial temperature and RH.

Keywords: Qinghai-Tibet Plateau, precipitation, data assimilation, GSI

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Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

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The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

Procedia PDF Downloads 200

Authors: D. Mokatsanyane, J. Jansen Van Rensburg

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This paper analyses the influence and impact of COVID-19 on major agricultural commodity prices in South Africa. According to a World Bank report, the agricultural sector in South Africa has been unable to reduce the domestic food crisis that has been occurring over the past years, hence the increased rate of poverty, which is currently at 55.5 percent as of April 2020. Despite the significance of this sector, empirical findings concluded that the agricultural sector now accounts for 1.88 percent of South Africa's gross domestic product (GDP). Suggesting that the agricultural sector's contribution to the economy has diminished. Despite the low contribution to GDP, this primary sector continues to play an essential role in the economy. Over the past years, multiple factors have contributed to the soaring commodities prices, namely, climate shocks, biofuel demand, demand and supply shocks, the exchange rate, speculation in commodity derivative markets, trade restrictions, and economic growth. The COVID-19 outbursts have currently disturbed the supply and demand of staple crops. To address the disruption, the government has exempted the agricultural sector from closure and restrictions on movement. The spread of COVID-19 has caused turmoil all around the world, but mostly in developing countries. According to Statistic South Africa, South Africa's economy decreased by seven percent in 2020. Consequently, this has arguably made the agricultural sector the most affected sector since slumped economic growth negatively impacts food security, trade, farm livelihood, and greenhouse gas emissions. South Africa is sensitive to the fruitfulness of global food chains. Restrictions in trade, reinforced sanitary control systems, and border controls have influenced food availability and prices internationally. The main objective of this study is to evaluate the behavior of agricultural commodity prices pre-and during-COVID to determine the impact of volatility drivers on these crops. Historical secondary data of spot prices for the top five major commodities, namely white maize, yellow maize, wheat, soybeans, and sunflower seeds, are analysed from 01 January 2017 to 1 September 2021. The timeframe was chosen to capture price fluctuations between pre-COVID-19 (01 January 2017 to 23 March 2020) and during-COVID-19 (24 March 2020 to 01 September 2021). The Generalised Autoregressive Conditional Heteroscedasticity (GARCH) statistical model will be used to measure the influence of price fluctuations. The results reveal that the commodity market has been experiencing volatility at different points. Extremely high volatility is represented during the first quarter of 2020. During this period, there was high uncertainty, and grain prices were very volatile. Despite the influence of COVID-19 on agricultural prices, the demand for these commodities is still existing and decent. During COVID-19, analysis indicates that prices were low and less volatile during the pandemic. The prices and returns of these commodities were low during COVID-19 because of the government's actions to respond to the virus's spread, which collapsed the market demand for food commodities.

Keywords: commodities market, commodity prices, generalised autoregressive conditional heteroscedasticity (GARCH), Price volatility, SAFEX

Procedia PDF Downloads 174

Authors: Aditya Sharma

Abstract:

Profits earned from relative strength strategy of zero-cost portfolio i.e. taking long position in winner stocks and short position in loser stocks from recent past are termed as momentum profits. In recent times, there has been lot of controversy and concern about sources of momentum profits, since the existence of these profits acts as an evidence of earning non-normal returns from publicly available information directly contradicting Efficient Market Hypothesis. Literature review reveals conflicting theories and differing evidences on sources of momentum profits. This paper aims at re-examining the sources of momentum profits in Indian capital markets. The study focuses on assessing the effect of fundamental as well as behavioral sources in order to understand the role of investor behavior in stock returns and suggest (if any) improvements to existing behavioral asset pricing models. This Paper adopts calendar time methodology to calculate momentum profits for 6 different strategies with and without skipping a month between ranking and holding period. For each J/K strategy, under this methodology, at the beginning of each month t stocks are ranked on past j month’s average returns and sorted in descending order. Stocks in upper decile are termed winners and bottom decile as losers. After ranking long and short positions are taken in winner and loser stocks respectively and both portfolios are held for next k months, in such manner that at any given point of time we have K overlapping long and short portfolios each, ranked from t-1 month to t-K month. At the end of period, returns of both long and short portfolios are calculated by taking equally weighted average across all months. Long minus short returns (LMS) are momentum profits for each strategy. Post testing for momentum profits, to study the role market risk plays in momentum profits, CAPM and Fama French three factor model adjusted LMS returns are calculated. In the final phase of studying sources, decomposing methodology has been used for breaking up the profits into unconditional means, serial correlations, and cross-serial correlations. This methodology is unbiased, can be used with the decile-based methodology and helps to test the effect of behavioral and fundamental sources altogether. From all the analysis, it was found that momentum profits do exist in Indian capital markets with market risk playing little role in defining them. Also, it was observed that though momentum profits have multiple sources (risk, serial correlations, and cross-serial correlations), cross-serial correlations plays a major role in defining these profits. The study revealed that momentum profits do have multiple sources however, cross-serial correlations i.e. the effect of returns of other stocks play a major role. This means that in addition to studying the investors` reactions to the information of the same firm it is also important to study how they react to the information of other firms. The analysis confirms that investor behavior does play an important role in stock returns and incorporating both the aspects of investors’ reactions in behavioral asset pricing models help make then better.

Keywords: investor behavior, momentum effect, sources of momentum, stock returns

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Authors: Farid Khosravikia, Patricia Clayton

Abstract:

The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Oksana Koval, Stephen Nabareseh

Abstract:

Talent attraction is identified as a top priority between 2015 – 2020 for Shared Service Centers (SSCs) based on an industry-wide studies. Due to market dynamics and the structure of labour force, shared service industries in Eastern and Central Europe strive for qualified graduates with appropriate and unique skills to occupy such job places. The inbuilt interest and course prescriptions undertaken by prospective job seekers determine whether SSCs will eventually admit such professionals. This paper assesses students’ overall perception of careers in the shared services industry and further diagnosis gender impact and influence on the job preferences among students. Questionnaires were distributed among students in the Czech Republic universities using an online mode. Respondents vary by study year, gender, age, course of study, and work preferences. A total of 1283 student responses has been analyzed using Stata data analytics software. It was discovered that over 70% of respondents who are aware of SSCs are quite ignorant of the job opportunities offered by the centers. While majority of respondents are interested in support positions (e.g. procurement specialist, planning specialist, human resource specialist, process improvement specialist and payroll specialist, etc.), around a third of respondents (32.8 percent) will decline a job offer from SSCs. The analysis also revealed that males are more likely than females to seek careers in international companies, hence, tend to be more favorable towards shared service jobs. Females, however, have stronger preferences towards marketing and PR jobs. The research results provide insights into the job aspirations of students interviewed. The findings provide a huge resource for recruitment agencies and shared service industries to renew and redirect their search for talents into SSCs. Based on the fact that great portion of respondents are planning to start their career within 6-12 months, the research provides important highlights for the talent attraction and recruitment strategies in the industry and provides a curriculum direction in academia.

Keywords: Czech Republic labour market, gender, talent attraction, shared service centers, students

Procedia PDF Downloads 229