Search results for: market prediction

Authors: Madhawa Basnayaka, Jouni Paltakari

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Fully passive backscattering chipless RFID tags are an emerging wireless technology with low cost, higher reading distance, and fast automatic identification without human interference, unlike already available technologies like optical barcodes. The design optimization of chipless RFID tags is crucial as it requires replacing integrated chips found in conventional RFID tags with printed geometric designs. These designs enable data encoding and decoding through backscattered electromagnetic (EM) signatures. The applications of chipless RFID tags have been limited due to the constraints of data encoding capacity and the ability to design accurate yet efficient configurations. The traditional approach to accomplishing design parameters for a desired EM response involves iterative adjustment of design parameters and simulating until the desired EM spectrum is achieved. However, traditional numerical simulation methods encounter limitations in optimizing design parameters efficiently due to the speed and resource consumption. In this work, a deep learning neural network (DNN) is utilized to establish a correlation between the EM spectrum and the dimensional parameters of nested centric rings, specifically square and octagonal. The proposed bi-directional DNN has two simultaneously running neural networks, namely spectrum prediction and design parameters prediction. First, spectrum prediction DNN was trained to minimize mean square error (MSE). After the training process was completed, the spectrum prediction DNN was able to accurately predict the EM spectrum according to the input design parameters within a few seconds. Then, the trained spectrum prediction DNN was connected to the design parameters prediction DNN and trained two networks simultaneously. For the first time in chipless tag design, design parameters were predicted accurately after training bi-directional DNN for a desired EM spectrum. The model was evaluated using a randomly generated spectrum and the tag was manufactured using the predicted geometrical parameters. The manufactured tags were successfully tested in the laboratory. The amount of iterative computer simulations has been significantly decreased by this approach. Therefore, highly efficient but ultrafast bi-directional DNN models allow rapid and complicated chipless RFID tag designs.

Keywords: artificial intelligence, chipless RFID, deep learning, machine learning

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Authors: A. Brahma

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The drying of hydraulics materials is unavoidable and conducted to important spontaneous deformations. In this study, we show that it is possible to describe the drying shrinkage of the high-performance concrete by a simple expression. A multiple regression model was developed for the prediction of the drying shrinkage of the high-performance concrete. The assessment of the proposed model has been done by a set of statistical tests. The model developed takes in consideration the main parameters of confection and conservation. There was a very good agreement between drying shrinkage predicted by the multiple regression model and experimental results. The developed model adjusts easily to all hydraulic concrete types.

Keywords: hydraulic concretes, drying, shrinkage, prediction, modeling

Procedia PDF Downloads 349

Authors: Umamaheswari Shanmugam, Silvia Ronchi, Radu Vornicu

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Software, artificial intelligence, and machine learning can improve healthcare through innovative and advanced technologies that are able to use the large amount and variety of data generated during healthcare services every day. As we read the news, over 500 machine learning or other artificial intelligence medical devices have now received FDA clearance or approval, the first ones even preceding the year 2000. One of the big advantages of these new technologies is the ability to get experience and knowledge from real-world use and to continuously improve their performance. Healthcare systems and institutions can have a great benefit because the use of advanced technologies improves the same time efficiency and efficacy of healthcare. Software-defined as a medical device, is stand-alone software that is intended to be used for patients for one or more of these specific medical intended uses: - diagnosis, prevention, monitoring, prediction, prognosis, treatment or alleviation of a disease, any other health conditions, replacing or modifying any part of a physiological or pathological process–manage the received information from in vitro specimens derived from the human samples (body) and without principal main action of its principal intended use by pharmacological, immunological or metabolic definition. Software qualified as medical devices must comply with the general safety and performance requirements applicable to medical devices. These requirements are necessary to ensure high performance and quality and also to protect patients’ safety. The evolution and the continuous improvement of software used in healthcare must take into consideration the increase in regulatory requirements, which are becoming more complex in each market. The gap between these advanced technologies and the new regulations is the biggest challenge for medical device manufacturers. Regulatory requirements can be considered a market barrier, as they can delay or obstacle the device approval, but they are necessary to ensure performance, quality, and safety, and at the same time, they can be a business opportunity if the manufacturer is able to define in advance the appropriate regulatory strategy. The abstract will provide an overview of the current regulatory framework, the evolution of the international requirements, and the standards applicable to medical device software in the potential market all over the world.

Keywords: artificial intelligence, machine learning, SaMD, regulatory, clinical evaluation, classification, international requirements, MDR, 510k, PMA, IMDRF, cyber security, health care systems.

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Authors: Hamed Afrasiab, Saeed Khodaygan

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Prediction of machining process capability in the design stage plays a key role to reach the precision design and manufacturing of mechanical products. Inaccuracies in machining process lead to errors in position and orientation of machined features on the part, and strongly affect the process capability in the final quality of the product. In this paper, an efficient systematic approach is given to investigate the machining errors to predict the manufacturing errors of the parts and capability prediction of corresponding machining processes. A mathematical formulation of fixture locators modeling is presented to establish the relationship between the part errors and the related sources. Based on this method, the final machining errors of the part can be accurately estimated by relating them to the combined dimensional and geometric tolerances of the workpiece – fixture system. This method is developed for uncertainty analysis based on the Worst Case and statistical approaches. The application of the presented method is illustrated through presenting an example and the computational results are compared with the Monte Carlo simulation results.

Keywords: process capability, machining error, dimensional and geometrical tolerances, uncertainty analysis

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Authors: Chaluntorn Vichasilp, Sutee Wangtueai

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This study aims to develop a new method to detect active compounds in Thai herbs (1-deoxynojirimycin (DNJ) in mulberry leave, anthocyanin in Mao and curcumin in turmeric) using near infrared spectroscopy (NIRs). NIRs is non-destructive technique that rapid, non-chemical involved and low-cost determination. By NIRs and chemometrics technique, it was found that the DNJ prediction equation conducted with partial least square regression with cross-validation had low accuracy R2 (0.42) and SEP (31.87 mg/100g). On the other hand, the anthocyanin prediction equation showed moderate good results (R2 and SEP of 0.78 and 0.51 mg/g) with Multiplication scattering correction at wavelength of 2000-2200 nm. The high absorption could be observed at wavelength of 2047 nm and this model could be used as screening level. For curcumin prediction, the good result was obtained when applied original spectra with smoothing technique. The wavelength of 1400-2500 nm was created regression model with R2 (0.68) and SEP (0.17 mg/g). This model had high NIRs absorption at a wavelength of 1476, 1665, 1986 and 2395 nm, respectively. NIRs showed prospective technique for detection of some active compounds in Thai herbs.

Keywords: anthocyanin, curcumin, 1-deoxynojirimycin (DNJ), near infrared spectroscopy (NIRs)

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Authors: Ruba Hamed, Khaled Hussainey, Basiem Al-Shattarat, Wasim Al-Shattarat

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This paper examines how the influence of mandating corporate social responsibility reporting (CSR) on subsequent financial performance through accounting-based measures and market-based measures. We provide evidence about the negative impact of reporting CSR voluntarily on the firm’s future performance due to the increased spending on and costs related to such activities. On the contrary, mandating CSR reporting enhances firms’ future performance by signalling to the market about the firm’s positive stance towards sustainability issues in the UK. Our findings are of interest to regulation setters and stakeholders with respect to mandatory CSR reporting and provide further insight and feedback into accounting and reporting practices.

Keywords: accounting-based performance, mandatory CSR, mandatory regulation, market-based performance

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Authors: Eze R. Onukwugha

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The presence of cyanide in wastewater is known to inhibit the normal functioning of bio-reactors since it has the tendency to poison reactor micro-organisms. Bench scale models of activated sludge reactors with varying aspect ratios were operated for the treatment of cassava wastewater at several values of hydraulic retention time (HRT). The different values of HRT were achieved by the use of a peristaltic pump to vary the rate of introduction of the wastewater into the reactor. The main parameters monitored are the cyanide concentration and respective COD values of the influent and effluent. These observed values were then transformed into a mathematical model for the prediction of treatment efficiency.

Keywords: wastewater, aspect ratio, cyanide-inhibited wastewater, modeling

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Authors: Gurli Olsen, Ida Bruheim Jensen

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Internationally, many young adults not in education, employment, or training (NEET) remain in temporary solutions such as labour market measures or other forms of welfare arrangements. These trends have been associated with ineffective labour market measures, an underfunded theoretical foundation for activation work, limited competence among social workers and labour market employees in using ordinary workplaces as job inclusion measures, and an overemphasis on young adults’ personal limitations such as health challenges and lack of motivation. Two competing models have been prominent in activation work: Place‐Then‐Train and Train‐Then‐Place. A traditional strategy for labour market measures has been to first motivate NEETs to sheltered work and training and then to the regular labour market (train then place). Measures such as Supported Employment (SE) and Individual Placement and Support (IPS) advocate for rapid entry into paid work at the regular labour market with close supervision and training from social workers, employees, and others (place then train). None of these models demonstrate unquestionable results. In this web of working life measures, young adults (NEETs) experience a lack of confidence in their own capabilities and coping strategies vis-á-vis labour market- and educational demands. Drawing on young adults’ own experiences, we argue that the Job Demands-Resources (JD-R) Theory can contribute to the theoretical and practical dimensions of activation work. This presentation will focus on what the JD-R theory entails and how it can be fruitful in activation work with NEETs (what and how). The overarching rationale of the JD-R theory is that an enduring balance between demands (e.g., deadlines, working hours) and resources (e.g., social support, enjoyable work tasks) is important for job performance for people in any job and potentially in other meaningful activities. Extensive research has demonstrated that a balance between demands and resources increases motivation and decreases stress. Nevertheless, we have not identified literature on the JD-R theory in activation work with young adults.

Keywords: activation work, job demands-resources theory, social work, theory development

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Authors: Lela Mirtskhulava, Mariam Khunjgurua, Nino Lomineishvili, Koba Bakuria

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Software reliability prediction gives a great opportunity to measure the software failure rate at any point throughout system test. A software reliability prediction model provides with the technique for improving reliability. Software reliability is very important factor for estimating overall system reliability, which depends on the individual component reliabilities. It differs from hardware reliability in that it reflects the design perfection. Main reason of software reliability problems is high complexity of software. Various approaches can be used to improve the reliability of software. We focus on software reliability model in this article, assuming that there is a time redundancy, the value of which (the number of repeated transmission of basic blocks) can be an optimization parameter. We consider given mathematical model in the assumption that in the system may occur not only irreversible failures, but also a failure that can be taken as self-repairing failures that significantly affect the reliability and accuracy of information transfer. Main task of the given paper is to find a time distribution function (DF) of instructions sequence transmission, which consists of random number of basic blocks. We consider the system software unreliable; the time between adjacent failures has exponential distribution.

Keywords: exponential distribution, conditional mean time to failure, distribution function, mathematical model, software reliability

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Authors: Akor Omachile Opaluwah

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Entrepreneurs are heralded as a very vital force in the growth of economies. This is because they create businesses, employ people, have direct access to the local consumer, and primarily utilize local sources of raw materials, have an understanding of the immediate need of consumers, and they have the capacity to keep in motion the economy. The rise of social enterprises takes these advantages further beyond the business and economic benefits. These Social enterprises help address developmental issues in the society while maintaining a profit for their investors and shareholders. These combined roles create a unique synergy between the civil society and the market, therefore placing the social enterprise in a position where they can access directly, the benefits of the market while meeting the needs of the citizens and their environment. With such a unique position, social enterprises hold a place in the development discourse that has previously been left unexplored. This hybridisation of the functions of civil societies and the market can provide to development, practices, and benefits that have previously been only available in trace amounts. It, therefore, is imperative to understand the efficacy of social enterprises. With the discourse of social enterprises still in its early stages. This paper looks at selected social enterprise cases in Nigeria and analyses their approach and contribution to development.

Keywords: business, civil society, development, entrepreneurs, innovation, market, Nigeria, social enterprise

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Authors: H. Florianová, J. Nešleha

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Financial derivatives are considered to be risky investment instruments which could possibly bring another financial crisis. As prevention, European Union and its member states have released new legal acts adjusting this area of law in recent years. There have been several cases in history of capital markets worldwide where it was shown that legislature may affect behavior of subjects on capital markets. In our paper we analyze main events on selected European stock exchanges in order to apply them on three chosen markets - Czech capital market represented by Prague Stock Exchange, German capital market represented by Deutsche Börse and Polish capital market represented by Warsaw Stock Exchange. We follow time series of development of the sum of listed derivatives on these three stock exchanges in order to evaluate popularity of those exchanges. Afterwards we compare newly listed derivatives in relation to the speed of development of these exchanges. We also make a comparison between trends in derivatives and shares development. We explain how a legal regulation may affect situation on capital markets. If the regulation is too strict, potential investors or traders are not willing to undertake it and move to other markets. On the other hand, if the regulation is too vague, trading scandals occur and the market is not reliable from the prospect of potential investors or issuers. We see that making the regulation stricter usually discourages subjects to stay on the market immediately although making the regulation vaguer to interest more subjects is usually much slower process.

Keywords: capital markets, financial derivatives, investors' behavior, regulation

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Authors: Claudia Troccoli

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Childbearing constitutes one of the key factors affecting labour market differences between men and women, accounting for almost a quarter of the gender wage gap. Family leave policies, such as maternity, paternity, and parental leave, represent potential key policy tools to address these inequalities, as they can promote mothers' job continuity and career progression. This paper analyses four major reforms implemented in Finland between the 1960s and the early 1980s. It studies the effects of these maternity and parental leave extensions on mothers' short- and long-run labour market outcomes. Eligibility to longer leave was determined on the basis of the child's date of birth. Therefore, estimation of the causal effects of the reforms is possible by exploiting random variation in children's birthdates and comparing the outcomes of mothers giving birth just before and just after the reform cutoff date. Overall, the three maternity leave reforms did not significantly improve mothers' earnings or employment rates. On the contrary, the estimates, although imprecise, seem to indicate negative effects on women's labour market outcomes. The extension of parental leave is, on the other hand, the only reform that improved mothers' short- and long-term labour market outcomes, both in terms of earnings and employment rate. At the same time, fathers appeared to be negatively affected by the reform. These results provide suggestive evidence that shareable parental leave might have more beneficial effects on mothers' job continuity, as it weakens the connotation of childcare as a task reserved for mothers.

Keywords: family policies, Finland, maternal labour market outcomes, maternity leave

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Authors: Hao Yan, Xiaobing Zhang

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The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.

Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model

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Authors: Andrea Paltrinieri, Alberto Dreassi, Stefano Miani, Alex Sclip

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The financial crises caused a collapse in prices of most asset classes, raising the attention on alternative investments such as Sukuk, a smaller, fast growing but often misunderstood market. We study diversification benefits of Sukuk, their correlation with other asset classes and the effects of their inclusion in investment portfolios of institutional and retail investors, through a comprehensive comparison of their risk/return profiles during and after the financial crisis. We find a beneficial performance adjusted for the specific volatility together with a lower correlation especially during the financial crisis. The distribution of Sukuk returns is positively skewed and leptokurtic, with a risk/return profile similarly to high yield bonds. Overall, our results suggest that Sukuk present diversification opportunities, a significant volatility-adjusted performance and lower correlations especially during the financial crisis. Our findings are relevant for a number of institutional investors. Long term investors, such as life insurers would benefit from Sukuk’s protective features during financial crisis yet keeping return and growth opportunities, whereas banks would gain due to their role of placers, advisors, market makers or underwriters.

Keywords: sukuk, zero beta asset, asset allocation, sukuk market

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Authors: A. Daftari, W. Kudla

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Liquefaction is a phenomenon in which the strength and stiffness of a soil is reduced by earthquake shaking or other rapid cyclic loading. Liquefaction and related phenomena have been responsible for huge amounts of damage in historical earthquakes around the world. Modelling of soil behaviour is the main step in soil liquefaction prediction process. Nowadays, several constitutive models for sand have been presented. Nevertheless, only some of them can satisfy this mechanism. One of the most useful models in this term is UBCSAND model. In this research, the capability of this model is considered by using PLAXIS software. The real data of superstition hills earthquake 1987 in the Imperial Valley was used. The results of the simulation have shown resembling trend of the UBC3D-PLM model.

Keywords: liquefaction, plaxis, pore-water pressure, UBC3D-PLM

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Authors: Ernest Nweke, Enkhtuya Bavuudorj

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The Mongolia Stock Exchange (MSE) serves as a vehicle for executing the privatization policy of Mongolian Government as it transitioned from socialist to free market economy. It was also the intention of the Government to develop the investment and securities market through its establishment and to further boost the ailing Mongolian economy. This paper focuses on the contributions of the Mongolian Stock Exchange (MSE) to the industrial and economic development of Mongolia via Corporate fund mobilization for listed companies in Mongolia. A study of this nature is imperative as economic development in Mongolia has been accelerated by corporate investments. The key purpose of the research was to critically analyze the operations of the MSE to ascertain the extent to which the objectives for which it was established have been accomplished and to assess its contributions to industrial and economic development of Mongolia. In achieving this, secondary data on the activities of the MSE; its market capitalization over the years were collected and analyzed vis-à-vis the figures for Mongolia’s macro-economic data for the same time period to determine whether the progressive increase in market capitalization of the MSE has positively impacted on Mongolia’s economic growth. Regression analysis package was utilized in dissecting the data. It was proven that the Mongolian Stock Exchange has contributed positively and significantly to Mongolia’s economic development though not yet to the desired level. Against the findings of this research, recommendations were made to address, the problems facing the MSE and to enhance its performance and ultimately its contributions to industrial and economic development of the Mongolian nation.

Keywords: Corporate Fund Mobilization, Gross Domestic Product (GDP), market capitalization, purchasing power, stock exchange

Procedia PDF Downloads 229

Authors: Yinglisong, Jingge Chen, Jingxuan Chen, Yan Wang, Hui Huang, Jing Zhnag, Qianqian Zhang, Zhenzhen Zhang, Ji Zhang

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Purpose: The study aims to establish a nomogram prediction model for postpartum hemorrhage (PPH) in vaginal delivery. Patients and Methods: Clinical data were retrospectively collected from vaginal delivery patients admitted to a hospital in Zhengzhou, China, from June 1, 2022 - October 31, 2022. Univariate and multivariate logistic regression were used to filter out independent risk factors. A nomogram model was established for PPH in vaginal delivery based on the risk factors coefficient. Bootstrapping was used for internal validation. To assess discrimination and calibration, receiver operator characteristics (ROC) and calibration curves were generated in the derivation and validation groups. Results: A total of 1340 cases of vaginal delivery were enrolled, with 81 (6.04%) having PPH. Logistic regression indicated that history of uterine surgery, induction of labor, duration of first labor, neonatal weight, WBC value (during the first stage of labor), and cervical lacerations were all independent risk factors of hemorrhage (P <0.05). The area-under-curve (AUC) of ROC curves of the derivation group and the validation group were 0.817 and 0.821, respectively, indicating good discrimination. Two calibration curves showed that nomogram prediction and practical results were highly consistent (P = 0.105, P = 0.113). Conclusion: The developed individualized risk prediction nomogram model can assist midwives in recognizing and diagnosing high-risk groups of PPH and initiating early warning to reduce PPH incidence.

Keywords: vaginal delivery, postpartum hemorrhage, risk factor, nomogram

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Authors: Soha A. Bahanshal, Vaibhav Verdhan, Bayong Kim

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Graduation rates at six-year colleges are becoming a more essential indicator for incoming fresh students and for university rankings. Predicting student graduation is extremely beneficial to schools and has a huge potential for targeted intervention. It is important for educational institutions since it enables the development of strategic plans that will assist or improve students' performance in achieving their degrees on time (GOT). A first step and a helping hand in extracting useful information from these data and gaining insights into the prediction of students' progress and performance is offered by machine learning techniques. Data analysis and visualization techniques are applied to understand and interpret the data. The data used for the analysis contains students who have graduated in 6 years in the academic year 2017-2018 for science majors. This analysis can be used to predict the graduation of students in the next academic year. Different Predictive modelings such as logistic regression, decision trees, support vector machines, Random Forest, Naïve Bayes, and KNeighborsClassifier are applied to predict whether a student will graduate. These classifiers were evaluated with k folds of 5. The performance of these classifiers was compared based on accuracy measurement. The results indicated that Ensemble Classifier achieves better accuracy, about 91.12%. This GOT prediction model would hopefully be useful to university administration and academics in developing measures for assisting and boosting students' academic performance and ensuring they graduate on time.

Keywords: prediction, decision trees, machine learning, support vector machine, ensemble model, student graduation, GOT graduate on time

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Authors: Ziwei Huang

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In order to promote Inventory development in Shenzhen, the market-oriented real estate development mode has occupied a dominant position in the urban renewal activities of Shenzhen. This research is based on the theory of role relationship and urban regime, taking the Mutoulong community as the research object. Carries on the case depth analysis found that: Under the situation of absence and dislocation of the government's role, land property rights disputes and lack of communication platforms is the main reason for the problems of nail households and market failures, and the long-term delay in the progress of old city reconstruction. Through the analysis of the cause of the transformation problem and the upper planning and interest coordination mechanism, the optimization strategy of the old city transformation is finally proposed as follows: the establishment of interest coordination platform, the risk assessment of the government's intervention in the preliminary construction of the land, the adaptive construction of laws and regulations, and the re-examination of the interest relationship between the government and the market.

Keywords: Shenzhen city, Mutoulong community, urban regeneration, urban regime theory, role relationship theory

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Authors: Simiso Fisokuhle Nyandeni

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Climate change has turned out to be a pandemic that has drawn the attention of many countries’ households around the globe, especially those whose livelihood and economic status depend on agricultural productivity. Hence, the agricultural sector is regarded as the sector that is most dependent on climate conditions for its productivity/harvest, yet in recent years this sector has been experiencing drought. However, adaptation seems to be a tool that every farmer looks upon as a solution to their challenges as their productivity keeps on being vulnerable to climate effects. Thus, exposure/access to the market seems to be a major challenge that faces especially small-scale farmers. We, therefore, examine the small-scale farmers’ constraints or challenges towards getting access to the market for them to get proper sales of their farming products. As a result, the adaptation capacity of every farm household varies on the financial status.

Keywords: climate change, small-scale farming, agriculture sector, adaptation

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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: Hasan Muslemani, Xi Liang, Kathi Kaesehage, Francisco Ascui, Jeffrey Wilson

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The creation of a market for lower-carbon steel products, here called ‘green steel’, has been identified as an important means to support the introduction of breakthrough emission reduction technologies into the steel sector. However, the definition of what ‘green’ entails in the context of steel production, the implications on the competitiveness of green steel products in local and international markets, and the necessary market mechanisms to support their successful market penetration remain poorly explored. This paper addresses this gap by holding semi-structured interviews with international sustainability experts and commercial managers from leading steel trade associations, research institutes and steelmakers. Our findings show that there is an urgent need to establish a set of standards to define what ‘greenness’ means in the steelmaking context; standards that avoid market disruptions, unintended consequences, and opportunities for greenwashing. We also highlight that the introduction of green steel products will have implications on product competitiveness on three different levels: 1) between primary and secondary steelmaking routes, 2) with traditional, lesser green steel, and 3) with other substitutable materials (e.g. cement and plastics). This paper emphasises the need for steelmakers to adopt a transitional approach in deploying different low-carbon technologies, based on their stage of technological maturity, applicability in certain country contexts, capacity to reduce emissions over time, and the ability of the investment community to support their deployment. We further identify market mechanisms to support green steel production, including carbon border adjustments and public procurement, highlighting a need for implementing a combination of complementary policies to ensure the products’ roll-out. The study further shows that the auto industry is a likely candidate for green steel consumption, where a market would be supported by price premiums paid by willing consumers, such as those of high-end luxury vehicles.

Keywords: green steel, decarbonisation, business model innovation, market analysis

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Authors: Shazia Ali, Yves Mard, Éric Severin

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To the best of our knowledge, this is the first study to have analyzed the impact of a) firm-specific product-market power and b) industry competition on earnings management behavior of European firms in distress versus healthy years while controlling for firm-level characteristics. We predicted a significant relationship between firms’ product market power and earnings management tools and their trade-off under the moderation effect of financial distress. We found that the firm-level market power hereinafter referred to as MP (proxied by the industry-adjusted Lerner Index) is positively associated with both real and accrual earnings management. However, MP is associated with a higher level of real earnings management compared to accrual earnings management in distress years compared to healthy years. On the other hand, industry product market power (representing low competition and proxied by the inverse of the total number of firms in an industry hereinafter referred to as NUMB) and firms product market power (proxied by firm market share hereinafter referred to as MS) are associated with lower inflationary accruals and higher deflationary accruals respectively. On the other hand, they are found to be linked with higher real earnings management in distress versus healthy years. When we divided the sample into small and big firms based on their respective industry-year median total assets, we found that all three measures of industry competition (Industry Median Lerner Index (hereinafter referred to as IMLI), NUMB, and Herfindahl–Hirschman Index (hereinafter referred to as HHI) indicate that small firms in low-competitive industries in financial distress are more likely to inflate their earnings through discretionary accruals. While big firms in this situation are more likely to lower the use of both inflationary and deflationary discretionary accruals as indicated by IMLI and HHI and trade-off accruals earnings management for real earnings management as indicated by NUMB. Moreover, IMLI and HHI did not show any interesting results when we divided the sample based on the firm Lerner Index/Market Power. However, the distressed firms with high market power (MP>industry median) are found to engage in income-decreasing discretionary accruals in low-competitive industries (high NUMB). Whereas firms with low market power in the same industry use downward discretionary accruals but inflate income using real activities (abnCFO). Our findings are robust across alternate measures of discretionary accruals and financial distress, such as the Altman Z-Score. The finding of the study is valuable for accounting standard setters, competition authorities, policymakers, and investors alike to help in informed decision-making.

Keywords: financial distress, earnings management, market competition

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Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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Authors: Yuichi Kida, Takuro Kida

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In a new smart society supported by the recent development of 5G and 6G Communication systems, the im- portance of wireless power transmission is increasing. These systems contain discrete sampling systems in the middle of the transmission path and equivalent circuit representation of lossless or lossy power transmission through these systems is an important issue in circuit theory. In this paper, for the given weight function, we show that a lossless power transmission system with the given weight is expressed by an equivalent circuit representation of the Kida’s optimal signal prediction system followed by a reactance multi-port circuit behind it. Further, it is shown that, when the system is lossy, the system has an equivalent circuit in the form of connecting a multi-port positive-real circuit behind the Kida’s optimal signal prediction system. Also, for the convenience of the reader, in this paper, the equivalent circuit expression of the reactance multi-port circuit and the positive- real multi-port circuit by Cauer and Ohno, whose information is currently being lost even in the world of the Internet.

Keywords: signal prediction, pseudo inverse matrix, artificial intelligence, power transmission

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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

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Authors: Abdullah M. Alodhaiani, Yasir A. Alturki, Mohamed A. Elkady

Abstract:

Complex power flow distribution factors, which relate line complex power flows to the bus injected complex powers, have been widely used in various power system planning and analysis studies. In particular, AC distribution factors have been used extensively in the recent power and energy pricing studies in free electricity market field. As was demonstrated in the existing literature, many of the electricity market related costing studies rely on the use of the distribution factors. These known distribution factors, whether the injection shift factors (ISF’s) or power transfer distribution factors (PTDF’s), are linear approximations of the first order sensitivities of the active power flows with respect to various variables. This paper presents a novel model for evaluating the universal distribution factors (UDF’s), which are appropriate for an extensive range of power systems analysis and free electricity market studies. These distribution factors are used for the calculations of lines complex power flows and its independent of bus power injections, they are compact matrix-form expressions with total flexibility in determining the position on the line at which line flows are measured. The proposed approach was tested on IEEE 9-Bus system. Numerical results demonstrate that the proposed approach is very accurate compared with exact method.

Keywords: distribution factors, power system, sensitivity factors, electricity market

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Authors: Adedeji Tejumola Olugboja

Abstract:

Night markets are physical locations in residential neighbourhoods where market parties interact. It is a kind of market where marketing activities commence by 6pm until after midnight. The problem of the study is to assess marketing activities in the night markets. Specific objectives for this study include determining volume of business activities, numbers of market parties etc in the selected night markets. The purposive sampling technique is adopted for this study and the four night markets in the area of study are selected as sample: Aggregate of 173 retailers and an average of 2583 consumers daily operate in these night markets. The use of tables, simple percentage and descriptive statistics were employed for data analysis and presentation. Findings revealed volume of marketing activities, sales per night, profit per night and savings per day in each of these night markets. Government should erect street lights and repair damaged ones in these night markets to make night markets more lucrative.

Keywords: marketing activities, night markets, Nigerian economy

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Authors: Omar M. Elmabrouk

Abstract:

The cutting tool, in the high-speed machining process, is consistently dealing with high localized stress at the tool tip, tip temperature exceeds 800°C and the chip slides along the rake face. These conditions are affecting the tool wear, the cutting tool performances, the quality of the produced parts and the tool life. Therefore, a thin film coating on the cutting tool should be considered to improve the tool surface properties while maintaining its bulks properties. One of the general coating processes in applying thin film for hard coating purpose is PVD magnetron sputtering. In this paper, the prediction of the effects of PVD magnetron sputtering coating process parameters, sputter power in the range of (4.81-7.19 kW), bias voltage in the range of (50.00-300.00 Volts) and substrate temperature in the range of (281.08-600.00 °C), were studied using artificial neural network (ANN). The results were compared with previously published results using RSM model. It was found that the ANN is more accurate in prediction of tool hardness, and hence, it will not only improve the tool life of the tool but also significantly enhances the efficiency of the machining processes.

Keywords: artificial neural network, hardness, prediction, titanium aluminium nitrate coating

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Authors: Amir M. Shikhli, Refaat H. Abdel-Razek, Salaheddine Bendak

Abstract:

Innovation is considered to be one of the key factors that influence long-term success of any company. The problem of many organizations in developing countries is trying to implement innovation without a strong basis within the organizational culture to support it. The objective of this study is to assess the effects of organizational culture on innovation in one of the biggest information technology organizations in UAE, Injazat Data System. First, an Organizational Culture Assessment Instrument (OCAI) was used as a survey and Competing Value Framework as a model to analyze the existing culture within the organization and determine its characteristics. Following that, a modified version of the Community Innovation Survey (CIS) was used to determine innovation types introduced by the organization. Then multiple linear regression analysis was used to find out the effects of existing organizational culture on innovation. Results show that existing organizational culture is composed of a combination of Hierarchy (29.4%), Clan (25.8%), Market (24.9%) and Adhocracy (19.9%). Results of the second survey show that the organization focuses on organizational innovation (26.8%) followed by market and product innovations (25.6%) and finally process innovation (22.0%). Regression analysis results reveal that for each innovation type there is a recommended combination of the four culture types. For product innovation, the combination is 47.4% Clan, 17.9% Adhocracy, 1.0% Market and 33.3% Hierarchy; for process innovation it is 19.7% Clan, 45.2% Adhocracy, 32.0% Market and 3.1% Hierarchy; for organizational innovation the combination is 5.4% Clan, 32.7% Adhocracy, 6.0% Market and 55.9% Hierarchy; and for market innovation it is 25.5% Clan, 42.6% Adhocracy, 32.6% Market and 8.4% Hierarchy. Based on these recommended combinations, this study suggests two ways to enhance the innovation culture in the organization. First, if the management decides on the innovation type to be enhanced, a comparison between the existing culture and the recommended combination of selected innovation types will lead to difference in percentages of each culture type. Then further analysis should show how to modify the existing culture to match the recommended combination. Second, if the innovation type is not selected, but the management wants to enhance innovation culture in the organization, the difference in percentages of each culture type will lead to finding out the recommended combination of culture types that gives the narrowest gap between existing culture and recommended combination.

Keywords: developing countries, organizational culture, innovation types, product innovation, process innovation, organizational innovation, marketing innovation

Procedia PDF Downloads 257