Search results for: forecast uncertainty

Authors: Milad Abbasi

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Carbon fiber-reinforced polymer (CFRP) composite structures are increasingly being utilized in the automotive industry due to their lightweight and specific energy absorption capabilities. Although it is impossible to predict composite mechanical properties directly using theoretical methods, various research has been conducted so far in the literature for accurate simulation of CFRP structures' energy-absorbing behavior. In this research, axial compression experiments were carried out on hand lay-up unidirectional CFRP composite tubes. The fabrication method allowed the authors to extract the material properties of the CFRPs using ASTM D3039, D3410, and D3518 standards. A neural network machine learning algorithm was then utilized to build a robust prediction model to forecast the axial compressive properties of CFRP tubes while reducing high-cost experimental efforts. The predicted results have been compared with the experimental outcomes in terms of load-carrying capacity and energy absorption capability. The results showed high accuracy and precision in the prediction of the energy-absorption capacity of the CFRP tubes. This research also demonstrates the effectiveness and challenges of machine learning techniques in the robust simulation of composites' energy-absorption behavior. Interestingly, the proposed method considerably condensed numerical and experimental efforts in the simulation and calibration of CFRP composite tubes subjected to compressive loading.

Keywords: CFRP composite tubes, energy absorption, crushing behavior, machine learning, neural network

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Authors: Farzaneh Ghandehari, Helen Lu, Lina El-Jahel, Dulani Jayasuriya

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Disagreement among investors is a fundamental aspect of financial markets, significantly influencing market dynamics. Previous research highlights the challenges of effectively measuring investor disagreement, often relying on traditional proxies like analyst forecast dispersion, which are limited by biases and infrequent updates. Recent movements in social media indicate that retail investors actively seek financial advice online and can influence the stock market. The evolution of the investing landscape, particularly the rise of social media as a hub for financial advice, provides a novel avenue for real-time measurement of investor sentiment and disagreement. Platforms like Reddit offer rich, community-driven discussions that reflect genuine investor opinions. This research explores how social media empowers retail investors and the potential of leveraging textual analysis of social media content to capture daily fluctuations in investor disagreement. This study investigates the relationship between daily investor disagreement and trading volume, focusing on the role of social media platforms in shaping market dynamics, specifically using data from WallStreetBets (WSB) on Reddit. This paper uses data from 2020 to 2023 from WSB and analyses 4,896 firms with enough social media activity in WSB to define stock-day level disagreement measures. Consistent with traditional theories that disagreement induces trading volume, the results show significant evidence supporting this claim through different disagreement measures derived from WSB discussions.

Keywords: retail investor, social media, disagreement, social finance, reddit, fintech

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Authors: Alireza Vafaei Sadr, Vida Abedi, Jiang Li, Ramin Zand

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Missing data is a common challenge in medical research and can lead to biased or incomplete results. When the data bias leaks into models, it further exacerbates health disparities; biased algorithms can lead to misclassification and reduced resource allocation and monitoring as part of prevention strategies for certain minorities and vulnerable segments of patient populations, which in turn further reduce data footprint from the same population – thus, a vicious cycle. This study compares the performance of six imputation techniques grouped into Linear and Non-Linear models on two different realworld electronic health records (EHRs) datasets, representing 17864 patient records. The mean absolute percentage error (MAPE) and root mean squared error (RMSE) are used as performance metrics, and the results show that the Linear models outperformed the Non-Linear models in terms of both metrics. These results suggest that sometimes Linear models might be an optimal choice for imputation in laboratory variables in terms of imputation efficiency and uncertainty of predicted values.

Keywords: EHR, machine learning, imputation, laboratory variables, algorithmic bias

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Authors: Sunil Dutta

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Synchronization is defined as ‘the sequencing and re-sequencing of all relative and absolute activities in time and space and continuous alignment of those actions with purposeful objective in a complex and dynamic atmosphere. In a complex and dynamic production / maintenance setup, no single group can work in isolation for long. In addition, many activities in projects take place simultaneously at the same time. Work of every section / group is interwoven with work of others. The various activities / interactions which take place in production / overhaul workshops are interlinked because of physical requirements (information, material, workforces, equipment, and space) and dependencies. The activity sequencing is determined by physical dependencies of various department / sections / units (e.g., inventory availability must be ensured before stripping and disassembling of equipment), whereas resource dependencies do not. Theory of constraint facilitates identification, analyses and exploitation of the constraint in methodical manner. These constraints (equipment, manpower, policies etc.) prevent the department / sections / units from getting optimum exploitation of available resources. The significance of theory of constraints for achieving synchronization at overhaul workshop is illustrated in this paper.

Keywords: synchronization, overhaul, throughput, obsolescence, uncertainty

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Authors: Osamu Komori, Shinto Eguchi, Hiroshi Okamura, Momoko Ichinokawa

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The long time-series data on population assessments are essential for global ecosystem assessment because the temporal change of biomass in such a database reflects the status of global ecosystem properly. However, the available assessment data usually have limited sample sizes and the ratio of populations with low abundance of biomass (collapsed) to those with high abundance (non-collapsed) is highly imbalanced. To allow for the imbalance and uncertainty involved in the ecological data, we propose a binary regression model with mixed effects for inferring ecosystem status through an asymmetric logistic model. In the estimation equation, we observe that the weights for the non-collapsed populations are relatively reduced, which in turn puts more importance on the small number of observations of collapsed populations. Moreover, we extend the asymmetric logistic regression model using propensity score to allow for the sample biases observed in the labeled and unlabeled datasets. It robustified the estimation procedure and improved the model fitting.

Keywords: double robust estimation, ecological binary data, mixed effect logistic regression model, propensity score

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Authors: Jang kyun Cho, Jeong-dong Lee

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The advent of the Internet, mobile communications, and social network services has stimulated social interactions among consumers, allowing people to affect one another’s innovation adoptions by exchanging information more frequently and more quickly. Previous diffusion models, such as the Bass model, however, face limitations in reflecting such recent phenomena in society. These models are weak in their ability to model interactions between agents; they model aggregated-level behaviors only. The agent based model, which is an alternative to the aggregate model, is good for individual modeling, but it is still not based on an economic perspective of social interactions so far. This study assumes the presence of social utility from other consumers in the adoption of innovation and investigates the effect of individual interactions on innovation diffusion by developing a new model called the interaction-based diffusion model. By comparing this model with previous diffusion models, the study also examines how the proposed model explains innovation diffusion from the perspective of economics. In addition, the study recommends the use of a small-world network topology instead of cellular automata to describe innovation diffusion. This study develops a model based on individual preference and heterogeneous social interactions using utility specification, which is expandable and, thus, able to encompass various issues in diffusion research, such as reservation price. Furthermore, the study proposes a new framework to forecast aggregated-level market demand from individual level modeling. The model also exhibits a good fit to real market data. It is expected that the study will contribute to our understanding of the innovation diffusion process through its microeconomic theoretical approach.

Keywords: innovation diffusion, agent based model, small-world network, demand forecasting

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Authors: Alexandros Pournaras, Anastasia Papadopoulou, Serafim Kontos, Anastasios Karakostas

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The air quality of modern cities is an important concern, as poor air quality contributes to human health and environmental issues. Reliable air quality forecasting has, thus, gained scientific and governmental attention as an essential tool that enables authorities to take proactive measures for public safety. In this study, the potential of Machine Learning (ML) models to forecast PM2.5 at local scale is investigated in the city of Thessaloniki, the second largest city in Greece, which has been struggling with the persistent issue of air pollution. ML models, with proven ability to address timeseries forecasting, are employed to predict the PM2.5 concentrations and the respective Air Quality Index 5-days ahead by learning from daily historical air quality and meteorological data from 2014 to 2016 and gathered from two stations with different land use characteristics in the urban fabric of Thessaloniki. The performance of the ML models on PM2.5 concentrations is evaluated with common statistical methods, such as R squared (r²) and Root Mean Squared Error (RMSE), utilizing a portion of the stations’ measurements as test set. A multi-categorical evaluation is utilized for the assessment of their performance on respective AQIs. Several conclusions were made from the experiments conducted. Experimenting on MLs’ configuration revealed a moderate effect of various parameters and training schemas on the model’s predictions. Their performance of all these models were found to produce satisfactory results on PM2.5 concentrations. In addition, their application on untrained stations showed that these models can perform well, indicating a generalized behavior. Moreover, their performance on AQI was even better, showing that the MLs can be used as predictors for AQI, which is the direct information provided to the general public.

Keywords: Air Quality, AQ Forecasting, AQI, Machine Learning, PM2.5

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Authors: Roien Qiam, Shoshi Mizokami

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Parking demand in the Central Business District (CBD) has enlarged with the increase of the number of private vehicles due to rapid economic growth, lack of an efficient public transport and traffic management system. This has resulted in low mobility, poor accessibility, serious congestion, high rates of traffic accident fatalities and injuries and air pollution, mainly because people have to drive slowly around to find a vacant spot. With parking pricing and enforcement policy, considerable advancement could be found, and on-street parking spaces could be managed efficiently and effectively. To evaluate parking demand and making parking policy, it is required to understand the current parking condition and driver’s behavior, understand how drivers choose their parking type and location as well as their behavior toward finding a vacant parking spot under parking charges and search times. This study illustrates the result from an observational, revealed and stated preference surveys and experiment. Attained data shows that there is a gap between supply and demand in parking and it has maximized. For the modeling of the parking decision, a choice model was constructed based on discrete choice modeling theory and multinomial logit model estimated by using SP survey data; the model represents the choice of an alternative among different alternatives which are priced on-street, off-street, and illegal parking. Individuals choose a parking type based on their preference concerning parking charges, searching times, access times and waiting times. The parking assignment model was obtained directly from behavioral model and is used in parking simulation. The study concludes with an evaluation of parking policy.

Keywords: CBD, parking demand forecast, parking policy, parking choice model

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Authors: M. A. Wanzala, L. A. Ogallo, F. J. Opijah, J. N. Mutemi

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The usefulness and limitations in climate information are due to uncertainty inherent in the climate system. For any given region to have sustainable development it is important to apply climate information into its socio-economic strategic plans. The overall objective of the study was to assess the performance of the Coupled Model Inter-comparison Project (CMIP5) over the Lake Victoria Basin. The datasets used included the observed point station data, gridded rainfall data from Climate Research Unit (CRU) and hindcast data from eight CMIP5. The methodology included trend analysis, spatial analysis, correlation analysis, Principal Component Analysis (PCA) regression analysis, and categorical statistical skill score. Analysis of the trends in the observed rainfall records indicated an increase in rainfall variability both in space and time for all the seasons. The spatial patterns of the individual models output from the models of MPI, MIROC, EC-EARTH and CNRM were closest to the observed rainfall patterns.

Keywords: categorical statistics, coupled model inter-comparison project, principal component analysis, statistical downscaling

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Authors: Yas Barzegar, Atrin Barzegar

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Drinking water quality assessment is a major issue today; technology and practices are continuously improving; Artificial Intelligence (AI) methods prove their efficiency in this domain. The current research seeks a hierarchical fuzzy model for predicting drinking water quality in Rome (Italy). The Mamdani fuzzy inference system (FIS) is applied with different defuzzification methods. The Proposed Model includes three fuzzy intermediate models and one fuzzy final model. Each fuzzy model consists of three input parameters and 27 fuzzy rules. The model is developed for water quality assessment with a dataset considering nine parameters (Alkalinity, Hardness, pH, Ca, Mg, Fluoride, Sulphate, Nitrates, and Iron). Fuzzy-logic-based methods have been demonstrated to be appropriate to address uncertainty and subjectivity in drinking water quality assessment; it is an effective method for managing complicated, uncertain water systems and predicting drinking water quality. The FIS method can provide an effective solution to complex systems; this method can be modified easily to improve performance.

Keywords: water quality, fuzzy logic, smart cities, water attribute, fuzzy inference system, membership function

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Authors: Sofia N. Gonçalves, Duarte M. S. Albuquerque, José C. F. Pereira

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Microwave technology has been gaining importance in contributing to decarbonization processes in high energy demand industries. Despite the several numerical models presented in the literature, a proper Verification and Validation exercise is still lacking. This is important and required to evaluate the physical process model accuracy and adequacy. Another issue addresses impedance matching, which is an important mechanism used in microwave experiments to increase electromagnetic efficiency. Such mechanism is not available in current computational tools, thus requiring an external numerical procedure. A numerical model was implemented to study the continuous processing of limestone with microwave heating. This process requires the material to be heated until a certain temperature that will prompt a highly endothermic reaction. Both a 2D and 3D model were built in COMSOL Multiphysics to solve the two-way coupling between Maxwell and Energy equations, along with the coupling between both heat transfer phenomena and limestone endothermic reaction. The 2D model was used to study and evaluate the required numerical procedure, being also a benchmark test, allowing other authors to implement impedance matching procedures. To achieve this goal, a controller built in MATLAB was used to continuously matching the cavity impedance and predicting the required energy for the system, thus successfully avoiding energy inefficiencies. The 3D model reproduces realistic results and therefore supports the main conclusions of this work. Limestone was modeled as a continuous flow under the transport of concentrated species, whose material and kinetics properties were taken from literature. Verification and Validation of the coupled model was taken separately from the chemical kinetic model. The chemical kinetic model was found to correctly describe the chosen kinetic equation by comparing numerical results with experimental data. A solution verification was made for the electromagnetic interface, where second order and fourth order accurate schemes were found for linear and quadratic elements, respectively, with numerical uncertainty lower than 0.03%. Regarding the coupled model, it was demonstrated that the numerical error would diverge for the heat transfer interface with the mapped mesh. Results showed numerical stability for the triangular mesh, and the numerical uncertainty was less than 0.1%. This study evaluated limestone velocity, heat transfer, and load influence on thermal decomposition and overall process efficiency. The velocity and heat transfer coefficient were studied with the 2D model, while different loads of material were studied with the 3D model. Both models demonstrated to be highly unstable when solving non-linear temperature distributions. High velocity flows exhibited propensity to thermal runways, and the thermal efficiency showed the tendency to stabilize for the higher velocities and higher filling ratio. Microwave efficiency denoted an optimal velocity for each heat transfer coefficient, pointing out that electromagnetic efficiency is a consequence of energy distribution uniformity. The 3D results indicated the inefficient development of the electric field for low filling ratios. Thermal efficiencies higher than 90% were found for the higher loads and microwave efficiencies up to 75% were accomplished. The 80% fill ratio was demonstrated to be the optimal load with an associated global efficiency of 70%.

Keywords: multiphysics modeling, microwave heating, verification and validation, endothermic reactions modeling, impedance matching, limestone continuous processing

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Authors: Hossein Gitinavard, Mohammad Hossein Fazel Zarandi

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In the last decades, concerns about the environmental issues lead to professional and academic efforts on green supplier selection problems. In this sake, one of the main issues in evaluating the green supplier selection problems, which could increase the uncertainty, is the preferences of the experts' judgments about the candidate green suppliers. Therefore, preparing an expert system to evaluate the problem based on the historical data and the experts' knowledge can be sensible. This study provides an expert evaluation system to assess the candidate green suppliers under selected criteria in a multi-period approach. In addition, a ranking approach under interval-valued hesitant fuzzy set (IVHFS) environment is proposed to select the most appropriate green supplier in planning horizon. In the proposed ranking approach, the IVHFS and the last aggregation approach are considered to margin the errors and to prevent data loss, respectively. Hence, a comparative analysis is provided based on an illustrative example to show the feasibility of the proposed approach.

Keywords: green supplier selection, expert system, ranking approach, interval-valued hesitant fuzzy setting

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Authors: Oladipo Joseph Ajayi, Yakubu Muhammed, Raufu Olusola Sanusi

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This study determined the perceived effect of livelihood diversification on welfare of rural household in Niger state, Nigeria. Multi-stage sampling technique was adopted for sampling the respondents. Data used for the study were obtained from primary source. Structured questionnaire with interview schedule was administered to 180 randomly selected rural farmers in the study area. Descriptive statistics analysis and z-test statistics were used to analyse the data collected. The study revealed the mean age of the household to be 43 years, mean years of schooling was 8.5, mean household size was 6 people, mean farming experience of 17.5 years and mean farm size of 1.8 hectares. The effect of livelihood diversification revealed that livelihood diversification had positive and significant effect on food security (65.6%) and income generation (66.8%) in the study area. The major constraints to diversification in the study area were poor infrastructure, unavailability of credit and climatic risk and uncertainty. The study, therefore, recommended that rural household should be sensitised to diversify their income source into non-farm activities.

Keywords: income, livelihood diversification , rural household, welfare

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Authors: Liang-Hsuan Chen, Sheng-Shing Nien

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This study aims to develop approaches to formulate intuitionistic fuzzy regression (IFR) models for many decision-making applications in the fuzzy environments using intuitionistic fuzzy observations. Intuitionistic fuzzy numbers (IFNs) are used to characterize the fuzzy input and output variables in the IFR formulation processes. A mathematical programming problem (MPP) is built up to optimally determine the IFR parameters. Each parameter in the MPP is defined as a couple of alternative numerical variables with opposite signs, and an intuitionistic fuzzy error term is added to the MPP to characterize the uncertainty of the model. The IFR model is formulated based on the distance measure to minimize the total distance errors between estimated and observed intuitionistic fuzzy responses in the MPP resolution processes. The proposed approaches are simple/efficient in the formulation/resolution processes, in which the sign of parameters can be determined so that the problem to predetermine the sign of parameters is avoided. Furthermore, the proposed approach has the advantage that the spread of the predicted IFN response will not be over-increased, since the parameters in the established IFR model are crisp. The performance of the obtained models is evaluated and compared with the existing approaches.

Keywords: fuzzy sets, intuitionistic fuzzy number, intuitionistic fuzzy regression, mathematical programming method

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Authors: Parul Sinha

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Electronic commerce facilitates increased choice and information on goods or services for consumers but at the same time it compounds the inequality of bargaining power many consumers face when contracting with sellers. Due to the ‘inequality of bargaining power’ experienced by consumers when contracting by electronic means with business sellers in different jurisdictions, it may be difficult to determine where either the consumer is domiciled or the place where the seller is situated or conducts its business. The question arises in such situation that if one party wants to sue the other, then where can one sue? Which court has jurisdiction to try international conflicts arising from electronic contracts concluded through the internet? Will the same rules applicable to conventional contracts apply? Or should other considerations be taken into account? In all these situations the degree of consumer protection in electronic contracts comes into picture. In the light of the above, the paper discusses the jurisdiction and choice of law rules applied in EU and United States. Further, the paper considers the current uncertainty plaguing questions of jurisdiction in India. Therefore, the jurisdiction and choice of law rules for electronic contracts must be applied consistently and provide an automatic, harmonised rule in favour of the consumer’s jurisdiction and law. Lastly, the paper suggests the need for a uniform law in order to achieve effective juridical protection.

Keywords: electronic commerce, electronic contracts, jurisdiction, consumer protection

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Authors: Kwaku Damoah

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The agricultural sector is characterized by continuous transformation, driven by factors such as demographic shifts, evolving consumer preferences, climate change, and migration trends. This dynamic environment presents complex challenges for key stakeholders including farmers, governments, and investors, who must navigate these changes to achieve optimal investment returns. To effectively predict market trends and uncover promising investment opportunities, a systematic, data-driven approach is essential. This paper introduces the Structural Evolution Index (SEI), a machine learning-based methodology. SEI is specifically designed to analyse long-term trends and forecast the potential of emerging agricultural products for investment. Versatile in application, it evaluates various agricultural metrics such as production, yield, trade, land use, and consumption, providing a comprehensive view of the evolution within agricultural markets. By harnessing data from the UN Food and Agricultural Organisation (FAOSTAT), this study demonstrates the SEI's capabilities through Comparative Exploratory Analysis and evaluation of international trade in agricultural products, focusing on Malaysia and Singapore. The SEI methodology reveals intricate patterns and transitions within the agricultural sector, enabling stakeholders to strategically identify and capitalize on emerging markets. This predictive framework is a powerful tool for decision-makers, offering crucial insights that help anticipate market shifts and align investments with anticipated returns.

Keywords: agricultural investment, algorithm, comparative exploratory analytics, machine learning, market trends, predictive analytics, structural evolution index

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Authors: Asmamaw Yehun, Tsegaye Kassa, Addisu Hunegnaw, Martin Vermeer

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There are various models to estimate the neutral atmospheric parameter values, such as in-suite and reanalysis datasets from numerical models. Accurate estimated values of the atmospheric parameters are useful for weather forecasting and, climate modeling and monitoring of climate change. Recently, Global Navigation Satellite System (GNSS) measurements have been applied for atmospheric sounding due to its robust data quality and wide horizontal and vertical coverage. The Global Positioning System (GPS) solutions that includes tropospheric parameters constitute a reliable set of data to be assimilated into climate models. The objective of this paper is, to estimate the neutral atmospheric parameters such as Wet Zenith Delay (WZD), Precipitable Water Vapour (PWV) and Total Zenith Delay (TZD) using six selected GPS stations in the equatorial regions, more precisely, the Ethiopian GPS stations from 2012 to 2015 observational data. Based on historic estimated GPS-derived values of PWV, we forecasted the PWV from 2015 to 2030. During data processing and analysis, we applied GAMIT-GLOBK software packages to estimate the atmospheric parameters. In the result, we found that the annual averaged minimum values of PWV are 9.72 mm for IISC and maximum 50.37 mm for BJCO stations. The annual averaged minimum values of WZD are 6 cm for IISC and maximum 31 cm for BDMT stations. In the long series of observations (from 2012 to 2015), we also found that there is a trend and cyclic patterns of WZD, PWV and TZD for all stations.

Keywords: atmosphere, GNSS, neutral atmosphere, precipitable water vapour

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Authors: Kaustav Kundu, Alberto Portioli Staudacher

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Construction industry plays a vital role in the economy of the world. But due to high uncertainty and variability in the industry, its performance is not as efficient in terms of quality, lead times, productivity and costs as of other industries. Moreover, there are continuous conflicts among the different actors in the construction supply chains in terms of profit sharing. Previous studies suggested partnership as an important approach to promote cooperation among the different actors in the construction supply chains and thereby it improves the overall performance. Construction practitioners tried to focus on partnership which can enhance the performance of construction supply chains but they are not fully aware of different approaches and techniques for improving partnership. In this research, a systematic review on partnership in relation to construction supply chains is carried out to understand different elements influencing the partnership. The research development of this domain is analyzed by reviewing selected articles published from 1996 to 2015. Based on the papers, three major elements influencing partnership in construction supply chains are identified: “Lean approach”, “Relationship building” and “E-commerce applications”. This study analyses the contributions in the areas within each element and provides suggestions for future developments of partnership in construction supply chains.

Keywords: partnership, construction, lean, SCM, supply chain management

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Authors: Jihoon Lim, Patrick Kirchen, Ryozo Nagamune

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This paper proposes a linear parameter-varying (LPV) controller capable of reducing nitrogen oxide (NOx) emissions with low ammonia (NH3) slip downstream of selective catalytic reduction (SCR) systems. SCR systems are widely adopted in diesel engines due to high NOx conversion efficiency. However, the nonlinearity of the SCR system and sensor uncertainty result in a challenging control problem. In order to overcome the control challenges, an LPV controller is proposed based on gain-scheduling parameters, that is, exhaust gas temperature and exhaust gas flow rate. Based on experimentally obtained data under the non-road transient driving cycle (NRTC), the simulations firstly show that the proposed controller yields high NOx conversion efficiency with a desired low NH3 slip. The performance of the proposed LPV controller is then compared with other controllers, including a gain-scheduling PID controller and a sliding mode controller. Additionally, the robustness is also demonstrated using the uncertainties ranging from 10 to 30%. The results show that the proposed controller is robustly stable under uncertainties.

Keywords: diesel engine, gain-scheduling control, linear parameter-varying, selective catalytic reduction

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Authors: Kiran Bala Das

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This paper examines the Ukraine wheat export condition after Russian-Ukrainian military confrontation. The political conflict in Ukraine and the recent military intervention of Russia in Crimea is raising concern full effect of the events there is still uncertain, but some hints can be seen in the wheat market by analyzing the trend and pattern of Ukraine wheat export. Crimea is extremely important as it is where most of Ukraine grain exported by ship from its ports of the black sea. Ukraine is again seeking to establish itself a significant exporter of agricultural product with its rich black soil, it is chornozem the top soil layer that makes the country soil so fertile and become one of the major exporter of wheat in the world, its generous supplier of wheat make Ukraine 'Bread basket of Europe'. Ukraine possesses 30% of the world’s richest black soil; its agricultural industry has huge potential especially in grains. European Union (EU) is a significant trading partner of Ukraine but geopolitical tension adversely affects the wheat trade from black sea, which threatens Europe breadbasket. This study also highlights an index of export intensity to analyze the intensity of existing trade for the period 2011-2014 between Ukraine and EU countries. The result show export has intensified over the years, but this year low trade intensity. The overall consequence is hard to determine but if the situation deteriorates and Ukraine cutoff export, international wheat price will hike and grain prices (wheat) also come under the current circumstances and the recent development indicates how the grain market get affected and Agri future now in danger in Ukraine, and its forecast that Ukraine harvest low wheat crop this year and projected decline in export of wheat.

Keywords: breadbasket of Europe, export intensity index, growth rate, wheat export

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Authors: Derick Shi-Chen Ou, Liang-Yu Chen

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Background: Acupuncture, a potential alternative, and complementary therapy revealed insufficient evidence in depression treatment. The efficacy of acupuncture treatment was still uncertainty. To evaluate the effect of acupuncture in treating depression, the randomized controlled trials (RCTs) were examined. Methods: RCTs of the acupuncture therapy in treating major depression were searched from MEDLINE from 2007 to 2017. Keywords used for searching strategy included acupuncture, acupoint, and major depressive disorder. Results: Among the nine RCTs, four studies demonstrated great improvement in acupuncture treatment and five studies revealed the effectiveness of acupuncture intervention in medication. General trends suggest that acupuncture treatment is as effective as antidepressants with minimal side effects. Conclusion: Despite the promising results from the RCTs, there are still a variety of limitations, including small sample size, imprecise enrollment criteria, difficulties with blinding, randomization, short duration of study and lack of longitudinal follow-up. Therefore, the evidence that acupuncture as an alternative therapy for depression is inconclusive. More rigorously designed RCTs should be conducted in the future.

Keywords: acupuncture, major depressive disorders, randomized clinical trials, antidepressants

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Authors: Miroslav Čulík, Petr Gurný

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This paper is aimed at the financial assessment of the hard coal mining in a given region by real option methodology application. Hard coal mining in this mine makes net loss for the owner during the last years due to the long-term unfavourable mining conditions and significant drop in the coal prices during the last years. Management is going to shut down the operation and abandon the project to reduce the loss of the company. The goal is to assess whether the shutting down the operation is the only and correct solution of the problem. Due to the uncertainty in the future hard coal price evolution, the production might be again restarted if the price raises enough to cover the cost of the production. For the assessment, real option methodology is applied, which captures two important aspect of the financial decision-making: risk and flexibility. The paper is structured as follows: first, current state is described and problem is analysed. Next, methodology of real options is described. At last, project is evaluated by applying real option methodology. The results are commented and recommendations are provided.

Keywords: real option, investment, option to abandon, option to shut down and restart, risk, flexibility

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Authors: Farhat Ullah

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It is very unfortunate that in Pakistani public schools political interference is on its peak. The present study tries to find out the effect of the interference of political elected members in the affairs of public schools. The objectives of the study were to find out, the degree of interference of political members in public school, the positive and negative effects of political members, influence in public schools, students, and its administrators. This study was quantitative in nature. All the public schools in Khyber Pakhtunkhwa were the population of this study. A sample of 400 teachers and 100 schools heads were selected for this study. A survey questionnaire consisted of 50 items related to objectives, was used for this study. The questionnaire consisted of five options based on Likert scale. Data were collected by the researcher himself from the respondents. Data were analyzed using chi square test. It was concluded from the analysis of data that recently the political members are involved in the process of school activities, which had badly affected the freedom and autonomy of school administrators. Mostly teachers are transferred from schools on political influence, which had created uncertainty among the schools teachers. Further, the student’s academic performance was also affected badly. It is recommended that schools must be free from political involvement for the smooth running of schools.

Keywords: public schools, politics, interference, performance

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Authors: Svanik Garg

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The center of our galaxy is often referred to as the ‘galactic center’ and has many theories associated with its true nature. Given the existence of interstellar dust and bright stars, it is nearly impossible to observe its position, about 24,000 light-years away. Due to this uncertainty, humans have often speculated what could exist at a vantage point upon which the entire galaxy spirals and revolves, with wild theories ranging from the presence of dark matter to black holes and wormholes. Data up till now on the same is very limited, and conclusions are to the best of the author's knowledge, as the only method to view the galactic center is through x-ray and infrared imaging, which counter the problems mentioned earlier. This paper examines, first, the existence of a galactic center, then the methods to identify what it might contain, and lastly, possible conclusions along with implications of the findings. Several secondary sources, along with a python tool to analyze x-ray readings were used to identify the true nature of what lies in the center of the galaxy, whether it be a void due to the existence of dark energy or a black hole. Using this roughly 4-part examination, as a result of this study, a plausible definition of the galactic center was formulated, keeping in mind the rather wild theories, data and different ideas proposed by researchers. This paper aims to dissect the theory of a galactic center and identify its nature to help understand what it shows about galaxies and our universe.

Keywords: milky way, galaxy, dark energy, stars

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Authors: F. Felipe

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Air Defense Systems contain high-value assets that are expected to fulfill their mission for several years - in many cases, even decades - while operating in a fast-changing, technology-driven environment. Thus, it is paramount that decision-makers can assess how effective an Air Defense System is in the face of new developing threats, as well as to identify the bottlenecks that could jeopardize the security of the airspace of a country. Given the broad extent of activities and the great variety of assets necessary to achieve the strategic objectives, a systems approach was taken in order to delineate the core requirements and the physical architecture of an Air Defense System. Then, value-focused thinking helped in the definition of the measures of effectiveness. Furthermore, analytical methods were applied to create a formal structure that preliminarily assesses such measures. To validate the proposed methodology, a powerful simulation was also used to determine the measures of effectiveness, now in more complex environments that incorporate both uncertainty and multiple interactions of the entities. The results regarding the validity of this methodology suggest that the approach can support decisions aimed at enhancing the capabilities of Air Defense Systems. In conclusion, this paper sheds some light on how consolidated approaches of Systems Engineering and Operations Research can be used as valid techniques for solving problems regarding a complex and yet vital matter.

Keywords: air defense, effectiveness, system, simulation, decision-support

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Authors: Kulsoom S. Bughio, Naeem K. Janjua, Gordana Dermody, Leslie F. Sikos, Shamsul Islam

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The advancement in information and communication technologies (ICT) and wireless sensor networks have played a pivotal role in the design and development of real-time healthcare solutions, mainly targeting the elderly living in health-assistive smart homes. Such smart homes are equipped with sensor technologies to detect and record activities of daily living (ADL). This survey reviews and evaluates existing approaches and techniques based on real-time sensor-based modeling and reasoning in single-user and multi-user environments. It classifies the approaches into three main categories: learning-based, knowledge-based, and hybrid, and evaluates how they handle temporal relations, granularity, and uncertainty. The survey also highlights open challenges across various disciplines (including computer and information sciences and health sciences) to encourage interdisciplinary research for the detection and recognition of ADLs and discusses future directions.

Keywords: daily living activities, smart homes, single-user environment, multi-user environment

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Authors: Benjamin Bae, Mahdy Elhusseiny, Sherif El-Halaby

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This study investigates the relationship between some cultural factors and the level of dividend payouts of banks in a number of Muslim countries. We examine whether cultural factors play any role in determining dividend payout policy in banks. The results suggest that banks in high masculinity countries tend to pay higher dividends than low masculinity countries. The results also show that banks in high uncertainty avoidance (UA) countries tend to pay lower dividends than high UA countries. Additionally, the results of this study indicate that banks in high long-term orientation (LTO) countries tend to pay lower dividends than low LTO countries. However, two other cultural factors of power distance (PD) and individualism do not have any incremental explanatory power on the dividend payouts. Overall, this research adds to our understanding of the bank’s dividend payout policies. First, evidence on the relationship between the cultural factors and bank’s level of dividend payouts should be useful to investors. Second, the findings of this study provide financial statement users with useful information about the bank’s dividend payout levels. Third, in general, it also adds to the accounting and finance literature on dividends.

Keywords: cultural factor, dividend payout, Hofstede index, bank industry

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Authors: Olga Bernikova, Oleg Redkin

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Globalization is a process of worldwide economic, political and cultural integration. Obviously, this phenomenon has both positive and negative issues. This article analyzes the impact of the modern process of globalization on the national traditions of language teaching and research. In this context, the problem of the ratio of local to global can be viewed from several sides. Firstly, since English is the language of over 80 percent of scientific and technical research worldwide, what should be the language of science in certain region? Secondly, language 'globality' is not always associated with English, because intercultural communications may have their regional peculiarities. For example, in the Arab world, Modern Standard Arabic can also be regarded as 'global' phenomenon, since the mother-tongue languages of the population are local Arabic dialects. In addition, the correlation 'local' versus 'global' is manifested not only in the linguistic sphere but also in the methodology used in language acquisition and research. Thus, the major principles of the Arabic philological tradition, which goes back to the 7th century, are still spread in the modern Arab world. At the same time, the terminology and methods of language research that are peculiar to this tradition are quite far from the issues of general linguistics that underlies the description of all the languages of the world. The present research relies on a comparative analysis of sources in Arabic linguistics, including original works in Arabic dating back to the 12th-13th centuries. As a case study, interaction of local and global is also considered on the example of the Arabic teaching and research in Russia. Speaking about the correlation between local and global it is possible to forecast development of two parallel tendencies: the spread of the phenomena of globalization on one hand, and local implementation of a language policy aimed at preserving native languages, including Arabic, on the other.

Keywords: Arabic, global, language, local, tradition

Procedia PDF Downloads 245

Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

Procedia PDF Downloads 151

Authors: Madara Apsalone

Abstract:

Knowledge management and innovation is key to competitive advantage and sustainable business development in advanced economies. Small and medium-sized enterprises (SMEs) have lower capacity and more constrained resources for long-term and high-uncertainty research and development investments. At the same time, SMEs can implement organizational innovation to improve their performance and further foster other types of innovation. The purpose of this study is to analyze, how socio-cultural factors such as shared values, organizational behaviors, work organization and decision making processes can influence knowledge management and help to develop organizational innovation via an empirical study. Surveying 600 SMEs in Latvia, the author explores the contribution of different socio-cultural factors to organizational innovation and the role of knowledge management and organizational learning in this process. A conceptual model, explaining the impact of organizational team, development, result-orientation and structure is created. The study also proposes insights that contribute to theoretical and practical discussions on fostering innovation of small businesses in small economies.

Keywords: knowledge management, organizational innovation, small and medium-sized enterprises, socio-cultural factors

Procedia PDF Downloads 374