Search results for: price discovery

Authors: Vinko Lešić, Mario Vašak, Anita Martinčević, Marko Gulin, Antonio Starčić, Hrvoje Novak

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With 40% of total world energy consumption, building systems are developing into technically complex large energy consumers suitable for application of sophisticated power management approaches to largely increase the energy efficiency and even make them active energy market participants. Centralized control system of building heating and cooling managed by economically-optimal model predictive control shows promising results with estimated 30% of energy efficiency increase. The research is focused on implementation of such a method on a case study performed on two floors of our faculty building with corresponding sensors wireless data acquisition, remote heating/cooling units and central climate controller. Building walls are mathematically modeled with corresponding material types, surface shapes and sizes. Models are then exploited to predict thermal characteristics and changes in different building zones. Exterior influences such as environmental conditions and weather forecast, people behavior and comfort demands are all taken into account for deriving price-optimal climate control. Finally, a DC microgrid with photovoltaics, wind turbine, supercapacitor, batteries and fuel cell stacks is added to make the building a unit capable of active participation in a price-varying energy market. Computational burden of applying model predictive control on such a complex system is relaxed through a hierarchical decomposition of the microgrid and climate control, where the former is designed as higher hierarchical level with pre-calculated price-optimal power flows control, and latter is designed as lower level control responsible to ensure thermal comfort and exploit the optimal supply conditions enabled by microgrid energy flows management. Such an approach is expected to enable the inclusion of more complex building subsystems into consideration in order to further increase the energy efficiency.

Keywords: price-optimal building climate control, Microgrid power flow optimisation, hierarchical model predictive control, energy efficient buildings, energy market participation

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Authors: Stefanie Jeanette Huber

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This paper explores the relevance and implications of preferences for housing services on house price fluctuations through the lens of an overlapping generation’s model. The model implies that an economy whose agents have lower preferences for housing services is characterized with lower expenditure shares on housing services and will tend to experience more frequent and more volatile housing bubbles. These model predictions are tested empirically in the companion paper Housing Booms and Busts - Convergences and Divergences across OECD countries. Between 1970 - 2013, countries who spend less on housing services as a share of total income experienced significantly more housing cycles and the associated housing boom-bust cycles were more violent. Finally, the model is used to study the impact of rental subsidies and help-to-buy schemes on rational housing bubbles. Rental subsidies are found to contribute to the control of housing bubbles, whereas help-to- buy scheme makes the economy more bubble-prone.

Keywords: housing bubbles, housing booms and busts, preference for housing services, expenditure shares for housing services, rental and purchase subsidies

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Authors: Ghada Mohammed, Mohamed Abdel Salam, Passent Tantawi

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The study aimed to investigate the impact of motives and barriers for Egyptian users to use Airbnb as a platform of peer-to-peer accommodation instead of hotels on overall attitude towards Airbnb. A sequential mixed-methods approach was adopted to this study and it proposed a comprehensive research model adapted from both literature and results of qualitative phase and then tested via an online questionnaire. The findings revealed that, motives, price, home benefits, privacy, and online reviews significantly explained overall attitude towards Airbnb, while the main barriers were respectively: perceived risk and distrust in which they can predict the overall attitude. While from the subjective norms, only social influence can predict behavioral intention to use Airbnb. The study may serve as a practical reference for practitioners as well as researchers when developing programs and strategies to manage Airbnb consumers' needs and decision process. Some of the main conclusions drawn from this study are that variety was one of the major things that users like about Airbnb and the most important motives are the functional ones like price rather than the experiential ones like authenticity.

Keywords: airbnb, barriers, disruptive innovation, motives, sharing economy

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Authors: Christina Williams, Mehari Weldemariam, Ann M. Farese, Thomas J. MacVittie, Maureen A. Kane

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Radiation exposure results in dose-dependent and time-dependent multi-organ damage. Drug development of medical countermeasures (MCM) for radiation-induced injury occurs under the FDA Animal Rule because human efficacy studies are not ethical or feasible. The FDA Animal Rule requires the representation of both sexes and describes several uses for biomarkers in MCM drug development studies. Currently, MCMs are limited and there is no FDA-approved biomarker for any radiation injury. Sex as a variable is essential to identifying biomarkers and developing effective MCMs for acute radiation exposure (ARS) and delayed effects of acute radiation exposure (DEARE). These studies aim to address the death of information on sex differences that have not been determined by studies that included only male, single-sex cohorts. Studies have reported differences in radiosensitivity according to sex. As such, biomarker discovery for radiation-induced damage must consider sex as a variable. This study evaluated the plasma proteomic profile of Rhesus macaque non-human primates after different exposures and doses, as well as time points after radiation. Exposures and doses included total body irradiation between 5-7.5 Gy and partial body irradiation with 5% bone marrow sparing at 9, 9.5 and 10 Gy. Timepoints after irradiation included days 1, 3, 60, and 180, which encompassed both acute radiation syndromes and delayed effects of acute radiation exposure. Bottom-up proteomic analyses of plasma included equal numbers of males and females. In the control animals, few proteomic differences are observed between the sexes. In the irradiated animals, there are a few sex differences, with changes mostly consisting of proteins upregulated in the female animals. Multiple canonical pathways were upregulated in irradiated animals relative to the control animals when subjected to pathway analysis, but differential responses between the sexes are limited. These data provide critical baseline differences according to sex and establish sex differences in non-human primate models relevant to drug development of MCM under the FDA Animal Rule.

Keywords: ionizing radiation, sex differences, plasma proteomics, biomarker discovery

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Navid Mohammadzadeh, Huy Truong-Ba, Michael Cholette

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The solar tower (ST) plant is a promising technology to exploit large-scale solar irradiation. With thermal energy storage, ST plant has the potential to shift generation to high electricity price periods. However, the size of storage limits the dispatchability of the plant, particularly when it should compete with uncertainty in forecasts of solar irradiation and electricity prices. The purpose of this study is to explore the size of storage when Rolling Horizon Control (RHC) is employed for dispatch scheduling. To this end, RHC is benchmarked against perfect knowledge (PK) forecast and two day-ahead dispatching policies. With optimisation of dispatch planning using PK policy, the optimal achievable profit for a specific size of the storage is determined. A sensitivity analysis using Monte-Carlo simulation is conducted, and the size of storage for RHC and day-ahead policies is determined with the objective of reaching the profit obtained from the PK policy. A case study is conducted for a hypothetical ST plant with thermal storage located in South Australia and intends to dispatch under two market scenarios: 1) fixed price and 2) wholesale spot price. The impact of each individual source of uncertainty on storage size is examined for January and August. The exploration of results shows that dispatching with RH controller reaches optimal achievable profit with ~15% smaller storage compared to that in day-ahead policies. The results of this study may be applied to the CSP plant design procedure.

Keywords: solar tower plant, spot market, thermal storage system, optimized dispatch planning, sensitivity analysis, Monte Carlo simulation

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Authors: Olanrewaju B. Lasisi

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The position of Ijebu-Ode, the historical capital of the Ijebu Kingdom, at the center of gravity of Ijebu land is enclosed by the 180-km-long earthwork and suggests a centrally controlled project. This paper reflects on the first stratigraphic drawing of the banks and ditches of this earthwork, and place its construction mechanism in a chronological framework. Nine radiocarbon dates obtained at the site suggest that the earthwork was built in the late 14th or early 15th century. This suggests a relationship with the Ijebu Kingdom, which pre-existed the opening of the Atlantic trade but first became visible only in the Portuguese records in the 1480s. In June 2017, more earthworks were found but within the core of Ijebu Land. This most recent finding points to an extension of territory from the center to the outlying villages. One central question about this discovery of monumental architectures that was functional around the 14th century or before is in its mode of construction. Apparently, iron tools must have been used in the construction of ‘a 20m deep ditch that runs 180km in circumference.’ Thus, the discovery of iron-working sites around the vicinity of the earthwork is a pointer to this building process that is up till now shrouded in mystery. By comparing the chronology of Ijebu earthworks with the evidence of Iron working in south western Nigeria around the first half of the first millennium AD, it can be thought that the rise in polity triggered the knowledge of metallurgy in the region.

Keywords: archaeology, earthworks, Ijebu, metallurgy

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Authors: Abdul Musaweer Habib, Habibul Hasan Mazumder, Saiful Islam, Sohel Sikder, Omar Faruk Sikder

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The plethora of genome sequence information of bacteria in recent times has ushered in many novel strategies for antibacterial drug discovery and facilitated medical science to take up the challenge of the increasing resistance of pathogenic bacteria to current antibiotics. In this study, we adopted subtractive genomics approach to analyze the whole genome sequence of the Fusobacterium nucleatum, a human oral pathogen having association with colorectal cancer. Our study divulged 1499 proteins of Fusobacterium nucleatum, which has no homolog in human genome. These proteins were subjected to screening further by using the Database of Essential Genes (DEG) that resulted in the identification of 32 vitally important proteins for the bacterium. Subsequent analysis of the identified pivotal proteins, using the KEGG Automated Annotation Server (KAAS) resulted in sorting 3 key enzymes of F. nucleatum that may be good candidates as potential drug targets, since they are unique for the bacterium and absent in humans. In addition, we have demonstrated the 3-D structure of these three proteins. Finally, determination of ligand binding sites of the key proteins as well as screening for functional inhibitors that best fitted with the ligands sites were conducted to discover effective novel therapeutic compounds against Fusobacterium nucleatum.

Keywords: colorectal cancer, drug target, Fusobacterium nucleatum, homology modeling, ligands

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Authors: Surajit Bhattacharya, Daniel Veltri, Atit A. Patel, Daniel N. Cox

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miRNAs have emerged as key post-transcriptional regulators of gene expression, however identification of biologically-relevant target genes for this epigenetic regulatory mechanism remains a significant challenge. To address this knowledge gap, we have developed a novel tool in R, Intra-miR-ExploreR, that facilitates integrated discovery of miRNA targets by incorporating target databases and novel target prediction algorithms, using statistical methods including Pearson and Distance Correlation on microarray data, to arrive at high confidence intragenic miRNA target predictions. We have explored the efficacy of this tool using Drosophila melanogaster as a model organism for bioinformatics analyses and functional validation. A number of putative targets were obtained which were also validated using qRT-PCR analysis. Additional features of the tool include downloadable text files containing GO analysis from DAVID and Pubmed links of literature related to gene sets. Moreover, we are constructing interaction maps of intragenic miRNAs, using both micro array and RNA-seq data, focusing on neural tissues to uncover regulatory codes via which these molecules regulate gene expression to direct cellular development.

Keywords: miRNA, miRNA:mRNA target prediction, statistical methods, miRNA:mRNA interaction network

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Authors: Rudi Kurniawan Arief

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Manufacturing cost and setup time are the hot topics to improve in Metal Stamping industry because material and components price are always rising up while costumer requires to cut down the component price year by year. The Single Minute Exchange of Die (SMED) is one of many methods to reduce waste in stamping industry. The Japanese Quick Die Change (QDC) dies system is one of SMED systems that could reduce both of setup time and manufacturing cost. However, this system is rarely used in stamping industries. This paper will analyze how deep the QDC dies system could reduce setup time and the manufacturing cost. The research is conducted by direct observation, simulating and comparing of QDC dies system with conventional dies system. In this research, we found that the QDC dies system could save up to 35% of manufacturing cost and reduce 70% of setup times. This simulation proved that the QDC die system is effective for cost reduction but must be applied in several parallel production processes.

Keywords: press die, metal stamping, QDC system, single minute exchange die, manufacturing cost saving, SMED

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Authors: Reza Ferdousi, Reza Safdari, Yadollah Omidi

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Drug-drug interactions (DDIs) are main cause of the adverse drug reactions and nature of the functional and molecular complexity of drugs behavior in human body make them hard to prevent and treat. With the aid of new technologies derived from mathematical and computational science the DDIs problems can be addressed with minimum cost and efforts. Market basket analysis is known as powerful method to identify co-occurrence of thing to discover patterns and frequency of the elements. In this research, we used market basket analysis to identify important bio-elements in DDIs occurrence. For this, we collected all known DDIs from DrugBank. The obtained data were analyzed by market basket analysis method. We investigated all drug-enzyme, drug-carrier, drug-transporter and drug-target associations. To determine the importance of the extracted bio-elements, extracted rules were evaluated in terms of confidence and support. Market basket analysis of the over 45,000 known DDIs reveals more than 300 important rules that can be used to identify DDIs, CYP 450 family were the most frequent shared bio-elements. We applied extracted rules over 2,000,000 unknown drug pairs that lead to discovery of more than 200,000 potential DDIs. Analysis of the underlying reason behind the DDI phenomena can help to predict and prevent DDI occurrence. Ranking of the extracted rules based on strangeness of them can be a supportive tool to predict the outcome of an unknown DDI.

Keywords: drug-drug interaction, market basket analysis, rule discovery, important bio-elements

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Authors: Kai-Cheng Hsu, Tzu-Ying Sung, Jinn-Moon Yang

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Colorectal cancer (CRC) is one of the main causes of cancer death in the world. Although several drugs have been developed to treat colorectal cancer, such as Regorafenib and 5-FU, their efficacy is often limited by the development of drug resistance. Therefore, development of new drugs with new scaffolds is necessary to treat CRC. Here, we used site-moiety maps to identify inhibitors against PIM1, LIMK1, SRC, and mTOR, which are often overexpressed in CRC. A site-moiety map represents physicochemical properties and moiety preferences of a binding site through anchors. An anchor contains three elements: (1) conserved interacting residues of a binding pocket; (2) moiety preference of the binding pocket; and (3) the type (e.g., hydrogen-bonding or van der Waals interactions) of interaction between the moieties and the binding pocket. Then, we performed a structure-based virtual screening of ~260,000 compounds and selected compound candidates with high site-moiety map scores for bioassays. Among these candidates, compound 1 and compound 2 inhibited the growth of CRC cells with IC50 values of <10 μM. The experimental result of enzyme-based assays indicated that compound 1 is a dual inhibitor against PIM1 (IC50 6 μM) and LIMK1(IC50 11 μM). Compound 2 was predicted as a SRC inhibitor and will be further validated. The compounds inhibited different protein targets compared to the current drugs. We believe that the compounds provide a starting point to design new drugs for CRC treatment.

Keywords: colorectal cancer, drug discovery, site-moiety map, virtual screening, PIM1, LIMK1

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Authors: Avinash Kishore, Suman Chakrabarti

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In September 2013, the parliament of India enacted the National Food Security Act (NFSA) which entitles two-thirds of India’s population to five kilograms of rice, wheat or coarse cereals per person per month at one to three rupees per kilogram. Five states in India—Andhra Pradesh, Chhattisgarh, Tamil Nadu, Odisha and West Bengal—had already implemented somewhat similar changes in the TPDS a few years earlier using their own budgetary resources. They made rice—coincidentally, all five states are predominantly rice-eating—available in fair price shops to a majority of their population at very low prices (less than Rs.3/kg). This paper tries to account for the changes in household consumption patterns associated with the change in TPDS policy in these states using data from household consumption surveys by the National Sample Survey Organization (NSSO). NSS data show improvement in the coverage of TPDS and average off-take of grains from fair price shops between 2004-05 and 2009-10 across all states of India. However, the increase in coverage and off-take was significantly higher in four out of these five states than in the rest of India. An average household in these states purchased three kilos more rice per month from fair price shops than its counterpart in non-treated states as a result of more generous TPDS policies backed by administrative reforms. The increase in consumption of PDS rice was the highest in Chhattisgarh, the poster state of PDS reforms. Households in Chhattisgarh used money saved on rice to spend more on pulses, edible oil, vegetables and sugar and other non-food items. We also find evidence that making TPDS more inclusive and more generous is not enough unless it is supported by administrative reforms to improve grain delivery and control diversion to open markets.

Keywords: public distribution system, social safety-net, national food security act, diet quality, Chhattisgarh

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Authors: Muna Jalal Ali, Essam A. Makky, Mashitah M. Yusoff

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Objectives: The aim of this study to evaluate the antimicrobial susceptibility of combined toothpaste with medicinal plants and the relations between the commercial toothpaste to its price and the patient age as well. Materials and Methods: Oral isolates of different patients aged 3 to 60 years were obtained, purified, and tested against four different ethnomedicinal plant extracts for antimicrobial activity. A total of 10 different commercial toothpastes (different brands and prices) were collected from the market, and the combined action of the medicinal plants and toothpaste was studied. Results: We found a higher bacterial population in the age group of 3–40 years than the group of 40–60 years, with approximately 44% and 32%, respectively. The combined action of ethanolic extract (alone) against oral isolates showed a synergistic effect, with 32.20, 30.50, and 25.42% for combinations A (Ci/Ca), B (Ci/Ca/P), and C (Ci/Ca/P/N), respectively. By contrast, the combined action of ethnomedicinal plants with 10 different toothpastes improved the antimicrobial sensitivity by 60, 100, and 0% for combinations A, B, and C respectively. Clinical relevance: The ethanolic extract of only combinations A and B with commercial toothpaste showed high antibacterial activity against oral isolates and the effectiveness of toothpaste is not related to the price.

Keywords: microbial evolution, oral isolates, ethnomedicinal plants, antimicrobial activity, toothpaste

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Authors: Mengna Hu

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This paper examines the impact of an anti-speculation tax policy on the trading activities and home price movements in the housing market in Hong Kong. The study focuses on the secondary residential property market where transactions dominate. The policy intervention substantially raised the transaction cost to speculators as well as genuine homeowners who dispose their homes within a certain period. Through the demonstration of structural breaks, our empirical results show that the rise in transaction cost effectively reduced speculative trading activities. However, it accelerated price increase in the small-sized segment by vastly demotivating existing homeowners from trading up to better homes, causing congestion in the lower-end market where the demand from first-time buyers is still strong. Apart from that, by employing regime switching approach, we further show that the unintended consequences are likely to be persistent due to this policy together with other strengthened cooling measures.

Keywords: transaction costs, housing market, structural breaks, regime switching

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Authors: Rodrigo Arnaldo Scarpel

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Trend following is a widely accepted investment strategy that adopts a rule-based trading mechanism that rather than striving to predict market direction or on information gathering to decide when to buy and when to sell a stock. Thus, in trend following one must respond to market’s movements that has recently happen and what is currently happening, rather than on what will happen. Optimally, in trend following strategy, is to catch a bull market at its early stage, ride the trend, and liquidate the position at the first evidence of the subsequent bear market. For applying the trend following strategy one needs to find the trend and identify trade signals. In order to avoid false signals, i.e., identify fluctuations of short, mid and long terms and to separate noise from real changes in the trend, most academic works rely on moving averages and other technical analysis indicators, such as the moving average convergence divergence (MACD) and the relative strength index (RSI) to uncover intelligible stock trading rules following trend following strategy philosophy. Recently, some works has applied machine learning techniques for trade rules discovery. In those works, the process of rule construction is based on evolutionary learning which aims to adapt the rules to the current environment and searches for the global optimum rules in the search space. In this work, instead of focusing on the usage of machine learning techniques for creating trading rules, a time series trend classification employing a semi-supervised approach was used to early identify both the beginning and the end of upward and downward trends. Such classification model can be employed to identify trade signals and the decision-making procedure is that if an up-trend (down-trend) is identified, a buy (sell) signal is generated. Semi-supervised learning is used for model training when only part of the data is labeled and Semi-supervised classification aims to train a classifier from both the labeled and unlabeled data, such that it is better than the supervised classifier trained only on the labeled data. For illustrating the proposed approach, it was employed daily trade information, including the open, high, low and closing values and volume from January 1, 2000 to December 31, 2022, of the São Paulo Exchange Composite index (IBOVESPA). Through this time period it was visually identified consistent changes in price, upwards or downwards, for assigning labels and leaving the rest of the days (when there is not a consistent change in price) unlabeled. For training the classification model, a pseudo-label semi-supervised learning strategy was used employing different technical analysis indicators. In this learning strategy, the core is to use unlabeled data to generate a pseudo-label for supervised training. For evaluating the achieved results, it was considered the annualized return and excess return, the Sortino and the Sharpe indicators. Through the evaluated time period, the obtained results were very consistent and can be considered promising for generating the intended trading signals.

Keywords: evolutionary learning, semi-supervised classification, time series data, trading signals generation

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Authors: Luyao Zhang, Tianyu Wu, Jiayi Li, Carlos-Gustavo Salas-Flores, Saad Lahrichi

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Recent advances in AI have made algorithmic trading a central role in finance. However, current research and applications are disconnected information islands. We propose a generally applicable pipeline for designing, programming, and evaluating algorithmic trading of stock and crypto tokens. Moreover, we provide comparative case studies for four conventional algorithms, including moving average crossover, volume-weighted average price, sentiment analysis, and statistical arbitrage. Our study offers a systematic way to program and compare different trading strategies. Moreover, we implement our algorithms by object-oriented programming in Python3, which serves as open-source software for future academic research and applications.

Keywords: algorithmic trading, AI for finance, fintech, machine learning, moving average crossover, volume weighted average price, sentiment analysis, statistical arbitrage, pair trading, object-oriented programming, python3

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Authors: Velly Anatasia

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The worldwide annual expenditure for cosmetics is estimated at U.S. $18 billion and many players in the field are competing aggressively to capture more and more markets. Players in the cosmetics industry strive to be the foremost by establish customer loyalty. Furthermore, customer loyalty is portrayed by brand loyalty. Therefore, brand loyalty is the key determine of winning the competition in tight market. This study examines the influence of brand loyalty on cosmetics buying behavior of female consumers in Jakarta as capital of Indonesia. The seven factors of brand loyalty are brand name, Product quality, price, design, promotion, servicesquality and store environment. The paper adopted descriptive analysis, factor loading and multiple regression approach to test the hypotheses. The data has been collected by using questionnaires which were distributed and self-administered to 125female respondents accustomed using cosmetics. The findings of this study indicated that promotion has shown strong correlation with brand loyalty. The research results showed that there is positive and significant relationship between factors of brand loyalty (brand name, product quality, price, design, promotion, services quality and store environment) with cosmetics brand loyalty.

Keywords: brand loyalty, brand name, product quality, service quality, promotion

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Authors: Gabriela V. Angeles Perez, Jose Castillejos Lopez, Araceli L. Reyes Cabello, Emilio Bravo Grajales, Adriana Perez Espinosa, Jose L. Quiroz Fabian

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Road traffic accidents are among the principal causes of traffic congestion, causing human losses, damages to health and the environment, economic losses and material damages. Studies about traditional road traffic accidents in urban zones represents very high inversion of time and money, additionally, the result are not current. However, nowadays in many countries, the crowdsourced GPS based traffic and navigation apps have emerged as an important source of information to low cost to studies of road traffic accidents and urban congestion caused by them. In this article we identified the zones, roads and specific time in the CDMX in which the largest number of road traffic accidents are concentrated during 2016. We built a database compiling information obtained from the social network known as Waze. The methodology employed was Discovery of knowledge in the database (KDD) for the discovery of patterns in the accidents reports. Furthermore, using data mining techniques with the help of Weka. The selected algorithms was the Maximization of Expectations (EM) to obtain the number ideal of clusters for the data and k-means as a grouping method. Finally, the results were visualized with the Geographic Information System QGIS.

Keywords: data mining, k-means, road traffic accidents, Waze, Weka

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Authors: Pisit Potjanajaruwit

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The objectives of the study were to determine the marketing mix factors that influencing tourist’s destination decision making for cultural tourism in the Chonburi province. Both quantitative and qualitative data were applied in this study. The samples of 400 cases for quantitative analysis were tourists (both Thai and foreign) who were interested in cultural tourism in the Chonburi province, and traveled to cultural sites in Chonburi and 14 representatives from provincial tourism committee of Chonburi and local tourism experts. Statistics utilized in this research included frequency, percentage, mean, standard deviation, and multiple regression analysis. The study found that Thai and foreign tourists are influenced by different important marketing mix factors. The important factors for Thai respondents were physical evidence, price, people, and place at high importance level. For foreign respondents, physical evidence, price, people, and process were high importance level, whereas, product, place, and promotion were moderate importance level.

Keywords: Chonburi Province, decision making, cultural tourism, marketing mixed

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Authors: Paolo Fantozzi, Luigi Laura, Umberto Nanni

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The problem of Entity relation discovery in structured data, a well covered topic in literature, consists in searching within unstructured sources (typically, text) in order to find connections among entities. These can be a whole dictionary, or a specific collection of named items. In many cases machine learning and/or text mining techniques are used for this goal. These approaches might be unfeasible in computationally challenging problems, such as processing massive data streams. A faster approach consists in collecting the cooccurrences of any two words (entities) in order to create a graph of relations - a cooccurrence graph. Indeed each cooccurrence highlights some grade of semantic correlation between the words because it is more common to have related words close each other than having them in the opposite sides of the text. Some authors have used sliding windows for such problem: they count all the occurrences within a sliding windows running over the whole text. In this paper we generalise such technique, coming up to a Weighted-Distance Sliding Window, where each occurrence of two named items within the window is accounted with a weight depending on the distance between items: a closer distance implies a stronger evidence of a relationship. We develop an experiment in order to support this intuition, by applying this technique to a data set consisting in the text of the Bible, split into verses.

Keywords: cooccurrence graph, entity relation graph, unstructured text, weighted distance

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Authors: Simon Huston, Ebraheim Lahbash, Ali Parsa

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The development of the United Arab Emirates (UAE) into a regional trade, tourism, finance and logistics hub has transformed its real estate markets. However, speculative activity and price volatility remain concerns. UAE residential market values (MV) are exposed to fluctuations in capital flows and migration which in turn are affected by geopolitical uncertainty, oil price volatility, and global investment market sentiment. Internally, a complex interplay between administrative boundaries, land tenure, building quality and evolving location characteristics fragments UAE residential property markets. In short, the UAE Residential Valuation System (UAE-RVS) confronts multiple challenges to collect, filter and analyze relevant information in complex and dynamic spatial and capital markets. A robust (RVS) can mitigate the risk of unhelpful volatility, speculative excess or investment mistakes. The research outlines the institutional, ontological, dynamic, and epistemological issues at play. We highlight the importance of system capabilities, valuation standard salience and stakeholders trust.

Keywords: valuation, property rights, information, institutions, trust, salience

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Authors: Murad A. Bein

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This paper empirically examines the time-varying relationship and spillover effects between the international crude oil price and ten emerging equity markets, namely three oil-exporting countries (Brazil, Mexico, and Russia) and seven Central and Eastern European (CEE) countries (Bulgaria, Croatia, Czech Republic, Hungary, Poland, Romania, and Slovakia). The results revealed that there are spillover effects from oil markets into almost all emerging equity markets save Slovakia. Besides, the oil supply glut had a homogenous effect on the emerging markets, both net oil-exporting, and oil-importing countries (CEE). Further, the time variance drastically increased during financial turmoil. Indeed, the time variance remained high from 2009 to 2012 in response to aggregate demand shocks (global financial crisis and Eurozone debt crisis) and quantitative easing measures. Interestingly, the time variance was slightly higher for the oil-exporting countries than for some of the CEE countries. Decision-makers in emerging economies should therefore seek policy coordination when dealing with financial turmoil.

Keywords: crude oil, spillover effects, emerging equity, time-varying, aggregate demand shock

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Authors: Abdullah M. Algarni, Yashwant K. Malaiya

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Some of the key aspects of vulnerability-discovery, dissemination, and disclosure-have received some attention recently. However, the role of interaction among the vulnerability discoverers and vulnerability acquirers has not yet been adequately addressed. Our study suggests that a major percentage of discoverers, a majority in some cases, are unaffiliated with the software developers and thus are free to disseminate the vulnerabilities they discover in any way they like. As a result, multiple vulnerability markets have emerged. In some of these markets, the exchange is regulated, but in others, there is little or no regulation. In recent vulnerability discovery literature, the vulnerability discoverers have remained anonymous individuals. Although there has been an attempt to model the level of their efforts, information regarding their identities, modes of operation, and what they are doing with the discovered vulnerabilities has not been explored. Reports of buying and selling of the vulnerabilities are now appearing in the press; however, the existence of such markets requires validation, and the natures of the markets need to be analysed. To address this need, we have attempted to collect detailed information. We have identified the most prolific vulnerability discoverers throughout the past decade and examined their motivation and methods. A large percentage of these discoverers are located in Eastern and Western Europe and in the Far East. We have contacted several of them in order to collect first-hand information regarding their techniques, motivations, and involvement in the vulnerability markets. We examine why many of the discoverers appear to retire after a highly successful vulnerability-finding career. The paper identifies the actual vulnerability markets, rather than the hypothetical ideal markets that are often examined. The emergence of worldwide government agencies as vulnerability buyers has significant implications. We discuss potential factors that can impact the risk to society and the need for detailed exploration.

Keywords: risk management, software security, vulnerability discoverers, vulnerability markets

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Authors: Maria Evnevich

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In modern conditions, in most markets, price competition is becoming less effective. On the one hand, there is a gradual decrease in the level of marginality in main traditional sectors of the economy, so further price reduction becomes too ‘expensive’ for the company. On the other hand, the effect of price reduction is leveled, and the reason for this phenomenon is likely to be informational. As a result, it turns out that even if the company reduces prices, making its products more accessible to the buyer, there is a high probability that this will not lead to increase in sales unless additional large-scale advertising and information campaigns are conducted. Similarly, a large-scale information and advertising campaign have a much greater effect itself than price reductions. At the same time, the cost of mass informing is growing every year, especially when using the main information channels. The article presents generalization, systematization and development of theoretical approaches and best practices in the field of customer focus approach to business management and in the field of relationship marketing in the modern digital economy. The research methodology is based on the synthesis and content-analysis of sociological and marketing research and on the study of the systems of working with consumer appeals and loyalty programs in the 50 largest client-oriented companies in Russia. Also, the analysis of internal documentation on customers’ purchases in one of the largest retail companies in Russia allowed to identify if buyers prefer to buy goods for complex purchases in one retail store with the best price image for them. The cost of attracting a new client is now quite high and continues to grow, so it becomes more important to keep him and increase the involvement through marketing tools. A huge role is played by modern digital technologies used both in advertising (e-mailing, SEO, contextual advertising, banner advertising, SMM, etc.) and in service. To implement the above-described client-oriented omnichannel service, it is necessary to identify the client and work with personal data provided when filling in the loyalty program application form. The analysis of loyalty programs of 50 companies identified the following types of cards: discount cards, bonus cards, mixed cards, coalition loyalty cards, bank loyalty programs, aviation loyalty programs, hybrid loyalty cards, situational loyalty cards. The use of loyalty cards allows not only to stimulate the customer to purchase ‘untargeted’, but also to provide individualized offers, as well as to produce more targeted information. The development of digital technologies and modern means of communication has significantly changed not only the sphere of marketing and promotion, but also the economic landscape as a whole. Factors of competitiveness are the digital opportunities of companies in the field of customer orientation: personalization of service, customization of advertising offers, optimization of marketing activity and improvement of logistics.

Keywords: customer focus, digital economy, loyalty program, relationship marketing

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Authors: Sara Sousa

Abstract:

The environmental goods and services have often been neglected in crucial decisions affecting the environment mainly because the difficulty in estimating their economic value, since we are dealing with non-market goods and, thus, without a price associated. Nevertheless, the inexistence of prices does not necessarily mean these goods have no value. The environment is a key element in today's society that seeks to be as sustainable as possible, where the environmental assets have both use and non-use values. To estimate the use value, researchers may apply the revealed preference methods. This paper provides a theoretical review of the main concepts and methodologies on the economic valuation of the environment, with particular emphasis on the revealed preference techniques. Based on a detailed literature review, this study concludes that, despite some inherent limitations, the revealed preference methodologies – travel cost, hedonic price, and averting behaviour – represent essential tools for the researchers who accept the challenge to estimate the use value of environmental goods and services based on the actual individuals` behaviour. The main purpose of this study is to contribute to an increased theoretical information on the economic valuation of environmental assets, allowing researchers and policymakers to improve future decisions regarding the environment.

Keywords: economic valuation, environmental goods, revealed preference methods, total economic value

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Authors: S. Niamkaeo, O. Robert, O. Chaowalit

Abstract:

In this investigation, we focus on discovering spatial relationship of drug smuggling along the northern border of Thailand. Thailand is no longer a drug production site, but Thailand is still one of the major drug trafficking hubs due to its topographic characteristics facilitating drug smuggling from neighboring countries. Our study areas cover three districts (Mae-jan, Mae-fahluang, and Mae-sai) in Chiangrai city and four districts (Chiangdao, Mae-eye, Chaiprakarn, and Wienghang) in Chiangmai city where drug smuggling of methamphetamine crystal and amphetamine occurs mostly. The data on drug smuggling incidents from 2011 to 2017 was collected from several national and local published news. Geo-spatial drug smuggling database was prepared. Decision tree algorithm was applied in order to discover the spatial relationship of factors related to drug smuggling, which was converted into rules using rule-based system. The factors including land use type, smuggling route, season and distance within 500 meters from check points were found that they were related to drug smuggling in terms of rules-based relationship. It was illustrated that drug smuggling was occurred mostly in forest area in winter. Drug smuggling exhibited was discovered mainly along topographic road where check points were not reachable. This spatial relationship of drug smuggling could support the Thai Office of Narcotics Control Board in surveillance drug smuggling.

Keywords: decision tree, drug smuggling, Geographic Information System, GIS knowledge discovery, rule-based system

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Authors: Phutthiwat Waiyawuththanapoom, Pimploi Tirastittam, Manop Tirastittam

Abstract:

Thailand is the agriculture country which mainly exports the agriculture product to the other countries in so many ways which are fresh vegetable, chilled vegetable or frozen vegetable. The gross export for Thailand’s vegetable is 30-40 billion baht per year, and the growth rate is about 15-20 percent per year. Ginger is one of the main vegetable product that Thailand export to Japan because Thailand’s Ginger has a good quality and be able to supply Japan’s demand with a reasonable price. This research paper is aimed to study the factors which affect the efficiency of the supply chain process of Thailand’s ginger to Japan. There are 5 factors which related to the exporting Thailand’s ginger to Japan which are quality, price, equipment and supply standard, custom process and distribution pattern. The result of the research showed that the factor which reached the 'very good' significant level is quality of Thailand’s ginger with the score of 4.86. The other 5 factors are in the 'good' significant level. So the most important factor for Thai ginger farmer to concern is the quality of the product.

Keywords: critical success factor, export, ginger, supply chain

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Authors: Waheed Akhtar

Abstract:

Bears in Asia facing multiple threats and challenges such as hunting, illegal trading, habitat loss, and human conflicts. According to IUCN Red List, the Asiatic black bear (Ursus thibetanus) is listed as Vulnerable since 1990, population declining by 49% during the last 30 years. The present study was conducted in Manglawar (DwaSaro Mountain) from April-August 2021, to collect all the information on Asiatic black bear observation, illegal hunting, and cub poaching. According to the response of the local community, very intensive illegal hunting and cub poaching were observed. Hunters usually installed many traps in the routes of black bears and when they move in the winter season the cubs get trapped and they collect them and kept in a specialized wooden box that is mainly helpful for further transportation. These cubs are then brought to the concerned Market where they sell them to many dealers. One of the potential observers of the illegal trading responds towards the Market price of the cubs, “The average price of the black bear cub is ranging from 45000-50000 Pakistani Rupees”. Apart from cubs' poaching, the black bear is also hunted for its skin, claws, and teeth.

Keywords: first report, illegal hunting, cub poaching, parts trading, Ursus thibetanus

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Authors: Siri-orn Champatong

Abstract:

The purpose of this research was to study factors that were related with travel behaviors of Thai tourists at the Ayothaya Floating Market, Phra Nakhon Sri Ayutthaya. The quantitative research was conducted with 400 samples of Thai tourists traveling to the Ayothaya Floating Market. The Questionnaire was a tool used to collect data, and the statistics used for data analysis were mean and Pearson product moment correlation coefficient. The results found that Thai tourists focused on attraction, easy access and facilities of the tourist spot at a high level. In addition, they gave priority to the marketing mix in the dimension of products, price, and distribution channels at a high level as well. For marketing promotion, it was at the moderate level. The results of hypothesis testing revealed that factors related to the attractions of the tourist destination, easy access to the tourist destination, the facilities of the tourist spot, and product and price of the marketing mix were associated with travel behaviors in the aspect of the number of visits used and the budget on tourism.

Keywords: floating market, marketing mix, tourism attractions, travelling behavior

Procedia PDF Downloads 264