Search results for: financial market prediction

Authors: Juan Pedro Aznar Alarcon, Josep Maria Sayeras Maspera

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Airbnb and other similar platforms are offering a near substitute to the traditional accommodation service supplied by the hotel sector. In this context, hotels can try to compete by offering higher quality and additional services, which imply the need for new investments or try to compete by reducing prices. The theoretical model presented in this paper analyzes the best response using a sequential game theory model. The main conclusion is that due to the financial constraints that small and medium hotels have these hotels have reduced prices whereas hotels that belong to international groups or have an easy access to financial resources have increased their investment to increase the quality of the service provided. To check the validity of the theoretical model financial data from Barcelona, London and Paris hotels have been used analyzing profitability, quality of the service provided, the investment propensity and the evolution of the gross profit. The model and the empirical data provide the base for some industrial policy in the hospitality industry. To address the extra cost that small hotels in Europe have to face compared by bigger firms would help to improve the level of quality provided and to some extent have positive externalities in terms of job creation and an increasing added value for the industry.

Keywords: Airbnb, profitability, hospitality industry, game theory

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Authors: J. Jayasuriya

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In this research paper, a procedure for installing and assessment of a solar PV plant to generate optimal solar energy SPAV hostel at Vijayawada city was analyzed. The hostel was experiencing power disruption and had a need for an unceasing energy source. The solar panel is one of the best solutions to obtain uninterrupted clean renewable energy for an institutional building as it neither makes din nor pollutes the atmosphere. The electricity usage per month was initially measured to discriminate the energy change. The solar array was installed with its financial and environmental assessment considering recent market prices. All the aspects related to a solar PV plant were considered for the feasibility and efficiency of PV plant near this site i.e., the orientation of the site, the size and shape of the terrace, the sun path were considered while installing panels. Various precautions were taken to intercept the factors which cause interference in energy generation, with respect to temperature, overshadowing, the wiring of panels, pollution etc. The solar panels were frequently installed, monitored and maintained properly to procure optimal energy output. Result obtained with the assessment of the proposed plant and deflation in the electric bill will show the maximal energy that can be generated in a month on that particular site.

Keywords: solar efficiency, building sustainability, PV panel, solar energy

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Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

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As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: apartment housing, machine learning, multi-objective optimization, performance prediction

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Authors: Ju-Hyung Kim, Dae-Ho Mun, Hong-Gun Park

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When an impact is applied to a floating floor, noise and vibration response of high-frequency range is reduced effectively, while amplifies the response at low-frequency range. This means floating floor can make worse noise condition when heavy-weight impact is applied. The amplified response is the result of interaction between finishing layer (mortar plate) and concrete slab. Because an impact force is not directly delivered to concrete slab, the impact force waveform or spectrum can be changed. In this paper, the changed impact spectrum was derived from several floating floor vibration tests. Based on the measured data, numerical modeling can describe the floating floor response, especially at low-frequency range. As a result, heavy-weight impact noise can be predicted using modified impact spectrum.

Keywords: floating floor, heavy-weight impact, prediction, vibration

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Authors: Faustina Masocha

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This study sought to determine if the Covid-19 pandemic played a disruptive role in the signalling effect of dividend increases for the Top 40 companies listed on the Johannesburg Stock Exchange. With the use of Event Study Methodologies, it was found that dividend increases that were announced in the 2018 and 2019 financial years resulted in Cumulative Abnormal Returns (CARs) that were significantly different from zero, as confirmed by a p-value of 0,0300. This resulted in the conclusion that, under normal circumstances, dividend increases follow the precepts outlined in signalling theories which indicate that the announcement of dividend increases sent positive signals about the expected financial performance of a company. To prove the notion that Covid-19 plays a disruptive role on the signalling hypothesis, it was found from both parametric and non-parametric tests of significance that CARs related to dividend increases that were announced during the 2020 and 2021 financial years, when the Covid-19 pandemic was at its peak, were not significantly different from zero. Therefore, although the dividend increases still resulted in some CARs, such CARs were not statistically different from zero to confirm the signalling hypothesis. A p-value of 0.9830 from parametric t-tests and a p-value of 0.8971 from the Wilcoxon signed-rank test were used as a gauge that led to the conclusion that Covid-19 plays a disruptive effect on the signalling process of dividend increases.

Keywords: cumulative abnormal returns, dividend increases, event study methodology, signalling

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Authors: Satinder Bhatia

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Limited Liability Partnerships (LLPs) was a concept introduced in India in 2009. Ever since then, there has been a fierce growth in the number of organizations registered as LLPs outpacing the number of registrations as private companies. Among other benefits extended to LLPs, the audit being mandated only for LLPs having a turnover of at least Rs 40 lakhs or capital contribution of Rs 25 lakhs, has been a major attraction. This has resulted in only about 10 per cent of LLPs coming under mandatory audit. Even for such companies, the accounting standards to be followed in the preparation of financial statements have not been specified. The Revised Indian Accounting Standards (Revised IndAS) which are aligned with IFRS to a great extent, may apply to LLPs only under limited conditions. Thus, the veracity of even the audited financial statements of LLPs can be questioned. If in future, these LLPs would like to list on a stock exchange to raise capital, there can be serious hurdles if investors do not find the financial statements to be reliable and consistent. LLPs are generally governed by country-specific rules in the area of accounts and audit. Thus, such rules vary across UK, EU and the USA. Some countries have adopted the IFRS for SMEs and since LLPs can be referred to as SMEs; they would come under the ambit of these IFRS provisions. Besides, as the scope of audit widens to cover qualitative information in addition to quantitative data, audit of LLPs has now acquired a new meaning and a new urgency as demands for at least limited purpose audits are arising from different stakeholders including lenders, suppliers, customers and joint venture partners.

Keywords: audit disclosures, audit quality, guidance for SMEs, non-audit services

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Authors: J. Ticona Chambi, E. A. De Almeida, C. A. Andrade Raymundo Gaiotto, A. M. Do Espírito Santo, L. Infantes, S. L. Cuffini

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The solubility of active pharmaceutical ingredients (APIs) is challenging for the pharmaceutical industry. The new multicomponent crystalline forms as cocrystal and solvates present an opportunity to improve the solubility of APIs. Commonly, the procedure to obtain multicomponent crystalline forms of a drug starts by screening the drug molecule with the different coformers/solvents. However, it is necessary to develop methods to obtain multicomponent forms in an efficient way and with the least possible environmental impact. The Hansen Solubility Parameters (HSPs) is considered a tool to obtain theoretical knowledge of the solubility of the target compound in the chosen solvent. H-Bond Propensity (HBP), Molecular Complementarity (MC), Coordination Values (CV) are tools used for statistical prediction of cocrystals developed by the Cambridge Crystallographic Data Center (CCDC). The HSPs and the CCDC tools are based on inter- and intra-molecular interactions. The curcumin (Cur), target molecule, is commonly used as an anti‐inflammatory. The demethoxycurcumin (Demcur) and bisdemethoxycurcumin (Bisdcur) are natural analogues of Cur from turmeric. Those target molecules have differences in their solubilities. In this way, the work aimed to analyze and compare different tools for multicomponent forms prediction (solvates) of Cur, Demcur and Biscur. The HSP values were calculated for Cur, Demcur, and Biscur using the chemical group contribution methods and the statistical optimization from experimental data. The HSPmol software was used. From the HSPs of the target molecules and fifty solvents (listed in the HSP books), the relative energy difference (RED) was determined. The probability of the target molecules would be interacting with the solvent molecule was determined using the CCDC tools. A dataset of fifty molecules of different organic solvents was ranked for each prediction method and by a consensus ranking of different combinations: HSP, CV, HBP and MC values. Based on the prediction, 15 solvents were selected as Dimethyl Sulfoxide (DMSO), Tetrahydrofuran (THF), Acetonitrile (ACN), 1,4-Dioxane (DOX) and others. In a starting analysis, the slow evaporation technique from 50°C at room temperature and 4°C was used to obtain solvates. The single crystals were collected by using a Bruker D8 Venture diffractometer, detector Photon100. The data processing and crystal structure determination were performed using APEX3 and Olex2-1.5 software. According to the results, the HSPs (theoretical and optimized) and the Hansen solubility sphere for Cur, Demcur and Biscur were obtained. With respect to prediction analyses, a way to evaluate the predicting method was through the ranking and the consensus ranking position of solvates already reported in the literature. It was observed that the combination of HSP-CV obtained the best results when compared to the other methods. Furthermore, as a result of solvent selected, six new solvates, Cur-DOX, Cur-DMSO, Bicur-DOX, Bircur-THF, Demcur-DOX, Demcur-ACN and a new Biscur hydrate, were obtained. Crystal structures were determined for Cur-DOX, Biscur-DOX, Demcur-DOX and Bicur-Water. Moreover, the unit-cell parameter information for Cur-DMSO, Biscur-THF and Demcur-ACN were obtained. The preliminary results showed that the prediction method is showing a promising strategy to evaluate the possibility of forming multicomponent. It is currently working on obtaining multicomponent single crystals.

Keywords: curcumin, HSPs, prediction, solvates, solubility

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Authors: Shreyas Kumar, Mili Shangari

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Integrating artificial intelligence (AI) in the cyber insurance sector represents a significant advancement, offering the potential to revolutionize risk assessment, fraud detection, and claims processing. However, this integration introduces a range of regulatory and economic challenges that must be addressed to ensure responsible and effective deployment of AI technologies. This paper examines the multifaceted regulatory landscape governing AI in cyber insurance and explores the economic implications of compliance, innovation, and market dynamics. AI's capabilities in processing vast amounts of data and identifying patterns make it an invaluable tool for insurers in managing cyber risks. Yet, the application of AI in this domain is subject to stringent regulatory scrutiny aimed at safeguarding data privacy, ensuring algorithmic transparency, and preventing biases. Regulatory bodies, such as the European Union with its General Data Protection Regulation (GDPR), mandate strict compliance requirements that can significantly impact the deployment of AI systems. These regulations necessitate robust data protection measures, ethical AI practices, and clear accountability frameworks, all of which entail substantial compliance costs for insurers. The economic implications of these regulatory requirements are profound. Insurers must invest heavily in upgrading their IT infrastructure, implementing robust data governance frameworks, and training personnel to handle AI systems ethically and effectively. These investments, while essential for regulatory compliance, can strain financial resources, particularly for smaller insurers, potentially leading to market consolidation. Furthermore, the cost of regulatory compliance can translate into higher premiums for policyholders, affecting the overall affordability and accessibility of cyber insurance. Despite these challenges, the potential economic benefits of AI integration in cyber insurance are significant. AI-enhanced risk assessment models can provide more accurate pricing, reduce the incidence of fraudulent claims, and expedite claims processing, leading to overall cost savings and increased efficiency. These efficiencies can improve the competitiveness of insurers and drive innovation in product offerings. However, balancing these benefits with regulatory compliance is crucial to avoid legal penalties and reputational damage. The paper also explores the potential risks associated with AI integration, such as algorithmic biases that could lead to unfair discrimination in policy underwriting and claims adjudication. Regulatory frameworks need to evolve to address these issues, promoting fairness and transparency in AI applications. Policymakers play a critical role in creating a balanced regulatory environment that fosters innovation while protecting consumer rights and ensuring market stability. In conclusion, the integration of AI in cyber insurance presents both regulatory and economic challenges that require a coordinated approach involving regulators, insurers, and other stakeholders. By navigating these challenges effectively, the industry can harness the transformative potential of AI, driving advancements in risk management and enhancing the resilience of the cyber insurance market. This paper provides insights and recommendations for policymakers and industry leaders to achieve a balanced and sustainable integration of AI technologies in cyber insurance.

Keywords: artificial intelligence (AI), cyber insurance, regulatory compliance, economic impact, risk assessment, fraud detection, cyber liability insurance, risk management, ransomware

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Authors: Pornnapat Berndt

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The purpose of this research is to study the tourism routes in Bangnoi Floating Market, Bangkhontee District, Samut Songkhram province, Thailand in order to create type and form of interpretation to promote cultural tourism based on local community and visitor requirement. To accomplish the goals and objectives, qualitative research will be applied. The research instruments used are observation, questionnaires, basic interviews, in-depth interviews, focus group, interviewed of key local informants including site visitors. The study also uses both primary data and secondary data. A Statistical Package for Social Sciences (SPSS) was used to analyze the data. Descriptive and inferential statistics such as tables, percentage, mean and standard deviation were used for data analysis and summary. From research result, it is revealed that the local community requirement on types of interpretation conforms to visitors require which need guide post, guide book, etc. with up to date and informally content to present Bangnoi Floating Market which got the most demand score (3.78) considered as most wanted demand.

Keywords: interpretation, cultural tourism, tourism route, local community, stakeholders participated

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Authors: Ahmed T. Farid, Muhammed Rizwan

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Geotechnical study for evaluating soil or rock permeability is a highly important parameter. Permeability values for rock formations are more difficult for determination than soil formation as it is an effect of the rock quality and its fracture values. In this research, the prediction of in situ permeability of limestone rock formations was predicted. The limestone rock permeability was evaluated using Lugeon tests (in-situ packer permeability). Different sites which spread all over the Riyadh region of Saudi Arabia were chosen to conduct our study of predicting the in-situ permeability of limestone rock. Correlations were deducted between the values of in-situ permeability of the limestone rock with the value of the rock quality designation (RQD) calculated during the execution of the boreholes of the study areas. The study was performed for different ranges of RQD values measured during drilling of the sites boreholes. The developed correlations are recommended for the onsite determination of the in-situ permeability of limestone rock only. For the other sedimentary formations of rock, more studies are needed for predicting the actual correlations related to each type.

Keywords: In situ, packer, permeability, rock, quality

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Authors: Roman Bieda

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The General Data Protection Regulation (GDPR) will come into force on 25 May 2018 which will create a new legal framework for the protection of personal data in the European Union. Article 20 of GDPR introduces a right to data portability. This right allows for data subjects to receive the personal data which they have provided to a data controller, in a structured, commonly used and machine-readable format, and to transmit this data to another data controller. The right to data portability, by facilitating transferring personal data between IT environments (e.g.: applications), will also facilitate changing the provider of services (e.g. changing a bank or a cloud computing service provider). Therefore, it will contribute to the development of competition and the digital market. The aim of this paper is to discuss the right to data portability and its influence on the development of new digital services.

Keywords: data portability, digital market, GDPR, personal data

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Authors: V. Benedico, C. Anacleto, A. Bearzi, L. Brice, V. Delahaye

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The aim in present paper is to construct an affordable and reliable commodity prices based on a recalculation of its cost through time which allows visualize the potential risks and thus, take more appropriate decisions regarding forecasts. Here attention has been focused on Levy model, more reliable and realistic than classical random Gaussian one as it takes into consideration observed abrupt jumps in case of sudden price variation. In application to Energy Trading sector where it has never been used before, equations corresponding to Levy model have been written for electricity pricing in European market. Parameters have been set in order to predict and simulate the price and its evolution through time to remarkable accuracy. As predicted by Levy model, the results show significant spikes which reach unconventional levels contrary to currently used Brownian model.

Keywords: commodity pricing, Lévy Model, price spikes, electricity market

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Authors: Andrea Ando

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The development of the blockchain has been a major innovation in the technology field. It opened the door to the creation of novel cyberassets and currencies. In more recent times, the non-fungible tokens have started to be at the centre of media attention. Their popularity has been increasing since 2021, and they represent the latest in the world of distributed ledger technologies and cryptocurrencies. It seems more and more likely that NFTs will play a more important role in our online interactions. They are indeed increasingly taking part in the arts and technology sectors. Their impact on society and the market is still very difficult to define, but it is very likely that there will be a turning point in the world of digital assets. There are some examples of their peculiar behaviour and effect in our contemporary tech-market: the former CEO of the famous social media site Twitter sold an NFT of his first tweet for around £2,1 million ($2,5 million), or the National Basketball Association has created a platform to sale unique moment and memorabilia from the history of basketball through the non-fungible token technology. Their growth, as imaginable, paved the way for civil disputes, mostly regarding their position under the current intellectual property law in each jurisdiction. In April 2022, the High Court of England and Wales ruled in the OpenSea case that non-fungible tokens can be considered properties. The judge, indeed, concluded that the cryptoasset had all the indicia of property under common law (National Provincial Bank v. Ainsworth). The research has demonstrated that the ruling of the High Court is not providing enough answers to the dilemma of whether minting an NFT is a violation or not of intellectual property and/or property rights. Indeed, if, on the one hand, the technology follows the framework set by the case law (e.g., the 4 criteria of Ainsworth), on the other hand, the question that arises is what is effectively protected and owned by both the creator and the purchaser. Then the question that arises is whether a person has ownership of the cryptographed code, that it is indeed definable, identifiable, intangible, distinct, and has a degree of permanence, or what is attached to this block-chain, hence even a physical object or piece of art. Indeed, a simple code would not have any financial importance if it were not attached to something that is widely recognised as valuable. This was demonstrated first through the analysis of the expectations of intellectual property law. Then, after having laid the foundation, the paper examined the OpenSea case, and finally, it analysed whether the expectations were met or not.

Keywords: technology, technology law, digital law, cryptoassets, NFTs, NFT, property law, intellectual property law, copyright law

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Authors: Mohammed Kafaji

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This paper aims to investigate the influence of quality of education and quality of research, provided by local educational institutions, on the adoption of Information and Communication Technology (ICT) in managing business operations for companies in Saudi market. A model was developed and tested using data collected from 138 CEO’s of foreign companies in diverse business sectors. The data is analysed and managed using multivariate approaches through standard statistical packages. The results showed that educational quality has little contribution to the ICT adoption while research quality seems to play a more prominent role. These results are analysed in terms of business environment and market constraints and further extended to the perceived effectiveness of applied pedagogical approaches in schools and universities.

Keywords: quality of education, quality of research, mediation, domestic competition, ICT adoption

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Authors: Markus Stralla

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This study employs a GMM framework to examine the impact of potential regulatory intervention regarding the occupations of supervisory board members in cooperative banking. To achieve insights, the study proceeds in two different ways. First, it investigates the changes in board structure prior and following to the German Act to Strengthen Financial Market and Insurance Supervision (FinVAG). Second, the study estimates the influence of Ph.D.Share, professional concentration and supervisory power on bank-risk changes in consideration of the implementation of FinVAG. Therefore, the study is based on a sample of 246 German cooperative banks from 2006-2011 while applying four different measures of bank risk, namely credit-, equity-, liquidity-risk, and Z-Score, with the former three also being addressed in FinVAG. Results indicate that the implementation of FinVAG results in (most likely unintentional) structural changes, especially at the expense of farmers, and affects all risk measures and relations between risk measures and supervisory board characteristics in a risk-reducing and therefore intended way. To disentangle the complex relationship between board characteristics and risk measures, the study utilizes two-step system GMM estimator to account for unobserved heterogeneity and simultaneity in order to reduce endogeneity problems. The findings may be especially relevant for stakeholders, regulators, supervisors and managers.

Keywords: bank governance, bank risk-taking, board of directors, regulation

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Authors: Peter Dzakah Fanam, Hong O. Nguyen, Stephen Cahoon

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The objective of this study is to explore the buying behavior of the freight forwarders and to evaluate how their buying decision affects the ocean container carriers’ market share. This study analysed the buying decisions of the freight forwarders and validated the process of stages that the freight forwarders’ pass through before choosing an ocean container carrier. Factor analysis was applied to data collected from 105 freight forwarding companies to unveil the influential factors the freight forwarders’ consider important when selecting an ocean container carrier. This study did not only analysed the buying behaviour of the freight forwarders but also unveiled the influential factors affecting the competitiveness of the ocean container carriers in their market share maximisation. Furthermore, the study have made a methodological contribution that helps in better understanding of the critical factors influencing the selection of the ocean container carriers from the freight forwarders’ perspective. The implications of the freight forwarders’ buying behaviour is important to the ocean container carriers because it have severe effect on the market share of the ocean container carriers and the percentage of customers they control within the liner shipping sector. The findings of this study will help the ocean container carriers to formulate relevant marketing strategies in attracting the freight forwarders in purchasing the liner shipping service.

Keywords: ocean carrier, freight forwarder, buying behaviour, influential factors

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Authors: Obadiah Nwabueze

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Bodija market is classified as Central Business District (CBD) of Ibadan North Local Government Area of Oyo State (Nigeria) because of socio economic activities, so Crime is a peculiar social issue that causes insecurity. The law enforcement agencies tasked with crime prevention and control such as the Nigerian Police have insufficient manpower, and a resultant effect is the emergence of Vigilante groups as citizen’s response to crime control and prevention (self-help). The research design adopted for this study is a case study design exploring Vigilante activities in Bodija Market. The study utilizes both quantitative and qualitative approach, sources of data includes primary and secondary sources. A sample of 127 respondents randomly picked from the 4 sections of Bodija Market through questionnaire, comprising of 50 male and 77 females which alienates issues of gender bias in addition to the 4 in-depth interview, making a total of 131 respondents. Statistical package for Social Sciences (SPSS) was used. The descriptive statistics of simple frequency, percentage, charts and graphs were computed for the analysis. Finding in the study shows that the market vigilante is able to deter and disrupt criminal activities through strategic spiritual intelligence (SSI), use of charm and juju, physical presence in strategic locations vulnerable to crime occurrence. Findings in the study also show that vigilantes collaborate with the police by assisting them in surveillance, tracking down criminals, identifying black spots, acting as informants to the police, arrest and handover criminal to police. Their challenges include poor equipment, motivation, unhealthy rivalry between the vigilante and the police. The study recommends that the government should support vigilantes with logistics and training, including patrol vehicle and radio communication. The study also recommends the integration of the informal mechanism (juju and charm) of crime detection and prevention into the formal policing strategy, an office should be created in the force commands for use of SSI.

Keywords: central business district, CBD, charm, Juju, strategic spiritual intelligence, SSI

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Authors: Saira Tajdar, Sajad Ahmad

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The pharmaceutical market of Pakistan showed a mixed trend since 1947. In these six decades various local and foreign pharmaceutical companies entered the market with their highly researched based formulas and brands for various diseases. It also created a very competitive market between local and foreign companies and brands. But this intense competition does not clear the picture that whether the customers (Doctors) are preferring/prescribing foreign or local brands more frequently. Previous research has been done in various markets for different brands that whether the customers in that industry prefer foreign or local brands. However, the pharmaceutical industry in this regard has been ignored by the researchers. Generally people don't know that for prescription brands of medicines what the preferences of customers (Doctors) are. Therefore, this study is conducted in two departments of Pharmaceutical industry by selecting the top recommended formulas in those departments that for those formulas whether the customers (Doctors) are prescribing either foreign brands or local brands. Secondary data has been collected from previous studies on the country of origin (COO), ethnocentrism and factors influencing brands preferences from authentic sources. Primary data was also collected through 100 self administered questionnaires from top five hospitals of Peshawar. The results of the study were analyzed through SPSS which shows that in some categories of pharmaceutical products the COO is very important but not for all.

Keywords: customer prescription, country of origin, empirical study, foreign versus local brands, pharmaceutical industry, Pakistan

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Authors: Hilya Mudrika Arini, Nur Aini Masruroh, Budi Hartono

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There are more than 20 terrorist threats from 2002 to 2012 in Indonesia. Despite of this fact, preventive solution through studies in the field of national security in Indonesia has not been conducted comprehensively. This study aims to provide a preventive solution by developing prediction model of the terrorist threat in Indonesia by using Bayesian network. There are eight stages to build the model, started from literature review, build and verify Bayesian belief network to what-if scenario. In order to build the model, four experts from different perspectives are utilized. This study finds several significant findings. First, news and the readiness of terrorist group are the most influent factor. Second, according to several scenarios of the news portion, it can be concluded that the higher positive news proportion, the higher probability of terrorist threat will occur. Therefore, the preventive solution to reduce the terrorist threat in Indonesia based on the model is by keeping the positive news portion to a maximum of 38%.

Keywords: Bayesian network, decision analysis, national security system, text mining

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Authors: Luisa Kynast

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It is obvious that buildings and infrastructure are highly impacted by climate change (CC). Both, design and material of buildings need to be resilient to weather events in order to shelter humans, animals, or goods. As well as buildings and infrastructure are exposed to weather events, the construction process itself is generally carried out outdoors without being protected from extreme temperatures, heavy rain, or storms. The production process is restricted by technical limitations for processing materials with machines and physical limitations due to human beings (“outdoor-worker”). In future due to CC, average weather patterns are expected to change as well as extreme weather events are expected to occur more frequently and more intense and therefore have a greater impact on production processes and on the construction businesses itself. This research aims to examine this impact by analyzing an association between responses to CC and financial performance of businesses within the construction industry. After having embedded the above depicted field of research into the resource dependency theory, a literature review was conducted to expound the state of research concerning a contingent relation between climate change adaptation measures (CCAM) and corporate financial performance for construction businesses. The examined studies prove that this field is rarely investigated, especially for construction businesses. Therefore, reports of the Carbon Disclosure Project (CDP) were analyzed by applying content analysis using the software tool MAXQDA. 58 construction companies – located worldwide – could be examined. To proceed even more systematically a coding scheme analogous to findings in literature was adopted. Out of qualitative analysis, data was quantified and a regression analysis containing corporate financial data was conducted. The results gained stress adaptation measures as a response to CC as a crucial proxy to handle climate change impacts (CCI) by mitigating risks and exploiting opportunities. In CDP reports the majority of answers stated increasing costs/expenses as a result of implemented measures. A link to sales/revenue was rarely drawn. Though, CCAM were connected to increasing sales/revenues. Nevertheless, this presumption is supported by the results of the regression analysis where a positive effect of implemented CCAM on construction businesses´ financial performance in the short-run was ascertained. These findings do refer to appropriate responses in terms of the implemented number of CCAM. Anyhow, still businesses show a reluctant attitude for implementing CCAM, which was confirmed by findings in literature as well as by findings in CDP reports. Businesses mainly associate CCAM with costs and expenses rather than with an effect on their corporate financial performance. Mostly companies underrate the effect of CCI and overrate the costs and expenditures for the implementation of CCAM and completely neglect the pay-off. Therefore, this research shall create a basis for bringing CC to the (financial) attention of corporate decision-makers, especially within the construction industry.

Keywords: climate change adaptation measures, construction businesses, financial implication, resource dependency theory

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Authors: Shashwat Gangwal

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This paper analyses the effect of adding Bitcoin, to the portfolio (stocks, bonds, Baltic index, MXEF, gold, real estate and crude oil) of an international investor by using daily data available from 2^nd of July, 2010 to 2^{nd of} August, 2016. We conclude that adding Bitcoin to portfolio, over the course of the considered period, always yielded a higher Sharpe ratio. This means that Bitcoin’s returns offset its high volatility. This paper, recognizing the fact that Bitcoin is a relatively new asset class, gives the readers a basic idea about the working of the virtual currency, the increasing number developments in the financial industry revolving around it, its unique features and the detailed look into its continuously growing acceptance across different fronts (Banks, Merchants and Countries) globally. We also construct optimal portfolios to reflect the highly lucrative and largely unexplored opportunities associated with investment in Bitcoin.

Keywords: bitcoin, financial instruments, portfolio management, risk adjusted return

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Authors: Gabrielle Peck, Ryan Hayes

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This research presents the design and creation of a drone gas analyser, aimed at addressing the need for independent data collection and analysis of gas emissions during large-scale fires, particularly wasteland fires. The analyser drone, comprising a lightweight gas analysis system attached to a remote-controlled drone, enables the real-time assessment of smoke toxicity and the monitoring of gases released into the atmosphere during such incidents. The key components of the analyser unit included two gas line inlets connected to glass wool filters, a pump with regulated flow controlled by a mass flow controller, and electrochemical cells for detecting nitrogen oxides, hydrogen cyanide, and oxygen levels. Additionally, a non-dispersive infrared (NDIR) analyser is employed to monitor carbon monoxide (CO), carbon dioxide (CO₂), and hydrocarbon concentrations. Thermocouples can be attached to the analyser to monitor temperature, as well as McCaffrey probes combined with pressure transducers to monitor air velocity and wind direction. These additions allow for monitoring of the large fire and can be used for predictions of fire spread. The innovative system not only provides crucial data for assessing smoke toxicity but also contributes to fire prediction and management. The remote-controlled drone's mobility allows for safe and efficient data collection in proximity to the fire source, reducing the need for human exposure to hazardous conditions. The data obtained from the gas analyser unit facilitates informed decision-making by emergency responders, aiding in the protection of both human health and the environment. This abstract highlights the successful development of a drone gas analyser, illustrating its potential for enhancing smoke toxicity analysis and fire prediction capabilities. The integration of this technology into fire management strategies offers a promising solution for addressing the challenges associated with wildfires and other large-scale fire incidents. The project's methodology and results contribute to the growing body of knowledge in the field of environmental monitoring and safety, emphasizing the practical utility of drones for critical applications.

Keywords: fire prediction, drone, smoke toxicity, analyser, fire management

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: Jawairia Umar, Tanveer Hussain, Zulfiqar Ali, Muhammad Maqsood

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Different knitted structures and knitted parameters play a vital role in the stretch and recovery management of compression sportswear in addition to the materials use to generate this stretch and recovery behavior of the fabric. The present work was planned to predict the different performance indicators of a compression sportswear fabric with some ground parameters i.e. base yarn stitch length (polyester as base yarn and spandex as plating yarn involve to make a compression fabric) and linear density of the spandex which is a key material of any sportswear fabric. The prediction models were generated by response surface method for performance indicators such as stretch & recovery percentage, compression generated by the garment on body, total elongation on application of high power force and load generated on certain percentage extension in fabric. Certain physical properties of the fabric were also modeled using these two parameters.

Keywords: Compression, sportswear, stretch and recovery, statistical model, kikuhime

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Authors: Wendu Pang, Yaxin Luo, Junhong Li, Yu Zhao, Danni Cheng, Yufang Rao, Minzi Mao, Ke Qiu, Yijun Dong, Fei Chen, Jun Liu, Jian Zou, Haiyang Wang, Wei Xu, Jianjun Ren

Abstract:

We aimed to compare the prognostic prediction value of positive lymph node number (PLNN) to the American Joint Committee on Cancer (AJCC) tumor, lymph node, and metastasis (TNM) staging system for patients with hypopharyngeal squamous cell carcinoma (HPSCC). A total of 826 patients with HPSCC from the Surveillance, Epidemiology, and End Results database (2004–2015) were identified and split into two independent cohorts: training (n=461) and validation (n=365). Univariate and multivariate Cox regression analyses were used to evaluate the prognostic effects of PLNN in patients with HPSCC. We further applied six Cox regression models to compare the survival predictive values of the PLNN and AJCC TNM staging system. PLNN showed a significant association with overall survival (OS) and cancer-specific survival (CSS) (P < 0.001) in both univariate and multivariable analyses, and was divided into three groups (PLNN 0, PLNN 1-5, and PLNN>5). In the training cohort, multivariate analysis revealed that the increased PLNN of HPSCC gave rise to significantly poor OS and CSS after adjusting for age, sex, tumor size, and cancer stage; this trend was also verified by the validation cohort. Additionally, the survival model incorporating a composite of PLNN and TNM classification (C-index, 0.705, 0.734) performed better than the PLNN and AJCC TNM models. PLNN can serve as a powerful survival predictor for patients with HPSCC and is a surrogate supplement for cancer staging systems.

Keywords: hypopharyngeal squamous cell carcinoma, positive lymph nodes number, prognosis, prediction models, survival predictive values

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

Abstract:

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

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

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Authors: Heeyong Noh, Seongryong Kang, Sungjoo Lee

Abstract:

As the world becomes increasingly interconnected, firms have tried to explore market opportunities not only in the domestic market but also abroad. In particular, transactions of intangible assets in the global market now take on great importance. Accordingly, technology transfer activities such as patent licensing, copyright transfer, or workforce trainings which are considered significant to leverage an organization’s internal capabilities, are occurring more frequently and briskly across the world than ever before. Though a number of studies have addressed the issues regarding technology transfer, most of them have focused on university-industry technology transfer. Of course, some have investigated international technology transfer phenomenon but used patent citations data as a proxy. In order to understand the phenomena more clearly, it would be necessary to collect and analyze data that can measure technology transfer activities between firms more directly. Therefore, this study aims to examine the patterns of international trade in technology by employing data about international technology in-licensing activities in Korean firms. We also investigate the effect of international technology in-licensing strategy on a firm’s innovation performance. The research findings are expected to help R&D managers understand how firms have absorbed technological knowledge from foreign firms in the form of licensing and further develop effective international collaboration strategies. In addition, significant implications can be offered for political decision-making regarding technology trade within increasing international interconnections.

Keywords: international technology trade, technology trade effect, technology transfer, R&D managers

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Authors: Alan Wan

Abstract:

In recent years, the body of literature on frequentist model averaging in statistics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this paper, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimisation of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favourable properties compared to some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function misspecification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two real data sets.

Keywords: heteroscedasticity-robust, model averaging, multiplicative heteroscedasticity, plug-in, squared prediction risk

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Authors: Wan Jiun Tin

Abstract:

Neighborhood regeneration has gained its popularity despite market-led urban redevelopment is still the main strategy in most of the countries in Asia. Area-based approach of neighborhood regeneration with the focus on people, place and system which covers the main sustainable aspects shall be studied as part of the solution. Project implementation in small scale without fully depending on the financial support from the government and main stakeholders is the advantage of neighborhood regeneration. This enables the improving and upgrading of living conditions to be ongoing even during the economy downturn. In addition to that, there will be no specific selection on the development areas as the entire nation share the similar opportunity to upgrade and to improve their neighborhood. This is important to narrow the income disparities in urban. The objective of this paper is to review and to summarize the urban regeneration in developed countries with the focus on Korea, Singapore and Hong Kong. The aim is to determine the direction of sustainable urban regeneration in Malaysia for post-Vision 2020 through the introduction of neighborhood regeneration. This paper is conducted via literature review and observations in those selected countries. In conclusion, neighborhood regeneration shall be one of the approach of sustainable urban regeneration in Malaysia. A few criteria have been identified and to be recommended for the adaptation in Malaysia.

Keywords: area-based regeneration, public participation, sustainable urban regeneration, urban redevelopment

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Authors: Hyon I. Paek, Sang Rim Kim, Hyon A. Ryu

Abstract:

In functional linear regression, one typical problem is to reduce dimension. Compared with multivariate linear regression, functional linear regression is regarded as an infinite-dimensional case, and the main task is to reduce dimensions of functional response and functional predictors. One common approach is to adapt functional principal component analysis (FPCA) on functional predictors and then use a few leading functional principal components (FPC) to predict the functional model. The leading FPCs estimated by the typical FPCA explain a major variation of the functional predictor, but these leading FPCs may not be mostly correlated with the functional response, so they may not be significant in the prediction for response. In this paper, we propose a supervised functional principal component analysis method for a functional response model with FPCs obtained by considering the correlation of the functional response. Our method would have a better prediction accuracy than the typical FPCA method.

Keywords: supervised, functional principal component analysis, functional response, functional linear regression

Procedia PDF Downloads 76