Search results for: natural disaster forecasting

Authors: Alan (Avi) Kirschenbaum, Carmit Rapaport

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The behavior of bystanders in emergencies and disasters have been examined for over 50 years. Such acts have been cited as contributing to saving lives in terms of providing first responder help until official emergency units can arrive. Several reasons have been suggested for this type of behavior but most focused on a broad segment of individual psychological decision-making processes. Recent theoretical evidence suggests that the external factors for such bystander decisions, mainly disaster community based social contexts factors, are also important. We aim to test these competing arguments. Specifically, we examine alternative explanatory perspectives by focusing on self-efficacy as a proxy for the accepted individual psychological case and contrast it with potential bystander characteristics of the individual as well factors as embedded in the social context of the disaster community. To do so, we will utilize a random sampling of the population from a field study of an urban community in Israel that experienced five years of continuous terror attacks. The results strongly suggest that self-efficacy, as well as external factors: preparedness and having skills for intervention during emergencies along with gender best, predict potential helping behaviors. These results broaden our view of bystander behavior and open a window for enhancing this phenomenon as another element in disaster and crisis management.

Keywords: bystander behavior, disasters emergencies, psychological motivation to help, social context for helping

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Authors: Lancer Scott

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Purpose: High quality clinical disaster medicine requires providers working collaboratively to care for multiple patients in chaotic environments; however, many providers lack adequate training. To address this deficit, we created a competency-based, 5-hour Emergency Preparedness Training (EPT) curriculum using didactics, small-group discussion, and kinetic learning. The goal was to evaluate the effect of a short course on improving provider knowledge, confidence and skills in disaster scenarios. Methods: Diverse groups of medical university students, health care professionals, and community members were enrolled between 2011 and 2014. The course consisted of didactic lectures, small group exercises, and two live, multi-patient mass casualty incident (MCI) scenarios. The outcome measures were based on core competencies and performance objectives developed by a curriculum task force and assessed via trained facilitator observation, pre- and post-testing, and a course evaluation. Results: 708 participants completed were trained between November 2011 and August 2014, including 49.9% physicians, 31.9% medical students, 7.2% nurses, and 11% various other healthcare professions. 100% of participants completed the pre-test and 71.9% completed the post-test, with average correct answers increasing from 39% to 60%. Following didactics, trainees met 73% and 96% of performance objectives for the two small group exercises and 68.5% and 61.1% of performance objectives for the two MCI scenarios. Average trainee self-assessment of both overall knowledge and skill with clinical disasters improved from 33/100 to 74/100 (overall knowledge) and 33/100 to 77/100 (overall skill). The course assessment was completed by 34.3% participants, of whom 91.5% highly recommended the course. Conclusion: A relatively short, intensive EPT course can improve the ability of a diverse group of disaster care providers to respond effectively to mass casualty scenarios.

Keywords: clinical disaster medicine, training, hospital preparedness, surge capacity, education, curriculum, research, performance, training, student, physicians, nurses, health care providers, health care

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Authors: Maleh Yassine, Ezzati Abdellah

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Wireless Sensor Networks (WSNs) are currently used in different industrial and consumer applications, such as earth monitoring, health related applications, natural disaster prevention, and many other areas. Security is one of the major aspects of wireless sensor networks due to the resource limitations of sensor nodes. However, these networks are facing several threats that affect their functioning and their life. In this paper we present security attacks in wireless sensor networks, and we focus on a review and analysis of the recent Intrusion Detection schemes in WSNs.

Keywords: wireless sensor networks, security attack, denial of service, IDS, cluster-based model, signature based IDS, hybrid IDS

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Authors: Jawad Taheri

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This paper investigates the impact of foreign aid-funded development projects on corruption perception in Afghanistan, utilizing instrumental variable (IV) approaches in regression analysis setting. Seven sub-categories of development projects are examined in order to have a glance over comparative usefulness of each project. The study incorporates natural disaster indicators as exogenous variables to mitigate endogeneity concerns and control variables. The findings reveal significant associations between certain types of projects and corruption perception. Road and bridge construction, irrigation, and agriculture projects are found to decrease corruption perception, fostering optimism and trust within affected communities. Mosque construction and healthcare initiatives aligned with religious beliefs also contribute to reduced corruption perception. However, drinking water projects are associated with increased corruption perception, highlighting potential challenges in their implementation. The study emphasizes the importance of well-implemented projects, cultural contexts, and effective governance in mitigating corruption and fostering trust within communities. These findings contribute to a refined understanding of the relationship between development projects and corruption perception in Afghanistan.

Keywords: foreign aid, development projects, corruption perception, Afghanistan, instrumental variable analysis, survey of Afghan people (SAP), natural disasters, exogenous variation

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Authors: Jonathan Iworiso

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Forecasting the equity premium out-of-sample is a major concern to researchers in finance and emerging markets. The quest for a superior model that can forecast the equity premium with significant economic gains has resulted in several controversies on the choice of variables and suitable techniques among scholars. This research focuses mainly on the application of Regression Training (RT) techniques to forecast monthly equity premium out-of-sample recursively with an expanding window method. A broad category of sophisticated regression models involving model complexity was employed. The RT models include Ridge, Forward-Backward (FOBA) Ridge, Least Absolute Shrinkage and Selection Operator (LASSO), Relaxed LASSO, Elastic Net, and Least Angle Regression were trained and used to forecast the equity premium out-of-sample. In this study, the empirical investigation of the RT models demonstrates significant evidence of equity premium predictability both statistically and economically relative to the benchmark historical average, delivering significant utility gains. They seek to provide meaningful economic information on mean-variance portfolio investment for investors who are timing the market to earn future gains at minimal risk. Thus, the forecasting models appeared to guarantee an investor in a market setting who optimally reallocates a monthly portfolio between equities and risk-free treasury bills using equity premium forecasts at minimal risk.

Keywords: regression training, out-of-sample forecasts, expanding window, statistical predictability, economic significance, utility gains

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Authors: Cabangile Ncengeni Ngwane, Gerald Mills

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Anthropogenic climate change is exacerbating natural hazards such as droughts, heat waves and sea-level rise. The associated risks are the greatest in places where socio-ecological systems are exposed to these changes and the populations and infrastructure are vulnerable. Identifying the communities at risk and enhancing local resilience are key issues in responding to the current and project climate changes. This paper explores the types of risks associated with multiple overlapping hazards in Durban, South Africa where the social, cultural and economic dimensions that contribute to exposure and vulnerability are compounded by its history of apartheid. As a result, climate change risks are highly concentrated in marginalized communities that have the least adaptive capacity. In this research, a Geographic Information System is to explore the spatial correspondence among geographic layers representing hazards, exposure and vulnerability across Durban. This quantitative analysis will allow authors to identify communities at high risk and focus our study on the nature of the current human-environment relationships that result in risk inequalities. This work will employ qualitative methods to critically examine policies (including educational practices and financial support systems) and on-the-ground actions that are designed to improve the adaptive capacity of these communities and meet UN Sustainable Development Goals. This work will contribute to a growing body of literature on disaster risk management, especially as it relates to developing economies where socio-economic inequalities are correlated with ethnicity and race.

Keywords: adaptive capacity, disaster risk reduction, exposure, resilience, South Africa

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Authors: Mounir Ait Belkacem, Mehdi Boukri, Omar Amellal, Nacim Yousfi, Abderrahmane Kibboua, Med Naboussi Farsi, Mounir Naili

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The North of Algeria is located in a seismic zone, then earthquakes are probably the most likely natural disaster that would lead to major lifeline disruption. The adequate operation of lifelines is vital for the economic development of regions under moderate to high seismic activity. After an earthquake, the proper operation of all vital systems is necessary, for instance hospitals for medical attention of the wounded and highways for communication and assistance for victims.In this work we apply the knowledge of pipeline vulnerability to the water supply system, sanitary sewer pipelines (waste water), and telephone in Constantine (Algeria).

Keywords: lifeline, earthquake, vulnerability, pipelines

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Authors: Khaled M. Alhawiti

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Natural Language Processing (NLP) is an effective approach for bringing improvement in educational setting. This involves initiating the process of learning through the natural acquisition in the educational systems. It is based on following effective approaches for providing the solution for various problems and issues in education. Natural Language Processing provides solution in a variety of different fields associated with the social and cultural context of language learning. It is based on involving various tools and techniques such as grammar, syntax, and structure of text. It is effective approach for teachers, students, authors, and educators for providing assistance for writing, analysis, and assessment procedure. Natural Language Processing is widely integrated in the large number of educational contexts such as research, science, linguistics, e-learning, evaluations system, and various other educational settings such as schools, higher education system, and universities. Natural Language Processing is based on applying scientific approach in the educational settings. In the educational settings, NLP is an effective approach to ensure that students can learn easily in the same way as they acquired language in the natural settings.

Keywords: natural language processing, education, application, e-learning, scientific studies, educational system

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Authors: Sejin Jung, Dongho Kang, Byungsik Kim

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Assessing the impact of climate change requires the use of a multi-model ensemble (MME) to quantify uncertainties between scenarios and produce downscaled outlines for simulation of climate under the influence of different factors, including topography. This study decreases climate change scenarios from the 13 global climate models (GCMs) to assess the impacts of future climate change. Unlike South Korea, North Korea lacks in studies using climate change scenarios of the CoupledModelIntercomparisonProject (CMIP5), and only recently did the country start the projection of extreme precipitation episodes. One of the main purposes of this study is to predict changes in the average climatic conditions of North Korea in the future. The result of comparing downscaled climate change scenarios with observation data for a reference period indicates high applicability of the Multi-Model Ensemble (MME). Furthermore, the study classifies climatic zones by applying the Köppen-Geiger climate classification system to the MME, which is validated for future precipitation and temperature. The result suggests that the continental climate (D) that covers the inland area for the reference climate is expected to shift into the temperate climate (C). The coefficient of variation (CVs) in the temperature ensemble is particularly low for the southern coast of the Korean peninsula, and accordingly, a high possibility of the shifting climatic zone of the coast is predicted. This research was supported by a grant (MOIS-DP-2015-05) of Disaster Prediction and Mitigation Technology Development Program funded by Ministry of Interior and Safety (MOIS, Korea).

Keywords: MME, North Korea, Koppen–Geiger, climatic zones, coefficient of variation, CV

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: Dalal Albudaiwi, Mike Allen, Talal Alhaji, Shahnaz Khadimehzadah

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As a pandemic, COVID-19 has impacted the whole world since November 2019. In other words, every organization, industry, and institution has been negatively affected by the Coronavirus. The uncertainty of how long the pandemic will last caused chaos at all levels. As with any other institution, public libraries were affected and transmitted into online services and resources. As internationally, have been witnessed that some public libraries were well-prepared for such disasters as the pandemic, and therefore, collections, users, services, technologies, staff, and budgets were all influenced. Public libraries’ policies did not mention any plan regarding such a pandemic. Instead, there are several rules in the guidelines about disasters in general, such as natural disasters. In this pandemic situation, libraries have been involved in different uneasy circumstances. However, it has always been apparent to public libraries the role they play in serving their communities in excellent and critical times. It dwells into the traditional role public libraries play in providing information services and sources to satisfy their information-based community needs. Remarkably increasing people’s awareness of the importance of informational enrichment and enhancing society’s skills in dealing with information and information sources. Under critical circumstances, libraries play a different role. It goes beyond the traditional part of information providers to the untraditional role of being a social institution that serves the community with whatever capabilities they have. This study takes two significant directions. The first focuses on investigating how libraries have responded to COVID-19 and how they manage disasters within their organization. The second direction focuses on how libraries help their communities to act during disasters and how to recover from the consequences. The current study examines how libraries prepare for disasters and the role of public libraries during disasters. We will also propose “measures” to be a model that libraries can use to evaluate the effectiveness of their response to disasters. We intend to focus on how libraries responded to this new disaster. Therefore, this study aims to develop a comprehensive policy that includes responding to a crisis such as Covid-19. An analytical lens inside the libraries as an organization and outside the organization walls will be documented based on analyzing disaster-related literature published in the LIS publication. The study employs content analysis (CA) methodology. CA is widely used in the library and information science. The critical contribution of this work is to propose solutions it provides to libraries and planers to prepare crisis management plans/ policies, specifically to face a new global disaster such as the COVID-19 pandemic. Moreover, the study will help library directors to evaluate their strategies and to improve them properly. The significance of this study lies in guiding libraries’ directors to enhance the goals of the libraries to guarantee crucial issues such as: saving time, avoiding loss, saving budget, acting quickly during a crisis, maintaining libraries’ role during pandemics, finding out the best response to disasters, and creating plan/policy as a sample for all libraries.

Keywords: Covid-19, policy, preparedness, public libraries

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Authors: Hatharasinghe Liyanage Saneetha Chathaurika, Shreenika De Silva Weliange

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Background and Significance of the Study: Natural and human-induced disasters have become more common due to rapid development and climate change. Therefore hospitalization due to injuries has increased in the midst of advancement in medicine. Prehospital trauma care is critical in reducing morbidity and mortality following injury. Army soldiers are one of the first responder categories after a major disaster causing injury. Thus, basic life support measures taken by trained lay first responders is life-saving, it is important to build up their capacities by updating their knowledge and practices while cultivating positive attitudes toward it. Objective: To describe knowledge, attitudes and practices on prehospital trauma care among army soldiers in Matara District. Methodology: A descriptive cross sectional study was carried out among army soldiers in Matara district. The whole population was studied belonging to the above group during the study period. Self-administered questionnaire was used as the study instrument. Cross tabulations were done to identify the possible associations using chi square statistics. Knowledge and practices were categorized in to two groups as “Poor” and “Good” taking 50% as the cut off. Results: The study population consists of 266 participants (response rate 97.79%).The overall level of knowledge on prehospital trauma care is poor (78.6%) while knowledge on golden hour of trauma (77.1%), triage system (74.4%), cardio pulmonary resuscitation (92.5%) and transportation of patients with spinal cord injury (69.2%) was markedly poor. Good knowledge is significantly associated with advance age, higher income and higher level of education whereas it has no significant association with work duration. More than 80% of them had positive attitudes on most aspects of prehospital trauma care while majority thinks it is good to have knowledge on this topic and they would have performed better in disaster situations if they were trained on pre-hospital trauma care. With regard to the practice, majority (62.8%) is included in the group of poor level of practice. They lack practice on first-aid, cardiopulmonary resuscitation and safe transportation of the patients. Moreover, they had less opportunity to participate in drills/simulation programs done on disaster events. Good practice is significantly associated with advance age and higher level of education but not associated with level of income and working duration of army soldiers. Highly significant association was observed between the level of knowledge and level of practice on prehospital trauma care of army soldiers. It is observed that higher the knowledge practices become better. Conclusion: A higher proportion of army soldiers had poor knowledge and practice on prehospital trauma care while majority had positive attitudes regarding it. Majority lacks knowledge and practice in first-aid and cardiopulmonary resuscitation. Due to significant association observed between knowledge and practice it can be recommended to include a training session on prehospital trauma care in the basic military curriculum which will enhance the ability to act as first responders effectively. Further research is needed in this area of prehospital trauma care to enhance the qualitative outcome.

Keywords: disaster, prehospital trauma care, first responders, army soldiers

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Authors: Matthew Arnaouti, Michael Baird, Gabrielle Cahill, Tamara Worlton, Michelle Joseph

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Introduction: Over ten years after the 7.0 magnitude Earthquake struck the capital of Haiti, impacting over three million people and leading to the deaths of over two hundred thousand, the multinational humanitarian response remains the largest disaster relief effort to date. This study critically evaluates the multi-sector and multinational disaster response to the Earthquake, looking at how the lessons learned from this analysis can be applied to future disaster response efforts. We put particular emphasis on assessing the interaction between civilian and military sectors during this humanitarian relief effort, with the hopes of highlighting how concrete guidelines are essential to improve future responses. Methods: An extensive scoping review of the relevant literature was conducted - where library scientists conducted reproducible, verified systematic searches of multiple databases. Grey literature and hand searches were utilised to identify additional unclassified military documents, for inclusion in the study. More than 100 documents were included for data extraction and analysis. Key domains were identified, these included: Humanitarian and Military Response, Communication, Coordination, Resources, Needs Assessment and Pre-Existing Policy. Corresponding information and lessons-learned pertaining to these domains was then extracted - detailing the barriers and facilitators to an effective response. Results: Multiple themes were noted which stratified all identified domains - including the lack of adequate pre-existing policy, as well as extensive ambiguity of actors’ roles. This ambiguity was continually influenced by the complex role the United States military played in the disaster response. At a deeper level, the effects of neo-colonialism and concern about infringements on Haitian sovereignty played a substantial role at all levels: setting the pre-existing conditions and determining the redevelopment efforts that followed. Furthermore, external factors significantly impacted the response, particularly the loss of life within the political and security sectors. This was compounded by the destruction of important infrastructure systems - particularly electricity supplies and telecommunication networks, as well as air and seaport capabilities. Conclusions: This study stands as one of the first and most comprehensive evaluations, systematically analysing the civilian and military response - including their collaborative efforts. This study offers vital information for improving future combined responses and provides a significant opportunity for advancing knowledge in disaster relief efforts - which remains a more pressing issue than ever. The categories and domains formulated serve to highlight interdependent factors that should be applied in future disaster responses, with significant potential to aid the effective performance of humanitarian actors. Further studies will be grounded in these findings, particularly the need for greater inclusion of the Haitian perspective in the literature, through additional qualitative research studies.

Keywords: civilian and military collaboration, combined response, disaster, disaster response, earthquake, Haiti, humanitarian response

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Authors: Zhenyu Zhang, Hsi-Hsien Wei

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Highway networks play a vital role in post-disaster recovery for disaster-damaged areas. Damaged bridges in such networks can disrupt the recovery activities by impeding the transportation of people, cargo, and reconstruction resources. Therefore, rapid restoration of damaged bridges is of paramount importance to long-term disaster recovery. In the post-disaster recovery phase, the key to restoration scheduling for a highway network is prioritization of bridge-repair tasks. Resilience is widely used as a measure of the ability to recover with which a network can return to its pre-disaster level of functionality. In practice, highways will be temporarily blocked during the downtime of bridge restoration, leading to the decrease of highway-network functionality. The failure to take downtime effects into account can lead to overestimation of network resilience. Additionally, post-disaster recovery of highway networks is generally divided into emergency bridge repair (EBR) in the response phase and long-term bridge repair (LBR) in the recovery phase, and both of EBR and LBR are different in terms of restoration objectives, restoration duration, budget, etc. Distinguish these two phases are important to precisely quantify highway network resilience and generate suitable restoration schedules for highway networks in the recovery phase. To address the above issues, this study proposes a novel resilience quantification method for the optimization of long-term bridge repair schedules (LBRS) taking into account the impact of EBR activities and restoration downtime on a highway network’s functionality. A time-dependent integer program with recursive functions is formulated for optimally scheduling LBR activities. Moreover, since uncertainty always exists in the LBRS problem, this paper extends the optimization model from the deterministic case to the stochastic case. A hybrid genetic algorithm that integrates a heuristic approach into a traditional genetic algorithm to accelerate the evolution process is developed. The proposed methods are tested using data from the 2008 Wenchuan earthquake, based on a regional highway network in Sichuan, China, consisting of 168 highway bridges on 36 highways connecting 25 cities/towns. The results show that, in this case, neglecting the bridge restoration downtime can lead to approximately 15% overestimation of highway network resilience. Moreover, accounting for the impact of EBR on network functionality can help to generate a more specific and reasonable LBRS. The theoretical and practical values are as follows. First, the proposed network recovery curve contributes to comprehensive quantification of highway network resilience by accounting for the impact of both restoration downtime and EBR activities on the recovery curves. Moreover, this study can improve the highway network resilience from the organizational dimension by providing bridge managers with optimal LBR strategies.

Keywords: disaster management, highway network, long-term bridge repair schedule, resilience, restoration downtime

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Authors: Md. Mahadi Hasan Sany, Mumenunnessa Keya, Sharun Khushbu, Sheikh Abujar

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Since the beginning of China's economic reform, trade between the U.S. and China has grown rapidly, and has increased since China's accession to the World Trade Organization in 2001. The US imports more than it exports from China, reducing the trade war between China and the U.S. for the 2019 trade deficit, but in 2020, the opposite happens. In international and U.S. trade, Washington launched a full-scale trade war against China in March 2016, which occurred a catastrophic epidemic. The main goal of our study is to measure and predict trade relations between China and the U.S., before and after the arrival of the COVID epidemic. The ML model uses different data as input but has no time dimension that is present in the time series models and is only able to predict the future from previously observed data. The LSTM (a well-known Recurrent Neural Network) model is applied as the best time series model for trading forecasting. We have been able to create a sustainable forecasting system in trade between China and the US by closely monitoring a dataset published by the State Website NZ Tatauranga Aotearoa from January 1, 2015, to April 30, 2021. Throughout the survey, we provided a 180-day forecast that outlined what would happen to trade between China and the US during COVID-19. In addition, we have illustrated that the LSTM model provides outstanding outcome in time series data analysis rather than RFR and SVR (e.g., both ML models). The study looks at how the current Covid outbreak affects China-US trade. As a comparative study, RMSE transmission rate is calculated for LSTM, RFR and SVR. From our time series analysis, it can be said that the LSTM model has given very favorable thoughts in terms of China-US trade on the future export situation.

Keywords: RFR, China-U.S. trade war, SVR, LSTM, deep learning, Covid-19, export value, forecasting, time series analysis

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Authors: Elias Kodjo Kekesi

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This study seeks to investigate a critical but neglected area in disaster and emergency management in Ghana. It explores aspects of work within one of the safety-critical work environments that expose workers to psychological, social and physical harm. With much attention to crises’ survivors, deceased and their families, this research attempts to answer a key question: ‘What happens to the rescuer’? Emergency response is associated with immense and unprecedented pressure that puts responders’ physical, mental and social well-being at risk. Despite the negative psychological outcomes, scholars argue that being in a traumatic situation may trigger positive outcomes for some people. Thus, the study also focuses on the positive impact of working in a risky crisis environment. Additionally, people’s interpretation of negative experiences or exposure to adverse conditions differ owing to their personal resources which explains why some people may be negatively affected whiles others are positively impacted. To examine these complex nuances, an exploratory sequential mixed method design is adopted. This paper will highlight the findings of study one, which explores the underlying themes emerging from the Ghanaian disaster and emergency response environment regarding psychosocial hazard exposures and the corresponding outcomes.

Keywords: psychosocial hazards, organisational outcomes, qualitative research, Ghana

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Authors: An-Shik Yang, Chiang-Ho Cheng, Jen-Hao Wu, Yu-Hsuan Juan

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Natural ventilation has played an important role for many low energy-building designs. It has been also noticed as a essential subject to persistently bring the fresh cool air from the outside into a building. This study carried out the computational fluid dynamics (CFD)-based simulations to examine the natural ventilation development of a work area in a public building. The simulated results can be useful to better understand the indoor microclimate and the interaction of wind with buildings. Besides, this CFD simulation procedure can serve as an effective analysis tool to characterize the airing performance, and thereby optimize the building ventilation for strengthening the architects, planners and other decision makers on improving the natural ventilation design of public buildings.

Keywords: CFD simulations, natural ventilation, microclimate, wind environment

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Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

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Authors: N. AlShaya, R. Alhomidan, S. Alromizan, W. Labib

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Plant-based natural fibers are used more increasingly in construction materials. It is done to reduce the pressure on the built environment, which has been increased dramatically due to the increases world population and their needs. Plant-based natural fibers are abundant in many countries. Despite the low-cost of such environmental friendly renewable material, it has the ability to enhance the mechanical properties of construction materials. This paper presents an extensive discussion on the use of plant-based natural fibers as reinforcement for cement-based composites, with a particular emphasis upon fiber types; fiber characteristics, and fiber-cement composites performance. It also covers a thorough overview on the main factors, affecting the properties of plant-based natural fiber cement composite in it fresh and hardened state. The feasibility of using plant-based natural fibers in producing various construction materials; such as, mud bricks and blocks is investigated. In addition, other applications of using such fibers as internal curing agents as well as durability enhancer are also discussed. Finally, recommendation for possible future work in this area is presented.

Keywords: natural fibres, cement composites, construction materia, sustainability, stregth, durability

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Authors: Darren Zou

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Forecasting the stock market is a very challenging task. Various economic indicators such as GDP, exchange rates, interest rates, and unemployment have a substantial impact on the stock market. Time series models are the traditional methods used to predict stock market changes. In this paper, a machine learning method, Bayesian Additive Regression Tree (BART) is used in predicting stock market indexes based on multiple economic indicators. BART can be used to model heterogeneous treatment effects, and thereby works well when models are misspecified. It also has the capability to handle non-linear main effects and multi-way interactions without much input from financial analysts. In this research, BART is proposed to provide a reliable prediction on day-to-day stock market activities. By comparing the analysis results from BART and with time series method, BART can perform well and has better prediction capability than the traditional methods.

Keywords: BART, Bayesian, predict, stock

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Authors: Gopi Kerekere Rangaraju, Madhu Puttegowda

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Natural fibres composites include coir, jute, bagasse, cotton, bamboo, and hemp. Natural fibers come from plants. These fibers contain lingo cellulose in nature. Natural fibers are eco-friendly; lightweight, strong, renewable, cheap, and biodegradable. The natural fibers can be used to reinforce both thermosetting and thermoplastic matrices. Thermosetting resins such as epoxy, polyester, polyurethane, and phenolic are commonly used composites requiring higher performance applications. They provide sufficient mechanical properties, in particular, stiffness and strength at acceptably low-price levels. Recent advances in natural fibers development are genetic engineering. The composites science offers significant opportunities for improved materials from renewable resources with enhanced support for global sustainability. Natural fibers composites are attractive to industry because of their low density and ecological advantages over conventional composites. These composites are gaining importance due to their non-carcinogenic and bio-degradable nature. Natural fibers composites are a very costeffective material, especially in building and construction, packaging, automobile and railway coach interiors, and storage devices. These composites are potential candidates for the replacement of high- cost glass fibers for low load bearing applications. Natural fibers have the advantages of low density, low cost, and biodegradability

Keywords: PMC, basalt, coir, carbon fibers

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Authors: Giriraj Achari, Malay Bhattacharyya

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In this paper, the effectiveness of Copula Markov Switching Multifractal (MSM) models at forecasting Value-at-Risk of a two-stock portfolio is studied. The innovations are allowed to be drawn from distributions that can capture skewness and leptokurtosis, which are well documented empirical characteristics observed in financial returns. The candidate distributions considered for this purpose are Johnson-SU, Pearson Type-IV and α-Stable distributions. The two univariate marginal distributions are combined using the Student-t copula. The estimation of all parameters is performed by Maximum Likelihood Estimation. Finally, the models are compared in terms of accurate Value-at-Risk (VaR) forecasts using tests of unconditional coverage and independence. It is found that Copula-MSM-models with leptokurtic innovation distributions perform slightly better than Copula-MSM model with Normal innovations. Copula-MSM models, in general, produce better VaR forecasts as compared to traditional methods like Historical Simulation method, Variance-Covariance approach and Copula-Generalized Autoregressive Conditional Heteroscedasticity (Copula-GARCH) models.

Keywords: Copula, Markov Switching, multifractal, value-at-risk

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Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Chansiri Singhtaun

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This paper proposes a mathematical model and examines the performance of an exact algorithm for a location–transportation problems in humanitarian relief. The model determines the number and location of distribution centers in a relief network, the amount of relief supplies to be stocked at each distribution center and the vehicles to take the supplies to meet the needs of disaster victims under capacity restriction, transportation and budgetary constraints. The computational experiments are conducted on the various sizes of problems that are generated. Branch and bound algorithm is applied for these problems. The results show that this algorithm can solve problem sizes of up to three candidate locations with five demand points and one candidate location with up to twenty demand points without premature termination.

Keywords: disaster response, facility location, humanitarian relief, transportation

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Authors: Fan Yu

Abstract:

An atrium is a place for natural climate exchanging of indoor and outdoor space of buildings, which plays an active role in the overall energy conservation, climate control and environmental purification of buildings. Its greatest contribution is serving as a natural light collector and distributor to solve the problem of natural lighting in large and deep spaces. However, in real situations, the atrium space often results in energy consumption due to improper design in considering its big size and large amount use of glass. Based on the purpose of energy conservation of buildings, this paper emphasizes the significance of natural lighting of atriums. Through literature research, case analysis and other methods, four factors, namely: the light transmittance through the top of the atrium, the geometric proportion of the atrium space, the size and position of windows and the material of the surface of walls in the atrium, were studied, and the influence of different architectural compositions on the natural light distribution of the atrium is discussed. Relying on the analysis of relevant cases, it is proposed that when designing the natural lighting of the atrium, the height and width of the atrium should be paid attention to, the atrium walls are required being rough surfaces and the atrium top-level windows need to be minimized in order to introduce more natural light into the buildings and achieve the purpose of energy conservation.

Keywords: energy conservation, atrium, natural lighting, architectural design

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Authors: Angeliki Peponi, Paulo Morgado, Jorge Trindade

Abstract:

Artificial Neural Networks (ANNs) and Geographic Information Systems (GIS) are applied as a robust tool for modeling and forecasting the erosion changes in Costa Caparica, Lisbon, Portugal, for 2021. ANNs present noteworthy advantages compared with other methods used for prediction and decision making in urban coastal areas. Multilayer perceptron type of ANNs was used. Sensitivity analysis was conducted on natural and social forces and dynamic relations in the dune-beach system of the study area. Variations in network’s parameters were performed in order to select the optimum topology of the network. The developed methodology appears fitted to reality; however further steps would make it better suited.

Keywords: artificial neural networks, backpropagation, coastal urban zones, erosion prediction

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Authors: Chao Jen-Chih, Hsu Kuo-Wei

Abstract:

Climate change and natural disasters take effect on urban development. It has been discussed urban renewals can prevent natural disasters. Integrating green infrastructure and urban renewals may have great effect on adapting the impact of climate change. To highlight the economic value of green infrastructure development on urban renewals, some strategies need to be carry on to reduce environmental impact. A number of urban renewals studies has been conducted on right transfer, financial risk, urban renewal policy, and public participation. Little research has been devoted on the subject of the economic value of green infrastructure development on urban renewals. The purpose of this study is to investigate the affecting factors on the economic value of green infrastructure development on urban renewals. This study will present the benefits of green infrastructure development and summarize the critical factors of green infrastructure develop on urban renewals for real essence and non-real essence on economic value from literature. Our results indicate that factors of housing price, land value, floor area incentive, and facilitation of the construction industry affect the outcome of real essence economic value. Factors of enhancement of urban disaster prevention, improvement of urban environment and landscape, crime reduction, climate control, pollution reduction, biological diversity, health impacts, and leisure space affects the outcome of non-real essence economic value.

Keywords: economic value, green infrastructure, urban renewals, urban development

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Authors: Mohammad Ruhul Amin, Nusrat Jahan

Abstract:

Natural gas is a cleaner fuel compared to the others. But it needs some treatment before it is in a state to be used. So natural gas purification is an integral part of any process where natural gas is used as raw material or fuel. There are several impurities in natural gas that have to be removed before use. CO2 is one of the major contaminants. In this project we have removed CO2 by amine process by using MEA solution. We have built up the whole amine process for removing CO2 in Aspen Hysys and simulated the process. At the end of simulation we have got very satisfactory results by using MEA solution for the removal of CO2. Simulation result shows that amine absorption process enables to reduce CO2 content from NG by 58%. HYSYS optimizer allowed us to get a perfect optimized plant. After optimization the profit of existing plant is increased by 2.34 %.Simulation and optimization by Aspen-HYSYS simulator makes available us to enormous information which will help us to further research in future.

Keywords: Aspen–Hysys, CO2 removal, flowsheet development, MEA solution, natural gas optimization

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Authors: Hugh Byrd, Steve Matthewnan

Abstract:

The devastation of the city centre of Christchurch, New Zealand, after the 2010 and 2011 earthquakes presented an opportunity to rebuild infrastructure in a coordinated and efficient manner to allow for a city that was energy efficient, low carbon, resilient and provided both energy security and justice. The research described in this paper records the processes taken to attempt to rebuild the energy infrastructure. The story is one of political decisions overriding appropriate technology and ultimately is a lesson in how not to handle the implementation of post-disaster energy infrastructure. Lack of clarity in decision making by central government and then not pursuing consultant’s recommendations led to a scheme that was effectively abandoned in 2016 and described as ‘a total failure’. The paper records the critical events that occurred and explains why the proposed energy infrastructure was both politically and technologically inappropriate.

Keywords: energy infrastructure, policy and governance, post-disaster rebuilding

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

Abstract:

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

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

Procedia PDF Downloads 52