Search results for: structural change model

Authors: Trevor N. Haas, Alexander Koen

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Columns have traditionally been constructed of reinforced concrete or structural steel. Much attention was allocated to estimate the axial capacity of the traditional column sections to the detriment of other forms of construction. Other forms of column construction such as Concrete Filled Double Skin Tubes received little research attention, and almost no attention when subjected to eccentric loading. This paper investigates the axial capacity of columns when subjected to eccentric loading. The experimental axial capacities are compared to other established theoretical formulae on concentric loading to determine a possible relationship. The study found a good correlation between the reduction in axial capacity for different column lengths and hollow section ratios.

Keywords: CSDST, CFST, axial capacity, hollow section ratios

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Authors: Joao Fialho

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The mathematical modelling of idiopathic scoliosis has been studied throughout the years. The models presented on those papers are based on the orthotic stabilization of the idiopathic scoliosis, which are based on a transversal force being applied to the human spine on a continuous form. When considering the ATT (Antalgic-Trak Technology) device, the existent models cannot be used, as the type of forces applied are no longer transversal nor applied in a continuous manner. In this device, vertical traction is applied. In this study we propose to model the idiopathic scoliosis, using the ATT (Antalgic-Trak Technology) device, and with the parameters obtained from the mathematical modeling, set up a case-by-case individualized therapy plan, for each patient.

Keywords: idiopathic scoliosis, mathematical modelling, human spine, Antalgic-Trak technology

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Authors: Qudratullah Muradi

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Geothermal energy is a renewable energy source that can be found in abundance on our planet. Only a small fraction of it is currently converted to electrical power, though in recent years installed geothermal capacity has increased considerably all over the world. In this paper, we assumed a model for designing of Enhanced Geothermal System, EGS. We used computer modeling group, CMG reservoir simulation software to create the typical Hot Dry Rock, HDR reservoir. In this research two wells, one injection of cold water and one production of hot water are included in the model. There are some hydraulic fractures created by the mentioned software. And cold water is injected in order to produce energy from the reservoir. The result of injecting cold water to the reservoir and extracting geothermal energy is defined by some graphs at the end of this research. The production of energy is quantified in a period of 10 years.

Keywords: geothermal energy, EGS, HDR, hydraulic fracturing

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Authors: Shiuh-Jer Huang, Shin-Ham Lee, Sheam-Chyun Lin

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A two wheel inverted pendulum (TWIP) vehicle is built with two hub DC motors for motion control evaluation. Arduino Nano micro-processor is chosen as the control kernel for this electric test plant. Accelerometer and gyroscope sensors are built in to measure the tilt angle and angular velocity of the inverted pendulum vehicle. Since the TWIP has significantly hub motor dead zone and nonlinear system dynamics characteristics, the vehicle system is difficult to control by traditional model based controller. The intelligent model-free fuzzy sliding mode controller (FSMC) was employed as the main control algorithm. Then, intelligent controllers are designed for TWIP balance control, and two wheels synchronization control purposes.

Keywords: balance control, synchronization control, two-wheel inverted pendulum, TWIP

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Authors: Karima Megdouli, Bourhan Taschtouch

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Supersonic ejectors have many applications in refrigeration systems. And improving ejector performance is the key to improve the efficiency of these systems. One of the main advantages of the ejector is its geometric simplicity and the absence of moving parts. This paper presents a theoretical model for evaluating the performance of a new supersonic ejector configuration for refrigeration system applications. The relationship between the flow field and the key parameters of the new configuration has been illustrated by analyzing the Mach number and flow velocity contours. The method of characteristics (MOC) is used to design the supersonic nozzle of the ejector. The results obtained are compared with those obtained by CFD. The ejector is optimized by minimizing exergy destruction due to irreversibility and shock waves. The optimization converges to an efficient optimum solution, ensuring improved and stable performance over the whole considered range of uncertain operating conditions.

Keywords: supersonic ejector, theoretical model, CFD, optimization, performance

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Authors: Siavash Eftekharifar, Tohid Yousefi Rezaii, Mahdi Shamsi

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The purpose of this paper is to exploit compressed sensing (CS) method in order to model and compress the electrocardiogram (ECG) signals at a high compression ratio. In order to obtain a sparse representation of the ECG signals, first a suitable basis matrix with Gaussian kernels, which are shown to nicely fit the ECG signals, is constructed. Then the sparse model is extracted by applying some optimization technique. Finally, the CS theory is utilized to obtain a compressed version of the sparse signal. Reconstruction of the ECG signal from the compressed version is also done to prove the reliability of the algorithm. At this stage, a greedy optimization technique is used to reconstruct the ECG signal and the Mean Square Error (MSE) is calculated to evaluate the precision of the proposed compression method.

Keywords: compressed sensing, ECG compression, Gaussian kernel, sparse representation

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Authors: Rishabh Srivastav, Divyam Sharma

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We propose an algorithm to cluster categorical data named as ‘Genetic algorithm initialized rough set based fuzzy K-Modes for categorical data’. We propose an amalgamation of the simple K-modes algorithm, the Rough and Fuzzy set based K-modes and the Genetic Algorithm to form a new algorithm,which we hypothesise, will provide better Centroid Model clustering results, than existing standard algorithms. In the proposed algorithm, the initialization and updation of modes is done by the use of genetic algorithms while the membership values are calculated using the rough set and fuzzy logic.

Keywords: categorical data, fuzzy logic, genetic algorithm, K modes clustering, rough sets

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Authors: Janaina Camile Pasqual Lofhagen, David Savarese, Veronika Vazhnik

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The urgency of the climate crisis requires both innovation and practicality. The energy transition framework allows industry to deliver resilient cities, enhance adaptability to change, pursue energy objectives such as growth or efficiencies, and increase renewable energy. This paper investigates a real-world application perspective for the use of biogas in Brazil and the U.S.. It will examine interventions to provide a foundation of infrastructure, as well as the tangible benefits for policy-makers crafting law and providing incentives.

Keywords: resilience, vulnerability, risks, biogas, sustainability.

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: Cromwell F. Gopo, Joy L. Picar

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This study aimed to predict the employability of senior high school graduates for S.Y. 2018- 2019 in the Davao del Norte Division through quantitative research design using the descriptive status and predictive approaches among the indicated parameters, namely gender, school type, academics, academic award recipient, skills, values, and strand. The respondents of the study were the 33 secondary schools offering senior high school programs identified through simple random sampling, which resulted in 1,530 cases of graduates’ secondary data, which were analyzed using frequency, percentage, mean, standard deviation, and binary logistic regression. Results showed that the majority of the senior high school graduates who come from large schools were females. Further, less than half of these graduates received any academic award in any semester. In general, the graduates’ performance in academics, skills, and values were proficient. Moreover, less than half of the graduates were not employed. Then, those who were employed were either contractual, casual, or part-time workers dominated by GAS graduates. Further, the predictors of employability were gender and the Information and Communications Technology (ICT) strand, while the remaining variables did not add significantly to the model. The null hypothesis had been rejected as the coefficients of the predictors in the binary logistic regression equation did not take the value of 0. After utilizing the model, it was concluded that Technical-Vocational-Livelihood (TVL) graduates except ICT had greater estimates of employability.

Keywords: employability, senior high school graduates, Davao del Norte, Philippines

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Authors: Md. Rezaul Karim, Andreas Thiel

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Bangladesh is known as one of the most climate vulnerable countries in the world. Innovative technologies are always the key responses to the management of climate impacts. The objectives of this study are to determine the farmer’s perception of climate variability, to compare farmers’ perceptions with metrological data, and to explore the determinants that affect the likelihood of adoption of the selected Climate Smart Agricultural (CSA) technologies. Data regarding climate change perception, determinants and adoption were collected based on the household survey from stratified and randomly selected 365 farmers of the Biral sub-district under Dinajpur district in drought-prone northern Bangladesh. The likelihood of adoption of CSA technologies was analyzed following a multivariate probit model. The findings show that about 82.5% of the farmers perceived increasing temperature, and 75.1 % of farmers perceived decreasing dry season rainfall over the years, which is similarly relevant to metrological data. About 76.4.7% and 80.85% of farmers were aware of the drought tolerance crops and vermicompost, respectively; more than half of the farmers adopted these practices. Around 70.7% of farmers were aware of perching for insect control, but 46.3% of farmers adopted this practice. Although two-thirds of farmers were aware of crop diversification and pheromone trap, adoption was lower compared to the other three CSAs. Results also indicate that the likelihood of adoption of the selected CSAs is significantly influenced by different factors such as socio-economic characteristics, institutional factors and perceived technological or innovation attributes. The likelihood of adopting drought tolerance crops is affected by 11, while crop diversification and perching method by 7, pheromone trap by 9 and vermicompost by 8 determining factors. Lack of information and unavailability of input appear to be major obstacles to the non-adoption of CSA technologies. This study suggests that policy implications are necessary to promote extension services and overcome the obstacles to the non-adoption of individual CSA technologies. It further recommends that the research study should be conducted in a diverse context, nationally or globally.

Keywords: determinants, adoption, climate smart agriculture, northern Bangladesh

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Authors: Thomas Wetere

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Background: The coronavirus disease 2019 (COVID-19) pandemic (COVID-19) virus infection is a severe infectious disease with the highly transmissible variant, which become the global public health treat now. It has taken the life of more than 4 million people so far. What makes the disease the worst of all is no specific effective treatment available, its dynamics is not much researched and understood. Methodology: To end the global COVID-19 pandemic, implementation of multiple population-wide strategies, including vaccination, environmental factors, Government action, testing, and contact tracing, is required. In this article, a new mathematical model incorporating both temperature and government action to study the dynamics of the COVID-19 pandemic has been developed and comprehensively analysed. The model considers eight stages of infection: susceptible (S), infected Asymptomatic and Undetected(IAU ), infected Asymptomatic and detected(IAD), infected symptomatic and Undetected(ISU ), infected Symptomatic and detected(ISD), Hospitalized or threatened(H), Recovered(R) and Died(D). Results: The existence as well as non-negativity of the solution to the model is also verified, and the basic reproduction number is calculated. Besides, stability conditions are also checked, and finally, simulation results are compared with real data. The results demonstrates that effective government action will need to be combined with vaccination to end the ongoing COVID-19 pandemic. Conclusion: Vaccination and Government action are highly the crucial measures to control the COVID-19 pandemic. Besides, as the cost of vaccination might be high, we recommend an optimal control to reduce the cost and number of infected individuals. Moreover, in order to prevent COVID-19 pandemic, through the analysis of the model, the government must strictly manage the policy on COVID-19 and carry it out. This, in turn, helps for health campaigning and raising health literacy which plays a role to control the quick spread of the disease. We finally strongly believe that our study will play its own role in the current effort of controlling the pandemic.

Keywords: modeling, COVID-19, MCMC, stability

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Authors: Masaki Sakai, Tsuyoshi Nakajima, Kazuya Takahashi

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In order to improve the productivity of a factory, it is often the case to create an inference model by collecting and analyzing operational data off-line and then to develop an edge application (EAP) that evaluates the quality of the products or diagnoses machine faults in real-time. To accelerate this development cycle, an edge application framework for the smart factory is proposed, which enables to create and modify EAPs based on prepared inference models. In the framework, the preprocessing component is the key part to make it work. This paper proposes a language for preprocessing of edge applications, called LaPEA, which can flexibly process several sensor data from machines into explanatory variables for an inference model, and proves that it meets the requirements for the preprocessing.

Keywords: edge application framework, edgecross, preprocessing language, smart factory

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Authors: Jeffrey Chase

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Historically the profession of Occupational Therapy was closely tied to the treatment of those suffering from mental illness; more recently, and especially in the U.S., the percentage of OTs identifying as working in the mental health area has declined significantly despite the estimate that by 2020 behavioral health disorders will surpass physical illnesses as the major cause of disability worldwide. In the U.S. less than 10% of OTs identify themselves as working with the mentally ill and/or practicing in mental health settings. Such a decline has implications for both those suffering from mental illness and the profession of Occupational Therapy. One reason cited for the decline of OT in mental health has been the limited research in the discipline addressing mental health practice. Despite significant advances in technology and growth in the field of neuroscience, major institutions and funding sources such as the National Institute of Mental Health (NIMH) have noted that research into the etiology and treatment of mental illness have met with limited success over the past 25 years. One major reason posited by NIMH is that research has been limited by how we classify individuals, that being mostly on what is observable. A new classification system being developed by NIMH, the Research Domain Criteria (RDoc), has the goal to look beyond just descriptors of disorders for common neural, genetic, and physiological characteristics that cut across multiple supposedly separate disorders. The hope is that by classifying individuals along RDoC measures that both reliability and validity will improve resulting in greater advances in the field. As a result of this change NIH and NIMH will prioritize research funding to those projects using the RDoC model. Multiple disciplines across many different setting will be required for RDoC or similar classification systems to be developed. During this shift in research methodology OT has an opportunity to reassert itself into the research and treatment of mental illness, both in developing new ways to more validly classify individuals, and to document the legitimacy of previously ill-defined and validated disorders such as sensory integration.

Keywords: global mental health and neuroscience, research opportunities for ot, greater integration of ot in mental health research, research and funding opportunities, research domain criteria (rdoc)

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Authors: Habibi Said Mustafa, Hiroko Ono

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Since 2006, Afghanistan is dealing with one of the most dramatic trend of urban movement in its history, cities and towns are expanding in size and number. Kabul is the capital of Afghanistan and as well as the fast-growing city in the Asia. The influx of the returnees from neighbor countries and other provinces of Afghanistan caused high rate of artificial growth which slums increased. As an unwanted consequence of this growth, today informal settlements have covered a vast portion of the city. Land Readjustment (LR) has proved to be an important tool for developing informal settlements and reorganizing urban areas but its implementation always varies from country to country and region to region within the countries. Consequently, to successfully develop the informal settlements in Kabul, we need to define an Afghan model of LR specifically for Afghanistan which needs to incorporate all those factors related to the socio-economic condition of the country. For this purpose, a part of the old city of Kabul has selected as a study area which is located near the Central Business District (CBD). After the further analysis and incorporating all needed factors, the result shows a positive potential for the implementation of an adaptable Land Readjustment model for Kabul city which is more sustainable and socio-economically friendly. It will enhance quality of life and provide better urban services for the residents. Moreover, it will set a vision and criteria by which sustainable developments shall proceed in other similar informal settlements of Kabul.

Keywords: adaptation, informal settlements, Kabul, land readjustment, preservation

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Authors: Sylvain Perrin, Thomas Saillour

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The Grande-Motte port and seafront development project on the French Mediterranean coastline entailed evaluating wave impact loads (pressures and forces) on the new beach seawall and comparing the resulting scour potential at the base of the existing and new seawall. A physical model was built at ARTELIA’s hydraulics laboratory in Grenoble (France) to provide insight into the evolution of scouring overtime at the front of the wall, quasi-static and impulsive wave force intensity and distribution on the wall, and water and sand overtopping discharges over the wall. The beach was constituted of fine sand and approximately 50 m wide above mean sea level (MSL). Seabed slopes were in the range of 0.5% offshore to 1.5% closer to the beach. A smooth concrete structure will replace the existing concrete seawall with an elevated curved crown wall. Prior the start of breaking (at -7 m MSL contour), storm-driven maximum spectral significant wave heights of 2.8 m and 3.2 m were estimated for the benchmark historical storm event dated of 1997 and the 50-year return period storms respectively, resulting in 1 m high waves at the beach. For the wave load assessment, a tensor scale measured wave forces and moments and five piezo / piezo-resistive pressure sensors were placed on the wall. Light-weight sediment physical model and pressure and force measurements were performed with scale 1:18. The polyvinyl chloride light-weight particles used to model the prototype silty sand had a density of approximately 1 400 kg/m3 and a median diameter (d50) of 0.3 mm. Quantitative assessments of the seabed evolution were made using a measuring rod and also a laser scan survey. Testing demonstrated the occurrence of numerous impulsive wave impacts on the reflector (22%), induced not by direct wave breaking but mostly by wave run-up slamming on the top curved part of the wall. Wave forces of up to 264 kilonewtons and impulsive pressure spikes of up to 127 kilonewtons were measured. Maximum scour of -0.9 m was measured for the new seawall versus -0.6 m for the existing seawall, which is imputable to increased wave reflection (coefficient was 25.7 - 30.4% vs 23.4 - 28.6%). This paper presents a methodology for the setup and operation of a physical model in order to assess the hydrodynamic and morphodynamic processes at a beach seawall during storms events. It discusses the pros and cons of such methodology versus others, notably regarding structures peculiarities and model effects.

Keywords: beach, impacts, scour, seawall, waves

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Authors: Etleva Hamzaraj, Eva Kica, Anila Paparisto, Pranvera Lazo

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Worldwide demographic developments of recent decades have been associated with negative environmental consequences. For this reason, there is a growing interest in assessing the state of natural ecosystems or assessing human impact on them. In this respect, this study aims to evaluate the change in water quality of the Mat River for a period of about ten years to highlight human impact. In one year, period of study, several biological and environmental parameters are determined to evaluate river water quality, and the data collected are compared with those of a similar study in 2007. Samples are collected every month in five stations evenly distributed along the river. Total coliform bacteria, the number of heterotrophic bacteria in water, and benthic macroinvertebrates are used as biological parameters of water quality. The most probable number index is used for evaluation of total coliform bacteria in water, while the number of heterotrophic bacteria is determined by counting colonies on plates with Plate Count Agar, cultivated with 0.1 ml sample after series dilutions. Benthic macroinvertebrates are analyzed by the number of individuals per taxa, the value of biotic index, EPT Richness Index value and tolerance value. Environmental parameters like pH, temperature, and electrical conductivity are measured onsite. As expected, the bacterial load was higher near urban areas, and the pollution increased with the course of the river. The maximum concentration of fecal coliforms was 1100 MPN/100 ml in summer and near the most urbanized area of the river. The data collected during this study show that after about ten years, there is a change in water quality of Mat River. According to a similar study carried out in 2007, the water of Mat River was of ‘excellent’ quality. But, according to this study, the water was classified as of ‘excellent’ quality only in one sampling site, near river source, while in all other stations was of ‘good’ quality. This result is based on biological and environmental parameters measured. The human impact on the quality of water of Mat River is more than evident.

Keywords: water quality, coliform bacteria, MPN index, benthic macroinvertebrates, biotic index

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Authors: Ali Muhammad

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The purpose of this study is to develop a theoretical framework which demonstrates the effect of four personal factors on employees rule following behavior in Kuwaiti business organizations. The model suggested in this study includes organizational citizenship behavior, affective organizational commitment, organizational trust, and procedural justice as possible predictors of rule following behavior. The study also attempts to compare the effects of the suggested factors on employees rule following behavior. The new model will, hopefully, extend previous research by adding new variables to the models used to explain employees rule following behavior. A discussion of issues related to rule-following behavior is presented, as well as recommendations for future research.

Keywords: employee adherence to rules, organizational justice, organizational commitment, organizational citizenship behavior

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Authors: Mandana Karimi, Martha McMahon

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One of the most important consequences of water scarcity worldwide is the increase in conflicts over water issues, reduced access to clean water, food shortages, energy shortages, and reduced economic development. The extreme weather conditions in British Columbia are because of climate change, which is leading to water scarcity becoming a serious issue affecting British Columbians, aquatic ecosystems, the BC water policy, agriculture, and the economy. In light of climate change and water stress, the British Columbia government introduced a new water legislation in 2016 named the Water Sustainability Act to manage water resources in British Columbia. So, this study aimed to present a deep understanding emanating from the political and social dimensions of the new water policy in BC in the agriculture sector and which sociological paradigm governs the current water policy (WSA) in BC. Policy analysis based on the water problem representation approach was used to present the problem and solutions identified by the water policy in the agricultural sector in BC. The results of the policy analysis highlighted that the Water Sustainability Act is governed by a positivist and modernist approach because the groundwater license is the measurable situation to access the adequate quantity of water for the farmers. In addition, by the positivist paradigm water resources are conceptualized as a commodity to be bought and sold. Under the positivist approach, the measurable parameter of groundwater is also applied based on the top-down approach for water management to show the use of water resources for economic development. In addition, the findings of the policy analysis suggest that alternative paradigms, such as relational ontology, ecofeminism, and indigenous knowledge, could be applied in introducing water policies to shift from the positivist or modernist paradigm. These new paradigms present the potential for environmental policies like the Water Sustainability Act, based on partnership, and collaboration and with an explicit emphasis on protecting water for nature.

Keywords: water governance, Water Sustainability Act, water policy, small-scale farmer, policy analysis

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Authors: Vasileiou Apostolos, Theodosiou Chrysa, Tsitroulis Ioannis, Maris Fotios

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The severity of extreme phenomena is due to their ability to cause severe damage in a small amount of time. It has been observed that floods affect the greatest number of people and induce the biggest damage when compared to the total of annual natural disasters. The detection of potential flood-prone areas constitutes one of the fundamental components of the European Natural Disaster Management Policy, directly connected to the European Directive 2007/60. The aim of the present paper is to develop a new methodology that combines geographical information, fuzzy logic and multi-criteria evaluation methods so that the most vulnerable areas are defined. Therefore, ten factors related to geophysical, morphological, climatological/meteorological and hydrological characteristics of the basin were selected. Afterwards, two models were created to detect the areas pronest to flooding. The first model defined the gravitas of each factor using Analytical Hierarchy Process (AHP) and the final map of possible flood spots were created using GIS and Boolean Algebra. The second model made use of the fuzzy logic and GIS combination and a respective map was created. The application area of the aforementioned methodologies was in Ardas basin due to the frequent and important floods that have taken place these last years. Then, the results were compared to the already observed floods. The result analysis shows that both models can detect with great precision possible flood spots. As the fuzzy logic model is less time-consuming, it is considered the ideal model to apply to other areas. The said results are capable of contributing to the delineation of high risk areas and to the creation of successful management plans dealing with floods.

Keywords: analytical hierarchy process, flood prone areas, fuzzy logic, geographic information system

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Authors: Juan Manuel Sanchez-Cartas, Gonzalo Leon

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A price competition algorithm for ABMs based on game theory principles is proposed to deal with the simulation of theoretical market models. The algorithm is applied to the classical Hotelling’s model and to a two-sided market model to show it leads to the optimal behavior predicted by theoretical models. However, when theoretical models fail to predict the equilibrium, the algorithm is capable of reaching a feasible outcome. Results highlight that the algorithm can be implemented in other simulation models to guarantee rational users and endogenous optimal behaviors. Also, it can be applied as a tool of verification given that is theoretically based.

Keywords: agent-based models, algorithmic game theory, multi-sided markets, price optimization

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Authors: Adel A. Ghobbar, Varun Raman

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The Corrective maintenance of components and systems is a problem plaguing almost every industry in the world today. Train operators’ and the maintenance repair and overhaul subsidiary of the Dutch railway company is also facing this problem. A considerable portion of the maintenance activities carried out by the company are unscheduled. This, in turn, severely stresses and stretches the workforce and resources available. One possible solution is to have a robust preventive maintenance plan. The other possible solution is to plan maintenance based on real-time data obtained from sensor-based ‘Health and Usage Monitoring Systems.’ The former has been investigated in this paper. The preventive maintenance model developed for train operator will subsequently be extended, to tackle the unscheduled maintenance problem also affecting the aerospace industry. The extension of the model to the aerospace sector will be dealt with in the second part of the research, and it would, in turn, validate the soundness of the model developed. Thus, there are distinct areas that will be addressed in this paper, including the mathematical modelling of preventive maintenance and optimization based on cost and system availability. The results of this research will help an organization to choose the right maintenance strategy, allowing it to save considerable sums of money as opposed to overspending under the guise of maintaining high asset availability. The concept of delay time modelling was used to address the practical problem of unscheduled maintenance in this paper. The delay time modelling can be used to help with support planning for a given asset. The model was run using MATLAB, and the results are shown that the ideal inspection intervals computed using the extended from a minimal cost perspective were 29 days, and from a minimum downtime, perspective was 14 days. Risk matrix integration was constructed to represent the risk in terms of the probability of a fault leading to breakdown maintenance and its consequences in terms of maintenance cost. Thus, the choice of an optimal inspection interval of 29 days, resulted in a cost of approximately 50 Euros and the corresponding value of b(T) was 0.011. These values ensure that the risk associated with component X being maintained at an inspection interval of 29 days is more than acceptable. Thus, a switch in maintenance frequency from 90 days to 29 days would be optimal from the point of view of cost, downtime and risk.

Keywords: delay time modelling, unscheduled maintenance, reliability, maintainability, availability

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Authors: Rimon Rafiah

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This paper will present a combination of two seemingly unrelated models, which are the one for estimating economic development impacts as a result of transportation investment and the other for increasing CAV penetration in order to reduce congestion. Measuring economic development impacts resulting from transportation investments is becoming more recognized around the world. Examples include the UK’s Wider Economic Benefits (WEB) model, Economic Impact Assessments in the USA, various input-output models, and additional models around the world. The economic impact model is based on WEB and is based on the following premise: investments in transportation will reduce the cost of personal travel, enabling firms to be more competitive, creating additional throughput (the same road allows more people to travel), and reducing the cost of travel of workers to a new workplace. This reduction in travel costs was estimated in out-of-pocket terms in a given localized area and was then translated into additional employment based on regional labor supply elasticity. This additional employment was conservatively assumed to be at minimum wage levels, translated into GDP terms, and from there into direct taxation (i.e., an increase in tax taken by the government). The CAV model is based on economic principles such as CAV usage, supply, and demand. Usage of CAVs can increase capacity using a variety of means – increased automation (known as Level I thru Level IV) and also by increased penetration and usage, which has been predicted to go up to 50% by 2030 according to several forecasts, with possible full conversion by 2045-2050. Several countries have passed policies and/or legislation on sales of gasoline-powered vehicles (none) starting in 2030 and later. Supply was measured via increased capacity on given infrastructure as a function of both CAV penetration and implemented technologies. The CAV model, as implemented in the USA, has shown significant savings in travel time and also in vehicle operating costs, which can be translated into economic development impacts in terms of job creation, GDP growth and salaries as well. The models have policy implications as well and can be adapted for use in Japan as well.

Keywords: CAV, economic development, WEB, transport economics

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Authors: Heiko Weiß, Andreas Meister, Christoph Ament, Nils Dreifke

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Linear actuators are deployed in a wide range of applications. This paper presents the modeling and system identification of a variable excited linear direct drive (LDD). The LDD is designed based on linear hybrid stepper technology exhibiting the characteristic tooth structure of mover and stator. A three-phase topology provides the thrust force caused by alternating strengthening and weakening of the flux of the legs. To achieve best possible synchronous operation, the phases are commutated sinusoidal. Despite the fact that these LDDs provide high dynamics and drive forces, noise emission limits their operation in calm workspaces. To overcome this drawback an additional excitation of the magnetic circuit is introduced to LDD using additional enabling coils instead of permanent magnets. The new degree of freedom can be used to reduce force variations and related noise by varying the excitation flux that is usually generated by permanent magnets. Hence, an identified simulation model is necessary to analyze the effects of this modification. Especially the force variations must be modeled well in order to reduce them sufficiently. The model can be divided into three parts: the current dynamics, the mechanics and the force functions. These subsystems are described with differential equations or nonlinear analytic functions, respectively. Ordinary nonlinear differential equations are derived and transformed into state space representation. Experiments have been carried out on a test rig to identify the system parameters of the complete model. Static and dynamic simulation based optimizations are utilized for identification. The results are verified in time and frequency domain. Finally, the identified model provides a basis for later design of control strategies to reduce existing force variations.

Keywords: force variations, linear direct drive, modeling and system identification, variable excitation flux

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Authors: Soheila Sadeghi

Abstract:

Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Neera Kapoor

Abstract:

Background: Knockdown Resistance (KDR) is one of the mechanisms of insecticide resistance in insects caused by the reduced target site sensitivity i.e. voltage gated sodium channel (VGSC) rendering it less sensitive to the toxic effects of DDT and pyrethroids. In this study, we evaluated insecticide susceptibility and its underlying KDR mechanism in eight Ae. aegypti and five Ae. albopictus field populations. Methodology: Field population was collected from four different geographical regions of India covering 18 districts of ten states. For genotyping of twelve KDR alleles in Ae. aegypti field populations, three PCR based assays were used; with DNA sequencing; ASPCR; PCR-RFLP. Genomic DNA was isolated, and three partial domains (II, III, and IV) of VGSC were amplified and sequenced. Results: Molecular screening for common KDR mutations, revealed the presence of five mutations viz. S989P, V1016G, T1520I, F1534C/L. Two novel mutations were observed, first at T1520 (ACC) residue where a C > T substitution at the second position of codon results in amino acid change to Isoleucine (ATC). Second mutation was an alternative point mutation at F1534 (TTC) residue where a substitution of T > C at the first position of codon results in an amino acid change to Leucine (CTC). ASPCRs were not accurate, so three PCR-RFLP assays were developed for genotyping of five KDR alleles in Ae. aegypti; viz. T1520I, F1534C/L. Representative samples of all genotypes (n=200) were sequenced to validate the newly developed PCR based assays for Ae. aegypti. Genotyping results showed that 989P is linked to 1016G and novel mutation 1520I was always found with 1534C allele. Conclusion: Present study confirmed the presence of DDT and pyrethroid resistance among Ae. aegypti populations in India and for the first time reported KDR mutations in this species from India including two novel mutations. Results of present study lead us to infer that, at least five KDR mutations (S989P, V1016G, T1530I, F1534C, and F1534L) can be seen as a potential marker for DDT/pyrethroid resistance.

Keywords: F1534C, F1534L, S989P, T1530I, V1016G

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Authors: Suin Seo, Sung-Il Cho

Abstract:

Background: The use of mobile apps for smoking cessation has grown exponentially in recent years. Yet, there were limited researches which evaluated the quality of smoking cessation apps to our knowledge. In most cases, a clinical practice guideline which is focused on clinical physician was used as an evaluation tool. Objective: The objective of this study was to develop a user-centered measure for quality of mobile smoking cessation apps. Methods: A literature search was conducted to identify articles containing explicit smoking cessation guideline for smoker published until January 2018. WHO’s guide for tobacco users to quit was adopted for evaluation tool which assesses smoker-oriented contents of smoking cessation apps. Compared to the clinical practice guideline, WHO guideline was designed for smokers (non-specialist). On the basis of existing criteria which was developed based on 2008 clinical practice guideline for Treating Tobacco Use and Dependence, evaluation tool was modified and developed by an expert panel. Results: There were five broad categories of criteria that were identified including five objective quality scales: enhancing motivation, assistance with a planning and making quit attempts, preparation for relapse, self-efficacy, connection to smoking. Enhancing motivation and assistance with planning and making quit attempts were similar to contents of clinical practice guideline, but preparation for relapse, self-efficacy and connection to smoking (environment or habit which reminds of smoking) only existed on WHO guideline. WHO guideline had more user-centered elements than clinical guideline. Especially, self-efficacy is the most important determinant of behavior change in accordance with many health behavior change models. With the WHO guideline, it is now possible to analyze the content of the app in the light of a health participant, not a provider. Conclusion: The WHO guideline evaluation tool is a simple, reliable and smoker-centered tool for assessing the quality of mobile smoking cessation apps. It can also be used to provide a checklist for the development of new high-quality smoking cessation apps.

Keywords: smoking cessation, evaluation, mobile application, WHO, guideline

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Authors: Sherly M. A., Subhankar Karmakar, Terence Chan, Christian Rau

Abstract:

Floods are one of the most frequent natural disasters, causing widespread devastation, economic damage and threat to human lives. Hydrologic impacts of climate change and intensification of urbanization are two root causes of increased flood occurrences, and recent research trends are oriented towards understanding these aspects. Due to rapid urbanization, population of cities across the world has increased exponentially leading to improperly planned developments. Climate change due to natural and anthropogenic activities on our environment has resulted in spatiotemporal changes in rainfall patterns. The combined effect of both aggravates the vulnerability of urban populations to floods. In this context, an efficient and effective flood risk management with its core component as flood risk mapping is essential in prevention and mitigation of flood disasters. Urban flood risk mapping involves zoning of an urban region based on its flood risk, which depicts the spatiotemporal pattern of frequency and severity of hazards, exposure to hazards, and degree of vulnerability of the population in terms of socio-economic, environmental and infrastructural aspects. Although vulnerability is a key component of risk, its assessment and mapping is often less advanced than hazard mapping and quantification. A synergic effort from technical experts and social scientists is vital for the effectiveness of flood risk management programs. Despite an increasing volume of quality research conducted on urban flood risk, a comprehensive multidisciplinary approach towards flood risk mapping still remains neglected due to which many of the input parameters and definitions of flood risk concepts are imprecise. Thus, the objectives of this review are to introduce and precisely define the relevant input parameters, concepts and terms in urban flood risk mapping, along with its methodology, current status and limitations. The review also aims at providing thought-provoking insights to potential future researchers and flood management professionals.

Keywords: flood risk, flood hazard, flood vulnerability, flood modeling, urban flooding, urban flood risk mapping

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Authors: Chinedu Peter, Dashrath Singh

Abstract:

Membrane computing is a computability model which abstracts its structures and functions from the biological cell. The main ingredient of membrane computing is the notion of a membrane structure, which consists of several cell-like membranes recurrently placed inside a unique skin membrane. The emergence of several variants of membrane computing gives rise to the notion of a P system. The paper presents a variant of P systems for arithmetic operations on non-negative integers based on the weak priorities for rule application. Consequently, we obtain deterministic P systems. Two membranes suffice. There are at most four objects for multiplication and five objects for division throughout the computation processes. The model is simple and has a potential for possible extension to non-negative integers and real numbers in general.

Keywords: P system, binary operation, determinism, weak rule priority

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Authors: Muhammed Ordu, Eren Demir, Chris Tofallis

Abstract:

The number of inpatient admissions in the UK has been significantly increasing over the past decade. These increases cause bed occupancy rates to exceed the target level (85%) set by the Department of Health in England. Therefore, hospital service managers are struggling to better manage key resource such as beds. On the other hand, this severe demand pressure might lead to confusion in wards. For example, patients can be admitted to the ward of another inpatient specialty due to lack of resources (i.e., bed). This study aims to develop a simulation-optimization model to reallocate the available number of beds in a mid-sized hospital in the UK. A hospital simulation model was developed to capture the stochastic behaviours of the hospital by taking into account the accident and emergency department, all outpatient and inpatient services, and the interactions between each other. A couple of outputs of the simulation model (e.g., average length of stay and revenue) were generated as inputs to be used in the optimization model. An integer linear programming was developed under a number of constraints (financial, demand, target level of bed occupancy rate and staffing level) with the aims of maximizing number of admitted patients. In addition, a sensitivity analysis was carried out by taking into account unexpected increases on inpatient demand over the next 12 months. As a result, the major findings of the approach proposed in this study optimally reallocate the available number of beds for each inpatient speciality and reveal that 74 beds are idle. In addition, the findings of the study indicate that the hospital wards will be able to cope with 14% demand increase at most in the projected year. In conclusion, this paper sheds a new light on how best to reallocate beds in order to cope with current and future demand for healthcare services.

Keywords: bed occupancy rate, bed reallocation, discrete event simulation, inpatient admissions, integer linear programming, projected usage

Procedia PDF Downloads 131