Search results for: regression models

Authors: Sakapas Saengchai, Pichamon Chansuchai

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Research Thai- Myanmar Border Trade Promotion Policy, Mae Sai District, Chiang Rai Province The objectives of this study were to study the policy of promoting Thai- Myanmar border trade in Mae Sai district, Chiang Rai province. And suitable models for the development of border trade in Mae Sai. Chiang Rai province This research uses qualitative methodology. The method of collecting data from research papers. Participatory Observation In-depth interviews in which the information is important, the governor of Chiang Rai. Chiang Rai Customs Service Executive Office of Mae Sai Immigration Bureau Maesai Chamber of Commerce and Private Entrepreneurs By specific sampling Data analysis uses content analysis. The study indicated that Border Trade Promotion Policy The direction taken by the government to focus on developing 1. Security is further reducing crime. Smuggling and human trafficking Including the preparation to protect people from terrorism and natural disasters. And cooperation with Burma on border security. 2. The development of wealth is the promotion of investment. The transport links, logistics value chain. Products and services across the Thai-Myanmar border. Improve the regulations and laws to promote fair trade. Convenient and fast 3. Sustainable development is the ability to generate income, quality of life of people in the Thai border to increase continuously. By using balanced natural resources, production and consumption are environmentally friendly. Which featured the participation of all sectors of the public and private sectors in the region to drive the development of the border with Thailand. Chiang Rai province To be more competitive .

Keywords: Border, Trade, Policy, Promote

Procedia PDF Downloads 153

Authors: Cesar Vazquez-Gonzalez, Sophie Avila-Foucat, Leonardo Ortiz-Lozano, Patricia Moreno-Casasola, Alejandro Granados-Barba

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In the last twenty years, academic and scientific literature has been focused on understanding the processes and factors of coastal social-ecological systems vulnerability and resilience. Some scholars argue that resilience and vulnerability are isolated concepts due to their epistemological origin, while others note the existence of a strong resilience-vulnerability relationship. Here we present an ordinal logistic regression model based on the analytical framework about dynamic resilience-vulnerability interaction along adaptive cycle of complex systems and disasters process phases (during, recovery and learning). In this way, we demonstrate that 1) during the disturbance, absorptive capacity (resilience as a core of attributes) and external response capacity explain the probability of households capitals to diminish the damage, and exposure sets the thresholds about the amount of disturbance that households can absorb, 2) at recovery, absorptive capacity and external response capacity explain the probability of households capitals to recovery faster (resilience as an outcome) from damage, and 3) at learning, adaptive capacity (resilience as a core of attributes) explains the probability of households adaptation measures based on the enhancement of physical capital. As a result, during the disturbance phase, exposure has the greatest weight in the probability of capital’s damage, and households with absorptive and external response capacity elements absorbed the impact of floods in comparison with households without these elements. At the recovery phase, households with absorptive and external response capacity showed a faster recovery on their capital; however, the damage sets the thresholds of recovery time. More importantly, diversity in financial capital increases the probability of recovering other capital, but it becomes a liability so that the probability of recovering the household finances in a longer time increases. At learning-reorganizing phase, adaptation (modifications to the house) increases the probability of having less damage on physical capital; however, it is not very relevant. As conclusion, resilience is an outcome but also core of attributes that interacts with vulnerability along the adaptive cycle and disaster process phases. Absorptive capacity can diminish the damage experienced by floods; however, when exposure overcomes thresholds, both absorptive and external response capacity are not enough. In the same way, absorptive and external response capacity diminish the recovery time of capital, but the damage sets the thresholds in where households are not capable of recovering their capital.

Keywords: absorptive capacity, adaptive capacity, capital, floods, recovery-learning, social-ecological systems

Procedia PDF Downloads 125

Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

Procedia PDF Downloads 126

Authors: Yen-Luan Chen

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Maintenance has a great impact on the capacity of production and on the quality of the products, and therefore, it deserves continuous improvement. Maintenance procedure done before a failure is called preventive maintenance (PM). Sequential PM, which specifies that a system should be maintained at a sequence of intervals with unequal lengths, is one of the commonly used PM policies. This article proposes a generalized sequential PM policy for a system subject to shocks with imperfect maintenance and random working time. The shocks arrive according to a non-homogeneous Poisson process (NHPP) with varied intensity function in each maintenance interval. As a shock occurs, the system suffers two types of failures with number-dependent probabilities: type-I (minor) failure, which is rectified by a minimal repair, and type-II (catastrophic) failure, which is removed by a corrective maintenance (CM). The imperfect maintenance is carried out to improve the system failure characteristic due to the altered shock process. The sequential preventive maintenance-last (PML) policy is defined as that the system is maintained before any CM occurs at a planned time Ti or at the completion of a working time in the i-th maintenance interval, whichever occurs last. At the N-th maintenance, the system is replaced rather than maintained. This article first takes up the sequential PML policy with random working time and imperfect maintenance in reliability engineering. The optimal preventive maintenance schedule that minimizes the mean cost rate of a replacement cycle is derived analytically and determined in terms of its existence and uniqueness. The proposed models provide a general framework for analyzing the maintenance policies in reliability theory.

Keywords: optimization, preventive maintenance, random working time, minimal repair, replacement, reliability

Procedia PDF Downloads 257

Authors: Joao Garretto, R. J. Yarwood, Vamsi Borra, Frank Li

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Bluetooth Low Energy (BLE) has emerged as one of the main wireless communication technologies used in low-power electronics, such as wearables, beacons, and Internet of Things (IoT) devices. BLE’s energy efficiency characteristic, smart mobiles interoperability, and Over the Air (OTA) capabilities are essential features for ultralow-power devices, which are usually designed with size and cost constraints. Most current research regarding the power analysis of BLE devices focuses on the theoretical aspects of the advertising and scanning cycles, with most results being presented in the form of mathematical models and computer software simulations. Such computer modeling and simulations are important for the comprehension of the technology, but hardware measurement is essential for the understanding of how BLE devices behave in real operation. In addition, recent literature focuses mostly on the BLE technology, leaving possible applications and its analysis out of scope. In this paper, a coin cell battery-powered BLE Data Acquisition Device, with a 4-in-1 sensor and one accelerometer, is proposed and evaluated with respect to its Power Consumption. First, evaluations of the device in advertising mode with the sensors turned off completely, followed by the power analysis when each of the sensors is individually turned on and data is being transmitted, and concluding with the power consumption evaluation when both sensors are on and respectively broadcasting the data to a mobile phone. The results presented in this paper are real-time measurements of the electrical current consumption of the BLE device, where the energy levels that are demonstrated are matched to the BLE behavior and sensor activity.

Keywords: bluetooth low energy, power analysis, BLE advertising cycle, wireless sensor node

Procedia PDF Downloads 77

Authors: Zair Nadje, Attoui Badra, Miloudi Abdelmonem

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This study aims to assess sensitivity to nitrate pollution and monitor the temporal evolution of nitrate contents in groundwater using statistical models and map their spatial distribution. The nitrate levels observed in the waters of the town of El-Oued differ from one aquifer to another. Indeed, the waters of the Quaternary aquifer are the richest in nitrates, with average annual contents varying from 6 mg/l to 85 mg/l, for an average of 37 mg/l. These levels are higher than the WHO standard (50 mg/l) for drinking water. At the water level of the Terminal Complex (CT) aquifer, the annual average nitrate levels vary from 14 mg/l to 37 mg/l, with an average of 18 mg/l. In the Terminal Complex, excessive nitrate levels are observed in the central localities of the study area. The spatial distribution of nitrates in the waters of the Quaternary aquifer shows that the majority of the catchment points of this aquifer are subject to nitrate pollution. This study shows that in the waters of the Terminal Complex aquifer, nitrate pollution evolves in two major areas. The first focus is South-North, following the direction of underground flow. The second is West-East, progressing towards the East zone. The temporal distribution of nitrate contents in the water of the Terminal Complex aquifer in the city of El-Oued showed that for decades, nitrate contents have suffered a decline after an increase. This evolution of nitrate levels is linked to demographic growth and the rapid urbanization of the city of El-Oued.

Keywords: anthropogenic activities, groundwater, nitrates, pollution, arid zones city of El-Oued, Algeria

Procedia PDF Downloads 40

Authors: Namik Kemal Sonmez, Sahriye Sonmez, Hasan Rasit Turkkan, Hatice Tuba Selcuk

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In this study, the spatial variation of plant and soil nutrition contents of tomato plants grown in greenhouses was investigated in Elmalı region of Antalya. For this purpose, total of 19 sampling points were determined. Coordinates of each sampling points were recorded by using a hand-held GPS device and were transferred to satellite data in GIS. Soil samples were collected from two different depths, 0-20 and 20-40 cm, and leaf were taken from different tomato greenhouses. The soil and plant samples were analyzed for N, P and K. Then, attribute tables were created with the analyses results by using GIS. Data were analyzed and semivariogram models and parameters (nugget, sill and range) of variables were determined by using GIS software. Kriged maps of variables were created by using nugget, sill and range values with geostatistical extension of ArcGIS software. Kriged maps of the N, P and K contents of plant and soil samples showed patchy or a relatively smooth distribution in the study areas. As a result, the N content of plants were sufficient approximately 66% portion of the tomato productions. It was determined that the P and K contents were sufficient of 70% and 80% portion of the areas, respectively. On the other hand, soil total K contents were generally adequate and available N and P contents were found to be highly good enough in two depths (0-20 and 20-40 cm) 90% portion of the areas.

Keywords: Elmali, nutrients, springs greenhouses, spatial variation, tomato

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Authors: Audrius Dedele, Aukse Miskinyte

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Air pollution is a major problem, especially in large cities, causing a variety of environmental issues and a risk to human health effects. In order to observe air quality, to reduce and control air pollution in the city, municipalities are responsible for the creation of air quality management plans, air quality monitoring and emission inventories. Atmospheric dispersion modelling systems, along with monitoring, are powerful tools, which can be used not only for air quality management, but for the assessment of human exposure to air pollution. These models are widely used in epidemiological studies, which try to determine the associations between exposure to air pollution and the adverse health effects. The purpose of this study was to determine the concentration of particulate matter smaller than 10 μm (PM10) in different districts of Kaunas city during winter season. ADMS-Urban dispersion model was used for the simulation of PM10 pollution. The inputs of the model were the characteristics of stationary, traffic and domestic sources, emission data, meteorology and background concentrations were entered in the model. To assess the modelled concentrations of PM10 in Kaunas districts, geographic information system (GIS) was used. More detailed analysis was made using Spatial Analyst tools. The modelling results showed that the average concentration of PM10 during winter season in Kaunas city was 24.8 µg/m3. The highest PM10 levels were determined in Zaliakalnis and Aleksotas districts with are the highest number of individual residential properties, 32.0±5.2 and 28.7±8.2 µg/m3, respectively. The lowest pollution of PM10 was modelled in Petrasiunai district (18.4 µg/m3), which is characterized as commercial and industrial neighbourhood.

Keywords: air pollution, dispersion model, GIS, Particulate matter

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Authors: Rebecca Hafner, David Elmes, Daniel Read

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The current research applies decision-making theory to the problem of increasing uptake of energy efficient technologies in the market place, where uptake is currently slower than one might predict following rational choice models. We apply the alignable/non-alignable features effect and explore the impact of varying information structure on the consumers’ preference for standard versus energy efficient technologies. In two studies we present participants with a choice between similar (boiler vs. boiler) vs. dissimilar (boiler vs. heat pump) technologies, described by a list of alignable and non-alignable attributes. In study One there is a preference for alignability when options are similar; an effect mediated by an increased tendency to infer missing information is the same. No effects of alignability on preference are found when options differ. One explanation for this split-shift in attentional focus is a change in construal levels potentially induced by the added consideration of environmental concern. Study two was designed to explore the interplay between alignability and construal level in greater detail. We manipulated construal level via a thought prime task prior to taking part in the same heating systems choice task, and find that there is a general preference for non-alignability, regardless of option type. We draw theoretical and applied implications for the type of information structure best suited for the promotion of energy efficient technologies.

Keywords: alignability effects, decision making, energy-efficient technologies, sustainable behaviour change

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Authors: D. Supaporn, B. Julaluk

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The purposes of this study were to study symptom cluster, functional status and quality of life of patients with chronic obstructive pulmonary disease (COPD), and to examine factors related to and predicting symptom cluster, functional status and quality of life of COPD patients. The sample was 180 COPD patients multi-stage random sampling from 4 hospitals in the eastern region, Thailand. The research instruments were 8 questionnaires and recorded forms measuring personal and illness data, co-morbidity, physical and psychological symptom, health status perception, social support, and regimen adherence, functional status and quality of life. Spearman rank and Pearson correlation coefficient, exploratory factors analysis and standard multiple regression were used to analyzed data. The findings revealed that two symptom clusters were generated: physical symptom cluster including dyspnea, fatigue and insomnia; and, psychological symptom cluster including anxiety and depression. Scores of physical symptom cluster was at moderate level while that of psychological symptom cluster was at low level. Scores on functional status, social support and overall regimen adherence were at good level whereas scores on quality of life and health status perception were at moderate level. Disease severity was positively related to physical symptom cluster, psychological symptom cluster and quality of life, and was negatively related to functional status at a moderate level (rs = .512, .509, .588 and -.611, respectively). Co-morbidity was positively related to physical symptom cluster and psychological symptom cluster at a low level (r = .179 and .176, respectively). Regimen adherence was negatively related to quality of life and psychological symptom cluster at a low level (r=-.277 and -.309, respectively), and was positively related to functional status at a moderate level (r=.331). Health status perception was negatively related to physical symptom cluster, psychological symptom cluster and quality of life at a moderate to high level (r = -.567, -.640 and -.721, respectively) and was positively related to functional status at a high level (r = .732). Social support was positively related to functional status (r=.235) and was negatively related to quality of life at a low level (r=-.178). Physical symptom cluster was negatively related to functional status (r= -.490) and was positively related to quality of life at a moderate level (r=.566). Psychological symptom cluster was negatively related to functional status and was positively related to quality of life at a moderate level (r= -.566 and .559, respectively). Disease severity, co-morbidity and health status perception could predict 40.2% of the variance of physical symptom cluster. Disease severity, co-morbidity, regimen adherence and health status perception could predict 49.8% of the variance of psychological symptom cluster. Co-morbidity, regimen adherence and health status perception could predict 65.0% of the variance of functional status. Disease severity, health status perception and physical symptom cluster could predict 60.0% of the variance of quality of life in COPD patients. The results of this study can be used for enhancing quality of life of COPD patients.

Keywords: chronic obstructive pulmonary disease, functional status, quality of life, symptom cluster

Procedia PDF Downloads 542

Authors: G. Edwards, N. Dybro, L. J. Munkholm, C. G. Sørensen

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Planning agricultural operations requires an understanding of when fields are ready for operations. However determining a field’s readiness is a difficult process that can involve large amounts of data and an experienced farm manager. A consequence of this is that operations are often executed when fields are unready, or partially unready, which can compromise results incurring environmental impacts, decreased yield and increased operational costs. In order to assess timeliness of operations’ execution, a new scheme is introduced to quantify the aptitude of farm managers to plan operations. Two criteria are presented by which the execution of operations can be evaluated as to their exploitation of a field’s readiness window. A dataset containing the execution dates of spring and autumn operations on 93 fields in Iowa, USA, over two years, was considered as an example and used to demonstrate how operations’ executions can be evaluated. The execution dates were compared with simulated data to gain a measure of how disparate the actual execution was from the ideal execution. The presented tool is able to evaluate the spring operations better than the autumn operations as required data was lacking to correctly parameterise the crop model. Further work is needed on the underlying models of the decision support tool in order for its situational knowledge to emulate reality more consistently. However the assessment methods and evaluation criteria presented offer a standard by which operations' execution proficiency can be quantified and could be used to identify farm managers who require decisional support when planning operations, or as a means of incentivising and promoting the use of sustainable farming practices.

Keywords: operation management, field readiness, sustainable farming, workability

Procedia PDF Downloads 381

Authors: Mrinalini Amritkar, Disha Patil, Swapna Kulkarni, Sukratu Barve, Suresh Gosavi

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Micro-mixers play an important role in the lab-on-a-chip applications and micro total analysis systems to acquire the correct level of mixing for any given process. The mixing process can be classified as active or passive according to the use of external energy. Literature of microfluidics reports that most of the work is done on the models of steady laminar flow; however, the study of unsteady laminar flow is an active area of research at present. There are wide applications of this, out of which, we consider nanoparticle synthesis in micro-mixers. In this work, we have developed a model for unsteady flow to study the mixing performance of a passive micro mixer for reactants used for such synthesis. The model is developed in Finite Volume Method (FVM)-based software, OpenFOAM. The model is tested by carrying out the simulations at Re of 0.5. Mixing performance of the micro-mixer is investigated using simulated concentration values of mixed species across the width of the micro-mixer and calculating the variance across a line profile. Experimental validation is done by passing dyes through a Y shape micro-mixer fabricated using polydimethylsiloxane (PDMS) polymer and comparing variances with the simulated ones. Gold nanoparticles are later synthesized through the micro-mixer and collected at two different times leading to significantly different size distributions. These times match with the time scales over which reactant concentrations vary as obtained from simulations. Our simulations could thus be used to create design aids for passive micro-mixers used in nanoparticle synthesis.

Keywords: Lab-on-chip, LOC, micro-mixer, OpenFOAM, PDMS

Procedia PDF Downloads 147

Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

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Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

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Authors: Wlodzimierz Ogryczak, Michal Przyluski, Tomasz Sliwinski

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In problems of portfolio selection, the reward-risk ratio criterion is optimized to search for a risky portfolio with the maximum increase of the mean return in proportion to the risk measure increase when compared to the risk-free investments. In the classical model, following Markowitz, the risk is measured by the variance thus representing the Sharpe ratio optimization and leading to the quadratic optimization problems. Several Linear Programming (LP) computable risk measures have been introduced and applied in portfolio optimization. In particular, the Mean Absolute Deviation (MAD) measure has been widely recognized. The reward-risk ratio optimization with the MAD measure can be transformed into the LP formulation with the number of constraints proportional to the number of scenarios and the number of variables proportional to the total of the number of scenarios and the number of instruments. This may lead to the LP models with huge number of variables and constraints in the case of real-life financial decisions based on several thousands scenarios, thus decreasing their computational efficiency and making them hardly solvable by general LP tools. We show that the computational efficiency can be then dramatically improved by an alternative model based on the inverse risk-reward ratio minimization and by taking advantages of the LP duality. In the introduced LP model the number of structural constraints is proportional to the number of instruments thus not affecting seriously the simplex method efficiency by the number of scenarios and therefore guaranteeing easy solvability. Moreover, we show that under natural restriction on the target value the MAD risk-reward ratio optimization is consistent with the second order stochastic dominance rules.

Keywords: portfolio optimization, reward-risk ratio, mean absolute deviation, linear programming

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Authors: Huong Nguyen Thi Thanh, Van Mai Thi Khanh

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For around the world, payments for environmental services have been implemented since the late 1970s in Europe and North America; then, it was spread to Latin America, Asia, Africa, and finally Oceania in 2008. In Vietnam, payments for environmental services are an interesting issue recently with the forest as the main focus and therefore known as the program on payment for forest environmental services (PFES). PFES was piloted in Lam Dong and Son La in 2008 and has been widely applied in many provinces after 2010. PFES is in the orientation for the socialization of national forest protection in Vietnam and has made great strides in the last decade. By using the primary data and secondary data simultaneously, the paper clarifies two cases of implementing PFES in the Vietnam North Central area with the different mechanisms of payment. In the first case at Phu Loc district (Thua Thien Hue province), PFES is an indirect method by a water supply company via the Forest Protection and Development Fund. In the second one at Phong Nha – Ke Bang National Park (Quang Binh Province), tourism companies are the direct payers to forest owners. The paper describes the PFES implementation process at each site, clarifies the payment mechanism, and models the relationship between stakeholders in PFES implementation. Based on the current status of PFES sites, the paper compares and analyzes the advantages and disadvantages of the two payment methods. Finally, the paper proposes recommendations to improve the existing shortcomings in each payment mechanism.

Keywords: advantages and disadvantages, forest environmental services, forest protection, payment mechanism

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Authors: Luiz Carlos Wrobel, Matjaz Hribersek, Jure Marn, Jurij Iljaz

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Dynamic thermography has been clinically proven to be a valuable diagnostic technique for skin tumor detection as well as for other medical applications such as breast cancer diagnostics, diagnostics of vascular diseases, fever screening, dermatological and other applications. Thermography for medical screening can be done in two different ways, observing the temperature response under steady-state conditions (passive or static thermography), and by inducing thermal stresses by cooling or heating the observed tissue and measuring the thermal response during the recovery phase (active or dynamic thermography). The numerical modelling of heat transfer phenomena in biological tissue during dynamic thermography can aid the technique by improving process parameters or by estimating unknown tissue parameters based on measured data. This paper presents a nonlinear numerical model of multilayer skin tissue containing a skin tumor, together with the thermoregulation response of the tissue during the cooling-rewarming processes of dynamic thermography. The model is based on the Pennes bioheat equation and solved numerically by using a subdomain boundary element method which treats the problem as axisymmetric. The paper includes computational tests and numerical results for Clark II and Clark IV tumors, comparing the models using constant and temperature-dependent thermophysical properties, which showed noticeable differences and highlighted the importance of using a local thermoregulation model.

Keywords: boundary element method, dynamic thermography, static thermography, skin tumor diagnostic

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Authors: A. Gopala Krishna, M. Lakshmi Chaitanya, V. Kalyana Manohar

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In the existent analysis, laser micro machining (LMM) of Silicon carbide (SiCp) reinforced Aluminum 7075 Metal Matrix Composite (Al7075/SiCp MMC) was studied. While machining, Because of the intense heat generated, A layer gets formed on the work piece surface which is called recast layer and this layer is detrimental to the surface quality of the component. The recast layer needs to be as small as possible for precise applications. Therefore, The height of recast layer and the depth of groove which are conflicting in nature were considered as the significant manufacturing criteria, Which determines the pursuit of a machining process obtained in LMM of Al7075/10%SiCp composite. The present work formulates the depth of groove and height of recast layer in relation to the machining parameters using the Response Surface Methodology (RSM) and correspondingly, The formulated mathematical models were put to use for optimization. Since the effect of machining parameters on the depth of groove and height of recast layer was contradictory, The problem was explicated as a multi objective optimization problem. Moreover, An evolutionary Non-dominated sorting genetic algorithm (NSGA-II) was employed to optimize the model established by RSM. Subsequently this algorithm was also adapted to achieve the Pareto optimal set of solutions that provide a detailed illustration for making the optimal solutions. Eventually experiments were conducted to affirm the results obtained from RSM and NSGA-II.

Keywords: Laser Micro Machining (LMM), depth of groove, Height of recast layer, Response Surface Methodology (RSM), non-dominated sorting genetic algorithm

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Authors: Zülfiye Bıkmaz, Aytolan Yıldırım

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This study was planned as a descriptive regressive study in order to determine the relationship between the competency levels of working nurses, the levels of competency expected by nursing students, the critical thinking disposition of nurses, their perceived autonomy levels, and certain socio demographic characteristics. It is also a methodological study with regard to the intercultural adaptation of the Nursing Competence Scale (NCS) in both working and student samples. The sample of the study group of nurses at a university hospital for at least 6 months working properly and consists of 443 people filled out questionnaires. The student group, consisting of 543 individuals from the 4 public university nursing 3rd and 4th grade students. Data collection tools consisted of a questionnaire prepared in order to define the socio demographic, economic, and personal characteristics of the participants, the ‘Nursing Competency Scale’, the ‘Autonomy Subscale of the Sociotropy – Autonomy Scale’, and the ‘California Critical Thinking Disposition Inventory’. In data evaluation, descriptive statistics, nonparametric tests, Rasch analysis and correlation and regression tests were used. The language validity of the ‘NCS’ was performed by translation and back translation, and the context validity of the scale was performed with expert views. The scale, which was formed into its final structure, was applied in a pilot application from a group consisting of graduate and student nurses. The time constancy of the test was obtained by analysis testing retesting method. In order to reduce the time problems with the two half reliability method was used. The Cronbach Alfa coefficient of the scale was found to be 0.980 for the nurse group and 0.986 for the student group. Statistically meaningful relationships between competence and critical thinking and variables such as age, gender, marital status, family structure, having had critical thinking training, education level, class of the students, service worked in, employment style and position, and employment duration were found. Statistically meaningful relationships between autonomy and certain variables of the student group such as year, employment status, decision making style regarding self, total duration of employment, employment style, and education status were found. As a result, it was determined that the NCS which was adapted interculturally was a valid and reliable measurement tool and was found to be associated with autonomy and critical thinking.

Keywords: nurse, nursing student, competence, autonomy, critical thinking, Rasch analysis

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Authors: Afshin Keshvadi, Johari Endan, Haniff Harun, Desa Ahmad, Farah Saleena

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Experiments were conducted to develop a model for analyzing the ripening process of oil palm fresh fruits in relation to oil yield and oil quality of palm oil produced. This research was carried out on 8-year-old Tenera (Dura × Pisifera) palms planted in 2003 at the Malaysian Palm Oil Board Research Station. Fresh fruit bunches were harvested from designated palms during January till May of 2010. The bunches were divided into three regions (top, middle and bottom), and fruits from the outer and inner layers were randomly sampled for analysis at 8, 12, 16 and 20 weeks after anthesis to establish relationships between maturity and oil development in the mesocarp and kernel. Computations on data related to ripening time, oil content and oil quality were performed using several computer software programs (MSTAT-C, SAS and Microsoft Excel). Nine nonlinear mathematical models were utilized using MATLAB software to fit the data collected. The results showed mean mesocarp oil percent increased from 1.24 % at 8 weeks after anthesis to 29.6 % at 20 weeks after anthesis. Fruits from the top part of the bunch had the highest mesocarp oil content of 10.09 %. The lowest kernel oil percent of 0.03 % was recorded at 12 weeks after anthesis. Palmitic acid and oleic acid comprised of more than 73 % of total mesocarp fatty acids at 8 weeks after anthesis, and increased to more than 80 % at fruit maturity at 20 weeks. The Logistic model with the highest R2 and the lowest root mean square error was found to be the best fit model.

Keywords: oil palm, oil yield, ripening process, anthesis, fatty acids, modeling

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Authors: Ernest Nweke

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Mongolia has made giant strides in the development of its auditing and accounting system from Soviet-style to a market-oriented system. High levels of domestic and foreign investment desired by the Mongolian government require that better and improved quality of corporate information and disclosure consistent with international standards be made available to investors. However, the Mongolian Certified Public Accountants (CPA) profession is still developing, and the quality of services provided by accounting firms in most cases do not comply with International Financial Reporting Standards (IFRS) framework approved by the government for use in financial reporting. Against this backdrop, Accounting and audit reforms, liberalization and deregulation, establishment of an efficient and effective professional monitoring and supervision regime are policy necessities. These will further enhance the Mongolian business environment, eliminate incompetence in the system, make the economy more attractive to investors and ultimately lift reporting standards and bring about improved accounting, auditing and disclosure practices among Mongolian firms. This paper examines the fundamental issues in the accounting and auditing environment in Mongolia and investigates the relationship between selected characteristics of Mongolian Stock Exchange (MSE) listed firms (profitability, leverage, firm size, firm auditor size, firm listing age, board size and proportion of independent directors) and voluntary accounting disclosures in their annual reports and accounts. The selected sample of firms for the research purpose consists of the top 20 indexes of the MSE, representing over 95% of the market capitalization. An empirical analysis of the hypothesized relationship was carried out using multiple regression in EViews analytical software. Research results lend credence to the fact that only a few of the company attributes positively impact voluntary accounting disclosures in Mongolian Stock Exchange-listed firms. The research is motivated by the absence of empirical evidence on the correlation between the quality of voluntary accounting disclosures made by listed companies in Mongolia and company characteristics and the findings thereof significantly useful to both firms and regulatory authorities. The concluding part of the paper precisely consists of useful research-based recommendations for listed firms and regulatory agencies on measures to put in place in order to enhance the quality of corporate financial reporting and disclosures in Mongolia.

Keywords: accounting, auditing, corporate disclosure, listed firms

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Authors: Havisha Banda

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Recent advancements in technology have facilitated commerce around the globe. The online medium of commerce has provided and will continue to provide great opportunities for consumers and businesses. Advancements in technology enable apparel stores, for instance, to improve their online services by using personalized virtual models allowing consumers to visualize the product on the model to determine correct sizing and fit. In addition to many advantages in online shopping the consumers will also have to undergo many types of switching costs in this process of buying apparel online. This study is to identify such switching costs and switching benefits from traditional shopping to online shopping and to understand what the consumers value the most. The scope of this study is to understand the types of switching costs and the factors that actually allow the consumers to shift from brick and mortar to online shopping and also to understand why a certain set of customers consider to purchase offline. Hence this study helps to understand the perceived cost and perceived benefit relation that the consumer draws in purchasing the garments online. This will help the upcoming e-commerce sites and brick and mortar store to understand the various factors and formulate new policies and implement strategies in their own ways to attract the customers and to retain them. A sample of 35 is considered for the process of laddered interviews. In the era of e-commerce there are people who feel comfortable to shop in a retail store rather than online purchase. Few respondents who shop online do not prefer to shop apparel online. Few respondents said that they shop online only for apparels. Most of the variables match in terms of switching costs and also in regard to benefits.

Keywords: e-commerce, switching costs, switching benefits, apparel shopping

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Authors: Asawari Ajay Avhad

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The Soil and Water Assessment Tool (SWAT) is a hydrological model designed to predict the complex interactions within natural and human-altered watersheds. This research applies the SWAT model to the Ulhas River basin, a small watershed undergoing urbanization and characterized by bowl-like topography. Three simulation scenarios (LC17, LC22, and LC27) are investigated, each representing different land use and land cover (LULC) configurations, to assess the impact of urbanization on runoff. The LULC for the year 2027 is generated using the MOLUSCE Plugin of QGIS, incorporating various spatial factors such as DEM, Distance from Road, Distance from River, Slope, and distance from settlements. Future climate data is simulated within the SWAT model using historical data spanning 30 years. A susceptibility map for runoff across the basin is created, classifying runoff into five susceptibility levels ranging from very low to very high. Sub-basins corresponding to major urban settlements are identified as highly susceptible to runoff. With consideration of future climate projections, a slight increase in runoff is forecasted. The reliability of the methodology was validated through the identification of sub-basins known for experiencing severe flood events, which were determined to be highly susceptible to runoff. The susceptibility map successfully pinpointed these sub-basins with a track record of extreme flood occurrences, thus reinforcing the credibility of the assessment methodology. This study suggests that the methodology employed could serve as a valuable tool in flood management planning.

Keywords: future land use impact, flood management, run off prediction, ArcSWAT

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Authors: Eduardo Costa

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Hospital strikes in the Portuguese National Health Service (NHS) are becoming increasingly frequent, raising concerns in what respects patient safety. In fact, data shows that mortality rates for patients admitted during strikes are up to 30% higher than for patients admitted in other days. This paper analyses the effects of hospital strikes on patients’ outcomes. Specifically, it analyzes the impact of different strikes (physicians, nurses and other health professionals), on in-hospital mortality rates, readmission rates and length of stay. The paper uses patient-level data containing all NHS hospital admissions in mainland Portugal from 2012 to 2017, together with a comprehensive strike dataset comprising over 250 strike days (19 physicians-strike days, 150 nurses-strike days and 50 other health professionals-strike days) from 135 different strikes. The paper uses a linear probability model and controls for hospital and regional characteristics, time trends, and changes in patients’ composition and diagnoses. Preliminary results suggest a 6-7% increase in in-hospital mortality rates for patients exposed to physicians’ strikes. The effect is smaller for patients exposed to nurses’ strikes (2-5%). Patients exposed to nurses strikes during their stay have, on average, higher 30-days urgent readmission rates (4%). Length of stay also seems to increase for patients exposed to any strike. Results – conditional on further testing, namely on non-linear models - suggest that hospital operations and service levels are partially disrupted during strikes.

Keywords: health sector strikes, in-hospital mortality rate, length of stay, readmission rate

Procedia PDF Downloads 127

Authors: Jiří. J. Urbánek, Hana Malachová, Josef Krahulec, Jitka Johanidisová

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The paper deals with crisis situations investigation and modelling within the organizations of critical infrastructure. Every crisis situation has an origin in the emergency event occurrence in the organizations of energetic critical infrastructure especially. Here, the emergency events can be both the expected events, then crisis scenarios can be pre-prepared by pertinent organizational crisis management authorities towards their coping or the unexpected event (Black Swan effect) – without pre-prepared scenario, but it needs operational coping of crisis situations as well. The forms, characteristics, behaviour and utilization of crisis scenarios have various qualities, depending on real critical infrastructure organization prevention and training processes. An aim is always better organizational security and continuity obtainment. This paper objective is to find and investigate critical/ crisis zones and functions in critical situations models of critical infrastructure organization. The DYVELOP (Dynamic Vector Logistics of Processes) method is able to identify problematic critical zones and functions, displaying critical interfaces among actors of crisis situations on the DYVELOP maps named Blazons. Firstly, for realization of this ability is necessary to derive and create identification algorithm of critical interfaces. The locations of critical interfaces are the flags of crisis situation in real organization of critical infrastructure. Conclusive, the model of critical interface will be displayed at real organization of Czech energetic crisis infrastructure subject in Black Out peril environment. The Blazons need live power Point presentation for better comprehension of this paper mission.

Keywords: algorithm, crisis, DYVELOP, infrastructure

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Authors: Suraj Jena, Rabindra Kumar Panda

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The study region of the reported study was in Eastern India, having a sub-humid sub-tropical climate and sandy loam soil. The rainfall in this region has wide temporal and spatial variation. Due to lack of adequate surface water to meet the irrigation and household demands, groundwater is being over exploited in that region leading to continuous depletion of groundwater level. Therefore, there is an obvious urgency in reversing the depleting groundwater level through induced recharge, which becomes more critical under the climate change scenarios. The major goal of the reported study was to investigate the effects of climate change on groundwater recharge and subsequent adaptation strategies. Groundwater recharge was modelled using HELP3, a quasi-two-dimensional, deterministic, water-routing model along with global climate models (GCMs) and three global warming scenarios, to examine the changes in groundwater recharge rates for a 2030 climate under a variety of soil and vegetation covers. The relationship between the changing mean annual recharge and mean annual rainfall was evaluated for every combination of soil and vegetation using sensitivity analysis. The relationship was found to be statistically significant (p<0.05) with a coefficient of determination of 0.81. Vegetation dynamics and water-use affected by the increase in potential evapotranspiration for large climate variability scenario led to significant decrease in recharge from 49–658 mm to 18–179 mm respectively. Therefore, appropriate conjunctive use, irrigation schedule and enhanced recharge practices under the climate variability and land use/land cover change scenarios impacting the groundwater recharge needs to be understood properly for groundwater sustainability.

Keywords: Groundwater recharge, climate variability, Land use/cover, GCM

Procedia PDF Downloads 265

Authors: Changde Zhang, Qiang Zhang, Shilong Zheng, Jiawang Liu, Shanchun Guo, Qiu Zhong, Guangdi Wang

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Fulvestrant was approved by FDA to treat breast cancer as a selective estrogen receptor downregulator (SERD) with intramuscular injection administration. ZB716, a fulvestarnt-3 boronic acid, is an SERD with comparable anticancer effect to fulvestrant, but could produce good pharmacokinetic properties under oral administration with mice or rat models. To understand why ZB716 produced much better oral bioavailability, it was proposed that the boronic acid blocked the phase II direct biotransformation with the hydroxyl group on the 3 position of the aromatic ring on fulvestrant. In this study, ZB716 or fulvestrant was incubated with human liver microsome and oxidation cofactor NADPH in vitro. Their metabolites after oxidation were profiled with the Q-Exactive, a high-resolution mass spectrometer. The result showed that ZB716 blocked the forming of hydroxyl groups on its benzene ring except for the oxidation of C-B bond forming fulvestrant in its metabolites, and the concentration of fulvestrant with one more hydroxyl group found in the metabolites from incubation with fulvestrant was about 34 fold high as that formed from incubation with ZB716. Compared to fulvestrant, ZB716 is expected to be much difficult to be further bio-transformed into more hydrophilic compounds, to be difficult excreted out of blood system, and to have longer residence time in blood, which can lead to higher oral bioavailability. This study provided evidence to explain the high bioavailability of ZB716 after oral administration from the perspective of its difficulty of oxidation, a phase I biotransformation, on positions on its aromatic ring.

Keywords: biotransformation, fulvestrant, metabolite profiling, ZB716

Procedia PDF Downloads 248

Authors: Yuqing Zou, Chunrui Zou, Yichong Cao

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Previous research has revealed relationships between self-efficacy (SE), engagement, and academic achievement among students in Western countries, but these relationships remain unknown in college math courses among college students in China. In addition, previous research has shown that test anxiety has a direct effect on engagement and academic achievement. However, how test anxiety affects the relationships between SE, engagement, and academic achievement is still unknown. In this study, the authors aimed to explore the mediating roles of behavioral engagement (BE), emotional engagement (EE), and cognitive engagement (CE) in the association between SE and academic achievement and the moderating role of test anxiety in college math courses. Our hypotheses are that the association between SE and academic achievement was mediated by engagement and that test anxiety played a moderating role in the association. To explore the research questions, the authors collected data through self-reported surveys among 147 students at a northwestern university in China. Self-reported surveys were used to collect data. The motivated strategies for learning questionnaire (MSLQ) (Pintrich, 1991), the metacognitive strategies questionnaire (Wolters, 2004), and the engagement versus disaffection with learning scale (Skinner et al., 2008) were used to assess SE, CE, and BE and EE, respectively. R software was used to analyze the data. The main analyses used were reliability and validity analysis of scales, descriptive statistics analysis of measured variables, correlation analysis, regression analysis, and structural equation modeling (SEM) analysis and moderated mediation analysis to look at the structural relationships between variables at the same time. The SEM analysis indicated that student SE was positively related to BE, EE, and CE and academic achievement. BE, EE, and CE were all positively associated with academic achievement. That is, as the authors expected, higher levels of SE led to higher levels of BE, EE, and CE, and greater academic achievement. Higher levels of BE, EE, and CE led to greater academic achievement. In addition, the moderated mediation analysis found that the path of SE to academic achievement in the model was as significant as expected, as was the moderating effect of test anxiety in the SE-Achievement association. Specifically, test anxiety was found to moderate the association between SE and BE, the association between SE and CE, and the association between EE and Achievement. The authors investigated possible mediating effects of BE, EE, and CE in the associations between SE and academic achievement, and all indirect effects were found to be significant. As for the magnitude of mediations, behavioral engagement was the most important mediator in the SE-Achievement association. This study has implications for college teachers, educators, and students in China regarding ways to promote academic achievement in college math courses, including increasing self-efficacy and engagement and lessening test anxiety toward math.

Keywords: academic engagement, self-efficacy, test anxiety, academic achievement, college math courses, behavioral engagement, cognitive engagement, emotional engagement

Procedia PDF Downloads 84

Authors: Tessa Sellis, Marike A. Wierda, Elke Tichelman, Mirjam T. Van Lohuizen, Marjolein Berger, François Schellevis, Claudi Bockting, Lilian Peters, Huib Burger

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Introduction: Most women develop a healthy bond with their children, however, adequate mother-to-infant bonding cannot be taken for granted. Mother-to-infant bonding refers to the feelings and emotions experienced by the mother towards her child. It is an ongoing process that starts during pregnancy and develops during the first year postpartum and likely throughout early childhood. The prevalence of inadequate bonding ranges from 7 to 11% in the first weeks postpartum. An impaired mother-to-infant bond can cause long-term complications for both mother and child. Very little research has been conducted on the direct relationship between the personality of the mother and mother-to-infant bonding. This study explores the associations between maternal personality and postnatal mother-to-infant bonding. The main hypothesis is that there is a relationship between neuroticism and mother-to-infant bonding. Methods: Data for this study were used from the Pregnancy Anxiety and Depression Study (2010-2014), which examined symptoms of and risk factors for anxiety or depression during pregnancy and the first year postpartum of 6220 pregnant women who received primary, secondary or tertiary care in the Netherlands. The study was expanded in 2015 to investigate postnatal mother-to-infant bonding. For the current research 3836 participants were included. During the first trimester of gestation, baseline characteristics, as well as personality, were measured through online questionnaires. Personality was measured by the NEO Five Factor Inventory (NEO-FFI), which covers the big five of personality (neuroticism, extraversion, openness, altruism and conscientiousness). Mother-to-infant bonding was measured postpartum by the Postpartum Bonding Questionnaire (PBQ). Univariate linear regression analysis was performed to estimate the associations. Results: 5% of the PBQ-respondents reported impaired bonding. A statistically significant association was found between neuroticism and mother-to-infant bonding (p < .001): mothers scoring higher on neuroticism, reported a lower score on mother-to-infant bonding. In addition, a positive correlation was found between the personality traits extraversion (b: -.081), openness (b: -.014), altruism (b: -.067), conscientiousness (b: -.060) and mother-to-infant bonding. Discussion: This study is one of the first to demonstrate a direct association between the personality of the mother and mother-to-infant bonding. A statistically significant relationship has been found between neuroticism and mother-to-infant bonding, however, the percentage of variance predictable by a personality dimension is very small. This study has examined one part of the multi-factorial topic of mother-to-infant bonding and offers more insight into the rarely investigated and complex matter of mother-to-infant bonding. For midwives, it is important recognize the risks for impaired bonding and subsequently improve policy for women at risk.

Keywords: mother-to-infant bonding, personality, postpartum, pregnancy

Procedia PDF Downloads 348

Authors: Qasim Munye, Juned Islam, Haseeb Qureshi, Syed Jung

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Lung nodules are commonly identified in computed tomography (CT) scans by experienced radiologists at a relatively late stage. Early diagnosis can greatly increase survival. We propose using a pix2pix conditional generative adversarial network to generate realistic images simulating early-stage lung nodule growth. We have applied deep images prior to 2341 slices from 895 computed tomography (CT) scans from the Lung Image Database Consortium (LIDC) dataset to generate pseudo-healthy medical images. From these images, 819 were chosen to train a pix2pix network. We observed that for most of the images, the pix2pix network was able to generate images where the nodule increased in size and intensity across epochs. To evaluate the images, 400 generated images were chosen at random and shown to a medical student beside their corresponding original image. Of these 400 generated images, 384 were defined as satisfactory - meaning they resembled a nodule and were visually similar to the corresponding image. We believe that this generated dataset could be used as training data for neural networks to detect lung nodules at an early stage or to improve the accuracy of such networks. This is particularly significant as datasets containing the growth of early-stage nodules are scarce. This project shows that the combination of deep image prior and generative models could potentially open the door to creating larger datasets than currently possible and has the potential to increase the accuracy of medical classification tasks.

Keywords: medical technology, artificial intelligence, radiology, lung cancer

Procedia PDF Downloads 54

Authors: Paula A. Cruise, Carvell McLeary

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Employee turnover leads to lowered productivity, decreased morale and work quality, and psychological effects associated with employee separation and replacement. Yet, it remains unknown why talented employees willingly withdraw from organizations. This uncertainty is worsened as studies; a) priorities organizational over individual predictors resulting in restriction in range in turnover measurement; b) focus on actual rather than intended turnover thereby limiting conceptual understanding of the turnover construct and its relationship with other variables and; c) produce inconsistent findings across cultures, contexts and industries despite a clear need for a unified perspective. The current study addressed these gaps by adopting the theory of planned behavior (TPB) framework to examine socio-cognitive factors in organizational trust and individual turnover intentions among bankers and energy employees in Jamaica. In a comparative study of n=369 [n_bank= 264; male=57 (22.73%); n_energy =105; male =45 (42.86)], it was hypothesized that organizational trust was a predictor of employee turnover intention, and the effect of individual, group, cognitive and socio-affective variables varied across industry. Findings from structural equation modelling confirmed the hypothesis, with a model of both cognitive and socio-affective variables being a better fit [CMIN (χ²) = 800.067, df = 364, p ≤ .000; CFI = 0.950; RMSEA = 0.057 with 90% C.I. (0.052 - 0.062); PCLOSE = 0.016; PNFI = 0.818 in predicting turnover intention. The findings are discussed in relation to socio-cognitive components of trust models and predicting negative employee behaviors across cultures and industries.

Keywords: context-specific organizational trust, cross-cultural psychology, theory of planned behavior, employee turnover intention

Procedia PDF Downloads 234