Search results for: data analyses

Authors: Feride Eskin Bacaksiz, Arzu K. Harmanci Seren

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The problems about the working hours of nursing personnel, such as overworking, numbers of night or daytime shifts, is stated one of the most complained issues by nurses. Otherwise, besides of nursing shortage, absenteeism of nurses because of sickness, some other health problems, or other reasons enforces nurse managers to make working schedules. In this study, it was aimed to analyse and evaluate the working schedules of nurses working in public hospitals. Working schedule lists of nursing personnel for the months of October and November in two public hospitals were analysed and evaluated. Approvals were acquired from the head nurse managers. Descriptive and comparative analyses were used. Totally 36 lists were analysed from two hospitals. There were totally 416 nurses (manager nurse: 25, nurse: 391) and 6-28 (12.6, SD=5.34) nurses in each list. It was found that nurses were working 8 (42.5%), 12 (27.9%) and 16 (20%) hours, in inpatient clinics (74.8%). Totally 20.2% of the nurses were absent during analysing time due to the annual, paid or unpaid leaves. Nurses were generally working 20-112 hours (80.27, SD=11.92). Most of the nurses over worked 1.5-443 hours (47.436, SD=60.78) the year before that year. It was determined that 11.8% of the nurses (n: 49) were working only night shifts and 42.1% (n: 175) of the nurses were working only daytime shifts. It was found that there were inequities in the working hours of nurses.

Keywords: nurse, public hospitals, working conditions, working schedules

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Authors: Filip Buruiana, Alexander Hall, Reimar Hofmann, Thomas Hofmann, Silviu Ganceanu, Alexandru Tudorica

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In-memory column-stores make interactive analysis feasible for many big data scenarios. PowerDrill is a system used internally at Google for exploration in logs data. Even though it is a highly parallelized column-store and uses in memory caching, interactive response times cannot be achieved for all datasets (note that it is common to analyze data with 50 billion records in PowerDrill). In this paper, we investigate two orthogonal approaches to optimize performance at the expense of an acceptable loss of accuracy. Both approaches can be implemented as outer wrappers around existing database engines and so they should be easily applicable to other systems. For the first optimization we show that memory is the limiting factor in executing queries at speed and therefore explore possibilities to improve memory efficiency. We adapt some of the theory behind data sketches to reduce the size of particularly expensive fields in our largest tables by a factor of 4.5 when compared to a standard compression algorithm. This saves 37% of the overall memory in PowerDrill and introduces a 0.4% relative error in the 90th percentile for results of queries with the expensive fields. We additionally evaluate the effects of using sampling on accuracy and propose a simple heuristic for annotating individual result-values as accurate (or not). Based on measurements of user behavior in our real production system, we show that these estimates are essential for interpreting intermediate results before final results are available. For a large set of queries this effectively brings down the 95th latency percentile from 30 to 4 seconds.

Keywords: big data, in-memory column-store, high-performance SQL queries, approximate SQL queries

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Authors: Eyob Seife

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Immunization is a life-saving intervention which prevents needless suffering through sickness, disability, and death. Emphasis on data quality and use will become even stronger with the development of the immunization agenda 2030 (IA2030). Quality of data is a key factor in generating reliable health information that enables monitoring progress, financial planning, vaccine forecasting capacities, and making decisions for continuous improvement of the national immunization program. However, ensuring data of sufficient quality and promoting an information-use culture at the point of the collection remains critical and challenging, especially in hard-to-reach and pastoralist areas where Degehabur district is selected based on a hypothesis of ‘there is no difference in reported and recounted immunization data consistency. Data quality is dependent on different factors where organizational, behavioral, technical, and contextual factors are the mentioned ones. A cross-sectional quantitative study was conducted on September 2022 in the Degehabur district. The study used the world health organization (WHO) recommended data quality self-assessment (DQS) tools. Immunization tally sheets, registers, and reporting documents were reviewed at 5 health facilities (2 health centers and 3 health posts) of primary health care units for one fiscal year (12 months) to determine the accuracy ratio. The data was collected by trained DQS assessors to explore the quality of monitoring systems at health posts, health centers, and the district health office. A quality index (QI) was assessed, and the accuracy ratio formulated were: the first and third doses of pentavalent vaccines, fully immunized (FI), and the first dose of measles-containing vaccines (MCV). In this study, facility-level results showed both over-reporting and under-reporting were observed at health posts when computing the accuracy ratio of the tally sheet to health post reports found at health centers for almost all antigens verified where pentavalent 1 was 88.3%, 60.4%, and 125.6% for Health posts A, B, and C respectively. For first-dose measles-containing vaccines (MCV), similarly, the accuracy ratio was found to be 126.6%, 42.6%, and 140.9% for Health posts A, B, and C, respectively. The accuracy ratio for fully immunized children also showed 0% for health posts A and B and 100% for health post-C. A relatively better accuracy ratio was seen at health centers where the first pentavalent dose was 97.4% and 103.3% for health centers A and B, while a first dose of measles-containing vaccines (MCV) was 89.2% and 100.9% for health centers A and B, respectively. A quality index (QI) of all facilities also showed results between the maximum of 33.33% and a minimum of 0%. Most of the verified immunization data accuracy ratios were found to be relatively better at the health center level. However, the quality of the monitoring system is poor at all levels, besides poor data accuracy at all health posts. So attention should be given to improving the capacity of staff and quality of monitoring system components, namely recording, reporting, archiving, data analysis, and using information for decision at all levels, especially in pastoralist areas where such kinds of study findings need to be improved beside to improving the data quality at root and health posts level.

Keywords: accuracy ratio, Degehabur District, regular childhood immunization program, quality of monitoring system, Somali Region-Ethiopia

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

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Authors: Eyad Almaita

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In this paper, a novel adaptive Radial Basis Function Neural Networks (RBFNN) algorithm is used to forecast the hour by hour electrical load demand in Jordan. A small and effective RBFNN model is used to forecast the hourly total load demand based on a small number of features. These features are; the load in the previous day, the load in the same day in the previous week, the temperature in the same hour, the hour number, the day number, and the day type. The proposed adaptive RBFNN model can enhance the reliability of the conventional RBFNN after embedding the network in the system. This is achieved by introducing an adaptive algorithm that allows the change of the weights of the RBFNN after the training process is completed, which will eliminates the need to retrain the RBFNN model again. The data used in this paper is real data measured by National Electrical Power co. (Jordan). The data for the period Jan./2012-April/2013 is used train the RBFNN models and the data for the period May/2013- Sep. /2013 is used to validate the models effectiveness.

Keywords: load forecasting, adaptive neural network, radial basis function, short-term, electricity consumption

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Authors: Ayudhia P. Gusti, Semin

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It is known that total operating cost of a vessel is dominated by the cost of fuel consumption. How to reduce the fuel cost of ship so that the operational costs of fuel can be minimized is the question that arises. As the basis of these kinds of problem, sailing speed determination is an important factor to be considered by a shipping company. Optimal speed determination will give a significant influence on the route and berth schedule of ships, which also affect vessel operating costs. The purpose of this paper is to clarify some important issues about ship speed optimization. Sailing speed, displacement, sailing time, and specific fuel consumption were obtained from shipping log data to be further analyzed for modeling the speed optimization. The presented speed optimization model is expected to affect the fuel consumption and to reduce the cost of fuel consumption.

Keywords: maritime transportation, reducing fuel, shipping log data, speed optimization

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Authors: Jan Muhammad, Saad Malik, Fadia W. Al-Azawi, Ali Imran

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In the era of climate variability, rising temperatures are the most significant aspect. In this study PRECIS model data and observed data are used for assessing the temperature change scenarios of Sindh province during the first half of present century. Observed data from various meteorological stations of Sindh are the primary source for temperature change detection. The current scenario (1961–1990) and the future one (2010-2050) are acted by the PRECIS Regional Climate Model at a spatial resolution of 25 * 25 km. Regional Climate Model (RCM) can yield reasonably suitable projections to be used for climate-scenario. The main objective of the study is to map the simulated temperature as obtained from climate model-PRECIS and their comparison with observed temperatures. The analysis is done on all the districts of Sindh in order to have a more precise picture of temperature change scenarios. According to results the temperature is likely to increases by 1.5 - 2.1°C by 2050, compared to the baseline temperature of 1961-1990. The model assesses more accurate values in northern districts of Sindh as compared to the coastal belt of Sindh. All the district of the Sindh province exhibit an increasing trend in the mean temperature scenarios and each decade seems to be warmer than the previous one. An understanding of the change in temperatures is very vital for various sectors such as weather forecasting, water, agriculture, and health, etc.

Keywords: PRECIS Model, real observed data, Arc GIS, interpolation techniques

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Authors: Nan Yang, Hye Pae

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English as a second language (ESL) learners are often observed to have transferred traits of their first languages (L1) and habits of using their L1s to their use of English (second language, L2), and this phenomenon is coined as language transfer. In addition to the transfer of linguistic features (e.g., grammar, vocabulary, etc.), which are relatively easy to observe and quantify, many cross-cultural theorists emphasized on a much subtle and fundamental transfer existing on a higher conceptual level that is referred to as conceptual transfer. Although a growing body of literature in linguistics has demonstrated evidence of L1 transfer in various discourse genres, very limited studies address the underlying conceptual transfer that is happening along with the language transfer, especially with the extended form of spontaneous discourses such as personal narrative. To address this issue, this study situates itself in the context of Chinese ESL learners’ written narratives, examines evidence of L1 conceptual transfer in comparison with native English speakers’ narratives, and provides discussion from the perspective of the conceptual transfer. It is hypothesized that Chinese ESL learners’ English narrative strategies are heavily influenced by the strategies that they use in Chinese as a result of the conceptual transfer. Understanding language transfer cognitively is of great significance in the realm of SLA, as it helps address challenges that ESL learners around the world are facing; allow native English speakers to develop a better understanding about how and why learners’ English is different; and also shed light in ESL pedagogy by providing linguistic and cultural expectations in native English-speaking countries. To achieve the goals, 40 college students were recruited (20 Chinese ESL learners and 20 native English speakers) in the United States, and their written narratives on the prompt 'The most frightening experience' were collected for quantitative discourse analysis. 40 written narratives (20 in Chinese and 20 in English) were collected from Chinese ESL learners, and 20 written narratives were collected from native English speakers. All written narratives were coded according to the coding scheme developed by the authors prior to data collection. Statistical descriptive analyses were conducted, and the preliminary results revealed that native English speakers included more narrative elements such as events and explicit evaluation comparing to Chinese ESL students’ both English and Chinese writings; the English group also utilized more evaluation device (i.e., physical state expressions, indirectly reported speeches, delineation) than Chinese ESL students’ both English and Chinese writings. It was also observed that Chinese ESL students included more orientation elements (i.e., the introduction of time/place, the introduction of character) in their Chinese and English writings than the native English-speaking participants. The findings suggest that a similar narrative strategy was observed in Chinese ESL learners’ Chinese narratives and English narratives, which is considered as the evidence of conceptual transfer from Chinese (L1) to English (L2). The results also indicate that distinct narrative strategies were used by Chinese ESL learners and native English speakers as a result of cross-cultural differences.

Keywords: Chinese ESL learners, language transfer, thinking-for-speaking, written narratives

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Authors: Toshanlal Meenpal, Ankita Meenpal

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A very important technique in reversible data hiding field is Difference expansion. Secret message as well as the cover image may be completely recovered without any distortion after data extraction process due to reversibility feature. In general, in any difference expansion scheme embedding is performed by integer transform in the difference image acquired by grouping two neighboring pixel values. This paper proposes an improved reversible difference expansion embedding scheme. We mainly consider edge direction for embedding by modifying the difference of two neighboring pixels values. In general, the larger difference tends to bring a degraded stego image quality than the smaller difference. Image quality in the range of 0.5 to 3.7 dB in average is achieved by the proposed scheme, which is shown through the experimental results. However payload wise it achieves almost similar capacity in comparisons with previous method.

Keywords: information hiding, wedge direction, difference expansion, integer transform

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Authors: Habes Sameh, Bettahar Asma, Nezli Imad Eddine

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The region of Touggourt is situated in the southern part is Algeria, it receives important quantities of waters, the latter are extracted from the fossil groundwater (the Intercalary Continental and the Terminal Complex). The mineralization of these waters of the Terminal Complex is between 3 and 6,5 g/l and for waters of Intercalary Continental is 1,8 and 8,7 g/l, thus it constitutes an obstacle as for its use. To highlight the origins of this mineralization, we used the hydrochemical tool. So the chemical analyses in our ownership, were treated by means of the software "Statistica", what allowed us to realize an analysis in main components (ACP), the latter showed a competition between sodic or magnesian chlorinated water and calcic bicarbonate water, rich in potassium for the TC, while for the IC, we have a competition between sodic or calcic chlorinated and magnesian water treated with copper sulphate waters. The simulation realized thermodynamics showed a variation of the index of saturation which do not exceed zero, for waters of two aquifer TC and IC, so indicating one under saturation of waters towards minerals, highlighting the influence of the geologic formation in the outcrop on the quality of waters. However, we notice that these waters remain acceptable for the irrigation of plants but must be treated before what are consumed by the human being.

Keywords: ACP, intercalary, continental, mineralization, SI, Terminal Complex

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Authors: Houman Tamaddon, Mehrdad Behnia, Masud Behnia

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An accurate study of blood flow is associated with an accurate vascular pattern and geometrical properties of the organ of interest. Due to the complexity of vascular networks and poor accessibility in vivo, it is challenging to reconstruct the entire vasculature of any organ experimentally. The objective of this study is to introduce an innovative approach for the reconstruction of a full vascular tree from available morphometric data. Our method consists of implementing morphometric data on those parts of the vascular tree that are smaller than the resolution of medical imaging methods. This technique reconstructs the entire arterial tree down to the capillaries. Vessels greater than 2 mm are obtained from direct volume and surface analysis using contrast enhanced computed tomography (CT). Vessels smaller than 2mm are reconstructed from available morphometric and distensibility data and rearranged by applying Murray’s Laws. Implementation of morphometric data to reconstruct the branching pattern and applying Murray’s Laws to every vessel bifurcation simultaneously, lead to an accurate vascular tree reconstruction. The reconstruction algorithm generates full arterial tree topography down to the first capillary bifurcation. Geometry of each order of the vascular tree is generated separately to minimize the construction and simulation time. The node-to-node connectivity along with the diameter and length of every vessel segment is established and order numbers, according to the diameter-defined Strahler system, are assigned. During the simulation, we used the averaged flow rate for each order to predict the pressure drop and once the pressure drop is predicted, the flow rate is corrected to match the computed pressure drop for each vessel. The final results for 3 cardiac cycles is presented and compared to the clinical data.

Keywords: blood flow, morphometric data, vascular tree, Strahler ordering system

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Authors: Oladejo Olagoke Peter, Adagunodo T. A., Ogunkoya C. O.

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The research investigated the arrangement of some geological features under Ilesa employing aeromagnetic data. The obtained data was subjected to various data filtering and processing techniques, which are Total Horizontal Derivative (THD), Depth Continuation and Analytical Signal Amplitude using Geosoft Oasis Montaj 6.4.2 software. The Reduced to the Equator –Total Magnetic Intensity (TRE-TMI) outcomes reveal significant magnetic anomalies, with high magnitude (55.1 to 155 nT) predominantly at the Northwest half of the area. Intermediate magnetic susceptibility, ranging between 6.0 to 55.1 nT, dominates the eastern part, separated by depressions and uplifts. The southern part of the area exhibits a magnetic field of low intensity, ranging from -76.6 to 6.0 nT. The lineaments exhibit varying lengths ranging from 2.5 and 16.0 km. Analyzing the Rose Diagram and the analytical signal amplitude indicates structural styles mainly of E-W and NE-SW orientations, particularly evident in the western, SW and NE regions with an amplitude of 0.0318nT/m. The identified faults in the area demonstrate orientations of NNW-SSE, NNE-SSW and WNW-ESE, situated at depths ranging from 500 to 750 m. Considering the divergence magnetic susceptibility, structural style or orientation of the lineaments, identified fault and their depth, these lithological features could serve as a valuable foundation for assessing ground motion, particularly in the presence of sufficient seismic energy.

Keywords: lineament, aeromagnetic, anomaly, fault, magnetic

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Authors: Napoleana-Anna Chaliasou, Andrew Heath, Kevin Paine

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Fly ash-slag based Geopolymer Cement (GPC) is presenting mechanical properties and environmental advantages that make it the predominant “green” alternative to Portland Cement (PC). Although numerous life-cycle analyses praising its environmental advantages, disposal after the end of its life remains as an issue that has been barely explored. The present study is investigating the recyclability of fly ash-slag GPC as aggregate in mortars. The purpose of the study was to evaluate the effect of GPC fine Recycled Aggregates (RA), at replacement levels of 25% and 50%, on the main mechanical properties of PC and GPC mortar mixes. The results were compared with those obtained by corresponding mixes incorporating natural and PC-RA. The main physical properties of GPC-RA were examined and proven to be comparable to those of PC-RA and slightly inferior to those of natural sand. A negligible effect was observed at 28-day compressive and flexural strength of PC mortars with GPC aggregates having a milder effect than PC. As far as GPC mortars are concerned, the influence of GPC aggregates was enhancing for the investigated mechanical properties. Additionally, a screening test showed that recycled geopolymer aggregates are not prone of inducing alkali silica reaction.

Keywords: concrete recycling, geopolymer cement, fly ash, construction wastes

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Authors: N. Othman, J. Ujang, M. N. Ismail, R. Noordin, B. H. Lim

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Amoebiasis is caused by the Entamoeba histolytica and still endemic in many parts of the tropical region, worldwide. Currently, there is no available vaccine against amoebiasis. Hence, there is an urgent need to develop a vaccine. The excretory secretory antigen (ESA) of E. histolytica is a suitable biomarker for the vaccine candidate since it can modulate the host immune response. Hence, the objective of this study is to identify the proteome of the ESA towards finding suitable biomarker for the vaccine candidate. The non-gel based and gel-based proteomics analyses were performed to identify proteins. Two kinds of mass spectrometry with different ionization systems were utilized i.e. LC-MS/MS (ESI) and MALDI-TOF/TOF. Then, the functional proteins classification analysis was performed using PANTHER software. Combination of the LC -MS/MS for the non-gel based and MALDI-TOF/TOF for the gel-based approaches identified a total of 273 proteins from the ESA. Both systems identified 29 similar proteins whereby 239 and 5 more proteins were identified by LC-MS/MS and MALDI-TOF/TOF, respectively. Functional classification analysis showed the majority of proteins involved in the metabolic process (24%), primary metabolic process (19%) and protein metabolic process (10%). Thus, this study has revealed the proteome the E. histolytica ESA and the identified proteins merit further investigations as a vaccine candidate.

Keywords: E. histolytica, ESA, proteomics, biomarker

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Authors: Kazeem Adesina Dauda, Waheed Babatunde Yahya

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To design a tree that maximizes within-node homogeneity, there is a need for a homogeneity measure that is appropriate for event history data with multiple risks. We consider the use of Deviance and Modified Cox-Snell residuals as a measure of impurity in Classification Regression Tree (CART) and compare our results with the results of Fiona (2008) in which homogeneity measures were based on Martingale Residual. Data structure approach was used to validate the performance of our proposed techniques via simulation and real life data. The results of univariate competing risk revealed that: using Deviance and Cox-Snell residuals as a response in within node homogeneity classification tree perform better than using other residuals irrespective of performance techniques. Bone marrow transplant data and double-blinded randomized clinical trial, conducted in other to compare two treatments for patients with prostate cancer were used to demonstrate the efficiency of our proposed method vis-à-vis the existing ones. Results from empirical studies of the bone marrow transplant data showed that the proposed model with Cox-Snell residual (Deviance=16.6498) performs better than both the Martingale residual (deviance=160.3592) and Deviance residual (Deviance=556.8822) in both event of interest and competing risks. Additionally, results from prostate cancer also reveal the performance of proposed model over the existing one in both causes, interestingly, Cox-Snell residual (MSE=0.01783563) outfit both the Martingale residual (MSE=0.1853148) and Deviance residual (MSE=0.8043366). Moreover, these results validate those obtained from the Monte-Carlo studies.

Keywords: within-node homogeneity, Martingale residual, modified Cox-Snell residual, classification and regression tree

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Authors: Grace Omowunmi Soyebi

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A computerized medical record system is a collection of medical information about a person that is stored on a computer. One principal problem of most hospitals in rural areas is using the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved; this may cause an unexpected to happen to the patient. This data mining application is to be designed using a structured system analysis and design method which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the design and implementation of a computerized medical record system. This computerized system will replace the file management system and help to quickly retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: programming, data, software development, innovation

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Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

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Authors: Ozan Kahraman, Hao Feng

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Several models such as Weibull, Modified Gompertz, Biphasic linear, and Log-logistic models have been proposed in order to describe non-linear inactivation kinetics and used to fit non-linear inactivation data of several microorganisms for inactivation by heat, high pressure processing or pulsed electric field. First-order kinetic parameters (D-values and z-values) have often been used in order to identify microbial inactivation by non-thermal processing methods such as ultrasound. Most ultrasonic inactivation studies employed first-order kinetic parameters (D-values and z-values) in order to describe the reduction on microbial survival count. This study was conducted to analyze the E. coli O157:H7 inactivation data by using five microbial survival models (First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic). First-order, Weibull, Modified Gompertz, Biphasic linear and Log-logistic kinetic models were used for fitting inactivation curves of Escherichia coli O157:H7. The residual sum of squares and the total sum of squares criteria were used to evaluate the models. The statistical indices of the kinetic models were used to fit inactivation data for E. coli O157:H7 by MTS at three temperatures (40, 50, and 60 0C) and three pressures (100, 200, and 300 kPa). Based on the statistical indices and visual observations, the Weibull and Biphasic models were best fitting of the data for MTS treatment as shown by high R2 values. The non-linear kinetic models, including the Modified Gompertz, First-order, and Log-logistic models did not provide any better fit to data from MTS compared the Weibull and Biphasic models. It was observed that the data found in this study did not follow the first-order kinetics. It is possibly because of the cells which are sensitive to ultrasound treatment were inactivated first, resulting in a fast inactivation period, while those resistant to ultrasound were killed slowly. The Weibull and biphasic models were found as more flexible in order to determine the survival curves of E. coli O157:H7 treated by MTS on apple-carrot juice.

Keywords: Weibull, Biphasic, MTS, kinetic models, E.coli O157:H7

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Authors: Dana Lee, Young Chan Kim

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The purpose of this paper is to examine the relationship between the fashion corporate social responsibility (CSR) ad framing and consumer purchase behaviours with the focus on consumer’s concern and involvement towards fashion brands. A self-completion questionnaire was administered to 200 respondents. Factor analysis and other statistical analyses were applied to test hypotheses. The results suggested that the quality of the product was the most important factor when consumers purchase fashion brand products with high level of responsibility towards unethical practices but surprisingly favourability for fast fashion. Unexpectedly, it was shown that consumers took the plenty of blame, but not much responsibility on buying fast fashion evading their responsibility to CSR ad, and their purchase intentions remained unchanged. The result, on the other hand, showed that fashion CSR ads can significantly moderate individuals’ emotions even though this had no significant correlation with the purchase intentions. Despite the limited sample size and geographical region, this research has important implications for contemporary fashion brands that use ad framing to understand how consumers’ involvement and concernedness toward the CSR actions in ad, influence their favourability (purchase intention) for fashion brands.

Keywords: framing effect, CSR advertisements, consumer behaviour, purchase intention

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Authors: Usman Ghani

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Feedback is a vital element for improving student learning in a simulation-based training as it guides and refines learning through scaffolding. A number of studies in literature have shown that students’ learning is enhanced when feedback is provided with personalized tutoring that offers specific guidance and adapts feedback to the learner in a one-to-one environment. Thus, emulating these adaptive aspects of human tutoring in simulation provides an effective methodology to train individuals. This paper presents the results of a study that investigated the effectiveness of automating different types of feedback techniques such as Knowledge-of-Correct-Response (KCR) and Answer-Until- Correct (AUC) in software simulation for learning basic information technology concepts. For the purpose of comparison, techniques like simulation with zero or no-feedback (NFB) and traditional hands-on (HON) learning environments are also examined. The paper presents the summary of findings based on quantitative analyses which reveal that the simulation based instructional strategies are at least as effective as hands-on teaching methodologies for the purpose of learning of IT concepts. The paper also compares the results of the study with the earlier studies and recommends strategies for using feedback mechanism to improve students’ learning in designing and simulation-based IT training.

Keywords: simulation, feedback, training, hands-on, labs

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Authors: Dawei Xia, Benyan Jiang, Yong Li

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Split-type air conditioners is widely used for indoor temperature regulation in urban residential buildings in summer in China. The energy-related occupant behavior has a great impact on building energy consumption. Obtaining the energy-related occupant behavior data of air conditioners is the research basis for the energy consumption prediction and simulation. Relying on the development of sensing and control technology, this paper selects Zigbee intelligent electronic equipment to monitor the energy-related occupant behavior of 20 households for 3 months in summer. Through analysis of data, it is found that people of different ages in the region have significant difference in the time, duration, frequency, and energy consumption of air conditioners, and form a data model of three basic energy-related occupant behavior patterns to provide an accurate simulation of energy.

Keywords: occupant behavior, Zigbee, split air conditioner, energy simulation

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Authors: Hana Janáčková

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Housing can be sorted as basic needs of households. Purchase of acceptable ownership housing is important investments for most them. For rental housing households must consider the part of rent expenditure paid in the total household income. For this reason, financial considerations of households in this area depend on the government innervations (public administration) in housing - on housing policy. Market system of housing allocation, whether ownership or tenancy, is based on the fact that housing is a scarce good. The allocation of housing is based on demand and supply. The market system of housing can sometimes have a negative impact on some households, the market is unable to satisfy certain groups of the population that are not able or willing to accept market price. For these reasons, there is a more or less regulation of the market. Regulation is both on the demand and supply side, and the state determines the rules of behaviour for all economic entities of the housing market. This article submits results of analysis of selected regulatory interference of the state in the housing market and assesses their implications deforming the market in the selected region of the Czech Republic. The first part describes tools of supports and the second part discusses deformations and analyses their consequences on the demand side of housing market and on supply side.

Keywords: housing, housing market, microeconomic consequences, deformation

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Authors: Priya Madhuri P., Kamini J., Jayanthi S. C.

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Rooftop rainwater harvesting is a crucial practice to address water scarcity, pollution, and flooding. This study aims to estimate the rooftop rainwater harvesting potential (RRWHP) for Suryapet, India, using building footprint data and average rainfall data. The study uses rainfall grids from the India Meteorological Department and Very High Resolution Satellite data to capture building footprints and calculate the RRWHP for a five-year period (2015-2020). Buildings with an area of more than 20 square meters are considered. A conservative figure of 60% efficiency for the catchment area is considered. The study chose 31,770 buildings with an effective rooftop area of around 1.56 sq. km. The city experiences annual rainfall values ranging from 791 mm to 987 mm, with August being the wettest month. The projected annual rooftop rainwater harvesting potential is 1.3 billion litres.

Keywords: buildings, rooftop rainwater harvesting, sustainable water management, urban

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Authors: S. Jeyarajan

Abstract:

Concept of knowledge is discovered from conducted study for successive Competition in Sri Lankan Non-State Higher Educational Institutes. The Concept discovered out of collected Knowledge Management Practices from Emerald inside likewise reputed literatures and of Non-State Higher Educational sector. A test is conducted to reveal existences and its reason behind of these collected practices in Sri Lankan Non-State Higher Education Institutes. Further, unavailability of such study and uncertain on number of participants for data collection in the Sri Lankan context contributed selection of research method as qualitative method, which used attributes of Delphi Method to manage those likewise uncertainty. Data are collected under Dramaturgical Method, which contributes efficient usage of the Delphi method. Grounded theory is selected as data analysis techniques, which is conducted in intermixed discourse to manage different perspectives of data that are collected systematically through perspective and modified snowball sampling techniques. Data are then analysed using Grounded Theory Development Techniques in Intermix discourses to manage differences in Data. Consequently, Agreement in the results of Grounded theories and of finding in the Foreign Study is discovered in the analysis whereas present study conducted as Qualitative Research and The Foreign Study conducted as Quantitative Research. As such, the Present study widens the discovery in the Foreign Study. Further, having discovered reason behind of the existences, the Present result shows Concept for Knowledge from Sri Lankan Non-State sector to manage higher educational Institutes in successful manner.

Keywords: adherence of snowball sampling into perspective sampling, Delphi method in qualitative method, grounded theory development in intermix discourses of analysis, knowledge management for success of higher educational institutes

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Authors: Jiangxia Han, Liang Xue, Keda Chen

Abstract:

Despite the significant progress over the last decades in reservoir simulation using numerical discretization, meshing is complex. Moreover, the high degree of freedom of the space-time flow field makes the solution process very time-consuming. Therefore, we present Physics-Informed Convolutional Neural Networks(PICNN) as a hybrid scientific theory and data method for reservoir modeling. Besides labeled data, the model is driven by the scientific theories of the underlying problem, such as governing equations, boundary conditions, and initial conditions. PICNN integrates governing equations and boundary conditions into the network architecture in the form of a customized convolution kernel. The loss function is composed of data matching, initial conditions, and other measurable prior knowledge. By customizing the convolution kernel and minimizing the loss function, the neural network parameters not only fit the data but also honor the governing equation. The PICNN provides a methodology to model and history-match flow and transport problems in porous media. Numerical results demonstrate that the proposed PICNN can provide an accurate physical solution from a limited dataset. We show how this method can be applied in the context of a forward simulation for continuous problems. Furthermore, several complex scenarios are tested, including the existence of data noise, different work schedules, and different good patterns.

Keywords: convolutional neural networks, deep learning, flow and transport in porous media, physics-informed neural networks, reservoir simulation

Procedia PDF Downloads 136

Authors: Won Il Jung, Gene Lee, Luis Rabelo

Abstract:

Since many weapon systems are getting more complex and diverse, various problems occur in terms of the acquisition cost, time, and performance limitation. As a matter of fact, the experiment execution in real world is costly, dangerous, and time-consuming to obtain Required Operational Characteristics (ROC) for a new weapon acquisition although enhancing the fidelity of experiment results. Also, until presently most of the research contained a large amount of assumptions so therefore a bias is present in the experiment results. At this moment, the new methodology is proposed to solve these problems without a variety of assumptions. ROC of the new weapon system is developed through the new methodology, which is a way to analyze big data generated by simulating various scenarios based on virtual and constructive models which are involving 6 Degrees of Freedom (6DoF). The new methodology enables us to identify unbiased ROC on new weapons by reducing assumptions and provide support in terms of the optimal weapon systems acquisition.

Keywords: big data, required operational characteristics (ROC), virtual and constructive models, weapon acquisition

Procedia PDF Downloads 286

Authors: Muhammad Tayyab, Saba Saira

Abstract:

It is an established fact that organizations across the globe consider employees as their assets and try to advance their well-being. However, the local firms of developing countries are mostly profit oriented and do not have much concern about their employees’ engagement or commitment. Like other developing countries, the local organizations of Pakistan are also less concerned about the well-being of their employees. Especially public sector organizations lack concern regarding engagement, satisfaction or commitment of the employees. Therefore, this study aimed at investigating the impact of job recognition and job motivation on organizational commitment in the mediation role of employee engagement. The data were collected from land record officers of board of revenue, Punjab, Pakistan. Structured questionnaire was used to collect data through physically visiting land record officers and also through the internet. A total of 318 land record officers’ responses were finalized to perform data analysis. The data were analyzed through confirmatory factor analysis and structural equation modeling technique. The findings revealed that job recognition and job motivation have direct as well as indirect positive and significant impact on organizational commitment. The limitations, practical implications and future research indications are also explained.

Keywords: job motivation, job recognition, employee engagement, employee commitment, public sector, land record officers

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Authors: Mahmoud Hassan Onsa, Saadelnour Abdueljabbar Adam

Abstract:

Wastes from slaughterhouses in most towns in Sudan are often poorly managed and sometimes discharged into adjoining streams due to poor implementation of standards, thus causing environmental and public health hazards and also there is a large amount of manure from dairy farms. This paper presents solution of organic waste from cow dairy farms and slaughterhouse the anaerobic digestion and biogas production. The paper presents the findings of experimental investigation of biogas production with and without pasteurization using cow manure, blood and rumen content were mixed at two proportions, 72.3% manure, 21.7%, rumen content and 6% blood for bio digester1with 62% dry matter at the beginning and without pasteurization and 72.3% manure, 21.7%, rumen content and 6% blood for bio-digester2 with 10% dry matter and pasteurization. The paper analyses the quantitative and qualitative composition of biogas: gas content, the concentration of methane. The highest biogas output 2.9 mL/g dry matter/day (from bio-digester2) together with a high quality biogas of 87.4% methane content which is useful for combustion and energy production and healthy bio-fertilizer but biodigester1 gave 1.68 mL/g dry matter/day with methane content 85% which is useful for combustion, energy production and can be considered as new technology of dryer bio-digesters.

Keywords: anaerobic digestion, bio-digester, blood, cow manure, rumen content

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Authors: Martin Georgiev, Milena Nanova, Damyan Damov

Abstract:

Architects, engineers, and developers need to analyse and implement a wide spectrum of data in different formats, if they want to produce viable residential developments. Usually, this data comes from a number of different sources and is not well structured. The main objective of this research project is to provide parametric tools working with real geodesic data that can generate residential solutions. Various codes, regulations and design constraints are described by variables and prioritized. In this way, we establish a common workflow for architects, geodesists, and other professionals involved in the building and investment process. This collaborative medium ensures that the generated design variants conform to various requirements, contributing to a more streamlined and informed decision-making process. The quantification of distinctive characteristics inherent to typical residential structures allows a systematic evaluation of the generated variants, focusing on factors crucial to designers, such as daylight simulation, circulation analysis, space utilization, view orientation, etc. Integrating real geodesic data offers a holistic view of the built environment, enhancing the accuracy and relevance of the design solutions. The use of generative algorithms and parametric models offers high productivity and flexibility of the design variants. It can be implemented in more conventional CAD and BIM workflow. Experts from different specialties can join their efforts, sharing a common digital workspace. In conclusion, our research demonstrates that a generative parametric approach based on real geodesic data and collaborative decision-making could be introduced in the early phases of the design process. This gives the designers powerful tools to explore diverse design possibilities, significantly improving the qualities of the building investment during its entire lifecycle.

Keywords: architectural design, residential buildings, urban development, geodesic data, generative design, parametric models, workflow optimization

Procedia PDF Downloads 46

Authors: Martin Pellon Consunji

Abstract:

Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

Procedia PDF Downloads 118