Search results for: error metrics

Authors: Benjamin Chijioke Agwah, Paulinus Chinaenye Eze

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Antilock braking system (ABS) is one of the important contributions by the automobile industry, designed to ensure road safety in such way that vehicles are kept steerable and stable when during emergency braking. This paper presents a wheel slip-based intelligent controller with variable zero lag compensation for ABS. It is required to achieve a very fast perfect wheel slip tracking during hard braking condition and eliminate chattering with improved transient and steady state performance, while shortening the stopping distance using effective braking torque less than maximum allowable torque to bring a braking vehicle to a stop. The dynamic of a vehicle braking with a braking velocity of 30 ms⁻¹ on a straight line was determined and modelled in MATLAB/Simulink environment to represent a conventional ABS system without a controller. Simulation results indicated that system without a controller was not able to track desired wheel slip and the stopping distance was 135.2 m. Hence, an intelligent control based on fuzzy logic controller (FLC) was designed with a variable zero lag compensator (VZLC) added to enhance the performance of FLC control variable by eliminating steady state error, provide improve bandwidth to eliminate the effect of high frequency noise such as chattering during braking. The simulation results showed that FLC- VZLC provided fast tracking of desired wheel slip, eliminate chattering, and reduced stopping distance by 70.5% (39.92 m), 63.3% (49.59 m), 57.6% (57.35 m) and 50% (69.13 m) on dry, wet, cobblestone and snow road surface conditions respectively. Generally, the proposed system used effective braking torque that is less than the maximum allowable braking torque to achieve efficient wheel slip tracking and overall robust control performance on different road surfaces.

Keywords: ABS, fuzzy logic controller, variable zero lag compensator, wheel slip tracking

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Authors: Harpal Singh, Sakshi Batra

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The widespread and easy accesses to multimedia contents and possibility to make numerous copies without loss of significant fidelity have roused the requirement of digital rights management. Thus this problem can be effectively solved by Digital watermarking technology. This is a concept of embedding some sort of data or special pattern (watermark) in the multimedia content; this information will later prove ownership in case of a dispute, trace the marked document’s dissemination, identify a misappropriating person or simply inform user about the rights-holder. The primary motive of digital watermarking is to embed the data imperceptibly and robustly in the host information. Extensive counts of watermarking techniques have been developed to embed copyright marks or data in digital images, video, audio and other multimedia objects. With the development of digital video-based innovations, copyright dilemma for the multimedia industry increases. Video watermarking had been proposed in recent years to serve the issue of illicit copying and allocation of videos. It is the process of embedding copyright information in video bit streams. Practically video watermarking schemes have to address some serious challenges as compared to image watermarking schemes like real-time requirements in the video broadcasting, large volume of inherently redundant data between frames, the unbalance between the motion and motionless regions etc. and they are particularly vulnerable to attacks, for example, frame swapping, statistical analysis, rotation, noise, median and crop attacks. In this paper, an effective, robust and imperceptible video watermarking algorithm is proposed based on hybridization of powerful mathematical transforms; Fractional Fourier Transform (FrFT), Discrete Wavelet transforms (DWT) and Singular Value Decomposition (SVD) using redundant wavelet. This scheme utilizes various transforms for embedding watermarks on different layers by using Hybrid systems. For this purpose, the video frames are portioned into layers (RGB) and the watermark is being embedded in two forms in the video frames using SVD portioning of the watermark, and DWT sub-band decomposition of host video, to facilitate copyright safeguard as well as reliability. The FrFT orders are used as the encryption key that allows the watermarking method to be more robust against various attacks. The fidelity of the scheme is enhanced by introducing key generation and wavelet based key embedding watermarking scheme. Thus, for watermark embedding and extraction, same key is required. Therefore the key must be shared between the owner and the verifier via some safe network. This paper demonstrates the performance by considering different qualitative metrics namely Peak Signal to Noise ratio, Structure similarity index and correlation values and also apply some attacks to prove the robustness. The Experimental results are presented to demonstrate that the proposed scheme can withstand a variety of video processing attacks as well as imperceptibility.

Keywords: discrete wavelet transform, robustness, video watermarking, watermark

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Authors: Saeid Gharechelou, Stuart Green, Fiona Cawkwell

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Hedgerow has an important role for a wide range of ecological habitats, landscape, agriculture management, carbon sequestration, wood production. Hedgerow detection accurately using satellite imagery is a challenging problem in remote sensing techniques, because in the special approach it is very similar to line object like a road, from a spectral viewpoint, a hedge is very similar to a forest. Remote sensors with very high spatial resolution (VHR) recently enable the automatic detection of hedges by the acquisition of images with enough spectral and spatial resolution. Indeed, recently VHR remote sensing data provided the opportunity to detect the hedgerow as line feature but still remain difficulties in monitoring the characterization in landscape scale. In this research is used the TerraSAR-x Spotlight and Staring mode with 3-5 m resolution in wet and dry season in the test site of Fermoy County, Ireland to detect the hedgerow by acquisition time of 2014-2015. Both dual polarization of Spotlight data in HH/VV is using for detection of hedgerow. The varied method of SAR image technique with try and error way by integration of classification algorithm like texture analysis, support vector machine, k-means and random forest are using to detect hedgerow and its characterization. We are applying the Shannon entropy (ShE) and backscattering analysis in single and double bounce in polarimetric analysis for processing the object-oriented classification and finally extracting the hedgerow network. The result still is in progress and need to apply the other method as well to find the best method in study area. Finally, this research is under way to ahead to get the best result and here just present the preliminary work that polarimetric image of TSX potentially can detect the hedgerow.

Keywords: TerraSAR-X, hedgerow detection, high resolution SAR image, dual polarization, polarimetric analysis

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Authors: Getaneh Berie Tarekegn

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A precise localization system is crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. The most common method for providing continuous positioning services in outdoor environments is by using a global navigation satellite system (GNSS). Due to nonline-of-sight, multipath, and weather conditions, GNSS systems do not perform well in dense urban, urban, and suburban areas.This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. It also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Shankaran Sitarama

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New Product Development (NPD) is invariably a team effort and involves effective teamwork. NPD team has members from different disciplines coming together and working through the different phases all the way from conceptual design phase till the production and product roll out. Creativity and Innovation are some of the key factors of successful NPD. Team members going through the different phases of NPD interact and work closely yet challenge each other during the design phases to brainstorm on ideas and later converge to work together. These two traits require the teams to have a divergent and a convergent thinking simultaneously. There needs to be a good balance. The team dynamics invariably result in conflicts among team members. While some amount of conflict (ideational conflict) is desirable in NPD teams to be creative as a group, relational conflicts (or discords among members) could be detrimental to teamwork. Team communication truly reflect these tensions and team dynamics. In this research, team communication (emails) between the members of the NPD teams is considered for analysis. The email communication is processed through a semantic analysis algorithm (LSA) to analyze the content of communication and a semantic similarity analysis to arrive at a social network graph that depicts the communication amongst team members based on the content of communication. The amount of communication (content and not frequency of communication) defines the interaction strength between the members. Social network adjacency matrix is thus obtained for the team. Standard social network analysis techniques based on the Adjacency Matrix (AM) and Dichotomized Adjacency Matrix (DAM) based on network density yield network graphs and network metrics like centrality. The social network graphs are then rendered for visual representation using a Metric Multi-Dimensional Scaling (MMDS) algorithm for node placements and arcs connecting the nodes (representing team members) are drawn. The distance of the nodes in the placement represents the tie-strength between the members. Stronger tie-strengths render nodes closer. Overall visual representation of the social network graph provides a clear picture of the team’s interactions. This research reveals four distinct patterns of team interaction that are clearly identifiable in the visual representation of the social network graph and have a clearly defined computational scheme. The four computational patterns of team interaction defined are Central Member Pattern (CMP), Subgroup and Aloof member Pattern (SAP), Isolate Member Pattern (IMP), and Pendant Member Pattern (PMP). Each of these patterns has a team dynamics implication in terms of the conflict level in the team. For instance, Isolate member pattern, clearly points to a near break-down in communication with the member and hence a possible high conflict level, whereas the subgroup or aloof member pattern points to a non-uniform information flow in the team and some moderate level of conflict. These pattern classifications of teams are then compared and correlated to the real level of conflict in the teams as indicated by the team members through an elaborate self-evaluation, team reflection, feedback form and results show a good correlation.

Keywords: team dynamics, team communication, team interactions, social network analysis, sna, new product development, latent semantic analysis, LSA, NPD teams

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Authors: Ramprasad Srinivasan

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Engineers create inventions and put their ideas in concrete terms to design new products. Design drivers must be established, which requires, among other things, a complete understanding of the product design, load paths, etc. For Aerospace Vehicles, weight/strength ratio, strength, stiffness and stability are the important design drivers. A complex built-up structure is made up of an assemblage of primitive structural forms of arbitrary shape, which include 1D structures like beams and frames, 2D structures like membranes, plate and shell structures, and 3D solid structures. Justification through simulation involves a check for all the quantities of interest, namely stresses, deformation, frequencies, and buckling loads and is normally achieved through the finite element (FE) method. Over the past few decades, Fiber-reinforced composites are fast replacing the traditional metallic structures in the weight-sensitive aerospace and aircraft industries due to their high specific strength, high specific stiffness, anisotropic properties, design freedom for tailoring etc. Composite panel constructions are used in aircraft to design primary structure components like wings, empennage, ailerons, etc., while thin-walled composite beams (TWCB) are used to model slender structures like stiffened panels, helicopter, and wind turbine rotor blades, etc. The TWCB demonstrates many non-classical effects like torsional and constrained warping, transverse shear, coupling effects, heterogeneity, etc., which makes the analysis of composite structures far more complex. Conventional FE formulations to model 1D structures suffer from many limitations like shear locking, particularly in slender beams, lower convergence rates due to material coupling in composites, inability to satisfy, equilibrium in the domain and natural boundary conditions (NBC) etc. For 2D structures, the limitations of conventional displacement-based FE formulations include the inability to satisfy NBC explicitly and many pathological problems such as shear and membrane locking, spurious modes, stress oscillations, lower convergence due to mesh distortion etc. This mandates frequent re-meshing to even achieve an acceptable mesh (satisfy stringent quality metrics) for analysis leading to significant cycle time. Besides, currently, there is a need for separate formulations (u/p) to model incompressible materials, and a single unified formulation is missing in the literature. Hence coupled field formulation (CFF) is a unified formulation proposed by the author for the solution of complex 1D and 2D structures addressing the gaps in the literature mentioned above. The salient features of CFF and its many advantages over other conventional methods shall be presented in this paper.

Keywords: coupled field formulation, kinematic and material coupling, natural boundary condition, locking free formulation

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Authors: David L. Olson, Stephen B. H. Bruder, Adam S. Watkins, Cleon E. Davis

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In global navigation satellite systems (GNSS), denied settings such as indoor environments, autonomous mobile robots are often limited to dead-reckoning navigation techniques to determine their position, velocity, and attitude (PVA). Localization is typically accomplished by employing an inertial measurement unit (IMU), which, while precise in nature, accumulates errors rapidly and severely degrades the localization solution. Standard sensor fusion methods, such as Kalman filtering, aim to fuse precise IMU measurements with accurate aiding sensors to establish a precise and accurate solution. In indoor environments, where GNSS and no other a priori information is known about the environment, effective sensor fusion is difficult to achieve, as accurate aiding sensor choices are sparse. However, an opportunity arises by employing a depth camera in the indoor environment. A depth camera can capture point clouds of the surrounding floors and walls. Extracting attitude from these surfaces can serve as an accurate aiding source, which directly combats errors that arise due to gyroscope imperfections. This configuration for sensor fusion leads to a dramatic reduction of PVA error compared to traditional aiding sensor configurations. This paper provides the theoretical basis for the depth camera aiding sensor method, initial expectations of performance benefit via simulation, and hardware implementation, thus verifying its veracity. Hardware implementation is performed on the Quanser Qbot 2™ mobile robot, with a Vector-Nav VN-200™ IMU and Kinect™ camera from Microsoft.

Keywords: autonomous mobile robotics, dead reckoning, depth camera, inertial navigation, Kalman filtering, localization, sensor fusion

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Authors: Shamini Dhana, G. Subrahmanya VRK Rao

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The fashion industry is advancing towards a mindset of zero waste, personalization, creativity, and circularity. The trend of upcycling clothing and materials into personalized fashion is being demanded by the next generation. There is a need for a digital tool to accelerate the process towards mass customization. Dhana’s D/Sphere fashion technology platform uses digital tools to accelerate upcycling. In essence, advanced fashion garments can be designed and developed via reuse, repurposing, recreating activities, and using existing fabric and circulating materials. The D/Sphere platform has the following objectives: to provide (1) An opportunity to develop modern fashion using existing, finished materials and clothing without chemicals or water consumption; (2) The potential for an everyday customer and designer to use the medium of fashion for creative expression; (3) A solution to address the global textile waste generated by pre- and post-consumer fashion; (4) A solution to reduce carbon emissions, water, and energy consumption with the participation of all stakeholders; (5) An opportunity for brands, manufacturers, retailers to work towards zero-waste designs and as an alternative revenue stream. Other benefits of this alternative approach include sustainability metrics, trend prediction, facilitation of disassembly and remanufacture deep learning, and hyperheuristics for high accuracy. A design tool for mass personalization and customization utilizing existing circulating materials and deadstock, targeted to fashion stakeholders will lower environmental costs, increase revenues through up to date upcycled apparel, produce less textile waste during the cut-sew-stitch process, and provide a real design solution for the end customer to be part of circular fashion. The broader impact of this technology will result in a different mindset to circular fashion, increase the value of the product through multiple life cycles, find alternatives towards zero waste, and reduce the textile waste that ends up in landfills. This technology platform will be of interest to brands and companies that have the responsibility to reduce their environmental impact and contribution to climate change as it pertains to the fashion and apparel industry. Today, over 70% of the $3 trillion fashion and apparel industry ends up in landfills. To this extent, the industry needs such alternative techniques to both address global textile waste as well as provide an opportunity to include all stakeholders and drive circular fashion with new personalized products. This type of modern systems thinking is currently being explored around the world by the private sector, organizations, research institutions, and governments. This technological innovation using digital tools has the potential to revolutionize the way we look at communication, capabilities, and collaborative opportunities amongst stakeholders in the development of new personalized and customized products, as well as its positive impacts on society, our environment, and global climate change.

Keywords: circular fashion, deep learning, digital technology platform, personalization

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of carbon dioxide (CO2) emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, electricity), CO2 emissions and gross domestic product (GDP) for Egypt using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Augmented Dickey-Fuller (ADF) test for stationarity, Johansen maximum likelihood method for co-integration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests some negative impacts of the CO2 emissions and the coal and natural gas use on the GDP. Conversely, a positive long-run causality from the electricity consumption to the GDP is found to be significant in Egypt during the period. In the short-run, some positive unidirectional causalities exist, running from the coal consumption to the GDP, and the CO2 emissions and the natural gas use. Further, the GDP and the electricity use are positively influenced by the consumption of petroleum products and the direct combustion of crude oil. Overall, the results support arguments that there are relationships among environmental quality, energy use, and economic output in both the short term and long term; however, the effects may differ due to the sources of energy, such as in the case of Egypt for the period of 1980-2010.

Keywords: CO2 emissions, Egypt, energy consumption, GDP, time series analysis

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Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

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Authors: Chukwuma S. Ezeonu, Ikechukwu N. E. Onwurah, Uchechukwu U. Nwodo, Chibuike S. Ubani, Chigozie M. Ejikeme

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Trichophyton soudanense and Trichophyton mentagrophyte were isolated from the rice mill environment, cultured and used singly and as di-culture in the treatment of measure quantities of preheated rice husk. Optimized conditions studied showed that carboxymethylcellulase (CMCellulase) activity of 57.61 µg/ml/min was optimum for Trichophyton mentagrophyte heat pretreated rice husk crude enzymes at 50oC and 80oC respectively. Duration of 120 hours (5 days) gave the highest CMcellulase activity of 75.84 µg/ml/min for crude enzyme of Trichophyton mentagrophyte heat pretreated rice husk. However, 96 hours (4 days) duration gave maximum activity of 58.21 µg/ml/min for crude enzyme of Trichophyton soudanense heat pretreated rice husk. Highest CMCellulase activities of 67.02 µg/ml/min and 69.02 µg/ml/min at pH of 5 were recorded for crude enzymes of monocultures of Trichophyton soudanense (TS) and Trichophyton mentagrophyte (TM) heat pretreated rice husk respectively. Biomass components showed that rice husk cooled after heating followed by treatment with Trichophyton mentagrophyte gave 44.50 ± 10.90 (% ± Standard Error of Mean) cellulose as the highest yield. Maximum total lignin value of 28.90 ± 1.80 (% ± SEM) was obtained from pre-heated rice husk treated with di-culture of Trichophyton soudanense and Trichophyton mentagrophyte (TS+TM). The hemicellulose content of 30.50 ± 2.12 (% ± SEM) from pre-heated rice husk treated with Trichophyton soudanense (TS); lignin value of 28.90 ± 1.80 from pre-heated rice husk treated with di-culture of Trichophyton soudanense and Trichophyton mentagrophyte (TS+TM); also carbohydrate content of 16.79 ± 9.14 (% ± SEM) , reducing and non-reducing sugar values of 2.66 ± 0.45 and 14.13 ± 8.69 (% ± SEM) were all obtained from for pre- heated rice husk treated with Trichophyton mentagrophyte (TM). All the values listed above were the highest values obtained from each rice husk treatment. The pre-heated rice husk treated with Trichophyton mentagrophyte (TM) fermented with palmwine yeast gave bio-ethanol value of 11.11 ± 0.21 (% ± Standard Deviation) as the highest yield.

Keywords: Trichophyton soudanense, Trichophyton mentagrophyte, biomass, bioethanol, rice husk

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Authors: Wei Xiao, Gangzhi Cai, Xingliang Qin, Hongyan Ren, Zaidong Hua, Zhe Zhu, Hongwei Xiao, Ximin Zheng, Jie Yao, Yanzhen Bi

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Myostatin (MSTN) is the master regulator of double muscling phenotype with overgrown muscle and decreased fatness in animals, but its action mode to regulate fat deposition remains to be elucidated. In this study a swin-specific pathway through which MSTN acts to regulate the fat deposition was deciphered. Deep sequenincing of the mRNA and miRNA of fat tissues of MSTN knockout (KO) and wildtype (WT) pigs discovered the positive correlation of myocyte enhancer factor 2C (MEF2C) and fat-inhibiting miR222 expression, and the inverse correlation of miR222 and stearoyl-CoA desaturase 5 (SCD5) expression. SCD5 is rodent-absent and expressed only in pig, sheep and cattle. Fatty acid spectrum of fat tissues revealed a lower percentage of oleoyl-CoA (18:1) and palmitoleyl CoA (16:1) in MSTN KO pigs, which are the catalyzing products of SCD5-mediated desaturation of steroyl CoA (18:0) and palmitoyl CoA (16:0). Blood metrics demonstrated a 45% decline of triglyceride (TG) content in MSTN KO pigs. In light of these observations we hypothesized that MSTN might act through MEF2C/miR222/SCD5 pathway to regulate desaturation of fatty acid as well as triglyceride synthesis in pigs. To this end, real-time PCR and Western blotting were carried out to detect the expression of the three genes stated above. These experiments showed that MEF2C expression was up-regulated by nearly 2-fold, miR222 up-regulated by nearly 3-fold and SCD5 down-regulated by nearly 50% in MSTN KO pigs. These data were consistent with the expression change in deep sequencing analysis. Dual luciferase reporter was then used to confirm the regulation of MEF2C upon the promoter of miR222. Ecotopic expression of MEF2C in preadipocyte cells enhanced miR222 expression by 3.48-fold. CHIP-PCR identified a putative binding site of MEF2C on -2077 to -2066 region of miR222 promoter. Electrophoretic mobility shift assay (EMSA) demonstrated the interaction of MEF2C and miR222 promoter in vitro. These data indicated that MEF2C transcriptionally regulates the expression of miR222. Next, the regulation of miR222 on SCD5 mRNA as well as its physiological consequences were examined. Dual luciferase reporter testing revealed the translational inhibition of miR222 upon the 3´ UTR (untranslated region) of SCD5 in preadipocyte cells. Transfection of miR222 mimics and inhibitors resulted in the down-regulation and up-regulation of SCD5 in preadipocyte cells respectively, consistent with the results from reporter testing. RNA interference of SCD5 in preadipocyte cells caused 26.2% reduction of TG, in agreement with the results of TG content in MSTN KO pigs. In summary, the results above supported the existence of a molecular pathway that MSTN signals through MEF2C/miR222/SCD5 to regulate the fat deposition in pigs. This swine-specific pathway offers potential molecular markers for the development and breeding of a new pig line with optimised fatty acid composition. This would benefit human health by decreasing the takeup of saturated fatty acid.

Keywords: fat deposition, MEF2C, miR222, myostatin, SCD5, pig

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Authors: Hanna Teklu Gebregziabher

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Background: A variety of techniques for medical termination of second-trimester pregnancy can be used, but there is no consensus about which is the best. Even though most evidence suggests the combined use of intracervical Foley catheter and vaginal misoprostol is safe, effective, and acceptable method for termination of second-trimester pregnancy, which is comparable to mifepristone-misoprostol combination regimen with lower cost and no additional maternal risks. The use of mifepristone and misoprostol alone with no other procedure is still the most common procedure in different institutions for 2nd-trimester pregnancy. Methods: A cross-sectional comparative prospective study design is employed on women who were admitted for 2nd-trimester medical abortion and medical abortion failed or if there was no change in cervical status after 24 hours of 1st dose of misoprostol. The study was conducted at St. Paulose Hospital Millennium Medical College. A sample of 44 participants in each arm was necessary to give a two-tailed test, a type 1 error of 5%, 80% statistical power, and a 1:1 ratio among groups. Thus, a total of 94 cases, 47 from each arm, were recruited. Data was entered and cleaned by using Epi-info and analyzed using SPSS version 29.0 statistical software and was presented in descriptive and tabular forms. Different variables were cross-tabulated and compared for significant differences and statistical analysis using the chi-square test and independent t-test, to conclude. Result: There was a significant difference between the two groups on induction to expulsion time and number of doses used. The mean ± SD of induction to expulsion time for those used misoprostol alone was 48.09 ± 11.86 and those who used trans-cervical catheter sequentially with misoprostol were 36.7 ±6.772. Conclusion: The use of a trans-cervical Foley catheter in conjunction with misoprostol in a sequential manner is a more effective, safe, and easily accessible procedure. In addition, the cost of utilizing the catheter is less compared to the cost of misoprostol and is readily available. As a good substitute, we advised using Trans-cervical Catether even for medical abortions performed in the second trimester.

Keywords: second trimester, medical abortion, catheter, misoprostol

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Authors: Mukisa Simon Peter Turker, Etomaru Irene

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This study examined factors determining the success of University-Industry Collaboration (UIC) in the science academic units (SAUs) at Makerere University. This was prompted by concerns about weak linkages between industry and the academic units at Makerere University. The study examined institutional, relational, output, and framework factors determining the success of UIC in the science academic units at Makerere University. The study adopted a predictive cross-sectional survey design. Data was collected using a questionnaire survey from 172 academic staff from the six SAUs at Makerere University. Stratified, proportionate, and simple random sampling techniques were used to select the samples. The study used descriptive statistics and linear multiple regression analysis to analyze data. The study findings reveal a coefficient of determination (R-square) of 0.403 at a significance level of 0.000, suggesting that UIC success was 40.3% at a standardized error of estimate of 0.60188. The strength of association between Institutional factors, Relational factors, Output factors, and Framework factors, taking into consideration all interactions among the study variables, was at 64% (R= 0.635). Institutional, Relational, Output and Framework factors accounted for 34% of the variance in the level of UIC success (adjusted R2 = 0.338). The remaining variance of 66% is explained by factors other than Institutional, Relational, Output, and Framework factors. The standardized coefficient statistics revealed that Relational factors (β = 0.454, t = 5.247, p = 0.000) and Framework factors (β = 0.311, t = 3.770, p = 0.000) are the only statistically significant determinants of the success of UIC in the SAU in Makerere University. Output factors (β = 0.082, t =1.096, p = 0.275) and Institutional factors β = 0.023, t = 0.292, p = 0.771) turned out to be statistically insignificant determinants of the success of UIC in the science academic units at Makerere University. The study concludes that Relational Factors and Framework Factors positively and significantly determine the success of UIC, but output factors and institutional factors are not statistically significant determinants of UIC in the SAUs at Makerere University. The study recommends strategies to consolidate Relational and Framework Factors to enhance UIC at Makerere University and further research on the effects of Institutional and Output factors on the success of UIC in universities.

Keywords: university-industry collaboration, output factors, relational factors, framework factors, institutional factors

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Authors: Liu Canqi, Zeng Junsheng

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This study identifies two typical bottlenecks that occur when a vehicle cannot change lanes: car following and car stopping. The ideas of traffic field and traffic mass are presented in this work. When there are other vehicles in front of the target vehicle within a particular distance, a force is created that affects the target vehicle's driving speed. The characteristics of the driver and the vehicle collectively determine the traffic mass; the driving speed of the vehicle and external variables have no bearing on this. From a physical level, this study examines the vehicle's bottleneck when following a car, identifies the outside factors that have an impact on how it drives, takes into account that the vehicle will transform kinetic energy into potential energy during deceleration, and builds a calculation model for traffic mass. The energy-time conversion coefficient is created from an economic standpoint utilizing the social average wage level and the average cost of motor fuel. Vissim simulation program measures the vehicle's deceleration distance and delays under the Wiedemann car-following model. The difference between the measured value of deceleration delay acquired by simulation and the theoretical value calculated by the model is compared using the conversion calculation model of traffic mass and deceleration delay. The experimental data demonstrate that the model is reliable since the error rate between the theoretical calculation value of the deceleration delay obtained by the model and the measured value of simulation results is less than 10%. The article's conclusion is that the traffic field has an impact on moving cars on the road and that physical and socioeconomic factors should be taken into account while studying vehicle-following behavior. The deceleration delay value of a vehicle's driving and traffic mass have a socioeconomic relationship that can be utilized to calculate the energy-time conversion coefficient when dealing with the bottleneck of cars stopping and starting.

Keywords: traffic field, social economics, traffic mass, bottleneck, deceleration delay

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Authors: D. Naranchimeg, G. Ulziisaikhan

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Student dropout problem has not been recently investigated within the Mongolian higher education. A student dropping out is a personal decision, but it may cause unemployment and other social problems including low quality of life because students who are not completed a degree cannot find better-paid jobs. The research aims to determine percentage of at-risk students, and understand reasons for dropouts and to find a way to predict. The study based on the students of the Mongolian National University of Education including its Arkhangai branch school, National University of Mongolia, Mongolian University of Life Sciences, Mongolian University of Science and Technology, Mongolian National University of Medical Science, Ikh Zasag International University, and Dornod University. We conducted the paper survey by method of random sampling and have surveyed about 100 students per university. The margin of error - 4 %, confidence level -90%, and sample size was 846, but we excluded 56 students from this study. Causes for exclusion were missing data on the questionnaire. The survey has totally 17 questions, 4 of which was demographic questions. The survey shows that 1.4% of the students always thought to dropout whereas 61.8% of them thought sometimes. Also, results of the research suggest that students’ dropouts from university do not have relationships with their sex, marital and social status, and peer and faculty climate, whereas it slightly depends on their chosen specialization. Finally, the paper presents the reasons for dropping out provided by the students. The main two reasons for dropouts are personal reasons related with choosing wrong study program, not liking the course they had chosen (50.38%), and financial difficulties (42.66%). These findings reveal the importance of early prevention of dropout where possible, combined with increased attention to high school students in choosing right for them study program, and targeted financial support for those who are at risk.

Keywords: at risk students, dropout, faculty climate, Mongolian universities, peer climate

Procedia PDF Downloads 381

Authors: Souvik Phadikar, Nidul Sinha, Rajdeep Ghosh

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In brainwaves research using electroencephalogram (EEG) signals, finding the most relevant and effective feature set for identification of activities in the human brain is a big challenge till today because of the random nature of the signals. The feature extraction method is a key issue to solve this problem. Finding those features that prove to give distinctive pictures for different activities and similar for the same activities is very difficult, especially for the number of activities. The performance of a classifier accuracy depends on this quality of feature set. Further, more number of features result in high computational complexity and less number of features compromise with the lower performance. In this paper, a novel idea of the selection of optimal feature set using a heuristically optimized deep autoencoder is presented. Using various feature extraction methods, a vast number of features are extracted from the EEG signals and fed to the autoencoder deep neural network. The autoencoder encodes the input features into a small set of codes. To avoid the gradient vanish problem and normalization of the dataset, a meta-heuristic search algorithm is used to minimize the mean square error (MSE) between encoder input and decoder output. To reduce the feature set into a smaller one, 4 hidden layers are considered in the autoencoder network; hence it is called Heuristically Optimized Deep Autoencoder (HO-DAE). In this method, no features are rejected; all the features are combined into the response of responses of the hidden layer. The results reveal that higher accuracy can be achieved using optimal reduced features. The proposed HO-DAE is also compared with the regular autoencoder to test the performance of both. The performance of the proposed method is validated and compared with the other two methods recently reported in the literature, which reveals that the proposed method is far better than the other two methods in terms of classification accuracy.

Keywords: autoencoder, brainwave signal analysis, electroencephalogram, feature extraction, feature selection, optimization

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Authors: Sima Modirkhameneh, Mohammad Mohammadpanah

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In the process of EFL teachers’ development, self-led reflection (SLR) is thought to have an imperative role because it may help teachers analyze, evaluate, and contemplate what is happening in their classes. Such contemplations can not only enhance the quality of their instruction and provide better learning environments for learners but also improve the quality of their classroom management (CM). Accordingly, understanding the effect of teachers’ SLR practices may help us gain valuable insights into what possible modifications SLR may bring about in all aspects of EFL teachers' practitioners, especially their CM. The main purpose of this case study was, thus, to investigate the impact of SLR practices of 12 Iranian EFL teachers on their CM based on the universal classroom management checklist (UCMC). In addition, another objective of the current study was to have a clear image of EFL teachers’ perceptions of their own SLR practices and their possible outcomes. By conducting repeated reflective interviews, observations, and feedback of the participants over five teaching sessions, the researcher analyzed the outcomes qualitatively through the process of meaning categorization and data interpretation based on the principles of Grounded Theory. The results demonstrated that EFL teachers utilized SLR practices to improve different aspects of their language teaching skills and CM in different contexts. Almost all participants had positive comments and reactions about the effect of SLR on their CM procedures in different aspects (expectations and routines, behavior-specific praise, error corrections, prompts and precorrections, opportunity to respond, strengths and weaknesses of CM, teachers’ perception, CM ability, and learning process). Otherwise stated, results implied that familiarity with the UCMC criteria and reflective practices contributes to modifying teacher participants’ perceptions about their CM procedure and utilizing the reflective practices in their teaching styles. The results are thought to be valuably beneficial for teachers, teacher educators, and policymakers, who are recommended to pay special attention to the contributions as well as the complexity of reflective teaching. The study concludes with more detailed results and implications and useful directions for future research.

Keywords: classroom management, EFL teachers, reflective practices, self-led reflection

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Authors: Abir Dey, Shams Noman

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Introduction: Offshore Island is the remote and isolated area from the terrestrial mainland. They are deprived of their needs. The children from an offshore island are usually underserved in the case of health care because it is a remote area where the health care systems are quite poor compared to mainland. So, the proper information is required for appropriate planning to reduce underlying causes behind visual deprivation among the surviving children of the Offshore Island. Purpose: The purpose of this study was to determine ocular morbidities, knowledge, and barriers of eye care services among children in an Offshore Island. Methods: The study team visited, and all data were collected from different rural communities at Sandwip Upazila, Chittagong district for screening the children aged 5-16 years old by doing spot examination. The whole study was conducted in both qualitative and quantitative methods. To determine ocular status of children, examinations were done under skilled Ophthalmologists and Optometrists. A focus group discussion was held. The sample size was 490. It was a community based descriptive study and the sampling method was purposive sampling. Results: In total 490 children, about 56.90% were female and 43.10% were male. Among them 456 were school-going children (93.1%) and 34 were non-school going children (6.9%). In this study the most common ocular morbidity was Allergic Conjunctivitis (35.2%). Other mentionable ocular morbidities were Refractive error (27.7%), Blepharitis (13.8%), Meibomian Gland Dysfunction (7.5%), Strabismus (6.3%) and Amblyopia (6.3%). Most of the non-school going children were involved in different types of domestic work like farming, fishing, etc. About 90.04% children who had different ocular abnormalities could not attend to the doctor due to various reasons. Conclusions: The ocular morbidity was high in rate on the offshore island. Eye health care facility was also not well established there. Awareness should be raised about necessity of maintaining hygiene and eye healthcare among the island people. Timely intervention through available eye care facilities and management can reduce the ocular morbidity rate in that area.

Keywords: morbidities, screening, barriers, offshore island, knowledge

Procedia PDF Downloads 133

Authors: Maria I. Nuñez-Hernández, Maria L. Riesco

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Background: Exclusive breastfeeding (EBF) up to 6 months old infant have been considered one of the most important factors in the overall development of children. Nevertheless, as resources are scarce, it is essential to identify the most vulnerable groups that have major risk of EBF abandonment, in order to deliver the best strategies. Children of adolescent mothers are within these groups. Aims: To determine the EBF abandonment rate among adolescent mothers and to analyze the associated factors. Methods: Prospective cohort study of adolescent mothers in the southern area of Santiago, Chile, conducted in primary care services of public health system. The cohort was established from 2014 to 2015, with a sample of 105 adolescent mothers and their children at 2 months of life. The inclusion criteria were: adolescent mother from 14 to 19 years old; not twin babies; mother and baby leaving the hospital together after birthchild; correct attachment of the baby to the breast; no difficulty understanding the Spanish language or communicating. Follow-up was performed at 4 and 6 months old infant. Data were collected by interviews, considering EBF as breastfeeding only, without adding other milk, tea, juice, water or other product that not breast milk, except drugs. Data were analyzed by descriptive and inferential statistics, by Kaplan-Meier estimator and Log-Rank test, admitting the probability of occurrence of type I error of 5% (p-value = 0.05). Results: The cumulative EBF abandonment rate at 2, 4 and 6 months was 33.3%, 52.2% and 63.8%, respectively. Factors associated with EBF abandonment were maternal perception of the quality of milk as poor (p < 0.001), maternal perception that the child was not satisfied after breastfeeding (p < 0.001), use of pacifier (p < 0.001), maternal consumption of illicit drugs after delivery (p < 0.001), mother return to school (p = 0.040) and presence of nipple trauma (p = 0.045). Conclusion: EBF abandonment rate was higher in the first 4 months of life and is superior to the population of women who breastfeed. Among the EBF abandonment factors, one of them is related to the adolescent condition, and two are related to the maternal subjective perception.

Keywords: adolescent, breastfeeding, midwifery, nursing

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Authors: Aschalew Cherie Workneh, K. S. Hari Prasad, Chandra Shekhar Prasad Ojha

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Due to the increase in water scarcity, the crop water stress index (CWSI) is receiving significant attention these days, especially in arid and semiarid regions, for quantifying water stress and effective irrigation scheduling. Nowadays, machine learning techniques such as neural networks are being widely used to determine CWSI. In the present study, the performance of two artificial neural networks, namely, Self-Organizing Maps (SOM) and Feed Forward-Back Propagation Artificial Neural Networks (FF-BP-ANN), are compared while determining the CWSI of rice crop. Irrigation field experiments with varying degrees of irrigation were conducted at the irrigation field laboratory of the Indian Institute of Technology, Roorkee, during the growing season of the rice crop. The CWSI of rice was computed empirically by measuring key meteorological variables (relative humidity, air temperature, wind speed, and canopy temperature) and crop parameters (crop height and root depth). The empirically computed CWSI was compared with SOM and FF-BP-ANN predicted CWSI. The upper and lower CWSI baselines are computed using multiple regression analysis. The regression analysis showed that the lower CWSI baseline for rice is a function of crop height (h), air vapor pressure deficit (AVPD), and wind speed (u), whereas the upper CWSI baseline is a function of crop height (h) and wind speed (u). The performance of SOM and FF-BP-ANN were compared by computing Nash-Sutcliffe efficiency (NSE), index of agreement (d), root mean squared error (RMSE), and coefficient of correlation (R²). It is found that FF-BP-ANN performs better than SOM while predicting the CWSI of rice crops.

Keywords: artificial neural networks; crop water stress index; canopy temperature, prediction capability

Procedia PDF Downloads 88

Authors: Kayode Ilesanmi Ebenezer Bowale, Dominic Azuh, Busayo Aderounmu, Alfred Ilesanmi

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There has been a debate among scholars and policymakers about the effects of oil exploration and production on industrial development. In Nigeria, there were many reforms resulting in an increase in crude oil production in the recent past. There is a controversy on the importance of oil production in the development of the manufacturing sector in Nigeria. Some scholars claim that oil has been a blessing to the development of the manufacturing sector, while others regard it as a curse. The objective of the study is to determine if empirical analysis supports the presence of Dutch Disease and de-industrialisation in the Nigerian manufacturing sector between 2019- 2022. The study employed data that were sourced from World Development Indicators, Nigeria Bureau of Statistics, and the Central Bank of Nigeria Statistical Bulletin on manufactured exports, manufacturing employment, agricultural employment, and service employment in line with the theory of Dutch Disease using the unit root test to establish their level of stationarity, Engel and Granger cointegration test to check their long-run relationship. Autoregressive. Distributed Lagged bound test was also used. The Vector Error Correction Model will be carried out to determine the speed of adjustment of the manufacturing export and resource movement effect. The results showed that the Nigerian manufacturing industry suffered from both direct and indirect de-industrialisation over the period. The findings also revealed that there was resource movement as labour moved away from the manufacturing sector to both the oil sector and the services sector. The study concluded that there was the presence of Dutch Disease in the manufacturing industry, and the problem of de-industrialisation led to the crowding out of manufacturing output. The study recommends that efforts should be made to diversify the Nigerian economy. Furthermore, a conducive business environment should be provided to encourage more involvement of the private sector in the agriculture and manufacturing sectors of the economy.

Keywords: Dutch disease, resource movement, manufacturing sector performance, Nigeria

Procedia PDF Downloads 55

Authors: Rindra Rantoson, Hichem Nouira, Nabil Anwer, Charyar Mehdi-Souzani

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Multiple measurements by means of various data acquisition systems are generally required to measure the shape of freeform workpieces for accuracy, reliability and holisticity. The obtained data are aligned and fused into a common coordinate system within a registration technique involving coarse and fine registrations. Standardized iterative methods have been established for fine registration such as Iterative Closest Points (ICP) and its variants. For coarse registration, no conventional method has been adopted yet despite a significant number of techniques which have been developed in the literature to supply an automatic rough matching between data sets. Two main issues are addressed in this paper: the coarse registration and the fine registration. For coarse registration, two novel automated methods based on the exploitation of discrete curvatures are presented: an enhanced Hough Transformation (HT) and an improved Ransac Transformation. The use of curvature features in both methods aims to reduce computational cost. For fine registration, a new variant of ICP method is proposed in order to reduce registration error using curvature parameters. A specific distance considering the curvature similarity has been combined with Euclidean distance to define the distance criterion used for correspondences searching. Additionally, the objective function has been improved by combining the point-to-point (P-P) minimization and the point-to-plane (P-Pl) minimization with automatic weights. These ones are determined from the preliminary calculated curvature features at each point of the workpiece surface. The algorithms are applied on simulated and real data performed by a computer tomography (CT) system. The obtained results reveal the benefit of the proposed novel curvature-based registration methods.

Keywords: discrete curvature, RANSAC transformation, hough transformation, coarse registration, ICP variant, point-to-point and point-to-plane minimization combination, computer tomography

Procedia PDF Downloads 406

Authors: Isaac Mugume, Charles Basalirwa, Daniel Waiswa, Triphonia Ngailo

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Rainfall is a major climatic parameter affecting many sectors such as health, agriculture and water resources. Its quantitative prediction remains a challenge to weather forecasters although numerical weather prediction models are increasingly being used for rainfall prediction. The performance of six convective parameterization schemes, namely the Kain-Fritsch scheme, the Betts-Miller-Janjic scheme, the Grell-Deveny scheme, the Grell-3D scheme, the Grell-Fretas scheme, the New Tiedke scheme of the weather research and forecast (WRF) model regarding quantitative rainfall prediction over Uganda is investigated using the root mean square error for the March-May (MAM) 2013 season. The MAM 2013 seasonal rainfall amount ranged from 200 mm to 900 mm over Uganda with northern region receiving comparatively lower rainfall amount (200–500 mm); western Uganda (270–550 mm); eastern Uganda (400–900 mm) and the lake Victoria basin (400–650 mm). A spatial variation in simulated rainfall amount by different convective parameterization schemes was noted with the Kain-Fritsch scheme over estimating the rainfall amount over northern Uganda (300–750 mm) but also presented comparable rainfall amounts over the eastern Uganda (400–900 mm). The Betts-Miller-Janjic, the Grell-Deveny, and the Grell-3D underestimated the rainfall amount over most parts of the country especially the eastern region (300–600 mm). The Grell-Fretas captured rainfall amount over the northern region (250–450 mm) but also underestimated rainfall over the lake Victoria Basin (150–300 mm) while the New Tiedke generally underestimated rainfall amount over many areas of Uganda. For deterministic rainfall prediction, the Grell-Fretas is recommended for rainfall prediction over northern Uganda while the Kain-Fritsch scheme is recommended over eastern region.

Keywords: convective parameterization schemes, March-May 2013 rainfall season, spatial variation of parameterization schemes over Uganda, WRF model

Procedia PDF Downloads 294

Authors: Chia-Wen Yeh, Ting-Sheng Lin, Chih-Han Chang

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Pressure garment has been used to prevent and treat the hypertrophic scars following serious burns since 1970s. The use of pressure garment is believed to hasten the maturation process and decrease the highness of scars. Pressure garment is custom made by reducing circumferential measurement of the patient by 10%~20%, called Reduction Factor. However the exact reducing value used depends on the subjective judgment of the therapist and the feeling of patients throughout the try and error process. The Laplace Law can be applied to calculate the pressure from the dimension of the pressure garment by the circumferential measurements of the patients and the tension profile of the fabrics. The tension profile currently obtained neglects the stress relaxation and hysteresis effect within most elastic fabrics. The purpose of this study was to investigate the influence of the tension attenuation, from stress relaxation and hysteresis effect of the fabrics. Samples of pressure garment were obtained from Sunshine Foundation Organization, a nonprofit organization for burn patients in Taiwan. The wall tension profile of pressure garments were measured on a material testing system. Specimens were extended to 10% of the original length, held for 1 hour for the influence of the stress relaxation effect to take place. Then, specimens were extended to 15% of the original length for 10 seconds, then reduced to 10% to simulate donning movement for the influence of the hysteresis effect to take place. The load history was recorded. The stress relaxation effect is obvious from the load curves. The wall tension is decreased by 8.5%~10% after 60mins of holding. The hysteresis effect is obvious from the load curves. The wall tension is increased slightly, then decreased by 1.5%~2.5% and lower than stress relaxation results after 60mins of holding. The wall tension attenuation of the fabric exists due to stress relaxation and hysteresis effect. The influence of hysteresis is more than stress relaxation. These effect should be considered in order to design and evaluate the pressure of pressure garment more accurately.

Keywords: hypertrophic scars, hysteresis, pressure garment, stress relaxation

Procedia PDF Downloads 492

Authors: Maryam Kheirollahpour, Mahmoud Danaee, Amir Faisal Merican, Asma Ahmad Shariff

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Background: The presence of nonlinearity among the risk factors of eating behavior causes a bias in the prediction models. The accuracy of estimation of eating behaviors risk factors in the primary prevention of obesity has been established. Objective: The aim of this study was to explore the potential of a hybrid model of structural equation modeling (SEM) and Artificial Neural Networks (ANN) to predict eating behaviors. Methods: The Partial Least Square-SEM (PLS-SEM) and a hybrid model (SEM-Artificial Neural Networks (SEM-ANN)) were applied to evaluate the factors affecting eating behavior patterns among university students. 340 university students participated in this study. The PLS-SEM analysis was used to check the effect of emotional eating scale (EES), body shape concern (BSC), and body appreciation scale (BAS) on different categories of eating behavior patterns (EBP). Then, the hybrid model was conducted using multilayer perceptron (MLP) with feedforward network topology. Moreover, Levenberg-Marquardt, which is a supervised learning model, was applied as a learning method for MLP training. The Tangent/sigmoid function was used for the input layer while the linear function applied for the output layer. The coefficient of determination (R²) and mean square error (MSE) was calculated. Results: It was proved that the hybrid model was superior to PLS-SEM methods. Using hybrid model, the optimal network happened at MPLP 3-17-8, while the R² of the model was increased by 27%, while, the MSE was decreased by 9.6%. Moreover, it was found that which one of these factors have significantly affected on healthy and unhealthy eating behavior patterns. The p-value was reported to be less than 0.01 for most of the paths. Conclusion/Importance: Thus, a hybrid approach could be suggested as a significant methodological contribution from a statistical standpoint, and it can be implemented as software to be able to predict models with the highest accuracy.

Keywords: hybrid model, structural equation modeling, artificial neural networks, eating behavior patterns

Procedia PDF Downloads 123

Authors: Shawndra Bowers, Allie Brandriet, Betsy Gilbertson

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This paper will highlight how Auburn Online’s instructional designers leveraged student and faculty data to update and improve online course design and instructional materials. When designing and revising online courses, it can be difficult for faculty to know what strategies are most likely to engage learners and improve educational outcomes in a specific discipline. It can also be difficult to identify which metrics are most useful for understanding and improving teaching, learning, and course design. At Auburn Online, the instructional designers use a suite of data based student’s performance, participation, satisfaction, and engagement, as well as faculty perceptions, to inform sound learning and design principles that guide growth-mindset consultations with faculty. The consultations allow the instructional designer, along with the faculty member, to co-create an actionable course improvement plan. Auburn Online gathers learning analytics from a variety of sources that any instructor or instructional design team may have access to at their own institutions. Participation and performance data, such as page: views, assignment submissions, and aggregate grade distributions, are collected from the learning management system. Engagement data is pulled from the video hosting platform, which includes unique viewers, views and downloads, the minutes delivered, and the average duration each video is viewed. Student satisfaction is also obtained through a short survey that is embedded at the end of each instructional module. This survey is included in each course every time it is taught. The survey data is then analyzed by an instructional designer for trends and pain points in order to identify areas that can be modified, such as course content and instructional strategies, to better support student learning. This analysis, along with the instructional designer’s recommendations, is presented in a comprehensive report to instructors in an hour-long consultation where instructional designers collaborate with the faculty member on how and when to implement improvements. Auburn Online has developed a triage strategy of priority 1 or 2 level changes that will be implemented in future course iterations. This data-informed decision-making process helps instructors focus on what will best work in their teaching environment while addressing which areas need additional attention. As a student-centered process, it has created improved learning environments for students and has been well received by faculty. It has also shown to be effective in addressing the need for improvement while removing the feeling the faculty’s teaching is being personally attacked. The process that Auburn Online uses is laid out, along with the three-tier maintenance and revision guide that will be used over a three-year implementation plan. This information can help others determine what components of the maintenance and revision plan they want to utilize, as well as guide them on how to create a similar approach. The data will be used to analyze, revise, and improve courses by providing recommendations and models of good practices through determining and disseminating best practices that demonstrate an impact on student success.

Keywords: data-driven, improvement, online courses, faculty development, analytics, course design

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Authors: Jinhoa Lee

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The relationships between environmental quality, energy use and economic output have created growing attention over the past decades among researchers and policy makers. Focusing on the empirical aspects of the role of CO2 emissions and energy use in affecting the economic output, this paper is an effort to fulfill the gap in a comprehensive case study at a country level using modern econometric techniques. To achieve the goal, this country-specific study examines the short-run and long-run relationships among energy consumption (using disaggregated energy sources: crude oil, coal, natural gas, electricity), carbon dioxide (CO2) emissions and gross domestic product (GDP) for Israel using time series analysis from the year 1980-2010. To investigate the relationships between the variables, this paper employs the Phillips–Perron (PP) test for stationarity, Johansen maximum likelihood method for cointegration and a Vector Error Correction Model (VECM) for both short- and long-run causality among the research variables for the sample. The long-run equilibrium in the VECM suggests significant positive impacts of coal and natural gas consumptions on GDP in Israel. In the short run, GDP positively affects coal consumption. While there exists a positive unidirectional causality running from coal consumption to consumption of petroleum products and the direct combustion of crude oil, there exists a negative unidirectional causality running from natural gas consumption to consumption of petroleum products and the direct combustion of crude oil in the short run. Overall, the results support arguments that there are relationships among environmental quality, energy use and economic output but the associations can to be differed by the sources of energy in the case of Israel over of period 1980-2010.

Keywords: CO2 emissions, energy consumption, GDP, Israel, time series analysis

Procedia PDF Downloads 629

Authors: Rebecca Lees, Deborah Anderson

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Whilst the majority of research into social media in education focuses on the applications for teaching and learning environments, this study looks at how such activities can be managed by investigating the current state of social media regulation within UK higher education. Social media has pervaded almost all aspects of higher education; from marketing, recruitment and alumni relations to both distance and classroom-based learning and teaching activities. In terms of who uses it and how it is used, social media is growing at an unprecedented rate, particularly amongst the target market for higher education. Whilst the platform presents opportunities not found in more traditional methods of communication and interaction, such as speed and reach, it also carries substantial risks that come with inappropriate use, lack of control and issues of privacy. Typically, organisations rely on the concept of a social contract to guide employee behaviour to conform to the expectations of that organisation. Yet, where academia and social media intersect applying the notion of a social contract to enforce governance may be problematic; firstly considering the emphasis on treating students as customers with a growing focus on the use and collection of satisfaction metrics; and secondly regarding the notion of academic’s freedom of speech, opinion and discussion, which is a long-held tradition of learning instruction. Therefore the need for sound governance procedures to support expectations over online behaviour is vital, especially when the speed and breadth of adoption of social media activities has in the past outrun organisations’ abilities to manage it. An analysis of the current level of governance was conducted by gathering relevant policies, guidelines and best practice documentation available online via internet search and institutional requests. The documents were then subjected to a content analysis in the second phase of this study to determine the approach taken by institutions to apply such governance. Documentation was separated according to audience, i.e.: applicable to staff, students or all users. Given many of these included guests and visitors to the institution within their scope being easily accessible was considered important. Yet, within the UK only about half of all education institutions had explicit social media governance documentation available online without requiring member access or considerable searching. Where they existed, the majority focused solely on employee activities and tended to be policy based rather than rooted in guidelines or best practices, or held a fallback position of governing online behaviour via implicit instructions within IT and computer regulations. Explicit instructions over expected online behaviours is therefore lacking within UK HE. Given the number of educational practices that now include significant online components, it is imperative that education organisations keep up to date with the progress of social media use. Initial results from the second phase of this study which analyses the content of the governance documentation suggests they require reading levels at or above the target audience, with some considerable variability in length and layout. Further analysis will add to this growing field of investigating social media governance within higher education.

Keywords: governance, higher education, policy, social media

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Authors: Jonathan Ham, Christopher Koch

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There are consistently observed associations between certain odors and colors, and there is an association between the ability to imagine vivid visual objects and imagine vivid odors. However, little has been done to investigate how the associations between odors and visual information effect visual processes. This study seeks to understand the relationship between odor imaging, color associations, and visual attention by utilizing a Stroop-task based on common odor-color associations. This Stroop-task was designed using three fruits with distinct odors that are associated with the color of the fruit: lime with green, strawberry with red, and lemon with yellow. Each possible word-color combination was presented in the experimental trials. When the word matched the associated color (lime written in green) it was considered congruent; if it did not, it was considered incongruent (lime written in red or yellow). In experiment I (n = 34) participants were asked to both imagine the odor of the fruit on the screen and identify which fruit it was, and each word-color combination was presented 20 times (a total of 180 trials, with 60 congruent and 120 incongruent instances). Response time and error rate of the participant responses were recorded. There was no significant difference in either measure between the congruent and incongruent trials. In experiment II participants (n = 18) followed the identical procedure as in the previous experiment with the addition of an odorant in the room. The odorant (orange) was not the fruit or color used in the experimental trials. With a fruit-based odorant in the room, the response times (measured in milliseconds) between congruent and incongruent trials were significantly different, with incongruent trials (M = 755.919, SD = 239.854) having significantly longer response times than congruent trials (M = 690.626, SD = 198.822), t (1, 17) = 4.154, p < 0.01. This suggests that odor imagery does affect visual attention to colors, and the ability to inhibit odor-color associations; however, odor imagery is difficult and appears to be facilitated in the presence of a related odorant.

Keywords: odor-color associations, odor imagery, visual attention, inhibition

Procedia PDF Downloads 154