Search results for: multimedia learning tools

Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Mehdi Khaki, Ehsan Forootan, Joseph Awange, Michael Kuhn

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Terrestrial water storage, as a source of freshwater, plays an important role in human lives. Hydrological models offer important tools for simulating and predicting water storages at global and regional scales. However, their comparisons with 'reality' are imperfect mainly due to a high level of uncertainty in input data and limitations in accounting for all complex water cycle processes, uncertainties of (unknown) empirical model parameters, as well as the absence of high resolution (both spatially and temporally) data. Data assimilation can mitigate this drawback by incorporating new sets of observations into models. In this effort, we use multi-mission satellite-derived remotely sensed observations to improve the performance of World-Wide Water Resources Assessment system (W3RA) hydrological model for estimating terrestrial water storages. For this purpose, we assimilate total water storage (TWS) data from the Gravity Recovery And Climate Experiment (GRACE) and surface soil moisture data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) into W3RA. This is done to (i) improve model estimations of water stored in ground and soil moisture, and (ii) assess the impacts of each satellite of data (from GRACE and AMSR-E) and their combination on the final terrestrial water storage estimations. These data are assimilated into W3RA using the Ensemble Square-Root Filter (EnSRF) filtering technique over Mississippi Basin (the United States) and Murray-Darling Basin (Australia) between 2002 and 2013. In order to evaluate the results, independent ground-based groundwater and soil moisture measurements within each basin are used.

Keywords: data assimilation, GRACE, AMSR-E, hydrological model, EnSRF

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Authors: Khadijat T. Bamigbade, Olufade F. W. Onifade

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The field of automatic facial expression analysis has been an active research area in the last two decades. Its vast applicability in various domains has drawn so much attention into developing techniques and dataset that mirror real life scenarios. Many techniques such as Local Binary Patterns and its variants (CLBP, LBP-TOP) and lately, deep learning techniques, have been used for facial expression recognition. However, the problem of occlusion has not been sufficiently handled, making their results not applicable in real life situations. This paper develops a simple, yet highly efficient method tagged Local Binary Pattern-Histogram of Gradient (LBP-HOG) with occlusion detection in face image, using a multi-class SVM for Action Unit and in turn expression recognition. Our method was evaluated on three publicly available datasets which are JAFFE, CK, SFEW. Experimental results showed that our approach performed considerably well when compared with state-of-the-art algorithms and gave insight to occlusion detection as a key step to handling expression in wild.

Keywords: automatic facial expression analysis, local binary pattern, LBP-HOG, occlusion detection

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Authors: Dinesh Khanduja, Pardeep Kumar Sharma

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Education as the most important resource in any country has multiplying effects on all facets of development in a society. The new social realities, particularly, the interplay between democratization of education; unprecedented developments in the IT sector; emergence of knowledge society, liberalization of economy, and globalization have greatly influenced the educational process of all nations. This turbulence entails upon education to undergo dramatic changes to keep up with the new expectations. Growth of entrepreneurship among Indian women is highly important for empowering them and this is highly essential for the socio-economic development of a society. Unfortunately, in India, there is poor acceptance of entrepreneurship among women as unfounded myths and fears restrain them to be enterprising. To remove these inhibitions, the education system needs to be re-engineered to make entrepreneurship more acceptable. This paper empirically analyses the results of a survey done on around 500 female graduates in North India to measure and evaluate various entrepreneurial traits present in them. A formative model has been devised in this context, which should improve the teaching-learning process in our education system, which can lead to a sustainable growth of women entrepreneurship in India.

Keywords: women empowerment, entrepreneurship, education system, women entrepreneurship, sustainable development

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Authors: Mikkel Snorre Wilms Boysen

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From a theoretical perspective, expertise is generally considered a precondition for creativity. The assumption is that an individual needs to master the common and accepted rules and techniques within a certain knowledge-domain in order to create something new and valuable. However, real life cases, and a limited amount of empirical studies, demonstrate that this assumption may be overly simple. In this article, this question is explored through a number of semi-experimental case studies conducted within the fields of music, technology, and youth culture. The studies indicate that, in various ways, expertise plays an important part in creative processes. However, the case studies also indicate that expertise sometimes leads to an entrenched perspective, in the sense that knowledge and experience may work as a path into the well-known rather than into the unknown. In this article, these issues are explored with reference to different theoretical approaches to creativity and learning, including actor-network theory, the theory of blind variation and selective retention, and Csikszentmihalyi’s system model. Finally, some educational aspects and implications of this are discussed.

Keywords: creativity, expertise , education, technology

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Authors: Laura Hervai

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One of the most significant phenomena of the 2000s was the emergence of social media sites and web 2.0 that revolutionized communication processes. Social networking platforms have fundamentally changed social and political participation of the public, which require organisations in the public and non-profit sector not only to adapt to these new trends but also to actively engage their audiences. Opportunity for interaction, freer expression of opinion and the proliferation of user generated content are major changes brought by web 2.0 technologies. Furthermore, due to the wide penetration of mobile technologies, social media sites are capable of connecting underdeveloped regions to the global flow of information. Taking advantage of these characteristics, organisations have the opportunity to engage much wider audiences, exploit new ways to raise awareness or reach out to regions that are difficult to access. The early adopters of these new communication tools soon recognized the need of developing social media guidelines for their organisations as well as the increased workload that they require. While ten years ago communication officers could handle their organisation’s social media presence, today it is a separate profession. International organisations face several challenges related to their social media presence. Early adopters have contributed to the development of best practices among which the ethics of social media usage still remained problematic. Another challenge for international organisations is to adapt to country-specific social media trends while they have to comply with the requirements of their parent organisation as well. However in the 21st century social media presence can be crucial to the successful operation of international organisations, their importance is still not taken seriously enough. The measurement of the effects and influence of social networking on the organisations’ productivity is an unsolved problem thus further research should focus on this matter. Research methods included primary research of major IGOs’ and NGOs’ social media presence and guidelines along with secondary research of social media statistics and scientific articles in the topic.

Keywords: international organisations, non-profit sector, NGO, social media, social network

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Authors: Farahnaz Karami

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Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio.

Keywords: mobile ad hoc networks, routing, quality of service, ant colony, fuzzy logic

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Authors: Proud Arunrangsiwed, Sarinya Kongtieng

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Teachers and university lecturers face an unsolved problem, which is students’ multitasking behaviors during class time, such as texting or playing a game. It is important to examine the most powerful predictor that can result in students’ educational performances. Meta-analysis was used to analyze the research articles, which were published with the keywords, multitasking, class performance, and texting. We selected 14 research articles published during 2008-2013 from online databases, and four articles met the predetermined inclusion criteria. Effect size of each pair of variables was used as the dependent variable. The findings revealed that the students’ expectancy and value on SNSs usages is the best significant predictor of their educational performances, followed by their motivation and ability in using SNSs, prior educational performances, usage behaviors of SNSs in class, and their personal characteristics, respectively. Future study should conduct a longitudinal design to better understand the effect of multitasking in the classroom.

Keywords: meta-regression analysis, social networking sites, academic Performances, multitasking, motivation

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Authors: E. N. Masri, E. Takács

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Amplitude Versus Offset (AVO) and simultaneous model-based impedance inversion techniques have not been utilized for geothermal exploration commonly; however, some recent publications called the attention that they can be very useful in the geothermal investigations. In this study, we present rock physical attributes obtained from 3D pre-stack seismic data and well logs collected in a study area of the NW part of Pannonian Basin where the geothermal reservoir is located in the fractured zones of Triassic basement and it was hit by three productive-injection well pairs. The holes were planned very successfully based on the conventional 3D migrated stack volume prior to this study. Subsequently, the available geophysical-geological datasets provided a great opportunity to test modern inversion procedures in the same area. In this presentation, we provide a summary of the theory and application of the most promising seismic inversion techniques from the viewpoint of geothermal exploration. We demonstrate P- and S-wave impedance, as well as the velocity (Vp and Vs), the density, and the Vp/Vs ratio attribute volumes calculated from the seismic and well-logging data sets. After a detailed discussion, we conclude that P-wave impedance and Vp/Vp ratio are the most helpful parameters for lithology discrimination in the study area. They detect the hot water saturated fracture zone very well thus they can be very useful in mapping the investigated reservoir. Integrated interpretation of all the obtained rock-physical parameters is essential. We are extending the above discussed pre-stack seismic tools by studying the possibilities of Elastic Impedance Inversion (EII) for geothermal exploration. That procedure provides two other useful rock-physical properties, the compressibility and the rigidity (Lamé parameters). Results of those newly created elastic parameters will also be demonstrated in the presentation. Geothermal extraction is of great interest nowadays; and we can adopt several methods have been successfully applied in the hydrocarbon exploration for decades to discover new reservoirs and reduce drilling risk and cost.

Keywords: fractured zone, seismic, well-logging, inversion

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Authors: L. C. Ansanelli, P. Alfredini

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The article presents the evaluation of the effectiveness of two groins located at Gonzaguinha and Milionários Beaches, situated on the southeast coast of Brazil. The effectiveness of these coastal defense structures is evaluated in terms of sedimentary dynamics, which is one of the most important environmental processes to be assessed in coastal engineering studies. The applied method is based on the implementation of the Delft3D numerical model system tools. Delft3D-WAVE module was used for waves modelling, Delft3D-FLOW for hydrodynamic modelling and Delft3D-SED for sediment transport modelling. The calibration of the models was carried out in a way that the simulations adequately represent the region studied, evaluating improvements in the model elements with the use of statistical comparisons of similarity between the results and waves, currents and tides data recorded in the study area. Analysis of the maximum wave heights was carried to select the months with higher accumulated energy to implement these conditions in the engineering scenarios. The engineering studies were performed for two scenarios: 1) numerical simulation of the area considering only the two existing groins; 2) conception of breakwaters coupled at the ends of the existing groins, resulting in two “T” shaped structures. The sediment model showed that, for the simulated period, the area is affected by erosive processes and that the existing groins have little effectiveness in defending the coast in question. The implemented T structures showed some effectiveness in protecting the beaches against erosion and provided the recovery of the portion directly covered by it on the Milionários Beach. In order to complement this study, it is suggested the conception of further engineering scenarios that might recover other areas of the studied region.

Keywords: coastal engineering, coastal erosion, Sao Vicente bay, Delft3D, coastal engineering works

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Authors: Enes Yasa, Guven Fidan

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Building Simulation tools need to better evaluate convective heat exchanges between external air and wall surfaces. Previous analysis demonstrated the significant effects of convective heat transfer coefficient values on the room energy balance. Some authors have pointed out that large discrepancies observed between widely used building thermal models can be attributed to the different correlations used to calculate or impose the value of the convective heat transfer coefficients. Moreover, numerous researchers have made sensitivity calculations and proved that the choice of Convective Heat Transfer Coefficient values can lead to differences from 20% to 40% of energy demands. The thermal losses to the ambient from a building surface or a roof mounted solar collector represent an important portion of the overall energy balance and depend heavily on the wind induced convection. In an effort to help designers make better use of the available correlations in the literature for the external convection coefficients due to the wind, a critical discussion and a suitable tabulation is presented, on the basis of algebraic form of the coefficients and their dependence upon characteristic length and wind direction, in addition to wind speed. Many research works have been conducted since early eighties focused on the convection heat transfer problems inside buildings. In this context, a Computational Fluid Dynamics (CFD) program has been used to predict external convective heat transfer coefficients at external building surfaces. For the building facades model, effects of wind speed and temperature differences between the surfaces and the external air have been analyzed, showing different heat transfer conditions and coefficients. In order to provide further information on external convective heat transfer coefficients, a numerical work is presented in this paper, using a Computational Fluid Dynamics (CFD) commercial package (CFX) to predict convective heat transfer coefficients at external building surface.

Keywords: CFD in buildings, external convective heat transfer coefficients, building facades, thermal modelling

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Authors: Aisulu Tileukulova, Zhexebay Dauren

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Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Ilma Robo, Saimir Heta, Nedja Hysi, Vera Ostreni

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Introduction: Marginal gingivitis is a disease with considerable frequency among patients who present routinely for periodontal control and treatment. In fact, this disease may not have alarming symptoms in patients and may go unnoticed by themselves when personal hygiene conditions are optimal. The aim of this study was to collect retrograde data on the prevalence of marginal gingiva in the respective group of patients, evaluated according to specific periodontal diagnostic tools. Materials and methods: The study was conducted in two patient groups. The first group was with 34 patients, during December 2019-January 2020, and the second group was with 64 patients during 2010-2018 (each year in the mentioned monthly period). Bacterial plaque index, hemorrhage index, amount of gingival fluid, presence of xerostomia and candidiasis were recorded in patients. Results: Analysis of the collected data showed that susceptibility to marginal gingivitis shows higher values according to retrograde data, compared to cross-sectional ones. Susceptibility to candidiasis and the occurrence of xerostomia, even in the combination of both pathologies, as risk factors for the occurrence of marginal gingivitis, show higher values ​​according to retrograde data. The female are presented with a reduced bacterial plaque index than the males, but more importantly, this index in the females is also associated with a reduced index of gingival hemorrhage, in contrast to the males. Conclusions: Cross-sectional data show that the prevalence of marginal gingivitis is more reduced, compared to retrograde data, based on the hemorrhage index and the bacterial plaque index together. Changes in production in the amount of gingival fluid show a higher prevalence of marginal gingivitis in cross-sectional data than in retrograde data; this is based on the sophistication of the way data are recorded, which evolves over time and also based on professional sensitivity to this phenomenon.

Keywords: marginal gingivitis, cross-sectional, retrograde, prevalence

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Authors: Edward Guillén, Jeisson Sánchez, Carlos Omar Ramos

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In recent years, a wide variety of applications are developed with Support Vector Machines -SVM- methods and Artificial Neural Networks -ANN-. In general, these methods depend on intrusion knowledge databases such as KDD99, ISCX, and CAIDA among others. New classes of detectors are generated by machine learning techniques, trained and tested over network databases. Thereafter, detectors are employed to detect anomalies in network communication scenarios according to user’s connections behavior. The first detector based on training dataset is deployed in different real-world networks with mobile and non-mobile devices to analyze the performance and accuracy over static detection. The vulnerabilities are based on previous work in telemedicine apps that were developed on the research group. This paper presents the differences on detections results between some network scenarios by applying traditional detectors deployed with artificial neural networks and support vector machines.

Keywords: anomaly detection, back-propagation neural networks, network intrusion detection systems, support vector machines

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Authors: Maatouk Mohamed, Nouira Mariem, Hamdi Kbir Gh, Mahjoubi M. F., Ben Moussa M.

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Background and aim: Preoperative biliary drainage (PBD) has been introduced to lower bilirubin levels and to control the negative effects of obstructive jaundice in patients with malignant obstructive jaundice undergoing pancreaticoduodenectomy (PD). The optimal time interval between PBD and PD is still not clear. Delaying surgery by 4 to 6 weeks is the commonly accepted practice. However, delayed PD has been shown to decrease the rate of resection and adversely affect the tumor grading and prognosis. Thus, the purpose of our systematic review and meta-analysis was to evaluate the optimal period for PBD prior to PD: short or prolonged in terms of postoperative morbidity and survival outcomes. Methods: Trials were searched in PubMed, Science Direct, Google Scholar, and Cochrane Library until November 2022. Studies using PBD in patients with malignant obstructive jaundice that compared short duration group (SDG) (surgery performed within 3-4 weeks) with prolonged duration group (PDG) (at least 3-4 weeks after PBD) were included in this study. The risk of bias was assessed using the Rob v2 and Robins-I tools. The priori protocol was published in PROSPERO (ID: CRD42022381405). Results: Seven studies comprising 1625 patients (SDG 870, PDG 882) were included. All studies were non-randomized, and only one was prospective. No significant differences were observed between the SDG and PDG in mortality (OR= 0.59; 95% CI [0.30, 1.17], p=0.13), major morbidity (Chi² = 30.28, p <0.00001; I² = 87%), pancreatic fistula (Chi² = 6.61, p = 0.25); I² = 24%), post pancreatectomy haemorrhage (OR= 1.16; 95% CI [0.67, 2.01], p=0.59), positive drainage culture (OR= 0.36; 95% CI [0.10, 1.32], p=0.12), septic complications (OR= 0.78; 95% CI [0.23, 2.72], p=0.70), wound infection (OR= 0.08, p=0.07), operative time (MD= 0.21; p=0.21). Conclusion: Early surgery within 3 or 4 weeks after biliary drainage is both safe and effective. Thus, it is reasonable to suggest early surgery following PBD for patients having resectable periampullary cancers.

Keywords: preoperative biliary drainage, pancreatic cancer, pancreatic surgery, complication

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Authors: Asrat M.Belachew, Tiago Pereira, Institute of Mathematics, Computer Sciences, Avenida Trabalhador São Carlense, 400, São Carlos, 13566-590, Brazil

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Infectious diseases are among the most prominent threats to human beings. They cause morbidity and mortality to an individual and collapse the social, economic, and political systems of the whole world collectively. Mathematical models are fundamental tools and provide a comprehensive understanding of how infectious diseases spread and designing the control strategy to mitigate infectious diseases from the host population. Modeling the spread of infectious diseases using a compartmental model of inhomogeneous populations is good in terms of complexity. However, in the real world, there is a situation that accounts for heterogeneity, such as ages, locations, and contact patterns of the population which are ignored in a homogeneous setting. In this work, we study how classical an SEIR infectious disease spreading of the compartmental model can be extended by incorporating the mobility of population between heterogeneous cities during an outbreak of infectious disease. We have formulated an SEIR multi-cities epidemic spreading model using a system of 4k ordinary differential equations to describe the disease transmission dynamics in k-cities during the day and night. We have shownthat the model is epidemiologically (i.e., variables have biological interpretation) and mathematically (i.e., a unique bounded solution exists all the time) well-posed. We constructed the next-generation matrix (NGM) for the model and calculated the basic reproduction number R0for SEIR-epidemic spreading model with cities mobility. R0of the disease depends on the spectral radius mobility operator, and it is a threshold between asymptotic stability of the disease-free equilibrium and disease persistence. Using the eigenvalue perturbation theorem, we showed that sending a fraction of the population between cities decreases the reproduction number of diseases in interconnected cities. As a result, disease transmissiondecreases in the population.

Keywords: SEIR-model, mathematical model, city mobility, epidemic spreading

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Authors: Modreck Gomo

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The evolution of groundwater chemistry and its quality is largely controlled by hydrogeochemical processes and their understanding is therefore important for groundwater quality assessments and protection of the water resources. A study was conducted in Bloemfontein town of South Africa to assess and compare the groundwater chemistry and quality characteristics in an alluvial aquifer and single-plane fractured-rock aquifers. 9 groundwater samples were collected from monitoring boreholes drilled into the two aquifer systems during a once-off sampling exercise. Samples were collected through low-flow purging technique and analysed for major ions and trace elements. In order to describe the hydrochemical facies and identify dominant hydrogeochemical processes, the groundwater chemistry data are interpreted using stiff diagrams and principal component analysis (PCA), as complimentary tools. The fitness of the groundwater quality for domestic and irrigation uses is also assessed. Results show that the alluvial aquifer is characterised by a Na-HCO₃ hydrochemical facie while fractured-rock aquifer has a Ca-HCO₃ facie. The groundwater in both aquifers originally evolved from the dissolution of calcite rocks that are common on land surface environments. However the groundwater in the alluvial aquifer further goes through another evolution as driven by cation exchange process in which Na in the sediments exchanges with Ca²⁺ in the Ca-HCO₃ hydrochemical type to result in the Na-HCO₃ hydrochemical type. Despite the difference in the hydrogeochemical processes between the alluvial aquifer and single-plane fractured-rock aquifer, this did not influence the groundwater quality. The groundwater in the two aquifers is very hard as influenced by the elevated magnesium and calcium ions that evolve from dissolution of carbonate minerals which typically occurs in surface environments. Based on total dissolved levels (600-900 mg/L), groundwater quality of the two aquifer systems is classified to be of fair quality. The negative potential impacts of the groundwater quality for domestic uses are highlighted.

Keywords: alluvial aquifer, fractured-rock aquifer, groundwater quality, hydrogeochemical processes

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Authors: Maryam Firouzmandi, Moosa Miri

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Smile is considered to be one of the most effective methods of influencing people. Increasing numbers of patients are requesting cosmetic dental procedures to achieve the perfect smile. Based on the patient’s age, oral and facial characteristics, and the dentist’s expertise, different concepts of treatment would be available. Orthodontics is the most conservative and the ideal treatment alternative for crowded anterior teeth; however, it may be rejected by patients due to occupational limitations of time, physical discomfort including pain and functional limitations, psychological discomfort, and appearance during treatment. In addition, orthodontic treatment will not resolve deficits of contour and color of the anterior teeth. In consequence, patients may demand restorative techniques to resolve their anterior mal-alignment instead, often called "instant orthodontics". Following its introduction, however, adhesive dentistry has suffered at times from overuse. Creating short-term attractive smiles at the expense of long-term dental health and optimal tooth biomechanics by using cosmetic techniques should not be considered an ethical approach. The objective of this narrative review was to investigate the literature for guidelines with regard to decision making and treatment planning for anterior tooth mal-alignment. In this regard, indications of orthodontic, restorative, combination of both treatments, and adjunctive periodontal surgery were discussed in clinical cases to achieve a proportional smile. Restorative modalities would include disking, cosmetic contouring, veneers, and crowns and were compared with limited or comprehensive orthodontic options. A rapid review was also presented on pros and cons of snap on smile to mask malalignments. Diagnostic tools such as mock up, wax up, and digital smile design were also considered to achieve more conservative and functional treatments with respect to biologic factors.

Keywords: crowding, misalignment, veneer, crown, orthodontics

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Authors: Greeshma Sharma, Sushil Chandra, Vijander Singh, Alok Prakash Mittal

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Navigational ability requires spatial representation, planning, and memory. It covers three interdependent domains, i.e. cognitive and perceptual factors, neural information processing, and variability in brain microstructure. Many attempts have been made to see the role of spatial representation in the navigational ability, and the individual differences have been identified in the neural substrate. But, there is also a need to address the influence of planning, memory on navigational ability. The present study aims to evaluate relations of aforementioned factors in the navigational ability. Total 30 participants volunteered in the study of a virtual shopping complex and subsequently were classified into good and bad navigators based on their performances. The result showed that planning ability was the most correlated factor for the navigational ability and also the discriminating factor between the good and bad navigators. There was also found the correlations between spatial memory recall and navigational ability. However, non-verbal episodic memory and spatial memory recall were also found to be correlated with the learning variable. This study attempts to identify differences between people with more and less navigational ability on the basis of planning and memory.

Keywords: memory, planning navigational ability, virtual reality

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Authors: Juan Bojórquez, Henry E. Reyes, Edén Bojórquez, Alfredo Reyes-Salazar

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This paper addressed the use of Artificial Intelligence to obtain the structural reliability of reinforced concrete buildings. For this purpose, artificial neuronal networks (ANN) are developed to predict seismic demand hazard curves. In order to have enough input-output data to train the ANN, a set of reinforced concrete buildings (low, mid, and high rise) are designed, then a probabilistic seismic hazard analysis is made to obtain the seismic demand hazard curves. The results are then used as input-output data to train the ANN in a feedforward backpropagation model. The predicted values of the seismic demand hazard curves found by the ANN are then compared. Finally, it is concluded that the computer time analysis is significantly lower and the predictions obtained from the ANN were accurate in comparison to the values obtained from the conventional methods.

Keywords: structural reliability, seismic design, machine learning, artificial neural network, probabilistic seismic hazard analysis, seismic demand hazard curves

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Authors: Haritha Saranga

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Service quality has become an important driver of competition in manufacturing industries of late, as many products are being sold in conjunction with service offerings. With increase in computational power and data capture capabilities, it has become possible to analyze and estimate various aspects of service quality at the granular level and determine their impact on business performance. In the current study context, dealer level, model-wise warranty data from one of the top two-wheeler manufacturers in India is used to estimate service quality of individual dealers and its impact on warranty related costs and sales performance. We collected primary data on warranty costs, number of complaints, monthly sales, type of quality upgrades, etc. from the two-wheeler automaker. In addition, we gathered secondary data on various regions in India, such as petrol and diesel prices, geographic and climatic conditions of various regions where the dealers are located, to control for customer usage patterns. We analyze this primary and secondary data with the help of a variety of analytics tools such as Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA and ARIMAX. Study results, after controlling for a variety of factors, such as size, age, region of the dealership, and customer usage pattern, show that service quality does influence sales of the products in a significant manner. A more nuanced analysis reveals the dynamics between product quality and service quality, and how their interaction affects sales performance in the Indian two-wheeler industry context. We also provide various managerial insights using descriptive analytics and build a model that can provide sales projections using a variety of forecasting techniques.

Keywords: service quality, product quality, automobile industry, business analytics, auto-regressive integrated moving average

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Authors: Laila Salah Seada, Ashraf Ibrahim, Fawaz Al Rashid, Ihab Abdo, Hassan Kasim, Waleed Al Mansi, Saud Al Shabli

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Background and Objectives: Colorectal carcinoma is increasing among both men and women worldwide. It has a multifactorial etiology including genetic factors, environmental factors and inflammatory conditions of the digestive tract. A clinicopathologic assessment of colorectal carcinoma in Hail region is done, considering any changing patterns in two 5-year periods from 2005-2009 (A) and from 2012 to 2017 (B). All data had been retrieved from histopathology files of King Khalid Hospital, Hail. Results: During period (A), 75 cases were diagnosed as colorectal carcinoma. Male patients comprised 56/75 (74.7%) of the study, with a mean age of 58.4 (36-97), while females were 19/75 (25.3%) with a mean age of 50.3(30-85) and the difference was significant (p = 0.05). M:F ratio was 2.9:1. Most common histological type was adenocarcioma in 68/75 (90.7%) patients mostly well differentiated in 44/68 (64.7%). Mucinous neoplasms comprised only 7/75 (9.3%) of cases and tended to have a higher stage (p = 0.04). During period (B), 115 cases were diagnosed with an increase of 53.3% in number of cases than period (A). Male to female ratio also decreased to 1.35:1, females being 44.83% more affected. Adenocarcinoma remained the prevalent type (93.9%), while mucinous type was still rare (5.2%). No distal metastases found at time of presentation. Localization of tumors was rectosigmoid in group (A) in 41.4%, which increased to 56.6% in group (B), with an increase of 15.2%. Iliocecal location also decreased from 8% to 3.5%, being 56.25% less. Other proximal areas of the colon were decreased by 25.75%, from 53.9% in group (A) to 40% in group (B). Conclusion: Colorectal carcinoma in Hail region has increased by 53.3% in the past 5 years, with more females being diagnosed. Localization has also shifted distally by 15.2%. These findings are different from Western world patterns which experienced a decrease in incidence and proximal shift of the colon cancer localization. This might be due to better diagnostic tools, population awareness of the disease, as well as changing of life style and/or food habits in the region.

Keywords: colorectal cancer, Hail Region, changing pattern, distal shift

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Authors: Ebere Ellison Obisike, Justina N. Adalikwu-Obisike

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Artificial intelligence (AI) is progressively transforming health and social care. With the rapid invention of various electronic devices, machine learning, and computing systems, the use of AI istraversing many health and social care practices. In this systematic review of journal and grey literature, this study explores how the applications of AI might promote the prevention of micro and macrovascular complications in type 1 diabetic patients. This review focuses on the use of a digitized blood glucose meter and the application of insulin pumps for the effective management of type 1 diabetes in low and middle-income countries. It is projected that the applications of AI may assist individuals with type 1 diabetes to monitor and control their blood glucose level and prevent the early onset of micro and macrovascular complications.

Keywords: artificial intelligence, blood glucose meter, insulin pump, low and middle-income countries, micro and macrovascular complications, type 1 diabetes

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Authors: Sanjay Adhikesaven

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Workplace accidents are a critical problem that causes many deaths, injuries, and financial losses. Climate change has a severe impact on industrial workers, partially caused by global warming. To reduce such casualties, it is important to proactively find unsafe environments where injuries could occur by detecting the use of personal protective equipment (PPE) and identifying unsafe activities. Thus, we propose an industrial workplace alerting and monitoring platform to detect PPE use and classify unsafe activity in group settings involving multiple humans and objects over a long period of time. Our proposed method is the first to analyze prolonged actions involving multiple people or objects. It benefits from combining pose estimation with PPE detection in one platform. Additionally, we propose the first open-source annotated data set with video data from industrial workplaces annotated with the action classifications and detected PPE. The proposed system can be implemented within the surveillance cameras already present in industrial settings, making it a practical and effective solution.

Keywords: computer vision, deep learning, workplace safety, automation

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Authors: Sara Franco Ortega, Alina Goldberg Cavalleri, Nawaporn Onkokesung, Richard Dale, Melissa Brazier-Hicks, Robert Edwards

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Safeners are agrochemicals that enhance the selective chemical control of wild grasses by increasing the ability of the crop to metabolise the herbicide. Although these compounds are widely used, their mode of action is not well understood. It is known that safeners enhance the metabolism of herbicides, by up-regulating the associated detoxification system we have termed the xenome. The xenome proteins involved in herbicide metabolism have been previously divided into four different phases, with cytochrome P450s (CYPs) playing a key role in phase I metabolism by catalysing hydroxylation and dealkylation reactions. Subsequently, glutathione S-transferases (GSTs) and UDP-glucosyltransferases lead to the formation of Phase II conjugates prior to their transport into the vacuole by ABCs transporters (Phase III). Maize (Zea mays), was been treated with different safeners to explore the selective induction of xenome proteins, with a special interest in the regulation of the CYP superfamily. Transcriptome analysis enabled the identification of key safener-inducible CYPs that were then functionally assessed to determine their role in herbicide detoxification. In order to do that, CYP’s were codon optimised, synthesised and inserted into the yeast expression vector pYES3 using in-fusion cloning. CYP’s expressed as recombinant proteins in a strain of yeast engineered to contain the P450 co-enzyme (cytochrome P450 reductase) from Arabidopsis. Microsomes were extracted and treated with herbicides of different chemical classes in the presence of the cofactor NADPH. The reaction products were then analysed by LCMS to identify any herbicide metabolites. The results of these studies will be presented with the key CYPs identified in maize used as the starting point to find orthologs in other crops and weeds to better understand their roles in herbicide selectivity and safening.

Keywords: CYPs, herbicide detoxification, LCMS, RNA-Seq, safeners

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Authors: Sean McCarthy, Patrice M. Ludwig, Will Watson

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This exploratory case study explores the deployment of Social Network Analysis (SNA) in mapping community assets in an interdisciplinary, undergraduate, team-taught course focused on income insecure populations in a rural area in the US. Specifically, it analyzes how students were taught to collect data on community assets and to visualize the connections between those assets using Kumu, an SNA data visualization tool. Further, the case study shows how social network data was also collected about student teams via their written communications in Slack, an enterprise messaging tool, which enabled instructors to manage and guide student research activity throughout the semester. The discussion presents how SNA methods can simultaneously inform both community-based research and social innovation pedagogy through the use of data visualization and collaboration-focused communication technologies.

Keywords: social innovation, social network analysis, pedagogy, problem-based learning, data visualization, information communication technologies

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Authors: Rashini Maduka, C. R. Wijesinghe, A. R. Weerasinghe

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Drug-drug interactions (DDIs) can happen when two or more drugs are taken together. Today DDIs have become a serious health issue due to adverse drug effects. In vivo and in vitro methods for identifying DDIs are time-consuming and costly. Therefore, in-silico-based approaches are preferred in DDI identification. Most machine learning models for DDI prediction are used chemical and biological drug properties as features. However, some drug features are not available and costly to extract. Therefore, it is better to make automatic feature engineering. Furthermore, people who have diabetes already suffer from other diseases and take more than one medicine together. Then adverse drug effects may happen to diabetic patients and cause unpleasant reactions in the body. In this study, we present a model with a graph convolutional autoencoder and a graph decoder using a dataset from DrugBank version 5.1.3. The main objective of the model is to identify unknown interactions between antidiabetic drugs and the drugs taken by diabetic patients for other diseases. We considered automatic feature engineering and used Known DDIs only as the input for the model. Our model has achieved 0.86 in AUC and 0.86 in AP.

Keywords: drug-drug interaction prediction, graph embedding, graph convolutional networks, adverse drug effects

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Authors: Erion Luga, Aksel Seitllari, Kemal Pervanqe

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This study investigates the relation between physical and mechanical properties of concrete paving stones using neuro-fuzzy approach. For this purpose 200 samples of concrete paving stones were selected randomly from different sources. The first phase included the determination of physical properties of the samples such as water absorption capacity, porosity and unit weight. After that the indirect tensile strength test and compressive strength test of the samples were performed. İn the second phase, adaptive neuro-fuzzy approach was employed to simulate nonlinear mapping between the above mentioned physical properties and mechanical properties of paving stones. The neuro-fuzzy models uses Sugeno type fuzzy inference system. The models parameters were adapted using hybrid learning algorithm and input space was fuzzyfied by considering grid partitioning. It is concluded based on the observed data and the estimated data through ANFIS models that neuro-fuzzy system exhibits a satisfactory performance.

Keywords: paving stones, physical properties, mechanical properties, ANFIS

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Authors: Sum Lau, Wing Chung Cleo Lau, Wing Yan Chu, Long Ching Ip, Wan Yin Lo, Jo Long Sam Yau, Ka Ho Hui, Sze Yi Mak

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Biomedical engineering (BME) is an interdisciplinary subject where knowledge about biology and medicine is applied to novel applications, solving clinical problems. This subject is crucial for cities such as Hong Kong, where the burden on the medical system is rising due to reasons like the ageing population. Hong Kong, who is actively boosting technological advancements in recent years, sets BME, or biotechnology, as a major category, as reflected in the 2018-19 Budget, where biotechnology was one of the four pillars for development. Over the years, while resources in terms of money and space have been provided, there has been a lack of talents expressed by both the academia and industry. While exogenous factors, such as COVID, may have hindered talents from outside Hong Kong to come, endogenous factors should also be considered. In particular, since there are already a few local universities offering BME programmes, their curriculum or style of education requires to be reviewed to intensify the network of the BME community and support post-academic career development. It was observed that while undergraduate (UG) studies focus on knowledge teaching with some technical training and postgraduate (PG) programmes concentrate on upstream research, the programmes are generally confined to the academic sector and lack connections to the industry. In light of that, a “Biomedical Innovation and Outreach Programme 2022” (“B.I.O.2022”) was held to connect students and professors from academia with clinicians and engineers from the industry, serving as a comparative approach to conventional education methods (UG and PG programmes from tertiary institutions). Over 100 participants, including undergraduates, postgraduates, secondary school students, researchers, engineers, and clinicians, took part in various outreach events such as conference and site visits, all held from June to July 2022. As a case study, this programme aimed to tackle the aforementioned problems with the theme of “4Cs” (connection, communication, collaboration, and commercialisation). The effectiveness of the programme is investigated by its ability to serve as an adult and continuing education and the effectiveness of causing social change to tackle current societal challenges, with the focus on tackling the lack of talents engaging in biomedical engineering. In this study, B.I.O.2022 is found to be able to complement the traditional educational methods, particularly in terms of knowledge exchange between the academia and the industry. With enhanced communications between participants from different career stages, there were students who followed up to visit or even work with the professionals after the programme. Furthermore, connections between the academia and industry could foster the generation of new knowledge, which ultimately pointed to commercialisation, adding value to the BME industry while filling the gap in terms of human resources. With the continuation of events like B.I.O.2022, it provides a promising starting point for the development and relationship strengthening of a BME community in Hong Kong, and shows potential as an alternative way of adult education or learning with societal benefits.

Keywords: biomedical engineering, adult education for social change, comparative methods and principles, lifelong learning, faced problems, promises, challenges and pitfalls

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Authors: Wanghongshu Zhou

Abstract:

This study aims to explore the relationship between the L2MSS and WTC among Chinese non-English major students in order to provide pedagogical implications for English as a Foreign Language (EFL) classrooms in Chinese universities. By employing a mixed methods approach, we involved 103 Chinese non-English major students from a typical university in China, conducted questionnaire survey to measure their levels of L2WTC and L2MSS level, and then analyzed the correlation between the two above mentioned variables. Semi-structured interviews were conducted with eight participants to provide a deeper understanding and explanation of the questionnaire data. Findings show that 1) Chinese non-English major students’ ideal L2 self and L2 learning experience could positively predict their L2 WTC in EFL class; 2) Chinese non-English major students’ ought-to L2 self might have no significant impact on their L2 WTC in EFL class; and 3) self-confidence might be another main factor that will influence Chinese non-English major students’ L2 WTC in EFL class. These findings might shed light on the second language acquisition field and provide pedagogical recommendations for pre-service as well as in-service EFL teachers.

Keywords: L2 willingness to communicate, L2 motivation, self-confidence, Chinese non-English major students

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