Search results for: feature film

Authors: Ming Liu, Chong Wu, Bingquan Liu, Lei Chen

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Internet users have to face the massive amount of textual data every day. Organizing texts into categories can help users dig the useful information from large-scale text collection. Clustering, in fact, is one of the most promising tools for categorizing texts due to its unsupervised characteristic. Unfortunately, most of traditional clustering algorithms lose their high qualities on large-scale text collection. This situation mainly attributes to the high- dimensional vectors generated from texts. To effectively and efficiently cluster large-scale text collection, this paper proposes a vector reconstruction based clustering algorithm. Only the features that can represent the cluster are preserved in cluster’s representative vector. This algorithm alternately repeats two sub-processes until it converges. One process is partial tuning sub-process, where feature’s weight is fine-tuned by iterative process. To accelerate clustering velocity, an intersection based similarity measurement and its corresponding neuron adjustment function are proposed and implemented in this sub-process. The other process is overall tuning sub-process, where the features are reallocated among different clusters. In this sub-process, the features useless to represent the cluster are removed from cluster’s representative vector. Experimental results on the three text collections (including two small-scale and one large-scale text collections) demonstrate that our algorithm obtains high quality on both small-scale and large-scale text collections.

Keywords: vector reconstruction, large-scale text clustering, partial tuning sub-process, overall tuning sub-process

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Authors: Cesar Torres, Robert Briber, Nam Sun Wang

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Eye drops are the most commonly used treatment for short-term and long-term ophthalmic diseases. However, eye drops could deliver only about 5% of the functional ingredients contained in a burst dosage. To address the limitations of eye drops, the use of therapeutic contact lenses has been introduced. Drug-loaded contact lenses provide drugs a longer residence time in the tear film and hence, decrease the potential risk of side effects. Nevertheless, a major limitation of contact lenses as drug delivery devices is that most of the drug absorbed is released within the first few hours. This fact limits their use for extended release. The present study demonstrates the application of long-alkyl chain ionic surfactants on extending drug release kinetics from commercially available silicone hydrogel contact lenses. In vitro release experiments were carried by immersing drug-containing contact lenses in phosphate buffer saline at physiological pH. The drug concentration as a function of time was monitored using ultraviolet-visible spectroscopy. The results of the study demonstrate that release kinetics is dependent on the ionic surfactant weight percent in the contact lenses, and on the length of the hydrophobic alkyl chain of the ionic surfactants. The use of ionic surfactants in contact lenses can extend the delivery of drugs from a few hours to a few weeks, depending on the physicochemical properties of the drugs. Contact lenses embedded with ionic surfactants could be potential biomaterials to be used for extended drug delivery and in the treatment of ophthalmic diseases. However, ocular irritation and toxicity studies would be needed to evaluate the safety of the approach.

Keywords: contact lenses, drug delivery, controlled release, ionic surfactant

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Authors: Siriporn Okonogi, Pimpak Phumat, Sakornrat Khongkhunthian

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Piper betle has been extensively reported for various pharmacological effects including antimicrobial activity. 4-Allylpyrocatechol (AC) is a principle active compound found in P. betle. However, AC has a problem of solubility in water. The aims of the present study were to prepare AC loaded polymeric micelles for enhancing its water solubility and to evaluate its anti-biofilm activity against oral phathogenic bacteria. AC was loaded in polymeric micelles (PM) of Pluronic F127 by using thin film hydration method to obtain AC loaded PM (PMAC). The results revealed that AC in the form of PMAC possessed high water solubility. PMAC particles were characterized using a transmission electron microscope and photon correlation spectroscopy. Determination of entrapment efficiency (EE) and loading capacity (LC) of PMAC was done by using high-performance liquid chromatography. The highest EE (86.33 ± 14.27 %) and LC (19.25 ± 3.18 %) of PMAC were found when the weight ratio of polymer to AC was 4 to 1. At this ratio, the particles showed spherical in shape with the size of 38.83 ± 1.36 nm and polydispersity index of 0.28 ± 0.10. Zeta potential of the particles is negative with the value of 16.43 ± 0.55 mV. Crystal violet assay and confocal microscopy were applied to evaluate the effects of PMAC on Streptococcus mutans biofilms using chlorhexidine (CHX) as a positive control. PMAC contained 1.5 mg/mL AC could potentially inhibit (102.01 ± 9.18%) and significantly eradicate (85.05 ± 2.03 %) these biofilms (p < 0.05). Comparison with CHX, PMAC showed slightly similar biofilm inhibition but significantly stronger biofilm eradication (p < 0.05) than CHX. It is concluded that PMAC can enhance water solubility and anti-biofilm activity of AC.

Keywords: pluronic, polymeric micelles, solubility, 4-allylpyrocathecol, Streptococcus mutans, anti-biofilm

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Authors: Emmanuel Osaze Oshoiribhor, Adetokunbo MacGregor John-Otumu

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In recent years, there has been a desire to forecast student academic achievement prior to graduation. This is to help them improve their grades, particularly for individuals with poor performance. The goal of this study is to employ supervised learning techniques to construct a predictive model for student academic achievement. Many academics have already constructed models that predict student academic achievement based on factors such as smoking, demography, culture, social media, parent educational background, parent finances, and family background, to name a few. This feature and the model employed may not have correctly classified the students in terms of their academic performance. This model is built using a logistic regression classifier with basic features such as the previous semester's course score, attendance to class, class participation, and the total number of course materials or resources the student is able to cover per semester as a prerequisite to predict if the student will perform well in future on related courses. The model outperformed other classifiers such as Naive bayes, Support vector machine (SVM), Decision Tree, Random forest, and Adaboost, returning a 96.7% accuracy. This model is available as a desktop application, allowing both instructors and students to benefit from user-friendly interfaces for predicting student academic achievement. As a result, it is recommended that both students and professors use this tool to better forecast outcomes.

Keywords: artificial intelligence, ML, logistic regression, performance, prediction

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Authors: Sohail Ahmad Umer

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Since last few decades a question is raising on the subject of the importance of women in different societies of the world particularly in the developing societies of Asia and Africa. Female population constitutes almost 50% of the total population of the world and is playing a significant role in the economy with male population. In Pakistan, a developing country of Asia with majority of Muslim population, working women role is more focused. Women of rural background who are working as voluntary workers and their working hours are neither recorded nor recognized. Agricultural statistics shows that the female participation rate is below 40% while other sources claim them below 20%. Here in present study, another effort has been made to compare the women role in two different provinces of Pakistan to analyze the participation of women in agricultural activities like sowing, picking, irrigating the fields, harvesting and threshing of crops, caring and feeding of the animals, collecting the firewood and etc,as without these activities the farming would be incomplete. One hundred villages in the district Khushab (Punjab) and one hundred villages in district D.I.Khan (KPK) were selected and 33% of the families of each village have been interviewed to study their input in agriculture work. Another important feature is the social stratification therefore the contribution by different variables like the ownership, tenancy, education and caste has also been studied.

Keywords: caste, social stratification, tenancy, voluntary workers

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Authors: Zhou Yang, Heli Sun, Jianbin Huang, Jizhong Zhao, Shaojie Qiao

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Traffic prediction is essential in a multitude of ways in modern urban life. The researchers of earlier work in this domain carry out the investigation chiefly with two major focuses: (1) the accurate forecast of future values in multiple time series and (2) knowledge extraction from spatial-temporal correlations. However, two key considerations for traffic prediction are often missed: the completeness of raw data and the full context of the prediction timestamp. Concentrating on the two drawbacks of earlier work, we devise an approach that can address these issues in a two-phase framework. First, we utilize the raw trajectories to a greater extent through building a VLA table and data compression. We obtain the intra-trajectory features with graph-based encoding and the intertrajectory ones with a grid-based model and the technique of back projection that restore their surrounding high-resolution spatial-temporal environment. To the best of our knowledge, we are the first to study direct feature extraction from raw trajectories for traffic prediction and attempt the use of raw data with the least degree of reduction. In the prediction phase, we provide a broader context for the prediction timestamp by taking into account the information that are around it in the training dataset. Extensive experiments on several well-known datasets have verified the effectiveness of our solution that combines the strength of raw trajectory data and prediction context. In terms of performance, our approach surpasses several state-of-the-art methods for traffic prediction.

Keywords: traffic prediction, raw data utilization, context building, data reduction

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: Akhila Sara Varughese

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In the postmodern society, visual narratives became an emerging genre in the field of literature. Graphic literature focuses on the literal and symbolic layer of interpretation. The most salient feature of graphic literature is its exploration of the public history of events and life narratives. The Indian graphic literature re-interprets the canon, style and the form of texts in Indian Writing in English and it demands a new literacy and the structure of the English literature. With the help of visual-verbal language, the graphic narratives discuss various facets of contemporary India. Graphic novels have firmly identified itself with the art of storytelling because of its capability of expressing human experiences to the most. In the textual novels, the author usually deserts the imagination of the readers, but in the case of graphic narratives, due to the presence of visual elements, the interpretation becomes simpler. India is the second most populous country in the world with a long tradition of history and culture. Indian literature always tries to reconstruct Indian history in various modes of representation. The present paper focuses on the fictional articulation of Indian history through the graphic narratives and analyses how some historical events in India portrays. The paper also traces the differences in rendering the history in graphic novels with that of textual novels. The paper discusses how much the blending of words and images helps in represent the Indian history by analyzing the graphic novels like Kashmir Pending by Naseer Ahmed, Delhi Calm by Vishwajyoti Ghosh and Munnu by Malik Sajad.

Keywords: graphic novels, Indian history, representation, visual-verbal literacy

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Authors: Pauli P.Y. Lai

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Students with Special Education Needs (SEN) face many challenges in learning. Various challenges include difficulty in handwriting, slow understanding and assimilation, difficulty in paying attention during class, and lack of communication skills. To engage students with Special Education Needs in class with general students, Blackboard Collaborate is used as a teaching and learning tool to deliver a lecture with interactive activities. Blackboard Collaborate provides a good platform to create and enhance active, collaborative and interactive learning experience whereby the SEN students can easily interact with their general peers and the instructor by using the features of drawing on a virtual whiteboard, file sharing, classroom chatter, breakout room, hand-raising feature, polling, etc. By integrating a blended learning approach with Blackboard Collaborate, the students with Special Education Needs could engage in interactive activities with ease in class. Our research aims at exploring and discovering the use of Blackboard Collaborate for inclusive education based on a qualitative design with in-depth interviews. Being served in a general education environment, three university students with different kinds of learning disabilities have participated in our study. All participants agreed that functions provided by Blackboard Collaborate have enhanced their learning experiences and helped them learn better. Their academic performances also showed that SEN students could perform well with the help of technology. This research studies different aspects of using Blackboard Collaborate to create an inclusive learning environment for SEN students.

Keywords: blackboard collaborate, enhanced learning experience, inclusive education, special education needs

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Jie Huo, Jonathan Wu

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Segmentation of anatomical structures in medical images is essential for scientific inquiry into the complex relationships between biological structure and clinical diagnosis, treatment and assessment. As a method of incorporating the prior knowledge and the anatomical structure similarity between a target image and atlases, multi-atlas segmentation has been successfully applied in segmenting a variety of medical images, including the brain, cardiac, and abdominal images. The basic idea of multi-atlas segmentation is to transfer the labels in atlases to the coordinate of the target image by matching the target patch to the atlas patch in the neighborhood. However, this technique is limited by the pairwise registration between target image and atlases. In this paper, a novel multi-atlas segmentation approach is proposed by introducing a dynamic energy model. First, the target is mapped to each atlas image by minimizing the dynamic energy function, then the segmentation of target image is generated by weighted fusion based on the energy. The method is tested on MICCAI 2012 Multi-Atlas Labeling Challenge dataset which includes 20 target images and 15 atlases images. The paper also analyzes the influence of different parameters of the dynamic energy model on the segmentation accuracy and measures the dice coefficient by using different feature terms with the energy model. The highest mean dice coefficient obtained with the proposed method is 0.861, which is competitive compared with the recently published method.

Keywords: brain MRI segmentation, dynamic energy model, multi-atlas segmentation, energy minimization

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Authors: Dalyah Y. Al-Jamal, Maryam H. Eshtaiwi, Liyakathunisa Syed

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Connecting health services with technology has a huge demand as people health situations are becoming worse day by day. In fact, engaging new technologies such as Internet of Things (IOT) into the medical services can enhance the patient care services. Specifically, patients suffering from chronic diseases such as cardiac patients need a special care and monitoring. In reality, some efforts were previously taken to automate and improve the patient monitoring systems. However, the previous efforts have some limitations and lack the real-time feature needed for chronic kind of diseases. In this paper, an improved process model for patient monitoring system specialized for cardiac patients is presented. A survey was distributed and interviews were conducted to gather the needed requirements to improve the cardiac patient monitoring system. Business Process Model and Notation (BPMN) language was used to model the proposed process. In fact, the proposed system uses the IOT Technology to assist doctors to remotely monitor and follow-up with their heart patients in real-time. In order to validate the effectiveness of the proposed solution, simulation analysis was performed using Bizagi Modeler tool. Analysis results show performance improvements in the heart monitoring process. For the future, authors suggest enhancing the proposed system to cover all the chronic diseases.

Keywords: IoT, process model, remote patient monitoring system, smart watch

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Authors: Kamran Dastafkan, Chuan Zhao

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Improving surface chemistry is a promising approach in addition to the rational alteration in the catalyst composition to advance water electrolysis. Here, we demonstrate an evident enhancement of oxygen evolution on an iron-nickel vanadate catalyst synthesized by a facile successive ionic adsorption and reaction method. The vanadate-modified catalyst demonstrates a highly efficient oxygen evolution in 1 M KOH by requiring low overpotentials of 274 and 310 mV for delivering large current densities of 100 and 400 mA cm⁻², respectively where vigorous gas bubble evolution occurs. Vanadate modification augments the OER activity from three aspects. (i) Both the electrochemical surface area (47.1 cm²) and intrinsic activity (318 mV to deliver 10 mA cm⁻² per unit ECSA) of the catalytic sites are improved. (ii) The amorphous and roughened nanoparticle-comprised catalyst film exhibits a high surface wettability and a low-gas bubble-adhesion, which is beneficial for the accelerated mass transport and gas bubble dissipation at large current densities. The gas bubble dissipation behavior is studied by operando dynamic specific resistance measurements where a significant change in the variation of the interfacial resistance during the OER is detected for the vanadate-modified catalyst. (iii) The introduced vanadate poly-oxo-anions with high charge density have electronic interplay with Fe and Ni catalytic centers. Raman study reveals the structural evolution of β-NiOOH and γ-FeOOH phases during the OER through the vanadate-active site synergistic interactions. Achievement of a high catalytic turnover of 0.12 s⁻¹ put the developed FeNi vanadate among the best recent catalysts for water oxidation.

Keywords: gas bubble dissipation, iron-nickel vanadate, low-gas bubble-adhesion catalyst, oxygen evolution reaction

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Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

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Authors: Kai-Wei Huang, Yi-Feng Lin

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The purpose of this study is to have chemical resistance, high heat resistance and mechanical strength of ceramic hollow fiber membrane into a membrane contactor, and the combustion process is applied (Post-combustion capture) of the carbon dioxide absorption device. In this paper, we would investigate the effect of the ceramic membrane hydrophobicity to the flux of the carbon dioxide adsorption. To improve the applicability of the ceramic film. We use the dry-wet spinning method with the high temperature sintering process for preparing a ceramic hollow fiber membranes to increase the filling density per unit volume of the membrane. The PESf/Al2O3 ratio of 1:5 was prepared ceramic hollow fibers membrane precursors and investigate the relationship of the different sintering temperature to the membrane pore size and porosity. It can be found that the membrane via the sintering temperature of 1400 °C prepared with the highest porosity of 70%, while the membrane via the sintering temperature of 1600 °C prepared although has a minimum porosity of about 54%, but also has the smallest average pore size of about 0.2 μm. The hydrophilic ceramic hollow fiber membranes which after high-temperature sintering were changed into hydrophobic successfully via the 0.02M FAS modifier. The hydrophobic ceramic hollow fiber membranes with different sintering temperature, the membrane which was prepared via 1400 °C sintering has the highest carbon dioxide adsorption about 4.2 × 10-4 (mole/m2s). The membrane prepared via 1500 °C sintering has the carbon dioxide adsorption about 3.8 × 10-3 (mole/m2s),and the membrane prepared via 1600 °C sintering has the lowest carbon dioxide adsorption about 2.68 × 10-3 (mole/m2s).All of them have reusability and in long time operation, the membrane which was prepared via 1600 °C sintering has the smallest pores and also could operate for three days. After the test, the 1600 °C sintering ceramic hollow fiber membrane was most suitable for the factory.

Keywords: carbon dioxide capture, membrane contactor, ceramic membrane, ceramic hollow fiber membrane

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Authors: Muhammad Rafi Ullah Khan, Yaodong Guo, Vanee Chonhenchob, Jinjin Pei, Chongxing Huang

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The effect of sodium alginate (1%) based edible coating incorporated natural essential oils; thymol, carvone and carvacrol as antimicrobial agents at different concentrations (0.1, 0.5 and 1.0 %) on the quality changes of fresh-cut pineapple were investigated. Pineapple dipped in distilled water was served as control. After coating, fruit were sealed in a modified atmosphere package (MAP) using high permeable film; and stored at 5 °C. Gas composition in package headspace, color values (L*, a*, b*, C*), TSS, pH, ethanol, browning, and microbial decay were monitored during storage. Oxygen concentration continuously decreased while carbon dioxide concentration inside all packages continuously increased over time. Color parameters (L*, b*, c*) decreased and a* values increased during storage. All essential oils significantly (p ≤ 0.05) prevented microbial growth than control. A significantly higher (p ≤ 0.05) ethanol content was found in the control than in all other treatments. Visible microbial growth, high ethanol, and low color values limited the shelf life to 6 days in control as compared to 9 days in all other treatments. Among all essential oils, thymol at all concentrations maintained the overall quality of the pineapple and could potentially be used commercially in fresh fruit industries for longer storage.

Keywords: essential oils, antibrowning agents, antimicrobial agents, modified atmosphere packaging, microbial decay, pineapple

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Authors: Amina Farooq, Nauman Zafar Butt

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This paper is about methods and tools for counting particles of interest, such as cells. A microfluidic system with interconnected electronics on a flexible substrate, inlet-outlet ports and interface schemes, sensitive and selective detection of cells specificity, and processing of cell counting at polymer interfaces in a microscale biosensor for use in the detection of target biological and non-biological cells. The development of fluidic channels, planar fluidic contact ports, integrated metal electrodes on a flexible substrate for impedance measurements, and a surface modification plasma treatment as an intermediate bonding layer are all part of the fabrication process. Magnetron DC sputtering is used to deposit a double metal layer (Ti/Pt) over the polypropylene film. Using a photoresist layer, specified and etched zones are established. Small fluid volumes, a reduced detection region, and electrical impedance measurements over a range of frequencies for cell counts improve detection sensitivity and specificity. The procedure involves continuous flow of fluid samples that contain particles of interest through the microfluidic channels, counting all types of particles in a portion of the sample using the electrical differential counter to generate a bipolar pulse for each passing cell—calculating the total number of particles of interest originally in the fluid sample by using MATLAB program and signal processing. It's indeed potential to develop a robust and economical kit for cell counting in whole-blood samples using these methods and similar devices.

Keywords: impedance, biochip, cell counting, microfluidics

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Authors: Ana Paula Esteves, Diego S. Caetano, Louise L. B. Lomardo

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This paper presents an approach on the advantages of using adequate coverage in the zenithal lighting typology in various areas of architectural production, while at the same time to encourage to the design concerns inherent in this choice of roofing in Brazil. Understanding that sustainability needs to cover several aspects, a roofing system such as zenithal lighting system can contribute to the provision of better quality natural light for the interior of the building, which is related to the good health and welfare; it will also be able to contribute for the sustainable aspects and environmental needs, when it allows the generation of energy in semitransparent or opacity photovoltaic solutions and economize the artificial lightning. When the energy balance in the building is positive, that is, when the building generates more energy than it consumes, it may fit into the Net Zero Energy Building concept. The zenithal lighting systems could be an important ally in Brazil, when solved the burden of heat gains, participate in the set of pro-efficiency actions in search of "zero energy buildings". The paper presents comparative three cases of buildings that have used this feature in search of better environmental performance, both in light comfort and sustainability as a whole. Two of these buildings are examples in Europe: the Notley Green School in the UK and the Isofóton factory in Spain. The third building with these principles of shed´s roof is located in Brazil: the Ipel´s factory in São Paulo.

Keywords: natural lighting, net zero energy building, sheds, semi-transparent photovoltaics

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Authors: Pratheesh Jakkala

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This paper addresses the use of breakout sessions to conduct successful weekly physics recitations for College Physics I and II at a large University in remote teaching method during COVID-19 pandemic. All breakout sessions are synchronous, conducted live, and handled by teaching assistants. A two-prong approach is used to maintain the integrity of recitations. Three different conference platforms WebEx, Zoom, and Canvas conferences, were tested, and BigBlue button using Canvas was adopted. The results and experiences of all three learning platforms are presented in this paper. Recitation questions were assigned on WebAssign learning platform and a standard five-question template developed by the instructor was used for group discussions and active peer-peer engagement. Breakout sessions feature of BigBlueButton in Canvas conferences was successfully implemented. Each breakout session consists of a team of 4 students. An online whiteboard, chat window options were used for live teamwork. Student peer-peer interactions, Teaching Assistants’ interaction with students were video and audio recorded. A total of 72 students in College Physics II and 55 students in College Physics I was enrolled. 82% of students agreed that method under study is better than previous methods. The study addressed the quality of student teamwork, student attitude towards problem-solving, and student performance in the exams.

Keywords: recitations, breakout rooms, online learning platforms, COVID pandemic

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Authors: Emiljan Karma

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One of the major problems of the present is young people who are not concerned with employment, education, or training (NEETs). Unfortunately, this group of people in Albania is a considerable part of working-age people, and despite the measures taken, they remain a major problem. NEETs in Albania are very heterogeneous. This is since youth unemployment and inactivity rate are at a very high level (Albania has the highest NEET rate among EU candidates/potential candidates’ countries and EU countries); the high level of NEET rate in Albania means that government agencies responsible for labour market regulation and other social actors interested in the phenomenon (representatives of employees, representatives of employers, non-governmental organizations, etc.) did not effectively materialize the policies in the field of youth employment promotion. The National Agency for Employment and Skills (NAES) delivers measures specifically designed to target unemployed youth, being the key stakeholder in the implementation of employment policies and skills development in Albania. In the context of identifying and assisting NEETs, this role becomes even stronger. The experience of different EU countries (e.g., Youth Guarantee) indicates that there are different policy-making structures and various outreach mechanisms for constraining the youth NEET phenomenon. The purpose of this research is to highlight: (1) The identification of NEETs feature in Albania; (2) The identification of tailored and efficient outreach mechanisms to assist vulnerable NEETs; (3) The fundamental importance of stakeholders’ partnership at central and regional level.

Keywords: labor market, NEETs, non-registered NEETs, unemployment

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Authors: Haruna Abdu Usman, Bashir Usman Mohammed, Mohammed Umar Jamil

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Municipal solid waste management constitutes a serious problem bedeviling environmental protection agencies in many cities of developing countries. Most agencies do not collect the totality of the waste generated in their cities. This study presents the current solid waste management practices and problems in Bauchi metropolis, Bauchi state Nigeria. The general feature is characterized by inefficient, insufficient and irrational collection and improper disposal alternatives. The consequent environmental effects of these problems depict clogged city drains, uncollected heap of waste on road sides of residential areas, vacant plots and uncompleted buildings and highways. This contributes immensely to flooding in the city. The major challenges facing the state environmental protection agency includes; lack of collection and disposal points, technical and institutional arrangements, financial resources and general attitude of the serving public among others. The study suggested a comprehensive and integrated approach to the solid waste management which recognizes and incorporates the interventionist role of the state government, the private formal and informal waste management operators and the serving public.

Keywords: municipal solid waste, bauchi metropolitan area, environmental protection agency, solid waste management, waste disposal

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Authors: Ravi Sheshala, Annie Lee, Ai Lin Ong, Ling Ling Cheu, Thiagarajan Madheswaran, Thankur R. R. Singh

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The objectives of the present research work were to develop and characterize biodegradable controlled release photo cross-linked implants of Triamcinolone Acetonide (TA) for the treatment of chronic ocular diseases. The photo cross-linked implants were prepared using film casting technique by mixing TA (2.5%) polyethylene glycol diacrylate (PEGDA 700), pore formers (mannitol, maltose, and gelatin) and the photoinitiator (Irgacure 2959). The resulting mixture was injected into moulds using 21 G and subjected to photocrosslinking at 365 nm. Scanning electron microscopy results demonstrated that more pores were formed in the films with the increase in the concentration of pore formers from 2%-10%. The maximum force required to break the films containing 2-10% of pore formers were determined in both dry and wet conditions using texture analyzer and found that films in a dry condition required a higher force to break compared to wet condition and blank films. In vitro drug release from photo cross-linked films were determined by incubating samples in 50 ml PBS pH 7.4 at 37 C and the samples were analyzed for drug release by HPLC. The films demonstrated a biphasic release profile i.e. an initial burst release (<20%) on the first day followed by a constant and continuous drug release in a controlled manner for 42 days. The drug release from all formulations followed the first-order release pattern and the combination of diffusion and erosion release mechanism. In conclusion, the developed formulations were able to provide controlled drug delivery to treat the chronic ocular diseases.

Keywords: controlled release, ophthalmic, PEGDA, photocrosslinking, pore formers

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Authors: Nana Adjoah Mbroh, Suares Clovis Oukouomi Noutchie

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Singularly perturbed problems are parameter dependent problems, and they play major roles in the modelling of real-life situational problems in applied sciences. Thus, designing efficient numerical schemes to solve these problems is of much interest since the exact solutions of such problems may not even exist. Generally, singularly perturbed problems are identified by a small parameter multiplying at least the highest derivative in the equation. The presence of this parameter causes the solution of these problems to be characterized by rapid oscillations. This unique feature renders classical numerical schemes inefficient since they are unable to capture the behaviour of the exact solution in the part of the domain where the rapid oscillations are present. In this paper, a numerical scheme is proposed to solve a singularly perturbed Volterra Integro-differential equation. The scheme is based on the midpoint rule and employs the non-standard finite difference scheme to solve the differential part whilst the composite trapezoidal rule is used for the integral part. A fully fledged error estimate is performed, and Richardson extrapolation is applied to accelerate the convergence of the scheme. Numerical simulations are conducted to confirm the theoretical findings before and after extrapolation.

Keywords: midpoint rule, non-standard finite difference schemes, Richardson extrapolation, singularly perturbed problems, trapezoidal rule, uniform convergence

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Authors: Tai Yuan Yu, Pei-Jen Wang

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A simulation scheme of rotational motions for predictions of bump-type gas foil bearings operating at steady-state is proposed; and, the scheme is based on multi-physics coupling computer aided engineering packages modularized with computational fluid dynamic model and structure elasticity model to numerically solve the dynamic equation of motions of a hydrodynamic loaded shaft supported by an elastic bump foil. The bump foil is assumed to be modelled as infinite number of Hookean springs mounted on stiff wall. Hence, the top foil stiffness is constant on the periphery of the bearing housing. The hydrodynamic pressure generated by the air film lubrication transfers to the top foil and induces elastic deformation needed to be solved by a finite element method program, whereas the pressure profile applied on the top foil must be solved by a finite element method program based on Reynolds Equation in lubrication theory. As a result, the equation of motions for the bearing shaft are iteratively solved via coupling of the two finite element method programs simultaneously. In conclusion, the two-dimensional center trajectory of the shaft plus the deformation map on top foil at constant rotational speed are calculated for comparisons with the experimental results.

Keywords: computational fluid dynamics, fluid structure interaction multi-physics simulations, gas foil bearing, load capacity

Procedia PDF Downloads 151

Authors: Aleksandra Twarda, Dobrosława Krzemień, Grzegorz Dubin, Tad A. Holak

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Stabilin-2 belongs to the group of scavenger receptors and plays a crucial role in clearance of more than 10 ligands from the bloodstream, including hyaluronic acid, products of degradation of extracellular matrix and metabolic products. The Link domain, a defining feature of stabilin-2, has a sequence similar to Link domains in other hyaluronic acid receptors, such as CD44 or TSG-6, and is responsible for most of ligands binding. Present knowledge of signal transduction by stabilin-2, as well as ligands’ recognition and binding mechanism, is limited. Until now, no experimental structures have been solved for any segments of stabilin-2. It has recently been demonstrated that the stabilin-2 knock-out or blocking of the receptor by an antibody effectively opposes cancer metastasis by elevating the level of circulating hyaluronic acid. Moreover, loss of expression of stabilin-2 in a peri-tumourous liver correlates with increased survival. Solving of the crystal structure of stabilin-2 and elucidation of the binding mechanism of hyaluronic acid could enable the precise characterization of the interactions in the binding site. These results may allow for designing specific small-molecule inhibitors of stabilin-2 that could be used in cancer therapy. To carry out screening for crystallization of stabilin-2, we cloned constructs of the Link domain of various lengths with or without surrounding domains. The folding properties of the constructs were checked by nuclear magnetic resonance (NMR). It is planned to show the binding of hyaluronic acid to the Link domain using several biochemical methods, i.a. NMR, isothermal titration calorimetry and fluorescence polarization assay.

Keywords: stabilin-2, Link domain, X-ray crystallography, NMR, hyaluronic acid, cancer

Procedia PDF Downloads 393

Authors: Marko Kolakovic

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Creative industries are an important sector of growth and development of knowledge economies. They have a positive impact on employment, economic growth, export and the quality of life in the areas where they are developed. Creative sectors include architecture, design, advertising, publishing, music, film, television and radio, video games, visual and performing arts and heritage. Following the positive trends of development of creative industries on the global and European level, this paper analyzes creative industries in general and specific characteristics of creative entrepreneurship. Special focus in this paper is put on the influence of the information communication technology on the development of new creative business models and protection of the intellectual property rights. One part of the paper is oriented on the analysis of the status of creative industries and creative entrepreneurship in Croatia. The main objective of the paper is by using the statistical analysis of creative industries in Croatia and information gained during the interviews with entrepreneurs, to make conclusions about potentials and development of creative industries in Croatia. Creative industries in Croatia are at the beginning of their development and growth strategy still does not exist at the national level. Statistical analysis pointed out that in 2015 creative enterprises made 9% of all enterprises in Croatia, employed 5,5% of employed people and their share in GDP was 4,01%. Croatian creative entrepreneurs are building competitive advantage using their creative resources and creating specific business models. The main obstacles they meet are lack of business experience and impossibility of focusing on the creative activities only. In their business, they use digital technologies and are focused on export. The conclusion is that creative industries in Croatia have development potential, but it is necessary to take adequate measures to use this potential in a right way.

Keywords: creative entrepreneurship, knowledge economy, business models, intellectual property

Procedia PDF Downloads 195

Authors: Mohadese Hashemi, Elham Akhoundi Kharanaghi, Fatemeh Haghiralsadat, Mojgan Yazdani, Omid Javani, Mahboobe Sharafodini, Davood Rajabi

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Niosomes are non-ionic surfactant vesicles in aqueous media resulting in closed bilayer structures that can be used as carriers of hydrophilic and hydrophobic compounds. The use of niosomes for encapsulation of essential oils (EOs) is an attractive new approach to overcome their physicochemical stability concerns include sensibility to oxygen, light, temperature, and volatility, and their reduced bioavailability which is due to low solubility in water. EOs are unstable and fragile volatile compounds which have strong interest in pharmaceutical due to their medicinal properties such as antiviral, anti-inflammatory, antifungal, and antioxidant activities without side effects. Trachyspermum copticum (ajwain) is an annual aromatic plant with important medicinal properties that grows widely around Mediterranean region and south-west Asian countries. The major components of the ajwain oil were reported as thymol, γ-terpinene, p-cymene, and carvacrol which provide antimicrobial and antioxidant activity. The aim of this work was to formulate ajwain essential oil-loaded niosomes to improve water solubility of natural product and evaluate its physico-chemical features and stability. Ajwain oil was obtained through steam distillation using a clevenger-type apparatus and GC/MS was applied to identify the main components of the essential oil. Niosomes were prepared by using thin film hydration method and nanoparticles were characterized for particle size, dispersity index, zeta potential, encapsulation efficiency, in vitro release, and morphology.

Keywords: trachyspermum copticum, ajwain, niosome, essential oil, encapsulation

Procedia PDF Downloads 473

Authors: Clara Shenderey, Helena Vitoshkin, Mordechai Barak, Avraham Arbel

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Greenhouse cultivation is an energy-intensive process due to the high demands on cooling or heating according to external climatic conditions, which could be extreme in the summer or winter seasons. The thermal radiation rate inside a greenhouse depends mainly on the type of covering material and greenhouse construction. Using additional thermal screens under a greenhouse covering combined with a dehumidification system improves the insulation and could be cost-effective. Greenhouse covering material usually contains protective ultraviolet (UV) radiation additives to prevent the film wear, insect harm, and crop diseases. This paper investigates the overall heat transfer coefficient, or U-value, for greenhouse polyethylene covering contains UV-additives and glass covering with or without a thermal screen supplement. The hot-box method was employed to evaluate overall heat transfer coefficients experimentally as a function of the type and number of the thermal screens. The results show that the overall heat transfer coefficient decreases with increasing the number of thermal screens as a hyperbolic function. The overall heat transfer coefficient highly depends on the ability of the material to reflect thermal radiation. Using a greenhouse covering, i.e., polyethylene films or glass, in combination with high reflective thermal screens, i.e., containing about 98% of aluminum stripes or aluminum foil, the U-value reduces by 61%-89% in the first case, whereas by 70%-92% in the second case, depending on the number of the thermal screen. Using thermal screens made from low reflective materials may reduce the U-value by 30%-57%. The heat transfer coefficient is an indicator of the thermal insulation properties of the materials, which allows farmers to make decisions on the use of appropriate thermal screens depending on the external and internal climate conditions in a greenhouse.

Keywords: energy-saving thermal screen, greenhouse cover material, heat transfer coefficient, hot box

Procedia PDF Downloads 136

Authors: John Lloyd Angolluan, Jennile Caday, Crystal Mae Estrella, Reike Alliyah Taladua, Zion Michael Ysulat

Abstract:

One of the requirements of having the ability to communicate in English is by having adequate vocabulary. Nowadays, people are more engaged in watching movie streams on which they can watch movies in a very portable way, such as Netflix. Wherein Netflix became global demand for online media has taken off in recent years. This research aims to know whether the use of bimodal subtitles on Netflix English movies can enhance vocabulary. This study is quantitative and utilizes a descriptive method, and this study aims to explore the use of bimodal subtitles on Netflix English movies to enhance the vocabulary of students. The respondents of the study were the selected Second-year English majors of Rizal Technological University Pasig and Boni Campus using the purposive sampling technique. The researcher conducted a survey questionnaire through the use of Google Forms. In this study, the weighted mean was used to evaluate the student's responses to the statement of the problems of the study of the use of bimodal subtitles on Netflix English movies. The findings of this study revealed that the bimodal subtitle on Netflix English movies enhanced students’ vocabulary learning acquisition by providing learners with access to large amounts of real and comprehensible language input, whether accidentally or intentionally, and it turns out that bimodal subtitles on Netflix English movies help students recognize vocabulary, which has a positive impact on their vocabulary building. Therefore, the researchers advocate that watching English Netflix movies enhances students' vocabulary by using bimodal subtitled movie material during their language learning process, which may increase their motivation and the usage of bimodal subtitles in learning new vocabulary. Bimodal subtitles need to be incorporated into educational film activities to provide students with a vast amount of input to expand their vocabulary.

Keywords: bimodal subtitles, Netflix, English movies, vocabulary, subtitle, language, media

Procedia PDF Downloads 71