Search results for: language function

Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo

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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.

Keywords: conversational AI, chatbot, dialog management, semantic analysis

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Authors: Igor A. Bessmertny, Bidru C. Enkomaryam

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Active involving patients and communities in health decisions can improve both people’s health and the healthcare system. Adopting artificial intelligence can lead to more accurate and complete patient record management. This review aims to identify the current state of researches conducted using artificial intelligence techniques to improve patient engagement and wellbeing, medical domains used in patient engagement context, and lastly, to assess opportunities and challenges for patient engagement in the wellness process. A search of peer-reviewed publications, reviews, conceptual analyses, white papers, author’s manuscripts and theses was undertaken. English language literature published in 2013– 2022 period and publications, report and guidelines of World Health Organization (WHO) were also assessed. About 281 papers were retrieved. Duplicate papers in the databases were removed. After application of the inclusion and exclusion criteria, 41 papers were included to the analysis. Patient counseling in preventing adverse drug events, in doctor-patient risk communication, surgical, drug development, mental healthcare, hypertension & diabetes, metabolic syndrome and non-communicable chronic diseases are implementation areas in healthcare where patient engagement can be implemented using artificial intelligence, particularly machine learning and deep learning techniques and tools. The five groups of factors that potentially affecting patient engagement in safety are related to: patient, health conditions, health care professionals, tasks and health care setting. Active involvement of patients and families can help accelerate the implementation of healthcare safety initiatives. In sub-Saharan Africa, using digital technologies like artificial intelligence in patient engagement context is low due to poor level of technological development and deployment. The opportunities and challenges available to implement patient engagement strategies vary greatly from country to country and from region to region. Thus, further investigation will be focused on methods and tools using the potential of artificial intelligence to support more simplified care that might be improve communication with patients and train health care professionals.

Keywords: artificial intelligence, patient engagement, machine learning, patient involvement

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Authors: Natan C. G. Silva, Carlos A. C. Girao Neto, Marcele M. S. Vasconcelos, Luciana R. B. Goncalves, Maria Valderez P. Rocha

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Galactooligosaccharides (GOS) are sugars with prebiotic function that can be synthesized chemically or enzymatically, and this last one can be promoted by the action of β-galactosidases. In addition to favoring the transgalactosylation reaction to form GOS, these enzymes can also catalyze the hydrolysis of lactose. A highly studied type of GOS is lactulose because it presents therapeutic properties and is a health promoter. Among the different raw materials that can be used to produce lactulose, whey stands out as the main by-product of cheese manufacturing, and its discarded is harmful to the environment due to the residual lactose present. Therefore, its use is a promising alternative to solve this environmental problem. Thus, lactose from whey is hydrolyzed into glucose and galactose by β-galactosidases. However, in order to favor the transgalactosylation reaction, the medium must contain fructose, due this sugar reacts with galactose to produce lactulose. Then, the glucose-isomerase enzyme can be used for this purpose, since it promotes the isomerization of glucose into fructose. In this scenario, the aim of the present work was first to develop β-galactosidase biocatalysts of Kluyveromyces lactis and to apply it in the integrated reactions of hydrolysis, isomerization (with the glucose-isomerase from Streptomyces murinus) and transgalactosylation reaction, using whey as a substrate. The immobilization of β-galactosidase in chitosan previously functionalized with 0.8% glutaraldehyde was evaluated using different enzymatic loads (2, 5, 7, 10, and 12 mg/g). Subsequently, the hydrolysis and transgalactosylation reactions were studied and conducted at 50°C, 120 RPM for 20 minutes. In parallel, the isomerization of glucose into fructose was evaluated under conditions of 70°C, 750 RPM for 90 min. After, the integration of the three processes for the production of lactulose was investigated. Among the evaluated loads, 7 mg/g was chosen because the best activity of the derivative (44.3 U/g) was obtained, being this parameter determinant for the reaction stages. The other parameters of immobilization yield (87.58%) and recovered activity (46.47%) were also satisfactory compared to the other conditions. Regarding the integrated process, 94.96% of lactose was converted, achieving 37.56 g/L and 37.97 g/L of glucose and galactose, respectively. In the isomerization step, conversion of 38.40% of glucose was observed, obtaining a concentration of 12.47 g/L fructose. In the transgalactosylation reaction was produced 13.15 g/L lactulose after 5 min. However, in the integrated process, there was no formation of lactulose, but it was produced other GOS at the same time. The high galactose concentration in the medium probably favored the reaction of synthesis of these other GOS. Therefore, the integrated process proved feasible for possible production of prebiotics. In addition, this process can be economically viable due to the use of an industrial residue as a substrate, but it is necessary a more detailed investigation of the transgalactosilation reaction.

Keywords: beta-galactosidase, glucose-isomerase, galactooligosaccharides, lactulose, whey

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Authors: Jeffrey Siphiwe Mkhize

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Twelve years of schooling for most South African children, particularly those children from disadvantaged past, are confronted with numerous and diverse challenges. These challenges range from infrastructural limitations, language of teaching, poor resources and varying family backgrounds. Likewise, schools are categorized to signify schools’ geographic location, poverty lines, societal class and type of students that the school are likely to enroll. Such categorization perpetuates particular lines of identities that are indirectly reinforced by the same system that seeks to redress. South African universities prefer point systems to determine students’ suitability to gain access to their programmes. Once students are admitted based on the qualifying points there is an assumed equity in the manner in which they receive tuition. They are assumed as equal; noting the widened access to South African universities as means to redress past inequalities. Given the challenges, inequalities, it is necessary to view higher education as a site for knowledge construction that is accessible to all students. Epistemological access is key to all students irrespective of their socio-economic status. This paper seeks to contribute to the discourse of student engagement using lecturer-student relationship as a lens to understand this phenomenon. Data were generated using South African Survey of Student Engagement, focus group interviews, semi-structured one-on-one-interviews as well as document analysis. The focus was on students registered for the first year of a Bachelor of Education degree as well as lecturers that teach high risk modules in this qualification at the same level. The findings suggest that lecturers are challenged by overcrowded classrooms and over-enrolled modules; this challenge hampers their good intentions to become more efficient and innovative in their teaching. Students lack confidence in approaching lecturers for assistance. Collaborative learning has stronger results and students believe in self-support to deal with their challenges based on their individual strengths. Collaborative learning is key to student academic performance.

Keywords: collaborative learning, consultations, student engagement, student performance

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Authors: Nabeel Mohammed Ali

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Background and significance of the study: This study focuses on the experiences, perspectives, and issues of the working-class Aboriginals and immigrants in Australia. In addition to dealing with their lives, struggles, and aspirations of working-class people, poetry of labor presents an insight into a neglected literary writing that goes beyond the social discourse of class distinction. In this contemporary context, it explores a broader spectrum of challenges and experiences, such as the complexities of modern labor, immigration, indigenous rights, social justice, multiculturalism, economic inequality, advocating for workers' rights and labor movements, the impact of globalization on local industries, and the evolution of labor in the digital age. Aims of the Study: The study will try to answer the following questions: What insights does poetics of labor provide to affect the literary creation of poetry at the time, as well as whether it can create a change in the social fabric of Australian diversity? What are the main themes and issues that Aboriginal and immigrant poets address in their works? How do they reflect the realities and challenges of working-class people in Australia? How do they use language, form, and style to convey their messages and emotions? How do the poets engage with and critique the dominant narratives and ideologies of Australian society and culture? How do they challenge or resist the stereotypes, prejudices, and discrimination that they face? And how do they show solidarity or empathy with others who share similar struggles or aspirations? Methodology: The study will utilize traditional Marxist paradigms to analyze the poetry of the selected poets in the context of the evolving sociopolitical landscape of the 21st century. The Neo-Marxist literary criticism is used as a theoretical tool to analyze the texts. The concept of Power dynamics to analyze the intersectionality of race, labor and class. Findings: The poetry of contemporary Australian Aboriginal and immigrant poets labor, represents a critical, yet under-explored, discussion of the intersection of labor, class, and a multicultural identity. The study will deal with the poetry of the Aboriginal poet Ali Cobby Eckermann (1963- ) and the immigrant Chinese poet Ouyang Yu ( 1955- ).

Keywords: aboriginals, immigrants, Australia, working-class, Ali eckermann, ouyang Yu

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Authors: Dumont Maxime, Correia Carlos, Sauvage Jean-François, Schwartz Noah, Gray Morgan

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Context: The concept of the segmented telescope is unavoidable to build extremely large telescopes (ELT) in the quest for spatial resolution, but it also allows one to fit a large telescope within a reduced volume of space (JWST) or into an even smaller volume (Standard Cubesat). Cubesats have tight constraints on the computational burden available and the small payload volume allowed. At the same time, they undergo thermal gradients leading to large and evolving optical aberrations. The pupil segmentation comes nevertheless with an obvious difficulty: to co-phase the different segments. The CubeSat constraints prevent the use of a dedicated wavefront sensor (WFS), making the focal-plane images acquired by the science detector the most practical alternative. Yet, one of the challenges for the wavefront sensing is the non-linearity between the image intensity and the phase aberrations. Plus, for Earth observation, the object is unknown and unrepeatable. Recently, several studies have suggested Neural Networks (NN) for wavefront sensing; especially convolutional NN, which are well known for being non-linear and image-friendly problem solvers. Aims: We study in this paper the prospect of using NN to measure the phasing aberrations of a segmented pupil from the focal-plane image directly without a dedicated wavefront sensing. Methods: In our application, we take the case of a deployable telescope fitting in a CubeSat for Earth observations which triples the aperture size (compared to the 10cm CubeSat standard) and therefore triples the angular resolution capacity. In order to reach the diffraction-limited regime in the visible wavelength, typically, a wavefront error below lambda/50 is required. The telescope focal-plane detector, used for imaging, will be used as a wavefront-sensor. In this work, we study a point source, i.e. the Point Spread Function [PSF] of the optical system as an input of a VGG-net neural network, an architecture designed for image regression/classification. Results: This approach shows some promising results (about 2nm RMS, which is sub lambda/50 of residual WFE with 40-100nm RMS of input WFE) using a relatively fast computational time less than 30 ms which translates a small computation burder. These results allow one further study for higher aberrations and noise.

Keywords: wavefront sensing, deep learning, deployable telescope, space telescope

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Authors: Monika Balach, Iwona Frydrych, Agnieszka Cichocka

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The objective of this research is to develop an algorithm, taking into account material type and body type that will describe the fabric properties and quality of fit of a garment to the body. One of the objectives of this research is to develop a new algorithm to simulate cloth draping within CAD/CAM software. Existing virtual fitting does not accurately simulate fabric draping behaviour. Part of the research into virtual fitting will focus on the mechanical properties of fabrics. Material behaviour depends on many factors including fibre, yarn, manufacturing process, fabric weight, textile finish, etc. For this study, several different fabric types with very different mechanical properties will be selected and evaluated for all of the above fabric characteristics. These fabrics include woven thick cotton fabric which is stiff and non-bending, woven with elastic content, which is elastic and bends on the body. Within the virtual simulation, the following mechanical properties can be specified: shear, bending, weight, thickness, and friction. To help calculate these properties, the KES system (Kawabata) can be used. This system was originally developed to calculate the mechanical properties of fabric. In this research, the author will focus on three properties: bending, shear, and roughness. This study will consider current research using the KES system to understand and simulate fabric folding on the virtual body. Testing will help to determine which material properties have the largest impact on the fit of the garment. By developing an algorithm which factors in body type, material type, and clothing function, it will be possible to determine how a specific type of clothing made from a particular type of material will fit on a specific body shape and size. A fit indicator will display areas of stress on the garment such as shoulders, chest waist, hips. From this data, CAD/CAM software can be used to develop garments that fit with a very high degree of accuracy. This research, therefore, aims to provide an innovative solution for garment fitting which will aid in the manufacture of clothing. This research will help the clothing industry by cutting the cost of the clothing manufacturing process and also reduce the cost spent on fitting. The manufacturing process can be made more efficient by virtual fitting of the garment before the real clothing sample is made. Fitting software could be integrated into clothing retailer websites allowing customers to enter their biometric data and determine how the particular garment and material type would fit their body.

Keywords: 3D scanning, fabric mechanical properties, quality of fit, virtual fitting

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Authors: Emre Bayamlıoğlu

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This paper aims to provide an account of the legal and the regulative aspects of the algorithmic reputation systems with a special emphasis on the sharing economy (i.e., Uber, Airbnb, Lyft) business model. The first section starts with an analysis of the legal and commercial nature of the tripartite relationship among the parties, namely, the host platform, individual sharers/service providers and the consumers/users. The section further examines to what extent an algorithmic system of reputational information could serve as an alternative to legal regulation. Shortcomings are explained and analyzed with specific examples from Airbnb Platform which is a pioneering success in the sharing economy. The following section focuses on the issue of governance and control of the reputational information. The section first analyzes the legal consequences of algorithmic filtering systems to detect undesired comments and how a delicate balance could be struck between the competing interests such as freedom of speech, privacy and the integrity of the commercial reputation. The third section deals with the problem of manipulation by users. Indeed many sharing economy businesses employ certain techniques of data mining and natural language processing to verify consistency of the feedback. Software agents referred as "bots" are employed by the users to "produce" fake reputation values. Such automated techniques are deceptive with significant negative effects for undermining the trust upon which the reputational system is built. The third section is devoted to explore the concerns with regard to data mobility, data ownership, and the privacy. Reputational information provided by the consumers in the form of textual comment may be regarded as a writing which is eligible to copyright protection. Algorithmic reputational systems also contain personal data pertaining both the individual entrepreneurs and the consumers. The final section starts with an overview of the notion of reputation as a communitarian and collective form of referential trust and further provides an evaluation of the above legal arguments from the perspective of public interest in the integrity of reputational information. The paper concludes with certain guidelines and design principles for algorithmic reputation systems, to address the above raised legal implications.

Keywords: sharing economy, design principles of algorithmic regulation, reputational systems, personal data protection, privacy

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Authors: Bello Abdullahi, Yahaya M. Ibrahim, Ahmed D. Ibrahim, Kabir Bala

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Public sector tendering has traditionally been conducted using manual paper-based processes which are known to be inefficient, less transparent and more prone to manipulations and errors. The advent of the Internet and the World Wide Web has led to the development of numerous e-Tendering systems that addressed some of the problems associated with the manual paper-based tendering system. However, most of these systems rarely support the evaluation of tenders and where they do it is mostly based on the single decision maker which is not suitable in public sector tendering, where for the sake of objectivity, transparency, and fairness, it is required that the evaluation is conducted through a tender evaluation committee. Currently, in Nigeria, the public tendering process in general and the evaluation of tenders, in particular, are largely conducted using manual paper-based processes. Automating these manual-based processes to digital-based processes can help in enhancing the proficiency of public sector tendering in Nigeria. This paper is part of a larger study to develop an electronic tendering system that supports the whole tendering lifecycle based on Nigerian procurement law. Specifically, this paper presents the design and implementation of part of the system that supports group evaluation of tenders based on a technique called fuzzy multi-attributes group decision making. The system was developed using Object-Oriented methodologies and Unified Modelling Language and hypothetically applied in the evaluation of technical and financial proposals submitted by bidders. The system was validated by professionals with extensive experiences in public sector procurement. The results of the validation showed that the system called NPS-eTender has an average rating of 74% with respect to correct and accurate modelling of the existing manual tendering domain and an average rating of 67.6% with respect to its potential to enhance the proficiency of public sector tendering in Nigeria. Thus, based on the results of the validation, the automation of the evaluation process to support tender evaluation committee is achievable and can lead to a more proficient public sector tendering system.

Keywords: e-Tendering, e-Procurement, group decision making, tender evaluation, tender evaluation committee, UML, object-oriented methodologies, system development

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Authors: Paola Causin, Andrea Aspri, Alessandro Benfenati

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Diffuse Optical Tomography (DOT) is an emergent medical imaging technique which employs NIR light to estimate the spatial distribution of optical coefficients in biological tissues for diagnostic purposes, in a noninvasive and non-ionizing manner. DOT reconstruction is a severely ill-conditioned problem due to prevalent scattering of light in the tissue. In this contribution, we present our research in adopting hybrid knowledgedriven/data-driven approaches which exploit the existence of well assessed physical models and build upon them neural networks integrating the availability of data. Namely, since in this context regularization procedures are mandatory to obtain a reasonable reconstruction [1], we explore the use of neural networks as tools to include prior information on the solution. 2. Materials and Methods The idea underlying our approach is to leverage neural networks to solve PDE-constrained inverse problems of the form 𝒒 ∗ = 𝒂𝒓𝒈 𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃), (1) where D is a loss function which typically contains a discrepancy measure (or data fidelity) term plus other possible ad-hoc designed terms enforcing specific constraints. In the context of inverse problems like (1), one seeks the optimal set of physical parameters q, given the set of observations y. Moreover, 𝑦̃ is the computable approximation of y, which may be as well obtained from a neural network but also in a classic way via the resolution of a PDE with given input coefficients (forward problem, Fig.1 box ). Due to the severe ill conditioning of the reconstruction problem, we adopt a two-fold approach: i) we restrict the solutions (optical coefficients) to lie in a lower-dimensional subspace generated by auto-decoder type networks. This procedure forms priors of the solution (Fig.1 box ); ii) we use regularization procedures of type 𝒒̂ ∗ = 𝒂𝒓𝒈𝒎𝒊𝒏𝒒 𝐃(𝒚, 𝒚̃)+ 𝑹(𝒒), where 𝑹(𝒒) is a regularization functional depending on regularization parameters which can be fixed a-priori or learned via a neural network in a data-driven modality. To further improve the generalizability of the proposed framework, we also infuse physics knowledge via soft penalty constraints (Fig.1 box ) in the overall optimization procedure (Fig.1 box ). 3. Discussion and Conclusion DOT reconstruction is severely hindered by ill-conditioning. The combined use of data-driven and knowledgedriven elements is beneficial and allows to obtain improved results, especially with a restricted dataset and in presence of variable sources of noise.

Keywords: inverse problem in tomography, deep learning, diffuse optical tomography, regularization

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Authors: Bulcha Belay Etana, Benny Malengier, Debelo Oljira, Janarthanan Krishnamoorthy, Lieva Vanlangenhove

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Electromyography (EMG) is a technique used to measure the electrical activity of muscles. EMG can be used to assess muscle function in a variety of settings, including clinical, research, and sports medicine. The aim of this study was to develop a wearable textile sensor for EMG monitoring. The sensor was designed to be soft, stretchable, and washable, making it suitable for long-term use. The sensor was fabricated using a conductive thread material that was embroidered onto a fabric substrate. The sensor was then connected to a microcontroller unit (MCU) and a Wi-Fi-enabled module. The MCU was programmed to acquire the EMG signal and transmit it wirelessly to the Wi-Fi-enabled module. The Wi-Fi-enabled module then sent the signal to a server, where it could be accessed by a computer or smartphone. The sensor was able to successfully acquire and transmit EMG signals from a variety of muscles. The signal quality was comparable to that of commercial EMG sensors. The development of this sensor has the potential to improve the way EMG is used in a variety of settings. The sensor is soft, stretchable, and washable, making it suitable for long-term use. This makes it ideal for use in clinical settings, where patients may need to wear the sensor for extended periods of time. The sensor is also small and lightweight, making it ideal for use in sports medicine and research settings. The data for this study was collected from a group of healthy volunteers. The volunteers were asked to perform a series of muscle contractions while the EMG signal was recorded. The data was then analyzed to assess the performance of the sensor. The EMG signals were analyzed using a variety of methods, including time-domain analysis and frequency-domain analysis. The time-domain analysis was used to extract features such as the root mean square (RMS) and average rectified value (ARV). The frequency-domain analysis was used to extract features such as the power spectrum. The question addressed by this study was whether a wearable textile sensor could be developed that is soft, stretchable, and washable and that can successfully acquire and transmit EMG signals. The results of this study demonstrate that a wearable textile sensor can be developed that meets the requirements of being soft, stretchable, washable, and capable of acquiring and transmitting EMG signals. This sensor has the potential to improve the way EMG is used in a variety of settings.

Keywords: EMG, electrode position, smart wearable, textile sensor, IoT, IoT-integrated textile sensor

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Authors: Marios Papaevripidou, Yvoni Pavlou, Zacharias Zacharia

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Embodied cognition and additional (touch) sensory channel theories indicate that physical manipulation is crucial to learning since it provides, among others, touch sensory input, which is needed for constructing knowledge. Given these theories, the use of Physical Manipulatives (PM) becomes a prerequisite for learning. On the other hand, empirical research on Virtual Manipulatives (VM) (e.g., simulations) learning has provided evidence showing that the use of PM, and thus haptic sensory input, is not always a prerequisite for learning. In order to investigate which means of experimentation, PM or VM, are required for enhancing student science learning at the kindergarten level, an empirical study was conducted that sought to investigate the impact of haptic feedback on the conceptual understanding of pre-school students (n=44, age mean=5,7) in three science domains: beam balance (D1), sinking/floating (D2) and springs (D3). The participants were equally divided in two groups according to the type of manipulatives used (PM: presence of haptic feedback, VM: absence of haptic feedback) during a semi-structured interview for each of the domains. All interviews followed the Predict-Observe-Explain (POE) strategy and consisted of three phases: initial evaluation, experimentation, final evaluation. The data collected through the interviews were analyzed qualitatively (open-coding for identifying students’ ideas in each domain) and quantitatively (use of non-parametric tests). Findings revealed that the haptic feedback enabled students to distinguish heavier to lighter objects when held in hands during experimentation. In D1 the haptic feedback did not differentiate PM and VM students' conceptual understanding of the function of the beam as a mean to compare the mass of objects. In D2 the haptic feedback appeared to have a negative impact on PM students’ learning. Feeling the weight of an object strengthen PM students’ misconception that heavier objects always sink, whereas the scientifically correct idea that the material of an object determines its sinking/floating behavior in the water was found to be significantly higher among the VM students than the PM ones. In D3 the PM students outperformed significantly the VM students with regard to the idea that the heavier an object is the more the spring will expand, indicating that the haptic input experienced by the PM students served as an advantage to their learning. These findings point to the fact that PMs, and thus touch sensory input, might not always be a requirement for science learning and that VMs could be considered, under certain circumstances, as a viable means for experimentation.

Keywords: haptic feedback, physical and virtual manipulatives, pre-school science learning, science experimentation

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Authors: Zachary Huffman, Joana Rocha

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Wall-pressure fluctuations induced by the turbulent boundary layer (TBL) developed over aircraft are a significant source of aircraft cabin noise. Since the power spectral density (PSD) of these pressure fluctuations is directly correlated with the amount of sound radiated into the cabin, the development of accurate empirical models that predict the PSD has been an important ongoing research topic. The sound emitted can be represented from the pressure fluctuations term in the Reynoldsaveraged Navier-Stokes equations (RANS). Therefore, early TBL empirical models (including those from Lowson, Robertson, Chase, and Howe) were primarily derived by simplifying and solving the RANS for pressure fluctuation and adding appropriate scales. Most subsequent models (including Goody, Efimtsov, Laganelli, Smol’yakov, and Rackl and Weston models) were derived by making modifications to these early models or by physical principles. Overall, these models have had varying levels of accuracy, but, in general, they are most accurate under the specific Reynolds and Mach numbers they were developed for, while being less accurate under other flow conditions. Despite this, recent research into the possibility of using alternative methods for deriving the models has been rather limited. More recent studies have demonstrated that an artificial neural network model was more accurate than traditional models and could be applied more generally, but the accuracy of other machine learning techniques has not been explored. In the current study, an original model is derived using a stepwise regression algorithm in the statistical programming language R, and TBL wall-pressure fluctuations PSD data gathered at the Carleton University wind tunnel. The theoretical advantage of a stepwise regression approach is that it will automatically filter out redundant or uncorrelated input variables (through the process of feature selection), and it is computationally faster than machine learning. The main disadvantage is the potential risk of overfitting. The accuracy of the developed model is assessed by comparing it to independently sourced datasets.

Keywords: aircraft noise, machine learning, power spectral density models, regression models, turbulent boundary layer wall-pressure fluctuations

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Authors: Abdullah Alassiri

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Prompted by the widespread saturation of Facebook usage in Saudi Arabia, among university students to socialize with online members, this study investigated the usage, self-presentation, psychological and sociological consequences of the Facebook social networking site among undergraduate students in Saudi Arabia. The problem statement of this study was addressed by answering the following questions: 1) What motivation do undergraduate students have for joining Facebook? 2) How do undergraduate students consume Facebook? 3) In what condition do undergraduate students need Facebook? 4) How do undergraduate students manage their self-presentation via Facebook? 5) What are the experiences obtained by the undergraduate students from Facebook psychologically? 6) What are the experiences obtained by the undergraduate students from Facebook sociologically? 7) How have Facebook activities affected the lifestyle of the undergraduate students?. These questions were answered by analyzing in-depth interview data collected from twenty male undergraduate students between the ages of 18 and 24 years selected from King Saud University (KSU) and King Khalid University (KKU) Saudi Arabia. Using thematic analysis, informants data were coded ‘R1 to R20’, validated and was transcribed to minimize error from translating into the study items from Arabic back to the English Language. Using purposive sampling method, informant perspective within the research context were explored. Data collection was confined to students’ motivations for engaging in online activities, self-presentation, psychological and sociological consequences to their everyday life was investigated based on the theoretical and philosophical perspective underpinnings media and gratification paradigm and social influence theory. The findings contributed to the development of important study themes that supported the development of a new research framework. Based on the analysis, all the study questions were answered. The findings of this study showed that the students use Facebook for the purpose of interacting with others, getting information and as knowledge sources. In terms of self-presentation, this study revealed that the students portray themselves in the real and not fake image while socializing with others. Psychological and sociological consequences from the usage of Facebook are recorded ranging from cheerful to stress and from loneliness to having many friends. As a conclusion, this study conclusively drew that Facebook is a very persuasive medium of communication among the University students in Saudi Arabia that bridges across socio-cultural boundaries and unite students to interact as a community.

Keywords: Saudi Arabia, Facebook, undergraduate students, social network

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Authors: Bao Li, Aike Qiao, Gaoyang Li, Youjun Liu

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Background: Lumped parameter model (LPM) is a common numerical model for hemodynamic calculation. LPM uses circuit elements to simulate the human blood circulatory system. Physiological indicators and characteristics can be acquired through the model. However, due to the different physiological indicators of each individual, parameters in LPM should be personalized in order for convincing calculated results, which can reflect the individual physiological information. This study aimed to develop an automatic and effective optimization method to personalize the parameters in LPM of the blood circulatory system, which is of great significance to the numerical simulation of individual hemodynamics. Methods: A closed-loop LPM of the human blood circulatory system that is applicable for most persons were established based on the anatomical structures and physiological parameters. The patient-specific physiological data of 5 volunteers were non-invasively collected as personalized objectives of individual LPM. In this study, the blood pressure and flow rate of heart, brain, and limbs were the main concerns. The collected systolic blood pressure, diastolic blood pressure, cardiac output, and heart rate were set as objective data, and the waveforms of carotid artery flow and ankle pressure were set as objective waveforms. Aiming at the collected data and waveforms, sensitivity analysis of each parameter in LPM was conducted to determine the sensitive parameters that have an obvious influence on the objectives. Simulated annealing was adopted to iteratively optimize the sensitive parameters, and the objective function during optimization was the root mean square error between the collected waveforms and data and simulated waveforms and data. Each parameter in LPM was optimized 500 times. Results: In this study, the sensitive parameters in LPM were optimized according to the collected data of 5 individuals. Results show a slight error between collected and simulated data. The average relative root mean square error of all optimization objectives of 5 samples were 2.21%, 3.59%, 4.75%, 4.24%, and 3.56%, respectively. Conclusions: Slight error demonstrated good effects of optimization. The individual modeling algorithm developed in this study can effectively achieve the individualization of LPM for the blood circulatory system. LPM with individual parameters can output the individual physiological indicators after optimization, which are applicable for the numerical simulation of patient-specific hemodynamics.

Keywords: blood circulatory system, individual physiological indicators, lumped parameter model, optimization algorithm

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Authors: Sabrina Howard

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'Window Seat' uses public transportation as an entry point for understanding the relationship between public space, politics, and social identity construction. This project argues that by bringing people of different races, classes, and genders in 'contact' with one another, public transit operates as a site of exposure, as people consciously and unconsciously perform social identity within these spaces. These performances offer a form of freedom that we associate with being in urban spaces while simultaneously rendering certain racialized, gendered, and classed bodies vulnerable to violence. Furthermore, due to its exposing function, public transit operates as a site through which we, as urbanites and scholars, can read social injustice and reflect on the work that is necessary to become a truly democratic society. The major questions guiding this research are: How does using public transit as the entry point provide unique insights into the relationship between social identity, politics, and public space? What ideas do Americans hold about public space and how might these ideas reflect a liberal yearning for a more democratic society? To address these research questions, 'Window Seat' critically examines ethnographic data collected on public buses and trains in Los Angeles, California, and online news media. It analyzes these sources through literature in socio-cultural psychology, sociology, and political science. It investigates the 'everyday urban hero' narrative or popular news stories that feature an individual or group of people acting against discriminatory or 'Anti-American' behavior on public buses and trains. 'Window Seat' studies these narratives to assert that by circulating stories of civility in news media, United Statsians construct and maintain ideas of the 'liberal city,' which is characterized by ideals of freedom and democracy. Furthermore, for those involved, these moments create an opportunity to perform the role of the Good Samaritan, an identity that is wrapped up in liberal beliefs in diversity and inclusion. This research expands conversations in urban studies by making a case for the political significance of urban public space. It demonstrates how these sites serve as spaces through which liberal beliefs are circulated and upheld through identity performance.

Keywords: social identity, public space, public transportation, liberalism

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Authors: H. M. El-Rafie, A. H. Abou Zeid, R. S. Mohammed, A. A. Sleem

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Acrocarpus is a genus of flowering plants in the legume family Fabaceae which considered as a large and economically important family. This study aimed to investigate the phytoconstituents of the petroleum ether extract (PEE) of Acrocarpus fraxinofolius bark by Gas chromatography coupled with mass spectrometry (GC/MS) analysis of its fractions (fatty acid and unsaponifiable matter). Concerning this, identification of 52 compounds constituting 97.03 % of the total composition of the unsaponifiable matter fraction. Cycloeucalenol was found to be the major compound representing 32.52% followed by 4a, 14a-dimethyl-A8~24(28)-ergostadien (26.50%) and ß-sitosterol(13.74%), furthermore Gas liquid chromatography (GLC) analysis of the sterol fraction revealed the identification of cholesterol (7.22 %), campesterol (13.30 %), stigmasterol (10.00 %) and β - sitosterol (69.48 %). Meanwhile, the identification of 33 fatty acids representing 90.71% of the total fatty acid constituents. Methyl-9,12-octadecadienoate (40.39%) followed by methyl hexadecanoate (23.64%) were found to be the major compounds. On the other hand, column chromatography and Thin layer chromatography (TLC) fractionation of PEE separate the triterpenoid: 21β-hydroxylup-20(29)-en-3-one and β- amyrin which were structurally identified by spectroscopic analysis (NMR, MS and IR). PEE has been biologically evaluated for 1: management of diabetes in alloxan induced diabetic rats 2: cytotoxic activity against four human tumor cell lines (Cervix carcinoma cell line[HELA], Breast carcinoma cell line [MCF7], Liver carcinoma cell line[HEPG2] and Colon carcinoma cell line[HCT-116] 3: hepatoprotective activity against CCl4-induced hepatotoxicity in rats and the activity was studied by assaying the serum marker enzymes like AST, ALT, and ALP. Concerning this, the anti-diabetic activity exhibited by 100mg of PEE extract was 74.38% relative to metformin (100% potency). It also showed a significant anti-proliferative activity against MCF-7 (IC50= 2.35µg), Hela(IC50=3.85µg) and HEPG-2 (IC50= 9.54µg) compared with Doxorubicin as reference drug. The hepatoprotective activity was evidenced by significant decrease in liver function enzymes, i.e. AST, ALT and ALP by (29.18%, 28.26%, and 34.11%, respectively using silymarin as the reference drug, compared to their concentration levels in an untreated group with liver damage induced by CCl₄. This study was performed for the first time on the bark of this species.

Keywords: Acrocarpus fraxinofolius, antidiabetic, cytotoxic, hepatoprotective

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Authors: Agus Hariyadi, Hiroatsu Fukuda

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In tropical country like Indonesia, especially in Jakarta, most of the energy consumption on building is for the cooling system, the second one is from lighting electric consumption. One of the passive design strategy that can be done is optimizing the use of natural light from the sun. In this area, natural light is always available almost every day around the year. Natural light have many effect on building. It can reduce the need of electrical lighting but also increase the external load. Another thing that have to be considered in the use of natural light is the visual comfort from occupant inside the room. To optimize the effectiveness of natural light need some modification of façade design. By using external shading device, it can minimize the external load that introduces into the room, especially from direct solar radiation which is the 80 % of the external energy load that introduces into the building. It also can control the distribution of natural light inside the room and minimize glare in the perimeter zone of the room. One of the horizontal blind that can be used for that purpose is Sudare. It is traditional Japanese blind that have been used long time in Japanese traditional house especially in summer. In its original function, Sudare is used to prevent direct solar radiation but still introducing natural ventilation. It has some physical characteristics that can be utilize to optimize the effectiveness of natural light. In this research, different scale of Sudare will be simulated using EnergyPlus and DAYSIM simulation software. EnergyPlus is a whole building energy simulation program to model both energy consumption—for heating, cooling, ventilation, lighting, and plug and process loads—and water use in buildings, while DAYSIM is a validated, RADIANCE-based daylighting analysis software that models the annual amount of daylight in and around buildings. The modelling will be done in Ladybug and Honeybee plugin. These are two open source plugins for Grasshopper and Rhinoceros 3D that help explore and evaluate environmental performance which will directly be connected to EnergyPlus and DAYSIM engines. Using the same model will maintain the consistency of the same geometry used both in EnergyPlus and DAYSIM. The aims of this research is to find the best configuration of façade design which can reduce the external load from the outside of the building to minimize the need of energy for cooling system but maintain the natural light distribution inside the room to maximize the visual comfort for occupant and minimize the need of electrical energy consumption.

Keywords: façade, natural light, blind, energy

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Authors: Nilanthi Ratnayake, Wen-Ling Liu

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Prior research indicates an increasing gap between the skills and capabilities of graduates in the contemporary workplace across the globe. The challenge of addressing this issue primarily lies on the hands of higher education institutes/universities. In particular, surveys of UK employers and retailers found that soft skills including communication, numeracy, teamwork, confidence, analytical ability, digital/IT skills, business sense, language, and social skills are highly valued by graduate employers, and in achieving this, there are various assessed and non-assessed learning exercises have already been embedded into the university curriculum. To this end, this research study aims to explore the reflections of postgraduate student participation in a live commercial project (i.e. designing an advertising campaign for open days, summer school etc.) implemented with the intention of offering a transformative experience by deploying this project. Qualitative research methodology has been followed in this study, collecting data from three types of target audiences; students, academics and employers via a series of personal interviews and focus group discussions. Recorded data were transcribed, entered into NVIVO, and analysed using meaning condensation and content analysis. Students reported that they had a very positive impact towards improving self-efficacy, especially in relation to soft skills and confidence in seeking employment opportunities. In addition, this project has reduced cultural barriers for international students in general communications. Academic staff and potential employers who attended on the presentation day expressed their gratitude for offering a lifelong experience for students, and indeed believed that these type of projects contribute significantly to enhance skills and capabilities of students to cater the demands of employers. In essence, key findings demonstrate that an integration of knowledge-based skills into a live commercial project facilitate individuals to make the transition from education to employment in terms of skills, abilities and work behaviours more effectively in comparison to some other activities/assuagements that are currently in place in higher education institutions/universities.

Keywords: soft skills, commercially live project, higher education, student participation

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Authors: Sara Nosari

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In the research field on adult education, the learning development project followed different itineraries: recently it has promoted adult transformation by practices focused on the reflexive oriented interaction. This perspective, that connects life stories and life-based methods, characterizes a transformative space between formal and informal education. Within this framework, in the Nursing Degree Courses of Turin University, it has been discussed and realized a formal reflexive path on the care work professional identity through group practices. This path compared the future care professionals with possible experiences staged by texts used with the function of a pre-tests: these texts, setting up real or believable professional situations, had the task to start a reflection on the different 'elements' of care work professional life (relationship, educational character of relationship, relationship between different care roles; or even human identity, aims and ultimate aim of care, …). The learning transformative aspect of this kind of experience-test is that it is impossible to anticipate the process or the conclusion of reflexion because they depend on two main conditions: the personal sensitivity and the specific situation. The narrated experience is not a device, it does not include any tricks to understand the answering advance; the text is not aimed at deepening the knowledge, but at being an active and creative force which takes the group to compare with problematic figures. In fact, the experience-text does not have the purpose to explain but to problematize: it creates a space of suspension to live for questioning, for discussing, for researching, for deciding. It creates a space 'open' and 'in connection' where each one, in comparing with others, has the possibility to build his/her position. In this space, everyone has to possibility to expose his/her own argumentations and to be aware of the others emerged points of view, aiming to research and find the own personal position. However, to define his/her position, it is necessary to learn to exercise character skills (conscientiousness, motivation, creativity, critical thinking, …): if these not-cognitive skills have an undisputed evidence, less evident is how to value them. The paper will reflect on the epistemological limits and possibility to 'measure' character skills, suggesting some evaluation criteria.

Keywords: transformative learning, educational role, formal/informal education, character-skills

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Authors: Javed Mohammed

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Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

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Authors: Yali Liu

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The ethnographic research involved a multi-sited field trip study in China to compile in-depth data from Chinese multilingual academics of Korean, Japanese, and Russian. It aimed to create a culturally-informed portrait of their values and perceptions regarding their choice of language for academic publishing. Extended and lengthy fieldwork, or known as ‘deep hanging out’, enabled the author to gain a comprehensive understanding of the research context at the macro-level and the participants’ experiences at the micro-level. This research involved multiple fieldwork sites, which the author selected in acknowledgment of the diversity in China’s regions with respect to their geopolitical context, socio-economic development, cultural traditions, and administrative status. The 14 weeks of data collection took the author over-land to five regions in northern China: Hebei province, Tianjin, Jilin province, Gansu province, and Xinjiang. Responding to the fieldwork dynamics, the author positioned herself at different degrees of insiderness and outsiderness. This occurred at three levels: the regional level, the individual level, and the within-individual level. To enhance the ability to reflect on the authors’ researcher subjectivity, the author explored her understanding of the five ‘I’s, derived from the authors’ natural attributes. This helped the author to monitor her subjectivity, particularly during critical decision-making. The methodological challenges the author navigated were related to interviewing elites; this involved the initial approach, establishing a relationship, and negotiating the unequal power relationship during our contact. The author developed a number of strategies to strengthen her authority, and to gain the confidence of her envisaged participants and secure their collaboration, and the author negotiated a form of reciprocity that reflected their needs and expectations. The current ethnographic research has both theoretical and practical significance. It contributes to the methodological development regarding multi-sited ethnographic research. The messiness and strategies about positioning and interviewing elites will provide practical lessons for researchers who conduct ethnographic research, especially from power-‘less’ positions.

Keywords: multi-sited ethnographic research, elite interview, multilingual China, subjectivity, reciprocity

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Authors: Sylwia Sumińska, Łukasz Kapica, Grzegorz Szczepański

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Introduction: Cognitive skills are a crucial part of everyday functioning. Cognitive skills include perception, attention, language, memory, executive functions, and higher cognitive skills. With the aging of societies, there is an increasing percentage of people whose cognitive skills decline. Cognitive skills affect work performance. The appropriate diagnosis of a worker’s cognitive skills reduces the risk of errors and accidents at work which is also important for senior workers. The study aimed to prepare new cognitive tests for adults aged 20-60 and assess the psychometric properties of the tests. The project responds to the need for reliable and accurate methods of assessing cognitive performance. Computer tests were developed to assess psychomotor performance, attention, and working memory. Method: Two hundred eighty people aged 20-60 will participate in the study in 4 age groups. Inclusion criteria for the study were: no subjective cognitive impairment, no history of severe head injuries, chronic diseases, psychiatric and neurological diseases. The research will be conducted from February - to June 2022. Cognitive tests: 1) Measurement of psychomotor performance: Reaction time, Reaction time with selective attention component; 2) Measurement of sustained attention: Visual search (dots), Visual search (numbers); 3) Measurement of working memory: Remembering words, Remembering letters. To assess the validity and the reliability subjects will perform the Vienna Test System, i.e., “Reaction Test” (reaction time), “Signal Detection” (sustained attention), “Corsi Block-Tapping Test” (working memory), and Perception and Attention Test (TUS), Colour Trails Test (CTT), Digit Span – subtest from The Wechsler Adult Intelligence Scale. Eighty people will be invited to a session after three months aimed to assess the consistency over time. Results: Due to ongoing research, the detailed results from 280 people will be shown at the conference separately in each age group. The results of correlation analysis with the Vienna Test System will be demonstrated as well.

Keywords: aging, attention, cognitive skills, cognitive tests, psychomotor performance, working memory

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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Authors: Bei Shan Lin, Tzu Yu Huang, Ya Ping Chen, Chun Mel Lu

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This research is divided into two parts. The first part is to adopt the method of supercritical carbon dioxide fluid extraction to extract sage essential oil (Salvia officinalis) and to find out the differences when the procedure is under different pressure conditions. Meanwhile, this research is going to probe into the composition of the extracted sage essential oil. The second part will talk about the effect of the aromatherapy with extracted sage essential oil to improve the sleeping quality for women in menopause. The extracted sage substance is tested by inhibiting DPPH radical to identify its antioxidant capacity, and the extracted component was analyzed by gas chromatography-mass spectrometer. Under two different pressure conditions, the extracted experiment gets different results. By 3000 psi, the extracted substance is IC50 180.94mg/L, which is higher than IC50 657.43mg/L by 1800 psi. By 3000 psi, the extracted yield is 1.05%, which is higher than 0.68% by 1800 psi. Through the experimental data, the researcher also can conclude that the extracted substance with 3000psi contains more materials than the one with 1800 psi. The main overlapped materials are the compounds of cyclic ether, flavonoid, and terpenes. Cyclic ether and flavonoids have the function of soothing and calming. They can be applied to relieve cramps and to eliminate menopause disorders. The second part of the research is to apply extracted sage essential oil to aromatherapy for women who are in menopause and to discuss the effect of the improvement for the sleeping quality. This research adopts the approaching of Swedish upper back massage, evaluates the sleeping quality with the Pittsburgh Sleep Quality Index, and detects the changes with heart rate variability apparatus. The experimental group intervenes with extracted sage essential oil to the aromatherapy. The average heart beats detected by the apparatus has a better result in SDNN, low frequency, and high frequency. The performance is better than the control group. According to the statistical analysis of the Pittsburgh Sleep Quality Index, this research has reached the effect of sleep quality improvement. It proves that extracted sage essential oil has a significant effect on increasing the activities of parasympathetic nerves. It is able to improve the sleeping quality for women in menopause

Keywords: supercritical carbon dioxide fluid extraction, Salvia officinalis, aromatherapy, Swedish massage, Pittsburgh sleep quality index, heart rate variability, parasympathetic nerves

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Authors: Girgis Younan, Ikram Eltayeb

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Geigeria alata belongs to the family Asteraceae, is an effective plant traditionally used in Sudan as a therapy for hepatic disease and as an antiepileptic, antispasmodic and to treat cough and intestinal complaints.The liver is responsible for many critical functions within the body and any liver disease or injury will result in the loss of those functions leading to significant damage in the body. Liver diseases cause increase in liver enzymes (AST, ALP ALT) and total bilirubin and a decrease in total blood protein level. The objective of this study is to investigate the hepato-protective activity of Geigeria alata leaves ethanolic extract. The plant leaves were extracted using 96% ethanol using Soxhlet apparatus. The hepatoprotective effect was determined using 25 wistar rats, the rats was divided to 5 groups, each group contain 5 rats: [Normal control group] receiving purified water, liver damage was induced in wistar rats by administering a 1:1 (v/v) mixture of CCl4 (1.25 ml/kg) and olive oil once at day four of the experiment [negative control group]. Two doses of extract [400mg/kg and 200mg/kg] was applied daily for 7 days, and standard drug Silymarin (200 mg/kg) were administered daily for 7 days to CCl4-treated rats. The degree of hepato-protective activity was evaluated by determining the hepatic marker enzymes AST, ALP, ALT, total Bilirubin and total proteins (TP). Results have shown that, the extract of G.alata leaves reduced the level of liver enzymes ALT, AST, ALP, total bilirubin and increased the level of total proteins. Since the levels of liver enzymes; bilirubin and total protein are considered as markers of liver function, the extract has proven to reduce the detrimental effects of liver toxicity induced using CCl4. The hepato-protective effect of extract on liver was found to be dose dependent, where the 400mg/kg dose of the extract exhibited higher activity than 200mg/kg dose. In addition, the effect of the higher dose (400mg/kg) of the extract was found to be higher than Silymarin standard drug. The result concludes that, G.alata leaves extract was found to exhibit profound hepato-protective activity, which justifies the traditional use of the plant for the treatment of hepatic diseases.

Keywords: alata, extract, geigeria, hepatoprotective

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Authors: Byeongcheol Kang, Junyi Kim, Hyungsik Jung, Hyungjun Yang, Jaewoo An, Jonggeun Choe

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It is important to figure out reservoir properties for better production management. Due to the limited information, there are geological uncertainties on very heterogeneous or channel reservoir. One of the solutions is to generate multiple equi-probable realizations using geostatistical methods. However, some models have wrong properties, which need to be excluded for simulation efficiency and reliability. We propose a novel method of model selection scheme, based on distance-based clustering for reliable application of production optimization algorithm. Distance is defined as a degree of dissimilarity between the data. We calculate Hausdorff distance to classify the models based on their similarity. Hausdorff distance is useful for shape matching of the reservoir models. We use multi-dimensional scaling (MDS) to describe the models on two dimensional space and group them by K-means clustering. Rather than simulating all models, we choose one representative model from each cluster and find out the best model, which has the similar production rates with the true values. From the process, we can select good reservoir models near the best model with high confidence. We make 100 channel reservoir models using single normal equation simulation (SNESIM). Since oil and gas prefer to flow through the sand facies, it is critical to characterize pattern and connectivity of the channels in the reservoir. After calculating Hausdorff distances and projecting the models by MDS, we can see that the models assemble depending on their channel patterns. These channel distributions affect operation controls of each production well so that the model selection scheme improves management optimization process. We use one of useful global search algorithms, particle swarm optimization (PSO), for our production optimization. PSO is good to find global optimum of objective function, but it takes too much time due to its usage of many particles and iterations. In addition, if we use multiple reservoir models, the simulation time for PSO will be soared. By using the proposed method, we can select good and reliable models that already matches production data. Considering geological uncertainty of the reservoir, we can get well-optimized production controls for maximum net present value. The proposed method shows one of novel solutions to select good cases among the various probabilities. The model selection schemes can be applied to not only production optimization but also history matching or other ensemble-based methods for efficient simulations.

Keywords: distance-based clustering, geological uncertainty, particle swarm optimization (PSO), production optimization

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Authors: Doron Markel

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Water availability in most of the Middle East (especially in Jordan) is among the lowest in the world and has been even further exacerbated by the regional climatic change and the reduced rainfall. The Araba Valley in Israel is disconnected from the national water system. On the other hand, the Araba Valley, both in Israel and Jordan, is an excellent area for solar energy gaining. The Dead Sea (Israel and Jordan) is a hypersaline lake which its level declines at a rate of more than 1 m/y. The decline stems from the increasing use of all available freshwater resources that discharge into the Dead Sea and decreasing natural precipitation due to climate change in the Middle East. As an adaptation tool for this humanmade and Climate Change results, a comprehensive water-energy and environmental project were suggested: The Red Sea-Dead Sea Conveyance. It is planned to desalinate the Red Sea water, supply the desalinated water to both Israel and Jordan, and convey the desalination brine to the Dead Sea to stabilize its water level. Therefore, the World Bank had led a multi-discipline feasibility study between 2008 and 2013, that had mainly dealt with the mixing of seawater and Dead Sea Water. The possible consequences of such mixing were precipitation and possible suspension of secondary Gypsum, as well as blooming of Dunaliella red algae. Using a comprehensive hydrodynamic-geochemical model for the Dead Sea, it was predicted that while conveying up to 400 Million Cubic Meters per year of seawater or desalination brine to the Dead Sea, the latter would not be stratified as it was until 1979; hence Gypsum precipitation and algal blooms would be neglecting. Using another hydrodynamic-biological model for the Red Sea, it was predicted the Seawater pump from the Gulf of Eilat would not harm the ecological system of the gulf (including the sensitive coral reef), giving a pump depth of 120-160 m. Based on these studies, a pipeline conveyance was recommended to convey desalination brine to the Dead Sea with the use of a hydropower plant, utilizing the elevation difference of 400 m between the Red Sea and the Dead Sea. The complementary energy would come from solar panels coupled with innovative storage technology, needed to produce a continuous energy production for an appropriate function of the desalination plant. The paper will describe the proposed project as well as the feasibility study results. The possibility to utilize this water-energy-environmental project as a climate change adaptation strategy for both Israel and Jordan will also be discussed.

Keywords: Red Sea, Dead Sea, water supply, hydro-power, Gypsum, algae

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Authors: Cherres Sara, Cajas Diego

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This paper explores how EFL teachers understand the process of Lesson Study in Ecuadorian schools and the challenges and opportunities that it brings to the improvement of their teaching practice. Using a narrative research methodology, this study presents the results of the application of the four steps of Lesson Study carried out by seven teachers in four different schools located in the Southern part of Ecuador during four months. Before starting the implementation of the lesson study, 30 teachers were trained on this model. This training was opened to EFL teachers working in public and private schools without any charge. The criteria to select these teachers were first, to be minimum a one-year in-service teacher, second, to have a b2 level of English, and third, to be able to commit to follow the course guidelines. After the course, seven teachers decided to continue with the implementation of the Lesson Study in their respective institutions. During the implementation of the Lesson Study, data was collected through observations, in-depth interviews and teachers’ planning meetings; and analyzed using a thematic analysis. The results of this study are presented using the lenses of seven EFL teachers that explained the challenges and opportunities that the implementation of Lesson Study conveyed. The challenges identified were the limited capacity of reflection and recognition of the activities that required improvement after the class, limited capacity to provide truthful peer feedback, teachers wrong notions about their performance in their classes, difficulties to follow a collaborative lesson plan; and, the disconnection between class activities and the class content. The opportunities identified were teachers’ predisposition to collaborate, teachers’ disposition to attend professional development courses, their commitment to work extra hours in planning meetings, their openness and their desired to be observed in their classes; and, their willingness to share class materials and knowledge. On the other hand, the results show that there is a disconnection between teachers’ knowledge of ELT and its proper application in class (from theory to practice). There are also, rigid institutional conceptions of teaching that do not allow teaching innovations. The authors concluded that there is a disconnection between teachers’ knowledge of ELT and its proper application in class (from theory to practice). There are also, rigid institutional conceptions of teaching that do not allow teaching innovations for example: excessive institutional paperwork and activities that are not connected to the development of students’ competences.

Keywords: ELT, lesson study, teachers’ professional development, teachers’ collaboration

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Authors: Junie B. Billones, Maria Constancia O. Carrillo, Voltaire G. Organo, Stephani Joy Y. Macalino, Inno A. Emnacen, Jamie Bernadette A. Sy

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The drug discovery and development process is generally known to be a very lengthy and labor-intensive process. Therefore, in order to be able to deliver prompt and effective responses to cure certain diseases, there is an urgent need to reduce the time and resources needed to design, develop, and optimize potential drugs. Computer-aided drug design (CADD) is able to alleviate this issue by applying computational power in order to streamline the whole drug discovery process, starting from target identification to lead optimization. This drug design approach can be predominantly applied to diseases that cause major public health concerns, such as tuberculosis. Hitherto, there has been no concrete cure for this disease, especially with the continuing emergence of drug resistant strains. In this study, CADD is employed for tuberculosis by first identifying a key enzyme in the mycobacterium’s metabolic pathway that would make a good drug target. One such potential target is the lipoate protein ligase B enzyme (LipB), which is a key enzyme in the M. tuberculosis metabolic pathway involved in the biosynthesis of the lipoic acid cofactor. Its expression is considerably up-regulated in patients with multi-drug resistant tuberculosis (MDR-TB) and it has no known back-up mechanism that can take over its function when inhibited, making it an extremely attractive target. Using cutting-edge computational methods, compounds from AnalytiCon Discovery Natural Derivatives database were screened and docked against the LipB enzyme in order to rank them based on their binding affinities. Compounds which have better binding affinities than LipB’s known inhibitor, decanoic acid, were subjected to in silico toxicity evaluation using the ADMET and TOPKAT protocols. Out of the 31,692 compounds in the database, 112 of these showed better binding energies than decanoic acid. Furthermore, 12 out of the 112 compounds showed highly promising ADMET and TOPKAT properties. Future studies involving in vitro or in vivo bioassays may be done to further confirm the therapeutic efficacy of these 12 compounds, which eventually may then lead to a novel class of anti-tuberculosis drugs.

Keywords: pharmacophore, molecular docking, lipoate protein ligase B (LipB), ADMET, TOPKAT

Procedia PDF Downloads 406