Search results for: distributed lag model

Authors: Suzan Sayegh, Izzeldin Ibrahim, Mercia Van Der Walt, Mohamed Al-Kuwari

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Background: Walking is the most common form of physical activity which can promote a healthy well-being among people of different age groups. In this regard, pedometers are becoming more popular within research and are considered useful tools in monitoring physical activity levels based on individuals’ daily steps. A value of ˂5,000 steps/day is identified as a sedentary lifestyle index where individuals are physically inactive. Those achieving 5,000-7,499 steps/day have a low active lifestyle as they do not meet the moderate-to-vigorous physical activity (MVPA) recommendations. Moreover, individuals achieving ≥7,500 steps/day are classified as physically active. The objective of this study is to assess the physical activity levels of adult population in Qatar through a pedometer-based program over a one-year period. Methods: A cross-sectional analysis, as part of a longitudinal study, was carried out over one year to assess the daily step count. “Step into Health” is a community-based program launched by Aspire as an approach for the purpose of improving physical activity across the population of Qatar. The program involves distribution of pedometers to registered members which is supported by a self-monitoring online account and linked to a web database. Daily habitual physical activity (daily total step count) was assessed through Omron HJ-324U pedometer. Analyses were done on data extracted from the web database. Results: A total of 1,988 members were included in this study (males: n=1,143, 57%; females: n=845, 43%). Average age was 37.8±10.9 years distributed as 60% of age between age 25-54 (n=1,186), 27% of age 45-64 (n=546), and 13% of age 18-24 years (n=256). Majority were non-Qataris, 81% (n=1,609) compared with 19% of the Qatari nationality (n=379). Average body mass index (BMI) was 27.8±6.1 (kg/m2) where most of them (41%, n=809) were found to be overweight, between 25-30 kg/m2. Total average step count was 5,469±3,884. Majority were found to be sedentary (n=1110, 55.8%). Middle aged individuals were more active than the other two age groups. Males were seen as more active than females. Those who were less active had a higher BMI. Older individuals were more active. There was a variation in the physical activity level throughout the year period. Conclusion: It is essential to further develop the available intervention programs and increase their physical activity behavior. Planning such physical activity interventions for female population should involve aspects such as time, environmental variables and aerobic steps.

Keywords: adults, pedometer, physical activity, step-count

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Authors: Jiaying Zhang, Xin Mu, Chang Ni, Jeff L. Zhang

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Magnetic resonance elastography (MRE) non-invasively assesses tissue elastic properties, such as shear modulus, by measuring tissue’s displacement in response to mechanical waves. The estimated metrics on tissue elasticity or stiffness have been shown to be valuable for monitoring physiologic or pathophysiologic status of tissue, such as a tumor or fatty liver. To quantify tissue shear modulus from MRE-acquired displacements (essentially an inverse problem), multiple approaches have been proposed, including Local Frequency Estimation (LFE) and Direct Inversion (DI). However, one common problem with these methods is that the estimates are severely noise-sensitive due to either the inverse-problem nature or noise propagation in the pixel-by-pixel process. With the advent of deep learning (DL) and its promise in solving inverse problems, a few groups in the field of MRE have explored the feasibility of using DL methods for quantifying shear modulus from MRE data. Most of the groups chose to use real MRE data for DL model training and to cut training images into smaller patches, which enriches feature characteristics of training data but inevitably increases computation time and results in outcomes with patched patterns. In this study, simulated wave images generated by Finite Differential Time Domain (FDTD) simulation are used for network training, and U-Net is used to extract features from each training image without cutting it into patches. The use of simulated data for model training has the flexibility of customizing training datasets to match specific applications. The proposed method aimed to estimate tissue shear modulus from MRE data with high robustness to noise and high model-training efficiency. Specifically, a set of 3000 maps of shear modulus (with a range of 1 kPa to 15 kPa) containing randomly positioned objects were simulated, and their corresponding wave images were generated. The two types of data were fed into the training of a U-Net model as its output and input, respectively. For an independently simulated set of 1000 images, the performance of the proposed method against DI and LFE was compared by the relative errors (root mean square error or RMSE divided by averaged shear modulus) between the true shear modulus map and the estimated ones. The results showed that the estimated shear modulus by the proposed method achieved a relative error of 4.91%±0.66%, substantially lower than 78.20%±1.11% by LFE. Using simulated data, the proposed method significantly outperformed LFE and DI in resilience to increasing noise levels and in resolving fine changes of shear modulus. The feasibility of the proposed method was also tested on MRE data acquired from phantoms and from human calf muscles, resulting in maps of shear modulus with low noise. In future work, the method’s performance on phantom and its repeatability on human data will be tested in a more quantitative manner. In conclusion, the proposed method showed much promise in quantifying tissue shear modulus from MRE with high robustness and efficiency.

Keywords: deep learning, magnetic resonance elastography, magnetic resonance imaging, shear modulus estimation

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Authors: Calister Wingang Makebe, Wilson Ambindei Agwanande, Emmanuel Jong Nso, P. Nisha

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Traditional liquid-state fermentation processes of Annona muricata L. juice can result in fluctuating product quality and quantity due to difficulties in control and scale up. This work describes a laboratory-scale batch fermentation process to produce a probiotic Annona muricata L. enzymatically extracted juice, which was modeled using the Doehlert design with independent extraction factors being incubation time, temperature, and enzyme concentration. It aimed at a better understanding of the traditional process as an initial step for future optimization. Annona muricata L. juice was fermented with L. acidophilus (NCDC 291) (LA), L. casei (NCDC 17) (LC), and a blend of LA and LC (LCA) for 72 h at 37 °C. Experimental data were fitted into mathematical models (Monod, Logistic and Luedeking and Piret models) using MATLAB software, to describe biomass growth, sugar utilization, and organic acid production. The optimal fermentation time was obtained based on cell viability, which was 24 h for LC and 36 h for LA and LCA. The model was particularly effective in estimating biomass growth, reducing sugar consumption, and lactic acid production. The values of the determination coefficient, R2, were 0.9946, 0.9913 and 0.9946, while the residual sum of square error, SSE, was 0.2876, 0.1738 and 0.1589 for LC, LA and LCA, respectively. The growth kinetic parameters included the maximum specific growth rate, µm, which was 0.2876 h-1, 0.1738 h-1 and 0.1589 h-1 as well as the substrate saturation, Ks, with 9.0680 g/L, 9.9337 g/L and 9.0709 g/L respectively for LC, LA and LCA. For the stoichiometric parameters, the yield of biomass based on utilized substrate (YXS) was 50.7932, 3.3940 and 61.0202, and the yield of product based on utilized substrate (YPS) was 2.4524, 0.2307 and 0.7415 for LC, LA, and LCA, respectively. In addition, the maintenance energy parameter (ms) was 0.0128, 0.0001 and 0.0004 with respect to LC, LA and LCA. With the kinetic model proposed by Luedeking and Piret for lactic acid production rate, the growth associated, and non-growth associated coefficients were determined as 1.0028 and 0.0109, respectively. The model was demonstrated for batch growth of LA, LC, and LCA in Annona muricata L. juice. The present investigation validates the potential of Annona muricata L. based medium for heightened economical production of a probiotic medium.

Keywords: L. acidophilus, L. casei, fermentation, modelling, kinetics

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Authors: Torsten Hermanns, Thoufik Al Khawli, Wolfgang Schulz

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In laser cutting as well as in long pulsed laser drilling of metals, it can be demonstrated that the ablation shape (the shape of cut faces respectively the hole shape) that is formed approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from the ultrashort pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in laser cutting and long pulse drilling of metals is identified, its underlying mechanism numerically implemented, tested and clearly confirmed by comparison with experimental data. In detail, there now is a model that allows the simulation of the temporal (pulse-resolved) evolution of the hole shape in laser drilling as well as the final (asymptotic) shape of the cut faces in laser cutting. This simulation especially requires much less in the way of resources, such that it can even run on common desktop PCs or laptops. Individual parameters can be adjusted using sliders – the simulation result appears in an adjacent window and changes in real time. This is made possible by an application-specific reduction of the underlying ablation model. Because this reduction dramatically decreases the complexity of calculation, it produces a result much more quickly. This means that the simulation can be carried out directly at the laser machine. Time-intensive experiments can be reduced and set-up processes can be completed much faster. The high speed of simulation also opens up a range of entirely different options, such as metamodeling. Suitable for complex applications with many parameters, metamodeling involves generating high-dimensional data sets with the parameters and several evaluation criteria for process and product quality. These sets can then be used to create individual process maps that show the dependency of individual parameter pairs. This advanced simulation makes it possible to find global and local extreme values through mathematical manipulation. Such simultaneous optimization of multiple parameters is scarcely possible by experimental means. This means that new methods in manufacturing such as self-optimization can be executed much faster. However, the software’s potential does not stop there; time-intensive calculations exist in many areas of industry. In laser welding or laser additive manufacturing, for example, the simulation of thermal induced residual stresses still uses up considerable computing capacity or is even not possible. Transferring the principle of reduced models promises substantial savings there, too.

Keywords: asymptotic ablation shape, interactive process simulation, laser drilling, laser cutting, metamodeling, reduced modeling

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Authors: Rabindra K. Panda, Gurjeet Singh

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The major objective of this study was to analyze the trend and variability of rainfall in the middle Mahandi river basin located in eastern India. The trend of variation of extreme rainfall events has predominant effect on agricultural water management and extreme hydrological events such as floods and droughts. Mahanadi river basin is one of the major river basins of India having an area of 1,41,589 km² and divided into three regions: Upper, middle and delta region. The middle region of Mahanadi river basin has an area of 48,700 km² and it is mostly dominated by agricultural land, where agriculture is mostly rainfed. The study region has five Agro-climatic zones namely: East and South Eastern Coastal Plain, North Eastern Ghat, Western Undulating Zone, Western Central Table Land and Mid Central Table Land, which were numbered as zones 1 to 5 respectively for convenience in reporting. In the present study, analysis of variability and trends of annual, seasonal, and monthly rainfall was carried out, using the daily rainfall data collected from the Indian Meteorological Department (IMD) for 35 years (1979-2013) for the 5 agro-climatic zones. The long term variability of rainfall was investigated by evaluating the mean, standard deviation and coefficient of variation. The long term trend of rainfall was analyzed using the Mann-Kendall test on monthly, seasonal and annual time scales. It was found that there is a decreasing trend in the rainfall during the winter and pre monsoon seasons for zones 2, 3 and 4; whereas in the monsoon (rainy) season there is an increasing trend for zones 1, 4 and 5 with a level of significance ranging between 90-95%. On the other hand, the mean annual rainfall has an increasing trend at 99% significance level. The estimated seasonality index showed that the rainfall distribution is asymmetric and distributed over 3-4 months period. The study will help to understand the spatio-temporal variation of rainfall and to determine the correlation between the current rainfall trend and climate change scenario of the study region for multifarious use.

Keywords: Eastern India, long-term variability and trends, Mann-Kendall test, seasonality index, spatio-temporal variation

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Authors: Beloulou Mohamed Lamine, Fedili Benamar, Meliani Walid, Chaid Dalila, Lamara Abdelhak

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Introduction: Post-surgical chronic pain (PSCP) is a pathological condition with a rather complex etiopathogenesis that extensively involves sensitization processes and neuronal damage. The neuropathic component of these pains is almost always present, with variable expression depending on the type of surgery. Objective: To assess the presumed beneficial effect of Regional Anesthesia-Analgesia Techniques (RAAT) on the development of post-surgical chronic neuropathic pain (PSCNP) in various surgical procedures. Patients and Methods: A comparative study involving 510 patients distributed across five surgical models (mastectomy, thoracotomy, hernioplasty, cholecystectomy, and major abdominal-pelvic surgery) and randomized into two groups: Group A (240) receiving conventional postoperative analgesia and Group B (270) receiving balanced analgesia, including the implementation of a Regional Anesthesia-Analgesia Technique (RAAT). These patients were longitudinally followed over a 6-month period, with postsurgical chronic neuropathic pain (PSCNP) defined by a Neuropathic Pain Score DN2≥ 3. Comparative measurements through univariate and multivariable analyses were performed to identify associations between the development of PSCNP and certain predictive factors, including the presumed preventive impact (protective effect) of RAAT. Results: At the 6th month post-surgery, 419 patients were analyzed (Group A= 196 and Group B= 223). The incidence of PSCNP was 32.2% (n=135). Among these patients with chronic pain, the prevalence of neuropathic pain was 37.8% (95% CI: [29.6; 46.5]), with n=51/135. It was significantly lower in Group B compared to Group A, with respective percentages of 31.4% vs. 48.8% (p-value = 0.035). The most significant differences were observed in breast and thoracopulmonary surgeries. In a multiple regression analysis, two predictors of PSCNP were identified: the presence of preoperative pain at the surgical site as a risk factor (OR: 3.198; 95% CI [1.326; 7.714]) and RAAT as a protective factor (OR: 0.408; 95% CI [0.173; 0.961]). Conclusion: The neuropathic component of PSCNP can be observed in different types of surgeries. Regional analgesia included in a multimodal approach to postoperative pain management has proven to be effective for acute pain and seems to have a preventive impact on the development of PSCNP and its neuropathic nature, particularly in surgeries that are more prone to chronicization.

Keywords: post-surgical chronic pain, post-surgical chronic neuropathic pain, regional anesthesia-analgesia techniques, neuropathic pain score DN2, preventive impact

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Authors: Tatiana Stanchuliak

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It is common knowledge that intonation is not laid over a ready text. On the contrary, intonation forms and accompanies the text on the level of its birth in the speaker’s mind. As a result, intonation plays one of the fundamental roles in the process of transferring a thought into external speech. Intonation structure can highlight the semantic significance of textual elements and become a ranging mark in understanding the information structure of the text. Intonation functions by means of prosodic characteristics, one of which is intonation salience, whose function in texts results in making some textual elements more prominent than others. This function of intonation, therefore, performs as organizing. It helps to form the frame of key elements of the text. The study under consideration made an attempt to look into the inner nature of salience and create a sort of a text intonation model. This general goal brought to some more specific intermediate results. First, there were established degrees of salience on the level of the smallest semantic element - intonation group, as well as prosodic means of creating salience, were examined. Second, the most frequent combinations of prosodic means made it possible to distinguish patterns of salience, which then became constituent elements of a text intonation model. Third, the analysis of the predicate structure allowed to divide the whole text into smaller parts, or units, which performed a specific function in the developing of the general communicative intention. It appeared that such units can be found in any text and they have common characteristics of their intonation arrangement. These findings are certainly very important both for the theory of intonation and their practical application.

Keywords: accentuation , inner speech, intention, intonation, intonation functions, models, patterns, predicate, salience, semantics, sentence stress, text

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Authors: Musarrat Jabeen

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Thoughtful intelligence offers a sustainable position to enhance the influence of decision-makers. Thoughtful Intelligence implies the understanding to realize the impact of one’s thoughts, words and actions on the survival, dignity and development of the individuals, groups and nations. Thoughtful intelligence has received minimal consideration in the area of Decision Support Systems, with an end goal to evaluate the quantity of knowledge and its viability. This pattern degraded the imbibed contribution of thoughtful intelligence required for sustainable decision making. Given the concern, this paper concentrates on the question: How to present a model of Thoughtful Decision Support System (TDSS)? The aim of this paper is to appreciate the concepts of thoughtful intelligence and insinuate a Decision Support System based on thoughtful intelligence. Thoughtful intelligence includes three dynamic competencies: i) Realization about long term impacts of decisions that are made in a specific time and space, ii) A great sense of taking actions, iii) Intense interconnectivity with people and nature and; seven associate competencies, of Righteousness, Purposefulness, Understanding, Contemplation, Sincerity, Mindfulness, and Nurturing. The study utilizes two methods: Focused group discussion to count prevailing Decision Support Systems; 70% results of focus group discussions found six decision support systems and the positive inexistence of thoughtful intelligence among decision support systems regarding sustainable decision making. Delphi focused on defining thoughtful intelligence to model (TDSS). 65% results helped to conceptualize (definition and description) of thoughtful intelligence. TDSS is offered here as an addition in the decision making literature. The clients are top leaders.

Keywords: thoughtful intelligence, sustainable decision making, thoughtful decision support system

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Authors: A. A. Dare, E. U. Iniegbedion

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Industrial furnaces generally involve enclosures of fire typically initiated by the combustion of gases. The fire leads to temperature distribution inside the enclosure. A proper understanding of the temperature and velocity distribution within the enclosure is often required for optimal design and use of the furnace. This study was therefore directed at numerical modeling of temperature distribution inside an enclosure as typical in a furnace. A mathematical model was developed from the conservation of mass, momentum and energy. The stream function-vorticity formulation of the governing equations was solved by an alternating direction implicit (ADI) finite difference technique. The finite difference formulation obtained were then developed into a computer code. This was used to determine the temperature, velocities, stream function and vorticity. The effect of the wall heat conduction was also considered, by assuming a one-dimensional heat flow through the wall. The computer code (MATLAB program) developed was used for the determination of the aforementioned variables. The results obtained showed that the transient temperature distribution assumed a uniform profile which becomes more chaotic with increasing time. The vertical velocity showed increasing turbulent behavior with time, while the horizontal velocity assumed decreasing laminar behavior with time. All of these behaviours were equally reported in the literature. The developed model has provided understanding of heat transfer process in an industrial furnace.

Keywords: heat source, modelling, enclosure, furnace

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Authors: Matthew B. Davis, Amit Kumar

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With potentially dangerous impacts of climate change on the horizon, Canada has an opportunity to take a lead role on the international stage to demonstrate how energy use intensity and greenhouse gas emission intensity may be effectively reduced. Through bottom-up modelling of Canada’s energy sector using Long-range Energy Alternative Planning (LEAP) software, it can be determined where efforts should to be concentrated to produce the most positive energy management results. By analyzing a provincially integrated Canada, one can develop strategies to minimize the country’s economic downfall while transitioning to lower-emission energy technologies. Canada’s electricity sector plays an important role in accommodating these transitionary technologies as fossil-fuel based power production is prevalent in many parts of the country and is responsible for a large portion (17%) of Canada’s greenhouse gas emissions. Current findings incorporate an in-depth model of Canada’s current energy supply and demand sectors, as well as a business-as-usual scenario up to the year 2035. This allows for in-depth analysis of energy flow from resource potential, to extraction, to fuel and electricity production, to energy end use and emissions in Canada’s residential, transportation, commercial, institutional, industrial, and agricultural sectors. Bottom-up modelling techniques such as these are useful to critically analyze and compare the various possible scenarios of implementing sustainable energy measures. This work can aid government in creating effective energy and environmental policies, as well as guide industry to what technology or process changes would be most worthwhile to pursue.

Keywords: energy management, LEAP, energy end-use, GHG emissions

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Authors: Han-Mei Chen, Rongxin Zhou, Taige Wang

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Glass is widely used in everyday life, from glass bottles for beverages to architectural glass for various forms of glazing. Although the mainstream of used glass is recycled in the UK, the single-use and then recycling procedure results in a lot of waste as it incorporates intact glass with smashing, re-melting, and remanufacturing. These processes bring massive energy consumption with a huge loss of high embodied energy and economic value, compared to re-use, which’s towards a ‘zero carbon’ target. As a tourism city, Liverpool has more glass bottle consumption than most less leisure-focused cities. It’s therefore vital for Liverpool to find an upgrading approach for the single-use glass bottles with low carbon output. This project aims to assess the feasibility of industrial symbiosis and upgrading the framework of glass and to investigate the ways of achieving them. It is significant to Liverpool’s future industrial strategy since it provides an opportunity to target economic recovery for post-COVID by industry symbiosis and up-grading waste management in Liverpool to respond to the climate emergency. In addition, it will influence the local government policy for glass bottle reuse and recycling in North West England and as a good practice to be further recommended to other areas of the UK. First, a critical literature review of glass waste strategies has been conducted in the UK and worldwide industrial symbiosis practices. Second, mapping, data collection, and analysis have shown the current life cycle chain and the strong links of glass reuse and upgrading potentials via site visits to 16 local waste recycling centres. The results of this research have demonstrated the understanding of the influence of key factors on the development of a circular industrial symbiosis business model for beverage glass bottles. The current waste management procedures of the glass bottle industry, its business model, supply chain, and material flow have been reviewed. The various potential opportunities for glass bottle up-valuing have been investigated towards an industrial symbiosis in Liverpool. Finally, an up-valuing business model has been developed for an industrial symbiosis framework of glass in Liverpool. For glass bottles, there are two possibilities 1) focus on upgrading processes towards re-use rather than single-use and recycling and 2) focus on ‘smart’ re-use and recycling, leading to optimised values in other sectors to create a wider industry symbiosis for a multi-level and circular economy.

Keywords: glass bottles, industry symbiosis, smart re-use, waste upgrading

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Authors: Fatema Akram, Mohammad G. Rasul, M. Masud K. Khan, M. Sharif I. I. Amir

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Australia is a country of some 7,700 million square kilometres with a population of about 22.6 million. At present water security is a major challenge for Australia. In some areas the use of water resources is approaching and in some parts it is exceeding the limits of sustainability. A focal point of proposed national water conservation programs is the recycling of both urban storm-water and treated wastewater. But till now it is not widely practiced in Australia, and particularly storm-water is neglected. In Australia, only 4% of storm-water and rainwater is recycled, whereas less than 1% of reclaimed wastewater is reused within urban areas. Therefore, accurately monitoring, assessing and predicting the availability, quality and use of this precious resource are required for better management. As storm-water is usually of better quality than untreated sewage or industrial discharge, it has better public acceptance for recycling and reuse, particularly for non-potable use such as irrigation, watering lawns, gardens, etc. Existing storm-water recycling practice is far behind of research and no robust technologies developed for this purpose. Therefore, there is a clear need for using modern technologies for assessing feasibility of storm-water harvesting and reuse. Numerical modelling has, in recent times, become a popular tool for doing this job. It includes complex hydrological and hydraulic processes of the study area. The hydrologic model computes storm-water quantity to design the system components, and the hydraulic model helps to route the flow through storm-water infrastructures. Nowadays water quality module is incorporated with these models. Integration of Geographic Information System (GIS) with these models provides extra advantage of managing spatial information. However for the overall management of a storm-water harvesting project, Decision Support System (DSS) plays an important role incorporating database with model and GIS for the proper management of temporal information. Additionally DSS includes evaluation tools and Graphical user interface. This research aims to critically review and discuss all the aspects of storm-water harvesting and reuse such as available guidelines of storm-water harvesting and reuse, public acceptance of water reuse, the scopes and recommendation for future studies. In addition to these, this paper identifies, understand and address the importance of modern technologies capable of proper management of storm-water harvesting and reuse.

Keywords: storm-water management, storm-water harvesting and reuse, numerical modelling, geographic information system, decision support system, database

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Authors: Walid Wahballah, Taher Bazan, Fawzy Eltohamy

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One of the important functions of the satellite attitude control system is to provide the required pointing accuracy and attitude stability for optical remote sensing satellites to achieve good image quality. Although offering noise reduction and increased sensitivity, time delay and integration (TDI) charge coupled devices (CCDs) utilized in high-resolution satellites (HRS) are prone to introduce large amounts of pixel smear due to the instability of the line of sight. During on-orbit imaging, as a result of the Earth’s rotation and the satellite platform instability, the moving direction of the TDI-CCD linear array and the imaging direction of the camera become different. The speed of the image moving on the image plane (focal plane) represents the image motion velocity whereas the angle between the two directions is known as the drift angle (β). The drift angle occurs due to the rotation of the earth around its axis during satellite imaging; affecting the geometric accuracy and, consequently, causing image quality degradation. Therefore, the image motion velocity vector and the drift angle are two important factors used in the assessment of the image quality of TDI-CCD based optical remote sensing satellites. A model for estimating the image motion velocity and the drift angle in HRS is derived. The six satellite attitude control parameters represented in the derived model are the (roll angle φ, pitch angle θ, yaw angle ψ, roll angular velocity φ֗, pitch angular velocity θ֗ and yaw angular velocity ψ֗ ). The influence of these attitude parameters on the image quality is analyzed by establishing a relationship between the image motion velocity vector, drift angle and the six satellite attitude parameters. The influence of the satellite attitude parameters on the image quality is assessed by the presented model in terms of modulation transfer function (MTF) in both cross- and along-track directions. Three different cases representing the effect of pointing accuracy (φ, θ, ψ) bias are considered using four different sets of pointing accuracy typical values, while the satellite attitude stability parameters are ideal. In the same manner, the influence of satellite attitude stability (φ֗, θ֗, ψ֗) on image quality is also analysed for ideal pointing accuracy parameters. The results reveal that cross-track image quality is influenced seriously by the yaw angle bias and the roll angular velocity bias, while along-track image quality is influenced only by the pitch angular velocity bias.

Keywords: high-resolution satellites, pointing accuracy, attitude stability, TDI-CCD, smear, MTF

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Authors: Ebrahim Shayesteh, Nikolaos Styliaras, Alin George Raducu, Ozan Sahin, Daniel Pombo VáZquez, Jonas Funkquist, Sotirios Thanopoulos

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One of the important requirements in many code generation projects is defining some of the model parameters tunable. This helps to update the model parameters without performing the code generation again. This paper studies the concept of embedded code generation by MATLAB/Simulink coder targeting the TwinCAT Simulink system. The generated runtime modules are then tested and deployed to the TwinCAT 3 engineering environment. However, defining the parameters tunable in MATLAB/Simulink code generation targeting TwinCAT is not very straightforward. This paper focuses on this subject and reviews some of the techniques tested here to make the parameters tunable in generated runtime modules. Three techniques are proposed for this purpose, including normal tunable parameters, callback functions, and mask subsystems. Moreover, some test Simulink models are developed and used to evaluate the results of proposed approaches. A brief summary of the study results is presented in the following. First of all, the parameters defined tunable and used in defining the values of other Simulink elements (e.g., gain value of a gain block) could be changed after the code generation and this value updating will affect the values of all elements defined based on the values of the tunable parameter. For instance, if parameter K=1 is defined as a tunable parameter in the code generation process and this parameter is used to gain a gain block in Simulink, the gain value for the gain block is equal to 1 in the gain block TwinCAT environment after the code generation. But, the value of K can be changed to a new value (e.g., K=2) in TwinCAT (without doing any new code generation in MATLAB). Then, the gain value of the gain block will change to 2. Secondly, adding a callback function in the form of “pre-load function,” “post-load function,” “start function,” and will not help to make the parameters tunable without performing a new code generation. This means that any MATLAB files should be run before performing the code generation. The parameters defined/calculated in this file will be used as fixed values in the generated code. Thus, adding these files as callback functions to the Simulink model will not make these parameters flexible since the MATLAB files will not be attached to the generated code. Therefore, to change the parameters defined/calculated in these files, the code generation should be done again. However, adding these files as callback functions forces MATLAB to run them before the code generation, and there is no need to define the parameters mentioned in these files separately. Finally, using a tunable parameter in defining/calculating the values of other parameters through the mask is an efficient method to change the value of the latter parameters after the code generation. For instance, if tunable parameter K is used in calculating the value of two other parameters K1 and K2 and, after the code generation, the value of K is updated in TwinCAT environment, the value of parameters K1 and K2 will also be updated (without any new code generation).

Keywords: code generation, MATLAB, tunable parameters, TwinCAT

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Authors: Maryam Hajrezaie, Mahmood Ameen Abdulla, Nazia AbdulMajid, Maryam Zahedifard

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Colon cancer is one of the most prevalent cancers in the world. Cancer chemoprevention is defined as the use of natural or synthetic compounds capable of inducing biological mechanisms necessary to preserve genomic fidelity. New schiff based compounds are reported to exhibit a wide spectrum of biological activities of therapeutic importance. To evaluate inhibitory properties of CdCl2(C14H21N3O2) complex on colonic aberrant crypt foci, five groups of 7-week-old male rats were used. Control group was fed with 10% Tween 20 once a day, cancer control group was intra-peritoneally injected with 15 mg/kg Azoxymethan, drug control group was injected with 15 mg/kg azoxymethan and 5-Flourouracil, experimental groups were fed with 2.5 and 5 mg/kg CdCl2(C14H21N3O2) compound each once a day. Administration of compound were found to be effectively chemoprotective. Andrographolide suppressed total colonic ACF formation up to 72% to 74%, respectively, when compared with control group. The results also showed a significant increase in glutathione peroxidase, superoxide dismutase, catalase activities and a decrease in malondialdehyde level. Immunohistochemical staining demonstrated down-regulation of PCNA protein. According to the Western blot comparison analysis, COX-2 and Bcl2 is up-regulated whilst the Bax is down-regulated. according to these data, this compound plays promising chemoprotective activity, in a model of AOM-induced in ACF.

Keywords: chemopreventive, Schiff based compound, aberrant crypt foci (ACF), immunohistochemical staining

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Authors: Ankit Sinha, Soham Banerjee, Pratik Chattopadhyay

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Automated product recognition in retail stores is an important real-world application in the domain of Computer Vision and Pattern Recognition. In this paper, we consider the problem of automatically identifying the classes of the products placed on racks in retail stores from an image of the rack and information about the query/product images. We improve upon the existing approaches in terms of effectiveness and memory requirement by developing a two-stage object detection and recognition pipeline comprising of a Faster-RCNN-based object localizer that detects the object regions in the rack image and a ResNet-18-based image encoder that classifies the detected regions into the appropriate classes. Each of the models is fine-tuned using appropriate data sets for better prediction and data augmentation is performed on each query image to prepare an extensive gallery set for fine-tuning the ResNet-18-based product recognition model. This encoder is trained using a triplet loss function following the strategy of online-hard-negative-mining for improved prediction. The proposed models are lightweight and can be connected in an end-to-end manner during deployment to automatically identify each product object placed in a rack image. Extensive experiments using Grozi-32k and GP-180 data sets verify the effectiveness of the proposed model.

Keywords: retail stores, faster-RCNN, object localization, ResNet-18, triplet loss, data augmentation, product recognition

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Authors: H. Arzani, Z. Arzani

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Every piece of rangeland has a different set of physical and biological characteristics. This requires the manager to synthesis various information for regular monitoring to define changes trend to get wright decision for sustainable management. So range managers need to use computerized technologies to monitor rangeland, and select. The best management practices. There are four examples of computerized technologies that can benefit sustainable management: (1) Photographic method for cover measurement: The method was tested in different vegetation communities in semi humid and arid regions. Interpretation of pictures of quadrats was done using Arc View software. Data analysis was done by SPSS software using paired t test. Based on the results, generally, photographic method can be used to measure ground cover in most vegetation communities. (2) GPS application for corresponding ground samples and satellite pixels: In two provinces of Tehran and Markazi, six reference points were selected and in each point, eight GPS models were tested. Significant relation among GPS model, time and location with accuracy of estimated coordinates was found. After selection of suitable method, in Markazi province coordinates of plots along four transects in each 6 sites of rangelands was recorded. The best time of GPS application was in the morning hours, Etrex Vista had less error than other models, and a significant relation among GPS model, time and location with accuracy of estimated coordinates was found. (3) Application of satellite data for rangeland monitoring: Focusing on the long term variation of vegetation parameters such as vegetation cover and production is essential. Our study in grass and shrub lands showed that there were significant correlations between quantitative vegetation characteristics and satellite data. So it is possible to monitor rangeland vegetation using digital data for sustainable utilization. (4) Rangeland suitability classification with GIS: Range suitability assessment can facilitate sustainable management planning. Three sub-models of sensitivity to erosion, water suitability and forage production out puts were entered to final range suitability classification model. GIS was facilitate classification of range suitability and produced suitability maps for sheep grazing. Generally digital computers assist range managers to interpret, modify, calibrate or integrating information for correct management.

Keywords: computer, GPS, GIS, remote sensing, photographic method, monitoring, rangeland ecosystem, management, suitability, sheep grazing

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Authors: Alastair Hales, Xi Jiang, Siming Zhang

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Numerical methods are used to generate vortices in a domain. Through considered design, two counter-rotating vortices may interact and effectively drive one another downstream. This phenomenon is comparable to the vortex interaction that occurs in a region immediately downstream from two counter-oscillating piezoelectric (PE) fan blades. PE fans are small blades clamped at one end and driven to oscillate at their first natural frequency by an extremely low powered actuator. In operation, the high oscillation amplitude and frequency generate sufficient blade tip speed through the surrounding air to create downstream air flow. PE fans are considered an ideal solution for low power hot spot cooling in a range of small electronic devices, but a single blade does not typically induce enough air flow to be considered a direct alternative to conventional air movers, such as axial fans. The development of face-to-face PE fan arrays containing multiple blades oscillating in counter-phase to one another is essential for expanding the range of potential PE fan applications regarding the cooling of power electronics. Even in an unoptimised state, these arrays are capable of moving air volumes comparable to axial fans with less than 50% of the power demand. Replicating the airflow generated by face-to-face PE fan arrays without including the actual blades in the model reduces the process’s computational demands and enhances the rate of innovation and development in the field. Vortices are generated at a defined inlet using a time-dependent velocity profile function, which pulsates the inlet air velocity magnitude. This induces vortex generation in the considered domain, and these vortices are shown to separate and propagate downstream in a regular manner. The generation and propagation of a single vortex are compared to an equivalent vortex generated from a PE fan blade in a previous experimental investigation. Vortex separation is found to be accurately replicated in the present numerical model. Additionally, the downstream trajectory of the vortices’ centres vary by just 10.5%, and size and strength of the vortices differ by a maximum of 10.6%. Through non-dimensionalisation, the numerical method is shown to be valid for PE fan blades with differing parameters to the specific case investigated. The thorough validation methods presented verify that the numerical model may be used to replicate vortex formation from an oscillating PE fans blade. An investigation is carried out to evaluate the effects of varying the distance between two PE fan blade, pitch. At small pitch, the vorticity in the domain is maximised, along with turbulence in the near vicinity of the inlet zones. It is proposed that face-to-face PE fan arrays, oscillating in counter-phase, should have a minimal pitch to optimally cool nearby heat sources. On the other hand, downstream airflow is maximised at a larger pitch, where the vortices can fully form and effectively drive one another downstream. As such, this should be implemented when bulk airflow generation is the desired result.

Keywords: piezoelectric fans, low energy cooling, vortex formation, computational fluid dynamics

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Authors: Deokho Kim, Wonwoo Lee, Jaewoong Lee, Teresa Ng, Gun-Ill Lee, Jiwon Jeong

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This paper presents a method for improving object search accuracy using a deep learning model. A major limitation to provide accurate similarity with deep learning is the requirement of huge amount of data for training pairwise similarity scores (metrics), which is impractical to collect. Thus, similarity scores are usually trained with a relatively small dataset, which comes from a different domain, causing limited accuracy on measuring similarity. For this reason, this paper proposes a deep learning model that can be trained with a significantly small amount of data, a clustered data which of each cluster contains a set of visually similar images. In order to measure similarity distance with the proposed method, visual features of two images are extracted from intermediate layers of a convolutional neural network with various pooling methods, and the network is trained with pairwise similarity scores which is defined zero for images in identical cluster. The proposed method outperforms the state-of-the-art object similarity scoring techniques on evaluation for finding exact items. The proposed method achieves 86.5% of accuracy compared to the accuracy of the state-of-the-art technique, which is 59.9%. That is, an exact item can be found among four retrieved images with an accuracy of 86.5%, and the rest can possibly be similar products more than the accuracy. Therefore, the proposed method can greatly reduce the amount of training data with an order of magnitude as well as providing a reliable similarity metric.

Keywords: visual search, deep learning, convolutional neural network, machine learning

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Authors: Anupriya, Bikramjit Sinfh, Radhay Shyam

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In order to improve mixing phenomena and combustion processes in supersonic flow, the current work has concentrated on identifying the ideal cavity parameters using CFD ANSYS Fluent. Offset ratios (OR) and aft ramp angles () have been manipulated in simulations of several models, but the length-to-depth ratio has remained the same. The length-to-depth ratio of all cavity flows is less than 10, making them all open. Hydrogen fuel was injected into a supersonic air flow with a Mach number of 3.75 using a chamber with a 1 mm diameter and a transverse slot nozzle. The free stream had conditions of a pressure of 1.2 MPa, a temperature of 299K, and a Reynolds number of 2.07x107. This method has the ability to retain a flame since the cavity facilitates rapid mixing of fuel and oxidizer and decreases total pressure losses. The impact of the cavity on combustion efficiency and total pressure loss is discussed, and the results are compared to those of a model without a cavity. Both the mixing qualities and the combustion processes were enhanced in the model with the cavity. The overall pressure loss as well as the effectiveness of the combustion process both increase with the increase in the ramp angle to the rear. When OR is increased, however, resistance to the supersonic flow field is reduced, which has a detrimental effect on both parameters. For a given ramp height, larger pressure losses were observed at steeper ramp angles due to increased eddy-viscous turbulent flow and increased wall drag.

Keywords: total pressure loss, flame holder, supersonic combustion, combustion efficiency, cavity, nozzle

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Authors: Souzan Al Sayegh, Izzeldin Ibrahim, Mercia Van Der Walt, Mohamed Al-Kuwari

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Background: Walking is the most common form of physical activity which can promote a healthy well-being among people of different age groups. In this regard, pedometers are becoming more popular within research and are considered useful tools in monitoring physical activity levels based on individuals’ daily steps. A value of ˂5,000 steps/day is identified as a sedentary lifestyle index where individuals are physically inactive. Those achieving 5,000-7,499 steps/day have a low active lifestyle as they do not meet the moderate-to-vigorous physical activity (MVPA) recommendations. Moreover, individuals achieving ≥7,500 steps/day are classified as physically active. The objective of this study is to assess the physical activity levels of adult population in Qatar through a pedometer-based program over a one-year period. Methods: A cross-sectional analysis, as part of a longitudinal study, was carried out over one year to assess the daily step count. 'Step into Health' is a community-based program launched by Aspire as an approach for the purpose of improving physical activity across the population of Qatar. The program involves the distribution of pedometers to registered members which is supported by a self-monitoring online account and linked to a web database. Daily habitual physical activity (daily total step count) was assessed through Omron HJ-324U pedometer. Analyses were done on data extracted from the web database. Results: A total of 1,988 members were included in this study (males: n=1,143, 57%; females: n=845, 43%). Average age was 37.8±10.9 years distributed as 60% of age between age 25-54 (n=1,186), 27% of age 45-64 (n=546), and 13% of age 18-24 years (n=256). Majority were non-Qataris, 81% (n=1,609) compared with 19% of the Qatari nationality (n=379). Average body mass index (BMI) was 27.8±6.1 (kg/m2) where most of them (41%, n=809) were found to be overweight, between 25-30 kg/m2. Total average step count was 5,469±3,884. Majority were found to be sedentary (n=1110, 55.8%). Middle aged individuals were more active than the other two age groups. Males were seen as more active than females. Those who were less active had a higher BMI. Older individuals were more active. There was a variation in the physical activity level throughout the year period. Conclusion: It is essential to further develop the available intervention programs and increase their physical activity behavior. Planning such physical activity interventions for female population should involve aspects such as time, environmental variables and aerobic steps.

Keywords: adults, pedometer, physical activity, step-count

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Authors: Fally Titikpina, Abderafi Charki, Antoine Caucheteux, David Bigaud

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The building sector is one of the largest energy consumer with about 40% of the final energy consumption in the European Union. Ensuring building energy performance is of scientific, technological and sociological matter. To assess a building energy performance, the consumption being predicted or estimated during the design stage is compared with the measured consumption when the building is operational. When valuing this performance, many buildings show significant differences between the calculated and measured consumption. In order to assess the performance accurately and ensure the thermal efficiency of the building, it is necessary to evaluate the uncertainties involved not only in measurement but also those induced by the propagation of dynamic and static input data in the model being used. The evaluation of measurement uncertainty is based on both the knowledge about the measurement process and the input quantities which influence the result of measurement. Measurement uncertainty can be evaluated within the framework of conventional statistics presented in the \textit{Guide to the Expression of Measurement Uncertainty (GUM)} as well as by Bayesian Statistical Theory (BST). Another choice is the use of numerical methods like Monte Carlo Simulation (MCS). In this paper, we proposed to evaluate the uncertainty associated to the use of a simplified model for the estimation of the energy consumption of a given building. A detailed review and discussion of these three approaches (GUM, MCS and BST) is given. Therefore, an office building has been monitored and multiple sensors have been mounted on candidate locations to get required data. The monitored zone is composed of six offices and has an overall surface of 102 $m^2$. Temperature data, electrical and heating consumption, windows opening and occupancy rate are the features for our research work.

Keywords: building energy performance, uncertainty evaluation, GUM, bayesian approach, monte carlo method

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Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

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Authors: Kulin Dave, Kapil Mohan

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Earthquakes are considered to be the most destructive rapid-onset disasters human beings are exposed to. The amount of loss it brings in is sufficient to take careful considerations for designing of structures and facilities. Seismic Hazard Analysis is one such tool which can be used for earthquake resistant design. Ground Response Analysis is one of the most crucial and decisive steps for seismic hazard analysis. Rapar district of Kutch, Gujarat falls in Zone 5 of earthquake zone map of India and thus has high seismicity because of which it is selected for analysis. In total 8 bore-log data were studied at different locations in and around Rapar district. Different soil engineering properties were analyzed and relevant empirical correlations were used to calculate maximum shear modulus (Gmax) and shear wave velocity (Vs) for the soil layers. The soil was modeled using Pressure-Dependent Modified Kodner Zelasko (MKZ) model and the reference curve used for fitting was Seed and Idriss (1970) for sand and Darendeli (2001) for clay. Both Equivalent linear (EL), as well as Non-linear (NL) ground response analysis, has been carried out with Masing Hysteretic Re/Unloading formulation for comparison. Commercially available DEEPSOIL v. 7.0 software is used for this analysis. In this study an attempt is made to quantify ground response regarding generated acceleration time-history at top of the soil column, Response spectra calculation at 5 % damping and Fourier amplitude spectrum calculation. Moreover, the variation of Peak Ground Acceleration (PGA), Maximum Displacement, Maximum Strain (in %), Maximum Stress Ratio, Mobilized Shear Stress with depth is also calculated. From the study, PGA values estimated in rocky strata are nearly same as bedrock motion and marginal amplification is observed in sandy silt and silty clays by both analyses. The NL analysis gives conservative results of maximum displacement as compared to EL analysis. Maximum strain predicted by both studies is very close to each other. And overall NL analysis is more efficient and realistic because it follows the actual hyperbolic stress-strain relationship, considers stiffness degradation and mobilizes stresses generated due to pore water pressure.

Keywords: DEEPSOIL v 7.0, ground response analysis, pressure-dependent modified Kodner Zelasko model, MKZ model, response spectra, shear wave velocity

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Authors: Sajjad Hussain, Sabir Khan, Maria Del Pilar Taboada Sotomayor

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This work demonstrates the synthesis of magnetic molecularly imprinted polymers (MMIPs) for determination of a selected pesticide (ametryne) using high performance liquid chromatography (HPLC). Computational simulation can assist the choice of the most suitable monomer for the synthesis of polymers. The (MMIPs) were polymerized at the surface of Fe3O4@SiO2 magnetic nanoparticles (MNPs) using 2-vinylpyradine as functional monomer, ethylene-glycol-dimethacrylate (EGDMA) is a cross-linking agent and 2,2-Azobisisobutyronitrile (AIBN) used as radical initiator. Magnetic non-molecularly imprinted polymer (MNIPs) was also prepared under the same conditions without analyte. The MMIPs were characterized by scanning electron microscopy (SEM), Brunauer, Emmett and Teller (BET) and Fourier transform infrared spectroscopy (FTIR). Pseudo first order and pseudo second order model were applied to study kinetics of adsorption and it was found that adsorption process followed the pseudo first order kinetic model. Adsorption equilibrium data was fitted to Freundlich and Langmuir isotherms and the sorption equilibrium process was well described by Langmuir isotherm mode. The selectivity coefficients (α) of MMIPs for ametryne with respect to atrazine, ciprofloxacin and folic acid were 4.28, 12.32, and 14.53 respectively. The spiked recoveries ranged between 91.33 and 106.80% were obtained. The results showed high affinity and selectivity of MMIPs for pesticide ametryne in the food samples.

Keywords: molecularly imprinted polymer, pesticides, magnetic nanoparticles, adsorption

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Authors: Mohamed Ouzzane, Mahmoud Bady

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Due to energy and environment context, research is looking for the use of clean and energy efficient system in cooling industry. In this regard, the ejector represents one of the promising solutions. The thermal ejector is a passive component used for thermal compression in refrigeration and cooling systems, usually activated by heat either waste or solar. The present study introduces a theoretical analysis of the cooling system which uses a gas ejector thermal compression. A theoretical model is developed and applied for the design and simulation of the ejector, as well as the whole cooling system. Besides the conservation equations of mass, energy and momentum, the gas dynamic equations, state equations, isentropic relations as well as some appropriate assumptions are applied to simulate the flow and mixing in the ejector. This model coupled with the equations of the other components (condenser, evaporator, pump, and generator) is used to analyze profiles of pressure and velocity (Mach number), as well as evaluation of the cycle cooling capacity. A FORTRAN program is developed to carry out the investigation. Properties of refrigerant R134a are calculated using real gas equations. Among many parameters, it is thought that the generator pressure is the cornerstone in the cycle, and hence considered as the key parameter in this investigation. Results show that the generator pressure has a great effect on the ejector and on the whole cooling system. At high generator pressures, strong shock waves inside the ejector are created, which lead to significant condenser pressure at the ejector exit. Additionally, at higher generator pressures, the designed system can deliver cooling capacity for high condensing pressure (hot season).

Keywords: air cooling system, refrigeration, thermal ejector, thermal compression

Procedia PDF Downloads 157

Authors: Milica Karadzic, Lidija Jevric, Sanja Podunavac-Kuzmanovic, Strahinja Kovacevic, Anamarija Mandic, Katarina Penov Gasi, Marija Sakac, Aleksandar Okljesa, Andrea Nikolic

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The present paper deals with chromatographic lipophilicity prediction of newly synthesized steroid derivatives. The prediction was achieved using in silico generated molecular descriptors and quantitative structure-retention relationship (QSRR) methodology with the artificial neural networks (ANN) approach. Chromatographic lipophilicity of the investigated compounds was expressed as retention factor value logk. For QSRR modeling, a feedforward back-propagation ANN with gradient descent learning algorithm was applied. Using the novel sum of ranking differences (SRD) method generated ANN models were ranked. The aim was to distinguish the most consistent QSRR model that can be found, and similarity or dissimilarity between the models that could be noticed. In this study, SRD was performed with average values of retention factor value logk as reference values. An excellent correlation between experimentally observed retention factor value logk and values predicted by the ANN was obtained with a correlation coefficient higher than 0.9890. Statistical results show that the established ANN models can be applied for required purpose. This article is based upon work from COST Action (TD1305), supported by COST (European Cooperation in Science and Technology).

Keywords: artificial neural networks, liquid chromatography, molecular descriptors, steroids, sum of ranking differences

Procedia PDF Downloads 319

Authors: Mershack O. Tetteh, Albert P. C. Chan, Amos Darko, Gabriel Nani

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International construction joint ventures (ICJVs) have evolved as an effective approach to sustainable development, given their myriad socio-economic and environmental benefits. Yet, they are not free of barriers and risks. In many studies, it is termed as risks for convenience’s sake. While the barriers and risks continue to affect the success of ICJVs, a systematic and reliable approach for managing them has yet to be developed. This study aims to identify and classify the barriers and risks factors affecting ICJVs through a systematic literature review. Based on a critical review of 54 papers published in peer-reviewed journals from 1990 to 2019, a conceptual framework was proposed for managing the barriers and risks in ICJV operations. The review showed that the barriers can be grouped into six including inter-organizational differences, lack of expertise and confidence, lack of effective planning and strategies, lack of knowledge of ICJV’s fundamentals, conflicts among ICJV entities, and management difficulties. The risks were also categorized into six: policy and political risks, legal risks, financial risks, management risks, project and technical risks, and market risks. The developed model would help practitioners achieve more efficient resource allocation and bring new perspectives for managerial practices in ICJVs. Moreover, it is positioned to alleviate the negligence of previous studies that combined the barriers and risks factors as one checklist.

Keywords: barriers, construction, international construction joint venture, risks, sustainable development

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Authors: Jocelyn L. Alimondo, Juna O. Sabelo

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This study explored the MTB-MLE implementation practices of teachers in one monolingual elementary school and one multilingual elementary school in Benguet, Philippines. It used phenomenological approach employing participant-observation, focus group discussion and individual interview. Data were gathered using a video camera, an audio recorder, and an FGD guide and were treated through triangulation and coding. From the data collected, varied ways in implementing the MTB-MLE program were noted. These are: Teaching using a hybrid first language, teaching using a foreign LOI, using translation and multilingual instruction, and using L2/L3 to unlock L1. However, these practices come with challenges such as the a conflict between the mandated LOI and what pupils need, lack of proficiency of teachers in the mandated LOI, facing unreceptive parents, stagnation of knowledge resulting from over-familiarity of input, and zero learning resulting from an incomprehensible language input. From the practices and challenges experienced by the teachers, a model of MTB-MLE approach, the 3L-in-one approach, to teaching was created to illustrate the practice which teachers claimed to be the best way to address the challenges besetting them while at the same time satisfying the academic needs of their pupils. From the findings, this paper concludes that despite the challenges besetting the teachers, they still displayed creativity in coming up with relevant teaching practices, the unreceptiveness of some teachers and parents sprung from the fact that they do not understand the real concept of MTB-MLE, greater challenges are being faced by teachers in multilingual school due to the diverse linguistic background of their clients, and the most effective approach in implementing MTB-MLE is the multilingual approach, allowing the use of the pupils’ mother tongue, L2 (Filipino), L3 (English), and other languages familiar to the students.

Keywords: MTB-MLE Philippines, MTB-MLE model, first language, multilingual instruction

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Authors: Polina Prokopovich

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Disease-modifying osteoarthritis drugs (DMOADs) are a new therapeutic class for OA, preventing or inhibiting OA development. Unfortunately, none of the DMOADs have been clinically approved due to their poor therapeutic effects in clinical trials. The joint environment has played a role in the poor clinical performance of these drugs by limiting the amount of drug effectively delivered as well as the time that the drug spends within the joint space. The current study aims to enhance the cartilage uptake and retention time of the DMOADs-model (licofelone), which showed a significant therapeutic effect against OA progression and is currently in phase III. Licofelone will be covalently conjugated to the hydrolysable, cytocompatible, and cationic poly beta-amino ester polymers (PBAE). The cationic polymers (A16 and A87) can be electrostatically attached to the negatively charged cartilage component (glycosaminoglycan), which will increase the drug penetration through the cartilage and extend the drug time within the cartilage. In the cartilage uptake and retention time studies, an increase of 18 to 37 times of the total conjugated licofelone to A87 and A16 was observed when compared to the free licofelone. Furthermore, the conjugated licofelone to A87 was detectable within the cartilage at 120 minutes, while the free licofelone was not detectable after 60 minutes. Additionally, the A87-licofelone conjugate showed no effect on the chondrocyte viability. In conclusion, the cationic A87 and A16 polymers increased the percentage of licofelone within the cartilage, which could potentially enhance the therapeutic effect and pharmacokinetic performance of licofelone or other DMOADs clinically.

Keywords: PBAE, cartilage., osteoarthritis, injectable biomaterials, drug delivery

Procedia PDF Downloads 72