Search results for: machine learning tools and techniques

Authors: Ghita Benayad

Abstract:

Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques.

Keywords: asset allocation, portfolio optimization, principle component analysis, multi-objective modelling, financial market

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Authors: Sumaira Zafar, Arjumand Zaidi

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During past few years, several floods have adversely affected the areas along lower Indus River. Besides other climate related anomalies, rapidly increasing urbanization and blockage of natural drains due to siltation or encroachments are two other critical causes that may be responsible for these disasters. Due to flat topography of river Indus plains and blockage of natural waterways, drainage of storm water takes time adversely affecting the crop health and soil properties of the area. Government of Sindh is taking a keen interest in revival of natural drainage network in the province and has initiated this work under Sindh Irrigation and Drainage Authority. In this paper, geospatial techniques are used to analyze landuse/land-cover changes of Larkana district over the past three decades (1980-present) and their impact on natural drainage system. Satellite derived Digital Elevation Model (DEM) and topographic sheets (recent and 1950) are used to delineate natural drainage pattern of the district. The urban landuse map developed in this study is further overlaid on drainage line layer to identify the critical areas where the natural floodwater flows are being inhibited by urbanization. Rainfall and flow data are utilized to identify areas of heavy flow, whereas, satellite data including Landsat 7 and Google Earth are used to map previous floods extent and landuse/cover of the study area. Alternatives to natural drainage systems are also suggested wherever possible. The output maps of natural drainage pattern can be used to develop a decision support system for urban planners, Sindh development authorities and flood mitigation and management agencies.

Keywords: geospatial techniques, satellite data, natural drainage, flood, urbanization

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Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Fikiru Temesgen Gelata

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Food security, adaptation, and climate change mitigation are all problems that can be resolved simultaneously with Climate-Smart Agriculture (CSA). This study examines determinants of climate-smart agriculture (CSA) practices among smallholder farmers, aiming to understand the factors guiding adoption decisions and evaluate the impact of CSA on smallholder farmer income in the study areas. For this study, three-stage sampling techniques were applied to select 230 smallholders randomly. Mann-Kendal test and multinomial endogenous switching regression model were used to analyze trends of decrease or increase within long-term temporal data and the impact of CSA on the smallholder farmer income, respectively. Findings revealed education level, household size, land ownership, off-farm income, climate information, and contact with extension agents found to be highly adopted CSA practices. On the contrary, erosion exerted a detrimental impact on all the agricultural practices examined within the study region. Various factors such as farming methods, the size of farms, proximity to irrigated farmlands, availability of extension services, distance to market hubs, and access to weather forecasts were recognized as key determinants influencing the adoption of CSA practices. The multinomial endogenous switching regression model (MESR) revealed that joint adoption of crop rotation and soil and water conservation practices significantly increased farm income by 1,107,245 ETB. The study recommends that counties and governments should prioritize addressing climate change in their development agendas to increase the adoption of climate-smart farming techniques.

Keywords: climate-smart practices, food security, Oincome, MERM, Ethiopia

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Authors: Razieh Karimi Aghcheh, Christian Kubicek, Joseph Strauss, Gerhard Braus

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Filamentous fungi are of considerable economic and social significance for human health, nutrition and in white biotechnology. These organisms are dominant producers of a range of primary metabolites such as citric acid, microbial lipids (biodiesel) and higher unsaturated fatty acids (HUFAs). In particular, they produce also important but structurally complex secondary metabolites with enormous therapeutic applications in pharmaceutical industry, for example: cephalosporin, penicillin, taxol, zeranol and ergot alkaloids. Several fungal secondary metabolites, which are significantly relevant to human health do not only include antibiotics, but also e.g. lovastatin, a well-known antihypercholesterolemic agent produced by Aspergillus. terreus, or aflatoxin, a carcinogen produced by A. flavus. In addition to their roles for human health and agriculture, some fungi are industrially and commercially important: Species of the ascomycete genus Hypocrea spp. (teleomorph of Trichoderma) have been demonstrated as efficient producer of highly active cellulolytic enzymes. This trait makes them effective in disrupting and depolymerization of lignocellulosic materials and thus applicable tools in number of biotechnological areas as diverse as clothes-washing detergent, animal feed, and pulp and fuel productions. Fungal LaeA/LAE1 (Loss of aflR Expression A) homologs their gene products act at the interphase between secondary metabolisms, cellulase production and development. Lack of the corresponding genes results in significant physiological changes including loss of secondary metabolite and lignocellulose degrading enzymes production. At the molecular level, the encoded proteins are presumably methyltransferases or demethylases which act directly or indirectly at heterochromatin and interact with velvet domain proteins. Velvet proteins bind to DNA and affect expression of secondary metabolites (SMs) genes and cellulases. The dynamic interplay between LaeA/LAE1, velvet proteins and additional interaction partners is the key for an understanding of the coordination of metabolic and morphological functions of fungi and is required for a biotechnological control of the formation of desired bioactive products. Aspergilli and Trichoderma represent different biotechnologically significant species with significant differences in the LaeA/LAE1-Velvet protein machinery and their target proteins. We, therefore, performed a comparative study of the interaction partners of this machinery and the dynamics of the various protein-protein interactions using our robust proteomic and mass spectrometry techniques. This enhances our knowledge about the fungal coordination of secondary metabolism, cellulase production and development and thereby will certainly improve recombinant fungal strain construction for the production of industrial secondary metabolite or lignocellulose hydrolytic enzymes.

Keywords: cellulases, LaeA/1, proteomics, secondary metabolites

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Authors: Liang Zhou, Qinke Sun

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Taking the Yangtze River Economic Belt as an example, using long-term nighttime lighting data from DMSP/OLS from 1992 to 2012, support vector machine classification (SVM) was used to quantitatively extract urban built-up areas of economic belts, and spatial analysis of expansion intensity index, standard deviation ellipse, etc. was introduced. The model conducts detailed and in-depth discussions on the strength, direction, and type of the expansion of the middle and lower reaches of the economic belt and the key node cities. The results show that: (1) From 1992 to 2012, the built-up areas of the major cities in the Yangtze River Valley showed a rapid expansion trend. The built-up area expanded by 60,392 km², and the average annual expansion rate was 31%, that is, from 9615 km² in 1992 to 70007 km² in 2012. The spatial gradient analysis of the watershed shows that the expansion of urban built-up areas in the middle and lower reaches of the river basin takes Shanghai as the leading force, and the 'bottom-up' model shows an expanding pattern of 'upstream-downstream-middle-range' declines. The average annual rate of expansion is 36% and 35%, respectively. 17% of which the midstream expansion rate is about 50% of the upstream and downstream. (2) The analysis of expansion intensity shows that the urban expansion intensity in the Yangtze River Basin has generally shown an upward trend, the downstream region has continued to rise, and the upper and middle reaches have experienced different amplitude fluctuations. To further analyze the strength of urban expansion at key nodes, Chengdu, Chongqing, and Wuhan in the upper and middle reaches maintain a high degree of consistency with the intensity of regional expansion. Node cities with Shanghai as the core downstream continue to maintain a high level of expansion. (3) The standard deviation ellipse analysis shows that the overall center of gravity of the Yangtze River basin city is located in Anqing City, Anhui Province, and it showed a phenomenon of reciprocating movement from 1992 to 2012. The nighttime standard deviation ellipse distribution range increased from 61.96 km² to 76.52 km². The growth of the major axis of the ellipse was significantly larger than that of the minor axis. It had obvious east-west axiality, in which the nighttime lights in the downstream area occupied in the entire luminosity scale urban system leading position.

Keywords: urban space, support vector machine, spatial characteristics, night lights, Yangtze River Economic Belt

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Authors: Han-Woong Ahn, In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

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It is important to accurately identify machine parameters for direct vector control. To obtain the parameter values, traditional methods can be used such as no-load and rotor locked tests. However, there are many differences between values obtained from the traditional tests and actual values. In addition, there are drawbacks that additional equipment and cost are required for the experiment. Therefore, it is hard to temporary operation to estimate induction motor parameters. Therefore, this paper deals with the estimation algorithm of induction motor parameters without a motor operation and the measurement from additional equipment such as sensors and dynamometer. The validity and usefulness of the estimation algorithm considering inverter nonlinearity is verified by comparing the conventional method with the proposed method.

Keywords: induction motor, parameter, off-line estimation, inverter nonlinearity

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Authors: Victoria Molina, Wendy Franco, Sergio Benavides, José M. Troncoso, Ricardo Luna, Jose R. PéRez-Correa

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Emamectin Benzoate (EB) is a crystal antiparasitic that belongs to the avermectin family. It is one of the most common treatments used in Chile to control Caligus rogercresseyi in Atlantic salmon. However, the sea lice acquired resistance to EB when it is exposed at sublethal EB doses. The low solubility rate of EB and its degradation at the acidic pH in the fish digestive tract are the causes of the slow absorption of EB in the intestine. To protect EB from degradation and enhance its absorption, specific microencapsulation technologies must be developed. Amorphous Solid Dispersion techniques such as Spray Drying (SD) and Ionic Gelation (IG) seem adequate for this purpose. Recently, Soluplus® (SOL) has been used to increase the solubility rate of several drugs with similar characteristics than EB. In addition, alginate (ALG) is a widely used polymer in IG for biomedical applications. Regardless of the encapsulation technique, the quality of the obtained microparticles is evaluated with the following responses, yield (Y%), encapsulation efficiency (EE%) and loading capacity (LC%). In addition, it is important to know the percentage of EB released from the microparticles in gastric (GD%) and intestinal (ID%) digestions. In this work, we microencapsulated EB with SOL (EB-SD) and with ALG (EB-IG) using SD and IG, respectively. Quality microencapsulation responses and in vitro gastric and intestinal digestions at pH 3.35 and 7.8, respectively, were obtained. A central composite design was used to find the optimum microencapsulation variables (amount of EB, amount of polymer and feed flow). In each formulation, the behavior of these variables was predicted with statistical models. Then, the response surface methodology was used to find the best combination of the factors that allowed a lower EB release in gastric conditions, while permitting a major release at intestinal digestion. Two approaches were used to determine this. The desirability approach (DA) and multi-objective optimization (MOO) with multi-criteria decision making (MCDM). Both microencapsulation techniques allowed to maintain the integrity of EB in acid pH, given the small amount of EB released in gastric medium, while EB-IG microparticles showed greater EB release at intestinal digestion. For EB-SD, optimal conditions obtained with MOO plus MCDM yielded a good compromise among the microencapsulation responses. In addition, using these conditions, it is possible to reduce microparticles costs due to the reduction of 60% of BE regard the optimal BE proposed by (DA). For EB-GI, the optimization techniques used (DA and MOO) yielded solutions with different advantages and limitations. Applying DA costs can be reduced 21%, while Y, GD and ID showed 9.5%, 84.8% and 2.6% lower values than the best condition. In turn, MOO yielded better microencapsulation responses, but at a higher cost. Overall, EB-SD with operating conditions selected by MOO seems the best option, since a good compromise between costs and encapsulation responses was obtained.

Keywords: microencapsulation, multiple decision-making criteria, multi-objective optimization, Soluplus®

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Authors: Jian Li

Abstract:

The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Asima Ranjan Parhi

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English language teaching-learning in higher education departments in Indian and Asian contexts needs to be one of innovation and experimentation rather than rigid prescription. The communicative language teaching has been proposing the context to be of primary importance in this process. Today, English print and electronic media have flooded the market with plenty of material suitable to the classroom context. The entries are poetic, catchy and contain a deliberate method in them which could be utilized to teach not only English language but literature, literary terms and the theory of literature. The Bollywood movies, especially through their songs have been propagating a package which may be useful to teach language and even theory in the sub-continent. While investigating, one may be fascinated to see how such material in the body of media (print and electronic), movies and popular songs generate a data for our classroom in our context, thereby developing a mass language with huge pedagogical implications. Harping on the four skills of teaching and learning of a language in general and English language in particular appears stale and mechanical in a decontextualised, matter of fact classroom. So this discussion visualizes a model beyond these skills as well as the conventional theory, literature, language classroom practices in order to build up a systematic pattern stressing the factors responsible in the particular context, that of specific language, society and culture in tune with language-literature teaching. This study intends to examine certain catchy use of the language entries in mass media which could be in the direction of inviting more such investigations in the Asian context in order to develop a common platform of decolonized pedagogy.

Keywords: pedagogy, electronic media, Bollywood, decolonized, mass media

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Authors: Elham Koohikerade, Silvio Jose Gumiere

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In this research, irrigation is highlighted as crucial for improving both the yield and quality of ‎potatoes due to their high sensitivity to soil moisture changes. The study presents a hybrid Long ‎Short-Term Memory (LSTM) model aimed at optimizing irrigation scheduling in potato fields in ‎Quebec City, Canada. This model integrates model-based and satellite-derived datasets to simulate ‎soil moisture content, addressing the limitations of field data. Developed under the guidance of the ‎Food and Agriculture Organization (FAO), the simulation approach compensates for the lack of direct ‎soil sensor data, enhancing the LSTM model's predictions. The model was calibrated using indices ‎like Surface Soil Moisture (SSM), Normalized Vegetation Difference Index (NDVI), Enhanced ‎Vegetation Index (EVI), and Normalized Multi-band Drought Index (NMDI) to effectively forecast ‎soil moisture reductions. Understanding soil moisture and plant development is crucial for assessing ‎drought conditions and determining irrigation needs. This study validated the spectral characteristics ‎of vegetation and soil using ECMWF Reanalysis v5 (ERA5) and Moderate Resolution Imaging ‎Spectrometer (MODIS) data from 2019 to 2023, collected from agricultural areas in Dolbeau and ‎Peribonka, Quebec. Parameters such as surface volumetric soil moisture (0-7 cm), NDVI, EVI, and ‎NMDI were extracted from these images. A regional four-year dataset of soil and vegetation moisture ‎was developed using a machine learning approach combining model-based and satellite-based ‎datasets. The LSTM model predicts soil moisture dynamics hourly across different locations and ‎times, with its accuracy verified through cross-validation and comparison with existing soil moisture ‎datasets. The model effectively captures temporal dynamics, making it valuable for applications ‎requiring soil moisture monitoring over time, such as anomaly detection and memory analysis. By ‎identifying typical peak soil moisture values and observing distribution shapes, irrigation can be ‎scheduled to maintain soil moisture within Volumetric Soil Moisture (VSM) values of 0.25 to 0.30 ‎m²/m², avoiding under and over-watering. The strong correlations between parcels suggest that a ‎uniform irrigation strategy might be effective across multiple parcels, with adjustments based on ‎specific parcel characteristics and historical data trends. The application of the LSTM model to ‎predict soil moisture and vegetation indices yielded mixed results. While the model effectively ‎captures the central tendency and temporal dynamics of soil moisture, it struggles with accurately ‎predicting EVI, NDVI, and NMDI.‎

Keywords: irrigation scheduling, LSTM neural network, remotely sensed indices, soil and vegetation ‎monitoring

Procedia PDF Downloads 38

Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: C. Fornacelli, P. Colomban, E. Mugnaioli, I. Memmi Turbanti

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When semiconductor particles are reduced in scale to nanometer dimension, their optical and electro-optical properties strongly differ from those of bulk crystals of the same composition. Since sampling is often not allowed concerning cultural heritage artefacts, the potentialities of two non-invasive techniques, such as Raman and Fiber Optic Reflectance Spectroscopy (FORS), have been investigated and the results of the analysis on some original glasses of different colours (from yellow to orange and deep red) and periods (from the second decade of the 20th century to present days) are reported in the present study. In order to evaluate the potentialities of the application of non-invasive techniques to the investigation of the structure and distribution of nanoparticles dispersed in a glass matrix, Scanning Electron Microscopy (SEM) and energy-disperse spectroscopy (EDS) mapping, together with Transmission Electron Microscopy (TEM) and Electron Diffraction Tomography (EDT) have also been used. Raman spectroscopy allows a fast and non-destructive measure of the quantum dots composition and size, thanks to the evaluation of the frequencies and the broadening/asymmetry of the LO phonons bands, respectively, though the important role of the compressive strain arising from the glass matrix and the possible diffusion of zinc from the matrix to the nanocrystals should be taken into account when considering the optical-phonons frequency values. The incorporation of Zn has been assumed by an upward shifting of the LO band related to the most abundant anion (S or Se), while the role of the surface phonons as well as the confinement-induced scattering by phonons with a non-zero wavevectors on the Raman peaks broadening has been verified. The optical band gap varies from 2.42 eV (pure CdS) to 1.70 eV (CdSe). For the compositional range between 0.5≤x≤0.2, the presence of two absorption edges has been related to the contribution of both pure CdS and the CdSxSe1-x solid solution; this particular feature is probably due to the presence of unaltered cubic zinc blende structures of CdS that is not taking part to the formation of the solid solution occurring only between hexagonal CdS and CdSe. Moreover, the band edge tailing originating from the disorder due to the formation of weak bonds and characterized by the Urbach edge energy has been studied and, together with the FWHM of the Raman signal, has been assumed as a good parameter to evaluate the degree of topological disorder. SEM-EDS mapping showed a peculiar distribution of the major constituents of the glass matrix (fluxes and stabilizers), especially concerning those samples where a layered structure has been assumed thanks to the spectroscopic study. Finally, TEM-EDS and EDT were used to get high-resolution information about nanocrystals (NCs) and heterogeneous glass layers. The presence of ZnO NCs (< 4 nm) dispersed in the matrix has been verified for most of the samples, while, for those samples where a disorder due to a more complex distribution of the size and/or composition of the NCs has been assumed, the TEM clearly verified most of the assumption made by the spectroscopic techniques.

Keywords: CdSxSe1-x, EDT, glass, spectroscopy, TEM-EDS

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Authors: Titanilla Komenda, Viktorio Malisa

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In this paper, the influence of diversity-related factors on the design of collaborative scenarios is analysed. Based on the evaluation, a framework for simulating human-robot-collaboration is presented that considers both human factors as well as the overall system performance. The implementation of the model is shown on a real-life scenario from industry and validated in terms of traceability, safety and physical limitations. By comparing scenarios that consider diversity with those only meeting system performance, an overall understanding of individually adapted human-robot-collaborative workspaces is reached. A diversity-related guideline for human-robot-collaborations provides a summary of the research and aids in optimizing future applications. Finally, limitations and future amendments of the model are discussed.

Keywords: diversity, human-machine system, human-robot collaboration, simulation

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Authors: Bintou Jobe

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Musculo-skeletal disorders (MSDs) are injuries or disorders of the spine disc, muscle strains, and low back injuries. It remains a major cause of occupational illness. Findings: Due to poor grips during handling, it is possible for neck, shoulder, arm, knees, ankle, fingers, waist, lower back injuries, and other muscle joints to be affected. Pregnant women are more prone to physical and hormonal changes, which lead to the relaxation of supporting ligaments. MSD continues to pose a global concern due to its impact on workers worldwide. The prevalence of the disorder is high, according to research into the workforce in Europe and developing countries. The causes are characterized by long working hours, insufficient rest breaks, poor posture, repetitive motion, poor manual handling techniques, psychological stress, and poor nutrition. To prevent MSD, the design mainly involves avoiding and assessing the risk. However, clinical solutions, policy governance, and minimizing manual labour are also an alternative. In addition, eating a balanced diet and teamwork force are key to elements in minimising the risk. This review aims to raise awareness and promote cost effectiveness prevention and understanding of MSD through research and identify proposed solutions to recognise the underlying causes of MSDs in the construction sectors. The methodology involves a literature review approach, engaging with the policy landscape of MSD, synthesising publications on MSD and a wider range of academic publications. In conclusion, training on effective manual handling techniques should be considered, and Personal Protective Equipment should be a last resort. The implementation of training guidelines has yielded significant benefits.

Keywords: musculoskeletal disorder work related, MSD, manual handling, work hazards

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Authors: Vinay Patil, Swapnil Kekade, Ashish Supare, Vinayak Pawar, Shital Jadhav, Rajkumar Singh

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In recent days, bainitic steel is used in automobile and non-automobile sectors due to its high strength. Bainitic steel is difficult to machine because of its high hardness, hence in this paper machinability of bainitic steel is studied by using Taguchi design of experiments (DOE) approach. Convectional turning experiments were done by using L16 orthogonal array for three input parameters viz. cutting speed, depth of cut and feed. The Taguchi method is applied to study the performance characteristics of machining parameters with surface roughness (Ra), cutting force and tool wear rate. By using Taguchi analysis, optimized process parameters for best surface finish and minimum cutting forces were analyzed.

Keywords: conventional turning, Taguchi method, S/N ratio, bainitic steel machining

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Authors: Molly Gerrish

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This work explores the physical, emotional, social, and cognitive risks and benefits related to nature-based teaching and highlights the importance of promoting a sustainable workforce within early childhood programs. Assessing and managing risks can help programs reimagine their approach to teaching, learning, recruitment, family connectivity, and staff motivation. The importance of staff sustainability and motivation/engagement related to social justice and the environment will be discussed. We will explore ways to manage fears and limitations faced by early childhood programs regarding nature experiences and risky play in a variety of locations using a lens of place-based learning. We will also examine the alignment of sustainability and social-emotional development, mental health supports, social awareness, and risk assessment. The work will discuss the varied perceptions of risk in diverse areas and the impact on the early childhood workforce. Motivational theory and compassion resiliency are hallmarks of both recruiting and retaining high-quality early childhood educators; the work will discuss how to balance programmatic constraints and healthy motivation for students and teachers while empowering individuals to advocate for their mental health and well-being. Finally, the work will highlight the positive impact of nature-based teaching practices and the overall benefit to young children and their educators.

Keywords: child’s rights, inclusion, nature-based education, risk assessment

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Authors: Jinhui Bak, Bumjin Kim

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The purpose of this study is to analyze how the scientific and mathematical fields (STEM) and liberal arts (A) work together in the STEAM program. In the future STEAM programs that have been designed and developed, the humanities will act not just as a 'tool' for science technology and mathematics, but as a 'core' content to have an equivalent status. STEAM was first introduced to the Republic of Korea in 2011 when the Ministry of Education emphasized fostering creative convergence talent. Many programs have since been developed under the name STEAM, but with the majority of programs focusing on technology education, arts and humanities are considered secondary. As a result, arts is most likely to be accepted as an option that can be excluded from the teachers who run the STEAM program. If what we ultimately pursue through STEAM education is in fostering STEAM literacy, we should no longer turn arts into a tooling area for STEM. Based on this consciousness, this study analyzed over 160 STEAM programs in middle and high schools, which were produced and distributed by the Ministry of Education and the Korea Science and Technology Foundation from 2012 to 2017. The framework of analyses referenced two criteria presented in the related prior studies: normative convergence and technological convergence. In addition, we divide Arts into fine arts and liberal arts and focused on Korean Language Course which is in liberal arts and analyzed what kind of curriculum standards were selected, and what kind of process the Korean language department participated in teaching and learning. In this study, to ensure the reliability of the analysis results, we have chosen to cross-check the individual analysis results of the two researchers and only if they are consistent. We also conducted a reliability check on the analysis results of three middle and high school teachers involved in the STEAM education program. Analyzing 10 programs selected randomly from the analyzed programs, Cronbach's α .853 showed a reliable level. The results of this study are summarized as follows. First, the convergence ratio of the liberal arts was lowest in the department of moral at 14.58%. Second, the normative convergence is 28.19%, which is lower than that of the technological convergence. Third, the language and achievement criteria selected for the program were limited to functional areas such as listening, talking, reading and writing. This means that the convergence of Korean language departments is made only by the necessary tools to communicate opinions or promote scientific products. In this study, we intend to compare these results with the STEAM programs in the United States and abroad to explore what elements or key concepts are required for the achievement criteria for Korean language and curriculum. This is meaningful in that the humanities field (A), including Korean, provides basic data that can be fused into 'equivalent qualifications' with science (S), technical engineering (TE) and mathematics (M).

Keywords: Korean STEAM Programme, liberal arts, STEAM curriculum, STEAM Literacy, STEM

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Authors: S. Bangphan, P. Bangphan, T.Boonkang

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Quality control helps industries in improvements of its product quality and productivity. Statistical Process Control (SPC) is one of the tools to control the quality of products that turning practice in bringing a department of industrial engineering process under control. In this research, the process control of a turning manufactured at workshops machines. The varying measurements have been recorded for a number of samples of a rice polished cylinder obtained from a number of trials with the turning practice. SPC technique has been adopted by the process is finally brought under control and process capability is improved.

Keywords: rice polished cylinder, statistical process control, control charts, process capability

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Authors: Retno Supriyanti, Ays Rahmadian Subhi, Yogi Ramadhani, Haris B. Widodo

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Alzheimer is one type of disease in the elderly that may occur in the world. The severity of the Alzheimer can be measured using a scale called Clinical Dementia Rating (CDR) based on a doctor's diagnosis of the patient's condition. Currently, diagnosis of Alzheimer often uses MRI machine, to know the condition of part of the brain called Hippocampus and Ventricle. MRI image itself consists of 3 slices, namely Coronal, Sagittal and Axial. In this paper, we discussed the measurement of the area of the ventricle especially in the Coronal slice based on the severity level referring to the CDR value. We use Active Contour method to segment the ventricle’s region, therefore that ventricle’s area can be calculated automatically. The results show that this method can be used for further development in the automatic diagnosis of Alzheimer.

Keywords: Alzheimer, CDR, coronal, ventricle, active contour

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Authors: Madhu Jain, Rakesh Kumar Meena

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This paper investigates a finite M/G/1 fault tolerant multi-component machining system. The system incorporates the features such as standby support, threshold recovery and imperfect coverage make the study closer to real time systems. The performance prediction of M/G/1 fault tolerant system is carried out using recursive approach by treating remaining service time as a supplementary variable. The numerical results are presented to illustrate the computational tractability of analytical results by taking three different service time distributions viz. exponential, 3-stage Erlang and deterministic. Moreover, the cost function is constructed to determine the optimal choice of system descriptors to upgrading the system.

Keywords: fault tolerant, machine repair, threshold recovery policy, imperfect coverage, supplementary variable technique

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Authors: Jarrod Sio Jyh Lih

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This paper discusses the factors contributing to student achievement in literacy and numeracy in primary schools within Sibu division. The study involved 694 level 1 primary schoolteachers. Using descriptive statistics, the study observed high levels of practice for teacher efficacy, instructional leadership and professional learning communities (PLCs). The differences between gender, teaching experience and academic qualification were analyzed using the t-test and one-way analysis of variance (ANOVA). The study reported significant differences in respondent perceptions based on teaching experience vis-à-vis teacher efficacy. Here, the post hoc Tukey test revealed that efficaciousness grows with experience. A correlation test observed positive and significant correlations between all independent variables. Binary logistic regression was applied to predict the independent variables’ influence on student achievement. The findings revealed that a dimension of instructional leadership – ‘monitoring student progress’ - emerged as the best predictor of student achievement for literacy and numeracy. The result indicated the students were more than 4 times more likely to achieve the national key performance index for both literacy and numeracy when student progress was monitored. In conclusion, ‘monitoring student progress’ had a positive influence on students’ achievement for literacy and numeracy, hence making it a possible course of action for school heads. However, more comprehensive studies are needed to ascertain its consistency within the context of Malaysia.

Keywords: efficacy, instructional, literacy, numeracy

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Authors: Zehra Gencel Efe, Fatma Susam Ozsayın, Satı Tas

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Background and Aim: It is not possible to teach all the knowledge related to operating room nursing in the nursing education process. Certified courses are organized by the Ministry of Health to compensate the lack of postgraduate training and the theoretical and practical training needs of working nurses. In this study; It is aimed to investigate the participants’ opinions and recommendations attending the certified course of operating room nursing that organized in İKCU AtaturkTraining and Research Hospital. Method: Two operating room nursing courses were organized in 2016. The 1st Operating Room Nursing Certified Course Program was organized between March 07, 2016 and April 6, 2016and the 2nd Operating Room Nursing Certified Course Program was organized between 07 November 2016 - 06 December 2016 at the İKCU Ataturk Training and Research Hospital. The first program was accepted for 29 participants, the second program was accepted for 30 participants. In the collection of the data, the 'Operating Room Nursing Certified Training Program Evaluation Form', 'Operating Room Nursing Certified Training Program Theoretical Training Evaluation Form' were used. Three point Likert-type scale is used for responses in the 'Operating Room Nursing Certified Training Program Evaluation Form’ (1=verygood, 2=good, 3=poor). Data is collected in five areas related to training program, operation room practice, communication, responsibility, experiences of learning. Four point Likert-type scale is used for responses in the 'Operating Room Nursing Certified Training Program Theoretical Training Evaluation Form' (1=verysatisfied, 2=quitesatisfied, 3=satisfied, 4=dissatisfied). Data is collected in two areas include presentation and content. Data were analyzed with SPSS 16 program. Findings and Conclusion: It was found that 93,22% of participants were female in addition, 62,7% had bachelor degree. It was seen that 33,87% of the work group had 1-5 years of experience in their field. It was found that; 88% of trainees participating in the first group to the operating room nursing-certified course program stated the training program was very good, 12% of them stated the training program was good. Nobody was signed the ‘poor’ choice. 81% of the trainees who participated in the 2nd group to the operating room nursing-certified course program stated the training program was very good, 19% of them stated the training program was good. Nobody was signed the ‘poor’ choice. It was found that there was no meaningful difference between the achievement ratios of the trainees and the learning status of the trainees when compared with the t test in the groups with success level of the operating room nursing certified course program according to the learning status of the participants (p ˃ 0,05). The trainees noted that the course was satisfied with theoretical and practical steps but the support services (lunch, coffee breaks etc.) were in adequate.

Keywords: certified courses, nursing certified courses, operating room nursing, training program

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Authors: dr. Bendes Ákos

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Digital skepticism, a critical stance towards digital technology and its pervasive influence on society, presents significant challenges when analyzed from a legal philosophical perspective. This abstract aims to explore the intersection of digital skepticism and legal philosophy, emphasizing the implications for justice, rights, and the rule of law in the digital age. Digital skepticism arises from concerns about privacy, security, and the ethical implications of digital technology. It questions the extent to which digital advancements enhance or undermine fundamental human values. Legal philosophy, which interrogates the foundations and purposes of law, provides a framework for examining these concerns critically. One key area where digital skepticism and legal philosophy intersect is in the realm of privacy. Digital technologies, particularly data collection and surveillance mechanisms, pose substantial threats to individual privacy. Legal philosophers must grapple with questions about the limits of state power and the protection of personal autonomy. They must consider how traditional legal principles, such as the right to privacy, can be adapted or reinterpreted in light of new technological realities. Security is another critical concern. Digital skepticism highlights vulnerabilities in cybersecurity and the potential for malicious activities, such as hacking and cybercrime, to disrupt legal systems and societal order. Legal philosophy must address how laws can evolve to protect against these new forms of threats while balancing security with civil liberties. Ethics plays a central role in this discourse. Digital technologies raise ethical dilemmas, such as the development and use of artificial intelligence and machine learning algorithms that may perpetuate biases or make decisions without human oversight. Legal philosophers must evaluate the moral responsibilities of those who design and implement these technologies and consider the implications for justice and fairness. Furthermore, digital skepticism prompts a reevaluation of the concept of the rule of law. In an increasingly digital world, maintaining transparency, accountability, and fairness becomes more complex. Legal philosophers must explore how legal frameworks can ensure that digital technologies serve the public good and do not entrench power imbalances or erode democratic principles. Finally, the intersection of digital skepticism and legal philosophy has practical implications for policy-making. Legal scholars and practitioners must work collaboratively to develop regulations and guidelines that address the challenges posed by digital technology. This includes crafting laws that protect individual rights, ensure security, and promote ethical standards in technology development and deployment. In conclusion, digital skepticism provides a crucial lens for examining the impact of digital technology on law and society. A legal philosophical approach offers valuable insights into how legal systems can adapt to protect fundamental values in the digital age. By addressing privacy, security, ethics, and the rule of law, legal philosophers can help shape a future where digital advancements enhance, rather than undermine, justice and human dignity.

Keywords: legal philosophy, privacy, security, ethics, digital skepticism

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Authors: Uraib Sharaha, Ahmad Salman, Eladio Rodriguez-Diaz, Elad Shufan, Klaris Riesenberg, Irving J. Bigio, Mahmoud Huleihel

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Antimicrobial drugs have played an indispensable role in controlling illness and death associated with infectious diseases in animals and humans. However, the increasing resistance of bacteria to a broad spectrum of commonly used antibiotics has become a global healthcare problem. Many antibiotics had lost their effectiveness since the beginning of the antibiotic era because many bacteria have adapted defenses against these antibiotics. Rapid determination of antimicrobial susceptibility of a clinical isolate is often crucial for the optimal antimicrobial therapy of infected patients and in many cases can save lives. The conventional methods for susceptibility testing require the isolation of the pathogen from a clinical specimen by culturing on the appropriate media (this culturing stage lasts 24 h-first culturing). Then, chosen colonies are grown on media containing antibiotic(s), using micro-diffusion discs (second culturing time is also 24 h) in order to determine its bacterial susceptibility. Other methods, genotyping methods, E-test and automated methods were also developed for testing antimicrobial susceptibility. Most of these methods are expensive and time-consuming. Fourier transform infrared (FTIR) microscopy is rapid, safe, effective and low cost method that was widely and successfully used in different studies for the identification of various biological samples including bacteria; nonetheless, its true potential in routine clinical diagnosis has not yet been established. The new modern infrared (IR) spectrometers with high spectral resolution enable measuring unprecedented biochemical information from cells at the molecular level. Moreover, the development of new bioinformatics analyses combined with IR spectroscopy becomes a powerful technique, which enables the detection of structural changes associated with resistivity. The main goal of this study is to evaluate the potential of the FTIR microscopy in tandem with machine learning algorithms for rapid and reliable identification of bacterial susceptibility to antibiotics in time span of few minutes. The UTI E.coli bacterial samples, which were identified at the species level by MALDI-TOF and examined for their susceptibility by the routine assay (micro-diffusion discs), are obtained from the bacteriology laboratories in Soroka University Medical Center (SUMC). These samples were examined by FTIR microscopy and analyzed by advanced statistical methods. Our results, based on 700 E.coli samples, were promising and showed that by using infrared spectroscopic technique together with multivariate analysis, it is possible to classify the tested bacteria into sensitive and resistant with success rate higher than 90% for eight different antibiotics. Based on these preliminary results, it is worthwhile to continue developing the FTIR microscopy technique as a rapid and reliable method for identification antibiotic susceptibility.

Keywords: antibiotics, E.coli, FTIR, multivariate analysis, susceptibility, UTI

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Authors: Ahmed Abdrabou, Medhat Abdalla

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The studied stele dates back to Third Intermediate Period (1075-664) B.C in an ancient Egypt. It is made of wood and covered with painted gesso layers. This study aims to use a combination of multi spectral imaging {visible, infrared (IR), Visible-induced infrared luminescence (VIL), Visible-induced ultraviolet luminescence (UVL) and ultraviolet reflected (UVR)}, along with portable x-ray fluorescence in order to map and identify the pigments as well as to provide a deeper understanding of the painting techniques. Moreover; the authors were significantly interested in the identification of wood species. Multispectral imaging acquired in 3 spectral bands, ultraviolet (360-400 nm), visible (400-780 nm) and infrared (780-1100 nm) using (UV Ultraviolet-induced luminescence (UVL), UV Reflected (UVR), Visible (VIS), Visible-induced infrared luminescence (VIL) and Infrared photography. False color images are made by digitally editing the VIS with IR or UV images using Adobe Photoshop. Optical Microscopy (OM), potable X-ray fluorescence spectroscopy (p-XRF) and Fourier Transform Infrared Spectroscopy (FTIR) were used in this study. Mapping and imaging techniques provided useful information about the spatial distribution of pigments, in particular visible-induced luminescence (VIL) which allowed the spatial distribution of Egyptian blue pigment to be mapped and every region containing Egyptian blue, even down to single crystals in some instances, is clearly visible as a bright white area; however complete characterization of the pigments requires the use of p. XRF spectroscopy. Based on the elemental analysis found by P.XRF, we conclude that the artists used mixtures of the basic mineral pigments to achieve a wider palette of hues. Identification of wood species Microscopic identification indicated that the wood used was Sycamore Fig (Ficus sycomorus L.) which is recorded as being native to Egypt and was used to make wooden artifacts since at least the Fifth Dynasty.

Keywords: polychrome wooden stele, multispectral imaging, IR luminescence, Wood identification, Sycamore Fig, p-XRF

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Authors: Xia Yang, Zheng Yang, Haiyong Sun, Yan Fang, Jingyu Liu, Jia Song

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The smart contract in Ethereum is a unique program deployed on the Ethereum Virtual Machine (EVM) to help manage cryptocurrency. The security of this smart contract is critical to Ethereum’s operation and highly sensitive. In this paper, we present a formal model for smart contract, using the separated term-obligation (STO) strategy to formalize and verify the smart contract. We use the IBM smart sponsor contract (SSC) as an example to elaborate the detail of the formalizing process. We also propose a formal smart sponsor contract model (FSSCM) and verify SSC’s security properties with an interactive theorem prover Coq. We found the 'Unchecked-Send' vulnerability in the SSC, using our formal model and verification method. Finally, we demonstrate how we can formalize and verify other smart contracts with this approach, and our work indicates that this formal verification can effectively verify the correctness and security of smart contracts.

Keywords: smart contract, formal verification, Ethereum, Coq

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Authors: Ilja Käch, Abdul R. Jandali, Nadja Zechmann-Müller

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Introduction: We discuss the treatment of two young male patients suffering from scaphoid non-union after a traumatic scaphoid fracture. The currently propagated techniques for treating a scaphoid non-union in children are either the operative reconstruction of the scaphoid or the conservative treatment with splinting in a scaphoid cast. Cases: In the first case, we operated on a 13 years old male patient with a posttraumatic scaphoid non-union in the middle third with a humpback deformity. We resected the middle third of the scaphoid and grafted the defect with an iliac crest bone, and the DISI-Deformity was reduced. Fixation was performed with K-Wires and immobilisation in a scaphoid cast. In the second case a 13 years old male patient also with a posttraumatic scaphoid non-union in the middle third and humpback deformity, DISI-deformity, was treated conservatively. Immobilisation in a scaphoid cast for four months was performed. Results: Operative: One year postoperatively the patient achieved a painless free arc of motion. Flexion/Extension 70/0/60°, Radial-/Ulnarduction 30/0/30° and Pro-/Supination 90/0/90°. The computer tomogram showed complete consolidation and bony fusion of the iliac crest bone. Conservative: Six to eight months after conservative treatment the patient demonstrated painless motion and AROM Flexion/Extension 80/0/80°, Radial-/Ulnarduction and Pro-/Supination in maximum range. Complete consolidation in the computer tomogram with persistent humpback- and DISI deformity. Conclusion: In the literature, both techniques are described, either the operative scaphoid reconstruction or the conservative treatment with splinting. In our cases, both the operative and conservative treatments showed comparable good results. However, the humpback- and DISI deformity can only be addressed with a surgical approach.

Keywords: scaphoid, non-union, trauma, operative vs. non operative

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Authors: Y. Hamaizi, H. Triki, A. El-Akrmi

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In this paper, the reflection spectra, group delay and dispersion of a uniform fiber Bragg grating (FBG) are obtained. FBGs with two types of apodized variations of the refractive index were modeled to show how the side-lobes can be suppressed. Apodization techniques are used to get optimized reflection spectra. The simulation is based on solving coupled mode equations together with the transfer matrix method.

Keywords: fiber bragg gratings, coupled-mode theory, reflectivity, apodization

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Authors: J. Knight, E. Barberà

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Language learning strategies have been defined as thoughts and actions, consciously chosen and operationalized by language learners, to help them in carrying out a multiplicity of tasks from the very outset of learning to the most advanced levels of target language performance. While research in the field of Second Language Acquisition has focused on ‘good’ language learners, the effectiveness of strategy-use and orchestration by effective learners in face-to-face classrooms much less research has attended to learner strategies in online contexts, particular strategies in relation to technological tool use which can be part of a task design. In addition, much research on learner strategies and strategy use has been explored focusing on cognitive, attitudinal and metacognitive behaviour with less research focusing on the social aspect of strategies. This study focuses on how learners mediate with a technological tool designed to support synchronous spoken interaction and how this shape their spoken interaction in the opening of their talk. A case study approach is used incorporating notions from communities of practice theory to analyse and understand learner strategies of dyads carrying out a role play task. The study employs analysis of transcripts of spoken interaction in the openings of the talk along with log files of tool use. The study draws on results of previous studies pertaining to the same tool as a form of triangulation. Findings show how learners gain pre-task planning time through technological tool control. The strategies involving learners’ choices to enter and exit the tool shape their spoken interaction qualitatively, with some cases demonstrating long silences whilst others appearing to start the pedagogical task immediately. Who/what learners orientate to in the openings of the talk: an audience (i.e. the teacher), each other and/or screen-based signifiers in the opening moments of the talk also becomes a focus. The study highlights how tool use as a social practice should be considered a learning strategy in online contexts whereby different usages may be understood in the light of the more usual asynchronous social practices of the online community. The teachers’ role in the community is also problematised as the evaluator of the practices of that community. Results are pertinent for task design for synchronous speaking tasks. The use of community of practice theory supports an understanding of strategy use that involves both metacognition alongside social context revealing how tool-use strategies may need to be orally (socially) negotiated by learners and may also differ from an online language community.

Keywords: learner strategy, tool use, community of practice, speaking task

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