Search results for: gradient training method

Authors: Wenjuan Du

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The phase compensation method was proposed based on the concept of the damping torque analysis (DTA). It is a method for the design of a PSS (power system stabilizer) to suppress local-mode power oscillations in a single-machine infinite-bus power system. This paper presents the application of the phase compensation method for the design of a PSS in a multi-machine power system. The application is achieved by examining the direct damping contribution of the stabilizer to the power oscillations. By using linearized equal area criterion, a theoretical proof to the application for the PSS design is presented. Hence PSS design in the paper is an example of stable tending control by localized method.

Keywords: phase compensation method, power system small-signal stability, power system stabilizer

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Authors: Charalambos C. Cleanthous, Ryan Sain, Tabitha Black, Stephen Vera, Suzanne Milton

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A concern when administering questionnaires is whether the participant is providing information that is accurate. The results may be invalid because the person is trying to present oneself in an unrealistic positive manner referred to as ‘faking good’, or in an unrealistic negative manner known as ‘faking bad’. The Balanced Inventory of Desirable Responding (BIDR) was used to assess commercial pilots’ responses on the two subscales of the BIDR: impression management (IM) and self-deceptive enhancement (SDE) that result in high or low scores. Thus, the BIDR produces four valid profiles: IM low and SDE low, IM high and SDE low, IM low and SDE high, and IM high and SDE high. The various profiles were used to compare the respondents’ answers to crew resource management (CRM) items developed from the USA Federal Aviation Administration’s (FAA) guidelines for CRM composition and training. Of particular interest were the results on the IM subscale. The comparisons between those scoring high (lying or faking) versus those low on the IM suggest that there were significant differences regarding their views of the various dimensions of CRM. One of the more disconcerting conclusions is that the high IM scores suggest that the pilots were trying to impress rather than honestly answer the questions regarding their CRM training and practice.

Keywords: USA commercial pilots, crew resource management, faking, social desirability

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Authors: Ahmet Erdoğan, Şahin Kesici, Mustafa Baloğlu

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Information technologies have lead to changes in the areas of communication, learning, and teaching. Besides offering many opportunities to the learners, these technologies have changed the teaching methods and beliefs of teachers. What the Technological Pedagogical Content Knowledge (TPACK) means to the teachers is considerably important to integrate technology successfully into teaching processes. It is necessary to understand how to plan and apply teacher training programs in order to balance students’ pedagogical and technological knowledge. Because of many inefficient teacher training programs, teachers have difficulties in relating technology, pedagogy and content knowledge each other. While providing an efficient training supported with technology, understanding the three main components (technology, pedagogy and content knowledge) and their relationship are very crucial. The purpose of this study is to determine whether motivational beliefs and self-regulated learning strategies are significant predictors of mathematics teacher candidates' TPACK perceptions. A hundred seventy five Turkish mathematics teachers candidates responded to the Motivated Strategies for Learning Questionnaire (MSLQ) and the Technological Pedagogical And Content Knowledge (TPACK) Scale. Of the group, 129 (73.7%) were women and 46 (26.3%) were men. Participants' ages ranged from 20 to 31 years with a mean of 23.04 years (SD = 2.001). In this study, a multiple linear regression analysis was used. In multiple linear regression analysis, the relationship between the predictor variables, mathematics teacher candidates' motivational beliefs, and self-regulated learning strategies, and the dependent variable, TPACK perceptions, were tested. It was determined that self-efficacy for learning and performance and intrinsic goal orientation are significant predictors of mathematics teacher candidates' TPACK perceptions. Additionally, mathematics teacher candidates' critical thinking, metacognitive self-regulation, organisation, time and study environment management, and help-seeking were found to be significant predictors for their TPACK perceptions.

Keywords: candidate mathematics teachers, motivational beliefs, self-regulated learning strategies, technological and pedagogical knowledge, content knowledge

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Authors: Renata L. C. Leão, João E. Nascimento, Natalia C. E. S. Nascimento, Elaine S. Vasconcelos, Mércia L. Oliveira

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The quality control procedures of a radiopharmaceutical include the assessment of its chemical purity. The method suggested by international pharmacopeias consists of a thin layer chromatographic run. In this paper, the method proposed by the United States Pharmacopeia (USP) is compared to a direct method to determine the final concentration of aminopolyether in Fludeoxyglucose (¹⁸F-FDG) preparations. The approach (no chromatographic run) was achieved by placing the thin-layer chromatography (TLC) plate directly on an iodine vapor chamber. Both methods were validated and they showed adequate results to determine the concentration of aminopolyether in ¹⁸F-FDG preparations. However, the direct method is more sensitive, faster and simpler when compared to the reference method (with chromatographic run), and it may be chosen for use in routine quality control of ¹⁸F-FDG.

Keywords: chemical purity, Kryptofix 222, thin layer chromatography, validation

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Authors: A. Afroogh, R. Ramazani omali, N. Hafezi Moghaddas, A. Nohegar

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The Burkh anticline with a length of 50 km and a width of 9 km is located 40 km to the north of Bastak in internal Fars zone in folded-trusted belt of Zagros. In order to assess the active tectonics in the area of study, morphometrical indexes such as V indexes (V), ratio of valley floor to valley width (Vf), the stream length-gradient ratio (Sl), channel sinuosity indexes (S), mountain front faceting indexes (F%) and mountain front sinuosity(Smf) have been studied. These investigations show that the activity is not equal in various sections of the length of Burkh anticline. The central part of this anticline is the most active one.

Keywords: anticline, internal fars zone, tectonic, morohometrical indexes, folded-trusted belt

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Authors: Mokdad Sakhri, Youcef Bouhadda

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Thin film chalcopyrite technology is thus nowadays a solid candidate for photovoltaic cells. The currently used window layer for the solar cell Cu(In,Ga)S2 is our interest point in this work. For this purpose, we have performed a first-principles study of structural, electronic and optical properties for both delafossite transparent conductive oxides Cu (In, Ga)O2 and absorbers films Cu(In,Ga)S2. The calculations have been carried out within the local density functional (LDA) and generalized gradient approximations (GGA) combined with the hubbard potential using norm-conserving pseudopotentials and a plane-wave basis with ABINIT code. We have found the energy gap is :1.6, 2.53, 3.6, 3.8 eV for CuInS2, CuGaS2, CuInO2 and CuGaO2 respectively. The results are in good agreement with experimental results.

Keywords: ABINIT code, DFT, electronic and optical properties, solar-cell absorbers, delafossite transparent conductive oxides

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Authors: Haniye Dehestani, Yadollah Ordokhani

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In this work, we present an efficient approach for solving variable-order time-fractional partial differential equations, which are based on Legendre and Laguerre polynomials. First, we introduced the pseudo-operational matrices of integer and variable fractional order of integration by use of some properties of Riemann-Liouville fractional integral. Then, applied together with collocation method and Legendre-Laguerre functions for solving variable-order time-fractional partial differential equations. Also, an estimation of the error is presented. At last, we investigate numerical examples which arise in physics to demonstrate the accuracy of the present method. In comparison results obtained by the present method with the exact solution and the other methods reveals that the method is very effective.

Keywords: collocation method, fractional partial differential equations, legendre-laguerre functions, pseudo-operational matrix of integration

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Authors: Alexey Abilov

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The article highlights the experience of training master's and doctoral students at Satbayev University by preparing their course works for disciplines "Principles of Sustainable Architecture", "Energy Efficiency in Urban planning", "Urban planning analysis, "Social foundations of Architecture". The purpose of these works is the acquisition by students of practical skills necessary in their future professional activities, which are achieved through comprehensive assessment of individual sections of the Almaty urban environment. The methodology of student’s researches carried out under the guidance of the author of this publication is based on an expert assessment of the territory through its full-scale survey, analysis of project documents and statistical data, as well as on a social assessment of the territory based on the results of a questionnaire survey of residents. A comprehensive qualitative and quantitative assessment of the selected sites according to the criteria of the quality of the living environment also allows to formulate specific recommendations for designers who carry out a pre-project analysis of the city territory in the process of preparing draft master plans and detailed planning projects.

Keywords: urban environment, expert/social assessment of the territory, questionnaire survey, comprehensive approach

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Authors: Andrey V. Timofeev

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An adaptive nonparametric method is proposed for stable real-time detection of seismoacoustic sources in multichannel C-OTDR systems with a significant number of channels. This method guarantees given upper boundaries for probabilities of Type I and Type II errors. Properties of the proposed method are rigorously proved. The results of practical applications of the proposed method in a real C-OTDR-system are presented in this report.

Keywords: guaranteed detection, multichannel monitoring systems, change point, interval estimation, adaptive detection

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Authors: E. M. Condori-Peñaloza, S. S. Costa

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Eleusine indica L, known as goose-grass, is considered a troublesome weed that can cause important economic losses in the agriculture worldwide. However, this grass is used as a medicinal plant in some regions of Brazil to treat influenza and pneumonia. In Africa and Asia, it is used to treat malaria and as diuretic, anti-helminthic, among other uses. Despite its therapeutic potential, little is known about the chemical composition and bioactive compounds of E. indica. Hitherto, two major flavonoids, schaftoside and vitexin, were isolated from aerial part of the species and showed inhibitory activity on lung neutrophil influxes in mice, suggesting a beneficial effect on airway inflammation. Therefore, the aim of this study was to analyze the chemical profile of aqueous extracts from aerial parts of Eleusine indica specimens using high performance liquid chromatography (HPLC-DAD) and 1H nuclear magnetic resonance spectroscopy (NMR), with emphasis on phenolic compounds. Specimens of E. indica were collected in Minas Gerais state, Brazil. Aerial parts of fresh plants were extracted by decoction (10% p/v). After spontaneous precipitation of the aqueous extract at 10-12°C for 24 hours, the supernatant obtained was frozen and lyophilized. After that, 1 g of the extract was dissolved into 25 mL of water and fractionated on a reverse phase chromatography column (RP-2), eluted with a gradient of H2O/EtOH. Five fractions were obtained. The extract and fractions had their chemical profile analyzed by using HPLC-DAD (C-18 column: 20 μL, 256 -365 nm; gradient water 0.01% phosphoric acid/ acetonitrile. The extract was also analyzed by NMR (1H, 500 MHz, D2O) in order to access its global chemical composition. HPLC-DAD analyses of crude extract allowed the identification of ten phenolic compounds. Fraction 1, eluted with 100% water, was poor in phenolic compounds and no major peak was detected. In fraction 2, eluted with 100% water, it was possible to observe one major peak at retention time (RT) of 23.75 minutes compatible with flavonoid; fraction 3, also eluted with 100% water, showed four peaks at RT= 21.47, 23.52, 24.33 and 25.84 minutes, all of them compatible with flavonoid. In fraction 4, eluted with 50%/ethanol/50% water, it was possible to observe 3 peaks compatible with flavonoids at RT=24.65, 26.81, 27.49 minutes, and one peak (28.83 min) compatible with a phenolic acid derivative. Finally, in fraction 5, eluted with 100% ethanol, no phenolic substance was detected. The UV spectra of all flavonoids detected were compatible with the flavone subclass (λ= 320-345 nm). The 1H NMR spectra of aerial parts extract showed signals in three regions: δ 0.8-3.0 ppm (aliphatic compounds), δ 3.0-5.5 ppm corresponding to carbohydrates (signals most abundant and overlapped), and δ 6.0-8.5 ppm (aromatic compounds). Signals compatible with flavonoids (rings A and B) could also be detected in the crude extract spectra. These results suggest the presence of several flavonoids in E. indica, which reinforces their therapeutic potential. The pharmacological activities of Eleusine indica extracts and fractions will be further evaluated.

Keywords: flavonoids, HPLC, NMR, phenolic compounds

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Authors: Wataru Nakamura, Tomoaki Hashimoto, Liang-Kuang Chen

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This paper provides a state estimation method for automatic control systems of nonlinear vehicle dynamics. A nonlinear tire model is employed to represent the realistic behavior of a vehicle. In general, all the state variables of control systems are not precisedly known, because those variables are observed through output sensors and limited parts of them might be only measurable. Hence, automatic control systems must incorporate some type of state estimation. It is needed to establish a state estimation method for nonlinear vehicle dynamics with restricted measurable state variables. For this purpose, unscented Kalman filter method is applied in this study for estimating the state variables of nonlinear vehicle dynamics. The objective of this paper is to propose a state estimation method using unscented Kalman filter for nonlinear vehicle dynamics. The effectiveness of the proposed method is verified by numerical simulations.

Keywords: state estimation, control systems, observer systems, nonlinear systems

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Authors: Nizar Tayem, AbuMuhammad Moinuddeen, Ahmed A. Hussain, Redha M. Radaydeh

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This research paper introduces an approach for estimating the direction of arrival (DOA) of multiple RF noncoherent sources in a uniform linear array (ULA). The proposed method utilizes a Capon-like estimation algorithm and incorporates LU decomposition to enhance the accuracy of DOA estimation while significantly reducing computational complexity compared to existing methods like the Capon method. Notably, the proposed method does not require prior knowledge of the number of sources. To validate its effectiveness, the proposed method undergoes validation through both software simulations and practical experimentation on a prototype testbed constructed using a software-defined radio (SDR) platform and GNU Radio software. The results obtained from MATLAB simulations and real-time experiments provide compelling evidence of the proposed method's efficacy.

Keywords: DOA estimation, real-time validation, software defined radio, computational complexity, Capon's method, GNU radio

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Authors: Young Hee Geum

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We propose the numerical method defined by xn+1 = xn − λ[f(xn − μh(xn))/]f'(xn) , n ∈ N, and determine the control parameter λ and μ to converge cubically. In addition, we derive the asymptotic error constant. Applying this proposed scheme to various test functions, numerical results show a good agreement with the theory analyzed in this paper and are proven using Mathematica with its high-precision computability.

Keywords: asymptotic error constant, iterative method, multiple root, root-finding

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Authors: Marcin Sowa

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The paper discusses the subinterval-based numerical method for fractional derivative computations. It is now referred to by its acronym – SubIval. The basis of the method is briefly recalled. The ability of the method to be applied in time stepping solvers is discussed. The possibility of implementing a time step size adaptive solver is also mentioned. The solver is tested on a transient circuit example. In order to display the accuracy of the solver – the results have been compared with those obtained by means of a semi-analytical method called gcdAlpha. The time step size adaptive solver applying SubIval has been proven to be very accurate as the results are very close to the referential solution. The solver is currently able to solve FDE (fractional differential equations) with various derivative orders for each equation and any type of source time functions.

Keywords: numerical method, SubIval, fractional calculus, numerical solver, circuit analysis

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Authors: A. M. Sagir

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The aim of this paper is to investigate the performance of the developed linear multistep block method for solving first order initial value problem of Ordinary Differential Equations (ODEs). The method calculates the numerical solution at three points simultaneously and produces three new equally spaced solution values within a block. The continuous formulations enable us to differentiate and evaluate at some selected points to obtain three discrete schemes, which were used in block form for parallel or sequential solutions of the problems. A stability analysis and efficiency of the block method are tested on ordinary differential equations involving practical applications, and the results obtained compared favorably with the exact solution. Furthermore, comparison of error analysis has been developed with the help of computer software.

Keywords: block method, first order ordinary differential equations, linear multistep, self-starting

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Authors: A. M. Sagir

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This paper derived four newly schemes which are combined in order to form an accurate and efficient block method for parallel or sequential solution of third order ordinary differential equations of the form y^'''= f(x,y,y^',y^'' ), y(α)=y_0,〖y〗^' (α)=β,y^('' ) (α)=μ with associated initial or boundary conditions. The implementation strategies of the derived method have shown that the block method is found to be consistent, zero stable and hence convergent. The derived schemes were tested on stiff and non-stiff ordinary differential equations, and the numerical results obtained compared favorably with the exact solution.

Keywords: block method, hybrid, linear multistep, self-starting, third order ordinary differential equations

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: L. Arumuru

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The paper investigates computer anxiety and the use of computerized library system by university librarians in Delta State University library, Abraka, Nigeria. Some of the root causes of computer anxiety among university librarians such as lack of exposure to computers at early age, inadequate computer skills, inadequate computer training, fear at the sight of a computer, lack of understanding of how computers work, etc. were pin-pointed in the study. Also, the different services rendered in the university libraries with the aid of computers such as reference services, circulation services, acquisition services, cataloguing and classification services, etc. were identified. The study employed the descriptive survey research design through the expo-facto method, with a population of 56 librarians, while the simple percentage and frequency counts were used to analyze the data generated from the administered copies of the questionnaire. Based on the aforementioned root causes of computer anxiety and the resultant effect on computerized library system, recommendations were proffered in the study.

Keywords: computer anxiety, computerized library system, library services, university librarians

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Authors: Aymen Laadhari

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We present in this paper a fully implicit finite element method tailored for the numerical modeling of inextensible fluidic membranes in a surrounding Newtonian fluid. We consider a highly simplified version of the Canham-Helfrich model for phospholipid membranes, in which the bending force and spontaneous curvature are disregarded. The coupled problem is formulated in a fully Eulerian framework and the membrane motion is tracked using the level set method. The resulting nonlinear problem is solved by a Newton-Raphson strategy, featuring a quadratic convergence behavior. A monolithic solver is implemented, and we report several numerical experiments aimed at model validation and illustrating the accuracy of the proposed method. We show that stability is maintained for significantly larger time steps with respect to an explicit decoupling method.

Keywords: finite element method, level set, Newton, membrane

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Authors: Poonam Malik, Ravi Bhushan

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This work describes a very efficient approach for synthesis of activated ester of (S)-naproxen which was characterized by UV, IR, ¹HNMR, elemental analysis and polarimetric studies. It was used as a C-N bond forming chiral derivatizing reagent for further synthesis of diastereomeric amides of (RS)-salbutamol (a β₂ agonist that belongs to the group β-adrenolytic and is marketed as racamate) under microwave irradiation. The diastereomeric pair was separated by achiral phase HPLC, using mobile phase in gradient mode containing methanol and aqueous triethylaminephosphate (TEAP); separation conditions were optimized with respect to pH, flow rate, and buffer concentration and the method of separation was validated as per International Council for Harmonisation (ICH) guidelines. The reagent proved to be very effective for on-line sensitive detection of the diastereomers with very low limit of detection (LOD) values of 0.69 and 0.57 ng mL⁻¹ for diastereomeric derivatives of (S)- and (R)-salbutamol, respectively. The retention times were greatly reduced (2.7 min) with less consumption of organic solvents and large (α) as compared to literature reports. Besides, the diastereomeric derivatives were separated and isolated by preparative HPLC; these were characterized and were used as standard reference samples for recording ¹HNMR and IR spectra for determining absolute configuration and elution order; it ensured the success of diastereomeric synthesis and established the reliability of enantioseparation and eliminated the requirement of pure enantiomer of the analyte which is generally not available. The newly developed reagent can suitably be applied to several other amino group containing compounds either from organic syntheses or pharmaceutical industries because the presence of (S)-Npx as a strong chromophore would allow sensitive detection.This work is significant not only in the area of enantioseparation and determination of absolute configuration of diastereomeric derivatives but also in the area of developing new chiral derivatizing reagents (CDRs).

Keywords: chiral derivatizing reagent, naproxen, salbutamol, synthesis

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Authors: Andrea ČopíKová

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Competency Based Management is a new approach to management, which solves organization’s challenges with complexity and with the aim to find and solve organization’s problems and learn how to avoid these in future. They teach the organizations to create, apart from the state of stability – that is temporary, vital organization, which is permanently able to utilize and profit from internal and external opportunities. The aim of this paper is to propose a process of competency model design, based on which a competency model for a financial department manager in a production company will be created. Competency models are very useful tool in many personnel processes in any organization. They are used for acquiring and selection of employees, designing training and development activities, employees’ evaluation, and they can be used as a guide for a career planning and as a tool for succession planning especially for managerial positions. When creating a competency model the method AHP (Analytic Hierarchy Process) and quantitative pair-wise comparison (Saaty’s method) will be used; these methods belong among the most used methods for the determination of weights, and it is used in the AHP procedure. The introduction part of the paper consists of the research results pertaining to the use of competency model in practice and then the issue of competency and competency models is explained. The application part describes in detail proposed methodology for the creation of competency models, based on which the competency model for the position of financial department manager in a foreign manufacturing company, will be created. In the conclusion of the paper, the final competency model will be shown for above mentioned position. The competency model divides selected competencies into three groups that are managerial, interpersonal and functional. The model describes in detail individual levels of competencies, their target value (required level) and the level of importance.

Keywords: analytic hierarchy process, competency, competency model, quantitative pairwise comparison

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Authors: Supria Sarkar, Vasit Sagan, Sourav Bhadra, Meghnath Pokharel, Felix B.Fritschi

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Soybean and their derivatives are very important agricultural commodities around the world because of their wide applicability in human food, animal feed, biofuel, and industries. However, the significance of soybean production depends on the quality of the soybean seeds rather than the yield alone. Seed composition is widely dependent on plant physiological properties, aerobic and anaerobic environmental conditions, nutrient content, and plant phenological characteristics, which can be captured by high temporal resolution remote sensing datasets. Planet scope (PS) satellite images have high potential in sequential information of crop growth due to their frequent revisit throughout the world. In this study, we estimate soybean seed composition while the plants are in the field by utilizing PlanetScope (PS) satellite images and different machine learning algorithms. Several experimental fields were established with varying genotypes and different seed compositions were measured from the samples as ground truth data. The PS images were processed to extract 462 hand-crafted vegetative and textural features. Four machine learning algorithms, i.e., partial least squares (PLSR), random forest (RFR), gradient boosting machine (GBM), support vector machine (SVM), and two recurrent neural network architectures, i.e., long short-term memory (LSTM) and gated recurrent unit (GRU) were used in this study to predict oil, protein, sucrose, ash, starch, and fiber of soybean seed samples. The GRU and LSTM architectures had two separate branches, one for vegetative features and the other for textures features, which were later concatenated together to predict seed composition. The results show that sucrose, ash, protein, and oil yielded comparable prediction results. Machine learning algorithms that best predicted the six seed composition traits differed. GRU worked well for oil (R-Squared: of 0.53) and protein (R-Squared: 0.36), whereas SVR and PLSR showed the best result for sucrose (R-Squared: 0.74) and ash (R-Squared: 0.60), respectively. Although, the RFR and GBM provided comparable performance, the models tended to extremely overfit. Among the features, vegetative features were found as the most important variables compared to texture features. It is suggested to utilize many vegetation indices for machine learning training and select the best ones by using feature selection methods. Overall, the study reveals the feasibility and efficiency of PS images and machine learning for plot-level seed composition estimation. However, special care should be given while designing the plot size in the experiments to avoid mixed pixel issues.

Keywords: agriculture, computer vision, data science, geospatial technology

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Authors: Sushmita Chaudhuri, Parthiba Basu

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Urbanization leads to habitat degradation and is responsible for the fast disappearance of native butterfly species. Random sampling of rural, suburban and urban sites in an around Kolkata metropolis revealed the presence of 28 species of butterfly belonging to 5 different families in winter (February-March). Butterfly diversity, species richness and abundance decreased with increase in urbanization. Psyche (Leptosia nina of family Pieridae) was the most predominant butterfly species found everywhere in Kolkata during the winter period. The most dominant family was Nymphalidae (11species), followed by Pieridae (6 species), Lycaenidae (5 species), Papilionidae (4 species) and Hesperiidae (2 species). The rural and suburban sites had butterfly species that were unique to those sites. Vegetation cover and flowering shrub density were significantly related to butterfly diversity.

Keywords: butterfly, Kolkata metropolis, Shannon-Weiner diversity index, species diversity

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Edgar Santoyo, Daniel Perez-Zarate, Agustin Acevedo, Lorena Diaz-Gonzalez, Mirna Guevara

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Four new gas geothermometers have been derived from a multivariate geo chemometric analysis of a geothermal fluid chemistry database, two of which use the natural logarithm of CO₂ and H2S concentrations (mmol/mol), respectively, and the other two use the natural logarithm of the H₂S/H₂ and CO₂/H₂ ratios. As a strict compilation criterion, the database was created with gas-phase composition of fluids and bottomhole temperatures (BHTM) measured in producing wells. The calibration of the geothermometers was based on the geochemical relationship existing between the gas-phase composition of well discharges and the equilibrium temperatures measured at bottomhole conditions. Multivariate statistical analysis together with the use of artificial neural networks (ANN) was successfully applied for correlating the gas-phase compositions and the BHTM. The predicted or simulated bottomhole temperatures (BHTANN), defined as output neurons or simulation targets, were statistically compared with measured temperatures (BHTM). The coefficients of the new geothermometers were obtained from an optimized self-adjusting training algorithm applied to approximately 2,080 ANN architectures with 15,000 simulation iterations each one. The self-adjusting training algorithm used the well-known Levenberg-Marquardt model, which was used to calculate: (i) the number of neurons of the hidden layer; (ii) the training factor and the training patterns of the ANN; (iii) the linear correlation coefficient, R; (iv) the synaptic weighting coefficients; and (v) the statistical parameter, Root Mean Squared Error (RMSE) to evaluate the prediction performance between the BHTM and the simulated BHTANN. The prediction performance of the new gas geothermometers together with those predictions inferred from sixteen well-known gas geothermometers (previously developed) was statistically evaluated by using an external database for avoiding a bias problem. Statistical evaluation was performed through the analysis of the lowest RMSE values computed among the predictions of all the gas geothermometers. The new gas geothermometers developed in this work have been successfully used for predicting subsurface temperatures in high-temperature geothermal systems of Mexico (e.g., Los Azufres, Mich., Los Humeros, Pue., and Cerro Prieto, B.C.) as well as in a blind geothermal system (known as Acoculco, Puebla). The last results of the gas geothermometers (inferred from gas-phase compositions of soil-gas bubble emissions) compare well with the temperature measured in two wells of the blind geothermal system of Acoculco, Puebla (México). Details of this new development are outlined in the present research work. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: artificial intelligence, gas geochemistry, geochemometrics, geothermal energy

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Authors: Kyung-Jin You, Kiwon Rhee, Marc H. Schieber, Nitish V. Thakor, Hyun-Chool Shin

Abstract:

It remains unknown whether M1 neurons encode multi-finger movements independently or as a certain neural network of single finger movements although multi-finger movements are physically a combination of single finger movements. We present an evidence of correlation between single and multi-finger movements and also attempt a challenging task of semi-blind decoding of neural data with minimum training of the neural decoder. Data were collected from 115 task-related neurons in M1 of a trained rhesus monkey performing flexion and extension of each finger and the wrist (12 single and 6 two-finger-movements). By exploiting correlation of temporal firing pattern between movements, we found that correlation coefficient for physically related movements pairs is greater than others; neurons tuned to single finger movements increased their firing rate when multi-finger commands were instructed. According to this knowledge, neural semi-blind decoding is done by choosing the greatest and the second greatest likelihood for canonical candidates. We achieved a decoding accuracy about 60% for multiple finger movement without corresponding training data set. this results suggest that only with the neural activities on single finger movements can be exploited to control dexterous multi-fingered neuroprosthetics.

Keywords: finger movement, neural activity, blind decoding, M1

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

Abstract:

Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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Authors: Ha Van Trung, Suchint Simaraks

Abstract:

Agro-tourism is a form of rural tourism that has actively developed in recent years. Tra Que vegetable village has developed this type of tourism to meet the needs of visitors to visit and experience. However, in the process of agricultural tourism development, Tra Que village is facing many issues related to the agricultural way of life, affecting the attraction of tourists. The purpose of this study is to find those issues. The survey questionnaire of 71 households and a semi-structured group interview of 30 households has been applied for the data collection. Research results show that there is a shortage of young workers, lack of training in tourism and agricultural production, and households only exploit a few agricultural activities for tourism. The number of households receiving tourists tends to decrease, and the number of households selling products to tourists at farms accounts for a small proportion. These will affect sustainable agro-tourism development in the future. Focusing on training local households in tourism and agricultural production, encourage young generation to preserve the agricultural way of life, upgrade infrastructure and public services, develop agro-products and tourism services will contribute to the sustainable development of agro-tourism in Tra Que vegetable village in the future.

Keywords: agro-tourism, way of life, Vietnamese tourists, Tra Que vegetable village

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Authors: Mamiko Orii

Abstract:

This study reports on primary school second language teachers’ sources of language insecurity. Furthermore, it aims to develop an in-service training course to reduce anxiety and build sufficient English communication skills. Language/Linguistic insecurity refers to a lack of confidence experienced by language speakers. In particular, second language/non-native learners often experience insecurity, influencing their learning efficacy. While language learner insecurity has been well-documented, research on the insecurity of language teaching professionals is limited. Teachers’ language insecurity or anxiety in target language use may adversely affect language instruction. For example, they may avoid classroom activities requiring intensive language use. Therefore, understanding teachers’ language insecurity and providing continuing education to help teachers to improve their proficiency is vital to improve teaching quality. This study investigated Japanese primary school teachers’ language insecurity. In Japan, teachers are responsible for teaching most subjects, including English, which was recently added as compulsory. Most teachers have never been professionally trained in second language instruction during college teacher certificate preparation, leading to low confidence in English teaching. Primary source of language insecurity is a lack of confidence regarding English communication skills. Their actual use of English in classrooms remains unclear. Teachers’ classroom speech remains a neglected area requiring improvement. A more refined programme for second language teachers could be constructed if we can identify areas of need. Two questionnaires were administered to primary school teachers in Tokyo: (1) Questionnaire A: 396 teachers answered questions (using a 5-point scale) concerning classroom teaching anxiety and general English use and needs for in-service training (Summer 2021); (2) Questionnaire B: 20 teachers answered detailed questions concerning their English use (Autumn 2022). Questionnaire A’s responses showed that over 80% of teachers have significant language insecurity and anxiety, mainly when speaking English in class or teaching independently. Most teachers relied on a team-teaching partner (e.g., ALT) and avoided speaking English. Over 70% of the teachers said they would like to participate in training courses in classroom English. Questionnaire B’s results showed that teachers could use simple classroom English, such as greetings and basic instructions (e.g., stand up, repeat after me), and initiate conversation (e.g., asking questions). In contrast, teachers reported that conversations were mainly carried on in a simple question-answer style. They had difficulty continuing conversations. Responding to learners’ ‘on-the-spot’ utterances was particularly difficult. Instruction in turn-taking patterns suitable in the classroom communication context is needed. Most teachers received grammar-based instruction during their entire English education. They were predominantly exposed to displayed questions and form-focused corrective feedback. Therefore, strategies such as encouraging teachers to ask genuine questions (i.e., referential questions) and responding to students with content feedback are crucial. When learners’ utterances are incorrect or unsatisfactory, teachers should rephrase or extend (recast) them instead of offering explicit corrections. These strategies support a continuous conversational flow. These results offer benefits beyond Japan’s English as a second Language context. They will be valuable in any context where primary school teachers are underprepared but must provide English-language instruction.

Keywords: english as a second/non-native language, in-service training, primary school, teachers’ language insecurity

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Authors: Atilla Bayram

Abstract:

This paper presents the trajectory tracking control of a spatial redundant hybrid manipulator. This manipulator consists of two parallel manipulators which are a variable geometry truss (VGT) module. In fact, each VGT module with 3-degress of freedom (DOF) is a planar parallel manipulator and their operational planes of these VGT modules are arranged to be orthogonal to each other. Also, the manipulator contains a twist motion part attached to the top of the second VGT module to supply the missing orientation of the endeffector. These three modules constitute totally 7-DOF hybrid (parallel-parallel) redundant spatial manipulator. The forward kinematics equations of this manipulator are obtained, then, according to these equations, the inverse kinematics is solved based on an optimization with the joint limit avoidance. The dynamic equations are formed by using virtual work method. In order to test the performance of the redundant manipulator and the controllers presented, two different desired trajectories are followed by using the computed force control method and a switching control method. The switching control method is combined with the computed force control method and genetic algorithm. In the switching control method, the genetic algorithm is only used for fine tuning in the compensation of the trajectory tracking errors.

Keywords: computed force method, genetic algorithm, hybrid manipulator, inverse kinematics of redundant manipulators, variable geometry truss

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