Search results for: comprehensive model

Authors: Rose Veitch

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Learning outcomes-based qualifications have been heralded as the means to raise vocational education and training (VET) standards, meet the needs of the changing workforce, and establish equivalence with existing academic qualifications. Characterized by explicit, measurable performance statements and atomistically specified assessment criteria, the outcomes model has been adopted by many VET systems worldwide since its inception in the United Kingdom in the 1980s. Debate to date centers on how the outcomes model treats knowledge. Flaws have been identified in terms of the overemphasis of end-points, neglect of process and a failure to treat curricula coherently. However, much of this censure has evaluated the outcomes model from a theoretical perspective; to date, there has been scant empirical research to support these criticisms. Various issues therefore remain unaddressed. This study investigates how the outcomes model impacts the teaching of subject literacies. This is of particular concern for subjects on the academic-vocational boundary such as Business Studies, since many of these students progress to higher education in the United Kingdom. This study also explores the extent to which the outcomes model is compatible with borderline vocational subjects. To fully understand if this qualification model is fit for purpose in the 16-18 year-old phase, it is necessary to investigate how teachers interpret their qualification specifications in terms of curriculum, pedagogy and assessment. Of particular concern is the nature of the interaction between the outcomes model and teachers’ understandings of their subject-procedural knowledge, and how this affects their capacity to embed literacy into their teaching. This present study is part of a broader doctoral research project which seeks to understand if and how content-area, disciplinary literacy and genre approaches can be adapted to outcomes-based VET qualifications. This qualitative research investigates the ‘what’ and ‘how’ of literacy embedding from the perspective of in-service teacher development in the 16-18 phase of education. Using ethnographic approaches, it is based on fieldwork carried out in one Further Education college in the United Kingdom. Emergent findings suggest that the outcomes model is not fit for purpose in the context of borderline vocational subjects. It is argued that the outcomes model produces inferior qualifications due to compositional fallacy; the sum of a subject’s components do not add up to the whole. Findings indicate that procedural knowledge, largely unspecified by some outcomes-based qualifications, is where subject-literacies are situated, and that this often gets lost in ‘delivery’. It seems that the outcomes model provokes an atomistic treatment of knowledge amongst teachers, along with the privileging of propositional knowledge over procedural knowledge. In other words, outcomes-based VET is a hostile environment for subject-literacy embedding. It is hoped that this research will produce useful suggestions for how this problem can be ameliorated, and will provide an empirical basis for the potential reforms required to address these issues in vocational education.

Keywords: literacy, outcomes-based, qualification design, vocational education

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Authors: Brittany Richardson, Ying Wang

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For physical therapy, exercise prescription, athlete training, and regular fitness training, it is crucial to perform health assessments or fitness assessments periodically. An accurate assessment is propitious for tracking recovery progress, preventing potential injury and making long-range training plans. Assessments include necessary measurements, height, weight, blood pressure, heart rate, body fat, etc. and advanced evaluation, muscle group strength, stability-mobility, and movement evaluation, etc. In the current standard assessment procedures, the accuracy of assessments, especially advanced evaluations, largely depends on the experience of physicians, coaches, and personal trainers. And it is challenging to track clients’ progress in the current assessment. Unlike the tradition assessment, in this paper, we present a deep learning based face recognition algorithm for accurate, comprehensive and trackable assessment. Based on the result from our assessment, physicians, coaches, and personal trainers are able to adjust the training targets and methods. The system categorizes the difficulty levels of the current activity for the client or user, furthermore make more comprehensive assessments based on tracking muscle group over time using a designed landmark detection method. The system also includes the function of grading and correcting the form of the clients during exercise. Experienced coaches and personal trainer can tell the clients' limit based on their facial expression and muscle group movements, even during the first several sessions. Similar to this, using a convolution neural network, the system is trained with people’s facial expression to differentiate challenge levels for clients. It uses landmark detection for subtle changes in muscle groups movements. It measures the proximal mobility of the hips and thoracic spine, the proximal stability of the scapulothoracic region and distal mobility of the glenohumeral joint, as well as distal mobility, and its effect on the kinetic chain. This system integrates data from other fitness assistant devices, including but not limited to Apple Watch, Fitbit, etc. for a improved training and testing performance. The system itself doesn’t require history data for an individual client, but the history data of a client can be used to create a more effective exercise plan. In order to validate the performance of the proposed work, an experimental design is presented. The results show that the proposed work contributes towards improving the quality of exercise plan, execution, progress tracking, and performance.

Keywords: exercise prescription, facial recognition, landmark detection, fitness assessments

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Authors: Aleš Florian, Lenka Ševelová

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Complex sensitivity analysis of stresses in a concrete slab of the real type of rigid pavement made from recycled materials is performed. The computational model of the pavement is designed as a spatial (3D) model, is based on a nonlinear variant of the finite element method that respects the structural nonlinearity, enables to model different arrangements of joints, and the entire model can be loaded by the thermal load. Interaction of adjacent slabs in joints and contact of the slab and the subsequent layer are modeled with the help of special contact elements. Four concrete slabs separated by transverse and longitudinal joints and the additional structural layers and soil to the depth of about 3m are modeled. The thickness of individual layers, physical and mechanical properties of materials, characteristics of joints, and the temperature of the upper and lower surface of slabs are supposed to be random variables. The modern simulation technique Updated Latin Hypercube Sampling with 20 simulations is used. For sensitivity analysis the sensitivity coefficient based on the Spearman rank correlation coefficient is utilized. As a result, the estimates of influence of random variability of individual input variables on the random variability of principal stresses s1 and s3 in 53 points on the upper and lower surface of the concrete slabs are obtained.

Keywords: concrete, FEM, pavement, sensitivity, simulation

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Authors: Hyun-Woo Cho

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Continuous monitoring of industrial plants is one of necessary tasks when it comes to ensuring high-quality final products. In terms of monitoring and diagnosis, it is quite critical and important to detect some incipient abnormal events of manufacturing processes in order to improve safety and reliability of operations involved and to reduce related losses. In this work a new multivariate statistical online diagnostic method is presented using a case study. For building some reference models an empirical discriminant model is constructed based on various past operation runs. When a fault is detected on-line, an on-line diagnostic module is initiated. Finally, the status of the current operating conditions is compared with the reference model to make a diagnostic decision. The performance of the presented framework is evaluated using a dataset from complex industrial processes. It has been shown that the proposed diagnostic method outperforms other techniques especially in terms of incipient detection of any faults occurred.

Keywords: data mining, empirical model, on-line diagnostics, process fault, process monitoring

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Authors: Orkide Donma, Mustafa M. Donma

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Basal metabolic rate is questioned as a risk factor for weight gain. The relations between basal metabolic rate and body composition have not been cleared yet. The impact of fat mass on basal metabolic rate is also uncertain. Within this context, indices based upon total body mass as well as total body fat mass are available. In this study, the aim is to investigate the potential clinical utility of these indices in the adult population. 287 individuals, aged from 18 to 79 years, were included into the scope of the study. Based upon body mass index values, 10 underweight, 88 normal, 88 overweight, 81 obese, and 20 morbid obese individuals participated. Anthropometric measurements including height (m), and weight (kg) were performed. Body mass index, diagnostic obesity notation model assessment index I, diagnostic obesity notation model assessment index II, basal metabolic rate-to-weight ratio were calculated. Total body fat mass (kg), fat percent (%), basal metabolic rate, metabolic age, visceral adiposity, fat mass of upper as well as lower extremities and trunk, obesity degree were measured by TANITA body composition monitor using bioelectrical impedance analysis technology. Statistical evaluations were performed by statistical package (SPSS) for Windows Version 16.0. Scatterplots of individual measurements for the parameters concerning correlations were drawn. Linear regression lines were displayed. The statistical significance degree was accepted as p < 0.05. The strong correlations between body mass index and diagnostic obesity notation model assessment index I as well as diagnostic obesity notation model assessment index II were obtained (p < 0.001). A much stronger correlation was detected between basal metabolic rate and diagnostic obesity notation model assessment index I in comparison with that calculated for basal metabolic rate and body mass index (p < 0.001). Upon consideration of the associations between basal metabolic rate-to-weight ratio and these three indices, the best association was observed between basal metabolic rate-to-weight and diagnostic obesity notation model assessment index II. In a similar manner, this index was highly correlated with fat percent (p < 0.001). Being independent of the indices, a strong correlation was found between fat percent and basal metabolic rate-to-weight ratio (p < 0.001). Visceral adiposity was much strongly correlated with metabolic age when compared to that with chronological age (p < 0.001). In conclusion, all three indices were associated with metabolic age, but not with chronological age. Diagnostic obesity notation model assessment index II values were highly correlated with body mass index values throughout all ranges starting with underweight going towards morbid obesity. This index is the best in terms of its association with basal metabolic rate-to-weight ratio, which can be interpreted as basal metabolic rate unit.

Keywords: basal metabolic rate, body mass index, children, diagnostic obesity notation model assessment index, obesity

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Authors: Chiao-Yi Chen, Dung-Ying Lin

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With the growing awareness of environmental protection and the implementation of government carbon reduction policies, the number of electric vehicles (EVs) has rapidly increased, leading to a surge in charging demand and imposing significant challenges on the existing power grid’s capacity. Traditional urban power grid planning has not adequately accounted for the additional load generated by EV charging, which often strains the infrastructure. This study aims to optimize grid operation and load management by dynamically adjusting EV charging prices based on real-time electricity supply and demand, leveraging consumer demand elasticity to enhance system efficiency. This study uniquely addresses the intricate interplay between urban traffic patterns and power grid dynamics in the context of electric vehicle (EV) adoption. By integrating Hsinchu City's road network with the IEEE 33-bus system, the research creates a comprehensive model that captures both the spatial and temporal aspects of EV charging demand. This approach allows for a nuanced analysis of how traffic flow directly influences the load distribution across the power grid. The strategic placement of charging stations at key nodes within the IEEE 33-bus system, informed by actual road traffic data, enables a realistic simulation of the dynamic relationship between vehicle movement and energy consumption. This integration of transportation and energy systems provides a holistic view of the challenges and opportunities in urban EV infrastructure planning, highlighting the critical need for solutions that can adapt to the ever-changing interplay between traffic patterns and grid capacity. The proposed dynamic pricing strategy effectively reduces peak charging loads, enhances the operational efficiency of charging stations, and maximizes operator profits, all while ensuring grid stability. These findings provide practical insights and a valuable framework for optimizing EV charging infrastructure and policies in future smart cities, contributing to more resilient and sustainable urban energy systems.

Keywords: dynamic pricing, demand elasticity, EV charging, grid load balancing, optimization

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Authors: Chiung-Ju Huang

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This study examines whether the Taiwan’s public debt is sustainable utilizing an unrestricted two-regime threshold autoregressive (TAR) model with an autoregressive unit root. The empirical results show that Taiwan’s public debt appears as a nonlinear series and is stationary in regime 1 but not in regime 2. This result implies that while Taiwan’s public debt was mostly sustainable over the 1996 to 2013 period examined in the study, it may no longer be sustainable in the most recent two years as the public debt ratio has increased cumulatively to 3.618%.

Keywords: nonlinearity, public debt, sustainability, threshold autoregressive model

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Authors: Rasim M. Alguliyev, Alovsat G. Aliyev, Roza O. Shahverdiyeva

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The paper investigated the role and position of high techno-parks, which is one of the priorities of Azerbaijan. The main objectives, functions and features of the establishment of high-techno parks, as well as organization of the activity of the structural elements, which are the parking complex and their interactions were analyzed. The development, organization and management of high techno-parks were studied. The key features and functions of innovative structures’ management were explained. The need for a comprehensive management system for the development of high-techno parks was emphasized and the major problems were analyzed. In addition, the methods were proposed for the development of information systems supporting decision making in systematic and sustainable management of the parks.

Keywords: innovative development, innovation processes, innovation economy, innovation infrastructure, high technology park, efficient management, management decisions, information insurance

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Authors: Ebrahim Hassan Kapeel, Ahmed Mohsen Kamel, Hossan Hendy, Yehia Z. Elhalwagy

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Interest in autopilot design has been raised intensely as a result of recent advancements in Unmanned Aerial vehicles (UAVs). Due to the enormous number of applications that UAVs can achieve, the number of applied control theories used for them has increased in recent years. These small fixed-wing UAVs are suffering high non-linearity, sensitivity to disturbances, and coupling effects between their channels. In this work, the nonlinear dynamic inversion (NDI) control lawisdesigned for a nonlinear small fixed-wing UAV model. The NDI is preferable for varied operating conditions, there is no need for a scheduling controller. Moreover, it’s applicable for high angles of attack. For the designed flight controller validation, a nonlinear Modified PI-D controller is performed with our model. A comparative study between both controllers is achieved to evaluate the NDI performance. Simulation results and analysis are proposed to illustrate the effectiveness of the designed controller based on NDI.

Keywords: UAV dynamic model, attitude control, nonlinear PID, dynamic inversion

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Authors: Belkheir Noura, Rabah Kerfah, Boumehani Abdellah

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The influence of solidity variations on the aerodynamic performance of H type vertical axis wind turbine is studied in this paper. The wind turbine model used in this paper is the three-blade wind turbine with the symmetrical airfoil, NACA0021. The length of the chord is 0.265m. Numerical investigations were implemented for the different solidity by changing the radius and blade number. A two-dimensional model of the wind turbine is employed. The approach a Reynolds-Averaged Navier–Stokes equations, completed by the K- ώ SST turbulence model, is used. Motion mesh model capability of a computational fluid dynamics (CFD) solver is used. For each value of the solidity, the aerodynamics performances and the characteristics of the flow field are studied at several values of the tip speed ratio, λ = 0.5 to λ = 3, with an incoming wind speed of 8 m/s. The results show that increasing the number of blades will reduce the maximum value of the power coefficient of the wind turbine. Also, for the VAWT with a lower solidity can obtain the maximum Cp at a high tip speed ratio. The effects of changing the radius and blade number on aerodynamic performance are almost the same. Finally, for the validation, experimental data from the literature and computational results were compared. In conclusion, to study the influence of the solidity in the performances of the wind turbine is to provide the reference for the design of H type vertical axis wind turbines.

Keywords: wind energy, darrieus h type vertical axis wind turbine, computational fluid dynamic, solidity

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Authors: Akshat Kumar, Vidushi

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This paper identifies 27 KOIs, 26 of which are currently classified as candidates and one as false positives that have a high probability of being confirmed. For this purpose, 11 machine learning algorithms were implemented on the cumulative kepler dataset sourced from the NASA exoplanet archive; it was observed that the best-performing model was HistGradientBoosting and XGBoost with a test accuracy of 93.5%, and the lowest-performing model was Gaussian NB with a test accuracy of 54%, to test model performance F1, cross-validation score and RUC curve was calculated. Based on the learned models, the significant characteristics for confirm exoplanets were identified, putting emphasis on the object’s transit and stellar properties; these characteristics were namely koi_count, koi_prad, koi_period, koi_dor, koi_ror, and koi_smass, which were later considered to filter out the potential KOIs. The paper also calculates the Earth similarity index based on the planetary radius and equilibrium temperature for each KOI identified to aid in their classification.

Keywords: Kepler objects of interest, exoplanets, space exploration, machine learning, earth similarity index, transit photometry

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Authors: Frew Yirgalem Mane

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This study has examined Africa’s experiment with authoritarian model of development drawing from the experience of Ethiopia. With the tectonic crisis of neoliberal ideology, the dominant policy agenda in Africa pertains to bringing the state back to development. More concretely, countries epitomized by Ethiopia, Rwanda and Uganda have been constructing a highly interventionist state with authoritarian character. The central motive appears to facilitate development and salvage people out of appalling and grinding poverty. Each country warrants closer inspection. However, this study focuses on Ethiopia- a country often applauded as ‘Africa’s Great Run’ for delivering socio-economic success over the past two decades. In fact, inspired by East Asia’s including Chinese model of authoritarian development, Ethiopia orchestrated a vanguard party, centralized rent control system with politicized bureaucracy and militaristic mobilization resources for development. This arrangement may explain Ethiopia economic success story as one the fastest growing countries in the world. However, this paper detected, Ethiopia’s attempt to bring the state back in development has precipitated institutionalization of a new breed of authoritarianism and informalization of public institutions. Ethiopia’s model of state-led development may constitute a noticeable shift away from the vengeful adherence to neoliberal policies. However, the manner the model has been practiced proved to be neither smooth nor appears to address Ethiopia’s aspiration for political and economic transformation. Partly, this can be illustrated by recent widespread grievances that fed into the popular uprising and animated opposition against the state. Sources of the grievance are complex, but they are highly ingrained with the way the authoritarian model of development is functioning and also the model’s dis-functioning in terms of benefiting people. In light of these findings, the study has arrived at the following conclusion. Africa’s attempt to emulate development models from other countries is not such a ‘bad’ thing. However, emulation makes sense if it is contextualized and sensitive to complex local socio-economic interests.

Keywords: Africa, authoritarianism, development, Ethiopia, neoliberalism

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Authors: Houda Najeh, Mahendra Pratap Singh, Stéphane Ploix, Antoine Caucheteux, Karim Chabir, Mohamed Naceur Abdelkrim

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Building system is highly vulnerable to different kinds of faults and human misbehaviors. Energy efficiency and user comfort are directly targeted due to abnormalities in building operation. The available fault diagnosis tools and methodologies particularly rely on rules or pure model-based approaches. It is assumed that model or rule-based test could be applied to any situation without taking into account actual testing contexts. Contextual tests with validity domain could reduce a lot of the design of detection tests. The main objective of this paper is to consider fault validity when validate the test model considering the non-modeled events such as occupancy, weather conditions, door and window openings and the integration of the knowledge of the expert on the state of the system. The concept of heterogeneous tests is combined with test validity to generate fault diagnoses. A combination of rules, range and model-based tests known as heterogeneous tests are proposed to reduce the modeling complexity. Calculation of logical diagnoses coming from artificial intelligence provides a global explanation consistent with the test result. An application example shows the efficiency of the proposed technique: an office setting at Grenoble Institute of Technology.

Keywords: heterogeneous tests, validity, building system, sensor grids, sensor fault, diagnosis, fault detection and isolation

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Authors: Pirta Palola, Richard Bailey, Lisa Wedding

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Societies and economies worldwide fundamentally depend on natural capital. Alarmingly, natural capital assets are quickly depreciating, posing an existential challenge for humanity. The development of robust natural capital accounting systems is essential for transitioning towards sustainable economic systems and ensuring sound management of capital assets. However, the accurate, equitable and comprehensive estimation of natural capital asset stocks and their accounting values still faces multiple challenges. In particular, the representation of socio-cultural values held by groups or communities has arguably been limited, as to date, the valuation of natural capital assets has primarily been based on monetary valuation methods and assumptions of individual rationality. People relate to and value the natural environment in multiple ways, and no single valuation method can provide a sufficiently comprehensive image of the range of values associated with the environment. Indeed, calls have been made to improve the representation of multiple types of value (instrumental, intrinsic, and relational) and diverse ontological and epistemological perspectives in environmental valuation. This study addresses this need by establishing a novel valuation framework, Environmental Value Functions (EVF), that allows for the integration of multiple types of value in natural capital accounting systems. The EVF framework is based on the estimation and application of value functions, each of which describes the relationship between the value and quantity (or quality) of an ecosystem component of interest. In this framework, values are estimated in terms of change relative to the current level instead of calculating absolute values. Furthermore, EVF was developed to also support non-marginalist conceptualizations of value: it is likely that some environmental values cannot be conceptualized in terms of marginal changes. For example, ecological resilience value may, in some cases, be best understood as a binary: it either exists (1) or is lost (0). In such cases, a logistic value function may be used as the discriminator. Uncertainty in the value function parameterization can be considered through, for example, Monte Carlo sampling analysis. The use of EVF is illustrated with two conceptual examples. For the first time, EVF offers a clear framework and concrete methodology for the representation of multiple types of value in natural capital accounting systems, simultaneously enabling 1) the complementary use and integration of multiple valuation methods (monetary and non-monetary); 2) the synthesis of information from diverse knowledge systems; 3) the recognition of value incommensurability; 4) marginalist and non-marginalist value analysis. Furthermore, with this advancement, the coupling of EVF and ecosystem modeling can offer novel insights to the study of spatial-temporal dynamics in natural capital asset values. For example, value time series can be produced, allowing for the prediction and analysis of volatility, long-term trends, and temporal trade-offs. This approach can provide essential information to help guide the transition to a sustainable economy.

Keywords: economics of biodiversity, environmental valuation, natural capital, value function

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Authors: H. A. Mansour

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The objective of this research work is studying the water use prediction, prediction technology for water use by action agencies, and others involved in conservation, planning, and environmental assessment of the Water Erosion Prediction Project (WEPP) simulation model. Models the important physical, processes governing erosion in Egypt (climate, infiltration, runoff, ET, detachment by raindrops, detachment by flowing water, deposition, etc.). Simulation of the non-uniform slope, soils, cropping/management., and Egyptian databases for climate, soils, and crops. The study included important parameters in Egyptian conditions as follows: Water Balance & Percolation, Soil Component (Tillage impacts), Plant Growth & Residue Decomposition, Overland Flow Hydraulics. It could be concluded that we can adapt the WEPP simulation model to determining the previous important parameters under Egyptian conditions.

Keywords: WEPP, adaptation, soil properties, tillage impacts, water balance, soil percolation

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Authors: D Bhargavi, J. Sharath Kumar Reddy

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The present investigation has been undertaken to assess the effect of viscous dissipation and axial conduction on forced convection heat transfer in the entrance region of a parallel plate channel with the porous insert attached to both walls of the channel. The flow field is unidirectional. Flow in the porous region corresponds to Darcy-Brinkman model and the clear fluid region to that of plane Poiseuille flow. The effects of the parameters Darcy number, Da, Peclet number, Pe, Brinkman number, Br and a porous fraction γ_p on the local heat transfer coefficient are analyzed graphically. Effects of viscous dissipation employing the Darcy model and the clear fluid compatible model have been studied.

Keywords: porous material, channel partially filled with a porous material, axial conduction, viscous dissipation

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Authors: Anujendra, Parag Kumar Guha Thakurta

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The data transmission between mobile hosts and base stations (BSs) in Mobile networks are often vulnerable to failure. Thus, efficient link connectivity, in terms of the services of both base stations and communication channels of the network, is required in wireless mobile networks to achieve highly reliable data transmission. In addition, it is observed that the number of blocked hosts is increased due to insufficient number of channels during heavy load in the network. Under such scenario, the channels are allocated accordingly to offer a reliable communication at any given time. Therefore, a reliability-based channel allocation model with acceptable system performance is proposed as a MOO problem in this paper. Two conflicting parameters known as Resource Reuse factor (RRF) and the number of blocked calls are optimized under reliability constraint in this problem. The solution to such MOO problem is obtained through NSGA-II (Non-dominated Sorting Genetic Algorithm). The effectiveness of the proposed model in this work is shown with a set of experimental results.

Keywords: base station, channel, GA, pareto-optimal, reliability

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Authors: M. Mohan, J. P. Roise, G. P. Catts

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Despite living in an era where conservation zones are de-facto the central element in any sustainable wildlife management strategy, we still find ourselves grappling with several pareto-optimal situations regarding resource allocation and area distribution for the same. In this paper, a multi-objective decision making (MODM) model is presented to answer the question of whether or not we can establish mutual relationships between these contradicting objectives. For our study, we considered a Red-cockaded woodpecker (Picoides borealis) habitat conservation scenario in the coastal plain of North Carolina, USA. Red-cockaded woodpecker (RCW) is a non-migratory territorial bird that excavates cavities in living pine trees for roosting and nesting. The RCW groups nest in an aggregation of cavity trees called ‘cluster’ and for our model we use the number of clusters to be established as a measure of evaluating the size of conservation zone required. The case study is formulated as a linear programming problem and the objective function optimises the Red-cockaded woodpecker clusters, carbon retention rate, biofuel, public safety and Net Present Value (NPV) of the forest. We studied the variation of individual objectives with respect to the amount of area available and plotted a two dimensional dynamic graph after establishing interrelations between the objectives. We further explore the concept of interdependency by integrating the MODM model with GIS, and derive a raster file representing carbon distribution from the existing forest dataset. Model results demonstrate the applicability of interdependency from both linear and spatial perspectives, and suggest that this approach holds immense potential for enhancing environmental investment decision making in future.

Keywords: conservation, interdependency, multi-objective decision making, red-cockaded woodpecker

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Authors: Ling Zhang, Weikai Li

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Recently three sufficient conditions for the three-way Candecomp/Parafac (CP) model which ensure uniqueness in one of the three modes(“uni-mode uniqueness”) are given. In this paper, we generalize these uniqueness conditions to n ≤ 3 and give a sufficient conditions for the n-way Candecomp/Parafac (CP) model, which ensure uniqueness in one of the n modes.

Keywords: uni-mode uniqueness, candecomp/parafac, n-way arrays, decomposition, tensor

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Authors: Pranav Gunhal., Krish Jhurani

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This study tackles the significant computational challenge of predicting excited state dynamics in organic photovoltaic (OPV) materials—a pivotal factor in the performance of solar energy solutions. Time-dependent density functional theory (TDDFT), though effective, is computationally prohibitive for larger and more complex molecules. As a solution, the research explores the application of transformer neural networks, a type of artificial intelligence (AI) model known for its superior performance in natural language processing, to predict excited state dynamics in OPV materials. The methodology involves a two-fold process. First, the transformer model is trained on an extensive dataset comprising over 10,000 TDDFT calculations of excited state dynamics from a diverse set of OPV materials. Each training example includes a molecular structure and the corresponding TDDFT-calculated excited state lifetimes and key electronic transitions. Second, the trained model is tested on a separate set of molecules, and its predictions are rigorously compared to independent TDDFT calculations. The results indicate a remarkable degree of predictive accuracy. Specifically, for a test set of 1,000 OPV materials, the transformer model predicted excited state lifetimes with a mean absolute error of 0.15 picoseconds, a negligible deviation from TDDFT-calculated values. The model also correctly identified key electronic transitions contributing to the excited state dynamics in 92% of the test cases, signifying a substantial concordance with the results obtained via conventional quantum chemistry calculations. The practical integration of the transformer model with existing quantum chemistry software was also realized, demonstrating its potential as a powerful tool in the arsenal of materials scientists and chemists. The implementation of this AI model is estimated to reduce the computational cost of predicting excited state dynamics by two orders of magnitude compared to conventional TDDFT calculations. The successful utilization of transformer neural networks to accurately predict excited state dynamics provides an efficient computational pathway for the accelerated discovery and design of new OPV materials, potentially catalyzing advancements in the realm of sustainable energy solutions.

Keywords: transformer neural networks, organic photovoltaic materials, excited state dynamics, time-dependent density functional theory, predictive modeling

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Authors: Basil Manos, Thomas Bournaris, Christina Moulogianni

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The European agricultural policy supports all member states to apply agricultural development plans for the development of their agricultural sectors. A specific measure of the agricultural development plans refers to young people in order to enter into the agricultural sector. This measure helps the participating young farmers in achieving maximum efficiency, using methods and environmentally friendly practices, by altering their farm plans. This study applies a Multicriteria Mathematical Programming (MCDA) model for the young farmers to find farm plans that achieve the maximum gross margin and the minimum environmental impacts (less use of fertilizers and irrigation water). The analysis was made in the region of Central Macedonia, Greece, among young farmers who have participated in the “Setting up Young Farmers” measure during 2007-2010. The analysis includes the implementation of the MCDA model for the farm plans optimization and the comparison of selected environmental indicators with those of the existent situation.

Keywords: multicriteria, optimum farm plans, environmental impacts, sustainable management

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Authors: Nick Farid, Roghoyeh Salmeh

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The planning and operation of the power grid is becoming much more complex because of the introduction of renewable energy resources, the digitalization of the electricity industry, as well as the coupling of efficiency and greener energy trends. These changes, along with new trends, make interactions between grid users and the other stakeholders more complex. This paper focuses on the main “physical” and “logical” interactions between grid users and the grid stakeholders, both from power system equipment and information management standpoints, and proposes a new interoperability model for Smart Grids.

Keywords: user interface, interoperability layers, grid architecture framework, smart grid

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Authors: Meirav Eshkol, Miri Blaufeld, Rinat Basal

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Aim: The working environment in rural clinics is often isolated and distant from major health centers. In these circumstances, rural health care faces numerous challenges. The hope is that, in the immediate future and in the medium and long range, the rural nursing staff will realize their full professional and personal potential to their own satisfaction and to the health and welfare of their patients. Background: Rural nurses work mostly alone or with very few colleagues, and have the authority to make professional decisions, a fact which often requires them to make critical decisions in pressure situations. In addition, the expectations set for these nurses are extremely high, a fact which requires them to be extremely skilled and to fulfill their professional potential. They are required to provide high-quality and comprehensive care to the individual, the family, and the community and to maintain close interaction with the community. Work in a rural setting requires the flexibility to perform multiple tasks in an isolated setting, often far removed from major health centers. In order to maintain professional satisfaction for the rural nurse, expanded direction and training are required in professional know-how, and in the development of new and existing skills, toward the goal of treating a diverse population and to obtain a comprehensive view of the components of a diagnosis for treatment and to develop an understanding appropriate to the presented reality. Objective: To provide knowledge and to expand and develop professional skills in the prevention and advancement of health in the care of a diverse patient population. The development of strategies and skills for work under pressure alone instills expertise in performing multiple tasks in diverse disciplines. To reduce feelings of stress and burnout. Methodology: This course is the first and one of a kind in Clalit - the biggest health organisation in Israel. Observing and identifying the needs of the nurses in the field relating to the development of professional and personal skills defining goals and objectives, and determining the content of a course designed for rural nurses and kibbutz nurses who are not Clalit employees. Results: 43 nurses participated and 30 answered the feedback questionnaire. The rating of their experience was 4.33 (on a scale of 1-5, with 5 being the highest ranking). 92% indicated the importance of meeting with additional nurses to teach their colleagues. 83% of the nurses indicated an increased sense of organizational belonging. 60% indicated that the course helped to reduce feelings of stress and burnout in becoming a better rural nurse. 80% indicated that the course helped them establish intra-organizational professional cooperation and initiating processes. Conclusion: The course is an instrument which aids in increasing the feeling of organizational belonging, reducing feelings of stress and burnout, creation of relationships and cooperation both within and outside of the organization, increased the realization of the potential of the village nurse.

Keywords: rural nurse, alone, burnout, multiple tasks

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Authors: Maymona Al-Husari, Craig Murdoch, Steven Webb

Abstract:

Some tumors are known to exhibit an extracellular pH that is more acidic than the intracellular, creating a 'reversed pH gradient' across the cell membrane and this has been shown to affect their invasive and metastatic potential. Tumour hypoxia also plays an important role in tumour development and has been directly linked to both tumour morphology and aggressiveness. In this paper, we present a hybrid mathematical model of intracellular pH regulation that examines the effect of oxygen and pH on tumour growth and morphology. In particular, we investigate the impact of pH regulatory mechanisms on the cellular pH gradient and tumour morphology. Analysis of the model shows that: low activity of the Na+/H+ exchanger or a high rate of anaerobic glycolysis can give rise to a 'fingering' tumour morphology; and a high activity of the lactate/H+ symporter can result in a reversed transmembrane pH gradient across a large portion of the tumour mass. Also, the reversed pH gradient is spatially heterogenous within the tumour, with a normal pH gradient observed within an intermediate growth layer, that is the layer between the proliferative inner and outermost layer of the tumour.

Keywords: acidic pH, cellular automaton, ebola, tumour growth

Procedia PDF Downloads 333

Authors: Wen-Chung Chiang, Lun-Jou Lo, Hsiu-Hsia Lin

Abstract:

This study aimed to improve the evaluation of facial symmetry, which is crucial for planning and assessing outcomes in orthognathic surgery (OGS). Facial symmetry plays a key role in both aesthetic and functional aspects of OGS, making its accurate evaluation essential for optimal surgical results. To address the limitations of traditional methods, a different approach was developed, combining three-dimensional (3D) facial contour mapping with hyperdimensional (HD) computing to enhance precision and efficiency in symmetry assessments. The study was conducted at Chang Gung Memorial Hospital, where data were collected from 2018 to 2023 using 3D cone beam computed tomography (CBCT), a highly detailed imaging technique. A large and comprehensive dataset was compiled, consisting of 150 normal individuals and 2,800 patients, totaling 5,750 preoperative and postoperative facial images. These data were critical for training a machine learning model designed to analyze and quantify facial symmetry. The machine learning model was trained to process 3D contour data from the CBCT images, with HD computing employed to power the facial symmetry quantification system. This combination of technologies allowed for an objective and detailed analysis of facial features, surpassing the accuracy and reliability of traditional symmetry assessments, which often rely on subjective visual evaluations by clinicians. In addition to developing the system, the researchers conducted a retrospective review of 3D CBCT data from 300 patients who had undergone OGS. The patients’ facial images were analyzed both before and after surgery to assess the clinical utility of the proposed system. The results showed that the facial symmetry algorithm achieved an overall accuracy of 82.5%, indicating its robustness in real-world clinical applications. Postoperative analysis revealed a significant improvement in facial symmetry, with an average score increase of 51%. The mean symmetry score rose from 2.53 preoperatively to 3.89 postoperatively, demonstrating the system's effectiveness in quantifying improvements after OGS. These results underscore the system's potential for providing valuable feedback to surgeons and aiding in the refinement of surgical techniques. The study also led to the development of a web-based system that automates facial symmetry assessment. This system integrates HD computing and 3D contour mapping into a user-friendly platform that allows for rapid and accurate evaluations. Clinicians can easily access this system to perform detailed symmetry assessments, making it a practical tool for clinical settings. Additionally, the system facilitates better communication between clinicians and patients by providing objective, easy-to-understand symmetry scores, which can help patients visualize the expected outcomes of their surgery. In conclusion, this study introduced a valuable and highly effective approach to facial symmetry evaluation in OGS, combining 3D contour mapping, HD computing, and machine learning. The resulting system achieved high accuracy and offers a streamlined, automated solution for clinical use. The development of the web-based platform further enhances its practicality, making it a valuable tool for improving surgical outcomes and patient satisfaction in orthognathic surgery.

Keywords: facial symmetry, orthognathic surgery, facial contour mapping, hyperdimensional computing

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Authors: Khenfer Koummich Fatma, Hendel Fatiha, Mesbahi Larbi

Abstract:

This paper presents an approach based on Hidden Markov Models (HMM: Hidden Markov Model) using HTK tools. The goal is to create a human-machine interface with a voice recognition system that allows the operator to teleoperate a mentor robot to execute specific tasks as rotate, raise, close, etc. This system should take into account different levels of environmental noise. This approach has been applied to isolated words representing the robot commands pronounced in two languages: French and Arabic. The obtained recognition rate is the same in both speeches, Arabic and French in the neutral words. However, there is a slight difference in favor of the Arabic speech when Gaussian white noise is added with a Signal to Noise Ratio (SNR) equals 30 dB, in this case; the Arabic speech recognition rate is 69%, and the French speech recognition rate is 80%. This can be explained by the ability of phonetic context of each speech when the noise is added.

Keywords: Arabic speech recognition, Hidden Markov Model (HMM), HTK, noise, TIMIT, voice command

Procedia PDF Downloads 393

Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

Abstract:

Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

Procedia PDF Downloads 119

Authors: Janet Holland

Abstract:

Eye tracking is a great way to triangulate multiple data sources for deeper, more complete knowledge of how instructional materials are really being used and emotional connections made. Using sensor based biometrics provides a detailed local analysis in real time expanding our ability to collect science based data for a more comprehensive level of understanding, not previously possible, for teaching and learning. The knowledge gained will be used to make future improvements to instructional materials, tools, and interactions. The literature has been examined and a preliminary pilot test was implemented to develop a methodology for research in Instructional Design and Technology. Eye tracking now offers the addition of objective metrics obtained from eye tracking and other biometric data collection with analysis for a fresh perspective.

Keywords: area of interest, eye tracking, biometrics, fixation, fixation count, fixation sequence, fixation time, gaze points, heat map, saccades, time to first fixation

Procedia PDF Downloads 134

Authors: Esha Saha, Pradip Kumar Ray

Abstract:

A hospital inventory comprises of a large number and great variety of items for the proper treatment and care of patients, such as pharmaceuticals, medical equipment, surgical items, etc. Improper management of these items, i.e. stockouts, may lead to delay in treatment or other fatal consequences, even death of the patient. So, generally the hospitals tend to overstock items to avoid the risk of stockout which leads to unnecessary investment of money, difficulty in storing, more expiration and wastage, etc. Thus, in such challenging environment, it is necessary for hospitals to follow an inventory policy considering the stochasticity of demand in a hospital. Statistical analysis captures the correlation of patient condition based on bed occupancy with the patient demand which changes stochastically. Due to the dependency on bed occupancy, the markov model is developed that helps to map the changes in demand of hospital inventory based on the changes in the patient condition represented by the movements of bed occupancy states (acute care state, rehabilitative state and long-care state) during the length-of-stay of patient in a hospital. An inventory policy is developed for a hospital based on the fulfillment of patient demand with the objective of minimizing the frequency and quantity of placement of orders of inventoried items. The analytical structure of the model based on probability calculation is provided to show the optimal inventory-related decisions. A case-study is illustrated in this paper for the development of hospital inventory model based on patient demand for multiple inpatient pharmaceutical items. A sensitivity analysis is conducted to investigate the impact of inventory-related parameters on the developed optimal inventory policy. Therefore, the developed model and solution approach may help the hospital managers and pharmacists in managing the hospital inventory in case of stochastic demand of inpatient pharmaceutical items.

Keywords: bed occupancy, hospital inventory, markov model, patient condition, pharmaceutical items

Procedia PDF Downloads 327

Authors: Aurelija Burinskiene

Abstract:

This paper presents the study for an e-commerce warehouse. The study is aiming to improve order fulfillment activity by identifying the strategy presenting the best performance. A simulation model was proposed to reach the target of this research. This model enables various scenario tests in an e-commerce warehouse, allowing them to find out for the best order fulfillment strategy. By using simulation, model authors investigated customers’ orders representing on-line purchases for one month. Experiments were designed to evaluate various order picking methods applicable to the fulfillment of customers’ orders. The research uses cost components analysis and helps to identify the best possible order picking method improving the overall performance of e-commerce warehouse and fulfillment service to the customers. The results presented show that the application of order batching strategy is the most applicable because it brings distance savings of around 6.7 percentage. This result could be improved by taking an assortment clustering action until 8.34 percentage. So, the recommendations were given to apply the method for future e-commerce warehouse operations.

Keywords: e-commerce, order, fulfilment, strategy, simulation

Procedia PDF Downloads 151