Search results for: computer assisted language learning

Authors: Yiea Funk Te, Daniel Mueller, Irena Pletikosa Cvijikj

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Small and medium enterprises (SMEs) play an important role in the economy of many countries. When the overall world economy is considered, SMEs represent 95% of all businesses in the world, accounting for 66% of the total employment. Existing studies show that the current business environment is characterized as highly turbulent and strongly influenced by modern information and communication technologies, thus forcing SMEs to experience more severe challenges in maintaining their existence and expanding their business. To support SMEs at improving their competitiveness, researchers recently turned their focus on applying data mining techniques to build risk and growth prediction models. However, data used to assess risk and growth indicators is primarily obtained via questionnaires, which is very laborious and time-consuming, or is provided by financial institutes, thus highly sensitive to privacy issues. Recently, web mining (WM) has emerged as a new approach towards obtaining valuable insights in the business world. WM enables automatic and large scale collection and analysis of potentially valuable data from various online platforms, including companies’ websites. While WM methods have been frequently studied to anticipate growth of sales volume for e-commerce platforms, their application for assessment of SME risk and growth indicators is still scarce. Considering that a vast proportion of SMEs own a website, WM bears a great potential in revealing valuable information hidden in SME websites, which can further be used to understand SME risk and growth indicators, as well as to enhance current SME risk and growth prediction models. This study aims at developing an automated system to collect business-relevant data from the Web and predict future growth trends of SMEs by means of WM and data mining techniques. The envisioned system should serve as an 'early recognition system' for future growth opportunities. In an initial step, we examine how structured and semi-structured Web data in governmental or SME websites can be used to explain the success of SMEs. WM methods are applied to extract Web data in a form of additional input features for the growth prediction model. The data on SMEs provided by a large Swiss insurance company is used as ground truth data (i.e. growth-labeled data) to train the growth prediction model. Different machine learning classification algorithms such as the Support Vector Machine, Random Forest and Artificial Neural Network are applied and compared, with the goal to optimize the prediction performance. The results are compared to those from previous studies, in order to assess the contribution of growth indicators retrieved from the Web for increasing the predictive power of the model.

Keywords: data mining, SME growth, success factors, web mining

Procedia PDF Downloads 247

Authors: Rehan Ullah

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The primary purpose of the study is to observe the relationship that exists between the organizational information technology (IT) capabilities and the organizational performance in China and Pakistan. Nations like China and Pakistan utilize modern techno-how to enhance their production endeavors. Therefore, making a wide-ranging comparison of the manufacturing services between China and Pakistan was chosen due to numerous reasons. One reason for carrying out this comparison is to determine how IT of the two countries enhances organizational competency on small and medium-sized manufacturing enterprises (SMEs). The study hypothesized that organizational IT capabilities (IT infrastructure, IT competence) have a positive influence on organizational performance and the strategic benefits of IT have a mediating effect on the relationship between IT capability and organizational performance. To investigate the relationship between IT capabilities and organizational performance, surveys were sent to managers of small, medium-sized manufacturing organizations located in the southwestern region, Sichuan province of China, and Pakistani companies, which are located in Islamabad, Lahore, and Karachi. These cities were selected as typical representatives of each country. Organizational performance has been measured in terms of profitability, organizational success, growth, market share, and innovativeness. Out of 400 surveys distributed to different manufacturing organizations, 303 usable and valid responses were received that are analyzed in this research. The data were examined using SPSS and Smart PLS computer software. The results of the study, including the descriptive statistics of each variable, are used. The outer model has been measured with considerations to content validity, discriminant validity, and convergent validity. The path coefficients among the constructs were also computed when analyzing the structural model using the bootstrapping technique. The analysis of data from both China and Pakistan yields an identical but unique result. The results show that IT infrastructure, IT competence, strategic benefits of IT are all correlated to the performance of the organizations. Moreover, strategic benefits of IT have been proved to mediate the relationship between IT capabilities and organization performance. The author, concerning the role of IT on the performance of an organization, highlights the different aspects as well as its benefits in an organization. The overall study concludes several implications for both managers and academicians. It also provides the limitations of the study and offers recommendations for future studies and practice.

Keywords: organizational performance, IT capabilities, IT infrastructure, IT competence, strategic benefits of IT, China, Pakistan

Procedia PDF Downloads 79

Authors: Tehrrie Konig, Cassiano Bortolozo, Daniel Metodiev, Rodolfo Mendes, Marcio Andrade, Marcio Moraes

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Gravitational mass movements are recurrent events in Brazil, usually triggered by intense rainfall. When these events occur in urban areas, they end up becoming disasters due to the economic damage, social impact, and loss of human life. To identify the landslide-susceptible areas, it is important to know the geotechnical parameters of the soil, such as cohesion, internal friction angle, unit weight, hydraulic conductivity, and hydraulic diffusivity. The measurement of these parameters is made by collecting soil samples to analyze in the laboratory and by using geophysical methodologies, such as Vertical Electrical Survey (VES). The geophysical surveys analyze the soil properties with minimal impact in its initial structure. Statistical analysis and mathematical models of physical basis are used to model and calculate the Factor of Safety for steep slope areas. In general, such mathematical models work from the combination of slope stability models and hydrological models. One example is the mathematical model TRIGRS (Transient Rainfall Infiltration and Grid-based Regional Slope- Stability Model) which calculates the variation of the Factor of Safety of a determined study area. The model relies on changes in pore-pressure and soil moisture during a rainfall event. TRIGRS was written in the Fortran programming language and associates the hydrological model, which is based on the Richards Equation, with the stability model based on the principle of equilibrium limit. Therefore, the aims of this work are modeling the slope stability of Campos do Jordão with TRIGRS, using geotechnical and geophysical methodologies to acquire the soil properties. The study area is located at southern-east of Sao Paulo State in the Mantiqueira Mountains and has a historic landslide register. During the fieldwork, soil samples were collected, and the VES method applied. These procedures provide the soil properties, which were used as input data in the TRIGRS model. The hydrological data (infiltration rate and initial water table height) and rainfall duration and intensity, were acquired from the eight rain gauges installed by Cemaden in the study area. A very high spatial resolution digital terrain model was used to identify the slopes declivity. The analyzed period is from March 6th to March 8th of 2017. As results, the TRIGRS model calculates the variation of the Factor of Safety within a 72-hour period in which two heavy rainfall events stroke the area and six landslides were registered. After each rainfall, the Factor of Safety declined, as expected. The landslides happened in areas identified by the model with low values of Factor of Safety, proving its efficiency on the identification of landslides susceptible areas. This study presents a critical threshold for landslides, in which an accumulated rainfall higher than 80mm/m² in 72 hours might trigger landslides in urban and natural slopes. The geotechnical and geophysics methods are shown to be very useful to identify the soil properties and provide the geological characteristics of the area. Therefore, the combine geotechnical and geophysical methods for soil characterization and the modeling of landslides susceptible areas with TRIGRS are useful for urban planning. Furthermore, early warning systems can be developed by combining the TRIGRS model and weather forecast, to prevent disasters in urban slopes.

Keywords: landslides, susceptibility, TRIGRS, vertical electrical survey

Procedia PDF Downloads 153

Authors: A. Schander, T. Tessmann, H. Stemmann, S. Strokov, A. Kreiter, W. Lang

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For the chronic application of neural prostheses and other brain-computer interfaces, long-term stable microelectrodes for electrical stimulation are essential. In recent years many developments were done to investigate different appropriate materials for these electrodes. One of these materials is the electrical conductive polymer poly(3,4-ethylenedioxythiophene) (PEDOT), which has lower impedance and higher charge injection capacity compared to noble metals like gold and platinum. However the long-term stability of this polymer is still unclear. Thus this paper reports on the in-vitro evaluation of the long-term stability of PEDOT coated gold microelectrodes. For this purpose a highly flexible electrocorticography (ECoG) electrode array, based on the polymer polyimide, is used. This array consists of circular gold electrodes with a diameter of 560 µm (0.25 mm2). In total 25 electrodes of this array were coated simultaneously with the polymer PEDOT:PSS in a cleanroom environment using a galvanostatic electropolymerization process. After the coating the array is additionally sterilized using a steam sterilization process (121°C, 1 bar, 20.5 min) to simulate autoclaving prior to the implantation of such an electrode array. The long-term measurements were performed in phosphate-buffered saline solution (PBS, pH 7.4) at the constant body temperature of 37°C. For the in-vitro electrical stimulation a one channel bipolar current stimulator is used. The stimulation protocol consists of a bipolar current amplitude of 5 mA (cathodal phase first), a pulse duration of 100 µs per phase, a pulse pause of 50 µs and a frequency of 1 kHz. A PEDOT:PSS coated gold electrode with an area of 1 cm2 serves as the counter electrode. The electrical stimulation is performed continuously with a total amount of 86.4 million bipolar current pulses per day. The condition of the PEDOT coated electrodes is monitored in between with electrical impedance spectroscopy measurements. The results of this study demonstrate that the PEDOT coated electrodes are stable for more than 3.6 billion bipolar current pulses. Also the unstimulated electrodes show currently no degradation after the time period of 5 months. These results indicate an appropriate long-term stability of this electrode coating for chronic recording and electrical stimulation. The long-term measurements are still continuing to investigate the life limit of this electrode coating.

Keywords: chronic recording, electrical stimulation, long-term stability, microelectrodes, PEDOT

Procedia PDF Downloads 571

Authors: Baha Eddine Aissou, Aichouche Belhadj Aissa

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Light detection and ranging (LiDAR) is an active remote sensing technology used for several applications. Airborne LiDAR is becoming an important technology for the acquisition of a highly accurate dense point cloud. A classification of airborne laser scanning (ALS) point cloud is a very important task that still remains a real challenge for many scientists. Support vector machine (SVM) is one of the most used statistical learning algorithms based on kernels. SVM is a non-parametric method, and it is recommended to be used in cases where the data distribution cannot be well modeled by a standard parametric probability density function. Using a kernel, it performs a robust non-linear classification of samples. Often, the data are rarely linearly separable. SVMs are able to map the data into a higher-dimensional space to become linearly separable, which allows performing all the computations in the original space. This is one of the main reasons that SVMs are well suited for high-dimensional classification problems. Only a few training samples, called support vectors, are required. SVM has also shown its potential to cope with uncertainty in data caused by noise and fluctuation, and it is computationally efficient as compared to several other methods. Such properties are particularly suited for remote sensing classification problems and explain their recent adoption. In this poster, the SVM classification of ALS LiDAR data is proposed. Firstly, connected component analysis is applied for clustering the point cloud. Secondly, the resulting clusters are incorporated in the SVM classifier. Radial basic function (RFB) kernel is used due to the few numbers of parameters (C and γ) that needs to be chosen, which decreases the computation time. In order to optimize the classification rates, the parameters selection is explored. It consists to find the parameters (C and γ) leading to the best overall accuracy using grid search and 5-fold cross-validation. The exploited LiDAR point cloud is provided by the German Society for Photogrammetry, Remote Sensing, and Geoinformation. The ALS data used is characterized by a low density (4-6 points/m²) and is covering an urban area located in residential parts of the city Vaihingen in southern Germany. The class ground and three other classes belonging to roof superstructures are considered, i.e., a total of 4 classes. The training and test sets are selected randomly several times. The obtained results demonstrated that a parameters selection can orient the selection in a restricted interval of (C and γ) that can be further explored but does not systematically lead to the optimal rates. The SVM classifier with hyper-parameters is compared with the most used classifiers in literature for LiDAR data, random forest, AdaBoost, and decision tree. The comparison showed the superiority of the SVM classifier using parameters selection for LiDAR data compared to other classifiers.

Keywords: classification, airborne LiDAR, parameters selection, support vector machine

Procedia PDF Downloads 134

Authors: Shuang Luo, Er-Xiang Song

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Many problems in geotechnical engineering, such as foundation deformation, groundwater seepage, seismic wave propagation and geothermal transfer problems, may involve analysis in the ground which can be seen as extending to infinity. To that end, consideration has to be given regarding how to deal with the unbounded domain to be analyzed by using numerical methods, such as finite element method (FEM), finite difference method (FDM) or finite volume method (FVM). A simple artificial boundary approach derived from the analytical solutions for transient radial seepage problems, is introduced. It should be noted, however, that the analytical solutions used to derive the artificial boundary are particular solutions under certain boundary conditions, such as constant hydraulic head at the origin or constant pumping rate of the well. When dealing with unbounded domains with unsteady boundary conditions, a more sophisticated artificial boundary approach to deal with the infinity of the domain is presented. By applying Laplace transforms and introducing some specially defined auxiliary variables, the global artificial boundary conditions (ABCs) are simplified to local ones so that the computational efficiency is enhanced significantly. The introduced two local ABCs are implemented in a finite element computer program so that various seepage problems can be calculated. The two approaches are first verified by the computation of a one-dimensional radial flow problem, and then tentatively applied to more general two-dimensional cylindrical problems and plane problems. Numerical calculations show that the local ABCs can not only give good results for one-dimensional axisymmetric transient flow, but also applicable for more general problems, such as axisymmetric two-dimensional cylindrical problems, and even more general planar two-dimensional flow problems for well doublet and well groups. An important advantage of the latter local boundary is its applicability for seepage under rapidly changing unsteady boundary conditions, and even the computational results on the truncated boundary are usually quite satisfactory. In this aspect, it is superior over the former local boundary. Simulation of relatively long operational time demonstrates to certain extents the numerical stability of the local boundary. The solutions of the two local ABCs are compared with each other and with those obtained by using large element mesh, which proves the satisfactory performance and obvious superiority over the large mesh model.

Keywords: transient seepage, unbounded domain, artificial boundary condition, numerical simulation

Procedia PDF Downloads 281

Authors: Armen Grigoryan, Milena Arakelyan

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The idea of smart logistics is still quite a complicated one. It can be used to market products to a large number of customers or to acquire raw materials of the highest quality at the lowest cost in geographically dispersed areas. The use of smart contracts in logistics and supply chain management has the potential to revolutionize the way that goods are tracked, transported, and managed. Smart contracts are simply computer programs written in one of the blockchain programming languages (Solidity, Rust, Vyper), which are capable of self-execution once the predetermined conditions are met. They can be used to automate and streamline many of the traditional manual processes that are currently used in logistics and supply chain management, including the tracking and movement of goods, the management of inventory, and the facilitation of payments and settlements between different parties in the supply chain. Currently, logistics is a core area for companies which is concerned with transporting products between parties. Still, the problem of this sector is that its scale may lead to detainments and defaults in the delivery of goods, as well as other issues. Moreover, large distributors require a large number of workers to meet all the needs of their stores. All this may contribute to big detainments in order processing and increases the potentiality of losing orders. In an attempt to break this problem, companies have automated all their procedures, contributing to a significant augmentation in the number of businesses and distributors in the logistics sector. Hence, blockchain technology and smart contracted legal agreements seem to be suitable concepts to redesign and optimize collaborative business processes and supply chains. The main purpose of this paper is to examine the scope of blockchain technology and smart contracts in the field of logistics and supply chain management. This study discusses the research question of how and to which extent smart contracts and blockchain technology can facilitate and improve the implementation of collaborative business structures for sustainable entrepreneurial activities in smart supply chains. The intention is to provide a comprehensive overview of the existing research on the use of smart contracts in logistics and supply chain management and to identify any gaps or limitations in the current knowledge on this topic. This review aims to provide a summary and evaluation of the key findings and themes that emerge from the research, as well as to suggest potential directions for future research on the use of smart contracts in logistics and supply chain management.

Keywords: smart contracts, smart logistics, smart supply chain management, blockchain and smart contracts in logistics, smart contracts for controlling supply chain management

Procedia PDF Downloads 75

Authors: Constanza Levicán, Andrés Aparicio, Rodrigo F. Cádiz

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The artistic usage of Brain-computer interfaces (BCI), initially intended for medical purposes, has increased in the past few years as they become more affordable and available for the general population. One interesting question that arises from this practice is whether it is possible to compose or perform music by using only the brain as a musical instrument. In order to approach this question, we propose a BCI for musical composition, based on the representation of some mental states as the musician thinks about sounds. We developed software, called Insight2OSC, that allows the usage of the Emotiv Insight device as a musical instrument, by sending the EEG data to audio processing software such as MaxMSP through the OSC protocol. We provide two compositional applications bundled with the software, which we call Mapping your Mental State and Thinking On. The signals produced by the brain have different frequencies (or rhythms) depending on the level of activity, and they are classified as one of the following waves: delta (0.5-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), gamma (30-50 Hz). These rhythms have been found to be related to some recognizable mental states. For example, the delta rhythm is predominant in a deep sleep, while beta and gamma rhythms have higher amplitudes when the person is awake and very concentrated. Our first application (Mapping your Mental State) produces different sounds representing the mental state of the person: focused, active, relaxed or in a state similar to a deep sleep by the selection of the dominants rhythms provided by the EEG device. The second application relies on the physiology of the brain, which is divided into several lobes: frontal, temporal, parietal and occipital. The frontal lobe is related to abstract thinking and high-level functions, the parietal lobe conveys the stimulus of the body senses, the occipital lobe contains the primary visual cortex and processes visual stimulus, the temporal lobe processes auditory information and it is important for memory tasks. In consequence, our second application (Thinking On) processes the audio output depending on the users’ brain activity as it activates a specific area of the brain that can be measured using the Insight device.

Keywords: BCI, music composition, emotiv insight, OSC

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Authors: Christopher Benedikt Jakob

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In our current society, there is no need to take a look at the wristwatch to know the exact time. Smartphones, the watch in the car or the computer watch, inform us about the time too. Over hundreds of years, luxury watches have held a fascination for human beings. Consumers buy watches that cost thousands of euros, although they could buy much cheaper watches which also fulfill the function to indicate the correct time. This shows that the functional value has got a minor meaning with reference to the buying-reasons as regards luxury watches. For a few years, people have an increased demand to track data like their walking distance per day or to track their sleep for example. Smart watches enable consumers to get information about these data. There exists a trend that people intend to optimise parts of their social life, and thus they get the impression that they are able to optimise themselves as human beings. With the help of smart watches, they are able to optimise parts of their productivity and to realise their targets at the same time. These smart watches are also offered as luxury models, and the question is: how will customers of traditional luxury watches react? Therefore this study has the intention to give answers to the question why people are willing to spend an enormous amount of money on the consumption of luxury watches. The self-expression model, the relationship basis model, the functional benefit representation model and the means-end-theory are chosen as an appropriate methodology to find reasons why human beings purchase specific luxury watches and luxury smart watches. This evaluative approach further discusses these strategies concerning for example if consumers buy luxury watches/smart watches to express the current self or the ideal self and if human beings make decisions on expected results. The research critically evaluates that relationships are compared on the basis of their advantages. Luxury brands offer socio-emotional advantages like social functions of identification and that the strong brand personality of luxury watches and luxury smart watches helps customers to structure and retrieve brand awareness which simplifies the process of decision-making. One of the goals is to identify if customers know why they like specific luxury watches and dislike others although they are produced in the same country and cost comparable prices. It is very obvious that the market for luxury watches especially for luxury smart watches is changing way faster than it has been in the past. Therefore the research examines the market changing parameters in detail.

Keywords: buying-behaviour, brand management, consumer, luxury watch, smart watch

Procedia PDF Downloads 187

Authors: Alexandra Tucaliuc, Alexandra C. Blaga, Anca I. Galaction, Lenuta Kloetzer, Dan Cascaval

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Ergosterol (ergosta-5,7,22-trien-3β-ol), also known as provitamin D2, is the precursor of vitamin D2 (ergocalciferol), because it is converted under UV radiation to this vitamin. The natural sources of ergosterol are mainly the yeasts (Saccharomyces sp., Candida sp.), but it can be also found in fungus (Claviceps sp.) or plants (orchids). In the yeasts cells, ergosterol is accumulated in membranes, especially in free form in the plasma membrane, but also as esters with fatty acids in membrane lipids. The chemical synthesis of ergosterol does not represent an efficient method for its production, in these circumstances, the most attractive alternative for producing ergosterol at larger-scale remains the aerobic fermentation using S. cerevisiae on glucose or by-products from agriculture of food industry as substrates, in batch or fed-batch operating systems. The aim of this work is to analyze comparatively the influence of aeration efficiency on ergosterol production by S. cerevisiae in batch and fed-batch fermentations, by considering different levels of mixing intensity, aeration rate, and n-dodecane concentration. The effects of the studied factors are quantitatively described by means of the mathematical correlations proposed for each of the two fermentation systems, valid both for the absence and presence of oxygen-vector inside the broth. The experiments were carried out in a laboratory stirred bioreactor, provided with computer-controlled and recorded parameters. n-Dodecane was used as oxygen-vector and the ergosterol content inside the yeasts cells has been considered at the fermentation moment related to the maximum concentration of ergosterol, 9 hrs for batch process and 20 hrs for fed-batch one. Ergosterol biosynthesis is strongly dependent on the dissolved oxygen concentration. The hydrocarbon concentration exhibits a significant influence on ergosterol production mainly by accelerating the oxygen transfer rate. Regardless of n-dodecane addition, by maintaining the glucose concentration at a constant level in the fed-batch process, the amount of ergosterol accumulated into the yeasts cells has been almost tripled. In the presence of hydrocarbon, the ergosterol concentration increased by over 50%. The value of oxygen-vector concentration corresponding to the maximum level of ergosterol depends mainly on biomass concentration, due to its negative influences on broth viscosity and interfacial phenomena of air bubbles blockage through the adsorption of hydrocarbon droplets–yeast cells associations. Therefore, for the batch process, the maximum ergosterol amount was reached for 5% vol. n-dodecane, while for the fed-batch process for 10% vol. hydrocarbon.

Keywords: bioreactors, ergosterol, fermentation, oxygen-vector

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Authors: Pradipta Chandra, Titas Bhattacharjee, Bhaskar Bhowmick

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The foundation of economy in India is primarily based on agriculture while this is the most neglected in the rural setting. More significantly, household women take part in agriculture with higher involvement. However, because of lower education of women they have limited access towards financial decisions, land ownership and technology but they have vital role towards the individual family level. There are limited studies on the institution-wise training barriers with the focus of gender disparity. The main purpose of this paper is to find out the factors of institution-wise training (non-formal education) barriers in technology transfer with the focus of participation of rural women in agriculture. For this study primary and secondary data were collected in the line of qualitative and quantitative approach. Qualitative data were collected by several field visits in the adjacent areas of Seva-Bharati, Seva Bharati Krishi Vigyan Kendra through semi-structured questionnaires. In the next level detailed field surveys were conducted with close-ended questionnaires scored on the seven-point Likert scale. Sample size was considered as 162. During the data collection the focus was to include women although some biasness from the end of respondents and interviewer might exist due to dissimilarity in observation, views etc. In addition to that the heterogeneity of sample is not very high although female participation is more than fifty percent. Data were analyzed using Exploratory Factor Analysis (EFA) technique with the outcome of three significant factors of training barriers in technology adoption by farmers: (a) Failure of technology transfer training (TTT) comprehension interprets that the technology takers, i.e., farmers can’t understand the technology either language barrier or way of demonstration exhibited by the experts/ trainers. (b) Failure of TTT customization, articulates that the training for individual farmer, gender crop or season-wise is not tailored. (c) Failure of TTT generalization conveys that absence of common training methods for individual trainers for specific crops is more prominent at the community level. The central finding is that the technology transfer training method can’t fulfill the need of the farmers under an economically challenged area. The impact of such study is very high in the area of dry lateritic and resource crunch area of Jangalmahal under Paschim Medinipur district, West Bengal and areas with similar socio-economy. Towards the policy level decision this research may help in framing digital agriculture for implementation of the appropriate information technology for the farming community, effective and timely investment by the government with the selection of beneficiary, formation of farmers club/ farm science club etc. The most important research implication of this study lies upon the contribution towards the knowledge diffusion mechanism of the agricultural sector in India. Farmers may overcome the barriers to achieve higher productivity through adoption of modern farm practices. Corporates will be interested in agro-sector through investment under corporate social responsibility (CSR). The research will help in framing public or industry policy and land use pattern. Consequently, a huge mass of rural farm-women will be empowered and farmer community will be benefitted.

Keywords: dry lateritic zone, institutional barriers, technology transfer in India, farm-women participation

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Authors: Avi Shrivastava

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In 2021, two heart-wrenching scenes, shown live on television screens across countries, painted a grim picture of refugees. One of them was of people clinging onto an airplane's wings in their desperate attempt to flee war-torn Afghanistan. They ultimately fell to their death. The other scene was the U.S. government authorities separating children from their parents or guardians to deter migrants/refugees from coming to the U.S. These events show the desperation refugees feel when they are trying to leave their homes in disaster zones. However, data paints a grave picture of the current refugee situation. It also indicates that a bleak future lies ahead for the refugees across the globe. Data and information are the two threads that intertwine to weave the shimmery fabric of modern society. Data and information are often used interchangeably, but they differ considerably. For example, information analysis reveals rationale, and logic, while data analysis, on the other hand, reveals a pattern. Moreover, patterns revealed by data can enable us to create the necessary tools to combat huge problems on our hands. Data analysis paints a clear picture so that the decision-making process becomes simple. Geopolitical and economic data can be used to predict future refugee hotspots. Accurately predicting the next refugee hotspots will allow governments and relief agencies to prepare better for future refugee crises. The refugee crisis does not have binary answers. Given the emotionally wrenching nature of the ground realities, experts often shy away from realistically stating things as they are. This hesitancy can cost lives. When decisions are based solely on data, emotions can be removed from the decision-making process. Data also presents irrefutable evidence and tells whether there is a solution or not. Moreover, it also responds to a nonbinary crisis with a binary answer. Because of all that, it becomes easier to tackle a problem. Data science and A.I. can predict future refugee crises. With the recent explosion of data due to the rise of social media platforms, data and insight into data has solved many social and political problems. Data science can also help solve many issues refugees face while staying in refugee camps or adopted countries. This paper looks into various ways data science can help solve refugee problems. A.I.-based chatbots can help refugees seek legal help to find asylum in the country they want to settle in. These chatbots can help them find a marketplace where they can find help from the people willing to help. Data science and technology can also help solve refugees' many problems, including food, shelter, employment, security, and assimilation. The refugee problem seems to be one of the most challenging for social and political reasons. Data science and machine learning can help prevent the refugee crisis and solve or alleviate some of the problems that refugees face in their journey to a better life. With the explosion of data in the last decade, data science has made it possible to solve many geopolitical and social issues.

Keywords: refugee crisis, artificial intelligence, data science, refugee camps, Afghanistan, Ukraine

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Authors: Shunyi Yang, Jingjing Yan, Siyu Dong, Xiangguo Cui

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Current architectural façade design practices incorporate various daylighting and solar radiation analysis methods. These emphasize the impact of geometry on façade design. There is scope to extend this knowledge into methods that address material translucency, porosity, and form. Such approaches can also achieve these conditions through adaptive robotic manufacturing approaches that exploit material dynamics within the design, and alleviate fabrication waste from molds, ultimately accelerating the autonomous manufacturing system. Besides analyzing the environmental solar radiant in building facade design, there is also a vacancy research area of how lighting effects can be precisely controlled by engaging the instant real-time data-driven robot control and manipulating the material properties. Ceramics carries a wide range of transmittance and deformation potentials for robotics control with the research of its material property. This paper presents one semi-autonomous system that engages with real-time data-driven robotics control, hardware kit design, environmental building studies, human interaction, and exploratory research and experiments. Our objectives are to investigate the relationship between different clay bodies or ceramics’ physio-material properties and their transmittance; to explore the feedback system of instant lighting data in robotic fabrication to achieve precise lighting effect; to design the sufficient end effector and robot behaviors for different stages of deformation. We experiment with architectural clay, as the material of the façade that is potentially translucent at a certain stage can respond to light. Studying the relationship between form, material properties, and porosity can help create different interior and exterior light effects and provide façade solutions for specific architectural functions. The key idea is to maximize the utilization of in-progress robotics fabrication and ceramics materiality to create a highly integrated autonomous system for lighting facade design and manufacture.

Keywords: light transmittance, data-driven fabrication, computational design, computer vision, gamification for manufacturing

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Authors: Olarinde Olaniran, Oluyomi A. Sowemimo

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Toxoplasmosis is frequently misdiagnosed or underdiagnosed, and it is the third most common cause of hospitalization due to food-borne infection. Intra-uterine infection with Toxoplasma gondii due to active parasitaemia during pregnancy can cause severe and often fatal cerebral damage, abortion, and stillbirth of a fetus. The aim of the study was to investigate the prevalence of T. gondii infection in women of childbearing age in selected communities of Osun State with a view to determining the risk factors which predispose to the T. gondii infection. Five (5) ml of blood was collected by venopuncture into a plain blood collection tube by a medical laboratory scientist. Serum samples were separated by centrifuging the blood samples at 3000 rpm for 5 mins. The sera were collected with Eppendorf tubes and stored at -20°C analysis for the presence of IgG and IgM antibodies against T. gondii by commercially available enzyme-linked immunosorbent assay (ELISA) kit (Demeditec Diagnostics GmbH, Germany) conducted according to the manufacturer’s instructions. The optical densities of wells were measured by a photometer at a wavelength of 450 nm. Data collected were analysed using appropriate computer software. The overall seroprevalence of T. gondii among the women of child-bearing age in selected seven communities in Osun state was 76.3%. Out of 76.3% positive for Toxoplasma gondii infection, 70.0% were positive for anti- T. gondii IgG, and 32.3% were positive for IgM, and 26.7% for both IgG and IgM. The prevalence of T. gondii was lowest (58.9%) among women from Ile Ife, a peri-urban community, and highest (100%) in women residing in Alajue, a rural community. The prevalence of infection was significantly higher (P= 0.000) among Islamic women (87.5%) than in Christian women (70.8%). The highest prevalence (86.3%) was recorded in women with primary education, while the lowest (61.2%) was recorded in women with tertiary education (p =0.016). The highest prevalence (79.7%) was recorded in women that reside in rural areas, and the lowest (70.1%) was recorded in women that reside in peri-urban area (p=0.025). The prevalence of T. gondii infection was highest (81.4%) in women with one miscarriage, while the prevalence was lowest in women with no miscarriages (75.9%). The age of the women (p=0.042), Islamic religion (p=0.001), the residence of the women (p=0.001), and water source were all positively associated with T. gondii infection. The study concluded that there was a high seroprevalence of T. gondii recorded among women of child-bearing age in the study area. Hence, there is a need for health education and create awareness of the disease and its transmission to women of reproductive age group in general and pregnant women in particular to reduce the risk of T. gondii in pregnant women.

Keywords: seroepidemiology, Toxoplasma gondii, women, child-bearing, age, communities, Ile -Ife, Nigeria

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Authors: Kalliopi Kanaki, Michael Kalogiannakis

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Nowadays, in the digital era, the early acquisition of basic programming skills and knowledge is encouraged, as it facilitates students’ exposure to computational thinking and empowers their creativity, problem-solving skills, and cognitive development. More and more researchers and educators investigate the introduction of computational thinking in K-12 since it is expected to be a fundamental skill for everyone by the middle of the 21st century, just like reading, writing and arithmetic are at the moment. In this paper, a doctoral research in the process is presented, which investigates the infusion of computational thinking into science curriculum in early childhood education. The whole attempt aims to develop young children’s computational thinking by introducing them to the fundamental concepts of object-oriented programming in an enjoyable, yet educational framework. The backbone of the research is the digital environment PhysGramming (an abbreviation of Physical Science Programming), which provides children the opportunity to create their own digital games, turning them from passive consumers to active creators of technology. PhysGramming deploys an innovative hybrid schema of visual and text-based programming techniques, with emphasis on object-orientation. Through PhysGramming, young students are familiarized with basic object-oriented programming concepts, such as classes, objects, and attributes, while, at the same time, get a view of object-oriented programming syntax. Nevertheless, the most noteworthy feature of PhysGramming is that children create their own digital games within the context of physical science courses, in a way that provides familiarization with the basic principles of object-oriented programming and computational thinking, even though no specific reference is made to these principles. Attuned to the ethical guidelines of educational research, interventions were conducted in two classes of second grade. The interventions were designed with respect to the thematic units of the curriculum of physical science courses, as a part of the learning activities of the class. PhysGramming was integrated into the classroom, after short introductory sessions. During the interventions, 6-7 years old children worked in pairs on computers and created their own digital games (group games, matching games, and puzzles). The authors participated in these interventions as observers in order to achieve a realistic evaluation of the proposed educational framework concerning its applicability in the classroom and its educational and pedagogical perspectives. To better examine if the objectives of the research are met, the investigation was focused on six criteria; the educational value of PhysGramming, its engaging and enjoyable characteristics, its child-friendliness, its appropriateness for the purpose that is proposed, its ability to monitor the user’s progress and its individualizing features. In this paper, the functionality of PhysGramming and the philosophy of its integration in the classroom are both described in detail. Information about the implemented interventions and the results obtained is also provided. Finally, several limitations of the research conducted that deserve attention are denoted.

Keywords: computational thinking, early childhood education, object-oriented programming, physical science courses

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Authors: Christo Swart, Lidia Pottas, David Maree

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Background: It is a sine qua non that the South African educational system finds itself in a profound crisis and that traditional school leadership styles are outdated and hinder quality education. New thinking is mandatory to improve the status quo and school leadership has an immense role to play to improve the current situation. It is believed that the servant leadership paradigm, when practiced by school leadership, may have a significant influence on the school environment in totality. This study investigates the private school segment in search of constructive answers to assist with the educational crises in South Africa. It is assumed that where school leadership can augment a supportive and empowering environment for teachers to constructively engage in their teaching and learning activities - then many challenges facing by school system may be subjugated in a productive manner. Aim: The aim of this study is fourfold. To outline the constructs of servant leadership which are perceived by teachers of private schools as priorities to enhance a successful school environment. To describe the constructs of organizational climate which are observed by teachers of private schools as priorities to enhance a successful school environment. To investigate whether the participants perceived a link between the constructs of servant leadership and organizational climate. To consider the process to be followed to introduce the constructs of SL and OC the school system in general as perceived by participants. Method: This study utilized a qualitative approach to explore the mediation between school leadership and the organizational climate in private schools in the search for amicable answers. The participants were purposefully selected for the study. Focus group interviews were held with participants from primary and secondary schools and a focus group discussion was conducted with principals of both primary and secondary schools. The interview data were transcribed and analyzed and identical patterns of coded data were grouped together under emerging themes. Findings: It was found that the practice of servant leadership by school leadership indeed mediates a constructive and positive school climate. It was found that the constructs of empowerment, accountability, humility and courage – interlinking with one other - are prominent of servant leadership concepts that are perceived by teachers of private schools as priorities for school leadership to enhance a successful school environment. It was confirmed that the groupings of training and development, communication, trust and work environment are perceived by teachers of private schools as prominent features of organizational climate as practiced by school leadership to augment a successful school environment. It can be concluded that the participants perceived several links between the constructs of servant leadership and organizational climate that encourage a constructive school environment and that there is a definite positive consideration and motivation that the two concepts be introduced to the school system in general. It is recommended that school leadership mentors and guides teachers to take ownership of the constructs of servant leadership as well as organizational climate and that public schools be researched and consider to implement the two paradigms. The study suggests that aspirant teachers be exposed to leadership as well as organizational paradigms during their studies at university.

Keywords: empowering environment for teachers and learners, new thinking required, organizational climate, school leadership, servant leadership

Procedia PDF Downloads 200

Authors: Ursula D. C. Resende, Yan G. Santos, Lucas M. de O. Andrade

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The recent and fast development of the internet, wireless, telecommunication technologies and low-power electronic devices has led to an expressive amount of electromagnetic energy available in the environment and the smart applications technology expansion. These applications have been used in the Internet of Things devices, 4G and 5G solutions. The main feature of this technology is the use of the wireless sensor. Although these sensors are low-power loads, their use imposes huge challenges in terms of an efficient and reliable way for power supply in order to avoid the traditional battery. The radio frequency based energy harvesting technology is especially suitable to wireless power sensors by using a rectenna since it can be completely integrated into the distributed hosting sensors structure, reducing its cost, maintenance and environmental impact. The rectenna is an equipment composed of an antenna and a rectifier circuit. The antenna function is to collect as much radio frequency radiation as possible and transfer it to the rectifier, which is a nonlinear circuit, that converts the very low input radio frequency energy into direct current voltage. In this work, a set of rectennas, mounted on a paper substrate, which can be used for the inner coating of buildings and simultaneously harvest electromagnetic energy from the environment, is proposed. Each proposed individual rectenna is composed of a 2.45 GHz patch antenna and a voltage doubler rectifier circuit, built in the same paper substrate. The antenna contains a rectangular radiator element and a microstrip transmission line that was projected and optimized by using the Computer Simulation Software (CST) in order to obtain values of S11 parameter below -10 dB in 2.45 GHz. In order to increase the amount of harvested power, eight individual rectennas, incorporating metamaterial cells, were connected in parallel forming a system, denominated Electromagnetic Wall (EW). In order to evaluate the EW performance, it was positioned at a variable distance from the internet router, and a 27 kΩ resistive load was fed. The results obtained showed that if more than one rectenna is associated in parallel, enough power level can be achieved in order to feed very low consumption sensors. The 0.12 m2 EW proposed in this work was able to harvest 0.6 mW from the environment. It also observed that the use of metamaterial structures provide an expressive growth in the amount of electromagnetic energy harvested, which was increased from 0. 2mW to 0.6 mW.

Keywords: electromagnetic energy harvesting, metamaterial, rectenna, rectifier circuit

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Authors: Hamad Almebayedh, Chuxia Lin, Yu wang

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The environmental impact assessment (EIA) must be improved, assessed, and quality checked for human and environmental health and safety. Soil contamination is expanding, and sites and soil remediation activities proceeding around the word which simplifies the answer “quality soil characterization” will lead to “quality EIA” to illuminate the contamination level and extent and reveal the unknown for the way forward to remediate, countifying, containing, minimizing and eliminating the environmental damage. Spatial interpolation methods play a significant role in decision making, planning remediation strategies, environmental management, and risk assessment, as it provides essential elements towards site characterization, which need to be informed into the EIA. The Innovative 3D soil mapping and soil characterization technology presented in this research paper reveal the unknown information and the extent of the contaminated soil in specific and enhance soil characterization information in general which will be reflected in improving the information provided in developing the EIA related to specific sites. The foremost aims of this research paper are to present novel 3D mapping technology to quality and cost-effectively characterize and estimate the distribution of key soil characteristics in contaminated sites and develop Innovative process/procedure “assessment measures” for EIA quality and assessment. The contaminated site and field investigation was conducted by innovative 3D mapping technology to characterize the composition of petroleum hydrocarbons contaminated soils in a decommissioned oilfield waste pit in Kuwait. The results show the depth and extent of the contamination, which has been interred into a developed assessment process and procedure for the EIA quality review checklist to enhance the EIA and drive remediation and risk assessment strategies. We have concluded that to minimize the possible adverse environmental impacts on the investigated site in Kuwait, the soil-capping approach may be sufficient and may represent a cost-effective management option as the environmental risk from the contaminated soils is considered to be relatively low. This research paper adopts a multi-method approach involving reviewing the existing literature related to the research area, case studies, and computer simulation.

Keywords: quality EIA, spatial interpolation, soil characterization, contaminated site

Procedia PDF Downloads 69

Authors: Elham Koohi, Silvio Jose Gumiere, Hossein Bonakdari, Saeid Homayouni

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Water used in agricultural crops can be managed by irrigation scheduling based on soil moisture levels and plant water stress thresholds. Automated irrigation scheduling limits crop physiological damage and yield reduction. Knowledge of crop water stress monitoring approaches can be effective in optimizing the use of agricultural water. Understanding the physiological mechanisms of crop responding and adapting to water deficit ensures sustainable agricultural management and food supply. This aim could be achieved by analyzing and diagnosing crop characteristics and their interlinkage with the surrounding environment. Assessments of plant functional types (e.g., leaf area and structure, tree height, rate of evapotranspiration, rate of photosynthesis), controlling changes, and irrigated areas mapping. Calculating thresholds of soil water content parameters, crop water use efficiency, and Nitrogen status make irrigation scheduling decisions more accurate by preventing water limitations between irrigations. Combining Remote Sensing (RS), the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning Algorithms (MLAs) can improve measurement accuracies and automate irrigation scheduling. This paper is a review structured by surveying about 100 recent research studies to analyze varied approaches in terms of providing high spatial and temporal resolution mapping, sensor-based Variable Rate Application (VRA) mapping, the relation between spectral and thermal reflectance and different features of crop and soil. The other objective is to assess RS indices formed by choosing specific reflectance bands and identifying the correct spectral band to optimize classification techniques and analyze Proximal Optical Sensors (POSs) to control changes. The innovation of this paper can be defined as categorizing evaluation methodologies of precision irrigation (applying the right practice, at the right place, at the right time, with the right quantity) controlled by soil moisture levels and sensitiveness of crops to water stress, into pre-processing, processing (retrieval algorithms), and post-processing parts. Then, the main idea of this research is to analyze the error reasons and/or values in employing different approaches in three proposed parts reported by recent studies. Additionally, as an overview conclusion tried to decompose different approaches to optimizing indices, calibration methods for the sensors, thresholding and prediction models prone to errors, and improvements in classification accuracy for mapping changes.

Keywords: agricultural crops, crop water stress detection, irrigation scheduling, precision agriculture, remote sensing

Procedia PDF Downloads 57

Authors: Dian Zhang, Brendan Heery, Maria O’Neill, Ciprian Briciu-Burghina, Noel E. O’Connor, Fiona Regan

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Recent developments in sensors, wireless data communication and the cloud computing have brought the sensor web to a whole new generation. The introduction of the concept of ‘Internet of Thing (IoT)’ has brought the sensor research into a new level, which involves the developing of long lasting, low cost, environment friendly and smart sensors; new wireless data communication technologies; big data analytics algorithms and cloud based solutions that are tailored to large scale smart sensor network. The next generation of smart sensor network consists of several layers: physical layer, where all the smart sensors resident and data pre-processes occur, either on the sensor itself or field gateway; data transmission layer, where data and instructions exchanges happen; the data process layer, where meaningful information is extracted and organized from the pre-process data stream. There are many definitions of smart sensor, however, to summarize all these definitions, a smart sensor must be Intelligent and Adaptable. In future large scale sensor network, collected data are far too large for traditional applications to send, store or process. The sensor unit must be intelligent that pre-processes collected data locally on board (this process may occur on field gateway depends on the sensor network structure). In this case study, three smart sensing methods, corresponding to simple thresholding, statistical model and machine learning based MoPBAS method, are introduced and their strength and weakness are discussed as an introduction to the smart sensing concept. Data fusion, the integration of data and knowledge from multiple sources, are key components of the next generation smart sensor network. For example, in the water level monitoring system, weather forecast can be extracted from external sources and if a heavy rainfall is expected, the server can send instructions to the sensor notes to, for instance, increase the sampling rate or switch on the sleeping mode vice versa. In this paper, we describe the deployment of 11 affordable water level sensors in the Dublin catchment. The objective of this paper is to use the deployed river level sensor network at the Dodder catchment in Dublin, Ireland as a case study to give a vision of the next generation of a smart sensor network for flood monitoring to assist agencies in making decisions about deploying resources in the case of a severe flood event. Some of the deployed sensors are located alongside traditional water level sensors for validation purposes. Using the 11 deployed river level sensors in a network as a case study, a vision of the next generation of smart sensor network is proposed. Each key component of the smart sensor network is discussed, which hopefully inspires the researchers who are working in the sensor research domain.

Keywords: smart sensing, internet of things, water level sensor, flooding

Procedia PDF Downloads 363

Authors: Sourabh Jain, S. S. Jain

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Intelligent transportation system (ITS) is the application of technologies for developing a user–friendly transportation system to extend the safety and efficiency of urban transportation systems in developing countries. These systems involve vehicles, drivers, passengers, road operators, managers of transport services; all interacting with each other and the surroundings to boost the security and capacity of road systems. The goal of urban corridor management using ITS in road transport is to achieve improvements in mobility, safety, and the productivity of the transportation system within the available facilities through the integrated application of advanced monitoring, communications, computer, display, and control process technologies, both in the vehicle and on the road. Intelligent transportation system is a product of the revolution in information and communications technologies that is the hallmark of the digital age. The basic ITS technology is oriented on three main directions: communications, information, integration. Information acquisition (collection), processing, integration, and sorting are the basic activities of ITS. In the paper, attempts have been made to present the endeavor that was made to interpret and evaluate the performance of the 27.4 Km long study corridor having eight intersections and four flyovers. The corridor consisting of six lanes as well as eight lanes divided road network. Two categories of data have been collected such as traffic data (traffic volume, spot speed, delay) and road characteristics data (no. of lanes, lane width, bus stops, mid-block sections, intersections, flyovers). The instruments used for collecting the data were video camera, stop watch, radar gun, and mobile GPS (GPS tracker lite). From the analysis, the performance interpretations incorporated were the identification of peak and off-peak hours, congestion and level of service (LOS) at midblock sections and delay followed by plotting the speed contours. The paper proposed the urban corridor management strategies based on sensors integrated into both vehicles and on the roads that those have to be efficiently executable, cost-effective, and familiar to road users. It will be useful to reduce congestion, fuel consumption, and pollution so as to provide comfort, safety, and efficiency to the users.

Keywords: ITS strategies, congestion, planning, mobility, safety

Procedia PDF Downloads 165

Authors: Angelis P. Barlampas

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Learning Objectives: The purpose of this study is to inform briefly the reader about the applications of AI in chest radiology. Background: Currently, there are 190 FDA-approved radiology AI applications, with 42 (22%) pertaining specifically to thoracic radiology. Imaging findings OR Procedure details Aids of AI in chest radiology1: Detects and segments pulmonary nodules. Subtracts bone to provide an unobstructed view of the underlying lung parenchyma and provides further information on nodule characteristics, such as nodule location, nodule two-dimensional size or three dimensional (3D) volume, change in nodule size over time, attenuation data (i.e., mean, minimum, and/or maximum Hounsfield units [HU]), morphological assessments, or combinations of the above. Reclassifies indeterminate pulmonary nodules into low or high risk with higher accuracy than conventional risk models. Detects pleural effusion . Differentiates tension pneumothorax from nontension pneumothorax. Detects cardiomegaly, calcification, consolidation, mediastinal widening, atelectasis, fibrosis and pneumoperitoneum. Localises automatically vertebrae segments, labels ribs and detects rib fractures. Measures the distance from the tube tip to the carina and localizes both endotracheal tubes and central vascular lines. Detects consolidation and progression of parenchymal diseases such as pulmonary fibrosis or chronic obstructive pulmonary disease (COPD).Can evaluate lobar volumes. Identifies and labels pulmonary bronchi and vasculature and quantifies air-trapping. Offers emphysema evaluation. Provides functional respiratory imaging, whereby high-resolution CT images are post-processed to quantify airflow by lung region and may be used to quantify key biomarkers such as airway resistance, air-trapping, ventilation mapping, lung and lobar volume, and blood vessel and airway volume. Assesses the lung parenchyma by way of density evaluation. Provides percentages of tissues within defined attenuation (HU) ranges besides furnishing automated lung segmentation and lung volume information. Improves image quality for noisy images with built-in denoising function. Detects emphysema, a common condition seen in patients with history of smoking and hyperdense or opacified regions, thereby aiding in the diagnosis of certain pathologies, such as COVID-19 pneumonia. It aids in cardiac segmentation and calcium detection, aorta segmentation and diameter measurements, and vertebral body segmentation and density measurements. Conclusion: The future is yet to come, but AI already is a helpful tool for the daily practice in radiology. It is assumed, that the continuing progression of the computerized systems and the improvements in software algorithms , will redder AI into the second hand of the radiologist.

Keywords: artificial intelligence, chest imaging, nodule detection, automated diagnoses

Procedia PDF Downloads 49

Authors: Elham Bagheri, Yalda Mohsenzadeh

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Image memorability refers to the phenomenon where certain images are more likely to be remembered by humans than others. It is a quantifiable and intrinsic attribute of an image. Understanding how visual perception and memory interact is important in both cognitive science and artificial intelligence. It reveals the complex processes that support human cognition and helps to improve machine learning algorithms by mimicking the brain's efficient data processing and storage mechanisms. To explore the computational underpinnings of image memorability, this study examines the relationship between an image's reconstruction error, distinctiveness in latent space, and its memorability score. A trained autoencoder is used to replicate human-like memorability assessment inspired by the visual memory game employed in memorability estimations. This study leverages a VGG-based autoencoder that is pre-trained on the vast ImageNet dataset, enabling it to recognize patterns and features that are common to a wide and diverse range of images. An empirical analysis is conducted using the MemCat dataset, which includes 10,000 images from five broad categories: animals, sports, food, landscapes, and vehicles, along with their corresponding memorability scores. The memorability score assigned to each image represents the probability of that image being remembered by participants after a single exposure. The autoencoder is finetuned for one epoch with a batch size of one, attempting to create a scenario similar to human memorability experiments where memorability is quantified by the likelihood of an image being remembered after being seen only once. The reconstruction error, which is quantified as the difference between the original and reconstructed images, serves as a measure of how well the autoencoder has learned to represent the data. The reconstruction error of each image, the error reduction, and its distinctiveness in latent space are calculated and correlated with the memorability score. Distinctiveness is measured as the Euclidean distance between each image's latent representation and its nearest neighbor within the autoencoder's latent space. Different structural and perceptual loss functions are considered to quantify the reconstruction error. The results indicate that there is a strong correlation between the reconstruction error and the distinctiveness of images and their memorability scores. This suggests that images with more unique distinct features that challenge the autoencoder's compressive capacities are inherently more memorable. There is also a negative correlation between the reduction in reconstruction error compared to the autoencoder pre-trained on ImageNet, which suggests that highly memorable images are harder to reconstruct, probably due to having features that are more difficult to learn by the autoencoder. These insights suggest a new pathway for evaluating image memorability, which could potentially impact industries reliant on visual content and mark a step forward in merging the fields of artificial intelligence and cognitive science. The current research opens avenues for utilizing neural representations as instruments for understanding and predicting visual memory.

Keywords: autoencoder, computational vision, image memorability, image reconstruction, memory retention, reconstruction error, visual perception

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Authors: Adriana De Serio, Adrian Korek

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Aims: This experiential research work presents a Group-MusicTherapy-Theatre-Plan (MusThePlan) the authors have carried out to support the actors. The MusicTherapy gives rise to individual psychophysical feedback and influences the emotional centres of the brain and the subconsciousness. Therefore, the authors underline the effectiveness of the preventive, educational, and training goals of the MusThePlan to lead theatre students and actors to deal with anxiety and to overcome psychophysical weaknesses, shyness, emotional stress in stage performances, to increase flexibility, awareness of one's identity and resources for a positive self-development and psychophysical health, to develop and strengthen social bonds, increasing a network of subjects working for social inclusion and reduction of stigma. Materials-Methods: Thirty students from the University Theatre Faculty participated in weekly music therapy sessions for two months; each session lasted 120 minutes. MusThePlan: Each session began with a free group rhythmic-sonorous-musical-production by body-percussion, voice-canto, instruments, to stimulate communication. Then, a synchronized-structured bodily-rhythmic-sonorous-musical production also involved acting, dances, movements of hands and arms, hearing, and more sensorial perceptions and speech to balance motor skills and the muscular tone. Each student could be the director-leader of the group indicating a story to inspire the group's musical production. The third step involved the students in rhythmic speech and singing drills and in vocal exercises focusing on the musical pitch to improve the intonation and on the diction to improve the articulation and lead up it to an increased intelligibility. At the end of each musictherapy session and of the two months, the Musictherapy Assessment Document was drawn up by analysis of observation protocols and two Indices by the authors: Patient-Environment-Music-Index (time to - tn) to estimate the behavior evolution, Somatic Pattern Index to monitor subject’s eye and mouth and limb motility, perspiration, before, during and after musictherapy sessions. Results: After the first month, the students (non musicians) learned to play percussion instruments and formed a musical band that played classical/modern music on the percussion instruments with the musictherapist/pianist/conductor in a public concert. At the end of the second month, the students performed a public musical theatre show, acting, dancing, singing, and playing percussion instruments. The students highlighted the importance of the playful aspects of the group musical production in order to achieve emotional contact and harmony within the group. The students said they had improved kinetic and vocal and all the skills useful for acting activity and the nourishment of the bodily and emotional balance. Conclusions: The MusThePlan makes use of some specific MusicTherapy methodological models, techniques, and strategies useful for the actors. The MusThePlan can destroy the individual "mask" and can be useful when the verbal language is unable to undermine the defense mechanisms of the subject. The MusThePlan improves actor’s psychophysical activation, motivation, gratification, knowledge of one's own possibilities, and the quality of life. Therefore, the MusThePlan could be useful to carry out targeted interventions for the actors with characteristics of repeatability, objectivity, and predictability of results. Furthermore, it would be useful to plan a University course/master in “MusicTherapy for the Theatre”.

Keywords: musictherapy, sonorous-musical energy, quality of life, theatre

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Authors: Kevin Baxter, Ron Grove, James Pitney, John Harrison, Ozlem Gumus

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The purpose of this research is to determine the mediating effect of effectiveness of the training provided by Northwestern University’s School of Police Staff and Command (SPSC), on the ability of law enforcement mid-level managers to learn transformational and authentic leadership traits. This study will also evaluate the leadership styles, of course, graduates compared to non-attendees using a static group comparison design. The Louisiana State Police pay approximately $40,000 in salary, tuition, housing, and meals for each state police lieutenant attending the 10-week program of the SPSC. This school lists the development of transformational leaders as an increasing element. Additionally, the SPSC curriculum addresses all four components of authentic leadership - self-awareness, transparency, ethical/moral, and balanced processing. Upon return to law enforcement in roles of mid-level management, there are questions as to whether or not students revert to an “autocratic” leadership style. Insufficient evidence exists to support claims for the effectiveness of management training or leadership development. Though it is widely recognized that transformational styles are beneficial to law enforcement, there is little evidence that suggests police leadership styles are changing. Police organizations continue to hold to a more transactional style (i.e., most senior police leaders remain autocrats). Additionally, research in the application of transformational, transactional, and laissez-faire leadership related to police organizations is minimal. The population of the study is law enforcement mid-level managers from various states within the United States who completed leadership training presented by the SPSC. The sample will be composed of 66 active law enforcement mid-level managers (lieutenants and captains) who have graduated from SPSC and 65 active law enforcement mid-level managers (lieutenants and captains) who have not attended SPSC. Participants will answer demographics questions, Multifactor Leadership Questionnaire, Authentic Leadership Questionnaire, and the Kirkpatrick Hybrid Evaluation Survey. Analysis from descriptive statistics, group comparison, one-way MANCOVA, and the Kirkpatrick Evaluation Model survey will be used to determine training effectiveness in the four levels of reaction, learning, behavior, and results. Independent variables are SPSC graduates (two groups: upper and lower) and no-SPSC attendees, and dependent variables are transformational and authentic leadership scores. SPSC graduates are expected to have higher MLQ scores for transformational leadership traits and higher ALQ scores for authentic leadership traits than SPSC non-attendees. We also expect the graduates to rate the efficacy of SPSC leadership training as high. This study will validate (or invalidate) the benefits, costs, and resources required for leadership development from a nationally recognized police leadership program, and it will also help fill the gap in the literature that exists between law enforcement professional development and transformational and authentic leadership styles.

Keywords: training effectiveness, transformational leadership, authentic leadership, law enforcement mid-level manager

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Authors: Vrinda Hari Ankolekar, Anne D. Souza, Mamatha Hosapatna

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Introduction: Precise anatomical assessment of femoral neck anteversion (FNA) and the neck shaft angles (NSA) would be essential in diagnosing the pathological conditions involving hip joint and its ligaments. FNA of greater than 20 degrees is considered excessive femoral anteversion, whereas a torsion angle of fewer than 10 degrees is considered femoral retroversion. Excessive femoral torsion is not uncommon and has been associated with certain neurologic and orthopedic conditions. The enlargement and maturation of the hip joint increases at the 20th week of gestation and the NSA ranges from 135- 140◦ at birth. Material and methods: 48 femurs were tagged according to the GA and two photographs for each femur were taken using Nikon digital camera. Each femur was kept on a horizontal hard desk and end on an image of the upper end was taken for the estimation of FNA and a photograph in a perpendicular plane was taken to calculate the NSA. The images were transferred to the computer and were stored in TIFF format. Microsoft Paint software was used to mark the points and Image J software was used to calculate the angles digitally. 1. Calculation of FNA: The midpoint of the femoral head and the neck were marked and a line was drawn joining these two points. The angle made by this line with the horizontal plane was measured as FNA. 2. Calculation of NSA: The midpoint of the femoral head and the neck were marked and a line was drawn joining these two points. A vertical line was drawn passing through the tip of the greater trochanter to the inter-condylar notch. The angle formed by these lines was calculated as NSA. Results: The paired t-test for the inter-observer variability showed no significant difference between the values of two observers. (FNA: t=-1.06 and p=0.31; NSA: t=-0.09 and p=0.9). The FNA ranged from 17.08º to 33.97 º on right and 17.32 º to 45.08 º on left. The NSA ranged from 139.33 º to 124.91 º on right and 143.98 º to 123.8 º on left. Unpaired t-test was applied to compare the mean angles between the second and third trimesters which did not show any statistical significance. This shows that the FNA and NSA of femur did not vary significantly during the third trimester. The FNA and NSA were correlated with the GA using Pearson’s correlation. FNA appeared to increase with the GA (r=0.5) but the increase was not statistically significant. A decrease in the NSA was also noted with the GA (r=-0.3) which was also statistically not significant. Conclusion: The present study evaluates the FNA and NSA of the femur in fetuses and correlates their development with the GA during second and third trimesters. The FNA and NSA did not vary significantly during the third trimester.

Keywords: anteversion, coxa antetorsa, femoral torsion, femur neck shaft angle

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Authors: Elham Koohikerade, Silvio Jose Gumiere

Abstract:

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

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

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Authors: Sogand Barghi

Abstract:

The integration of artificial intelligence (AI) in accounting practices is reshaping the landscape of financial management. This paper explores the innovative applications of AI in the realm of accounting, emphasizing its transformative impact on efficiency, accuracy, decision-making, and financial insights. By harnessing AI's capabilities in data analysis, pattern recognition, and automation, accounting professionals can redefine their roles, elevate strategic decision-making, and unlock unparalleled value for businesses. This paper delves into AI-driven solutions such as automated data entry, fraud detection, predictive analytics, and intelligent financial reporting, highlighting their potential to revolutionize the accounting profession. Artificial intelligence has swiftly emerged as a game-changer across industries, and accounting is no exception. This paper seeks to illuminate the profound ways in which AI is reshaping accounting practices, transcending conventional boundaries, and propelling the profession toward a new era of efficiency and insight-driven decision-making. One of the most impactful applications of AI in accounting is automation. Tasks that were once labor-intensive and time-consuming, such as data entry and reconciliation, can now be streamlined through AI-driven algorithms. This not only reduces the risk of errors but also allows accountants to allocate their valuable time to more strategic and analytical tasks. AI's ability to analyze vast amounts of data in real time enables it to detect irregularities and anomalies that might go unnoticed by traditional methods. Fraud detection algorithms can continuously monitor financial transactions, flagging any suspicious patterns and thereby bolstering financial security. AI-driven predictive analytics can forecast future financial trends based on historical data and market variables. This empowers organizations to make informed decisions, optimize resource allocation, and develop proactive strategies that enhance profitability and sustainability. Traditional financial reporting often involves extensive manual effort and data manipulation. With AI, reporting becomes more intelligent and intuitive. Automated report generation not only saves time but also ensures accuracy and consistency in financial statements. While the potential benefits of AI in accounting are undeniable, there are challenges to address. Data privacy and security concerns, the need for continuous learning to keep up with evolving AI technologies, and potential biases within algorithms demand careful attention. The convergence of AI and accounting marks a pivotal juncture in the evolution of financial management. By harnessing the capabilities of AI, accounting professionals can transcend routine tasks, becoming strategic advisors and data-driven decision-makers. The applications discussed in this paper underline the transformative power of AI, setting the stage for an accounting landscape that is smarter, more efficient, and more insightful than ever before. The future of accounting is here, and it's driven by artificial intelligence.

Keywords: artificial intelligence, accounting, automation, predictive analytics, financial reporting

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Authors: Jalil ur Rehman, Ramesh C, Tailor, Isa Khan, Jahanzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott

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The purpose of this analysis is to estimate secondary cancer risks after VMAT compared to other modalities of head and neck radiotherapy (IMRT, 3DCRT). Computer tomography (CT) scans of Radiological Physics Center (RPC) head and neck phantom were acquired with CT scanner and exported via DICOM to the treatment planning system (TPS). Treatment planning was done using four arc (182-178 and 180-184, clockwise and anticlockwise) for volumetric modulated arc therapy (VMAT) , Nine fields (200, 240, 280, 320,0,40,80,120 and 160), which has been commonly used at MD Anderson Cancer Center Houston for intensity modulated radiation therapy (IMRT) and four fields for three dimensional radiation therapy (3DCRT) were used. True beam linear accelerator of 6MV photon energy was used for dose delivery, and dose calculation was done with CC convolution algorithm with prescription dose of 6.6 Gy. Primary Target Volume (PTV) coverage, mean and maximal doses, DVHs and volumes receiving more than 2 Gy and 3.8 Gy of OARs were calculated and compared. Absolute point dose and planar dose were measured with thermoluminescent dosimeters (TLDs) and GafChromic EBT2 film, respectively. Quality Assurance of VMAT and IMRT were performed by using ArcCHECK method with gamma index criteria of 3%/3mm dose difference to distance to agreement (DD/DTA). PTV coverage was found 90.80 %, 95.80 % and 95.82 % for 3DCRT, IMRT and VMAT respectively. VMAT delivered the lowest maximal doses to esophagus (2.3 Gy), brain (4.0 Gy) and thyroid (2.3 Gy) compared to all other studied techniques. In comparison, maximal doses for 3DCRT were found higher than VMAT for all studied OARs. Whereas, IMRT delivered maximal higher doses 26%, 5% and 26% for esophagus, normal brain and thyroid, respectively, compared to VMAT. It was noted that esophagus volume receiving more than 2 Gy was 3.6 % for VMAT, 23.6 % for IMRT and up to 100 % for 3DCRT. Good agreement was observed between measured doses and those calculated with TPS. The averages relative standard errors (RSE) of three deliveries within eight TLD capsule locations were, 0.9%, 0.8% and 0.6% for 3DCRT, IMRT and VMAT, respectively. The gamma analysis for all plans met the ±5%/3 mm criteria (over 90% passed) and results of QA were greater than 98%. The calculations for maximal doses and volumes of OARs suggest that the estimated risk of secondary cancer induction after VMAT is considerably lower than IMRT and 3DCRT.

Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD

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Authors: Ana Handel, Jamal Ellerbe, Sarah Kanzaki, Natalie White, Nathan Ousey, Sean Gallagher

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A sense of belonging is the ability for individuals to feel they are a necessary part of whatever organization or community they find themselves in. In an academic setting, a sense of belonging is key to a student’s success. The collected research points to this sense of belonging in academic settings as a significant contributor of students’ levels of engagement and trust. When universities leverage the arts, students are provided with more opportunities to engage and feel confident in their surroundings. This allows for greater potential to develop within academic and social settings. The arts also call for the promotion of diversity, equity, and inclusion by showcasing works of artists from all different backgrounds, thus allowing students to gain cultural knowledge and be able to embrace differences. Equity, diversity, and inclusion are all emotional facets of belonging. Equity relates to the concept of making the conscious choice to recognize opportunities to incorporate inclusive and diverse ideals into different thought processes and collaboration. Inclusion involves providing equal access to opportunities and resources for people of all ‘ingroups. In an inclusive culture, individuals are able to maximize their potential with the confidence they have gained through an accepting environment. A variety of members in academic communities have noted it may be beneficial to make certain events surrounding the arts to be built into course requirements in order to ensure students are expanding their horizons and exposing themselves to the arts. These academics also recommend incorporating the arts into extracurricular activities, such as Greek life, in order to appeal to large groups of students. Once students have an understanding of the rich knowledge cultivated through exploring the arts, they will feel more comfortable in their surroundings and thus more confident to become involved in other areas of their university. A number of universities, including West Chester and Carnegie Mellon, have instituted programs aiming to provide students with the necessary tools and resources to feel comfortable in their educational settings. Different programs include references to hotlines for discrimination and office for diversity, equity, and inclusion. Staff members have also been provided with means of combating biases and increasing feelings of belongingness in order to properly support and communicate with students. These tools have successfully allowed universities to foster inviting environments for students of all backgrounds to feel belong as well as strengthening the community’s diversity, equity, and inclusion. Through demonstrating concepts of diversity, equity, and inclusion by introducing the arts into learning spaces, students can find a sense of belonging within their academic environments. It is essential to understand these topics and how they work together to achieve a common goal. The efforts of universities have made much progress in shedding light on different cultures and ideas to show students their full potential and opportunities. Once students feel more comfortable within their organizations, engagement will increase substantially.

Keywords: arts, belonging, engagement, inclusion

Procedia PDF Downloads 154