Search results for: learning methods

Authors: Vedant Janapaty

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Estuaries are wetlands where fresh water from streams mixes with salt water from the sea. Also known as “kidneys of our planet”- they are extremely productive environments that filter pollutants, absorb floods from sea level rise, and shelter a unique ecosystem. However, eutrophication and loss of native species are ailing our wetlands. There is a lack of uniform data collection and sparse research on correlations between satellite data and in situ measurements. Remote sensing (RS) has shown great promise in environmental monitoring. This project attempts to use satellite data and correlate metrics with in situ observations collected at five estuaries. Images for satellite data were processed to calculate 7 bands (SIs) using Python. Average SI values were calculated per month for 23 years. Publicly available data from 6 sites at ELK was used to obtain 10 parameters (OPs). Average OP values were calculated per month for 23 years. Linear correlations between the 7 SIs and 10 OPs were made and found to be inadequate (correlation = 1 to 64%). Fourier transform analysis on 7 SIs was performed. Dominant frequencies and amplitudes were extracted for 7 SIs, and a machine learning(ML) model was trained, validated, and tested for 10 OPs. Better correlations were observed between SIs and OPs, with certain time delays (0, 3, 4, 6 month delay), and ML was again performed. The OPs saw improved R² values in the range of 0.2 to 0.93. This approach can be used to get periodic analyses of overall wetland health with satellite indices. It proves that remote sensing can be used to develop correlations with critical parameters that measure eutrophication in situ data and can be used by practitioners to easily monitor wetland health.

Keywords: estuary, remote sensing, machine learning, Fourier transform

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Authors: Mohammadali Tavakoli, Atefeh Heydari, Zuriati Ismail, Naomie Salim

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With the increasing number of people reviewing products online in recent years, opinion sharing websites has become the most important source of customers’ opinions. Unfortunately, spammers generate and post fake reviews in order to promote or demote brands and mislead potential customers. These are notably destructive not only for potential customers but also for business holders and manufacturers. However, research in this area is not adequate, and many critical problems related to spam detection have not been solved to date. To provide green researchers in the domain with a great aid, in this paper, we have attempted to create a high-quality framework to make a clear vision on review spam-detection methods. In addition, this report contains a comprehensive collection of detection metrics used in proposed spam-detection approaches. These metrics are extremely applicable for developing novel detection methods.

Keywords: fake reviews, feature collection, opinion spam, spam detection

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Authors: Amanda Kavner, Richard Lamb

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Faster growth in science and technology of other nations may make staying globally competitive more difficult without shifting focus on how science is taught in US classes. An integral part of learning science involves visual and spatial thinking since complex, and real-world phenomena are often expressed in visual, symbolic, and concrete modes. The primary barrier to spatial thinking and visual literacy in Science, Technology, Engineering, and Math (STEM) fields is representational competence, which includes the ability to generate, transform, analyze and explain representations, as opposed to generic spatial ability. Although the relationship is known between the foundational visual literacy and the domain-specific science literacy, science literacy as a function of science learning is still not well understood. Moreover, the need for a more reliable measure is necessary to design resources which enhance the fundamental visuospatial cognitive processes behind scientific literacy. To support the improvement of students’ representational competence, first visualization skills necessary to process these science representations needed to be identified, which necessitates the development of an instrument to quantitatively measure visual literacy. With such a measure, schools, teachers, and curriculum designers can target the individual skills necessary to improve students’ visual literacy, thereby increasing science achievement. This project details the development of an artificial neural network capable of measuring science literacy using functional Near-Infrared Spectroscopy (fNIR) data. This data was previously collected by Project LENS standing for Leveraging Expertise in Neurotechnologies, a Science of Learning Collaborative Network (SL-CN) of scholars of STEM Education from three US universities (NSF award 1540888), utilizing mental rotation tasks, to assess student visual literacy. Hemodynamic response data from fNIRsoft was exported as an Excel file, with 80 of both 2D Wedge and Dash models (dash) and 3D Stick and Ball models (BL). Complexity data were in an Excel workbook separated by the participant (ID), containing information for both types of tasks. After changing strings to numbers for analysis, spreadsheets with measurement data and complexity data were uploaded to RapidMiner’s TurboPrep and merged. Using RapidMiner Studio, a Gradient Boosted Trees artificial neural network (ANN) consisting of 140 trees with a maximum depth of 7 branches was developed, and 99.7% of the ANN predictions are accurate. The ANN determined the biggest predictors to a successful mental rotation are the individual problem number, the response time and fNIR optode #16, located along the right prefrontal cortex important in processing visuospatial working memory and episodic memory retrieval; both vital for science literacy. With an unbiased measurement of science literacy provided by psychophysiological measurements with an ANN for analysis, educators and curriculum designers will be able to create targeted classroom resources to help improve student visuospatial literacy, therefore improving science literacy.

Keywords: artificial intelligence, artificial neural network, machine learning, science literacy, neuroscience

Procedia PDF Downloads 115

Authors: Bouchenak Fatima, Lmegharbi Abdelbaki, Houssem Degaichia, Benrebiha Fatima

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The essential oil composition of the leaf of Artemisia absinthium from region of Cherchell (The south of Algeria) was investigated by GC, GC-MS. 27 constituents were identified correspond to 84, 63% of the total oil. The major components are Thujone (60, 82%), Chamazulènel (16, 62%), ρ-cymène (4, 29%) and 2-carène (4.25%). The antimicrobial activity of oil was tested in vitro by two methods (agar diffusion and microdilution) on three plant pathogenic fungi. This oil has been tested for antimicrobial activity against three pathogenic fungi (Botrytis cinerea, Fusarium culmorum and Helminthosporium Sp.).The study of activity was evaluated by two methods: Method of diffusion in gelose and the minimum inhibitory concentration MIC. This oil exhibited an interesting antimicrobial activity. A preliminary study showed that this oil presented high toxicity against this fungus. These results, although preliminary show a good antifungal activity, to limit and inhibit stop the development of those pathogen agent.

Keywords: artemisia absinthian, extraction process, chemical study, antifungal activity

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Authors: Chandan Deep Singh

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In present days, companies are facing the rapid competition in terms of customer requirements to be satisfied, new technologies to be integrated into future products, new safety regulations to be followed, new computer-based tools to be introduced into design activities that becomes more scientific. In today’s highly competitive market, survival focuses on various factors such as quality, innovation, adherence to standards, and rapid response as the basis for competitive advantage. For competitive advantage, companies have to produce various competencies: for improving the capability of suppliers and for strengthening the process of integrating technology. For more competitiveness, organizations should operate in a strategy driven way and have a strategic architecture for developing core competencies. Traditional ways to take such experience and develop competencies tend to take a lot of time and they are expensive. A new learning environment, which is built around a gaming engine, supports the development of competences in specific subject areas. Technology competencies have a significant role in firm innovation and competitiveness; they interact with the competitive environment. Technological competencies vary according to the type of competitive environment, thus enhancing firm innovativeness. Technological competency is gained through extensive experimentation and learning in its research, development and employment in manufacturing. This is a review paper based on competency and strategic success of automobile industry. The aim here is to study strategy formulation and competency tools in the industry. This work is a review of literature related to competency and strategy in automobile industry. This study involves review of 34 papers related to competency and strategy.

Keywords: manufacturing competency, strategic success, competitiveness, strategy formulation

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Authors: Michał Glet, Kamil Kaczyński

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Counterfeit goods and documents are a global problem, which needs more and more sophisticated methods of resolving it. Existing techniques using watermarking or embedding symbols on objects are not suitable for all use cases. To address those special needs, we created complete system allowing authentication of paper documents and physical objects with flat surface. Objects are marked using orientation independent and resistant to camera noise 2D graphic codes, named DotAuth. Based on the identifier stored in 2D code, the system is able to perform basic authentication and allows to conduct more sophisticated analysis methods, e.g., relying on augmented reality and physical properties of the object. In this paper, we present the complete architecture, algorithms and applications of the proposed system. Results of the features comparison of the proposed solution and other products are presented as well, pointing to the existence of many advantages that increase usability and efficiency in the means of protecting physical objects.

Keywords: anti-forgery, authentication, paper documents, security

Procedia PDF Downloads 128

Authors: Madhuri S. Ghate, Rahul P. Chavhan, Shriya S. Nahar

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Learning objective: •To describe Magnetic Resonance Imaging (MRI) imaging appearances of typical and atypical uterine fibroids with emphasis on differentiating it from other similar conditions. •To classify uterine fibroids according to International Federation of Gynecology and Obstetrics (FIGO) Sub-classifications system and emphasis on its clinical significance. •To show cases with atypical imaging appearances atypical fibroids Material and methods: MRI of Pelvis had been performed in symptomatic women of child bearing age group on 1.5T and 3T MRI using T1, T2, STIR, FAT SAT, DWI sequences. Contrast was administered when degeneration was suspected. Imaging appearances of Atypical fibroids and various degenerations in fibroids were studied. Fibroids were classified using FIGO Sub-classification system. Its impact on surgical decision making and clinical outcome were also studied qualitatively. Results: Intramural fibroids were most common (14 patients), subserosal 7 patients, submucosal 5 patients . 6 patients were having multiple fibroids. 7 were having atypical fibroids. (1 hyaline degeneration, 1 cystic degeneration, 1 fatty, 1 necrosis and hemorrhage, 1 red degeneration, 1 calcification, 1 unusual large bilobed growth). Fibroids were classified using FIGO system. In uterus conservative surgeries, the lesser was the degree of myometrial invasion of fibroid, better was the fertility outcome. Conclusion: Relationship of fibroid with mucosal and serosal layers is important in the management of symptomatic fibroid cases. Risk to fertility involved in uterus conservative surgeries in women of child bearing age group depends on the extent of myometrial invasion of fibroids. FIGO system provides better insight into the degree of myometrial invasion. Knowledge about the atypical appearances of fibroids is important to avoid diagnostic confusion and untoward treatment.

Keywords: degeneration, FIGO sub-classification, MRI pelvis, uterine fibroids

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Authors: John Gaber

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Objectives: Complementary and alternative medicine (CAM) is a medicine or health practice that is used alongside conventional practice. Nowadays, CAM is commonly used in North America and other countries, and there is a need for more scientific study to understand its efficacy in different clinical cases. This retrospective study explores the effectiveness and recovery time of CAMs such as cupping, acupuncture, and sotai to treat cases of non-specific low back pain (ANLBP). Methods: We assessed the effectiveness of acupuncture, cupping, and sotai methods on pain and for the treatment of ANLBP. We have compared the magnitude of pain relief using a pain scale assessment method to compare the efficacy of each treatment. The Face Pain Scale assessment was conducted before and 24 hours post-treatment. This retrospective study analyzed 40 patients and categorized them according to the treatment they received. The study included the control group, and the three intervention groups, each with ten patients. Each of the three intervention groups received one of the intervention methods. The first group received the cupping treatment, where cups were placed on the lower back of both sides on points: BL23, BL25, BL26, BL54, BL37, BL40, and BL57. After vacuuming, the cups will stay for 10-15 minutes under infrared light (IR) heating. IR heating is applied by an infrared heat lamp. The second group received the acupuncture treatment, placing needles on points: BL23, BL25, BL26, BL52BL54, GB30, BL37, BL40, BL57, BL59, BL60, and KI3. The needles will be simulated with IR light. The final group received the sotai treatment, a Japanese form of structural realignment that relieves pain, balance, and mobility -moving the body naturally and spontaneously towards a comfortable direction by focusing on the inner feeling and synchronizing with the patient’s breathing. The SPSS statistical software was used to analyze the data using repeated-measures ANOVA. The data collected demonstrates the change in the FPS assessment method value over the course of treatment. p<0.05 was considered statistically significant. Results: In the cupping, acupuncture, and sotai therapy groups, the mean of the FPS value reduced from 8.7±1.2, 8.8±1.2, 9.0±0.8 before the intervention to 3.5±1.4, 4.3±1.4, 3.3±1.3, 24 hours after the intervention, respectively. The data collected shows that the CAM methods included in this study all show improvements in pain relief 24 hours after treatment. Conclusion: Complementary and alternative medicine were developed to treat injuries and illnesses with the whole body in mind, designed to be used in addition to standard treatments. The data above shows that the use of these treatments can have a pain-relieving effect, but more research should be done on the matter, as finding CAM methods that are efficacious is crucial in the landscape of health sciences.

Keywords: acupuncture, cupping, alternative medicine, rehabilitation, acute injury

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Authors: Lourenço José, Elsa Melo, Sandra Viera, Ana Pinheiro

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Introduction: People with stroke in Angola face challenges in accessing appropriate healthcare and rehabilitation services. There is a lack of information on the quality of care provided and the development of early intervention plans Methods: Two different methods will be chosen. The exploratory, descriptive, and longitudinal study (E1) to characterize health care for people with stroke, housed in 2 hospitals in Luanda; the quality and transverse study (E2) concerning the development and evaluation of a strategic early intervention plan for a stroke patient. Ethical and deontological principles for an investigation will be proposed. Results: Contributor to the knowledge of the reality of providing care to the person after a stroke, in Angola; Propose and develop an early action plan. Contribute to integration to influence policy makers on the need for assistance with stroke, aiming at their functional, family and social rehabilitation, particularly in the labor market.

Keywords: stroke, functional recovery, quality of life, health

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Authors: Chen Zhang; Qiang Wang

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With the rapid development of the new generation of digital technology, various industries have created more and more value by using digital technology, accelerating the digital transformation of various industries. The economic form of human society has evolved with the progress of technology, and in this context, the power conversion, transformation and upgrading of the manufacturing industry in terms of quality, efficiency and energy change has become a top priority. Based on the digitalization background, this paper analyzes the transformation and upgrading efficiency of the manufacturing industry and evaluates the impact of the driving factors, which have very important theoretical and practical significance. This paper utilizes qualitative research methods, entropy methods, data envelopment analysis methods and econometric models to explore the transformation and upgrading efficiency of manufacturing enterprises and driving factors. The study shows that the transformation and upgrading efficiency of the manufacturing industry shows a steady increase, and regions rich in natural resources and social resources provide certain resources for transformation and upgrading. The ability of scientific and technological innovation has been improved, but there is still much room for progress in the transformation of scientific and technological innovation achievements. Most manufacturing industries pay more attention to green manufacturing and sustainable development. In addition, based on the existing problems, this paper puts forward suggestions for improving infrastructure construction, developing the technological innovation capacity of enterprises, green production and sustainable development.

Keywords: digitization, manufacturing firms, transformation and upgrading, efficiency, driving factors

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Authors: Trung Le Thanh

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Soil permeability coefficient is an important parameter that affects the effectiveness of mortar restoration work to reinforce soft soil. Currently, there are many methods to determine the permeability coefficient of ground through laboratory and field experiments. However, the value of the permeability coefficient is determined very differently depending on the geology in general and the sand base in particular. This article presents how to determine the permeability coefficient of sand foundation in Phu My Ward, Tan Uyen City, Binh Duong. The author analyzes and evaluates the advantages and disadvantages of assessment methods based on the data and results obtained, and on that basis recommends a suitable method for determining the permeability coefficient for sand foundations. The research results serve the evaluation of the effectiveness of grouting to reinforce soft ground in general, and grouting of bored piles in particular.

Keywords: permeability coefficient, soft soil, shaft grouting, post grouting, jet grouting

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Authors: Prashansa Singh, Rohit Bajaj, Manjot Kaur

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In the modern agricultural landscape, precision farming has emerged as a pivotal strategy for enhancing crop yield and optimizing resource utilization. This paper introduces an innovative Crop Recommendation System (CRS) that leverages the Internet of Things (IoT) technology and the Message Queuing Telemetry Transport (MQTT) protocol to collect critical environmental and soil data via sensors deployed across agricultural fields. The system is designed to address the challenges of real-time data acquisition, efficient data transmission, and dynamic crop recommendation through the application of advanced Artificial Intelligence (AI) and Machine Learning (ML) models. The CRS architecture encompasses a network of sensors that continuously monitor environmental parameters such as temperature, humidity, soil moisture, and nutrient levels. This sensor data is then transmitted to a central MQTT server, ensuring reliable and low-latency communication even in bandwidth-constrained scenarios typical of rural agricultural settings. Upon reaching the server, the data is processed and analyzed by AI/ML models trained to correlate specific environmental conditions with optimal crop choices and cultivation practices. These models consider historical crop performance data, current agricultural research, and real-time field conditions to generate tailored crop recommendations. This implementation gets 99% accuracy.

Keywords: Iot, MQTT protocol, machine learning, sensor, publish, subscriber, agriculture, humidity

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Authors: Ma Hongqiang, Yang Hui, Wen Haoju, Feng Jiabo, Bi Zhixian, Nie Ying

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In order to measure thrust and torque of airship propeller, a two-component balance and data acquisition system was developed in China Academy of Aerospace Aerodynamics(CAAA) in early time. During the development, some problems were encountered. At first, the measuring system and its protective parts made the weight of whole system increase significantly. Secondly, more parts might induce more failures, so the reliability of the system was decreased. In addition, the rigidity of the system was lowered, and the structure was more possible to vibrate. Therefore, CAAA and the Academy of Opto-Electronics, Chinese Academy of Science(AOECAS) developed a new technology, use the propeller supporting rack as a spring element, attach strain gages onto it, sum up as a generalized balance. And new math models, new calibration methods and new load determining methods were developed.

Keywords: airship, propeller, thrust and torque, flight test

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Authors: M. M. Sadeghi, S. Vahdani

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Masonry dome structures had been widely used for covering large spans in the past. The seismic assessment of these historical structures is very complicated due to the nonlinear behavior of the material, their rigidness, and special stability configuration. The assessment method based on energy balance concept, as well as the standard pushover analysis, is used to evaluate the effectiveness of these methods in the case of masonry dome structures. The Soltanieh dome building is used as an example to which two methods are applied. The performance points are given from superimposing the capacity, and demand curves in Acceleration Displacement Response Spectra (ADRS) and energy coordination are compared with the nonlinear time history analysis as the exact result. The results show a good agreement between the dynamic analysis and the energy balance method, but standard pushover method does not provide an acceptable estimation.

Keywords: energy balance method, pushover analysis, time history analysis, masonry dome

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Authors: Tarun Chand, Michael Jurczyk

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With the increasing need and popularity of IoT devices and how integrated they are becoming in our daily lives and industries, these devices make for a very lucrative target for malicious actors. And since these devices have such limited resources, the implementation of robust security features is a tradeoff to be made for the actual functionality the device was intended for. This makes them an easy target with high returns. Several frameworks for the secure firmware update of these devices have been recently proposed in the literature. They focus on methods such as blockchains and distributed file systems to secure firmware updates, but do not go into the details of the actual implementation of these frameworks and the lower-level interactions among these methods used. This work integrates some of these security measures into one overall framework and details the actual lower-level implementation of this framework in a virtual dockerized testbed running on AWS.

Keywords: blockchain, Ethereum, Geth, IPFS, secure IoT-firmware update, virtual testbed development

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Authors: Khaled Elbehiery, Hussam Elbehiery

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The success of education is dependent on evolution and adaptation, while the traditional system has worked before, one type of education evolved with the digital age is virtual education that has influenced efficiency in today’s learning environments. Virtual learning has indeed proved its efficiency to overcome the drawbacks of the physical environment such as time, facilities, location, etc., but despite what it had accomplished, the educational system over all is not adequate for being a productive system yet. Earning a degree is not anymore enough to obtain a career job; it is simply missing the skills and creativity. There are always two sides of a coin; a college degree or a specialized certificate, each has its own merits, but having both can put you on a successful IT career path. For many of job-seeking individuals across world to have a clear meaningful goal for work and education and positively contribute the community, a productive correlation and cooperation among employers, universities alongside with the individual technical skills is a must for generations to come. Fortunately, the proposed research “Entrepreneur Universal Education System” is an evolution to meet the needs of both employers and students, in addition to gaining vital and real-world experience in the chosen fields is easier than ever. The new vision is to empower the education to improve organizations’ needs which means improving the world as its primary goal, adopting universal skills of effective thinking, effective action, effective relationships, preparing the students through real-world accomplishment and encouraging them to better serve their organization and their communities faster and more efficiently.

Keywords: virtual education, academic degree, certificates, internship, amazon web services, Microsoft Azure, Google Cloud Platform, hybrid models

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Authors: Vildan Kistik, Tuncay Can

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From a business perspective, cost and profit are two key factors for businesses. The intent of most businesses is to minimize the cost to maximize or equalize the profit, so as to provide the greatest benefit to itself. However, the physical system is very complicated because of technological constructions, rapid increase of competitive environments and similar factors. In such a system it is not easy to maximize profits or to minimize costs. Businesses must decide on the competence and competence of the personnel to be recruited, taking into consideration many criteria in selecting personnel. There are many criteria to determine the competence and competence of a staff member. Factors such as the level of education, experience, psychological and sociological position, and human relationships that exist in the field are just some of the important factors in selecting a staff for a firm. Personnel selection is a very important and costly process in terms of businesses in today's competitive market. Although there are many mathematical methods developed for the selection of personnel, unfortunately the use of these mathematical methods is rarely encountered in real life. In this study, unlike other methods, an exponential programming model was established based on the possibilities of failing in case the selected personnel was started to work. With the necessary transformations, the problem has been transformed into unconstrained Geometrical Programming problem and personnel selection problem is approached with geometric programming technique. Personnel selection scenarios for a classroom were established with the help of normal distribution and optimum solutions were obtained. In the most appropriate solutions, the personnel selection process for the classroom has been achieved with minimum cost.

Keywords: geometric programming, personnel selection, non-linear programming, operations research

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Authors: Ekin Nurbaş

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One of the essential challenges in array signal processing, which has drawn enormous research interest over the past several decades, is estimating the direction of arrival (DOA) of plane waves impinging on an array of sensors. In recent years, the Compressive Sensing based DoA estimation methods have been proposed by researchers, and it has been discovered that the Compressive Sensing (CS)-based algorithms achieved significant performances for DoA estimation even in scenarios where there are multiple coherent sources. On the other hand, the Genetic Algorithm, which is a method that provides a solution strategy inspired by natural selection, has been used in sparse representation problems in recent years and provides significant improvements in performance. With all of those in consideration, in this paper, a method that combines the Genetic Algorithm (GA) and the Multi-Criteria Decision Making (MCDM) approaches for Direction of Arrival (DoA) estimation in the Compressive Sensing (CS) framework is proposed. In this method, we generate a multi-objective optimization problem by splitting the norm minimization and reconstruction loss minimization parts of the Compressive Sensing algorithm. With the help of the Genetic Algorithm, multiple non-dominated solutions are achieved for the defined multi-objective optimization problem. Among the pareto-frontier solutions, the final solution is obtained with the multiple MCDM methods. Moreover, the performance of the proposed method is compared with the CS-based methods in the literature.

Keywords: genetic algorithm, direction of arrival esitmation, multi criteria decision making, compressive sensing

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Authors: Antti Lanki, Justiina Halonen, Juuso Punnonen, Emmi Rantavuo

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Marine oil spill response operation requires extensive vessel maneuvering and navigation skills. At-sea oil containment and recovery include both single vessel and multi-vessel operations. Towing long oil containment booms that are several hundreds of meters in length, is a challenge in itself. Boom deployment and towing in multi-vessel configurations is an added challenge that requires precise coordination and control of the vessels. Efficient communication, as a prerequisite for shared situational awareness, is needed in order to execute the response task effectively. To gain and maintain adequate maritime skills, practical training is needed. Field exercises are the most effective way of learning, but especially the related vessel operations are resource-intensive and costly. Field exercises may also be affected by environmental limitations such as high sea-state or other adverse weather conditions. In Finland, the seasonal ice-coverage also limits the training period to summer seasons only. In addition, environmental sensitiveness of the sea area restricts the use of real oil or other target substances. This paper examines, whether maritime simulator training can offer a complementary method to overcome the training challenges related to field exercises. The objective is to assess the efficiency and the learning impact of simulator training, and the specific skills that can be trained most effectively in simulators. This paper provides an overview of learning results from two oil spill response pilot courses, in which maritime navigational bridge simulators were used to train the oil spill response authorities. The simulators were equipped with an oil spill functionality module. The courses were targeted at coastal Fire and Rescue Services responsible for near shore oil spill response in Finland. The competence levels of the participants were surveyed before and after the course in order to measure potential shifts in competencies due to the simulator training. In addition to the quantitative analysis, the efficiency of the simulator training is evaluated qualitatively through feedback from the participants. The results indicate that simulator training is a valid and effective method for developing marine oil spill response competencies that complement traditional field exercises. Simulator training provides a safe environment for assessing various oil containment and recovery tactics. One of the main benefits of the simulator training was found to be the immediate feedback the spill modelling software provides on the oil spill behaviour as a reaction to response measures.

Keywords: maritime training, oil spill response, simulation, vessel manoeuvring

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Katerina Florou

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Two decades after coined the term "learner corpora" as collections of texts created by foreign or second language learners across various language contexts, and some years following suggestion to incorporate "focusing on form" within a Task-Based Learning framework, this study aims to explore how learner corpora, whether annotated with errors or not, can facilitate a focus on form in an educational setting. Argues that analyzing linguistic form serves the purpose of enabling students to delve into language and gain an understanding of different facets of the foreign language. This same objective is applicable when analyzing learner corpora marked with errors or in their raw state, but in this scenario, the emphasis lies on identifying incorrect forms. Teachers should aim to address errors or gaps in the students' second language knowledge while they engage in a task. Building on this recommendation, we compared the written output of two student groups: the first group (G1) employed the focusing on form phase by studying a specific aspect of the Italian language, namely the past participle, through examples from native speakers and grammar rules; the second group (G2) focused on form by scrutinizing their own errors and comparing them with analogous examples from a native speaker corpus. In order to test our hypothesis, we created four learner corpora. The initial two were generated during the task phase, with one representing each group of students, while the remaining two were produced as a follow-up activity at the end of the lesson. The results of the first comparison indicated that students' exposure to their own errors can enhance their grasp of a grammatical element. The study is in its second stage and more results are to be announced.

Keywords: Corpus interlanguage analysis, task based learning, Italian language as F1, learner corpora

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Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

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In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

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Authors: Miracle Ajah

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One of the biggest challenges facing Theological/Religious Education in Nigeria is access to quality learning materials. For instance at the Trinity (Union) Theological College, Umuahia, it was difficult for lecturers to access suitable and qualitative materials for instruction especially the ones that would suit the African context and stimulate a deep rooted interest among the students. Some textbooks written by foreign authors were readily available in the School Library, but were lacking in the College bookshops for students to own copies. Even when the College was able to order some of the books from abroad, it did not usher in the needed enthusiasm expected from the students because they were either very expensive or very difficult to understand during private studies. So it became necessary to develop contextual materials which were affordable and understandable, though with little success. The National Open University of Nigeria (NOUN)’s innovation in the development and sharing of learning resources through its Open Course ware is a welcome development and of great assistance to students. Apart from NOUN students who could easily access the materials, many others from various theological/religious institutes across the nation have benefited immensely. So, the thesis of this paper is that the promotion of open educational resources in theological/religious education in Nigeria would facilitate a better informed/equipped religious leadership, which would in turn impact its adherents for a healthier society and national development. Adopting a narrative and historical approach within the context of Nigeria’s educational system, the paper discusses: educational traditions in Nigeria; challenges facing theological/religious education in Nigeria; and benefits of open educational resources. The study goes further to making recommendations on how OER could positively influence theological/religious education in Nigeria. It is expected that theologians, religious educators, and ODL practitioners would find this work very useful.

Keywords: OER, theological education, religious education, Nigeria

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Authors: Shafait Hussain Ali

Abstract:

Since its outbreak in China, the Covid_19 19 disease has been caused by the corona virus SARS N coyote 2. Italy was the first Western country to be severely affected, and the first country to take drastic measures to control the disease. In start of December 2019, the sudden outbreaks of the Coronary Virus Disease was caused by a new Corona 2 virus (SARS-CO2) of acute respiratory syndrome in china city Wuhan. The World Health Organization declared the epidemic a public health emergency of international concern on January 30, 2020,. On February 14, 2020, 49,053 laboratory-confirmed deaths and 1481 deaths have been reported worldwide. The threat of the disease has forced most of the governments to implement various control measures. Therefore it becomes necessary to analyze the Italian data very carefully, in particular to investigates and to find out the present condition and the number of infected persons in the form of positive cases, death, hospitalized or some other features of infected persons will clear in simple form. So used such a model that will clearly shows the real facts and figures and also understandable to every readable person which can get some real benefit after reading it. The model used must includes(total positive cases, current positive cases, hospitalized patients, death, recovered peoples frequency rates ) all features that explains and clear the wide range facts in very simple form and helpful to administration of that country.

Keywords: machine learning tools and techniques, rapid miner tool, Naive-Bayes algorithm, predictions

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Authors: Abdulmjeid Aljerawi

Abstract:

This study aimed to investigate the students' attitudes towards the use of social networking in education. Due to the nature of the study, and on the basis of its problem, objectives, and questions, the researcher used the descriptive approach. An appropriate questionnaire was prepared and validity and reliability were ensured. The questionnaire was then applied to the study sample of 434 students from King Saud University.

Keywords: social networks, education, learning, students

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Authors: Riin Seema

Abstract:

Guidance and counseling as a whole-school responsibility is a global trend. Counseling is a specific competence, that consist of cognitive, emotional, attitudinal, and behavioral components. To authors best knowledge, there are no self-assessment scales for teachers in the whole world to measure teachers’ counseling competency. In 2016 an Estonian scale on teachers counseling competence was developed during an Interdisciplinary Project at Tallinn University. The team consisted of 10 interdisciplinary students (psychology, nursery school, special and adult education) and their supervisor. In 2017 another international Interdisciplinary Project was carried out for adapting the scale in English for international students. Firstly, the Estonian scale was translated by 2 professional translators, and then a group of international Erasmus students (again from psychology, nursery school, special and adult education) selected the most suitable translation for the scale. The developed ‘Teacher’s Counselling Competence Self-Efficacy Scale’ measures teacher’s self-efficacy beliefs in their own competence to perform different counseling tasks (creating a counseling relationship, using different reflection techniques, etc.). The scale consists of 47 questions in a 5-point numeric scale. The scale is created based on counseling theory and scale development and validation theory. The scale has been used as a teaching and learning material for counseling courses by 174 Estonian and 10 international student teachers. After filling out the scale, the students also reflected on the scale and their own counseling competencies. The study showed that the scale is unidimensional and has an excellent Cronbach alpha coefficient. Student’s qualitative feedback on the scale has been very positive, as the scale supports their self-reflection. In conclusion, the developed ‘Teacher’s Counselling Competence Self-Efficacy Scale’ is a useful tool for supporting student teachers’ learning.

Keywords: competency, counseling, self-efficacy, teacher students

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Authors: Gaurav Kumar Sinha

Abstract:

In an era marked by increasing concerns about energy sustainability, this research endeavors to address the pressing challenge of energy consumption in buildings and industries. This study delves into the transformative potential of AWS services in optimizing energy efficiency. The research is founded on the recognition that effective management of energy consumption is imperative for both environmental conservation and economic viability. Buildings and industries account for a substantial portion of global energy use, making it crucial to develop advanced techniques for analysis and reduction. This study sets out to explore the integration of AWS services with big data analytics to provide innovative solutions for energy consumption analysis. Leveraging AWS's cloud computing capabilities, scalable infrastructure, and data analytics tools, the research aims to develop efficient methods for collecting, processing, and analyzing energy data from diverse sources. The core focus is on creating predictive models and real-time monitoring systems that enable proactive energy management. By harnessing AWS's machine learning and data analytics capabilities, the research seeks to identify patterns, anomalies, and optimization opportunities within energy consumption data. Furthermore, this study aims to propose actionable recommendations for reducing energy consumption in buildings and industries. By combining AWS services with metrics-driven insights, the research strives to facilitate the implementation of energy-efficient practices, ultimately leading to reduced carbon emissions and cost savings. The integration of AWS services not only enhances the analytical capabilities but also offers scalable solutions that can be customized for different building and industrial contexts. The research also recognizes the potential for AWS-powered solutions to promote sustainable practices and support environmental stewardship.

Keywords: energy consumption analysis, big data analytics, AWS services, energy efficiency

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Authors: Ajay Pinjani, Minha Khan, Ayesha Mehkeri, Anum Iftikhar

Abstract:

There is much research available on the benefits of adopting mother tongue-based multilingual education (MTB MLE) in primary school classrooms, but there is limited guidance available on how to design such programs for low-resource and linguistically diverse contexts. This paper is an effort to bridge the gap between theory and practice by offering a practitioner’s perspective on designing an MTB MLE program for linguistically heterogeneous contexts. The research compounds findings from current academic literature on MTB MLE, the study of global MTB MLE programs, interviews with practitioners, policy-makers, and academics worldwide, and a socio-linguistic survey carried out in parts of Tharparkar, Pakistan, the area selected for envisioned pilot implementation. These findings enabled the creation of ‘guiding principles’ which provide structure for the development of a contextualized and holistic MTB-MLE program. The guiding principles direct the creation of teaching and learning materials, creating effective teaching and learning environment, community engagement, and program evaluation. Additionally, the paper demonstrates the development of a context-specific language ladder framework which outlines the language journey of a child’s education, beginning with the mother tongue/ most familiar language in the early years and then gradually transitioning into other languages. Both the guiding principles and language ladder can be adapted to any multilingual context. Thus, this research provides MTB MLE practitioners with assistance in developing an MTB MLE model, which is best suited for their context.

Keywords: mother tongue based multilingual education, education design, language ladder, language issues, heterogeneous contexts

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Authors: Klara Królewiak-Detsi, Anna Orylska, Anna Gorgolewska, Marta Boczkowska, Agata Graczykowska

Abstract:

This study investigates the communication between students and teachers in inclusive education in Poland. Specifically, we examine the communication and interaction of students with special educational needs during online learning compared to traditional face-to-face instruction. Our research questions are (1) how children with special educational needs communicate with their teachers and peers during online learning, and (2) what strategies can improve their communication skills. We conducted five focus groups with: (1) 55 children with special educational needs, (2) 65 typically developing pupils, (3) 28 professionals (psychologists and special education therapists), (4) 16 teachers, and (5) 16 parents of children with special educational needs. Our analysis focused on primary schools and used thematic analysis according to the 6-step procedure of Braun and Clarke. Our findings reveal that children with disabilities faced more difficulties communicating and interacting with others online than in face-to-face lessons. The online tools used for education were not adapted to the needs of children with disabilities, and schools lacked clear guidelines on how to pursue inclusive education online. Based on the results, we offer recommendations for online communication training and tools that are dedicated to children with special educational needs. Additionally, our results demonstrate that typically developing pupils are better in interpersonal relations and more often and effectively use social support. Children with special educational needs had similar emotional and communication challenges compared to their typically developing peers. In conclusion, our study highlights the importance of providing adequate support for the online education of children with special educational needs in inclusive classrooms.

Keywords: Inclusive education, Special educational needs, Social skills development, Online communication

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Authors: Larbi Belagraa, Miloud Beddar, Abderrazak Bouzid

Abstract:

The needs of the construction sector still increasing for concrete. However, the shortage of natural resources of aggregate could be a problem for the concrete industry, in addition to the negative impact on the environment due to the demolition wastes. Recycling aggregate from construction and demolition (C&D) waste presents a major interest for users and researchers of concrete since this constituent can occupies more than 70% of concrete volume. The aim of the study here in is to assess the effect of sulfate resistant cement combined with the local mineral addition of marble waste fillers on the mechanical behavior of a recycled aggregate concrete (RAC). Physical and mechanical properties of RAC including the density, the flexural and the compressive strength were studied. The non destructive test methods (pulse-velocity, rebound hammer) were performed . The results obtained were compared to crushed aggregate concrete (CAC) using the normal compressive testing machine test method. The optimal content of 5% marble fillers showed an improvement for both used test methods (compression, flexion and NDT). Non-destructive methods (ultrasonic and rebound hammer test) can be used to assess the strength of RAC, but a correction coefficient is required to obtain a similar value to the compressive strength given by the compression tests. The study emphasizes that these waste materials can be successfully and economically utilized as additional inert filler in RAC formulation within similar performances compared to a conventional concrete.

Keywords: marble waste fillers, mechanical strength, natural aggregate, non-destructive testing (NDT), recycled aggregate concrete

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