Search results for: student network

Authors: Ravneet Kaur, Joydeep Acharya, Sudhanshu Gaur

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Artificial Intelligence (AI) has the potential to transform manufacturing by improving shop floor processes such as production, maintenance and quality. However, industrial datasets are notoriously difficult to extract in a real-time, streaming fashion thus, negating potential AI benefits. The main example is some specialized industrial controllers that are operated by custom software which complicates the process of connecting them to an Information Technology (IT) based data acquisition network. Security concerns may also limit direct physical access to these controllers for data acquisition. To connect the Operational Technology (OT) data stored in these controllers to an AI application in a secure, reliable and available way, we propose a novel Industrial IoT (IIoT) solution in this paper. In this solution, we demonstrate how video cameras can be installed in a factory shop floor to continuously obtain images of the controller HMIs. We propose image pre-processing to segment the HMI into regions of streaming data and regions of fixed meta-data. We then evaluate the performance of multiple Optical Character Recognition (OCR) technologies such as Tesseract and Google vision to recognize the streaming data and test it for typical factory HMIs and realistic lighting conditions. Finally, we use the meta-data to match the OCR output with the temporal, domain-dependent context of the data to improve the accuracy of the output. Our IIoT solution enables reliable and efficient data extraction which will improve the performance of subsequent AI applications.

Keywords: human machine interface, industrial internet of things, internet of things, optical character recognition, video analytics

Procedia PDF Downloads 99

Authors: Jamal Radaideh

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Al-Asfar Lake is located about 14 km east of Al-Ahsa and is one of the most important wetland lakes in the Al Ahsa/Eastern Province of Saudi Arabia. Al-Ahsa is may be the largest oasis in the world, having an area of 20,000 hectares, in addition, it is of the largest and oldest agricultural centers in the region. The surplus farm irrigation water beside additional water supplied by treated wastewater from Al-Hofuf sewage station is collected by a drainage network and discharged into Al-Asfar Lake. The lake has good wetlands, sand dunes as well as large expanses of open and shallow water. Salt tolerant vegetation is present in some of the shallow areas around the lake, and huge stands of Phragmites reeds occur around the lake. The lake presents an important habitat for wildlife and birds, something not expected to find in a large desert. Although high evaporation rates in the range of 3250 mm are common, the water remains in the evaporation lakes during all seasons of the year is used to supply cattle with drinking water and for aquifer recharge. Investigations showed that high concentrations of nitrogen (N), phosphorus (P), biological oxygen demand (BOD), chemical oxygen demand (COD) and salinity discharge to Al Asfar Lake from the D2 drain exist. It is expected that the majority of BOD, COD and N originates from wastewater discharge and leachate from surplus irrigation water which also contribute to the majority of P and salinity. The significant content of nutrients and biological oxygen demand reduces available oxygen in the water. The present project aimed to improve the water quality of the lake using constructed wetland trains which will be built around the lake. Phragmites reeds, which already occur around the lake, will be used.

Keywords: Al Asfar lake, constructed wetland, water quality, water treatment

Procedia PDF Downloads 429

Authors: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

Procedia PDF Downloads 91

Authors: Rune Bjerke

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Psychologists incline to see creativity as a mental and psychological process. However, creativity is as well results of cultural and social interactions. Therefore, creativity is not a product of individuals in isolation, but of social systems. Creative people get ideas from the influence of others and the immediate cultural environment – a space of knowledge, situations, and practices. Therefore, in this study we apply the systems theory in practice to activate creativity processes in the production of our novel and screenplay writing. We, as storytellers actively seek to get into situations in our everyday lives, our systems, to generate ideas. Within our personal systems, we have the potential to induce situations to realise ideas to our texts, which may be accepted by our gate-keepers and can become socially validated. This is our method of writing – get into situations, get ideas to texts, and test them with family and friends in our social systems. Example of novel text as an outcome of our method is as follows: “Is it a matter of obviousness or had I read it somewhere, that the one who increases his knowledge increases his pain? And also, the other way around, with increased pain, knowledge increases, I thought. Perhaps such a chain of effects explains why the rebel August Strindberg wrote seven plays in ten months after the divorce with Siri von Essen. Shortly after, he tried painting. Neither the seven theatre plays were shown, nor the paintings were exhibited. I was standing in front of Munch's painting Women in Three Stages with chaotic mental images of myself crumpled in a church and a laughing x-girlfriend watching my suffering. My stomach was turning at unpredictable intervals and the subsequent vomiting almost suffocated me. Love grief at the worst. Was it this pain Strindberg felt? Despite the failure of his first plays, the pain must have triggered a form of creative energy that turned pain into ideas. Suffering, thoughts, feelings, words, text, and then, the reader experience. Maybe this negative force can be transformed into something positive, I asked myself. The question eased my pain. At that moment, I forgot the damp, humid air in the Munch Museum. Is it the similar type of Strindberg-pain that could explain the recurring, depressive themes in Munch's paintings? Illness, death, love and jealousy. As a beginning art student at the master's level, I had decided to find the answer. Was it the same with Munch's pain, as with Strindberg - a woman behind? There had to be women in the case of Munch - therefore, the painting “Women in Three Stages”? Who are they, what personality types are they – the women in red, black and white dresses from left to the right?” We, the writers, are using persons, situations and elements in our systems, in a systems theory perspective, to prompt creative ideas. A conceptual model is provided to advance creativity theory.

Keywords: creativity theory, systems theory, novel writing, screenplay writing, sources of creativity in social systems

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Authors: Genevaive Melendres

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School administrators face the reality of teachers losing their engagement, or schools losing the teachers. This study is then conducted to identify a structural model that best predict work engagement of private secondary teachers in Davao City. Ninety-three teachers from four sectarian schools and 56 teachers from four non-sectarian schools were involved in the completion of four survey instruments namely Organizational Climate Questionnaire, Leader Behavior Descriptive Questionnaire, Organizational Commitment Scales, and Utrecht Work Engagement Scales. Data were analyzed using frequency distribution, mean, standardized deviation, t-test for independent sample, Pearson r, stepwise multiple regression analysis, and structural equation modeling. Results show that schools have high level of organizational climate dimensions; leaders oftentimes show work-oriented and people-oriented behavior; teachers have high normative commitment and they are very often engaged at their work. Teachers from non-sectarian schools have higher organizational commitment than those from sectarian schools. Organizational climate and leadership behavior are positively related to and predict work engagement whereas commitment did not show any relationship. This study underscores the relative effects of three variables on the work engagement of teachers. After testing network of relationships and evaluating several models, a best-fitting model was found between leadership behavior and work engagement. The noteworthy findings suggest that principals pay attention and consistently evaluate their behavior for this best predicts the work engagement of the teachers. The study provides value to administrators who take decisions and create conditions in which teachers derive fulfillment.

Keywords: leadership behavior, organizational climate, organizational commitment, private secondary school teachers, structural model on work engagement

Procedia PDF Downloads 250

Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

Procedia PDF Downloads 166

Authors: Aishvarya Gupta, Keerthana Anand, Sasirekha Rengaraj, Latha Chathurvedula

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Introduction: Advances in assisted reproductive technology and increase in the proportion of infertile couples have both contributed to the steep increase in the incidence of twin pregnancies in past decades. Maternal and perinatal complications are higher in twins than in singleton pregnancies. Studies comparing the maternal and perinatal outcomes of ART twin pregnancies versus spontaneously conceived twin pregnancies report heterogeneous results making it unclear whether the complications are due to twin gestation per se or because of assisted reproductive techniques. The present study aims to compare both maternal and perinatal outcomes in twin pregnancies which are spontaneously conceived and after assisted conception methods, so that targeted steps can be undertaken in order to improve maternal and perinatal outcome of twins. Objectives: To study perinatal and maternal outcome in twin pregnancies conceived spontaneously as well as with assisted methods and compare the outcomes between the two groups. Setting: Women delivering at JIPMER (tertiary care institute), Pondicherry. Population: 380 women with twin pregnancies who delivered in JIPMER between June 2015 and March 2017 were included in the study. Methods: The study population was divided into two cohorts – one conceived by spontaneous conception and other by assisted reproductive methods. Association of various maternal and perinatal outcomes with the method of conception was assessed using chi square test or Student's t test as appropriate. Multiple logistic regression analysis was done to assess the independent association of assisted conception with maternal outcomes after adjusting for age, parity and BMI. Multiple logistic regression analysis was done to assess the independent association of assisted conception with perinatal outcomes after adjusting for age, parity, BMI, chorionicity, gestational age at delivery and presence of hypertension or gestational diabetes in the mother. A p value of < 0.05 was considered as significant. Result: There was increased proportion of women with GDM (21% v/s 4.29%) and premature rupture of membranes (35% v/s 22.85%) in the assisted conception group and more anemic women in the spontaneous group (71.27% v/s 55.1%). However assisted conception per se increased the incidence of GDM among twin gestations (OR 3.39, 95% CI 1.34 – 8.61) and did not influence any of the other maternal outcomes. Among the perinatal outcomes, assisted conception per se increased the risk of having very preterm (<32 weeks) neonates (OR 3.013, 95% CI 1.432 – 6.337). The mean birth weight did not significantly differ between the two groups (p = 0.429). Though there were higher proportion of babies admitted to NICU in the assisted conception group (48.48% v/s 36.43%), assisted conception per se did not increase the risk of admission to NICU (OR 1.23, 95% CI 0.76 – 1.98). There was no significant difference in perinatal mortality rates between the two groups (p = 0.829). Conclusion: Assisted conception per se increases the risk of developing GDM in women with twin gestation and increases the risk of delivering very preterm babies. Hence measures should be taken to ensure appropriate screening methods for GDM and suitable neonatal care in such pregnancies.

Keywords: assisted conception, maternal outcomes, perinatal outcomes, twin gestation

Procedia PDF Downloads 192

Authors: Gulnaz Erkomaishvili

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Introduction: The pandemic and the current economic crisis have created problems for entrepreneurship and, therefore for start-up entrepreneurs. The paper presents the challenges of start-up entrepreneurs in Georgia in the time of pandemic and the analysis of the state economic policy measures. Despite many problems, the study found that in 54.2% of start-ups surveyed under the pandemic, innovation opportunities were growing. It can be stated that the pandemic was a good opportunity to increase the innovative capacity of the enterprise. 52% of the surveyed start-up entrepreneurs managed to adapt to the current situation and increase the sale of their products/services through remote channels. As for the assessment of state support measures by start-up entrepreneurs, a large number of Georgian start-ups do not assess the measures implemented by the state positively. Methodology: The research process uses methods of analysis and synthesis, quantitative and qualitative, interview/survey, grouping, relative and average values, graphing, comparison, data analysis, and others. Main Findings: Studies have shown that for the start-up entrepreneurs, the main problem remains: inaccessible funding, workers' qualifications gap, inflation, taxes, regulation, political instability, inadequate provision of infrastructure, amount of taxes, and other factors. Conclusions: The state should take the following measures to support business start-ups: create an attractive environment for investment, availability of soft loans, creation of an insurance system, infrastructure development, increase the effectiveness of tax policy (simplicity of the tax system, clarity, optimal tax level ); promote export growth (develop strategy for opening up international markets, build up a broad marketing network, etc.).

Keywords: start-up entrepreneurs, startups, start-up entrepreneurs support programs, start-up entrepreneurs support economic policy

Procedia PDF Downloads 104

Authors: Sreetama Ghosh, Sundara Ramaprabhu

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Carbon dioxide gas has been a major cause for the worldwide increase in green house effect, which leads to climate change and global warming. So CO₂ capture & sequestration has become an effective way to reduce the concentration of CO₂ in the environment. One such way to capture CO₂ in porous materials is by adsorption process. A potential material in this aspect is porous hexagonal boron nitride or 'white graphene' which is a well-known two-dimensional layered material with very high thermal stability. It had been investigated that the sample with hierarchical pore structure and high specific surface area shows excellent performance in capturing carbon dioxide gas and thereby mitigating the problem of environmental pollution to the certain extent. Besides, the presence of sulfur as well as nitrogen in the sample synergistically helps in the increase in adsorption capacity. In this work, a cost effective single step synthesis of highly porous boron nitride nanosheets doped with sulfur had been demonstrated. Besides, the CO₂ adsorption-desorption studies were carried on using a pressure reduction technique. The studies show that the nanosheets exhibit excellent cyclic stability in storage performance. Thermodynamic studies suggest that the adsorption takes place mainly through physisorption. The studies show that the nanosheets exhibit excellent cyclic stability in storage performance. Further, the surface modification of the highly porous nano sheets carried out by incorporating ionic liquids had further enhanced the capturing capability of CO₂ gas in the nanocomposite, revealing that this particular material has the potential to be an excellent adsorbent of carbon dioxide gas.

Keywords: CO₂ capture, hexagonal boron nitride nanosheets, porous network, sulfur doping

Procedia PDF Downloads 233

Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

Procedia PDF Downloads 307

Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The educational system faces a significant concern with regards to Dyslexia and Dysgraphia, which are learning disabilities impacting reading and writing abilities. This is particularly challenging for children who speak the Sinhala language due to its complexity and uniqueness. Commonly used methods to detect the risk of Dyslexia and Dysgraphia rely on subjective assessments, leading to limited coverage and time-consuming processes. Consequently, delays in diagnoses and missed opportunities for early intervention can occur. To address this issue, the project developed a hybrid model that incorporates various deep learning techniques to detect the risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16, and YOLOv8 models were integrated to identify handwriting issues. The outputs of these models were then combined with other input data and fed into an MLP model. Hyperparameters of the MLP model were fine-tuned using Grid Search CV, enabling the identification of optimal values for the model. This approach proved to be highly effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention. The Resnet50 model exhibited a training accuracy of 0.9804 and a validation accuracy of 0.9653. The VGG16 model achieved a training accuracy of 0.9991 and a validation accuracy of 0.9891. The MLP model demonstrated impressive results with a training accuracy of 0.99918, a testing accuracy of 0.99223, and a loss of 0.01371. These outcomes showcase the high accuracy achieved by the proposed hybrid model in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, dyslexia, dysgraphia, deep learning, learning disabilities, data science

Procedia PDF Downloads 51

Authors: Fernando Cabo Aseguinolaza, Víctor Bouzas Blanco, Alberto Martí Ezpeleta

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Santiago de Compostela became a stable object of literary representation during the period between 1840 and 1915, approximately. This study offers a partial cartographical look at this process, suggesting that a cultural space like Compostela’s becoming an object of literary representation paralleled the first stages of its becoming a tourist destination. We use maps as a method of analysis to show the interaction between a corpus of novels and the emerging tradition of tourist guides on Compostela during the selected period. Often, the novels constitute ways to present a city to the outside, marking it for the gaze of others, as guidebooks do. That leads us to examine the ways of constructing and rendering communicable the local in other contexts. For that matter, we should also acknowledge the fact that a good number of the narratives in the corpus evoke the representation of the city through the figure of one who comes from elsewhere: a traveler, a student or a professor. The guidebooks coincide in this with the emerging fiction, of which the mimesis of a city is a key characteristic. The local cannot define itself except through a process of symbolic negotiation, in which recognition and self-recognition play important roles. Cartography shows some of the forms that these processes of symbolic representation take through the treatment of space. The research uses GIS to find significant models of representation. We used the program ArcGIS for the mapping, defining the databases starting from an adapted version of the methodology applied by Barbara Piatti and Lorenz Hurni’s team at the University of Zurich. First, we designed maps that emphasize the peripheral position of Compostela from a historical and institutional perspective using elements found in the texts of our corpus (novels and tourist guides). Second, other maps delve into the parallels between recurring techniques in the fictional texts and characteristic devices of the guidebooks (sketching itineraries and the selection of zones and indexicalization), like a foreigner’s visit guided by someone who knows the city or the description of one’s first entrance into the city’s premises. Last, we offer a cartography that demonstrates the connection between the best known of the novels in our corpus (Alejandro Pérez Lugín’s 1915 novel La casa de la Troya) and the first attempt to create package tourist tours with Galicia as a destination, in a joint venture of Galician and British business owners, in the years immediately preceding the Great War. Literary cartography becomes a crucial instrument for digging deeply into the methods of cultural production of places. Through maps, the interaction between discursive forms seemingly so far removed from each other as novels and tourist guides becomes obvious and suggests the need to go deeper into a complex process through which a city like Compostela becomes visible on the contemporary cultural horizon.

Keywords: compostela, literary geography, literary cartography, tourism

Procedia PDF Downloads 382

Authors: Zhongmin Wang, Wudong Fan, Hengshan Zhang, Yimin Zhou

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In data-driven prognostic methods, the prediction accuracy of the estimation for remaining useful life of bearings mainly depends on the performance of health indicators, which are usually fused some statistical features extracted from vibrating signals. However, the existing health indicators have the following two drawbacks: (1) The differnet ranges of the statistical features have the different contributions to construct the health indicators, the expert knowledge is required to extract the features. (2) When convolutional neural networks are utilized to tackle time-frequency features of signals, the time-series of signals are not considered. To overcome these drawbacks, in this study, the method combining convolutional neural network with gated recurrent unit is proposed to extract the time-frequency image features. The extracted features are utilized to construct health indicator and predict remaining useful life of bearings. First, original signals are converted into time-frequency images by using continuous wavelet transform so as to form the original feature sets. Second, with convolutional and pooling layers of convolutional neural networks, the most sensitive features of time-frequency images are selected from the original feature sets. Finally, these selected features are fed into the gated recurrent unit to construct the health indicator. The results state that the proposed method shows the enhance performance than the related studies which have used the same bearing dataset provided by PRONOSTIA.

Keywords: continuous wavelet transform, convolution neural net-work, gated recurrent unit, health indicators, remaining useful life

Procedia PDF Downloads 121

Authors: Cathal Ffrench, Ryan Barrett, Mike Quayle

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It is accepted in social psychology that categorization leads to ingroup favoritism, without further thought given to the processes that may co-occur or even precede categorization. These categorizations move away from the conceptualization of the self as a unique social being toward an increasingly collective identity. Subsequently, many individuals derive much of their self-evaluations from these collective identities. The seminal literature on this topic argues that it is primarily categorization that evokes instances of ingroup favoritism. Apropos to these theories, we argue that categorization acts to enhance and further intergroup processes rather than defining them. More accurately, we propose categorization aids initial ingroup contact and this first contact is predictive of subsequent favoritism on individual and collective levels. This analysis focuses on Virtual Interaction APPLication (VIAPPL) based studies, a software interface that builds on the flaws of the original minimal group studies. The VIAPPL allows the exchange of tokens in an intra and inter-group manner. This token exchange is how we classified the first contact. The study involves binary longitudinal analysis to better understand the subsequent exchanges of individuals based on who they first interacted with. Studies were selected on the criteria of evidence of explicit first interactions and two-group designs. Our findings paint a compelling picture in support of a motivated contact hypothesis, which suggests that an individual’s first motivated contact toward another has strong predictive capabilities for future behavior. This contact can lead to habit formation and specific favoritism towards individuals where contact has been established. This has important implications for understanding how group conflict occurs, and how intra-group individual bias can develop.

Keywords: categorization, group dynamics, initial contact, minimal social networks, momentary contact

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Authors: Vilma Zydziunaite, Gitana Balezentiene, Vilma Zydziunaite

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Teaching is one of most responsible profession, and it is not only a job of an artisan. This profes-sion needs a developed ability to identify oneself with the chosen teaching profession. Research questions: How teachers characterize their authentic individual professional identity? What factors teachers exclude, which support and limit the professional identity? Aim was to develop the grounded theory (GT) about teacher’s professional identity (TPI). Research methodology is based on Charmaz GT version. Data were collected via semi-structured interviews with the he sample of 12 teachers. Findings. 15 extracted categories revealed that the core of TPI is teacher’s professional calling. Premises of TPI are family support, motives for choos-ing teacher’s profession, teacher’s didactic competence. Context of TPI consists of teacher compli-ance with the profession, purposeful preparation for pedagogical studies, professional growth. The strategy of TPI is based on teacher relationship with school community strengthening. The profes-sional frustration limits the TPI. TPI outcome includes teacher recognition, authority; professional mastership, professionalism, professional satisfaction. Dimensions of TPI GT the past (reaching teacher’s profession), present (teacher’s commitment to professional activity) and future (teacher’s profession reconsideration). Conclusions. The substantive GT describes professional identity as complex, changing and life-long process, which develops together with teacher’s personal identity and is connected to professional activity. The professional decision "to be a teacher" is determined by the interaction of internal (professional vocation, personal characteristics, values, self-image, talents, abilities) and external (family, friends, school community, labor market, working condi-tions) factors. The dimensions of the TPI development includes: the past (the pursuit of the teaching profession), the present (the teacher's commitment to professional activity) and the future (the revi-sion of the teaching profession). A significant connection emerged - as the teacher's professional commitment strengthens (creating a self-image, growing the teacher's professional experience, recognition, professionalism, mastery, satisfaction with pedagogical activity), the dimension of re-thinking the teacher's profession weakens. This proves that professional identity occupies an im-portant place in a teacher's life and it affects his professional success and job satisfaction. Teachers singled out the main factors supporting a teacher's professional identity: their own self-image per-ception, professional vocation, positive personal qualities, internal motivation, teacher recognition, confidence in choosing a teaching profession, job satisfaction, professional knowledge, professional growth, good relations with the school community, pleasant experiences, quality education process, excellent student achievements.

Keywords: grounded theory, teacher professional identity, semi-structured interview, school, students, school community, family

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Authors: Jin Hwan Cheong, Mina Hwang, Myung Hoon Han, Je Il Ryu, Young ha Oh, Seong Ho Koh, Wu Duck Won, Byung Jin Ha

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Leucine-rich repeat-containing G-protein coupled receptor 5 (LGR5) has been reported to play critical roles in the proliferation of various cancer cells. However, the roles of LGR5 in brain tumors and the specific intracellular signaling proteins directly associated with it remain unknown. Expression of LGR5 was first measured in normal brain tissue, meningioma, and pituitary adenoma of humans. To identify the downstream signaling pathways of LGR5, siRNA-mediated knockdown of LGR5 was performed in SH-SY5Y neuroblastoma cells followed by proteomics analysis with 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE). In addition, the expression of LGR5-associated proteins was evaluated in LGR5-inꠓhibited neuroblastoma cells and in human normal brain, meningioma, and pituitary adenoma tissue. Proteomics analysis showed 12 protein spots were significantly different in expression level (more than two-fold change) and subsequently identified by peptide mass fingerprinting. A protein association network was constructed from the 12 identified proteins altered by LGR5 knockdown. Direct and indirect interactions were identified among the 12 proteins. HSP 90-beta was one of the proteins whose expression was altered by LGR5 knockdown. Likewise, we observed decreased expression of proteins in the hnRNP subfamily following LGR5 knockdown. In addition, we have for the first time identified significantly higher hnRNP family expression in meningioma and pituitary adenoma compared to normal brain tissue. Taken together, LGR5 and its downstream sigꠓnaling play critical roles in neuroblastoma and brain tumors such as meningioma and pituitary adenoma.

Keywords: LGR5, neuroblastoma, meningioma, pituitary adenoma, hnRNP

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Authors: Jonas Hamann, Siavash Saki, Tobias Hagen

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Discretization of urban areas is a crucial aspect in many spatial analyses. The process of discretization of space into subspaces without overlaps and gaps is called tessellation. It helps understanding spatial space and provides a framework for analyzing geospatial data. Tessellation methods can be divided into two groups: regular tessellations and irregular tessellations. While regular tessellation methods, like squares-grids or hexagons-grids, are suitable for addressing pure geometry problems, they cannot take the unique characteristics of different subareas into account. However, irregular tessellation methods allow the border between the subareas to be defined more realistically based on urban features like a road network or Points of Interest (POI). Even though Python is one of the most used programming languages when it comes to spatial analysis, there is currently no library that combines different tessellation methods to enable users and researchers to compare different techniques. To close this gap, we are proposing TessPy, an open-source Python package, which combines all above-mentioned tessellation methods and makes them easily accessible to everyone. The core functions of TessPy represent the five different tessellation methods: squares, hexagons, adaptive squares, Voronoi polygons, and city blocks. By using regular methods, users can set the resolution of the tessellation which defines the finesse of the discretization and the desired number of tiles. Irregular tessellation methods allow users to define which spatial data to consider (e.g., amenity, building, office) and how fine the tessellation should be. The spatial data used is open-source and provided by OpenStreetMap. This data can be easily extracted and used for further analyses. Besides the methodology of the different techniques, the state-of-the-art, including examples and future work, will be discussed. All dependencies can be installed using conda or pip; however, the former is more recommended.

Keywords: geospatial data science, geospatial data analysis, tessellations, urban studies

Procedia PDF Downloads 117

Authors: Alicia Regodon Puyalto, Alfonso Garcia-Santos

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This article presents a method to analyze the use of indoor spaces based on data analytics obtained from inbuilt digital devices. The study uses the data generated by the in-place devices, such as smart locks, Wi-Fi routers, and electrical sensors, to gain additional insights on space occupancy, user behaviour, and comfort. Those devices, originally installed to facilitate remote operations, report data through the internet that the research uses to analyze information on human real-time use of spaces. Using an in-place Internet of Things (IoT) network enables a faster, more affordable, seamless, and scalable solution to analyze building interior spaces without incorporating external data collection systems such as sensors. The methodology is applied to a real case study of coliving, a residential building of 3000m², 7 floors, and 80 users in the centre of Madrid. The case study applies the method to classify IoT devices, assess, clean, and analyze collected data based on the analysis framework. The information is collected remotely, through the different platforms devices' platforms; the first step is to curate the data, understand what insights can be provided from each device according to the objectives of the study, this generates an analysis framework to be escalated for future building assessment even beyond the residential sector. The method will adjust the parameters to be analyzed tailored to the dataset available in the IoT of each building. The research demonstrates how human-centered data analytics can improve the future spatial design of indoor spaces.

Keywords: in-place devices, IoT, human-centred data-analytics, spatial design

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Authors: Armend Kelmendi, Adnan Ahmeti

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The purpose of our study was to assess the impact of soccer in the overall wealth fare (education, health, and economic prosperity) of youth in Kosovo (age: 7-18). The research conducted measured a number of parameters (training methodologies, conditions, community leadership impact) in a sample consisting of 6 different football clubs’ academies across the country. Fifty (50) male and female football youngsters volunteered in this study. To generate more reliable results, the analysis was conducted with the help of a set of effective project management tools and techniques (Gantt chart, Logic Network, PERT chart, Work Breakdown Structure, and Budgeting Analysis). The interviewees were interviewed under a specific lens of categories (impact in education, health, and economic prosperity). A set of questions were asked i.e. what has football provided to you and the community you live in?; Did football increase your confidence and shaped your life for better?; What was the main reason you started training in football? The results generated explain how a single sport, namely that of football in Kosovo can make a huge social change, improving key social factors in a society. There was a considerable difference between the youth clubs as far as training conditions are concerned. The study found out that despite financial constraints, two out of six clubs managed to produce twice as more talented players that were introduced to professional primary league teams in Kosovo and Albania, including other soccer teams in the region, Europe, and Asia. The study indicates that better sports policy must be formulated and associated with important financial investments in soccer for it to be considered fruitful and beneficial for players of 18 plus years of age, namely professionals.

Keywords: youth, prosperity, conditions, investments, growth, free movement

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Authors: Adelaide Rethabile Motshabi Pitso-Mbili

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The disparity between universities in rural and urban areas of South Africa is still an ongoing issue. There are a lot of variations in these universities, such as the performance of the students and the lecturers, which is viewed as a worrying discrepancy related to knowledge gaps or educational inequality. According to research, rural students routinely perform worse than urban students in sub-Saharan Africa, and the disparity is wide when compared to the global average. This may be a result of the various challenges that universities in rural and urban areas face. Hence, the aim of this study was to compare the challenges faced by rural and urban universities, especially in hospitality and tourism programs, and recommend possible solutions. This study used a qualitative methodology and included focus groups and in-depth interviews. Eight focus groups of final-year students in hospitality and tourism programs from four institutions and four department heads of those programs participated in in-depth interviews. Additionally, the study was motivated by the teacher collaboration theory, which proposes that colleagues can help one another for the benefit of students and the institution. It was revealed that rural universities face more challenges than urban universities when it comes to hospitality and tourism education. The results of the interviews showed that universities in rural areas have a high staff turnover rate and offer fewer courses due to a lack of resources, such as the infrastructure, staff, equipment, and materials needed to give students hands-on training on the campus and in various hospitality and tourism programs. Urban universities, on the other hand, provide a variety of courses in the hospitality and tourism areas, and while resources are seldom an issue, they must deal with classes that have large enrolments and insufficient funding to support them all. Additionally, students in remote locations noted that having a lack of water and electricity makes it difficult for them to perform practical lessons. It is recommended that universities work together to collaborate or develop partnerships to help one another overcome obstacles and that universities in rural areas visit those in urban areas to observe how things are done there and to determine where they can improve themselves. The significance of the study is that it will truly bring rural and urban educational processes and practices into greater alignment of standards, benefits, and achievements; this will also help retain staff members within the rural area universities. The present study contributes to the literature by increasing the accumulation of knowledge on research topics, challenges, trends and innovation in hospitality and tourism education and setting forth an agenda for future research. The current study adds to the body of literature by expanding the accumulation of knowledge on research topics that contribute to trends and innovations in hospitality and tourism education and by laying out a plan for future research.

Keywords: hospitality and tourism education, rural and urban universities, collaboration, teacher and student performance, educational inequality

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Authors: Migael van Zyl, Stefanie Visser, Awelani Phaswana

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The electricity landscape in South Africa is characterized by frequent occurrences of loadshedding, a measure implemented by Eskom to manage electricity generation shortages by curtailing demand. Loadshedding, classified into stages ranging from 1 to 8 based on severity, involves the systematic rotation of power cuts across municipalities according to predefined schedules. However, this practice introduces distortions in recorded electricity demand, posing challenges to accurate forecasting essential for budgeting, network planning, and generation scheduling. Addressing this challenge requires the development of a methodology to quantify the impact of loadshedding and integrate it back into metered electricity demand data. Fortunately, comprehensive records of loadshedding impacts are maintained in a database, enabling the alignment of Loadshedding effects with hourly demand data. This adjustment ensures that forecasts accurately reflect true demand patterns, independent of loadshedding's influence, thereby enhancing the reliability of electricity supply management in South Africa. This paper presents a methodology for determining the hourly impact of load scheduling and subsequently adjusting historical demand data to account for it. Furthermore, two forecasting models are developed: one utilizing the original dataset and the other using the adjusted data. A comparative analysis is conducted to evaluate forecast accuracy improvements resulting from the adjustment process. By implementing this methodology, stakeholders can make more informed decisions regarding electricity infrastructure investments, resource allocation, and operational planning, contributing to the overall stability and efficiency of South Africa's electricity supply system.

Keywords: electricity demand forecasting, load shedding, demand side management, data science

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Authors: Oto J. Okwu, Ornan Henry, Victor A. Otene

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The agricultural sector contributes a great deal to the sustenance of Nigeria’s food security and economy, with an attendant impact on rural development. In spite of the relatively high number of farmers in the country, self-sufficiency in food production is still a challenge. Farmers are faced with myriad problems which hinder their production efficiency, one of which is their access to agricultural inputs required for optimum production. To meet the challenges faced by farmers, the government at the federal level has come up with many agricultural policies, one of which is the Agricultural Transformation Agenda (ATA). The Growth Enhancement Support Scheme (GESS) is one of the critical components of ATA, which is aimed at ensuring the effective distribution of agricultural inputs delivered directly to farmers, and at a regulated cost. After about 8 years of launching this policy, it will be necessary to carry out an assessment of GESS and determine the impact it has made on rural farmers with respect to their access to farm inputs. This study was carried out to assess the Growth Enhancement Support Scheme (GESS) in Adamawa State, Nigeria. Crop farmers who registered under the GESS in Adamawa State, Nigeria, formed the population for the study. Primary data for the study were obtained through a survey, and the use of a structured questionnaire. A sample size of 167 respondents was selected using multi-stage, purposive, and random sampling techniques. The validity and reliability of the research instrument (questionnaire) were obtained through pilot testing and test-retest method, respectively. The objectives of the study were to determine the difference in the level of access to agricultural inputs before and after GESS, determine the difference in cost of agricultural inputs before and after GESS, and to determine the challenges faced by rural farmers in accessing agricultural inputs through GESS. Both descriptive and inferential statistics were used in analyzing the collected data. Specifically, Mann-Whitney, student t-test, and factor analysis were used to test the stated hypotheses. Research findings revealed there was a significant difference in the level of access to farm inputs after the introduction of GESS (Z=14.216). Also, there was a significant difference in the cost of agro-inputs after the introduction of GESS (Pr |T| > |t|= 0.0000). The challenges faced by respondents in accessing agro-inputs through GESS were administrative and technical in nature. Based on the findings of the research, it was recommended that efforts be made by the government to sustain the GESS, as it has significantly improved the level of farmers’ access to agricultural inputs and has reduced the cost of agro-inputs, while administrative challenges faced by the respondents in accessing inputs be addressed by the government, and extension agents assist the farmers to overcome the technical challenges they face in accessing inputs.

Keywords: agricultural policy, agro-inputs, assessment, growth enhancement support scheme, rural farmers

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Authors: Mohamed Zellagui, Heba Ahmed Hassan

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This paper proposes a new approach for the calculation of short-circuit parameters in the presence of Pulse Width Modulated based Series Compensator (PWMSC). PWMSC is a newly Flexible Alternating Current Transmission System (FACTS) device that can modulate the impedance of a transmission line through applying a variation to the duty cycle (D) of a train of pulses with fixed frequency. This results in an improvement of the system performance as it provides virtual compensation of distribution line impedance by injecting controllable apparent reactance in series with the distribution line. This controllable reactance can operate in both capacitive and inductive modes and this makes PWMSC highly effective in controlling the power flow and increasing system stability in the system. The purpose of this work is to study the impact of fault resistance (RF) which varies between 0 to 30 Ω on the fault current calculations in case of a ground fault and a fixed fault location. The case study is for a medium voltage (MV) Algerian distribution line which is compensated by PWMSC in the 30 kV Algerian distribution power network. The analysis is based on symmetrical components method which involves the calculations of symmetrical components of currents and voltages, without and with PWMSC in both cases of maximum and minimum duty cycle value for capacitive and inductive modes. The paper presents simulation results which are verified by the theoretical analysis.

Keywords: pulse width modulated series compensator (pwmsc), duty cycle, distribution line, short-circuit calculations, ground fault, symmetrical components method

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Authors: A. F. Alves, L. Schmidt, J. Ferrao

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Portugal is one of the more vulnerable European countries to the impacts of climate change. These include: temperature increase; coastal sea level rise; desertification and drought in the countryside; and frequent and intense extreme weather events. Hence, adaptation strategies to climate change are of great importance. This is what was addressed by ClimAdaPT.Local. This policy-oriented project had the main goal of developing 26 Municipal Adaptation Strategies for Climate Change, through the identification of local specific present and future vulnerabilities, the training of municipal officials, and the engagement of local communities. It is intended to be replicated throughout the whole territory and to stimulate the creation of a national network of local adaptation in Portugal. Supported by methodologies and tools specifically developed for this project, our paper is based on the surveys, training and stakeholder engagement workshops implemented at municipal level. In an 'adaptation-as-learning' process, these tools functioned as a social-learning platform and an exercise in knowledge and policy co-production. The results allowed us to explore the nature of local vulnerabilities and the exposure of gaps in the context of reappraisal of both future climate change adaptation opportunities and possible dysfunctionalities in the governance arrangements of municipal Portugal. Development issues are highlighted when we address the sectors and social groups that are both more sensitive and more vulnerable to the impacts of climate change. We argue that a pluralistic dialogue and a common framing can be established between them, with great potential for transformational adaptation. Observed climate change, present-day climate variability and future expectations of change are great societal challenges which should be understood in the context of the sustainable development agenda.

Keywords: adaptation, ClimAdaPT.Local, climate change, Portugal, sustainable development

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Authors: S. Hussain, C. Grieco, M. Brosnan

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Background and Aims: Autism is a complex neurobehavioural disorder, characterised by impairments in the development of language and communication skills. The study involved a state-of-art systematic review, in addition to qualitative data analysis, to establish the evidence for social stories as an intervention strategy for autistic children. An up-to-date review of the use of digital technologies in the delivery of interventions to autistic children was also carried out; to propose the efficacy of digital technologies and the use of social stories to improve intervention outcomes for autistic children. Methods: Two student researchers reviewed a range of randomised control trials and observational studies. The aim of the review was to establish if there was adequate evidence to justify recommending social stories to autistic patients. Students devised their own search strategies to be used across a range of search engines, including Ovid-Medline, Google Scholar and PubMed. Students then critically appraised the generated literature. Additionally, qualitative data obtained from a comprehensive online questionnaire on social stories was also thematically analysed. The thematic analysis was carried out independently by each researcher, using a ‘bottom-up’ approach, meaning contributors read and analysed responses to questions and devised semantic themes from reading the responses to a given question. The researchers then placed each response into a semantic theme or sub-theme. The students then joined to discuss the merging of their theme headings. The Inter-rater reliability (IRR) was calculated before and after theme headings were merged, giving IRR for pre- and post-discussion. Lastly, the thematic analysis was assessed by a third researcher, who is a professor of psychology and the director for the ‘Centre for Applied Autism Research’ at the University of Bath. Results: A review of the literature, as well as thematic analysis of qualitative data found supporting evidence for social story use. The thematic analysis uncovered some interesting themes from the questionnaire responses, relating to the reasons why social stories were used and the factors influencing their effectiveness in each case. However, overall, the evidence for digital technologies interventions was limited, and the literature could not prove a causal link between better intervention outcomes for autistic children and the use of technologies. However, they did offer valid proposed theories for the suitability of digital technologies for autistic children. Conclusions: Overall, the review concluded that there was adequate evidence to justify advising the use of social stories with autistic children. The role of digital technologies is clearly a fast-emerging field and appears to be a promising method of intervention for autistic children; however, it should not yet be considered an evidence-based approach. The students, using this research, developed ideas on social story interventions which aim to help autistic children.

Keywords: autistic children, digital technologies, intervention, social stories

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Authors: Keenoo Bibi Sumera, Alsheikh Mona, Mubarak Jan Beebee Zeba Mahetaab

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Background: Due to the busy schedule of the practicing clinicians at the hospitals, students may not always be attended to, which is to their detriment. The clinicians at the hospitals are also not always acquainted with teaching and/or supervising students on their placements. Additionally, there is a high student-patient ratio. Since they are the prospective clinical doctors under training, they need to reach the competence levels in clinical decision-making skills to be able to serve the healthcare system of the country and to be safe doctors. Aims and Objectives: A reflective portfolio was used to provide a means for students to learn by reflecting on their experiences and obtaining continuous feedback. This practice is an attempt to compensate for the scarcity of lack of resources, that is, clinical placement supervisors and patients. It is also anticipated that it will provide learners with a continuous monitoring and learning gap analysis tool for their clinical skills. Methodology: A hardcopy reflective portfolio was designed and validated. The portfolio incorporated a mini clinical evaluation exercise (mini-CEX), direct observation of procedural skills and reflection sections. Workshops were organized for the stakeholders, that is the management, faculty and students, separately. The rationale of reflection was emphasized. Students were given samples of reflective writing. The portfolio was then implemented amongst the undergraduate medical students of years four, five and six during clinical clerkship. After 16 weeks of implementation of the portfolio, a survey questionnaire was introduced to explore how undergraduate students perceive the educational value of the reflective portfolio and its impact on their deep information processing. Results: The majority of the respondents are in MD Year 5. Out of 52 respondents, 57.7% were doing the internal medicine clinical placement rotation, and 42.3% were in Otorhinolaryngology clinical placement rotation. The respondents believe that the implementation of a reflective portfolio helped them identify their weaknesses, gain professional development in terms of helping them to identify areas where the knowledge is good, increase the learning value if it is used as a formative assessment, try to relate to different courses and in improving their professional skills. However, it is not necessary that the portfolio will improve the self-esteem of respondents or help in developing their critical thinking, The portfolio takes time to complete, and the supervisors are not useful. They had to chase supervisors for feedback. 53.8% of the respondents followed the Gibbs reflective model to write the reflection, whilst the others did not follow any guidelines to write the reflection 48.1% said that the feedback was helpful, 17.3% preferred the use of written feedback, whilst 11.5% preferred oral feedback. Most of them suggested more frequent feedback. 59.6% of respondents found the current portfolio user-friendly, and 28.8% thought it was too bulky. 27.5% have mentioned that for a mobile application. Conclusion: The reflective portfolio, through the reflection of their work and regular feedback from supervisors, has an overall positive impact on the learning process of undergraduate medical students during their clinical clerkship.

Keywords: Portfolio, Reflection, Feedback, Clinical Placement, Undergraduate Medical Education

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Authors: Bhat Geetalaxmi Jairam, D. V. Ashoka

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Wireless Sensor Networks are used in many applications to collect sensed data from different sources. Sensed data has to be delivered through sensors wireless interface using multi-hop communication towards the sink. The data collection in wireless sensor networks consumes energy. Energy consumption is the major constraints in WSN .Reducing the energy consumption while increasing the amount of generated data is a great challenge. In this paper, we have implemented two data gathering protocols with multiple mobile sinks/elements to collect data from sensor nodes. First, is Energy-Efficient Data Gathering with Tour Length-Constrained Mobile Elements in Wireless Sensor Networks (EEDG), in which mobile sinks uses vehicle routing protocol to collect data. Second is An Intelligent Agent-based Routing Structure for Mobile Sinks in WSNs (IAR), in which mobile sinks uses prim’s algorithm to collect data. Authors have implemented concepts which are common to both protocols like deployment of mobile sinks, generating visiting schedule, collecting data from the cluster member. Authors have compared the performance of both protocols by taking statistics based on performance parameters like Delay, Packet Drop, Packet Delivery Ratio, Energy Available, Control Overhead. Authors have concluded this paper by proving EEDG is more efficient than IAR protocol but with few limitations which include unaddressed issues likes Redundancy removal, Idle listening, Mobile Sink’s pause/wait state at the node. In future work, we plan to concentrate more on these limitations to avail a new energy efficient protocol which will help in improving the life time of the WSN.

Keywords: aggregation, consumption, data gathering, efficiency

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Authors: Budhvin T. Withana, Sulochana Rupasinghe

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The problem of Dyslexia and Dysgraphia, two learning disabilities that affect reading and writing abilities, respectively, is a major concern for the educational system. Due to the complexity and uniqueness of the Sinhala language, these conditions are especially difficult for children who speak it. The traditional risk detection methods for Dyslexia and Dysgraphia frequently rely on subjective assessments, making it difficult to cover a wide range of risk detection and time-consuming. As a result, diagnoses may be delayed and opportunities for early intervention may be lost. The project was approached by developing a hybrid model that utilized various deep learning techniques for detecting risk of Dyslexia and Dysgraphia. Specifically, Resnet50, VGG16 and YOLOv8 were integrated to detect the handwriting issues, and their outputs were fed into an MLP model along with several other input data. The hyperparameters of the MLP model were fine-tuned using Grid Search CV, which allowed for the optimal values to be identified for the model. This approach proved to be effective in accurately predicting the risk of Dyslexia and Dysgraphia, providing a valuable tool for early detection and intervention of these conditions. The Resnet50 model achieved an accuracy of 0.9804 on the training data and 0.9653 on the validation data. The VGG16 model achieved an accuracy of 0.9991 on the training data and 0.9891 on the validation data. The MLP model achieved an impressive training accuracy of 0.99918 and a testing accuracy of 0.99223, with a loss of 0.01371. These results demonstrate that the proposed hybrid model achieved a high level of accuracy in predicting the risk of Dyslexia and Dysgraphia.

Keywords: neural networks, risk detection system, Dyslexia, Dysgraphia, deep learning, learning disabilities, data science

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Authors: Julianne Mullen-Williams

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This paper will provide an overview of a three year mixed methods research project that explores if methods from the supervision of dramatherapy can augment the occupational psychology of newly qualified secondary school teachers. It will consider how creativity and the use of metaphor, as applied in the supervision of dramatherapists, can be translated to an educational context in order to explore the explicit / implicit dynamics between the teacher trainee/ newly qualified teacher and the organisation in order to support the super objective in training for teaching; how to ‘be a teacher.’ There is growing evidence that attrition rates among teachers are rising after only five years of service owing to too many national initiatives, an unmanageable curriculum and deteriorating student discipline. The fieldwork conducted entailed facilitating a reflective space for Newly Qualified Teachers from all subject areas, using methods from the supervision of dramatherapy, to explore the social and emotional aspects of teaching and learning with the ultimate aim of improving the occupational psychology of teachers. Clinical supervision is a formal process of professional support and learning which permits individual practitioners in frontline service jobs; counsellors, psychologists, dramatherapists, social workers and nurses to expand their knowledge and proficiency, take responsibility for their own practice, and improve client protection and safety of care in complex clinical situations. It is deemed integral to continued professional practice to safeguard vulnerable people and to reduce practitioner burnout. Dramatherapy supervision incorporates all of the above but utilises creative methods as a tool to gain insight and a deeper understanding of the situation. Creativity and the use of metaphor enable the supervisee to gain an aerial view of the situation they are exploring. The word metaphor in Greek means to ‘carry across’ indicating a transfer of meaning form one frame of reference to another. The supervision support was incorporated into each group’s induction training programme. The first year group attended fortnightly one hour sessions, the second group received two one hour sessions every term. The existing literature on the supervision and mentoring of secondary school teacher trainees calls for changes in pre-service teacher education and in the induction period. There is a particular emphasis on the need to include reflective and experiential learning, within training programmes and within the induction period, in order to help teachers manage the interpersonal dynamics and emotional impact within a high pressurised environment

Keywords: dramatherapy supervision, newly qualified secondary school teachers, professional development, teacher education

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Authors: Zouhair Haddi, Stephane Delliaux, Jean-Francois Pons, Ismail Kechaf, Jean-Claude De Haro, Mustapha Ouladsine

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Atrial fibrillation (AF) has been considered as the most common cardiac arrhythmia, and a major public health burden associated with significant morbidity and mortality. Nowadays, telemedical approaches targeting cardiac outpatients situate AF among the most challenged medical issues. The automatic, early, and fast AF detection is still a major concern for the healthcare professional. Several algorithms based on univariate analysis have been developed to detect atrial fibrillation. However, the published results do not show satisfactory classification accuracy. This work was aimed at resolving this shortcoming by proposing multivariate data analysis methods for automatic AF detection. Four publicly-accessible sets of clinical data (AF Termination Challenge Database, MIT-BIH AF, Normal Sinus Rhythm RR Interval Database, and MIT-BIH Normal Sinus Rhythm Databases) were used for assessment. All time series were segmented in 1 min RR intervals window and then four specific features were calculated. Two pattern recognition methods, i.e., Principal Component Analysis (PCA) and Learning Vector Quantization (LVQ) neural network were used to develop classification models. PCA, as a feature reduction method, was employed to find important features to discriminate between AF and Normal Sinus Rhythm. Despite its very simple structure, the results show that the LVQ model performs better on the analyzed databases than do existing algorithms, with high sensitivity and specificity (99.19% and 99.39%, respectively). The proposed AF detection holds several interesting properties, and can be implemented with just a few arithmetical operations which make it a suitable choice for telecare applications.

Keywords: atrial fibrillation, multivariate data analysis, automatic detection, telemedicine

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