Search results for: visual observation

Authors: Jatziri Y. Moreno-Martínez, Arturo Galván, Israel Enrique Herrera Díaz, José Ramón Gasca Tirado

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In recent years, for the elevated viaducts in Mexico City, a construction system based on precast/pre-stressed concrete elements has been used, in which the bridge girders are divided in two parts by imposing a hinged support in sections where the bending moments that are originated by the gravity loads in a continuous beam are minimal. Precast concrete girders with dapped ends are a representative sample of a behavior that has complex configurations of stresses that make them more vulnerable to cracking due to flexure–shear interaction. The design procedures for ends of the dapped girders are well established and are based primarily on experimental tests performed for different configurations of reinforcement. The critical failure modes that can govern the design have been identified, and for each of them, the methods for computing the reinforcing steel that is needed to achieve adequate safety against failure have been proposed. Nevertheless, the design recommendations do not include procedures for controlling diagonal cracking at the entrant corner under service loading. These cracks could cause water penetration and degradation because of the corrosion of the steel reinforcement. The lack of visual access to the area makes it difficult to detect this damage and take timely corrective actions. Three-dimensional non-linear numerical models based on Finite Element Method to study the cracking at the entrant corner of dapped-end beams were performed using the software package ANSYS v. 11.0. The cracking was numerically simulated by using the smeared crack approach. The concrete structure was modeled using three-dimensional solid elements SOLID65 capable of cracking in tension and crushing in compression. Drucker-Prager yield surface was used to include the plastic deformations. The longitudinal post-tension was modeled using LINK8 elements with multilinear isotropic hardening behavior using von Misses plasticity. The reinforcement was introduced with smeared approach. The numerical models were calibrated using experimental tests carried out in “Instituto de Ingeniería, Universidad Nacional Autónoma de México”. In these numerical models the characteristics of the specimens were considered: typical solution based on vertical stirrups (hangers) and on vertical and horizontal hoops with a post-tensioned steel which contributed to a 74% of the flexural resistance. The post-tension is given by four steel wires with a 5/8’’ (16 mm) diameter. Each wire was tensioned to 147 kN and induced an average compressive stress of 4.90 MPa on the concrete section of the dapped end. The loading protocol consisted on applying symmetrical loading to reach the service load (180 kN). Due to the good correlation between experimental and numerical models some additional numerical models were proposed by considering different percentages of post-tension in order to find out how much it influences in the appearance of the cracking in the reentrant corner of the dapped-end beams. It was concluded that the increasing of percentage of post-tension decreases the displacements and the cracking in the reentrant corner takes longer to appear. The authors acknowledge at “Universidad de Guanajuato, Campus Celaya-Salvatierra” and the financial support of PRODEP-SEP (UGTO-PTC-460) of the Mexican government. The first author acknowledges at “Instituto de Ingeniería, Universidad Nacional Autónoma de México”.

Keywords: concrete dapped-end beams, cracking control, finite element analysis, postension

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Authors: Suparna Bagchi

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Education as a discipline will probably be shaped by the importance it places on a conceptual, curricular, and pedagogical need to shift the emphasis toward transformative classrooms working for positive change through cultural diversity. Awareness of cultural diversity and race equality has heightened following George Floyd’s killing in the USA in 2020. This increasing awareness is particularly relevant in areas of historically low ethnic diversity which have lately experienced a rise in ethnic minority populations and where inclusive growth is a challenge. This research study aims to explore the perspectives of practitioners, students, and parents towards multiculturalism in four South West England primary schools. A qualitative case study methodology has been adopted framed by sociocultural theory. Data were collected through virtually conducted semi-structured interviews with school practitioners and parents, observation of students’ classroom activities, and documentary analysis of classroom displays. Although one-third of the school population includes ethnically diverse children, BAME (Black, Asian, and Minority Ethnic) characters featured in children's books published in Britain in 2019 were almost invisible, let alone a BAME main character. The Office for Standards in Education, Children's Services and Skills (Ofsted) are vocal about extending the Curriculum beyond the academic and technical arenas for pupils’ broader development and creation of an understanding and appreciation of cultural diversity. However, race equality and community cohesion which could help in the students’ broader development are not Ofsted’s school inspection criteria. The absence of culturally diverse content in the school curriculum highlighted by the 1985 Swann Report and 2007 Ajegbo Report makes England’s National Curriculum look like a Brexit policy three decades before Brexit. A revised National Curriculum may be the starting point with the teachers as curriculum framers playing a significant part. The task design is crucial where teachers can place equal importance on the interwoven elements of “how”, “what” and “why” the task is taught. Teachers need to build confidence in encouraging difficult conversations around racism, fear, indifference, and ignorance breaking the stereotypical barriers, thus helping to create students’ conception of a multicultural Britain. Research showed that trainee teachers in predominantly White areas often exhibit confined perspectives while educating children. Irrespective of the geographical location, school teachers can be equipped with culturally responsive initial and continuous professional development necessary to impart multicultural education. This may aid in the reduction of employees’ unconscious bias. This becomes distinctly pertinent to avoid horrific cases in the future like the recent one in Hackney where a Black teenager was strip-searched during period wrongly suspected of cannabis possession. Early research findings show participants’ eagerness for more ethnic diversity content incorporated in teaching and learning. However, schools are considerably dependent on the knowledge-focused Primary National Curriculum in England. Moreover, they handle issues around the intersectionality of disability, poverty, and gender. Teachers were trained in times when foregrounding ethnicity matters was not happening. Therefore, preoccupied with Curriculum requirements, intersectionality issues, and teacher preparations, schools exhibit an incapacity due to which keeping momentum on ethnic diversity is somewhat endangered.

Keywords: case study, curriculum decolonisation, inclusive education, multiculturalism, qualitative research in Covid19 times

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Authors: Pei Yi Lin

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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.

Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model

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Authors: Ali Kazemi

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Those within the volatile and interconnected international economic markets, appropriately predicting market trends, hold substantial fees for traders and financial establishments. Traditional device mastering strategies have made full-size strides in forecasting marketplace movements; however, monetary data's complicated and networked nature calls for extra sophisticated processes. This observation offers a groundbreaking method for monetary marketplace prediction that leverages the synergistic capability of Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks. Our suggested algorithm is meticulously designed to forecast the traits of inventory market indices and cryptocurrency costs, utilizing a comprehensive dataset spanning from January 1, 2015, to December 31, 2023. This era, marked by sizable volatility and transformation in financial markets, affords a solid basis for schooling and checking out our predictive version. Our algorithm integrates diverse facts to construct a dynamic economic graph that correctly reflects market intricacies. We meticulously collect opening, closing, and high and low costs daily for key inventory marketplace indices (e.g., S&P 500, NASDAQ) and widespread cryptocurrencies (e.g., Bitcoin, Ethereum), ensuring a holistic view of marketplace traits. Daily trading volumes are also incorporated to seize marketplace pastime and liquidity, providing critical insights into the market's shopping for and selling dynamics. Furthermore, recognizing the profound influence of the monetary surroundings on financial markets, we integrate critical macroeconomic signs with hobby fees, inflation rates, GDP increase, and unemployment costs into our model. Our GCN algorithm is adept at learning the relational patterns amongst specific financial devices represented as nodes in a comprehensive market graph. Edges in this graph encapsulate the relationships based totally on co-movement styles and sentiment correlations, enabling our version to grasp the complicated community of influences governing marketplace moves. Complementing this, our LSTM algorithm is trained on sequences of the spatial-temporal illustration discovered through the GCN, enriched with historic fee and extent records. This lets the LSTM seize and expect temporal marketplace developments accurately. Inside the complete assessment of our GCN-LSTM algorithm across the inventory marketplace and cryptocurrency datasets, the version confirmed advanced predictive accuracy and profitability compared to conventional and opportunity machine learning to know benchmarks. Specifically, the model performed a Mean Absolute Error (MAE) of 0.85%, indicating high precision in predicting day-by-day charge movements. The RMSE was recorded at 1.2%, underscoring the model's effectiveness in minimizing tremendous prediction mistakes, which is vital in volatile markets. Furthermore, when assessing the model's predictive performance on directional market movements, it achieved an accuracy rate of 78%, significantly outperforming the benchmark models, averaging an accuracy of 65%. This high degree of accuracy is instrumental for techniques that predict the course of price moves. This study showcases the efficacy of mixing graph-based totally and sequential deep learning knowledge in economic marketplace prediction and highlights the fee of a comprehensive, records-pushed evaluation framework. Our findings promise to revolutionize investment techniques and hazard management practices, offering investors and economic analysts a powerful device to navigate the complexities of cutting-edge economic markets.

Keywords: financial market prediction, graph convolutional networks (GCNs), long short-term memory (LSTM), cryptocurrency forecasting

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Authors: Chau So Ling

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I.INTRODUCTION: Educators have always been interested in exploring factors that contribute to students’ academic success. It is beyond question that language, as a medium of instruction, will affect student learning. This paper tries to investigate whether language is a crucial factor in determining students’ achievement in their studies. II. BACKGROUND AND SIGNIFICANCE OF STUDY: The issue of using English as a medium of instruction in Hong Kong is a special topic because Hong Kong is a post-colonial and international city which a British colony. In such a specific language environment, researchers in the education field have always been interested in investigating students’ language proficiency and its relation to academic achievement and other related educational indicators such as motivation to learn, self-esteem, learning effectiveness, self-efficacy, etc. Along this line of thought, this study specifically focused on business education. III. METHODOLOGY: The methodology in this study involved two sequential stages, namely, a focus group interview and a data analysis. The whole study was directed towards both qualitative and quantitative aspects. The subjects of the study were divided into two groups. For the first group participating in the interview, a total of ten high school students were invited. They studied Business Studies, and their English standard was varied. The theme of the discussion was “Does English affect your learning and examination results of Business Studies?” The students were facilitated to discuss the extent to which English standard affected their learning of Business subjects and requested to rate the correlation between English and performance of Business Studies on a five-point scale. The second stage of the study involved another group of students. They were high school graduates who had taken the public examination for entering universities. A database containing their public examination results for different subjects has been obtained for the purpose of statistical analysis. Hypotheses were tested and evidence was obtained from the focus group interview to triangulate the findings. V. MAJOR FINDINGS AND CONCLUSION: By sharing of personal experience, the discussion of focus group interviews indicated that higher English standards could help the students achieve better learning and examination performance. In order to end the interview, the students were asked to indicate the correlation between English proficiency and performance of Business Studies on a five-point scale. With point one meant least correlated, ninety percent of the students gave point four for the correlation. The preliminary results illustrated that English plays an important role in students’ learning of Business Studies, or at least this was what the students perceived, which set the hypotheses for the study. After conducting the focus group interview, further evidence had to be gathered to support the hypotheses. The data analysis part tried to find out the relationship by correlating the students’ public examination results of Business Studies and levels of English standard. The results indicated a positive correlation between their English standard and Business Studies examination performance. In order to highlight the importance of the English language to the study of Business Studies, the correlation between the public examination results of other non-business subjects was also tested. Statistical results showed that language does play a role in affecting students’ performance in studying Business subjects than the other subjects. The explanation includes the dynamic subject nature, examination format and study requirements, the specialist language used, etc. Unlike Science and Geography, students in their learning process might find it more difficult to relate business concepts or terminologies to their own experience, and there are not many obvious physical or practical activities or visual aids to serve as evidence or experiments. It is well-researched in Hong Kong that English proficiency is a determinant of academic success. Other research studies verified such a notion. For example, research revealed that the more enriched the language experience, the better the cognitive performance in conceptual tasks. The ability to perform this kind of task is particularly important to students taking Business subjects. Another research was carried out in the UK, which was geared towards identifying and analyzing the reasons for underachievement across a cohort of GCSE students taking Business Studies. Results showed that weak language ability was the main barrier to raising students’ performance levels. It seemed that the interview result was successfully triangulated with data findings. Although education failure cannot be restricted to linguistic failure and language is just one of the variables to play in determining academic achievement, it is generally accepted that language does affect students’ academic performance. It is just a matter of extent. This paper provides recommendations for business educators on students’ language training and sheds light on more research possibilities in this area.

Keywords: academic performance, language, learning, medium of instruction

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Authors: Swaleh Aboud, Mishal Gudka, David Obura

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Background: Coral reefs are becoming increasingly vulnerable due to several threats ranging from climate change to overfishing. This has resulted in increased management and conservation efforts to protect reefs from degradation and facilitate recovery. Recruitmentof new individuals are isimportant in the recovery process and critical for the persistence of coral reef ecosystems. Local coral community structure can be influenced by successful recruit settlement, survival, and growth Understanding coral recruitment patterns can help quantify reef resilience and connectivity, establish baselines and track changes and evaluate the effectiveness of reef restoration and conservation efforts. This study will examine the abundance and spatial pattern of coral recruits and how this relates to adult community structure, including the distribution of thermal resistance and sensitive genera and their distribution in different management regimes. Methods: Coral recruit and demography surveys were conducted from 2020 to 2022, covering 35 sites in 19coral reef locations along the Kenyan coast. These included marine parks, reserves, community conservation areas (CMAs), and open access areas from the north (Marereni) to the south (Kisite) coast of Kenya and across different reef habitats. The data was collected through the underwater visual census (UVC) technique. We counted adult corals (>10 cm diameter)of23 selected genera using belt transects (25 by 1 m) and sampling of 1 m2 quadrat (at an interval of 5m) for all coloniesless than 10 cm diameter. The benthic cover was collected using photo quadrats. The surveys were only done during the northeast monsoon season. The data wereanalyzed using the R program to see the distribution patterns and the Kruskal Wallis test to see whether there was a significant difference. Spearman correlation was also applied to assess the relationship between the distribution of coral genera in recruits and adults. Results: A total of 44 different coral genera were recorded for recruits, ranging from 3at Marereni to 30at Watamu Marine Reserve. Recruit densities ranged from 1.2±1.5recruit m-2 (mean±SD) at Likoni to 10.3± 8.4 recruit m-2 at Kisite Marine Park. The overall densityof recruitssignificantly differed between reef locations, with Kisite Marine Park and Reserve and Likonihaving significantly large differences from all the other locations, while Vuma, Watamu, Malindi, and Kilifi had significantly lower differences from all the other locations. The recruit generadensity along the Kenya coastwas divided into two clusters, one of which only included sites inKisite Marine Park. Adult colonies were dominated by Porites massive, Acropora, Platygyra, and Favites, whereas recruits were dominated by Porites branching, Porites massive, Galaxea, and Acropora. However, correlation analysis revealed a statistically significant positive correlation (r=0.81, p<0.05) between recruit and adult coral densities across the 23 coral genera. Marereni, which had the lowest densityof recruits, has only thermallyresistant coral genera, while Kisite Marine Park, with the highest recruit densities, has over 90% thermal sensitive coral genera. A weak positive correlation was found between recruit density and coralline algae, dead standing corals, and turf algae, whereas a weak negative correlation was found between recruit density and bare substrate and macroalgae. Between management regimes, marine reserves were found to have more recruits than no-take zones (marine parks and CMAs) and open access areas, although the difference was not significant. Conclusion: There was a statistically significant difference in the density of recruits between different reef locations along the Kenyan coast. Although the dominating genera of adults and recruits were different, there was a strong positive correlation between their coral communities, which could indicate self-recruitment processes or consistent distance seedings (of the same recruit genera). Sites such as Kisite Marine Park, with high recruit densities but dominated by thermally sensitive genera, will, on the other hand, be adversely affected by future thermal stress. This could imply that reducing the threats to coral reefs such as overfishingcould allow for their natural regeneration and recovery.

Keywords: coral recruits, coral adult size-class, cora demography, resilience

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