Search results for: respiratory rate

Authors: Alejandro Molina-Villegas, Silvia Fernández-Sabido, Eduardo Mendoza-Vargas, Fátima Miranda-Pestaña

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The automatic detection of indigenous languages ​​in digital texts is essential to promote their inclusion in digital media. Underrepresented languages, such as Maya, are often excluded from language detection tools like Google’s language-detection library, LANGDETECT. This study addresses these limitations by developing a hybrid language detection solution that accurately distinguishes Maya (YUA) from Spanish (ES). Two strategies are employed: the first focuses on creating a profile for the Maya language within the LANGDETECT library, while the second involves training a Naive Bayes classification model with two categories, YUA and ES. The process includes comprehensive data preprocessing steps, such as cleaning, normalization, tokenization, and n-gram counting, applied to text samples collected from various sources, including articles from La Jornada Maya, a major newspaper in Mexico and the only media outlet that includes a Maya section. After the training phase, a portion of the data is used to create the YUA profile within LANGDETECT, which achieves an accuracy rate above 95% in identifying the Maya language during testing. Additionally, the Naive Bayes classifier, trained and tested on the same database, achieves an accuracy close to 98% in distinguishing between Maya and Spanish, with further validation through F1 score, recall, and logarithmic scoring, without signs of overfitting. This strategy, which combines the LANGDETECT profile with a Naive Bayes model, highlights an adaptable framework that can be extended to other underrepresented languages in future research. This fills a gap in Natural Language Processing and supports the preservation and revitalization of these languages.

Keywords: indigenous languages, language detection, Maya language, Naive Bayes classifier, natural language processing, low-resource languages

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Authors: Shuyu Zhang

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The waterfront historical district, as a type of historical districts on the verge of waters such as the sea, lake, and river, have a relatively special urban form. In the past preservation and renewal of traditional historic districts, there have been many discussions on the land range, and the waterfront and marginal spaces are easily overlooked. However, the waterfront space of the historic districts, as a cultural landscape heritage combining historical buildings and landscape elements, has strong ecological and sustainable values. At the same time, Suzhou and Venice, as sister water cities in history, have more waterfront spaces that can be compared in urban form and other levels. Therefore, this paper focuses on the waterfront historic districts in Venice and Suzhou, establishes quantitative evaluation indicators for environmental perception, makes analogies, and promotes the renewal and activation of the entire historical district by improving the spatial quality and vitality of the waterfront area. First, this paper uses multi-source data for analysis, such as Baidu Maps and Google Maps API to crawl the street view of the waterfront historic districts, uses machine learning algorithms to analyze the proportion of cultural landscape elements such as green viewing rate in the street view pictures, and uses space syntax software to make quantitative selectivity analysis, so as to establish environmental perception evaluation indicators for the waterfront historic districts. Finally, by comparing and summarizing the waterfront historic districts in Venice and Suzhou, it reveals their similarities and differences, characteristics and conclusions, and hopes to provide a reference for the heritage preservation and renewal of other waterfront historic districts.

Keywords: waterfront historical district, cultural landscape, perception, multi-source Data

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Authors: Abhay Asthana, Shally Toshkhani, Gyati Shilakari

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The present research was aimed to develop a biodegradable dermal patch formulation for wound healing in a novel, sustained and systematic manner. The goal is to reduce the frequency of dressings with improved drug delivery and thereby enhance therapeutic performance. In present study optimized formulation was designed using component polymers and excipients (e.g. Hydroxypropyl methyl cellulose, Ethylcellulose, and Gelatin) to impart significant folding endurance, elasticity and strength. Gelatin was used to get a mixture using ethylene glycol. Chitosan dissolved in suitable medium was mixed with stirring to gelatin mixture. With continued stirring to the mixture Curcumin was added in optimized ratio to get homogeneous dispersion. Polymers were dispersed with stirring in final formulation. The mixture was sonicated casted to get the film form. All steps were carried out under under strict aseptic conditions. The final formulation was a thin uniformly smooth textured film with dark brown-yellow color. The film was found to have folding endurance was around 20 to 21 times without a crack in an optimized formulation at RT (23C). The drug content was in range 96 to 102% and it passed the content uniform test. The final moisture content of the optimized formulation film was NMT 9.0%. The films passed stability study conducted at refrigerated conditions (4±0.2C) and at room temperature (23 ± 2C) for 30 days. Further, the drug content and texture remained undisturbed with stability study conducted at RT 23±2C for 45 and 90 days. Percentage cumulative drug release was found to be 80% in 12 h and matched the biodegradation rate as drug release with correlation factor R2 > 0.9. The film based formulation developed shows promising results in terms of stability and release profiles.

Keywords: biodegradable, patch, bioactive, polymer

Procedia PDF Downloads 517

Authors: Chenxi Liao, Mizuho Akimoto, Mariya Bivolarova, Sekhar Chandra, Xiaojun Fan, Li Lan, Jelle Laverge, Pawel Wargocki

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The bedroom environment plays an important role in maintaining good sleep quality, which is vital for humans health and next-day performance. A survey of the sleep-disturbed bedroom environmental factors and the occupants’ bedroom windows (BW) or bedroom door (BD) opening behaviors was launched in the capital region of Denmark in 2020 by an online questionnaire. People were asked if they were disturbed by too warm temperature, too cool temperature, noise, or stuffy air during sleep. Also, they reported their BW or the BD opening behaviors in the morning, afternoon, evening, and during sleep. A total of 512 responses were received. Too warm temperature was reported the most among the four sleep-disturbed factors, following too cool temperature, noise, and stuffy air. Whether or not opening BW or the BD was commonly used to improve or change the bedroom environment. The respondents who were disturbed by too warm temperature during sleep opened BW for a longer time in the morning compared to those who were never disturbed by it (OR, 1.28; 95% CI, 1.01-1.62). Those who were disturbed by too cool temperatures tended to open BW less frequently in the morning (OR, 1.24; 95% CI, 0.97-1.57). They preferred keeping BW open in the whole day if they realized stuffy air disturbing their sleep, although only a few of them still opened BW during sleep. Those who were disturbed by too cool temperature (OR, 0.76; 95% CI, 0.63-0.92) and noise (OR, 0.80; 95% CI, 0.66-0.96) were more likely to sleep with the BD open in a lesser frequency. Opening BW, increasing ventilation rates, could relieve disturbing by stuffy air during sleep, but induced other sleep-disturbed factors such as too cool in winter and noise. Also, opening BW only when people were not sleep was not sufficient to exempt disturbing by stuffy air during sleep. Using mechanical ventilation in bedrooms is necessary to ensure good air quality and meanwhile to avoid thermal discomfort and noise during sleep. Future studies are required to figure out the required flow rate of fresh air of mechanical ventilation during sleep.

Keywords: bedroom environmental, survey, occupants behaviors, windows, door

Procedia PDF Downloads 209

Authors: Rachel Shukrun, Szilvia Baron, Victoria Fidel, Anna Shusterman, Osnat Sher, Netanya Kollender, Dror Levin, Yair Peled, Yair Gortzak, Yoav Ben-Shahar, Revital Caspi, Sagi Gordon, Michal Manisterski, Ronit Elhasid

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Ewing sarcoma (EWS) is a highly aggressive cancer with a survival rate of 70–80% for patients with localized disease and under 30% for those with metastatic disease. Tumor-infiltrating neutrophils (TIN) can generate extracellular net-like DNA structures known as neutrophil extracellular traps (NETs). However, little is known about the presence and prognostic significance of tumor-infiltrating NETs in EWS. Herein, we investigated 46 patients diagnosed with EWS and treated in the Tel Aviv Medical Center between 2010 and 2021. TINs and NETs were identified in diagnostic biopsies of EWS by immunofluorescent. In addition, NETs were investigated in neutrophils isolated from peripheral blood samples of EWS patients at diagnosis and following neoadjuvant chemotherapy. The relationships between the presence of TINs and NETs, pathological and clinical features, and outcomes were analyzed. Our results demonstrate that TIN and NETs at diagnosis were higher in EWS patients with metastatic disease compared to those with local disease. High NETs formation at diagnosis predicted poor response to neo-adjuvant chemotherapy, relapse, and death from disease (P < .05). NETs formation in peripheral blood samples at diagnosis was significantly elevated among patients with EWS compared to pediatric controls and decreased significantly following neoadjuvant chemotherapy. In conclusion, NETs formation seems to have a role in the EWS immune microenvironment. Their presence can refine risk stratification, predict chemotherapy resistance and survival, and serve as a therapeutic target in patients with EWS.

Keywords: Ewing sarcoma, tumor microenvironment, neutrophil, neutrophil extracellular traps (NETs), prognosis

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Authors: Isaac Kofi Mensah

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The manual system of voting has been the most widely used system of electing representatives around the globe, particularly in Africa. Due to the known numerous problems and challenges associated with the manual system of voting, many countries are migrating to the electronic voting system as a suitable and credible means of electing representatives over the manual paper-based system. This research paper therefore investigated the factors influencing adoption and use of an electronic voting system in Ghana. A total of 400 Questionnaire Instruments (QI) were administered to potential respondents in Ghana, of which 387 responded representing a response rate of 96.75%. The Technology Acceptance Model was used as the theoretical framework for the study. The research model was tested using a simple linear regression analysis with SPSS. A little of over 71.1% of the respondents recommended the Electoral Commission (EC) of Ghana to adopt an electronic voting system in the conduct of public elections in Ghana. The results indicated that all the six predictors such as perceived usefulness (PU), perceived ease of use (PEOU), perceived free and fair elections (PFFF), perceived credible elections (PCE), perceived system integrity (PSI) and citizens trust in the election management body (CTEM) were all positively significant in predicting the readiness of citizens to adopt and use an electronic voting system in Ghana. However, jointly, the hypotheses tested revealed that apart from Perceived Free and Fair Elections and Perceived Credible and Transparent Elections, all the other factors such as PU, Perceived System Integrity and Security and Citizen Trust in the Election Management Body were found to be significant predictors of the Willingness of Ghanaians to use an electronic voting system. All the six factors considered in this study jointly account for about 53.1% of the reasons determining the readiness to adopt and use an electronic voting system in Ghana. The implications of this research finding on elections in Ghana are discussed.

Keywords: credible elections, Election Management Body (EMB), electronic voting, Ghana, Technology Acceptance Model (TAM)

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Authors: Mohamed Aly, Aliaa Rehan Youssef, Emad Sawerees, Mounir Guirgis

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Relevance: Osteoarthritis (OA) is the most common degenerative disease seen in all populations. It causes disability and substantial socioeconomic burden. Evidence supports that exercise are the most effective conservative treatment for patients with OA. Therapists experience and clinical judgment play major role in exercise prescription and scientific evidence for this regard is lacking. The development of clinical prediction rules to identify patients who are most likely benefit from exercise may help solving this dilemma. Purpose: This study investigated whether body mass index and functional ability at baseline can predict patients’ response to a selected exercise program. Approach: Fifty-six patients, aged 35 to 65 years, completed an exercise program consisting of open kinetic chain strengthening and passive stretching exercises. The program was given for 3 sessions per week, 45 minutes per session, for 6 weeks Evaluation: At baseline and post treatment, pain severity was assessed using the numerical pain rating scale, whereas functional ability was being assessed by step test (ST), time up and go test (TUG) and 50 feet time walk test (50 FTW). After completing the program, global rate of change (GROC) score of greater than 4 was used to categorize patients as successful and non-successful. Thirty-eight patients (68%) had successful response to the intervention. Logistic regression showed that BMI and 50 FTW test were the only significant predictors. Based on the results, patients with BMI less than 34.71 kg/m2 and 50 FTW test less than 25.64 sec are 68% to 89% more likely to benefit from the exercise program. Conclusions: Clinicians should consider the described strengthening and flexibility exercise program for patents with BMI less than 34.7 Kg/m2 and 50 FTW faster than 25.6 seconds. The validity of these predictors should be investigated for other exercise.

Keywords: clinical prediction rule, knee osteoarthritis, physical therapy exercises, validity

Procedia PDF Downloads 425

Authors: Dharshini S.

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India is currently experiencing a shift in wage discrimination from gender, caste and religion to different age groups in both formal and informal sectors. In this milieu, this study examines wage discrimination in the informal labour market between young people (15-29 years) and middle-aged people (30-59 years) among regular and casual employees in the Indian informal sector. The data was collected using periodic labour force (PLFS), and the original data was extracted from the National Sample Survey Office (NSSO) under the Ministry of Statistics and Programme Implementation (MOSPI), Government of India. The OLS regression model explores the determinants of wages for both regular and casual employees. Moreover, the Blinder Oaxaca decomposition method is used to explore the explained and unexplained components of this wage discrimination. The younger people (regular and casual employees) get lower wages as compared to middle-aged employees in the informal sector. The regression result follows the human capital theory, where education, job experience and higher occupation help to raise the wage rate of middle-aged people more than young-aged people in regular work. Furthermore, we found the rising trend of wage discrimination between the above groups over the years from 2017-18 to 2022-23. Unexplained factors (discrimination effects) contribute more to the wage differentiation between the young and middle age groups. It indicates that wage discrimination persists among regular and casual employees in the informal labour market, which is not a good sign for the economy. For the betterment of workers who face discrimination for age, the policies and programs should be implemented like other countries such as the U.S.A to stop age discrimination due to stereotypes in India.

Keywords: wage discrimination, young workers, middle workers, Informal sector, blinder oaxaca decomposition, PLFS.

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Authors: Shraddha Srivastava, N. K. Painuly, S. P. Mishra, Navin Singh, Muhsin Punchankandy, Kirti Srivastava, M. L. B. Bhatt

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Various optimization methods used in interstitial brachytherapy are based on dwell positions and dwell weights alteration to produce dose distribution based on the implant geometry. Since these optimization schemes are not anatomy based, they could lead to deviations from the desired plan. This study was henceforth carried out to compare anatomy-based Inverse Planning Simulated Annealing (IPSA) optimization technique with graphical and geometrical optimization methods in interstitial high dose rate brachytherapy planning of cervical carcinoma. Six patients with 12 CT data sets of MUPIT implants in HDR brachytherapy of cervical cancer were prospectively studied. HR-CTV and organs at risk (OARs) were contoured in Oncentra treatment planning system (TPS) using GYN GEC-ESTRO guidelines on cervical carcinoma. Three sets of plans were generated for each fraction using IPSA, graphical optimization (GrOPT) and geometrical optimization (GOPT) methods. All patients were treated to a dose of 20 Gy in 2 fractions. The main objective was to cover at least 95% of HR-CTV with 100% of the prescribed dose (V100 ≥ 95% of HR-CTV). IPSA, GrOPT, and GOPT based plans were compared in terms of target coverage, OAR doses, homogeneity index (HI) and conformity index (COIN) using dose-volume histogram (DVH). Target volume coverage (mean V100) was found to be 93.980.87%, 91.341.02% and 85.052.84% for IPSA, GrOPT and GOPT plans respectively. Mean D90 (minimum dose received by 90% of HR-CTV) values for IPSA, GrOPT and GOPT plans were 10.19 ± 1.07 Gy, 10.17 ± 0.12 Gy and 7.99 ± 1.0 Gy respectively, while D100 (minimum dose received by 100% volume of HR-CTV) for IPSA, GrOPT and GOPT plans was 6.55 ± 0.85 Gy, 6.55 ± 0.65 Gy, 4.73 ± 0.14 Gy respectively. IPSA plans resulted in lower doses to the bladder (D₂

Keywords: cervical cancer, HDR brachytherapy, IPSA, MUPIT

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Authors: Ayten Bakhtiyar Khasayeva

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Lower Kura depression is a part of the South Caspian Basin (SCB), located between the folded regions of the Greater and Lesser Caucasus. The region is characterized by thick sedimentary cover 22 km (SCB up to 30 km), high sedimentation rate, low geothermal gradient (average value corresponds to 2 °C / 100m). There is Quaternary, Pliocene, Miocene and Oligocene deposits take part in geological structure. Miocene and Oligocene deposits are opened by prospecting and exploratory wells in the areas of Kalamaddin and Garabagli. There are 25 mud volcanoes within the territory of the Lower Kura depression, which are the unique source of information about hydrocarbons contenting great depths. During the wells data research, solid erupted products and mud volcano fluids, and according to the geological and thermal characteristics of the region, it was determined that the main phase of the hydrocarbon generation (MK1-AK2) corresponds to a wide range of depths from 10 to 14 km, which corresponds to the Pliocene-Miocene sediments, and to the "oil and gas windows" according to the intended meaning of R0 ≈ 0,65-0,85%. Fluids of mud volcanoes comprise by the following phases - gas, water. Gas phase consists mainly of methane (99%) of heavy hydrocarbons (С2+ hydrocarbons), CO2, N2, inert components He, Ar. The content of the С2+ hydrocarbons in the gases of mud volcanoes associated with oil deposits is increased. Carbon isotopic composition of methane for the Lower Kura depression varies from -40 ‰ to -60 ‰. Water of mud volcanoes are represented by all four genetic types. However the most typical types of water are HCN type. According to the Mg-Li geothermometer formation of mud waters corresponds to the temperature range from 20 °C to 140 °C (PC2). The solid product emissions of mud volcanoes identified 90 minerals and 30 trace elements. As a result geochemical investigation, thermobaric and geological conditions, zone oil and gas generation - the prospect of the Lower Kura depression is projected to depths greater than 10 km.

Keywords: geology, geochemistry, mud volcanoes, petroleum potential

Procedia PDF Downloads 367

Authors: M. S. Wickramarachchi, L. S. Nawarathna, C. M. B. Dematawewa

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Hatchery performance is critical for the profitability of poultry breeder operations. Some extrinsic parameters of eggs and breeders cause to increase or decrease the hatchability. This study aims to identify the affecting extrinsic parameters on the commercial hatchability of local chicken's eggs and determine the most efficient classification model with a hatchability rate greater than 90%. In this study, seven extrinsic parameters were considered: egg weight, moisture loss, breeders age, number of fertilised eggs, shell width, shell length, and shell thickness. Multiple linear regression was performed to determine the most influencing variable on hatchability. First, the correlation between each parameter and hatchability were checked. Then a multiple regression model was developed, and the accuracy of the fitted model was evaluated. Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), k-Nearest Neighbors (kNN), Support Vector Machines (SVM) with a linear kernel, and Random Forest (RF) algorithms were applied to classify the hatchability. This grouping process was conducted using binary classification techniques. Hatchability was negatively correlated with egg weight, breeders' age, shell width, shell length, and positive correlations were identified with moisture loss, number of fertilised eggs, and shell thickness. Multiple linear regression models were more accurate than single linear models regarding the highest coefficient of determination (R²) with 94% and minimum AIC and BIC values. According to the classification results, RF, CART, and kNN had performed the highest accuracy values 0.99, 0.975, and 0.972, respectively, for the commercial hatchery process. Therefore, the RF is the most appropriate machine learning algorithm for classifying the breeder outcomes, which are economically profitable or not, in a commercial hatchery.

Keywords: classification models, egg weight, fertilised eggs, multiple linear regression

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Authors: Tsuneyuki Namekata, Yoko Namekata

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It is known that the mean investment evolves from a very low initial value to some high level in the Continuous Prisoner's Dilemma. We examine how the cooperation level evolves from a low initial level to a high level in our Demographic Multi-level Donor-Recipient situation. In the Multi-level Donor-Recipient game, one player is selected as a Donor and the other as a Recipient randomly. The Donor has multiple cooperative moves and one defective move. A cooperative move means the Donor pays some cost for the Recipient to receive some benefit. The more cooperative move the Donor takes, the higher cost the Donor pays and the higher benefit the Recipient receives. The defective move has no effect on them. Two consecutive Multi-level Donor-Recipient games, one as a Donor and the other as a Recipient, can be viewed as a discrete version of the Continuous Prisoner's Dilemma. In the Demographic Multi-level Donor-Recipient game, players are initially distributed spatially. In each period, players play multiple Multi-level Donor-Recipient games against other players. He leaves offspring if possible and dies because of negative accumulated payoff of him or his lifespan. Cooperative moves are necessary for the survival of the whole population. There is only a low level of cooperative move besides the defective move initially available in strategies of players. A player may modify and expand his strategy by his recent experiences or practices. We distinguish several types of a player about modification and expansion. We show, by Agent-Based Simulation, that introducing only the modification increases the emergence rate of cooperation and introducing both the modification and the expansion further increases it and a high level of cooperation does emerge in our Demographic Multi-level Donor-Recipient Game.

Keywords: agent-based simulation, donor-recipient game, emergence of cooperation, spatial structure, TFT, TF2T

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Authors: M. R. Ahmad, I. Musirin, M. M. Othman, N. A. Rahmat

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Plug-in Hybrid-Electric Vehicle (PHEV) has gained immense popularity in recent years. PHEV offers numerous advantages compared to the conventional internal-combustion engine (ICE) vehicle. Millions of PHEVs are estimated to be on the road in the USA by 2020. Uncoordinated PHEV charging is believed to cause severe impacts to the power grid; i.e. feeders, lines and transformers overload and voltage drop. Nevertheless, improper PHEV data model used in such studies may cause the findings of their works is in appropriated. Although smart charging is more attractive to researchers in recent years, its implementation is not yet attainable on the street due to its requirement for physical infrastructure readiness and technology advancement. As the first step, it is finest to study the impact of charging PHEV based on real vehicle travel data from National Household Travel Survey (NHTS) and at present charging rate. Due to the lack of charging station on the street at the moment, charging PHEV at home is the best option and has been considered in this work. This paper proposed a technique that comprehensively presents the impact of charging PHEV on power system networks considering huge numbers of PHEV samples with its traveling data pattern. Vehicles Charging Load Profile (VCLP) is developed and implemented in IEEE 30-bus test system that represents a portion of American Electric Power System (Midwestern US). Normalization technique is used to correspond to real time loads at all buses. Results from the study indicated that charging PHEV using opportunity charging will have significant impacts on power system networks, especially whereas bigger battery capacity (kWh) is used as well as for higher penetration level.

Keywords: plug-in hybrid electric vehicle, transportation electrification, impact of charging PHEV, electricity demand profile, load profile

Procedia PDF Downloads 288

Authors: Brian Kwok

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Hong Kong is well-known for its name as "the Pearl of the Orient", due to its spectacular night-view with vast amount of decorative neon lights on the streets. Neon signs are first used as the pervasive media of communication for all kinds of commercial advertising, ranging from movie theatres to nightclubs and department stores, and later appropriated by artists as medium of artwork. As a well-established visual language, it displays texts in bilingual format due to British's colonial influence, which are sometimes arranged in an opposite reading order. Research on neon signs as a visual representation is rare but significant because they are part of people’s collective memories of the unique cityscapes which associate the shifting values of people's daily lives and culture identity. Nevertheless, with the current policy to remove abandoned neon signs, their total number dramatically declines recently. The Buildings Department found an estimation of 120,000 unauthorized signboards (including neon signs) in Hong Kong in 2013, and the removal of such is at a rate of estimated 1,600 per year since 2006. In other words, the vernacular cultural values and historical continuity of neon signs will gradually be vanished if no immediate action is taken in documenting them for the purpose of research and cultural preservation. Therefore, the Hong Kong Neon Signs Archive project was established in June of 2015, and over 100 neon signs are photo-documented so far. By content analysis, this project will explore the two components of neon signs – the use of visual languages and vernacular tacit knowledge of neon makers. It attempts to answer these questions about Hong Kong's neon signs: 'What are the ways in which visual representations are used to produce our cityscapes and streetscapes?'; 'What are the visual languages and conventions of usage in different business types?'; 'What the intact knowledge are applied when producing these visual forms of neon signs?'

Keywords: cityscapes, neon signs, tacit knowledge, visual representation

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Authors: Dilliram Paudyal

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The history of taxation in Nepal dates back to antiquity. However, the modern tax system gained its momentum after the establishment of democracy in 1951, which initially focused only land tax and tariff on foreign trade. In the due time, several taxes were introduced, such as direct taxes, indirect taxes, and non-taxes. However, the tax structure in Nepal is heavily dominated by indirect taxes that contribute more than 60 % of the total revenue. The government has been mobilizing revenues through a series of tax reforms during the Tenth Five-year Plan (2002 – 2007) and successive Three-year Interim Development Plans by introducing several tax measures. However, these reforms are regressive in nature, which does not lead the overall economy towards short-run stability as well as in the long run development. Based on the literature review and discussion among government officials and few taxpayers individually and groups, this paper aims to major issues and challenges that hinder the tax reform effective in Nepal. Additionally, this paper identifies potential way and process of tax reform in Nepal. The results of the study indicate that transparency in a major problem in Nepalese tax system in Nepal, where serious structural constraints with administrative and procedural complexities envisaged in the Income Tax Act and taxpayers are often unaware of the specific size of tax which is to comply them. Some other issues include high tax rate, limited tax base, leakages in tax collection, rigid and complex Income Tax Act, inefficient and corrupt tax administration, limited potentialities of direct taxes and negative responsiveness of land tax with higher administrative costs. In the context, modality of tax structure and mobilize additional resources is to be rectified on a greater quantum by establishing an effective, dynamic and highly power driven Autonomous Revenue Board.

Keywords: corrupt, development, inefficient, taxation

Procedia PDF Downloads 180

Authors: Maryam Ghavam Sadri, Kimia Moiniafshari

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Objectives: Autism is a neuro-developmental disorder that has negative effects on verbal, mental and behavioral development. Studies have shown the role of a maternal dietary pattern before and during pregnancy. The relation of exerting of nutritional management programs in prevention of Autism has been approved. This review article has been made to investigate the role of nutritional management strategies before and during pregnancy in the prevention of Autism. Methods: This review study was accomplished by using the keywords related to the topic, 67 articles were found (2000-2015) and finally 20 article with criteria such as including maternal lifestyle, nutritional deficiencies and Autism prevention were selected. Results: Maternal dietary pattern and health before and during pregnancy have important roles in the incidence of Autism. Studies have suggested that high dietary fat intake and obesity can increase the risk of Autism in offspring. Maternal metabolic condition specially gestational diabetes (GDM) (p-value < 0.04) and folate deficiency (p-value = 0.04) is associated with risk of Autism. Studies have shown that folate intake in mothers with autistic children is less than mothers who have typically developing children (TYP) (p-value<0.01). As folate is an essential micronutrient for fetus mental development, consumption of average 600 mcg/day especially in P1 phase of pregnancy results in significant reduction in incidence of Autism (OR:1.53, 95%CI=0.42-0.92, p-value = 0.02). furthermore, essential fatty acid deficiency especially omega-3 fatty acid increases the rate of Autism and consumption of supplements and food sources of omega-3 can decrease the risk of Autism up to 34% (RR=1.53, 95%CI=1-2.32). Conclusion: regards to nutritional deficiency and maternal metabolic condition before and during pregnancy in prevalence of Autism, carrying out the appropriate nutritional strategies such as well-timed folate supplementation before pregnancy and healthy lifestyle adherence for prevention of metabolic syndrome (GDM) seems to help Autism prevention.

Keywords: autism, autism prevention, dietary inadequacy, maternal lifestyle

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Authors: Mehak Nagpal, T. S. Sathyanarayan Rao

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Background: Sexual functioning is a neglected aspect of health in women with diabetes, though it contributes greatly towards quality of life and feeling of wellbeing. Also women with DM are at higher risk than men of developing sexual dysfunction and depression. Materials and Methods: Cross-sectional comparison study. Sample size: 100 previously diagnosed type 2DM patients attending Outpatient Diabetic Clinic at Medicine department JSS Hospital Mysore; aged 20-65 years and 60 normal healthy female subjects for Control group. Data was collected with ethical approval over a period of 2 years. Tools Used: 1) Hamilton Depression Rating Scale (HAMD – 17 item) 2) Female Sexual Functioning Index (FSFI) 3) Arizona Sexual Experience Scale (ASEX-F) for female-for screening. 4) The Appraisal of Diabetes Scale (ADS). Results: Statistically significant differences were observed in prevalence rate and severity of depression between diabetic group (45% vs 11% syndromal depression) and controls. Depression scores correlated significantly with glycaemic control, adherence to treatment, BMI and the cognitive appraisal of diabetes. There was significantly greater impairment in the sexual functioning of women with type 2 diabetes mellitus as compared to controls; both prevalence (62% vs 38.3%) and severity (p value < 0.01). Arousal (74.2% vs 53.3%), Desire (76.3% vs 50%) and Satisfaction (76.7% vs 63.7%) were most affected and 64.5% were affected in 2 or more domains. A negative illness appraisal on ADS correlated significantly with poor glycaemic control, higher rates of depression and also more severe female sexual dysfunction (p value < 0.05). Conclusion: Diabetes specific factors that correlated significantly with FSD in this study included the psychological appraisal of diabetes, duration of diabetes, presence of complications and BMI.

Keywords: depression, female sexual dysfunction, India, type 2 diabetes mellitus

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Authors: Fatemeh Daneshvartarigh

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New technologies and applications have radically altered construction techniques in recent years. In order to anticipate how the building will act, perform, and appear, these technologies encompass a wide range of visualization, simulation, and analytic tools. These new technologies and applications have a considerable impact on completing construction projects in today's (architecture, engineering and construction)AEC industries. The rate of changes in BIM-related topics is different worldwide, and it depends on many factors, e.g., the national policies of each country. Therefore, there is a need for comprehensive research focused on a specific area with common characteristics. Therefore, one of the necessary measures to increase the use of this new approach is to examine the challenges and obstacles facing it. In this research, based on the Delphi method, at first, the background and related literature are reviewed. Then, using the knowledge obtained from the literature, a primary questionnaire is generated and filled by experts who are selected using snowball sampling. It covered the experts' attitudes towards implementing BIM in renovation projects and their view of the benefits and obstacles in this regard. By analyzing the primary questionnaire, the second group of experts is selected among the participants to be interviewed. The results are analyzed using Theme analysis. Six themes, including Management support, staff resistance, client willingness, Cost of software and implementation, the difficulty of implementation, and other reasons, are obtained. Then a final questionnaire is generated from the themes and filled by the same group of experts. The result is analyzed by the Fuzzy Delphi method, showing the exact ranking of the obtained themes. The final results show that management support, staff resistance, and client willingness are the most critical barrier to BIM usage in renovation projects.

Keywords: building information modeling, BIM, BIM implementation, BIM barriers, BIM in renovation

Procedia PDF Downloads 167

Authors: Andreas Ateke Njoh, Shalom Tchokfe Ndoula, Amani Adidja, Germain Nguessan Menan, Annie Mengue, Eric Mboke, Hassan Ben Bachir, Sangwe Clovis Nchinjoh, Yauba Saidu, Laurent Cleenewerck De Kiev

Abstract:

Vaccines serve a great deal in protecting the population globally. Vaccine products are subject to rigorous quality control and approval before use to ensure safety. Even if all actors take the required precautions, some people could still have adverse events following immunization (AEFI) caused by the vaccine composition or an error in its administration. AEFI underreporting is pronounced in low-income settings like Cameroon. The Country introduced electronic platforms to strengthen surveillance. With the introduction of many novel vaccines, like COVID-19 and the novel Oral Polio Vaccine (nOPV) 2, there was a need to monitor AEFI in the Country. A cross-sectional study was conducted from July to December 2022. Data on AEFI per region of Cameroon were reviewed for the past five years. Data were analyzed with MS Excel, and the results were presented in proportions. AEFI reporting was uncommon in Cameroon. With the introduction of novel vaccines in 2021, the health authorities engaged in new tools and training to capture cases. AEFI detected almost doubled using the open data kit (ODK) compared to previous platforms, especially following the introduction of the nOPV2 and COVID-19 vaccines. The AEFI rate was 1.9 and 160 per administered 100 000 doses of nOPV2 and COVID-19 vaccines, respectively. This mobile tool captured individual information for people with AEFI from all regions. The platform helped to identify common AEFI following the use of these new vaccines. The ODK mobile technology was vital in improving AEFI reporting and providing data to monitor using new vaccines in Cameroon.

Keywords: adverse events following immunization, cameroon, COVID-19 vaccines, nOPV, ODK

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Authors: Joanne Sin Wei Yeoh, Quynh Lê, Daniel R. Terry, Rosa Mc Manamey

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Food security is a global issue and one of the concerns in Australia, particularly in regional and rural areas. Despite Australia’s current ability to produce enough food to feed more than its current population, evidence has been accumulating over the last decade to demonstrate many Australians struggle to feed themselves, including immigrants from cultural and linguistically diverse (CALD) backgrounds. This study aims to identify the acculturation strategies used by migrants to enhance their approach to food security in Tasmania. The study employed a mixed methods approach that used both questionnaires and semi-structured interviews with migrants living in Tasmania. Descriptive and inferential statistics was used to analyse data collected from questionnaire, whereas, thematic analysis was employed to analyse the interview data. Migrants (n=301) completed the questionnaire with a response rate of 50.2% and 33 follow-up interviews were conducted. We found that majority of the migrants (70.0%) replaced food ingredients and went without the food they could not buy from shops with similar ingredients. Support and advice from friends were effective ways to improve their food access. Additionally, length of stays in Tasmania and region of origin were significantly associated with the ways migrants dealing with food security. The interview results revealed that migrants managed to adapt to the new food culture by using different acculturation strategies, including access food ingredients from other country; adjusting or adapting; home gardening and access to technology. In addition, social and cultural capitals were also treated as vital roles in improving migrants’ food security. To summarize, migrants employed different strategies for food security while acculturating into the new environment. Our findings could become the guidelines for migrants and relevant government or private sectors that address food security.

Keywords: food security, migrants, strategies, inferential statistics

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Authors: Eyob Messele Sefene, Atinkut Atinafu Yilma

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The highest strength-to-weight ratio criterion has fascinated increasing curiosity in virtually all areas where weight reduction is indispensable. One of the recent advances in manufacturing to achieve this intention endears friction stir welding (FSW). The process is widely used for joining similar and dissimilar non-ferrous materials. In FSW, the mechanical properties of the weld joints are impelled by property-selected process parameters. This paper presents verdicts of optimum process parameters in attempting to attain enhanced mechanical properties of the weld joint. The experiment was conducted on a 5 mm 6061 aluminum alloy sheet. A butt joint configuration was employed. Process parameters, rotational speed, traverse speed or feed rate, axial force, dwell time, tool material and tool profiles were utilized. Process parameters were also optimized, making use of a mixed L18 orthogonal array and the Grey relation analysis method with larger is better quality characteristics. The mechanical properties of the weld joint are examined through the tensile test, hardness test and liquid penetrant test at ambient temperature. ANOVA was conducted in order to investigate the significant process parameters. This research shows that dwell time, rotational speed, tool shape, and traverse speed have become significant, with a joint efficiency of about 82.58%. Nine confirmatory tests are conducted, and the results indicate that the average values of the grey relational grade fall within the 99% confidence interval. Hence the experiment is proven reliable.

Keywords: friction stir welding, optimization, 6061 AA, Taguchi

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Authors: Chen-Ya Juan

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In recent years, the increasing number of children with special needs has brought a lot of attention by many scholars and experts in education, which enforced the preschool teachers face the harsh challenge in the classroom. To protect the right of equal education for all children, enhance the quality of children learning, and take care of the needs of children with special needs, the special education paraprofessional becomes one of the future employment trends for students of the department of the early childhood care and education. Problem-based learning is a problem-oriented instruction, which is different from traditional instruction. The instructor first designed an ambiguous problem direction, following the basic knowledge of early intervention, students had to find clues to solve the problem defined by themselves. In the class, the total instruction included 20 hours, two hours per week. The primary purpose of this paper is to investigate the relationship of student academic scores, self-awareness, learning motivation, learning attitudes, and early intervention knowledge. A total of 105 college students participated in this study and 97 questionnaires were effective. The effective response rate was 90%. The student participants included 95 females and two males. The average age of the participants was 19 years old. The questionnaires included 125 questions divided into four major dimensions: (1) Self-awareness, (2) learning motivation, (3) learning attitudes, and (4) early intervention knowledge. The results indicated (1) the scores of self-awareness were 58%; the scores of the learning motivations was 64.9%; the scores of the learning attitudes was 55.3%. (2) After the instruction, the early intervention knowledge has been increased to 64.2% from 38.4%. (3) Student’s academic performance has positive relationship with self-awareness (p < 0.05; R = 0.506), learning motivation (p < 0.05; R = 0.487), learning attitudes (p < 0.05; R = 0.527). The results implied that although students had gained early intervention knowledge by using PBL instruction, students had medium scores on self-awareness and learning attitudes, medium high in learning motivations.

Keywords: college students, children with special needs, problem-based learning, learning motivation

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Authors: Sadia Bibi, Karim Ullah

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Islamic banks have excess liquidity, which needs proper management to earn a high rate of return on them to remain competitive. However, they lack assets-backed avenues and rely on a few sukuks, which led them to liquidity management issues. Financial engineering comes forward to innovate and develop instruments for the requisite financial problem. Still, they face many challenges, explored in the context of liquidity management in Islamic banks. The rigorous literature review shows that Shariah compliance, competition from the conventional banks, lack of sufficient instruments, derivatives are still not accepted as legitimate products, the inter-bank market being less developed, and no possibility of lender of last resort is the six significant constraints to financial engineering for liquidity management of Islamic banks. To further explore the problem, a multiple case study strategy is used to extend and develop the theory with the philosophical stance of social constructivism. Narrative in-depth interviews over the telephone are conducted with key personnel at treasury departments of selected banks. Data is segregated and displayed using NVivo 11 software, and the thematic analysis approach identifies themes related to the constraints. The exploration of further constraints to financial engineering for liquidity management of Islamic banks achieves the research aim. The theory is further developed by the addition of three more constraints to the theoretical framework, which are i) lack of skilled human resources, ii) lack of unified vision, and iii) lack of government support to the Islamic banks. These study findings are fruitful for the use of the government, regulatory authorities of the banking sector, the State Bank of Pakistan (Central Bank), and the product design & development division of Islamic banks to make the financial engineering process feasible and resolve liquidity management issues of Islamic banks.

Keywords: financial engineering, liquidity management, Islamic banks, shariah compliance

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Authors: Hester Fass, Ofir Pinto

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Research indicates gaps in education, employment rates and wages between people with disabilities and those without disabilities. One of the main tools available to reduce these gaps is vocational rehabilitation. In order to examine the effects of vocational rehabilitation, a follow-up study, based on comprehensive administrative data, was conducted. The study included 88,286 people with disabilities who participated in vocational rehabilitation of the National Insurance Institute of Israel (NII), and completed the process between 1999 and 2012. Research variables included: employment rates, job persistence and wage levels. This research, the first of its kind in Israel, has several unique aspects: a)a long-range follow-up study on people who completed vocational rehabilitation; b) examination of a broad population spectrum, including also people that are not eligible to disability pensions ; c) a comparison among those with work-related injuries, those injured in hostile acts and those injured in other circumstances; and finally d) the identification of the characteristics of those who are entitled to vocational rehabilitation but who do not participate in any vocational rehabilitation plan. The most notable results include: 1. Vocational rehabilitation contributed to employment, job persistence and wage levels. Participation in vocational rehabilitation resulted in an employment rate of 65% within two years after completing the program, and 73% eventually. Participation in a vocational rehabilitation plan also contributed to job persistence and wage levels. 2. Vocational rehabilitation plans aimed at integration in universal frameworks increased the chances of being employed, persisting at the job and receiving a higher wage than did the vocational rehabilitation aimed at selective frameworks (such as sheltered workshops). 3. The type of disability affected the chances of integration in a vocational rehabilitation plan and in the labor market. People with a disability from birth had greater chances of integration in a vocational rehabilitation plan, while the type of disability and its severity affected the chances of the person with disabilities to find employment.

Keywords: vocational rehabilitation, employment, job persistence, wages

Procedia PDF Downloads 455

Authors: Oumaima Khlifati, Khadija Baba

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Pavement distress is the main factor responsible for the deterioration of road structure durability, damage vehicles, and driver comfort. Transportation agencies spend a high proportion of their funds on pavement monitoring and maintenance. The auscultation of pavement distress was based on the manual survey, which was extremely time consuming, labor intensive, and required domain expertise. Therefore, the automatic distress detection is needed to reduce the cost of manual inspection and avoid more serious damage by implementing the appropriate remediation actions at the right time. Inspired by recent deep learning applications, this paper proposes an algorithm for automatic road distress detection and classification using on the Deep Convolutional Neural Network (DCNN). In this study, the types of pavement distress are classified as transverse or longitudinal cracking, alligator, pothole, and intact pavement. The dataset used in this work is composed of public asphalt pavement images. In order to learn the structure of the different type of distress, the DCNN models are trained and tested as a multi-label classification task. In addition, to get the highest accuracy for our model, we adjust the structural optimization hyper parameters such as the number of convolutions and max pooling, filers, size of filters, loss functions, activation functions, and optimizer and fine-tuning hyper parameters that conclude batch size and learning rate. The optimization of the model is executed by checking all feasible combinations and selecting the best performing one. The model, after being optimized, performance metrics is calculated, which describe the training and validation accuracies, precision, recall, and F1 score.

Keywords: distress pavement, hyperparameters, automatic classification, deep learning

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Authors: Abdulmohsen Alruwaili

Abstract:

A partial differential system featuring momentum and energy balance is often used to describe simulations of flow initiation and thermal shifting in boundary layers. The buoyancy force in terms of temperature is factored in the momentum balance equation. Buoyancy force causes the flow quantity to fluctuate along the streamwise direction 𝑋; therefore, the problem can be, to our best knowledge, analyzed through nonsimilar modeling. In this analysis, a nonsimilar model is evolved for radiative mixed convection of a magnetized power-law nanoliquid flow on top of a vertical plate installed in a stationary fluid. The upward linear stretching initiated the flow in the vertical direction. Assuming nanofluids are composite of copper (Cu) and alumina (Al₂O₃) nanoparticles, the viscous dissipation in this case is negligible. The nonsimilar system is dealt with analytically by local nonsimilarity (LNS) via numerical algorithm bvp4c. Surface temperature and flow field are shown visually in relation to factors like mixed convection, magnetic field strength, nanoparticle volume fraction, radiation parameters, and Prandtl number. The repercussions of magnetic and mixed convection parameters on the rate of energy transfer and friction coefficient are represented in tabular forms. The results obtained are compared to the published literature. It is found that the existence of nanoparticles significantly improves the temperature profile of considered nanoliquid. It is also observed that when the estimates of the magnetic parameter increase, the velocity profile decreases. Enhancement in nanoparticle concentration and mixed convection parameter improves the velocity profile.

Keywords: nanofluid, power law model, mixed convection, thermal radiation

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Authors: Herath M. P. C. Jayaweera, Samer Hanoun

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Path planning for on-time obstacle avoidance is an essential and challenging task that enables drones to achieve safe operation in any application domain. The level of challenge increases significantly on the obstacle avoidance technique when the drone is following a ground mobile entity (GME). This is mainly due to the change in direction and magnitude of the GME′s velocity in dynamic and unstructured environments. Force field techniques are the most widely used obstacle avoidance methods due to their simplicity, ease of use, and potential to be adopted for three-dimensional dynamic environments. However, the existing force field obstacle avoidance techniques suffer many drawbacks, including their tendency to generate longer routes when the obstacles are sideways of the drone′s route, poor ability to find the shortest flyable path, propensity to fall into local minima, producing a non-smooth path, and high failure rate in the presence of symmetrical obstacles. To overcome these shortcomings, this paper proposes an on-time three-dimensional obstacle avoidance method for drones to effectively and efficiently avoid dynamic and static obstacles in unknown environments while pursuing a GME. This on-time obstacle avoidance technique generates velocity waypoints for its obstacle-free and efficient path based on the shape of the encountered obstacles. This method can be utilized on most types of drones that have basic distance measurement sensors and autopilot-supported flight controllers. The proposed obstacle avoidance technique is validated and evaluated against existing force field methods for different simulation scenarios in Gazebo and ROS-supported PX4-SITL. The simulation results show that the proposed obstacle avoidance technique outperforms the existing force field techniques and is better suited for real-world applications.

Keywords: drones, force field methods, obstacle avoidance, path planning

Procedia PDF Downloads 94

Authors: Ruphi Naz, Altaf Ahmad, Mohammad Anis

Abstract:

Sialic acid (5-Acetylneuraminic acid, Neu5Ac) is a common sugar found as a terminal residue on glycoconjugates in many animals. Humans brain and the central nervous system contain the highest concentration of sialic acid (as N-acetylneuraminic acid) where these acids play an important role in neural transmission and ganglioside structure in synaptogenesis. Due to its important biological function, sialic acid is attracting increasing attention. To understand metabolic networks, fluxes and regulation, it is essential to be able to determine the cellular and subcellular levels of metabolites. Genetically-encoded fluorescence resonance energy transfer (FRET) sensors represent a promising technology for measuring metabolite levels and corresponding rate changes in live cells. Taking this, we developed a genetically encoded FRET (fluorescence resonance energy transfer) based nanosensor to analyse the sialic acid level in living cells. Sialic acid periplasmic binding protein (sia P) from Haemophilus influenzae was taken and ligated between the FRET pair, the cyan fluorescent protein (eCFP) and Venus. The chimeric sensor protein was expressed in E. coli BL21 (DE3) and purified by affinity chromatography. Conformational changes in the binding protein clearly confirmed the changes in FRET efficiency. So any change in the concentration of sialic acid is associated with the change in FRET ratio. This sensor is very specific to sialic acid and found stable with the different range of pH. This nanosensor successfully reported the intracellular level of sialic acid in bacterial cell. The data suggest that the nanosensors may be a versatile tool for studying the in vivo dynamics of sialic acid level non-invasively in living cells

Keywords: nanosensor, FRET, Haemophilus influenzae, metabolic networks

Procedia PDF Downloads 133

Authors: Hadjer Sadoune, Liza Lamini, Scherazade Krim, Amel Djouadi, Rachida Rihani

Abstract:

Deep neural networks are highly regarded for their accuracy in predicting intricate fermentation processes. Their ability to learn from a large amount of datasets through artificial intelligence makes them particularly effective models. The primary obstacle in improving the performance of these models is to carefully choose the suitable hyperparameters, including the neural network architecture (number of hidden layers and hidden units), activation function, optimizer, learning rate, and other relevant factors. This study predicts biogas production from real wastewater treatment plant data using a sophisticated approach: hybrid Bayesian optimization with a tree-structured Parzen estimator (BO-TPE) for an optimised deep neural network (DNN) model. The plant utilizes an Upflow Anaerobic Sludge Blanket (UASB) digester that treats industrial wastewater from soft drinks and breweries. The digester has a working volume of 1574 m3 and a total volume of 1914 m3. Its internal diameter and height were 19 and 7.14 m, respectively. The data preprocessing was conducted with meticulous attention to preserving data quality while avoiding data reduction. Three normalization techniques were applied to the pre-processed data (MinMaxScaler, RobustScaler and StandardScaler) and compared with the Non-Normalized data. The RobustScaler approach has strong predictive ability for estimating the volume of biogas produced. The highest predicted biogas volume was 2236.105 Nm³/d, with coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE) values of 0.712, 164.610, and 223.429, respectively.

Keywords: anaerobic digestion, biogas production, deep neural network, hybrid bo-tpe, hyperparameters tuning

Procedia PDF Downloads 39

Authors: Yi Yang, Jiasong Sun

Abstract:

Almost every aspect of our daily lives is now intertwined with some degree of human-computer interaction (HCI). HCI courses draw on knowledge from disciplines as diverse as computer science, psychology, design principles, anthropology, and more. Our HCI courses, named the Media and Cognition course, are constantly updated to reflect state-of-the-art technological advancements such as virtual reality, augmented reality, and artificial intelligence-based interactions. For more than a decade, our course has used an interest-based approach to teaching, in which students proactively propose some research-based questions and collaborate with teachers, using course knowledge to explore potential solutions. Semantic communication plays a key role in facilitating understanding and interaction between users and computer systems, ultimately enhancing system usability and user experience. The advancements in AI-generated technology, which have gained significant attention from both academia and industry in recent years, are exemplified by language models like GPT-3 that generate human-like dialogues from given prompts. Our latest version of the Human-Computer Interaction course practices a semantic communication platform based on AI-generated techniques. The purpose of this semantic communication is twofold: to extract and transmit task-specific information while ensuring efficient end-to-end communication with minimal latency. An AI-generated semantic communication platform evaluates the retention of signal sources and converts low-retain ability visual signals into textual prompts. These data are transmitted through AI-generated techniques and reconstructed at the receiving end; on the other hand, visual signals with a high retain ability rate are compressed and transmitted according to their respective regions. The platform and associated research are a testament to our students' growing ability to independently investigate state-of-the-art technologies.

Keywords: human-computer interaction, media and cognition course, semantic communication, retainability, prompts

Procedia PDF Downloads 116