Search results for: project integrated knowledge sharing

Authors: F. Clara Fang, Hernan Castaneda

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The Connecticut Department of Transportation (ConnDOT) has constructed four roundabouts in the State of Connecticut within the past ten years. VISSIM traffic simulation software was utilized to analyze these roundabouts during their design phase. The queue length and level of service observed in the field appear to be better than predicted by the VISSIM model. The objectives of this project are to: identify VISSIM input variables most critical to accurate modeling; recommend VISSIM calibration factors; and, provide other recommendations for roundabout traffic operations modeling. Traffic data were collected at these roundabouts using Miovision Technologies. Cameras were set up to capture vehicle circulating activity and entry behavior for two weekdays. A large sample size of filed data was analyzed to achieve accurate and statistically significant results. The data extracted from the videos include: vehicle circulating speed; critical gap estimated by Maximum Likelihood Method; peak hour volume; follow-up headway; travel time; and, vehicle queue length. A VISSIM simulation of existing roundabouts was built to compare both queue length and travel time predicted from simulation with measured in the field. The research investigated a variety of simulation parameters as calibration factors for describing driver behaviors at roundabouts. Among them, critical gap is the most effective calibration variable in roundabout simulation. It has a significant impact to queue length, particularly when the volume is higher. The results will improve the design of future roundabouts in Connecticut and provide decision makers with insights on the relationship between various choices and future performance.

Keywords: driver critical gap, roundabout analysis, simulation, VISSIM modeling

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Authors: Santosh Kumar Yadav, Shobha Keswani, Nishat Quddus, Sohrab Ahmad Khan, Zuheb Ahmad Shiddiqui, Varsha Chorsiya

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Introduction: Early onset diabetes is more aggressive than the late onset diabetes. Diabetic individual has a greater spectrum of life period to suffer from its damage, complications, and long-term disability. This study aimed at assessing knee joint muscle performance under various durations of diabetes. Method and Materials: A total of 30 diabetic subjects (18 male and 12 females) without diabetic neuropathy were included for the study. They were divided into three groups with 5 years, 10 years and 15 years of duration of disease each. Muscle performance was evaluated through strength and flexibility. Peak torque for quadriceps muscle was measured using isokinetic dynamometer. Flexibility for quadriceps and hamstring muscles were measured through Ducan’s Elys test and 90/90 test. Results: The result showed significant difference in muscle strength (p<0.05), flexibility (p≤0.05) between groups. Discussion: Optimal muscle strength and flexibility are vital for musculoskeletal health and functional independence. Conclusion: The reduced muscle performance and functional impairment in nonneuropathic diabetic patients suggest that other mechanism besides neuropathy that contribute to altered biomechanics. These findings of this study project early management of these altered parameters through disease-specific physical therapy and assessment-based intervention. Clinical Relevance: Managing disability is more costly than managing disease. Prompt and timely identification and management strategy can dramatically reduce the cost of care for diabetic patients.

Keywords: muscle flexibility, muscle performance, muscle torque, type 2 diabetes

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Authors: Bright E. Owusu, Nittaya McNeil

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Rainfall is one of the most essential quantities in meteorology and hydrology. It has important impacts on people’s daily life and excess or inadequate of it could bring tremendous losses in economy and cause fatalities. Population increase around the globe tends to have a corresponding increase in settlement and industrialization. Some countries are affected by flood and drought occasionally due to climate change, which disrupt most of the daily activities. Knowledge of trends in spatial and temporal rainfall variability and their physical explanations would be beneficial in climate change assessment and to determine erosivity. This study describes the spatial-temporal variability of daily rainfall in Australia and their corresponding long-term trend during 1950-2013. The spatial patterns were investigated by using exploratory factor analysis and the long term trend in rainfall time series were determined by linear regression, Mann-Kendall rank statistics and the Sen’s slope test. The exploratory factor analysis explained most of the variations in the data and grouped Australia into eight distinct rainfall regions with different rainfall patterns. Significant increasing trends in annual rainfall were observed in the northern regions of Australia. However, the northeastern part was the wettest of all the eight rainfall regions.

Keywords: climate change, explanatory factor analysis, Mann-Kendall and Sen’s slope test, rainfall.

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Authors: Amal E. Ahmed, Levent Trabzon

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Due to the low concentration of the analytes in biological samples, the use of Biological Microelectromechanical System (Bio-MEMS) biosensors for biomolecules detection results in a minuscule output signal that is not good enough for practical applications. In response to this, a need has arisen for an optimized biosensor capable of giving high output signal in response the detection of few analytes in the sample; the ultimate goal is being able to convert the attachment of a single biomolecule into a measurable quantity. For this purpose, MEMS microcantilevers based biosensors emerged as a promising sensing solution because it is simple, cheap, very sensitive and more importantly does not need analytes optical labeling (Label-free). Among the different microcantilever transducing techniques, piezoresistive based microcantilever biosensors became more prominent because it works well in liquid environments and has an integrated readout system. However, the design of piezoresistive microcantilevers is not a straightforward problem due to coupling between the design parameters, constraints, process conditions, and performance. It was found that the parameters that can be optimized to enhance the sensitivity of Piezoresistive microcantilever-based sensors are: cantilever dimensions, cantilever material, cantilever shape, piezoresistor material, piezoresistor doping level, piezoresistor dimensions, piezoresistor position, Stress Concentration Region's (SCR) shape and position. After a systematic analyzation of the effect of each design and process parameters on the sensitivity, a step-wise optimization approach was developed in which almost all these parameters were variated one at each step while fixing the others to get the maximum possible sensitivity at the end. At each step, the goal was to optimize the parameter in a way that it maximizes and concentrates the stress in the piezoresistor region for the same applied force thus get the higher sensitivity. Using this approach, an optimized sensor that has 73.5x times higher electrical sensitivity (ΔR⁄R) than the starting sensor was obtained. In addition to that, this piezoresistive microcantilever biosensor it is more sensitive than the other similar sensors previously reported in the open literature. The mechanical sensitivity of the final senior is -1.5×10-8 Ω/Ω ⁄pN; which means that for each 1pN (10-10 g) biomolecules attach to this biosensor; the piezoresistor resistivity will decrease by 1.5×10-8 Ω. Throughout this work COMSOL Multiphysics 5.0, a commercial Finite Element Analysis (FEA) tool, has been used to simulate the sensor performance.

Keywords: biosensor, microcantilever, piezoresistive, stress concentration region (SCR)

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Authors: Zeenat Rupawalla, Nicole Robinson, Susanne Schmidt, Sijie Li, Selina Carruthers, Elodie Buisset, John Roles, Ben Hankamer, Juliane Wolf

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Agriculture has radically changed the global biogeochemical cycle of nitrogen (N). Fossil fuel-enabled synthetic N-fertiliser is a foundation of modern agriculture but applied to soil crops only use about half of it. To address N-pollution from cropping and the large carbon and energy footprint of N-fertiliser synthesis, new technologies delivering enhanced energy efficiency, decarbonisation, and a circular nutrient economy are needed. We characterised algae fertiliser (AF) as an alternative to synthetic N-fertiliser (SF) using empirical and modelling approaches. We cultivated microalgae in nutrient solution and modelled up-scaled production in nutrient-rich wastewater. Over four weeks, AF released 63.5% of N as ammonium and nitrate, and 25% of phosphorous (P) as phosphate to the growth substrate, while SF released 100% N and 20% P. To maximise crop N-use and minimise N-leaching, we explored AF and SF dose-response-curves with spinach in glasshouse conditions. AF-grown spinach produced 36% less biomass than SF-grown plants due to AF’s slower and linear N-release, while SF resulted in 5-times higher N-leaching loss than AF. Optimised blends of AF and SF boosted crop yield and minimised N-loss due to greater synchrony of N-release and crop uptake. Additional benefits of AF included greener leaves, lower leaf nitrate concentration, and higher microbial diversity and water holding capacity in the growth substrate. Life-cycle-analysis showed that replacing the most effective SF dosage with AF lowered the carbon footprint of fertiliser production from 2.02 g CO₂ (C-producing) to -4.62 g CO₂ (C-sequestering), with a further 12% reduction when AF is produced on wastewater. Embodied energy was lowest for AF-SF blends and could be reduced by 32% when cultivating algae on wastewater. We conclude that (i) microalgae offer a sustainable alternative to synthetic N-fertiliser in spinach production and potentially other crop systems, and (ii) microalgae biofertilisers support the circular nutrient economy and several sustainable development goals.

Keywords: bioeconomy, decarbonisation, energy footprint, microalgae

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Authors: Oyediran Wasiu Oyeleke

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Melon is a popular economic cucurbit in Southwest, Nigeria. In recent time, many young farmers are shifting from melon to watermelon farming due to poor yield and low monetary returns. Hence, this study was carried out to assess the decline in melon yield and its contribution to young farmers’ diversification into watermelon farming in Oyo state, Nigeria. Purposive sampling technique was used in selecting 75 respondents from five villages in Ibarapa block of the Oyo State Agricultural Development Project (ADP). Data collected were analyzed using descriptive statistics and Pearson Product Moment Correlation (PPMC). Results show that majority of the respondents (77.3%) were between 31-40 years of age and 46.70% had secondary school education. Most of the respondents (80%) cultivated more than 3 ha of land for watermelon. Majority of the respondents (74.7%) intercropped melon with other crops while watermelon was cultivated as a sole crop. None of the respondents either grew improved melon seeds (certified seeds) or applied fertilizers but all respondents cultivated treated watermelon seeds, applied fertilizers, and agro-chemicals. The average yields of melon fell from 376.53kg/ha in 2009 to 280.70kg/ha in 2011. However, the respondents were shifting into watermelon production because of available quality seeds and its early maturity, easy harvest, and high sales. There was a significant relationship between melon output and young farmers’ diversification to watermelon in the study area at p < 0.05. The study concluded that decline in the melon yield discouraged youth to continue melon farming in the study area. It is hereby recommended that certified melon seeds should be made available while extension service providers should provide training support for the young farmers in order to reposition and boost melon production in the study area.

Keywords: decline, melon yield, contribution, watermelon, diversification, young farmers

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Authors: Grygorii Kostromin, Vladyslav Povoroznyuk

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Chronic venous diseases (CVD) are one of the common, though controversial problems in medicine. It is generally accepted that this pathology predominantly occurs in women. The issue of excessive weight as a risk factor for CVD is still considered debatable. To the author's best knowledge, today in Ukraine, there are barely any studies that describe the relationship between CVD and obesity. Our study aims to determine the association between age, body composition, obesity and CVD in postmenopausal women. The study was conducted in D. F. Chebotarev Institute of Gerontology, National Academy of Medical Sciences of Ukraine. We have examined 96 postmenopausal women aged 46-85 years (mean age – 66.19 ± 0.96 years), who were divided into two groups depending on the presence of CVD. The women were examined by vascular surgeons. For the diagnosis of CVD, we used clinical, anatomic and pathophysiologic classifications. We also performed clinical, ultrasound and densitometry examinations. We found that the CVD frequency in postmenopausal women increased with age (from 72% in those aged 45-59 years to 84% in those aged 75-89 years). A significant correlation between the total fat mass and age was determined in postmenopausal women with CVD. We also observed a significant correlation between the lower extremities’ fat mass and age in both examined groups. A significant correlation between body mass index and age was determined only in postmenopausal women without CVD.

Keywords: chronic venous disease, risk factors, age, obesity, postmenopausal women

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Authors: Somoshree Datta, Chithra A. V., Sandeep Nithyanandan, Smitha K. K.

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Space exploration has always been at the forefront of technological innovation, pushing the boundaries of human knowledge and capabilities. In recent years, the integration of Artificial Intelligence (AI) has revolutionized the field, offering unprecedented opportunities to enhance the efficiency, autonomy and intelligence of space missions. This survey paper aims to provide a comprehensive overview of the multifaceted applications of AI in space exploration, exploring the evolution of this synergy and its impact on mission success, scientific discovery, and the future of space endeavors. Indian Space Research Organization (ISRO) has achieved great feats in the recent moon mission (Chandrayaan-3) and sun mission (Aditya L1) by using artificial intelligence to enhance moon navigation as well as help young scientists to study the Sun even before the launch by creating AI-generated image visualizations. Throughout this survey, we will review key advancements, challenges and prospects in the intersection of AI and space exploration. As humanity continues its quest to explore the cosmos, the integration of AI promises to unlock new frontiers, reshape mission architectures, and redefine our understanding of the universe. This survey aims to serve as a comprehensive resource for researchers, engineers and enthusiasts interested in the dynamic and evolving landscape of AI applications in space exploration.

Keywords: artificial intelligence, space exploration, space missions, deep learning

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Authors: Zuhaina Zakaria, Noraliza Hamzah, Siti Halijjah Shariff, Noor Aizah Abdul Karim

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The residential sector in Malaysia has become the single largest energy sector accounting for 21% of the entire energy usage of the country. In the past 10 years, a number of energy efficiency initiatives in the residential sector had been undertaken by the government including. However, there is no clear evidence that the total residential energy consumption has been reduced substantially via these strategies. Household electrical appliances such as air conditioners, refrigerators, lighting and televisions are used depending on the consumers’ activities. The behavior of household occupants played an important role in energy consumption and influenced the operation of the physical devices. Therefore, in order to ensure success in energy efficiency program, it requires not only the technological aspect but also the consumers’ behaviors component. This paper focuses on the challenges and opportunities in modelling residential consumer behavior in Malaysia. A field survey to residential consumers was carried out and responses from the survey were analyzed to determine the consumers’ level of knowledge and awareness on energy efficiency. The analyses will be used in determining a right framework to explain household energy use intentions and behavior. These findings will be beneficial to power utility company and energy regulator in addressing energy efficiency related issues.

Keywords: consumer behavior theories, energy efficiency, household occupants, residential consumer

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Authors: Shivankar Agrawal, Sunil Kumar Deshmukh, Colin Barrow, Alok Adholeya

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A variety of genetic and environmental factors cause various cosmetics and dermatological problems. There are already claimed drugs available in market for treating these problems. However, the challenge remains in finding more potent, environmental friendly, causing minimal side effects and economical cosmeceuticals. This leads to an increased demand for natural cosmeceutical products in the last few decades. Plant derived ingredients are limited because plants either contain toxic metabolites, grow too slow or seasonal harvesting is a problem. The research work carried out in this project aims at isolation, characterization of marine fungal secondary metabolite and evaluating their potential use in future cosmetic skin care products. We have isolated and purified 35 morphologically different fungal isolates from various marine habitats of the India. These isolates have been functionally characterized for anti-tyrosinase, antioxidant and anti-acne activities. For molecular characterization, the Internal Transcribed spacer (ITS) region of 15 functionally active marine fungal isolates was amplified using universal primers, ITS1 and ITS4 and sequenced. Out of 15 marine fungal isolates crude extract of strains D4 (Aspergillus terreus) and P2 (Talaromyces stipitatus) showed 70% and 57% tyrosinase inhibition at 1mg/mL respectively. Strain D5 (Simplicillium lamellicola) has showed significant inhibition against Propionibacterium acnes and Staphylococcus epidermidis. In addition, all these strains also displayed DPPH- radical scavenging activity and may be utilized as skin cosmeceutical applications. Purification and characterization of crude extracts for identification of active lead molecule is under process.

Keywords: anti-acne, anti-tyrosinase, cosmeceutical, marine fungi

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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

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Authors: Allen Seylani

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Lysosome-dependent autophagy is a nutrient-deprivation-induced evolutionarily conserved intracellular recycling program that sequestrates intracellular cargo into autophagosomes (AP), which then fuse with lysosomes to form autolysosomes (ALs) for cargo digestion. To restore free lysosomes, autophagic lysosome reformation (ALR) is initiated by extrusion of tubular structures from autolysosomes at the final stage of autophagy, in a process called lysosomal tubulation (LT). This project aimed to investigate the molecular mechanism of GCN5L1 in LT and the following lysosomal signaling. GCN5L1 belongs to the BORC multiprotein complexes and is involved in controlling lysosomal trafficking; however, the effect of GCN5L1 on lysosome tubulation remains largely unknown. Genetic ablation of GCN5L1 in the mouse primary hepatocytes showed dramatically increased autolysosomes (ALs), decreased lysosome regeneration and absence of lysosomal tubulation. This phenotype suggests the possibility of disruption in lysosome tubulation, which results in the disturbance of the overall lysosome homeostasis. The formation of tubulars from ALs requires kinesin motor protein KIF5B. Immunoprecipitation was employed and confirmed the interaction of GCN5L1 with the ARL8B-KIF5B complex, which recruited KIF5B to ALs. At the same time, GCN5L1 interacted with WHAMM, which promotes the actin nucleation factor, which brings actin cytoskeleton to ALs and initiates LT. Furthermore, impaired LT in GCN5L1 deficient hepatocytes was restored by overexpression of GCN5L1, and this rescue effect was attenuated by knockdown of KIF5B. Additionally, lysosomal mTORC1 activity was upregulated in GCN5L1-/- hepatocytes, while inhibition of mTORC1 abrogated the GCN5L1 mediated rescue of LT in knockout hepatocytes. Altogether these findings revealed a novel mechanism of ALR, in which a simultaneous interaction of GCN5L1 with KIF5B and WHAMM is required for LT and downstream mTORC1 signaling.

Keywords: autophagy, autolysosome, GCN5L1, lysosome

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Authors: Azizeh Chalak, Firouzeh Baktash

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Reticence is a prominent and complex phenomenon which occurs in foreign language classrooms and influences students’ oral passivity. The present study investigated the extent in which students experience reticence in the EFL classrooms and explored the underlying factors triggering reticence. The participants were 104 Iranian freshmen undergraduate male and female EFL students, who enrolled in listening and speaking courses, all majoring in English studying at Islamic Azad University Isfahan (Khorasgan) Branch and University of Isfahan, Isfahan, Iran. To collect the data, the Reticence Scale-12 (RS-12) questionnaire which measures the level of reticence consisting of six dimensions (anxiety, knowledge, timing, organization, skills, and memory) was administered to the participants. The statistical analyses showed that the reticent level was high among the Iranian EFL undergraduate students, and their major problems were feelings of anxiety and delivery skills. Moreover, the results revealed that factors such as low English proficiency, the teaching method, and lack of confidence contributed to the students’ reticence in Iranian EFL classrooms. It can be implied that language teachers’ awareness of learners’ reticence can help them choose more appropriate activities and provide a friendly environment enhancing hopefully more effective participation of EFL learners. The findings can have implications for EFL teachers, learners and policy makers.

Keywords: anxiety, Iranian EFL learners, reticence, reticence scale-12

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: Juni C. Kim, Anna R. Robuck, Douglas I. Walker

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The human exposome, which includes chemical exposures over the lifetime and their effects, is now recognized as an important measure for understanding human health; however, the complexity of the data makes the identification of environmental chemicals challenging. The goal of our project was to establish a computational workflow for the improved identification of environmental pollutants containing chlorine or bromine. Using the “pattern. search” function available in the R package NonTarget, we wrote a multifunctional script that searches mass spectral clusters from untargeted gas-chromatography high-resolution mass spectrometry (GC-HRMS) for the presence of spectra consistent with chlorine and bromine-containing organic compounds. The “pattern. search” function was incorporated into a different function that allows the evaluation of clusters containing multiple analyte fragments, has multi-core support, and provides a simplified output identifying listing compounds containing chlorine and/or bromine. The new function was able to process 46,000 spectral clusters in under 8 seconds and identified over 150 potential halogenated spectra. We next applied our function to a deidentified dataset from patients diagnosed with primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC), and healthy controls. Twenty-two spectra corresponded to potential halogenated compounds in the PSC and PBC dataset, including six significantly different in PBC patients, while four differed in PSC patients. We have developed an improved algorithm for detecting halogenated compounds in GC-HRMS data, providing a strategy for prioritizing exposures in the study of human disease.

Keywords: exposome, metabolome, computational metabolomics, high-resolution mass spectrometry, exposure, pollutants

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Authors: Ho-Wa Li, Sai-Wing Tsang

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The excitonic effect in organic semiconductors plays a key role in determining the electronic devices performance. Strong exciton binding energy has been regarded as the detrimental factor limiting the further improvement in organic photovoltaic cells. To the best of our knowledge, only limited reported can be found in measuring the exciton binding energy in organic photovoltaic materials. Conventional sophisticated approach using photoemission spectroscopy (UPS and IPES) would limit the wide access of the investigation. Here, we demonstrate a facile approach to study the electrical and optical quantum efficiencies of a series of conjugated photovoltaic polymer, fullerene and non-fullerene materials. Quantitative values of the exciton binding energy in those prototypical materials were obtained with concise photovoltaic device structure. And the extracted binding energies have excellent agreement with those determined by the conventional photoemission technique. More importantly, our findings can provide valuable information on the excitonic dissociation in the first excited state. Particularly, we find that the high binding energy of some non-fullerene acceptors limits the combination of polymer acceptors for efficiency exciton dissociation. The results bring insight into the engineering of excitonic effect for the development of efficient organic photovoltaic cells.

Keywords: organic photovoltaics, quantum efficiency, exciton binding energy, device physics

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Authors: Jayati Das

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Kolkata, one of the fastest-growing metropolises in India, has been suffering from air pollution for many decades. Mismanagement of government and an increase in automobiles have been fuelling this problem. The study aims to portray the quality of air along with the influence of traffic flow and vehicular growth and the effects on human health. It further shows the correlation between the emission of pollution during weekdays and weekends with the help of a scatter diagram and trend line. An assessment of Kolkata air quality is done where the listed pollutants’ (RPM, SPM, NO2, and SO2) annual average concentrations are classified into four different categories. Our observed association between childhood Acute Respiratory disorder and early life exposure to traffic-related air pollutants is biologically plausible. The period of in utero and the first year of life is critical in the development of the immune and respiratory systems and potentially harmful effects of toxic pollutants during this period might result in the long-lasting impaired capacity to fight infections and increased risk of allergic manifestations. Up-to-date knowledge about the seasonal and spatial variation of asthma and studying the air quality of the area is done through Geographical Information System (GIS). Steps are taken by the government to control air pollution by alternative public transport like the metro and compulsory certification of period-driven vehicles which test for Carbon mono oxide.

Keywords: air pollution, asthma, GIS, hotspots, governance

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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

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Authors: Maryam Maleki, Esther Mead, Mohammad Arani, Nitin Agarwal

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The proliferation of social media platforms like Twitter has heightened the consequences of the spread of misinformation. To understand and model the spread of misinformation, in this paper, we leveraged the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological model to describe the underlying process that delineates the spread of misinformation on Twitter. Compared to the other epidemiological models, this model produces broader results because it includes the additional Skeptics (Z) compartment, wherein a user may be Exposed to an item of misinformation but not engage in any reaction to it, and the additional Exposed (E) compartment, wherein the user may need some time before deciding to spread a misinformation item. We analyzed misinformation regarding the unrest in Washington, D.C. in the month of March 2020, which was propagated by the use of the #DCblackout hashtag by different users across the U.S. on Twitter. Our analysis shows that misinformation can be modeled using the concept of epidemiology. To the best of our knowledge, this research is the first to attempt to apply the SEIZ epidemiological model to the spread of a specific item of misinformation, which is a category distinct from that of rumor and hoax on online social media platforms. Applying a mathematical model can help to understand the trends and dynamics of the spread of misinformation on Twitter and ultimately help to develop techniques to quickly identify and control it.

Keywords: Black Lives Matter, epidemiological model, mathematical modeling, misinformation, SEIZ model, Twitter

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Authors: K. Shaloom Mbambu, M. Pascal Tshimbalanga, K. Ruth Mutala, K. Roger Kabuya, N. Dieudonné Kabeya, Y. L. Kabeya Mukeba

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In this article, the authors examine the even more worrying situation of girls in sub-Saharan Africa. Two-girls on five are private of Global Education, which represents a real loss to the development of communities and countries. Cultural traditions, poverty, violence, early and forced marriages, early pregnancies, and many other gender inequalities were the causes of this cancer development. Namely, "it is no more efficient development tool that is educating girls." The non-schooling of girls and their lack of supervision by liberal professions have serious consequences for the life of each of them. To improve the conditions of their inferior status, girls to men introduce poverty and health risks. Raising awareness among parents and communities on the importance of girls' education, improving children's access to school, girl-boy equality with their rights, creating income, and generating activities for girls, girls, and girls learning of liberal trades to make them self-sufficient. Organizations such as the United Nations Organization can save the children. ASEAD and the AEDA group are predicting the impact of this cancer on the development of a nation's future generation must be preserved.

Keywords: young girl, Sub-Saharan Africa, higher and vocational education, development, society, environment

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Authors: José Batista de Souza Neto

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This research project has as its theme the implementation of public policies to support micro and small businesses (MSEs). The research problem defined was how public policies for access to markets that drive the entrepreneurial ecosystem of MSEs are implemented. The general objective of this research is to understand the process of implementing a public policy to support the entrepreneurial ecosystem of MSEs by the Support Service for Micro and Small Enterprises of the State of São Paulo (SEBRAESP). Public policies are constituent elements of entrepreneurship ecosystems that influence the creation and development of ventures from the action of the entrepreneur. At the end of the research, it is expected to achieve the results for the following specific objectives: (a) understand how the entrepreneurial ecosystem of MSEs is constituted; (b) understand how market access public policies for MSEs are designed and implemented; (c) understand SEBRAE's role in the entrepreneurship ecosystem; and (d) offer an action plan and monitor its execution up to march, 2023. The field research will be conducted based on Action Research, with a qualitative and longitudinal approach to the data. Data collection will be based on narratives produced since 2019 when the decision to implement Comércio Brasil program, a public policy focused on generating market access for 4280 MSEs yearly, was made. The narratives will be analyzed by the method of document analysis and narrative analysis. It is expected that the research will consolidate the relevance of public policies to market access for MSEs and the role of SEBRAE as a protagonist in the implementation of these public policies in the entrepreneurship ecosystem will be demonstrated. Action research is recognized as an intervention method, it is expected that this research will corroborate its role in supporting management processes.

Keywords: entrepreneurship, entrepreneurship ecosystem, public policies, SEBRAE, action research

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Authors: Gobinathan Devathasan, Kezia Devathasan

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Objective: To improve the current modified Broca-Wernicke-Lichtheim-Kussmaul speech schema and provide insight into autism. Methods: We reviewed the pertinent literature. Current findings, involving Brodmann areas 22, 46, 9,44,45,6,4 are based on neuropathology and functional MRI studies. However, in primary autism, there is no lucid explanation and changes described, whether neuropathology or functional MRI, appear consequential. Findings: We forward an enhanced model which may explain the enigma related to autism. Vowel output is subcortical and does need cortical representation whereas consonant speech is cortical in origin. Left lateralization is needed to commence the circuitry spin as our life have evolved with L-amino acids and left spin of electrons. A fundamental species difference is we are capable of three syllable-consonants and bi-syllable expression whereas cetaceans and songbirds are confined to single or dual consonants. The 4 key sites for speech are superior auditory cortex, Broca’s two areas, and the supplementary motor cortex. Using the Argand’s diagram and Reimann’s projection, we theorize that the Euclidean three dimensional synaptic neuronal circuits of speech are quantized to coherent waves, and then decoherence takes place at area 6 (spherical representation). In this quantum state complex, 3-consonant languages are instantaneously integrated and multiple languages can be learned, verbalized and differentiated. Conclusion: We postulate that evolutionary human speech is elevated to quantum interaction unlike cetaceans and birds to achieve the three consonants/bi-syllable speech. In classical primary autism, the sudden speech switches off and on noted in several cases could now be explained not by any anatomical lesion but failure of coherence. Area 6 projects directly into prefrontal saccadic area (8); and this further explains the second primary feature in autism: lack of eye contact. The third feature which is repetitive finger gestures, located adjacent to the speech/motor areas, are actual attempts to communicate with the autistic child akin to sign language for the deaf.

Keywords: quantum neuronal paradigm, cetaceans and human speech, autism and rapid magnetic stimulation, coherence and decoherence of speech

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Authors: Maria Medrano, Cristina Cadenas-Sanchez, Maddi Oses, Lide Arenaza, Maria Amasene, Idoia Labayen

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The COVID-19 outbreak began in December 2019 in China and was rapidly expanded globally. Emergency measures, such as social distance or home confinement, were adopted by many country governments to prevent its transmission. In Spain, one of the most affected countries, the schools were closed, and one of the most severe mandatory home confinement was established for children from 14th March to 26th April 2020. The hypothesis of this study was that the measures adopted during the COVID-19 pandemic may have negatively affected physical activity and screen time of children. However, few studies have examined the effects of COVID-19 pandemic on lifestyle behaviours. Thus, the aim of the current work was to analyse the effects of the COVID-19 confinement on physical activity and screen time in Spanish children. For the current purpose, a total of 113 children and adolescents (12.0 ± 2.6 yr., 51.3% boys, 24.0% with overweight/obesity according to the World Obesity Federation) of the MUGI project were included in the analyses. Physical activity and screen time were longitudinally assessed by 'The Youth Activity Profile' questionnaire (YAP). Differences in physical activity and screen time before and during the confinement were assessed by dependent t-test. Before the confinement, 60% did not meet physical activity recommendations ( ≥ 60/min/day of moderate to vigorous physical activity), and 61% used screens ≥ 2 h/day. During the COVID-19 confinement, children decreased their physical activity levels (-91 ± 55 min/day, p < 0.001) and increased screen time ( ± 2.6 h/day, p < 0.001). The prevalence of children that worsened physical activity and screen time during the COVID-19 confinement were 95.2% and 69.8%, respectively. The current study evidence the negative effects of the COVID-19 confinement on physical activity and screen time in Spanish children. These findings should be taken into account to develop and implement future public health strategies for preserving children's lifestyle behaviours and health during and after the COVID-19 pandemic.

Keywords: COVID-19, lifestyle changes, paediatric, physical activity, screen time

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Authors: Joseph Carl Macalintal, Erlinda Armovit

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Magnesium sulfate has been a mainstay in the management of preeclampsia and is associated with a decreased incidence of morbidity and mortality. The syndrome has an unpredictable course, sometimes rapidly evolving to full-blown disease. In patients with deteriorating status, it is indicated to terminate the pregnancy via cesarean section. The anesthesiologists would prefer to have the procedure done under regional anesthesia; however, there may be cases when neuraxial anesthesia is contraindicated, or a general anesthesia would permit prompt delivery of the fetus. A patient with severe preeclampsia was given magnesium sulfate intrapartum, wherein a primary cesarean section was indicated for arrest in cervical dilatation, and was performed under general anesthesia. The patient developed acute respiratory failure and the causes of this occurrence were investigated in this report. It was later found out that neither the hypermagnesemia nor the muscle relaxant alone caused the patient’s condition but the interaction between the two. The patient was managed expectantly at the intensive care unit (ICU) and was eventually extubated during the 1st post-operative day. Knowledge of this drug interaction would allow obstetricians to advise their patients and their family about the possibility of prolonged intubation and ICU admission. This would also bring to the anesthesiologists’ attention the need to decrease the dose of muscle relaxant and to prepare drugs for immediate decurarisation.

Keywords: eclampsia, magnesium sulfate, preeclampsia, rocuronium bromide

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Authors: Faris Algahtani

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Every child has a right to education. This is one of the laws in the constitution and it empowers every child to access knowledge but it does not, however, allocate special interest to the rights of education for children with disabilities. It also does not address the challenges that teachers of such children face while trying to educate them. This study was conducted at government schools of Saudi Arabia. As the teaching profession is the most valuable profession and deserves to have its challenges tackled. This paper explores the challenges that teachers face as they try to teach students who have intellectual disabilities (ID). It looks at the daily challenges of a teacher who has to teach both children with disabilities and those without. The literature review shed light on the various aspects of mainstream education from the classroom to the outside environment to the teachers involved in mainstream education. The study employed qualitative methods in which Focus Group Discussions were utilized and Twenty (N=20) special education teachers were randomly sampled from primary schools through 6 groups of teachers from 6 different schools were interviewed through semi-structured interviews with the aim of drawing collective perceptions rather than personal perceptions about the challenges. The study found that most teachers had similar perceptions about the challenges that teachers face as they educate students with intellectual disabilities. The study recommends that The Ministry of Education should consider increasing the availability of special needs courses, workshops and conference for special education teachers.

Keywords: intellectual disabilities, inclusion, mainstream schools, disabilities, special education teachers

Procedia PDF Downloads 138

Authors: Navid Mirzaei Varzeghani, Mahmoud Saffarzadeh, Ali Naderan, Amirhossein Taheri

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Countries are progressing, and the world's busiest airports see year-on-year increases in travel demand. Passenger acceptability of an airport depends on the airport's appeals, which may include one of these routes between the city and the airport, as well as the facilities to reach them. One of the critical roles of transportation planners is to predict future transportation demand so that an integrated, multi-purpose system can be provided and diverse modes of transportation (rail, air, and land) can be delivered to a destination like an airport. In this study, 356 questionnaires were filled out in person over six days. First, the attraction of business and non-business trips was studied using data and a linear regression model. Lower travel costs, a range of ages more significant than 55, and other factors are essential for business trips. Non-business travelers, on the other hand, have prioritized using personal vehicles to get to the airport and ensuring convenient access to the airport. Business travelers are also less price-sensitive than non-business travelers regarding airport travel. Furthermore, carrying additional luggage (for example, more than one suitcase per person) undoubtedly decreases the attractiveness of public transit. Afterward, based on the manner and purpose of the trip, the locations with the highest trip generation to the airport were identified. The most famous district in Tehran was District 2, with 23 visits, while the most popular mode of transportation was an online taxi, with 12 trips from that location. Then, significant variables in separation and behavior of travel methods to access the airport were investigated for all systems. In this scenario, the most crucial factor is the time it takes to get to the airport, followed by the method's user-friendliness as a component of passenger preference. It has also been demonstrated that enhancing public transportation trip times reduces private transportation's market share, including taxicabs. Based on the responses of personal and semi-public vehicles, the desire of passengers to approach the airport via public transportation systems was explored to enhance present techniques and develop new strategies for providing the most efficient modes of transportation. Using the binary model, it was clear that business travelers and people who had already driven to the airport were the least likely to change.

Keywords: multimodal transportation, demand modeling, travel behavior, statistical models

Procedia PDF Downloads 179

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

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

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

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Authors: Steffon Campbell

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This research explores the potential of using artificial intelligence (AI) autoethnographies to study, document, explore, and understand aspects of Caribbean culture. As a digital research methodology, AI autoethnography merges computer science and technology with ethnography, providing a fresh approach to collecting and analyzing data to generate novel insights. This research investigates how AI autoethnography can best be applied to understanding the various complexities and nuances of Caribbean culture, as well as examining how technology can be a valuable tool for enriching study of the region. By applying AI autoethnography to Caribbean studies, the research aims to produce new and innovative ways of discovering, understanding, and appreciating the Caribbean. The study found that AI autoethnographies can offer a valuable method for exploring Caribbean culture. Specifically, AI autoethnographies can facilitate experiences of self-reflection, facilitate reconciliation with the past, and provide a platform to explore and understand the cultural, social, political, and economic concerns of Caribbean people. Findings also reveal that these autoethnographies can create a space for people to reimagine and reframe the conversation around Caribbean culture by enabling them to actively participate in the process of knowledge creation. The study also finds that AI autoethnography offers the potential for cross-cultural dialogue, allowing participants to connect with one another over cultural considerations and engage in meaningful discourse.

Keywords: artificial intelligence, autoethnography, caribbean, culture

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Authors: Nicolás Rudas

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Sociologists have brought to light the fascination of contemporary societies with numbers but fall short of explaining it. In their accounts, people generally misunderstand the technical intricacies of statistical knowledge and therefore accept numbers as unassailable “facts”. It is due to such pervasive fascination, furthermore, that both old and new forms of social control find fertile ground. By focusing on the process whereby the fetishization of numbers reaches its zenith, i.e., when specific statistics become emblematic of an entire society, it is asserted that numbers primarily function as moral symbols with immense potential for galvanizing collective action. Their “facticity” is not solely a cognitive problem but one that is deeply rooted in myth and connected with social experiences of epiphany and ritual. Evidence from Colombia is used to illustrate how certain quantifications become canonical. In 2021, Colombia’s Peace Court revealed that the national army had executed 6,402 innocent civilians to later report them as members of illegal armed groups. Rapidly, “6,402” transformed into a prominent item in the country’s political landscape. This article reconstructs such a process by following the first six months of the figure’s circulation, both in traditional and social media. In doing so, it is developed a new cultural-sociological conceptualization of numbers as “fact-icons” that departs from traditional understandings of statistics as “technical” objects. Numbers are icons whose appropriation is less rational than aesthetic.

Keywords: culture, statistics, collective memory, social movements

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Authors: Bhaargav Gupta P., Vignesh N., Nithish Kumar R., Rahul J., Nivetha Ruvah D.

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Delivering essential medical supplies to rural and underserved areas is challenging due to infrastructure limitations and logistical barriers, often resulting in inefficiencies and delays. Traditional delivery methods are hindered by poor road networks, long distances, and difficult terrains, compromising timely access to vital resources, especially in emergencies. This paper introduces an autonomous drone system engineered to optimize last-mile delivery. By utilizing advanced navigation and object-detection algorithms, such as region-based convolutional neural networks (R-CNN), our drones efficiently avoid obstacles, identify safe landing zones, and adapt dynamically to varying environments. Equipped with high-precision GPS and autonomous capabilities, the drones effectively navigate complex, remote areas with minimal dependence on established infrastructure. The system includes a dedicated mobile application for secure order placement and real-time tracking, and a secure payload box with OTP verification ensures tamper-resistant delivery to authorized recipients. This project demonstrates the potential of automated drone technology in healthcare logistics, offering a scalable and eco-friendly approach to enhance accessibility and service delivery in underserved regions. By addressing logistical gaps through advanced automation, this system represents a significant advancement toward sustainable, accessible healthcare in remote areas.

Keywords: region-based convolutional neural network, one time password, global positioning system, autonomous drones, healthcare logistics

Procedia PDF Downloads 19