Search results for: academic speed and accuracy

Authors: Joseph Marae Djouda, Ashraf Kasmi, François Hild

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Fused filament fabrication is one of the most widespread additive manufacturing techniques because of its low-cost implementation. Its initial development was based on part fabrication with thermoplastic materials. The influence of the manufacturing parameters such as the filament orientation through the nozzle, the deposited layer thickness, or the speed deposition on the mechanical properties of the parts has been widely experimentally investigated. It has been recorded the remarkable variations of the anisotropy in the function of the filament path during the fabrication process. However, there is a lack in the development of constitutive models describing the mechanical properties. In this study, integrated digital image correlation (I-DIC) is used for the identification of mechanical constitutive parameters of two configurations of ABS samples: +/-45° and so-called “oriented deposition.” In this last, the filament was deposited in order to follow the principal strain of the sample. The identification scheme based on the gap reduction between simulation and the experiment directly from images recorded from a single sample (single edge notched tension specimen) is developed. The macroscopic and mesoscopic analysis are conducted from images recorded in both sample surfaces during the tensile test. The elastic and elastoplastic models in isotropic and orthotropic frameworks have been established. It appears that independently of the sample configurations (filament orientation during the fabrication), the elastoplastic isotropic model gives the correct description of the behavior of samples. It is worth noting that in this model, the number of constitutive parameters is limited to the one considered in the elastoplastic orthotropic model. This leads to the fact that the anisotropy of the architectured 3D printed ABS parts can be neglected in the establishment of the macroscopic behavior description.

Keywords: elastic anisotropy, fused filament fabrication, Acrylonitrile butadiene styrene, I-DIC identification

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Authors: Fangyan Li, Lin Min Lee, Hui Zhu Peh, Shoet Harn Chan

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Advantame, a derivative of aspartame, is the latest addition to a family of low caloric and high potent dipeptide sweeteners which include aspartame, neotame and alitame. The use of advantame as a high-intensity sweetener in food was first accepted by Food Standards Australia New Zealand in 2011 and subsequently by US and EU food authorities in 2014, with the results from toxicity and exposure studies showing advantame poses no safety concern to the public at regulated levels. To our knowledge, currently there is barely any detailed information on the analytical method of advantame in food matrix, except for one report published in Japanese, stating a high performance liquid chromatography (HPLC) and liquid chromatography/ mass spectrometry (LC-MS) method with a detection limit at ppm level. However, the use of acid in sample preparation and instrumental analysis in the report raised doubt over the reliability of the method, as there is indication that stability of advantame is compromised under acidic conditions. Besides, the method may not be suitable for analyzing food matrices containing advantame at low ppm or sub-ppm level. In this presentation, a simple, specific and sensitive method for the determination of advantame in food is described. The method involved extraction with water and clean-up via solid phase extraction (SPE) followed by detection using liquid chromatography tandem mass spectrometry (LC-MS/MS) in negative electrospray ionization mode. No acid was used in the entire procedure. Single laboratory validation of the method was performed in terms of linearity, precision and accuracy. A low detection limit at ppb level was achieved. Satisfactory recoveries were obtained using spiked samples at three different concentration levels. This validated method could be used in the routine inspection of the advantame level in food.

Keywords: advantame, food, LC-MS/MS, sweetener

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Michelle W. T. Cheng, Yau Y.

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Hong Kong continues to be ranked as the least affordable housing market in the world, making co-living a feasible alternative in this high-density city. Although the number of co-living residences has increased in Hong Kong, co-living as a housing typology is still a new concept for many. Little research has been conducted on this new housing typology, let alone the co-living experience. To address this gap, this study targeted student residents in university residential halls as it is a more controlled environment (e.g., with established rules and guidelines regarding the use of communal facilitates and housing management) for studying co-living experiences in Hong Kong. To date, no research study has systematically identified anti-social behavior (ASB) in co-living spaces. Since ASB can be influenced by factors such as social norms and individual interpretation, it has an elastic definition that results in different levels of acceptance. Unlike other types of housing, co-living spaces can be potentially more influenced by the neighborhood as residents share more time and space. As a pilot study, this research targeted one university residential hall to examine student co-living experiences. To clarify, the research question is focused on identifying the social factors that impact the residential satisfaction of those who co-living in residential halls. Quantitative data (n=100) were collected via a structured questionnaire to measure the residential environment, including ASB, social neighboring, community attachment, and perceived hall management efficacy. The survey was distributed at the end of the academic year to ensure that respondents had at least one year of first-hand experience living in a co-living space. To gather qualitative data, follow-up focus group interviews were conducted with 16 participants who completed the survey. The semi-structured interviews aimed to elicit the participants' perspectives on their co-living experience. Through analyzing their co-living experiences, the researcher identified factors that affected their residential satisfaction and provided recommendations to enhance their co-living experience.

Keywords: co-living, university residential hall, anti-social behabiour, neighbour relationship, community attachement

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Authors: Xiaogang Li, Jieqiong Miao

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As for the problem of the grey forecasting model prediction accuracy is low, an improved grey prediction model is put forward. Firstly, use trigonometric function transform the original data sequence in order to improve the smoothness of data , this model called SGM( smoothness of grey prediction model), then combine the improved grey model with support vector machine , and put forward the grey support vector machine model (SGM - SVM).Before the establishment of the model, we use trigonometric functions and accumulation generation operation preprocessing data in order to enhance the smoothness of the data and weaken the randomness of the data, then use support vector machine (SVM) to establish a prediction model for pre-processed data and select model parameters using genetic algorithms to obtain the optimum value of the global search. Finally, restore data through the "regressive generate" operation to get forecasting data. In order to prove that the SGM-SVM model is superior to other models, we select the battery life data from calce. The presented model is used to predict life of battery and the predicted result was compared with that of grey model and support vector machines．For a more intuitive comparison of the three models, this paper presents root mean square error of this three different models .The results show that the effect of grey support vector machine (SGM-SVM) to predict life is optimal, and the root mean square error is only 3.18%. Keywords: grey forecasting model, trigonometric function, support vector machine, genetic algorithms, root mean square error

Keywords: Grey prediction model, trigonometric functions, support vector machines, genetic algorithms, root mean square error

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Authors: Molnar Catalina, Lexi Frankel, Amalia Ardeljan, Enoch Kim, Marissa Dallara, Omar Rashid

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Introduction: Clostridium difficile (C. diff) is a toxin-producing bacteria that can cause diarrhea and colitis. U.S. Center for Disease Control and Prevention revealed that C. difficile infection (CDI) has increased from 31 cases per 100,000 persons per year in 1996 to 61 per 100,000 in 2003. Approximately 500,000 cases per year occur in the United States. After exposure, the bacteria colonize the colon, where it adheres to the intestinal epithelium where it produces two toxins: TcdA and TcdB. TcdA affects the intestinal epithelium, causing fluid secretion, inflammation, and tissue necrosis, while TcdB acts as a cytotoxin purpose of this study was to evaluate the association between C diff infection and bronchial lung cancer development. Methods: Using ICD- 9 and ICD-10 codes, the data was provided by a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to assess the patients infected with C diff as opposed to the non-infected patients. The Holy Cross Health, Fort Lauderdale, granted access to the database for the purpose of academic research. Patients were matched for age and Charlson Comorbidity Index (CCI). Standard statistical methods were used. Results: Bronchial lung cancer occurrence in the population not infected with C diff infection was 4741, as opposed to the population infected with C. diff, where 2039 cases of lung cancer were observed. The difference was statistically significant (p-value < 2.2x10^e-16), which reveals that C diff might be protective against bronchial lung cancer. The data was then matched by treatment to create to minimize the effect of treatment bias. Bronchial cancer incidence was 422 and 861 in infected vs. non-infected (p-value of < 2.2x10^e-16), which once more indicates that C diff infection could be beneficial in diminishing bronchial cancer development. Conclusion: This retrospective study conveys a statistical correlation between C diff infection and decreased incidence of lung bronchial cancer. Further studies are needed to comprehend the protective mechanisms of C. Diff infection on lung cancer.

Keywords: C. diff, lung cancer, protective, microbiology

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Authors: Abdallah Al-ZoubI, Mamoun Dmour

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The spectrum of IoT devices is becoming widely diversified, entering almost all possible fields and finding applications in industry, health, finance, logistics, education, to name a few. The IoT active endpoint sensors and devices exceeded the 12 billion mark in 2021 and are expected to reach 27 billion in 2025, with over $34 billion in total market value. This sheer rise in numbers and use of IoT devices bring with it considerable concerns regarding data storage, analysis, manipulation and protection. IoT Blockchain-based systems have recently been proposed as a decentralized solution for large-scale data storage and protection. COVID-19 has actually accelerated the desire to utilize IoT devices as it impacted both demand and supply and significantly affected several regions due to logistic reasons such as supply chain interruptions, shortage of shipping containers and port congestion. An IoT-blockchain system is proposed to handle big data generated by a distributed network of sensors and controllers in an interactive manner. The system is designed using the Ethereum platform, which utilizes smart contracts, programmed in solidity to execute and manage data generated by IoT sensors and devices. such as Raspberry Pi 4, Rasbpian, and add-on hardware security modules. The proposed system will run a number of applications hosted by a local machine used to validate transactions. It then sends data to the rest of the network through InterPlanetary File System (IPFS) and Ethereum Swarm, forming a closed IoT ecosystem run by blockchain where a number of distributed IoT devices can communicate and interact, thus forming a closed, controlled environment. A prototype has been deployed with three IoT handling units distributed over a wide geographical space in order to examine its feasibility, performance and costs. Initial results indicated that big IoT data retrieval and storage is feasible and interactivity is possible, provided that certain conditions of cost, speed and thorough put are met.

Keywords: IoT devices, blockchain, Ethereum, big data

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Authors: Edison A. Bonifaz

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In this work, a numerical procedure is proposed to design dense multi-material structures using the Directed Energy Deposition (DED) process. A thermo-mechanical finite element model to predict thermal cycles and residual stresses is presented. A numerical layer build-up procedure coupled with a moving heat flux was constructed to minimize strains and residual stresses that result in the multi-layer deposition of an AISI 316 austenitic steel on an AISI 304 austenitic steel substrate. To simulate the DED process, the automated interface of the ABAQUS AM module was used to define element activation and heat input event data as a function of time and position. Of this manner, the construction of ABAQUS user-defined subroutines was not necessary. Thermal cycles and thermally induced stresses created during the multi-layer deposition metal AM pool crystallization were predicted and validated. Results were analyzed in three independent metal layers of three different experiments. The one-way heat and material deposition toolpath used in the analysis was created with a MatLab path script. An optimal combination of feedstock and heat input printing parameters suitable for fabricating multi-material dense structures in the directed energy deposition metal AM process was established. At constant power, it can be concluded that the lower the heat input, the lower the peak temperatures and residual stresses. It means that from a design point of view, the one-way heat and material deposition processing toolpath with the higher welding speed should be selected.

Keywords: event series, thermal cycles, residual stresses, multi-pass welding, abaqus am modeler

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Authors: Olive P. Khaliq, Stephen C. Brown, Boitumelo Pitso, Nomakhuwa E. Tabane

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Context: The research focuses on the prevalence of SARS-CoV-2 infection in hospitalized children during the Omicron variant wave in South Africa, specifically in the Free State Province. Research Aim: This study aimed to investigate the prevalence of COVID-19 infection in asymptomatic, unvaccinated children during the Omicron variant wave in the Free State Province of South Africa. Methods: A prospective cross-sectional study was conducted on children aged 0-12 admitted to hospitals using nucleocapsid antibody rapid testing for SARS-CoV-2 presence. Data on parent/caregiver vaccination and patient conditions were collected. Results: 46.8% of hospitalized children tested positive for SARS-CoV-2, with the highest rates in neonates. Most infected children had unrelated conditions and were asymptomatic. The Omicron variant was characterized as highly infectious but less virulent, leading to mild disease. Theoretical Importance: The study highlights the significant SARS-CoV-2 infection rates in hospitalized children during the Omicron variant surge, emphasizing the variant's unique characteristics in causing mild or asymptomatic infections. Data Collection: Data were collected through nucleocapsid antibody rapid testing for SARS-CoV-2 and the compilation of parent/caregiver vaccination status and patient conditions. Analysis Procedures: The data were analyzed to determine the prevalence of SARS-CoV-2 infection in hospitalized children, focusing on demographics, infection rates, and associated conditions. Questions Addressed: The study addressed the prevalence of SARS-CoV-2 in hospitalized children, the impact of the Omicron variant, the asymptomatic nature of infections, and the potential role of vaccination status in transmission. Conclusion: The research revealed a high rate of SARS-CoV-2 infections among hospitalized children, mostly asymptomatic and with unrelated conditions, indicating the unique infectiousness and clinical presentation of the Omicron variant in this demographic.

Keywords: SARS-CoV-2, Omicron variant, antibodies, children, admission diagnosis

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Authors: David Mason-Cox

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Existing academic analyses of The Beatles cover a huge array of topics. This research explores one clear but multifaceted aspect of The Beatles: the development of their creativity. While its importance cannot be underestimated, a thorough appraisal of the roots of the group’s individual and collective artistic blossoming deserves more attention. This paper investigates the mechanisms that caused or enabled the group to eventually exert such an immense and long-lasting influence on popular music and culture. It suggests that the artistic inspiration of Astrid Kirchherr during their time in Hamburg may be much more far-reaching than has previously been credited. It further addresses the effect of the confluence of conditions and events which essentially ‘hot-housed’ the four working-class Liverpudlians, providing them with the incentives and the means to far exceed their apparent potential. Thirdly, it looks at the competitive nature of The Beatles, both as a group and as individuals, and how that competitive streak sparked them to improve as musicians, songwriters, and showmen. In viewing these triggers through the lens of creative theory, the research attempts to analyse what made The Beatles’ innovative ascendancy so extraordinary and why creativity can be misunderstood. This then is the tale of impressionable youths from post-war austerity Britain; the lure of an artist with strong aesthetic sensibilities in an exotic locale, the media boom of the early 1960s, the machinations of the music business, the national grief in the US following Kennedy’s assassination, and, finally the resilience and determination of four young men who were prepared to take advantage of every opportunity to prove, and improve, themselves -the harbingers of a new creative paradigm. This paper is part of a broader study which also examines how their growth toward artistic maturity informs The Beatles’ significance and impact on the culture and the counterculture during the 1960s and beyond. It will eventually combine critical textual analysis with a series of interviews of musicians, other creatives, and intellectuals. These will be conducted to advance the existing erudition and to develop a more accurate understanding of the group’s cultural influence upon real-world individuals.

Keywords: artistic influence, Beatles, competition, creative theory, new creative paradigm

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Authors: Jianjun Wu, Fanhui Guo, Yixin Zhang

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In this study, gasification-coke were produced by blending the middlings (MC), and coking coal (CC) and a large proportion of long flame coal (Shenfu coal, SC), the effects of blending ratio were investigated. Mineral evolution and crystalline order obtained by XRD methods were reproduced within reasonable accuracy. Structure characteristics of partially gasification-coke such as surface area and porosity were determined using the N₂ adsorption and mercury porosimetry. Experimental data of gasification-coke was dominated by the TGA results provided trend, reactivity differences between gasification-cokes are discussed in terms of structure characteristic, crystallinity, and alkali index (AI). The first-order reaction equation was suitable for the gasification reaction kinetics of CO₂ atmosphere which was represented by the volumetric reaction model with linear correlation coefficient above 0.985. The differences in the microporous structure of gasification-coke and catalysis caused by the minerals in parent coals were supposed to be the main factors which affect its reactivity. The addition of MC made the samples enriched with a large amount of ash causing a higher surface area and a lower crystalline order to gasification-coke which was beneficial to gasification reaction. The higher SiO₂ and Al₂O₃ contents, causing a decreasing AI value and increasing activation energy, which reduced the gasification reaction activity. It was found that the increasing amount of MC got a better performance on the coke gasification reactivity by blending > 30% SC with this coking process.

Keywords: low-rank coal, middlings, structure characteristic, mineral evolution, alkali index, gasification-coke, gasification kinetics

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Authors: ShyamKrishna Kirithivasan, Shreyas Battula, Aditi Soori, Richa Ramesh, Ramamoorthy Srinath

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The human brain, among the most complex and mysterious aspects of the body, harbors vast potential for extensive exploration. Unraveling these enigmas, especially within neural perception and cognition, delves into the realm of neural decoding. Harnessing advancements in generative AI, particularly in Visual Computing, seeks to elucidate how the brain comprehends visual stimuli observed by humans. The paper endeavors to reconstruct human-perceived visual stimuli using Functional Magnetic Resonance Imaging (fMRI). This fMRI data is then processed through pre-trained deep-learning models to recreate the stimuli. Introducing a new architecture named LatentNeuroNet, the aim is to achieve the utmost semantic fidelity in stimuli reconstruction. The approach employs a Latent Diffusion Model (LDM) - Stable Diffusion v1.5, emphasizing semantic accuracy and generating superior quality outputs. This addresses the limitations of prior methods, such as GANs, known for poor semantic performance and inherent instability. Text conditioning within the LDM's denoising process is handled by extracting text from the brain's ventral visual cortex region. This extracted text undergoes processing through a Bootstrapping Language-Image Pre-training (BLIP) encoder before it is injected into the denoising process. In conclusion, a successful architecture is developed that reconstructs the visual stimuli perceived and finally, this research provides us with enough evidence to identify the most influential regions of the brain responsible for cognition and perception.

Keywords: BLIP, fMRI, latent diffusion model, neural perception.

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Authors: Carla Pérez-Chirinos Buxadé, Bruno Fernández-Valdés, Mónica Morral-Yepes, Sílvia Tuyà Viñas, Josep Maria Padullés Riu, Gerard Moras Feliu

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There is a long way to explore all the possible applications inertial measurement units (IMUs) have in the sports field. The aim of this study was to evaluate the validity of a new application on the use of these wearable sensors, specifically it was to evaluate a magnet-based timing system (M-BTS) for timing gate-to-gate in an alpine ski slalom using the magnetometer embedded in an IMU. This was a validation study. The criterion validity of time measured by the M-BTS was assessed using the 95% error range against actual time obtained from photocells. The experiment was carried out with first-and second-year junior skiers performing a ski slalom on a ski training slope. Eight alpine skiers (17.4 ± 0.8 years, 176.4 ± 4.9 cm, 67.7 ± 2.0 kg, 128.8 ± 26.6 slalom FIS-Points) participated in the study. An IMU device was attached to the skier’s lower back. Skiers performed a 40-gate slalom from which four gates were assessed. The M-BTS consisted of placing four bar magnets buried into the snow surface on the inner side of each gate’s turning pole; the magnetometer built into the IMU detected the peak-shaped magnetic field when passing near the magnets at a certain speed. Four magnetic peaks were detected. The time compressed between peaks was calculated. Three inter-gate times were obtained for each system: photocells and M-BTS. The total time was defined as the time sum of the inter-gate times. The 95% error interval for the total time was 0.050 s for the ski slalom. The M-BTS is valid for timing gate-to-gate in an alpine ski slalom. Inter-gate times can provide additional data for analyzing a skier’s performance, such as asymmetries between left and right foot.

Keywords: gate crossing time, inertial measurement unit, timing system, wearable sensor

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Authors: Chitra Rajagopal, Indra Deo Kumar, Ruchi Joshi, Binoy Bhargavan

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Role of fighter planes in modern network centric military warfare scenarios has changed significantly in the recent past. New generation fighter planes have multirole capability of engaging both air and ground targets with high precision. Multirole aircraft undertakes missions such as Air to Air combat, Air defense, Air to Surface role (including Air interdiction, Close air support, Maritime attack, Suppression and Destruction of enemy air defense), Reconnaissance, Electronic warfare missions, etc. Designers have primarily focused on development of technologies to enhance the combat performance of the fighter planes and very little attention is given to human factor aspects of technologies. Unique physical and psychological challenges are imposed on the pilots to meet operational requirements during these missions. Newly evolved technologies have enhanced aircraft performance in terms of its speed, firepower, stealth, electronic warfare, situational awareness, and vulnerability reduction capabilities. This paper highlights the impact of emerging technologies on human factors for various military operations and missions. Technologies such as ‘cooperative knowledge-based systems’ to aid pilot’s decision making in military conflict scenarios as well as simulation technologies to enhance human performance is also studied as a part of research work. Current and emerging pilot protection technologies and systems which form part of the integrated life support systems in new generation fighter planes is discussed. System safety analysis application to quantify the human reliability in military operations is also studied.

Keywords: combat effectiveness, emerging technologies, human factors, systems safety analysis

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Authors: Priyatma Singh, Manpreet Kaur

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Climate change is progressively being identified as a global crisis and this has immediate repercussions for Fiji Islands due to its geographical location being prone to natural disasters. In the Pacific, it is common to find significant differences between men and women, in terms of their roles and responsibilities. In the pursuit of prudent preparedness before disasters, Fijian women’s engagement is constrained due to socially constructed roles and expectation of women here in Fiji. This vulnerability is aggravated by viewing women as victims, rather than as key people who have vital information of their society, economy, and environment, as well as useful skills, which, when recognized and used, can be effective in disaster risk reduction. The focus of this study on disaster management is to outline ways in which Fijian women can be actively engaged in disaster risk management, articulating in decision-making, negating the perceived ideology of women’s constricted roles in Fiji and unveiling social constraints that limit women’s access to practical disaster management strategic plan. This paper outlines the importance of gender mainstreaming in disaster risk reduction and the ways of mainstreaming gender based on a literature review. It analyses theoretical study of academic literature as well as papers and reports produced by various national and international institutions and explores ways to better inform and engage women for climate change per ser disaster management in Fiji. The empowerment of women is believed to be a critical element in constructing disaster resilience as women are often considered to be the designers of community resilience at the local level. Gender mainstreaming as a way of bringing a gender perspective into climate related disasters can be applied to distinguish the varying needs and capacities of women, and integrate them into climate change adaptation strategies. This study will advocate women articulation in disaster risk management, thus giving equal standing to females in Fiji and also identify the gaps and inform national and local Disaster Risk Management authorities to implement processes that enhance gender equality and women’s empowerment towards a more equitable and effective disaster practice.

Keywords: disaster risk management, climate change, gender mainstreaming, women empowerment

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Authors: Abbas Pourreza

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MicroRNAs (miRNAs) are small single-stranded non-coding RNAs. They are almost 18-25 nucleotides long and very conservative through evolution. They are involved in adjusting the expression of numerous genes due to the existence of a complementary region, generally in the 3' untranslated regions (UTR) of target genes, against particular mRNAs in the cell. Also, miRNAs have been proven to be involved in cell development, differentiation, proliferation, and apoptosis. More than 2000 miRNAs have been recognized in human cells, and these miRNAs adjust approximately one-third of all genes in human cells. Dysregulation of miRNA originated from abnormal DNA methylation patterns of the locus, cause to down-regulated or overexpression of miRNAs, and it may affect tumor formation or development of it. Breast cancer (BC) is the most commonly identified cancer, the most prevalent cancer (23%), and the second-leading (14%) mortality in all types of cancer in females. BC can be classified based on the status (+/−) of the hormone receptors, including estrogen receptor (ER), progesterone receptor (PR), and the Receptor tyrosine-protein kinase erbB-2 (ERBB2 or HER2). Currently, there are four main molecular subtypes of BC: luminal A, approximately 50–60 % of BCs; luminal B, 10–20 %; HER2 positive, 15–20 %, and 10–20 % considered Basal (triple-negative breast cancer (TNBC)) subtype. Aberrant expression of miR-145, miR-21, miR-10b, miR-125a, and miR-206 was detected by Stem-loop real-time RT-PCR in BC cases. Breast tumor formation and development may result from down-regulation of a tumor suppressor miRNA such as miR-145, miR-125a, and miR-206 and/or overexpression of an oncogenic miRNA such as miR-21 and miR-10b. MiR-125a, miR-206, miR-145, miR-21, and miR-10b are hugely predicted to be new tumor markers for the diagnosis and prognosis of BC. MiR-21 and miR-125a could play a part in the treatment of HER-2-positive breast cancer cells, while miR-145 and miR-206 could speed up the evolution of cure techniques for TNBC. To conclude, miRNAs will be presented as hopeful molecules to be used in the primary diagnosis, prognosis, and treatment of BC and battle as opposed to its developed drug resistance.

Keywords: breast cancer, HER2 positive, miRNA, TNBC

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Authors: Dirson Santos De Campos, Deller James Ferreira, Anderson Cavalcante Gonçalves, Uyara Ferreira Silva

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Rubrics to learning research provide many evaluation criteria and expected performance standards linked to defined student activity for learning and pedagogical objectives. Despite the rubric being used in education at all levels, academic literature on rubrics as a tool to support research in SQL Education is quite rare. There is a large class of SQL queries is syntactically correct, but certainly, not all are semantically correct. Detecting and correcting errors is a recurring problem in SQL education. In this paper, we usthe Rubric Abstract Framework (RAF), which consists of steps, that allows us to map the information to measure student performance guided by didactic objectives defined by the teacher as long as it is contextualized domain modeling by rubric. An empirical study was done that demonstrates how rubrics can mitigate student difficulties in finding logical errors and easing teacher workload in SQL education. Detecting and correcting logical errors is an important skill for students. Researchers have proposed several ways to improve SQL education because understanding this paradigm skills are crucial in software engineering and computer science. The RAF instantiation was using in an empirical study developed during the COVID-19 pandemic in database course. The pandemic transformed face-to-face and remote education, without presential classes. The lab activities were conducted remotely, which hinders the teaching-learning process, in particular for this research, in verifying the evidence or statements of knowledge, skills, and abilities (KSAs) of students. Various research in academia and industry involved databases. The innovation proposed in this paper is the approach used where the results obtained when using rubrics to map logical errors in query formulation have been analyzed with gains obtained by students empirically verified. The research approach can be used in the post-pandemic period in both classroom and distance learning.

Keywords: rubric, logical error, structured query language (SQL), empirical study, SQL education

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Authors: Vladimir Isakov, Evgeniy Rykov, Lubov Magerramova, Nikolay Emmaussky

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For the manufacture of critical hollow products, a laser narrow-seam welding scheme based on the supply of a laser beam into the inner cavity has been developed. The report presents the results of comprehensive studies aimed at creating a sealed weld that repeats the geometric shape of the inner cavity using a rotary mirror. Laser welding of hard-to-reach places requires preliminary modeling of the process to identify defect-free modes performed at the highest possible welding speed. Optimization of the technological modes of the welded joint with a ratio of the seam width to its depth equal to 1/5 of the thickness of the Ni superalloy 6.0 mm was performed using the Verhulst limited growth model in a discrete representation. This mathematical model in the form of a recurrence relation made it possible to numerically investigate the entire variety of laser melting modes: chaotic; self-oscillating; stationary and attenuated. The control parameters and the parameter of the order to which other variables of the technological system of laser welding are subordinated are established. In it, the coefficient of relative heat capacity of the melt bath was used as a control parameter, characterizing the competition between the heat input by the laser and the heat sink into the surrounding metal. The parameter of the order of the narrow–seam laser welding process, in this interpretation, is a dimensionless value of the penetration depth, which is an argument of the function of the desired logistic equation. Experimental studies of narrow-seam welding were performed using a copper, water-cooled mirror by radiation from a powerful fiber laser. The obtained results were used to validate the evolutionary mathematical model of the laser welding process.

Keywords: laser welding, internal cavity, limited growth model, ni-superalloy

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Authors: A. Cerrato Casado, C. Guigou, P. Jean

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In this investigation, the particle swarm optimization (PSO) algorithm is used to perform the inversion of the dispersion curves in the spectral analysis of surface waves (SASW) method. This inverse problem usually presents complicated solution spaces with many local minima that make difficult the convergence to the correct solution. PSO is a metaheuristic method that was originally designed to simulate social behavior but has demonstrated powerful capabilities to solve inverse problems with complex space solution and a high number of variables. The dispersion curve of the synthetic soils is constructed by the vertical flexibility coefficient method, which is especially convenient for soils where the stiffness does not increase gradually with depth. The reason is that these types of soil profiles are not normally dispersive since the dominant mode of Rayleigh waves is usually not coincident with the fundamental mode. Multiple synthetic soil profiles have been tested to show the characteristics of the convergence process and assess the accuracy of the final soil profile. In addition, the inversion procedure is applied to multiple real soils and the final profile compared with the available information. The combination of the vertical flexibility coefficient method to obtain the dispersion curve and the PSO algorithm to carry out the inversion process proves to be a robust procedure that is able to provide good solutions for complex soil profiles even with scarce prior information.

Keywords: dispersion, inverse problem, particle swarm optimization, SASW, soil profile

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Authors: Shih-Tsung Chen, Yenchin Hsiao

Abstract:

Evergreen Lily is a newly-built school after Morakot Typhoon took place. The school is located on Majia farm, which is surrounded by mountains. The fund in the construction of the school is solely sponsored by Chang Yung-Fa Foundation. There are 483 permanent houses near the school belonging to three tribes, Dashe, Majia, and Haocha. Due to the most ancient heritages of Paiwan and Rukai in these three tribes, the school is full of cultural atmosphere. From modern and traditional perspectives, Evergreen Lily strives to establish and develop a long-lasting educational model to meet the expectation of the tribes, parents, and the public. This study is a case study of how to develop indigenous education in newly established schools after the Morakot Hurricane disaster to meet the concept of environmental education. The systematic curriculum construction of education and cultural integration and the systematic practice of curriculum practice will be discussed, and the concept and practice of tribal education curriculum and sustainable environmental education will be understood. This study found that this school integrates the spirit of natural philosophy, democratic education, ethnic and experimental education, and constructs a knowledge system that includes three levels of spiritual culture, institutional culture, and material culture, as well as six dimensions of life philosophy, natural ecology, organizational system, tribal literature and history, song and dance, and technical and artistic methods. Adhering to the concept of harmonious education and the sustainable common good, the development of school-based tribal academic courses accounts for about one-third of the total number of teaching sessions, and there are different cultural themes in grades one to six, and there are clear teaching modules to effectively enhance students' potential inspiration. The complete curriculum implementation model can be described as a model for the development of indigenous schools to sustainable environmental education.

Keywords: environmental education, indigenous education, sustainable development, school-based curriculum

Procedia PDF Downloads 163

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 83

Authors: Fahad Abbasi

Abstract:

Flow of electrically conducting nanofluids is of pivotal importance in countless industrial and medical appliances. Fluctuations in thermophysical properties of such fluids due to variations in temperature have not received due attention in the available literature. Present investigation aims to fill this void by analyzing the flow of copper-water nanofluid with temperature dependent viscosity past a Riga plate. Strong wall suction and viscous dissipation have also been taken into account. Numerical solutions for the resulting nonlinear system have been obtained. Results are presented in the graphical and tabular format in order to facilitate the physical analysis. An estimated expression for skin friction coefficient and Nusselt number are obtained by performing linear regression on numerical data for embedded parameters. Results indicate that the temperature dependent viscosity alters the velocity, as well as the temperature of the nanofluid and, is of considerable importance in the processes where high accuracy is desired. Addition of copper nanoparticles makes the momentum boundary layer thinner whereas viscosity parameter does not affect the boundary layer thickness. Moreover, the regression expressions indicate that magnitude of rate of change in effective skin friction coefficient and Nusselt number with respect to nanoparticles volume fraction is prominent when compared with the rate of change with variable viscosity parameter and modified Hartmann number.

Keywords: heat transfer, peristaltic flows, radially varying magnetic field, curved channel

Procedia PDF Downloads 167

Authors: Jadon Matthiasa, Cindy Donga, Ali Al Jibouria, Hsin Kuo

Abstract:

The environmental impact of emissions from the Covanta Waste-to-Energy facility in Burnaby, BC, was comprehensively evaluated, focusing on the dispersion of air pollutants and the subsequent assessment of heavy metal contamination in surrounding soils. A Gaussian Plume Model, implemented in MATLAB, was utilized to simulate the dispersion of key pollutants to understand their atmospheric behaviour and potential deposition patterns. The MATLAB code developed for this study enhanced the accuracy of pollutant concentration predictions and provided capabilities for visualizing pollutant dispersion in 3D plots. Furthermore, the code could predict the maximum concentration of pollutants at ground level, eliminating the need to use the Ranchoux model for predictions. Complementing the modelling approach, empirical soil sampling and analysis were conducted to evaluate heavy metal concentrations in the vicinity of the facility. This integrated methodology underscored the importance of computational modelling in air pollution assessment and highlighted the necessity of soil analysis to obtain a holistic understanding of environmental impacts. The findings emphasized the effectiveness of current emissions controls while advocating for ongoing monitoring to safeguard public health and environmental integrity.

Keywords: air emissions, Gaussian Plume Model, MATLAB, soil contamination, air pollution monitoring, waste-to-energy, pollutant dispersion visualization, heavy metal analysis, environmental impact assessment, emission control effectiveness

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Authors: Ouafa Amira, Jiangshe Zhang

Abstract:

Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

Procedia PDF Downloads 264

Authors: Neha Intikhab Khan, Alanoud AlBalwi, Farah Alqazlan, Tala Almadoudi

Abstract:

Background: Grammarly, a widely utilized writing assistant launched in 2009, leverages advanced artificial intelligence and natural language processing to enhance writing quality across various platforms. Methods: To collect data on user perceptions of Grammarly, a structured survey was designed and distributed via Google Forms. The survey included a series of quantitative and qualitative questions aimed at assessing various aspects of Grammarly's performance. The survey comprised multiple-choice questions, Likert scale items (ranging from "strongly disagree" to "strongly agree"), and open-ended questions to capture detailed user feedback. The target population included students, friends, and family members. The collected responses were analyzed using statistical methods to quantify user satisfaction. Participation in the survey was voluntary, and respondents were assured anonymity and confidentiality. Results: The survey of 28 respondents revealed a generally favorable perception of Grammarly's AI capabilities. A significant 39.3% strongly agreed that it effectively improves text tone, with an additional 46.4% agreeing, while 10.7% remained neutral. For clarity suggestions, 28.6% strongly agreed, and 57.1% agreed, totaling 85.7% recognition of its value. Regarding grammatical accuracy across various genres, 46.4% rated it a perfect score of 5, contributing to 78.5% who found it highly effective. Conclusion: The evolution of Grammarly from a basic grammar checker to a robust AI-driven application underscores its adaptability and commitment to helping users develop their writing skills.

Keywords: Grammarly, writing tool, user engagement, AI capabilities, effectiveness

Procedia PDF Downloads 10

Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

Abstract:

Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

Procedia PDF Downloads 73

Authors: Siliang Luan, Zhongtai Jiang

Abstract:

The risk of highway incidents is commonly recognized as a major concern for transportation authorities due to the hazardous consequences and negative influence. It is crucial to respond to these unpredictable events as soon as possible faced by emergency management decision makers. In this paper, we focus on path planning for emergency vehicles, one of the most significant processes to avoid congestion and reduce rescue time. A Mixed-Integer Linear Programming with Semi-Soft Time Windows Model (MIPSSTWM) is conducted to plan an optimal routing respectively considering the time consumption of arcs and nodes of the urban road network and the highway network, especially in developing countries with an enormous population. Here, the arcs indicate the road segments and the nodes include the intersections of the urban road network and the on-ramp and off-ramp of the highway networks. An attempt in this research has been made to develop a comprehensive and executive strategy for emergency vehicle routing in heavy traffic conditions. The proposed Cuckoo Search (CS) algorithm is designed by imitating obligate brood parasitic behaviors of cuckoos and Lévy Flights (LF) to solve this hard and combinatorial problem. Using a Chinese city as our case study, the numerical results demonstrate the approach we applied in this paper outperforms the previous method without considering the nodes of the road network for a real-world situation. Meanwhile, the accuracy and validity of the CS algorithm also show better performances than the traditional algorithm.

Keywords: emergency vehicle, path planning, cs algorithm, urban traffic management and urban planning

Procedia PDF Downloads 87

Authors: Hu Ding, Kai Liu, Guoan Tang

Abstract:

The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

Procedia PDF Downloads 222

Authors: Guillaume Mullier, Jean François Chatelain

Abstract:

Carbon Fiber Reinforced Plastics (CFRPs) are widely used for advanced applications, in particular in aerospace, automotive and wind energy industries. Once cured to near net shape, CFRP parts need several finishing operations such as trimming, milling or drilling in order to accommodate fastening hardware and meeting the final dimensions. The present research aims to study the effect of the cutting temperature in trimming on the mechanical strength of high performance CFRP laminates used for aeronautics applications. The cutting temperature is of great importance when dealing with trimming of CFRP. Temperatures higher than the glass-transition temperature (Tg) of the resin matrix are highly undesirable: they cause degradation of the matrix in the trimmed edges area, which can severely affect the mechanical performance of the entire component. In this study, a 9.50 mm diameter CVD diamond coated carbide tool with six flutes was used to trim 24-plies CFRP laminates. A 300 m/min cutting speed and 1140 mm/min feed rate were used in the experiments. The tool was heated prior to trimming using a blowtorch, for temperatures ranging from 20°C to 300°C. The temperature at the cutting edge was measured using embedded K-Type thermocouples. Samples trimmed for different cutting temperatures, below and above Tg, were mechanically tested using three-points bending short-beam loading configurations. New cutting tools as well as worn cutting tools were utilized for the experiments. The experiments with the new tools could not prove any correlation between the length of cut, the cutting temperature and the mechanical performance. Thus mechanical strength was constant, regardless of the cutting temperature. However, for worn tools, producing a cutting temperature rising up to 450°C, thermal damage of the resin was observed. The mechanical tests showed a reduced mean resistance in short beam configuration, while the resistance in three point bending decreases with increase of the cutting temperature.

Keywords: composites, trimming, thermal damage, surface quality

Procedia PDF Downloads 323

Authors: Aymen Laadhari

Abstract:

The cardiovascular system is centered on the heart and is characterized by a very complex structure with different physical scales in space (e.g. micrometers for erythrocytes and centimeters for organs) and time (e.g. milliseconds for human brain activity and several years for development of some pathologies). The development and numerical implementation of mathematical models of the cardiovascular system is a tremendously challenging topic at the theoretical and computational levels, inducing consequently a growing interest over the past decade. The accurate computational investigations in both healthy and pathological cases of processes related to the functioning of the human cardiovascular system can be of great potential in tackling several problems of clinical relevance and in improving the diagnosis of specific diseases. In this talk, we focus on the specific task of simulating three particular phenomena related to the cardiovascular system on the macroscopic, mesoscopic and microscopic scales, respectively. Namely, we develop numerical methodologies tailored for the simulation of (i) the haemodynamics (i.e., fluid mechanics of blood) in the aorta and sinus of Valsalva interacting with highly deformable thin leaflets, (ii) the hyperelastic anisotropic behaviour of cardiomyocytes and the influence of calcium concentrations on the contraction of single cells, and (iii) the dynamics of red blood cells in microvasculature. For each problem, we present an appropriate fully Eulerian finite element methodology. We report several numerical examples to address in detail the relevance of the mathematical models in terms of physiological meaning and to illustrate the accuracy and efficiency of the numerical methods.

Keywords: finite element method, cardiovascular system, Eulerian framework, haemodynamics, heart valve, cardiomyocyte, red blood cell

Procedia PDF Downloads 257