Search results for: heatmap visualization techniques

Authors: Soheila Sadeghi

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In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

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Authors: S. Tebibel, C. Mechati, S. Messaoudi

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Algeria is currently experiencing the same rate of cancer progression as that registered these last years in the western countries. Colorectal cancer, constituting increasingly a major public health problem, is the most common form of cancer after breast and Neck-womb cancer at the woman and prostate cancer at the man. Our work is based on a retrospective study to determine the cases of colorectal cancer through eastern Algeria. Our goal is to carry out an epidemiological, histological and immune- histochemical study to investigate different techniques for the diagnosis of colorectal cancer and their interests and specific in detecting the disease. The study includes 110 patients (aged between 20 to 87 years) with colorectal cancer where the inclusions and exclusions criteria were established. In our study, colorectal cancer, expresses a male predominance, with a sex ratio of 1, 99 and the most affected age group is between 50 and 59 years. We noted that the colon cancer rate is higher than rectal cancer rate, whose frequencies are respectively 60,91 % and 39,09 %. In the series of colon cancer, the ADK lieberkunien is histological the most represented type, or 85,07 % of all cases. In contrast, the proportion of ADK mucinous (colloid mucous) is only 1,49% only. Well-differentiated ADKS, are very significant in our series, they represent 83,58 % of cases. Adenocarcinoma moderately and poorly differentiated, whose proportions are respectively 2,99 % and 0.05 %. For histological varieties of rectal ADK, we see in our workforce that ADK lieberkunien represent the most common histological form, or 76,74%, while the mucosal colloid is 13,95 %. Research of the mutation on the gene encoding K-ras, a major step in the targeted therapy of colorectal cancers, is underway in our study. Colorectal cancer is the subject of much promising research concern: the evaluation of new therapies (antiangiogenic monoclonal antibodies), the search for predictors of sensitivity to chemotherapy and new prognostic markers using techniques of molecular biology and proteomics.

Keywords: adenocarcinoma, age, colorectal cancer, epidemiology, histological section, sex

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Authors: Dileep K. Verma, Sunil K. Lal

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Influenza still remains one of the most challenging diseases posing significant threat to public health causing seasonal epidemics and pandemics. Previous studies suggest that influenza hemagglutinin is essential for viral attachment to host sialic acid receptors and concentrate in lipid rafts for efficient viral fusion. Studies also reported selective nature of Influenza virus to utilize rafts micro-domain for efficient virus assembly and budding. However, the detailed mechanism of Influenza A Virus (IAV) binding to host cell membrane and entry inside the host remains elusive. In the present study, we investigated if host membrane lipid rafts play any significant role in early life cycle events of influenza A virus. Role of host lipid rafts was studied using raft disruption method by extraction of cholesterol and Methyl-β-Cyclodextrin was used to remove membrane cholesterol. We observed co-localization of Influenza A Virus to lipid rafts by visualization of known lipid raft marker GM1 on host cell membrane. Co-localization suggest direct involvement of these micro-domain in initiation of IAV life cycle. We found significant reduction in influenza A virus adsorption in raft disrupted target host cells indicating poor binding and attachment in absence of coherent membrane rafts. Taken together, the results of present study provide evidence for critical involvement of host lipid rafts and its constituents in adsorption process of Influenza A Virus and suggests crucial involvement in other early events of IAV life cycle. The present study opens a new domain to study influenza virus-host interaction and to combat flu at the very early steps of viral life cycle.

Keywords: lipid raft, adsorption, cholesterol, methyl-β-cyclodextrin, GM1

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Authors: Jalil ur Rehman, Ramesh C. Tailor, Muhammad Isa Khan, Jahnzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott

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Purpose: The purpose of this analysis was to investigate dose distribution of different techniques (3D-CRT, IMRT and VMAT) of head and neck cancer using 3-dimensional dosimeter called PRESAGETM Dosimeter. Materials and Methods: Computer tomography (CT) scans of radiological physics center (RPC) head and neck anthropomorphic phantom with both RPC standard insert and PRESAGETM insert were acquired separated with Philipp’s CT scanner and both CT scans were exported via DICOM to the Pinnacle version 9.4 treatment planning system (TPS). Each plan was delivered twice to the RPC phantom first containing the RPC standard insert having TLD and film dosimeters and then again containing the Presage insert having 3-D dosimeter (PRESAGETM) by using a Varian True Beam linear accelerator. After irradiation, the standard insert including point dose measurements (TLD) and planar Gafchromic® EBT film measurement were read using RPC standard procedure. The 3D dose distribution from PRESAGETM was read out with the Duke Midsized optical scanner dedicated to RPC (DMOS-RPC). Dose volume histogram (DVH), mean and maximal doses for organs at risk were calculated and compared among each head and neck technique. The prescription dose was same for all head and neck radiotherapy techniques which was 6.60 Gy/friction. Beam profile comparison and gamma analysis were used to quantify agreements among film measurement, PRESAGETM measurement and calculated dose distribution. Quality assurances of all plans were performed by using ArcCHECK method. Results: VMAT delivered the lowest mean and maximum doses to organ at risk (spinal cord, parotid) than IMRT and 3DCRT. Such dose distribution was verified by absolute dose distribution using thermoluminescent dosimeter (TLD) system. The central axial, sagittal and coronal planes were evaluated using 2D gamma map criteria(± 5%/3 mm) and results were 99.82% (axial), 99.78% (sagital), 98.38% (coronal) for VMAT plan and found the agreement between PRESAGE and pinnacle was better than IMRT and 3D-CRT plan excludes a 7 mm rim at the edge of the dosimeter. Profile showed good agreement for all plans between film, PRESAGE and pinnacle and 3D gamma was performed for PTV and OARs, VMAT and 3DCRT endow with better agreement than IMRT. Conclusion: VMAT delivered lowered mean and maximal doses to organs at risk and better PTV coverage during head and neck radiotherapy. TLD, EBT film and PRESAGETM dosimeters suggest that VMAT was better for the treatment of head and neck cancer than IMRT and 3D-CRT.

Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD, PRESAGETM

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Authors: Ghadeer A.Al-Jabaree, Husam Aldeen Al-Khadash, M. Nassar

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This study aimed at investigating the effect of well-designed corporate governance system on the financial stability of insurance companies listed in ASE. Further, this study provides a comprehensive model for evaluating and analyzing insurance companies' financial position and prospective for comparing the degree of corporate governance application provisions among Jordanian insurance companies. In order to achieve the goals of the study, a whole population that consist of (27) listed insurance companies was introduced through the variables of (board of director, audit committee, internal and external auditor, board and management ownership and block holder's identities). Statistical methods were used with alternative techniques by (SPSS); where descriptive statistical techniques such as means, standard deviations were used to describe the variables, while (F) test and ANOVA analysis of variance were used to test the hypotheses of the study. The study revealed the existence of significant effect of corporate governance variables except local companies that are not listed in ASE on financial stability within control variables especially debt ratio (leverage),where it's also showed that concentration in motor third party doesn't have significant effect on insurance companies' financial stability during study period. Moreover, the study concludes that Global financial crisis affect the investment side of insurance companies with insignificant effect on the technical side. Finally, some recommendations were presented such as enhancing the laws and regulation that help the appropriate application of corporate governance, and work on activating the transparency in the disclosures of the financial statements and focusing on supporting the technical provisions for the companies, rather than focusing only on profit side.

Keywords: corporate governance, financial stability and solvency margin, insurance companies, Jordan

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Authors: Emad A. Mohammed

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The concept of hydraulic flow units (HFU) has been used for decades in the petroleum industry to improve the prediction of permeability. This concept is strongly related to the flow zone indicator (FZI) which is a function of the reservoir rock quality index (RQI). Both indices are based on reservoir porosity and permeability of core samples. It is assumed that core samples with similar FZI values belong to the same HFU. Thus, after dividing the porosity-permeability data based on the HFU, transformations can be done in order to estimate the permeability from the porosity. The conventional practice is to use the power law transformation using conventional HFU where percentage of error is considerably high. In this paper, neural network technique is employed as a soft computing transformation method to predict permeability instead of power law method to avoid higher percentage of error. This technique is based on HFU identification where Amaefule et al. (1993) method is utilized. In this regard, Kozeny and Carman (K–C) model, and modified K–C model by Hasan and Hossain (2011) are employed. A comparison is made between the two transformation techniques for the two porosity-permeability models. Results show that the modified K-C model helps in getting better results with lower percentage of error in predicting permeability. The results also show that the use of artificial intelligence techniques give more accurate prediction than power law method. This study was conducted on a heterogeneous complex carbonate reservoir in Oman. Data were collected from seven wells to obtain the permeability correlations for the whole field. The findings of this study will help in getting better estimation of permeability of a complex reservoir.

Keywords: permeability, hydraulic flow units, artificial intelligence, correlation

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Authors: Terentes Printzios Dimitrios, Loanna Gourgouli, Vlachopoulos Charalambos

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Aims: Atrial fibrillation (AF) remains one of the major causes of stroke, heart failure, sudden death and cardiovascular morbidity. Ablation techniques are becoming more appealing after the latest results of randomized trials showing the overall clinical benefit. On the other hand, imaging techniques and the frontier application of 3D printing are emerging as a valuable ally for cardiac procedures. However, no randomized trial has directly assessed the impact of preprocedural imaging and especially 3D printing guidance for AF ablation. The present study is designed to investigate for the first time the effect of 3D printing of the heart on the safety and effectiveness of the ablation procedure. Methods and design: The 3D-GALA trial is a randomized, open-label, controlled, multicentre clinical trial of 2 parallel groups designed to enroll a total of 100 patients undergoing ablation using cryo-balloon for paroxysmal and persistent AF. Patients will be randomized with a patient allocation ratio of 1: 1 to preprocedural MRI scan of the heart and 3D printing of left atrium and pulmonary veins and cryoablation versus standard cryoablation without imaging. Patients will be followed up to 6 months after the index procedure. The primary outcome measure is the reduction of radiation dose and contrast amount during pulmonary veins isolation. Secondary endpoints will include the percentage of atrial fibrillation relapse at 24h-Holter electrocardiogram monitoring at 6 months after initial treatment. Discussion: To our knowledge, the 3D-GALA trial will be the first study to provide evidence about the clinical impact of preprocedural imaging and 3D printing before cryoablation.

Keywords: atrial fibrillation, cardiac MRI, cryoablation, 3-d printing

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Authors: Rihab Hamdi, Amel Hadri Hamida, Fatiha Khelili, Sakina Zerouali, Ouafae Bennis

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This paper features a comparative study performance of sliding mode controller (SMC) for closed-loop voltage control of direct current to direct current (DC-DC) three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM) with SMC based on hysteresis modulation (HM) where an adaptive feedforward technique is adopted. On one hand, for the PWM-based SM, the approach is to incorporate a fixed-frequency PWM scheme which is effectively a variant of SM control. On the other hand, for the HM-based SM, oncoming an adaptive feedforward control that makes the hysteresis band variable in the hysteresis modulator of the SM controller in the aim to restrict the switching frequency variation in the case of any change of the line input voltage or output load variation are introduced. The results obtained under load change, input change and reference change clearly demonstrates a similar dynamic response of both proposed techniques, their effectiveness is fast and smooth tracking of the desired output voltage. The PWM-based SM technique has greatly improved the dynamic behavior with a bit advantageous compared to the HM-based SM technique, as well as provide stability in any operating conditions. Simulation studies in MATLAB/Simulink environment have been performed to verify the concept.

Keywords: DC-DC converter, hysteresis modulation, parallel multi-cells converter, pulse-width modulation, robustness, sliding mode control

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Authors: Masomeh Jamshid Nejad

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Nowadays, statistical literacy is no longer a necessary skill but an essential skill with broad applications across diverse fields, especially in operational decision areas such as business management, finance, and economics. As such, learning and deep understanding of statistical concepts are essential in the context of business studies. One of the crucial topics in statistical theory and its application is the normal distribution, often called a bell-shaped curve. To interpret data and conduct hypothesis tests, comprehending the properties of normal distribution (the mean and standard deviation) is essential for business students. This requires undergraduate students in the field of economics and business management to visualize and work with data following a normal distribution. Since technology is interconnected with education these days, it is important to teach statistics topics in the context of Python, R-studio, and Microsoft Excel to undergraduate students. This research endeavours to shed light on the effect of Excel-based instruction on learners’ knowledge of statistics, specifically the central concept of normal distribution. As such, two groups of undergraduate students (from the Business Management program) were compared in this research study. One group underwent Excel-based instruction and another group relied only on traditional teaching methods. We analyzed experiential data and BBA participants’ responses to statistic-related questions focusing on the normal distribution, including its key attributes, such as the mean and standard deviation. The results of our study indicate that exposing students to Excel-based learning supports learners in comprehending statistical concepts more effectively compared with the other group of learners (teaching with the traditional method). In addition, students in the context of Excel-based instruction showed ability in picturing and interpreting data concentrated on normal distribution.

Keywords: statistics, excel-based instruction, data visualization, pedagogy

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Authors: Micheale Yifter Weldemichael, Hailay Mehari Gebremedhn, Teklehaimanot Hailesslasie Teklu

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Recent developments in targeted genome editing accelerated genetic research and opened new potentials to improve crops for better yields and quality. Given the significance of cereal crops as a primary source of food for the global population, the utilization of contemporary genome editing techniques like CRISPR/Cas9 is timely and crucial. CRISPR/Cas technology has enabled targeted genomic modifications, revolutionizing genetic research and exploration. Application of gene editing through CRISPR/Cas9 in enhancing sorghum is particularly vital given the current ecological, environmental, and agricultural challenges exacerbated by climate change. As sorghum is one of the main staple foods of our region and is known to be a resilient crop with a high potential to overcome the above challenges, the application of genome editing technology will enhance the investigation of gene functionality. CRISPR/Cas9 enables the improvement of desirable sorghum traits, including nutritional value, yield, resistance to pests and diseases, and tolerance to various abiotic stresses. Furthermore, CRISPR/Cas9 has the potential to perform intricate editing and reshape the existing elite sorghum varieties, and introduce new genetic variations. However, current research primarily focuses on improving the efficacy of the CRISPR/Cas9 system in successfully editing endogenous sorghum genes, making it a feasible and successful undertaking in sorghum improvement. Recent advancements and developments in CRISPR/Cas9 techniques have further empowered researchers to modify additional genes in sorghum with greater efficiency. Successful application and advancement of CRISPR techniques in sorghum will aid not only in gene discovery and the creation of novel traits that regulate gene expression and functional genomics but also in facilitating site-specific integration events. The purpose of this review is, therefore, to elucidate the current advances in sorghum genome editing and highlight its potential in addressing food security issues. It also assesses the efficiency of CRISPR-mediated improvement and its long-term effects on crop improvement and host resistance against parasites, including tissue-specific activity and the ability to induce resistance. This review ends by emphasizing the challenges and opportunities of CRISPR technology in combating parasitic plants and proposing directions for future research to safeguard global agricultural productivity.

Keywords: CRISPR/Cas9, genome editing, quality, sorghum, stress, yield

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Authors: Bitewulign Mekonnen

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Context: This scientific paper focuses on the use of near-infrared (NIR) spectroscopy to determine glucose concentration in aqueous solutions accurately and rapidly. The study compares six different machine learning methods for predicting glucose concentration and also explores the development of a deep learning model for classifying NIR spectra. The objective is to optimize the detection model and improve the accuracy of glucose prediction. This research is important because it provides a comprehensive analysis of various machine-learning techniques for estimating aqueous glucose concentrations. Research Aim: The aim of this study is to compare and evaluate different machine-learning methods for predicting glucose concentration from NIR spectra. Additionally, the study aims to develop and assess a deep-learning model for classifying NIR spectra. Methodology: The research methodology involves the use of machine learning and deep learning techniques. Six machine learning regression models, including support vector machine regression, partial least squares regression, extra tree regression, random forest regression, extreme gradient boosting, and principal component analysis-neural network, are employed to predict glucose concentration. The NIR spectra data is randomly divided into train and test sets, and the process is repeated ten times to increase generalization ability. In addition, a convolutional neural network is developed for classifying NIR spectra. Findings: The study reveals that the SVMR, ETR, and PCA-NN models exhibit excellent performance in predicting glucose concentration, with correlation coefficients (R) > 0.99 and determination coefficients (R²)> 0.985. The deep learning model achieves high macro-averaging scores for precision, recall, and F1-measure. These findings demonstrate the effectiveness of machine learning and deep learning methods in optimizing the detection model and improving glucose prediction accuracy. Theoretical Importance: This research contributes to the field by providing a comprehensive analysis of various machine-learning techniques for estimating glucose concentrations from NIR spectra. It also explores the use of deep learning for the classification of indistinguishable NIR spectra. The findings highlight the potential of machine learning and deep learning in enhancing the prediction accuracy of glucose-relevant features. Data Collection and Analysis Procedures: The NIR spectra and corresponding references for glucose concentration are measured in increments of 20 mg/dl. The data is randomly divided into train and test sets, and the models are evaluated using regression analysis and classification metrics. The performance of each model is assessed based on correlation coefficients, determination coefficients, precision, recall, and F1-measure. Question Addressed: The study addresses the question of whether machine learning and deep learning methods can optimize the detection model and improve the accuracy of glucose prediction from NIR spectra. Conclusion: The research demonstrates that machine learning and deep learning methods can effectively predict glucose concentration from NIR spectra. The SVMR, ETR, and PCA-NN models exhibit superior performance, while the deep learning model achieves high classification scores. These findings suggest that machine learning and deep learning techniques can be used to improve the prediction accuracy of glucose-relevant features. Further research is needed to explore their clinical utility in analyzing complex matrices, such as blood glucose levels.

Keywords: machine learning, signal processing, near-infrared spectroscopy, support vector machine, neural network

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Authors: Balakrishna Shetty

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Introduction: Stem Cell Imaging is a challenging field since the advent of Stem Cell treatment in humans. Series of research on tagging and tracking the stem cells has not been very effective. The present study is an effort by the authors to track the stem cells injected into calf muscles by Magnetic Resonance Diffusion Weighted Imaging. Materials and methods: Stem Cell injection deep into the calf muscles of patients with peripheral vascular disease is one of the recent treatment modalities followed in our institution. 5 patients who underwent deep intramuscular injection of stem cells as treatment were included for this study. Pre and two hours Post injection MRI of bilateral calf regions was done using 1.5 T Philips Achieva, 16 channel system using 16 channel torso coils. Axial STIR, Axial Diffusion weighted images with b=0 and b=1000 values with back ground suppression (DWIBS sequence of Philips MR Imaging Systems) were obtained at 5 mm interval covering the entire calf. The invert images were obtained for better visualization. 120ml of autologous bone marrow derived stem cells were processed and enriched under c-GMP conditions and reduced to 40ml solution containing mixture of above stem cells. Approximately 40 to 50 injections, each containing 0.75ml of processed stem cells, was injected with marked grids over the calf region. Around 40 injections, each of 1ml normal saline, is injected into contralateral leg as control. Results: Significant Diffusion hyper intensity is noted at the site of injected stem cells. No hyper intensity noted before the injection and also in the control side where saline was injected conclusion: This is one of the earliest studies in literature showing diffusion hyper intensity in intramuscularly injected stem cells. The advantages and deficiencies in this study will be discussed during the presentation.

Keywords: stem cells, imaging, DWI, peripheral vascular disease

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Authors: Ozgu Hafizoglu

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An analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with physical, behavioral, principal relations that are essential to learning, discovery, and innovation. The Cognitive Model of Analogy (CMA) leads and creates patterns of pathways to transfer information within and between domains in science, just as happens in the brain. The connectome of the brain shows how the brain operates with mental leaps between domains and mental hops within domains and the way how analogical reasoning mechanism operates. This paper demonstrates the CMA as an evolutionary approach to science, technology, and life. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions in the new era, especially post-pandemic. In this paper, we will reveal how to draw an analogy to scientific research to discover new systems that reveal the fractal schema of analogical reasoning within and between the systems like within and between the brain regions. Distinct phases of the problem-solving processes are divided thusly: stimulus, encoding, mapping, inference, and response. Based on the brain research so far, the system is revealed to be relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain’s mechanism in macro context; brain and spinal cord, and micro context: glia and neurons, relative to matching conditions of analogical reasoning and relational information, encoding, mapping, inference and response processes, and verification of perceptual responses in four-term analogical reasoning. Finally, we will relate all these terminologies with these mental leaps, mental maps, mental hops, and mental loops to make the mental model of CMA clear.

Keywords: analogy, analogical reasoning, brain connectome, cognitive model, neurons and glia, mental leaps, mental hops, mental loops

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Authors: Saikiran Subbagari, Avinash Malladhi

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Anti Money Laundering (AML) regulations are designed to prevent money laundering and terrorist financing activities worldwide. Financial institutions around the world are legally obligated to identify, assess and mitigate the risks associated with money laundering and report any suspicious transactions to governing authorities. With increasing volumes of data to analyze, financial institutions seek to automate their AML processes. In the rise of financial crimes, optical character recognition (OCR), in combination with machine learning (ML) algorithms, serves as a crucial tool for automating AML processes by extracting the data from documents and identifying suspicious transactions. In this paper, we examine the utilization of OCR for AML and delve into various OCR techniques employed in AML processes. These techniques encompass template-based, feature-based, neural network-based, natural language processing (NLP), hidden markov models (HMMs), conditional random fields (CRFs), binarizations, pattern matching and stroke width transform (SWT). We evaluate each technique, discussing their strengths and constraints. Also, we emphasize on how OCR can improve the accuracy of customer identity verification by comparing the extracted text with the office of foreign assets control (OFAC) watchlist. We will also discuss how OCR helps to overcome language barriers in AML compliance. We also address the implementation challenges that OCR-based AML systems may face and offer recommendations for financial institutions based on the data from previous research studies, which illustrate the effectiveness of OCR-based AML.

Keywords: anti-money laundering, compliance, financial crimes, fraud detection, machine learning, optical character recognition

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Authors: Raana Babadi Fathipour

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Tea, a beverage widely cherished across the globe, has captured the interest of scholars with its recent acknowledgement for possessing noteworthy health advantages. Of particular significance is its proven ability to ward off ailments such as cancer and cardiovascular afflictions. Moreover, within the realm of culinary creations, lipid oxidation poses a significant challenge for food product development. In light of these aforementioned concerns, this present discourse turns its attention towards exploring diverse methodologies employed in extracting polyphenols from various types of tea leaves and examining their utility within the vast landscape of the ever-evolving food industry. Based on the discoveries unearthed in this comprehensive investigation, it has been determined that the fundamental constituents of tea are polyphenols possessed of intrinsic health-enhancing properties. This includes an assortment of catechins, namely epicatechin, epigallocatechin, epicatechin gallate, and epigallocatechin gallate. Moreover, gallic acid, flavonoids, flavonols and theaphlavins have also been detected within this aromatic beverage. Of these myriad components examined vigorously in this study's analysis, catechin emerges as particularly beneficial. Multiple techniques have emerged over time to successfully extract key compounds from tea plants, including solvent-based extraction methodologies, microwave-assisted water extraction approaches and ultrasound-assisted extraction techniques. In particular, consideration is given to microwave-assisted water extraction method as a viable scheme which effectively procures valuable polyphenols from tea extracts. This methodology appears adaptable for implementation within sectors such as dairy production along with meat and oil industries alike.

Keywords: camellia sinensis, extraction, food application, shelf life, tea

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Authors: A. Al Hajeri, M. E. Minton, B. Haskins, F. H. Cummins

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The pre-hospital endotracheal intubation is fraught with difficulties; one solution offered has been video laryngoscopy (VL) which permits better visualization of the glottis than the standard method of direct laryngoscopy (DL). This method has resulted in a higher first attempt success rate and fewer failed intubations. However, VL has mainly been evaluated by experienced providers (experienced anesthetists), and as such the utility of this device for those whom infrequently intubate has not been thoroughly assessed. We sought to evaluate this equipment to determine whether in the hands of novice providers this equipment could prove an effective airway management adjunct. DL and two VL methods (C-Mac with distal screen/C-Mac with attached screen) were evaluated by simulating practice on a Laerdal airway management trainer manikin. Twenty Emergency Medical Technicians (basics) were recruited as novice practitioners. This group was used to eliminate bias, as these clinicians had no pre-hospital experience of intubation (although they did have basic airway skills). The following areas were assessed: Time taken to intubate, number of attempts required to successfully intubate, ease of use of equipment VL (attached screen) took on average longer for novice clinicians to successfully intubate and had a lower success rate and reported higher rating of difficulty compared to DL. However, VL (with distal screen) and DL were comparable on intubation times, success rate, gastric inflation rate and rating of difficulty by the user. This study highlights the routine use of VL by inexperienced clinicians would be of no added benefit over DL. Further studies are required to determine whether Emergency Medical Technicians (Paramedics) would benefit from this airway adjunct, and ascertain whether after initial mastery of VL (with a distal screen), lower intubation times and difficulty rating may be achievable.

Keywords: direct laryngoscopy, endotracheal intubation, pre-hospital, video laryngoscopy

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Authors: Faisal Muhibuddin, Ani Dijah Rahajoe

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This in-depth exploration, through a systematic literature review, scrutinizes the nuanced role of data mining in the classification of criminal activities. The research focuses on investigating various methodological aspects and recent developments in leveraging data mining techniques to enhance the effectiveness and precision of crime categorization. Commencing with an exposition of the foundational concepts of crime classification and its evolutionary dynamics, this study details the paradigm shift from conventional methods towards approaches supported by data mining, addressing the challenges and complexities inherent in the modern crime landscape. Specifically, the research delves into various data mining techniques, including K-means clustering, Naïve Bayes, K-nearest neighbour, and clustering methods. A comprehensive review of the strengths and limitations of each technique provides insights into their respective contributions to improving crime classification models. The integration of diverse data sources takes centre stage in this research. A detailed analysis explores how the amalgamation of structured data (such as criminal records) and unstructured data (such as social media) can offer a holistic understanding of crime, enriching classification models with more profound insights. Furthermore, the study explores the temporal implications in crime classification, emphasizing the significance of considering temporal factors to comprehend long-term trends and seasonality. The availability of real-time data is also elucidated as a crucial element in enhancing responsiveness and accuracy in crime classification.

Keywords: data mining, classification algorithm, naïve bayes, k-means clustering, k-nearest neigbhor, crime, data analysis, sistematic literature review

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Authors: Gichun Cha, Changgil Lee, Seunghee Park

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The objective of the study is to construct the shape management method contributing to the safety of the large structure. In Korea, the research of the shape management is lack because of the new attempted technology. Terrestrial Laser Scanning (TLS) is used for measurements of large structures. TLS provides an efficient way to actively acquire accurate the point clouds of object surfaces or environments. The point clouds provide a basis for rapid modeling in the industrial automation, architecture, construction or maintenance of the civil infrastructures. TLS produce a huge amount of point clouds. Registration, Extraction and Visualization of data require the processing of a massive amount of scan data. The octree can be applied to the shape management of the large structure because the scan data is reduced in the size but, the data attributes are maintained. The octree space partitioning generates the voxel of 3D space, and the voxel is recursively subdivided into eight sub-voxels. The point cloud of scan data was converted to voxel and sampled. The experimental site is located at Sungkyunkwan University. The scanned structure is the steel-frame bridge. The used TLS is Leica ScanStation C10/C5. The scan data was condensed 92%, and the octree model was constructed with 2 millimeter in resolution. This study presents octree space partitioning for handling the point clouds. The basis is created by shape management of the large structures such as double-deck tunnel, building and bridge. The research will be expected to improve the efficiency of structural health monitoring and maintenance. "This work is financially supported by 'U-City Master and Doctor Course Grant Program' and the National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIP) (NRF- 2015R1D1A1A01059291)."

Keywords: 3D scan data, octree space partitioning, shape management, structural health monitoring, terrestrial laser scanning

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Authors: Mandlenkosi George Robert Mahlobo, Tumelo Seadira, Major Melusi Mabuza, Peter Apata Olubambi

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Cathodic protection (CP) has been widely considered a suitable technique for mitigating corrosion of buried metal structures. Plenty of efforts have been made in developing techniques, in particular non-destructive techniques, for monitoring and quantifying the effectiveness of CP to ensure the sustainability and performance of buried steel structures. The aim of this study was to investigate the evolution of the electrochemical processes at the steel/soil interface during the application of CP on steel in simulated soil. Carbon steel was subjected to electrochemical tests with NS4 solution used as simulated soil conditions for 4 days before applying CP for a further 11 days. A previously modified non-destructive voltammetry technique was applied before and after the application of CP to measure the corrosion rate. Electrochemical impedance spectroscopy (EIS), in combination with mathematical modeling through equivalent electric circuits, was applied to determine the electrochemical behavior at the steel/soil interface. The measured corrosion rate was found to have decreased from 410 µm/yr to 8 µm/yr between days 5 and 14 because of the applied CP. Equivalent electrical circuits were successfully constructed and used to adequately model the EIS results. The modeling of the obtained EIS results revealed the formation of corrosion products via a mixed activation-diffusion mechanism during the first 4 days, while the activation mechanism prevailed in the presence of CP, resulting in a protective film. The x-ray diffraction analysis confirmed the presence of corrosion products and the predominant protective film corresponding to the calcareous deposit.

Keywords: carbon steel, cathodic protection, NS4 solution, voltammetry, EIS

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Authors: Nishi Bhuvandas, P. V. Timbadiya, P. L. Patel, P. D. Porey

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Recent perceived climate variability raises concerns with unprecedented hydrological phenomena and extremes. Distribution and circulation of the waters of the Earth become increasingly difficult to determine because of additional uncertainty related to anthropogenic emissions. According to the sixth Intergovernmental Panel on Climate Change (IPCC) Technical Paper on Climate Change and water, changes in the large-scale hydrological cycle have been related to an increase in the observed temperature over several decades. Although many previous research carried on effect of change in climate on hydrology provides a general picture of possible hydrological global change, new tools and frameworks for modelling hydrological series with nonstationary characteristics at finer scales, are required for assessing climate change impacts. Of the downscaling techniques, dynamic downscaling is usually based on the use of Regional Climate Models (RCMs), which generate finer resolution output based on atmospheric physics over a region using General Circulation Model (GCM) fields as boundary conditions. However, RCMs are not expected to capture the observed spatial precipitation extremes at a fine cell scale or at a basin scale. Statistical downscaling derives a statistical or empirical relationship between the variables simulated by the GCMs, called predictors, and station-scale hydrologic variables, called predictands. The main focus of the paper is on the need for using statistical downscaling techniques for projection of local hydrometeorological variables under climate change scenarios. The projections can be then served as a means of input source to various hydrologic models to obtain streamflow, evapotranspiration, soil moisture and other hydrological variables of interest.

Keywords: climate change, downscaling, GCM, RCM

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Authors: Hira Jabbar

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Education is vital for inclusive growth of an economy and a critical contributor for investment in human capital. Like other developing countries, Pakistan is facing enormous challenges regarding the provision of public facilities, improper infrastructure planning, accelerating rate of population and poor accessibility. The influence of the rapid advancement and innovations in GIS and RS techniques have proved to be a useful tool for better planning and decision making to encounter these challenges. Therefore present study incorporates GIS and RS techniques to investigate the spatial distribution of school facilities, identifies settlements with served and unserved population, finds potential areas for new schools based on population and develops an accessibility index to evaluate the higher accessibility for schools. For this purpose high-resolution worldview imagery was used to develop road network, settlements and school facilities and to generate school accessibility for each level. Landsat 8 imagery was utilized to extract built-up area by applying pre and post-processing models and Landscan 2015 was used to analyze population statistics. Service area analysis was performed using network analyst extension in ArcGIS 10.3v and results were evaluated for served and underserved areas and population. An accessibility tool was used to evaluate a set of potential destinations to determine which is the most accessible with the given population distribution. Findings of the study may contribute to facilitating the town planners and education authorities for understanding the existing patterns of school facilities. It is concluded that GIS and remote sensing can be effectively used in urban transport and facility planning.

Keywords: accessibility, geographic information system, landscan, worldview

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Authors: Chen Wang, Qiuxiao Chen

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With the advent of the information age and the era of big data, urban planning research is no longer satisfied with the analysis and application of traditional data. Because of the limitations of traditional urban commercial center system research, big data provides new opportunities for urban research. Therefore, based on the quantitative evaluation method of big data, the commercial center system of the main city of Hangzhou is analyzed and evaluated, and the scale and hierarchical structure characteristics of the urban commercial center system are studied. In order to make up for the shortcomings of the existing POI extraction method, it proposes a POI extraction method based on adaptive adjustment of search window, which can accurately and efficiently extract the POI data of commercial business in the main city of Hangzhou. Through the visualization and nuclear density analysis of the extracted Point of Interest (POI) data, the current situation of the commercial center system in the main city of Hangzhou is evaluated. Then it compares with the commercial center system structure of 'Hangzhou City Master Plan (2001-2020)', analyzes the problems existing in the planned urban commercial center system, and provides corresponding suggestions and optimization strategy for the optimization of the planning of Hangzhou commercial center system. Then get the following conclusions: The status quo of the commercial center system in the main city of Hangzhou presents a first-level main center, a two-level main center, three third-level sub-centers, and multiple community-level business centers. Generally speaking, the construction of the main center in the commercial center system is basically up to standard, and there is still a big gap in the construction of the sub-center and the regional-level commercial center, further construction is needed. Therefore, it proposes an optimized hierarchical functional system, organizes commercial centers in an orderly manner; strengthens the central radiation to drive surrounding areas; implements the construction guidance of the center, effectively promotes the development of group formation and further improves the commercial center system structure of the main city of Hangzhou.

Keywords: business center system, business format, main city of Hangzhou, POI extraction method

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Authors: Salman Amur Alhajri

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Purpose: This paper explores the art of contemporary Arabic calligraphy in Oman. It explains the aesthetic features of Arabic calligraphy as a unique icon of Islamic art. This paper also explores the profile of one Omani artist, Salman Alhajri, as an example of Omani artists who have developed unique styles in this art stream. Methodology and approach: The paper is based on a theoretical study using a descriptive and case-study approach. Omani artists are fascinated by the art forms of Arabic calligraphy, which combine both spiritual meaning and aesthetic beauty. Artist Salman Alhajri is an example of a contemporary Arabic artist who uses Arabic calligraphy as the main theme in his art. Dr. Alhajri is trying to introduce the beauty of Arabic letters from a new aesthetic point of view. He also aims to create unusual visual effects that viewers can easily interact with. Even though words and phrases appear in Alhajri’s artwork, they are not conveying direct meanings: viewers can create their own meaning or expressions from them by appreciating the compositions of the artwork. Results: Arabic writing is directly related to the identity of Omani artists and their cultural background. This paper shows how the beauty of Arabic letters comes from its indefinite possibilities in designing calligraphic expressions, even within a single word, because letters can be stretched and transformed in various ways to create different compositions. Omani artists are interested in employing new media applications in this kind of practice to find new techniques for creating artwork based on Arabic writing. It is really important for all Omani artists to practice this art style because Arabic calligraphy and its flexibility introduce infinite possibilities that involve further exploration and investigation.

Keywords: Islamic art, contemporary Arabic calligraphy, new techniques, Omani artist

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Authors: Shahad Nagoor, Lucy Hederman, Kevin Koidl, Annalina Caputo

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Doctors’ notes reflect their impressions, attitudes, clinical sense, and opinions about patients’ conditions and progress, and other information that is essential for doctors’ daily clinical decisions. Despite their value, clinical notes are insufficiently researched within the language processing community. Automatically extracting information from unstructured text data is known to be a difficult task as opposed to dealing with structured information such as vital physiological signs, images, and laboratory results. The aim of this research is to investigate how Natural Language Processing (NLP) techniques and machine learning techniques applied to clinician notes can assist in doctors’ decision-making in Intensive Care Unit (ICU) for coronavirus disease 2019 (COVID-19) patients. The hypothesis is that clinical outcomes like survival or mortality can be useful in influencing the judgement of clinical sentiment in ICU clinical notes. This paper introduces two contributions: first, we introduce COVID_ICU_BERT, a fine-tuned version of clinical transformer models that can reliably predict clinical sentiment for notes of COVID patients in the ICU. We train the model on clinical notes for COVID-19 patients, a type of notes that were not previously seen by clinicalBERT, and Bio_Discharge_Summary_BERT. The model, which was based on clinicalBERT achieves higher predictive accuracy (Acc 93.33%, AUC 0.98, and precision 0.96 ). Second, we perform data augmentation using clinical contextual word embedding that is based on a pre-trained clinical model to balance the samples in each class in the data (survived vs. deceased patients). Data augmentation improves the accuracy of prediction slightly (Acc 96.67%, AUC 0.98, and precision 0.92 ).

Keywords: BERT fine-tuning, clinical sentiment, COVID-19, data augmentation

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Authors: Addisu Tadesse Sahile, Tennyson Mgutshini

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Background: Periodontal disease is a common, complex, inflammatory disease characterized by the destruction of tooth-supporting soft and hard tissues of the periodontium and a major public health problem across developed and developing countries. Objectives: The study was aimed at assessing the prevalence of periodontal disease and associated factors among diabetes patients in Addis Ababa, Ethiopia, 2018. Methods: Institutional based cross-sectional study was conducted on 388 diabetes patients selected by systematic random sampling method from March to May 2018. The study was conducted at two conveniently selected public hospitals in Addis Ababa. Data were collected with pre-tested, structured and translated questionnaire then entered to SPSS version 23 software for analysis. Descriptive statistics as a summary, in line with chi-square and binary logistics regression to identify factors associated with periodontal disease, were applied. A 95% CI with a p-value less than 5% was used as a level of significance. Results: Ninety-one percent (n=353) of participants had periodontal disease while oral examination was done in six regions. While only 9% (n=35) of participants were free of periodontal disease. The number of tooth brushings per day, correct techniques of brushing, malocclusion, and fillings that are defective were associated with periodontal disease at p < 0.05. Conclusion and recommendation: A higher prevalence of periodontal disease among diabetes patient was observed. The frequency of tooth brushing, correct techniques of brushing, malocclusion and defective fillings were associated with periodontal disease. Emphasis has to be given to oral health of diabetes patients by every concerned body so as to control the current higher burden of periodontal disease in diabetes.

Keywords: periodontal disease, risk factors, diabetes mellitus, Addis Ababa

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Authors: Famuwagun A. A., Abiona O. O., Gbadamosi S.O., Adeboye O. A., Adebooye O. C.

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The need to provide more information on the perceived bioactive status of the peel of plantain fruit informed the design of this research. Matured Plantain fruits were harvested, and fruits were allowed to ripen spontaneously. Samples of plantain fruit were taken every fortnight, and the peels were removed. The peels were dried using two different drying techniques (Oven drying and sun drying) and milled into powdery forms. Other samples were picked and processed in a similar manner on the first, third, seventh and tenth day until the peels of the fruits were fully ripped, resulting in eight different samples. The anti-oxidative properties of the samples using different assays (DPPH, FRAP, MCA, HRSA, SRSA, ABTS, ORAC), inhibitory activities against enzymes related to diabetes (alpha-amylase and glucosidase) and inhibition against angiotensin-converting enzymes (ACE) were evaluated. The result showed that peels of plantain fruits on the 7th day of ripening and sundried exhibited greater inhibitions against free radicals, which enhanced its antioxidant activities, resulting in greater inhibitions against alpha-amylase and alpha-glucosidase enzymes. Also, oven oven-dried sample of the peel of plantain fruit on the 7th day of ripening had greater phenolic contents than the other samples, which also resulted in higher inhibition against angiotensin converting enzymes when compared with other samples. The results showed that even though the unripe peel of plantain fruit is assumed to contain excellent bioactive activities, consumption of the peel should be allowed to ripen for seven days after maturity and harvesting so as to derive maximum benefit from the peel.

Keywords: functional ingredient, diabetics, hypertension, functional foods

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Authors: Manuel Alejandro Taborda Ceballos, Martin Sommerfeld

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Transport phenomena and dispersion of non-spherical particle in turbulent flows are found everywhere in industrial application and processes. Powder handling, pollution control, pneumatic transport, particle separation are just some examples where the particle encountered are not only spherical. These types of multiphase flows are wall bounded and mostly highly turbulent. The particles found in these processes are rarely spherical but may have various shapes (e.g., fibers, and rods). Although research related to the behavior of regular non-spherical particles in turbulent flows has been carried out for many years, it is still necessary to refine models, especially near walls where the interaction fiber-wall changes completely its behavior. Imaging-based experimental studies on dispersed particle-laden flows have been applied for many decades for a detailed experimental analysis. These techniques have the advantages that they provide field information in two or three dimensions, but have a lower temporal resolution compared to point-wise techniques such as PDA (phase-Doppler anemometry) and derivations therefrom. The applied imaging techniques in dispersed two-phase flows are extensions from classical PIV (particle image velocimetry) and PTV (particle tracking velocimetry) and the main emphasis was simultaneous measurement of the velocity fields of both phases. In a similar way, such data should also provide adequate information for validating the proposed models. Available experimental studies on the behavior of non-spherical particles are uncommon and mostly based on planar light-sheet measurements. Especially for elongated non-spherical particles, however, three-dimensional measurements are needed to fully describe their motion and to provide sufficient information for validation of numerical computations. For further providing detailed experimental results allowing a validation of numerical calculations of non-spherical particle dispersion in turbulent flows, a water channel test facility was built around a horizontal closed water channel. Into this horizontal main flow, a small cross-jet laden with fiber-like particles was injected, which was also solely driven by gravity. The dispersion of the fibers was measured by applying imaging techniques based on a LED array for backlighting and high-speed cameras. For obtaining the fluid velocity fields, almost neutrally buoyant tracer was used. The discrimination between tracer and fibers was done based on image size which was also the basis to determine fiber orientation with respect to the inertial coordinate system. The synchronous measurement of fluid velocity and fiber properties also allow the collection of statistics of fiber orientation, velocity fields of tracer and fibers, the angular velocity of the fibers and the orientation between fiber and instantaneous relative velocity. Consequently, an experimental study the behavior of elongated non-spherical particles in wall bounded turbulent flows was achieved. The development of a comprehensive analysis was succeeded, especially near the wall region, where exists hydrodynamic wall interaction effects (e.g., collision or lubrication) and abrupt changes of particle rotational velocity. This allowed us to predict numerically afterwards the behavior of non-spherical particles within the frame of the Euler/Lagrange approach, where the particles are therein treated as “point-particles”.

Keywords: crossflow, non-spherical particles, particle tracking velocimetry, PIV

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Authors: Saritdikhun Somasa

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3D printing fabrication has empowered many artists in many fields. Artists who work in stop motion, 3D modeling, toy design, product design, sculpture, and fine arts become one-stop shop operations–where they can design, prototype, and distribute their designs for commercial or fine art purposes. The author has developed a digital sculpting course that fosters digital software, peripheral hardware, and 3D printing with traditional sculpting concept techniques to address the complexities of this multifaceted process, allowing the students to produce complex 3d-printed work. The author will detail the preparation and planning for pre- to post-process 3D printing elements, including software, materials, space, equipment, tools, and schedule consideration for small to medium figurine design statues in a semester-long class. In addition, the author provides insight into teaching challenges in the non-studio space that requires students to work intensively on post-printed models to assemble parts, finish, and refine the 3D printed surface. Even though this paper focuses on the 3D printing processes and techniques for small to medium design statue projects for the Digital Media program, the author hopes the paper will benefit other fields of study such as craft practices, product design, and fine-arts programs. Other schools that might implement 3D printing and fabrication in their programs will find helpful information in this paper, such as a teaching plan, choices of equipment and materials, adaptation for non-studio spaces, and putting together a complete and well-resolved project for students.

Keywords: 3D digital modeling, 3D digital sculpting, 3D modeling, 3D printing, 3D digital fabrication

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Authors: R. L. P. De Lima, F. C. B. Boogaard, R. E. De Graaf-Van Dinther

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Blue-green algae blooms (cyanobacteria) is currently a relevant ecological problem that is being addressed by most water authorities in the Netherlands. These can affect recreation areas by originating unpleasant smells and toxins that can poison humans and animals (e.g. fish, ducks, dogs). Contamination events usually take place during summer months, and their frequency is increasing with climate change. Traditional monitoring of this bacteria is expensive, labor-intensive and provides only limited (point sampling) information about the spatial distribution of algae concentrations. Recently, a novel handheld sensor allowed water authorities to quicken their algae surveying and alarm systems. This study converted the mentioned algae sensor into a mobile platform, by combining it with an underwater remotely operated vehicle (also equipped with other sensors and cameras). This provides a spatial visualization (mapping) of algae concentrations variations within the area covered with the drone, and also in depth. Measurements took place in different locations in the Netherlands: i) lake with thick silt layers at the bottom, very eutrophic former bottom of the sea and frequent / intense mowing regime; ii) outlet of waste water into large reservoir; iii) urban canal system. Results allowed to identify probable dominant causes of blooms (i), provide recommendations for the placement of an outlet, day-night differences in algae behavior (ii), or the highlight / pinpoint higher algae concentration areas (iii). Although further research is still needed to fully characterize these processes and to optimize the measuring tool (underwater drone developments / improvements), the method here presented can already provide valuable information about algae behavior and spatial / temporal variability and shows potential as an efficient monitoring system.

Keywords: blue-green algae, cyanobacteria, underwater drones / ROV / AUV, water quality monitoring

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Authors: Moriom Akter Mousumi, Spyridon Paparrizos, Fulco Ludwig

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Rice yield reduction due to climate change-induced disease occurrence is becoming a great concern for coastal farmers of Bangladesh. The development of participatory climate information services (CIS) based on farmers’ needs could implicitly facilitate farmers to get disease forecasts and make better decisions to manage diseases. Therefore, this study aimed to investigate how participatory climate information services assist coastal rice farmers to take up rice disease forecasts and better manage rice diseases by improving their informed decision-making. Through participatory approaches, we developed a tailor-made agrometeorological service through the DROP app to forecast rice diseases and manage them in advance. During farmers field schools (FFS) we communicated 7-day disease forecasts during face-to-face weekly meetings using printed paper and, messenger app derived from DROP app. Results show that the majority of the farmers understand disease forecasts through visualization, symbols, and text. The majority of them use disease forecast information directly from the DROP app followed by face-to-face meetings, messenger app, and printed paper. Farmers participation and engagement during capacity building training at FFS also assist them in making more informed decisions and improved management of diseases using both preventive measures and chemical measures throughout the rice cultivation period. We conclude that the development of participatory CIS and the associated capacity-building and training of farmers has increased farmers' understanding and uptake of disease forecasts to better manage of rice diseases. Participatory services such as the DROP app offer great potential as an adaptation option for climate-smart rice production under changing climatic conditions.

Keywords: participatory climate service, disease forecast, disease management, informed decision making, coastal Bangladesg

Procedia PDF Downloads 47