Search results for: multi-sensory approach

Authors: Wondmyibza Tsegaye Bayou, Johannes C. Nonner, Joost Heijkers

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This study aimed to identify and estimate dynamic groundwater seepage rates using four comparative methods; the Darcian approach, the water balance approach, the tracer method, and modeling. The theoretical background to these methods is put together in this study. The methodology was applied to a case study area at Zegveld following the advice of the Water Board Stichtse Rijnlanden. Data collection has been from various offices and a field campaign in the winter of 2008/09. In this complex confining layer of the study area, the location of the phreatic groundwater table is at a shallow depth compared to the piezometric water level. Data were available for the model years 1989 to 2000 and winter 2008/09. The higher groundwater table shows predominately-downward seepage in the study area. Results of the study indicated that net recharge to the groundwater table (precipitation excess) and the ditch system are the principal sources for seepage across the complex confining layer. Especially in the summer season, the contribution from the ditches is significant. Water is supplied from River Meije through a pumping system to meet the ditches' water demand. The groundwater seepage rate was distributed unevenly throughout the study area at the nature reserve averaging 0.60 mm/day for the model years 1989 to 2000 and 0.70 mm/day for winter 2008/09. Due to data restrictions, the seepage rates were mainly determined based on the Darcian method. Furthermore, the water balance approach and the tracer methods are applied to compute the flow exchange within the ditch system. The site had various validated groundwater levels and vertical flow resistance data sources. The phreatic groundwater level map compared with TNO-DINO groundwater level data values overestimated the groundwater level depth by 28 cm. The hydraulic resistance values obtained based on the 3D geological map compared with the TNO-DINO data agreed with the model values before calibration. On the other hand, the calibrated model significantly underestimated the downward seepage in the area compared with the field-based computations following the Darcian approach.

Keywords: groundwater seepage, phreatic water table, piezometric water level, nature reserve, Zegveld, The Netherlands

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Authors: Gianni Jacucci

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Epistemology traditionally finds its place within human research philosophies and methodologies. Artificial intelligence methods pose challenges, particularly given the unresolved relationship between AI and pivotal concepts in social arenas such as hermeneutics and accountability. We begin by examining the essential criteria governing scientific rigor in the human sciences. We revisit the three foundational philosophies underpinning qualitative research methods: empiricism, hermeneutics, and phenomenology. We elucidate the distinct attributes, merits, and vulnerabilities inherent in the methodologies they inspire. The integration of AI, e.g., deep learning algorithms, sparks an interest in evaluating these criteria against the diverse forms of AI architectures. For instance, Interpreted AI could be viewed as a hermeneutic approach, relying on a priori interpretations, while straight AI may be perceived as a descriptive phenomenological approach, processing original and uncontaminated data. This paper serves as groundwork for such explorations, offering preliminary reflections to lay the foundation and outline the initial landscape.

Keywords: artificial intelligence, deep learning, epistemology, qualitative research, methodology, hermeneutics, accountability

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Authors: Y. Benabid, K. Benfriha, V. Rieuf, J. F. Omhover

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This paper describes an approach to create custom concepts for innovative products; this approach describes relations between innovation tools and Computer Aided Design environment (use creativity session and design tools). A model for the design process is proposed and explored in order to describe the power tool used to create and ameliorate an innovative product all based upon a range of data (cloud points) in this study. Comparison between traditional method and innovative method we help to generate and put forward a new model of the design process in order to create a custom Ankle Foot Orthosis (AFO) in a CAD environment in order to ameliorate and controlling the motion. The custom concept needs big development in different environments; the relation between these environments is described. The results can help the surgeons in the upstream treatment phases. CAD models can be applied and accepted by professionals in the design and manufacture systems. This development is based on the anatomy of the population of North Africa.

Keywords: ankle foot orthosis, CAD, reverse engineering, sketch

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Authors: Tarela J. Ike

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Over the years, the hemorrhagic acts of Boko Haram have led to the adoption of counter-terrorism measures which mostly takes the form of military repressive measures. These measures have wrought flagrant violation of human rights worthy of concern. Hence, the need to examine the efficacy of the counter-terrorism measures adopted by the Nigeria government in combatting terrorism. This article addresses this issue by relying on a systematic literature review which examines the impact of Nigeria counter-terrorism measures from 2009 to 2016 in combating terrorism. The review of literature includes 42 article. Of the 42 articles, 14 met the peer-reviewed requirement which finds that most of Nigeria’s counter-terrorism policies are geared toward the use of state repressive military approach which violates the human right. Thus, the study concludes that to effectively address the terrorist uprising; Nigeria should adopt a non-aggressive counter-terrorism approach which incorporates religious clerics, and community active engagement strategy in combatting terrorism as opposed to military retaliation which violates human right and so far proved ineffective.

Keywords: Boko Haram, counter-terrorism, human rights, military retaliation

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Authors: Sonia Ben Hamza, Sabra Habli, Nejla Mahjoub Saïd, Hervé Bournot, Georges Le Palec

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Free surface flows caused by dam breaks in channels or rivers is an attention-getting subject to the engineering practice, however, the studies are few to be reported. In this paper, a numerical investigation of unsteady free surface flows resulting from a dam-breaking in a rectangular channel is studied. Numerical computations were carried out using ANSYS Fluent which is based on the finite volume approach. The air/water interface was modeled with the volume of fluid method (VOF). Verification for a typical dam-break problem is analyzed by comparing the present results with others and very good agreement is obtained. The present approach is then used to predict the characteristics of free surface flow due to the dam breaking in channel. The characteristics of complex unsteady free surface flow in these examples are clearly explained. The numerical results show that the flow became more disturbed after impacting the vertical wall, then a recirculation zone, as well as turbulence phenomena, were created. At this instant, a cavity of air was included on the flow. The results agree well with the experimental data found in the literature.

Keywords: CFD, dam-break, free surface, turbulent flows, VOF

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Authors: Matich Lyubov

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Today roadmaps are becoming commonly used tools for detecting and designing a desired future for companies, states and the international community. The growing popularity of this method puts tasks such as identifying basic roadmapping principles, creation of concepts and determination of the characteristics of the use of roadmaps depending on the objectives as well as restrictions and opportunities specific to the study area on the agenda. However, the system approach, e.g. the elements which are recognized to be major for high-quality roadmapping, remains one of the main fields for improving the methodology and practice of their development as limited research was devoted to the detailed analysis of the roadmaps from the view of system approach. Therefore, this article is an attempt to examine roadmaps from the view of the system analysis, to compare areas, where, as a rule, roadmaps and systems analysis are considered the most effective tools. To compare the structure and composition of roadmaps and systems models the identification of common points between construction stages of roadmaps and system modeling and the determination of future directions for research roadmaps from a systems perspective are of special importance.

Keywords: technology roadmap, roadmapping, systems analysis, system modeling, innovation management

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Authors: Nurulhuda A. Manaf, Nor Najihah Zainal Abidin, Nur Amalina Jamaludin

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Coordinating service interactions are a vital part of developing distributed applications that are built up as networks of autonomous participants, e.g., software components, web services, online resources, involve a collaboration between a diverse number of participant services on different providers. The complexity in coordinating service interactions reflects how important the techniques and approaches require for designing and coordinating the interaction between participant services to ensure the overall goal of a collaboration between participant services is achieved. The objective of this research is to develop capability of steering a complex service interaction towards a desired outcome. Therefore, an efficient technique for modelling, generating, and verifying the coordination of service interactions is developed. The developed model describes service interactions using service choreographies approach and focusing on a declarative approach, advocating an Object Management Group (OMG) standard, Semantics of Business Vocabulary and Rules (SBVR). This model, namely, SBVR model for service choreographies focuses on a declarative deontic rule expressing both obligation and prohibition, which can be more useful in working with coordinating service interactions. The generated SBVR model is then be formulated and be transformed into Alloy model using Alloy Analyzer for verifying the generated SBVR model. The transformation of SBVR into Alloy allows to automatically generate the corresponding coordination of service interactions (service choreography), hence producing an immediate instance of execution that satisfies the constraints of the specification and verifies whether a specific request can be realised in the given choreography in the generated choreography.

Keywords: service choreography, service coordination, behavioural modelling, complex interactions, declarative specification, verification, model transformation, semantics of business vocabulary and rules, SBVR

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Authors: Iqbaldeep Kaur, Amarjeet Kaur

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Software Reusability is important part of software development. So component based software development in case of software testing has gained a lot of practical importance in the field of software engineering from academic researcher and also from software development industry perspective. Finding test cases for efficient reuse of test cases is one of the important problems aimed by researcher. Clustering reduce the search space, reuse test cases by grouping similar entities according to requirements ensuring reduced time complexity as it reduce the search time for retrieval the test cases. In this research paper we proposed approach for re-usability of test cases by unsupervised approach. In unsupervised learning we proposed k-mean and Support Vector Machine. We have designed the algorithm for requirement and test case document clustering according to its tf-idf vector space and the output is set of highly cohesive pattern groups.

Keywords: software testing, reusability, clustering, k-mean, SVM

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Authors: J. L. Chukwuneke, C. H. Achebe, S. N. Omenyi

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This research work presents the surface thermodynamics approach to M-TB/HIV-Human sputum interactions. This involved the use of the Hamaker coefficient concept as a surface energetics tool in determining the interaction processes, with the surface interfacial energies explained using van der Waals concept of particle interactions. The Lifshitz derivation for van der Waals forces was applied as an alternative to the contact angle approach which has been widely used in other biological systems. The methodology involved taking sputum samples from twenty infected persons and from twenty uninfected persons for absorbance measurement using a digital Ultraviolet visible Spectrophotometer. The variables required for the computations with the Lifshitz formula were derived from the absorbance data. The Matlab software tools were used in the mathematical analysis of the data produced from the experiments (absorbance values). The Hamaker constants and the combined Hamaker coefficients were obtained using the values of the dielectric constant together with the Lifshitz equation. The absolute combined Hamaker coefficients A132abs and A131abs on both infected and uninfected sputum samples gave the values of A132abs = 0.21631x10-21Joule for M-TB infected sputum and Ã132abs = 0.18825x10-21Joule for M-TB/HIV infected sputum. The significance of this result is the positive value of the absolute combined Hamaker coefficient which suggests the existence of net positive van der waals forces demonstrating an attraction between the bacteria and the macrophage. This however, implies that infection can occur. It was also shown that in the presence of HIV, the interaction energy is reduced by 13% conforming adverse effects observed in HIV patients suffering from tuberculosis.

Keywords: absorbance, dielectric constant, hamaker coefficient, lifshitz formula, macrophage, mycobacterium tuberculosis, van der waals forces

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Authors: Penina Orenstein

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In today’s world, particularly with COVID-19 a constant worldwide threat, organizations need greater visibility over their supply chains more than ever before, in order to find areas for improvement and greater efficiency, reduce the chances of disruption and stay competitive. The concept of supply chain mapping is one where every process and route is mapped in detail between each vendor and supplier. The simplest method of mapping involves sourcing publicly available data including news and financial information concerning relationships between suppliers. An additional layer of information would be disclosed by large, direct suppliers about their production and logistics sites. While this method has the advantage of not requiring any input from suppliers, it also doesn’t allow for much transparency beyond the first supplier tier and may generate irrelevant data—noise—that must be filtered out to find the actionable data. The primary goal of this research is to build data maps of supply chains by focusing on a layered approach. Using these maps, the secondary goal is to address the question as to whether the supply chain is re-engineered to make improvements, for example, to lower the carbon footprint. Using a drill-down approach, the end result is a comprehensive map detailing the linkages between tier-one, tier-two, and tier-three suppliers super-imposed on a geographical map. The driving force behind this idea is to be able to trace individual parts to the exact site where they’re manufactured. In this way, companies can ensure sustainability practices from the production of raw materials through the finished goods. The approach allows companies to identify and anticipate vulnerabilities in their supply chain. It unlocks predictive analytics capabilities and enables them to act proactively. The research is particularly compelling because it unites network science theory with empirical data and presents the results in a visual, intuitive manner.

Keywords: data mining, supply chain, empirical research, data mapping

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Authors: Fabrice Setephin Atedjio, Jean-Pierre Lienou, Frederica F. Nelson, Sachin S. Shetty

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Malicious users exploit vulnerabilities in computer systems, significantly disrupting their performance and revealing the inadequacies of existing protective solutions. Even machine learning-based approaches, designed to ensure reliability, can be compromised by adversarial attacks that undermine their robustness. This paper addresses two critical aspects of enhancing model reliability. First, we focus on improving model performance and robustness against adversarial threats. To achieve this, we propose a strategy by harnessing deep reinforcement learning. Second, we introduce an approach leveraging generative adversarial networks to counter adversarial attacks effectively. Our results demonstrate substantial improvements over previous works in the literature, with classifiers exhibiting enhanced accuracy in classification tasks, even in the presence of adversarial perturbations. These findings underscore the efficacy of the proposed model in mitigating intrusions and adversarial attacks within the machine learning landscape.

Keywords: machine learning, reliability, adversarial attacks, deep-reinforcement learning, robustness

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Authors: Amedeo D'Angiulli, Kylie Schibli

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Traditionally, children’s brain development research has emphasized the limitative aspects of disability and impairment, electing as an explanatory model the classical clinical notions of brain lesion or functional deficit. In contrast, Positive Educational Neuroscience (PEN) is a new approach that emphasizes strengths and human flourishing related to the brain by exploring how learning practices have the potential to enhance neurocognitive flexibility through neuroplastic overcompensation. This mini-review provides an overview of PEN and shows how it links to the concept of neurocognitive flexibility. We provide examples of how the present concept of neurocognitive flexibility can be applied to special education by exploring examples of neuroplasticity in the learning domain, including: (1) learning to draw in congenitally totally blind children, and (2) music training in children from disadvantaged neighborhoods. PEN encourages educators to focus on children’s strengths by recognizing the brain’s capacity for positive change and to incorporate activities that support children’s individual development.

Keywords: neurocognitive development, positive educational neuroscience, sociocultural approach, special education

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Authors: Angel Champion, Sadia Kanwal, Rafat Siddiqui

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Harnessing phytochemicals from plant sources presents a novel opportunity to prevent or treat malignant diseases, including breast cancer. Chemotherapy lacks precision in targeting cancerous cells while sparing normal cells, but a phytopharmaceutical approach may offer a solution. Dandelion, a common weed plant, is rich in phytochemicals and provides a safer, more cost-effective alternative with lower toxicity than traditional pharmaceuticals for conditions such as breast cancer. In this study, an in-vitro experiment will be conducted using the ethanol extract of Dandelion on triple-negative MDA-231 breast cancer cell lines. The polyphenolic analysis revealed that the Dandelion extract, particularly from the root and leaf (both cut and sifted), had the most potent antioxidant properties and exhibited the most potent antioxidation activity from the powdered leaf extract. The extract exhibits prospective promising effects for inducing cell proliferation and apoptosis in breast cancer cells, highlighting its potential for targeted therapeutic interventions. Standardizing methods for Dandelion use is crucial for future clinical applications in cancer treatment. Combining plant-derived compounds with cancer nanotechnology holds the potential for effective strategies in battling malignant diseases. Utilizing liposomes as carriers for phytoconstituent anti-cancer agents offers improved solubility, bioavailability, immunoregulatory effects, advancing anticancer immune function, and reducing toxicity. This integrated approach of natural products and nanotechnology has significant potential to revolutionize healthcare globally, especially in underserved communities where herbal medicine is prevalent.

Keywords: apoptosis, antioxidant activity, cancer nanotechnology, phytopharmaceutical

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Authors: Naila Nasreen, Dianchen Lu

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This paper explores the dynamical behavior of the (2+1)-dimensional Boussinesq equation, which is a nonlinear water wave equation and is used to model wave packets in dispersive media with weak nonlinearity. This equation depicts how long wave made in shallow water propagates due to the influence of gravity. The (2+1)- dimensional Boussinesq equation combines the two-way propagation of the classical Boussinesq equation with the dependence on a second spatial variable, as that occurs in the two-dimensional Kadomstev- Petviashvili equation. This equation provides a description of head- on collision of oblique waves and it possesses some interesting properties. The governing model is discussed by the assistance of Ricatti equation mapping method, a relatively integration tool. The solutions have been extracted in different forms the solitary wave solutions as well as hyperbolic and periodic solutions. Moreover, the sensitivity analysis is demonstrated for the designed dynamical structural system’s wave profiles, where the soliton wave velocity and wave number parameters regulate the water wave singularity. In addition to being helpful for elucidating nonlinear partial differential equations, the method in use gives previously extracted solutions and extracts fresh exact solutions. Assuming the right values for the parameters, various graph in different shapes are sketched to provide information about the visual format of the earned results. This paper’s findings support the efficacy of the approach taken in enhancing nonlinear dynamical behavior. We believe this research will be of interest to a wide variety of engineers that work with engineering models. Findings show the effectiveness simplicity, and generalizability of the chosen computational approach, even when applied to complicated systems in a variety of fields, especially in ocean engineering.

Keywords: (2+1)-dimensional Boussinesq equation, solitary wave solutions, Ricatti equation mapping approach, nonlinear phenomena

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Authors: L. Hamsaveni, Navya Prakash, Suresha

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Document Image Analysis recognizes text and graphics in documents acquired as images. An approach without Optical Character Recognition (OCR) for degraded document image analysis has been adopted in this paper. The technique involves document imaging methods such as Image Fusing and Speeded Up Robust Features (SURF) Detection to identify and extract the degraded regions from a set of document images to obtain an original document with complete information. In case, degraded document image captured is skewed, it has to be straightened (deskew) to perform further process. A special format of image storing known as YCbCr is used as a tool to convert the Grayscale image to RGB image format. The presented algorithm is tested on various types of degraded documents such as printed documents, handwritten documents, old script documents and handwritten image sketches in documents. The purpose of this research is to obtain an original document for a given set of degraded documents of the same source.

Keywords: grayscale image format, image fusing, RGB image format, SURF detection, YCbCr image format

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Authors: Jia Xin Low, Keng Wah Choo

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This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

Keywords: convolutional neural network, discrete wavelet transform, deep learning, heart sound classification

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Authors: K. Sathishkumar, V. Thiagarasu

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Due to recent advances in DNA microarray technology, it is now feasible to obtain gene expression profiles of tissue samples at relatively low costs. Many scientists around the world use the advantage of this gene profiling to characterize complex biological circumstances and diseases. Microarray techniques that are used in genome-wide gene expression and genome mutation analysis help scientists and physicians in understanding of the pathophysiological mechanisms, in diagnoses and prognoses, and choosing treatment plans. DNA microarray technology has now made it possible to simultaneously monitor the expression levels of thousands of genes during important biological processes and across collections of related samples. Elucidating the patterns hidden in gene expression data offers a tremendous opportunity for an enhanced understanding of functional genomics. However, the large number of genes and the complexity of biological networks greatly increase the challenges of comprehending and interpreting the resulting mass of data, which often consists of millions of measurements. A first step toward addressing this challenge is the use of clustering techniques, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. This work presents an analysis of several clustering algorithms proposed to deals with the gene expression data effectively. The existing clustering algorithms like Support Vector Machine (SVM), K-means algorithm and evolutionary algorithm etc. are analyzed thoroughly to identify the advantages and limitations. The performance evaluation of the existing algorithms is carried out to determine the best approach. In order to improve the classification performance of the best approach in terms of Accuracy, Convergence Behavior and processing time, a hybrid clustering based optimization approach has been proposed.

Keywords: microarray technology, gene expression data, clustering, gene Selection

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Authors: Rupu Yang, Cong Toan Tran, Assaad Zoughaib

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The work provides an iterative nonlinear programming method to synthesize a heat exchanger network by manipulating the trade-offs between the heat load of process heat exchangers (HEs) and utilities. We consider for the synthesis problem two cases, the first one without fixed cost for HEs, and the second one with fixed cost. For the no fixed cost problem, the nonlinear programming (NLP) model with all the potential HEs is optimized to obtain the global optimum. For the case with fixed cost, the NLP model is iterated through adding/removing HEs. The method was applied in five case studies and illustrated quite well effectiveness. Among which, the approach reaches the lowest TAC (2,904,026$/year) compared with the best record for the famous Aromatic plants problem. It also locates a slightly better design than records in literature for a 10 streams case without fixed cost with only 1/9 computational time. Moreover, compared to the traditional mixed-integer nonlinear programming approach, the iterative NLP method opens a possibility to consider constraints (such as controllability or dynamic performances) that require knowing the structure of the network to be calculated.

Keywords: heat exchanger network, synthesis, NLP, optimization

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Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

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Authors: Mohd Ridzwan Yaakub, Muhammad Iqbal Abu Latiffi

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Online business has become popular recently due to the massive amount of information and medium available on the Internet. This has resulted in the huge number of reviews where the consumers share their opinion, criticisms, and satisfaction on the products they have purchased on the websites or the social media such as Facebook and Twitter. However, to analyze customer’s behavior has become very important for organizations to find new market trends and insights. The reviews from the websites or the social media are in structured and unstructured data that need a sentiment analysis approach in analyzing customer’s review. In this article, techniques used in will be defined. Definition of the ontology and description of its possible usage in sentiment analysis will be defined. It will lead to empirical research that related to mobile phones used in research and the ontology used in the experiment. The researcher also will explore the role of preprocessing data and feature selection methodology. As the result, ontology-based approach in sentiment analysis can help in achieving high accuracy for the classification task.

Keywords: feature selection, ontology, opinion, preprocessing data, sentiment analysis

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Authors: Benjamin Poku

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Purpose: The purpose of the research is to conduct systematic reviews and synthesis of existing knowledge that addresses the growing incidence and prevalence of chronic diseases across the world and its impact on public health in relation to communicable diseases. Principal results: A careful compilation and summary of 15-20 peer-reviewed publications from reputable databases such as PubMed, MEDLINE, CINAHL, and other peer-reviewed journals indicate that the Global pandemic of Chronic diseases (such as diabetes, high blood pressure, etc.) have become a greater public health burden in proportion as compared to communicable diseases. Significant conclusions: Given the complexity of the situation, efforts and strategies to mitigate the negative effect of the Global Pandemic on chronic diseases within the global community must include not only urgent and binding commitment of all stakeholders but also a multi-sectorial long-term approach to increase the public health educational approach to meet the increasing world population of over 8 billion people and also the aging population as well to meet the complex challenges of chronic diseases.

Keywords: pandemic, chronic disease, public health, health challenges

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Authors: Sunil Chandra, Himanshu Rawat, Vikas Gusain, Triparna Barman

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Forest fires are a recurrent phenomenon in the Himalayan region owing to the presence of vulnerable forest types, topographical gradients, climatic weather conditions, and anthropogenic pressure. The present study focuses on the identification of forest fire-affected areas in a small part of the West Himalayan region using a differential normalized burnt ratio method and spectral unmixing methods. The study area has a rugged terrain with the presence of sub-tropical pine forest, montane temperate forest, and sub-alpine forest and scrub. The major reason for fires in this region is anthropogenic in nature, with the practice of human-induced fires for getting fresh leaves, scaring wild animals to protect agricultural crops, grazing practices within reserved forests, and igniting fires for cooking and other reasons. The fires caused by the above reasons affect a large area on the ground, necessitating its precise estimation for further management and policy making. In the present study, two approaches have been used for carrying out a burnt area analysis. The first approach followed for burnt area analysis uses a differenced normalized burnt ratio (dNBR) index approach that uses the burnt ratio values generated using the Short-Wave Infrared (SWIR) band and Near Infrared (NIR) bands of the Sentinel-2 image. The results of the dNBR have been compared with the outputs of the spectral mixing methods. It has been found that the dNBR is able to create good results in fire-affected areas having homogenous forest stratum and with slope degree <5 degrees. However, in a rugged terrain where the landscape is largely influenced by the topographical variations, vegetation types, tree density, the results may be largely influenced by the effects of topography, complexity in tree composition, fuel load composition, and soil moisture. Hence, such variations in the factors influencing burnt area assessment may not be effectively carried out using a dNBR approach which is commonly followed for burnt area assessment over a large area. Hence, another approach that has been attempted in the present study utilizes a spectral mixing method where the individual pixel is tested before assigning an information class to it. The method uses a neural network approach utilizing Sentinel-2 bands. The training and testing data are generated from the Sentinel-2 data and the national field inventory, which is further used for generating outputs using ML tools. The analysis of the results indicates that the fire-affected regions and their severity can be better estimated using spectral unmixing methods, which have the capability to resolve the noise in the data and can classify the individual pixel to the precise burnt/unburnt class.

Keywords: categorical data, log linear modeling, neural network, shifting cultivation

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Authors: Gastão Soares Ximenes de Oliveira, Richar Nicolás Durán, Romeo Micah Szmoski, Eloiza Aparecida Avila de Matos, Elano Gustavo Rein

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This article aims to present an experimental method to determine Planck's constant by calculating the cutting potential V₀ from LEDs with different wavelengths. The experiment is designed using Arduino as a central tool in order to make the experimental activity more engaging and attractive for students with the use of digital technologies. From the characteristic curves of each LED, graphical analysis was used to obtain the cutting potential, and knowing the corresponding wavelength, it was possible to calculate Planck's constant. This constant was also obtained from the linear adjustment of the cutting potential graph by the frequency of each LED. Given the relevance of Planck's constant in physics, it is believed that this experiment can offer teachers the opportunity to approach concepts from modern physics, such as the quantization of energy, in a more accessible and applied way in the classroom. This will not only enrich students' understanding of the fundamental nature of matter but also encourage deeper engagement with the principles of quantum physics.

Keywords: physics teaching, educational technology, modern physics, Planck constant, Arduino

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Authors: Dongyeob Han, Jungwon Huh, Quang Huy Tran, Choonghyun Kang

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Unmanned Aerial Vehicles (UAVs) have been used for surveillance, monitoring, inspection, and mapping. In this paper, we present a systematic approach for automatic registration of UAV images for monitoring facilities such as building, green house, and civil structures. The two-step process is applied; 1) an image matching technique based on SURF (Speeded up Robust Feature) and RANSAC (Random Sample Consensus), 2) bundle adjustment of multi-temporal images. Image matching to find corresponding points is one of the most important steps for the precise registration of multi-temporal images. We used the SURF algorithm to find a quick and effective matching points. RANSAC algorithm was used in the process of finding matching points between images and in the bundle adjustment process. Experimental results from UAV images showed that our approach has a good accuracy to be applied to the change detection of facility.

Keywords: building, image matching, temperature, unmanned aerial vehicle

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Authors: W. Rowan, Y. O’Connor, L. Lynch, C. Heavin

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Situation Awareness can offer the potential for conscious dynamic reflection. In an era of online health data sharing, it is becoming increasingly important that users of health social networks (HSNs) have the information necessary to make informed decisions as part of the registration process and in the provision of eConsent. This research aims to leverage an adapted Situation Awareness (SA) model to explore users’ decision making processes in the provision of eConsent. A HSN platform was used to investigate these behaviours. A mixed methods approach was taken. This involved the observation of registration behaviours followed by a questionnaire and focus group/s. Early results suggest that users are apt to automatically accept eConsent, and only later consider the long-term implications of sharing their personal health information. Further steps are required to continue developing knowledge and understanding of this important eConsent process. The next step in this research will be to develop a set of guidelines for the improved presentation of eConsent on the HSN platform.

Keywords: eConsent, health social network, mixed methods, situation awareness

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Authors: Oksana Razina, Shakeel Ahmad, Jessie Qun Ren, Olufemi Isiaq

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The modern life of businesses is categorically reliant on their established position online, where digital (and particularly website) content plays a significant role as the first point of information. Digital content, therefore, becomes essential – from making the first impression to the building and development of client relationships. Despite a number of valuable papers suggesting a strategic approach when dealing with digital data, other sources often do not view or accept the approach to digital content as a holistic or continuous process. Associations are frequently made with merely a one-off marketing campaign or similar. The challenge is to establish an agreed definition for the notion of Digital Content Strategy, which currently does not exist, as DCS is viewed from an excessive number of different angles. A strategic approach to content, nonetheless, is required, both practically and contextually. The researchers, therefore, aimed at attempting to identify the key content components comprising a digital content strategy to ensure all the aspects were covered and strategically applied – from the company’s understanding of the content value to the ability to display flexibility of content and advances in technology. This conceptual project evaluated existing literature on the topic of Digital Content Strategy (DCS) and related aspects, using the PRISMA Systematic Review Method, Document Analysis, Inclusion and Exclusion Criteria, Scoping Review, Snow-Balling Technique and Thematic Analysis. The data was collected from academic and statistical sources, government and relevant trade publications. Based on the suggestions from academics and trading sources related to the issues discussed, the researchers revealed the key actions for content creation and attempted to define the notion of DCS. The major finding of the study presented Key Content Components of Digital Content Strategy and can be considered for implementation in a business retail setting.

Keywords: digital content strategy, key content components, websites, digital marketing strategy

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Authors: Chijindu Okpalaoka, Charles Chinedu Onuselogu

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COVID-19, humanity's coronavirus disease caused by SARS-CoV-2, was first detected in late 2019 in Wuhan, China. COVID-19 lacked an established conventional pharmaceutical therapy, and as a result, the outbreak quickly became an epidemic affecting the entire World. Only a qPCR assay is reliable for diagnosing COVID-19. Clustered, regularly interspaced short palindromic repeats (CRISPR) technology is being researched for speedy and specific identification of COVID-19, among other therapeutic techniques. Apart from its therapeutic capabilities, the CRISPR technique is being evaluated to develop antiviral therapies; nevertheless, no CRISPR-based medication has been approved for human use to date. Prophylactic antiviral CRISPR in living being cells, a Cas 13-based approach against coronavirus, has been developed. While this method can be evolved into a treatment approach, it may face substantial obstacles in human clinical trials for licensure. This study discussed the potential applications of CRISPR-based techniques for developing a speedy and accurate feasible treatment alternative for the COVID-19 virus.

Keywords: COVID-19, CRISPR technique, Cas13, SARS-CoV-2, prophylactic antiviral

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Authors: Sumaira Taj

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Blended learning is emerged as a prerequisite approach for teaching in all schools after the outbreak of the COVID-19 pandemic. However, how much public elementary and secondary schools in Pakistan are ready for adapting this approach and what should be done to prepare schools and students for blended learning are the questions that this paper attempts to answer. Mixed-method research methodology was used to collect data from 40 teachers, 500 students, and 10 mothers. Descriptive statistics was used to analyze quantitative data. As for as readiness is concerned, schools lack resources for blended/ virtual/ online classes from infra-structure to skills, parents’ literacy level hindered students’ learning process and teachers’ skills presented challenges in a smooth and swift shift of the schools from face-to-face learning to blended learning. It is recommended to establish a conducive environment in schools by providing all required resources and skills. Special trainings should be organized for low literacy level parents. Multiple ways should be adopted to benefit all students.

Keywords: blended learning, challenges in online classes, education in covid-19, public schools in pakistan

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Authors: Chahrazed Zouaoui

Abstract:

In recent years, the volume of geospatial information is increasing due to the evolution of communication technologies and information, this information is presented often by geographic information systems (GIS) and stored on of spatial databases (BDS). The classical data mining revealed a weakness in knowledge extraction at these enormous amounts of data due to the particularity of these spatial entities, which are characterized by the interdependence between them (1st law of geography). This gave rise to spatial data mining. Spatial data mining is a process of analyzing geographic data, which allows the extraction of knowledge and spatial relationships from geospatial data, including methods of this process we distinguish the monothematic and thematic, geo- Clustering is one of the main tasks of spatial data mining, which is registered in the part of the monothematic method. It includes geo-spatial entities similar in the same class and it affects more dissimilar to the different classes. In other words, maximize intra-class similarity and minimize inter similarity classes. Taking account of the particularity of geo-spatial data. Two approaches to geo-clustering exist, the dynamic processing of data involves applying algorithms designed for the direct treatment of spatial data, and the approach based on the spatial data pre-processing, which consists of applying clustering algorithms classic pre-processed data (by integration of spatial relationships). This approach (based on pre-treatment) is quite complex in different cases, so the search for approximate solutions involves the use of approximation algorithms, including the algorithms we are interested in dedicated approaches (clustering methods for partitioning and methods for density) and approaching bees (biomimetic approach), our study is proposed to design very significant to this problem, using different algorithms for automatically detecting geo-spatial neighborhood in order to implement the method of geo- clustering by pre-treatment, and the application of the bees algorithm to this problem for the first time in the field of geo-spatial.

Keywords: mining, GIS, geo-clustering, neighborhood

Procedia PDF Downloads 375

Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

Abstract:

Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

Procedia PDF Downloads 125