Search results for: post classification change detection

Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Hamza Zidoum

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This paper investigates the impact of human genetic variation on the function of human proteins using machine-learning algorithms. Single-Nucleotide Polymorphism represents the most common form of human genome variation. We focus on the single amino-acid polymorphism located in the coding region as they can affect the protein function leading to pathologic phenotypic change. We use several supervised Machine Learning methods to identify structural properties correlated with increased risk of the missense mutation being damaging. SVM associated with Principal Component Analysis give the best performance.

Keywords: single-nucleotide polymorphism, machine learning, feature selection, SVM

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Authors: Nnedum Obiajuru Anthony Ugochukwu, Ezechukwu Emmanuel Ntomchukwu

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The research investigated the importance of marketing and customer relationship management in post-consolidated banks in achieving success and survival in the face of intense competition and global economic meltdown. The problem lies in the fact that during the pre-consolidation era in the banking industry in Nigeria, banks were comfortable transacting their businesses from their armchairs. Little attention was paid to marketing by banks as a veritable means of achieving and consolidating their profit position. This situation, no doubt sustained because banks were more or less currency exchange centers where customers buy and sell foreign exchange which was highly demanded, but in very short supply. Today, deregulation and consolidation of banks in Nigeria have tremendously increased the tempo of activities in the banking industry, and competition has become very severe among banks. The weak link in the success of post-consolidated banks in Nigeria is the utter neglect, and light or unserious consideration of customer relationship marketing by banks. Armchair banking which banks have been practicing has no regard for marketing as a means to survival. However, in order to survive, post-consolidated banks must take relationship marketing and customer relationship management seriously especially in the face of the current global economic crisis. This paper aims at exploring the role of marketing in building and managing customer relationships as a means to survival in post-consolidation banking in Nigeria.

Keywords: marketing, customer relationships, banking sector, Nigeria

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Authors: Malek Jamaliah, Robert Powell

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In spite of the influence of climate change on tourism destinations, particularly those rely heavily on natural resources, little attention paid to study the appropriate adaptation efforts to cope with, moderate and benefit from the impacts of climate change. The existing literature indicated that the research of climate change adaptation in the tourism and outdoor recreation field is at least 5-7 years behind other sectors such as water resources and agriculture. In Jordan, there are many observed changes in climate patterns such as higher temperatures, decreased precipitation and increased severity and frequency of drought. Dana Biosphere Reserve (DBR), the largest protected area and the major eco-tourism destination in Jordan, is facing climate change, which gradually degrading environment, shifting tourism seasons and changing livelihood and lifestyle of local communities. This study aims to assess climate change adaptation practices and policies used in DBR to cope with climate change related-risks. We conducted qualitative semi-structured interviews with key informants in DBR to assess climate change adaptation practices. Direct content analysis (or a priori content analysis) was used to determine the components and indicators of climate change adaptation. The results found that DBR has implemented a wide range of adaptation practices, including infrastructure development, diversification of tourism products, environmentally-friendly practices, visitor management, land use management, rainwater collection, environmental monitoring and research, environmental education and collaboration with stakeholders. These diverse practices implicitly and explicitly play an important role in coping with the social, economic and environmental impacts caused by climate change. Finally, this study demonstrated that climate change adaptation is closely related to sustainable management of eco-tourism.

Keywords: climate change adaptation, dana biosphere reserve, ecotourism, sustainable management

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Authors: Abdul Khalique Baloch, Ali Okatan

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The quality analysis of food and vegetable from image is hot topic now a day, where researchers make them better then pervious findings through different technique and methods. In this research we have review the literature, and find gape from them, and suggest better proposed approach, design the algorithm, developed a software to measure the quality from images, where accuracy of image show better results, and compare the results with Perouse work done so for. The Application we uses an open-source dataset and python language with tensor flow lite framework. In this research we focus to sort food and vegetable from image, in the images, the application can sorts and make them grading after process the images, it could create less errors them human base sorting errors by manual grading. Digital pictures datasets were created. The collected images arranged by classes. The classification accuracy of the system was about 94%. As fruits and vegetables play main role in day-to-day life, the quality of fruits and vegetables is necessary in evaluating agricultural produce, the customer always buy good quality fruits and vegetables. This document is about quality detection of fruit and vegetables using images. Most of customers suffering due to unhealthy foods and vegetables by suppliers, so there is no proper quality measurement level followed by hotel managements. it have developed software to measure the quality of the fruits and vegetables by using images, it will tell you how is your fruits and vegetables are fresh or rotten. Some algorithms reviewed in this thesis including digital images, ResNet, VGG16, CNN and Transfer Learning grading feature extraction. This application used an open source dataset of images and language used python, and designs a framework of system.

Keywords: deep learning, computer vision, image processing, rotten fruit detection, fruits quality criteria, vegetables quality criteria

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Authors: Ahmed El-Banbi

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Several PVT models exist to represent how PVT properties are handled in sub-surface and surface engineering calculations for oil and gas production. The most commonly used models include black oil, modified black oil (MBO), and compositional models. These models are used in calculations that allow engineers to optimize and forecast well and reservoir performance (e.g., reservoir simulation calculations, material balance, nodal analysis, surface facilities, etc.). The choice of which model is dependent on fluid type and the production process (e.g., depletion, water injection, gas injection, etc.). Based on close to 2,000 reservoir fluid samples collected from different basins and locations, this paper presents some conclusions on the occurrence of reservoir fluids. It also reviews the common methods used to classify reservoir fluid types. Based on new criteria related to the production behavior of different fluids and economic considerations, an updated classification of reservoir fluid types is presented in the paper. Recommendations on the use of different PVT models to simulate the behavior of different reservoir fluid types are discussed. Each PVT model requirement is highlighted. Available methods for the calculation of PVT properties from each model are also discussed. Practical recommendations and tips on how to control the calculations to achieve the most accurate results are given.

Keywords: PVT models, fluid types, PVT properties, fluids classification

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Authors: Jamison Duckworth, Shankarachary Ragi

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Detection and segmentation of wildfire smoke plumes from remote sensing imagery are being pursued as a solution for early fire detection and response. Smoke plume detection can be automated and made robust by the application of artificial intelligence methods. Specifically, in this study, the deep learning approach Mask Region-based Convolutional Neural Network (RCNN) is being proposed to learn smoke patterns across different spectral bands. This method is proposed to separate the smoke regions from the background and return masks placed over the smoke plumes. Multispectral data was acquired using NASA’s Earthdata and WorldView and services and satellite imagery. Due to the use of multispectral bands along with the three visual bands, we show that Mask R-CNN can be applied to distinguish smoke plumes from clouds and other landscape features that resemble smoke.

Keywords: deep learning, mask-RCNN, smoke plumes, spectral bands

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Authors: Li Hui, Riyadh Hindi

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Concrete structures frequently suffer from crack formation, a critical issue that can significantly reduce their lifespan by allowing damaging agents to enter. Traditional methods of crack detection depend on manual visual inspections, which heavily relies on the experience and expertise of inspectors using tools. In this study, a more efficient, computer vision-based approach is introduced by using the lightweight MobileNetV2 neural network. A dataset of 40,000 images was used to develop a specialized crack evaluation algorithm. The analysis indicates that MobileNetV2 matches the accuracy of traditional CNN methods but is more efficient due to its smaller size, making it well-suited for mobile device applications. The effectiveness and reliability of this new method were validated through experimental testing, highlighting its potential as an automated solution for crack detection in concrete structures.

Keywords: Concrete crack, computer vision, deep learning, MobileNetV2 neural network

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Authors: Zheng Yuxun

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This review critically assesses the advancements and prospective developments in defect detection methodologies within the semiconductor industry, an essential domain that significantly affects the operational efficiency and reliability of electronic components. As semiconductor devices continue to decrease in size and increase in complexity, the precision and efficacy of defect detection strategies become increasingly critical. Tracing the evolution from traditional manual inspections to the adoption of advanced technologies employing automated vision systems, artificial intelligence (AI), and machine learning (ML), the paper highlights the significance of precise defect detection in semiconductor manufacturing by discussing various defect types, such as crystallographic errors, surface anomalies, and chemical impurities, which profoundly influence the functionality and durability of semiconductor devices, underscoring the necessity for their precise identification. The narrative transitions to the technological evolution in defect detection, depicting a shift from rudimentary methods like optical microscopy and basic electronic tests to more sophisticated techniques including electron microscopy, X-ray imaging, and infrared spectroscopy. The incorporation of AI and ML marks a pivotal advancement towards more adaptive, accurate, and expedited defect detection mechanisms. The paper addresses current challenges, particularly the constraints imposed by the diminutive scale of contemporary semiconductor devices, the elevated costs associated with advanced imaging technologies, and the demand for rapid processing that aligns with mass production standards. A critical gap is identified between the capabilities of existing technologies and the industry's requirements, especially concerning scalability and processing velocities. Future research directions are proposed to bridge these gaps, suggesting enhancements in the computational efficiency of AI algorithms, the development of novel materials to improve imaging contrast in defect detection, and the seamless integration of these systems into semiconductor production lines. By offering a synthesis of existing technologies and forecasting upcoming trends, this review aims to foster the dialogue and development of more effective defect detection methods, thereby facilitating the production of more dependable and robust semiconductor devices. This thorough analysis not only elucidates the current technological landscape but also paves the way for forthcoming innovations in semiconductor defect detection.

Keywords: semiconductor defect detection, artificial intelligence in semiconductor manufacturing, machine learning applications, technological evolution in defect analysis

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Authors: Asma A. Alsufyani, Maram A. Alharthi, Maha J. Althobaiti, Manal S. Alharthi, Huda Rizq

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Twitter is one of the most popular microblogging sites that allows users to publish short text messages called 'tweets'. Increasing the number of accounts to follow (followings) increases the number of tweets that will be displayed from different topics in an unclassified manner in the timeline of the user. Therefore, it can be a vital solution for many Twitter users to have their tweets in a timeline classified into general categories to save the user’s time and to provide easy and quick access to tweets based on topics. In this paper, we developed a classifier for timeline tweets trained on a dataset consisting of 3600 tweets in total, which were collected from Saudi Twitter and annotated manually. We experimented with the well-known Bag-of-Words approach to text classification, and we used support vector machines (SVM) in the training process. The trained classifier performed well on a test dataset, with an average F1-measure equal to 92.3%. The classifier has been integrated into an application, which practically proved the classifier’s ability to classify timeline tweets of the user.

Keywords: corpus creation, feature extraction, machine learning, short text classification, social media, support vector machine, Twitter

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Authors: Nhleko Monique Chiloane

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The gold mining industry has predominantly used ammonium nitrate fuel oil (ANFO) explosives for decades, although these are known to be “gassier” and their detonation results in toxic fumes, for example, carbon monoxide (CO), nitrogen oxides (NOx) and ammonia. Re-entry into underground workings too soon after blasting can lead to fatal exposure to toxic fumes. It is, therefore, required that the polluted air be removed from the affected areas within a reasonable period before employees' re-entry into the working area. Post-blast re-entry times have therefore been described as a productivity bottleneck. The known causes of post-blast fumes are water ingress, incorrect fuel to oxygen ratio, confinement, explosive additives etc. To prevent or minimize post-blast fumes, some researchers have used neutralization, re-burning technique and non-explosive products or different oxidizing agents. The use of commercial explosives without nitrate oxidizing agents can also minimize the production of blasting fumes and thereby reduce the time needed for the clearance of these fumes to allow workers to re-enter the underground workings safely. The reduction in non-production time directly contributes to an increase in the available time per shift for productive work, thus leading to continuous mining. However, owing to its low cost and ease of use, ANFO is still widely used in South African underground blasting operations.

Keywords: post-blast fumes, continuous mining, ammonium nitrate explosive, non-explosive blasting, re-entry period

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Authors: Shivani Dhande, Sanjiv Choudhary, Adarshlata Singh

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Introduction: Chemical peeling is a popular, relatively inexpensive day procedure and generally safe method for treatment of pigmentary skin disorders and for skin rejuvenation. Chemical peels are classified by the depth of action into superficial, medium, and deep peels.Various facial pigmentary conditions have significant impact on quality of life causing psychological stress, necessitating its safe and effective treatment.Aim & Objectives:To compare the efficacy of Salicylic acid, Trichloroaceticacid & Glycolic Acid in facial melanosis(melasma,photomelanosis& post acne pigmentation).To study the side effects of above mentioned peeling agents. Method and Materials:It was a randomized parallel control single blind study consisting of total of 36 cases, 12 cases each of melasma, photo melanosis and post acne pigmentation within age group 20-50 years having fitzpatrick’s skin type4. Woods lamp examination was done to confirm the type of melasma.Patients with keloidal tendency, active herpes infection or past history of hypersensitivity to salicylic acid, trichloroaceticand glycolic acid as well aspatients on systemic isotretinoin were excluded.Clinical photographs at the beginning of therapy and then serially, were taken to assess the clinical response. Prior to application a written informed consent was obtained. A post auricular test peel was performed. Patients were divided into 3 groups, containing 12 patients each of melasma, photomelanosis and post acnepigmentation.All the three peels SA peel 20% (done once in 2 weeks), GA peel 50% (done once in 3 weeks) and TCA 15% (done once in 3 weeks) were used with total six settings for each patient. Before application of peel patients were counseled to wash the face with soap and water. Then face was dried and cleaned with spirit and acetone to remove all cutaneous oils. GA, TCA, SA were applied with cotton buds/gauze withmild strokes. After a contact period off 5-10mins neutralization was done with cold water. Post peel topical sunscreen application was mandatory. MASI was used pre and post treatment to assess melasma. Investigator’s global improvement scale- overall hyperpigmentation (4-significant, 3-moderate, 2-mild, 1-minimal, 0-no change ) and Patient’s satisfaction grading scale (>70%- excellent response, 50-70%- good response, <50%- average response) was used to assess improvement in all the three facial melanosis.Results:In our study of 12 patients of melasma, 4 (33.33%)patients showed excellent results;3 (25%) with GAand 1(8.33%) of TCA.Good response was seen in 4 (33.33%) patients;1(8.33%) each for GA & SA and 2(16.66%) for TCA.Poor response was seen in 4(33.33%) patients;1(8.33%) for TCA and 3 (25%) for SA.Of 12 patients of photomelanosis, excellent resultswas seen in 3(25%)patients of TCA. Good response was seen in 4 (33.33%) patients, 1(8.33%) each of TCA &SA and 2(16.66%) of GA.Poor responsewas seen in 5(41.66%) patients;3 (25%) for SA and 2(16.66%) of GA.Of 12 patients of post acne pigmentation, excellent responsein 3 (25%) patients;2(16.66%) of SA and 1(8.33%) of TCA.Good responsewas seen in 5(41.66%) patients;2(16.66%) of SA and GA and1(8.33%) of TCA.Poor response was seen in 4 (33.33%) patients; 2 (16.66%) for SA and TCA both. No major side effects in the form of scarring or persistant pigmentation was seen. Transient blackening of skin with burning sensation was seen in cases treated with TCA and SA. Post procedural itching and redness was noted with GA peel. Conclusion- In our study GA(50%),TCA(15%) & SA(20%) peels showed excellent response in melasma, photomelanosis and post-acne pigmentation respectively.All the 3 peeling agents were well tolerated without any significant side-effects in the above specified concentrations.

Keywords: facial melanosis, gycolic acid, salicylic acid, trichloroacetic acid

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Authors: Elene Zhuravliova, Tamar Barbakadze, Irina Kalandadze, Elnari Zaalishvili, Lali Shanshiashvili, David Mikeladze

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Background: Multiple sclerosis (MS) is an inflammatory, neurodegenerative disease, which is accompanied by demyelination and autoimmune response to myelin proteins. Among post-translational modifications, which mediate the modulation of inflammatory pathways during MS, methylation is the main one. The methylation of DNA, also amino acids lysine and arginine, occurs in the cell. It was found that decreased trans-methylation is associated with neuroinflammatory diseases. Therefore, abnormal regulation of the methyl cycle could induce demyelination through the action on PAD (peptidyl-arginine-deiminase) gene promoter. PAD takes part in protein citrullination and targets myelin basic protein (MBP), which is affected during demyelination. To determine whether MBP charge isomers are changing the methyl cycle, we have estimated the concentrations of methyl cycle metabolites in MBP-activated primary astrocytes and oligodendrocytes. For this purpose, the action of the citrullinated MBP- C8 and the most cationic MBP-C1 isomers on the primary cells were investigated. Methods: Primary oligodendrocyte and astrocyte cell cultures were prepared from whole brains of 2-day-old Wistar rats. The methyl cycle metabolites, including homocysteine, S-adenosylmethionine (SAM), and S-adenosylhomocysteine (SAH), were estimated by HPLC analysis using fluorescence detection and prior derivatization. Results: We found that the action of MBP-C8 and MBP-C1 induces a decrease in the concentration of both methyl cycle metabolites, S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), in astrocytes compared to the control cells. As for oligodendrocytes, the concentration of SAM was increased by the addition of MBP-C1, while MBP-C8 has no significant effect. As for SAH, its concentration was increased compared to the control cells by the action of both MBP-C1 and MBP-C8. A significant increase in homocysteine concentration was observed by the action of the MBP-C8 isomer in both oligodendrocytes and astrocytes. Conclusion: These data suggest that MBP charge isomers change the concentration of methyl cycle metabolites. MBP-C8 citrullinated isomer causes elevation of homocysteine in astrocytes and oligodendrocytes, which may be the reason for decreased astrocyte proliferation and increased oligodendrocyte cell death which takes place in neurodegenerative processes. Elevated homocysteine levels and subsequent abnormal regulation of methyl cycles in oligodendrocytes possibly change the methylation of DNA that activates PAD gene promoter and induces the synthesis of PAD, which in turn provokes the process of citrullination, which is the accompanying process of demyelination. Acknowledgment: This research was supported by the SRNSF Georgia RF17_534 grant.

Keywords: myelin basic protein, astrocytes, methyl cycle metabolites, homocysteine, oligodendrocytes

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Authors: Murat Colak, Berkay Cakir Saridogan

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Since the dawn of modern corporations, almost every single employee has been working in the same loop, which contains three basic steps: going to work, providing the needs for the work, and getting back home. Only a small amount of people were able to break that standard and live outside the box. As the 2019 pandemic hit the Earth and most companies shut down their physical offices, that loop had to change for everyone. This means that the old management styles had to be significantly re-arranged to the "work from home" type of business methods. The methods include online conferences and meetings, time and task tracking using algorithms, globalization of the work, and, most importantly, remote working. After the global epidemic started, even the tech giants were concerned. Now, it can be seen those technology companies have an incredible step-up in their shares compared to the other companies because they know how to manage such situations even better than every other industry. This study aims to take the old traditional management styles in big companies and compare them with the post-covid methods (2019-2022). As a result of this comparison made using the annual reports and shared statistics, this study aims to explain why the winners of this crisis are the technology companies.

Keywords: Covid-19, technology companies, business models, remote work

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Authors: Nadia Theuma

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Climate change is a reality that has started to leave an impact on the physical environment as well as on the built environment, in particular built heritage. This paper explores the argument that climate change is also a trigger which can lead to identifying a number of creative solutions that can transform built heritage into sustainable buildings. Using the Maltese Islands, and in particular the city of Valletta which is also a World Heritage Site, this paper illustrates some of the innovative solutions that are being developed to make heritage buildings more sustainable and in doing so, mitigating the negative impacts of climate change. The paper looks in detail at the most notable initiatives being developed, their implementation and application, which at times is not easy considering the restrictions within protected built heritage areas and the positive impacts that they will have on visitor experience and overall sustainability of the Maltese tourism product. The paper will conclude by outlining how these solutions can be adapted to buildings with similar climatic conditions.

Keywords: built heritage, creative solutions, climate change, Maltese Islands

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Authors: Chiara Cesali

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The effects of climate change are increasingly evident: increases in temperature (i.e. global warming), greater frequency of extreme weather events, i.e. storms, floods, which often affect transport infrastructures. Large-scale climatological models with long-term horizons (up to 2100) show the possibility of significant increases in precipitation in the future, according to the greenhouse gas emissions scenarios from IPCC. Consequently, the insufficiency of existing hydraulic works (i.e. bridges, culverts, drainage systems) may be more frequent, or those currently being designed may become insufficient in the future. Thus, the hydraulic design methods of transport infrastructure must begin to take into account the influence of climate change. To this purpose, criteria for applying to the hydraulic design of a railway infrastructure some of the approaches currently available for determining design rainfall intensity and/or peak discharge flow on the basis of possible climate change scenarios are defined and proposed in the paper. Some application cases are also described.

Keywords: climate change, hydraulic design, precipitation, railway

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Authors: Mehdi Moradi Sarmeidani, Peyman Keyhani, Hasan Momtaz

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Chlamydophila psittaci is a avian pathogen that may cause respiratory disorders in humans. Conjunctival and cloacal swabs from 54 captive psittacine birds presented at veterinary clinics were collected to determine the prevalence of C. psittaci in domestic birds in Isfahan. Samples were collected during 2014 from a total of 10 different species of parrots, with African gray(33), Cockatiel lutino(3), Cockatiel gray(2), Cockatiel cinnamon(1), Pearl cockatiel(6), Timneh African grey(1), Ringneck parakeet(2), Melopsittacus undulatus(1), Alexander parakeet(2), Green Parakeet(3) being the most representative species sampled. C. psittaci was detected in 27 (50%) birds using molecular detection (PCR) method. The detection of this bacterium in captive psittacine birds shows that there is a potential risk for human whom has a direct contact and there is a possibility of infecting other birds.

Keywords: chlamydophila psittaci, psittacine birds, PCR, Isfahan

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Authors: Mohamed Gaith, Zaid Haddadin, Abdulah Wahbe, Mahmoud Hamam, Mahmoud Qunees, Mohammad Al Khatib, Mohammad Bsaileh, Abd Al-Aziz Jaber, Ahmad Aqra’a

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The crack is one of the most common types of failure in pipelines that convey fluid, and early detection of the crack may assist to avoid the piping system from experiencing catastrophic damage, which would otherwise be fatal. The influence of flow velocity and the presence of a crack on the performance of a tapered simply supported pipe containing moving fluid is explored using the finite element approach in this study. ANSYS software is used to simulate the pipe as Bernoulli's beam theory. In this paper, the fluctuation of natural frequencies and matching mode shapes for various scenarios owing to changes in fluid speed and the presence of damage is discussed in detail.

Keywords: damage detection, finite element, tapered pipe, vibration characteristics

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Authors: Muqaddas Butt, Allah Bakhsh Malik

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The changing educational environment, the diverse educational needs of students, the high expectations from the public, and the policy reports demand a strong change & innovation in the curriculum. Effective change and innovation could not be possible without the involvement of two major tenants i.e. teachers and educational managers. Thus, the locus of this research was to explore the secondary school principals and teachers priorities regarding change and innovation in curriculum. The main research objectives were included to explore the secondary school teachers’ and principals’ views about existing Humanities group curriculum; to identify their priorities regarding change and innovation in curriculum and to make a comparison between the priorities of both (the teachers & principals). A total sample of 150 Secondary school teachers and 24 principals from Federal Government Secondary Schools was drawn. The data was obtained through a five point Likert scale questionnaire. The findings indicated a huge difference between principals and teachers priorities. Related to prevailed curriculum, teachers showed more satisfactory views than principals. It was also found that the principals in comparison with teachers showed more inclination towards change and innovation and emphasized on an interdisciplinary, practical and ICT Integrated curriculum. Inclusion of local environmental issues; creativity based and practical activities based curriculum; and orientation to citizenship education into curriculum were some of the aspects highly prioritized by both teachers and principals.

Keywords: curriculum change, curriculum innovation, humanities curriculum, curriculum priorities

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Authors: M. Kastek, M. Kowalski, M. Szustakowski, H. Polakowski, T. Sosnowski

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Development of highly efficient security systems is one of the most urgent topics for science and engineering. There are many kinds of threats and many methods of prevention. It is very important to detect a threat as early as possible in order to neutralize it. One of the very challenging problems is detection of dangerous objects hidden under human’s clothing. This problem is particularly important for safety of airport passengers. In order to develop methods and algorithms to detect hidden objects it is necessary to determine the thermal signatures of such objects of interest. The laboratory measurements were conducted to determine the thermal signatures of dangerous tools hidden under various clothes in different ambient conditions. Cameras used for measurements were working in spectral range 0.6-12.5 μm An infrared imaging Fourier transform spectroradiometer was also used, working in spectral range 7.7-11.7 μm. Analysis of registered thermograms and hyperspectral datacubes has yielded the thermal signatures for two types of guns, two types of knives and home-made explosive bombs. The determined thermal signatures will be used in the development of method and algorithms of image analysis implemented in proposed monitoring systems.

Keywords: hyperspectral detection, nultispectral detection, image processing, monitoring systems

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Authors: Noha Seddik, Sherine Youssef, Mohamed Kholeif

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The automated epileptic seizure detection research field has emerged in the recent years; this involves analyzing the Electroencephalogram (EEG) signals instead of the traditional visual inspection performed by expert neurologists. In this study, a Support Vector Machine (SVM) that uses Weighted Permutation Entropy (WPE) as the input feature is proposed for classifying normal and seizure EEG records. WPE is a modified statistical parameter of the permutation entropy (PE) that measures the complexity and irregularity of a time series. It incorporates both the mapped ordinal pattern of the time series and the information contained in the amplitude of its sample points. The proposed system utilizes the fact that entropy based measures for the EEG segments during epileptic seizure are lower than in normal EEG.

Keywords: electroencephalogram (EEG), epileptic seizure detection, weighted permutation entropy (WPE), support vector machine (SVM)

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Authors: Seyhan Karaçavuş, Bülent Yılmaz, Ömer Kayaaltı, Semra İçer, Arzu Taşdemir, Oğuzhan Ayyıldız, Kübra Eset, Eser Kaya

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In this study, our goal was to perform tumor staging and subtype determination automatically using different texture analysis approaches for a very common cancer type, i.e., non-small cell lung carcinoma (NSCLC). Especially, we introduced a texture analysis approach, called Law’s texture filter, to be used in this context for the first time. The 18F-FDG PET images of 42 patients with NSCLC were evaluated. The number of patients for each tumor stage, i.e., I-II, III or IV, was 14. The patients had ~45% adenocarcinoma (ADC) and ~55% squamous cell carcinoma (SqCCs). MATLAB technical computing language was employed in the extraction of 51 features by using first order statistics (FOS), gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and Laws’ texture filters. The feature selection method employed was the sequential forward selection (SFS). Selected textural features were used in the automatic classification by k-nearest neighbors (k-NN) and support vector machines (SVM). In the automatic classification of tumor stage, the accuracy was approximately 59.5% with k-NN classifier (k=3) and 69% with SVM (with one versus one paradigm), using 5 features. In the automatic classification of tumor subtype, the accuracy was around 92.7% with SVM one vs. one. Texture analysis of FDG-PET images might be used, in addition to metabolic parameters as an objective tool to assess tumor histopathological characteristics and in automatic classification of tumor stage and subtype.

Keywords: cancer stage, cancer cell type, non-small cell lung carcinoma, PET, texture analysis

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Authors: Koray U. Erbas

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Neural networks are appealing for many applications since they are able to learn complex non-linear relationships between input and output data. As the number of neurons and layers in a neural network increase, it is possible to represent more complex relationships with automatically extracted features. Nowadays Deep Neural Networks (DNNs) are widely used in Computer Vision problems such as; classification, object detection, segmentation image editing etc. In this work, Facial Emotion Recognition task is performed by proposed Convolutional Neural Network (CNN)-based DNN architecture using FER2013 Dataset. Moreover, the effects of different hyperparameters (activation function, kernel size, initializer, batch size and network size) are investigated and ablation study results for Pooling Layer, Dropout and Batch Normalization are presented.

Keywords: convolutional neural network, deep learning, deep learning based FER, facial emotion recognition

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Authors: Shenghui Wu, Patricia Chalela, Amelie G. Ramirez

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Background: Cervical cancer (CC), colorectal cancer (CRC), and breast cancer (BC) are diseases that can be prevented/detected through early test. Through educational programs, individuals can become better informed about these cancers and understand the importance of screening and early detection. A community-based educational program was developed to improve knowledge and awareness toward the screening of the three cancer types in a South Texas underserved population. Methods: Residents living in Laredo, Texas were invited to participate in the present study. From January 2020 to April 2021, participants were recruited using social media and flyer distributions in general community. Participants received a free live web cancer education presentation delivered by bilingual community health educators, and online pre- and post-education surveys for CC, CRC, and BC separately. Pre-post changes in knowledge for individual items were compared using McNemar’s chi-squared tests. Results: Overall, participants demonstrated increases in CC (n=237), CRC (n=59), and BC (n=56) screening knowledge and awareness after receiving the cancer screening education (Ps<0.05). After receiving the cancer screening education, 85-97% of participants had an intent to talk to a healthcare provider about CC/CRC/BC screening, 88-97% had an intent to get a CC/CRC/BC screening test in the next 12 months or at the next routine appointment, and 90-97% had an intent to talk about CC/CRC/BC with their family members or friends. Conclusion: A community-based educational program can help increase knowledge and awareness about cervical, colorectal, and breast cancer screening, promote positive changes in population's knowledge and awareness about the benefits of cancer screening.

Keywords: cervical cancer, colorectal cancer, breast cancer, educational program, health knowledge, awareness, Hispanics, screening, health education

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Authors: Andreea Campu, Ilinica Muresan, Simona Cainap, Simion Astilean, Monica Focsan

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Acute myocardial infarction (AMI) is the most severe cardiovascular disease, which has threatened human lives for decades, thus a continuous interest is directed towards the detection of cardiac biomarkers such as cardiac troponin I (cTnI) in order to predict risk and, implicitly, fulfill the early diagnosis requirements in AMI settings. Microfluidics is a major technology involved in the development of efficient sensing devices with real-time fast responses and on-site applicability. Microfluidic devices have gathered a lot of attention recently due to their advantageous features such as high sensitivity and specificity, miniaturization and portability, ease-of-use, low-cost, facile fabrication, and reduced sample manipulation. The integration of gold nanoparticles into the structure of microfluidic sensors has led to the development of highly effective detection systems, considering the unique properties of the metallic nanostructures, specifically the Localized Surface Plasmon Resonance (LSPR), which makes them highly sensitive to their microenvironment. In this scientific context, herein, we propose the implementation of a novel detection device, which successfully combines the efficiency of gold bipyramids (AuBPs) as signal transducers and thermal generators with the sample-driven advantages of the microfluidic channels into a miniaturized, portable, low-cost, specific, and sensitive test for the dual LSPR-thermographic cTnI detection. Specifically, AuBPs with longitudinal LSPR response at 830 nm were chemically synthesized using the seed-mediated growth approach and characterized in terms of optical and morphological properties. Further, the colloidal AuBPs were deposited onto pre-treated silanized glass substrates thus, a uniform nanoparticle coverage of the substrate was obtained and confirmed by extinction measurements showing a 43 nm blue-shift of the LSPR response as a consequence of the refractive index change. The as-obtained plasmonic substrate was then integrated into a microfluidic “Y”-shaped polydimethylsiloxane (PDMS) channel, fabricated using a Laser Cutter system. Both plasmonic and microfluidic elements were plasma treated in order to achieve a permanent bond. The as-developed microfluidic plasmonic chip was further coupled to an automated syringe pump system. The proposed biosensing protocol implicates the successive injection inside the microfluidic channel as follows: p-aminothiophenol and glutaraldehyde, to achieve a covalent bond between the metallic surface and cTnI antibody, anti-cTnI, as a recognition element, and target cTnI biomarker. The successful functionalization and capture of cTnI was monitored by LSPR detection thus, after each step, a red-shift of the optical response was recorded. Furthermore, as an innovative detection technique, thermal determinations were made after each injection by exposing the microfluidic plasmonic chip to 785 nm laser excitation, considering that the AuBPs exhibit high light-to-heat conversion performances. By the analysis of the thermographic images, thermal curves were obtained, showing a decrease in the thermal efficiency after the anti-cTnI-cTnI reaction was realized. Thus, we developed a microfluidic plasmonic chip able to operate as both LSPR and thermal sensor for the detection of the cardiac troponin I biomarker, leading thus to the progress of diagnostic devices.

Keywords: gold nanobipyramids, microfluidic device, localized surface plasmon resonance detection, thermographic detection

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Authors: Yanchun Zuo, Yingao Liu, Wei Liu, Le Yu, Run Huang, Lixin Guo

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Electric field detection is important in many application scenarios. The traditional strategy is measuring the electric field with a man walking around in the area under test. This strategy cannot provide a satisfactory measurement accuracy. To solve the mentioned problem, an autopilot measurement system is divided. A mini-car is produced, which can travel in the area under test according to respect to the program within the CPU. The electric field measurement platform (EFMP) carries a central computer, two horn antennas, and a vector network analyzer. The mini-car stop at the sampling points according to the preset. When the car stops, the EFMP probes the electric field and stores data on the hard disk. After all the sampling points are traversed, an electric field map can be plotted. The proposed system can give an accurate field distribution description of the chamber.

Keywords: autopilot mini-car measurement system, electric field detection, field map, static zone measurement

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Authors: Eiman Kattan, Hong Wei

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In using Convolutional Neural Network (CNN) for classification, there is a set of hyperparameters available for the configuration purpose. This study aims to evaluate the impact of a range of parameters in CNN architecture i.e. AlexNet on land cover classification based on four remotely sensed datasets. The evaluation tests the influence of a set of hyperparameters on the classification performance. The parameters concerned are epoch values, batch size, and convolutional filter size against input image size. Thus, a set of experiments were conducted to specify the effectiveness of the selected parameters using two implementing approaches, named pertained and fine-tuned. We first explore the number of epochs under several selected batch size values (32, 64, 128 and 200). The impact of kernel size of convolutional filters (1, 3, 5, 7, 10, 15, 20, 25 and 30) was evaluated against the image size under testing (64, 96, 128, 180 and 224), which gave us insight of the relationship between the size of convolutional filters and image size. To generalise the validation, four remote sensing datasets, AID, RSD, UCMerced and RSCCN, which have different land covers and are publicly available, were used in the experiments. These datasets have a wide diversity of input data, such as number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in both training and testing. The results have shown that increasing the number of epochs leads to a higher accuracy rate, as expected. However, the convergence state is highly related to datasets. For the batch size evaluation, it has shown that a larger batch size slightly decreases the classification accuracy compared to a small batch size. For example, selecting the value 32 as the batch size on the RSCCN dataset achieves the accuracy rate of 90.34 % at the 11th epoch while decreasing the epoch value to one makes the accuracy rate drop to 74%. On the other extreme, setting an increased value of batch size to 200 decreases the accuracy rate at the 11th epoch is 86.5%, and 63% when using one epoch only. On the other hand, selecting the kernel size is loosely related to data set. From a practical point of view, the filter size 20 produces 70.4286%. The last performed image size experiment shows a dependency in the accuracy improvement. However, an expensive performance gain had been noticed. The represented conclusion opens the opportunities toward a better classification performance in various applications such as planetary remote sensing.

Keywords: CNNs, hyperparamters, remote sensing, land cover, land use

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Authors: Nita Mitra, Pranab Chanda

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In this paper, the issue of attitude-behavior consistency has been addressed in the context of climate change. Scientists (about 98 percent) opine that human behavior has a significant role in climate change. Such climate changes are harmful for human life. Thus, it is natural to conclude that only change of human behavior can avoid harmful consequences. Government and Non-Government Organizations are taking steps to bring in the desired changes in behavior. However, it seems that although the efforts are achieving changes in the attitudes to some degree, those steps are failing to materialize the corresponding behavioral changes. This has been a great concern for environmentalists. Psychologists have noticed the problem as a particular case of the general psychological problem of making attitude and behavior consistent with each other. The present study is in continuation of a previous work of the same author based upon descriptive research on the status of attitude and behavior of the people of a foot-hill region of the Himalayas in India regarding climate change. The observations confirm the mismatch of attitude and behavior of the people of the region with respect to climate change. While doing so an attitude-behavior mismatch has been noticed with respect to the acceptance of psychological findings by the public. People have been found to be interested in Psychology as an important subject, but they are reluctant to take the observations of psychologists seriously. A comparative study in this regard has been made with similar studies done elsewhere. Finally, an attempt has been made to perceive observations in the framework of observational learning due to Bandura's and behavior change due to Lewin.

Keywords: acceptance of psychological variables, attitude-behavior consistency, behavior change, climate change, observational learning

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Authors: Joanne DiFrancisco-Donoghue, Seth E. Jenny, Peter C. Douris, Sophia Ahmad, Kyle Yuen, Hillary Gan, Kenney Abraham, Amber Sousa

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Background: Guidelines for the maintenance of health of esports players and the cognitive changes that accompany competitive gaming are understudied. Executive functioning is an important cognitive skill for an esports player. The relationship between executive functions and physical exercise has been well established. However, the effects of prolonged sitting regardless of physical activity level have not been established. Prolonged uninterrupted sitting reduces cerebral blood flow. Reduced cerebral blood flow is associated with lower cognitive function and fatigue. This decrease in cerebral blood flow has been shown to be offset by frequent and short walking breaks. These short breaks can be as little as 2 minutes at the 30-minute mark and 6 minutes following 60 minutes of prolonged sitting. The rationale is the increase in blood flow and the positive effects this has on metabolic responses. The primary purpose of this study was to evaluate executive function changes following 6-minute bouts of walking and complete rest mid-session, compared to no break, during prolonged gameplay in competitive first-person shooter (FPS) esports players. Methods: This study was conducted virtually due to the Covid-19 pandemic and was approved by the New York Institute of Technology IRB. Twelve competitive FPS participants signed written consent to participate in this randomized pilot study. All participants held a gold ranking or higher. Participants were asked to play for 2 hours on three separate days. Outcome measures to test executive function included the Color Stroop and the Tower of London tests which were administered online each day prior to gaming and at the completion of gaming. All participants completed the tests prior to testing for familiarization. One day of testing consisted of a 6-minute walk break after 60-75 minutes of play. The Rate of Perceived Exertion (RPE) was recorded. The participant continued to play for another 60-75 minutes and completed the tests again. Another day the participants repeated the same methods replacing the 6-minute walk with lying down and resting for 6 minutes. On the last day, the participant played continuously with no break for 2 hours and repeated the outcome tests pre and post-play. A Latin square was used to randomize the treatment order. Results: Using descriptive statistics, the largest change in mean reaction time incorrect congruent pre to post play was seen following the 6-minute walk (662.0 (609.6) ms pre to 602.8 (539.2) ms post), followed by the 6-minute rest group (681.7(618.1) ms pre to 666.3 (607.9) ms post), and with minimal change in the continuous group (594.0(534.1) ms pre to 589.6(552.9) ms post). The mean solution time was fastest in the resting condition (7774.6(6302.8)ms), followed by the walk condition (7929.4 (5992.8)ms), with the continuous condition being slowest (9337.3(7228.7)ms). the continuous group 9337.3(7228.7) ms; 7929.4 (5992.8 ) ms 774.6(6302.8) ms. Conclusion: Short walking breaks improve blood flow and reduce the risk of venous thromboembolism during prolonged sitting. This pilot study demonstrated that a low intensity 6 -minute walk break, following 60 minutes of play, may also improve executive function in FPS gamers.

Keywords: executive function, FPS, physical activity, prolonged sitting

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Authors: Richard Mbatu

Abstract:

Implementation of the Reducing Emissions from Deforestation and forest Degradation (REDD+) program will not only lead to significant net gains in greenhouse gas reduction but also gains in biodiversity conservation. However, the looming paradigm shift in the program in the form of the proposed landscape governance approach could change this inclination. The concern lies with the fact that pursue of carbon credits by governments and private entities under the proposed landscape approach could encourage obstinate land use behaviors that are detrimental to the cause of biodiversity conservation and ecosystem services. Yet, the landscape approach could also stimulate governments to develop and implement land use management policies for climate change adaptation and mitigation. Using two potential areas of land use under the proposed landscape approach – carbon farming in grasslands and carbon farming in plantations – this paper provides a balanced analytical review of conservation challenges and opportunities for forest governance and beyond under the proposed landscape approach to REDD+. The paper argues that such a balanced view will enable policymakers and other stakeholders to better present their arguments in their efforts to shape the course of the REDD+ program in the post-Paris Agreement era.

Keywords: biodiversity conservation, REDD+, forest governance, grasslands, landscape approach, plantations

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