Search results for: image coding standards

Authors: B. Sathyanarayana

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As per the report of World Health Organization, more than 80% of world population uses medicines made from herbal and natural materials. They have stood the test of time for their safety, efficacy, cultural acceptability and lesser side effects. Quality assurance and control measures, such as national quality specification and standards for herbal materials, good manufacturing practices (GMP) for herbal medicines, labelling, and licensing schemes for manufacturing, imports and marketing, should be in place in every country where herbal medicines are regulated. These measures are vital for ensuring the safety and efficacy of herbal medicines. In the case of herbal products challenge begins at the stage of designing itself except the classical products. Selection of herbal ingredients, officinal parts to be used, proportions are vital. Once the formulation is designed one should take utmost care to produce the standardized product of assured quality and safety. Quality control measures should cover the validation of quality and identity of raw materials, in process control (as per SOP and GMP norms) and at the level of final product. Quality testing, safety and efficacy studies of the final product are required to ensure the safe and effective use of the herbal products in human beings. Medicinal plants being the materials of natural resource are subjected to great variation making it really difficult to fix quality standards especially in the case of polyherbal preparations. Manufacturing also needs modification according to the type of ingredients present. Hence, it becomes essential to develop Standard operative Procedure for a specific herbal product. Present paper throws a light on the challenges that are encountered during the design and development of herbal products.

Keywords: herbal product, challenges, quality, safety, efficacy

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Authors: Vahid Bayrami Rad

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In recent years, the use of artificial intelligence techniques, tools, and methods in processing medical images and health-related applications has been highlighted and a lot of research has been done in this regard. For example, colonoscopy and diagnosis of colon lesions are some cases in which the process of diagnosis of lesions can be improved by using image processing and artificial intelligence algorithms, which help doctors a lot. Due to the lack of accurate measurements and the variety of injuries in colonoscopy images, the process of diagnosing the type of lesions is a little difficult even for expert doctors. Therefore, by using different software and image processing, doctors can be helped to increase the accuracy of their observations and ultimately improve their diagnosis. Also, by using automatic methods, the process of diagnosing the type of disease can be improved. Therefore, in this paper, a deep learning framework called KCBA is proposed to classify colonoscopy lesions which are composed of several methods such as K-means clustering, a bag of features and deep auto-encoder. Finally, according to the experimental results, the proposed method's performance in classifying colonoscopy images is depicted considering the accuracy criterion.

Keywords: colorectal cancer, colonoscopy, region of interest, narrow band imaging, texture analysis, bag of feature

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Authors: Seyedeh Fatemeh Nabavi, Hamid Dalir, Anooshiravan Farshidianfar

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Laser welding is pivotal in modern manufacturing, offering unmatched precision, speed, and efficiency. Its versatility in minimizing heat-affected zones, seamlessly joining dissimilar materials, and working with various metals makes it indispensable for crafting intricate automotive components. Integration into automated systems ensures consistent delivery of high-quality welds, thereby enhancing overall production efficiency. Noteworthy are the safety benefits of laser welding, including reduced fumes and consumable materials, which align with industry standards and environmental sustainability goals. As the automotive sector increasingly demands advanced materials and stringent safety and quality standards, laser welding emerges as a cornerstone technology. A comprehensive model encompassing thermal dynamic and characteristics models accurately predicts geometrical, metallurgical, and mechanical aspects of the laser beam welding process. Notably, Model 2 showcases exceptional accuracy, achieving remarkably low error rates in predicting primary and secondary dendrite arm spacing (PDAS and SDAS). These findings underscore the model's reliability and effectiveness, providing invaluable insights and predictive capabilities crucial for optimizing welding processes and ensuring superior productivity, efficiency, and quality in the automotive industry.

Keywords: laser welding process, geometrical characteristics, mechanical characteristics, metallurgical characteristics, comprehensive model, thermal dynamic

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Authors: P. Abtahi, B. Samali

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Façade panels of various shapes, weights, and connections usually act as a barrier between the indoor and outdoor environments. They also play a major role in enhancing the aesthetics of building structures. They are attached by different types of connections to the primary structure or inner panels in double skin façade skins. Structural buildings designed to withstand seismic shocks have been undergoing a critical appraisal in recent years, with the emphasis changing from ‘strength’ to ‘performance’. Performance based design and analysis have found their way into research, development, and practice of earthquake engineering, particularly after the 1994 Northridge and 1995 Kobe earthquakes. The design performance of facades as non-structural elements has now focused mainly on evaluating the damage sustained by façade frames with fixed connections, not movable ones. This paper will review current design standards for structural buildings, including the performance of structural and non-structural components during earthquake excitations in order to overview and evaluate the damage assessment and behaviour of various façade systems in building structures during seismic activities. The proposed solutions for each facade system will be discussed case by case to evaluate their potential for incorporation with newly designed connections. Finally, Double-Skin-Facade systems can potentially be combined with movable facade technology, although other glazing systems would require minor to major changes in their design before being integrated into the system.

Keywords: building performance, earthquake engineering, glazing system, movable façade technology

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Authors: Jiangbo Shi, Zeyu Gao, Chen Li

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Nuclei segmentation is a fundamental task in digital pathology analysis and can be automated by deep learning-based methods. However, the development of such an automated method requires a large amount of data with precisely annotated masks which is hard to obtain. Training with weakly labeled data is a popular solution for reducing the workload of annotation. In this paper, we propose a novel meta-learning-based nuclei segmentation method which follows the label correction paradigm to leverage data with noisy masks. Specifically, we design a fully conventional meta-model that can correct noisy masks by using a small amount of clean meta-data. Then the corrected masks are used to supervise the training of the segmentation model. Meanwhile, a bi-level optimization method is adopted to alternately update the parameters of the main segmentation model and the meta-model. Extensive experimental results on two nuclear segmentation datasets show that our method achieves the state-of-the-art result. In particular, in some noise scenarios, it even exceeds the performance of training on supervised data.

Keywords: deep learning, histopathological image, meta-learning, nuclei segmentation, weak annotations

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Authors: Ralph Lugon, Randolf Ramseyer

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The beauty and quality of the natural heritage can be appreciated in different ways by different users, but the delicate balance of the environment in a nature reserve must be respected. The case of the territorial anchorage of the Grangettes natural reserve gives an interesting insight into the users' perception of the environmental constraints and standards of tourist activities. The nature reserve was once conceived as a sanctuary of natural heritage, a place where flora and fauna could flourish with minimal human interference. However, over time and with the transition to modernity, the values and meanings of the reserve have changed for visitors and the people living in the surrounding area. Today, The Grangettes nature reserve is a place of relaxation for urban dwellers with limited knowledge of nature and a lack of awareness of conservation issues. As a result, the reserve is now threatened by the negative impacts of human activities and mass tourism on its environment. Les Grangettes is a nature reserve that faces the challenge of preserving biodiversity while managing tourist flows. Ways must be found to accommodate new types of visitors from towns and cities who are looking for new activities, quality services and facilities, as well as aesthetic inspiration. To ensure the long-term conservation of the area, the flow of tourists must be carefully controlled. Through a dual qualitative-quantitative approach in 2021-22, this paper explores new visitor trends, changes in the reserve, and potential consequences for other stakeholders in the ecosystem. The purpose of this research is to assess users' perceptions of environmental constraints and standards on tourist activities in a nature reserve.

Keywords: outdoor recreation, nature-based tourism, over tourism, protected area, user's perceptions

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Authors: Sajjad Baghernezhad, Saeideh Baghernezhad

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Nowadays considering human involved in increasing data development some methods such as data mining to extract science are unavoidable. One of the discussions of data mining is inherent distribution of the data usually the bases creating or receiving such data belong to corporate or non-corporate persons and do not give their information freely to others. Yet there is no guarantee to enable someone to mine special data without entering in the owner’s privacy. Sending data and then gathering them by each vertical or horizontal software depends on the type of their preserving type and also executed to improve data privacy. In this study it was attempted to compare comprehensively preserving data methods; also general methods such as random data, coding and strong and weak points of each one are examined.

Keywords: data mining, distributed data mining, privacy protection, privacy preserving

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Authors: Edward Chamisa, Colin C. Smith, Hamutyinei H. Pamburai, Abdul C. Abdulla

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South Africa (SA) adopted International Financial Reporting Standards (IFRS) for listed firms effective 1 January 2005. However, it was not until 2011 that substantial financial reporting enforcement changes were introduced, which were meant to ensure compliance with IFRS. This innovative setting allows us to examine the value relevance of accounting amounts during the (1) pre-IFRS adoption period (2002-2004); (2) post-IFRS adoption, but pre-enforcement changes period (2006-2010); and (3) post-enforcement changes period (2011-2012). The results show that accounting amounts were most value relevant in the post-enforcement changes period (R2, 75.5%) compared to both the pre-IFRS adoption period (adjusted R2 is 24.3%) and the period after IFRS adoption but before enforcement changes (adjusted R2 is 37.5%). Also, during the 2008 financial crisis, the equity book value per share was significantly value relevant (at 1%) but not earnings per share, whereas before the crisis, the opposite was true. We make two important contributions to the literature. First, we identify SA as an innovative setting that allows researchers to examine separately the effects of IFRS adoption and enforcement changes on capital markets and accounting quality. This is a departure from prior studies that are dominated by the European Union setting, where IFRS adoption occurred contemporaneously with enforcement and other regulatory changes. Second, we provide preliminary findings which suggest that while the adoption of IFRS seems to have improved the financial reporting quality of accounting amounts of SA listed firms, its impact appears to be limited unless combined with effective enforcement.

Keywords: international financial reporting standards (ifrs), ifrs adoption, financial reporting enforcement, value relevance, price model, equity book value, earnings per share

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Authors: Rajendar Kandan, Mohammad Zakaria Alli, Hong Ong

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Cloud computing is a business model which provides an easier management of computing resources. Cloud users can request virtual machine and install additional softwares and configure them if needed. However, user can also request virtual appliance which provides a better solution to deploy application in much faster time, as it is ready-built image of operating system with necessary softwares installed and configured. Large numbers of virtual appliances are available in different image format. User can download available appliances from public marketplace and start using it. However, information published about the virtual appliance differs from each providers leading to the difficulty in choosing required virtual appliance as it is composed of specific OS with standard software version. However, even if user choses the appliance from respective providers, user doesn’t have any flexibility to choose their own set of softwares with required OS and application. In this paper, we propose a referenced architecture for dynamically customizing virtual appliance and provision them in an easier manner. We also add our experience in integrating our proposed architecture with public marketplace and Mi-Cloud, a cloud management software.

Keywords: cloud computing, marketplace, virtualization, virtual appliance

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Miky Lee, K. Kim, D. Lim, D. Cho

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This paper presents the planning, rationale for test specification derivation, sampling requirements, test facilities, and result analysis used to conduct lifetime expectancy endurance tests on power window switches (PWS) considering thermally induced mechanical stress under diurnal cyclic temperatures during normal operation (power cycling). The detail process of analysis and test results on the selected PWS set were discussed in this paper. A statistical approach to ‘life time expectancy’ was given to the measurement standards dealing with PWS lifetime determination through endurance tests. The approach choice, within the framework of the task, was explained. The present task was dedicated to voltage drop measurement to derive lifetime expectancy while others mostly consider contact or surface resistance. The measurements to perform and the main instruments to measure were fully described accordingly. The failure data from tests were analyzed to conclude lifetime expectancy through statistical method using Weibull cumulative distribution function. The first goal of this task is to develop realistic worst case lifetime endurance test specification because existing large number of switch test standards cannot induce degradation mechanism which makes the switches less reliable. 2nd goal is to assess quantitative reliability status of PWS currently manufactured based on test specification newly developed thru this project. The last and most important goal is to satisfy customer’ requirement regarding product reliability.

Keywords: power window switch, endurance test, Weibull function, reliability, degradation mechanism

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Authors: Joanna Dzieńdziora

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Lobbying is an instrument of influence in various decision-making processes. It is also the underestimated issue as a research problem. The lack of research on the modern lobbyist competencies is the most crucial element. The paper presents attempts of finding answers to the following questions: Who should run the lobbying activity? What competencies should a lobbyist possess in order to implement lobbying activities effectively? Searching for answers for the mentioned above questions requires positioning the opportunity to change the image of lobbying in the area of competencies of entities that provide lobbying activities. The aim of the paper is presenting the lobbyist competencies profile in the framework of his professional role. The essence of lobbying activity and its significance in the modern economy as well as areas, the scope of lobbying activities, diagnosis of a modern lobbyist’s competences, lobbyist’s competencies profile that is focused on the professionalization of the lobbying activity, will have been presented in this paper. Indicated research tasks let emerge lobbyist’s competencies in the way that allows identifying and elaborating the lobbyist competencies profile. The profile lets improve lobbying activities. Its elaboration is based on the author’s research results analysis. Taking into consideration the shortages within the theory and research on the lobbying activity, the implementation of this research enables to fill the cognitive gap existing in the theory of management sciences.

Keywords: competencies, competencies profile, lobbying, lobbyist

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Authors: Minbale Aschale, Bitew K. Dessie, Endaweke Assegide, Yosef Abebe, Tena Alamirew, Claire L. Walsh, Gete Zeleke

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The upper Awash Basin faces pollution challenges due to urbanization, population growth, and expanding industries. It receives various pollutants from its catchments. The study aimed to assess the impact of wastewater irrigation on vegetables and inform stakeholders about pollution challenges and consequences. Eighty-two composite samples of matured vegetables were randomly collected from twenty-one agricultural farm sites. These samples were analyzed for potentially toxic elements, including Cd, Pb, Cr, Hg, As, Ni, Sr, B, Co, Cu, Mn, Fe, Zn, and Se. The results indicated significant variations in concentrations across different sites, with localized contributions from various contaminants. Cr, Cd, and Pb concentrations in most vegetables exceeded recommended levels. Pollution levels varied with metals and vegetable types. Different vegetables contribute differently to health risks. The relative contributions of Ethiopian kale, cabbage, red beet, lettuce, Swiss chard, Gurage cabbage, tomato, zucchini, carrot, onion, watermelon, and potato to the aggregated risk were 12.69%, 12.25%, 11.83%, 11.20%, 10.21%, 9.91%, 8.49%, 5.66%, 3.96%, 3.35%, 3.10%, and 2.72%, respectively. Comparison with permissible standards revealed inadequate environmental management by relevant regulatory bodies and industries. Despite good laws and standards at the federal and regional levels, they are ineffectively implemented or enforced to prevent environmental pollution. Mitigation measures are urgently recommended to address the potential health implications of toxic substances.

Keywords: pollution, upper Awash Basin, health risk, Ethiopia

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Authors: Mahdi Farajzadeh Ajirlou

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Prior evidence of normal cardiac anatomy is desirable to relieve the anxiety of cases with a family history of congenital heart disease or to offer the option of early gestation termination or close follow-up should a cardiac anomaly be proved. Fetal heart discovery plays an important part in the opinion of the fetus, and it can reflect the fetal heart function of the fetus, which is regulated by the central nervous system. Acquisition of ventricular volume and inflow data would be useful to quantify more valve regurgitation and ventricular function to determine the degree of cardiovascular concession in fetal conditions at threat for hydrops fetalis. This study discusses imaging the fetal heart with transvaginal ultrasound, Doppler ultrasound, three-dimensional ultrasound (3DUS) and four-dimensional (4D) ultrasound, spatiotemporal image correlation (STIC), glamorous resonance imaging and cardiac catheterization. Doppler ultrasound (DUS) image is a kind of real- time image with a better imaging effect on blood vessels and soft tissues. DUS imaging can observe the shape of the fetus, but it cannot show whether the fetus is hypoxic or distressed. Spatiotemporal image correlation (STIC) enables the acquisition of a volume of data concomitant with the beating heart. The automated volume accession is made possible by the array in the transducer performing a slow single reach, recording a single 3D data set conforming to numerous 2D frames one behind the other. The volume accession can be done in a stationary 3D, either online 4D (direct volume scan, live 3D ultrasound or a so-called 4D (3D/ 4D)), or either spatiotemporal image correlation-STIC (off-line 4D, which is a circular volume check-up). Fetal cardiovascular MRI would appear to be an ideal approach to the noninvasive disquisition of the impact of abnormal cardiovascular hemodynamics on antenatal brain growth and development. Still, there are practical limitations to the use of conventional MRI for fetal cardiovascular assessment, including the small size and high heart rate of the mortal fetus, the lack of conventional cardiac gating styles to attend data accession, and the implicit corruption of MRI data due to motherly respiration and unpredictable fetal movements. Fetal cardiac MRI has the implicit to complement ultrasound in detecting cardiovascular deformations and extracardiac lesions. Fetal cardiac intervention (FCI), minimally invasive catheter interventions, is a new and evolving fashion that allows for in-utero treatment of a subset of severe forms of congenital heart deficiency. In special cases, it may be possible to modify the natural history of congenital heart disorders. It's entirely possible that future generations will ‘repair’ congenital heart deficiency in utero using nanotechnologies or remote computer-guided micro-robots that work in the cellular layer.

Keywords: fetal, cardiac MRI, ultrasound, 3D, 4D, heart disease, invasive, noninvasive, catheter

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Authors: Murat Demir Aydin, Elanur Celebi

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Adhesively bonded joints are used as an alternative to traditional joining methods due to the important advantages they provide. The most important consideration in the use of adhesively bonded joints is that these joints have appropriate requirements for their use in terms of safety. In order to ensure control of this condition, damage analysis of the adhesively bonded joints should be performed by determining the mechanical properties of the adhesives. When the literature is investigated; it is generally seen that the mechanical properties of adhesives are determined by traditional measurement methods. In this study, to determine the mechanical properties of adhesives, the Digital Image Correlation (DIC) method, which can be an alternative to traditional measurement methods, has been used. The DIC method is a new optical measurement method which is used to determine the parameters of displacement and strain in an appropriate and correct way. In this study, tensile tests of Thick Adherent Shear Test (TAST) samples formed using DP410 liquid structural adhesive and steel materials and bulk tensile specimens formed using and DP410 liquid structural adhesive was performed. The displacement and strain values of the samples were determined by DIC method and the shear stress-strain curves of the adhesive for TAST specimens and the tensile strain curves of the bulk adhesive specimens were obtained. Various methods such as numerical methods are required as conventional measurement methods (strain gauge, mechanic extensometer, etc.) are not sufficient in determining the strain and displacement values of the very thin adhesive layer such as TAST samples. As a result, the DIC method removes these requirements and easily achieves displacement measurements with sufficient accuracy.

Keywords: structural adhesive, adhesively bonded joints, digital image correlation, thick adhered shear test (TAST)

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Authors: Tienfuan Kerh, Hsienchang Lu, Rob Saunders

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Shoreline erosion problems caused by global warming and sea level rising may result in losing of land areas, so it should be examined regularly to reduce possible negative impacts. Initially in this study, three sets of survey images obtained from the years of 1990, 2001, and 2010, respectively, are digitalized by using graphical software to establish the spatial coordinates of six major beaches around the island of Taiwan. Then, by overlaying the known multi-period images, the change of shoreline can be observed from their distribution of coordinates. In addition, the neural network approximation is used to develop a model for predicting shoreline variation in the years of 2015 and 2020. The comparison results show that there is no significant change of total sandy area for all beaches in the three different periods. However, the prediction results show that two beaches may exhibit an increasing of total sandy areas under a statistical 95% confidence interval. The proposed method adopted in this study may be applicable to other shorelines of interest around the world.

Keywords: digitalized shoreline coordinates, survey image overlaying, neural network approximation, total beach sandy areas

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Authors: Olurankinse Felix, Fatukasi Bayo

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Over the years, the medium through which government financial statements are prepared has been on cash basis of accounting. This basis was characterised with some shortcomings ranging from non- disclosure of quality and detail information relating to government financial transactions, ill informed assessment of government resource allocation, weak internal control system that inhibits accountability and transparency and non- standardisation of reporting ethics for the purpose of comparability. The emergence of international public sector accounting standards (IPSAS) is therefore seen as leverage as it aims at improving the quality of general purpose financial reporting by public sector entities thereby increasing transparency and accountability. IPSAS is a new concept that all institutions must fully adopts. The crux of this paper is to find out to what extent is the awareness and adoption of IPSAS to both students and lecturers interms of teaching, learning and inclusion in the curriculum of accounting education. The methodology involved the use of well designed questionnaires to obtain information from some selected institutions and the analysis was done with the use of maximum likelihood ordered probit regression. The result of the analysis shows that despite a high level of sensitisation/awareness of IPSAS, the degree of adoption is still low due to low level of desirability by students and lecturers. The paper recommend the need for the government to enact an enabling law to back up the adoption and more importantly to institute appropriate sanctions to ensure full compliance.

Keywords: assessment, awareness, adoption, IPSAS, cash basis

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Masood Karamoozian, Zhang Hong

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The necessity and complexity of the decision-making process and the interference of the various factors to make decisions and consider all the relevant factors in a problem are very obvious nowadays. Hence, researchers show their interest in multi-criteria decision-making methods. In this research, the Analytical Hierarchy Process (AHP), Simple Additive Weighting (SAW), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods of multi-criteria decision-making have been used to solve the problem of optimal construction systems. Systems being evaluated in this problem include; Light Steel Frames (LSF), a case study of designs by Zhang Hong studio in the Southeast University of Nanjing, Insulating Concrete Form (ICF), Ordinary Construction System (OCS), and Prefabricated Concrete System (PRCS) as another case study designs in Zhang Hong studio in the Southeast University of Nanjing. Crowdsourcing was done by using a questionnaire at the sample level (200 people). Questionnaires were distributed among experts, university centers, and conferences. According to the results of the research, the use of different methods of decision-making led to relatively the same results. In this way, with the use of all three multi-criteria decision-making methods mentioned above, the Prefabricated Concrete System (PRCS) was in the first rank, and the Light Steel Frame (LSF) system ranked second. Also, the Prefabricated Concrete System (PRCS), in terms of performance standards and economics, was ranked first, and the Light Steel Frame (LSF) system was allocated the first rank in terms of environmental standards.

Keywords: multi-criteria decision making, AHP, SAW, TOPSIS

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Authors: Sofia Cramerotti, Angela Cattoni, Laura Biancato, Dario Ianes

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The recognition of specific standards for new professionals, within the teaching profile, is a necessary process in order to foster an innovative school vision in accordance with the change that school is experiencing. In line with the reform of the national education and training system and with the National Training Plan for teachers, our Research and Development department developed a training project based on a framework (Syllabus) of skills that each 'Expert Teacher' should master in order to fulfill what the different specific profiles request. The syllabus is a fundamental tool for a training process consistent with the teaching profiles, both to guide the to-become teachers entering in service and to provide the in-service teachers with a system of evaluation and improvement of their skills. According to the national and international literature about professional standards for teachers, we aggregated the skills of the syllabus in three macro areas: (1) Area of professional skills related to the teacher profile and their continuous training; (2) area of teaching skills related to the school innovation; (3) area of organizing skills related to school participation for its improvement. The syllabus is a framework that identifies and describes the skills of the expert teacher in all of their roles. However, the various skills take on different importance in the different profiles involved in the school; some of those skills are determining a role, others could be secondary. Therefore, the characterization of the different profiles is represented by suitably weighted skills sets. In this way, the same skill could differently characterize each profile. In the future, we hope that the skills development and training for the teacher could evolve in a skills development and training for the whole school staff ('Expert Team'). In this perspective, the school will, therefore, benefit from a solid team, in which the skills of the various profiles are all properly developed and well represented.

Keywords: framework, skills, teachers, training

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Authors: Hadeer R. M. Tawfik, Rania A. K. Birry, Amani A. Saad

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Eyes are considered to be the most sensitive and important organ for human being. Thus, any eye disorder will affect the patient in all aspects of life. Cataract is one of those eye disorders that lead to blindness if not treated correctly and quickly. This paper demonstrates a model for automatic detection, classification, and grading of cataracts based on image processing techniques and artificial intelligence. The proposed system is developed to ease the cataract diagnosis process for both ophthalmologists and patients. The wavelet transform combined with 2D Log Gabor Wavelet transform was used as feature extraction techniques for a dataset of 120 eye images followed by a classification process that classified the image set into three classes; normal, early, and advanced stage. A comparison between the two used classifiers, the support vector machine SVM and the artificial neural network ANN were done for the same dataset of 120 eye images. It was concluded that SVM gave better results than ANN. SVM success rate result was 96.8% accuracy where ANN success rate result was 92.3% accuracy.

Keywords: cataract, classification, detection, feature extraction, grading, log-gabor, neural networks, support vector machines, wavelet

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Authors: Joseph Falzon, Margaret Nelson

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Background: Regeneration and re-use of abandoned historic buildings present a continuous challenge for policy makers and stakeholders in the tourism and leisure industry. Obsolete historic buildings provide great potential for tourism and leisure accommodation, presenting unique heritage experiences to travellers and host communities. Contemporary demands in the hospitality industry continuously require higher standards, some of which are in conflict with heritage conservation principles. Objective: The aim of this research paper is to critically discuss regeneration policies with stakeholders of the tourism and leisure industry and to examine current practices in policy development and the resultant impact of policies on the Maltese tourism and leisure industry. Research Design: Six semi-structured interviews with stakeholders involved in the tourism and leisure industry participated in the research. A number of measures were taken to reduce bias and thus improve trustworthiness. Clear statements of the purpose of the research study were provided at the start of each interview to reduce expectancy bias. The interviews were semi-structured to minimise interviewer bias. Interviewees were allowed to expand and elaborate as necessary, with only necessary probing questions, to allow free expression of opinion and practices. Interview guide was submitted to participants at least two weeks before the interview to allow participants to prepare for the interview and prevent recall bias during the interview as much as possible. Interview questions and probes contained both positive and negative aspects to prevent interviewer bias. Policy documents were available during the interview to prevent recall bias. Interview recordings were transcribed ‘intelligent’ verbatim. Analysis was carried out using thematic analysis with the coding frame developed independently by two researchers. All phases of the study were governed by research ethics. Findings: Findings were grouped in main themes: financing of regeneration, governance, legislation and policies. Other key issues included value of historic buildings and approaches for regeneration. Whist regeneration of historic buildings was noted, participants discussed a number of barriers that hindered regeneration. Stakeholders identified gaps in policies and gaps at policy implementation stages. European Union funding policies facilitated regeneration initiatives but funding criteria based on economic deliverables presented the intangible heritage gap. Stakeholders identified niche markets for heritage tourism accommodation. Lack of research-based policies was also identified. Conclusion: Potential of regeneration is hindered by inadequate legal framework that supports contemporary needs of the tourism industry. Policies should be developed by active stakeholder participation. Adequate funding schemes have to support the tangible and intangible components of the built heritage.

Keywords: governance, historic buildings, policy, tourism

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Authors: Sher Muhammad, Mirza Muhammad Waqar

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It is necessary to monitor and identify mangroves types and spatial extent near coastal areas because it plays an important role in coastal ecosystem and environmental protection. This research aims at identifying and mapping mangroves types along Karachi coast ranging from 24.79 to 24.85 degree in latitude and 66.91 to 66.97 degree in longitude using hyperspectral remote sensing data and techniques. Image acquired during February, 2012 through Hyperion sensor have been used for this research. Image preprocessing includes geometric and radiometric correction followed by Minimum Noise Fraction (MNF) and Pixel Purity Index (PPI). The output of MNF and PPI has been analyzed by visualizing it in n-dimensions for end-member extraction. Well-distributed clusters on the n-dimensional scatter plot have been selected with the region of interest (ROI) tool as end members. These end members have been used as an input for classification techniques applied to identify and map mangroves species including Spectral Angle Mapper (SAM), Spectral Feature Fitting (SFF), and Spectral Information Diversion (SID). Only two types of mangroves namely Avicennia Marina (white mangroves) and Avicennia Germinans (black mangroves) have been observed throughout the study area.

Keywords: mangrove, hyperspectral, hyperion, SAM, SFF, SID

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Authors: Babar Ali, Mohammad Rashid, Showkat Rasool Mir, Mohammad Ali, Saiba Shams

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Background: The plant Camellia sinensis (Theaceae) is an evergreen shrub indigenous to Assam (India) and parts of China and Japan. Traditional Chinese medicine has recommended green tea for headaches, body aches and pains, digestion, enhancement of immune defense, detoxification, as an energizer and to prolong life. The leaves have more than 700 chemical constituents, among which flavanoids, amino acids, vitamins (C, E, K), caffeine and polysaccharides. Adulteration and substitution may affect the quality of formulation containing tea leaves. Standardization of medicinal preparation is essential for further therapeutic results and for global acceptance. Hence, chromatographic fingerprint profiles were carried out for establishing the standards. Materials and methods: TLC studies for methanolic extracts of the leaves of Camellia sinensis were carried out in a new developed solvent system, Toluene: Ethyl acetate: Formic acid (7:3:1). TLC plates were dried in air, visualized in UV at wavelengths 254 nm and 366 nm and photographed. Results: Results provide valuable clue regarding their polarity and selection of solvents for separation of phytochemicals. Fingerprinting of methanolic extract of Camellia sinensis leaves revealed the presence of various phytochemicals in UV at 254 nm and 366 nm. Conclusion: Fingerprint profile is quite helpful in setting up of standards and thus to keep a check on intentional/unintentional adulteration. TLC offers major advantages over other conventional chromatographic techniques such as unsurpassed flexibility (esp. stationary and mobile phase), choice of detection wavelength, user friendly, rapid and cost effective.

Keywords: Cammelia sinensis Linn., standardization, methanolic extract, thin layer chromatography

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Authors: M. Owlia, M. H. Azarsa, M. Khabbazan, A. Mirbagheri

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Proprioceptive neuromuscular facilitation exercises are a series of stretching techniques that are commonly used in rehabilitation and exercise therapy. Assessment of these exercises for true maneuvering requires extensive experience in this field and could not be down with patients themselves. In this paper, we developed software that uses Microsoft Kinect sensor, a spherical color marker, and real-time image processing methods to evaluate patient’s performance in generating true patterns of movements. The software also provides the patient with a visual feedback by showing his/her avatar in a Virtual Reality environment along with the correct path of moving hand, wrist and marker. Primary results during PNF exercise therapy of a patient in a room environment shows the ability of the system to identify any deviation of maneuvering path and direction of the hand from the one that has been performed by an expert physician.

Keywords: image processing, Microsoft Kinect, proprioceptive neuromuscular facilitation, upper extremities assessment, virtual reality

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Authors: Insaf Bellamine, Hamid Tairi

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Detecting moving objects in sequences is an essential step for video analysis. This paper mainly contributes to the Color Space-Time Interest Points (CSTIP) extraction and detection. We propose a new method for detection of moving objects. Two main steps compose the proposed method. First, we suggest to apply the algorithm of the detection of Color Space-Time Interest Points (CSTIP) on both components of the Color Structure-Texture Image Decomposition which is based on a Partial Differential Equation (PDE): a color geometric structure component and a color texture component. A descriptor is associated to each of these points. In a second stage, we address the problem of grouping the points (CSTIP) into clusters. Experiments and comparison to other motion detection methods on challenging sequences show the performance of the proposed method and its utility for video analysis. Experimental results are obtained from very different types of videos, namely sport videos and animation movies.

Keywords: Color Space-Time Interest Points (CSTIP), Color Structure-Texture Image Decomposition, Motion Detection, clustering

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Authors: Zachary Pan

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Our general mission is to solve the problem of classifying images into different body part types and deciding if each of them is healthy or not. However, for now, we will determine healthiness for only one-sixth of the body parts, specifically the chest. We will detect pneumonia in X-ray scans of those chest images. With this type of AI, doctors can use it as a second opinion when they are taking CT or X-ray scans of their patients. Another ad-vantage of using this machine learning classifier is that it has no human weaknesses like fatigue. The overall ap-proach to this problem is to split the problem into two parts: first, classify the image, then determine if it is healthy. In order to classify the image into a specific body part class, the body parts dataset must be split into test and training sets. We can then use many models, like neural networks or logistic regression models, and fit them using the training set. Now, using the test set, we can obtain a realistic accuracy the models will have on images in the real world since these testing images have never been seen by the models before. In order to increase this testing accuracy, we can also apply many complex algorithms to the models, like multiplicative weight update. For the second part of the problem, to determine if the body part is healthy, we can have another dataset consisting of healthy and non-healthy images of the specific body part and once again split that into the test and training sets. We then use another neural network to train on those training set images and use the testing set to figure out its accuracy. We will do this process only for the chest images. A major conclusion reached is that convolutional neural networks are the most reliable and accurate at image classification. In classifying the images, the logistic regression model, the neural network, neural networks with multiplicative weight update, neural networks with the black box algorithm, and the convolutional neural network achieved 96.83 percent accuracy, 97.33 percent accuracy, 97.83 percent accuracy, 96.67 percent accuracy, and 98.83 percent accuracy, respectively. On the other hand, the overall accuracy of the model that de-termines if the images are healthy or not is around 78.37 percent accuracy.

Keywords: body part, healthcare, machine learning, neural networks

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Authors: Alejandra Cardenas, Ona Flores, Fabiola Gretzinger

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Since the 1990s, legal strategies for the promotion and protection of maternal health rights have achieved significant gains. Successful litigation in courts around the world have shown that these rights can be judicially enforceable. Governments and international organizations have acknowledged the importance of a human rights-based approach to maternal mortality and morbidity, and obstetric violence has been recognized as a human rights issue. Despite the progress made, maternal mortality has worsened in some regions of the world, while progress has stagnated elsewhere, and mistreatment in maternal care is reported almost universally. In this context, issues of maternal autonomy and decision-making during pregnancy, labor, and delivery as a critical barrier to access quality maternal health have been largely overlooked. Indeed, despite the principles of autonomy and informed consent in medical interventions being well-established in international and regional norms, how they are applied particularly during childbirth and pregnancy remains underdeveloped. National and global legal standards and decisions related to maternal health were reviewed and analyzed to determine how maternal autonomy and decision-making during pregnancy, labor, and delivery have been protected (or not) by international and national courts. The results of this legal research and analysis lead to the conclusion that a few standards have been set by courts regarding pregnant people’s rights to make choices during pregnancy and birth; however, most undermine the agency of pregnant people. These decisions recognize obstetric violence and gender-based discrimination, but fail to protect pregnant people’s autonomy, privacy, and their right to informed consent. As current human rights standards stand today, maternal health is the only field in medicine and law in which informed consent can be overridden, and patients can be forced to submit to treatments against their will. Unconsented treatment and loss of agency during pregnancy and childbirth can have long-term physical and mental impacts, reduce satisfaction and trust in health systems, and may deter future health-seeking behaviors. This research proposes a path forward that focuses on the pregnant person as an independent agent, relying on the doctrine of self-determination during pregnancy and childbirth, which includes access to the necessary conditions to enable autonomy and choice throughout pregnancy and childbirth as a critical step towards our approaches to reduce maternal mortality, morbidity, and mistreatment, and realize the promise of access to quality maternal health as a human right.

Keywords: autonomy in childbirth and pregnancy, choice, informed consent, jurisprudential analysis

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Authors: Hsiao-Yung Wang

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This paper aims to elaborate methodology of visual rhetoric with constant reference to Mikhail Bakhtin’s concept of “chronotope”. First, it attempts to outline Ronald Barthes, the most representative scholar of visual rhetoric and structuralism, perspective on visual rhetoric and its time-space category by referring to the concurrent word-image, the symbolic systematicity, the outer dialogicity. Second, an alternative approach is explored for grasping the dynamics and functions of visual rhetoric by articulating Mikhail Bakhtin’s concept of “chronotope.” Furthermore, that visual rhetorical consciousness could be identified as “the meaning parabola which projects from word to image,” “the symbolic system which proceeds from sequence to disorder,” “the ideological environment which struggles from the local to the global.” Last but not least, primary vision of the 2014 Taipei LGBT parade would be analyzed preliminarily to evaluate the effectiveness and persuasiveness embodied by specific visual rhetorical strategies. How Bakhtin’s concept of “chronotope” to explain the potential or possible ideological struggle deployed by visual rhetoric might be interpreted empirically and extensively.

Keywords: barthes, chronotope, Mikhail Bakhtin, Taipei LGBT parade, visual rhetoric

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