Search results for: subtle change detection and quantification

Authors: Sophy Ann Xavier, Lakshmi A

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Gentrification is the process of altering a neighborhood's character through the influx of wealthier people and establishments. This idea has subsequently been expanded to encompass brand-new, high-status construction projects that involve regenerating brownfield sites or demolishing and rebuilding residential neighborhoods. Inequality is made worse by Gentrification in ways that go beyond socioeconomic position. The elderly, members of racial and ethnic minorities, individuals with disabilities, and mental health all suffer disproportionately when they are displaced. Cities must cultivate openness, diversity, and inclusion in their collaborations, as well as cooperation on objectives and results. The papers compiled in this issue concentrate on the new gentrification discussions, the rising residential allure of central cities, and the indices to measure this process according to its various varieties. The study makes an effort to fill the research gap in the area of gentrification studies, which is the absence of a set of indices for measuring Gentrification in a specific area. Studies on Gentrification that contain maps of historical change highlight trends that will aid in the production of displacement risk maps, which will guide future interventions by allowing residents and policymakers to extrapolate into the future. Additionally, these maps give locals a glimpse into the future of their communities and serve as a political call to action in areas where residents are expected to be displaced. This study intends to pinpoint metrics and approaches for measuring Gentrification that can then be applied to create a spatiotemporal map of a region and tactics for its inclusive planning. An understanding of various approaches will enable planners and policymakers to select the best approach and create the appropriate plans.

Keywords: gentrification, indices, methods, quantification

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Authors: Maleh Yassine, Ezzati Abdellah

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Wireless Sensor Networks (WSNs) are currently used in different industrial and consumer applications, such as earth monitoring, health related applications, natural disaster prevention, and many other areas. Security is one of the major aspects of wireless sensor networks due to the resource limitations of sensor nodes. However, these networks are facing several threats that affect their functioning and their life. In this paper we present security attacks in wireless sensor networks, and we focus on a review and analysis of the recent Intrusion Detection schemes in WSNs.

Keywords: wireless sensor networks, security attack, denial of service, IDS, cluster-based model, signature based IDS, hybrid IDS

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Authors: V. Jafari, M. Jafari

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When long-term changes in normal weather patterns happen in a certain area, it generally could be identified as climate change. Concentration of principal's greenhouse gases such as carbon dioxide, nitrous oxide, methane, ozone, and water vapor will cause climate change and perhaps climate variability. Main climate factors are temperature, precipitation, air pressure, and humidity. Extreme events may be the result of the changing of carbon dioxide concentration levels in the atmosphere which cause a change in temperature. Extreme events in some ways will affect the productivity of crop and dairy livestock. In this research, the correlation of milk production and temperature as the main climate factor in ChaharMahal and Bakhtiari province in Iran has been considered. The methodology employed for this study consists, collect reports and published national and provincial data, available recorded data on climate factors and analyzing collected data using statistical software. Milk production in ChaharMahal and Bakhtiari province is in the same pattern as national milk production in Iran. According to the current study results, there is a significant negative correlation between milk production in ChaharMahal and Bakhtiari provinces and temperature as the main climate change factor.

Keywords: Chaharmahal and Bakhtiari, climate change, impacts, Iran, milk production

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Authors: Shivi Agarwal, Trilok Mathur

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This paper proposes the method to measure the total factor productivity (TFP) change by credibility theory for fuzzy input and output variables. Total factor productivity change has been widely studied with crisp input and output variables, however, in some cases, input and output data of decision-making units (DMUs) can be measured with uncertainty. These data can be represented as linguistic variable characterized by fuzzy numbers. Malmquist productivity index (MPI) is widely used to estimate the TFP change by calculating the total factor productivity of a DMU for different time periods using data envelopment analysis (DEA). The fuzzy DEA (FDEA) model is solved using the credibility theory. The results of FDEA is used to measure the TFP change for fuzzy input and output variables. Finally, numerical examples are presented to illustrate the proposed method to measure the TFP change input and output variables. The suggested methodology can be utilized for performance evaluation of DMUs and help to assess the level of integration. The methodology can also apply to rank the DMUs and can find out the DMUs that are lagging behind and make recommendations as to how they can improve their performance to bring them at par with other DMUs.

Keywords: chance-constrained programming, credibility theory, data envelopment analysis, fuzzy data, Malmquist productivity index

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Authors: Nemanja Kidzin, Danka Puric

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Recently, the field of personality change has received an increasing amount of attention. Previously under-researched variables, such as the intention to change or taking on new social roles (in a working environment, education, family, etc.), have been shown to be relevant for personality change. Following this line of research, our study aimed to determine whether the process of acting can bring about personality changes in students of acting and, if yes, in which way. We hypothesized that there will be a significant difference between self-reported personality traits of students acting at the beginning and the end of preparing for a role. Additionally, as potential moderator variables, we measured the reported personality traits of the roles the students were acting, as well as empathy, disintegration, and years of formal education. The sample (N = 47) was composed of students of acting from the Faculty of Dramatic Arts (first- to fourth-year) and the Faculty of Modern Arts (first-year students only). Participants' mean age was 20.2 (SD = 1.47), and there were 64% of females. The procedure included two waves of testing (T1 at the beginning and T2 at the end of the semester), and students’ acting exercises and character immersion comprised the pseudo-experimental procedure. Students’ personality traits (HEXACO-60, self-report version), empathy (Questionnaire of Cognitive and Affective Empathy, QCAE), and disintegration (DELTA9, 10-item version) were measured at both T1 and T2, while the personality of the role (HEXACO-60 observer version) was measured at T2. Responses to all instruments were given on a 5-point Likert scale. A series of repeated-measures T-tests showed significant differences in emotionality (t(46) = 2.56, p = 0.014) and conscientiousness (t(46) = -2.39, p = 0.021) between T1 and T2. Moreover, an index of absolute personality change was significantly different from 0 for all traits (range .53 to .34, t(46) = 4.20, p < .001 for the lowest index. The average test-retest correlation for HEXACO traits was 0.57, which is lower than proposed by other similar researches. As for moderator variables, neither the personality of the role nor empathy or disintegration explained the change in students’ personality traits. The magnitude of personality change was the highest in fourth-year students, with no significant differences between the remaining three years of studying. Overall, our results seem to indicate some personality changes in students of acting. However, these changes cannot be unequivocally related to the process of preparing for a role. Further and methodologically stricter research is needed to unravel the role of acting in personality change.

Keywords: theater, personality change, acting, HEXACO

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Authors: Indu Gupta

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Climate change and global warming is most burning issue for “our common future”. For this common global interest. Countries organize conferences of government and nongovernment type. Human being destroying the non-renewable resources and polluting the renewable resources of planet for economic growth. Air pollution is mainly responsible for global warming and climate change .Due to global warming ice glaciers are shrinking and melting. Forests are shrinking, deserts expanding and soil eroding. The depletion of stratospheric ozone layer is depleting and hole in ozone layer that protect us from harmful ultra violet radiation. Extreme high temperature in summer and extreme low temperature and smog in winters, floods in rainy season. These all are indication of climate change. The level of carbon dioxide and other heat trapping gases in the atmosphere is increasing at high speed. Nation’s are worried about environmental degradation.

Keywords: environmental degradation, global warming, soil eroding, ultra-Violate radiation

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Ravi Kant, Banshi D. Gupta

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The unbridled application of insecticides to enhance agricultural yield has become a matter of grave concern to both the environment and the human health and, thus pose a potential threat to sustainable development. Fenitrothion is an extensively used organophosphate insecticide whose residues are reported to be extremely toxic for birds, humans and aquatic life. A sensitive, swift and accurate detection protocol for fenitrothion is, thus, highly demanded. In this work, we report an SPR based fiber optic sensor for the detection of fenitrothion, where a nanocomposite arrangement of Ta2O5 and reduced graphene oxide (rGO) (Ta₂O₅: rGO) decorated on silver coated unclad core region of an optical fiber forms the sensing channel. A nanocomposite arrangement synergistically integrates the properties of involved components and consequently furnishes a conducive framework for sensing applications. The modification of the dielectric function of the sensing layer on exposure to fenitrothion solutions of diverse concentration forms the sensing mechanism. This modification is reflected in terms of the shift in resonance wavelength. Experimental variables such as the concentration of rGO in the nanocomposite configuration, dip time of silver coated fiber optic probe for deposition of sensing layer and influence of pH on the performance of the sensor have been optimized to extract the best performance of the sensor. SPR studies on the optimized sensing probe reveal the high sensitivity, wide operating range and good reproducibility of the fabricated sensor, which unveil the promising utility of Ta₂O₅: rGO nanocomposite framework for developing an efficient detection methodology for fenitrothion. FOSPR approach in cooperation with nanomaterials projects the present work as a beneficial approach for fenitrothion detection by imparting numerous useful advantages such as sensitivity, selectivity, compactness and cost-effectiveness.

Keywords: surface plasmon resonance, optical fiber, sensor, fenitrothion

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

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Authors: Jesus Alfredo Ortega Granados, Pandiyan Thangarasu

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Antibiotic tetracycline presents as a micro-contaminant in fresh water, wastewater and soils, causing environmental and health problems. In this work, tetracycline (TC) has been employed as chemo-sensor for the recognition of Al³⁺ without interring other ions, and the results show that it enhances the fluorescence intensity for Al³⁺ and there is no interference from other coexisting cation ions (Cd²⁺, Ni²⁺, Co²⁺, Sr²⁺, Mg²⁺, Fe³⁺, K⁺, Sm³⁺, Ag⁺, Na⁺, Ba²⁺, Zn²⁺, and Mn²⁺). For the addition of Cu²⁺ to [TET-Al³⁺], it appears that the intensity of fluorescence has been quenched. Other combinations of metal ions in addition to TC do not change the fluorescence behavior. The stoichiometry determined by Job´s plot for the interaction of TC with Al³⁺ was found to be 1:1. Importantly, the detection of Al³⁺⁺ successfully employed in the real samples like living cells, and it was found that TC efficiently performs as a fluorescent probe for Al³⁺ ion in living systems, especially in Saccharomyces cerevisiae; this is confirmed by confocal laser scanning microscopy.

Keywords: chemo-sensor, recognition of Al³⁺ ion, Saccharomyces cerevisiae, tetracycline,

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Authors: Mrinal Kanti Bhowmik, Shawli Bardhan Jr., Debotosh Bhattacharjee

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Human skin containing temperature value more than absolute zero, discharges infrared radiation related to the frequency of the body temperature. The difference in infrared radiation from the skin surface reflects the abnormality present in human body. Considering the difference, detection and forecasting the temperature variation of the skin surface is the main objective of using Medical Infrared Thermography(MIT) as a diagnostic tool for pain detection. Medical Infrared Thermography(MIT) is a non-invasive imaging technique that records and monitors the temperature flow in the body by receiving the infrared radiated from the skin and represent it through thermogram. The intensity of the thermogram measures the inflammation from the skin surface related to pain in human body. Analysis of thermograms provides automated anomaly detection associated with suspicious pain regions by following several image processing steps. The paper represents a rigorous study based survey related to the processing and analysis of thermograms based on the previous works published in the area of infrared thermal imaging for detecting inflammatory pain diseases like arthritis, spondylosis, shoulder impingement, etc. The study also explores the performance analysis of thermogram processing accompanied by thermogram acquisition protocols, thermography camera specification and the types of pain detected by thermography in summarized tabular format. The tabular format provides a clear structural vision of the past works. The major contribution of the paper introduces a new thermogram acquisition standard associated with inflammatory pain detection in human body to enhance the performance rate. The FLIR T650sc infrared camera with high sensitivity and resolution is adopted to increase the accuracy of thermogram acquisition and analysis. The survey of previous research work highlights that intensity distribution based comparison of comparable and symmetric region of interest and their statistical analysis assigns adequate result in case of identifying and detecting physiological disorder related to inflammatory diseases.

Keywords: acquisition protocol, inflammatory pain detection, medical infrared thermography (MIT), statistical analysis

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Authors: Andrea Trevino Gavito, Diego Klabjan, Sanjiv Shah

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Echocardiograms serve as pivotal resources for clinicians in diagnosing cardiac conditions, offering non-invasive insights into a heart’s structure and function. When echocardiographic studies are conducted, no standardized labeling of the acquired views is performed. Employing machine learning algorithms for automated echocardiogram view detection has emerged as a promising solution to enhance efficiency in echocardiogram use for diagnosis. However, existing approaches predominantly rely on supervised learning, necessitating labor-intensive expert labeling. In this paper, we introduce a fully unsupervised echocardiographic view detection framework that leverages convolutional autoencoders to obtain lower dimensional representations and the K-means algorithm for clustering them into view-related groups. Our approach focuses on discriminative patches from echocardiographic frames. Additionally, we propose a trainable inverse average layer to optimize the decoding of average operations. By integrating both public and proprietary datasets, we obtain a marked improvement in model performance when compared to utilizing a proprietary dataset alone. Our experiments show boosts of 15.5% in accuracy and 9.0% in the F-1 score for frame-based clustering, 25.9% in accuracy, and 19.8% in the F-1 score for view-based clustering. Our research highlights the potential of unsupervised learning methodologies and the utilization of open-sourced data in addressing the complexities of echocardiogram interpretation, paving the way for more accurate and efficient cardiac diagnoses.

Keywords: artificial intelligence, machine learning, unsupervised learning, self-supervised representation learning, echocardiography, echocardiographic view detection

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Authors: Ruijia Hu, Susanna T.Y. Tong

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Light detection and ranging (LiDAR) and four-channel red, green, blue, and near-infrared (RGBI) remote sensed imageries allow an accurate quantification and contiguous measurement of vegetation characteristics and forest structures. This information facilitates the generation of habitat structure variables for forest species distribution modelling. However, applications of remote sensing data, especially the combination of structural and spectral information, to support evidence-based decisions in forest managements and conservation practices at local scale are not widely adopted. In this study, we examined the habitat requirements of pileated woodpecker (Dryocopus pileatus) (PW) in Hamilton County, Ohio, using ecologically relevant forest structural and vegetation characteristics derived from LiDAR and RGBI data. We hypothesized that the habitat of PW is shaped by vegetation characteristics that are directly associated with the availability of food, hiding and nesting resources, the spatial arrangement of habitat patches within home range, as well as proximity to water sources. We used 186 PW presence or absence locations to model their presence and absence in generalized additive model (GAM) at two scales, representing foraging and home range size, respectively. The results confirm PW’s preference for tall and large mature stands with structural complexity, typical of late-successional or old-growth forests. Besides, the crown size of dead trees shows a positive relationship with PW occurrence, therefore indicating the importance of declining living trees or early-stage dead trees within PW home range. These locations are preferred by PW for nest cavity excavation as it attempts to balance the ease of excavation and tree security. In addition, we found that PW can adjust its travel distance to the nearest water resource, suggesting that habitat fragmentation can have certain impacts on PW. Based on our findings, we recommend that forest managers should use different priorities to manage nesting, roosting, and feeding habitats. Particularly, when devising forest management and hazard tree removal plans, one needs to consider retaining enough cavity trees within high-quality PW habitat. By mapping PW habitat suitability for the study area, we highlight the importance of riparian corridor in facilitating PW to adjust to the fragmented urban landscape. Indeed, habitat improvement for PW in the study area could be achieved by conserving riparian corridors and promoting riparian forest succession along major rivers in Hamilton County.

Keywords: deadwood detection, generalized additive model, individual tree crown delineation, LiDAR, pileated woodpecker, RGBI aerial imagery, species distribution models

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Authors: Min Jindong, Wang Mingxia

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With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: M. Govindarajan

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One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection. 

Keywords: data mining, ensemble, radial basis function, support vector machine, accuracy

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Authors: Gabriel Jinga, Madalina Dumitru

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The objective of this paper is to present the process of change in management accounting in Romania, a former communist country from Eastern Europe. In order to explain this process, we used the contingency and institutional theories. We focused on the following directions: the presentation of the scientific context and motivation of this research and the case study. We presented the state of the art in the process of change in the management accounting from the international and national perspective. We also described the evolution of management accounting in Romania in the context of economic and political changes. An important moment was the fall of communism in 1989. This represents a starting point for a new economic environment and for new management accounting. Accordingly, we developed a case study which presented this evolution. The conclusion of our research was that the changes in the management accounting system of the company analysed occurred in the same time with the institutionalisation of some elements (e.g. degree of competition, training and competencies in management accounting). The management accounting system was modelled by the contingencies specific to this company (e.g. environment, industry, strategy).

Keywords: management accounting, change, Romania, contingency and institutional theory

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Authors: Radhika Ranjan Roy

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Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classification tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets.

Keywords: Mahalanobis distance, machine learning, deep learning, NS-KDD, local intrinsic dimensionality, chi-square, positive semi-definite, area under the curve

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Authors: Sabahat Zahra

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The purpose of this research is to present the changing global environment and its potential impacts on sustainable food and energy production at global level, particularly in Pakistan. The food and energy related-economic sector has been subjected to negative consequences due to recent extreme changes in weather conditions, particularly in developing countries. Besides continuous modifications in weather, population is also increasing by time, therefore it is necessary to take special steps and start effective initiatives to cope with the challenges of food and energy security to fight hunger and for economic stability of country. Severe increase in temperature and heat waves has also negative impacts on food production as well as energy sustainability. Energy (in terms of electricity) consumption has grown up than the production potential of the country as a consequence of increasing warm weather. Ultimately prices gone up when there is more consumption than production. Therefore, all these aspects of climate change are interrelated with socio-economic issues. There is a need to develop long-term policies on regional and national levels for maintainable economic growth. This research presents a framework-plan and recommendations for implementation needed to mitigate the potential threats due to global climate change sustainable food and energy production under climate change in the country.

Keywords: climate changes, energy security, food security, global climate change

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Authors: Falak Nawaz

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The Climate Change Vulnerability and Capacity Assessment carried out in the coastal regions of Thatta and Malir districts underscore the potential risks and challenges associated with climate change affecting water resources. This study was conducted by the author using participatory rural appraisal tools, with a greater focus on conducting focus group discussions, direct observations, key informant interviews, and other PRA tools. The assessment delves into the specific impacts of climate change along the coastal belt, concentrating on aspects such as rising sea levels, depletion of freshwater, alterations in precipitation patterns, fluctuations in water table levels, and the intrusion of saltwater into rivers. These factors have significant consequences for the availability and quality of water resources in coastal areas, manifesting in frequent migration and alterations in agriculture-based livelihood practices. Furthermore, the assessment assesses the adaptive capacity of communities and organizations in these coastal regions to effectively confront and alleviate the effects of climate change on water resources. It considers various measures, including infrastructure enhancements, water management practices, adjustments in agricultural approaches, and disaster preparedness, aiming to bolster adaptive capacity. The study's findings emphasize the necessity for prompt actions to address identified vulnerabilities and fortify the adaptive capacities of Sindh's coastal areas. This calls for comprehensive strategies and policies promoting sustainable water resource management, integrating climate change considerations, and providing essential resources and support to vulnerable communities.

Keywords: climate, climate change adaptation, disaster reselience, vulnerability, capacity, assessment

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Authors: Hussein Lujuo Mohamed

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Introduction: Public health has always been influenced by climate and weather. Changes in climate and climate variability, particularly changes in weather extremes affect the environment that provides people with clean air, food, water, shelter, and security. Tanzania is not an exception to the threats of climate change. The health sector is mostly affected due to emergence and proliferation of infectious diseases, thereby affecting health of the population and thus impacting achievement of sustainable development goals. Methodology: A desk review on documented issues pertaining to climate change and health in Tanzania was done using Google search engine. Keywords included climate change, link, health, climate initiatives. In cases where information was not available, documents from Ministry of Health, Vice Presidents Office-Environment, Local Government Authority, Ministry of Water, WHO, research, and training institutions were reviewed. Some of the reviewed documents from these institutions include policy brief papers, fieldwork activity reports, training manuals, and guidelines. Results: Six main climate resilience activities were identified in Tanzania. These were development and implementation of climate resilient water safety plans guidelines both for rural and urban water authorities, capacity building of rural and urban water authorities on implementation of climate-resilient water safety plans, and capacity strengthening of local environmental health practitioners on mainstreaming climate change and health into comprehensive council health plans. Others were vulnerability and adaptation assessment for the health sector, mainstreaming climate change in the National Health Policy, and development of risk communication strategy on climate. In addition information, education, and communication materials on climate change and to create awareness were developed aiming to sensitize and create awareness among communities on climate change issues and its effect on public health. Conclusion: Proper implementation of these interventions will help the country become resilient to many impacts of climate change in the health sector and become a good example for other least developed countries.

Keywords: climate, change, Tanzania, health

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Authors: Marwa Qaraqe, Muhammad Ismail, Erchin Serpedin

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This paper presents a novel architecture for a patient-specific epileptic seizure onset detector using scalp electroencephalography (EEG). The proposed architecture is based on the decision fusion calculated from energy and neural synchronization related features. Specifically, one level of the detector calculates the condition number (CN) of an EEG matrix to evaluate the amount of neural synchronization present within the EEG channels. On a parallel level, the detector evaluates the energy contained in four EEG frequency subbands. The information is then fed into two independent (parallel) classification units based on support vector machines to determine the onset of a seizure event. The decisions from the two classifiers are then combined together according to two fusion techniques to determine a global decision. Experimental results demonstrate that the detector based on the AND fusion technique outperforms existing detectors with a sensitivity of 100%, detection latency of 3 seconds, while it achieves a 2:76 false alarm rate per hour. The OR fusion technique achieves a sensitivity of 100%, and significantly improves delay latency (0:17 seconds), yet it achieves 12 false alarms per hour.

Keywords: epilepsy, EEG, seizure onset, electroencephalography, neuron, detection

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Authors: Wenwen Liu, Robert Burton, Simon McKeown

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Climate change is seriously threatening human security and development, leading to global warming and economic, political, and social chaos. Many artists have created visual responses that challenge perceptions on climate change, actively guiding people to think about the climate issues and potential crises after urban industrialization and explore positive solutions. This project is an interdisciplinary and intertextual study where art practice is informed by culture, philosophy, psychology, ecology, and science. By correlating theory and artistic practice, it studies how art practice creates a new way of understanding climate issues and uses art as a way of exploring speculative futures. In the context of practical-based research, arts-based practice as research and creative practice as interdisciplinary research are applied alternately to seek the original solution and new knowledge. Through creative art practice, this project has established new visual ways of looking at climate change and has developed it into a new model to generate more possibilities, an alternative social imagination. It not only encourages people to think and find a sustainable speculative future conducive to all species but also proves that people have the ability to realize positive futures.

Keywords: climate change, creative practice as interdisciplinary research, arts-based practice as research, creative art practice, speculative future

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Authors: Pshtiwan Shafiq Ahmed

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The invasion of Iraq was a turning point in Iraq, destroying the economic infrastructure of several important strategic and historic cities, including Mosul, Anbar and Diyala, which will take decades to rebuild It left 18,805 people dead and 37,000 injured, destroying hundreds of villages and cities, displacing 2.3 million people, and increasing the number of orphans The increase in the number of windows and the destruction of society and the structure of the population so that the number of children, women and the elderly has increased. Religious clashes have increased and religious cleansing has begun, trying to eradicate Christianity, Yazidis and Kakais from the whole of Iraq, causing the largest number of Christians, Yazidis and Kakais to leave Iraq and many of them went missing.

Keywords: ISIS, population change, geographical change, Iraq

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Authors: C. Iitake, K. Iitake

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Background and Aims: The number of diabetic patients based on obesity is increasing drastically in Asia. Since most patients have multiple complications, if one medicine can treat those at the same time, it would contribute to financial savings and patients’ compliance. A Japanese-made DPP-4 inhibitor, Anagliptin is only sold in Japan and South Korea. It is said to have its unique aspect of lowering LDL-cholesterol (LDL-C) levels together with lowering blood glucose. We have assessed 63 patients in our faculty to investigate this fact clinically and statistically. Method: Patients with type 2 diabetes who has been treated with Anagliptin for the first time was investigated changes in HbA1c, fasting and random blood glucose and LDL-C levels from the baseline at 1 month, 6 months and 1 year. Results: 29 patients (46.1%) were given DPP-4 inhibitors for the first time (original group), and 34 patients (53.9%) were using other DPP-4 inhibitors before Anagliptin (exchanged group). The change in HbA1c and fasting glucose from the baseline were -2.0% (P < 0.001) and -38.3mg/dl (P < 0.01) respectively with original group, -0.5% (P < 0.01) and -29.4mg/dl (P < 0.01) respectively with exchanged group. 23 patients (36.5%) were using statins or fibrates and 28 patients (44.4%) were using none, and its LDL-C change were -8.1mg/dl (P = 0.2582) and -10.1mg/dl(P < 0.05) respectively. 16 patients(25%) with LDL-C level ≥ 140mg/dl, change were -21.7mg/dl(P < 0.05). LDL-C change did not have a correlation coefficient (=-0.03238) with change in HbA1c and was not affected by other diabetic drugs. Conclusion: These findings indicate that Anagliptin is a potential treatment option for type 2 diabetes complicated by hyperlipidemia.

Keywords: DPP-4 inhibitors, anagliptin, LDL-cholesterol, type 2 diabetes

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Authors: Chang Li Yu

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Resistances commonly happened during sexual offenders treatment program (SOTP), and removing resistances was one of the treatment goals on it. Studies concerning treatment effectiveness relied on pre- and post-treatment evaluations, however, no significant difference on resistance revealed after treatment, and the above consequences generally contributed to the low motivation for change instead. Therefore, the aim of this study was to investigate the resistance across each stage of change among sexual offenders (SO). The present study recruited prisoned SO in Taiwan, excluding those with literacy difficulties; finally, 272 participants were included. Of all participants completed revised version of URICA (University of Rhode Island Change Assessment) and resistance scale specifically for SO. The former included four stages of change: pre-contemplation (PC), contemplation (C), action (A), and maintain (M); the later composed eight types of resistance: system blaming, victims blaming, problems with treatment alliance, social justification, hopelessness, isolation, psychological reactance, and passive reactance. Both of the instruments were with well reliability and validity. Descriptive statistics and ANOVA were performed. All of 272 participants, age under 25 were 18(6.6%), 25-39 were 133(48.9%), 40-54 were 102(37.5%), and age over 55 were 19(7.0%); college level and above were 53(19.5%), high school level were 110(40.4%), and under high school level were 109(40.1%); first offended were 117(43.0%), and recidivist were 23(8.5%). Further deleting data with missing values and invalid questionnaires, SO with stage of change on PC were 43(18.9%), C were 109(47.8%), A were 70(30.7%), and on M were 6(2.6%). One-way ANOVA showed significant differences on every kind of resistances, excepting isolation and passive reactance. Post-hoc analysis showed that SO with different stages had their main resistance. There are two contributions to the present study. First, this study provided a clinical and theoretical measurement of evaluation that was never used in the past. Second, this study used an evidence-based methodology to prove a clinical perspective differed from the past, suggesting that resistances to treatment on SO appear the whole therapeutic process, when SO progress into the next stage of change, clinicians have to deal with their main resistance for working through the therapy.

Keywords: resistance, sexual offenders treatment program (SOTP), motivation for change, prisoned sexual offender

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Authors: Alaa A. Almarzuki, Nora A. Farraj, Aisha M. Alshiky, Omar A. Batarfi

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The number of internet users is dramatically increased every year. Most of these users are exposed to the dangers of attackers in one way or another. The reason for this lies in the presence of many weaknesses that are not known for native users. In addition, the lack of user awareness is considered as the main reason for falling into the attackers’ snares. Cross Site Request Forgery (CSRF) has placed in the list of the most dangerous threats to security in OWASP Top Ten for 2013. CSRF is an attack that forces the user’s browser to send or perform unwanted request or action without user awareness by exploiting a valid session between the browser and the server. When CSRF attack successes, it leads to many bad consequences. An attacker may reach private and personal information and modify it. This paper aims to detect and prevent a specific type of CSRF, called reflected CSRF. In a reflected CSRF, a malicious code could be injected by the attackers. This paper explores how CSRF Detection Extension prevents the reflected CSRF by checking browser specific information. Our evaluation shows that the proposed solution succeeds in preventing this type of attack.

Keywords: CSRF, CSRF detection extension, attackers, attacks

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Authors: Ahmed Ashit

Abstract:

Image fusion is a significant and efficient image processing method used for detecting different types of tumors. This method has been used as an effective combination technique for obtaining high quality images that combine anatomy and physiology of an organ. It is the main key in the huge biomedical machines for diagnosing cancer such as PET-CT machine. This thesis aims to develop an image analysis system for the detection of the eye tumor. Different image processing methods are used to extract the tumor and then mark it on the original image. The images are first smoothed using median filtering. The background of the image is subtracted, to be then added to the original, results in a brighter area of interest or tumor area. The images are adjusted in order to increase the intensity of their pixels which lead to clearer and brighter images. once the images are enhanced, the edges of the images are detected using canny operators results in a segmented image comprises only of the pupil and the tumor for the abnormal images, and the pupil only for the normal images that have no tumor. The images of normal and abnormal images are collected from two sources: “Miles Research” and “Eye Cancer”. The computerized experimental results show that the developed image fusion based eye tumor detection system is capable of detecting the eye tumor and segment it to be superimposed on the original image.

Keywords: image fusion, eye tumor, canny operators, superimposed

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Authors: Suresh Hettiarachchi, Conrad Wasko, Ashish Sharma

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The ever changing and expanding urban landscape increases the stress on urban systems to support and maintain safe and functional living spaces. Flooding presents one of the more serious threats to this safety, putting a larger number of people in harm’s way in congested urban settings. Climate change is adding to this stress by creating a dichotomy in the urban flood response. On the one hand, climate change is causing storms to intensify, resulting in more destructive, rarer floods, while on the other hand, longer dry periods are decreasing the severity of more frequent, less intense floods. This variability is creating a need to be more agile and innovative in how we design for and manage urban flooding. Here, we argue that to cope with this challenge climate change brings, we need to move towards urban flood management through resilience rather than flood prevention. We also argue that dealing with the larger variation in flood response to climate change means that we need to look at flooding from all aspects rather than the single-dimensional focus of flood depths and extents. In essence, we need to rethink how we manage flooding in the urban space. This change in our thought process and approach to flood management requires a practical way to assess and quantify resilience that is built into the urban landscape so that informed decision-making can support the required changes in planning and infrastructure design. Towards that end, we propose a Simple Urban Flood Resilience Index (SUFRI) based on a robust definition of resilience as a tool to assess flood resilience. The application of a simple resilience index such as the SUFRI can provide a practical tool that considers urban flood management in a multi-dimensional way and can present solutions that were not previously considered. When such an index is grounded on a clear and relevant definition of resilience, it can be a reliable and defensible way to assess and assist the process of adapting to the increasing challenges in urban flood management with climate change.

Keywords: urban flood resilience, climate change, flood management, flood modelling

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Authors: Mohamed Aljarallah, Mohamed Nurullah, George Saridakis

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This paper aims and objectives are to investigate how the structural change of the financial regulatory bodies affect the financial supervision and how the regulators can design such a structure with taking into account; the Central Bank, the conduct of business and the prudential regulators, it will also consider looking at the structure of the international regulatory bodies and what barriers are found. There will be five questions to be answered; should conduct of business and prudential regulation be separated? Should the financial supervision and financial stability be separated? Should the financial supervision be under the Central Bank? To what extent the politician should intervene in changing the regulatory and supervisory structure? What should be the regulatory and supervisory structure when there is financial conglomerate? Semi structure interview design will be applied. This research sample selection contains a collective of financial regulators and supervisors from the emerged and emerging countries. Moreover, financial regulators and supervisors must be at a senior level at their organisations. Additionally, senior financial regulators and supervisors would come from different authorities and from around the world. For instance, one of the participants comes from the International Bank Settlements, others come from European Central Bank, and an additional one will come from Hong Kong Monetary Authority and others. Such a variety aims to fulfil the aims and objectives of the research and cover the research questions. The analysis process starts with transcription of the interview, using Nvivo software for coding, applying thematic interview to generate the main themes. The major findings of the study are as follow. First, organisational structure changes quite frequently if the mandates are not clear. Second, measuring structural change is difficult, which makes the whole process unclear. Third, effective coordination and communication are what regulators looking for when they change the structure and that requires; openness, trust, and incentive. In addition to that, issues appear during the event of crisis tend to be the reason why the structure change. Also, the development of the market sometime causes a change in the regulatory structure. And, some structural change occurs simply because of the international trend, fashion, or other countries' experiences. Furthermore, when the top management change the structure tends to change. Moreover, the structure change due to the political change, or politicians try to show they are doing something. Finally, fear of being blamed can be a driver of structural change. In conclusion, this research aims to provide an insight from the senior regulators and supervisors from fifty different countries to have a clear understanding of why the regulatory structure keeps changing from time to time through a qualitative approach, namely, semi-structure interview.

Keywords: financial regulation bodies, financial regulatory structure, global financial regulation, financial crisis

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Authors: Andreas Livera, Spyros Theocharides, Michalis Florides, Charalambos Anastassiou

Abstract:

A main challenge in the quest for ensuring quality of operation, especially for photovoltaic (PV) systems, is to safeguard the reliability and optimal performance by detecting and diagnosing potential failures and performance losses at early stages or before the occurrence through real-time monitoring, supervision, fault detection, and predictive maintenance. The purpose of this work is to present the functionalities and results related to the development and validation of a software platform for PV assets diagnosis and maintenance. The platform brings together proprietary hardware sensors and software algorithms to enable the early detection and prediction of the most common and critical faults in PV systems. It was validated using field measurements from operating PV systems. The results showed the effectiveness of the platform for detecting faults and losses (e.g., inverter failures, string disconnections, and potential induced degradation) at early stages, forecasting PV power production while also providing recommendations for maintenance actions. Increased PV energy yield production and revenue can be thus achieved while also minimizing operation and maintenance (O&M) costs.

Keywords: failure detection and prediction, operation and maintenance, performance monitoring, photovoltaic, platform, recommendations, predictive maintenance

Procedia PDF Downloads 35