Search results for: image dataset

Authors: Wei-Chih Huang, Pei-Lung Chung, Ching-Heng Lin, Hsuan-Chia Yang, Der-Ming Liou

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Background: In-Hospital Cardiac Arrest (IHCA) threaten life of the inpatients, cause serious effect to patient safety, quality of inpatients care and hospital service. Health providers must identify the signs of IHCA early to avoid the occurrence of IHCA. This study will consider the potential association between early signs of IHCA and the essence of patient care provided by nurses and other professionals before an IHCA occurs. The aim of this study is to identify significant associations between nursing interventions and abnormal early evaluation results of IHCA that can assist health care providers in monitoring inpatients at risk of IHCA to increase opportunities of IHCA early detection and prevention. Materials and Methods: This study used one of the data mining techniques called association rules mining to compute associations between nursing interventions and abnormal early evaluation results of IHCA. The nursing interventions and abnormal early evaluation results of IHCA were considered to be co-occurring if nursing interventions were provided within 24 hours of last being observed in abnormal early evaluation results of IHCA. The rule based methods were utilized 23.6 million electronic medical records (EMR) from a medical center in Taipei, Taiwan. This dataset includes 733 concepts of nursing interventions that coded by clinical care classification (CCC) codes and 13 early evaluation results of IHCA with binary codes. The values of interestingness and lift were computed as Q values to measure the co-occurrence and associations’ strength between all in-hospital patient care measures and abnormal early evaluation results of IHCA. The associations were evaluated by comparing the results of Q values and verified by medical experts. Results and Conclusions: The results show that there are 4195 pairs of associations between nursing interventions and abnormal early evaluation results of IHCA with their Q values. The indication of positive association is 203 pairs with Q values greater than 5. Inpatients with high blood sugar level (hyperglycemia) have positive association with having heart rate lower than 50 beats per minute or higher than 120 beats per minute, Q value is 6.636. Inpatients with temporary pacemaker (TPM) have significant association with high risk of IHCA, Q value is 47.403. There is significant positive correlation between inpatients with hypovolemia and happened abnormal heart rhythms (arrhythmias), Q value is 127.49. The results of this study can help to prevent IHCA from occurring by making health care providers early recognition of inpatients at risk of IHCA, assist with monitoring patients for providing quality of care to patients, improve IHCA surveillance and quality of in-hospital care.

Keywords: in-hospital cardiac arrest, patient safety, nursing intervention, association rule mining

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Authors: Ouafa Amira, Jiangshe Zhang

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Clustering is an unsupervised machine learning technique; its aim is to extract the data structures, in which similar data objects are grouped in the same cluster, whereas dissimilar objects are grouped in different clusters. Clustering methods are widely utilized in different fields, such as: image processing, computer vision , and pattern recognition, etc. Fuzzy c-means clustering (fcm) is one of the most well known fuzzy clustering methods. It is based on solving an optimization problem, in which a minimization of a given cost function has been studied. This minimization aims to decrease the dissimilarity inside clusters, where the dissimilarity here is measured by the distances between data objects and cluster centers. The degree of belonging of a data point in a cluster is measured by a membership function which is included in the interval [0, 1]. In fcm clustering, the membership degree is constrained with the condition that the sum of a data object’s memberships in all clusters must be equal to one. This constraint can cause several problems, specially when our data objects are included in a noisy space. Regularization approach took a part in fuzzy c-means clustering technique. This process introduces an additional information in order to solve an ill-posed optimization problem. In this study, we focus on regularization by relative entropy approach, where in our optimization problem we aim to minimize the dissimilarity inside clusters. Finding an appropriate membership degree to each data object is our objective, because an appropriate membership degree leads to an accurate clustering result. Our clustering results in synthetic data sets, gaussian based data sets, and real world data sets show that our proposed model achieves a good accuracy.

Keywords: clustering, fuzzy c-means, regularization, relative entropy

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Authors: Manjitsingh Rajput

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Visual communication plays an important role in artificial intelligence (AI) because it enables machines to understand and interpret visual information, similar to how humans do. This abstract explores the importance of visual communication in AI and emphasizes the importance of various applications such as computer vision, object emphasis recognition, image classification and autonomous systems. In going deeper, with deep learning techniques and neural networks that modify visual understanding, In addition to AI programming, the abstract discusses challenges facing visual interfaces for AI, such as data scarcity, domain optimization, and interpretability. Visual communication and other approaches, such as natural language processing and speech recognition, have also been explored. Overall, this abstract highlights the critical role that visual communication plays in advancing AI capabilities and enabling machines to perceive and understand the world around them. The abstract also explores the integration of visual communication with other modalities like natural language processing and speech recognition, emphasizing the critical role of visual communication in AI capabilities. This methodology explores the importance of visual communication in AI development and implementation, highlighting its potential to enhance the effectiveness and accessibility of AI systems. It provides a comprehensive approach to integrating visual elements into AI systems, making them more user-friendly and efficient. In conclusion, Visual communication is crucial in AI systems for object recognition, facial analysis, and augmented reality, but challenges like data quality, interpretability, and ethics must be addressed. Visual communication enhances user experience, decision-making, accessibility, and collaboration. Developers can integrate visual elements for efficient and accessible AI systems.

Keywords: visual communication AI, computer vision, visual aid in communication, essence of visual communication.

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Authors: Marcela Ondova, Adriana Estokova

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Rapid industrialization results in increased use of natural resources bring along serious ecological and environmental imbalance due to the dumping of industrial wastes. Principles of sustainable construction have to be accepted with regard to the consumption of natural resources and the production of harmful emissions. Cement is a great importance raw material in the building industry and today is its large amount used in the construction of concrete pavements. Concerning raw materials cost and producing CO2 emission the replacing of cement in concrete mixtures with more sustainable materials is necessary. To reduce this environmental impact people all over the world are looking for a solution. Over a period of last ten years, the image of fly ash has completely been changed from a polluting waste to resource material and it can solve the major problems of cement use. Fly ash concretes are proposed as a potential approach for achieving substantial reductions in cement. It is known that it improves the workability of concrete, extends the life cycle of concrete roads, and reduces energy use and greenhouse gas as well as amount of coal combustion products that must be disposed in landfills. Life cycle assessment also proved that a concrete pavement with fly ash cement replacement is considerably more environmentally friendly compared to standard concrete roads. In addition, fly ash is cheap raw material, and the costs saving are guaranteed. The strength properties, resistance to a frost or de-icing salts, which are important characteristics in the construction of concrete pavements, have reached the required standards as well. In terms of human health it can´t be stated that a concrete cover with fly ash could be dangerous compared with a cover without fly ash. Final Multi-criteria analysis also pointed that a concrete with fly ash is a clearly proper solution.

Keywords: life cycle assessment, fly ash, waste, concrete pavements

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Authors: Hamidreza Hoshyarmanesh, Hosein Madieh, Sanju Lama, Yaser Maddahi, Garnette R. Sutherland, Kourosh Zareinia

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This paper illustrates the concept of an algorithm to register specified markers on the neuroArm surgical manipulators, an image-guided MR-compatible tele-operated robot for microsurgery and stereotaxy. Two range-finding algorithms, namely time-of-flight and phase-shift, are evaluated for registration and supervisory control. The time-of-flight approach is implemented in a semi-field experiment to determine the precise position of a tiny retro-reflective moving object. The moving object simulates a surgical tool tip. The tool is a target that would be connected to the neuroArm end-effector during surgery inside the magnet bore of the MR imaging system. In order to apply flight approach, a 905-nm pulsed laser diode and an avalanche photodiode are utilized as the transmitter and receiver, respectively. For the experiment, a high frequency time to digital converter was designed using a field-programmable gate arrays. In the phase-shift approach, a continuous green laser beam with a wavelength of 530 nm was used as the transmitter. Results showed that a positioning error of 0.1 mm occurred when the scanner-target point distance was set in the range of 2.5 to 3 meters. The effectiveness of this non-contact approach exhibited that the method could be employed as an alternative for conventional mechanical registration arm. Furthermore, the approach is not limited by physical contact and extension of joint angles.

Keywords: 3D laser scanner, intraoperative MR imaging, neuroArm, real time registration, robot-assisted surgery, supervisory control

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Authors: Alexander K. Guo

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PM2.5 poses a large threat to people’s health and the environment and is an issue of large concern in Beijing, brought to the attention of the government by the media. In addition, both the United States Embassy in Beijing and the government of China have increased monitoring of PM2.5 in recent years, and have made real-time data available to the public. This report utilizes hourly historical data (2008-2016) from the U.S. Embassy in Beijing for the first time. The first objective was to attempt to fit probability distributions to the data to better predict a number of days exceeding the standard, and the second was to uncover any yearly, seasonal, monthly, daily, and hourly patterns and trends that may arise to better understand of air control policy. In these data, 66,650 hours and 2687 days provided valid data. Lognormal, gamma, and Weibull distributions were fit to the data through an estimation of parameters. The Chi-squared test was employed to compare the actual data with the fitted distributions. The data were used to uncover trends, patterns, and improvements in PM2.5 concentration over the period of time with valid data in addition to specific periods of time that received large amounts of media attention, analyzed to gain a better understanding of causes of air pollution. The data show a clear indication that Beijing’s air quality is unhealthy, with an average of 94.07µg/m3 across all 66,650 hours with valid data. It was found that no distribution fit the entire dataset of all 2687 days well, but each of the three above distribution types was optimal in at least one of the yearly data sets, with the lognormal distribution found to fit recent years better. An improvement in air quality beginning in 2014 was discovered, with the first five months of 2016 reporting an average PM2.5 concentration that is 23.8% lower than the average of the same period in all years, perhaps the result of various new pollution-control policies. It was also found that the winter and fall months contained more days in both good and extremely polluted categories, leading to a higher average but a comparable median in these months. Additionally, the evening hours, especially in the winter, reported much higher PM2.5 concentrations than the afternoon hours, possibly due to the prohibition of trucks in the city in the daytime and the increased use of coal for heating in the colder months when residents are home in the evening. Lastly, through analysis of special intervals that attracted media attention for either unnaturally good or bad air quality, the government’s temporary pollution control measures, such as more intensive road-space rationing and factory closures, are shown to be effective. In summary, air quality in Beijing is improving steadily and do follow standard probability distributions to an extent, but still needs improvement. Analysis will be updated when new data become available.

Keywords: Beijing, distribution, patterns, pm2.5, trends

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Authors: Nour Tawil, Sandra Weber, Kirsten K. Roessler, Martin Mau, Simone Kuhn

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The link between “quality” residential environments and human health and well-being has long been proposed. While the physical properties of a sound environment have been fairly defined, little focus has been given to the psychological impact of architectural elements. Recently, studies have investigated the response to architectural parameters, using measures of physiology, brain activity, and emotion. Results showed different aspects of interest: detailed and open versus blank and closed facades, patterns in perceiving different elements, and a visual bias for capturing faces in buildings. However, in the absence of a consensus on methodologies, the available studies remain unsystematic and face many limitations regarding the underpinning psychological mechanisms. To bridge some of these gaps, an online study was launched to investigate design features that influence the aesthetic judgement and emotional evaluation of house facades, using a well-controlled stimulus set of Canadian houses. A methodical modelling of design features will be performed to extract both high and low level image properties, in addition to segmentation of layout-related features. 300 participants from Canada, Denmark, and Germany will rate the images on twelve psychological dimensions representing appealing aspects of a house. Subjective ratings are expected to correlate with specific architectural elements while controlling for typicality and familiarity, and other individual differences. With the lack of relevant studies, this research aims to identify architectural elements of beneficial qualities that can inform design strategies for optimized residential spaces.

Keywords: architectural elements, emotions, psychological response, residential facades.

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Authors: Jude K. Safo

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Knowledge Graphs KG) and their relation to Graph Embeddings (GE) represent a unique data structure in the landscape of machine learning (relative to image, text and acoustic data). Unlike the latter, GEs are the only data structure sufficient for representing hierarchically dense, semantic information needed for use-cases like supply chain data and protein folding where the search space exceeds the limits traditional search methods (e.g. page-rank, Dijkstra, etc.). While GEs are effective for compressing low rank tensor data, at scale, they begin to introduce a new problem of ’data retreival’ which we observe in Large Language Models. Notable attempts by transE, TransR and other prominent industry standards have shown a peak performance just north of 57% on WN18 and FB15K benchmarks, insufficient practical industry applications. They’re also limited, in scope, to next node/link predictions. Traditional linear methods like Tucker, CP, PARAFAC and CANDECOMP quickly hit memory limits on tensors exceeding 6.4 million nodes. This paper outlines a topological framework for linear mapping between concepts in KG space and GE space that preserve cardinality. Most importantly we introduce a traceable framework for composing dense linguistic strcutures. We demonstrate performance on WN18 benchmark this model hits. This model does not rely on Large Langauge Models (LLM) though the applications are certainy relevant here as well.

Keywords: representation theory, large language models, graph embeddings, applied algebraic topology, applied knot theory, combinatorics

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Authors: Meng Su

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High-dimensional data analysis often presents challenges in capturing the complex, nonlinear relationships and manifold structures inherent to the data. This article presents a novel approach that leverages the strengths of two powerful techniques, Diffusion Maps and Diffusion Probabilistic Models (DPMs), to address these challenges. By integrating the dimensionality reduction capability of Diffusion Maps with the data modeling ability of DPMs, the proposed method aims to provide a comprehensive solution for analyzing and generating high-dimensional data. The Diffusion Map technique preserves the nonlinear relationships and manifold structure of the data by mapping it to a lower-dimensional space using the eigenvectors of the graph Laplacian matrix. Meanwhile, DPMs capture the dependencies within the data, enabling effective modeling and generation of new data points in the low-dimensional space. The generated data points can then be mapped back to the original high-dimensional space, ensuring consistency with the underlying manifold structure. Through a detailed example implementation, the article demonstrates the potential of the proposed hybrid approach to achieve more accurate and effective modeling and generation of complex, high-dimensional data. Furthermore, it discusses possible applications in various domains, such as image synthesis, time-series forecasting, and anomaly detection, and outlines future research directions for enhancing the scalability, performance, and integration with other machine learning techniques. By combining the strengths of Diffusion Maps and DPMs, this work paves the way for more advanced and robust data analysis methods.

Keywords: diffusion maps, diffusion probabilistic models (DPMs), manifold learning, high-dimensional data analysis

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Authors: Ju-Hong Lee, Yi-Lin Shieh

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Two-dimensional (2-D) quadrature mirror filter (QMF) banks have been widely considered for high-quality coding of image and video data at low bit rates. Without implementing subband coding, a 2-D QMF bank is required to have an exactly linear-phase response without magnitude distortion, i.e., the perfect reconstruction (PR) characteristics. The design problem of 2-D QMF banks with the PR characteristics has been considered in the literature for many years. This paper presents a transformation approach for designing 2-D two-channel QMF banks. Under a suitable one-dimensional (1-D) to two-dimensional (2-D) transformation with a specified decimation/interpolation matrix, the analysis and synthesis filters of the QMF bank are composed of 1-D causal and stable digital allpass filters (DAFs) and possess the 2-D doubly complementary half-band (DC-HB) property. This facilitates the design problem of the two-channel QMF banks by finding the real coefficients of the 1-D recursive DAFs. The design problem is formulated based on the minimax phase approximation for the 1-D DAFs. A novel objective function is then derived to obtain an optimization for 1-D minimax phase approximation. As a result, the problem of minimizing the objective function can be simply solved by using the well-known weighted least-squares (WLS) algorithm in the minimax (L∞) optimal sense. The novelty of the proposed design method is that the design procedure is very simple and the designed 2-D QMF bank achieves perfect magnitude response and possesses satisfactory phase response. Simulation results show that the proposed design method provides much better design performance and much less design complexity as compared with the existing techniques.

Keywords: Quincunx QMF bank, doubly complementary filter, digital allpass filter, WLS algorithm

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Authors: L. Petrosyan

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The problem of psychologist training remains a key priority in Armenia. During the Soviet period, the notion of a psychologist was obscure not only in Armenia but also in other Soviet republics. The breakup of the Soviet Union triggered a gradual change in this area activating the cooperation with specialists from other countries. The need for recovery from the psychological trauma caused by the 1988 earthquake pushed forward the development of practical psychology in Armenia. This phenomenon led to positive changes in perception of and interest to a psychologist profession.Armenian universities started designing special programs for psychologists’ preparation. Armenian psychologists combined their efforts in the field of training relevant specialists. During the recent years, the Bologna educational system was introduced in Armenia which led to implementation of education quality improvement programs. Nevertheless, even today the issue of psychologists’ training is not yet settled in Armenian universities. So far graduate psychologists haven’t got a clear idea of personal and professional qualities of a psychologist. Recently, as a result of educational reforms, the psychology curricula underwent changes, but so far they have not led to a desired outcome. Almost all curricula in certain specialties are aimed to form professional competencies and strengthen practical skills. A survey conducted in Armenia aimed to identify what are the ideas of young psychology specialists on the image of a psychologist. The survey respondents were 45 specialists holding bachelor’s degree as well as 30 master degree graduates, who have not been working yet. The research reveals that we need to change the approach of preparing psychology practitioners in the universities of Armenia. Such an approach to psychologist training will make it possible to train qualified specialists for enhancement of modern psychology theory and practice.

Keywords: practitioners, psychology degree, study, professional competencies

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Authors: David M. Lucas, Charles W. Jarrett

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In the culture of Thailand, the Yak serve as a mediated icon representing strength, power, and mystical protection not only for the Buddha, but for population of worshipers. Originating from the forests of China, the Yak continue to stand guard at the gates of Buddhist temples. The Yak represents Thai culture in the hearts of Thai people. This paper presents a qualitative study regarding the curious mix of media, culture, and religion that projects the Yak of Thailand as a larger than life message throughout the political, cultural, and religious spheres. The gate guardians, or gods as they are sometimes called, appear throughout the religious temples of Asian cultures. However, the Asian cultures demonstrate differences in artistic renditions (or presentations) of such sentinels. Thailand gate guards (the Yak) stand in front of many Buddhist temples, and these iconic figures display unique features with varied symbolic significance. The temple (or wat), plays a vital role in every community; and, for many people, Thailand’s temples are the country’s most endearing sights. The authors applied folk-nography as a methodology to illustrate the importance of the Thai Yak in serving as meaningful icons that transcend not only time, but the culture, religion, and mass media. The Yak represent mythical, religious, artistic, cultural, and militaristic significance for the Thai people. Data collection included interviews, focus groups, and natural observations. This paper summarizes the perceptions of the Thai people concerning their gate sentries and the relationship, communication, connection, and the enduring respect that Thai people hold for their guardians of the gates.

Keywords: communication, culture, folknography, icon, image, media, protection, religion, yak

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Authors: Mrinal Kanti Bhowmik, Kakali Das Jr., Barin Kumar De, Debotosh Bhattacharjee

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Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection.

Keywords: fractal, tumor, thermography, mammography

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Authors: A. Anastasiou, K. S. Tingay

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Background: Discovering suitable datasets is an important part of health research, particularly for projects working with clinical data from patients organized in cohorts (cohort data), but with the proliferation of so many national and international initiatives, it is becoming increasingly difficult for research teams to locate real world datasets that are most relevant to their project objectives. We present a method for identifying healthcare institutes in the European Union (EU) which may hold haemato-oncology (HO) data. A key enabler of this research was the bibInsight platform, a scientometric data management and analysis system developed by the authors at Swansea University. Method: A PubMed search was conducted using HO clinical terms taken from previous work. The resulting XML file was processed using the bibInsight platform, linking affiliations to the Global Research Identifier Database (GRID). GRID is an international, standardized list of institutions, including the city and country in which the institution exists, as well as a category of the main business type, e.g., Academic, Healthcare, Government, Company. Countries were limited to the 28 current EU members, and institute type to 'Healthcare'. An article was considered valid if at least one author was affiliated with an EU-based healthcare institute. Results: The PubMed search produced 21,310 articles, consisting of 9,885 distinct affiliations with correspondence in GRID. Of these articles, 760 were from EU countries, and 390 of these were healthcare institutes. One affiliation was excluded as being a veterinary hospital. Two EU countries did not have any publications in our analysis dataset. The results were analysed by country and by individual healthcare institute. Networks both within the EU and internationally show institutional collaborations, which may suggest a willingness to share data for research purposes. Geographical mapping can ensure that data has broad population coverage. Collaborations with industry or government may exclude healthcare institutes that may have embargos or additional costs associated with data access. Conclusions: Data reuse is becoming increasingly important both for ensuring the validity of results, and economy of available resources. The ability to identify potential, specific data sources from over twenty thousand articles in less than an hour could assist in improving knowledge of, and access to, data sources. As our method has not yet specified if these healthcare institutes are holding data, or merely publishing on that topic, future work will involve text mining of data-specific concordant terms to identify numbers of participants, demographics, study methodologies, and sub-topics of interest.

Keywords: data reuse, data discovery, data linkage, journal articles, text mining

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Authors: Silvia Elias

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The 'Femme Fatale' figure has become synonymous with a mysterious and seductive woman whose charms captivate her lovers into bonds of irresistible desire, often leading them to compromise or downfall. Originally, a Femme Fatale typically uses her beauty to lead men to their destruction but in modern literature, she represents a direct attack on traditional womanhood and the nuclear family as she refuses to abide by the pillars of mainstream society creating an image of a strong independent woman who defies the control of men and rejects the institution of the family. This research aims at discussing the differences and similarities between the femme fatale and the New Woman and how they are perceived by the audience. There is often confusion between the characteristics that define a New Woman and a Femme Fatale since both women desire independence, challenge typical gender role casting, push against the limits of the patriarchal society and take control of their sexuality. The study of the femme fatale remains appealing in modern times because the fear of gender equality gives life to modern femme fatale versions and post-modern literary works introduce their readers to new versions of the deadly seductress. One that does not fully depend on her looks to destroy men. The idea behind writing this paper was born from reading David Mamet's two-character play Oleanna (1992) and tracing the main female protagonist/antagonist's transformation from a helpless inarticulate girl into a powerful controlling negotiator who knows how to lead a bargain and maintain the upper hand.

Keywords: Circe, David, Eve, evolution, feminist, femme fatale, gender, Mamet, new, Odysseus, Oleanna, power, Salome, schema, seduction, temptress, woman

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Authors: Abdellah Hanafi, G. M. Mostafa, Mohamed Mortada, Ahmed Hamed

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The steam ejectors are the heart of most of the desalination systems that employ vacuum. The systems that employ low grade thermal energy sources like solar energy and geothermal energy use the ejector to drive the system instead of high grade electric energy. The jet-ejector is used to create vacuum employing the flow of steam or air and using the severe pressure drop at the outlet of the main nozzle. The present work involves developing a one dimensional mathematical model for designing jet-ejectors and transform it into computer code using Engineering Equation solver (EES) software. The model receives the required operating conditions at the inlets and outlet of the ejector as inputs and produces the corresponding dimensions required to reach these conditions. The one-dimensional model has been validated using an existed model working on Abu-Qir power station. A prototype has been designed according to the one-dimensional model and attached to a special test bench to be tested before using it in the solar desalination pilot plant. The tested ejector will be responsible for the startup evacuation of the system and adjusting the vacuum of the evaporating effects. The tested prototype has shown a good agreement with the results of the code. In addition a numerical analysis has been applied on one of the designed geometry to give an image of the pressure and velocity distribution inside the ejector from a side, and from other side, to show the difference in results between the two-dimensional ideal gas model and real prototype. The commercial edition of ANSYS Fluent v.14 software is used to solve the two-dimensional axisymmetric case.

Keywords: solar energy, jet ejector, vacuum, evaporating effects

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Authors: Zheng Wang, Zhenhong Li, Jon Mills

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Ground-based synthetic aperture radar (GBSAR) represents a powerful remote sensing tool for deformation monitoring towards various geohazards, e.g. landslides, mudflows, avalanches, infrastructure failures, and the subsidence of residential areas. Unlike spaceborne SAR with a fixed revisit period, GBSAR data can be acquired with an adjustable temporal resolution through either continuous or discontinuous operation. However, challenges arise from processing high temporal-resolution continuous GBSAR data, including the extreme cost of computational random-access-memory (RAM), the delay of displacement maps, and the loss of temporal evolution. Moreover, repositioning errors between discontinuous campaigns impede the accurate measurement of surface displacements. Therefore, a versatile package with two complete chains is developed in this study in order to process both continuous and discontinuous GBSAR data and address the aforementioned issues. The first chain is based on a small-baseline subset concept and it processes continuous GBSAR images unit by unit. Images within a window form a basic unit. By taking this strategy, the RAM requirement is reduced to only one unit of images and the chain can theoretically process an infinite number of images. The evolution of surface displacements can be detected as it keeps temporarily-coherent pixels which are present only in some certain units but not in the whole observation period. The chain supports real-time processing of the continuous data and the delay of creating displacement maps can be shortened without waiting for the entire dataset. The other chain aims to measure deformation between discontinuous campaigns. Temporal averaging is carried out on a stack of images in a single campaign in order to improve the signal-to-noise ratio of discontinuous data and minimise the loss of coherence. The temporal-averaged images are then processed by a particular interferometry procedure integrated with advanced interferometric SAR algorithms such as robust coherence estimation, non-local filtering, and selection of partially-coherent pixels. Experiments are conducted using both synthetic and real-world GBSAR data. Displacement time series at the level of a few sub-millimetres are achieved in several applications (e.g. a coastal cliff, a sand dune, a bridge, and a residential area), indicating the feasibility of the developed GBSAR data processing package for deformation monitoring of a wide range of scientific and practical applications.

Keywords: ground-based synthetic aperture radar, interferometry, small baseline subset algorithm, deformation monitoring

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Authors: Nnadi Ezekiel Ejiofor

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The construction industry plays a decisive role in the healthy development of any nation. Not only large but even small construction projects contribute to a country’s economic growth. There is a need for good management to ensure successful delivery and sustainability because of the plethora of risks that have resulted in low-profit margins for contractors, cost and schedule overruns, poor quality delivery, and abandoned projects. This research reviewed Capitalization on Sustainable Risk Management. Questionnaires and oral interviews conducted were utilized as means of data collection. One hundred and ninety-eight (198) large construction firms in Nigeria form the population of this study. 15 (fifteen) companies that emanated from merger and acquisition were used for the study. The instruments used for data collection were a researcher-developed structured questionnaire based on a five-point rating scale, interviews, focus group discussion, and secondary sources (bill of quantities and stock and exchange commission). The instrument was validated by two experts in the field. The reliability of the instrument was established by applying the split-half method. Kendall’s coefficient of concordance was used to test the data, and a degree of agreement was obtained. Data were subjected to descriptive statistics and analyzed using analysis of variance, t-test, and SPSS. The identified impacts of capitalization were an increase in turnover (24.5%), improvement in the image (24.5%), risk reduction (20%), business expansion (17.3%), and geographical spread (13.6%). The study strongly advocates the inclusion of risk management evaluation as part of the construction procurement process.

Keywords: capitalization, project delivery, risks, risk management, sustainability

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Authors: Gorazd Karer

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When performing a diagnostic procedure or surgery in general anesthesia (GA), a proper introduction and dosing of anesthetic agents are one of the main tasks of the anesthesiologist. However, depth of anesthesia (DoA) also seems to be a suitable process for closed-loop control implementation. To implement such a system, one must be able to acquire the relevant signals online and in real-time, as well as stream the calculated control signal to the infusion pump. However, during a procedure, patient monitors and infusion pumps are purposely unable to connect to an external (possibly medically unapproved) device for safety reasons, thus preventing closed-loop control. The paper proposes a conceptual solution to the aforementioned problem. First, it presents some important aspects of contemporary clinical practice. Next, it introduces the closed-loop-control-system structure and the relevant information flow. Focusing on transferring the data from the patient to the computer, it presents a non-invasive image-based system for signal acquisition from a patient monitor for online depth-of-anesthesia assessment. Furthermore, it introduces a UDP-based communication method that can be used for transmitting the calculated anesthetic inflow to the infusion pump. The proposed system is independent of a medical device manufacturer and is implemented in Matlab-Simulink, which can be conveniently used for DoA control implementation. The proposed scheme has been tested in a simulated GA setting and is ready to be evaluated in an operating theatre. However, the proposed system is only a step towards a proper closed-loop control system for DoA, which could routinely be used in clinical practice.

Keywords: closed-loop control, depth of anesthesia (DoA), modeling, optical signal acquisition, patient state index (PSi), UDP communication protocol

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Authors: Majda El Moufarej

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Higher education plays a central role in socio-economic development. However, with the wave of change mainly due to the extensive use of technology in the workplace, the rate of unemployment among graduates rises because they lack the appropriate competencies and skills currently required in professional life. This situation has led higher education institutions worldwide to reconsider their missions, strategic planning, and curricula, among other elements to redress the image of the university as expected. When it comes to practice, there are many obstacles that hinder the achievement of the expected objectives, especially in public universities with free access, as in the case of Morocco. Nevertheless, huge efforts have been made by educational managers to improve the quality of education by focusing on the issue of pedagogical approaches, where university teachers assume more responsibility to save the situation. In this paper, the focus will be placed on the issue of pedagogical approaches to be adopted, depending on the nature of the subject, the size of the class, the available equipment, the students’ level and degree of motivation. Before elaborating on this idea, it may be more insightful to begin by addressing another variable, which concerns the new role of university teachers and their qualification in pedagogical competence. Then, the discussion will revolve around five pedagogical approaches currently adopted in western universities and the focus will be exclusively placed on the one which is called “the Systematic Approach to course Design”, due to its crucial relevance in the teaching of subjects in the schools of humanities, as it can guide the teacher in the development of an explicit program for purposeful teaching and learning. The study is based on a qualitative method, and the findings will be analyzed and followed by some recommendations about how to overcome difficulties in teaching large groups, while transmitting the relevant knowledge and skills on demand in the workplace.

Keywords: higher education, public universities, pedagogical approaches, pedagogical competence

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Authors: Yongqin Zhang, John Lett

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Geospatial technologies have been increasingly used in agriculture for various applications and purposes in recent years. Unmanned aerial vehicles (drones) fit the needs of farmers in farming operations, from field spraying to grow cycles and crop health. In this research, we conducted a practical research project that used drone technology to design and map optimal locations and layouts of irrigation systems for agriculture farms. We flew a DJI Mavic 2 Pro drone to acquire aerial remote sensing images over two agriculture fields in Forest, Mississippi, in 2022. Flight plans were first designed to capture multiple high-resolution images via a 20-megapixel RGB camera mounted on the drone over the agriculture fields. The Drone Deploy web application was then utilized to develop flight plans and subsequent image processing and measurements. The images were orthorectified and processed to estimate the area of the area and measure the locations of the water line and sprinkle heads. Field measurements were conducted to measure the ground targets and validate the aerial measurements. Geospatial analysis and photogrammetric measurements were performed for the study area to determine optimal layout and quantitative estimates for irrigation systems. We created maps and tabular estimates to demonstrate the locations, spacing, amount, and layout of sprinkler heads and water lines to cover the agricultural fields. This research project provides scientific guidance to Mississippi farmers for a precision agricultural irrigation practice.

Keywords: drone images, agriculture, irrigation, geospatial analysis, photogrammetric measurements

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Authors: Akbar Cahyadhi Pratama Putra, Tantri Utami Widhaningtyas, M. Randy Aswin

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Indonesia as an archipelagic nation have very long coastline which have large potential marine resources, one of that is the mangrove ecosystems. Lapindo mudflow disaster in Sidoarjo, East Java requires mudflow flowed into the sea through the river Brantas and Porong. Mud material that transported by river flow is feared dangerous because they contain harmful substances such as heavy metals. This study aims to map the mangrove ecosystem seen from its density and knowing how big the impact of a disaster on the Lapindo mud to mangrove ecosystem and accompanied by efforts to address the mangrove ecosystem that maintained continuity. Mapping coastal mangrove conditions of Sidoarjo was done using remote sensing products that Landsat 7 ETM + images with dry months of recording time in 2002, 2006, 2009, and 2014. The density of mangrove detected using NDVI that uses the band 3 that is the red channel and band 4 that is near IR channel. Image processing was used to produce NDVI using ENVI 5.1 software. NDVI results were used for the detection of mangrove density is 0-1. The development of mangrove ecosystems of both area and density from year to year experienced has a significant increase. Mangrove ecosystems growths are affected by material deposition area of Lapindo mud on Porong and Brantas river estuary, where the silt is growing medium suitable mangrove ecosystem and increasingly growing. Increasing the density caused support by public awareness to prevent heavy metals in the material so that the Lapindo mud mangrove breeding done around the farm.

Keywords: archipelagic nation, mangrove, Lapindo mudflow disaster, NDVI

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Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Sherehan Hamdalla

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Egypt has experienced a series of terrifying love crimes in the last few months. This ‘trend’ of love crimes started with a young man caught on video slaughtering his ex-girlfriend in the street in the city of El Mansoura. The crime shocked all Egyptian citizens at all levels; unfortunately, not less than three similar crimes took place in other different Egyptian cities with the same killing trigger. The characteristics and easy access and reach of social media consider the reason why it is one of the most crucial online communication channels; users utilize social media platforms for sharing and exchanging ideas, news, and many other activities; they can freely share posts that reflect their mindset or personal views regarding any issues, these posts are going viral in all social media account by reposting or numbers of shares for these posts to support the content included, or even to attack. The repetition of sharing certain posts could mobilize other supporters with the same point of view, especially when that crowd’s online participation is confronting a public opinion case’s consequences. The death of that young woman was followed by similar crimes in other cities, such as El Sharkia and Port Said. These love crimes provoked a massive wave of contention among all social classes in Egypt. Strangely, some were supporting the criminal and defending his side for several reasons, which the study will uncover. Facebook, the most popular social media platform for Egyptians, reflects the debate between supporters of the victim and supporters of the criminal. Facebook pages were created specifically to disseminate certain viewpoints online, for example, asking for the maximum penalty to be given to criminals. These pages aimed to mobilize the maximum number of supporters and to affect the outcome of the trials.

Keywords: love crimes, victim, criminal, social media

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Authors: Zarabizan Zakaria, Syuhaida Ismail, Aminah Md. Yusof

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Project management process starts from the planning stage up to the stage of completion (handover of buildings, preparation of the final accounts and the closing balance). This process is not easy to implement efficiently and effectively. The issue of delays in construction is a major problem for construction projects. These delays have been blamed mainly on inefficient traditional construction practices that continue to dominate the current industry. This is due to several factors, such as environments of construction technology, sophisticated design and customer demands that are constantly changing and influencing, either directly or indirectly, the practice of management. Among the identified influences are physical environment, social environment, information environment, political and moral atmosphere. Therefore, this paper is emerged to determine the problem and issues in the final account closing in construction projects, and it establishes the need to embrace Supply Chain Management (SCM) and then elucidates the need and strategies for the development of a delay reduction framework. At the same time, this paper provides effective measures to avoid or at least reduce the delay to the optimum level. Allowing problems in the closure declaration to occur without proper monitoring and control can leave negative impact on the cost and time of delivery to the end user. Besides, it can also affect the reputation or image of the agency/department that manages the implementation of a contract and consequently may reduce customer's trust towards the agencies/departments. It is anticipated that the findings reported in this paper could address root delay contributors and apply SCM tools for their mitigation for the better development of construction project.

Keywords: final account closing, construction project, construction delay, supply chain management

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Authors: Mulugeta K. Tefera, Xiaolong Yang, Jian Liu

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Detection and tracking of moving objects are very important in many application contexts such as detection and recognition of people, visual surveillance and automatic generation of video effect and so on. However, the task of detecting a real shape of an object in motion becomes tricky due to various challenges like dynamic scene changes, presence of shadow, and illumination variations due to light switch. For such systems, once the moving object is detected, tracking is also a crucial step for those applications that used in military defense, video surveillance, human computer interaction, and medical diagnostics as well as in commercial fields such as video games. In this paper, an object presents in dynamic background is detected using adaptive mixture of Gaussian based analysis of the video sequences. Then the detected moving object is tracked using the region based moving object tracking and inter-frame differential mechanisms to address the partial overlapping and occlusion problems. Firstly, the detection algorithm effectively detects and extracts the moving object target by enhancing and post processing morphological operations. Secondly, the extracted object uses region based moving object tracking and inter-frame difference to improve the tracking speed of real-time moving objects in different video frames. Finally, the plotting method was applied to detect the moving objects effectively and describes the object’s motion being tracked. The experiment has been performed on image sequences acquired both indoor and outdoor environments and one stationary and web camera has been used.

Keywords: background modeling, Gaussian mixture model, inter-frame difference, object detection and tracking, video surveillance

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Authors: Gizem Eser Erdek

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This study is research on predicting the remaining life of industrial cutting tools used in the industrial production process with deep learning methods. When the life of cutting tools decreases, they cause destruction to the raw material they are processing. This study it is aimed to predict the remaining life of the cutting tool based on the damage caused by the cutting tools to the raw material. For this, hole photos were collected from the hole-drilling machine for 8 months. Photos were labeled in 5 classes according to hole quality. In this way, the problem was transformed into a classification problem. Using the prepared data set, a model was created with convolutional neural networks, which is a deep learning method. In addition, VGGNet and ResNet architectures, which have been successful in the literature, have been tested on the data set. A hybrid model using convolutional neural networks and support vector machines is also used for comparison. When all models are compared, it has been determined that the model in which convolutional neural networks are used gives successful results of a %74 accuracy rate. In the preliminary studies, the data set was arranged to include only the best and worst classes, and the study gave ~93% accuracy when the binary classification model was applied. The results of this study showed that the remaining life of the cutting tools could be predicted by deep learning methods based on the damage to the raw material. Experiments have proven that deep learning methods can be used as an alternative for cutting tool life estimation.

Keywords: classification, convolutional neural network, deep learning, remaining life of industrial cutting tools, ResNet, support vector machine, VggNet

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Authors: Iman Shawky, Mohamed Elsayed

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Every hotel works in the hospitality market tends to have its own merit and character in its products marketing in order to maintain both its brand's identity and image among guests. According to the growth of global competition in the hospitality industry; the concept of competitive advantage is becoming increasingly important in hotels' marketing world as it examines reasons for outweighing hotels in their dimensions of strategic and marketing plans. In fact, Egypt is the land of appeared and submerged secrets as a result of its ancient civilization ongoing explorations. Although underwater antiquities represent ambiguous treasures, they have auspicious future in it, particularly in Alexandria. The study aims at examining to what extent underwater antiquities represent a competitive advantage of four and five-star hotels in Alexandria. For achieving this aim, an exploratory study conducted by currying out the investigation and comparison of the closest and most popular landmarks mentioned on both hotels' official websites and on common used reservations' websites. In addition to that, two different questionnaire forms designed; one for both revenue and sales and marketing hotels' managers while the other for their guests. The results indicate that both official hotels' websites and the most common used reservations' websites totally ignore mentioning underwater antiquities as attractive landmarks surrounding Alexandria hotels. Furthermore, most managers expect that underwater antiquities can furnish distinguished competitive advantage to their hotels. Also, they can help exceeding guests' expectations during their accommodation as long as they included on both official hotels' and reservations' websites as the most surrounding famous landmarks. Moreover, most managers foresee that high awareness of underwater antiquities can enhance the guests' accommodation frequencies and improve the financial performance of their hotels.

Keywords: competitive advantage, financial performance, hotels' websites, underwater antiquities

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Authors: Chih Jou Hsiao, Chung Ming Lo, Li Chun Hsieh

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Brain tumor is not the cancer having high incidence rate, but its high mortality rate and poor prognosis still make it as a big concern. On clinical examination, the grading of brain tumors depends on pathological features. However, there are some weak points of histopathological analysis which can cause misgrading. For example, the interpretations can be various without a well-known definition. Furthermore, the heterogeneity of malignant tumors is a challenge to extract meaningful tissues under surgical biopsy. With the development of magnetic resonance imaging (MRI), tumor grading can be accomplished by a noninvasive procedure. To improve the diagnostic accuracy further, this study proposed a computer-aided diagnosis (CAD) system based on MRI features to provide suggestions of tumor grading. Gliomas are the most common type of malignant brain tumors (about 70%). This study collected 34 glioblastomas (GBMs) and 73 lower-grade gliomas (LGGs) from The Cancer Imaging Archive. After defining the region-of-interests in MRI images, multiple quantitative morphological features such as region perimeter, region area, compactness, the mean and standard deviation of the normalized radial length, and moment features were extracted from the tumors for classification. As results, two of five morphological features and three of four image moment features achieved p values of <0.001, and the remaining moment feature had p value <0.05. Performance of the CAD system using the combination of all features achieved the accuracy of 83.18% in classifying the gliomas into LGG and GBM. The sensitivity is 70.59% and the specificity is 89.04%. The proposed system can become a second viewer on clinical examinations for radiologists.

Keywords: brain tumor, computer-aided diagnosis, gliomas, magnetic resonance imaging

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Authors: Michael McCann

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Managerial theories propose that, in joint stock companies, executives may be tempted to waste excess free cash on unprofitable projects to keep control of resources. In order to conceal their projects' poor performance, they may seek to engage in earnings management. On the one hand, managers may manipulate earnings upwards in order to post ‘good’ performances and safeguard their position. On the other, since managers pursuit of unrewarding investments are likely to lead to low long-term profitability, managers will use negative accruals to reduce current year’s earnings, smoothing earnings over time in order to conceal the negative effects. Agency models argue that boards of directors are delegated by shareholders to ensure that companies are governed properly. Part of that responsibility is ensuring the reliability of financial information. Analyses of the impact of board characteristics, particularly board independence on the misuse of free cash flow and earnings management finds conflicting evidence. However, existing characterizations of board independence do not account for such directors gaining firm-specific knowledge over time, influencing their monitoring ability. Further, there is little analysis of the influence of the relative experience of independent directors and executives on decisions surrounding the use of free cash. This paper contributes to this literature regarding the heterogeneous characteristics of boards by investigating the influence of independent director tenure on earnings management and the relative tenures of independent directors and Chief Executives. A balanced panel dataset comprising 51 companies across 11 annual periods from 2005 to 2015 is used for the analysis. In each annual period, firms were classified as conducting earnings management if they had discretionary accruals in the bottom quartile (downwards) and top quartile (upwards) of the distributed values for the sample. Logistical regressions were conducted to determine the marginal impact of independent board tenure and a number of control variables on the probability of conducting earnings management. The findings indicate that both absolute and relative measures of board independence and experience do not have a significant impact on the likelihood of earnings management. It is the level of free cash flow which is the major influence on the probability of earnings management. Higher free cash flow increases the probability of earnings management significantly. The research also investigates whether board monitoring of earnings management is contingent on the level of free cash flow. However, the results suggest that board monitoring is not amplified when free cash flow is higher. This suggests that the extent of earnings management in companies is determined by a range of company, industry and situation-specific factors.

Keywords: corporate governance, boards of directors, agency theory, earnings management

Procedia PDF Downloads 237