Search results for: post classification change detection

Authors: Cheng Zeng, George Michailidis, Hitoshi Iyatomi, Leo L. Duan

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The conditional density characterizes the distribution of a response variable y given other predictor x and plays a key role in many statistical tasks, including classification and outlier detection. Although there has been abundant work on the problem of Conditional Density Estimation (CDE) for a low-dimensional response in the presence of a high-dimensional predictor, little work has been done for a high-dimensional response such as images. The promising performance of normalizing flow (NF) neural networks in unconditional density estimation acts as a motivating starting point. In this work, the authors extend NF neural networks when external x is present. Specifically, they use the NF to parameterize a one-to-one transform between a high-dimensional y and a latent z that comprises two components [zₚ, zₙ]. The zₚ component is a low-dimensional subvector obtained from the posterior distribution of an elementary predictive model for x, such as logistic/linear regression. The zₙ component is a high-dimensional independent Gaussian vector, which explains the variations in y not or less related to x. Unlike existing CDE methods, the proposed approach coined Augmented Posterior CDE (AP-CDE) only requires a simple modification of the common normalizing flow framework while significantly improving the interpretation of the latent component since zₚ represents a supervised dimension reduction. In image analytics applications, AP-CDE shows good separation of 𝑥-related variations due to factors such as lighting condition and subject id from the other random variations. Further, the experiments show that an unconditional NF neural network based on an unsupervised model of z, such as a Gaussian mixture, fails to generate interpretable results.

Keywords: conditional density estimation, image generation, normalizing flow, supervised dimension reduction

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Authors: Faisal Anwar, Dawar Zaidi, Shubham Dixit, Nafees Ahmedii

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This paper analyzes the various properties of biodiesel such as pour point, cloud point, viscosity, calorific value, etc produced from different feedstocks. The aim of the work is to analyze change in these properties after converting feedstocks to biodiesel and then comparring it with ASTM 6751-02 standards to check whether they are suitable for diesel engines or not. The conversion of feedstocks is carried out by a process called transesterification. This conversion is carried out to reduce viscosity, pour point, etc. It has been observed that there is some remarkable change in the properties of oil after conversion.

Keywords: biodiesel, ethyl ester, free fatty acid, production

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Authors: Eva Savy, Anthony Ruiz

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In extrusion blow molding, there is great variability in product quality due to the sensitivity of the machine settings. These variations lead to unnecessary rejects and loss of time. Yet production control is a major challenge for companies in this sector to remain competitive within their market. Current quality control methods only apply to finished products (vision control, leak test...). It has been shown that material melt temperature, blowing pressure, and ambient temperature have a significant impact on the variability of product quality. Since blowing is a key step in the process, we have studied this parameter in this paper. The objective is to determine if airflow analysis allows the identification of quality problems before the full completion of the manufacturing process. We conducted tests to determine if it was possible to identify a leakage defect and an obstructed defect, two common defects on products. The results showed that it was possible to identify a leakage defect by airflow analysis.

Keywords: extrusion blow molding, signal, sensor, defects, detection

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Authors: Thomas L. McCullough, John D. Hague, Marylesa M. Howard, Matthew K. Kiser, Michael A. Mazur, Lance K. McLean, Johanna L. Turk

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Radiological search and mapping depends on the successful recognition of anomalies in large data sets which contain varied and dynamic backgrounds. We present a new algorithmic approach for real-time anomaly detection which is resistant to common detector imperfections, avoids the limitations of a source template library and provides immediate, and easily interpretable, user feedback. This algorithm is based on a continuous wavelet transform for variance reduction and evaluates the deviation between a foreground measurement and a local background expectation using methods from linear algebra. We also present a technique for recognizing and visualizing spectrally similar clusters of data. This technique uses Laplacian Eigenmap Manifold Learning to perform dimensional reduction which preserves the geometric "closeness" of the data while maintaining sensitivity to outlying data. We illustrate the utility of both techniques on real-world data sets.

Keywords: radiological search, radiological mapping, radioactivity, radiation protection

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Authors: Katarzyna Wyka, Dana Sylvan, JoAnn Difede

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Background and significance: The following disaster, population-based screening programs are routinely established to assess physical and psychological consequences of exposure. These data sets are highly skewed as only a small percentage of trauma-exposed individuals develop health issues. Commonly used statistical methodology in post-disaster mental health generally involves population-averaged models. Such models aim to capture the overall response to the disaster and its aftermath; however, they may not be sensitive enough to accommodate population heterogeneity in symptomatology, such as post-traumatic stress or depressive symptoms. Methods: We use an archival longitudinal data set from Weill-Cornell 9/11 Mental Health Screening Program established following the World Trade Center (WTC) terrorist attacks in New York in 2001. Participants are rescue and recovery workers who participated in the site cleanup and restoration (n=2960). The main outcome is the post-traumatic stress symptoms (PTSD) severity score assessed via clinician interviews (CAPS). For a detailed understanding of response to the disaster and its aftermath, we are adapting quantile regression methodology with particular focus on predictors of extreme distress and resilience to trauma. Results: The response variable was defined as the quantile of the CAPS score for each individual under two different scenarios specifying the unconditional quantiles based on: 1) clinically meaningful CAPS cutoff values and 2) CAPS distribution in the population. We present graphical summaries of the differential effects. For instance, we found that the effect of the WTC exposures, namely seeing bodies and feeling that life was in danger during rescue/recovery work was associated with very high PTSD symptoms. A similar effect was apparent in individuals with prior psychiatric history. Differential effects were also present for age and education level of the individuals. Conclusion: We evaluate the utility of quantile regression in disaster research in contrast to the commonly used population-averaged models. We focused on assessing the distribution of risk factors for post-traumatic stress symptoms across quantiles. This innovative approach provides a comprehensive understanding of the relationship between dependent and independent variables and could be used for developing tailored training programs and response plans for different vulnerability groups.

Keywords: disaster workers, post traumatic stress, PTSD, quantile regression

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Authors: Judit Perjessy, Zsolt Zalan, Ferenc Hegyi, Eniko Horvath-Szanics, Krisztina Takacs, Andras Nagy, Adel Klupacs, Erika Koppany-Szabo, Zhirong Wang, Kaituo Wang, Muying Du, Jianquan Kan

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The post-harvest diseases cause great economic losses in the fruit and vegetables; the prevention of these deterioration has great importance. Against the fungi, which cause most of the diseases, are extensively used the fungicides. However, there are increasing consumer concerns over the presence of pesticide residues in food. An alternative and in recent years, increasingly studied method for the prevention of the diseases is biocontrol, where antagonistic microorganisms are used for the control of fungi. The genera of Lactobacillus is well known and extensively studied, but its applicability as biocontrol agents in post-harvest preservation of fruit and vegetables is poorly investigated. However these bacteria can be found on the surface of the plants and have great antimicrobial activity. In our study we have investigated the chitinase activity, the antifungal effect and the applicability of several Lactobacillus strains to select potential biocontrol agents. We investigated the determination of the environmental parameters of a gene (encoding chitinase) expression and we also investigated the relationship between actual antifungal activity and potential chitinase activity. Mixed cultures were also developed to enhance the antifungal activity and determined the optimal mold spore and bacteria concentration ratio for the appropriate efficacy. Five Lactobacillus strains (L. acidophilus N2, L. delbrueckii subsp. bulgaricus B397, L. sp. 2231, L. sake subsp. sake 2471, L. buchneri 1145) possess chitinase-coding gene from the 43 investigated Lactobacillus strains. Proteins with similar molecular weight and separation properties like bacterial chitinases were detected from these strains, which also possess chitin-binding property. Nevertheless, they were inactive, lacks the chitinolytic activity. In point of the cumulative activity of inhibition, our results showed that certain strains were statistically significant in a positive direction compared to other strains, e.g., L. rhamnosus VT1 and L. Casey 154 have shown great general antifungal effect against 11 molds from the genera Penicillium and Botrytis and isolated from spoiled fruit and vegetables. Also, some mixed cultures (L. rhamnosus VT1 - L. Plantarum 299v) showed significant antifungal effects against the indigenous molds on the surface of apple fruit during the industrial storage experiment. Thus, they could be promising for post-harvest biopreservation.

Keywords: biocontrol, chitinase, Lactobacillus, post-harvest

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Authors: Kika Ioannou Kazamia

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This paper examines the action research cycle of the second phase of longitudinal research on sustainable interior design practices, between two groups of stakeholders, designers and clients. During this phase of the action research, the second step - the change stage - of Lewin’s change management model has been utilized to change values, approaches, and attitudes toward sustainable design practices among the participants. Affective domain learning theory is utilized to attach new values. Learning with the use of information technology, collaborative learning, and problem-based learning are the learning methods implemented toward the acquisition of the objectives. Learning methods, and aims, require the design of interventions with participants' involvement in activities that would lead to the acknowledgment of the benefits of sustainable practices. Interventions are steered to measure participants’ decisions for the worth and relevance of ideas, and experiences; accept or commit to a particular stance or action. The data collection methods used in this action research are observers’ reports, participants' questionnaires, and interviews. The data analyses use both quantitative and qualitative methods. The main beneficial aspect of the quantitative method was to provide the means to separate many factors that obscured the main qualitative findings. The qualitative method allowed data to be categorized, to adapt the deductive approach, and then examine for commonalities that could reflect relevant categories or themes. The results from the data indicate that during the second phase, designers and clients' participants altered their behaviours.

Keywords: design, change, sustainability, learning, practices

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Authors: Daniel Scherman, Daniel Madani, Shanu Gambhir, Marcus Ling Zhixing, Yingda Li

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Objective: This study aims to identify clinical factors that may predict failed endoscopic lumbar spine surgery to guide surgeons with patient selection during the initial learning curve. Methods: This is an Australasian prospective analysis of the first 105 patients to undergo lumbar endoscopic spine decompression by 3 surgeons. Modified MacNab outcomes, Oswestry Disability Index (ODI) and Visual Analogue Score (VAS) scores were utilized to evaluate clinical outcomes at 6 months postoperatively. Descriptive statistics and Anova t-tests were performed to measure statistically significant (p<0.05) associations between variables using GraphPad Prism v10. Results: Patients undergoing endoscopic lumbar surgery via an interlaminar or transforaminal approach have overall good/excellent modified MacNab outcomes and a significant reduction in post-operative VAS and ODI scores. Regardless of the anatomical location of disc herniations, good/excellent modified MacNab outcomes and significant reductions in VAS and ODI were reported post-operatively; however, not in patients with calcified disc herniations. Patients with central and foraminal stenosis overall reported poor/fair modified MacNab outcomes. However, there were significant reductions in VAS and ODI scores post-operatively. Patients with subarticular stenosis or an associated spondylolisthesis reported good/excellent modified MacNab outcomes and significant reductions in VAS and ODI scores post-operatively. Patients with disc herniation and concurrent degenerative stenosis had generally poor/fair modified MacNab outcomes. Conclusion: The outcomes of endoscopic spine surgery are encouraging, with a low complication and reoperation rate. However, patients with calcified disc herniations, central canal stenosis or a disc herniation with concurrent degenerative stenosis present challenges during the initial learning curve and may benefit from traditional open or other minimally invasive techniques.

Keywords: complications, lumbar disc herniation, lumbar endoscopic spine surgery, predictors of failed endoscopic spine surgery

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Authors: Deanna Ruano-Meas, Laura Shenkman

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Variances are seen in the way healthcare personnel (HCP) don and doff PPE risking contamination to self and others. By standardizing practice, variances in technique decrease, and so does the risk of contamination. To implement this change, the Model for Improvement will be used. A system change will be developed that will outline the role of the organizational leader’s support of HCP in the proper donning and doffing of PPE. Interventions will include environmental surveys to assess the safety and work situation ensuring a permissible environment, plan audits to confirm consistency, and the assessment of PPE wear for standardization. The change will also include an educational plan that will involve instruction of the current guidelines recommended by the Centers for Disease Control and Prevention (CDC) to all pertinent HCP, and the incorporation of PPE education in yearly educational training. The goal is a standardized practice and a reduced risk of contamination through education and organizational support. Personal protective equipment has had recent attention with the coming of the SARS-CoV-2. The realization that proper technique is important to decreasing contamination of pathogens has led to the revising of current processes.

Keywords: donning and doffing, HAI, infection control, PPE

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Authors: Omid Kalatpour, Mohammadreza Ajdari

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This article studied the possible causes of human error-induced incidents in the petrochemical industry maintenance activities by using Human Factors Analysis and Classification System (HFACS). The purpose of the study was anticipating and identifying these causes and proposing corrective and preventive actions. Maintenance department in a petrochemical company was selected for research. A checklist of human error-induced incidents was developed based on four HFACS main levels and nineteen sub-groups. Hierarchical task analysis (HTA) technique was used to identify maintenance activities and tasks. The main causes of possible incidents were identified by checklist and recorded. Corrective and preventive actions were defined depending on priority. Analyzing the worksheets of 444 activities in four levels of HFACS showed 37.6% of the causes were at the level of unsafe actions, 27.5% at the level of unsafe supervision, 20.9% at the level of preconditions for unsafe acts and 14% of the causes were at the level of organizational effects. The HFACS sub-groups showed errors (24.36%) inadequate supervision (14.89%) and violations (13.26%) with the most frequency. According to findings of this study, increasing the training effectiveness of operators and supervision improvement respectively are the most important measures in decreasing the human error-induced incidents in petrochemical industry maintenance.

Keywords: human error, petrochemical industry, maintenance, HFACS

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Authors: Isah Mohammed Patizhiko

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This paper aimed at discussing the important role learning resources play in enhancing the teaching and learning of science and technology courses in post primary institution in Nigeria. The paper highlighted the importance learning resources contributed to the effective understanding of the learners. The use of learning resources in the teaching of these courses will encourage teachers to be more exploratory and the learners to have more understanding. In this paper, different range of learning resources particularly common learning resources (learning resources not design primarily for education purposes) to enrich their teaching. The paper also highlighted how ordinary resource can be turned into an educational resource. Recommendations were proffered in the sourcing of learning resources ie from the market, library, institutions, museums, and dump refuse and concluded that good demonstration on the use of resources will engage the learner’s interest and will develop higher level of conceptual understanding in the learning area.

Keywords: enhance, learning, resources, science and technology, teaching

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Authors: Amani Abdallah, Isam Shahrour

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The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration.

Keywords: distribution system, drinking water, refraction index, sensor, real-time

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Authors: Nahla Elsherbiny, Ahmed Ahmed, Hamada Mohammed, Mohamed Ali

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Background: The enterococci may be considered as opportunistic agents particularly in immunocompromised patients. It is one of the top three pathogens causing many healthcare associated infections (HAIs). Resistance to several commonly used antimicrobial agents is a remarkable characteristic of most species which may carry various genes contributing to virulence. Objectives: to determine the prevalence of enterococci species in different intensive care units (ICUs) causing health care-associated infections (HAIs), intestinal carriage and environmental contamination. Also, to study the antimicrobial susceptibility pattern of the isolates with special reference to vancomycin resistance. In addition to phenotypic and genotypic detection of gelatinase, cytolysin and biofilm formation among isolates. Patients and Methods: This study was carried out in the infection control laboratory at Assiut University Hospitals over a period of one year. Clinical samples were collected from 285 patients with various (HAIs) acquired after admission to different ICUs. Rectal swabs were taken from 14 cases for detection of enterococci carriage. In addition, 1377 environmental samples were collected from the surroundings of the patients. Identification was done by conventional bacteriological methods and confirmed by analytical profile index (API). Antimicrobial sensitivity testing was performed by Kirby Bauer disc diffusion method and detection of vancomycin resistance was done by agar screen method. For the isolates, phenotypic detection of cytolysin, gelatinase production and detection of biofilm by tube method, Congo red method and microtiter plate. We performed polymerase chain reaction (PCR) for detection of some virulence genes (gelE, cylA, vanA, vanB and esp). Results: Enterococci caused 10.5% of the HAIs. Respiratory tract infection was the predominant type (86.7%). The commonest species were E.gallinarum (36.7%), E.casseliflavus (30%), E.faecalis (30%), and E.durans (3.4 %). Vancomycin resistance was detected in a total of 40% (12/30) of those isolates. The risk factors associated with acquiring vancomycin resistant enterococci (VRE) were immune suppression (P= 0.031) and artificial feeding (P= 0.008). For the rectal swabs, enterococci species were detected in 71.4% of samples with the predominance of E. casseliflavus (50%). Most of the isolates were vancomycin resistant (70%). Out of a total 1377 environmental samples, 577 (42%) samples were contaminated with different microorganisms. Enterococci were detected in 1.7% (10/577) of total contaminated samples, 50% of which were vancomycin resistant. All isolates were resistant to penicillin, ampicillin, oxacillin, ciprofloxacin, amikacin, erythromycin, clindamycin and trimethoprim-sulfamethaxazole. For the remaining antibiotics, variable percentages of resistance were reported. Cytolysin and gelatinase were detected phenotypically in 16% and 48 % of the isolates respectively. The microtiter plate method showed the highest percentages of detection of biofilm among all isolated species (100%). The studied virulence genes gelE, esp, vanA and vanB were detected in 62%, 12%, 2% and 12% respectively, while cylA gene was not detected in any isolates. Conclusions: A significant percentage of enterococci was isolated from patients and environments in the ICUs. Many virulence factors were detected phenotypically and genotypically among isolates. The high percentage of resistance, coupled with the risk of cross transmission to other patients make enterococci infections a significant infection control issue in hospitals.

Keywords: antimicrobial resistance, enterococci, ICUs, virulence factors

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Authors: Mary Farrell, Maria Soubra, Sandra Vega, Dorothy Kakraba, Joanne Fontanilla, Moira Kendra, Danielle Tonzola, Stephanie Chiu

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Pneumonia is the most common infectious cause of hospitalizations in the United States, with more than one million admissions annually and costs of $10 billion every year, making it the 8th leading cause of death. Aspiration pneumonia is an aggressive type of pneumonia that results from inhalation of oropharyngeal secretions and/or gastric contents and is preventable. The authors hypothesized that an evidence-based aspiration pneumonia clinical care pathway could reduce 30-day hospital readmissions and mortality rates, while improving the overall care of patients. We conducted a retrospective chart review on 979 patients discharged with aspiration pneumonia from January 2021 to December 2022 at Overlook Medical Center. The authors identified patients who were coded with aspiration pneumonia and/or stable sepsis. Secondarily, we identified 30-day readmission rates for aspiration pneumonia from a SNF. The Aspiration Pneumonia Clinical Care Pathway starts in the emergency department (ED) with the initiation of antimicrobials within 4 hours of admission and early recognition of aspiration. Once this is identified, a swallow test is initiated by the bedside nurse, and if the patient demonstrates dysphagia, they are maintained on strict nothing by mouth (NPO) followed by a speech and language pathologist (SLP) referral for an appropriate modified diet recommendation. Aspiration prevention techniques included the avoidance of straws, 45-degree positioning, no talking during meals, taking small bites, placement of the aspiration wrist band, and consuming meals out of the bed in a chair. Nursing education was conducted with a newly created online learning module about aspiration pneumonia. The authors identified 979 patients, with an average age of 73.5 years old, who were diagnosed with aspiration pneumonia on the index hospitalization. These patients were reviewed for a 30-day readmission for aspiration pneumonia or stable sepsis, and mortality rates from January 2021 to December 2022 at Overlook Medical Center (OMC). The 30-day readmission rates were significantly lower in the cohort that received the clinical care pathway (35.0% vs. 27.5%, p = 0.011). When evaluating the mortality rates in the pre and post intervention cohort the authors discovered the mortality rates were lower in the post intervention cohort (23.7% vs 22.4%, p = 0.61) Mortality among non-white (self-reported as non-white) patients were lower in the post intervention cohort (34.4% vs. 21.0% , p = 0.05). Patients who reported as a current smoker/vaper in the pre and post cohorts had increased mortality rates (5.9% vs 22%). There was a decrease in mortality for the male population but an increase in mortality for women in the pre and post cohorts (19% vs. 25%). The authors attributed this increase in mortality in the post intervention cohort to more active smokers, more former smokers, and more being admitted from a SNF. This research identified that implementation of an Aspiration Pneumonia Clinical Care Pathway showed a statistically significant decrease in readmission rates and mortality rates in non-whites. The 30-day readmission rates were lower in the cohort that received the clinical care pathway (35.0% vs. 27.5%, p = 0.011).

Keywords: aspiration pneumonia, mortality, quality improvement, 30-day pneumonia readmissions

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Authors: Vaidehi Murarka, Sneha Mehta, Dishant Upadhyay

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Our project topic is “Alphabet Recognition using pixel probability distribution”. The project uses techniques of Image Processing and Machine Learning in Computer Vision. Alphabet recognition is the mechanical or electronic translation of scanned images of handwritten, typewritten or printed text into machine-encoded text. It is widely used to convert books and documents into electronic files etc. Alphabet Recognition based OCR application is sometimes used in signature recognition which is used in bank and other high security buildings. One of the popular mobile applications includes reading a visiting card and directly storing it to the contacts. OCR's are known to be used in radar systems for reading speeders license plates and lots of other things. The implementation of our project has been done using Visual Studio and Open CV (Open Source Computer Vision). Our algorithm is based on Neural Networks (machine learning). The project was implemented in three modules: (1) Training: This module aims “Database Generation”. Database was generated using two methods: (a) Run-time generation included database generation at compilation time using inbuilt fonts of OpenCV library. Human intervention is not necessary for generating this database. (b) Contour–detection: ‘jpeg’ template containing different fonts of an alphabet is converted to the weighted matrix using specialized functions (contour detection and blob detection) of OpenCV. The main advantage of this type of database generation is that the algorithm becomes self-learning and the final database requires little memory to be stored (119kb precisely). (2) Preprocessing: Input image is pre-processed using image processing concepts such as adaptive thresholding, binarizing, dilating etc. and is made ready for segmentation. “Segmentation” includes extraction of lines, words, and letters from the processed text image. (3) Testing and prediction: The extracted letters are classified and predicted using the neural networks algorithm. The algorithm recognizes an alphabet based on certain mathematical parameters calculated using the database and weight matrix of the segmented image.

Keywords: contour-detection, neural networks, pre-processing, recognition coefficient, runtime-template generation, segmentation, weight matrix

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

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

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

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Authors: Djehich Mohamed Yousri

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Religious Reform and Secularism present the title of the research paper (Religious Reform and Secularism) is the subject of this research paper. The researcher proceeded to address it through three main axes, in addition to an introduction and a conclusion that indicated the most important results of the study. Where the first axis dealt with the concept of the secular, while the second axis dealt with religious reform, and we devoted the third axis to discussing the relationship between them. It is a treatment that requires the researcher, at the level of methodology, to critically rethink the concepts of (religious) and (secular), and accompany the radical revisions that have been made in the field of (post-secular) studies in this regard. The paper concluded that caution should be exercised in dealing with the terms "religious reform" and "secularism". There are different and diverse viewpoints on (religious reform) and on (secularism) as well, and therefore it is wrong, according to the perspective of the paper, to deal with either of them as representing one comprehensive, homogeneous, closed and semantically stable category. ) or (secularism) with a set of diverse and divergent points of view from each other, a path that ultimately leads to confusion, confusion, ambiguity and misunderstanding.

Keywords: secularism, post-secularism, religious reform, concept of religion, the concept of secularism

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Authors: Hemalatha Rallapalli, K. Lal Kishore

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In comparison to systems that are typically designed to provide capabilities over a narrow frequency range through hardware elements, the next generation cognitive radios are intended to implement a broader range of capabilities through efficient spectrum exploitation. This offers the user the promise of greater flexibility, seamless roaming possible on different networks, countries, frequencies, etc. It requires true paradigm shift i.e., liberalization over a wide band of spectrum as well as a growth path to more and greater capability. This work contributes towards the design and implementation of an open spectrum access (OSA) feature to unlicensed users thus offering a frequency agile radio platform that is capable of performing spectrum sensing over a wideband. Thus, an ultra-wideband (UWB) radio, which has the intelligence of spectrum sensing only, unlike the cognitive radio with complete intelligence, is named as a Smart Software Radio (SSR). The spectrum sensing mechanism is implemented based on energy detection. Simulation results show the accuracy and validity of this method.

Keywords: cognitive radio, energy detection, software radio, spectrum sensing

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Authors: Rosaline Govender

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This research study is an exploration of the self-directed professional development of teachers who teach in public schools in an era of democracy and educational change in South Africa. Amidst an ever-changing educational system, the teachers in this study position themselves as self-directed teacher-learners where they adopt particular learning practices which enable change within the broader discourses of public schooling. Life-story interviews were used to enter into the private and public spaces of five teachers which offer glimpses of how particular systems shaped their identities, and how the meanings of self-directed teacher-learner shaped their learning practices. Through the Multidimensional framework of analysis and interpretation the teachers’ stories were analysed through three lenses: restorying the field texts - the self through story; the teacher-learner in relation to social contexts, and practices of self-directed learning.This study shows that as teacher-learners learn for change through self-directed learning practices, they develop their agency as transformative intellectuals, which is necessary for the reworking of South African public schools.

Keywords: professional development, professionality, professionalism, self-directed learning

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Authors: Sneha Leslie

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The current situation in the cyber world is that crimes performed by Botnets are increasing and the masterminds (botmaster) are not detectable easily. The botmaster in the botnet compromises the legitimate host machines in the network and make them bots or zombies to initiate the cyber-attacks. This paper will focus on the live detection of the botmaster in the network by using the strong framework 'metasploit', when distributed denial of service (DDOS) attack is performed by the botnet. The affected victim machine will be continuously monitoring its incoming packets. Once the victim machine gets to know about the excessive count of packets from any IP, that particular IP is noted and details of the noted systems are gathered. Using the vulnerabilities present in the zombie machines (already compromised by botmaster), the victim machine will compromise them. By gaining access to the compromised systems, applications are run remotely. By analyzing the incoming packets of the zombies, the victim comes to know the address of the botmaster. This is an effective and a simple system where no specific features of communication protocol are considered.

Keywords: bonet, DDoS attack, network security, detection system, metasploit framework

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Authors: Fei Gao, Xinyang Dong, Yisu Ge, Shufang Lu, Libo Weng

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Vehicle color recognition plays an important role in traffic accident investigation. However, due to the influence of illumination, weather, and noise, vehicle color recognition still faces challenges. In this paper, a vehicle color classification network based on spatial cluster loss and channel attention mechanism (SCNet) is proposed for vehicle color recognition. A channel attention module is applied to extract the features of vehicle color representative regions and reduce the weight of nonrepresentative color regions in the channel. The proposed loss function, called spatial clustering loss (SC-loss), consists of two channel-specific components, such as a concentration component and a diversity component. The concentration component forces all feature channels belonging to the same class to be concentrated through the channel cluster. The diversity components impose additional constraints on the channels through the mean distance coefficient, making them mutually exclusive in spatial dimensions. In the comparison experiments, the proposed method can achieve state-of-the-art performance on the public datasets, VCD, and VeRi, which are 96.1% and 96.2%, respectively. In addition, the ablation experiment further proves that SC-loss can effectively improve the accuracy of vehicle color recognition.

Keywords: feature extraction, convolutional neural networks, intelligent transportation, vehicle color recognition

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Authors: Marko Batic, Nikola Tomasevic, Sanja Vranes

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One of the strongholds in the ubiquitous efforts related to the energy conservation and energy efficiency improvement is represented by the retrofit of high energy consumers in buildings. In general, HVAC systems represent the highest energy consumers in buildings. However they usually suffer from mal-operation and/or malfunction, causing even higher energy consumption than necessary. Various Fault Detection and Diagnosis (FDD) systems can be successfully employed for this purpose, especially when it comes to the application at a single device/unit level. In the case of more complex systems, where multiple devices are operating in the context of the same building, significant energy efficiency improvements can only be achieved through application of comprehensive FDD systems relying on additional higher level knowledge, such as their geographical location, served area, their intra- and inter- system dependencies etc. This paper presents a comprehensive FDD system that relies on the utilization of common knowledge repository that stores all critical information. The discussed system is deployed as a test-bed platform at the two at Fiumicino and Malpensa airports in Italy. This paper aims at presenting advantages of implementation of the knowledge base through the utilization of ontology and offers improved functionalities of such system through examples of typical queries and reasoning that enable derivation of high level energy conservation measures (ECM). Therefore, key SPARQL queries and SWRL rules, based on the two instantiated airport ontologies, are elaborated. The detection of high level irregularities in the operation of airport heating/cooling plants is discussed and estimation of energy savings is reported.

Keywords: airport ontology, knowledge management, ontology modeling, reasoning

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Authors: E. A. Babiker, S. A. Makawi

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The influence of the season (autumn, winter, and summer) on semen production in Nubian and Saanen bucks was studied. Seven mature bucks (4 Nubian and 3 Saanen) were used in this study to prepare semen samples which were collected with an artificial vagina. The samples were extended in Tris-egg yolk-glycerol-glucose extender, frozen, and stored in liquid nitrogen at –196 0C for 48 hours. Straws were thawed in water at –37 0C for 15 seconds before sperm evaluation (post-thaw sperm motility). There was a significant seasonal variation in both semen quantity (volume, concentration, and the total number of spermatozoa per ejaculate) and quality (percentage of sperm motility, percentage of post-thaw sperm motility, and dead spermatozoa). Greater ejaculate volumes were observed during summer and autumn in comparison to winter. Higher values of sperms concentration were observed during autumn, while the lowest sperm concentration values were observed during summer. Higher values of sperm motility were observed during autumn in comparison to summer. Lower values of dead spermatozoa were recorded during autumn, while the highest percentages of dead spermatozoa were observed during summer for the two breeds of bucks. The influence of season on post-thaw sperm motility was significant. Semen frozen during autumn and winter had the highest values, while during summer, lower mean values were observed. The best semen was produced during autumn and winter, while during summer, poor semen quality was recorded.

Keywords: season, Nubian, Saanen, semen production, Sudan

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Sakulsri Srisaracam

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Social media has become an important source of information for the public and the media profession. Some social issues raised on social media are picked up by journalists to report on other platforms. This relationship between social media and mainstream media can sometimes drive public debate or stimulate social movements. The question to examine is in what situations can social media conversations raise awareness and stimulate change on public issues. This study addresses the communication patterns of social media conversations driving covert issues into mainstream media and leading to social advocacy movements. In methodological terms, the study findings are based on a content analysis of Facebook, Twitter, news websites and television media reports on three different case studies – saving Bryde’s whale, protests against a government proposal to downsize the Office of Knowledge Management and Development in Thailand, and a dengue fever campaign. These case studies were chosen because they represent issues that most members of the public do not pay much attention to but social media conversations stimulated public debate and calls to action. This study found: 1) Collective social media conversations can stimulate public debate and encourage change at three levels – awareness, public debate, and action of policy and social change. The level depends on the communication patterns of online users and media coverage. 2) Patterns of communication have to be designed to combine social media conversations, online opinion leaders, mainstream media coverage and call to both online and offline action to motivate social change. Thus, this result suggests that social media is a powerful platform for collective communication and setting the agenda on public issues for mainstream media. However, for social change to succeed, social media should be used to mobilize online movements to move offline too.

Keywords: public issues, mainstream media, social media, social movement

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Authors: Yali Liu, Maria Avello Iturriagagoitia

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Background: Fitness applications (apps) are one of the most popular mobile health (mHealth) apps. These apps can help prevent/control health issues such as obesity, which is one of the most serious public health challenges in the developed world in recent decades. Compared with the traditional intervention like face-to-face treatment, it is cheaper and more convenient to use fitness apps to interfere with physical activities and healthy behaviors. Nevertheless, fitness applications apps tend to have high abandonment rates and low levels of user engagement. Therefore, maintaining the endurance of users' usage is challenging. In fact, previous research shows a variety of strategies -goal-setting, self-monitoring, coaching, etc.- for promoting fitness and health behavior change. These strategies can influence the users’ perseverance and self-monitoring of the program as well as favoring their adherence to routines that involve a long-term behavioral change. However, commercial fitness apps rarely incorporate these strategies into their design, thus leading to a lack of engagement with the apps. Most of today’s mobile services and brands engage their users proactively via push notifications. Push notifications. These notifications are visual or auditory alerts to inform mobile users about a wide range of topics that entails an effective and personal mean of communication between the app and the user. One of the research purposes of this article is to implement the application of behavior change strategies through push notifications. Proposes: This study aims to better understand the influence that effective use of push notifications combined with the behavioral change strategies will have on users’ engagement with the fitness app. And the secondary objectives are 1) to discuss the sociodemographic differences in utilization of push notifications of fitness apps; 2) to determine the impact of each strategy in customer engagement. Methods: The study uses a combination of the Consumer Engagement Theory and UTAUT2 based model to conduct an online survey among current users of fitness apps. The questionnaire assessed attitudes to each behavioral change strategy, and sociodemographic variables. Findings: Results show the positive effect of push notifications in the generation of consumer engagement and the different impacts of each strategy among different groups of population in customer engagement. Conclusions: Fitness apps with behavior change strategies have a positive impact on increasing users’ usage time and customer engagement. Theoretical experts can participate in designing fitness applications, along with technical designers.

Keywords: behavioral change, customer engagement, fitness app, push notification, UTAUT2

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Authors: Gulfam Hasan, Ijaz Ashraf, Saleem Ashraf, Muhammad Rafay Muzammil, Salman Asghar, Shafiq-Ur-Rehman Zia

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The word “climate change” has become the most popular term when anyone observes any uncertain climate variation in their respective region. Asian countries are more prone to the impact of this phenomenon, and Pakistan is the leading affected country. Last few years, governments all over the world have been trying to cater to this issue for the best entrust of their population, especially agriculture. Now the farmers in Pakistan are fully aware of the term “climate change” and are more concerned about its solutions. On the other hand, developed countries like the USA are setting a benchmark for developing countries in every sphere of life. Based on cultural and other variations, the research was carried out to identify the behavior of farmers regarding the same issue. Cross-sectional survey research was designed for an in-depth study of relevant research questions. Face-to-face interviews were conducted in Pakistan, while virtual and face-to-face interviews were conducted in the Indiana State of the USA. The results of the present study and the responses of farmers were very interesting. The common climate change mitigation strategies suggested by farmers of both countries were less use of motor vehicles (replacement with bicycles in the circle of 10 Km), less dependency on chemical fertilizers (increased use of Manure, Bio-fertilizer, Compost), and plantation of the tree. The difference of opinion was in less government interest, lack of farmers’ education, political instability (views of Pakistani farmers), awareness of local communities, self-satisfaction, and economic disparities (views of USA farmers). Based on the given evidence, it was recommended that there is a dire need to address the climate change issue all over the world without discrimination of race, color, region, or religion. Because it will affect not only agriculture but also the real effect will be on HUMANITY.

Keywords: climate change, mitigation strategies, forests, biodiversity

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Authors: Dhanuja S. Patil, Sanjay B. Waykar

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Event detection is a standout amongst the most key parts for distinctive sorts of area applications of video data framework. Recently, it has picked up an extensive interest of experts and in scholastics from different zones. While detecting video event has been the subject of broad study efforts recently, impressively less existing methodology has considered multi-model data and issues related efficiency. Start of soccer matches different doubtful circumstances rise that can't be effectively judged by the referee committee. A framework that checks objectively image arrangements would prevent not right interpretations because of some errors, or high velocity of the events. Bayesian networks give a structure for dealing with this vulnerability using an essential graphical structure likewise the probability analytics. We propose an efficient structure for analysing and summarization of soccer videos utilizing object-based features. The proposed work utilizes the t-cherry junction tree, an exceptionally recent advancement in probabilistic graphical models, to create a compact representation and great approximation intractable model for client’s relationships in an interpersonal organization. There are various advantages in this approach firstly; the t-cherry gives best approximation by means of junction trees class. Secondly, to construct a t-cherry junction tree can be to a great extent parallelized; and at last inference can be performed utilizing distributed computation. Examination results demonstrates the effectiveness, adequacy, and the strength of the proposed work which is shown over a far reaching information set, comprising more soccer feature, caught at better places.

Keywords: summarization, detection, Bayesian network, t-cherry tree

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Authors: Camille Plant, Katherine McGill, Pek Ang

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Oncology patients face a host of unique challenges, which are physical, psychological and philosophical in nature. This preliminary study aimed to explore the psychiatric morbidity of oncology patients in an outpatient setting at a major public hospital in Australia. The study found that 33 patients were referred to a Psychiatrist by a Clinical Psychologist or treating Oncologist. These patients attended an outpatient Psychiatry appointment at the Calvary Mater Hospital, Newcastle, over a 7 month period (June 2017-January 2018). Of these, 45% went on to have a follow-up appointment. The Clinical Global Impressions Scale (CGI) was used to gather symptom severity scores at baseline and at follow-up. The CGI is a clinician determined instrument that provides an assessment of global functioning. It is comprised of two companion one-item measures: the CGI-Severity (CGI-S) rates mental illness severity, and the CGI-Improvement (CGI-I) rates change in condition or improvement from initiation of treatment. Patients referred to a Psychiatrist were observed to be on average in the Markedly ill approaching Severely ill range (CGI-S average of 5.5). However, those patients who attended a follow-up appointment were on average only Moderately Ill at baseline (CGI-S average of 3.9). Despite these follow patients not being severely mentally ill initially, the contact was helpful, as their CGI-S scores improved on average to the Mildly Ill range (CGI-S average of 2.8). A Mixed ANOVA revealed that there was a significant improvement in mental illness severity post-follow-up appointment (Greenhouse-Geisser .000). There was a near even proportion of males and females attending appointments (58% female), and slightly more females attended a follow-up (60% female). Males were on average more mentally ill at baseline compared to females at baseline (male average M=3.86, female average M=3.56), and males had a greater reduction in mental illness severity on average compared to females (male average M=2.71, female average 3.00). This was approaching significance (.073) and would be important to explore with a larger sample size. Change in clinical condition for follow-up patients was also recorded. It was found that more than half of patients (53%) were observed to experience Minimal improvement in attending at least one follow-up appointment. There was no change for 27% of patients, and there were no patients who were worse at follow up. As this was a preliminary study with small sample size, future research conducted could explore whether there are any significant gender differences, such as whether males experience the significantly greater reduction in symptoms of mental illness compared to females, as well as any effects of cancer stage or type on psychiatric outcomes. Future research could also investigate outcomes for those patients who concurrently access a Clinical Psychologist alongside the Psychiatrist. A limitation of the study is that the outcome measure is a brief item rating completed by the clinician.

Keywords: clinical global impressions scale, psychiatry, morbidity, oncology, outcomes, psychiatry

Procedia PDF Downloads 149