Search results for: multi-temporal image classification

Authors: Suruchi

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This study aims to examine the spatial and temporal patterns of fog occurrences across Czech Republic. It utilizes satellite imagery and other data sources to achieve this goal. The main objective is to understand the role of fog in atmospheric deposition processes and its potential impact on the environment and ecosystems. Through satellite image analysis, the study will identify and categorize different types of fog, including radiation fog, orographic fog, and mountain fog. Fog detection algorithms and cloud type products will be evaluated to assess the frequency and distribution of fog events throughout the Czech Republic. Furthermore, the regions covered by fog will be classified based on their fog type and associated pollution levels. This will provide insights into the variability in fog characteristics and its implications for atmospheric deposition. Spatial analysis techniques will be used to pinpoint areas prone to frequent fog events and evaluate their pollution levels. Statistical methods will be employed to analyze patterns in fog occurrence over time and its connection with environmental factors. The ultimate goal of this research is to offer fresh perspectives on fog's role in atmospheric deposition processes, enhancing our understanding of its environmental significance and informing future research and environmental management initiatives.

Keywords: pollution, GIS, FOG, satellie, atmospheric deposition

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Authors: Ki-Yeo Kim

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Trends in genetics are transforming in order to identify differential coexpressions of correlated gene expression rather than the significant individual gene. Moreover, it is known that a combined biomarker pattern improves the discrimination of a specific cancer. The identification of the combined biomarker is also necessary for the early detection of invasive oral squamous cell carcinoma (OSCC). To identify the combined biomarker that could improve the discrimination of OSCC, we explored an appropriate number of genes in a combined gene set in order to attain the highest level of accuracy. After detecting a significant gene set, including the pre-defined number of genes, a combined expression was identified using the weights of genes in a gene set. We used the Principal Component Analysis (PCA) for the weight calculation. In this process, we used three public microarray datasets. One dataset was used for identifying the combined biomarker, and the other two datasets were used for validation. The discrimination accuracy was measured by the out-of-bag (OOB) error. There was no relation between the significance and the discrimination accuracy in each individual gene. The identified gene set included both significant and insignificant genes. One of the most significant gene sets in the classification of normal and OSCC included MMP1, SOCS3 and ACOX1. Furthermore, in the case of oral dysplasia and OSCC discrimination, two combined biomarkers were identified. The combined genomic expression achieved better performance in the discrimination of different conditions than in a single significant gene. Therefore, it could be expected that accurate diagnosis for cancer could be possible with a combined biomarker.

Keywords: oral squamous cell carcinoma, combined biomarker, microarray dataset, correlated genes

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: Endrick Barnacin, Jean-Luc Henry, Jack Molinié, Jimmy Nagau, Hélène Delatte, Gérard Lebreton

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Palynology is a field of interest for many disciplines. It has multiple applications such as chronological dating, climatology, allergy treatment, and even honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time-consuming task that requires the intervention of experts in the field, which is becoming increasingly rare due to economic and social conditions. So, the automation of this task is a necessity. Pollen slides analysis is mainly a visual process as it is carried out with the naked eye. That is the reason why a primary method to automate palynology is the use of digital image processing. This method presents the lowest cost and has relatively good accuracy in pollen retrieval. In this work, we propose a system combining recognition and grouping of pollen. It consists of using a Logistic Model Tree to classify pollen already known by the proposed system while detecting any unknown species. Then, the unknown pollen species are divided using a cluster-based approach. Success rates for the recognition of known species have been achieved, and automated clustering seems to be a promising approach.

Keywords: pollen recognition, logistic model tree, expectation-maximization, local binary pattern

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Authors: Ayman Hegab

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Increasing evidence supports the use of platelet-rich plasma (PRP) combined with hyaluronic acid (HA) for the treatment of knee osteoarthritis, which effectively promotes cartilage repair. This study aimed to determine whether injection of PRP+HA following arthrocentesis reduces pain and improves maximum incisal opening. This was a single-blind, prospective, randomized control study. The patients were selected based on the Hegab classification: Group I: patients treated with arthrocentesis followed by a single PRP injection; Group II (Control): patients treated with arthrocentesis followed by a single HA injection; and Group III: patients treated with arthrocentesis followed by a single PRP+HA combination injection. The primary predictor variable was the medication used for injection. The primary outcome variables were the maximum voluntary mouth opening and pain index scores. The secondary outcome variable was joint sounds. All outcome variables were assessed and compared among the three groups at baseline and at 1-, 3-, 6-, and 12-month intervals. Other variables, including patients’ age and sex, were evaluated in relation to the patient outcomes. Injecting PRP+HA showed statistically significant improvement in the primary and secondary treatment outcomes over PRP or HA injection throughout the study period (P<0.005). Injection of PRP+HA following arthrocentesis had significant long-term clinical efficacy regarding pain relief that was considered the main concern of both the patient and clinician.

Keywords: TMJ, HA, PRP, osteoarthritis

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Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: Rahul Mohite, Lahcen Ouarbya

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This paper focuses on the interplay between humans and computer systems and the ability of these systems to understand and respond to human emotions, including non-verbal communication. Current emotion recognition systems are based solely on either facial or verbal expressions. The limitation of these systems is that it requires large training data sets. The paper proposes a system for recognizing human emotions that combines both speech and emotion recognition. The system utilizes advanced techniques such as deep learning and image recognition to identify facial expressions and comprehend emotions. The results show that the proposed system, based on the combination of facial expression and speech, outperforms existing ones, which are based solely either on facial or verbal expressions. The proposed system detects human emotion with an accuracy of 86%, whereas the existing systems have an accuracy of 70% using verbal expression only and 76% using facial expression only. In this paper, the increasing significance and demand for facial recognition technology in emotion recognition are also discussed.

Keywords: facial reputation, expression reputation, deep gaining knowledge of, photo reputation, facial technology, sign processing, photo type

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Authors: Titik Wahyuningsih

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The folktale to discuss is Ande-Ande Lumut, a story that is believed to be a history of two kingdoms in East Java, Indonesia. The title refers to the main male character in the story. This research is a library research which aims to know the patriarchal values in Indonesia. The data for the research is the song in the story that is actually the conversation between Ande-Ande Lumut and a mom who adopts him. It is told in the lines that many beautiful girls come to propose Ande-Ande Lumut but he does not want to accept them and keeps on staying in his upstairs room. Finally, he says yes for Klething Kuning to whom his mom describes as a girl with ugly face. Ande-Ande Lumut's decision is taken as Klething Kuning is the only girl who doesn't let Yuyu Kangkang help her. Yuyu Kangkang is described as a very big crab that helps the girls to cross the river but ask them to kiss him. Through the lense of feminist approach, Ande-Ande Lumut shows the men’s preference and dominance to make final decision over women. Even though the girls are actively propose their future husband, but they do it without giving any requirements. Meanwhile, the future husband chooses a girl with a criterion that no male has ever touched her, although the male is a crab.

Keywords: future spouse, Indonesian folktale, acceptance, patriarchal

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Authors: Emmanuel Okechukwu Nwafor, Folake Olubunmi Akintayo, Denis Alcides Rezende

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Decision tree models are versatile and interpretable machine learning algorithms widely used for both classification and regression tasks, which can be related to cities, whether physical or digital. The aim of this research is to assess how well decision tree algorithms can predict trip purposes in Makurdi, Nigeria, while also exploring their connection to the strategic digital city initiative. The research methodology involves formalizing household demographic and trips information datasets obtained from extensive survey process. Modelling and Prediction were achieved using Python Programming Language and the evaluation metrics like R-squared and mean absolute error were used to assess the decision tree algorithm's performance. The results indicate that the model performed well, with accuracies of 84% and 68%, and low MAE values of 0.188 and 0.314, on training and validation data, respectively. This suggests the model can be relied upon for future prediction. The conclusion reiterates that This model will assist decision-makers, including urban planners, transportation engineers, government officials, and commuters, in making informed decisions on transportation planning and management within the framework of a strategic digital city. Its application will enhance the efficiency, sustainability, and overall quality of transportation services in Makurdi, Nigeria.

Keywords: decision tree algorithm, trip purpose, intelligent transport, strategic digital city, travel pattern, sustainable transport

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Authors: A. Bouzekri, H. Benmassaud

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Aurès region is one of the arid and semi-arid areas that have suffered climate crises and overexploitation of natural resources they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and its spatiotemporal changes in the Aurès between 1987 and 2013, for this work, we adopted a method of analysis based on the exploitation of the images satellite Landsat TM 1987 and Landsat OLI 2013, from the supervised classification likelihood coupled with field surveys of the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover maps from 1987 and 2013, one can extract a spatial map change of different land cover units. The results show that between 1987 and 2013 vegetation has suffered negative changes are the significant degradation of forests and steppe rangelands, and sandy soils and bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013 allows us to understand the extensive or regressive orientation of vegetation and soil, this map shows that dense forests give his place to clear forests and steppe vegetation develops from a degraded forest vegetation and bare, sandy soils earn big steppe surfaces that explain its remarkable extension. The analysis of remote sensing data highlights the profound changes in our environment over time and quantitative monitoring of the risk of desertification.

Keywords: remote sensing, spatiotemporal, land use, Aurès

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Authors: Tianqiao Zhang, Ye Tian, Yanliang Yin, Yichao Tian, Suzhai Tian, Weige Sun, Shuhui Gong, Limei Tang, Ruoliang Tang

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Introduction: Healthcare workers, especially the nurses all over the world, are highly vulnerable to work-related musculoskeletal disorders (WMSDs), experiencing high rates of neck, shoulder, and low back injuries, due to the unfavorable working conditions. To reduce WMSDs among nursing personnel, many workplace interventions have been developed and implemented. Unfortunately, the ongoing Covid-19 (SARS-CoV-2) pandemic has posed great challenges to the ergonomic practices and interventions in healthcare facilities, particularly the hospitals, since current Covid-19 mitigation measures, such as social distancing and working remotely, has substantially minimized in-person gatherings and trainings. On the other hand, hospitals throughout the world have been short-staffed, resulting in disturbance of shift scheduling and more importantly, the increased job demand among the available caregivers, particularly the doctors and nurses. With the latest development in communication technology, remote intervention measures have been developed as an alternative, without the necessity of in-person meetings. The Omaha System (OS) is a standardized classification system for nursing practices, including a problem classification system, an intervention system, and an outcome evaluation system. This paper describes the development of an OS-based ergonomic intervention program. Methods: First, a comprehensive literature search was performed among worldwide electronic databases, including PubMed, Web of Science, Cochrane Library, China National Knowledge Infrastructure (CNKI), between journal inception to May 2020, resulting in a total of 1,418 scientific articles. After two independent screening processes, the final knowledge pool included eleven randomized controlled trial studies to develop the draft of the intervention program with Omaha intervention subsystem as the framework. After the determination of sample size needed for statistical power and the potential loss to follow-up, a total of 94 nurses from eight clinical departments agreed to provide written, informed consent to participate in the study, which were subsequently assigned into two random groups (i.e., intervention vs. control). A subgroup of twelve nurses were randomly selected to participate in a semi-structured interview, during which their general understanding and awareness of musculoskeletal disorders and potential interventions was assessed. Then, the first draft was modified to reflect the findings from these interviews. Meanwhile, the tentative program schedule was also assessed. Next, two rounds of consultation were conducted among experts in nursing management, occupational health, psychology, and rehabilitation, to further adjust and finalize the intervention program. The control group had access to all the information and exercise modules at baseline, while an interdisciplinary research team was formed and supervised the implementation of the on-line intervention program through multiple social media groups. Outcome measures of this comparative study included biomechanical load assessed by the Quick Exposure Check and stresses due to awkward body postures. Results and Discussion: Modification to the draft included (1) supplementing traditional Chinese medicine practices, (2) adding the use of assistive patient handling equipment, and (3) revising the on-line training method. Information module should be once a week, lasting about 20 to 30 minutes, for a total of 6 weeks, while the exercise module should be 5 times a week, each lasting about 15 to 20 minutes, for a total of 6 weeks.

Keywords: ergonomic interventions, musculoskeletal disorders (MSDs), omaha system, nurses, Covid-19

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Authors: Ugo Galvanetto, Pirto G. Pavan, Mirco Zaccariotto

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The discipline of Computer-integrated surgery (CIS) will provide equipment able to improve the efficiency of healthcare systems and, which is more important, clinical results. Surgeons and machines will cooperate in new ways that will extend surgeons’ ability to train, plan and carry out surgery. Patient specific CIS of the brain requires several steps: 1 - Fast generation of brain models. Based on image recognition of MR images and equipped with artificial intelligence, image recognition techniques should differentiate among all brain tissues and segment them. After that, automatic mesh generation should create the mathematical model of the brain in which the various tissues (white matter, grey matter, cerebrospinal fluid …) are clearly located in the correct positions. 2 – Reliable and fast simulation of the surgical process. Computational mechanics will be the crucial aspect of the entire procedure. New algorithms will be used to simulate the mechanical behaviour of cutting through cerebral tissues. 3 – Real time provision of visual and haptic feedback A sophisticated human-machine interface based on ergonomics and psychology will provide the feedback to the surgeon. The present work will address in particular point 2. Modelling the cutting of soft tissue in a structure as complex as the human brain is an extremely challenging problem in computational mechanics. The finite element method (FEM), that accurately represents complex geometries and accounts for material and geometrical nonlinearities, is the most used computational tool to simulate the mechanical response of soft tissues. However, the main drawback of FEM lies in the mechanics theory on which it is based, classical continuum Mechanics, which assumes matter is a continuum with no discontinuity. FEM must resort to complex tools such as pre-defined cohesive zones, external phase-field variables, and demanding remeshing techniques to include discontinuities. However, all approaches to equip FEM computational methods with the capability to describe material separation, such as interface elements with cohesive zone models, X-FEM, element erosion, phase-field, have some drawbacks that make them unsuitable for surgery simulation. Interface elements require a-priori knowledge of crack paths. The use of XFEM in 3D is cumbersome. Element erosion does not conserve mass. The Phase Field approach adopts a diffusive crack model instead of describing true tissue separation typical of surgical procedures. Modelling discontinuities, so difficult when using computational approaches based on classical continuum Mechanics, is instead easy for novel computational methods based on Peridynamics (PD). PD is a non-local theory of mechanics formulated with no use of spatial derivatives. Its governing equations are valid at points or surfaces of discontinuity, and it is, therefore especially suited to describe crack propagation and fragmentation problems. Moreover, PD does not require any criterium to decide the direction of crack propagation or the conditions for crack branching or coalescence; in the PD-based computational methods, cracks develop spontaneously in the way which is the most convenient from an energy point of view. Therefore, in PD computational methods, crack propagation in 3D is as easy as it is in 2D, with a remarkable advantage with respect to all other computational techniques.

Keywords: computational mechanics, peridynamics, finite element, biomechanics

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Authors: Rajvir Kaur, Jeewani Anupama Ginige

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With recent trends in Big Data and advancements in Information and Communication Technologies, the healthcare industry is at the stage of its transition from clinician oriented to technology oriented. Many people around the world die of cancer because the diagnosis of disease was not done at an early stage. Nowadays, the computational methods in the form of Machine Learning (ML) are used to develop automated decision support systems that can diagnose cancer with high confidence in a timely manner. This paper aims to carry out the comparative evaluation of a selected set of ML classifiers on two existing datasets: breast cancer and cervical cancer. The ML classifiers compared in this study are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression, Ensemble (Bagged Tree) and Artificial Neural Networks (ANN). The evaluation is carried out based on standard evaluation metrics Precision (P), Recall (R), F1-score and Accuracy. The experimental results based on the evaluation metrics show that ANN showed the highest-level accuracy (99.4%) when tested with breast cancer dataset. On the other hand, when these ML classifiers are tested with the cervical cancer dataset, Ensemble (Bagged Tree) technique gave better accuracy (93.1%) in comparison to other classifiers.

Keywords: artificial neural networks, breast cancer, classifiers, cervical cancer, f-score, machine learning, precision, recall

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Authors: M. Pomianek, M. Piszczek, M. Maciejewski

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The binocular eye tracking technology is increasingly being used in industry, entertainment and marketing analysis. In the case of virtual reality, eye tracking systems are already the basis for user interaction with the environment. In such systems, the high accuracy of determining the user's eye fixation point is very important due to the specificity of the virtual reality head-mounted display (HMD). Often, however, there are unknown errors occurring in the used eye tracking technology, as well as those resulting from the positioning of the devices in relation to the user's eyes. However, can the virtual environment itself influence estimation errors? The paper presents mathematical analyses and empirical studies of the determination of the fixation point and errors resulting from the change in the size of the pupil in response to the intensity of the displayed scene. The article contains both static laboratory tests as well as on the real user. Based on the research results, optimization solutions were proposed that would reduce the errors of gaze estimation errors. Studies show that errors in estimating the fixation point of vision can be minimized both by improving the pupil positioning algorithm in the video image and by using more precise methods to calibrate the eye tracking system in three-dimensional space.

Keywords: eye tracking, fixation point, pupil size, virtual reality

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Authors: Ligia Florio, João Mauricio Castaldelli-Maia

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Background: Food addiction (FA) emerged in the 1990s as a possible contributor to the increasing prevalence of obesity and overweight, in conjunction with changing food environments and mental health conditions. However, FA is not yet listed as one of the disorders in the DSM-5 and/or the ICD-11. Although there are controversies and debates in the literature about the classification and construct of FA, the most common approach to access it is the use of a research tool - the Yale Food Addiction Scale (YFAS) - which approximates the concept of FA to the concept diagnosis of dependence on psychoactive substances. There is a need to explore the dimensional phenotypes accessed by YFAS in different population groups for a better understanding and scientific support of FA diagnoses. Methods: The primary objective of this project was to investigate the construct validity of the FA concept by mYFAS 2.0 in individuals who underwent bariatric surgery (n = 100) at the Hospital Estadual Mário Covas since 2011. Statistical analyzes were conducted using the STATA software. In this sense, structural or factor validity was the type of construct validity investigated using exploratory factor analysis (EFA) and item response theory (IRT) techniques. Results: EFA showed that the one-dimensional model was the most parsimonious. The IRT showed that all criteria contributed to the latent structure, presenting discrimination values greater than 0.5, with most presenting values greater than 2. Conclusion: This study reinforces a FA dimension in patients who underwent bariatric surgery. Within this dimension, we identified the most severe and discriminating criteria for the diagnosis of FA.

Keywords: obesity, food addiction, bariatric surgery, regain

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Authors: Marcell Serra de Almeida Martins, Benedito de Souza Ribeiro Neto, Gerson Lima Serejo, Carlos Gustavo Resque Dos Santos

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Indoor positioning systems use sensors such as Bluetooth, ZigBee, and Wi-Fi, as well as cameras for image capture, which can be fixed or mobile. These computer vision-based positioning approaches are low-cost to implement, mainly when it uses a mobile camera. The present study aims to create a design of a fiducial marker for a low-cost indoor localization system. The marker is tape-shaped to perform a continuous reading employing two detection algorithms, one for greater distances and another for smaller distances. Therefore, the location service is always operational, even with variations in capture distance. A minimal localization and reading algorithm were implemented for the proposed marker design, aiming to validate it. The accuracy tests consider readings varying the capture distance between [0.5, 10] meters, comparing the proposed marker with others. The tests showed that the proposed marker has a broader capture range than the ArUco and QRCode, maintaining the same size. Therefore, reducing the visual pollution and maximizing the tracking since the ambient can be covered entirely.

Keywords: multiscale recognition, indoor localization, tape-shaped marker, fiducial marker

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Authors: Kei Okubo, Yosuke Kurihara, Takashi Kaburagi, Kajiro Watanabe

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Many of the ever-growing elderly population require exercise, such as running, for health management. One important element of a runner’s training is the choice of shoes for exercise; shoes are important because they provide the interface between the feet and road. When we purchase shoes, we may instinctively choose a pair after trying on many different pairs of shoes. Selecting the shoes instinctively may work, but it does not guarantee a suitable fit for running activities. Therefore, if we could select suitable shoes for each runner from the viewpoint of brain activities, it would be helpful for validating shoe selection. In this paper, we describe how brain activities show different characteristics during particular task, corresponding to different properties of shoes. Using five subjects, we performed a verification experiment, applying weight, softness, and flexibility as shoe properties. In order to affect the shoe property’s differences to the brain, subjects run for ten min. Before and after running, subjects conducted a paced auditory serial addition task (PASAT) as the particular task; and the subjects’ brain activities during the PASAT are evaluated based on oxyhemoglobin and deoxyhemoglobin relative concentration changes, measured by near-infrared spectroscopy (NIRS). When the brain works actively, oxihemoglobin and deoxyhemoglobin concentration drastically changes; therefore, we calculate the maximum values of concentration changes. In order to normalize relative concentration changes after running, the maximum value are divided by before running maximum value as evaluation parameters. The classification of the groups of shoes is expressed on a self-organizing map (SOM). As a result, deoxyhemoglobin can make clusters for two of the three types of shoes.

Keywords: brain activities, NIRS, PASAT, running shoes

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Authors: Angelina A. Tzacheva, Jaishree Ranganathan

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Interest in (STEM) Science Technology Engineering Mathematics education especially Computer Science education has seen a drastic increase across the country. This fuels effort towards recruiting and admitting a diverse population of students. Thus the changing conditions in terms of the student population, diversity and the expected teaching and learning outcomes give the platform for use of Innovative Teaching models and technologies. It is necessary that these methods adapted should also concentrate on raising quality of such innovations and have positive impact on student learning. Light-Weight Team is an Active Learning Pedagogy, which is considered to be low-stake activity and has very little or no direct impact on student grades. Emotion plays a major role in student’s motivation to learning. In this work we use the student feedback data with emotion classification using surveys at a public research institution in the United States. We use Actionable Pattern Discovery method for this purpose. Actionable patterns are patterns that provide suggestions in the form of rules to help the user achieve better outcomes. The proposed method provides meaningful insight in terms of changes that can be incorporated in the Light-Weight team activities, resources utilized in the course. The results suggest how to enhance student emotions to a more positive state, in particular focuses on the emotions ‘Trust’ and ‘Joy’.

Keywords: actionable pattern discovery, education, emotion, data mining

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Authors: Emily Rivera, Toni DiDona

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The Big Five personality traits is a hierarchical classification of personality traits that applies factor analysis to a personality survey data in order to describe human personality using five broad dimensions: Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness (Fetvadjiev & Van de Vijer, 2015). Research shows that personality constructs underline individual differences in processing conflict and interpersonal relations. (Graziano et al., 1996). This research explores the understudied correlation between the Big Five personality traits and perceived interpersonal conflict in the workplace. It revises social psychological literature on Big Five personality traits within a social context and discusses organizational development journal articles on the perceived efficacy of conflict tactics and approach to interpersonal relationships. The study also presents research undertaken on a survey group of 867 subjects over the age of 18 that were recruited by means of convenience sampling through social media, email, and text messaging. The central finding of this study is that only two of the Big Five personality traits had a significant correlation with perceiving interpersonal conflict in the workplace. Individuals who score higher on agreeableness and neuroticism, perceive more interpersonal conflict in the workplace compared to those that score lower on each dimension. The relationship between both constructs is worthy of research due to its everyday frequency and unique individual psycho-social consequences. This multimethod research associated the Big Five personality dimensions to interpersonal conflict. Its findings that can be utilized to further understand social cognition, person perception, complex social behavior and social relationships in the work environment.

Keywords: five-factor model, interpersonal conflict, personality, The Big Five personality traits

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Authors: Lazhar Belkhiri, Ammar Tiri, Lotfi Mouni, Fatma Elhadj Lakouas

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This study evaluates the surface water quality in the Kebir Rhumel Basin by analyzing hydrochemical parameters. To assess spatial and temporal variations in water quality, we applied the Water Quality Index (WQI), Mann-Kendall (MK) trend analysis, and hierarchical cluster analysis (HCA). Monthly measurements of eleven hydrochemical parameters were collected across eight stations from January 2016 to December 2020. Calcium and sulfate emerged as the dominant cation and anion, respectively. WQI analysis indicated a high incidence of poor water quality at stations Ain Smara (AS), Beni Haroune (BH), Grarem (GR), and Sidi Khalifa (SK), where 89.5%, 90.6%, 78.2%, and 62.7% of samples, respectively, fell into this category. The MK trend analysis revealed a significant upward trend in WQI at Oued Boumerzoug (ON) and SK stations, signaling temporal deterioration in these areas. HCA grouped the dataset into three clusters, covering approximately 22%, 30%, and 48% of the months, respectively. Within these clusters, specific stations exhibited elevated WQI values: GR and ON in the first cluster, OB and SK in the second, and AS, BH, El Milia (EM), and Hammam Grouz (HG) in the third. Furthermore, approximately 38%, 41%, and 38% of samples in clusters one, two, and three, respectively, were classified as having poor water quality. These findings provide essential insights for policymakers in formulating strategies to restore and manage surface water quality in the region.

Keywords: surface water quality, water quality index (WQI), Mann-Kendall Trend Analysis, hierarchical cluster analysis (HCA), spatial-temporal distribution, Kebir Rhumel Basin

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Authors: S. R. Shamili, C. Natarajan, J. Karthikeyan

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Rapid growth of world population and widespread urbanization has remarkably increased the development of the construction industry which caused a huge demand for sand and gravels. Environmental problems occur when the rate of extraction of sand, gravels, and other materials exceeds the rate of generation of natural resources; therefore, an alternative source is essential to replace the materials used in concrete. Now-a-days, electronic products have become an integral part of daily life which provides more comfort, security, and ease of exchange of information. These electronic waste (E-Waste) materials have serious human health concerns and require extreme care in its disposal to avoid any adverse impacts. Disposal or dumping of these E-Wastes also causes major issues because it is highly complex to handle and often contains highly toxic chemicals such as lead, cadmium, mercury, beryllium, brominates flame retardants (BFRs), polyvinyl chloride (PVC), and phosphorus compounds. Hence, E-Waste can be incorporated in concrete to make a sustainable environment. This paper deals with the composition, preparation, properties, classification of E-Waste. All these processes avoid dumping to landfills whilst conserving natural aggregate resources, and providing a better environmental option. This paper also provides a detailed literature review on the behaviour of concrete with incorporation of E-Wastes. Many research shows the strong possibility of using E-Waste as a substitute of aggregates eventually it reduces the use of natural aggregates in concrete.

Keywords: dumping, electronic waste, landfill, toxic chemicals

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Authors: Sankaran Rajendran

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Remote sensing plays a vital role in mapping of resources and monitoring of environments of the earth. In the present research study, mapping and monitoring of clay siltations occurred in the Alkhod Dam of Muscat, Sultanate of Oman are carried out using low-cost multispectral Landsat and ASTER data. The dam is constructed across the Wadi Samail catchment for ground water recharge. The occurrence and spatial distribution of siltations in the dam are studied with five years of interval from the year 1987 of construction to 2014. The deposits are mainly due to the clay, sand, and silt occurrences derived from the weathering rocks of ophiolite sequences occurred in the Wadi Samail catchment. The occurrences of clays are confirmed by minerals identification using ASTER VNIR-SWIR spectral bands and Spectral Angle Mapper supervised image processing method. The presence of clays and their spatial distribution are verified in the field. The study recommends the technique and the low-cost satellite data to similar region of the world.

Keywords: Alkhod Dam, ASTER siltation, Landsat, remote sensing, Oman

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Authors: Nabeel Safdar, Qian Aimin

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Corporate philanthropy is important, as the donations have been considered as a source to improve the image of business entity in modern era of high competition. We used data on annual basis from 2000 to 2014 for 1,248 firms listed at Shanghai and Shenzhen stock exchanges. Results for giving firms reveal that there is curve linear relation of profitability and CP, as profitable firms utilize cash in an efficient way and have fewer amounts of slack resource and tradeoff among stakeholder and agency cost made it more justifiable. We found that more profitability does not mean that the cash flows are available, actually good performing firms or profitable firm also good at cash management. Cash is utilized in an effective way by profitable firms, and have fewer extents of slack resources which generate curvilinear relationship of profitability with Corporate Philanthropy. We found that the trend of Corporate Philanthropy also got affected due to natural disasters. Analysis made by innovation, slack resources and directors salary revealed the positive significant relationship. It is not compulsory that firm should be only profitable for engaging in philanthropy rather they should have abundant slack resources to donate.

Keywords: corporate philanthropy, free cash flows, natural disasters, profitability

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Authors: Larry Powell, Seth Polsley, Drew Casey, Tracy Hammond

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Human activity recognition (HAR) systems have shown positive performance when recognizing repetitive activities like walking, running, and sleeping. Water-based activities are a reasonably new area for activity recognition. However, water-based activity recognition has largely focused on supporting the elite and competitive swimming population, which already has amazing coordination and proper form. Beginner swimmers are not perfect, and activity recognition needs to support the individual motions to help beginners. Activity recognition algorithms are traditionally built around short segments of timed sensor data. Using a time window input can cause performance issues in the machine learning model. The window’s size can be too small or large, requiring careful tuning and precise data segmentation. In this work, we present a method that uses a time window as the initial segmentation, then separates the data based on the change in the sensor value. Our system uses a multi-phase segmentation method that pulls all peaks and valleys for each axis of an accelerometer placed on the swimmer’s lower back. This results in high recognition performance using leave-one-subject-out validation on our study with 20 beginner swimmers, with our model optimized from our final dataset resulting in an F-Score of 0.95.

Keywords: time window, peak/valley segmentation, feature extraction, beginner swimming, activity recognition

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Authors: Alex Kizuk

Abstract:

This paper studies the trope or meme of the Siren in terms of what long-standing cultural continuities can be found in college classrooms today. Those who have raised children may remember reading from Hans Christian Anderson's 'The Little Mermaid' (1836), not to mention regaling them with colorful Disneyesque versions when they were younger. Though Anderson tempered the darker first ending of the story to give the little mermaid more agency in her salvation—a prognostic developed in Disney adaptations—nonetheless, the tale pivots on an image of a 'heavenly realm' that the mermaid may eventually come to know or comprehend as a beloved woman on dry land. Only after 300 years, however, may she hope to see that 'which lives forever' and 'rises through thin air, up to the shining stars. Just as [sea-people] rise through the water to see the lands on earth.' What students today can see in this example is a trope of the agonistic soul in a hard-won disembarkation at a harbour of knowledge--where the seeker after truth may come to know through persistence (300 years)—all that is good and true concerning human life. This paper discusses several such examples from the Great Books and popular culture to suggest that teaching in the world of the 21st century could do worse than accede to some such perennial seeking.

Keywords: the Great Books, tradition, popular culture, 21st century directions in teaching

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Authors: Nassib Abdallah, Pierre Chauvet, Abd El Salam Hajjar, Bassam Daya

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In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

Keywords: brain-computer interface, speech recognition, artificial neural network, electroencephalography, EEG, wernicke area

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Authors: Hamdi Amroun, Yacine Benziani, Mehdi Ammi

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In this paper, we propose an approach of detecting the behavior of the viewers of a TV program in a non-controlled environment. The experiment we propose is based on the use of three types of connected objects (smartphone, smart watch, and a connected remote control). 23 participants were observed while watching their TV programs during three phases: before, during and after watching a TV program. Their behaviors were detected using an approach based on The Dempster Shafer Theory (DST) in two phases. The first phase is to approximate dynamically the mass functions using an approach based on the correlation coefficient. The second phase is to calculate the approximate mass functions. To approximate the mass functions, two approaches have been tested: the first approach was to divide each features data space into cells; each one has a specific probability distribution over the behaviors. The probability distributions were computed statistically (estimated by empirical distribution). The second approach was to predict the TV-viewing behaviors through the use of classifiers algorithms and add uncertainty to the prediction based on the uncertainty of the model. Results showed that mixing the fusion rule with the computation of the initial approximate mass functions using a classifier led to an overall of 96%, 95% and 96% success rate for the first, second and third TV-viewing phase respectively. The results were also compared to those found in the literature. This study aims to anticipate certain actions in order to maintain the attention of TV viewers towards the proposed TV programs with usual connected objects, taking into account the various uncertainties that can be generated.

Keywords: Iot, TV-viewing behaviors identification, automatic classification, unconstrained environment

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Authors: Xu Han, Shanxiong Chen, Shiyu Zhu, Xiaoyu Lin, Fujia Zhao, Dingwang Wang

Abstract:

Character detection is an important issue for character recognition of ancient Yi books. The accuracy of detection directly affects the recognition effect of ancient Yi books. Considering the complex layout, the lack of standard typesetting and the mixed arrangement between images and texts, we propose a character detection method for ancient Yi books based on connected components and regressive character segmentation. First, the scanned images of ancient Yi books are preprocessed with nonlocal mean filtering, and then a modified local adaptive threshold binarization algorithm is used to obtain the binary images to segment the foreground and background for the images. Second, the non-text areas are removed by the method based on connected components. Finally, the single character in the ancient Yi books is segmented by our method. The experimental results show that the method can effectively separate the text areas and non-text areas for ancient Yi books and achieve higher accuracy and recall rate in the experiment of character detection, and effectively solve the problem of character detection and segmentation in character recognition of ancient books.

Keywords: CCS concepts, computing methodologies, interest point, salient region detections, image segmentation

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Authors: Helen Frialde-Dupale

Abstract:

Critical thinking Index (CTI) of 150 third year college students from five State Colleges and Universities (SUCs) in Region I were determined. Only students with Grade Point Average (GPA) of at least 2.0 from four general classification of degree courses, namely: Education, Arts and Sciences, Engineering and Agriculture were included. Specific problem No.1 dealt with the profile variables, namely: age, sex, degree course, monthly family income, number of siblings, high school graduated from, grade point average, personality type, highest educational attainment of parents, and occupation of parents. Problem No. 2 determined the critical thinking index among the respondents. Problem No. 3 investigated whether or not there are significant differences in the critical thinking index among the respondents across the profile variables. While problem No.4 determined whether or not there are significant relationship between the critical thinking index and selected profile variables, namely: age, monthly family income, number of siblings, and grade point average of the respondents. Finally, on problem No. 5, the critical thinking instrument which obtained the lowest rates, were used as basis for outlining an intervention program for enhancing critical thinking index (CTI) of students. The following null hypotheses were tested at 0.05 level of significance: there are no significant differences in the critical thinking index of the third college students across the profile variables; there are no significant relationships between the critical thinking index of the respondents and selected variables, namely: age, monthly family income, number of siblings, and grade point average.

Keywords: attitude as critical thinker, critical thinking applied, critical thinking index, self-perception as critical thinker

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Authors: Sivakumar Singaravelu, Giriprasath Ramanathan, M. D. Raja, Uma Tirichurapalli Sivagnanam

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The goal of the present study was to develop and evaluate composite film for tissue engineering application. The keratin was extracted from bovine horn and used for preparation of keratin (HK), physiologically clotted fibrin (PCF) and gelatin (G) blend films in different stoichiometric ratios (1:1:1, 1:1:2 and 1:1:3) by using solvent casting method. The composite films (HK-PCF-G) were characterized physiochemically using Fourier Transform Infrared Spectroscopy (FTIR), Differential Scanning Calorimetry (DSC), Thermogravimetric Analysis (TGA) and Scanning Electron Microscopy (SEM). The mechanical properties of the composite films were analyzed. The results of tensile strength show that ultimate strength and elongation were 10.72 Mpa and 4.83 MPA respectively for 1:1:3 ratio combination. The SEM image showed a slight smooth surface for 1:1:3 ratio combination compared to other films. In order to impart antibacterial activities, the composite films were loaded with Mupirocin (MP) to act against infection. The composite films acted as a suitable carrier to protect and release the drug in a controlled manner. This developed composite film would be a suitable alternative material for tissue engineering application.

Keywords: bovine horn, keratin, fibrin, gelatin, tensile strength

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