Search results for: schema recognition

Authors: Aadhya Kaul, Gautam Manocha, Preeti Nagrath

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Our environment comprises of a wide variety of plants that are similar to each other and sometimes the similarity between the plants makes the identification process tedious thus increasing the workload of the botanist all over the world. Now all the botanists cannot be accessible all the time for such laborious plant identification; therefore, there is an urge for a quick classification model. Also, along with the identification of the plants, it is also necessary to classify the plant as healthy or not as for a good lifestyle, humans require good food and this food comes from healthy plants. A large number of techniques have been applied to classify the plants as healthy or diseased in order to provide the solution. This paper proposes one such method known as anomaly detection using autoencoders using a set of collections of leaves. In this method, an autoencoder model is built using Keras and then the reconstruction of the original images of the leaves is done and the threshold loss is found in order to classify the plant leaves as healthy or diseased. A dataset of plant leaves is considered to judge the reconstructed performance by convolutional autoencoders and the average accuracy obtained is 71.55% for the purpose.

Keywords: convolutional autoencoder, anomaly detection, web application, FLASK

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Authors: Bienvenu Gael Fouda Mbanga, Zikhona Tywabi-Ngeva, Kriveshini Pillay

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This work highlighted a new method for preparing Nitrogen carbon nanoparticles fused on zinc oxide nanoparticle nanocomposite (N-CNPs/ZnONPsNC) to remove copper ions (Cu²+) from wastewater by sol-gel method and applying the metal-loaded adsorbent in latent fingerprint application. The N-CNPs/ZnONPsNC showed to be an effective sorbent for optimum Cu²+ sorption at pH 8 and 0.05 g dose. The Langmuir isotherm was found to best fit the process, with a maximum adsorption capacity of 285.71 mg/g, which was higher than most values found in other research for Cu²+ removal. Adsorption was spontaneous and endothermic at 25oC. In addition, the Cu²+-N-CNPs/ZnONPsNC was found to be sensitive and selective for latent fingerprint (LFP) recognition on a range of porous surfaces. As a result, in forensic research, it is an effective distinguishing chemical for latent fingerprint detection.

Keywords: latent fingerprint, nanocomposite, adsorption, copper ions, metal loaded adsorption, adsorbent

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Authors: Nebojša Janićijević

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The paper presents the results of a study on work-related values of Generation Z, comprised of young people born between the late 1990s and 2010. Following Millennials, Generation Z is the first generation of digital natives. This is the reason, along with some other circumstances that accompanied them during their growing up, why Generation Z has somewhat different work-related values than previous generations. Since they are just beginning to enter the labor market and will be the majority of the workforce in the next decade or two, it is very important and useful for their employers to understand what Generation Z values when it comes to work. The study was conducted by surveying the students of the Faculty of Economics, University of Belgrade, Serbia, during 2022 and 2023. The research results show that Generation Z values safety, achievement, and status the most in the workplace. From the individual perspective, future employees consider it most important that their job provides good working conditions, recognition for the work performed, and the possibility of achievement. It is noticeable that Generation Z students, to a significant extent, expect to be protected and safe at work in the future, both in terms of the job itself and in terms of social relations. According to the research findings, Generation Z is relatively homogeneous, and no significant differences in work-related values were found among them, except by gender.

Keywords: generation Z, work related values, students, Serbia

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Authors: Sokol Isaraj, Lorela Bendo

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Botulinum toxin is the most commonly used treatment to reduce the appearance of dynamic facial wrinkles. It can smooth out wrinkles and restore a more youthful appearance. Although allergic reactions after botox injection are rare, care should be taken by the physician to diagnose the condition and provide suitable treatment in time. The authors report a case of allergic reaction with angioedema to abobotulinumtoxin A. A 50-year-old woman complaining of dynamic wrinkles was injected in a private clinic with Dysport. After two weeks, she returned to the clinic for the touch-up session. Thirty minutes after the completion of the injections in the crow’s feet area, she described the feeling of mild pain and warmth in the injected area, followed by angioedema. The symptoms couldn’t be controlled by IM corticosteroid, and the patient was referred to a hospital center. After adequate systemic treatment for four days, there was a resolution of the symptoms. Despite the reported safety of abobotulinumtoxin A, this case warns practitioners of unpredictably adverse reactions, which require rapid recognition and intravenous support.

Keywords: botulinum toxin, side effects, angioedema, injections

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Authors: R. Biondi, G. Dys, G. Ferone, T. Renard, M. Zysman

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This paper describes an automated implementable system for impulsive signals detection and recognition. The system uses a Digital Signal Processing device for the detection and identification process. Here the system analyses the signals in real time in order to produce a particular response if needed. The system analyses the signals in real time in order to produce a specific output if needed. Detection is achieved through normalizing the inputs and comparing the read signals to a dynamic threshold and thus avoiding detections linked to loud or fluctuating environing noise. Identification is done through neuronal network algorithms. As a setup our system can receive signals to “learn” certain patterns. Through “learning” the system can recognize signals faster, inducing flexibility to new patterns similar to those known. Sound is captured through a simple jack input, and could be changed for an enhanced recording surface such as a wide-area recorder. Furthermore a communication module can be added to the apparatus to send alerts to another interface if needed.

Keywords: sound detection, impulsive signal, background noise, neural network

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Authors: Benghenia Hadj Abd El Kader

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Sleep apnea events as obstructive, central, mixed or hypopnea are characterized by frequent breathing cessations or reduction in upper airflow during sleep. An advanced method for analyzing the patterning of biomedical signals to recognize obstructive sleep apnea and hypopnea is presented. In the aim to extract characteristic parameters, which will be used for classifying the above stated (obstructive, central, mixed) sleep apnea and hypopnea, the proposed method is based first on the analysis of polysomnography signals such as electrocardiogram signal (ECG) and electromyogram (EMG), then classification of the (obstructive, central, mixed) sleep apnea and hypopnea. The analysis is carried out using the wavelet transform technique in order to extract characteristic parameters whereas classification is carried out by applying the SVM (support vector machine) technique. The obtained results show good recognition rates using characteristic parameters.

Keywords: obstructive, central, mixed, sleep apnea, hypopnea, ECG, EMG, wavelet transform, SVM classifier

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Authors: Mohd Zamri Osman, Mohd Aizaini Maarof, Mohd Foad Rohani

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Human skin detection recognized as the primary step in most of the applications such as face detection, illicit image filtering, hand recognition and video surveillance. The performance of any skin detection applications greatly relies on the two components: feature extraction and classification method. Skin color is the most vital information used for skin detection purpose. However, color feature alone sometimes could not handle images with having same color distribution with skin color. A color feature of pixel-based does not eliminate the skin-like color due to the intensity of skin and skin-like color fall under the same distribution. Hence, the statistical color analysis will be exploited such mean and standard deviation as an additional feature to increase the reliability of skin detector. In this paper, we studied the effectiveness of statistical color feature for human skin detection. Furthermore, the paper analyzed the integrated color and texture using eight classifiers with three color spaces of RGB, YCbCr, and HSV. The experimental results show that the integrating statistical feature using Random Forest classifier achieved a significant performance with an F1-score 0.969.

Keywords: color space, neural network, random forest, skin detection, statistical feature

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Authors: Budi Annisa Sidi

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Present-day Indonesia maintains a narrative of a culturally plural but unified nation. At the same time, multicultural policies extend different degrees of recognition, accommodation, toleration and even discrimination towards different socio-cultural groups. In conjunction with different ethnographic landscapes across regions in Indonesia, this approach leads to a varied experience and understanding of national identity and multiculturalism among people. As a result, governments seeking to maintain national unity while practicing multiculturalism have to juggle different expectations. This situation is examined through the microcosms of university students using questionnaires followed up by focus group discussions and personal interviews. A comparison between university students across four different provinces in Indonesia (Aceh, Jakarta, West Java and the Moluccas) highlights the influence of one’s surroundings on their perception of multiculturalism. Students in the more heterogeneous areas generally show more acceptance towards diversity compared to students in primarily homogenous areas who have little actual experience in dealing with diversity. Regardless of their environment, students claim to have positive feelings and a strong sense of attachment to Indonesia but hold different ideas of what constitutes an ideal Indonesian national identity.

Keywords: Indonesia, multiculturalism, national identity, nationalism

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Authors: Sylvie Michel, Emmanuelle Gagnou, Joanne Hamet

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Researchers in the fields of AI and Robotics frequently encounter ethical dilemmas throughout their research endeavors. Various ethical challenges have been pinpointed in the existing literature, including biases and discriminatory outcomes, diffusion of responsibility, and a deficit in transparency within AI operations. This research aims to pinpoint these ethical quandaries faced by researchers and shed light on the mechanisms behind ethical decision-making in the research process. By synthesizing insights from existing literature and acknowledging prevalent shortcomings, such as overlooking the heterogeneous nature of decision-making, non-accumulative results, and a lack of consensus on numerous factors due to limited empirical research, the objective is to conceptualize and validate a model. This model will incorporate influences from individual perspectives and situational contexts, considering potential moderating factors in the ethical decision-making process. Qualitative analyses were conducted based on direct observation of an AI/Robotics research team focusing on collaborative robotics for several months. Subsequently, semi-structured interviews with 16 team members were conducted. The entire process took place during the first semester of 2023. Observations were analyzed using an analysis grid, and the interviews underwent thematic analysis using Nvivo software. An initial finding involves identifying the ethical challenges that AI/robotics researchers confront, underlining a disparity between practical applications and theoretical considerations regarding ethical dilemmas in the realm of AI. Notably, researchers in AI prioritize the publication and recognition of their work, sparking the genesis of these ethical inquiries. Furthermore, this article illustrated that researchers tend to embrace a consequentialist ethical framework concerning safety (for humans engaging with robots/AI), worker autonomy in relation to robots, and the societal implications of labor (can robots displace jobs?). A second significant contribution entails proposing a model for ethical decision-making within the AI/Robotics research sphere. The model proposed adopts a process-oriented approach, delineating various research stages (topic proposal, hypothesis formulation, experimentation, conclusion, and valorization). Across these stages and the ethical queries, they entail, a comprehensive four-point comprehension of ethical decision-making is presented: recognition of the moral quandary; moral judgment, signifying the decision-maker's aptitude to discern the morally righteous course of action; moral intention, reflecting the ability to prioritize moral values above others; and moral behavior, denoting the application of moral intention to the situation. Variables such as political inclinations ((anti)-capitalism, environmentalism, veganism) seem to wield significant influence. Moreover, age emerges as a noteworthy moderating factor. AI and robotics researchers are continually confronted with ethical dilemmas during their research endeavors, necessitating thoughtful decision-making. The contribution involves introducing a contextually tailored model, derived from meticulous observations and insightful interviews, enabling the identification of factors that shape ethical decision-making at different stages of the research process.

Keywords: ethical decision making, artificial intelligence, robotics, research

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Authors: Hakan Bozdoğan

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The goal of this paper is to evaluate the species richness, diversity and structure of in different habitats in the Kahramanmaraş Province in Turkey by using a mathematical program called as Geo-Gebra Software. The Ascalaphidae family comprises the most visually remarkable members of the order Neuroptera due to large dimensions, aerial predatory behaviour and dragonfly-like (or even butterfly-like) habits, allowing an immediate recognition also for occasional observers. Otherwise, they are one of the more poorly known families of the order in respect to biology, ecology and especially larval morphology. This discrepancy appears particularly noteworthy considering that it is a fairly large family (ca. 430 species) widely distributed in tropical and temperate areas of the World. The use of Dynamic Geometry, Analytical Softwares provides researchers a great way of visualising mathematical objects and encourage them to carry out tasks to interact with such objects and add to support of their researching. In this study we implemented; Circle with Center Through Point, Perpendicular Line, Vectors and Rays, Segments and Locus to elucidate the ecological and habitat behaviours of Butterfly-like lacewings in an analytical plane by using Geo-Gebra.

Keywords: neuroptera, Ascalaphidae, geo-gebra software, habitat selectivity

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Authors: Yujie Yuan, Yiyang Fan, Hong Fan

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Objective: The RNA methylation recognition protein Aly/REF export factor (ALYREF) is considered one type of “reader” protein acting as a recognition protein of m5C, has been reported involved in several biological progresses including cancer initiation and progression. 5-methylcytosine (m5C) is a conserved and prevalent RNA modification in all species, as accumulating evidence suggests its role in the promotion of tumorigenesis. It has been claimed that ALYREF mediates nuclear export of mRNA with m5C modification and regulates biological effects of cancer cells. However, the systematical regulatory pathways of ALYREF in cancer tissues have not been clarified, yet. Methods: The expression level of ALYREF in pan-cancer and their normal tissues was compared through the data acquired from The Cancer Genome Atlas (TCGA). The University of Alabama at Birmingham Cancer data analysis Portal UALCAN was used to analyze the relationship between ALYREF and clinical pathological features. The relationship between the expression level of ALYREF and prognosis of pan-cancer, and the correlation genes of ALYREF were figured out by using Gene Expression Correlation Analysis database GEPIA. Immune related genes were obtained from TISIDB (an integrated repository portal for tumor-immune system interactions). Immune-related research was conducted by using Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and TIMER. Results: Based on the data acquired from TCGA, ALYREF has an obviously higher-level expression in various types of cancers compared with relevant normal tissues excluding thyroid carcinoma and kidney chromophobe. The immunohistochemical images on The Human Protein Atlas showed that ALYREF can be detected in cytoplasm, membrane, but mainly located in nuclear. In addition, a higher expression level of ALYREF in tumor tissue generates a poor prognosis in majority of cancers. According to the above results, cancers with a higher expression level of ALYREF compared with normal tissues and a significant correlation between ALYREF and prognosis were selected for further analysis. By using TISIDB, we found that portion of ALYREF co-expression genes (such as BIRC5, H2AFZ, CCDC137, TK1, and PPM1G) with high Pearson correlation coefficient (PCC) were involved in anti-tumor immunity or affect resistance or sensitivity to T cell-mediated killing. Furthermore, based on the results acquired from GEPIA, there was significant correlation between ALYREF and PD-L1. It was exposed that there is a negative correlation between the expression level of ALYREF and ESTIMATE score. Conclusion: The present study indicated that ALYREF plays a vital and universal role in cancer initiation and progression of pan-cancer through regulating mitotic progression, DNA synthesis and metabolic process, and RNA processing. The correlation between ALYREF and PD-L1 implied ALYREF may affect the therapeutic effect of immunotherapy of tumor. More evidence revealed that ALYREF may play an important role in tumor immunomodulation. The correlation between ALYREF and immune cell infiltration level indicated that ALYREF can be a potential therapeutic target. Exploring the regulatory mechanism of ALYREF in tumor tissues may expose the reason for poor efficacy of immunotherapy and offer more directions of tumor treatment.

Keywords: ALYREF, pan-cancer, immunotherapy, PD-L1

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Authors: Anna Bjällmark, Victoria Persson, Bodil Karlsson, May Bazzi

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Introduction: Interventional radiology (IR) is a continuously growing discipline that allows minimally invasive treatments of various medical conditions. The IR environment is, in several ways, comparable to the complex and accident-prone operation room (OR) environment. This implies that the IR environment may also be associated with various types of risks related to the work process and communication in the team. Patient safety is a central aspect of healthcare and involves the prevention and reduction of adverse events related to patient care. To maintain patient safety, it is crucial to build a safety culture where the staff are encouraged to report events and incidents that may have affected patient safety. It is also important to continuously evaluate the staff´s attitudes to patient safety. Despite the increasing number of IR procedures, research on the staff´s view regarding patients is lacking. Therefore, the main aim of the study was to describe and compare the IR team members' attitudes to patient safety. The secondary aim was to evaluate whether the WHO safety checklist was routinely used for IR procedures. Methods: An electronic survey was distributed to 25 interventional units in Sweden. The target population was the staff working in the IR team, i.e., physicians, radiographers, nurses, and assistant nurses. A modified version of the Safety Attitudes Questionnaire (SAQ) was used. Responses from 19 of 25 IR units (44 radiographers, 18 physicians, 5 assistant nurses, and 1 nurse) were received. The respondents rated their level of agreement for 27 items related to safety culture on a five-point Likert scale ranging from “Disagree strongly” to “Agree strongly.” Data were analyzed statistically using SPSS. The percentage of positive responses (PPR) was calculated by taking the percentage of respondents who got a scale score of 75 or higher. The respondents rated which corresponded to response options “Agree slightly” or “Agree strongly”. Thus, average scores ≥ 75% were classified as “positive” and average scores < 75% were classified as “non-positive”. Findings: The results indicated that the IR team had the highest factor scores and the highest percentages of positive responses in relation to job satisfaction (90/94%), followed by teamwork climate (85/92%). In contrast, stress recognition received the lowest ratings (54/25%). Attitudes related to these factors were relatively consistent between different professions, with only a few significant differences noted (Factor score: p=0.039 for job satisfaction, p=0.050 for working conditions. Percentage of positive responses: p=0.027 for perception of management). Radiographers tended to report slightly lower values compared to other professions for these factors (p<0.05). The respondents reported that the WHO safety checklist was not routinely used at their IR unit but acknowledged its importance for patient safety. Conclusion: This study reported high scores concerning job satisfaction and teamwork climate but lower scores concerning perception of management and stress recognition indicating that the latter are areas of improvement. Attitudes remained relatively consistent among the professions, but the radiographers reported slightly lower values in terms of job satisfaction and perception of the management. The WHO safety checklist was considered important for patient safety.

Keywords: interventional radiology, patient safety, safety attitudes questionnaire, WHO safety checklist

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Authors: Tathiana Yesenia Coy Mondragón, Jossitt William Vargas Cruz, Cristian Leonardo Gutiérrez Gómez

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The study of cosmic rays is based on two fundamental pillars: the detection of secondary cosmic rays on the Earth's surface and the detection of the source and origin of the cascade. In addition, the constant flow of RC generates a lot of interest for study due to the incidence of various natural phenomena, which makes it relevant to characterize their incidence parameters to determine their effect not only at subsoil or terrestrial surface levels but also throughout the atmosphere. To determine the physical parameters of the primary cosmic ray, the implementation of robust algorithms capable of reconstructing the cascade from the measured values is required, with a high level of reliability. Therefore, it is proposed to build a machine learning system that will be fed from the cosmic ray simulations in CORSIKA at different energies that lie in a range [10⁹-10¹²] eV. in order to generate a trained particle and pattern recognition system to obtain greater efficiency when inferring the nature of the origin of the cascade for EAS in the atmosphere considering atmospheric models.

Keywords: CORSIKA, cosmic rays, eas, Colombia

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Authors: Bin Liu

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As one of the most important tasks in protein sequence analysis, protein remote homology detection has been studied for decades. Currently, the profile-based methods show state-of-the-art performance. Position-Specific Frequency Matrix (PSFM) is widely used profile. However, there exists noise information in the profiles introduced by the amino acids with low frequencies. In this study, we propose a method to remove the noise information in the PSFM by removing the amino acids with low frequencies called Top frequency profile (TFP). Three new matrix transformation methods, including Autocross covariance (ACC) transformation, Tri-gram, and K-separated bigram (KSB), are performed on these profiles to convert them into fixed length feature vectors. Combined with Support Vector Machines (SVMs), the predictors are constructed. Evaluated on two benchmark datasets, and experimental results show that these proposed methods outperform other state-of-the-art predictors.

Keywords: protein remote homology detection, protein fold recognition, top frequency profile, support vector machines

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Authors: Chirag Kothawade, Gourie Jagtap, PreetKaur Relusinghani, Vedang Chavan, Smitha S. Bhosale

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Psychological research has proven that music relieves stress, elevates mood, and is responsible for the release of “feel-good” chemicals like oxytocin, serotonin, and dopamine. It comes as no surprise that music has been a popular tool in rehabilitation centers and therapy for various disorders, thus with the interminably rising numbers of people facing mental health-related issues across the globe, addressing mental health concerns is more crucial than ever. Despite the existing music recommendation systems, there is a dearth of holistically curated algorithms that take care of the needs of users. Given that, an undeniable majority of people turn to music on a regular basis and that music has been proven to increase cognition, memory, and sleep quality while reducing anxiety, pain, and blood pressure, it is the need of the hour to fashion a product that extracts all the benefits of music in the most extensive and deployable method possible. Our project aims to ameliorate our users’ mental state by building a comprehensive mood-based music recommendation system called “Viby”.

Keywords: language, communication, speech recognition, interaction

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Authors: Nathakhun Wiroonsri

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There are various cluster validity measures used for evaluating clustering results. One of the main objectives of using these measures is to seek the optimal unknown number of clusters. Some measures work well for clusters with different densities, sizes and shapes. Yet, one of the weaknesses that those validity measures share is that they sometimes provide only one clear optimal number of clusters. That number is actually unknown and there might be more than one potential sub-optimal option that a user may wish to choose based on different applications. We develop two new cluster validity indices based on a correlation between an actual distance between a pair of data points and a centroid distance of clusters that the two points are located in. Our proposed indices constantly yield several peaks at different numbers of clusters which overcome the weakness previously stated. Furthermore, the introduced correlation can also be used for evaluating the quality of a selected clustering result. Several experiments in different scenarios, including the well-known iris data set and a real-world marketing application, have been conducted to compare the proposed validity indices with several well-known ones.

Keywords: clustering algorithm, cluster validity measure, correlation, data partitions, iris data set, marketing, pattern recognition

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Authors: Seyedmohammad Mousavian, Hanlu Fan, Quingliang Tang

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Climate change is emerging as a severe threat to our society, so businesses are expected to take actions to mitigate carbon emissions. However, the actions to be taken depend on managers’ perceptions of climate change risks. Yet, there is scant research on this issue, and understanding of the determinants of corporate perceptions of climate change is extremely limited. The purpose of this study is to close this gap by examining the relationship between perceptions of climate risk and firm-level and country-level factors. In this study, climate change risk captures physical, regulatory, and other risks, and we use data from European companies that participated in CDP from 2010 to 2017. This study reveals those perceptions of climate change risk are significantly positively associated with the environmental, social, and governance score, firm size, and membership in a carbon-intensive sector. In addition, we find that managers in firms operating in a geographic area that is sensitive to the consequences of global warming are more likely to perceive and formally recognize carbon-related risks in their CDP reports.

Keywords: carbon actions, CDP, climate change risk, risk perception

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Authors: Hamed Ouattara, Pierre Duthon, Frédéric Bernardin, Omar Ait Aider, Pascal Salmane

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In this article, we present three neural network architectures for real-time classification of weather conditions (sunny, rainy, snowy, foggy) from images. Inspired by recent advances in style transfer, two of these architectures -Truncated ResNet50 and Truncated ResNet50 with Gram Matrix and Attention- surpass the state of the art and demonstrate re-markable generalization capability on several public databases, including Kaggle (2000 images), Kaggle 850 images, MWI (1996 images) [1], and Image2Weather [2]. Although developed for weather detection, these architectures are also suitable for other appearance-based classification tasks, such as animal species recognition, texture classification, disease detection in medical images, and industrial defect identification. We illustrate these applications in the section “Applications of Our Models to Other Tasks” with the “SIIM-ISIC Melanoma Classification Challenge 2020” [3].

Keywords: weather simulation, weather measurement, weather classification, weather detection, style transfer, Pix2Pix, CycleGAN, CUT, neural style transfer

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Authors: Peter Adu

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Although research on the concept of Mental Health Literacy (MHL) is growing internationally, to the authors’ best of knowledge, the beliefs and knowledge of Ghanaians on specific mental disorders have not yet been explored. This vignette study was conducted to explore the relationships between religiosity, education, stigmatization, and MHL among Ghanaians using a sample of laypeople (N = 409). The adapted questionnaire presented two vignettes (depression and schizophrenia) about a hypothetical person. The results revealed that more participants were able to recognize depression (47.4%) than schizophrenia (15.9%). Religiosity was not significantly associated with recognition of mental disorders (MHL) but was positively related with both social and personal stigma for depression and negatively associated with personal and perceived stigma for schizophrenia. Moreover, education was found to relate positively with MHL and negatively with perceived stigma. Finally, perceived stigma was positively associated with MHL, whereas personal stigma for schizophrenia related negatively to MHL. In conclusion, education but not religiosity predicted identification accuracy, but both predictors were associated with various forms of stigma. Findings from this study have implications for MHL and anti-stigma campaigns in Ghana and other developing countries in the region.

Keywords: depression, education, mental health literacy, religiosity, schizophrenia

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Authors: Sarah M. Bolger

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There is a clear recognition that humanitarian and development workers are operating more and more frequently in situations of protracted crises, with conflict and violence undermining long-term development efforts. First coined at the World Humanitarian Summit in 2016, the humanitarian-development-peacebuilding nexus – or 'Triple Nexus' - seeks to promote greater cooperation and policy and program coherence amongst organizations working within and across the nexus. However, despite the clear need for such an approach, the Triple Nexus has failed to gain much traction. This is largely due to the lack of conceptual clarity for actors on the ground and the disconnect between the theory of the Triple Nexus and what that means in practice. This paper seeks to identify the key challenges in shifting from the conceptual definition of the Triple Nexus and what that can look like, particularly for multi-mandated organizations, to the operationalization of the Triple Nexus approach. It adopts a case study approach, examining a selection of organizations and programs and their approaches to the Triple Nexus in order to extract key challenges and lessons learned. Finally, key recommendations are provided on how these challenges can be overcome, allowing for the operationalization of the Triple Nexus and ultimately for a more integrated and sustainable approach to humanitarian, development, and peacebuilding work.

Keywords: development, humanitarian, peacebuilding, triple nexus

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Authors: Maria Tagarelli De Monte

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Since its formal recognition in 2021, Italian Sign Language (LIS) and interpreters’ education has become a topic for higher education in Italian universities. In April 2022, Italian universities have been invited to present their proposals to create sign language courses for interpreters’ training for both LIS and tactile LIS. As a result, a few universities have presented a three-year course leading candidate students from the introductory level to interpreters. In such a context, there is an open debate not only on the fact that three years may not be enough to prepare skillful interpreters but also on the need to refer to international standards in the definition of the training path to follow. Among these, are the Common European Framework of Reference (CEFR) for languages and Dublin’s descriptors. This contribution will discuss the potentials and the challenges given by LIS training in academic settings, by comparing traditional studies to the requests coming from universities. Particular attention will be given to the use of CEFR as a reference document for the Italian Sign Language Curriculum. Its use has given me the chance to reflect on how LIS can be taught in higher education, and the adaptations that need to be addressed to respect the visual-gestural nature of sign language and the formal requirements of academic settings.

Keywords: Italian sign language, higher education, sign language curriculum, interpreters education, CEFR

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Authors: Ghafor Ahmadi, Nader Bohloli Zynab

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Rapid scientific changes are the driving force of upheaval. As new technologies arrive, human’s life changes and information becomes one of the productive sources besides other factors. Optimum application of each technology depends on precise recognition of that technology. Options of mobile phones are constantly developing and evolving. Meanwhile, one of the influential variables for improving the performance and eternity of organizations is creativity. One of the new technologies tied with development and innovation is mobile phone. In this research, the contribution of different dimensions of mobile technologies such as perceived use, perceived enjoyment, continuance intention, confirmation and satisfaction to creativity of employees were investigated. Statistical population included 510 employees of Kermanshah Petrochemical Company. Sample size was defined 217 based on Morgan and Krejcie table. This study is descriptive and data gathering instrument was a questionnaire. Applying SPSS software, linear regression was analyzed. It was found out that all dimensions of mobile technologies except satisfaction affect on creativity of employees.

Keywords: mobile technologies, continuance intention, perceived enjoyment, perceived use, confirmation, satisfaction, creativity

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Authors: Teodora Mocan, Matea Cristian, Cornel Iancu, Flaviu A. Tabaran, Florin Zaharie, Bartos Dana, Lucian Mocan

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Colon cancer (CC) a lethal human malignancy, is one of the most commonly diagnosed cancer. With its high increased mortality rate, as well as low survival rate combined with high resistance to chemotherapy CC, represents one of the most important global health issues. In the presented research, we have developed a distinct nanostructured colon carcinoma vaccine model based on a nano-biosystem composed of 39 nm gold nanoparticles conjugated to colon cancer epitopes. We prove by means of proteomic analysis, immunocytochemistry, flow cytometry and hyperspectral microscopy that our developed nanobioconjugate was able to contribute to an optimal prophylactic effect against CC by promoting major histocompatibility complex mediated (MHC) antigen presentation by dendritic cells. We may conclude that the proposed immunoprophylactic approach could be more effective than the current treatments of CC because it promotes recognition of the tumoral antigens by the immune system.

Keywords: anticancer vaccine, colon cancer, gold nanoparticles, tumor antigen

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Authors: Fakhruddin Esa, Yusri Yusof

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The confirmatory of Total Quality Management (TQM) implementation is most vital in quality management. This paper focuses on employees' perceptions towards TQM implementation in a local automotive company supplier. The objectives of this study are first and foremost to determine the perception of TQM implementation among the staff, and secondly to ascertain the correlation between the variables, and lastly to identify the relative influence of the 10 TQM variables on the cost of poor quality (COPQ). The TQM implementation is perceived to be moderate. All correlation is found to be significant and five variables having positively moderate to high correlation. Out of 10 variables, quality system improvement, reward and recognition and customer focus influence the perceived COPQ. This study extended a discussion on these three variables contribution to TQM in general and the human resource development in the organization. A significant recommendation to lowering costs of internal error, such as trouble shooting and scraps are also discussed. Certain components of further research that would add value to this study have also been suggested and perhaps could be implemented at policy-level initiatives.

Keywords: cost of poor quality (COPQ), correlation, total quality management (TQM), variables

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Authors: Morten Stigaard Laursen, Rasmus Nyholm Jørgensen, Mads Dyrmann, Robert Poulsen

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For the past three years, the Danish project, RoboWeedSupport, has sought to bridge the gap between the potential herbicide savings using a decision support system and the required weed inspections. In order to automate the weed inspections it is desired to generate a map of the weed species present within the field, to generate the map images must be captured with samples covering the field. This paper investigates the economical cost of performing this data collection based on a camera system mounted on a all-terain vehicle (ATV) able to drive and collect data at up to 50 km/h while still maintaining a image quality sufficient for identifying newly emerged grass weeds. The economical estimates are based on approximately 100 hectares recorded at three different locations in Denmark. With an average image density of 99 images per hectare the ATV had an capacity of 28 ha per hour, which is estimated to cost 6.6 EUR/ha. Alternatively relying on a boom solution for an existing tracktor it was estimated that a cost of 2.4 EUR/ha is obtainable under equal conditions.

Keywords: weed mapping, integrated weed management, weed recognition, image acquisition

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Authors: Ollie Hall, Salma ElSayed

Abstract:

Affective adaptation is a novel way for game designers to add an extra layer of engagement to their productions. When player’s emotions factor in game design, endless possibilities for creative gameplay emerge. Whilst gaining popularity, existing affective game research mostly runs controlled experiments carried in restrictive settings and relies on one or more specialist devices for measuring a player’s emotional state. These conditions, albeit effective, are not necessarily realistic. Moreover, the simplified narrative and intrusive wearables may not be suitable for the average player. This exploratory study investigates delivering an immersive affective experience in the wild with minimal requirements in an attempt for the average developer to reach the average player. A puzzle game is created with a rich narrative and creative mechanics. It employs both explicit and implicit adaptation and only requires a web camera. Participants played the game on their own machines in various settings. Whilst it was rated feasible, very engaging, and enjoyable, it remains questionable whether a fully immersive experience was delivered due to the limited sample size.

Keywords: affective games, dynamic adaptation, emotion recognition, game design

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Authors: Reihaneh Ahmadzadeh Ghavidel

Abstract:

Free radicals such as reactive oxygen species (ROS) have detrimental effects on human health through several mechanisms. On the other hand, antioxidant molecules reduce free radical generation in biologic systems. Synthetic antioxidants, which are used in food industry, have also negative impact on human health. Therefore recognition of natural antioxidants such as anthocyanins can solve these problems simultaneously. Coleus (Solenostemon scutellarioides) with red leaves is a rich source of anthocyanins compounds. In this study we evaluated the effect of time (10, 20 and 30 min) and temperature (40, 50 and 60° C) on optimization of anthocyanin extraction using surface response method. In addition, the study was aimed to determine maximum extraction for anthocyanin from coleus plant using ultrasound method. The results indicated that the optimum conditions for extraction were 39.84 min at 69.25° C. At this point, total compounds were achieved 3.7451 mg 100 ml⁻¹. Furthermore, under optimum conditions, anthocyanin concentration, extraction efficiency, ferric reducing ability, total phenolic compounds and EC50 were registered 3.221931, 6.692765, 223.062, 3355.605 and 2.614045, respectively.

Keywords: anthocyanin, antioxidant, coleus, extraction, sonication

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Authors: Aisulu Tileukulova, Zhexebay Dauren

Abstract:

Radar plays an important role because it is widely used in civil and military fields. Target detection is one of the most important radar applications. The accuracy of detecting inconspicuous aerial objects in radar facilities is lower against the background of noise. Convolutional neural networks can be used to improve the recognition of this type of aerial object. The purpose of this work is to develop an algorithm for recognizing aerial objects using convolutional neural networks, as well as training a neural network. In this paper, the structure of a convolutional neural network (CNN) consists of different types of layers: 8 convolutional layers and 3 layers of a fully connected perceptron. ReLU is used as an activation function in convolutional layers, while the last layer uses softmax. It is necessary to form a data set for training a neural network in order to detect a target. We built a Confusion Matrix of the CNN model to measure the effectiveness of our model. The results showed that the accuracy when testing the model was 95.7%. Classification of echo signals using CNN shows high accuracy and significantly speeds up the process of predicting the target.

Keywords: radar, neural network, convolutional neural network, echo signals

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Authors: Waleed Al-Nuaimy, Luke Anastassiou, Manjinder Kainth

Abstract:

As the process of teaching has evolved with the advent of new technologies over the ages, so has the process of learning. Educators have perpetually found themselves on the lookout for new technology-enhanced methods of teaching in order to increase learning efficiency and decrease ever expanding workloads. Shortly after the invention of the internet, web-based learning started to pick up in the late 1990s and educators quickly found that the process of providing learning material and marking assignments could change thanks to the connectivity offered by the internet. With the creation of early web-based virtual learning environments (VLEs) such as SPIDER and Blackboard, it soon became apparent that VLEs resulted in higher reported computer self-efficacy among students, but at the cost of students being less satisfied with the learning process . It may be argued that the impersonal nature of VLEs, and their limited functionality may have been the leading factors contributing to this reported dissatisfaction. To this day, often faced with the prospects of assigning colossal engineering cohorts their homework and assessments, educators may frequently choose optimally curated assessment formats, such as multiple-choice quizzes and numerical answer input boxes, so that automated grading software embedded in the VLEs can save time and mark student submissions instantaneously. A crucial skill that is meant to be learnt during most science and engineering undergraduate degrees is gaining the confidence in using, solving and deriving mathematical equations. Equations underpin a significant portion of the topics taught in many STEM subjects, and it is in homework assignments and assessments that this understanding is tested. It is not hard to see that this can become challenging if the majority of assignment formats students are engaging with are multiple-choice questions, and educators end up with a reduced perspective of their students’ ability to manipulate equations. Artificial intelligence (AI) has in recent times been shown to be an important consideration for many technologies. In our paper, we explore the use of new AI based software designed to work in conjunction with current VLEs. Using our experience with the software, we discuss its potential to solve a selection of problems ranging from impersonality to the reduction of educator workloads by speeding up the marking process. We examine the software’s potential to increase learning efficiency through its features which claim to allow more customized and higher-quality feedback. We investigate the usability of features allowing students to input equation derivations in a range of different forms, and discuss relevant observations associated with these input methods. Furthermore, we make ethical considerations and discuss potential drawbacks to the software, including the extent to which optical character recognition (OCR) could play a part in the perpetuation of errors and create disagreements between student intent and their submitted assignment answers. It is the intention of the authors that this study will be useful as an example of the implementation of AI in a practical assessment scenario insofar as serving as a springboard for further considerations and studies that utilise AI in the setting and marking of science and engineering assignments.

Keywords: engineering education, assessment, artificial intelligence, optical character recognition (OCR)

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Authors: Tamene Keneni, Tibebu Yohannes

Abstract:

The prevalence of autism spectrum disorder (ASD) in general and autism in particular is on the rise globally, and the need for evidence-based intervention and care for children with autism has grown, too. However, evidence on autism is scanty in developing countries, including Ethiopia. With the aim to help fill the gap and paucity in research into the issue, the main purpose of this study is to explore, better understand, and document the experiences and perceptions of parents of children with autism. To this end, we used a qualitative survey to collect data from a convenient sample of parents raising a child with autism. The data collected were subjected to qualitative analysis that yielded several themes and subthemes, including late diagnosis, parents’ reactions to diagnosis, sources of information during and after diagnosis, differing reactions to having a child with autism from siblings, extended family members, and the larger community, attribution of autism to several causes by the community, lack of recognition and open discussion of autism and lack of appropriated public educational and health care services for children with autism and their parents. The themes and subthemes identified were discussed in light of existing literature, and implications for practice were drawn.

Keywords: ASD, autism, children with autism, raising children with autism

Procedia PDF Downloads 169