Search results for: enantiomeric recognition

Authors: Maryam Panahiazar, Andrew M. Bishara, Yorick Chern, Roohallah Alizadehsani, Dexter Hadleye, Ramin E. Beygui

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Early detection plays a crucial role in enhancing the outcome for a patient with coronary artery disease (CAD). We utilized a big data analytics platform on ~23,000 patients with CAD from a total of 960,129 UCSF patients in 8 years. We traced the patients from their first encounter with a physician to diagnose and treat CAD. Characteristics such as demographic information, comorbidities, vital, lab tests, medications, and procedures are included. There are statistically significant gender-based differences in patients younger than 60 years old from the time of the first physician encounter to coronary artery bypass grafting (CABG) with a p-value=0.03. There are no significant differences between the patients between 60 and 80 years old (p-value=0.8) and older than 80 (p-value=0.4) with a 95% confidence interval. This recognition would affect significant changes in the guideline for referral of the patients for diagnostic tests expeditiously to improve the outcome by avoiding the delay in treatment.

Keywords: electronic medical records, coronary artery disease, data analytics, young women

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Authors: Reza Rezaeipour Honarmandzad

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Wavelet analysis is an exciting method for solving difficult problems in mathematics, physics, and engineering, with modern applications as diverse as wave propagation, data compression, signal processing, image processing, pattern recognition, etc. Wavelets allow complex information such as signals, images and patterns to be decomposed into elementary forms at different positions and scales and subsequently reconstructed with high precision. In this paper a wavelet-based signal processing algorithm for airplane cable fault location is proposed. An orthogonal discrete wavelet decomposition and reconstruction algorithm is used to eliminate the noise in the aircraft cable fault signal. The experiment result has shown that the character of emission pulse and reflect pulse used to test the aircraft cable fault point are reserved and the high-frequency noise are eliminated by means of the proposed algorithm in this paper.

Keywords: wavelet analysis, signal processing, orthogonal discrete wavelet, noise, aircraft cable fault signal

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Authors: Shierwin Agagen Cabunilas

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The unitary form of Philippine government has built a tradition of bureaucracy that strengthened oligarch and clientele politics. Consequently, the Philippines is lagged behind development. There is so much poverty, unemployment, and inadequate social services. In addition, it seems that the rights of national ethnic minority groups like the Igorots to develop their political and economic interests, linguistic and cultural heritage are neglected. Given these circumstances, a paradigm shift is inevitable. The author advocates a transition from a unitary to a federal system of government. Contrary to the notion that a unitary system facilitates better governance, it actually stifles it. As a unitary government, the Philippines seems (a) to exhibit incompetence in delivering efficient, necessary services to the people and (b) to exclude the minority from political participation and policy making. This shows that Philippine unitary system is highly centralized and operates from a top-bottom scheme. However, a federal system encourages decentralization, plurality and political participation. In my view, federalism is beneficial to the Philippine society and congenial to the Igorot indigenous peoples insofar as participative decision-making and development goals are concerned. This research employs critical and constructive analyses. The former interprets some complex practices of Philippine politics while the latter investigates how theories of federalism can be appropriated to deal with political deficits, ethnic diversity, and indigenous peoples’ rights to self-determination. The topic is developed accordingly: First, the author briefly examines the unitary structure of the Philippines and its impact on inter-governmental affairs and processes, asserting that bureaucracy and corruption, for example, are counterproductive to a participative political life, to economic development and to the recognition of national ethnic minorities. Second, he scrutinizes why federalism might transform this. Here, he assesses various opposing philosophical contentions on federal system in managing ethnically diverse society, like the Philippines, and argue that decentralization of political power, economic and cultural developments are reasons to exit from unitary government. Third, he suggests that federalism can be instrumental to Igorots self-determination. Self-determination is neither opposed to national development nor to the ideals of democracy – liberty, justice, solidarity. For example, as others have already noted, a politics in the vernacular facilitates greater participation among the people. Hence, there is a greater chance to arrive at policies that serve the interest of the people. Some may wary that decentralization disintegrates a nation. According to the author, however, the recognition of minority rights which includes self-determination may promote filial devotion to the state. If Igorot indigenous peoples have access to suitable institutions to determine their political life, economic goals, social needs, i.e., education, culture, language, chances are it moves the country forward to development fostering national unity. Remarkably, federal system thus best responds to the Philippines’s democratic and development deficits. Federalism can also significantly rectify the practices that oppress and dislocate national ethnic minorities as it ensures the creation of localized institutions for optimum political, economic, cultural determination and maximizes representation in the public sphere.

Keywords: federalism, Igorot, indigenous peoples, self-determination

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Authors: P. S. Jagadeesh Kumar, Tracy Lin Huan, S. Meenakshi Sundaram

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Alzheimer’s disease is one of the prevalent kind of ailment, expected for impudent reconciliation or an effectual therapy is to be accredited hitherto. Probable detonation of patients in the upcoming years, and consequently an enormous deal of apprehension in early discovery of the disorder, this will conceivably chaperon to enhanced healing outcomes. Complex impetuosity of the brain is an observant symbolic of the disease and a unique recognition of genetic sign of the disease. Machine learning alongside deep learning and decision tree reinforces the aptitude to absorb characteristics from multi-dimensional data’s and thus simplifies automatic classification of Alzheimer’s disease. Susceptible testing was prophesied and realized in training the prospect of Alzheimer’s disease classification built on machine learning advances. It was shrewd that the decision trees trained with deep neural network fashioned the excellent results parallel to related pattern classification.

Keywords: Alzheimer's diagnosis, decision trees, deep neural network, machine learning, pattern classification

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Authors: Chia-Chun Wu, Yu-Hsien Sung

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This study aims to examine the consistency between students’ predictions and their actual observations on the bullying prevalence rate among different types of high-risk victims, thereby clarifying the reliability of students’ reports on the identification of bullying. A total of 1,732 Taiwanese students (734 males and 998 females) participated in this study. A Rasch model was adopted for data analysis. The results showed that students with “personality or behavioral issues” are more likely to be bullied in schools, based on both students’ predictions and actual observations. Moreover, the results differed significantly between genders and between various educational levels in students’ predictions and their actual observations on the bullying prevalence rate of different types of high-risk victims. To summarize, this study not only suggests that students’ reports on the identification of bullying are accurate and could be a valuable reference in terms of recognizing a bullying incident, but it also argues that more attention should be paid to students’ gender and educational level when taking their perspectives into consideration when it comes to identifying bullying behaviors.

Keywords: school bullying, student, bullying recognition, high-risk victims

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Authors: Fei Jia, Xingjian Bai, Xiaowei Zhang, Wenjie Yan, Ruitong Dai, Xingmin Li, Jozef Kokini

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Pseudomonas aeruginosa is a Gram-negative, aerobic, opportunistic human pathogen that is present in the soil, water, and food. This microbe has been recognized as a representative food-borne spoilage bacterium that can lead to many types of infections. Considering the casualties and property loss caused by P. aeruginosa, the development of a rapid and reliable technique for the detection of P. aeruginosa is crucial. The whole-cell aptasensor, an emerging biosensor using aptamer as a capture probe to bind to the whole cell, for food-borne pathogens detection has attracted much attention due to its convenience and high sensitivity. Here, a low-field magnetic resonance imaging (LF-MRI) aptasensor for the rapid detection of P. aeruginosa was developed. The basic detection principle of the magnetic relaxation switch (MRSw) nanosensor lies on the ‘T₂-shortening’ effect of magnetic nanoparticles in NMR measurements. Briefly speaking, the transverse relaxation time (T₂) of neighboring water protons get shortened when magnetic nanoparticles are clustered due to the cross-linking upon the recognition and binding of biological targets, or simply when the concentration of the magnetic nanoparticles increased. Such shortening is related to both the state change (aggregation or dissociation) and the concentration change of magnetic nanoparticles and can be detected using NMR relaxometry or MRI scanners. In this work, two different sizes of magnetic nanoparticles, which are 10 nm (MN₁₀) and 400 nm (MN₄₀₀) in diameter, were first immobilized with anti- P. aeruginosa aptamer through 1-Ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDC)/N-hydroxysuccinimide (NHS) chemistry separately, to capture and enrich the P. aeruginosa cells. When incubating with the target, a ‘sandwich’ (MN₁₀-bacteria-MN₄₀₀) complex are formed driven by the bonding of MN400 with P. aeruginosa through aptamer recognition, as well as the conjugate aggregation of MN₁₀ on the surface of P. aeruginosa. Due to the different magnetic performance of the MN₁₀ and MN₄₀₀ in the magnetic field caused by their different saturation magnetization, the MN₁₀-bacteria-MN₄₀₀ complex, as well as the unreacted MN₄₀₀ in the solution, can be quickly removed by magnetic separation, and as a result, only unreacted MN₁₀ remain in the solution. The remaining MN₁₀, which are superparamagnetic and stable in low field magnetic field, work as a signal readout for T₂ measurement. Under the optimum condition, the LF-MRI platform provides both image analysis and quantitative detection of P. aeruginosa, with the detection limit as low as 100 cfu/mL. The feasibility and specificity of the aptasensor are demonstrated in detecting real food samples and validated by using plate counting methods. Only two steps and less than 2 hours needed for the detection procedure, this robust aptasensor can detect P. aeruginosa with a wide linear range from 3.1 ×10² cfu/mL to 3.1 ×10⁷ cfu/mL, which is superior to conventional plate counting method and other molecular biology testing assay. Moreover, the aptasensor has a potential to detect other bacteria or toxins by changing suitable aptamers. Considering the excellent accuracy, feasibility, and practicality, the whole-cell aptasensor provides a promising platform for a quick, direct and accurate determination of food-borne pathogens at cell-level.

Keywords: magnetic resonance imaging, meat spoilage, P. aeruginosa, transverse relaxation time

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Authors: E. Younsi, H. Andriamboavonjy, A. David, S. Dokou, B. Lemrabet

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Software and time optimization are very important factors in financial markets, which are competitive fields, and emergence of new computer tools further stresses the challenge. In this context, any improvement of technical indicators which generate a buy or sell signal is a major issue. Thus, many tools have been created to make them more effective. This worry about efficiency has been leading in present paper to seek best (and most innovative) way giving largest improvement in these indicators. The approach consists in attaching a signature to frequent market configurations by application of frequent patterns extraction method which is here most appropriate to optimize investment strategies. The goal of proposed trading algorithm is to find most accurate signatures using back testing procedure applied to technical indicators for improving their performance. The problem is then to determine the signatures which, combined with an indicator, outperform this indicator alone. To do this, the FP-Tree algorithm has been preferred, as it appears to be the most efficient algorithm to perform this task.

Keywords: quantitative analysis, back-testing, computational models, apriori algorithm, pattern recognition, data mining, FP-tree

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Authors: L. Steponavičiūtė, L. Steponavičienė

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Despite the rapid nanotechnology progress and recognition, its potential impact in ecosystems and health of humans is still not fully known. In this paper, the study of ecotoxicological dangers of nanomaterials is presented. By chemical reduction method, silver (AgNPs) and gold (AuNPs) nanoparticles were synthesized, characterized and used in experiments to examine their impact on microorganisms (Escherichia coli, Staphylococcus aureus and Candida albicans) and terrestrial flora (Phaseolus vulgaris and Lepidium sativum). The results collected during experiments with terrestrial flora show tendentious growth stimulations caused by gold nanoparticles. In contrast to these results, silver nanoparticle solutions inhibited growth of beans and garden cress, compared to control samples. The results obtained from experiments with microorganisms show similarities with ones collected from experiments with terrestrial plants. Samples treated with AuNPs of size 13 nm showed stimulation in the growth of the colonies compared with 3,5 nm size nanoparticles.

Keywords: nanomaterials, ecotoxicology, nanoparticles, ecosystems

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Authors: Mansouri Nabila, Ben Jemaa Yousra, Motamed Cina, Watelain Eric

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Research and development efforts in intelligent Advanced Driver Assistance Systems (ADAS) seek to save lives and reduce the number of on-road fatalities. For traffic and emergency monitoring, the essential but challenging task is vehicle detection and tracking in reasonably short time. This purpose needs first of all a powerful dynamic car detector model. In fact, this paper presents an optimized HOG process based on shape and motion parameters fusion. Our proposed approach mains to compute HOG by bloc feature from foreground blobs using configurable research window and pathway in order to overcome the shortcoming in term of computing time of HOG descriptor and improve their dynamic application performance. Indeed we prove in this paper that HOG by bloc descriptor combined with motion parameters is a very suitable car detector which reaches in record time a satisfactory recognition rate in dynamic outside area and bypasses several popular works without using sophisticated and expensive architectures such as GPU and FPGA.

Keywords: car-detector, HOG, motion, computing time

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Authors: Christoph Linse, Thomas Martinetz

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Recent findings have shown that Neural Networks generalize also in over-parametrized regimes with zero training error. This is surprising, since it is completely against traditional machine learning wisdom. In our empirical study we fortify these findings in the domain of fine-grained image classification. We show that very large Convolutional Neural Networks with millions of weights do learn with only a handful of training samples and without image augmentation, explicit regularization or pretraining. We train the architectures ResNet018, ResNet101 and VGG19 on subsets of the difficult benchmark datasets Caltech101, CUB_200_2011, FGVCAircraft, Flowers102 and StanfordCars with 100 classes and more, perform a comprehensive comparative study and draw implications for the practical application of CNNs. Finally, we show that VGG19 with 140 million weights learns to distinguish airplanes and motorbikes with up to 95% accuracy using only 20 training samples per class.

Keywords: convolutional neural networks, fine-grained image classification, generalization, image recognition, over-parameterized, small data sets

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Authors: Okoro Ngozi Priscilla

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Education is the bedrock of development in every nation of the world, and it is very useful in ensuring quality of life for every individual and a better world for the people. Education, therefore, is the basic instrument of economic growth and technological advancement in any society. It is in recognition of this fact that the Nigerian government commits immense resources to ensuring that its citizens acquire education and also policies are being made to ensure the accessibility of education, qualitative higher education is highly recognized as a vital driving force for the socio-economic growth and technological development of nations yet the problem of access to University education in the country persists and therefore brought about the introduction of Open and Distance Learning (ODL) which has as its main objective, the attainment of mass literacy and providing opportunities for those who could not gain admission through designated entrance examination agencies as well as those who could not afford to leave their job to attend a full-time educational programme. Open and distance learning seeks to improve skilled manpower and also improve the skills for those already at work.

Keywords: accessibility, open and distant learning programme, fulltime educational programme, distance learning

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Authors: Guilherme Oliveira

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The assimilation of images and their potential symbolism into lived experiences is inherent. It is through this exercise of recognition via imagistic records that the questioning of the origins of a constant narrative stimulated by the media arises. The construction of the "Man" archetype and the reflections of active masculine imagery in the 21st century, when conveyed through media channels, could potentially have detrimental effects. Addressing this systematic behavioral chronology of virile cisgender, permeated imagistically through these means, involves exploring potential resolutions. Thus, an investigation process is initiated into the potential representation of the 'hero' in this media emulation through idols contextualized in the political sphere, with the purpose of elucidating the processes of simulation and emulation of narratives based on mythical, historical, and sacred accounts. In this process of sharing, the narratives contained in the imagistic structuring offered by information dissemination channels seek validation through a process of public acceptance. To achieve this consensus, a visual set adorned with mythological and sacred symbolisms adapted to the intended environment is promoted, thus utilizing sociocultural characteristics in favor of political marketing. Visual recognition, therefore, becomes a direct reflection of a cultural heritage acquired through lived human experience, stimulated by continuous representations throughout history. Echoes of imagery and narratives undergo a constant process of resignification of their concepts, sharpened by their premises, and adapted to the environment in which they seek to establish themselves. Political figures analyzed in this article employ the practice of taking possession of symbolisms, mythological stories, and heroisms and adapt their visual construction through a continuous praxis of emulation. Thus, they utilize iconic mythological narratives to gain credibility through belief. Utilizing iconic mythological narratives for credibility through belief, the idol becomes the very act of release of trauma, offering believers liberation from preconceived concepts and allowing for the attribution of new meanings. To dissolve this issue and highlight the subjectivities within the intention of the image, a linguistic, semiotic, and anthropological methodology is created. Linguistics uses expressions like 'Blaming the Image' to create a mechanism of expressive action in questioning why to blame a construction or visual composition and thus seek answers in the first act. Semiotics and anthropology develop an imagistic atlas of graphic analysis, seeking to make connections, comparisons, and relations between modern and sacred/mystical narratives, emphasizing the different subjective layers of embedded symbolism. Thus, it constitutes a performative act of disarming the image. It creates a disenchantment of the superficial gaze under the constant reproduction of visual content stimulated by virtual networks, enabling a discussion about the acceptance of caricatures characterized by past fables.

Keywords: image, heroic narrative, media heroism, virile politics, political, myth, sacred performance, visual mythmaking, characterization dynamics

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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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Abstract:

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

Procedia PDF Downloads 290

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

Abstract:

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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