Search results for: background segmentation

Authors: Jaroslav Kusnir

Abstract:

In her short story collection, Sabrina & Corina, Kali Fajardo-Anstine depicts mostly Latina characters of indigenous background living and travelling in the American West and the Southwest. In all the stories, place and the environment plays an important role in the construction of cultural identity of these characters that is influenced by their indigenous background, a specificity of the American West, its culture and environment, as well as a contemporary (modern) American culture, position of women and gender roles in a Latino community in the USA. This paper will analyze Fajardo-Anstine´s depiction of a specificity of place, especially of the American West and its role in a construction of Latino/a cultural identity in a modern American society as manifested especially in Fajardo-Anstine´s stories Any Further West and Sabrina & Corina. At the same time, the paper will point out Fajardo-Anstine´s construction of cultural identity of female characters and their gender roles in both Latino and a contemporary American societies. The research results show that the formation of Latina cultural identity is closely connected with both place, that is the American West and the Soutwest as well as with Latina and contemporary American cultures.

Keywords: American culture, american west, cultural identity, female identity, latina identity, place

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, Bayesian, echocardiographic image, feature vector

Procedia PDF Downloads 450

Authors: Adebayo-Victoria Tobi Dada, Dada Emmanuel

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This study assessed households’ food security and hunger levels among different communities with varying educational and economic background in Ile-Ife, Nigeria, and its environment. It also examined the impacts of varying demography on the household food security level in the area. This was with a view to providing information on the food security status of the subjects within the study area. Ten different communities with varying demography (Parakin, Mokuro, Ilare, Obafemi Awolowo University (OAU) Staff Quarters, Ibadan Road, Aba-Iya Gani, Eleweran, Iraye, Boosa, and Eku-Isobo) were identified within the study area. Fieldwork was then carried out from 7th to 14th of March, 2016 in each of these communities through survey of market prices of food stuff, diet, and nutrition, social well-being, food accessibility and affordability as well as price fluctuation and variation in household’s social background. Selection of households for the survey was done using stratified random sampling method. Key informants included community heads, landlords, tenants, and household heads. Similarly, information on food security levels with respect to demographic backgrounds was obtained from the use of modified Food and Hunger Insecurity Module (FHIM) structured questionnaire. The questionnaire was administered to one percent of the households’ population per community. The results showed that communities such as Parakin and OAU Senior Staff Quarters were dominated by civil servants, while community such as Boosa was dominated by artisans. Respondents earning between ₦11,000 and ₦20,000 per month, during the study period, had the highest percentage across the selected communities. The household food security indices showed that about 41% of the investigated respondents could not guarantee their household food for a month, while 18% reduced or skipped meals. There were positive significant relationships between monthly income (F-value = 132.04), educational status (F-value = 102.30), occupation (F-value = 104.05) and food budget (F-value = 122.09), all at p < 0.05. However, there was no significant relationship between the monthly food budget and household sizes (t-value = -1.4074, p > 0.05). Food secured households’ had the household heads with a higher level of educational attainment. The study concluded that large variations which existed between socio-economic and educational background among the communities had significant effects on households’ food security level in the study area.

Keywords: food security, households, hunger level, market prices

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Authors: U. S. N. Raju, Kothuri Sai Kiran, Meena G. Kamal, Vinay Nikhil Pabba, Suresh Kanaparthi

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There is huge amount of lecture video data available for public use, and many more lecture videos are being created and uploaded every day. Searching for videos on required topics from this huge database is a challenging task. Therefore, an efficient method for video retrieval is needed. An approach for automated video indexing and video search in large lecture video archives is presented. As the amount of video lecture data is huge, it is very inefficient to do the processing in a centralized computation framework. Hence, Hadoop Framework for distributed computing for Big Video Data is used. First, step in the process is automatic video segmentation and key-frame detection to offer a visual guideline for the video content navigation. In the next step, we extract textual metadata by applying video Optical Character Recognition (OCR) technology on key-frames. The OCR and detected slide text line types are adopted for keyword extraction, by which both video- and segment-level keywords are extracted for content-based video browsing and search. The performance of the indexing process can be improved for a large database by using distributed computing on Hadoop framework.

Keywords: video lectures, big video data, video retrieval, hadoop

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Authors: Mohammad Ayub Khan

Abstract:

With increased market segmentation and competition in the hotel industry, a hotel’s ability to constantly renovate its services and amenities is a business practice that can be termed as an attitude that is not only flexible but also malleable as a result of which a hotel/property is continually poised to face the ever-changing nature of the hospitality industry and upgrades that keep the hotel or brand in competition with current competitors. One such challenge is to competitively and creatively market value-added amenities, upgraded technology, and marketing all of these as a package to not only stay relevant in the market but also to retain and enhance revenues to ensure the future financial health of a hotel. This delicate balance between staying relevant and financially viable is a crucial challenge that this poster will explore, analyze, and present by specifically looking at the ability of a hotel/brand to effectively translate its theoretical need and practice of constantly staying updated, including strategically renovating, upgrading, modifying its services, into a tangible business practice. In what ways do hotels face this challenge? In what areas of the hotel is this business concept/action most effective and profitable are just some questions that this paper will attempt to answer.

Keywords: hospitality theory, renovations, value-added amenities, strategic planning

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Authors: Sung Ryong Kim, Hyung Sup Han, Jae-Yup Kim

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Many engineering students feel uncomfortable in solving the industry related problems. There are many ways to strengthen the engineering student’s ability to solve the assigned problem when they get a job. Korea National University of Transportation has developed an industry-oriented undergraduate research program (URP). An URP program is designed for engineering students to provide an experience of solving a company’s research problem. The URP project is carried out for 6 months. Each URP team consisted of 1 company mentor, 1 professor, and 3-4 engineering students. A team of different majors is strongly encouraged to integrate different perspectives of multidisciplinary background. The corporate research projects proposed by companies are chosen by the major-related student teams. A company mentor gives the detailed technical background of the project to the students, and he/she also provides a basic data, raw materials and so forth. The company allows students to use the company's research equipment. An assigned professor has adjusted the project scope and level to the student’s ability after discussing with a company mentor. Monthly meeting is used to check the progress, to exchange ideas, and to help the students. It is proven as an effective engineering education program not only to provide an experience of company research but also to motivate the students in their course work. This program provides a premier interdisciplinary platform for undergraduate students to perform the practical challenges encountered in their major-related companies and it is especially helpful for students who want to get a job from a company that proposed the project.

Keywords: company mentor, industry oriented, interdisciplinary platform, undergraduate research program

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Authors: Niall Fennessy, Charlotte Yin, Vineet Gorolay, Michael Chan, Ilias Drivas

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Background: The aim of this study was to evaluate the effect of manual aspiration of air from the pleural cavity in mitigating the need for chest drain placement after a CT-guided lung biopsy. Method: This is a single institution retrospective review of CT-guided lung biopsies performed on 799 patients between September 2013 and May 2021 in a major tertiary hospital. Percutaneous manual aspiration of air was performed in 104/306 patients (34%) with pneumothoraxes as a preventative measure. Simple and multivariate analysis was performed to identify independent risk factors (modifiable and nonmodifiable) for the success of manual aspiration in mitigating the need for chest drain insertion. Results: The overall incidence of pneumothorax was 37% (295/799). Chest drains were inserted for 81/295 (27%) of the pneumothoraxes, representing 81/799 (10%) of all CT-guided lung biopsies. Of patients with pneumothoraces, 104 (36%) underwent percutaneous aspiration via either the coaxial guide needle or an 18 or 20G intravenous catheter attached to a three-way stopcock and syringe. Amongst this group, 13 patients (13%) subsequently required chest drain insertion. The success of percutaneous aspiration in avoiding subsequent pleural drain insertion decreased with aspiration volume >500mL, radial pneumothorax depth >3cm, increased subpleural depth of the lesion, and the presence of background emphysema.

Keywords: computed tomography, lung biopsy, pneumothorax, manual aspiration, chest drainage

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Authors: Ahmed Negm, George Aggidis, Xiandong Ma

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With the diverse nature of urban cities, customer demand patterns, landscape topologies or even seasonal weather trends; managing our water distribution networks (WDNs) has proved a complex task. These unpredictable circumstances manifest as pipe failures, intermittent supply and burst events thus adding to water loss, energy waste and increased carbon emissions. Whilst these events are unavoidable, advanced pressure management has proved an effective tool to control and mitigate them. Henceforth, water utilities have struggled with developing a real-time control method that is resilient when confronting the challenges of water distribution. In this paper we use deep reinforcement learning (DRL) algorithms as a novel pressure control strategy to minimise pressure violations and leakage under both burst and background leakage conditions. Agents based on asynchronous actor critic (A2C) and recurrent proximal policy optimisation (Recurrent PPO) were trained and compared to benchmarked optimisation algorithms (differential evolution, particle swarm optimisation. A2C manages to minimise leakage by 32.48% under burst conditions and 67.17% under background conditions which was the highest performance in the DRL algorithms. A2C and Recurrent PPO performed well in comparison to the benchmarks with higher processing speed and lower computational effort.

Keywords: deep reinforcement learning, pressure management, water distribution networks, leakage management

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Authors: Elena M. Kopteva, Pavlina Jaluvkova, Zdenek Stuchlik

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In spite of the numerous attempts to close the discussion about the influence of cosmological expansion on local gravitationally bounded systems, this question arises in literature again and again and remains still far from its final resolution. Here one of the main problems is the problem of obtaining a physically adequate model of strongly gravitating object immersed in non-static cosmological background. Such objects are usually called ‘cosmological’ black holes and are of great interest in wide set of cosmological and astrophysical areas. In this work the set of new exact solutions of the Einstein equations is derived for the flat space that generalizes the known Lemaitre-Tolman-Bondi solution for the case of nonzero pressure. The solutions obtained are pretending to describe the black hole immersed in nonstatic cosmological background and give a possibility to investigate the hot problems concerning the effects of the cosmological expansion in gravitationally bounded systems, the structure formation in the early universe, black hole thermodynamics and other related problems. It is shown that each of the solutions obtained contains either the Reissner-Nordstrom or the Schwarzschild black hole in the central region of the space. It is demonstrated that the approach of the mass function use in solving of the Einstein equations allows clear physical interpretation of the resulting solutions, that is of much benefit to any their concrete application.

Keywords: exact solutions of the Einstein equations, cosmological black holes, generalized Lemaitre-Tolman-Bondi solutions, nonzero pressure

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Authors: Chiharu Miyata, Hidenori Arai

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Background: The quality of care in geriatric intermediate facilities (GIFs) in Japan is not in a satisfied level. To improve it, it is crucial to reconsider nurses’ professionalism. Our goal is to create an organizational system that allows nurses to succeed professionally. To do this, we must first discuss the relationship between nurses’ characteristics and the organization. Objectives: The aim of the present study was to determine the extent to which demographic and work-related factors are related to organizational commitment among nurses in GIFs. Method: A quantitative, cross-sectional method was adopted, using a self-completion questionnaire survey. The questionnaires consisted of 49 items for job satisfaction, the three-dimensional commitment model of organizational commitment and the background information of respondents. Results: A total of 1,189 nurses participated. Of those, 91% (n=1084) were women, and mean age was 48.2 years. Most participants were staff nurses (n=791; 66%). Significant differences in 'affective commitment' (AC) scores were found for age (p < .001), overall work experience (p < .001), and work status (p < .001). For work experience in the current facility, significant differences were found in all organizational commitment scores (p < .001). The group with high job satisfaction scored significantly higher in all types of organizational commitment (p < 0.001). Conclusions: These results led to a conclusion that understanding the expectations of nurses at the workplace to adapt with the organization, and creating a work environment that clarifies contents of tasks, especially allowing for nurses to feel significance and achievement with tasks, would increase AC.

Keywords: geriatric intermediate care facilities, geriatric nursing, job satisfaction, organizational commitment

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Authors: Yuntao Liu, Lei Wang, Haoran Xia

Abstract:

Machine learning has shown extensive applications in the development of classification models for autism spectrum disorder (ASD) using neural image data. This paper proposes a fusion multi-modal classification network based on a graph neural network. First, the brain is segmented into 116 regions of interest using a medical segmentation template (AAL, Anatomical Automatic Labeling). The image features of sMRI and the signal features of fMRI are extracted, which build the node and edge embedding representations of the brain map. Then, we construct a dynamically updated brain map neural network and propose a method based on a dynamic brain map adjacency matrix update mechanism and learnable graph to further improve the accuracy of autism diagnosis and recognition results. Based on the Autism Brain Imaging Data Exchange I dataset(ABIDE I), we reached a prediction accuracy of 74% between ASD and TD subjects. Besides, to study the biomarkers that can help doctors analyze diseases and interpretability, we used the features by extracting the top five maximum and minimum ROI weights. This work provides a meaningful way for brain disorder identification.

Keywords: autism spectrum disorder, brain map, supervised machine learning, graph network, multimodal data, model interpretability

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Authors: S. Nandagopalan, N. Pradeep

Abstract:

The aim of this paper is to propose a general framework for storing, analyzing, and extracting knowledge from two-dimensional echocardiographic images, color Doppler images, non-medical images, and general data sets. A number of high performance data mining algorithms have been used to carry out this task. Our framework encompasses four layers namely physical storage, object identification, knowledge discovery, user level. Techniques such as active contour model to identify the cardiac chambers, pixel classification to segment the color Doppler echo image, universal model for image retrieval, Bayesian method for classification, parallel algorithms for image segmentation, etc., were employed. Using the feature vector database that have been efficiently constructed, one can perform various data mining tasks like clustering, classification, etc. with efficient algorithms along with image mining given a query image. All these facilities are included in the framework that is supported by state-of-the-art user interface (UI). The algorithms were tested with actual patient data and Coral image database and the results show that their performance is better than the results reported already.

Keywords: active contour, bayesian, echocardiographic image, feature vector

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Authors: Hala Zaghloul, Taymoor Nazmy

Abstract:

One of the great challenges of the 21st century is to build a robot that can perceive and act within its environment and communicate with people, while also exhibiting the cognitive capabilities that lead to performance like that of people. The Pulse Coupled Neural Network, PCNN, is a relative new ANN model that derived from a neural mammal model with a great potential in the area of image processing as well as target recognition, feature extraction, speech recognition, combinatorial optimization, compressed encoding. PCNN has unique feature among other types of neural network, which make it a candid to be an important approach for perceiving in cognitive systems. This work show and emphasis on the potentials of PCNN to perform different tasks related to image processing. The main drawback or the obstacle that prevent the direct implementation of such technique, is the need to find away to control the PCNN parameters toward perform a specific task. This paper will evaluate the performance of PCNN standard model for processing images with different properties, and select the important parameters that give a significant result, also, the approaches towards find a way for the adaptation of the PCNN parameters to perform a specific task.

Keywords: cognitive system, image processing, segmentation, PCNN kernels

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Authors: Renáta Nagy, Alexandra Csongor, Jon Marquette, Vilmos Warta

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The presentation aims at eliciting insight into the results of ongoing research regarding evolving trends and attitudes towards online assessment of English for Medical Purposes. The focus pinpointsonline as one of the most trending formsavailable during the global pandemic. The study was first initiated in 2019 in which its main target was to reveal the intriguing question of students’ and assessors’ attitudes towards online assessment. The research questions the attitudes towards the latest trends, possible online task types, their advantagesand disadvantages through an in-depth experimental process currently undergoing implementation. Material and methods include surveys, needs and wants analysis, and thorough investigations regarding candidates’ and assessors’ attitudes towards online tests in the field of Medicine. The examined test tasks include various online tests drafted in both English and Hungarian by student volunteers at the Medical School of the University of Pécs, Hungary. Over 400 respondents from more than 28 countries participated in the survey, which gives us an international and intercultural insight into how students with different cultural and educational background deal with the evolving online world. The results show the pandemic’s impact, which brought the slumbering online world of assessing roaring alive, fully operational andnowbearsphenomenalrelevancein today’s global education. Undeniably, the results can be used as a perspective in a vast array of contents. The survey hypothesized the generation of the 21st century expect everything readily available online, however, questions whether they are ready for this challenge are lurking in the background.

Keywords: assessment, changes, english, ESP, online assessment, online, trends

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Authors: Yuan-Hsiang Chang, Zhong-Xian Peng, Li-Der Jeng

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Music has always been an integral part of human’s daily lives. But, for the most people, reading musical score and turning it into melody is not easy. This study aims to develop an Automatic music score recognition system using digital image processing, which can be used to read and analyze musical score images automatically. The technical approaches included: (1) staff region segmentation; (2) image preprocessing; (3) note recognition; and (4) accidental and rest recognition. Digital image processing techniques (e.g., horizontal /vertical projections, connected component labeling, morphological processing, template matching, etc.) were applied according to musical notes, accidents, and rests in staff notations. Preliminary results showed that our system could achieve detection and recognition rates of 96.3% and 91.7%, respectively. In conclusion, we presented an effective automated musical score recognition system that could be integrated in a system with a media player to play music/songs given input images of musical score. Ultimately, this system could also be incorporated in applications for mobile devices as a learning tool, such that a music player could learn to play music/songs.

Keywords: connected component labeling, image processing, morphological processing, optical musical recognition

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Authors: Meghamala Nugehally

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This paper hypothesises and draws inferences on the impact of minimalism in dance education on the development of artistic and aesthetic sensibilities in individuals in the age group of 5-18 yrs of age. This research and conclusions are within the context of Indian Classical Dance, which is based on Indian theories of aesthetics drawn from the Natyashastra, an ancient treatise on Indian dance and drama. The research employs training methods handed down through a strict one-on-one teacher-student tradition known as the Guru-Shishya Parampara. Aesthetic principles used are defined, and basic theories from the Natyashastra are explained to provide background for the research design. The paper also discusses dance curriculum design and training methodology design within the context of these aesthetic theories. The scope of the research is limited to two genres of Indian classical forms: Bharatanatyam and Odissi. A brief description of these dance forms is given as background and dance aesthetics specific to these forms are described. The research design includes individual case studies of subjects studied, independent predetermined attributes for observations and a qualitative scoring methodology devised for the purpose of the study. The study describes the training techniques used and contrasts minimal solo training techniques with the more elaborate group training techniques. Study groups were divided and the basis for the division are discussed. Study observations are recorded and presented as evidences. The results inform the conclusion and set the stage for further research in this area.

Keywords: dance aesthetics, dance education, Indian classical dance, minimalism

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Authors: Shinhao Yang, Hsiao-Chien Huang, Chin-Hsiang Luo

Abstract:

The leakage of protective clothing is an important issue in the occupational health field. There is no quantitative method for measuring the leakage of personal protective equipment. This work aims to measure the quantitative leakage of the personal protective equipment by using the fluorochrome aerosol tracer. The fluorescent aerosols were employed as airborne particulates in a controlled chamber with ultraviolet (UV) light-detectable stickers. After an exposure-and-leakage test, the protective equipment was removed and photographed with UV-scanning to evaluate areas, color depth ratio, and aerosol deposition and shielding effects of the areas where fluorescent aerosols had adhered to the body through the protective equipment. Thus, this work built a calculation software for quantitative leakage ratio of protective clothing based on fluorescent illumination depth/aerosol concentration ratio, illumination/Fa ratio, aerosol deposition and shielding effects, and the leakage area ratio on the segmentation. The results indicated that the two-repetition total leakage rate of the X, Y, and Z type protective clothing for subject T were about 3.05, 4.21, and 3.52 (mg/m2). For five-repetition, the leakage rate of T were about 4.12, 4.52, and 5.11 (mg/m2).

Keywords: fluorochrome, deposition, shielding effects, digital image processing, leakage ratio, personal protective equipment

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Authors: Maged El-Sayed Mohamed

Abstract:

Plant tissue culture practices are presented nowadays as the most promising substitute to a whole plant in the terms of secondary metabolites production. They offer the advantages of high production, tunability and they have less effect on plant ecosystems. Lantana camara is a weed, which is common all over the world as an ornamental plant. Weeds can adapt to any type of soil and climate due to their rich cellular machinery for secondary metabolites’ production. This characteristic is found in Lantana camara as a plant of very rich diversity of secondary metabolites with no dominant class of compounds. Aim: This trait has encouraged the author to develop tissue culture experiments for Lantana camara to be a platform for production and manipulation of secondary metabolites through biotransformation. Methodology: The plant was collected in its flowering stage in September 2014, from which explants were prepared from shoot tip, auxiliary bud and leaf. Different types of culture media were tried as well as four phytohormones and their combinations; NAA, 2,4-D, BAP and kinetin. Explants were grown in dark or in 12 hours dark and light cycles at 25°C. A metabolic profile for the produced callus was made and then compared to the whole plant profile. The metabolic profile was made using GC-MS for volatile constituents (extracted by n-hexane) and by HPLC-MS and capillary electrophoresis-mass spectrometry (CE-MS) for non-volatile constituents (extracted by ethanol and water). Results: The best conditions for the callus induction was achieved using MS media supplied with 30 gm sucrose and NAA/BAP (1:0.2 mg/L). Initiation of callus was favoured by incubation in dark for 20 day. The callus produced under these conditions showed yellow colour, which changed to brownish after 30 days. The rate of callus growth was high, expressed in the callus diameter, which reached to 1.15±0.2 cm in 30 days; however, the induction of callus delayed for 15 days. The metabolic profile for both volatile and non-volatile constituents of callus showed more simple background metabolites than the whole plant with two new (unresolved) peaks in the callus’ nonvolatile constituents’ chromatogram. Conclusion: Lantana camara callus production can be itself a source of new secondary metabolites and could be used for biotransformation studies due to its simple metabolic background, which allow easy identification of newly formed metabolites. The callus production gathered the simple metabolic background with the rich cellular secondary metabolite machinery of the plant, which could be elicited to produce valuable medicinally active products.

Keywords: capillary electrophoresis-mass spectrometry, gas chromatography, metabolic profile, plant tissue culture

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Authors: Cheikh Bamba Dione, Alla Lo, Elhadji Mamadou Nguer, Siley O. Ba

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In this paper, we propose two neural machine translation (NMT) systems (French-to-Wolof and Wolof-to-French) based on sequence-to-sequence with attention and transformer architectures. We trained our models on a parallel French-Wolof corpus of about 83k sentence pairs. Because of the low-resource setting, we experimented with advanced methods for handling data sparsity, including subword segmentation, back translation, and the copied corpus method. We evaluate the models using the BLEU score and find that transformer outperforms the classic seq2seq model in all settings, in addition to being less sensitive to noise. In general, the best scores are achieved when training the models on word-level-based units. For subword-level models, using back translation proves to be slightly beneficial in low-resource (WO) to high-resource (FR) language translation for the transformer (but not for the seq2seq) models. A slight improvement can also be observed when injecting copied monolingual text in the target language. Moreover, combining the copied method data with back translation leads to a substantial improvement of the translation quality.

Keywords: backtranslation, low-resource language, neural machine translation, sequence-to-sequence, transformer, Wolof

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Authors: Shimaa Holail, Tamer Saleh, Xiongwu Xiao

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Building change detection (BCD) from satellite imagery is an essential topic in urbanization monitoring, agricultural land management, and updating geospatial databases. Recently, methods for detecting changes based on deep learning have made significant progress and impressive results. However, it has the problem of being insensitive to changes in buildings with complex spectral differences, and the features being extracted are not discriminatory enough, resulting in incomplete buildings and irregular boundaries. To overcome these problems, we propose a dual Siamese network based on the Unet model with the addition of a deep supervision strategy (DS) in this paper. This network consists of a backbone (encoder) based on ImageNet pre-training, a fusion block, and feature pyramid networks (FPN) to enhance the step-by-step information of the changing regions and obtain a more accurate BCD map. To train the proposed method, we created a new dataset (EGY-BCD) of high-resolution and multi-temporal aerial images captured over New Cairo in Egypt to detect building changes for this purpose. The experimental results showed that the proposed method is effective and performs well with the EGY-BCD dataset regarding the overall accuracy, F1-score, and mIoU, which were 91.6 %, 80.1 %, and 73.5 %, respectively.

Keywords: building change detection, deep supervision, semantic segmentation, EGY-BCD dataset

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Authors: Janos Farkas, Maria Czeller, Ildiko Tar

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A remarkable feature of today’s language teaching is the learners’ language learning motivation. It is always considered as a very important factor and has been widely discussed and investigated. This paper aims to present a research study conducted in higher education institutions among students majoring in business and administration in Hungary. The aim of the research was to investigate the motivational factors of students learning languages for business purposes and set up a multivariate statistical model of language learning motivation, and examine the model's main components by different social background variables. The research question sought to answer the question of whether the motivation of students of business learning LSP could be characterized through some main components. The principal components of LSP have been created, and the correlations with social background variables have been explored. The main principal components of learning a language for business purposes were "professional future", "abroad", "performance", and "external". In the online voluntary questionnaire, 28 questions were asked about students’ motivational attitudes. 449 students have filled in the questionnaire. Descriptive statistical calculations were performed, then the difference between the highest and lowest mean was analyzed by one-sample t-test. The assessment of LSP learning was examined by one-way analysis of variance and Tukey post-hoc test among students of parents with different qualifications. The correlations between student motivation statements and various social background variables and other variables related to LSP learning motivation (gender, place of residence, mother’s education, father’s education, family financial situation, etc.) have also been examined. The attitudes related to motivation were seperated by principal component analysis, and then the different language learning motivation between socio-economic variables and other variables using principal component values were examined using an independent two-sample t-test. The descriptive statistical analysis of language learning motivation revealed that students learn LSP because this knowledge will come in handy in the future. It can be concluded that students consider learning the language for business purposes to be essential and see its future benefits. Therefore, LSP teaching has an important role and place in higher education. The results verify the second linguistic motivational self-system where the ideal linguistic self embraces the ideas and desires that the foreign language learner wants to achieve in the future. One such desire is to recognize that students will need technical language skills in the future, and it is a powerful motivation for them to learn a language.

Keywords: higher education, language learning motivation, LSP, statistical analysis

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Authors: Mahdi Motahari, Mojtaba Farzaneh, Ebrahim Sepidbar

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Tracking of moving and flying targets is one of the most important issues in image processing topic. Estimating of trajectory of desired object in short-term and long-term scale is more important than tracking of moving and flying targets. In this paper, a new way of identifying and estimating of future trajectory of a moving ball in long-term scale is estimated by using synthesis and interaction of image processing algorithms including noise removal and image segmentation, Kalman filter algorithm in order to estimating of trajectory of ball in football game in short-term scale and intelligent adaptive neuro-fuzzy algorithm based on time series of traverse distance. The proposed system attain more than 96% identify accuracy by using aforesaid methods and relaying on aforesaid algorithms and data base video in format of synthesis and interaction. Although the present method has high precision, it is time consuming. By comparing this method with other methods we realize the accuracy and efficiency of that.

Keywords: tracking, signal processing, moving targets and flying, artificial intelligent systems, estimating of trajectory, Kalman filter

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Authors: H. D. Arora, Anjali Dhiman

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Uncertainty is found everywhere and its understanding is central to decision making. Uncertainty emerges as one has less information than the total information required describing a system and its environment. Uncertainty and information are so closely associated that the information provided by an experiment for example, is equal to the amount of uncertainty removed. It may be pertinent to point out that uncertainty manifests itself in several forms and various kinds of uncertainties may arise from random fluctuations, incomplete information, imprecise perception, vagueness etc. For instance, one encounters uncertainty due to vagueness in communication through natural language. Uncertainty in this sense is represented by fuzziness resulting from imprecision of meaning of a concept expressed by linguistic terms. Fuzzy set concept provides an appropriate mathematical framework for dealing with the vagueness. Both information theory, proposed by Shannon (1948) and fuzzy set theory given by Zadeh (1965) plays an important role in human intelligence and various practical problems such as image segmentation, medical diagnosis etc. Numerous approaches and theories dealing with inaccuracy and uncertainty have been proposed by different researcher. In the present communication, we generalize fuzzy entropy proposed by De Luca and Termini (1972) corresponding to Shannon entropy(1948). Further, some of the basic properties of the proposed measure were examined. We also applied the proposed measure to the real life decision making problem.

Keywords: entropy, fuzzy sets, fuzzy entropy, generalized fuzzy entropy, decision making

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Authors: Aditya Karade, Sharada Falane, Dhananjay Deshmukh, Vijaykumar Mantri

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Brain tumors pose a significant challenge in healthcare due to their complex nature and impact on patient outcomes. The application of deep learning (DL) algorithms in medical imaging have shown promise in accurate and efficient brain tumour detection. This paper explores the performance of various pre-trained DL models ResNet50, Xception, InceptionV3, EfficientNetB0, DenseNet121, NASNetMobile, VGG19, VGG16, and MobileNet on a brain tumour dataset sourced from Figshare. The dataset consists of MRI scans categorizing different types of brain tumours, including meningioma, pituitary, glioma, and no tumour. The study involves a comprehensive evaluation of these models’ accuracy and effectiveness in classifying brain tumour images. Data preprocessing, augmentation, and finetuning techniques are employed to optimize model performance. Among the evaluated deep learning models for brain tumour detection, ResNet50 emerges as the top performer with an accuracy of 98.86%. Following closely is Xception, exhibiting a strong accuracy of 97.33%. These models showcase robust capabilities in accurately classifying brain tumour images. On the other end of the spectrum, VGG16 trails with the lowest accuracy at 89.02%.

Keywords: brain tumour, MRI image, detecting and classifying tumour, pre-trained models, transfer learning, image segmentation, data augmentation

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Authors: Charlotte Bach, Albrecht Jahn, Mahnaz Motamedi, Maryam Karimi-Ghahfarokhi

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Background: Maternal and infant deaths are most prevalent in the first month after birth, emphasizing the critical need for quality healthcare services during this period. Immigrant women, who are more susceptible to adverse pregnancy outcomes, often face neglect in accessing proper healthcare. The lack of adequate postpartum care significantly contributes to mortality rates. Therefore, utilizing maternal health care services and implementing postpartum care is crucial in reducing maternal and child mortality. Aims: This study aims to evaluate the assessment of pre- and postnatal care among women with and without migration background. In addition, the study explores the impact of COVID-19 procedures on women's experiences during pregnancy, birth, and the postpartum period. Methods: This research employs a cross-sectional Mixed-Method design. Data collection was facilitated through structured questionnaires administered to participants, alongside the utilization of patient bases, including Maternity and child medical records. Following the assumption that the investigator aimed to gain comprehensive insights, qualitative sampling focused on individuals with substantial experiences related to COVID-19, regarded as rich cases. Results: our study highlighted the influence of educational level, marital status, and consensual partnerships on the likelihood of Cesarean deliveries. Regarding breastfeeding practices, migrant women exhibited higher rates of breastfeeding initiation and continuation. Contraception utilization revealed interesting patterns, with non-migrants displaying higher odds of contraceptive use. The qualitative component of our research adds depth to the exploration of women's experiences during the COVID-19 pandemic, revealing nuanced challenges related to anxiety, hospital restrictions, breastfeeding support, and postnatal ward routines. Conclusion: Dissimilarity among studies toward cesarean rate between migrants and non-migrants underscores the importance of targeted interventions considering the diverse needs of distinct population groups. It also acknowledges potential cultural, contextual, and healthcare system influences on the association between mode of delivery and infant feeding practices. Studies acknowledge the influence of contextual variables on contraceptive preferences among migrants and non-migrants, emphasizing the need for tailored healthcare policies. The findings contribute to existing research, highlighting the need for a nuanced understanding of the impact of birth preparation courses on maternal and infant outcomes. Furthermore, they emphasize the universality of certain maternity care experiences, regardless of pandemic contexts, reinforcing the importance of patient-centred approaches in healthcare delivery.

Keywords: migration background, pregnancy outcome, covid-19, postpartum

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Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

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Authors: Estela Díaz

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This article aims to make three contributions. First, build on the literature on ethical decision-making literature by exploring factors that influence the intention of adopting veganism. Second, study the superiority of extended models of the Theory of Planned Behavior (TPB) for understanding the process involved in forming the intention of adopting veganism. Third, analyze the moderating effect of gender on TPB given that attitudes and behavior towards animals are gender-sensitive. No study, to our knowledge, has examined these questions. Veganism is not a diet but a political and moral stand that exclude, for moral reasons, the use of animals. Although there is a growing interest in studying veganism, it continues being overlooked in empirical research, especially within the domain of social psychology. TPB has been widely used to study a broad range of human behaviors, including moral issues. Nonetheless, TPB has rarely been applied to examine ethical decisions about animals and, even less, to veganism. Hence, the validity of TPB in predicting the intention of adopting veganism remains unanswered. A total of 476 non-vegan Spanish university students (55.6% female; the mean age was 23.26 years, SD= 6.1) responded to online and pencil-and-paper self-reported questionnaire based on previous studies. TPB extended models incorporated two background factors: ‘general attitudes towards humanlike-attributes ascribed to animals’ (AHA) (capacity for reason/emotions/suffer, moral consideration, and affect-towards-animals); and ‘general attitudes towards 11 uses of animals’ (AUA). SPSS 22 and SmartPLS 3.0 were used for statistical analyses. This study constructed a second-order reflective-formative model and took the multi-group analysis (MGA) approach to study gender effects. Six models of TPB (the standard and five competing) were tested. No a priori hypotheses were formulated. The results gave partial support to TPB. Attitudes (ATTV) (β = .207, p < .001), subjective norms (SNV) (β = .323, p < .001), and perceived control behavior (PCB) (β = .149, p < .001) had a significant direct effect on intentions (INTV). This model accounted for 27,9% of the variance in intention (R2Adj = .275) and had a small predictive relevance (Q2 = .261). However, findings from this study reveal that contrary to what TPB generally proposes, the effect of the background factors on intentions was not fully mediated by the proximal constructs of intentions. For instance, in the final model (Model#6), both factors had significant multiple indirect effect on INTV (β = .074, 95% C = .030, .126 [AHA:INTV]; β = .101, 95% C = .055, .155 [AUA:INTV]) and significant direct effect on INTV (β = .175, p < .001 [AHA:INTV]; β = .100, p = .003 [AUA:INTV]). Furthermore, the addition of direct paths from background factors to intentions improved the explained variance in intention (R2 = .324; R2Adj = .317) and the predictive relevance (Q2 = .300) over the base-model. This supports existing literature on the superiority of enhanced TPB models to predict ethical issues; which suggests that moral behavior may add additional complexity to decision-making. Regarding gender effect, MGA showed that gender only moderated the influence of AHA on ATTV (e.g., βWomen−βMen = .296, p < .001 [Model #6]). However, other observed gender differences (e.g. the explained variance of the model for intentions were always higher for men that for women, for instance, R2Women = .298; R2Men = .394 [Model #6]) deserve further considerations, especially for developing more effective communication strategies.

Keywords: veganism, Theory of Planned Behavior, background factors, gender moderation

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Authors: Zuwaira Abdullahi

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In this increasing changing world, it is important to be aware of the needs of clients when it comes to race and ethnic diversities. These diversities create difficulties for multicultural counselling: the counsellor’s own culture, attitudes, and theoretical perspective; the client's culture; and the multiplicity of variables comprising an individual's identity. This paper examines the level of realization, sensitization and attitude of counsellors towards individuals that come from different cultural, social and economic background.

Keywords: multicultural, diversities, counselling, needs

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Authors: Tatsiana Haiden

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The role of the translator in the publishing process has been underestimated for centuries. Any translation is produced in a certain socio-political context by agents with different background, interests, and opinions, i.e., no translation is neutral. Any translation goes beyond the text; it is not only an interlingual transfer of signs but a social phenomenon. The case study shows how Jewish social networks influence publishing translations and aims to explain the unexpected success of the Jewish publishing house in pre-Anschluss Austria. The research shows that translators play a central role (‘Translator’s visibility’ - Pym, ‘Activist turn’ - Wolf, ‘Translator studies’ - Chesterman) in choosing what has to be translated and establishing communication between the author and the publisher. The concept of Translationskultur of Prunc is being historized and applied to the publishing house for the first time by analyzing business correspondence between the main actors of translation (publisher-translator-author). The translation studies project has become interdisciplinary –it encompasses sociology (concepts of Bourdieu’s ‘Field theory’ are used) and history. The historical research method Histoire croiseé is being used to avoid subjectivity and to introduce a new ‘translator-oriented’ vision in translation studies instead of the author-oriented one. In the course of the archival research, it was established that Jewish background plays an essential role in the destiny of the translators and the publishing house, so the Jewish studies have been added to the project. The study goes beyond the Austrian translational culture; it can be used as an example of dealing with publishing houses policies, publishing translations, and translator studies.

Keywords: history of translation, Jewish studies, publishing translations, translation sociology, translator studies, translators as actors

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Authors: Dong Chen, Han Song, Tingting Wei

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Industrial zones have made significant contributions to the economic development of Chinese urban areas for decades. In the background of China's New Normal, numbers of mature industrial zones are stepping into a new stage of inventory development instead of increment development. The aim of this study is to discover new characteristics and problems and corresponding inventory planning guidance of mature industrial zones. A case of Yangzhou Hi-Tech Industrial Development Zone is reported in this study. Based on a historical analysis and data analysis of land-use, it is found that land-use of the zone is near saturation and signs of land updating have begun to appear. It is observed that the zone is facing problems including disorder of land development, low economic productivity and single function. Through the data of economic output, tax contribution, industrial category, industry life cycle and environmental influence, a comprehensive assessment based on two dimensions, economic benefits and industrial matchup, is made upon every parcel in the zone. According to the assessment, the zone is divided into spatial units of the update with specific planning guidance. It comes to a conclusion as four directions of inventory planning guidance in mature industrial zones: moving industries with poor economic benefit and negative environmental influence, adding urban function and new industrial function to the zone, optimizing the function of important space, and restricting the mass layout of the real estate industry to provide space for industrial upgrading.

Keywords: China's new normal, mature industrial zones, land-use, inventory planning

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