Search results for: international ovarian tumor analysis classification

Authors: Hyeon Su Kim, Sungjin Park, Hae Ryung Chang, Hae Rim Jung, Young Zoo Ahn, Yon Hui Kim, Seungyoon Nam

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Ependymoma, one of the most common parenchymal spinal cord tumor, represents 3-6% of all CNS tumor. Especially intracranial ependymomas, which are more frequent in childhood, have a more poor prognosis and more malignant than spinal ependymomas. Although there are growing needs to understand pathogenesis, detailed molecular understanding of pathogenesis remains to be explored. A cancer cell is composed of complex signaling pathway networks, and identifying interaction between genes and/or proteins are crucial for understanding these pathways. Therefore, we explored each ependymoma in terms of differential expressed genes and signaling networks. We used Microsoft Excel™ to manipulate microarray data gathered from NCBI’s GEO Database. To analyze and visualize signaling network, we used web-based PATHOME algorithm and Cytoscape. We show HOX family and NEFL are down-regulated but SCL family is up-regulated in cerebrum and posterior fossa cancers over a spinal cancer, and JAK/STAT signaling pathway and Chemokine signaling pathway are significantly different in the both intracranial ependymoma comparing to spinal ependymoma. We are considering there may be an age-dependent mechanism under different histological pathogenesis. We annotated mutation data of each gene subsequently in order to find potential target genes.

Keywords: systems biology, ependymoma, deg, network analysis

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Authors: Aijing Luo, Zirui Xin, Yifeng Yuan

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Background: With the rapid development of the online health community, more and more patients or families are seeking health information on the Internet. Objective: This study aimed to discuss how to fully reveal the health information needs expressed by hypertensive patients in their questions in the online environment. Methods: This study randomly selected 1,000 text records from the question data of hypertensive patients from 2008 to 2018 collected from the website www.haodf.com and constructed a classification system through literature research and content analysis. This paper identified the background characteristics and questioning the intention of each hypertensive patient based on the patient’s question and used co-occurrence network analysis to explore the features of the health information needs of hypertensive patients. Results: The classification system for health information needs of patients with hypertension is composed of 9 parts: 355 kinds of drugs, 395 kinds of symptoms and signs, 545 kinds of tests and examinations , 526 kinds of demographic data, 80 kinds of diseases, 37 kinds of risk factors, 43 kinds of emotions, 6 kinds of lifestyles, 49 kinds of questions. The characteristics of the explored online health information needs of the hypertensive patients include: i)more than 49% of patients describe the features such as drugs, symptoms and signs, tests and examinations, demographic data, diseases, etc. ii) these groups are most concerned about treatment (77.8%), followed by diagnosis (32.3%); iii) 65.8% of hypertensive patients will ask doctors online several questions at the same time. 28.3% of the patients are very concerned about how to adjust the medication, and they will ask other treatment-related questions at the same time, including drug side effects, whether to take drugs, how to treat a disease, etc.; secondly, 17.6% of the patients will consult the doctors online about the causes of the clinical findings, including the relationship between the clinical findings and a disease, the treatment of a disease, medication, and examinations. Conclusion: In the online environment, the health information needs expressed by Chinese hypertensive patients to doctors are personalized; that is, patients with different background features express their questioning intentions to doctors. The classification system constructed in this study can guide health information service providers in the construction of online health resources, to help solve the problem of information asymmetry in communication between doctors and patients.

Keywords: online health community, health information needs, hypertensive patients, doctor-patient communication

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Authors: Hugo M. Gonzales, Ke Ni Chai, Deanne Mary King

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International students are underrepresented in Australian health literature, and this population is especially vulnerable to the well-documented negative impacts associated with racial microaggressions in their adjustment to settling in the new society, as well as to the many challenges they already face as international students. This study investigated the prevalence of racial microaggressions among international students and their impact on stress and psychological well-being. This research was conducted during the COVID-19 pandemic, which has been documented to contribute to anti-Asian racism. Participants included 54 international students, of which 72% were Asian. The Racial and Ethnic Microaggressions Scale (REMS), Perceived Stress Scale (PSS), and the Perceived General Wellbeing Indicator (PGWBI) were used to measure the participants’ responses. All participants reported experiencing racial microaggression in the last six months, and significant correlations and regression models were found between REMS, certain elements of the PSS scale, and time in Australia. Despite the small sample size, this research corroborated outcomes from recent studies and provided insight into the prevalence and impact of racial microaggressions among such populations, highlighting the need for further exploration.

Keywords: racial microaggressions, international students, racism, REMS, microaggressions in Australia, stress, psychological wellbeing

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Authors: Pei-Jyun Hsieh, Cheng-Hsuan Li, Bor-Chen Kuo

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For hyperspectral image classification, feature reduction is an important pre-processing for avoiding the Hughes phenomena due to the difficulty for collecting training samples. Hence, lots of researches developed feature selection methods such as F-score, HSIC (Hilbert-Schmidt Independence Criterion), and etc., to improve hyperspectral image classification. However, most of them only consider the class separability in the original space, i.e., a linear class separability. In this study, we proposed a nonlinear class separability measure based on kernel trick for selecting an appropriate feature subset. The proposed nonlinear class separability was formed by a generalized RBF kernel with different bandwidths with respect to different features. Moreover, it considered the within-class separability and the between-class separability. A genetic algorithm was applied to tune these bandwidths such that the smallest with-class separability and the largest between-class separability simultaneously. This indicates the corresponding feature space is more suitable for classification. In addition, the corresponding nonlinear classification boundary can separate classes very well. These optimal bandwidths also show the importance of bands for hyperspectral image classification. The reciprocals of these bandwidths can be viewed as weights of bands. The smaller bandwidth, the larger weight of the band, and the more importance for classification. Hence, the descending order of the reciprocals of the bands gives an order for selecting the appropriate feature subsets. In the experiments, three hyperspectral image data sets, the Indian Pine Site data set, the PAVIA data set, and the Salinas A data set, were used to demonstrate the selected feature subsets by the proposed nonlinear feature selection method are more appropriate for hyperspectral image classification. Only ten percent of samples were randomly selected to form the training dataset. All non-background samples were used to form the testing dataset. The support vector machine was applied to classify these testing samples based on selected feature subsets. According to the experiments on the Indian Pine Site data set with 220 bands, the highest accuracies by applying the proposed method, F-score, and HSIC are 0.8795, 0.8795, and 0.87404, respectively. However, the proposed method selects 158 features. F-score and HSIC select 168 features and 217 features, respectively. Moreover, the classification accuracies increase dramatically only using first few features. The classification accuracies with respect to feature subsets of 10 features, 20 features, 50 features, and 110 features are 0.69587, 0.7348, 0.79217, and 0.84164, respectively. Furthermore, only using half selected features (110 features) of the proposed method, the corresponding classification accuracy (0.84168) is approximate to the highest classification accuracy, 0.8795. For other two hyperspectral image data sets, the PAVIA data set and Salinas A data set, we can obtain the similar results. These results illustrate our proposed method can efficiently find feature subsets to improve hyperspectral image classification. One can apply the proposed method to determine the suitable feature subset first according to specific purposes. Then researchers can only use the corresponding sensors to obtain the hyperspectral image and classify the samples. This can not only improve the classification performance but also reduce the cost for obtaining hyperspectral images.

Keywords: hyperspectral image classification, nonlinear feature selection, kernel trick, support vector machine

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Authors: Shunzuo Wu, Xudong Luo, Yuanxiu Liao

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Recently, the rapid development of deep learning makes artificial intelligence (AI) penetrate into many fields, replacing manual work there. In particular, AI systems also become a research focus in the field of automatic office. To meet real needs in automatic officiating, in this paper we develop an automatic form filling system. Specifically, it uses two classical neural network models and several word embedding models to classify various relevant information elicited from the Internet. When training the neural network models, we use less noisy and balanced data for training. We conduct a series of experiments to test my systems and the results show that our system can achieve better classification results.

Keywords: artificial intelligence and office, NLP, deep learning, text classification

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Authors: Mayuva Youngsabanant, Suphaphorn Rabiab, Hatairuk Tungkasen, Nongnuch Gumlungpat, Mayuree Pumipaiboon

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The porcine follicular fluid proteins (pFF) of healthy small size ovarian follicles (1-3 mm in diameters) of Large White pig ovaries were collected by sterile technique. They were used for testing the effect on cell viability and characterization of Vero cell lines using MTT assay. Two hundred microliter of round shape Vero cell lines were culture in 96 well plates with DMEM for 24 h. After that, they were attachment to substrate and some changed into fibroblast shape and spread over the surface after culture for 48 h. Then, Vero cell lines were treated with pFF at concentration of 2, 4, 20, 40, 200, 400, 500, and 600 µg proteins/mL for 24 h. Yields of the best results were analyzed by using one-way ANOVA. MTT assay reviewed an increasing in percentage of viability of Vero cell lines indicated that at concentration of 400-600 µg proteins/mL showed higher percentage of viability (115.64 ± 6.95, 106.91 ± 5.27 and 116.73 ± 20.15) than control group. They were significantly different from the control group (p < 0.05) but lower than the positive control group (DMEM with 10% heat treated fetal bovine serum). Cell lines showed normal character in fibroblast elongate shape after treated with pFF except in high concentration of pFF. This result implies that pFF of small size ovarian follicle at concentration of 400-600 µg proteins/mL could be optimized concentration for using as a supplement in Vero cell line culture medium to promote cell viability instead of growth hormone from fetal bovine serum. This merit could be applied in other cell biotechnology researches. Acknowledgements: This work was funded by a grant from Silpakorn University and Faculty of Science, Silpakorn University, Thailand.

Keywords: cell viability, porcine follicular fluid, MTT assay, Vero cell line

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Authors: Abdulmajeed Abdullah Alqarni

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This paper is devoted a study of the current frameworks applicable to foreign investments at the levels of domestic and international law, with a particular focus on the legitimate balance to be achieved between the rights of the host state and the legal protections owed to foreign investors. At the wider level of analysis, the paper attempts to map and critically examine the relationship between foreign investment and economic development. In doing so, the paper offers a study in how current discourses and practices on investment law can reconcile the competing interests of developing and developed countries. The study draws on the growing economic imperative for developing nations to create a favorable investment climate capable of attracting private foreign investment. It notes that that over the past decades, an abundance of legal standards that establish substantive and procedural protections for legal forms of foreign investments in the host countries have evolved and crystalized. The study then goes on to offer a substantive analysis of legal reforms at the domestic level in countries such as Saudi Arabia before going on to provide an in- depth and substantive examination of the most important instruments developed at the levels of international law: bilateral investment agreements and double taxation agreements. As to its methods, the study draws on case studies and from data assessing the link between double taxation and economic development. Drawing from the extant literature and doctrinal research, and international and comparative jurisprudence, the paper excavates and critically examines contemporary definitions and norms of international investment law, many of which have been given concrete form and specificity in an ever-expanding number of bilateral and multilateral investment treaties. By reconsidering the wider challenges of conflicts of law and jurisdiction, and the competing aims of the modern investment law regime, the study reflects on how bilateral investment treaties might succeed in achieving the dual aims of rights protection and economic sovereignty. Through its examination of the double taxation phenomena, the study goes on to identify key practical challenges raised by the implementation of bilateral treaties whilst also assessing the sufficiency of the domestic and international legal solutions that are proposed in response. In its final analysis, the study aims to contribute to existing scholarship by assessing contemporary legal and economic barriers to the free flow of investment with due regard for the legitimate concerns and diversity of developing nations. It does by situating its analysis of the domestic enforcement of international investment instrument in its wider historical and normative context. By focusing on the economic and legal dimensions of foreign investment, the paper also aims to offer an interdisciplinary and holistic perspective on contemporary issues and developments in investment law while offering practical reform proposals that can be used to be achieve a more equitable balance between the rights and interests of states and private entities in an increasingly trans nationalized sphere of investment regulation and treaty arbitration.

Keywords: foreign investment, bilateral investment treaties, international tax law, double taxation treaties

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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Authors: Dercan Gencbas, Hatice Bebis, Sue Moorhead

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In the nursing process, many of the nursing classification systems were created to be used in international. From these, NANDA-I, Nursing Outcomes Classification (NOC) and Nursing Interventions Classification (NIC). In this direction, the main objective of this study is to establish a model for caregivers in hospitals and communities in Turkey and to ensure that nursing outputs are assessed by NOC-based measures. There are many scales to measure Urinary Incontinence (UI), which is very common in children, in old age, vaginal birth, NOC scales are ideal for use in the nursing process for comprehensive and holistic assessment, with surveys available. For this reason, the purpose of this study is to evaluate the validity of the NOC outputs and indicators used for UI NANDA-I. This research is a methodological study. In addition to the validity of scale indicators in the study, how much they will contribute to recovery after the nursing intervention was assessed by experts. Scope validations have been applied and calculated according to Fehring 1987 work model. According to this, nursing inclusion criteria and scores were determined. For example, if experts have at least four years of clinical experience, their score was 4 points or have at least one year of the nursing classification system, their score was 1 point. The experts were a publication experience about nursing classification, their score was 1 point, or have a doctoral degree in nursing, their score was 2 points. If the expert has a master degree, their score was 1 point. Total of 55 experts rated Fehring as a “senior degree” with a score of 90 according to the expert scoring. The nursing interventions to be applied were asked to what extent these indicators would contribute to recovery. For coverage validity tailored to Fehring's model, each NOC and NOC indicator from specialists was asked to score between 1-5. Score for the significance of indicators was from 1=no precaution to 5=very important. After the expert opinion, these weighted scores obtained for each NOC and NOC indicator were classified as 0.8 critical, 0.8 > 0.5 complements, > 0.5 are excluded. In the NANDA-I / NOC / NIC system (guideline), 5 NOCs proposed for nursing diagnoses for UI were proposed. These outputs are; Urinary Continence, Urinary Elimination, Tissue Integrity, Self CareToileting, Medication Response. After the scales are translated into Turkish, the weighted average of the scores obtained from specialists for the coverage of all 5 NOCs and the contribution of nursing initiatives exceeded 0.8. After the opinions of the experts, 79 of the 82 indicators were calculated as critical, 3 of the indicators were calculated as supplemental. Because of 0.5 > was not obtained, no substance was removed. All NOC outputs were identified as valid and usable scales in Turkey. In this study, five NOC outcomes were verified for the evaluation of the output of individuals who have received nursing knowledge of UI and variant types. Nurses in Turkey can benefit from the outputs of the NOC scale to perform the care of the elderly incontinence.

Keywords: nursing outcomes, content validity, nursing diagnosis, urinary incontinence

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Authors: Bingchun Liu, Pei-Chann Chang, Natasha Huang, Dun Li

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Machine Learning and Data Mining are the two important tools for extracting useful information and knowledge from large datasets. In machine learning, classification is a wildly used technique to predict qualitative variables and is generally preferred over regression from an operational point of view. Due to the enormous increase in air pollution in various countries especially China, Air Quality Classification has become one of the most important topics in air quality research and modelling. This study aims at introducing a hybrid classification model based on information theory and Support Vector Machine (SVM) using the air quality data of four cities in China namely Beijing, Guangzhou, Shanghai and Tianjin from Jan 1, 2014 to April 30, 2016. China's Ministry of Environmental Protection has classified the daily air quality into 6 levels namely Serious Pollution, Severe Pollution, Moderate Pollution, Light Pollution, Good and Excellent based on their respective Air Quality Index (AQI) values. Using the information theory, information gain (IG) is calculated and feature selection is done for both categorical features and continuous numeric features. Then SVM Machine Learning algorithm is implemented on the selected features with cross-validation. The final evaluation reveals that the IG and SVM hybrid model performs better than SVM (alone), Artificial Neural Network (ANN) and K-Nearest Neighbours (KNN) models in terms of accuracy as well as complexity.

Keywords: machine learning, air quality classification, air quality index, information gain, support vector machine, cross-validation

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Authors: S. Vlasyan

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The article deals with the implementation of the right to self-determination of peoples on the example of Nagorno-Karabakh Republic. The problem of correlation of two fundamental principles of international law i. e. territorial integrity and the right to self-determination of peoples is considered to be one of the vital issues in the field of international law for several decades. So, in this article, the author analyzes the decision of the Supreme Court of Canada regarding specific issues of secession of Quebec from Canada, as well as the decision of the International Court of Justice in the case concerning East Timor (Portugal v. Australia), and in the case of Western Sahara. The author formulates legal conditions of Nagorno-Karabakh secession.

Keywords: right of self-determination, territorial integrity, the principles of International Law, Nagorno-Karabakh Republic

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Ratih Indraswari

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As it occupies a new status in international relations, Indonesia needs to re-organize its resources in projecting the preferred image internationally. Attractiveness becomes crucial as Indonesia needs to maintain its posture as a reliable contributor to the world. This paper tries to scrutinize the un-tap potential of ideational powers Indonesia possesses. Herein the ideational power is assumed to be translated into a soft power, intangible and rely on its influential degree to persuade and attract other countries, through its public diplomacy activities. A specific correlation will be dedicated to the effort of Indonesia public diplomacy on international education. It is believed that international education progresses mutual understanding in disseminating Indonesia values and engages public audience. As a result these exchanges and engagements support the attainment of Indonesia’s interests and forwarding Indonesia’s foreign policies. A case study on KNB (Kemitraan Negara berkembang) scholarship scheme will be provided and its impact towards building people-to-people connections.

Keywords: Indonesia, international education, KNB (Kemitraan Negara Berkembang), public diplomacy

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Authors: Katsumi Hirata

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Certain systems can function well only if they recognize the sound environment as humans do. In this research, we focus on sound classification by adopting a convolutional neural network and aim to develop a method that automatically classifies various environmental sounds. Although the neural network is a powerful technique, the performance depends on the type of input data. Therefore, we propose an approach via a slice bispectrogram, which is a third-order spectrogram and is a slice version of the amplitude for the short-time bispectrum. This paper explains the slice bispectrogram and discusses the effectiveness of the derived method by evaluating the experimental results using the ESC‑50 sound dataset. As a result, the proposed scheme gives high accuracy and stability. Furthermore, some relationship between the accuracy and non-Gaussianity of sound signals was confirmed.

Keywords: environmental sound, bispectrum, spectrogram, slice bispectrogram, convolutional neural network

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Authors: Mehdi Ghaemi

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In principle, foreign investment security is enshrined in International Investment Agreements (IIAs) and Bilateral Investment Treaties (BITs) in the form of protection standards such as the Full Protection and Security Standard (FPS). Accordingly, the host countries undertake to provide the necessary security for the economic activities of foreign investment. With the outbreak of coronavirus, the international community called COVID-19 a threat to international peace security, as well as to the public interest and national security of nations; and to deal with, they proposed several solutions, generally including quarantine, creating social distances, and restricting businesses. This article first studies the security of foreign investment in international investment law. In the following, it analyzes the consequences of the COVID-19 pandemic for foreign investment security so that if there is a threat to that security, solutions could be offered to reduce it.

Keywords: foreign investment, FPS standard, host country, public health, COVID-19

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Authors: Mohamed Mounir Tellache, Hiroyuki Kambara, Yasuharu Koike, Makoto Miyakoshi, Natsue Yoshimura

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This study explores the practicality of using electroencephalographic (EEG) independent components to predict eight-direction finger movements in pseudo-real-time. Six healthy participants with individual-head MRI images performed finger movements in eight directions with two different arm configurations. The analysis was performed in two stages. The first stage consisted of using independent component analysis (ICA) to separate the signals representing brain activity from non-brain activity signals and to obtain the unmixing matrix. The resulting independent components (ICs) were checked, and those reflecting brain-activity were selected. Finally, the time series of the selected ICs were used to predict eight finger-movement directions using Sparse Logistic Regression (SLR). The second stage consisted of using the previously obtained unmixing matrix, the selected ICs, and the model obtained by applying SLR to classify a different EEG dataset. This method was applied to two different settings, namely the single-participant level and the group-level. For the single-participant level, the EEG dataset used in the first stage and the EEG dataset used in the second stage originated from the same participant. For the group-level, the EEG datasets used in the first stage were constructed by temporally concatenating each combination without repetition of the EEG datasets of five participants out of six, whereas the EEG dataset used in the second stage originated from the remaining participants. The average test classification results across datasets (mean ± S.D.) were 38.62 ± 8.36% for the single-participant, which was significantly higher than the chance level (12.50 ± 0.01%), and 27.26 ± 4.39% for the group-level which was also significantly higher than the chance level (12.49% ± 0.01%). The classification accuracy within [–45°, 45°] of the true direction is 70.03 ± 8.14% for single-participant and 62.63 ± 6.07% for group-level which may be promising for some real-life applications. Clustering and contribution analyses further revealed the brain regions involved in finger movement and the temporal aspect of their contribution to the classification. These results showed the possibility of using the ICA-based method in combination with other methods to build a real-time system to control prostheses.

Keywords: brain-computer interface, electroencephalography, finger motion decoding, independent component analysis, pseudo real-time motion decoding

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Authors: Mateusz Bilski, Jakub Klas, Emilia Kowalczyk, Sylwia Koziej, Katarzyna Kulszo, Ludmiła Grzybowska- Szatkowska

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Background: Liver metastases are a common complication of primary solid tumors and sig-nificantly reduce patient survival. In the era of increasing diagnosis of oligometastatic disease and oligoprogression, methods of local treatment of metastases, i.e. MDT, are becoming more important. Implementation of such treatment can be considered for liver metastases, which are a common complication of primary solid tumors and significantly reduce patient survival. To date, the mainstay of treatment for oligometastatic disease has been surgical resection, but not all patients qualify for the procedure. As an alternative to surgical resection, radiotherapy techniques have become available, including stereotactic body radiation therapy (SBRT) or high-dose interstitial brachytherapy (iBT). iBT is an invasive method that emits very high doses of radiation from the inside of the tumor to the outside. This technique provides better tumor coverage than SBRT while having little impact on surrounding healthy tissue and elim-inates some concerns involving respiratory motion. Methods: We conducted a systematic re-view of the scientific literature on the use of brachytherapy in the treatment of liver metasta-ses from 2018 - 2023 using PubMed and ResearchGate browsers according to PRISMA rules. Results: From 111 articles, 18 publications containing information on 729 patients with liver metastases were selected. iBT has been shown to provide high rates of tumor control. Among 14 patients with 54 unresectable RCC liver metastases, after iBT LTC was 92.6% during a median follow-up of 10.2 months, PFS was 3.4 months. In analysis of 167 patients after treatment with a single fractional dose of 15-25 Gy with brachytherapy at 6- and 12-month follow-up, LRFS rates of 88,4-88.7% and 70.7 - 71,5%, PFS of 78.1 and 53.8%, and OS of 92.3 - 96.7% and 76,3% - 79.6%, respectively, were achieved. No serious complications were observed in all patients. Distant intrahepatic progression occurred later in patients with unre-sectable liver metastases after brachytherapy (PFS: 19.80 months) than in HCC patients (PFS: 13.50 months). A significant difference in LRFS between CRC patients (84.1% vs. 50.6%) and other histologies (92.4% vs. 92.4%) was noted, suggesting a higher treatment dose is necessary for CRC patients. The average target dose for metastatic colorectal cancer was 40 - 60 Gy (compared to 100 - 250 Gy for HCC). To better assess sensitivity to therapy and pre-dict side effects, it has been suggested that humoral mediators be evaluated. It was also shown that baseline levels of TNF-α, MCP-1 and VEGF, as well as NGF and CX3CL corre-lated with both tumor volume and radiation-induced liver damage, one of the most serious complications of iBT, indicating their potential role as biomarkers of therapy outcome. Con-clusions: The use of brachytherapy methods in the treatment of liver metastases of various cancers appears to be an interesting and relatively safe therapeutic method alternative to sur-gery. An important challenge remains the selection of an appropriate brachytherapy method and radiation dose for the corresponding initial tumor type from which the metastasis origi-nated.

Keywords: liver metastases, brachytherapy, CT-HDRBT, iBT

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Marwa Abdulsalam, Osamu Suzuki, Wirawan Dony Dahana

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One of the remarkable and unique industries in Japan is the food samples industry which can be noticed in most of the restaurants located around Japan. However, the market is getting saturated, which has pushed Japanese food sample manufacturers to start exploring new international markets. Most of the markets they explored were in the East Asian region, such as China or Korea. In this research, we examine the feasibility and the potential adoption rate of food samples in the international market outside the East Asian region. The main focus of this study is on the Saudi Arabian market. Nonetheless, since Saudi Arabia is a big market, the study results could possibly be applied to the international market as well. The study has conducted a quantitative survey to test the potential of the food samples industry in Saudi Arabia especially in 4 major cities: Jeddah, Mecca, Riyadh, and Dammam. The survey also tests the willingness to purchase, the average price point that the consumer is willing to pay for food samples, and the factors that drive restaurant owners to adopt the food samples system. The study created a correlation analysis between different factors, such as the geographic factor and the size of the restaurant factor, to examine the effect of different aspects on the purchasing decision. The study has found that the Japanese food samples system is predicted to adapt successfully in the Saudi Arabian market and in the international market alike due to the high importance of the food culture and the existence of the communication challenges that the food samples can solve. Additionally, the market survey stated in this study indicated that 83% of the restaurants’ managers are willing to adopt this system in their restaurants.

Keywords: food samples, innovative marketing, international market, marketing method

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Moschos Vogiatzis, K. Perakis

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Spatial-explicit and up-to-date land use/land cover information is fundamental for spatial planning, land management, sustainable development, and sound decision-making. In the last decade, many satellite-derived land cover products at different spatial, spectral, and temporal resolutions have been developed, such as the European Copernicus Land Cover product. However, more efficient and detailed information for land use/land cover is required at the regional or local scale. A typical Mediterranean basin with a complex landscape comprised of various forest types, crops, artificial surfaces, and wetlands was selected to test and develop our approach. In this study, we investigate the improvement of Copernicus Land Cover product (CLC2018) using Landsat 8 and Sentinel-2 pixel-based classification based on all available existing geospatial data (Forest Maps, LPIS, Natura2000 habitats, cadastral parcels, etc.). We examined and compared the performance of the Random Forest classifier for land use/land cover mapping. In total, 10 land use/land cover categories were recognized in Landsat 8 and 11 in Sentinel-2A. A comparison of the overall classification accuracies for 2018 shows that Landsat 8 classification accuracy was slightly higher than Sentinel-2A (82,99% vs. 80,30%). We concluded that the main land use/land cover types of CLC2018, even within a heterogeneous area, can be successfully mapped and updated according to CLC nomenclature. Future research should be oriented toward integrating spatiotemporal information from seasonal bands and spectral indexes in the classification process.

Keywords: classification, land use/land cover, mapping, random forest

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Authors: T. M. Al Muhareb

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Any organization should take into consideration the customers’ satisfaction while providing any service to their customers. The quality of services is always considered as the main aspect that attracts the customers’ attention and helps the airports to develop their services and operations. King Khalid International Airport is considered as the gateway of the Kingdom of Saudi Arabia. Therefore, the aim of this paper was to identify the quality service in the departure area at in King Khalid Airport. The SERVQUAL questionnaire was distributed among the passengers in King Khalid International Airport and the respondents have reached to 500 passengers. The results that are obtained from the SERVQUAL questionnaire showed that the quality of airport’s services is low.

Keywords: service quality, SERVQUAL methodology, King Khalid International Airport (KKIA), customers’ satisfaction

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Authors: Bernd Kiefer, Abraham Gebru Tesfay, Dietrich Klakow

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The motivation of our work is to detect different terrain types traversed by a robot based on acoustic data from the robot-terrain interaction. Different acoustic features and classifiers were investigated, such as Mel-frequency cepstral coefficient and Gamma-tone frequency cepstral coefficient for the feature extraction, and Gaussian mixture model and Feed forward neural network for the classification. We analyze the system’s performance by comparing our proposed techniques with some other features surveyed from distinct related works. We achieve precision and recall values between 87% and 100% per class, and an average accuracy at 95.2%. We also study the effect of varying audio chunk size in the application phase of the models and find only a mild impact on performance.

Keywords: acoustic features, autonomous robots, feature extraction, terrain classification

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Authors: Mohamed R. Mhereeg

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The paper discusses the implementation of the MultiAgent classification System (MACS) and utilizing it to provide an automated and accurate classification of end users developing applications in the spreadsheet domain. However, different technologies have been brought together to build MACS. The strength of the system is the integration of the agent technology with the FIPA specifications together with other technologies, which are the .NET widows service based agents, the Windows Communication Foundation (WCF) services, the Service Oriented Architecture (SOA), and Oracle Data Mining (ODM). Microsoft's .NET windows service based agents were utilized to develop the monitoring agents of MACS, the .NET WCF services together with SOA approach allowed the distribution and communication between agents over the WWW. The Monitoring Agents (MAs) were configured to execute automatically to monitor excel spreadsheets development activities by content. Data gathered by the Monitoring Agents from various resources over a period of time was collected and filtered by a Database Updater Agent (DUA) residing in the .NET client application of the system. This agent then transfers and stores the data in Oracle server database via Oracle stored procedures for further processing that leads to the classification of the end user developers.

Keywords: MACS, implementation, multi-agent, SOA, autonomous, WCF

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Authors: Safa Safdar

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Breast cancer is a type of cancer which is developed by the formation of a tumor on the breast. This tumor invades and causes different electrolyte imbalance. The present study was designed to measure the serum calcium and inorganic phosphorous levels and to check the frequency of hypercalcemia and hypophosphatemia in breast cancer patients. Serum calcium and phosphorous levels of fifty breast cancer women of 18-70 years of age group and fifty healthy women of same age group were measured by using semi-automated chemistry analyzer ( Humalyzer 3000, Human, Germany ). Significant variation in these levels was observed. The mean calcium value in BC patients was higher 9.398 mg/dl as compared to controls which were 8.694 mg/dl. Whereas the mean value of inorganic phosphorus level was lower 4.060 mg/dl in BC patients as compared to controls having 4.456 mg/dl. In this study, the frequency of hypercalcemia in Breast cancer patients was 10% i.e. only 10 out of 50 Breast cancer patients were suffering from hypercalcemia. Whereas the frequency of hypophosphatemia in this study was only 2 % i.e. only 1 out of 50 patients was suffering from hypophosphatemia. Thus it is concluded that there is a significant change in serum calcium and inorganic phosphorous levels in Breast cancer patients as the disease progresses. So, this study will be helpful for the clinicians to maintain serum calcium and phosphorous levels in Breast cancer patients and also preventing them from further complications.

Keywords: serum analysis, calcium, inorganic phosphorus, hpercalcemia hypophosphatemia

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Authors: Romeu Bonk Mesquita

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Argentina is the South American number 1 country of destiny to intraregional migration flows. This research aims to shed light on the main trends of the Argentine immigrant policy from 2016 on, when Mauricio Marci was elected President, taking the approval of the current and fairly protective of human rights Ley de Migraciones (2003) as an analytical starting point. Foreign Policy Analysis (FPA) serves as the theoretical background, highlighting decision-making processes and institutional designs that encourage or constraint political and social actors. The analysis goes through domestic and international levels, observing how immigration policy is formulated as a public policy and is simultaneously connected to Mercosur and other international organizations, such as the International Organization for Migration (IOM) and the United Nations High Commissioner for Refugees (UNHCR). Thus, the study revolves around the Direccion Nacional de Migraciones, which is the state agency in charge of executing the country’s immigrant policy, as to comprehend how its internal processes and the connections it has with both domestic and international institutions shape Argentina’s immigrant policy formulation and execution. Also, it aims to locate the migration agenda within the country’s contemporary social and political context. The methodology is qualitative, case-based and oriented by process-tracing techniques. Empirical evidence gathered includes official documents and data, media coverage and interviews to key-informants. Recent events, such as the Decreto de Necesidad y Urgencia 70/2017 issued by President Macri, and the return of discursive association between migration and criminality, indicate a trend of nationalization and securitization of the immigration policy in contemporary Argentina.

Keywords: Argentine foreign policy, human rights, immigrant policy, Mercosur

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Authors: Syue-Liang Lin, C. Allen Chang

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Luminescence imaging is an important technique used in biomedical research and clinical diagnostic applications in recent years. Concurrently, the development of NIR luminescence probes / imaging contrast agents has helped the understanding of the structural and functional properties of cells and animals. Photodynamic therapy (PDT) is used clinically to treat a wide range of medical conditions, but the therapeutic efficacy of general PDT for deeper tumor was limited by the penetration of excitation source. The tumor targeting biomedical nanomaterials UCNP@PS (upconversion nanoparticle conjugated with photosensitizer) for photodynamic therapy and near-infrared imaging of cancer will be developed in our study. Synthesis and characterization of biomedical nanomaterials were completed in this studies. The spectrum of UCNP was characterized by photoluminescence spectroscopy and the morphology was characterized by Transmission Electron Microscope (TEM). TEM and XRD analyses indicated that these nanoparticles are about 20~50 nm with hexagonal phase. NaYF₄:Ln³⁺ (Ln= Yb, Nd, Er) upconversion nanoparticles (UCNPs) with core / shell structure, synthesized by thermal decomposition method in 300°C, have the ability to emit visible light (upconversion: 540 nm, 660 nm) and near-infrared with longer wavelength (downconversion: NIR: 980 nm, 1525 nm) by absorbing 800 nm NIR laser. The information obtained from these studies would be very useful for applications of these nanomaterials for bio-luminescence imaging and photodynamic therapy of deep tumor tissue in the future.

Keywords: Near Infrared (NIR), lanthanide, core-shell structure, upconversion, theranostics

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Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: machine learning, text classification, NLP techniques, semantic representation

Procedia PDF Downloads 78

Authors: Eugene Y. J. Aw, J. W. Koh, S. H. Chew, K. E. Chua, Lionel L. J. Ang, Algernon C. S. Hong, Danette S. E. Tan, Grace H. B. Foo, K. Q. Hong, L. M. Cheng, M. L. Leong

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This paper presents a novel rapid soil classification technique that combines computer vision with four-probe soil electrical resistivity method and cone penetration test (CPT), to improve the accuracy and productivity of on-site classification of excavated soil. In Singapore, excavated soils from local construction projects are transported to Staging Grounds (SGs) to be reused as fill material for land reclamation. Excavated soils are mainly categorized into two groups (“Good Earth” and “Soft Clay”) based on particle size distribution (PSD) and water content (w) from soil investigation reports and on-site visual survey, such that proper treatment and usage can be exercised. However, this process is time-consuming and labour-intensive. Thus, a rapid classification method is needed at the SGs. Computer vision, four-probe soil electrical resistivity and CPT were combined into an innovative non-destructive and instantaneous classification method for this purpose. The computer vision technique comprises soil image acquisition using industrial grade camera; image processing and analysis via calculation of Grey Level Co-occurrence Matrix (GLCM) textural parameters; and decision-making using an Artificial Neural Network (ANN). Complementing the computer vision technique, the apparent electrical resistivity of soil (ρ) is measured using a set of four probes arranged in Wenner’s array. It was found from the previous study that the ANN model coupled with ρ can classify soils into “Good Earth” and “Soft Clay” in less than a minute, with an accuracy of 85% based on selected representative soil images. To further improve the technique, the soil strength is measured using a modified mini cone penetrometer, and w is measured using a set of time-domain reflectometry (TDR) probes. Laboratory proof-of-concept was conducted through a series of seven tests with three types of soils – “Good Earth”, “Soft Clay” and an even mix of the two. Validation was performed against the PSD and w of each soil type obtained from conventional laboratory tests. The results show that ρ, w and CPT measurements can be collectively analyzed to classify soils into “Good Earth” or “Soft Clay”. It is also found that these parameters can be integrated with the computer vision technique on-site to complete the rapid soil classification in less than three minutes.

Keywords: Computer vision technique, cone penetration test, electrical resistivity, rapid and non-destructive, soil classification

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Authors: O. Yu Shchelkova, E. B. Usmanova

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Introduction: Last decades scientific research of quality of life (QoL) is developing fast worldwide. QoL concept pays attention to emotional experience of disease in patients, particularly to personal sense of possibility to satisfy actual needs and possibility of full social functioning in spite of disease limitations. QoL in oncological patients is studied intensively. Nevertheless, the issue of QoL in patients with bone tumors focused on psychological factors of QoL and relation to disease impact on QoL is not discussed. The aim of the study was to reveal the basic aspects and personality factors of QoL in patients with bone tumor. Results: Study participants were 139 patients with bone tumors. The diagnoses were osteosarcoma (n=42), giant cell tumor (n=32), chondrosarcoma (n=32), Ewing sarcoma (n=10) and bone metastases (n=23). The study revealed that patients with bone metastases assess their health significantly worse than other patients. Besides patients with osteosarcoma evaluate their general health higher than patients with giant cell tumors. Social functioning in patients with chondrosarcoma is higher than in patients with bone metastases and patients with giant cell tumor. Patients with chondrosarcoma have higher physical functioning and less restricted in daily activities than patients with bone metastases. Patients with bone metastases characterize their pain as more widespread than patients with primary bone tumors and have more functional restrictions due to bone incision. Moreover, the study revealed personality significant influence on QoL related to bone tumors. Such characteristics in structure of personality as high degree of self-consciousness, personal resources, cooperation and disposition to positive reappraisal in difficult situation correspond to higher QoL. Otherwise low personal resources and slight problem solving behaviour, low degree of self-consciousness and high social dependence correspond to decrease of QoL in patients with bone tumors. Conclusion: Patients with bone metastasis have lower QoL compared to patients with primary bone tumors. Patients with giant cell tumor have the worth quality of life among patients with primary bone tumors. Furthermore, the results revealed differences in QoL parameters associated with personality characteristics in patients with bone tumors. Such psychological factors as future goals, interest in life and emotional saturation, besides high degree of personal resources and cooperation influence on increasing QoL in patients with bone tumors.

Keywords: quality of life, psychological factors, bone tumor, personality

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