Search results for: encrypted traffic classification

Authors: Sandra Mitrovic, Gaurav Singh

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In many industries, including telecommunications, churn prediction has been a topic of active research. A lot of attention has been drawn on devising the most informative features, and this area of research has gained even more focus with spread of (social) network analytics. The call detail records (CDRs) have been used to construct customer networks and extract potentially useful features. However, to the best of our knowledge, no studies including network features have yet proposed a generic way of representing network information. Instead, ad-hoc and dataset dependent solutions have been suggested. In this work, we build upon a recently presented method (node2vec) to obtain representations for nodes in observed network. The proposed approach is generic and applicable to any network and domain. Unlike node2vec, which assumes a static network, we consider a dynamic and time-evolving network. To account for this, we propose an approach that constructs the feature representation of each node by generating its node2vec representations at different timestamps, concatenating them and finally compressing using an auto-encoder-like method in order to retain reasonably long and informative feature vectors. We test the proposed method on churn prediction task in telco domain. To predict churners at timestamp ts+1, we construct training and testing datasets consisting of feature vectors from time intervals [t1, ts-1] and [t2, ts] respectively, and use traditional supervised classification models like SVM and Logistic Regression. Observed results show the effectiveness of proposed approach as compared to ad-hoc feature selection based approaches and static node2vec.

Keywords: churn prediction, dynamic networks, node2vec, auto-encoders

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Authors: Hamed Nafar, Sajad Rezaei, Hamid Behbahani

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Safety analysis of the roads through the accident rates which is one of the widely used tools has been resulted from the direct exposure method which is based on the ratio of the vehicle-kilometers traveled and vehicle-travel time. However, due to some fundamental flaws in its theories and difficulties in gaining access to the data required such as traffic volume, distance and duration of the trip, and various problems in determining the exposure in a specific time, place, and individual categories, there is a need for an algorithm for prioritizing the road safety so that with a new exposure method, the problems of the previous approaches would be resolved. In this way, an efficient application may lead to have more realistic comparisons and the new method would be applicable to a wider range of time, place, and individual categories. Therefore, an algorithm was introduced to prioritize the safety of roads using the quasi-induced exposure method and utilizing the analytical hierarchy process. For this research, 11 provinces of Iran were chosen as case study locations. A rural accidents database was created for these provinces, the validity of quasi-induced exposure method for Iran’s accidents database was explored, and the involvement ratio for different characteristics of the drivers and the vehicles was measured. Results showed that the quasi-induced exposure method was valid in determining the real exposure in the provinces under study. Results also showed a significant difference in the prioritization based on the new and traditional approaches. This difference mostly would stem from the perspective of the quasi-induced exposure method in determining the exposure, opinion of experts, and the quantity of accidents data. Overall, the results for this research showed that prioritization based on the new approach is more comprehensive and reliable compared to the prioritization in the traditional approach which is dependent on various parameters including the driver-vehicle characteristics.

Keywords: road safety, prioritizing, Quasi-induced exposure, Analytical Hierarchy Process

Procedia PDF Downloads 331

Authors: Cam Nguyen

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Rapid, accurate, and safe sensing and imaging of physical quantities or structures finds many applications and is of significant interest to society. Sensing and imaging using radio-frequency (RF) techniques, particularly, has gone through significant development and subsequently established itself as a unique territory in the sensing world. RF sensing and imaging has played a critical role in providing us many sensing and imaging abilities beyond our human capabilities, benefiting both civilian and military applications - for example, from sensing abnormal conditions underneath some structures’ surfaces to detection and classification of concealed items, hidden activities, and buried objects. We present the developments of several sensing and imaging systems implementing RF technologies like ultra-wide band (UWB), synthetic-pulse, and interferometry. These systems are fabricated completely using RF integrated circuits. The UWB impulse system operates over multiple pulse durations from 450 to 1170 ps with 5.5-GHz RF bandwidth. It performs well through tests of various samples, demonstrating its usefulness for subsurface sensing. The synthetic-pulse system operating from 0.6 to 5.6 GHz can assess accurately subsurface structures. The synthetic-pulse system operating from 29.72-37.7 GHz demonstrates abilities for various surface and near-surface sensing such as profile mapping, liquid-level monitoring, and anti-personnel mine locating. The interferometric system operating at 35.6 GHz demonstrates its multi-functional capability for measurement of displacements and slow velocities. These RF sensors are attractive and useful for various surface and subsurface sensing applications. This paper was made possible by NPRP grant # 6-241-2-102 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.

Keywords: RF sensors, radars, surface sensing, subsurface sensing

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Authors: Beatrice Fenu, Francesco Niosi, Giovanni Bracco, Giuliana Mattiazzo

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In recent years, Europe has seen a great development of renewable energy, in a perspective of reducing polluting emissions and transitioning to cleaner forms of energy, as established by the European Green New Deal. Wind energy has come to cover almost 15% of European electricity needs andis constantly growing. In particular, far-offshore wind turbines are attractive from the point of view of exploiting high-speed winds and high wind availability. Considering offshore wind turbine siting that combines the resources analysis, the bathymetry, environmental regulations, and maritime traffic and considering the waves influence in the stability of the platform, the hydrodynamic characteristics of the platform become fundamental for the evaluation of the performances of the turbine, especially for the pitch motion. Many platform's geometries have been studied and used in the last few years. Their concept is based upon different considerations as hydrostatic stability, material, cost and mooring system. A new method to reach a high-performances substructure for different kinds of wind turbines is proposed. The system that considers substructure, mooring, and wind turbine is implemented in Orcaflex, and the simulations are performed considering several sea states and wind speeds. An external dynamic library is implemented for the turbine control system. The study shows the comparison among different substructures and the new concepts developed. In order to validate the model, CFD simulations will be performed by mean of STAR CCM+, and a comparison between rigid and elastic body for what concerns blades and tower will be carried out. A global model will be built to predict the productivity of the floating turbine according to siting, resources, substructure, and mooring. The Levelized Cost of Electricity (LCOE) of the system is estimated, giving a complete overview about the advantages of floating offshore wind turbine plants. Different case studies will be presented.

Keywords: aero-hydrodynamic model, computational fluid dynamics, floating offshore wind, siting, verification, and validation

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Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

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New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

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Authors: Joseph Gear, Yue Xu, Ernest Foo, Praveen Gauravaran, Zahra Jadidi, Leonie Simpson

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Deep learning methods have been seeing an increasing application to the long-standing security research goal of automatic vulnerability detection for source code. Attention, however, must still be paid to the task of producing vector representations for source code (code embeddings) as input for these deep learning models. Graphical representations of code, most predominantly Abstract Syntax Trees and Code Property Graphs, have received some use in this task of late; however, for very large graphs representing very large code snip- pets, learning becomes prohibitively computationally expensive. This expense may be reduced by intelligently pruning this input to only vulnerability-relevant information; however, little research in this area has been performed. Additionally, most existing work comprehends code based solely on the structure of the graph at the expense of the information contained by the node in the graph. This paper proposes Semantic-enhanced Code Embedding for Vulnerability Discovery (SCEVD), a deep learning model which uses semantic-based feature selection for its vulnerability classification model. It uses information from the nodes as well as the structure of the code graph in order to select features which are most indicative of the presence or absence of vulnerabilities. This model is implemented and experimentally tested using the SARD Juliet vulnerability test suite to determine its efficacy. It is able to improve on existing code graph feature selection methods, as demonstrated by its improved ability to discover vulnerabilities.

Keywords: code representation, deep learning, source code semantics, vulnerability discovery

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Authors: Abdullah Saeidi, Aydin Gülses, Christan Flörke

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Skin cancer of the face region is the most common type of malignancy and surgical excision is the preferred approach. However, the clinical long term results reported in the literature are still controversial. Objectives: To describe; 1. Demographical characteristics 2. Affected site, distribution and TNM classification regarding tumor type 3. Surgical aspects • Surgical removal: excision principles, safety margins, the need for secondary resection, primary reconstruction/ defect closure, anesthesia protocol, duration of hospital stay (if any) • Secondary intervention for defect closure/reconstruction: Flap technique, anesthesia protocol, duration of hospital stay (if any), postoperative wound management etc. 4. Tumor recurrences 5. Clinical outcomes 6. Studying the possible therapy approach throw Biostatistical relation and correlation between multiple Histological, diagnostics and clinical Faktors. following surgical ablation of the skin cancer of the head and neck region. Methods: Selection and statistical analysis of medical records of patients who had admitted to the Department of Oral and Maxillofacial Surgery, Universitätsklinikum Schleswig Holstein, Campus Kiel during the period of 2015-2019 will be retrospectively evaluated. Data will be collected via ORBIS Information-Management-System (ORBIS AG, Saarbrücken, Germany).

Keywords: non melanoma skin cancer, face skin cancer, skin reconstruction, non melanoma skin cancer recurrence, non melanoma skin cancer metastases

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Authors: Chi Wu, Dean Wu, Wen-Te Liu, Cheng-Yu Tsai, Shin-Mei Hsu, Yin-Tzu Lin, Ru-Yin Yang

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Background: The results of previous studies compared the benefits and risks of receiving insomnia medication. However, the effects between hypnotic drugs used and enhancement of sleep quality were still unclear. Objective: The aim of this study is to establish a prediction model for hypnotic drugs' dosage used for insomnia subjects and associated the relationship between sleep stage ratio change and drug types. Methodologies: According to American Academy of Sleep Medicine (AASM) guideline, sleep stages were classified and transformed to hypnogram via the polysomnography (PSG) in a hospital in New Taipei City (Taiwan). The subjects with diagnosis for insomnia without receiving hypnotic drugs treatment were be set as the comparison group. Conversely, hypnotic drugs dosage within the past three months was obtained from the clinical registration for each subject. Furthermore, the collecting subjects were divided into two groups for training and testing. After training convolution neuron network (CNN) to predict types of hypnotics used and dosages are taken, the test group was used to evaluate the accuracy of classification. Results: We recruited 76 subjects in this study, who had been done PSG for transforming hypnogram from their sleep stages. The accuracy of dosages obtained from confusion matrix on the test group by CNN is 81.94%, and accuracy of hypnotic drug types used is 74.22%. Moreover, the subjects with high ratio of wake stage were correctly classified as requiring medical treatment. Conclusion: CNN with hypnogram was potentially used for adjusting the dosage of hypnotic drugs and providing subjects to pre-screening the types of hypnotic drugs taken.

Keywords: convolution neuron network, hypnotic drugs, insomnia, polysomnography

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Authors: H. El-Messiry, M. El-Zainy, D. Abdelkhalek

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Cone-beam computed tomography (CBCT) provides high-quality 3-dimensional images of dental structures because of its high spatial resolution. The study of dental morphology is important in research as it provides information about diversities within a population. Many studies have shown different shapes and numbers of roots canals among different races, especially in molars. The aim of this study was to determine the morphology of root canals of mandibular first and third molars in a sample of Egyptian population using CBCT scanning. Fifty mandibular first Molars (M1) and fifty mandibular third (M3) extracted molars were collected. Thick rectangular molds were made using pink wax to hold the samples. Molars were embedded in the wax mold by aligning them in rows leaving arbitrary 0.5cm space between them. The molds with the samples in were submitted for CBCT scan. The number and morphology of root canals were assessed and classified according to Vertucci's classification. The mesial and the distal roots were examined separately. Finally, data was analyzed using Fisher exact test. The most prevalent mesial root canal frequency in M1 was type IV (60%) and type II (40 %), while M3 showed prevalence of type I (40%) and II (40%). Distal root canal morphology showed prevalence of type I in both M1 (66%) and M3 (86%). So, it can be concluded that CBCT scanning provides supplemental information about the root canal configurations of mandibular molars in a sample of Egyptian population. This study may help clinicians in the root canal treatment of mandibular molars.

Keywords: cone beam computed tomography, mandibular first molar, mandibular third molar, root canal morphology

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Authors: Rachna Agarwal, R. Naveen, Darpan Thakre, Rohit Shahi, Maryam Abbasi, Upendra Rathore, Latika Gupta

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Aim: Retinal changes are the window to systemic vasculature. Therefore, we explored retinal changes in patients with idiopathic inflammatory myopathies (IIM) as a surrogate for vascular health. Methods: Adult and juvenile IIM patients visiting a tertiary care centre in 2021 satisfying the International Myositis Classification Criteria were enrolled for detailed ophthalmic examination in comparison with healthy controls (HC). Patients with conditions that precluded thorough posterior chamber examination were excluded. Scale variables are expressed as median (IQR). Multivariate analysis (binary logistic regression-BLR) was conducted, adjusting for age, gender, and comorbidities besides factors significant in univariate analysis. Results: 43 patients with IIM [31 females; age 36 (23-45) years; disease duration 5.5 (2-12) months] were enrolled for participation. DM (44%) was the most common diagnosis. IIM patients exhibited frequent attenuation of retinal vessels (32.6% vs. 4.3%, p <0.001), AV nicking (14% vs. 2.2%, p=0.053), and vascular tortuosity (18.6% vs. 2.2%, p=0.012), besides decreased visual acuity (53.5% vs. 10.9%, p<0.001) and immature cataracts (34.9% vs. 2.2%, p<0.001). Attenuation of vessels [OR 10.9 (1.7-71), p=0.004] emerged as significantly different from HC after adjusting for covariates in BLR. Notably, adults with IIM were more predisposed to retinal abnormalities [21 (57%) vs. 1 (16%), p=0.068], especially attenuation of vessels [14(38%) vs. 0(0), p=0.067] than jIIM. However, no difference was found in retinal features amongst the subtypes of adult IIM, nor did they correlate with MDAAT, MDI, or HAQ-DI. Conclusion: Retinal microvasculopathy and diminution of vision occur in nearly one-third to half of the patients with IIM. Microvasculopathy occurs across subtypes of IIM, and more so in adults, calling for further investigation as a surrogate for damage assessment and potentially even systemic vascular health.

Keywords: idiopathic inflammatory myopathies, vascular health, retinal microvasculopathy, arterial attenuation

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Authors: Orose Rugchati, Khumthong Mahawongwiriya

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Dendrobium hybrid cv. Ear-Sakul has become one of the important commercial commodities in Thailand agricultural industry worldwide, either as potted plants or as cut flowers due to the attractive color produced in flower petals. Anthocyanins are the main flower pigments and responsible for the natural attractive display of petal colors. These pigments play an important role in functionality, such as to attract animal pollinators, classification, and grading of these orchids. Dendrobium hybrid cv. Ear-Sakul has been collected from local area farm in different stage flowers (F1, F2-F5, and F6). Anthocyanins pigment were extracted from the fresh flower by solvent extraction (MeOH–TFA 99.5:0.5v/v at 4ºC) and purification with ethyl acetate. The main anthocyanins components are cyanidin, pelargonidin, and delphinidin. Pure anthocyanin contents were analysis by UV-Visible spectroscopy technique at λ max 535, 520 and 546 nm respectively. The anthocyanins contents were converted in term of monomeric anthocyanins pigment (mg/L). The anthocyanins contents of all sample were compared with standard pigments cyanidin, pelargonidin and delphinidin. From this experiment is a simple extraction and analysis anthocyanins content in different stage of flowers results shown that monomeric anthocyanins pigment contents of different stage flowers (F1, F2-F5 and F6 ): cyanidin – 3 – glucoside (mg/l) are 0.85+0.08, 24.22+0.12 and 62.12+0.6; Pelargonidin 3,5-di- glucoside(mg/l) 10.37+0.12, 31.06+0.8 and 81.58+ 0.5; Delphinidin (mg/l) 6.34+0.17, 18.98+0.56 and 49.87+0.7; and the appearance of extraction pure anthocyanins in L(a, b): 2.71(1.38, -0.48), 1.06(0.39,-0.66) and 2.64(2.71,-3.61) respectively. Dendrobium Hybrid cv. Ear-Sakul could be used as a source of anthocyanins by simple solvent extraction and stage of flowers as a guideline for the prediction amount of main anthocyanins components are cyanidin, pelargonidin, and delphinidin could be application and development in quantities, and qualities with the advantage for food pharmaceutical and cosmetic industries.

Keywords: analysis, anthocyanins contents, different stage flowers, Dendrobium Hybrid cv. Ear-Sakul

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Authors: ChunYi Peng, Wei Hsuan CHeng, Shyh Kuang Ueng

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: J. K. Ebigwai, E. Asuquo

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Systematic affinities among Cucurbitaceae members are highly debatable as exemplified by diverging views on their phylogenies. Worst still is the overriding reliance on morphometric marker in the delimitation of cucurbitoideae members. Considerable symplesiomorphic and synapmorphic character states have been observed among some members of same genera than do with some members of other genera. The broad study aims at establishing phylogenies among species of Cucumis (Melothrieae), Momordica, Telfairia (Jolliffieae), Trichosanthes (Trichosantheae), Citrullus, Lagenaria, Luffa (Benincaseae) and Cucurbita (Cucurbita) using anatomical, cytological, Palynological, serological, and phytolith markers. However, this paper shall present preliminary findings on the phytolith character states for Cucumis melo, Momordica charantia, Telfairia occidentales, Trichosanthes dioica, Citrullus vulgaris, Lagenaria siceraria, Luffa cylindrical, Cucurbita pepo and Cucurbita maxima. Heavy liquid floatation method was employed in the extraction of the phytolith matter from the leaf tissues of these species. The result revealed that a bilobate short cell and a trapeziform sinuate form were absent in all the species except in Cucumis melo, Citrullus vulgaris and Lagenaria siceraria. Also a globular granulate form was observed exclusively in Telfairia occidentales, Cucurbita maxima, Momordica charantia and Luffa cylindrical. Other forms of phytolith observed were not diagnostic as they were not species specific. The results tentatively suggests a closer examination of the existing classification system.

Keywords: bilobate short cell, cucums, phytolith, telfairia, trapeziform sinuate

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Authors: Andressa S. T. Gomes, Luiza A. Souza, Luciana H. Yamane, Renato R. Siman

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The segregation of waste of electrical and electronic equipment (WEEE) in the generating source, its characterization (quali-quantitative) and identification of origin, besides being integral parts of classification reports, are crucial steps to the success of its integrated management. The aim of this paper was to count WEEE generation at the Federal University of Espírito Santo (UFES), Brazil, as well as to define sources, temporary storage sites, main transportations routes and destinations, the most generated WEEE and its recycling potential. Quantification of WEEE generated at the University in the years between 2010 and 2015 was performed using data analysis provided by UFES’s sector of assets management. EEE and WEEE flow in the campuses information were obtained through questionnaires applied to the University workers. It was recorded 6028 WEEEs units of data processing equipment disposed by the university between 2010 and 2015. Among these waste, the most generated were CRT screens, desktops, keyboards and printers. Furthermore, it was observed that these WEEEs are temporarily stored in inappropriate places at the University campuses. In general, these WEEE units are donated to NGOs of the city, or sold through auctions (2010 and 2013). As for recycling potential, from the primary processing and further sale of printed circuit boards (PCB) from the computers, the amount collected could reach U$ 27,839.23. The results highlight the importance of a WEEE management policy at the University.

Keywords: solid waste, waste of electrical and electronic equipment, waste management, institutional solid waste generation

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Authors: J. Pazourek, K. Šmejkal, P. Kollár, J. Rajchard, J. Šinko, Z. Balounová, E. Vlková, H. Salmonová

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Pectinatella magnifica (Leidy, 1851) is an invasive freshwater animal that lives in colonies. A colony of Pectinatella magnifica (a gelatinous blob) can be up to several feet in diameter large and under favorable conditions it exhibits an extreme growth rate. Recently European countries around rivers of Elbe, Oder, Danube, Rhine and Vltava have confirmed invasion of Pectinatella magnifica, including freshwater reservoirs in South Bohemia (Czech Republic). Our project (Czech Science Foundation, GAČR P503/12/0337) is focused onto biology and chemistry of Pectinatella magnifica. We monitor the organism occurrence in selected South Bohemia ponds and sandpits during the last years, collecting information about physical properties of surrounding water, and sampling the colonies for various analyses (classification, maps of secondary metabolites, toxicity tests). Because the gelatinous matrix is during the colony lifetime also a host for algae, bacteria and cyanobacteria (co-habitants), in this contribution, we also applied a high performance liquid chromatography (HPLC) method for determination of potentially present cyanobacterial toxins (microcystin-LR, microcystin-RR, nodularin). Results from the last 3-year monitoring show that these toxins are under limit of detection (LOD), so that they do not represent a danger yet. The final goal of our study is to assess toxicity risks related to fresh water resources invaded by Pectinatella magnifica, and to understand the process of invasion, which can enable to control it.

Keywords: cyanobacteria, fresh water resources, Pectinatella magnifica invasion, toxicity monitoring

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Authors: Erhong Sun, Xuchun Ye

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Technophobia, namely the fear or dislike of modern advanced technologies, plays a central role in age-related digital divides and is considered a new risk factor for older adults, which can affect the daily lives of people through low adherence to digital living. Indeed, there is considerable heterogeneity in the group of older adults who feel technophobia. Therefore, the aim of this study was to identify different technophobia typologies of older people and to examine their associations with the subjective age factor. A sample of 704 retired elderly over the age of 55 was recruited in China. Technophobia and subjective age were assessed with a questionnaire, respectively. Latent profile analysis was used to identify technophobia subgroups, using three dimensions including techno-anxiety, techno-paranoia, and privacy concerns as indicators. The association between the identified technophobia subgroups and subjective age was explored. In summary, four different technophobia typologies were identified among older adults in China. Combined with an investigation of personal background characteristics and subjective age, it draws a more nuanced image of the technophobia phenome among older adults in China. First, not all older adults suffer from technophobia, with about half of the elderly subjects belonging to the profiles of “Low-technophobia” and “Medium-technophobia.” Second, privacy concern plays an important role in the classification of technophobia among older adults. Third, subjective age might be a protective factor for technophobia in older adults. Although the causal direction between identified technophobia typologies and subjective age remains uncertain, our suggests that future interventions should better focus on subjective age by breaking the age stereotype of technology to reduce the negative effect of technophobia on older. Future development of this research will involve extensive investigation of the detailed impact of technophobia in senior populations, measurement of the negative outcomes, as well as formulation of innovative educational and clinical pathways.

Keywords: technophobia, older adults, latent profile analysis, subjective age

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Authors: R. Harihara Prakash, Shweta R. Parikh, Sangna S. Sheth

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The purpose of this study was to explore the usefulness of the Mini-BESTest compared to the Berg Balance Scale in evaluating balance in people with Parkinson's Disease (PD) of varying severity. Evaluation were done to obtain (1) the distribution of patients scores to look for ceiling effects, (2) concurrent validity with severity of disease, and (3) the sensitivity & specificity of separating people with or without postural response deficits. Methods and Material: Seventy-seven(77) people with Parkinson's Disease were tested for balance deficits using the Berg Balance Scale, Mini-BESTest. Unified Parkinson’s Disease Rating Scale (UPDRS) III and the Hoehn & Yahr (H&Y) disease severity scales were used for classification. Materials used in this study were case record sheet, chair without arm rests or wheels, Incline ramp, stopwatch, a box, 3 meter distance measured out and marked on the floor with tape [from chair]. Statistical analysis used: Multiple Linear regression was carried out of UPDRS jointly on the two scores for the Berg and Mini-BESTest. Receiver operating characteristic curves for classifying people into two groups based on a threshold for the H&Y score, to discriminate between mild PD versus more severe PD.Correlation co-efficient to find relativeness between the two variables. Results: The Mini-BESTest is highly correlated with the Berg (r = 0.732,P < 0.001), but avoids the ceiling compression effect of the Berg for mild PD (skewness −0.714 Berg, −0.512 Mini-BESTest). Consequently, the Mini-BESTest is more effective than the Berg for predicting UPDRS Motor score (P < 0.001 Mini-BESTest versus P = 0.72 Berg), and for discriminating between those with and without postural response deficits as measured by the H&Y (ROC).

Keywords: balance, berg balance scale, MINI BESTest, parkinson's disease

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Authors: R. Shayamapiki

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This study explores the applicability of applying new urbanism principles in cities of developing countries as a panacea towards building sustainable cities through implementing smart transportation. Smart transportation approach to planning has been growing remarkably around the globe in the past decade. In conquest to curb traffic congestion and reducing automobile dependency in the inner-city Harare, Smart Transportation has been a strong drive towards building sustainable cities. Conceptually, Smart Transportation constitutes of principles which include walking, cycling and mass transit. The Smart Transportation approach has been a success story in the cities of developing world but its application in the cities of developing countries has been doubtful. Cities of developing countries being multifaceted with several urban sustainability challenges, the study consolidates that there are no robust policy, legislative and institutional frameworks to govern the application of Smart Transportation in urban planning hence no clear roadway towards its success story. Questions regarding this investigation proliferate to; how capable are cities of developing countries to transform Smart Transportation principles to a success story? What victory can Smart Transportation bring to sustainable urban development? What are constraints of embracing the principles and how can they be manipulated? Methodologically the case study of urban syntax in Harare Central Business District and arterial roads of the city, legislation and institutional settings underpins various research outcomes. The study finds out the hindrances of policy, legislative and institutional incapacities cooked with economic constraints, lack of political will and technically inflexible zoning regulations. The study also elucidates that there is need to adopt a localized approach to Smart Transportation. The paper then calls for strengthening of institutional and legal reform in conquest to embrace the concept, policy and legislative support, feasible financial mechanism, coordination of responsible stakeholders, planning standards and regulatory frameworks reform to celebrate the success story of Smart Transportation in the developing world.

Keywords: inner-city Harare, new urbanism, smart transportation, sustainable cities

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Authors: Jakub Czyżycki, Paweł Twardowski

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Thin-walled components made of aluminum alloys are increasingly found in many fields of industry, and they dominate the aerospace industry. The machining of thinwalled structures encounters many difficulties related to the high susceptibility of the workpiece, which causes vibrations including the most unfavorable ones called chatter. The effect of these phenomena is the difficulty in obtaining the required geometric dimensions and surface quality. The purpose of this study is to analyze vibrations arising during machining of thin-walled workpieces made of aluminum alloy EN AW-7075. Samples representing actual thin-walled workpieces were examined in a different range of dimensions characterizing thin-walled workpieces. The tests were carried out in HSM high-speed machining (cutting speed vc = 1400 m/min) using a monolithic solid carbide endmill. Measurement of vibration was realized using a singlecomponent piezoelectric accelerometer 4508C from Brüel&Kjær which was mounted directly on the sample before machining, the measurement was made in the normal feed direction AfN. In addition, the natural frequency of the tested thin-walled components was investigated using a laser vibrometer for an broader analysis of the tested samples. The effect of vibrations on machining accuracy was presented in the form of surface images taken with an optical measuring device from Alicona. A classification of the vibrations produced during the test was carried out, and were analyzed in both the time and frequency domains. Observed significant influence of the thickness of the thin-walled component on the course of vibrations during machining.

Keywords: high-speed machining, thin-walled elements, thin-walled components, milling, vibrations

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Authors: Peng Chun-Yi, Chen Wei-Hsuan, Ueng Shyh-Kuang

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This study focuses on the application of self-organizing maps (SOM) technology in analyzing credit card transaction data, aiming to enhance the accuracy and efficiency of fraud detection. Som, as an artificial neural network, is particularly suited for pattern recognition and data classification, making it highly effective for the complex and variable nature of credit card transaction data. By analyzing transaction characteristics with SOM, the research identifies abnormal transaction patterns that could indicate potentially fraudulent activities. Moreover, this study has developed a specialized visualization tool to intuitively present the relationships between SOM analysis outcomes and transaction data, aiding financial institution personnel in quickly identifying and responding to potential fraud, thereby reducing financial losses. Additionally, the research explores the integration of SOM technology with composite intelligent system technologies (including finite state machines, fuzzy logic, and decision trees) to further improve fraud detection accuracy. This multimodal approach provides a comprehensive perspective for identifying and understanding various types of fraud within credit card transactions. In summary, by integrating SOM technology with visualization tools and composite intelligent system technologies, this research offers a more effective method of fraud detection for the financial industry, not only enhancing detection accuracy but also deepening the overall understanding of fraudulent activities.

Keywords: self-organizing map technology, fraud detection, information visualization, data analysis, composite intelligent system technologies, decision support technologies

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Authors: Alemaw Kefale, Aramde Fetene, Hayal Desta

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Monitoring the amount of green space in urban areas is important for ensuring sustainable development and proper management. The study analyzed changes in urban green space coverage over the past 20 years in two rapidly urbanizing cities in Ethiopia, Debre Berhan and Debre Markos, using GIS and remote sensing. The researchers used Landsat 5 and 8 data with a spatial resolution of 30 m to determine different land use and land cover classes, including urban green spaces, barren and croplands, built-up areas, and water bodies. The classification accuracy ranged between 90% and 91.4%, with a Kappa Statistic of 0.85 to 0.88. The results showed that both cities experienced significant decreases in vegetation cover in their urban cores between 2000 and 2020, with radical changes observed from green spaces and croplands to built-up areas. In Debre Berhan, barren and croplands decreased by 32.96%, while built-up and green spaces increased by 357.9% and 37.4%, respectively, in 2020. In Debre Markos, built-up areas increased by 224.2%, while green spaces and barren and croplands decreased by 41% and 5.71%, respectively. The spatial structure of cities and planning policies were noticed as the major factors for big green cover change. Thus it has an implication for other rapidly urbanized cities in Africa and Asia. Overall, rapid urbanization threatens green spaces and agricultural areas, highlighting the need for ecological-based spatial planning in rapidly urbanizing cities.

Keywords: green space coverage, GIS and remote sensing, Landsat, LULC, Ethiopia

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Authors: Amirreza Fazely Hamedani, Muzzamil Aziz, Philipp Wieder, Ramin Yahyapour

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Software-defined networking in recent years came into the sight of so many network designers as a successor to the traditional networking. Unlike traditional networks where control and data planes engage together within a single device in the network infrastructure such as switches and routers, the two planes are kept separated in software-defined networks (SDNs). All critical decisions about packet routing are made on the network controller, and the data level devices forward the packets based on these decisions. This type of network is vulnerable to DDoS attacks, degrading the overall functioning and performance of the network by continuously injecting the fake flows into it. This increases substantial burden on the controller side, and the result ultimately leads to the inaccessibility of the controller and the lack of network service to the legitimate users. Thus, the protection of this novel network architecture against denial of service attacks is essential. In the world of cybersecurity, attacks and new threats emerge every day. It is essential to have tools capable of managing and analyzing all this new information to detect possible attacks in real-time. These tools should provide a comprehensive solution to automatically detect, predict and prevent abnormalities in the network. Big data encompasses a wide range of studies, but it mainly refers to the massive amounts of structured and unstructured data that organizations deal with on a regular basis. On the other hand, it regards not only the volume of the data; but also that how data-driven information can be used to enhance decision-making processes, security, and the overall efficiency of a business. This paper presents an intelligent big data framework as a solution to handle illegitimate traffic burden on the SDN network created by the numerous DDoS attacks. The framework entails an efficient defence and monitoring mechanism against DDoS attacks by employing the state of the art machine learning techniques.

Keywords: apache spark, apache kafka, big data, DDoS attack, machine learning, SDN network

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Authors: Bouayachi Ali, Alikouss Saida, Baroudi Zouhir, Zerhouni Youssef, Zouhair Mohammed, El Idrissi Assia, Essalhi Mourad

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The world-class Imiter silver deposit is located on the northern flank of the Precambrian Imiter buttonhole. This deposit is formed by epithermal veins hosted in the sandstone-pelite formations of the lower complex and in the basic conglomerates of the upper complex, these veins are controlled by a regional scale fault cluster, oriented N70°E to N90°E. The present work on the contribution of remote sensing on the geological mapping of the Imiter buttonhole and application to the mineralized structures of the Principal Corps B3. Mapping on satellite images is a very important tool in mineral prospecting. It allows the localization of the zones of interest in order to orientate the field missions by helping the localization of the major structures which facilitates the interpretation, the programming and the orientation of the mining works. The predictive map also allows for the correction of field mapping work, especially the direction and dimensions of structures such as dykes, corridors or scrapings. The use of a series of processing such as SAM, PCA, MNF and unsupervised and supervised classification on a Landsat 8 satellite image of the study area allowed us to highlight the main facies of the Imite area. To improve the exploration research, we used another processing that allows to realize a spatial distribution of the alteration mineral indices, and the application of several filters on the different bands to have lineament maps.

Keywords: principal corps B3, teledetection, Landsat 8, Imiter II, silver mineralization, lineaments

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Authors: Xiaocong Liu, Huazhen Wang, Ting He, Xiaozheng Li, Weihan Zhang, Jian Chen

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The utilization of electronic medical record (EMR) data to establish the disease diagnosis model has become an important research content of biomedical informatics. Deep learning can automatically extract features from the massive data, which brings about breakthroughs in the study of EMR data. The challenge is that deep learning lacks semantic knowledge, which leads to impracticability in medical science. This research proposes a method of incorporating lexical-semantic knowledge from abundant entities into a convolutional neural network (CNN) framework for pediatric disease diagnosis. Firstly, medical terms are vectorized into Lexical Semantic Vectors (LSV), which are concatenated with the embedded word vectors of word2vec to enrich the feature representation. Secondly, the semantic distribution of medical terms serves as Semantic Decision Guide (SDG) for the optimization of deep learning models. The study evaluate the performance of LSV-SDG-CNN model on four kinds of Chinese EMR datasets. Additionally, CNN, LSV-CNN, and SDG-CNN are designed as baseline models for comparison. The experimental results show that LSV-SDG-CNN model outperforms baseline models on four kinds of Chinese EMR datasets. The best configuration of the model yielded an F1 score of 86.20%. The results clearly demonstrate that CNN has been effectively guided and optimized by lexical-semantic knowledge, and LSV-SDG-CNN model improves the disease classification accuracy with a clear margin.

Keywords: convolutional neural network, electronic medical record, feature representation, lexical semantics, semantic decision

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Authors: Harold Mauricio Díaz-Vargas, Cristian Alfonso Jimenez-Castaño, David Augusto Cárdenas-Peña, Guillermo Alberto Ortiz-Gómez, Alvaro Angel Orozco-Gutierrez

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Precise identification of the nerves is a crucial task performed by anesthesiologists for an effective Peripheral Nerve Blocking (PNB). Now, anesthesiologists use ultrasound imaging equipment to guide the PNB and detect nervous structures. However, visual identification of the nerves from ultrasound images is difficult, even for trained specialists, due to artifacts and low contrast. The recent advances in deep learning make neural networks a potential tool for accurate nerve segmentation systems, so addressing the above issues from raw data. The most widely spread U-Net network yields pixel-by-pixel segmentation by encoding the input image and decoding the attained feature vector into a semantic image. This work proposes a conditioning approach and encoder pre-training to enhance the nerve segmentation of traditional U-Nets. Conditioning is achieved by the one-hot encoding of the kind of target nerve a the network input, while the pre-training considers five well-known deep networks for image classification. The proposed approach is tested in a collection of 619 US images, where the best C-UNet architecture yields an 81% Dice coefficient, outperforming the 74% of the best traditional U-Net. Results prove that pre-trained models with the conditional approach outperform their equivalent baseline by supporting learning new features and enriching the discriminant capability of the tested networks.

Keywords: nerve segmentation, U-Net, deep learning, ultrasound imaging, peripheral nerve blocking

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Authors: Rigas Dimakopoulos, Marianna Papadopoulou, Roser Pons

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Background: Participation in family, recreational activities and self-care is an integral part of health. It is also the main outcome of rehabilitation services for children and adolescents with motor disabilities. There are currently no tools in Greek to assess participation in young children. Purpose: To culturally adapt and validate the Greek version of the Child Engagement in Daily Living (CEDL). Method: The CEDL was cross-culturally translated into Greek using forward-backward translation, review by the expert committee, pretest application and final review. Internal consistency was evaluated using the Cronbach alpha and test-retest reliability using the intra-class correlation coefficient (ICC). Parents of children aged 18 months to 5 years and with motor disabilities were recruited. Participants completed the CEDL and the children’s gross motor function was classified using the Gross Motor Function Classification System (GMFCS). Results: Eighty-three children were included, GMFCS I-V. Mean ± standard deviation of the CEDL domains “frequency of participation” “enjoyment of participation” and “self-care” were 58.4±14.0, 3.8±1.0 and 49.9±24, respectively. Internal consistency of all domains was high; Cronbach alpha for “frequency of participation” was 0.83, for “enjoyment of participation” was 0.76 and for “self-care” was 0.92. Test-retest reliability (ICC) was excellent for the “self-care” (0.95) and good for “frequency of participation” and “enjoyment of participation” domains (0.90 and 0.88, respectively). Conclusion: The Greek CEDL has good reliability. It can be used to evaluate participation in Greek young children with motor disabilities GMFCS levels I-V.

Keywords: participation, child, disabilities, child engagement in daily living

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Authors: Sushil Kumar Singh, Ashok Kumar, Ankit Ganeshpurkar, Ravi Singh, Devendra Kumar

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In spectrum of neurodegenerative diseases, Alzheimer’s disease (AD) is characterized by the presence of amyloid β plaques and neurofibrillary tangles in the brain. It results in cognitive and memory impairment due to loss of cholinergic neurons, which is considered to be one of the contributing factors. Donepezil, an acetylcholinesterase (AChE) inhibitor which also inhibits butyrylcholinesterase (BuChE) and improves the memory and brain’s cognitive functions, is the most successful and prescribed drug to treat the symptoms of AD. The present work is based on designing of the selective BuChE inhibitors using computational techniques. In this work, machine learning models were trained using classification algorithms followed by screening of diverse chemical library of compounds. The various molecular modelling and simulation techniques were used to obtain the virtual hits. The amide derivatives of 4-(phenylsulfonamido) benzoic acid were synthesized and characterized using 1H & 13C NMR, FTIR and mass spectrometry. The enzyme inhibition assays were performed on equine plasma BuChE and electric eel’s AChE by method developed by Ellman et al. Compounds 31, 34, 37, 42, 49, 52 and 54 were found to be active against equine BuChE. N-(2-chlorophenyl)-4-(phenylsulfonamido)benzamide and N-(2-bromophenyl)-4-(phenylsulfonamido)benzamide (compounds 34 and 37) displayed IC50 of 61.32 ± 7.21 and 42.64 ± 2.17 nM against equine plasma BuChE. Ortho-substituted derivatives were more active against BuChE. Further, the ortho-halogen and ortho-alkyl substituted derivatives were found to be most active among all with minimal AChE inhibition. The compounds were selective toward BuChE.

Keywords: Alzheimer disease, butyrylcholinesterase, machine learning, sulfonamides

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Authors: Sizhu Wang, Cuisong Tang, Lin Zhang, Guangyu Tang

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Objective: We aimed to detect the composition of bone marrow fatty acids early after ovariectomized (OVX) surgery and explore the potential mechanism. Methods: Thirty-two female Sprague-Dawley (SD) rats (12 weeks) were randomly divided into OVX group and Sham group (N=16/group), and received ovariectomy or sham surgery respectively. After 3 and 28 days, eight rats in each group were sacrificed to detect the composition of bone marrow fatty acids by gas chromatography–mass spectrometry (GC–MS) and evaluate the trabecular bone microarchitecture by micro-CT. Significant different fatty acids in the early stage of post-menopausal osteoporosis were selected by OPLS-DA and t test. Then selected fatty acids were further studied in the process of osteogenic differentiation through RT-PCR and Alizarin Red S staining. Results: An apparent sample clustering and group separation were observed between OVX group and sham group three days after surgery, which suggested the role of bone marrow fatty acids in the early stage of postmenopausal osteoporosis. Specifically, myristate, palmitoleate and arachidonate were found to play an important role in classification between OVX group and sham group. We further investigated the effect of palmitoleate and arachidonate on osteogenic differentiation and found that palmitoleate promoted the osteogenic differentiation of MC3T3-E1 cells while arachidonate inhibited this process. Conclusion: Profound bone marrow fatty acids changes have taken place in the early stage of post-menopausal osteoporosis. Bone marrow fatty acids may begin to affect osteogenic differentiation shortly after deficiency of estrogen.

Keywords: bone marrow fatty acids, GC-MS, osteoblast, osteoporosis, post-menopausal

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Authors: Shao-Chi Chien, Ying-Ju Chen, Chiao-Yu Tseng, Wan-Ting Lee, Yi-Wen Cheng

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Dr. George Leslie Mackay arrived at Takoukang (now Port of Kaohsiung) in Taiwan on December 30, 1871. When Dr. Mackay dedicated at Taiwan for 30 years, he has been an important factor in such areas as preaching, medical and engaged in education. Many researchers have thoroughly studied Dr. Mackay's travels to understand his impact on the state of education, medicine and religion in Taiwan. In the 30-year period of hard work, Dr. Mackay's made outstanding influence on the church in Taiwan. Therefore, the present study will be the mission of the establishment of hospitals, schools, churches which preaching, education, and medicine whether there are related the number of comparisons to explore. According to The Diaries of George Leslie Mackay, our research uses the Geographic Information System (GIS) to map the location of Dr. Mackay's travel in Taiwan and compares it with today's local churches, hospitals, and schools whether there are related the number of comparisons to explore. Therefore, our research focuses on the whole of Taiwan, divided into missionary, medical and education as the main content of the three major parts. Additionally, use of point layer, the surface layer of the property table to establish, in-depth mission of Dr. Mackay's development in Taiwan and Today's comparison. The results will be based on the classification of three different colors pictures that the distance of Mackay's contribution of preaching, medicine, and education. Our research will be compared with the current churches, hospitals, schools and the past churches, hospitals, schools. The results of the present study will provide a reference for future research.

Keywords: George Leslie Mackay, geographic information system, spatial distribution, color categories analysis

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Authors: Intissar Farid

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Groundwater quality assessment is crucial for sustainable water use, especially in semi-arid regions like the Jeloula basin in Tunisia, where groundwater is essential for domestic and agricultural needs. The present research aims to characterize the suitability of groundwater for irrigational purposes by considering various parameters: total salt concentration as measured by Electrical Conductivity EC, relative proportions of Na⁺ as expressed by %Na and SAR, Kelly’s ratio, Permeability Index, Magnesium hazard and Residual Sodium chloride. Chemical data indicate that the percent sodium (%Na) in the study area ranged from 26.3 to 45.3%. According to the Wilcox diagram, the quality classification of irrigation water suggests that analyzed groundwaters are suitable for irrigation purposes. The SAR values vary between 2.1 and 5. Most of the groundwater samples plot in the Richards’C3S1 water class and indicate little danger from sodium content to soil and plant growth. The Kelly’s ratio of the analyzed samples ranged from 0.3 to 0.8. These values indicate that the waters are fit for agricultural purposes. Magnesium hazard (MH) values range from 27.5 to 52.6, with an average of 38.9 in the analyzed waters. Hence, the Mg²⁺ content of the groundwater from the shallow aquifer cannot cause any problem to the soil permeability. Permeability index (PI) values computed for the area ranged from 33.6 to 52.7%. The above result, therefore, suggests that most of the water samples fall within class I of the Doneen chart and can be categorized as good irrigation water. The groundwaters collected from the Jeloula shallow aquifer were found to be within the safe limits and thus suitable for irrigation purposes.

Keywords: Kelly's ratio, magnesium hazard, permeability index, residual sodium chloride

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