Search results for: mobility features

Authors: Abdolghani Ebrahimi, Diego Klabjan, Chenxi Ge, Daniela Ladner, Parker Stride

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In this work, we use machine learning and novel data analysis techniques to predict the one-year mortality of cirrhotic patients. Data from 2,322 patients with liver cirrhosis are collected at a single medical center. Different machine learning models are applied to predict one-year mortality. A comprehensive feature space including demographic information, comorbidity, clinical procedure and laboratory tests is being analyzed. A temporal pattern mining technic called Frequent Subgraph Mining (FSM) is being used. Model for End-stage liver disease (MELD) prediction of mortality is used as a comparator. All of our models statistically significantly outperform the MELD-score model and show an average 10% improvement of the area under the curve (AUC). The FSM technic itself does not improve the model significantly, but FSM, together with a machine learning technique called an ensemble, further improves the model performance. With the abundance of data available in healthcare through electronic health records (EHR), existing predictive models can be refined to identify and treat patients at risk for higher mortality. However, due to the sparsity of the temporal information needed by FSM, the FSM model does not yield significant improvements. To the best of our knowledge, this is the first work to apply modern machine learning algorithms and data analysis methods on predicting one-year mortality of cirrhotic patients and builds a model that predicts one-year mortality significantly more accurate than the MELD score. We have also tested the potential of FSM and provided a new perspective of the importance of clinical features.

Keywords: machine learning, liver cirrhosis, subgraph mining, supervised learning

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Authors: Hu Ding, Kai Liu, Guoan Tang

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The Chinese Loess Plateau suffers from serious gully erosion induced by natural and human causes. Gully features detection including gully-affected area and its two dimension parameters (length, width, area et al.), is a significant task not only for researchers but also for policy-makers. This study aims at gully-affected area detection in three catchments of Chinese Loess Plateau, which were selected in Changwu, Ansai, and Suide by using unmanned aerial vehicle (UAV). The methodology includes a sequence of UAV data generation, image segmentation, feature calculation and selection, and random forest classification. Two experiments were conducted to investigate the influences of segmentation strategy and feature selection. Results showed that vertical and horizontal root-mean-square errors were below 0.5 and 0.2 m, respectively, which were ideal for the Loess Plateau region. The segmentation strategy adopted in this paper, which considers the topographic information, and optimal parameter combination can improve the segmentation results. Besides, the overall extraction accuracy in Changwu, Ansai, and Suide achieved was 84.62%, 86.46%, and 93.06%, respectively, which indicated that the proposed method for detecting gully-affected area is more objective and effective than traditional methods. This study demonstrated that UAV can bridge the gap between field measurement and satellite-based remote sensing, obtaining a balance in resolution and efficiency for catchment-scale gully erosion research.

Keywords: unmanned aerial vehicle (UAV), object-analysis image analysis, gully erosion, gully-affected area, Loess Plateau, random forest

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Authors: Abigail Tan, Heng Liang Tan, Vanessa Ding, James Hui, Eng Hin Lee, Andre Choo

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Introduction: Comparative oncology investigates the similarities in spontaneous carcinogenesis between humans and animals, in order to identify treatments that can benefit these patients. Companion animals (CA), like canines and felines, are of special interest when it comes to studying human cancers due to their exposure to the same environmental factors and develop tumours with similar features. The purpose of this study is to explore the cross-reactivity of monoclonal antibodies (mAbs) across cancers in humans and CA. Material and Methods: A panel of CA mAbs generated in the lab was screened on multiple human cancer cell lines through flow cytometry to identify for positive binders. Shortlisted candidates were then characterised by biochemical and functional assays e.g., antibody-drug conjugate (ADC) and western blot assays, including glycan studies. Results: Candidate mAb KP52 was generated from whole-cell immunisation using feline mammary carcinoma. KP52 showed strong positive binding to human cancer cells, such as breast cancer and ovarian cancer. Furthermore, KP52 demonstrated strong killing ( > 50%) as an ADC with Saporin as the payload. Western blot results revealed the molecular weight of the antigen targets to be approximately 45kD and 50kD under reduced conditions. Glycan studies suggest that the epitope is glycan in nature, specifically an O-linked glycan. Conclusion: Candidate mAb KP52 has a therapeutic potential as an ADC against feline mammary cancer, human ovarian cancer, human mammary cancer, human pancreatic cancer, and human gastric cancer.

Keywords: ADC, comparative oncology, mAb, therapeutic

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Authors: Mostafa Mabrouk, Basma E. Abdel-Ghany, Mona Moaness, Bothaina M. Abdel-Hady, Hanan H. Beherei

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Tissue engineering became a promising field for bone repair and regenerative medicine in which cultured cells, scaffolds and osteogenic inductive signals are used to regenerate tissues. The annual cost of treating bone defects in Egypt has been estimated to be many billions, while enormous costs are spent on imported bone grafts for bone injuries, tumors, and other pathologies associated with defective fracture healing. The current study is aimed at developing a more strategic approach in order to speed-up recovery after bone damage. This will reduce the risk of fatal surgical complications and improve the quality of life of people affected with such fractures. 3D scaffolds loaded with cheap nano-particles that possess an osteogenic effect were prepared by nano-electrospinning. The Microstructure and morphology characterizations of the 3D scaffolds were monitored using scanning electron microscopy (SEM). The physicochemical characterization was investigated using X-ray diffractometry (XRD) and infrared spectroscopy (IR). The Physicomechanical properties of the 3D scaffold were determined by a universal testing machine. The in vitro bioactivity of the 3D scaffold was assessed in simulated body fluid (SBF). The bone-bonding ability of novel 3D scaffolds was also evaluated. The obtained nanofibrous scaffolds demonstrated promising microstructure, physicochemical and physicomechanical features appropriate for enhanced bone regeneration. Therefore, the utilized nanomaterials loaded with the drug are greatly recommended as cheap alternatives to growth factors.

Keywords: bone regeneration, cheap scaffolds, nanomaterials, active molecules

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Authors: Shetie Gatew

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Current soil maps of Ethiopia do not represent accurately the soils of Nech Sar National Park. In the framework of studies on the ecology of the park, we prepared a soil map based on field observations and a digital terrain model derived from SRTM data with a 30-m resolution. The landscape comprises volcanic cones, lava and basalt outflows, undulating plains, horsts, alluvial plains and river deltas. SOTER-like terrain mapping units were identified. First, the DTM was classified into 128 terrain classes defined by slope gradient (4 classes), relief intensity (4 classes), potential drainage density (2 classes), and hypsometry (4 classes). A soil-landscape relation between the terrain mapping units and WRB soil units was established based on 34 soil profile pits. Based on this relation, the terrain mapping units were either merged or split to represent a comprehensive soil and terrain map. The soil map indicates that Leptosols (30 %), Cambisols (26%), Andosols (21%), Fluvisols (12 %), and Vertisols (9%) are the most widespread Reference Soil Groups of the park. In contrast, the harmonized soil map of Africa derived from the FAO soil map of the world indicates that Luvisols (70%), Vertisols (14%) and Fluvisols (16%) would be the most common Reference Soil Groups. However, these latter mapping units are not consistent with the topography, nor did we find such extensive areas occupied by Luvisols during the field survey. This case study shows that with the now freely available SRTM data, it is possible to improve current soil information layers with relatively limited resources, even in a complex terrain like Nech Sar National Park.

Keywords: andosols, cambisols, digital elevation model, leptosols, soil-landscaps relation

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Authors: Behnaz Sohani, Sahand Shahalinezhad, Amir Rahmani, Aliyu Aliyu

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Recently, medical imaging and specifically medical image processing is becoming one of the most dynamically developing areas of medical science. It has led to the emergence of new approaches in terms of the prevention, diagnosis, and treatment of various diseases. In the process of diagnosis of lung cancer, medical professionals rely on computed tomography (CT) scans, in which failure to correctly identify masses can lead to incorrect diagnosis or sampling of lung tissue. Identification and demarcation of masses in terms of detecting cancer within lung tissue are critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images, including further details available to machine vision systems to recognise objects in lung CT scan images.

Keywords: lung cancer detection, image segmentation, lung computed tomography (CT) images, medical image processing

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Authors: A. Kourou, A. Ioakeimidou, E. Pelli, M. Panoutsopoulou, V. Abramea

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Worldwide there is a growing recognition that education is a critical component of any disaster impacts reduction effort and a great challenge too. Given this challenge, a broad range of awareness raising projects at all levels are implemented and are continuously evaluated by Earthquake Planning and Protection Organization (EPPO). This paper presents an overview of EPPO educational initiative (seminars, lectures, workshops, campaigns and educational material) and its evaluation results. The abovementioned initiative is focused to aware the public, train teachers and civil protection staff, inform students and educate people with disabilities on subjects related to earthquake reduction issues. The better understating of how human activity can link to disaster and what can be done at the individual, family or workplace level to contribute to seismic reduction are the main issues of EPPO projects. Survey results revealed that a high percentage of teachers (included the ones of special schools) from all over the country have taken the appropriate preparedness measures at schools. On the other hand, the implementation of earthquake preparedness measures at various workplaces (kindergartens, banks, utilities etc.) has still significant room for improvement. Results show that the employees in banks and public utilities have substantially higher rates in preventive and preparedness actions in their workplaces than workers in kindergartens and other workplaces. One of the EPPO educational priorities is to enhance earthquake preparedness of people with disabilities. Booklets, posters and applications have been created with the financial support of the Council of Europe, addressed to people who have mobility impairments, learning difficulties or cognitive disability (ή intellectual disabilities). Part of the educational material was developed using the «easy-to-read» method and Makaton language program with the collaboration of experts on special needs education and teams of people with cognitive disability. Furthermore, earthquake safety seminars and earthquake drills have been implemented in order to develop children’s, parents’ and teachers abilities and skills on earthquake impacts reduction. To enhance the abovementioned efforts, EPPO is a partner at prevention and preparedness projects supported by EU Civil Protection Financial Instrument. One of them is E-PreS’ project (Monitoring and Evaluation of Natural Hazard Preparedness at School Environment). The main objectives of E-PreS project are: 1) to create smart tools which define, simulate and evaluate drills procedure at schools, centers of vocational training of people with disabilities or other workplaces, and 2) to involve students or adults with disabilities in the E-PreS system evacuation procedure in case of earthquake, flood, or volcanic occurrence. Two other EU projects (RACCE educational kit and EVANDE educational platform) are also with the aim of contributing to raising awareness among people with disabilities, students, teachers, volunteers etc. It is worth mentioning that even though in Greece many efforts have been done till now to build awareness towards earthquakes and establish preparedness status for prospective earthquakes, there are still actions to be taken.

Keywords: earthquake, emergency plans, E-PreS project, people with disabilities, special needs education

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Authors: Faiza Babur Khan, Sohail Asghar

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With an emergence of distributed environment, cloud computing is proving to be the most stimulating computing paradigm shift in computer technology, resulting in spectacular expansion in IT industry. Many companies have augmented their technical infrastructure by adopting cloud resource sharing architecture. Cloud computing has opened doors to unlimited opportunities from application to platform availability, expandable storage and provision of computing environment. However, from a security viewpoint, an added risk level is introduced from clouds, weakening the protection mechanisms, and hardening the availability of privacy, data security and on demand service. Issues of trust, confidentiality, and integrity are elevated due to multitenant resource sharing architecture of cloud. Trust or reliability of cloud refers to its capability of providing the needed services precisely and unfailingly. Confidentiality is the ability of the architecture to ensure authorization of the relevant party to access its private data. It also guarantees integrity to protect the data from being fabricated by an unauthorized user. So in order to assure provision of secured cloud, a roadmap or model is obligatory to analyze a security problem, design mitigation strategies, and evaluate solutions. The aim of the paper is twofold; first to enlighten the factors which make cloud security critical along with alleviation strategies and secondly to propose an intrusion detection model that identifies the attackers in a preventive way using machine learning Random Forest classifier with an accuracy of 99.8%. This model uses less number of features. A comparison with other classifiers is also presented.

Keywords: cloud security, threats, machine learning, random forest, classification

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Authors: Yildiray Korkmaz, Mehmet Aksoy

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In today's operations, countries want to reach their aims in the shortest way due to economical, political and humanitarian aspects. The most effective way of achieving this goal is to be able to penetrate strategic targets. Strategic targets are generally located deep inside of the countries and are defended by modern and efficient surface to air missiles (SAM) platforms which are operated as integrated with Intelligence, Surveillance and Reconnaissance (ISR) systems. On the other hand, these high valued targets are buried deep underground and hardened with strong materials against attacks. Therefore, to penetrate these targets requires very detailed intelligence. This intelligence process should include a wide range that is from weaponry to threat assessment. Accordingly, the framework of the attack package will be determined. This mission package has to execute missions in a high threat environment. The way to minimize the risk which depends on loss of life is to use packages which are formed by UAVs. However, some limitations arising from the characteristics of UAVs restricts the performance of the mission package consisted of UAVs. So, the mission package should be formed with UAVs under the leadership of a fifth generation manned aircraft. Thus, we can minimize the limitations, easily penetrate in the deep inside of the enemy territory with minimum risk, make a decision according to ever-changing conditions and finally destroy the strategic targets. In this article, the strengthens and weakness aspects of UAVs are examined by SWOT analysis. And also, it revealed features of a mission package and presented as an example what kind of a mission package we should form in order to get marginal benefit and penetrate into strategic targets with the development of autonomous mission execution capability in the near future.

Keywords: UAV, autonomy, mission package, strategic attack, mission planning

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Authors: Arup Saha Chaudhuri

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Structural steel is a very ductile material with high strength carrying capacity, thus it is very useful to make earthquake resistant buildings. It is a homogeneous material also. The member section and the structural system can be made very efficient for economical design. As the steel is recyclable and reused, it is a green material. The embodied energy for the efficiently designed steel structure is less than the RC structure. For sustainable green building steel is the best material nowadays. Moreover, pre-engineered and pre-fabricated faster construction methodologies help the development work to complete within the stipulated time. In this paper, the usefulness of Eccentric Bracing Frame (EBF) in steel structure over Moment Resisting Frame (MRF) and Concentric Bracing Frame (CBF) is shown. Stability of the steel structures against horizontal forces especially in seismic condition is efficiently possible by Eccentric bracing systems with economic connection details. The EBF is pin–ended, but the beam-column joints are designed for pin ended or for full connectivity. The EBF has several desirable features for seismic resistance. In comparison with CBF system, EBF system can be designed for appropriate stiffness and drift control. The link beam is supposed to yield in shear or flexure before initiation of yielding or buckling of the bracing member in tension or compression. The behavior of a 2-D steel frame is observed under seismic loading condition in the present paper. Ductility and brittleness of the frames are compared with respect to time period of vibration and dynamic base shear. It is observed that the EBF system is better than MRF system comparing the time period of vibration and base shear participation.

Keywords: steel building, green and sustainable, earthquake resistant, EBF system

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Authors: Hannah Nagel, Hayley Lang, Albert Sole Guitart, Natasha Lean, Rachel Allavena, Cleide Sprohnie-Barrera, Alex Young

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Aggressive neoplasia of the hoof is a rare occurrence in horses and has been only sporadically described in the literature. In the few cases reported intra-hoof wall, aggressive neoplasia has been documented radiographically and has been described with variable imaging characteristics. These include a well-defined osteolytic area, a smoothly outlined semi-circular defect, an extensive draining tract beneath the hoof wall, as well as an additional large area of osteolysis or an extensive central lytic region. A 20-year-old Quarterhorse gelding and a 10-year-old Thoroughbred gelding were both presented for chronic reoccurring lameness in the left forelimb and left hindlimb, respectively. Both of the cases displayed radiographic lesions that have been previously described but also displayed osteoproliferative expansile regions of additional bone formation. Changes associated with hoof neoplasia are often non-specific due to the nature and capacity of bone to react to pathological insult, which is either to proliferate or be absorbed. Both cases depict and describe imaging findings seen on radiography, contrast radiography, computed tomography, and magnetic resonance imaging before reaching a histological diagnosis of malignant melanoma and squamous cell carcinoma. Although aggressive hoof wall neoplasia is rare, there are some imaging features which may raise our index of suspicion for an aggressive hoof wall lesion. This case report documents two horses with similar imaging findings who underwent multiple assessments, surgical interventions, and imaging modalities with a final diagnosis of malignant neoplasia.

Keywords: horse, hoof, imaging, radiography, neoplasia

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Authors: Min Jindong, Wang Mingxia

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With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: Wong Kean Hin, Kumarason A. L. V. Rasiah

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A building provides shelter and protection for an individual to live, work, sleep, procreate or engage in leisurely activities comfortably. Currently, a very popular term related to building was often stated by many parties, which is sustainability. A sustainable building is environmental friendly, healthy to the occupants, as well as efficient in electricity and water. This particular research is important to any parties that are involved in the construction industry. This research will provide the awareness and acceptability of Malaysian public towards sustainable residential building. It will also provide the developers about which sustainable features that the people usually want so that the developers can build a sustainable housing that suits the needs of people. Then, propose solutions to solve the difficulties of implementing sustainability in Malaysian housing industry. Qualitative and quantitative research methods were used throughout the process of data collection. The quantitative research method was distribution of questionnaires to 100 Malaysian public and 50 individuals that worked in developer companies. Then, the qualitative method was an interview session with experienced personnel in Malaysian construction industry. From the data collected, there is increasingly Malaysian public and developers are aware about the existence of sustainability. Moreover, the public is willing to invest on sustainable residential building with minimum additional cost. However, there is a mismatch in between sustainable elements provided by developers and the public needs. Some recommendations to improve the progression of sustainability had been proposed in this study, which include laws enforcement, cooperation between the both government sector with private sector, and private sector with private sector, and learn from modern countries. These information will be helpful and useful for the future of sustainability development in Malaysia.

Keywords: acceptability, awareness, Malaysian housing industry, sustainable elements, green building index

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Authors: Lufei Mao, Hongwei Chen, Zijiao Chai

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Informal housing is a widespread phenomenon in developing countries. In many newly-emerging cities in China, rapid urbanization leads to an influx of population as well as a shortage of housing. Under this background, container housing, a new form of informal housing, gradually appears on a small scale on urban temporary land in recent years. Container housing, just as its name implies, transforms containers into small houses that allow migrant workers group to live in it. Scholars in other countries have established sound theoretical frameworks for informal housing study, but the research fruits seem rather limited on this small scale housing form. Unlike the cases in developed countries, these houses, which are outside urban planning, bring about various environmental, economic, social and governance issues. Aiming to figure out this new-born housing form, a survey mainly on two container housing settlements in Hangzhou, China was carried out to gather the information of them. Based on this thorough survey, the paper concludes the features and problems of infrastructure, environment and social communication of container housing settlements. The result shows that these containers were lacking of basic facilities and were restricted in a small mess temporary land. Moreover, because of the deficiency in management, the rental rights of these containers might not be guaranteed. Then the paper analyzes the factors affecting the formation and evolution of container housing settlements. It turns out that institutional and policy factors, market factors and social factors were the main three factors that affect the formation. At last, the paper proposes some suggestions for the governance of container housing and the utility pattern of urban temporary land.

Keywords: container housing, informal housing, urban temporary land, urban governance

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Authors: Parisa Mansour

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Cystic fibrosis (CF) is one of the most common autosomal recessive diseases among whites. It mostly affects the lungs, causing infections and inflammation that account for 90% of deaths in CF patients. Because of this high variability in clinical presentation and organ involvement, investigating treatment responses and evaluating lung changes over time is critical to preventing CF progression. High-resolution computed tomography (HRCT) greatly facilitates the assessment of lung disease progression in CF patients. Recently, artificial intelligence was used to analyze chest CT scans of CF patients. In this paper, we propose a convolutional neural network (CNN) approach to classify CF lung patterns in HRCT images. The proposed network consists of two convolutional layers with 3 × 3 kernels and maximally connected in each layer, followed by two dense layers with 1024 and 10 neurons, respectively. The softmax layer prepares a predicted output probability distribution between classes. This layer has three exits corresponding to the categories of normal (healthy), bronchitis and inflammation. To train and evaluate the network, we constructed a patch-based dataset extracted from more than 1100 lung HRCT slices obtained from 45 CF patients. Comparative evaluation showed the effectiveness of the proposed CNN compared to its close peers. Classification accuracy, average sensitivity and specificity of 93.64%, 93.47% and 96.61% were achieved, indicating the potential of CNNs in analyzing lung CF patterns and monitoring lung health. In addition, the visual features extracted by our proposed method can be useful for automatic measurement and finally evaluation of the severity of CF patterns in lung HRCT images.

Keywords: HRCT, CF, cystic fibrosis, chest CT, artificial intelligence

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Authors: C. Xavier Mendieta, J. J McArthur

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Governments are playing an increasingly active role in reducing carbon emissions, and a key strategy has been the introduction of mandatory energy disclosure policies. These policies have resulted in a significant amount of publicly available data, providing researchers with a unique opportunity to develop location-specific energy and carbon emission benchmarks from this data set, which can then be used to develop building archetypes and used to inform urban energy models. This study presents the development of such a benchmark using the public reporting data. The data from Ontario’s Ministry of Energy for Post-Secondary Educational Institutions are being used to develop a series of building archetype dynamic building loads and energy benchmarks to fill a gap in the currently available building database. This paper presents the development of a benchmark for college and university residences within ASHRAE climate zone 6 areas in Ontario using the mandatory disclosure energy and greenhouse gas emissions data. The methodology presented includes data cleaning, statistical analysis, and benchmark development, and lessons learned from this investigation are presented and discussed to inform the development of future energy benchmarks from this larger data set. The key findings from this initial benchmarking study are: (1) the importance of careful data screening and outlier identification to develop a valid dataset; (2) the key features used to develop a model of the data are building age, size, and occupancy schedules and these can be used to estimate energy consumption; and (3) policy changes affecting the primary energy generation significantly affected greenhouse gas emissions, and consideration of these factors was critical to evaluate the validity of the reported data.

Keywords: building archetypes, data analysis, energy benchmarks, GHG emissions

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Authors: Byung Keun Yang, Jae Eun Ku, Young Seon Joo, Je Sung You, Sung Phil Chung, Hahn Shick Lee

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The purpose of this study is to review the evidence comparing the efficacy and safety between L-carnitine and extracorporeal elimination therapy in the management of acute valproic acid L-carnitine vs Extracorporeal Elimination for Acute Valproic acid Intoxication. PubMed, Embase, Cochrane library, Web of Science, KoreaMed, KMbase, and KISS were searched, using the terms carnitine and valproic acid. All studies, regardless of design, reporting efficacy or safety endpoints were included. Reference citations from identified publications were reviewed. Both English and Korean languages were included. Two authors extracted primary data elements including poisoning severity, presenting features, clinical management, and outcomes. Thirty two articles including 33 cases were identified. Poisoning severity was classified as 3 mild, 11 moderate, and 19 severe cases. Nine cases were treated with L-carnitine while 24 cases received extracorporeal therapy without L-carnitine. All patients except one expired patient treated with hemodialysis recovered clinically and no adverse effects were noted. A case report comparing two patients who ingested the same amount of valproic acid showed increased ICU stay (3 vs. 11 days) in case of delayed extracorporeal therapy. Published evidence comparing L-carnitine with extracorporeal therapy is limited. Based on the available evidence, it is reasonable to consider L-carnitine for patients with acute valproic acid overdose. In case of severe poisoning, extracorporeal therapy would also be considered in the early phase of treatment.

Keywords: carnitine, overdose, poisoning, renal dialysis, valproic acid

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Authors: Dachuan Shi, M. Hecht, Y. Ye

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With the trend of digitalization in railway freight transport, a large number of freight wagons in Germany have been equipped with telematics devices, commonly placed on the wagon body. A telematics device contains a GPS module for tracking and a 3-axis accelerometer for shock detection. Besides these basic functions, it is desired to use the integrated accelerometer for condition monitoring without any additional sensors. Wheel flats as a common type of failure on wheel tread cause large impacts on wagons and infrastructure as well as impulsive noise. A large wheel flat may even cause safety issues such as derailments. In this sense, this paper proposes a machine learning approach for wheel flat detection by using car body accelerations. Due to suspension systems, impulsive signals caused by wheel flats are damped significantly and thus could be buried in signal noise and disturbances. Therefore, it is very challenging to detect wheel flats using car body accelerations. The proposed algorithm considers the envelope spectrum of car body accelerations to eliminate the effect of noise and disturbances. Subsequently, a 1-D convolutional neural network (CNN), which is well known as a deep learning method, is constructed to automatically extract features in the envelope-frequency domain and conduct classification. The constructed CNN is trained and tested on field test data, which are measured on the underframe of a tank wagon with a wheel flat of 20 mm length in the operational condition. The test results demonstrate the good performance of the proposed algorithm for real-time fault detection.

Keywords: fault detection, wheel flat, convolutional neural network, machine learning

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Authors: David M. Lucas, Charles W. Jarrett

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In the culture of Thailand, the Yak serve as a mediated icon representing strength, power, and mystical protection not only for the Buddha, but for population of worshipers. Originating from the forests of China, the Yak continue to stand guard at the gates of Buddhist temples. The Yak represents Thai culture in the hearts of Thai people. This paper presents a qualitative study regarding the curious mix of media, culture, and religion that projects the Yak of Thailand as a larger than life message throughout the political, cultural, and religious spheres. The gate guardians, or gods as they are sometimes called, appear throughout the religious temples of Asian cultures. However, the Asian cultures demonstrate differences in artistic renditions (or presentations) of such sentinels. Thailand gate guards (the Yak) stand in front of many Buddhist temples, and these iconic figures display unique features with varied symbolic significance. The temple (or wat), plays a vital role in every community; and, for many people, Thailand’s temples are the country’s most endearing sights. The authors applied folk-nography as a methodology to illustrate the importance of the Thai Yak in serving as meaningful icons that transcend not only time, but the culture, religion, and mass media. The Yak represent mythical, religious, artistic, cultural, and militaristic significance for the Thai people. Data collection included interviews, focus groups, and natural observations. This paper summarizes the perceptions of the Thai people concerning their gate sentries and the relationship, communication, connection, and the enduring respect that Thai people hold for their guardians of the gates.

Keywords: communication, culture, folknography, icon, image, media, protection, religion, yak

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Authors: Alanoud Moraya Aldalan, Abdulaziz Almaleh

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A crucial component of maintaining a customer-oriented business as in the telecom industry is understanding the reasons and factors that lead to customer churn. Competition between telecom companies has greatly increased in recent years. It has become more important to understand customers’ needs in this strong market of telecom industries, especially for those who are looking to turn over their service providers. So, predictive churn is now a mandatory requirement for retaining those customers. Machine learning can be utilized to accomplish this. Churn Prediction has become a very important topic in terms of machine learning classification in the telecommunications industry. Understanding the factors of customer churn and how they behave is very important to building an effective churn prediction model. This paper aims to predict churn and identify factors of customers’ churn based on their past service usage history. Aiming at this objective, the study makes use of feature selection, normalization, and feature engineering. Then, this study compared the performance of four different machine learning algorithms on the Orange dataset: Logistic Regression, Random Forest, Decision Tree, and Gradient Boosting. Evaluation of the performance was conducted by using the F1 score and ROC-AUC. Comparing the results of this study with existing models has proven to produce better results. The results showed the Gradients Boosting with feature selection technique outperformed in this study by achieving a 99% F1-score and 99% AUC, and all other experiments achieved good results as well.

Keywords: machine learning, gradient boosting, logistic regression, churn, random forest, decision tree, ROC, AUC, F1-score

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Authors: Giuseppe Timperio, Robert De Souza

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The combination of factors such as temperamental climate change, rampant urbanization of risk exposed areas, political and social instabilities, is posing an alarming base for the further growth of number and magnitude of humanitarian crises worldwide. Given the unique features of humanitarian supply chain such as unpredictability of demand in space, time, and geography, spike in the number of requests for relief items in the first days after the calamity, uncertain state of logistics infrastructures, large volumes of unsolicited low-priority items, a proactive approach towards design of disaster response operations is needed to achieve high agility in mobilization of emergency supplies in the immediate aftermath of the event. This paper is an attempt in that direction, and it provides decision makers with crucial strategic insights for a more effective network design for disaster response. Decision sciences and ICT are integrated to analyse the robustness and resilience of a prepositioned network of emergency strategic stockpiles for a real-life case about Indonesia, one of the most vulnerable countries in Asia-Pacific, with the model being built upon a rich set of quantitative data. At this aim, a network optimization approach was implemented, with several what-if scenarios being accurately developed and tested. Findings of this study are able to support decision makers facing challenges related with disaster relief chains resilience, particularly about optimal configuration of supply chain facilities and optimal flows across the nodes, while considering the network structure from an end-to-end in-country distribution perspective.

Keywords: disaster preparedness, humanitarian logistics, network optimization, resilience

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Authors: Motoki Seguchi, Fumiko Harada, Hiromitsu Shimakawa

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There have been many local companies in countryside that carefully produce and sell products, which include crafts and foods produced with traditional methods. These companies are likely to use commercials to advertise their products. However, it is difficult for companies to judge whether the commercials they create are having an impact on consumers. Therefore, to create effective commercials, this study researches what kind of gimmicks in commercials affect what kind of consumers. This study proposes a method for extracting psychological change points from the physiological characteristics of consumers while they are watching commercials and estimating the gimmicks in the commercial that affect consumer engagement. In this method, change point detection is applied to pupil size for estimating gimmicks that affect consumers’ emotional engagement, and to EDA for estimating gimmicks that affect cognitive engagement. A questionnaire is also used to estimate the commercials that influence behavioral engagement. As a result of estimating the gimmicks that influence consumer engagement using this method, it was found that there are some common features among the gimmicks. To influence cognitive engagement, it was found that it was useful to include flashback scenes, messages to be appealed to, the company’s name, and the company’s logos as gimmicks. It was also found that flashback scenes and story climaxes were useful in influencing emotional engagement. Furthermore, it was found that the use of storytelling commercials may or may not be useful, depending on which consumers are desired to take which behaviors. It also estimated the gimmicks that influence consumers for each target and found that the useful gimmicks are slightly different for students and working adults. By using this method, it can understand which gimmicks in the commercial affect which engagement of the consumers. Therefore, the results of this study can be used as a reference for the gimmicks that should be included in commercials when companies create their commercials in the future.

Keywords: change point detection, estimating engagement, physiological characteristics, psychological changes, watching commercials

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Authors: Tom Wanyama

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We have developed a weight-based parts inventory monitoring system utilizing the Industrial Internet of Things (IIoT) to enhance operational efficiencies in manufacturing. The system addresses various challenges, including eliminating downtimes caused by stock-outs, preventing human errors in parts delivery and product assembly, and minimizing motion waste by reducing unnecessary worker movements. The system incorporates custom QR codes for simplified inventory tracking and retrieval processes. The generated data serves a dual purpose by enabling real-time optimization of parts flow within manufacturing facilities and facilitating retroactive optimization of stock levels for informed decision-making in inventory management. The pilot implementation at SEPT Learning Factory successfully eradicated data entry errors, optimized parts delivery, and minimized workstation downtimes, resulting in a remarkable increase of over 10% in overall equipment efficiency across all workstations. Leveraging the IIoT features, the system seamlessly integrates information into the process control system, contributing to the enhancement of product quality. This approach underscores the importance of effective tracking of parts inventory in manufacturing to achieve transparency, improved inventory control, and overall profitability. In the broader context, our inventory monitoring system aligns with the evolving focus on optimizing supply chains and maintaining well-managed warehouses to ensure maximum efficiency in the manufacturing industry.

Keywords: industrial Internet of things, industrial systems integration, inventory monitoring, inventory control in manufacturing

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Authors: Anton V. Bourdine, Vladimir A. Burdin, Oleg R. Delmukhametov

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This work presents a fast and simple method for the design of large core silica optical fibers with differential mode delay (DMD) management. Some results are reported concerned with refractive index profile optimization for 42 µm core 16-LP-mode optical fiber for next-generation optical networks. Here special refractive index profile form provides total DMD reducing over all mode staff under desired enhanced mode effective area. Method for the simulation of 'real manufactured' few-mode optical fiber (FMF) core geometry differing from the desired optimized structure by core non-symmetrical ellipticity and refractive index profile deviation including local fluctuations is proposed. Results of the following analysis of optimized FMF with inserted geometry distortions performed by earlier on developed modification of rigorous mixed finite-element method showed strong DMD degradation that requires additional higher-order mode management. In addition, this work also presents a method for design mode division multiplexer channel precision spatial positioning scheme at FMF core end that provides one of the potentiality solutions of described DMD degradation problem concerned with 'distorted' core geometry due to features of optical fiber manufacturing techniques.

Keywords: differential mode delay, few-mode optical fibers, nonlinear Shannon limit, optical fiber non-circularity, ‘real manufactured’ optical fiber core geometry simulation, refractive index profile optimization

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Authors: Hanan Yousef, Nisreen Naji Al-Khawaldeh

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The present study investigates the use of the expression of refusal by native speakers of Jordanian Arabic (NSsJA) in different social situations (i.e. invitations, suggestions, and offers). It also investigates the influence of gender on the refusal realization patterns within the Jordanian culture to provide a better insight into the relation between situations, strategies and gender in the Jordanian culture. To that end, a group of 70 participants, including 35 male and 35 female students from different departments at the Hashemite University (HU) participated in this study using mixed methods (i.e. Discourse Completion Test (DCT), interviews and naturally occurring data). Data were analyzed in light of a developed coding scheme. The results showed that NSsJA preferred indirect strategies which mitigate the interaction such as "excuse, reason and, explanation" strategy more than other strategies which aggravate the interaction such as "face-threatening" strategy. Moreover, the analysis of this study has revealed a considerable impact of gender on the use of linguistic forms expressing refusal among NSsJA. Significant differences in the results of the Chi-square test relating the effect of participants' gender indicate that both males and females were conscious of the gender of their interlocutors. The findings provide worthwhile insights into the relation amongst types of communicative acts and the rapport between people in social interaction. They assert that refusal should not be labeled as face threatening act since it does not always pose a threat in some cases especially where refusal is expressed among friends, relatives and family members. They highlight some distinctive culture-specific features of the communicative acts of refusal.

Keywords: gender, Jordanian Arabic, politeness, refusals, speech act

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Authors: Tingyu Zhang

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This paper utilizes the Super-SBM model to assess water resources green efficiency (WRGE) among provinces in China and investigate its spatial and temporal features, based on the characteristic framework of “economy-environment-society.” The social network analysis is employed to examine the network pattern and spatial interaction of WRGE. Further, the quadratic assignment procedure method is utilized for examining the influencing factors of the spatial association of WRGE regarding “relationship.” The study reveals that: (1) the spatial distribution of WRGE demonstrates a distribution pattern of Eastern>Western>Central; (2) a remarkable spatial association exists among provinces; however, no strict hierarchical structure is observed. The internal structure of the WRGE network is characterized by the feature of "Eastern strong and Western weak". The block model analysis discovers that the members of the “net spillover” and “two-way spillover” blocks are mostly in the eastern and central provinces; “broker” block, which plays an intermediary role, is mostly in the central provinces; and members of the “net beneficiary” block are mostly in the western region. (3) Differences in economic development, degree of urbanization, water use environment, and water management have significant impacts on the spatial connection of WRGE. This study is dedicated to the realization of regional linkages and synergistic enhancement of WRGE, which provides a meaningful basis for building a harmonious society of human and water coexistence.

Keywords: water resources green efficiency, super-SBM model, social network analysis, quadratic assignment procedure

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Authors: B. Vinod, L. J. Sudev

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Natural fibers as reinforcement in polymer matrix material are gaining lot of attention in recent years. Natural fibers like jute, sisal, coir, hemp, banana etc. have attracted substantial importance as a potential structural material because of its attractive features along with its good mechanical properties. Cryogenic applications of natural fiber reinforced polymer composites are gaining importance. These materials need to possess good mechanical and physical properties at cryogenic temperatures to meet the high requirements by the cryogenic engineering applications. The objective of this work is to investigate the mechanical behavior of hybrid hemp/jute fibers reinforced epoxy composite material at liquid nitrogen temperature. Hybrid hemp/jute fibers reinforced polymer composite is prepared by hand lay-up method and test specimens are cut according to ASTM standards. These test specimens are dipped in liquid nitrogen for different time durations. The tensile properties, flexural properties and impact strength of the specimen are tested immediately after the specimens are removed from liquid nitrogen container. The experimental results indicate that the cryogenic treatment of the polymer composite has a significant effect on the mechanical properties of this material. The tensile properties and flexural properties of the hybrid hemp/jute fibers epoxy composite at liquid nitrogen temperature is higher than at room temperature. The impact strength of the material decreased after subjecting it to liquid nitrogen temperature.

Keywords: liquid nitrogen temperature, polymer composite, tensile properties, flexural properties

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Authors: Rihab Hamdi, Amel Hadri Hamida, Fatiha Khelili, Sakina Zerouali, Ouafae Bennis

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This paper features a comparative study performance of sliding mode controller (SMC) for closed-loop voltage control of direct current to direct current (DC-DC) three-cells buck converter connected in parallel, operating in continuous conduction mode (CCM), based on pulse-width modulation (PWM) with SMC based on hysteresis modulation (HM) where an adaptive feedforward technique is adopted. On one hand, for the PWM-based SM, the approach is to incorporate a fixed-frequency PWM scheme which is effectively a variant of SM control. On the other hand, for the HM-based SM, oncoming an adaptive feedforward control that makes the hysteresis band variable in the hysteresis modulator of the SM controller in the aim to restrict the switching frequency variation in the case of any change of the line input voltage or output load variation are introduced. The results obtained under load change, input change and reference change clearly demonstrates a similar dynamic response of both proposed techniques, their effectiveness is fast and smooth tracking of the desired output voltage. The PWM-based SM technique has greatly improved the dynamic behavior with a bit advantageous compared to the HM-based SM technique, as well as provide stability in any operating conditions. Simulation studies in MATLAB/Simulink environment have been performed to verify the concept.

Keywords: DC-DC converter, hysteresis modulation, parallel multi-cells converter, pulse-width modulation, robustness, sliding mode control

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Authors: Mrinal Kanti Bhowmik, Kakali Das Jr., Barin Kumar De, Debotosh Bhattacharjee

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Breast cancer is the most common cancer and a leading cause of death for women in the 35 to 55 age group. Early detection of breast cancer can decrease the mortality rate of breast cancer. Mammography is considered as a ‘Gold Standard’ for breast cancer detection and a very popular modality, presently used for breast cancer screening and detection. The screening of digital mammograms often leads to over diagnosis and a consequence to unnecessary traumatic & painful biopsies. For that reason recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. Tumor is a significant sign of breast cancer in both mammography and thermography. The tumors are complex in structure and they also exhibit a different statistical and textural features compared to the breast background tissue. Fractal geometry is a geometry which is used to describe this type of complex structure as per their main characteristic, where traditional Euclidean geometry fails. Over the last few years, fractal geometrics have been applied mostly in many medical image (1D, 2D, or 3D) analysis applications. In breast cancer detection using digital mammogram images, also it plays a significant role. Fractal is also used in thermography for early detection of the masses using the thermal texture. This paper presents an overview of the recent aspects and initiatives of fractals in breast cancer detection in both mammography and thermography. The scope of fractal geometry in automatic breast cancer detection using digital mammogram and thermogram images are analysed, which forms a foundation for further study on application of fractal geometry in medical imaging for improving the efficiency of automatic detection.

Keywords: fractal, tumor, thermography, mammography

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Authors: Cristina Busuioc, Georgeta Voicu, Sorin-Ion Jinga

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The human bone presents in its dry form piezoelectric properties, which means that a mechanical stress results in electric polarization and an applied electric field causes strain. The immediate consequence was the revealing of piezoelectricity role in bone remodelling, as well as the integration of ceramic materials with piezoelectric behaviour in the composition of unitary or composite biomaterials. Thus, we prepared hydroxyapatite - collagen hybrid materials with barium titanate addition in order to achieve a better osseointegration. Barium titanate powder synthesized by a combined sol-gel-hydrothermal method, commercial hydroxyapatite and laboratory extracted collagen gel were employed as starting materials. Before the composites, fabrication, the powder with piezoelectric features was characterized in detail from the compositional, structural, morphological and electrical point of view. The next step was to elucidate the influence of barium titanate presence especially on the biological properties of the final materials. The biocompatibility of the hybrid supports without or with piezoelectric addition was investigated on mouse osteoblast cells through LDH cytotoxicity assay, LIVE/DEAD cell viability assay, and MTT cell proliferation assay. All results indicated that the analysed materials do not exert cytotoxic effects and present the ability to sustain cell survival and to promote their proliferation. In conclusion, barium titanate nanoparticles exhibit a good biocompatibility and osteoinductive properties, while the derived composite materials based on hydroxyapatite as oxide phase and collagen as polymeric phase can be successfully used for tissue engineering applications.

Keywords: barium titanate, hybrid composites, piezoelectricity, tissue engineering

Procedia PDF Downloads 322