Search results for: Big Data Movement

Authors: Reza Rezaeipour Honarmandzad

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In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

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Authors: Nino Gogatishvili, Davit Kvernadze, Giorgi Japharidze

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Background and aims: Landau Kleffner syndrome (LKS) is a rare disorder with epileptic seizures and acquired aphasia. It usually starts in initially healthy children. The first symptoms are language regression and behavioral disturbances, and the sleep EEG reveals abnormal epileptiform activity. The aim was to discuss the efficacy of Clobazam for Landau Kleffner syndrome. Case report: We report a case of an 11-year-old boy with an uneventful pregnancy and delivery. He began to walk at 11 months and speak with simple phrases at the age of 2,5 years. At the age of 18 months, he had febrile convulsions; at the age of 5 years, the parents noticed language regression, stuttering, and serious behavioral dysfunction, including hyperactivity, temper outbursts. The epileptic seizure was not noticed. MRI was without any abnormality. Neuropsychological testing revealed verbal auditory agnosia. Sleep EEG showed abundant left fronto-temporal spikes, reaching over 85% during non-rapid eye movement sleep (non-REM sleep). Treatment was started with Clobazam. After ten weeks, EEG was improved. Stuttering and behavior also improved. Results: Since the start of Clobazam treatment, stuttering and behavior improved. Now, he is 11 years old, without antiseizure medication. Sleep EEG shows fronto-temporal spikes on the left side, over 10-49 % of non-REM sleep, bioccipital spikes, and slow-wave discharges and spike-waves. Conclusions: This case provides further support for the efficacy of Clobazam in patients with LKS.

Keywords: Landau-Kleffner syndrome, antiseizure medication, stuttering, aphasia

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Authors: Natalie Malone

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This study explores the role of a public library in the creation of social spaces for vulnerable populations. The data stems from a longitudinal ethnographic study of the Anderson Library community, which included field notes, artifacts, and interview data. Thematic analysis revealed multiple meanings and thematic relationships within and among the data sources -interviews, field notes, and artifacts. Initial analysis suggests the Anderson Library serves as a space for vulnerable populations, with the sub-themes of fostering interpersonal communication to create a social space for children and fostering interpersonal communication to create a social space for parents and adults. These findings are important as they illustrate the potential of public libraries to serve as community empowering institutions.

Keywords: capital, immigrant families, public libraries, space, vulnerable

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Authors: Nguyen-Thanh Son

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Flood is among the most frequent and costliest natural hazards. The flood disasters especially affect the poor people in rural areas, who are heavily dependent on agriculture and have lower incomes. Cambodia is identified as one of the most climate-vulnerable countries in the world, ranked 13th out of 181 countries most affected by the impacts of climate change. Flood monitoring is thus a strategic priority at national and regional levels because policymakers need reliable spatial and temporal information on flood-prone areas to form successful monitoring programs to reduce possible impacts on the country’s economy and people’s likelihood. This study aims to develop methods for flood mapping and assessment from MODIS data in Cambodia. We processed the data for the period from 2000 to 2017, following three main steps: (1) data pre-processing to construct smooth time-series vegetation and water surface indices, (2) delineation of flood-prone areas, and (3) accuracy assessment. The results of flood mapping were verified with the ground reference data, indicating the overall accuracy of 88.7% and a Kappa coefficient of 0.77, respectively. These results were reaffirmed by close agreement between the flood-mapping area and ground reference data, with the correlation coefficient of determination (R²) of 0.94. The seasonally flooded areas observed for 2010, 2015, and 2016 were remarkably smaller than other years, mainly attributed to the El Niño weather phenomenon exacerbated by impacts of climate change. Eventually, although several sources potentially lowered the mapping accuracy of flood-prone areas, including image cloud contamination, mixed-pixel issues, and low-resolution bias between the mapping results and ground reference data, our methods indicated the satisfactory results for delineating spatiotemporal evolutions of floods. The results in the form of quantitative information on spatiotemporal flood distributions could be beneficial to policymakers in evaluating their management strategies for mitigating the negative effects of floods on agriculture and people’s likelihood in the country.

Keywords: MODIS, flood, mapping, Cambodia

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Authors: Samuel Nii Tackie, Oyebade K. Oyedotun, Ebenezer O. Olaniyi, Adnan Khashman

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Artificial neural networks have been used in different fields of artificial intelligence, and more specifically in machine learning. Although, other machine learning options are feasible in most situations, but the ease with which neural networks lend themselves to different problems which include pattern recognition, image compression, classification, computer vision, regression etc. has earned it a remarkable place in the machine learning field. This research exploits neural networks as a data mining tool in predicting the number of times a student repeats a course, considering some attributes relating to the course itself, the teacher, and the particular student. Neural networks were used in this work to map the relationship between some attributes related to students’ course assessment and the number of times a student will possibly repeat a course before he passes. It is the hope that the possibility to predict students’ performance from such complex relationships can help facilitate the fine-tuning of academic systems and policies implemented in learning environments. To validate the power of neural networks in data mining, Turkish students’ performance database has been used; feedforward and radial basis function networks were trained for this task; and the performances obtained from these networks evaluated in consideration of achieved recognition rates and training time.

Keywords: artificial neural network, data mining, classification, students’ evaluation

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Authors: Anu Malhotra

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An Ad-hoc network is one that is an autonomous, self configuring network made up of mobile nodes connected via wireless links. Ad-hoc networks often consist of nodes, mobile hosts (MH) or mobile stations (MS, also serving as routers) connected by wireless links. Different routing protocols are used for data transmission in between the nodes in an adhoc network. In this paper two protocols (OLSR and AODV) are analyzed on the basis of two parameters i.e. time delay and throughput with different data rates. On the basis of these analysis, we observed that with same data rate, AODV protocol is having more time delay than the OLSR protocol whereas throughput for the OLSR protocol is less compared to the AODV protocol.

Keywords: routing adhoc, mobile hosts, mobile stations, OLSR protocol, AODV protocol

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Authors: Aref Aasi, Sadegh Mehdi Aghaei, Balaji Panchapakesan

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This work aims to evaluate the free and forced vibration of a beam with two end joints subjected to a concentrated moving mass and a load using the Euler-Bernoulli method. The natural frequency is calculated for different locations of the concentrated mass and load on the beam. The analytical results are verified by the experimental data. The variations of natural frequency as a function of the location of the mass, the effect of the forced frequency on the vibrational amplitude, and the displacement amplitude versus time are investigated. It is discovered that as the concentrated mass moves toward the center of the beam, the natural frequency of the beam and the relative error between experimental and analytical data decreases. There is a close resemblance between analytical data and experimental observations.

Keywords: Euler-Bernoulli beam, natural frequency, forced vibration, experimental setup

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Authors: NeisaKuonuo Tungoe

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Tenyidie, also known as Angami, is spoken by the Angami tribe of Nagaland, North-East India, bordering Myanmar (Burma). It belongs to the Tibeto-Burman language group, falling under the Kuki-Chin-Naga sub-family. Tenyidie studies have seen random attempts at explaining the phonemic inventory of Tenyidie. Different scholars have variously emphasized the grammar or the history of Tenyidie. Many of these claims have been stimulating, but they were often based on a small amount of merely suggestive data or on auditory perception only. The principal objective of this paper is to analyse the affricate segments of Tenyidie as an acoustic study. There are seven categories to the inventory of Tenyidie; Plosives, Nasals, Affricates, Laterals, Rhotics, Fricatives, Semi vowels and Vowels. In all, there are sixty phonemes in the inventory. As mentioned above, the only prominent readings on Tenyidie or affricates in particular are only reflected through auditory perception. As noted above, this study aims to lay out the affricate segments based only on acoustic conclusions. There are seven affricates found in Tenyidie. They are: 1) Voiceless Labiodental Affricate - / pf /, 2) Voiceless Aspirated Labiodental Affricate- / pfh /, 3) Voiceless Alveolar Affricate - / ts /, 4) Voiceless Aspirated Alveolar Affricate - / tsh /, 5) Voiced Alveolar Affricate - / dz /, 6) Voiceless Post-Alveolar Affricate / tʃ / and 7) Voiced Post- Alveolar Affricate- / dʒ /. Since the study is based on acoustic features of affricates, five informants were asked to record their voice with Tenyidie phonemes and English phonemes. Throughout the study of the recorded data, PRAAT, a scientific software program that has made itself indispensible for the analyses of speech in phonetics, have been used as the main software. This data was then used as a comparative study between Tenyidie and English affricates. Comparisons have also been drawn between this study and the work of another author who has stated that there are only six affricates in Tenyidie. The study has been quite detailed regarding the specifics of the data. Detailed accounts of the duration and acoustic cues have been noted. The data will be presented in the form of spectrograms. Since there aren’t any other acoustic related data done on Tenyidie, this study will be the first in the long line of acoustic researches on Tenyidie.

Keywords: tenyidie, affricates, praat, phonemic inventory

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Authors: Alveena Khan

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The objective of this research is to explore students’ understanding about bullying and different bullying types. Nowadays bullying is considered as an important social issue around the world because it has long lasting effects on students’ lives. Sometimes due to bullying students commit suicide, they lose confidence and become isolated. This research used qualitative research approach. In order to generate data, triangulation was considered for the verification and reliability of the generated data. Semi-structured interview, non-participant observation, and case studies were conducted. This research focused on five major private colleges and 20 students (both female and male) participated in Rawalpindi, Pakistan. The data generated included approximately 45 hours of total interviews. Thematic analysis was used for data analysis and followed grounded theory to generate themes. The findings of the research highlights that bullying does prevail in studied private colleges, mostly in the form of verbal and physical bullying. No specific gender difference was found in experiencing verbal and physical bullying. Furthermore, from students’ point of view, college administrators are responsible to deal with bullying. The researcher suggests that there must be a proper check and balance system and anti-bullying programs should be held in colleges to create a protective and healthy environment in which students do not face bullying.

Keywords: bullying, college student, physical and verbal bullying, qualitative research

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Authors: Anna Triwijayati, Etsa Astridya Setiyati, Titik Desi Harsoyo

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Culture is the important determinant of human behavior and desire. Culture influences the consumer through the norms and values established by the society in which they live and reflect it. The cultural values of Javanese society certainly have united in the Javanese society behavior in consumption. This research is expected to give big enough theoretical benefits in the findings of cultural value in consumption in Javanese society. These can be an incentive in finding the local cultural value in many tribes in Indonesia, so one time, the local cultural value in Indonesia about consumption can be fundamental part in education and consumption practice in Indonesia. The approach used in this research is non positivist research or is known as qualitative approach. The method or type of research used in this research is ethnomethodology. The collection data is done in Central Java region. The research subject or informant is determined by the purposive technique by certain criteria determined by the researcher. The data is collected by deep interview and observation. Before the data analysis, the researcher does the storing method data stage and implements the data validity procedures. Then, the data is analyzed by the theme and interactive analysis technique. The Javanese Mataraman society has such consumption values such as has to be sufficient, be careful, economical, submit to the one who creates the life, the way life flow, and the present problem is thought in the present also. In the financial management for consumption, the consumer should have the simple life principles, has to be sufficient, has to be able to eat, has to be able to self-press, well-managed/diligent/accurate/careful, the open or transparent management, has the struggle effort, like to self-sacrifice and think about the future. The meaning of consumption value in family is centered to the submission and full-trust to God. These consumption values are applied in consumer behavior in self, family, investment and credit need in short term and long term perspective.

Keywords: values, consumer, consumption, Javanese Mataraman, ethnomethodology

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Authors: Nitin Lahkar, Rishiraj Goswami

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“Oil Well Logging” or the practice of making a detailed record (a well log) of the geologic formations penetrated by a borehole is an important practice in the Oil and Gas industry. Although a lot of research has been undertaken in this field, some basic limitations still exist. One of the main arenas or venues where plethora of problems arises is in logistically challenged areas. Accessibility and availability of efficient manpower, resources and technology is very time consuming, restricted and often costly in these areas. So, in this regard, the main challenge is to decrease the Non Productive Time (NPT) involved in the conventional logging process. The thought for the solution to this problem has given rise to a revolutionary concept called the “Robotic Logging Technology”. Robotic logging technology promises the advent of successful logging in all kinds of wells and trajectories. It consists of a wireless logging tool controlled from the surface. This eliminates the need for the logging truck to be summoned which in turn saves precious rig time. The robotic logging tool here, is designed such that it can move inside the well by different proposed mechanisms and models listed in the full paper as TYPE A, TYPE B and TYPE C. These types are classified on the basis of their operational technology, movement and conditions/wells in which the tool is to be used. Thus, depending on subsurface conditions, energy sources available and convenience the TYPE of Robotic model will be selected. Advantages over Conventional Logging Techniques: Reduction in Non-Productive time, lesser energy requirements, very fast action as compared to all other forms of logging, can perform well in all kinds of well trajectories (vertical/horizontal/inclined).

Keywords: robotic logging technology, innovation, geology, geophysics

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Authors: Mustafa Cam, Nedim Ongun, Ufuk Kutluana

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Introduction: Restless LegsSyndrome (RLS) is a common, under-recognized disorder disrupts sleep and diminishes quality of life (1). The most common conditions highly associated with RLS include renalfailure, iron and folic acid deficiency, peripheral neuropathy, pregnancy, celiacdisease, Crohn’sdiseaseandrarelymalignancy (2).Despite a clear relation between low peripheral iron and increased prevalence and severity of RLS, the prevalence and clinical significance of RLS in iron-deficientanemic populations is unknown (2). We report here a case of RLS due to iron deficiency in the setting of neuroendocrinetumor. Report of Case: A 35 year-old man was referred to our clinic with general weakness, weight loss (10 kg in 2 months)and 2-month history of uncomfortable sensations in his legs with urge to move, partially relieved by movement. The symptoms were presented very day, worsening in the evening; the discomfort forced the patient to getup and walk around at night. RLS was severe, with a score of 22 at the International RLS ratingscale. The patient had no past medical history. The patient underwent a complete set of blood analyses and the following ab normal values were found (normal limitswithinbrackets): hemoglobin 9.9 g/dl (14-18), MCV 70 fL (80-94), ferritin 3,5 ng/mL (13-150). Brain and spinemagnetic resonance imaging was normal. The patient consultated with gastroenterology clinic and gastointestinal systemendoscopy was performed for theetiology of the iron deficiency anemia. After the gastricbiopsy, results allowed us to reach the diagnosis of neuroen docrine tumor and the patient referred to oncology clinic. Discussion: The first important consideration from this case report is that the patient was referred to our clinic because of his severe RLS symptoms dramatically reducing his quality of life. However, our clinical study clearly demonstrated that RLS was not the primary disease. Considering the information available for this patient, we believe that the most likely possibility is that RLS was secondary to iron deficiency, a very well-known and established cause of RLS in theliterature (3,4). Neuroendocrine tumors (NETs) are rare epithelial neoplasms with neuroendocrine differentiation that most commonly originate in the lungs and gastrointestinal tract (5). NETs vary widely in their clinical presentation; symptoms are often nonspecific and can be mistaken for those of other more common conditions (6). 50% of patients with reported disease stage have either regional or distant metastases at diagnosis (7). Accurate and earlier NET diagnosis is the first step in shortening the time to optimal care and improved outcomes for patients (8). The most important message from this case report is that RLS symptoms can sometimes be thesign of a life-threatening condition. Conclusion: Careful and complete collection of clinical and laboratory data should be carried out in RLS patients. Inparticular, if RLS onset coincides with weight loss and iron deficieny anemia, gastricendos copy should be performed. It is known about that malignancy is a rare etiology in RLS patients and to our knowledge; it is the first case with neuro endocrine tumor presenting with RLS.

Keywords: neurology, neuroendocrine tumor, restless legs syndrome, sleep

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Authors: Arindam Chaudhuri

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Classification of data has been actively used for most effective and efficient means of conveying knowledge and information to users. The prima face has always been upon techniques for extracting useful knowledge from data such that returns are maximized. With emergence of huge datasets the existing classification techniques often fail to produce desirable results. The challenge lies in analyzing and understanding characteristics of massive data sets by retrieving useful geometric and statistical patterns. We propose a supervised parallel fuzzy rough support vector machine (PFRSVM) for data classification in cloud environment. The classification is performed by PFRSVM using hyperbolic tangent kernel. The fuzzy rough set model takes care of sensitiveness of noisy samples and handles impreciseness in training samples bringing robustness to results. The membership function is function of center and radius of each class in feature space and is represented with kernel. It plays an important role towards sampling the decision surface. The success of PFRSVM is governed by choosing appropriate parameter values. The training samples are either linear or nonlinear separable. The different input points make unique contributions to decision surface. The algorithm is parallelized with a view to reduce training times. The system is built on support vector machine library using Hadoop implementation of MapReduce. The algorithm is tested on large data sets to check its feasibility and convergence. The performance of classifier is also assessed in terms of number of support vectors. The challenges encountered towards implementing big data classification in machine learning frameworks are also discussed. The experiments are done on the cloud environment available at University of Technology and Management, India. The results are illustrated for Gaussian RBF and Bayesian kernels. The effect of variability in prediction and generalization of PFRSVM is examined with respect to values of parameter C. It effectively resolves outliers’ effects, imbalance and overlapping class problems, normalizes to unseen data and relaxes dependency between features and labels. The average classification accuracy for PFRSVM is better than other classifiers for both Gaussian RBF and Bayesian kernels. The experimental results on both synthetic and real data sets clearly demonstrate the superiority of the proposed technique.

Keywords: FRSVM, Hadoop, MapReduce, PFRSVM

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Authors: Grace Omowunmi Soyebi

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A computerized medical record system is a collection of medical information about a person that is stored on a computer. One principal problem of most hospitals in rural areas is using the file management system for keeping records. A lot of time is wasted when a patient visits the hospital, probably in an emergency, and the nurse or attendant has to search through voluminous files before the patient's file can be retrieved, this may cause an unexpected to happen to the patient. This Data Mining application is to be designed using a Structured System Analysis and design method which will help in a well-articulated analysis of the existing file management system, feasibility study, and proper documentation of the Design and Implementation of a Computerized medical record system. This Computerized system will replace the file management system and help to quickly retrieve a patient's record with increased data security, access clinical records for decision-making, and reduce the time range at which a patient gets attended to.

Keywords: programming, computing, data, innovation

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Authors: Shirin Jalili, Ramin Ghasemi

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Lumbar microdiscectomy is the most commonly performed spinal surgery strategy; it is regularly performed to lighten the indications and signs of sciatica within the lower back and leg caused by a lumbar disc herniation. This surgery aims to progress leg pain, reestablish function, and enable a return to ordinary day-by-day exercises. Rates of lumbar disc surgery show critical geographic varieties recommending changing treatment criteria among working specialists. Few population-based considers have investigated the hazard of reoperation after disc surgery, and regional or inter specialty varieties within the reoperations are obscure. The conventional approach to recouping from lumbar microdiscectomy has been to restrain bending, lifting, or turning for a least 6 weeks in arrange to anticipate the disc from herniating once more. Traditionally, patients were exhorted to limit post-operative action, which was accepted to decrease the hazard of disc herniation and progressive insecurity. In modern hone, numerous specialists don't limit understanding of postoperative action due to the discernment this practice is pointless. There's a need of thinks about highlighting the result by distinctive scores or parameters after surgery for repetitive circle herniations of the lumbar spine at the starting herniation location. This study will evaluate the quality of life after surgical treatment of recurrent herniations with distinctive standardized approved result instruments.

Keywords: post-operative activity, disc, quality of life, treatment, movements

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Authors: Victoria Siriaki Jorry, I. S. Mbalawata, Hayong Shin

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The main objective in a change detection problem is to develop algorithms for efficient detection of gradual and/or abrupt changes in the parameter distribution of a process or time series data. In this paper, we present a modified cumulative (MCUSUM) algorithm to detect the start and end of a time-varying linear drift in mean value of a time series data based on likelihood ratio test procedure. The design, implementation and performance of the proposed algorithm for a linear drift detection is evaluated and compared to the existing CUSUM algorithm using different performance measures. An approach to accurately approximate the threshold of the MCUSUM is also provided. Performance of the MCUSUM for gradual change-point detection is compared to that of standard cumulative sum (CUSUM) control chart designed for abrupt shift detection using Monte Carlo Simulations. In terms of the expected time for detection, the MCUSUM procedure is found to have a better performance than a standard CUSUM chart for detection of the gradual change in mean. The algorithm is then applied and tested to a randomly generated time series data with a gradual linear trend in mean to demonstrate its usefulness.

Keywords: average run length, CUSUM control chart, gradual change detection, likelihood ratio test

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Authors: Julia Ive, Lucia Specia

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Understanding and detecting toxicity is an important problem to support safer human interactions online. Our work focuses on the important problem of contextual toxicity detection, where automated classifiers are tasked with determining whether a short textual segment (usually a sentence) is toxic within its conversational context. We use “toxicity” as an umbrella term to denote a number of variants commonly named in the literature, including hate, abuse, offence, among others. Detecting toxicity in context is a non-trivial problem and has been addressed by very few previous studies. These previous studies have analysed the influence of conversational context in human perception of toxicity in controlled experiments and concluded that humans rarely change their judgements in the presence of context. They have also evaluated contextual detection models based on state-of-the-art Deep Learning and Natural Language Processing (NLP) techniques. Counterintuitively, they reached the general conclusion that computational models tend to suffer performance degradation in the presence of context. We challenge these empirical observations by devising better contextual predictive models that also rely on NLP data augmentation techniques to create larger and better data. In our study, we start by further analysing the human perception of toxicity in conversational data (i.e., tweets), in the absence versus presence of context, in this case, previous tweets in the same conversational thread. We observed that the conclusions of previous work on human perception are mainly due to data issues: The contextual data available does not provide sufficient evidence that context is indeed important (even for humans). The data problem is common in current toxicity datasets: cases labelled as toxic are either obviously toxic (i.e., overt toxicity with swear, racist, etc. words), and thus context does is not needed for a decision, or are ambiguous, vague or unclear even in the presence of context; in addition, the data contains labeling inconsistencies. To address this problem, we propose to automatically generate contextual samples where toxicity is not obvious (i.e., covert cases) without context or where different contexts can lead to different toxicity judgements for the same tweet. We generate toxic and non-toxic utterances conditioned on the context or on target tweets using a range of techniques for controlled text generation(e.g., Generative Adversarial Networks and steering techniques). On the contextual detection models, we posit that their poor performance is due to limitations on both of the data they are trained on (same problems stated above) and the architectures they use, which are not able to leverage context in effective ways. To improve on that, we propose text classification architectures that take the hierarchy of conversational utterances into account. In experiments benchmarking ours against previous models on existing and automatically generated data, we show that both data and architectural choices are very important. Our model achieves substantial performance improvements as compared to the baselines that are non-contextual or contextual but agnostic of the conversation structure.

Keywords: contextual toxicity detection, data augmentation, hierarchical text classification models, natural language processing

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Authors: Irfan Khairi

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It is arguable that today's social media has dramatically redefined human contact, and chiefly because the platforms enable communication opportunities unprecedented. Without question, billions of individuals globally engage in the media, a reality by no means lost on businesses and social entrepreneurs desirous of generating interest in a cause, movement, or other social effort. If, however, the opportunities are immense, so too is the competition. Private persons and entrepreneurial concerns alike virtually saturate the popular sites of Facebook, Twitter, and Instagram, and most are intent on capturing as much external interest as possible. At the same time, however, the social entrepreneur possesses an advantage over the individual concerned only the social aspects of the sites, as they express interests in, and measures applicable to, important causes of which the public at large may be unaware. There is, unfortunately, no single means of assuring success in using the media outlets to generate interest. Nonetheless, a general awareness of how social media sites function, as well as the psychological elements relevant to the functioning, is necessary. It is as important to comprehend basic realities of the platforms and approaches that fail as it is to develop strategy, for the latter relies on knowledge of the former. This awareness in place, the social entrepreneur is then better enabled to determine strategy, in terms of which sites to focus upon and how to most effectively convey their message. What is required is familiarity with the online communities, with attention to the specific advantages each provides. Ultimately, today's social entrepreneur may establish a highly effective platform of promotion and engagement, provided they fully comprehend the social investment necessary for success.

Keywords: social media, marketing, e-commerce, internet business

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Authors: Jing Fang, Yao Cui, Mingming Wang, Shengli She, Jianping Yuan

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Wearable robotics is a potential solution in aiding gait rehabilitation of lower limbs dyskinesia patients, such as knee osteoarthritis or stroke afflicted patients. Many wearable robots have been developed in the form of rigid exoskeletons, but their bulk devices, high cost and control complexity hinder their popularity in the field of gait rehabilitation. Thus, the development of a portable, compliant and low-cost wearable robot for gait rehabilitation is necessary. Inspired by Chinese traditional folding fans and balloon inflators, the authors present an inflatable, foldable and variable stiffness knee exosuit (IFVSKE) in this paper. The pneumatic actuator of IFVSKE was fabricated in the shape of folding fans by using thermoplastic polyurethane (TPU) fabric materials. The geometric and mechanical properties of IFVSKE were characterized with experimental methods. To assist the knee joint smartly, an intelligent control profile for IFVSKE was proposed based on the concept of full-cycle energy management of the biomechanical energy during human movement. The biomechanical energy of knee joints in a walking gait cycle of patients could be collected and released to assist the joint motion just by adjusting the inner pressure of IFVSKE. Finally, a healthy subject was involved to walk with and without the IFVSKE to evaluate the assisting effects.

Keywords: biomechanical energy management, knee exosuit, gait rehabilitation, wearable robotics

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Authors: Loveneet Kaur

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Introduction: Osteoarthritis (OA) is one of the leading causes of disability, and the knee is the most commonly affected joint in the body. The last resort for treatment of knee OA is Total Knee Replacement (TKR) surgery. Despite numerous advances in prosthetic design, patients do not reach normal function after surgery. Current surgical decisions are made on 2D radiographs and patient interviews. Aims: The aim of this study was to compare knee kinematics pre and post-TKR surgery using computer-animated images of patient-specific models under everyday conditions. Methods: 7 subjects were recruited for the study. Subjects underwent 3D gait analysis during 4 everyday activities and medical imaging of the knee joint pre- and one-month post-surgery. A 3D model was created from each of the scans, and the kinematic gait analysis data was used to animate the images. Results: Improvements were seen in a range of motion in all 4 activities 1-year post-surgery. The preoperative 3D images provide detailed information on the anatomy of the osteoarthritic knee. The postoperative images demonstrate potential future problems associated with the implant. Although not accurate enough to be of clinical use, the animated data can provide valuable insight into what conditions cause damage to both the osteoarthritic and prosthetic knee joints. As the animated data does not require specialist training to view, the images can be utilized across the fields of health professionals and manufacturing in the assessment and treatment of patients pre and post-knee replacement surgery. Future improvements in the collection and processing of data may yield clinically useful data. Conclusion: Although not yet of clinical use, the potential application of 3D animations of the knee joint pre and post-surgery is widespread.

Keywords: Orthoporosis, Ortharthritis, knee replacement, TKR

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Authors: Jiahao Tian, Michael D. Porter

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Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval-censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of the week. This approach presented here provides accurate intensity estimates and can also uncover day-of-week clusters that share the same intensity patterns. Anticipating where and when crimes might occur is a key element to successful policing strategies. However, this task is complicated by the presence of interval-censored data. The censored data refers to the type of data that the event time is only known to lie within an interval instead of being observed exactly. This type of data is prevailing in the field of criminology because of the absence of victims for certain types of crime. Despite its importance, the research in temporal analysis of crime has lagged behind the spatial component. Inspired by the success of solving crime-related problems with a statistical approach, we propose a statistical model for the temporal intensity estimation of crime with censored data. The model is built on Poisson regression and has special penalty terms added to the likelihood. An EM algorithm was derived to obtain maximum likelihood estimates, and the resulting model shows superior performance to the competing model. Our research is in line with the smart policing initiative (SPI) proposed by the Bureau Justice of Assistance (BJA) as an effort to support law enforcement agencies in building evidence-based, data-driven law enforcement tactics. The goal is to identify strategic approaches that are effective in crime prevention and reduction. In our case, we allow agencies to deploy their resources for a relatively short period of time to achieve the maximum level of crime reduction. By analyzing a particular area within cities where data are available, our proposed approach could not only provide an accurate estimate of intensities for the time unit considered but a time-variation crime incidence pattern. Both will be helpful in the allocation of limited resources by either improving the existing patrol plan with the understanding of the discovery of the day of week cluster or supporting extra resources available.

Keywords: cluster detection, EM algorithm, interval censoring, intensity estimation

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Authors: A. S. Akinbani

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Overdependence on petroleum is causing set back in Nigeria economy. Field survey was carried out to assess the profitability and production of selected arable crops in six selected towns and villages of Ondo southwestern. Data were collected from 240 arable crop farmers with the aid of both primary and secondary data. Data were collected with the use of oral interview and structured questionnaires. Data collected were analyzed using both descriptive and inferential statistics. Forty farmers were randomly selected to give a total number of 240 respondents. 84 farmers interviewed had no formal education, 72 had primary education, 50 farmers attained secondary education while 38 attained beyond secondary education. The majority of the farmers hold less than 10 acres of land. The data collected from the field showed that 192 farmers practiced mixed cropping which includes mixtures of yam, cowpea, cocoyam, vegetable, cassava and maize while only 48 farmers practiced monocropping. Among the sampled farmers, 93% agreed that arable production is profitable while 7% disagreed. The findings show that managerial practices that conserve the soil fertility and reduce labor cost such as planting of leguminous crops and herbicide application instead of using hand held hoe for weeding should be encouraged. All the respondents agreed that yam, cowpea, cocoyam, sweet potato, rice, maize and vegetable production will solve the problem of hunger and increase standard of living compared with petroleum product that Nigeria relied on as means of livelihood.

Keywords: farmers, arable crop, cocoyam, respondents, maize

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Authors: Nkeiruka Queendarline Nwaizugbu

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The use of social media and mobile devices has become acceptable in virtually all areas of today’s world. Hence, this study is a survey that was carried out to find out if students and lecturers in public universities in Rivers State use social networking for educational purposes. The sample of the study comprised of 240 students and 99 lecturers from the University of Port Harcourt and the Rivers State University of science and Technology. The study had five research questions, two hypotheses and the instrument for data collection was a 4-point Likert-type rating scale questionnaire. The data was analysed using mean, standard deviation and z-test. The findings gotten from the analysed data shows that students participate in social networking using different types of web applications but they hardly use them for educational purposes. Some recommendations were also made.

Keywords: internet access, mobile learning, participation, social media, social networking, technology

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Authors: Adilah Abdul Ghapor, Yong Zulina Zubairi, A. H. M. R. Imon

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Missing value problem is common in statistics and has been of interest for years. This article considers two modern techniques in handling missing data for linear functional relationship model (LFRM) namely the Expectation-Maximization (EM) algorithm and Expectation-Maximization with Bootstrapping (EMB) algorithm using three performance indicators; namely the mean absolute error (MAE), root mean square error (RMSE) and estimated biased (EB). In this study, we applied the methods of imputing missing values in two types of LFRM namely the full model of LFRM and in LFRM when the slope is estimated using a nonparametric method. Results of the simulation study suggest that EMB algorithm performs much better than EM algorithm in both models. We also illustrate the applicability of the approach in a real data set.

Keywords: expectation-maximization, expectation-maximization with bootstrapping, linear functional relationship model, performance indicators

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Authors: Syeda Fahria Hoque Mimmi, Aparna Islam

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Genetically engineered (GE) plants are the need of time for increased demand for food. A complete set of regulations need to be followed from the development of a GE plant to its release into the environment. The whole regulation system is categorized into separate stages for maintaining the proper biosafety. Environmental risk assessment (ERA) is one of such crucial stages in the whole process. ERA identifies potential risks and their impacts through science-based evaluation where it is done in a case-by-case study. All the countries which deal with GE plants follow specific guidelines to conduct a successful ERA. In this study, ERA guidelines of 4 developing and 4 developed countries, including Bangladesh, were compared. ERA guidelines of countries such as India, Canada, Australia, the European Union, Argentina, Brazil, and the US were considered as a model to conduct the comparison study with Bangladesh. Initially, ten parameters were detected to compare the required data and information among all the guidelines. Surprisingly, an adequate amount of data and information requirements (e.g., if the intended modification/new traits of interest has been achieved or not, the growth habit of GE plants, consequences of any potential gene flow upon the cultivation of GE plants to sexually compatible plant species, potential adverse effects on the human health, etc.) matched between all the countries. However, a few differences in data requirement (e.g., agronomic conventions of non-transformed plants, applicants should clearly describe experimental procedures followed, etc.) were also observed in the study. Moreover, it was found that only a few countries provide instructions on the quality of the data used for ERA. If these similarities are recognized in a more framed manner, then the approval pathway of GE plants can be shared.

Keywords: GE plants, ERA, harmonization, ERA guidelines, Information and data requirements

Procedia PDF Downloads 172

Authors: Erol Bayraktarov, Asya Ilgun, Thomas Schickl, Alexandre Campo, Nicolis Stamatios

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Traditional means of harvesting honey are often stressful for honeybees. Each time honey is collected a portion of the colony can die. In consequence, the colonies’ resilience to environmental stressors will decrease and this ultimately contributes to the global problem of honeybee colony losses. As part of the project HIVEOPOLIS, we design and build a different kind of beehive, incorporating technology to reduce negative impacts of beekeeping procedures, including honey harvesting. A first step in maintaining more sustainable honey harvesting practices is to design honey storage frames that can automate the honey collection procedures. This way, beekeepers save time, money, and labor by not having to open the hive and remove frames, and the honeybees' nest stays undisturbed.This system shows promising features, e.g., high reliability which could be a key advantage compared to current honey harvesting technologies.Our original concept of fractional honey harvesting has been to encourage the removal of honey only from "safe" locations and at levels that would leave the bees enough high-nutritional-value honey. In this abstract, we describe the current state of our honey harvester, its technology and areas to improve. The honey harvester works by separating the honeycomb cells away from the comb foundation; the movement and the elastic nature of honey supports this functionality. The honey sticks to the foundation, because of the surface tension forces amplified by the geometry. In the future, by monitoring the weight and therefore the capped honey cells on our honey harvester frames, we will be able to remove honey as soon as the weight measuring system reports that the comb is ready for harvesting. Higher viscosity honey or crystalized honey cause challenges in temperate locations when a smooth flow of honey is required. We use resistive heaters to soften the propolis and wax to unglue the moving parts during extraction. These heaters can also melt the honey slightly to the needed flow state. Precise control of these heaters allows us to operate the device for several purposes. We use ‘Nitinol’ springs that are activated by heat as an actuation method. Unlike conventional stepper or servo motors, which we also evaluated throughout development, the springs and heaters take up less space and reduce the overall system complexity. Honeybee acceptance was unknown until we actually inserted a device inside a hive. We not only observed bees walking on the artificial comb but also building wax, filling gaps with propolis and storing honey. This also shows that bees don’t mind living in spaces and hives built from 3D printed materials. We do not have data yet to prove that the plastic materials do not affect the chemical composition of the honey. We succeeded in automatically extracting stored honey from the device, demonstrating a useful extraction flow and overall effective operation this way.

Keywords: honey harvesting, honeybee, hiveopolis, nitinol

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Authors: Lukholo Raxangana

Abstract:

Fourth Industrial Revolution (4IR)-era teaching offers in-service mathematics teachers opportunities to use blended approaches to engage learners while teaching mathematics. This study explores in-service high school teachers' experiences with a blended teaching approach to mathematics. This qualitative case study involved eight pre-service teachers from four selected schools in the Sedibeng West District of the Gauteng Province. The study used the community of inquiry model as its analytical framework for data analysis. Data collection was through semi-structured interviews and focus-group discussions to explore in-service teachers' experiences with the influence of blended teaching (BT) on learning mathematics. The study results are the impact of load-shedding, benefits of BT, and perceptions of in-service and hindrances of BT. Based on these findings, the study recommends that further research should focus on developing data-free BT tools to assist during load-shedding, regardless of location.

Keywords: bended teaching, teachers, in-service, and mathematics

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Authors: Maria Isabel Garcia-Planas, Maria Victoria Garcia-Camba

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The use of brain stem auditory evoked potential (BAEP) is a common way to study the auditory function of people, a way to learn the functionality of a part of the brain neuronal groups that intervene in the learning process by studying the behaviour of wave VI. The latest advances in neuroscience have revealed the existence of different brain activity in the learning process that can be highlighted through the use of innocuous, low-cost, and easy-access techniques such as, among others, the BAEP that can help us to detect early possible neurodevelopmental difficulties for their subsequent assessment and cure. To date and to the authors' best knowledge, only the latency data obtained, observing the first to V waves and mainly in the left ear, were taken into account. This work shows that it is essential to take into account both ears; with these latest data, it has been possible had diagnosed more precise some cases than with the previous data had been diagnosed as 'normal' despite showing signs of some alteration that motivated the new consultation to the specialist.

Keywords: ear, neurodevelopment, auditory evoked potentials, intervals of normality, learning disabilities

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Authors: Prateek Saxena, Sanjeev Singh, Julius Roy

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The security of the power distribution grid remains a paramount to the utility professionals while enhancing and making it more efficient. The most serious threat to the system can be maintaining the transformers, as the load is ever increasing with the addition of elements like electric vehicles. In this paper, intelligent transformer monitoring and grid management has been proposed. The engineering is done to use the evolving data from the smart meter for grid analytics and diagnostics for preventive maintenance. The two-tier architecture for hardware and software integration is coupled to form a robust system for the smart grid. The proposal also presents interoperable meter standards for easy integration. Distribution transformer analytics based on real-time data benefits utilities preventing outages, protects the revenue loss, improves the return on asset and reduces overall maintenance cost by predictive monitoring.

Keywords: electric vehicle charging, transformer monitoring, data analytics, intelligent grid

Procedia PDF Downloads 776

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

Abstract:

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

Procedia PDF Downloads 34