Search results for: multiple template matching

Authors: Libor Zachoval, Daire O Broin, Oisin Cawley

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E-learning platforms, such as Blackboard have two major shortcomings: limited data capture as a result of the limitations of SCORM (Shareable Content Object Reference Model), and lack of incorporation of Artificial Intelligence (AI) and machine learning algorithms which could lead to better course adaptations. With the recent development of Experience Application Programming Interface (xAPI), a large amount of additional types of data can be captured and that opens a window of possibilities from which online education can benefit. In a corporate setting, where companies invest billions on the learning and development of their employees, some learner behaviours can be troublesome for they can hinder the knowledge development of a learner. Behaviours that hinder the knowledge development also raise ambiguity about learner’s knowledge mastery, specifically those related to gaming the system. Furthermore, a company receives little benefit from their investment if employees are passing courses without possessing the required knowledge and potential compliance risks may arise. Using xAPI and rules derived from a state-of-the-art review, we identified three learner behaviours, primarily related to guessing, in a corporate compliance course. The identified behaviours are: trying each option for a question, specifically for multiple-choice questions; selecting a single option for all the questions on the test; and continuously repeating tests upon failing as opposed to going over the learning material. These behaviours were detected on learners who repeated the test at least 4 times before passing the course. These findings suggest that gauging the mastery of a learner from multiple-choice questions test scores alone is a naive approach. Thus, next steps will consider the incorporation of additional data points, knowledge estimation models to model knowledge mastery of a learner more accurately, and analysis of the data for correlations between knowledge development and identified learner behaviours. Additional work could explore how learner behaviours could be utilised to make changes to a course. For example, course content may require modifications (certain sections of learning material may be shown to not be helpful to many learners to master the learning outcomes aimed at) or course design (such as the type and duration of feedback).

Keywords: artificial intelligence, corporate e-learning environment, knowledge maintenance, xAPI

Procedia PDF Downloads 108

Authors: Jafar S. Noori, Jan Romano-deGea, Maria Dimaki, John Mortensen, Winnie E. Svendsen

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Pesticides have been intensively used in agriculture to control weeds, insects, fungi, and pest. One of the most commonly used pesticides is glyphosate. Glyphosate has the ability to attach to the soil colloids and degraded by the soil microorganisms. As glyphosate led to the appearance of resistant species, the pesticide was used more intensively. As a consequence of the heavy use of glyphosate, residues of this compound are increasingly observed in food and water. Recent studies reported a direct link between glyphosate and chronic effects such as teratogenic, tumorigenic and hepatorenal effects although the exposure was below the lowest regulatory limit. Today, pesticides are detected in water by complicated and costly manual procedures conducted by highly skilled personnel. It can take up to several days to get an answer regarding the pesticide content in water. An alternative to this demanding procedure is offered by electrochemical measuring techniques. Electrochemistry is an emerging technology that has the potential of identifying and quantifying several compounds in few minutes. It is currently not possible to detect glyphosate directly in water samples, and intensive research is underway to enable direct selective and quantitative detection of glyphosate in water. This study focuses on developing and modifying a sensor chip that has the ability to selectively measure glyphosate and minimize the signal interference from other compounds. The sensor is a silicon-based chip that is fabricated in a cleanroom facility with dimensions of 10×20 mm. The chip is comprised of a three-electrode configuration. The deposited electrodes consist of a 20 nm layer chromium and 200 nm gold. The working electrode is 4 mm in diameter. The working electrodes are modified by creating molecularly imprinted polymers (MIP) using electrodeposition technique that allows the chip to selectively measure glyphosate at low concentrations. The modification included using gold nanoparticles with a diameter of 10 nm functionalized with 4-aminothiophenol. This configuration allows the nanoparticles to bind to the working electrode surface and create the template for the glyphosate. The chip was modified using electrodeposition technique. An initial potential for the identification of glyphosate was estimated to be around -0.2 V. The developed sensor was used on 6 different concentrations and it was able to detect glyphosate down to 0.5 mgL⁻¹. This value is below the accepted pesticide limit of 0.7 mgL⁻¹ set by the US regulation. The current focus is to optimize the functionalizing procedure in order to achieve glyphosate detection at the EU regulatory limit of 0.1 µgL⁻¹. To the best of our knowledge, this is the first attempt to modify miniaturized sensor electrodes with functionalized nanoparticles for glyphosate detection.

Keywords: pesticides, glyphosate, rapid, detection, modified, sensor

Procedia PDF Downloads 164

Authors: Arslan Ellahi, Syed Amjad Hussain, Fawaz Saleem Bokhari

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Optimization of software architecture can be done with respect to a quality attributes (QA). In this paper, there is an analysis of multiple research papers from different dimensions that have been used to classify those attributes. We have proposed a technique of swarm intelligence Meta heuristic ant colony optimization algorithm as a contribution to solve this critical optimization problem of software architecture. We have ranked quality attributes and run our algorithm on every QA, and then we will rank those on the basis of accuracy. At the end, we have selected the most accurate quality attributes. Ant colony algorithm is an effective algorithm and will perform best in optimizing the QA’s and ranking them.

Keywords: complexity, rapid evolution, swarm intelligence, dimensions

Procedia PDF Downloads 238

Authors: F. Fekak, A. Gravouil, M. Brun, B. Depale

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During an earthquake, a bridge crane may be subjected to multiple impacts between crane wheels and rail. In order to model such phenomena, a time-history dynamic analysis with a multi-scale approach is performed. The high frequency aspect of the impacts between wheels and rails is taken into account by a Lagrange explicit event-capturing algorithm based on a velocity-impulse formulation to resolve contacts and impacts. An implicit temporal scheme is used for the rest of the structure. The numerical coupling between the implicit and the explicit schemes is achieved with a heterogeneous asynchronous time-integrator.

Keywords: bridge crane, earthquake, dynamic analysis, explicit, implicit, impact

Procedia PDF Downloads 288

Authors: Hend Alrasheed

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Due to their different properties that have led to avoid several limitations of classic client/server systems, there has been a great interest in the development and the improvement of different peer to peer systems. Understanding the properties of complex peer to peer networks is essential for their future improvements. It was shown that the performances of peer to peer protocols are directly related to their underlying topologies. Therefore, multiple efforts have analyzed the topologies of different peer to peer systems. This study presents an overview of major findings of close experimental analyses to different topologies of three unstructured peer to peer systems: BitTorrent, Gnutella, and FreeNet.

Keywords: peer to peer networks, network topology, graph diameter, clustering coefficient, small-world property, random graph, degree distribution

Procedia PDF Downloads 362

Authors: Ramanish Ravishankar, Azar Hussain, Mahmoud Loubani, Mubarak Chaudhry

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Background and Aims: Currently, there are no strict guidelines in cardiopulmonary bypass temperature management in cardiac surgery not involving the aortic arch. This study aims to compare patient outcomes undergoing mild hypothermia and normothermia. The aim of this study was to compare patient outcomes between mild hypothermia and normothermia undergoing on-pump cardiac surgery not involving the aortic arch. Methods: This was a retrospective cohort study from January 2015 until May 2023. Patients who underwent cardiac surgery with cardiopulmonary bypass temperatures ≥32oC were included and stratified into mild hypothermia (32oC – 35oC) and normothermia (>35oC) cohorts. Propensity matching was applied through the nearest neighbour method (1:1) using the risk factors detailed in the EuroScore using RStudio. The primary outcome was mortality. Secondary outcomes included post-op stay, intensive care unit readmission, re-admission, stroke, and renal complications. Patients who had major aortic surgery and off-pump operations were excluded. Results: Each cohort had 1675 patients. There was a significant increase in overall mortality with the mild hypothermia cohort (3.59% vs. 2.32%; p=0.04912). There was also a greater stroke incidence (2.09% vs. 1.13%; p=0.0396) and transient ischaemic attack (TIA) risk (3.1% vs. 1.49%; p=0.0027). There was no significant difference in renal complications (9.13% vs. 7.88%; p=0.2155). Conclusions: Patient’s who underwent mild hypothermia during cardiopulmonary bypass have a significantly greater mortality, stroke, and transient ischaemic attack incidence. Mild hypothermia does not appear to provide any benefit over normothermia and does not appear to provide any neuroprotective benefits. This shows different results to that of other major studies; further trials and studies need to be conducted to reach a consensus.

Keywords: cardiac surgery, therapeutic hypothermia, neuroprotection, cardiopulmonary bypass

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Authors: Matthew Baucum

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With the increasing popularity of fMRI as an experimental method, psychology and neuroscience can greatly benefit from advanced techniques for summarizing and synthesizing large amounts of data from brain imaging studies. One promising avenue is automated meta-analyses, in which natural language processing methods are used to identify the brain regions consistently associated with certain semantic concepts (e.g. “social”, “reward’) across large corpora of studies. This study builds on this approach by demonstrating how, in fMRI meta-analyses, individual voxels can be treated as vectors in a semantic space and evaluated for their “proximity” to terms of interest. In this technique, a low-dimensional semantic space is built from brain imaging study texts, allowing words in each text to be represented as vectors (where words that frequently appear together are near each other in the semantic space). Consequently, each voxel in a brain mask can be represented as a normalized vector sum of all of the words in the studies that showed activation in that voxel. The entire brain mask can then be visualized in terms of each voxel’s proximity to a given term of interest (e.g., “vision”, “decision making”) or collection of terms (e.g., “theory of mind”, “social”, “agent”), as measured by the cosine similarity between the voxel’s vector and the term vector (or the average of multiple term vectors). Analysis can also proceed in the opposite direction, allowing word cloud visualizations of the nearest semantic neighbors for a given brain region. This approach allows for continuous, fine-grained metrics of voxel-term associations, and relies on state-of-the-art “open vocabulary” methods that go beyond mere word-counts. An analysis of over 11,000 neuroimaging studies from an existing meta-analytic fMRI database demonstrates that this technique can be used to recover known neural bases for multiple psychological functions, suggesting this method’s utility for efficient, high-level meta-analyses of localized brain function. While automated text analytic methods are no replacement for deliberate, manual meta-analyses, they seem to show promise for the efficient aggregation of large bodies of scientific knowledge, at least on a relatively general level.

Keywords: FMRI, machine learning, meta-analysis, text analysis

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Authors: Jenee Gooden, Kevin Vasquez-monterroso, Lady Paula Dejesus, Sandra Wainwright, Daniel Kim, Mackenzie Walker

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Introduction: Pyoderma gangrenosum (PG) is a rare, rapidly progressive, neutrophilic ulcerative colitis condition with an incidence of 3 to 10 cases per year ¹ ². Due to the similar appearance, PG is often misdiagnosed as a diabetic ulcer in diabetic patients. Though they may clinically appear similar in appearance, the treatment protocol and diagnostic criteria differ. Also, end-stage renal disease (ESRD) is often a condition seen in diabetic patients, which can have a significant impact on wound healing due to the wide range of uremic toxins³. This case study demonstrates a multidisciplinary team and multimodal treatment approach by podiatric surgery, general surgery, rheumatology, infectious disease, interventional cardiology, wound care and hyperbaric medicine for an uncontrolled diabetic with pyoderma gangrenosum of a significant circumferential wound, covering almost the entire right lower extremity. Methods:56 y.o male presents with multiple PG ulcerations, including the chest, right posterior lower extremity and sacrum. All ulcerations were previously managed by the same wound care specialist. His chief complaint was worsening PG ulcerations accompanied by a fever of 103 °F . This case study focuses on the wound to his RLE. Past medical history significant for diabetes mellitus type 2 with hemoglobin A1c of 10% and end stage renal disease (ESRD) on hemodialysis. A multidisciplinary team approach by podiatric surgery, general surgery, rheumatology, infectious disease, interventional cardiology, wound care and hyperbaric medicine was successfully used to perform right lower extremity limb salvage. The patient was managed by rheumatology for the continuation of prior medication, as well as the mutual agreement with wound care for the addition of dapsone. A coronary CT angiogram was performed by interventional cardiology, but no significant disease was noted, and no further vascular workup was necessary. Multiple surgical sharp wide excisional debridements with application of allografts and split thickness skin grafts for the circumferential ulceration that encompassed almost the entire right lower extremity were performed by both podiatric surgery and general surgery. Wound cultures and soft tissue biopsies were performed, and infectious disease managed antibiotic therapy. Hyperbaric oxygen therapy and wound vac therapy by wound care were also completed as adjunct management. Results: Prevention of leg amputation by limb salvage of the RLE was accomplished by a multidisciplinary team approach, with the wound size decreasing over a total of 29 weeks from 600 cm² to 12.0 x 3.5 x 0.2 cm. Our multidisciplinary team included podiatric surgery, general surgery, rheumatology, infectious disease, interventional cardiology, wound care and hyperbaric medicine. Discussion: Wound healing, in general, can have its challenges, and those challenges are only magnified when accompanied by multiple systemic illnesses. Though the negative impact of diabetes on wound healing is well known, the compound impact of being a diabetic with ESRD and having pyoderma gangrenosum is not. This case demonstrates the necessity for a multidisciplinary team approach with a wide array of treatment modalities to optimize wound healing and perform limb salvage with prevention of lower extremity amputation.

Keywords: diabetes, podiatry, pyoderma gangrenosum, end stage renal disease

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Authors: Ryan Adams

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Introduction: Major trauma is the leading cause of death in 15-45 year olds and a significant human, social and economic costs. Resuscitation is a stalwart of trauma management, especially in the pre-hospital environment and packed red blood cells (pRBC) are being increasingly used with the advent of permissive hypotension. The evidence in this area is lacking and further research is required to determine its efficacy. Aim: The aim of this retrospective, matched cohort study was to determine if major trauma patients, who received pre-hospital pRBC, have a difference in their initial emergency department cardiovascular status; when compared with injury-profile matched controls. Methods: The trauma databases of the Royal Brisbane and Women's Hospital, Royal Children's Hospital (Herston) and Queensland Ambulance Service were accessed and major trauma patient (ISS>12) data, who received pre-hospital pRBC, from January 2011 to August 2014 was collected. Patients were then matched against control patients that had not received pRBC, by their injury profile. The primary outcomes was cardiovascular status; defined as shock index and Revised Trauma Score. Results: Data for 25 patients who received pre-hospital pRBC was accessed and the injury profiles matched against suitable controls. On admittance to the emergency department, a statistically significant difference was seen in the blood group (Blood = 1.42 and Control = 0.97, p-value = 0.0449). However, the same was not seen with the RTS (Blood = 4.15 and Control 5.56, p-value = 0.291). Discussion: A worsening shock index and revised trauma score was associated with pre-hospital administration of pRBC. However, due to the small sample size, limited matching protocol and associated confounding factors it is difficult to draw any solid conclusions. Further studies, with larger patient numbers, are required to enable adequate conclusions to be drawn on the efficacy of pre-hospital packed red blood cell transfusion.

Keywords: pre-hospital, packed red blood cells, severe trauma, emergency medicine

Procedia PDF Downloads 378

Authors: Magri Hicham, Noreddine Abghour, Mohamed Ouzzif

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The 5th generation of mobile networks is term used in various research papers and projects to identify the next major phase of mobile telecommunications standards. 5G wireless networks will support higher peak data rate, lower latency and provide best connections with QoS guarenty. In this article, we discuss various promising technologies for 5G wireless communication systems, such as IPv6 support, World Wide Wireless Web (WWWW), Dynamic Adhoc Wireless Networks (DAWN), BEAM DIVISION MULTIPLE ACCESS (BDMA), Cloud Computing and cognitive radio technology.

Keywords: WWWW, BDMA, DAWN, 5G, 4G, IPv6, Cloud Computing

Procedia PDF Downloads 496

Authors: Jamel Slaughter

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Father involvement across the development of a child has been linked to children’s psychological adjustment, fewer behavioral problems, and higher educational attainment. Conversely, there is much less research that highlights father involvement in relation to childhood development during early childhood period prior to preschool age (ages 1-3 years). Most research on fathers and child outcomes have been limited by its focus on the stages of adolescence, middle childhood, and infancy. This study examined the influence of father involvement, during the toddler stage, on 5th grade cognitive development, rule-breaking, and behavior outcomes measured by Child Behavior Checklist (CBCL) scores. Using data from the Early Head Start Research and Evaluation (EHSRE) Study, 1996-2010: United States, a total of 3,001 children and families were identified in 17 sites (cities), representing a diverse demographic sample. An independent samples t-test was run to compare cognitive development, aggressive, and rule-breaking behavior mean scores among children who had early continuous father involvement for the first 14 – 36 months to children who did not have early continuous father involvement for the first 14 – 36 months. Multiple linear regression was conducted to determine if continuous, or non-continuous father involvement (14 month-36 months), can be used to predict outcome scores on the Child Behavior Checklist in aggressive behavior, rule-breaking behavior, and cognitive development, at 5th grade. A statistically significant mean difference in cognitive development scores were found for children who had continuous father involvement (M=1.92, SD=2.41, t (1009) =2.81, p =.005, 95% CI=.146 to .828) compared to those who did not (M=2.60, SD=3.06, t (1009) =-2.38, p=.017, 95% CI= -1.08 to -.105). There was also a statistically significant mean difference in rule-breaking behavior scores between children who had early continuous father involvement (M=1.95, SD=2.33, t (1009) = 3.69, p <.001, 95% CI= .287 to .940), compared to those that did not (M=2.87, SD=2.93, t (1009) = -3.49, p =.001, 95% CI= -1.30 to -.364). No statistically significant difference was found in aggressive behavior scores. Multiple linear regression was performed using continuous father involvement to determine which has the largest relationship to rule-breaking behavior and cognitive development based on CBCL scores. Rule-breaking behavior was found to be significant (F (2, 1008) = 8.353, p<.001), with an R2 of .016. Cognitive development was also significant (F (2, 1008) = 4.44, p=.012), with an R2 of .009. Early continuous father involvement was a significant predictor of rule-breaking behavior and cognitive development at middle childhood. Findings suggest early continuous father involvement during the first 14 – 36 months of their children’s life, may lead to lower levels of rule-breaking behaviors and thought problems at 5th grade.

Keywords: cognitive development, early continuous father involvement, middle childhood, rule-breaking behavior

Procedia PDF Downloads 279

Authors: Khalid Alshallash

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Small fragments of the roots of some Rumex species including R. obtusifolius and R. crispus have been found to regenerate readily, contributing to the severity of infestations by these very common, widespread and difficult to control perennial weeds of agricultural crops and grasslands. Their root fragments are usually created during routine agricultural practices. We found that fresh root fragments of both species containing 65-70 % of moisture, progressively lose their moisture content when desiccated under controlled growth room conditions matching summer weather of southeast England, with the greatest reduction occurring in the first 48 hours. Probability of shoot emergence and the time taken for emergence in glasshouse conditions were also reduced significantly by desiccation, with R. obtusifolius least affected up to 48-hour. However, the effects converged after 120 hours. In contrast, R. obtusifolius was significantly slower to emerge after up to 48 hours desiccation, again effects converging after longer periods, R. crispus entirely failed to emerge at 120 hours. The dry weight of emerged shoots was not significantly different between the species, until desiccated for 96 hours when R. obtusifolius was significantly reduced. At 120 hours, R. obtusifolius did not emerge. In outdoor trials, desiccation for 24 or 48 hours had less effect on emergence when planted at the soil surface or up to 10 cm of depth, compared to deeper plantings. In both species, emergence was significantly lower when desiccated fragments were planted at 15 or 20 cm. Time taken for emergence was not significantly different between the species until planted at 15 or 20 cm when R. obtusifolius was slower than R. crispus and reduced further by increasing desiccation. Similar variation in effects of increasing soil depth interacting with increasing desiccation was found in reductions in dry weight, the number of tillers and leaf area, with R obtusifolius generally but not exclusively better able to withstand more extreme trial conditions. Our findings suggest that infestations of these highly troublesome weeds may be partly controlled by appropriate agricultural practices, notably exposing cut fragments to drying environmental conditions followed by deep burial.

Keywords: regeneration, root fragment, rumex crispus, rumex obtusifolius

Procedia PDF Downloads 80

Authors: Ibrahim Turfanda, Soniya Rai, Karan Vadher

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Objectives— The study aims to compare real-world adherence to and persistence with dulaglutide and liraglutide in patients with type 2 diabetes (T2D) initiating treatment in UAE. Methods— This was a retrospective, non-interventional study (observation period: 01 March 2017–31 August 2019) using the UAE Dubai e-Claims database. Included: adult patients initiating dulaglutide/liraglutide 01 September 2017–31 August 2018 (index period) with: ≥1 claim for T2D in the 6 months before index date (ID); ≥1 claim for dulaglutide/liraglutide during index period; and continuous medical enrolment for ≥6 months before and ≥12 months after ID. Key endpoints, assessed 3/6/12 months after ID: adherence to treatment (proportion of days covered [PDC; PDC ≥80% considered ‘adherent’], per-group mean±standard deviation [SD] PDC); and persistence (number of continuous therapy days from ID until discontinuation [i.e., >45 days gap] or end of observation period). Patients initiating dulaglutide/liraglutide were propensity score matched (1:1) based on baseline characteristics. Between-group comparison of adherence was analysed using the McNemar test (α=0.025). Persistence was analysed using Kaplan–Meier estimates with log-rank tests (α=0.025) for between-group comparisons. This study presents 12-month outcomes. Results— Following propensity score matching, 263 patients were included in each group. Mean±SD PDC for all patients at 12 months was significantly higher in the dulaglutide versus the liraglutide group (dulaglutide=0.48±0.30, liraglutide=0.39±0.28, p=0.0002). The proportion of adherent patients favored dulaglutide (dulaglutide=20.2%, liraglutide=12.9%, p=0.0302), as did the probability of being adherent to treatment (odds ratio [97.5% CI]: 1.70 [0.99, 2.91]; p=0.03). Proportion of persistent patients also favoured dulaglutide (dulaglutide=15.2%, liraglutide=9.1%, p=0.0528), as did the probability of discontinuing treatment 12 months after ID (p=0.027). Conclusions— Based on the UAE Dubai e-Claims database data, dulaglutide initiators exhibited significantly greater adherence in terms of mean PDC versus liraglutide initiators. The proportion of adherent patients and the probability of being adherent favored the dulaglutide group, as did treatment persistence.

Keywords: adherence, dulaglutide, effectiveness, liraglutide, persistence

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Authors: Jung-In Baik, Seung-Jun Yu, Young-Min Ko, Hyoung-Kyu Song

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This paper proposes an efficient relaying algorithm to obtain diversity for improving the reliability of a signal. The algorithm achieves time or space diversity gain by multiple versions of the same signal through two routes. Relays are separated between a source and destination. The routes between the source and destination are set adaptive in order to deal with different channels and noises. The routes consist of one or more relays and the source transmits its signal to the destination through the routes. The signals from the relays are combined and detected at the destination. The proposed algorithm provides a better performance than the conventional algorithms in bit error rate (BER).

Keywords: multi-hop, OFDM, relay, relaying selection

Procedia PDF Downloads 432

Authors: Mohammed R. Elkobaisi, Abdelsalam Maatuk

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The search engines are the large scale information retrieval tools from the Web that are currently freely available to all. This paper explains how to convert the raw resulted number of search engines into useful information. This represents a new method for data gathering comparing with traditional methods. When a query is submitted for a multiple numbers of keywords, this take a long time and effort, hence we develop a user interface program to automatic search by taking multi-keywords at the same time and leave this program to collect wanted data automatically. The collected raw data is processed using mathematical and statistical theories to eliminate unwanted data and converting it to usable data.

Keywords: search engines, information extraction, agent system

Procedia PDF Downloads 412

Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

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The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

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Authors: Loke Mun Sei

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Software testing has become a mandatory process in assuring the software product quality. Hence, test management is needed in order to manage the test activities conducted in the software test life cycle. This paper discusses on the challenges faced in the software test life cycle, and how the test processes and test activities, mainly on test cases creation, test execution, and test reporting is being managed and automated using several test automation tools, i.e. Jira, Robot Framework, and Jenkins.

Keywords: test automation tools, test case, test execution, test reporting

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Authors: Pedro Acevedo, Carlos Díaz, Mónica Vázquez, Joel Durán

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This paper describes the design of a pulse generator based on the Programmable System-on-Chip (PSoC) module. In this module, using programmable logic is possible to implement different pulses which are required for ultrasonic applications, either in a single channel or multiple channels. This module can operate with programmable frequencies from 3-74 MHz; its programming may be versatile covering a wide range of ultrasonic applications. It is ideal for low-power ultrasonic applications where PZT or PVDF transducers are used.

Keywords: PSoC, pulse generator, PVDF, ultrasonic transducer

Procedia PDF Downloads 271

Authors: Ahmet Ozcan, Onder Alparslan, Anil Sezgin, Omer Cetin

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The use of unmanned systems for different purposes has become very popular over the past decade. Expectations from these systems have also shown an incredible increase in this parallel. But meeting the demands of the tasks are often not possible with the usage of a single unmanned vehicle in a mission, so it is necessary to use multiple autonomous vehicles with different abilities together in coordination. Therefore the usage of the same type of vehicles together as a swarm is helped especially to satisfy the time constraints of the missions effectively. In other words, it allows sharing the workload by the various numbers of homogenous platforms together. Besides, it is possible to say there are many kinds of problems that require the usage of the different capabilities of the heterogeneous platforms together cooperatively to achieve successful results. In this case, cooperative working brings additional problems beyond the homogeneous clusters. In the scenario presented as an example problem, it is expected that an autonomous ground vehicle, which is lack of its position information, manage to perform point-to-point navigation without losing its way in a previously unknown labyrinth. Furthermore, the ground vehicle is equipped with very limited sensors such as ultrasonic sensors that can detect obstacles. It is very hard to plan or complete the mission for the ground vehicle by self without lost its way in the unknown labyrinth. Thus, in order to assist the ground vehicle, the autonomous air drone is also used to solve the problem cooperatively. The autonomous drone also has limited sensors like downward looking camera and IMU, and it also lacks computing its global position. In this context, it is aimed to solve the problem effectively without taking additional support or input from the outside, just benefiting capabilities of two autonomous vehicles. To manage the point-to-point navigation in a previously unknown labyrinth, the platforms have to work together coordinated. In this paper, cooperative work of heterogeneous unmanned systems is handled in an applied sample scenario, and it is mentioned that how to work together with an autonomous ground vehicle and the autonomous flying platform together in a harmony to take advantage of different platform-specific capabilities. The difficulties of using heterogeneous multiple autonomous platforms in a mission are put forward, and the successful solutions are defined and implemented against the problems like spatially distributed tasks planning, simultaneous coordinated motion, effective communication, and sensor fusion.

Keywords: unmanned systems, heterogeneous autonomous vehicles, coordination, task planning

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Authors: Kyriacos Clerides, Panagiotis Herodotou, Constantina Polycarpou, Evagoras Xydas

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Background: Musculoskeletal disorders (MSDs) in the workplace is a very common problem in Europe which are caused by multiple risk factors. In recent years, wearable devices and exoskeletons for the workplace have been trying to address the various risk factors that are associated with strenuous tasks in the workplace. The logistics sector is a huge sector that includes warehousing, storage, and transportation. However, the task associated with logistics is not well-studied in terms of MSDs risk. This study was aimed at looking into the MSDs affecting workers of logistics companies. It compares the prevalence of MSDs among workers and evaluates multiple risk factors that contribute to the development of MSDs. Moreover, this study seeks to obtain user feedback on the adoption of exoskeletons in such a work environment. Materials and Methods: The study was conducted among workers in logistics companies in Nicosia, Cyprus, from July to September 2022. A set of standardized questionnaires was used for collecting different types of data. Results: A high proportion of logistics professionals reported MSDs in one or more other body regions, the lower back being the most commonly affected area. Working in the same position for long periods, working in awkward postures, and handling an excessive load, were found to be the most commonly reported job risk factor that contributed to the development of MSDs, in this study. A significant number of participants consider the back region as the most to be benefited from a wearable exoskeleton device. Half of the participants would like to have at least a 50% reduction in their daily effort. The most important characteristics for the adoption of exoskeleton devices were found to be how comfortable the device is and its weight. Conclusion: Lower back and posture were the highest risk factors among all logistics professionals assessed in this study. A larger scale study using quantitative analytical tools may give a more accurate estimate of MSDs, which would pave the way for making more precise recommendations to eliminate the risk factors and thereby prevent MSDs. A follow-up study using exoskeletons in the workplace should be done to assess whether they assist in MSD prevention.

Keywords: musculoskeletal disorders, occupational health, safety, occupational risk, logistic companies, workers, Cyprus, industrial exoskeletons, wearable devices

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Authors: Kaddi Mohammed, Benahmed Khelifa D. Benatiallah

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A sensor network consists of a large number of sensors deployed in areas to monitor and communicate with each other through a wireless medium. The collected routing data in the network consumes most of the energy of the sensor nodes. For this purpose, multiple routing approaches have been proposed to conserve energy resource at the sensors and to overcome the challenges of its limitation. In this work, we propose a new low energy consumption routing protocol for wireless sensor networks based on a meta-heuristic methods. Our protocol is to operate more fairly energy when routing captured data to the base station.

Keywords: WSN, routing, energy, heuristic

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Authors: Zeina Bou Diab, Arij Daou

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The neural mechanisms of sequential behaviors are intensively studied, with songbirds a focus for learned vocal production. We are studying the premotor nucleus HVC at a nexus of multiple pathways contributing to song learning and production. The HVC consists of multiple classes of neuronal populations, each has its own cellular, electrophysiological and functional properties. During singing, a large subset of motor cortex analog-projecting HVCRA neurons emit a single 6-10 ms burst of spikes at the same time during each rendition of song, a large subset of basal ganglia-projecting HVCX neurons fire 1 to 4 bursts that are similarly time locked to vocalizations, while HVCINT neurons fire tonically at average high frequency throughout song with prominent modulations whose timing in relation to song remains unresolved. This opens the opportunity to define models relating explicit HVC circuitry to how these neurons work cooperatively to control learning and singing. We developed conductance-based Hodgkin-Huxley models for the three classes of HVC neurons (based on the ion channels previously identified from in vitro recordings) and connected them in several physiologically realistic networks (based on the known synaptic connectivity and specific glutaminergic and gabaergic pharmacology) via different architecture patterning scenarios with the aim to replicate the in vivo firing patterning behaviors. We are able, through these networks, to reproduce the in vivo behavior of each class of HVC neurons, as shown by the experimental recordings. The different network architectures developed highlight different mechanisms that might be contributing to the propagation of sequential neural activity (continuous or punctate) in the HVC and to the distinctive firing patterns that each class exhibits during singing. Examples of such possible mechanisms include: 1) post-inhibitory rebound in HVCX and their population patterns during singing, 2) different subclasses of HVCINT interacting via inhibitory-inhibitory loops, 3) mono-synaptic HVCX to HVCRA excitatory connectivity, and 4) structured many-to-one inhibitory synapses from interneurons to projection neurons, and others. Replication is only a preliminary step that must be followed by model prediction and testing.

Keywords: computational modeling, neural networks, temporal neural sequences, ionic currents, songbird

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Authors: Dávid Csercsik, Péter Kádár

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In the case of the proposed method, the problem is parallelized by considering multiple possible mode of operation profiles, which determine the range in which the generators operate in each period. For each of these profiles, the optimization is carried out independently, and the best resulting dispatch is chosen. For each such profile, the resulting problem is a quadratic programming (QP) problem with a potentially negative definite Q quadratic term, and constraints depending on the actual operation profile. In this paper we analyze the performance of available MATLAB optimization methods and solvers for the corresponding QP.

Keywords: optimization, MATLAB, quadratic programming, economic dispatch

Procedia PDF Downloads 531

Authors: Genevieve Lim, Arthur Shelley, Dongcheol Heo

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Abstract—Mobile technologies can enhance the learning process as it enables social engagement around concepts beyond the classroom and the curriculum. Early results in this ongoing research is showing that when learning interventions are designed specifically to generate new insights, mobile devices support regulated learning and encourage learners to collaborate, socialize and co-create new knowledge. As students navigate across the space and time boundaries, the fundamental social nature of learning transforms into mobile computer supported collaborative learning (mCSCL). The metacognitive interaction in mCSCL via mobile applications reflects the regulation of learning among the students. These metacognitive experiences whether self-, co- or shared-regulated are significant to the learning outcomes. Despite some insightful empirical studies, there has not yet been significant research that investigates the actual practice and processes of the new knowledge co-creation. This leads to question as to whether mobile learning provides a new channel to leverage learning? Alternatively, does mobile interaction create new types of learning experiences and how do these experiences co-create new knowledge. The purpose of this research is to explore these questions and seek evidence to support one or the other. This paper addresses these questions from the students’ perspective to understand how students interact when constructing knowledge in mCSCL and how students’ self-regulated learning (SRL) strategies support the co-creation of new knowledge in mCSCL. A pilot study has been conducted among international undergraduates to understand students’ perspective of mobile learning and concurrently develops a definition in an appropriate context. Using classroom action research (CAR) with multiple case studies, this study is being carried out in a private university in Thailand to narrow the research gaps in mCSCL and SRL. The findings will allow teachers to see the importance of social interaction for meaningful student engagement and envisage learning outcomes from a knowledge management perspective and what role mobile devices can play in these. The findings will signify important indicators for academics to rethink what is to be learned and how it should be learned. Ultimately, the study will bring new light into the co-creation of new knowledge in a social interactive learning environment and challenges teachers to embrace the 21st century of learning with mobile technologies to deepen and extend learning opportunities.

Keywords: mobile computer supported collaborative learning, mobile instant messaging, mobile learning, new knowledge co-creation, self-regulated learning

Procedia PDF Downloads 216

Authors: S. A. N. Danushka, T. A. Weerasinghe

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The diverse necessities of instruction could be addressed effectively with the support of new dimensions of ICT integrated learning such as blended learning –which is a combination of face-to-face and online instruction which ensures greater flexibility in student learning and congruity of course delivery. As blended learning has been the ‘new normality' in education, many experimental and quasi-experimental research studies provide ample of evidence on its successful implementation in many fields of studies, but it is hard to justify whether blended learning could work similarly in the delivery of technology-teacher development programmes (TTDPs). The present study is bound with the particular research uncertainty, and having considered existing research approaches, the study methodology was set to decide the efficient instructional strategies for flipped classroom learning in TTDPs. In a quasi-experimental pre-test and post-test design with a mix-method research approach, the major study objective was tested with two heterogeneous samples (N=135) identified in a virtual learning environment in a Sri Lankan university. Non-randomized informal ‘before-and-after without control group’ design was employed, and two data collection methods, identical pre-test and post-test and Likert-scale questionnaires were used in the study. Selected two instructional strategies, self-directed learning (SDL) and self-regulated learning (SRL), were tested in an appropriate instructional framework with two heterogeneous samples (pre-service and in-service teachers). Data were statistically analyzed, and an efficient instructional strategy was decided via t-test, ANOVA, ANCOVA. The effectiveness of the two instructional strategy implementation models was decided via multiple linear regression analysis. ANOVA (p < 0.05) shows that age, prior-educational qualifications, gender, and work-experiences do not impact on learning achievements of the two diverse groups of learners through the instructional strategy is changed. ANCOVA (p < 0.05) analysis shows that SDL is efficient for two diverse groups of technology-teachers than SRL. Multiple linear regression (p < 0.05) analysis shows that the staged self-directed learning (SSDL) model and four-phased model of motivated self-regulated learning (COPES Model) are efficient in the delivery of course content in flipped classroom learning.

Keywords: COPES model, flipped classroom learning, self-directed learning, self-regulated learning, SSDL model

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Authors: Salisu ibrahim, Abdalla Rababah

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In this research, we use Weighted G2-Multi-degree reduction of Bezier curve of degree n to a Bezier curve of degree m, m < n. The degree reduction of Bezier curves is used to represent a given Bezier curve of n by a Bezier curve of degree m, m < n. Exact degree reduction is not possible, and degree reduction is approximate process in nature. We derive a weighted degree reducing method that is geometrically continuous at the end points. Different norms will be considered, several error minimizations will be given. The proposed methods produce error function that are less than the errors of existing methods.

Keywords: Bezier curves, multiple degree reduction, geometric continuity, error function

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Authors: Marco Sewald

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Comparing present to past the numbers of Americans expatriating U. S. citizenship have risen. Even though these numbers are small compared to the immigrants, U. S. citizens expatriations have historically been much lower, making the uptick worrisome. In addition, the published lists and numbers from the U.S. government seems incomplete, with many not counted. Different branches of the U. S. government report different numbers and no one seems to know exactly how big the real number is, even though the IRS and the FBI both track and/or publish numbers of Americans who renounce. Since there is no single explanation, anecdotal evidence suggests this uptick is caused by global tax law and increased compliance burdens imposed by the U.S. lawmakers on U.S. citizens abroad. Within a research project the question arose about the reasons why a constant growing number of U.S. citizens are expatriating – the answers are believed helping to explain the underlying governmental policy rational, leading to such activities. While it is impossible to locate former U.S. citizens to conduct a survey on the reasons and the U.S. government is not commenting on the reasons given within the process of expatriation, the chosen methodology is Structural Equation Modeling (SEM), in the first step by re-using current surveys conducted by different researchers within the population of U. S. citizens residing abroad during the last years. Surveys questioning the personal situation in the context of tax, compliance, citizenship and likelihood to repatriate to the U. S. In general SEM allows: (1) Representing, estimating and validating a theoretical model with linear (unidirectional or not) relationships. (2) Modeling causal relationships between multiple predictors (exogenous) and multiple dependent variables (endogenous). (3) Including unobservable latent variables. (4) Modeling measurement error: the degree to which observable variables describe latent variables. Moreover SEM seems very appealing since the results can be represented either by matrix equations or graphically. Results: the observed variables (items) of the construct are caused by various latent variables. The given surveys delivered a high correlation and it is therefore impossible to identify the distinct effect of each indicator on the latent variable – which was one desired result. Since every SEM comprises two parts: (1) measurement model (outer model) and (2) structural model (inner model), it seems necessary to extend the given data by conducting additional research and surveys to validate the outer model to gain the desired results.

Keywords: expatriation of U. S. citizens, SEM, structural equation modeling, validating

Procedia PDF Downloads 196

Authors: Mai Ashraf Talaat

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Bullying is an aggressive behavior and a serious issue that should be addressed by everyone and should be avoided at all costs. It is very common among adolescents and schoolchildren and the effects can be devastating and long-lasting. Students are most commonly bullied about physical appearance, race, gender, disability, ethnicity, religion, and sexual orientation. Appearance-targeted bullying is a form of bullying that targets an aspect of a person's appearance, which includes facial and dental features. Deviation from accepted dentofacial aesthetics leads to elevated incidences of bullying in schoolchildren. The aim of this review article is to assess the prevalence of bullying due to dentofacial characteristics and evaluate the importance of dentofacial appearance on perceived social attractiveness based on multiple studies.

Keywords: dentofacial features, orthodontics, malocclusion, adolescents, bullying

Procedia PDF Downloads 60

Authors: Jennifer Gallup, Joel Bocanegra, Greg Callan, Abigail Vaughn

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Students with disabilities (SWD) engaged in a distance summer program delivered over multiple virtual mediums that used gaming principles to teach and practice self-regulated learning (SRL) through the process of exploring possible jobs. Gaming quests were developed to explore jobs and teach transition skills. Students completed specially designed quests that taught and reinforced SRL and problem-solving through individual, group, and teacher-led experiences. SRL skills learned were reinforced through guided job explorations over the context of MinecraftEDU, zoom with experts in the career, collaborations with a team over Marco Polo, and Zoom. The quests were developed and laid out on an accessible web page, with active learning opportunities and feedback conducted within multiple virtual mediums including MinecraftEDU. Gaming mediums actively engage players in role-playing, problem-solving, critical thinking, and collaboration. Gaming has been used as a medium for education since the inception of formal education. Games, and specifically board games, are pre-historic, meaning we had board games before we had written language. Today, games are widely used in education, often as a reinforcer for behavior or for rewards for work completion. Games are not often used as a direct method of instruction and assessment; however, the inclusion of games as an assessment tool and as a form of instruction increases student engagement and participation. Games naturally include collaboration, problem-solving, and communication. Therefore, our summer program was developed using gaming principles and MinecraftEDU. This manuscript describes a virtual learning summer program called Virtual Academy New and Exciting Transitions (VAN) that was redesigned from a face-to-face setting to a completely online setting with a focus on SWD aged 14-21. The focus of VAN was to address transition planning needs such as problem-solving skills, self-regulation, interviewing, job exploration, and communication for transition-aged youth diagnosed with various disabilities (e.g., learning disabilities, attention-deficit hyperactivity disorder, intellectual disability, down syndrome, autism spectrum disorder).

Keywords: autism, disabilities, transition, summer program, gaming, simulations

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Authors: Andrey V. Timofeev

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The paper presents new results concerning selection of optimal information fusion formula for ensembles of C-OTDR channels. The goal of information fusion is to create an integral classificator designed for effective classification of seismoacoustic target events. The LPBoost (LP-β and LP-B variants), the Multiple Kernel Learning, and Weighing of Inversely as Lipschitz Constants (WILC) approaches were compared. The WILC is a brand new approach to optimal fusion of Lipschitz Classifiers Ensembles. Results of practical usage are presented.

Keywords: Lipschitz Classifier, classifiers ensembles, LPBoost, C-OTDR systems

Procedia PDF Downloads 441