Search results for: learning methods

Authors: Drew Commandeur, Ryan Brodie, Sandra Hundza, Marc Klimstra

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The amplitude of raw electromyography (EMG) is affected by recording conditions and often requires normalization to make meaningful comparisons. Bioelectric methods normalize with an EMG signal recorded during a standardized task or from the experimental protocol itself, while biomechanical methods often involve measurements with an additional sensor such as a force transducer. Common bioelectric normalization techniques for treadmill walking include maximum voluntary isometric contraction (MVIC), dynamic EMG peak (EMGPeak) or dynamic EMG mean (EMGMean). There are several concerns with using MVICs to normalize EMG, including poor reliability and potential discomfort. A limitation of bioelectric normalization techniques is that they could result in a misrepresentation of the absolute magnitude of force generated by the muscle and impact the interpretation of EMG between functionally disparate groups. Additionally, methods that normalize to EMG recorded during the task may eliminate some real inter-individual variability due to biological variation. This study compared biomechanical and bioelectric EMG normalization techniques during treadmill walking to assess the impact of the normalization method on the functional interpretation of EMG data. For the biomechanical method, we normalized EMG to a target torque (EMGTS) and the bioelectric methods used were normalization to the mean and peak of the signal during the walking task (EMGMean and EMGPeak). The effect of normalization on muscle activation pattern, EMG amplitude, and inter-individual variability were compared between disparate cohorts of OLD (76.6 yrs N=11) and YOUNG (26.6 yrs N=11) adults. Participants walked on a treadmill at a self-selected pace while EMG was recorded from the right lower limb. EMG data from the soleus (SOL), medial gastrocnemius (MG), tibialis anterior (TA), vastus lateralis (VL), and biceps femoris (BF) were phase averaged into 16 bins (phases) representing the gait cycle with bins 1-10 associated with right stance and bins 11-16 with right swing. Pearson’s correlations showed that activation patterns across the gait cycle were similar between all methods, ranging from r =0.86 to r=1.00 with p<0.05. This indicates that each method can characterize the muscle activation pattern during walking. Repeated measures ANOVA showed a main effect for age in MG for EMGPeak but no other main effects were observed. Interactions between age*phase of EMG amplitude between YOUNG and OLD with each method resulted in different statistical interpretation between methods. EMGTS normalization characterized the fewest differences (four phases across all 5 muscles) while EMGMean (11 phases) and EMGPeak (19 phases) showed considerably more differences between cohorts. The second notable finding was that coefficient of variation, the representation of inter-individual variability, was greatest for EMGTS and lowest for EMGMean while EMGPeak was slightly higher than EMGMean for all muscles. This finding supports our expectation that EMGTS normalization would retain inter-individual variability which may be desirable, however, it also suggests that even when large differences are expected, a larger sample size may be required to observe the differences. Our findings clearly indicate that interpretation of EMG is highly dependent on the normalization method used, and it is essential to consider the strengths and limitations of each method when drawing conclusions.

Keywords: electromyography, EMG normalization, functional EMG, older adults

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Authors: Napat Hataivichian

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The effect of transition metal doping on Pt/Al2O3 catalyst used in propane dehydrogenation reaction at 500˚C was studied. The preparation methods investigated were sequential impregnation (Pt followed by the 2nd metal or the 2nd metal followed by Pt) and co-impregnation. The metal contents of these catalysts were fixed as the weight ratio of Pt per the 2nd metal of around 0.075. These catalysts were characterized by N2-physisorption, TPR, CO-chemisorption and NH3-TPD. It was found that the impregnated 2nd metal had an effect upon reducibility of Pt due to its interaction with transition metal-containing structure. This was in agreement with the CO-chemisorption result that the presence of Pt metal, which is a result from Pt species reduction, was decreased. The total acidity of bimetallic catalysts is decreased but the strong acidity is slightly increased. It was found that the stability of bimetallic catalysts prepared by co-impregnation and sequential impregnation where the 2nd metal was impregnated before Pt were better than that of monometallic catalyst (undoped Pt one) due to the forming of Pt sites located on the transition metal-oxide modified surface. Among all preparation methods, the sequential impregnation method- having Pt impregnated before the 2nd metal gave the worst stability because this catalyst lacked the modified Pt sites and some fraction of Pt sites was covered by the 2nd metal.

Keywords: alumina, dehydrogenation, platinum, transition metal

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Authors: Folasade R. Sulaiman

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Pupils’ health and nutrition are basically important to their schooling. The preponderance of avoidable deaths among children in Africa (WHO, 2000) may not be unconnected with the nutritional and health education status of families and communities that have their children as school clients. This study adopted a descriptive survey design focusing on the assessment of the level of nutritional and health education of families and community members in the rural riverine areas of Ogun State. Two research questions were raised. The Nutritional and Health Education of Families and Communities Inventory (NHEFCI) was used to collect data from 250 rural child-bearing aged women, and 0.73 test-retest reliability coefficient was established to determine the strength of the instrument. Data collected were analysed using descriptive statistics of frequency counts, percentages and mean in accordance with research questions raised in the study. The findings revealed amongst others: that 65% of the respondents had low level of nutritional and health education among the families and community members; while 72% had low level of awareness of the possible influence of nutritional and health education on the learning outcomes of the children. Based on the findings, it was recommended among others that government should intensify efforts on sensitization, mass literacy campaign etc.; also improve upon the already existing School Feeding Programme in Nigerian primary schools to provide at least one balanced diet for children while in school; community health workers, social workers, Non-Governmental Organizations (NGO) should collaborate with international Organizations like UNICEF, UNESCO, WHO etc. to organize sensitization programmes for members of the rural riverine communities on the importance of meeting the health and nutritional needs of their children in order to attain their educational potentials.

Keywords: nutritional and health education, learning capacities, school-age children, universal basic education, rural riverine areas

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Authors: Timothy Whitehill

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This paper presents an innovative chat-based focus group method for collecting qualitative data to construct a cause-and-effect analysis in business research. This method was developed in response to the research and data collection challenges faced by the Covid-19 outbreak in the United Kingdom during 2020-21. This paper discusses the methodological approaches and builds a contemporary argument for its effectiveness in exploring cause-and-effect relationships in the context of focus group research, systems thinking and problem structuring methods. The pilot for this method was conducted between October 2020 and March 2021 and collected more than 7,000 words of chat-based data which was used to construct a consensus drawn cause-and-effect analysis. This method was developed in support of an ongoing Doctorate in Business Administration (DBA) thesis, which is using Design Science Research methodology to operationalize organisational resilience in UK construction sector firms.

Keywords: cause-and-effect analysis, focus group research, problem structuring methods, qualitative research, systems thinking

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Authors: Shatirah Akib, Hossein Basser, Hojat Karami, Afshin Jahangirzadeh

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Bridge piers which are constructed in the track of high water rivers cause some variations in the flow patterns. This variation mostly is a result of the changes in river sections. Decreasing the river section, bridge piers significantly impress the flow patterns. Once the flow approaches the piers, the stream lines change their order, causing the appearance of different flow patterns around the bridge piers. New flow patterns are created following the geometry and the other technical characteristics of the piers. One of the most significant consequences of this event is the scour generated around the bridge piers which threatens the safety of the structure. In order to determine the properties of scour holes, to find maximum depth of the scour is an important factor. In this manuscript a numerical simulation of the scour around Marand-Soofian route bridge piers has been carried out via SSIIM 2.0 Software and the amount of maximum scour has been achieved subsequently. Eventually the methods for retrofitting of bridge piers against scours and also the methods for decreasing the amount of scour have been offered.

Keywords: scour, bridge pier, numerical simulation, SSIIM 2.0

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Authors: Denis Jordan, Daniel Golkowski, Mathias Lukas, Katharina Merz, Caroline Mlynarcik, Max Maurer, Valentin Riedl, Stefan Foerster, Eberhard F. Kochs, Andreas Bender, Ruediger Ilg

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Introduction: So far, clinical assessments that rely on behavioral responses to differentiate coma states or even predict outcome in coma patients are unreliable, e.g. because of some patients’ motor disabilities. The present study was aimed to provide prognosis in coma patients using markers from electroencephalogram (EEG), blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) and [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET). Unsuperwised principal component analysis (PCA) was used for multimodal integration of markers. Methods: Approved by the local ethics committee of the Technical University of Munich (Germany) 20 patients (aged 18-89) with severe brain damage were acquired through intensive care units at the Klinikum rechts der Isar in Munich and at the Therapiezentrum Burgau (Germany). At the day of EEG/fMRI/PET measurement (date I) patients (<3.5 month in coma) were grouped in the minimal conscious state (MCS) or vegetative state (VS) on the basis of their clinical presentation (coma recovery scale-revised, CRS-R). Follow-up assessment (date II) was also based on CRS-R in a period of 8 to 24 month after date I. At date I, 63 channel EEG (Brain Products, Gilching, Germany) was recorded outside the scanner, and subsequently simultaneous FDG-PET/fMRI was acquired on an integrated Siemens Biograph mMR 3T scanner (Siemens Healthineers, Erlangen Germany). Power spectral densities, permutation entropy (PE) and symbolic transfer entropy (STE) were calculated in/between frontal, temporal, parietal and occipital EEG channels. PE and STE are based on symbolic time series analysis and were already introduced as robust markers separating wakefulness from unconsciousness in EEG during general anesthesia. While PE quantifies the regularity structure of the neighboring order of signal values (a surrogate of cortical information processing), STE reflects information transfer between two signals (a surrogate of directed connectivity in cortical networks). fMRI was carried out using SPM12 (Wellcome Trust Center for Neuroimaging, University of London, UK). Functional images were realigned, segmented, normalized and smoothed. PET was acquired for 45 minutes in list-mode. For absolute quantification of brain’s glucose consumption rate in FDG-PET, kinetic modelling was performed with Patlak’s plot method. BOLD signal intensity in fMRI and glucose uptake in PET was calculated in 8 distinct cortical areas. PCA was performed over all markers from EEG/fMRI/PET. Prognosis (persistent VS and deceased patients vs. recovery to MCS/awake from date I to date II) was evaluated using the area under the curve (AUC) including bootstrap confidence intervals (CI, *: p<0.05). Results: Prognosis was reliably indicated by the first component of PCA (AUC=0.99*, CI=0.92-1.00) showing a higher AUC when compared to the best single markers (EEG: AUC<0.96*, fMRI: AUC<0.86*, PET: AUC<0.60). CRS-R did not show prediction (AUC=0.51, CI=0.29-0.78). Conclusion: In a multimodal analysis of EEG/fMRI/PET in coma patients, PCA lead to a reliable prognosis. The impact of this result is evident, as clinical estimates of prognosis are inapt at time and could be supported by quantitative biomarkers from EEG, fMRI and PET. Due to the small sample size, further investigations are required, in particular allowing superwised learning instead of the basic approach of unsuperwised PCA.

Keywords: coma states and prognosis, electroencephalogram, entropy, functional magnetic resonance imaging, machine learning, positron emission tomography, principal component analysis

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Authors: Muneeb Ullah, Daishihan, Xiadong Young

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Deep learning plays a crucial role in identifying COVID-19 and preventing its spread. To improve the accuracy of COVID-19 diagnoses, it is important to have access to a sufficient number of training images of CXRs (chest X-rays) depicting the disease. However, there is currently a shortage of such images. To address this issue, this paper introduces COVID-19 GAN, a model that uses generative adversarial networks (GANs) to generate realistic CXR images of COVID-19, which can be used to train identification models. Initially, a generator model is created that uses digressive channels to generate images of CXR scans for COVID-19. To differentiate between real and fake disease images, an efficient discriminator is developed by combining the dense connectivity strategy and instance normalization. This approach makes use of their feature extraction capabilities on CXR hazy areas. Lastly, the deep regret gradient penalty technique is utilized to ensure stable training of the model. With the use of 4,062 grape leaf disease images, the Leaf GAN model successfully produces 8,124 COVID-19 CXR images. The COVID-19 GAN model produces COVID-19 CXR images that outperform DCGAN and WGAN in terms of the Fréchet inception distance. Experimental findings suggest that the COVID-19 GAN-generated CXR images possess noticeable haziness, offering a promising approach to address the limited training data available for COVID-19 model training. When the dataset was expanded, CNN-based classification models outperformed other models, yielding higher accuracy rates than those of the initial dataset and other augmentation techniques. Among these models, ImagNet exhibited the best recognition accuracy of 99.70% on the testing set. These findings suggest that the proposed augmentation method is a solution to address overfitting issues in disease identification and can enhance identification accuracy effectively.

Keywords: classification, deep learning, medical images, CXR, GAN.

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Authors: Faisal A. Salih, V. V. Egorov

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Verapamil is a drug used in medicine for arrhythmia, angina, and hypertension as a calcium channel blocker. In this study, a Verapamil-selective electrode was prepared, and the concentrations of the components in the membrane were as follows: PVC (32.8 wt %), O-NPhOE (66.6 wt %), and KTPClPB (0.6 wt % or approximately 0.01 M). The inner solution containing verapamil hydrochloride 1 x 10⁻³ M was introduced, and the electrodes were conditioned overnight in 1 x 10⁻³ M verapamil hydrochloride solution in 1 x 10⁻³ M orthophosphoric acid. These studies have demonstrated that O-NPhOE and KTPClPB are the best plasticizers and ion exchangers, while both direct potentiometry and potentiometric titration methods can be used for the determination of verapamil hydrochloride in tablets and injection solutions. Normalized weights of verapamil per tablet (80.4±0.2, 80.7±0.2, 81.0±0.4 mg) were determined by direct potentiometry and potentiometric titration, respectively. Weights of verapamil per average tablet weight determined by the methods of direct potentiometry and potentiometric titration were" 80.4±0.2, 80.7±0.2 mg determined for the same set of tablets, respectively. The masses of verapamil in solutions for injection, determined by direct potentiometry for two ampoules from one set, were (5.00±0.015, 5.004±0.006) mg. In all cases, good reproducibility and excellent correspondence with the declared quantities were observed.

Keywords: verapamil, potentiometry, ion-selective electrode, lipophilic physiologically active amines

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Authors: Anika Chebrolu

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Mono-targeted drugs can be of limited efficacy against complex diseases. Recently, multi-target drug design has been approached as a promising tool to fight against these challenging diseases. However, the scope of current computational approaches for multi-target drug design is limited. DeepLig presents a de-novo drug discovery platform that uses reinforcement learning to generate and optimize novel, potent, and multitargeted drug candidates against protein targets. DeepLig’s model consists of two networks in interplay: a generative network and a predictive network. The generative network, a Stack- Augmented Recurrent Neural Network, utilizes a stack memory unit to remember and recognize molecular patterns when generating novel ligands from scratch. The generative network passes each newly created ligand to the predictive network, which then uses multiple Graph Attention Networks simultaneously to forecast the average binding affinity of the generated ligand towards multiple target proteins. With each iteration, given feedback from the predictive network, the generative network learns to optimize itself to create molecules with a higher average binding affinity towards multiple proteins. DeepLig was evaluated based on its ability to generate multi-target ligands against two distinct proteins, multi-target ligands against three distinct proteins, and multi-target ligands against two distinct binding pockets on the same protein. With each test case, DeepLig was able to create a library of valid, synthetically accessible, and novel molecules with optimal and equipotent binding energies. We propose that DeepLig provides an effective approach to design multi-targeted drug therapies that can potentially show higher success rates during in-vitro trials.

Keywords: drug design, multitargeticity, de-novo, reinforcement learning

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Authors: Sanat Ramesh, Tarun Dutt, Abhilasha Aswal, Anushka Chandrababu, G. N. Srinivasa Prasanna

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Optimal Schedule Generation for a large national railway network operating thousands of passenger trains with tens of thousands of kilometers of track is a grand computational challenge in itself. We present heuristics based on a Mixed Integer Program (MIP) formulation for local optimization. These methods provide flexibility in scheduling new trains with varying speed and delays and improve utilization of infrastructure. We propose methods that provide a robust solution with hundreds of trains being scheduled over a portion of the railway network without significant increases in delay. We also provide techniques to validate the nominal schedules thus generated over global correlated variations in travel times thereby enabling us to detect conflicts arising due to delays. Our validation results which assume only the support of the arrival and departure time distributions takes an order of few minutes for a portion of the network and is computationally efficient to handle the entire network.

Keywords: mixed integer programming, optimization, railway network, train scheduling

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Authors: E. A. Tkachenko, E. M. Rogova, A. S. Osipenko

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The problems of technological development for the Russian Federation take on special significance in the context of modernization of the production base. The complexity of the position of the Russian economy is that it cannot be attributed fully to developing ones. Russia is a strong industrial power that has gone through the processes of destructive de-industrialization in the conditions of changing its economic and political structure. The need to find ways for re-industrialization is not a unique task for the economies of industrially developed countries. Under the influence of production outsourcing for 20 years, the industrial potential of leading economies of the world was regressed against the backdrop of the ascent of China, a new industrial giant. Therefore, methods, tools, and techniques utilized for industrial renaissance in EU may be used to achieve a technological leap in the Russian Federation, especially since the temporary gap of 5-7 years makes it possible to analyze best practices and use those technological transfer tools that have shown the greatest efficiency. In this article, methods of technological transfer are analyzed, the role of technological audit is justified, and factors are analyzed that influence the successful process of commercialization of technologies.

Keywords: technological transfer, productivity, technological audit, commercialization of technologies

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Authors: Roseanne Sharon Borromeo, Mariana Carvalho, Mariia Karizhenskaia

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Healthcare providers need to comprehend cultural diversity for optimal patient-centered care, especially near the end of life. Although a universal method for navigating cultural differences would be ideal, culture’s high complexity makes this strategy impossible. Adding cultural humility, a process of self-reflection to understand personal and systemic biases and humbly acknowledging oneself as a learner when it comes to understanding another's experience leads to a meaningful process in palliative care generating respectful, honest, and trustworthy relationships. This study is a systematic review of the literature on cultural humility in palliative care research and best practices. Race, religion, language, values, and beliefs can affect an individual’s access to palliative care, underscoring the importance of culture in palliative care. Cultural influences affect end-of-life care perceptions, impacting bereavement rituals, decision-making, and attitudes toward death. Cultural factors affecting the delivery of care identified in a scoping review of Canadian literature include cultural competency, cultural sensitivity, and cultural accessibility. As the different parts of the world become exponentially diverse and multicultural, healthcare providers have been encouraged to give culturally competent care at the bedside. Therefore, many organizations have made cultural competence training required to expose professionals to the special needs and vulnerability of diverse populations. Cultural competence is easily standardized, taught, and implemented; however, this theoretically finite form of knowledge can dangerously lead to false assumptions or stereotyping, generating poor communication, loss of bonds and trust, and poor healthcare provider-patient relationship. In contrast, Cultural humility is a dynamic process that includes self-reflection, personal critique, and growth, allowing healthcare providers to respond to these differences with an open mind, curiosity, and awareness that one is never truly a “cultural” expert and requires life-long learning to overcome common biases and ingrained societal influences. Cultural humility concepts include self-awareness and power imbalances. While being culturally competent requires being skilled and knowledgeable in one’s culture, being culturally humble involves the sometimes-uncomfortable position of healthcare providers as students of the patient. Incorporating cultural humility emphasizes the need to approach end-of-life care with openness and responsiveness to various cultural perspectives. Thus, healthcare workers need to embrace lifelong learning in individual beliefs and values on suffering, death, and dying. There have been different approaches to this as well. Some adopt strategies for cultural humility, addressing conflicts and challenges through relational and health system approaches. In practice and research, clinicians and researchers must embrace cultural humility to advance palliative care practices, using qualitative methods to capture culturally nuanced experiences. Cultural diversity significantly impacts patient-centered care, particularly in end-of-life contexts. Cultural factors also shape end-of-life perceptions, impacting rituals, decision-making, and attitudes toward death. Cultural humility encourages openness and acknowledges the limitations of expertise in one’s culture. A consistent self-awareness and a desire to understand patients’ beliefs drive the practice of cultural humility. This dynamic process requires practitioners to learn continuously, fostering empathy and understanding. Cultural humility enhances palliative care, ensuring it resonates genuinely across cultural backgrounds and enriches patient-provider interactions.

Keywords: cultural competency, cultural diversity, cultural humility, palliative care, self-awareness

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Authors: Karolina Wieczorek, Zofia Przypaśniak

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Climate change is a rapidly growing threat to global health, and part of the responsibility to combat it lies within the healthcare sector itself, including adequate education of future medical professionals. To mitigate the consequences, the General Medical Council (GMC) has equipped medical schools with a list of outcomes regarding sustainability teaching. Students are expected to analyze the impact of the healthcare sector’s emissions on climate change. The delivery of the related teaching content is, however, often inadequate and insufficient time is devoted for exploration of the topics. Teaching curricula lack in-depth exploration of the learning objectives. This study aims to assess the extent and characteristics of climate change and sustainability subjects teaching in the curriculum of a chosen UK medical school (Barts and The London School of Medicine and Dentistry). It compares the data to the national average scores from the Climate Change and Sustainability Teaching (C.A.S.T.) in Medical Education Audit to draw conclusions about teaching on a regional level. This is a single-center audit of the timetabled sessions of teaching in the medical course. The study looked at the academic year 2020/2021 which included a review of all non-elective, core curriculum teaching materials including tutorials, lectures, written resources, and assignments in all five years of the undergraduate and graduate degrees, focusing only on mandatory teaching attended by all students (excluding elective modules). The topics covered were crosschecked with GMC Outcomes for graduates: “Educating for Sustainable Healthcare – Priority Learning Outcomes” as gold standard to look for coverage of the outcomes and gaps in teaching. Quantitative data was collected in form of time allocated for teaching as proxy of time spent per individual outcomes. The data was collected independently by two students (KW and ZP) who have received prior training and assessed two separate data sets to increase interrater reliability. In terms of coverage of learning outcomes, 12 out of 13 were taught (with the national average being 9.7). The school ranked sixth in the UK for time spent per topic and second in terms of overall coverage, meaning the school has a broad range of topics taught with some being explored in more detail than others. For the first outcome 4 out of 4 objectives covered (average 3.5) with 47 minutes spent per outcome (average 84 min), for the second objective 5 out of 5 covered (average 3.5) with 46 minutes spent (average 20), for the third 3 out of 4 (average 2.5) with 10 mins pent (average 19 min). A disproportionately large amount of time is spent delivering teaching regarding air pollution (respiratory illnesses), which resulted in the topic of sustainability in other specialties being excluded from teaching (musculoskeletal, ophthalmology, pediatrics, renal). Conclusions: Currently, there is no coherent strategy on national teaching of climate change topics and as a result an unstandardized amount of time spent on teaching and coverage of objectives can be observed.

Keywords: audit, climate change, sustainability, education

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Authors: Monica Gonzalez Machorro

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Dementia is hard to diagnose because of the lack of early physical symptoms. Early dementia recognition is key to improving the living condition of patients. Speech technology is considered a valuable biomarker for this challenge. Recent works have utilized conventional acoustic features and machine learning methods to detect dementia in speech. BERT-like classifiers have reported the most promising performance. One constraint, nonetheless, is that these studies are either based on human transcripts or on transcripts produced by automatic speech recognition (ASR) systems. This research contribution is to explore a method that does not require transcriptions to detect early Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This is achieved by fine-tuning a pre-trained ASR model for the downstream early AD and MCI tasks. To do so, a subset of the thoroughly studied Pitt Corpus is customized. The subset is balanced for class, age, and gender. Data processing also involves cropping the samples into 10-second segments. For comparison purposes, a baseline model is defined by training and testing a Random Forest with 20 extracted acoustic features using the librosa library implemented in Python. These are: zero-crossing rate, MFCCs, spectral bandwidth, spectral centroid, root mean square, and short-time Fourier transform. The baseline model achieved a 58% accuracy. To fine-tune HuBERT as a classifier, an average pooling strategy is employed to merge the 3D representations from audio into 2D representations, and a linear layer is added. The pre-trained model used is ‘hubert-large-ls960-ft’. Empirically, the number of epochs selected is 5, and the batch size defined is 1. Experiments show that our proposed method reaches a 69% balanced accuracy. This suggests that the linguistic and speech information encoded in the self-supervised ASR-based model is able to learn acoustic cues of AD and MCI.

Keywords: automatic speech recognition, early Alzheimer’s recognition, mild cognitive impairment, speech impairment

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Authors: Ivan Yudianto

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The research objectives are to analyze the factors that impede the Parking Tax and Parking Retribution collection in Bekasi City Government, analyzing the factors that can increase local own revenue from the tax sector of parking tax and parking retribution, analyze monitoring the parking retribution collection by the Bekasi City Government, analyze strategies Bekasi City Government through the preparation of a roadmap and action plan to increase parking tax and parking retribution revenues. The approach used in this research is a qualitative approach. Qualitative research is used because the problem is not yet clear and the object to be studied will be holistic, complex, and dynamic, and the relationship will be interactive symptoms. Methods of data collection and technical analysis of the data was in-depth interviews, participant observation, documentary materials, literature, and triangulation, as well as new methods such as the methods of visual materials and internet browsing. The results showed that there are several factors that become an obstacle such as the parking taxpayer does not disclose the actual parking revenue, the parking taxpayer are late or do not pay Parking Tax, many parking locations controlled by illegal organizations, shortage of human resources in charge levy and supervise the parking tax and parking retribution collection in the Bekasi City Government, surveillance parking tax and parking retribution are not scheduled on a regular basis. Several strategic priorities in order to develop the potential of the Parking Tax and Parking Retribution in the Bekasi City Government, namely through increased controling and monitoring of the Parking Taxpayer, forming a team of auditors to audit the Parking Taxpayer, seek law enforcement persuasive and educative to reduce Parking Taxpayer wayward, providing strict sanctions against the Parking Taxpayer disobedient, revised regulations mayors about locations of parking in Bekasi City, rationalize revenues target of Parking Retribution, conducting takeover attempts parking location on the roadside of the individual or specific group, and drafting regional regulations on parking subscribe.

Keywords: local own revenue, parking retribution, parking tax, parking taxpayer

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Authors: Babak Kamrani, Nozar Kishi

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Stochastic catalog represents the events module of the earthquake loss estimation models. It includes series of events with different magnitudes and corresponding frequencies/probabilities. For the development of the stochastic catalog, random or uniform sampling methods are used to sample the events from the seismicity model. For covering all the Magnitude Frequency Distribution (MFD), a huge number of events should be generated for the above-mentioned methods. Characteristic Event (CE) method chooses the events based on the interest of the insurance industry. We divide the MFD of each source into bins. We have chosen the bins based on the probability of the interest by the insurance industry. First, we have collected the information for the available seismic sources. Sources are divided into Fault sources, subduction, and events without specific fault source. We have developed the MFD for each of the individual and areal source based on the seismicity of the sources. Afterward, we have calculated the CE magnitudes based on the desired probability. To develop the stochastic catalog, we have introduced uncertainty to the location of the events too.

Keywords: stochastic catalogue, earthquake loss, uncertainty, characteristic event

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Authors: Herath M. P. C. Jayaweera, Samer Hanoun

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Path planning for on-time obstacle avoidance is an essential and challenging task that enables drones to achieve safe operation in any application domain. The level of challenge increases significantly on the obstacle avoidance technique when the drone is following a ground mobile entity (GME). This is mainly due to the change in direction and magnitude of the GME′s velocity in dynamic and unstructured environments. Force field techniques are the most widely used obstacle avoidance methods due to their simplicity, ease of use, and potential to be adopted for three-dimensional dynamic environments. However, the existing force field obstacle avoidance techniques suffer many drawbacks, including their tendency to generate longer routes when the obstacles are sideways of the drone′s route, poor ability to find the shortest flyable path, propensity to fall into local minima, producing a non-smooth path, and high failure rate in the presence of symmetrical obstacles. To overcome these shortcomings, this paper proposes an on-time three-dimensional obstacle avoidance method for drones to effectively and efficiently avoid dynamic and static obstacles in unknown environments while pursuing a GME. This on-time obstacle avoidance technique generates velocity waypoints for its obstacle-free and efficient path based on the shape of the encountered obstacles. This method can be utilized on most types of drones that have basic distance measurement sensors and autopilot-supported flight controllers. The proposed obstacle avoidance technique is validated and evaluated against existing force field methods for different simulation scenarios in Gazebo and ROS-supported PX4-SITL. The simulation results show that the proposed obstacle avoidance technique outperforms the existing force field techniques and is better suited for real-world applications.

Keywords: drones, force field methods, obstacle avoidance, path planning

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Authors: Felix Baumann, Aleksandar Milutinovic, Dieter Roller

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Up to this point business process management projects in general and business process modelling projects in particular could not rely on a practical and scientifically validated method to estimate cost and effort. Especially the model development phase is not covered by a cost estimation method or model. Further phases of business process modelling starting with implementation are covered by initial solutions which are discussed in the literature. This article proposes a method of filling this gap by deriving a cost estimation method from available methods in similar domains namely software development or software engineering. Software development is regarded as closely similar to process modelling as we show. After the proposition of this method different ideas for further analysis and validation of the method are proposed. We derive this method from COCOMO II and Function Point which are established methods of effort estimation in the domain of software development. For this we lay out similarities of the software development rocess and the process of process modelling which is a phase of the Business Process Management life-cycle.

Keywords: COCOMO II, busines process modeling, cost estimation method, BPM COCOMO

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Authors: Musa Alghamdi

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Although Saudi students’ motivation to learn English language as a foreign language in Saudi Arabia have been investigated by a number of studies; these have appeared almost completely as using the quantitative research paradigm. There is a significant lack of research that explores the Saudi students’ motivation using qualitative methods. It was essential, as an investigator, to be immersed in the community to understand the individuals under study via their actions and words, their thoughts, views and beliefs, and how those individuals credited to activities. Thus, the study aims to explore the Saudi students’ motivation to learn English language as a foreign language in Saudi Arabia employing qualitative methodology via applying ethnography. The study will be carried out in Saudi Arabia. Ethnography qualitative approach will be used in the current study by employing formal and informal interview instruments. Gardner’s motivation theory is used as frameworks for this study to aid the understanding of the research findings. The author, an English language lecturer, will undertake participant observations for 4 months. He will work as teaching-assistant (on an unpaid basis) with EFL lecturers in different discipline department at a Saudi university where students study English language as a minor course. The researcher will start with informal ethnographical interview with students during his existence with the informants in their natural context. Then the researcher will utilize the semi-structural interview. The informal interview will be with 14-16 students, then, he will carry out semi-structural interview with the same informants to go deep in their natural context to find out to what extent the Saudi university students are motivated to learn English as a foreign language. As well as, to find out the reasons that played roles in that. The findings of this study will add new knowledge about what factors motivate universities’ Saudi students to learn English language in Saudi Arabia. Very few chances have given to students to express themselves and to speak about their feelings in a more comfortable way in order to gain a clear image of those factors. The working author as an EFL teacher and lecturer will provide him secure access into EFL teaching and learning setting. It will help him attain richer insights into the nature EFL context in universities what will provide him with richer insights into the reasons behind the weakness of EFL level among Saudi students.

Keywords: motivation, ethnography, Saudi, language

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Authors: Foo Shen Hwang, Thomas Confrey, Stephen Scully, Barry Flannery

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Thermal characterization plays an important role in battery pack design. Lithium-ion batteries have to be maintained between 15-35 °C to operate optimally. Heat is generated (Q) internally within the batteries during both the charging and discharging phases. This can be quantified using several standard methods. The most common method of calculating the batteries heat generation is through the addition of both the joule heating effects and the entropic changes across the battery. In addition, such values can be derived by identifying the open-circuit voltage (OCV), nominal voltage (V), operating current (I), battery temperature (T) and the rate of change of the open-circuit voltage in relation to temperature (dOCV/dT). This paper focuses on experimental characterization and comparative modelling of the heat generation rate (Q) across several current discharge rates (0.5C, 1C, and 1.5C) of a 18650 cell. The analysis is conducted utilizing several non-linear mathematical functions methods, including polynomial, exponential, and power models. Parameter fitting is carried out over the respective function orders; polynomial (n = 3~7), exponential (n = 2) and power function. The generated parameter fitting functions are then used as heat source functions in a 3-D computational fluid dynamics (CFD) solver under natural convection conditions. Generated temperature profiles are analyzed for errors based on experimental discharge tests, conducted at standard room temperature (25°C). Initial experimental results display low deviation between both experimental and CFD temperature plots. As such, the heat generation function formulated could be easier utilized for larger battery applications than other methods available.

Keywords: computational fluid dynamics, curve fitting, lithium-ion battery, voltage drop

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Authors: Jehad Al Dallal

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Attributes and methods are the basic contents of an object-oriented class. The connectivity among these class members and the relationship between the class and other classes play an important role in determining the quality of an object-oriented system. Class cohesion evaluates the degree of relatedness of class attributes and methods, whereas class coupling refers to the degree to which a class is related to other classes. Researchers have proposed several class cohesion and class coupling measures. However, the correlation between class coupling and class cohesion measures have not been thoroughly studied. In this paper, using classes of three open-source Java systems, we empirically investigate the correlation between several measures of connectivity-based class cohesion and coupling. Four connectivity-based cohesion measures and eight coupling measures are considered in the empirical study. The empirical study results show that class connectivity-based cohesion and coupling internal quality attributes are inversely correlated. The strength of the correlation depends highly on the cohesion and coupling measurement approaches.

Keywords: object-oriented class, software quality, class cohesion measure, class coupling measure

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Authors: Hong Beom Hur

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Politics in America is polarized. The best illustration of this is the 2016 presidential election result map. States with megacities like California, New York, Illinois, Virginia, and others are marked blue to signify the color of the Democratic party. States located in inland and south like Texas, Florida, Tennesse, Kansas and others are marked red to signify the color of the Republican party. Such a stark difference between two colors, red and blue, combined with geolocations of each state with their borderline remarks one central message; America is divided into two colors between urban Democrats and rural Republicans. This paper seeks to defy the visualization by pointing out its limitations and search for alternative ways to visualize the 2016 election result. One such limitation is that geolocations of each state and state borderlines limit the visualization of population density. As a result, the election result map does not convey the fact that Clinton won the popular vote and only accentuates the voting patterns of urban and rural states. The paper seeks whether an alternative narrative can be observed by factoring in the population number into the size of each state and manipulating the state borderline according to the normalization. Yet another alternative narrative may be reached by factoring the size of each state by the number of the electoral college of each state by voting and visualize the number. Other alternatives will be discussed but are not implemented in visualization. Such methods include dividing the land of America into about 120 million cubes each representing a voter or by the number of whole population 300 million cubes. By exploring these alternative methods to visualize the politics of the 2016 election map, the public may be able to question whether it is possible to be free from the narrative of the divide-conquer when interpreting the election map and to look at both parties as a story of the United States of America.

Keywords: 2016 U.S. presidential election, data visualization, population scale, geo-political

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Authors: Mohammad Khosravi, Ali Kiani, Behroz Dastar, Parvin Showrang

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Review was carried out on important anti-nutritional compounds of livestock and poultry foods and the effect of radiation usage. Nowadays, with advancement in technology, different methods have been considered for the optimum usage of nutrients in livestock and poultry foods. Steaming, extruding, pelleting, and the use of chemicals are the most common and popular methods in food processing. Use of radiation in food processing researches in the livestock and poultry industry is currently highly regarded. Ionizing (electrons, gamma) and non-ionizing beams (microwave and infrared) are the most useable rays in animal food processing. In recent researches, these beams have been used to remove and reduce the anti-nutritional factors and microbial contamination and improve the digestibility of nutrients in poultry and livestock food. The evidence presented will help researchers to recognize techniques of relevance to them. Simplification of some of these techniques, especially in developing countries, must be addressed so that they can be used more widely.

Keywords: antitrypsin, gamma anti-nutritional components, phytic acid, radiation

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Authors: Vangmayee V. Subban, Suzan Deelan. Pinto, Somashekara Haralakatta Shivananjappa, Shwetha Prabhu, Jayashree S. Bhat

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Introduction and Need: During early years of schooling, the instruction in the schools mainly focus on children’s word decoding abilities. However, the skilled readers should master all the components of reading such as word decoding, reading fluency and comprehension. Nevertheless, the relationship between each component during the process of learning to read is less clear. The studies conducted in alphabetical languages have mixed opinion on relative contribution of word decoding and reading fluency on reading comprehension. However, the scenarios in alphasyllabary languages are unexplored. Aim and Objectives: The aim of the study was to explore the role of word decoding, reading fluency on reading comprehension abilities in children learning to read Kannada between the age ranges of 5.6 to 8.6 years. Method: In this cross sectional study, a total of 60 typically developing children, 20 each from Grade I, Grade II, Grade III maintaining equal gender ratio between the age range of 5.6 to 6.6 years, 6.7 to 7.6 years and 7.7 to 8.6 years respectively were selected from Kannada medium schools. The reading fluency and reading comprehension abilities of the children were assessed using Grade level passages selected from the Kannada text book of children core curriculum. All the passages consist of five questions to assess reading comprehension. The pseudoword decoding skills were assessed using 40 pseudowords with varying syllable length and their Akshara composition. Pseudowords are formed by interchanging the syllables within the meaningful word while maintaining the phonotactic constraints of Kannada language. The assessment material was subjected to content validation and reliability measures before collecting the data on the study samples. The data were collected individually, and reading fluency was assessed for words correctly read per minute. Pseudoword decoding was scored for the accuracy of reading. Results: The descriptive statistics indicated that the mean pseudoword reading, reading comprehension, words accurately read per minute increased with the Grades. The performance of Grade III children found to be higher, Grade I lower and Grade II remained intermediate of Grade III and Grade I. The trend indicated that reading skills gradually improve with the Grades. Pearson’s correlation co-efficient showed moderate and highly significant (p=0.00) positive co-relation between the variables, indicating the interdependency of all the three components required for reading. The hierarchical regression analysis revealed 37% variance in reading comprehension was explained by pseudoword decoding and was highly significant. Subsequent entry of reading fluency measure, there was no significant change in R-square and was only change 3%. Therefore, pseudoword-decoding evolved as a single most significant predictor of reading comprehension during early Grades of reading acquisition. Conclusion: The present study concludes that the pseudoword decoding skills contribute significantly to reading comprehension than reading fluency during initial years of schooling in children learning to read Kannada language.

Keywords: alphasyllabary, pseudo-word decoding, reading comprehension, reading fluency

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Authors: Carolina Zambrana, Grover Zurita

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The food loss production that occurs in deficient agricultural production is one of the major problems worldwide. This puts the population's food security and the efficiency of farming investments at risk. It is to be expected that this food security will be achieved with the own and efficient production of each country. It will have an impact on the well-being of its population and, thus, also on food sovereignty. The production losses in quantity and quality occur due to the lack of efficient detection of diseases at an early stage. It is very difficult to solve the agriculture efficiency using traditional methods since it takes a long time to be carried out due to detection imprecision of the main diseases, especially when the production areas are extensive. Therefore, the main objective of this research study is to perform a systematic literature review, of the latest five years, of Precision Agriculture (PA) to be able to understand the state of the art of the set of new technologies, procedures, and optimization processes with Artificial Intelligence (AI). This study will focus on Corns, Potatoes, and Rice diagnostic diseases. The extensive literature review will be performed on Elsevier, Scopus, and IEEE databases. In addition, this research will focus on advanced digital imaging processing and the development of software and hardware for PA. The convolution neural network will be handling special attention due to its outstanding diagnostic results. Moreover, the studied data will be incorporated with artificial intelligence algorithms for the automatic diagnosis of crop quality. Finally, precision agriculture with technology applied to the agricultural sector allows the land to be exploited efficiently. This system requires sensors, drones, data acquisition cards, and global positioning systems. This research seeks to merge different areas of science, control engineering, electronics, digital image processing, and artificial intelligence for the development, in the near future, of a low-cost image measurement system that allows the optimization of crops with AI.

Keywords: precision agriculture, convolutional neural network, deep learning, artificial intelligence

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Authors: Jefunnie Matahum, Yu-Chi Kuo, Chao-Ming Su, Tzong-Rong Ger

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Magnetic cell labeling is of great importance in various applications in biomedical fields such as cell separation and cell sorting. Since analytical methods for quantification of cell uptake of magnetic nanoparticles (MNPs) are already well established, image analysis on single cell level still needs more characterization. This study reports an alternative non-destructive quantification methods of single-cell uptake of positively charged MNPs. Magnetophoresis experiments were performed to calculate the number of MNPs in a single cell. Mobility of magnetic cells and the area of intracellular MNP stained by Prussian blue were quantified by image processing software. ICP-MS experiments were also performed to confirm the internalization of MNPs to cells. Initial results showed that the magnetic cells incubated at 100 µg and 50 µg MNPs/mL concentration move at 18.3 and 16.7 µm/sec, respectively. There is also an increasing trend in the number and area of intracellular MNP with increasing concentration. These results could be useful in assessing the nanoparticle uptake in a single cell level.

Keywords: magnetic nanoparticles, single cell, magnetophoresis, image analysis

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Authors: Nuur Insan Tangkelangi

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Needs on academic writing competence as the primary focus in higher education encourage the university institutions around the world to provide academic writing courses to support their students dealing with their tasks pertaining to this competence. However, a pilot study conducted previously in one of the universities in Palopo, a city in South Sulawesi, revealed that even though the institution has provided academic writing courses, supported by some workshops related to academic writing and some supporting facilities at campus, the students still face difficulties in completing their assignments related to academic writing, particularly in writing their theses. The present study focuses on investigating the specific needs of the students in the same institution in terms of competences required in academic writing. It is also carried out to examine whether the syllabus exists and accommodates the students’ needs or not. Questionnaire and interview were used to collect data from sixty students of sixth semester and two lecturers of the academic courses. The results reveal that the students need to learn all aspects of linguistic competence (language features, lexical phrases, academic language and vocabulary, and proper language) and some aspects in discourse competence (how to write introduction, search for appropriate literature, design research method, write coherent paragraphs, refer to sources, summarize and display data, and link sentences smoothly). Regarding the syllabus, it is found that the academic writing courses provided in the institution, where this study takes place, do not have syllabus. This condition is different from other institutions which provide syllabi for all courses. However, at the commencement of the course, the students and the lecturers have negotiated their learning goals, topics discussed, learning activities, and assessment criteria for the course. Therefore, even though the syllabus does not exist, but the elements of the syllabus are there. The negotiation between the students and the lecturers contributes to the students’ attitude toward the courses. The students are contented with the course and they feel that their needs in academic writing have been accommodated. However, some suggestions for the next academic writing courses are stated by the students. Considering the results of this study, a syllabus is then proposed which is expected to accommodate the specific needs of students in that institution.

Keywords: Students' needs, academic writing, syllabus design for instruction, case study

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Authors: Muhammmad Riandhy Anindika Yudhy, Harry Patria, Ramadhani Santoso

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Loss of containment is the primary hazard that process safety management is concerned within the oil and gas industry. Escalation to more serious consequences all begins with the loss of containment, starting with oil and gas release from leakage or spillage from primary containment resulting in pool fire, jet fire and even explosion when reacted with various ignition sources in the operations. Therefore, the heart of process safety management is avoiding loss of containment and mitigating its impact through the implementation of safeguards. The most effective safeguard for the case is an early detection system to alert Operations to take action prior to a potential case of loss of containment. The detection system value increases when applied to a long surface pipeline that is naturally difficult to monitor at all times and is exposed to multiple causes of loss of containment, from natural corrosion to illegal tapping. Based on prior researches and studies, detecting loss of containment accurately in the surface pipeline is difficult. The trade-off between cost-effectiveness and high accuracy has been the main issue when selecting the traditional detection method. The current best-performing method, Real-Time Transient Model (RTTM), requires analysis of closely positioned pressure, flow and temperature (PVT) points in the pipeline to be accurate. Having multiple adjacent PVT sensors along the pipeline is expensive, hence generally not a viable alternative from an economic standpoint.A conceptual approach to combine mathematical modeling using computational fluid dynamics and a supervised machine learning model has shown promising results to predict leakage in the pipeline. Mathematical modeling is used to generate simulation data where this data is used to train the leak detection and localization models. Mathematical models and simulation software have also been shown to provide comparable results with experimental data with very high levels of accuracy. While the supervised machine learning model requires a large training dataset for the development of accurate models, mathematical modeling has been shown to be able to generate the required datasets to justify the application of data analytics for the development of model-based leak detection systems for petroleum pipelines. This paper presents a review of key leak detection strategies for oil and gas pipelines, with a specific focus on crude oil applications, and presents the opportunities for the use of data analytics tools and mathematical modeling for the development of robust real-time leak detection and localization system for surface pipelines. A case study is also presented.

Keywords: pipeline, leakage, detection, AI

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Authors: Vassilis Argyropoulos, Magda Nikolaraizi, Maria Papazafiri

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This study describes a series of learning workshops, which took place within CUIDAR project. The workshops aimed to empower children to share their experiences and views in relation to natural hazards and disasters. The participants in the workshops were ten primary school students who had severe visual impairments or multiple disabilities and visual impairments (MDVI). The main objectives of the workshops were: a) to promote access of the children through the use of appropriate educational material such as texts in braille, enlarged text, tactile maps and the implementation of differentiated instruction, b) to make children aware regarding their rights to have access to information and to participate in planning and decision-making especially in relation to disaster education programs, and c) to encourage children to have an active role during the workshops through child-led and experiential learning activities. The children expressed their views regarding the meaning of hazards and disasters. Following, they discussed their experiences and emotions regarding natural hazards and disasters, and they chose to place the emphasis on a hazard, which was more pertinent to them, their community and their region, namely fires. Therefore, they recalled fires that have caused major disasters, and they discussed about the impact that these fires had on their community or on their country. Furthermore, they were encouraged to become aware regarding their own role and responsibility to prevent a fire or get prepared and know how to behave if a fire occurs. They realized that prevention and preparation are a matter of personal responsibility. They also felt the responsibility to inform their own families. Finally, they met important people involved in fire protection such as rescuers and firefighters and had the opportunity to carry dialogues. In conclusion, through child led workshops, experiential and accessible activities, the students had the opportunity to share their own experiences, to express their views and their questions, to broaden their knowledge and to realize their personal responsibility in disaster risk reduction, specifically in relation to fires.

Keywords: accessibility, children, disasters, visual impairment

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Authors: N. Fusun Oyman Serteller

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In this paper, the techniques to solve time dependent electromagnetic wave propagation equations based on the Finite Difference Method (FDM) are proposed by comparing the results with Finite Element Method (FEM) in 2D while discussing some special simulation examples.  Here, 2D dynamical wave equations for lossy media, even with a constant source, are discussed for establishing symbolic manipulation of wave propagation problems. The main objective of this contribution is to introduce a comparative study of two suitable numerical methods and to show that both methods can be applied effectively and efficiently to all types of wave propagation problems, both linear and nonlinear cases, by using symbolic computation. However, the results show that the FDM is more appropriate for solving the nonlinear cases in the symbolic solution. Furthermore, some specific complex domain examples of the comparison of electromagnetic waves equations are considered. Calculations are performed through Mathematica software by making some useful contribution to the programme and leveraging symbolic evaluations of FEM and FDM.

Keywords: finite difference method, finite element method, linear-nonlinear PDEs, symbolic computation, wave propagation equations

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