Search results for: train

Authors: A. Basu, A. A. Purohit, M. M. Vaidya, M. D. Kudale

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Mumbai, being traditionally the epicenter of India's trade and commerce, the existing major ports such as Mumbai and Jawaharlal Nehru Ports (JN) situated in Thane estuary are also developing its waterfront facilities. Various developments over the passage of decades in this region have changed the tidal flux entering/leaving the estuary. The intake at Pir-Pau is facing the problem of shortage of water in view of advancement of shoreline, while jetty near Ulwe faces the problem of ship scheduling due to existence of shallower depths between JN Port and Ulwe Bunder. In order to solve these problems, it is inevitable to have information about tide levels over a long duration by field measurements. However, field measurement is a tedious and costly affair; application of artificial intelligence was used to predict water levels by training the network for the measured tide data for one lunar tidal cycle. The application of two layered feed forward Artificial Neural Network (ANN) with back-propagation training algorithms such as Gradient Descent (GD) and Levenberg-Marquardt (LM) was used to predict the yearly tide levels at waterfront structures namely at Ulwe Bunder and Pir-Pau. The tide data collected at Apollo Bunder, Ulwe, and Vashi for a period of lunar tidal cycle (2013) was used to train, validate and test the neural networks. These trained networks having high co-relation coefficients (R= 0.998) were used to predict the tide at Ulwe, and Vashi for its verification with the measured tide for the year 2000 & 2013. The results indicate that the predicted tide levels by ANN give reasonably accurate estimation of tide. Hence, the trained network is used to predict the yearly tide data (2015) for Ulwe. Subsequently, the yearly tide data (2015) at Pir-Pau was predicted by using the neural network which was trained with the help of measured tide data (2000) of Apollo and Pir-Pau. The analysis of measured data and study reveals that: The measured tidal data at Pir-Pau, Vashi and Ulwe indicate that there is maximum amplification of tide by about 10-20 cm with a phase lag of 10-20 minutes with reference to the tide at Apollo Bunder (Mumbai). LM training algorithm is faster than GD and with increase in number of neurons in hidden layer and the performance of the network increases. The predicted tide levels by ANN at Pir-Pau and Ulwe provides valuable information about the occurrence of high and low water levels to plan the operation of pumping at Pir-Pau and improve ship schedule at Ulwe.

Keywords: artificial neural network, back-propagation, tide data, training algorithm

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Authors: O. V. Jurieva, O. U. Jurieva, R. H. Yagudin, P. B. Chursin

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In the recent scientific researches on the problems of public service the idea of the use of innovative technologies of management of personnel processes is accurately formulated. Authors made an attempt to analyze the changes in the public service organizations and to understand how the studied situation is interpreted by the government employees themselves. For this purpose the strategy of sociological research was carried out on the basis of application of questionnaire developed by M. Rokich and focus group research. For the research purposes it was necessary to get to microlevel in order to include daily activities of employees of an organization, their life experience and values in the focus of the analysis. Based on P. Bourdieu's methodology, authors investigated the established patterns of consciousness and behavior of officials (doxa) and also analyzed the tendencies of re-thinking (change) of the settled content of values (heterodoxy) by them. The distinctive feature of the conducted research is that the public servants who have different length of service in the public service took part in the research procedure. The obtained data helped to answer the following question: what are the specifics of doxs of the public servants who work in the public civil service more than 7-10 years and what perception of values of civil service have junior experts whose work experience doesn't exceed 3 years. Respondents were presented by two groups: (1) public servants of the level of main positions in the public civil service of the Republic of Tatarstan. (2) Public servants of the level of lower positions in the ministries and departments of the Republic of Tatarstan. For the study of doxa or of the existing values of public servants, the research with use of the questionnaire based on M. Rokich's system is conducted. Two types of values are emphasised: terminal and instrumental, which are united by us in the collective concept doxa. Doxa: the instrument of research of the established patterns of consciousness and behavior which can either resist to changes in the organization or, on the contrary, support their implementation. In the following stage an attempt to deepen our understanding of the essence and specifics of doxa of officials by means of the applied sociological research which is carried out by focus group method is made. Information obtained by authors during the research convinces that for the success of policy of changes in the organizations of public service it is necessary to develop special technologies of informing employees about the essence and inevitability of the developed innovations, to involve them in the process of changes, to train and to develop the younger generation of civil servants, seriously to perceive additional training and retraining of officials.

Keywords: innovative technologies, public service organizations, public servants

Procedia PDF Downloads 242

Authors: Mohsen Bahrami, Banafsheh Nikmehr, Yueqiang Song, Anuradha Koduru, Ayse K. Vuruskan, Hongkun Lu, Tamer M. Yalcinkaya

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In recent years, we have witnessed an explosion of studies aimed at using a combination of artificial intelligence (AI) and time-lapse imaging data on embryos to improve IVF outcomes. However, despite promising results, no study has used a matched cohort of transferred embryos which only differ in pregnancy outcome, i.e., embryos from a single clinic which are similar in parameters, such as: morphokinetic condition, patient age, and overall clinic and lab performance. Here, we used time-lapse data on embryos with known pregnancy outcomes to see if the rich spatiotemporal information embedded in this data would allow the prediction of the pregnancy outcome regardless of such critical parameters. Methodology—We did a retrospective analysis of time-lapse data from our IVF clinic utilizing Embryoscope 100% of the time for embryo culture to blastocyst stage with known clinical outcomes, including live birth vs nonpregnant (embryos with spontaneous abortion outcomes were excluded). We used time-lapse data from 200 elective single transfer embryos randomly selected from January 2019 to June 2021. Our sample included 100 embryos in each group with no significant difference in patient age (P=0.9550) and morphokinetic scores (P=0.4032). Data from all patients were combined to make a 4th order tensor, and feature extraction were subsequently carried out by a tensor decomposition methodology. The features were then used in a machine learning classifier to classify the two groups. Major Findings—The performance of the model was evaluated using 100 random subsampling cross validation (train (80%) - test (20%)). The prediction accuracy, averaged across 100 permutations, exceeded 80%. We also did a random grouping analysis, in which labels (live birth, nonpregnant) were randomly assigned to embryos, which yielded 50% accuracy. Conclusion—The high accuracy in the main analysis and the low accuracy in random grouping analysis suggest a consistent spatiotemporal pattern which is associated with pregnancy outcomes, regardless of patient age and embryo morphokinetic condition, and beyond already known parameters, such as: early cleavage or early blastulation. Despite small samples size, this ongoing analysis is the first to show the potential of AI methods in capturing the complex morphokinetic changes embedded in embryo time-lapse data, which contribute to successful pregnancy outcomes, regardless of already known parameters. The results on a larger sample size with complementary analysis on prediction of other key outcomes, such as: euploidy and aneuploidy of embryos will be presented at the meeting.

Keywords: IVF, embryo, machine learning, time-lapse imaging data

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Authors: Olivia A. Wilson, Hannah E. Power, Murray Kendall

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Sydney Harbour is an iconic location with a dense population and low-lying development. On the east coast of Australia, facing the Pacific Ocean, it is exposed to several tsunamigenic trenches. This paper presents a component of the most detailed assessment of the potential for earthquake-generated tsunami impact on Sydney Harbour to date. Models in this study use dynamic tides to account for tide-tsunami interaction. Sydney Harbour’s tidal range is 1.5 m, and the spring tides from January 2015 that are used in the modelling for this study are close to the full tidal range. The tsunami wave trains modelled include hypothetical tsunami generated from earthquakes of magnitude 7.5, 8.0, 8.5, and 9.0 M_W from the Puysegur and New Hebrides trenches as well as representations of the historical 1960 Chilean and 2011 Tohoku events. All wave trains are modelled for the peak wave to coincide with both a low tide and a high tide. A single wave train, representing a 9.0 M_W earthquake at the Puysegur trench, is modelled for peak waves to coincide with every hour across a 12-hour tidal phase. Using the hydrodynamic model ANUGA, results are compared according to the impact parameters of inundation area, depth variation and current speeds. Results show that both maximum inundation area and depth variation are tide dependent. Maximum inundation area increases when coincident with a higher tide, however, hazardous inundation is only observed for the larger waves modelled: NH90high and P90high. The maximum and minimum depths are deeper on higher tides and shallower on lower tides. The difference between maximum and minimum depths varies across different tidal phases although the differences are slight. Maximum current speeds are shown to be a significant hazard for Sydney Harbour; however, they do not show consistent patterns according to tide-tsunami phasing. The maximum current speed hazard is shown to be greater in specific locations such as Spit Bridge, a narrow channel with extensive marine infrastructure. The results presented for Sydney Harbour are novel, and the conclusions are consistent with previous modelling efforts in the greater area. It is shown that tide must be a consideration for both tsunami modelling and emergency management planning. Modelling with peak tsunami waves coinciding with a high tide would be a conservative approach; however, it must be considered that maximum current speeds may be higher on other tides.

Keywords: emergency management, sydney, tide-tsunami interaction, tsunami impact

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Kabuga Daniel

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Road accidents resulting to deaths and injuries have become a new public health challenge especially in developing countries including Tanzania. Reports from Tanzania Traffic Police Force shows that last year 2016 accidents increased compare to previous year 2015, accident happened from 3710 up to 5219, accidents and safety data indicate that children are the most vulnerable to road crashes where 78 pupils died and 182 others were seriously injured in separate roads accident last year. A survey done by Amend indicates that Pupil mode of transport in Dar es salaam schools are by walk 87%, bus 9.21%, car 1.32%, motorcycle 0.88%, 3-wheeler 0.24%, train 0.14%, bicycle 0.10%, ferry 0.07%, and combined mode 0.44%. According to this study, majority of school children’s uses walking mode, most of school children’s agreed to continue using walking mode and request to have signs for traffic control during crossing road like STOP sign and CHILD CROSSING sign for safe crossing. Because children not only sit inside this buses (Daladala) but also they walk in a group to/from school, and few (33.2%) parents or adults are willing to supervise their children’s during working to school while 50% of parents agree to let their children walking alone to school if the public transport started from nearby street. The study used both qualitative and quantitative methods of research by conducting physical surveying on sample districts. The main objectives of this research are to carries out all factors affecting school children’s when they use public road, to promote and encourage the safe use of public road by all classes especially pupil or student through the circulation of advice, information and knowledge gain from research and to recommends future direction for the developments for road design or plan to vulnerable users. The research also critically analyze the problems causing death and injuries to school children’s in Dar es Salaam Region. This study determines the relationship between road traffic accidents and factors, such as socio-economic, status, and distance from school, number of sibling, behavioral problems, knowledge and attitudes of public and their parents towards road safety and parent educational study traffic. The study comes up with some of recommendations including Infrastructure Improvements like, safe footpaths, Safe crossings, Speed humps, Speed limits, Road signs. However, Planners and policymakers wishing to increase walking and cycling among children need to consider options that address distance constraints, the land use planners and transport professionals use better understanding of the various factors that affect children’s choices of school travel mode, results suggest that all school travel attributes should be considered during school location.

Keywords: accidents, childrens, school, Tanzania

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Authors: Jared Beard, Ali Baheri

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As autonomous systems become more prominent in society, ensuring their safe application becomes increasingly important. This is clearly demonstrated with autonomous cars traveling through a crowded city or robots traversing a warehouse with heavy equipment. Human environments can be complex, having high dimensional state and action spaces. This gives rise to two problems. One being that analytic solutions may not be possible. The other is that in simulation based approaches, searching the entirety of the problem space could be computationally intractable, ruling out formal methods. To overcome this, approximate solutions may seek to find failures or estimate their likelihood of occurrence. One such approach is adaptive stress testing (AST) which uses reinforcement learning to induce failures in the system. The premise of which is that a learned model can be used to help find new failure scenarios, making better use of simulations. In spite of these failures AST fails to find particularly sparse failures and can be inclined to find similar solutions to those found previously. To help overcome this, multi-fidelity learning can be used to alleviate this overuse of information. That is, information in lower fidelity can simulations can be used to build up samples less expensively, and more effectively cover the solution space to find a broader set of failures. Recent work in multi-fidelity learning has passed information bidirectionally using “knows what it knows” (KWIK) reinforcement learners to minimize the number of samples in high fidelity simulators (thereby reducing computation time and load). The contribution of this work, then, is development of the bidirectional multi-fidelity AST framework. Such an algorithm, uses multi-fidelity KWIK learners in an adversarial context to find failure modes. Thus far, a KWIK learner has been used to train an adversary in a grid world to prevent an agent from reaching its goal; thus demonstrating the utility of KWIK learners in an AST framework. The next step is implementation of the bidirectional multi-fidelity AST framework described. Testing will be conducted in a grid world containing an agent attempting to reach a goal position and adversary tasked with intercepting the agent as demonstrated previously. Fidelities will be modified by adjusting the size of a time-step, with higher-fidelity effectively allowing for more responsive closed loop feedback. Results will compare the single KWIK AST learner with the multi-fidelity algorithm with respect to number of samples, distinct failure modes found, and relative effect of learning after a number of trials.

Keywords: multi-fidelity reinforcement learning, multi-fidelity simulation, safety validation, falsification

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Authors: Dewan Ahsan

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Denmark is well ahead in generating electricity from renewable sources. The offshore wind sector is playing the pivotal role to achieve this target. Though there is a rapid growth of offshore wind sector in Denmark, still there is a dearth of synchronization in OHS (occupational health and safety) regulation and standards. Therefore, this paper attempts to ascertain: i) what are the major challenges of the company specific OHS standards? ii) why does the offshore wind industry need a standardized OHS management system? and iii) who can play the key role in this process? To achieve these objectives, this research applies the interview and survey techniques. This study has identified several key challenges in OHS management system which are; gaps in coordination and communication among the stakeholders, gaps in incident reporting systems, absence of a harmonized OHS standard and blame culture. Furthermore, this research has identified eleven key stakeholders who are actively involve with the offshore wind business in Denmark. As noticed, the relationships among these stakeholders are very complex specially between operators and sub-contractors. The respondent technicians are concerned with the compliance of various third-party OHS standards (e.g. ISO 31000, ISO 29400, Good practice guidelines by G+) which are applying by various offshore companies. On top of these standards, operators also impose their own OHS standards. From the technicians point of angle, many of these standards are not even specific for the offshore wind sector. So, it is a big challenge for the technicians and sub-contractors to comply with different company specific standards which also elevate the price of their services offer to the operators. For instance, when a sub-contractor is competing for a bidding, it must fulfill a number of OHS requirements (which demands many extra documantions) set by the individual operator and/the turbine supplier. According to sub-contractors’ point of view these extra works consume too much time to prepare the bidding documents and they also need to train their employees to pass the specific OHS certification courses to accomplish the demand for individual clients and individual project. The sub-contractors argued that in many cases these extra documentations and OHS certificates are inessential to ensure the quality service. So, a standardized OHS management procedure (which could be applicable for all the clients) can easily solve this problem. In conclusion, this study highlights that i) development of a harmonized OHS standard applicable for all the operators and turbine suppliers, ii) encouragement of technicians’ active participation in the OHS management, iii) development of a good safety leadership, and, iv) sharing of experiences among the stakeholders (specially operators-operators-sub contractors) are the most vital strategies to overcome the existing challenges and to achieve the goal of 'zero accident/harm' in the offshore wind industry.

Keywords: green energy, offshore, safety, Denmark

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Authors: Szymon Rutkowski, Tomasz Korbak

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We present a reinforcement-learning-based method of training neural machine translation models without parallel corpora. The standard encoder-decoder approach to machine translation suffers from two problems we aim to address. First, it needs parallel corpora, which are scarce, especially for low-resource languages. Second, it lacks psychological plausibility of learning procedure: learning a foreign language is about learning to communicate useful information, not merely learning to transduce from one language’s 'encoding' to another. We instead pose the problem of learning to translate as learning a policy in a communication game between two agents: the translator and the classifier. The classifier is trained beforehand on a natural language inference task (determining the entailment relation between a premise and a hypothesis) in the target language. The translator produces a sequence of actions that correspond to generating translations of both the hypothesis and premise, which are then passed to the classifier. The translator is rewarded for classifier’s performance on determining entailment between sentences translated by the translator to disciple’s native language. Translator’s performance thus reflects its ability to communicate useful information to the classifier. In effect, we train a machine translation model without the need for parallel corpora altogether. While similar reinforcement learning formulations for zero-shot translation were proposed before, there is a number of improvements we introduce. While prior research aimed at grounding the translation task in the physical world by evaluating agents on an image captioning task, we found that using a linguistic task is more sample-efficient. Natural language inference (also known as recognizing textual entailment) captures semantic properties of sentence pairs that are poorly correlated with semantic similarity, thus enforcing basic understanding of the role played by compositionality. It has been shown that models trained recognizing textual entailment produce high-quality general-purpose sentence embeddings transferrable to other tasks. We use stanford natural language inference (SNLI) dataset as well as its analogous datasets for French (XNLI) and Polish (CDSCorpus). Textual entailment corpora can be obtained relatively easily for any language, which makes our approach more extensible to low-resource languages than traditional approaches based on parallel corpora. We evaluated a number of reinforcement learning algorithms (including policy gradients and actor-critic) to solve the problem of translator’s policy optimization and found that our attempts yield some promising improvements over previous approaches to reinforcement-learning based zero-shot machine translation.

Keywords: agent-based language learning, low-resource translation, natural language inference, neural machine translation, reinforcement learning

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Authors: Malcolm Richardson, Gabriele Richardson

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This paper illustrates how historical problems can be studied successfully using GIS even in cases in which data, in the modern sense, is fragmentary. The overall problem under investigation is how early (1300-1500) English schools of Common Law moved from apprenticeship training in random individual London inns run in part by clerks of the royal chancery to become what is widely called 'the Third University of England,' a recognized system of independent but connected legal inns. This paper focuses on the preparatory legal inns, called the Inns of Chancery, rather than the senior (and still existing) Inns of Court. The immediate problem studied in this paper is how the junior legal inns were organized, staffed, and located from 1292 to about 1500, and what maps tell us about the role of the chancery clerks as managers of legal inns. The authors first uncovered the names of all chancery clerks of the period, most of them unrecorded in histories, from archival sources in the National Archives, Kew. Then they matched the names with London property leases. Using ArcGIS, the legal inns and their owners were plotted on a series of maps covering the period 1292 to 1500. The results show a distinct pattern of ownership of the legal inns and suggest a narrative that would help explain why the Inns of Chancery became serious centers of learning during the fifteenth century. In brief, lower-ranking chancery clerks, always looking for sources of income, discovered by 1370 that legal inns could be a source of income. Since chancery clerks were intimately involved with writs and other legal forms, and since the chancery itself had a long-standing training system, these clerks opened their own legal inns to train fledgling lawyers, estate managers, and scriveners. The maps clearly show growth patterns of ownership by the chancery clerks for both legal inns and other London properties in the areas of Holborn and The Strand between 1450 and 1417. However, the maps also show that a royal ordinance of 1417 forbidding chancery clerks to live with lawyers, law students, and other non-chancery personnel had an immediate effect, and properties in that area of London leased by chancery clerks simply stop after 1417. The long-term importance of the patterns shown in the maps is that while the presence of chancery clerks in the legal inns likely created a more coherent education system, their removal forced the legal profession, suddenly without a hostelry managerial class, to professionalize the inns and legal education themselves. Given the number and social status of members of the legal inns, the effect on English education was to free legal education from the limits of chancery clerk education (the clerks were not practicing common lawyers) and to enable it to become broader in theory and practice, in fact, a kind of 'finishing school' for the governing (if not noble) class.

Keywords: GIS, law, London, education

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Neha Pranav Kolhe, Harithapriya Vijaye, Arushi Kamle

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The economic growth engines are urban regions. As the economy expands, so does the need for peace and quiet, and noise pollution is one of the important social and environmental issue. Health and wellbeing are at risk from environmental noise pollution. Because of urbanisation, population growth, and the consequent rise in the usage of increasingly potent, diverse, and highly mobile sources of noise, it is now more severe and pervasive than ever before, and it will only become worse. Additionally, it will expand as long as there is an increase in air, train, and highway traffic, which continue to be the main contributors of noise pollution. The current study will be conducted in two zones of class I city of central India (population range: 1 million–4 million). Total 56 measuring points were chosen to assess noise pollution. The first objective evaluates the noise pollution in various urban habitats determined as formal and informal settlement. It identifies the comparison of noise pollution within the settlements using T- Test analysis. The second objective assess the noise pollution in silent zones (as stated in Central Pollution Control Board) in a hierarchical way. It also assesses the noise pollution in the settlements and compares with prescribed permissible limits using class I sound level equipment. As appropriate indices, equivalent noise level on the (A) frequency weighting network, minimum sound pressure level and maximum sound pressure level were computed. The survey is conducted for a period of 1 week. Arc GIS is used to plot and map the temporal and spatial variability in urban settings. It is discovered that noise levels at most stations, particularly at heavily trafficked crossroads and subway stations, were significantly different and higher than acceptable limits and squares. The study highlights the vulnerable areas that should be considered while city planning. The study demands area level planning while preparing a development plan. It also demands attention to noise pollution from the perspective of residential and silent zones. The city planning in urban areas neglects the noise pollution assessment at city level. This contributes to that, irrespective of noise pollution guidelines, the ground reality is far away from its applicability. The result produces incompatible land use on a neighbourhood scale with respect to noise pollution. The study's final results will be useful to policymakers, architects and administrators in developing countries. This will be useful for noise pollution in urban habitat governance by efficient decision making and policy formulation to increase the profitability of these systems.

Keywords: noise pollution, formal settlements, informal settlements, built environment, silent zone, residential area

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Authors: M. Maier, I. Dedigama, J. Majasan, Y. Wu, Q. Meyer, L. Castanheira, G. Hinds, P. R. Shearing, D. J. L. Brett

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Increasing the efficiency of electrolyser technology is commonly seen as one of the main challenges on the way to the Hydrogen Economy. There is a significant lack of understanding of the different states of operation of polymer electrolyte membrane water electrolysers (PEMWE) and how these influence the overall efficiency. This in particular means the two-phase flow through the membrane, gas diffusion layers (GDL) and flow channels. In order to increase the efficiency of PEMWE and facilitate their spread as commercial hydrogen production technology, new analytic approaches have to be found. Acoustic emission (AE) offers the possibility to analyse the processes within a PEMWE in a non-destructive, fast and cheap in-situ way. This work describes the generation and analysis of AE data coming from a PEM water electrolyser, for, to the best of our knowledge, the first time in literature. Different experiments are carried out. Each experiment is designed so that only specific physical processes occur and AE solely related to one process can be measured. Therefore, a range of experimental conditions is used to induce different flow regimes within flow channels and GDL. The resulting AE data is first separated into different events, which are defined by exceeding the noise threshold. Each acoustic event consists of a number of consequent peaks and ends when the wave diminishes under the noise threshold. For all these acoustic events the following key attributes are extracted: maximum peak amplitude, duration, number of peaks, peaks before the maximum, average intensity of a peak and time till the maximum is reached. Each event is then expressed as a vector containing the normalized values for all criteria. Principal Component Analysis is performed on the resulting data, which orders the criteria by the eigenvalues of their covariance matrix. This can be used as an easy way of determining which criteria convey the most information on the acoustic data. In the following, the data is ordered in the two- or three-dimensional space formed by the most relevant criteria axes. By finding spaces in the two- or three-dimensional space only occupied by acoustic events originating from one of the three experiments it is possible to relate physical processes to certain acoustic patterns. Due to the complex nature of the AE data modern machine learning techniques are needed to recognize these patterns in-situ. Using the AE data produced before allows to train a self-learning algorithm and develop an analytical tool to diagnose different operational states in a PEMWE. Combining this technique with the measurement of polarization curves and electrochemical impedance spectroscopy allows for in-situ optimization and recognition of suboptimal states of operation.

Keywords: acoustic emission, gas diffusion layers, in-situ diagnosis, PEM water electrolyser

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Authors: Matias Hargreaves, Martin Del Valle, Diego Rodriguez, Riveros Jose Luis

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Trough years, all kind of social development projects have tried to solve social problems such as hunger, poverty, malnutrition, food insecurity, among others, with poor success. Both private and state initiatives have invested resources in several countries and communities. Nevertheless, most of these initiatives are scientific or external developers-centered, with a lack of local participation. This compromises the sustainability of any intervention and also leads to a poor empowerment of local community. The participatory approach aims to rescue and enhance the local knowledge since it recognizes that this kind of problems are better known by native actors. The objective of the study was to describe the role played by the community empowerment on food security improvement in the NGO “O Viveiro” (15°43'37.77"S; 32°46'27.53"E) and Barrio Broma village (15°43'58.78"S; 32°46'7.27"E) in Chitima, Mozambique. A center for training in goat livestock and orchard was build. A community orchard was co-constructed between foreign technicians and local actors. The prototype was installed in February, 2016 by the technician team and local community with 16 m2 as a nursery garden. Two orchard workshops were conducted in order to design a sustainable productive model which mixes both local and technological approaches. Two goat meat workshops were conducted in order to describe local methods and train the community to conduce their own techniques with high sanitary and productive standards. Technician team stayed in Mozambique until May, 2016. The quorum for the orchard workshops was 20 and 14 persons respectively, which represents 100% and 70%of the total requested quorum (20). For the goat meat workshops were 4 and 5 persons, which representa80% and 100% of the total requested quorum (5). Until August, 2016, the orchard is 3.219 m2 and it grows several vegetables as beans, chili pepper, garlic, onion, tomatoes, lettuce, sweet potato, yuca potato, cabbage, eggplant, papaya trees, mango, and cassava. The process of increasing in size and diversification of vegetables grown was led entirely by the local community. In connection with this, the local community started to harvest and began to sell the vegetable products at the local market. At the meat goat workshops, local participants rescued a local knowledge by describing and practicing a traditional way to process goat meat by drying it outdoors and then doing a smoked treatment. This information might contribute to describe the level of empowerment of this community, and thus give evidence of acceptance of foreign intervention for improving their own proceedings and traditions.

Keywords: children malnutrition, food security, Local community, participatory approach

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Authors: Hossein Havaeji, Tony Wong, Thien-My Dao

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1. Research Problems and Research Objectives: Blockchain Technology-enabled Supply Chain System (BT-enabled SCS) is the system using BT to drive SCS transparency, security, durability, and process integrity as SCS data is not always visible, available, or trusted. The costs of operating BT in the SCS are a common problem in several organizations. The costs must be estimated as they can impact existing cost control strategies. To account for system and deployment costs, it is necessary to overcome the following hurdle. The problem is that the costs of developing and running a BT in SCS are not yet clear in most cases. Many industries aiming to use BT have special attention to the importance of BT installation cost which has a direct impact on the total costs of SCS. Predicting BT installation cost in SCS may help managers decide whether BT is to be an economic advantage. The purpose of the research is to identify some main BT installation cost components in SCS needed for deeper cost analysis. We then identify and categorize the main groups of cost components in more detail to utilize them in the prediction process. The second objective is to determine the suitable Supervised Learning technique in order to predict the costs of developing and running BT in SCS in a particular case study. The last aim is to investigate how the running BT cost can be involved in the total cost of SCS. 2. Work Performed: Applied successfully in various fields, Supervised Learning is a method to set the data frame, treat the data, and train/practice the method sort. It is a learning model directed to make predictions of an outcome measurement based on a set of unforeseen input data. The following steps must be conducted to search for the objectives of our subject. The first step is to make a literature review to identify the different cost components of BT installation in SCS. Based on the literature review, we should choose some Supervised Learning methods which are suitable for BT installation cost prediction in SCS. According to the literature review, some Supervised Learning algorithms which provide us with a powerful tool to classify BT installation components and predict BT installation cost are the Support Vector Regression (SVR) algorithm, Back Propagation (BP) neural network, and Artificial Neural Network (ANN). Choosing a case study to feed data into the models comes into the third step. Finally, we will propose the best predictive performance to find the minimum BT installation costs in SCS. 3. Expected Results and Conclusion: This study tends to propose a cost prediction of BT installation in SCS with the help of Supervised Learning algorithms. At first attempt, we will select a case study in the field of BT-enabled SCS, and then use some Supervised Learning algorithms to predict BT installation cost in SCS. We continue to find the best predictive performance for developing and running BT in SCS. Finally, the paper will be presented at the conference.

Keywords: blockchain technology, blockchain technology-enabled supply chain system, installation cost, supervised learning

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Authors: Sara Bonetti

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The use of quantitative data to support policies and justify investments has become imperative in many fields including the field of education. However, the topic of data literacy has only marginally touched the early care and education (ECE) field. In California, within the ECE workforce, there is a group of professionals working in policy and advocacy that use quantitative data regularly and whose educational and professional experiences have been neglected by existing research. This study aimed at analyzing these experiences in accessing, using, and producing quantitative data. This study utilized semi-structured interviews to capture the differences in educational and professional backgrounds, policy contexts, and power relations. The participants were three key professionals from county-level organizations and one working at a State Department to allow for a broader perspective at systems level. The study followed Núñez’s multilevel model of intersectionality. The key in Núñez’s model is the intersection of multiple levels of analysis and influence, from the individual to the system level, and the identification of institutional power dynamics that perpetuate the marginalization of certain groups within society. In a similar manner, this study looked at the dynamic interaction of different influences at individual, organizational, and system levels that might intersect and affect ECE professionals’ experiences with quantitative data. At the individual level, an important element identified was the participants’ educational background, as it was possible to observe a relationship between that and their positionality, both with respect to working with data and also with respect to their power within an organization and at the policy table. For example, those with a background in child development were aware of how their formal education failed to train them in the skills that are necessary to work in policy and advocacy, and especially to work with quantitative data, compared to those with a background in administration and/or business. At the organizational level, the interviews showed a connection between the participants’ position within the organization and their organization’s position with respect to others and their degree of access to quantitative data. This in turn affected their sense of empowerment and agency in dealing with data, such as shaping what data is collected and available. These differences reflected on the interviewees’ perceptions and expectations for the ECE workforce. For example, one of the interviewees pointed out that many ECE professionals happen to use data out of the necessity of the moment. This lack of intentionality is a cause for, and at the same time translates into missed training opportunities. Another interviewee pointed out issues related to the professionalism of the ECE workforce by remarking the inadequacy of ECE students’ training in working with data. In conclusion, Núñez’s model helped understand the different elements that affect ECE professionals’ experiences with quantitative data. In particular, what was clear is that these professionals are not being provided with the necessary support and that we are not being intentional in creating data literacy skills for them, despite what is asked of them and their work.

Keywords: data literacy, early childhood professionals, intersectionality, quantitative data

Procedia PDF Downloads 218

Authors: Thomas S. Abia II

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Intel Chandler Site has internally developed its first-of-kind (FOK) facility-scale wastewater treatment system to achieve multi-metal ion exchange. The process was carried out using a serial process train of carbon filtration, pH / ORP adjustment, and cationic exchange purification to treat dilute metal wastewater (DMW) discharged from a substrate packaging factory. Spanning a trial period of 10 months, a total of 3,271 samples were collected and statistically analyzed (average baseline + standard deviation) to evaluate the performance of a 95-gpm, multi-reactor continuous copper ion exchange treatment system that was consequently retrofitted for manganese ion exchange to meet environmental regulations. The system is also equipped with an inline acid and hot caustic regeneration system to rejuvenate exhausted IX resins and occasionally remove surface crud. Data generated from lab-scale studies was transferred to system operating modifications following multiple trial-and-error experiments. Despite the DMW treatment system failing to meet internal performance specifications for manganese output, it was observed to remove the cation notwithstanding the prevalence of copper in the waste stream. Accordingly, the average manganese output declined from 6.5 + 5.6 mg¹L⁻¹ at pre-pilot to 1.1 + 1.2 mg¹L⁻¹ post-pilot (83% baseline reduction). This milestone was achieved regardless of the average influent manganese to DMW increasing from 1.0 + 13.7 mg¹L⁻¹ at pre-pilot to 2.1 + 0.2 mg¹L⁻¹ post-pilot (110% baseline uptick). Likewise, the pre-trial and post-trial average influent copper values to DMW were 22.4 + 10.2 mg¹L⁻¹ and 32.1 + 39.1 mg¹L⁻¹, respectively (43% baseline increase). As a result, the pre-trial and post-trial average copper output values were 0.1 + 0.5 mg¹L⁻¹ and 0.4 + 1.2 mg¹L⁻¹, respectively (300% baseline uptick). Conclusively, the operating pH range upstream of treatment (between 3.5 and 5) was shown to be the largest single point of influence for optimizing manganese uptake during multi-metal ion exchange. However, the high variability of the influent copper-to-manganese ratio was observed to adversely impact the system functionality. The journal herein intends to discuss the operating parameters such as pH and oxidation-reduction potential (ORP) that were shown to influence the functional versatility of the ion exchange system significantly. The literature also proposes to discuss limitations of the treatment system such as influent copper-to-manganese ratio variations, operational configuration, waste by-product management, and system recovery requirements to provide a balanced assessment of the multi-metal ion exchange process. The take-away from this literature is intended to analyze the overall feasibility of ion exchange for metals manufacturing facilities that lack the capability to expand hardware due to real estate restrictions, aggressive schedules, or budgetary constraints.

Keywords: copper, industrial wastewater treatment, multi-metal ion exchange, manganese

Procedia PDF Downloads 112

Authors: E. O. Egieyeh, N. Butler, R. Coetzee, M. Van Huyssteen, A. Bheekie

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Background: High rate of maternal and child mortality is a global concern. Nationally, it constitutes one of South Africa’s quadruple burdens of diseases. Pharmacists have a crucial role in maternal and child health care delivery and as such should be equipped with adequate knowledge and skill required to contribute to maternal and child well-being. The International Pharmaceutical Federation statement of policy (2013) outlines pharmacist-led interventions in accordance with the World Health Organisation’s interventions in maternal, new-born and child health care. The South African Pharmacy Council’s guideline on Good Pharmacy Practice (2010) also stipulates the minimum standards required to participate in reproductive, maternal and child care. Pharmacy schools are obliged to train pharmacy students to meet priority health needs of the population so that graduates are ‘fit for purpose’. The purpose of the study is to evaluate the knowledge and skill of final year pharmacy students at a university in South Africa to determine their preparedness to contribute effectively to maternal and child health care. Method: A quantitative, descriptive, non-randomized baseline study was conducted among the final year students at the School of Pharmacy. Data was collected using a questionnaire designed in sections to assess knowledge of contraception, maternal and child health directed at the primary care level and framed within the scope of practice required of an entry-level generalist pharmacist. Participants’ skill in infant growth assessment was assessed in a section of the questionnaire in a written format. Participants ticked the topics they had been exposed to on a curriculum content assessment tool which was not graded. A pilot study examined the clarity and suitability of question items, and duration to complete the questionnaire. A score of 50% in each section of the questionnaire indicated a pass. The questionnaire was delivered in campus lecture venue. Results: Of the 102 students in final year, 53 (52%) students consented to participate in the study. Only 13.2% of participants scored above 50% in each section. Forty five (85%) participants scored above 50% in the contraception section while 40 (75%) scored less than 50% in the skills assessment. Less than half (45.3%) of the participants had a total score above 50%. Being a parent or working part-time as pharmacist assistance did not have any influence on the performance of the participants. Evaluation of participants’ curriculum content exposure showed differences in exposure to the various topics. Exposure to contraception teaching received the most recognition. Conclusion: Maternal and child health curriculum content should be reviewed at the university to enhance the knowledge and skill of pharmacy graduates.

Keywords: final year pharmacy students, knowledge and skill, maternal and child health, South Africa

Procedia PDF Downloads 119

Authors: C. M. Sapkota, B. P. Sapkota

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Background: Continuous developments in science and technology are increasing the availability of thousands of medical devices – all of which should be of good quality and used appropriately to address global health challenges. It is obvious that bio medical devices are becoming ever more indispensable in health service delivery and among the key workforce responsible for their design, development, regulation, evaluation and training in their use: biomedical technician (BMET) is the crucial. As a pivotal member of health workforce, biomedical technicians are an essential component of the quality health service delivery mechanism supporting the attainment of the Sustainable Development Goals. Methods: The study was based on cross sectional descriptive design. Indicators measuring the quality of health services were assessed in Mechi Zonal Hospital (MZH) and Sagarmatha Zonal Hospital (SZH). Indicators were calculated based on the data about hospital utilization and performance of 2018 available in Medical record section of both hospitals. MZH had employed the BMET during 2018 but SZH had no BMET in 2018.Focus Group Discussion with health workers in both hospitals was conducted to validate the hospital records. Client exit interview was conducted to assess the level of client satisfaction in both the hospitals. Results: In MZH there was round the clock availability and utilization of Radio diagnostics equipment, Laboratory equipment. Operation Theater was functional throughout the year. Bed Occupancy rate in MZH was 97% but in SZH it was only 63%.In SZH, OT was functional only 54% of the days in 2018. CT scan machine was just installed but not functional. Computerized X-Ray in SZH was functional only in 72% of the days. Level of client satisfaction was 87% in MZH but was just 43% in SZH. MZH performed all (256) the Caesarean Sections but SZH performed only 36% of 210 Caesarean Sections in 2018. In annual performance ranking of Government Hospitals, MZH was placed in 1st rank while as SZH was placed in 19th rank out of 32 referral hospitals nationwide in 2018. Conclusion: Biomedical technicians are the crucial member of the human resource for health team with the pivotal role. Trained and qualified BMET professionals are required within health-care systems in order to design, evaluate, regulate, acquire, maintain, manage and train on safe medical technologies. Applying knowledge of engineering and technology to health-care systems to ensure availability, affordability, accessibility, acceptability and utilization of the safer, higher quality, effective, appropriate and socially acceptable bio medical technology to populations for preventive, promotive, curative, rehabilitative and palliative care across all levels of the health service delivery.

Keywords: biomedical equipment technicians, BMET, human resources for health, HRH, quality health service, rural hospitals

Procedia PDF Downloads 96

Authors: Rochelle Yun Ge

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University has played an active role in the country’s development in China. There has been an increasing research interest on the development of higher education cooperation, talent cultivation and attraction, and innovation in the regional development. The Triple Helix model, which indicates that regional innovation and development can be engendered by collaboration among university, industry and government, is often adopted as research framework. The research using triple helix model emphasizes the active and often leading role of university in knowledge-based economy. Within this framework, universities are conceptualized as key institutions of knowledge production, transmission and transference potentially making critical contributions to regional development. Recent research almost uniformly consistent in indicating the high-level research labours (i.e., doctoral, post-doctoral researchers and academics) as important actors in the innovation ecosystem with their cross-geographical human capital and resources presented. In 2019, the development of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) was officially launched as an important strategy by the Chinese government to boost the regional development of the Pearl River Delta and to support the realization of “One Belt One Road” strategy. Human Capital formation is at the center of this plan. One of the strategic goals of the GBA development is set to evolve into an international educational hub and innovation center with high-level talents. A number of policies have been issued to attract and cultivate human resources in different GBA cities, in particular for the high-level R&D (research and development) talents such as doctoral and post-doctoral researchers. To better understand the development of high-level talents hub in the GBA, more empirical considerations should be given to explore the approaches of talents cultivation and attraction in the GBA. What remains to explore is the ways to better attract, train, support and retain these talents in the cross-systems context. This paper aims to investigate the role of university in human capital development under China’s national agenda of GBA integration through the lens of universities and actors. Two flagship comprehensive universities are selected to be the cases and 30 interviews with university officials, research leaders, post-doctors and doctoral candidates are used for analysis. In particular, we look at in what ways have universities aligned their strategies and practices to the Chinese government’s GBA development strategy? What strategies and practices have been developed by universities for the cultivation and attraction of high-level research labor? And what impacts the universities have made for the regional development? The main arguments of this research highlights the specific ways in which universities in smaller sub-regions can collaborate in high-level human capital formation and the role policy can play in facilitating such collaborations.

Keywords: university, human capital, regional development, triple-helix model

Procedia PDF Downloads 73

Authors: Tam Viet Hoang

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With a plethora of new mobility services and payment systems found in our cities and across modern public transportation systems, several cities globally have turned to common ticketing systems to help navigate this complexity. Helping to create time and space-differentiated fare structures and tariff schemes, common ticketing systems can optimize transport utilization rates, achieve cost efficiencies, and provide key incentives to specific target groups. However, not all cities and transportation systems have enjoyed a smooth journey towards the adoption, roll-out, and servicing of common ticketing systems, with both the experiences of success and failure being attributed to a wide variety of critical factors. Using case study research as a methodology and cities as the main unit of analysis, this research will seek to address the fundamental question of “what are the critical factors for the success and failure of common ticketing systems?” Using rail/train systems as the entry point for this study will start by providing a background to the evolution of transport ticketing and justify the improvements in operational efficiency that can be achieved through common ticketing systems. Examining the socio-economic benefits of common ticketing, the research will also help to articulate the value derived for different key identified stakeholder groups. By reviewing case studies of the implementation of common ticketing systems in different cities, the research will explore lessons learned from cities with the aim to elicit factors to ensure seamless connectivity integrated e-ticketing platforms. In an increasingly digital age and where cities are now coming online, this paper seeks to unpack these critical factors, undertaking case study research drawing from literature and lived experiences. Offering us a better understanding of the enabling environment and ideal mixture of ingredients to facilitate the successful roll-out of a common ticketing system, interviews will be conducted with transport operators from several selected cities to better appreciate the challenges and strategies employed to overcome those challenges in relation to common ticketing systems. Meanwhile, as we begin to see the introduction of new mobile applications and user interfaces to facilitate ticketing and payment as part of the transport journey, we take stock of numerous policy challenges ahead and implications on city-wide and system-wide urban planning. It is hoped that this study will help to identify the critical factors for the success and failure of common ticketing systems for cities set to embark on their implementation while serving to fine-tune processes in those cities where common ticketing systems are already in place. Outcomes from the study will help to facilitate an improved understanding of common pitfalls and essential milestones towards the roll-out of a common ticketing system for railway systems, especially for emerging countries where mass rapid transit transport systems are being considered or in the process of construction.

Keywords: common ticketing, public transport, urban strategies, Bangkok, Fukuoka, Sydney

Procedia PDF Downloads 47

Authors: Christina Klippel, Lucia Pezzi, Silvio Neto, Rafael Bertani, Priscila Mendes, Flavio Machado, Aline Szeliga, Maria Cosendey, Adilson Mariz, Raquel Santos, Lys Bendett, Pedro Velasco, Thalita Rolleigh, Bruna Bellote, Daria Coelho, Bruna Martins, Julia Almeida, Juliana Cerqueira

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This project presents a framework for teaching intracranial pressure monitoring (ICP) concepts using a low-cost experimental model in a neurointensive care education program. Data concerning ICP monitoring contribute to the patient's clinical assessment and may dictate the course of action of a health team (nursing, medical staff) and influence decisions to determine the appropriate intervention. This study aims to present a safe method for teaching ICP monitoring to medical students in a Simulation Center. Methodology: Medical school teachers, along with students from the 4th year, built an experimental model for teaching ICP measurement. The model consists of a mannequin's head with a plastic bag inside simulating the cerebral ventricle and an inserted ventricular catheter connected to the ICP monitoring system. The bag simulating the ventricle can also be changed for others containing bloody or infected simulated cerebrospinal fluid. On the mannequin's ear, there is a blue point indicating the right place to set the "zero point" for accurate pressure reading. The educational program includes four steps: 1st - Students receive a script on ICP measurement for reading before training; 2nd - Students watch a video about the subject created in the Simulation Center demonstrating each step of the ICP monitoring and the proper care, such as: correct positioning of the patient, anatomical structures to establish the zero point for ICP measurement and a secure range of ICP; 3rd - Students train the procedure in the model. Teachers help students during training; 4th - Student assessment based on a checklist form. Feedback and correction of wrong actions. Results: Students expressed interest in learning ICP monitoring. Tests concerning the hit rate are still being performed. ICP's final results and video will be shown at the event. Conclusion: The study of intracranial pressure measurement based on an experimental model consists of an effective and controlled method of learning and research, more appropriate for teaching neurointensive care practices. Assessment based on a checklist form helps teachers keep track of student learning progress. This project offers medical students a safe method to develop intensive neurological monitoring skills for clinical assessment of patients with neurological disorders.

Keywords: neurology, intracranial pressure, medical education, simulation

Procedia PDF Downloads 136

Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

Procedia PDF Downloads 77

Authors: André Augusto Ceballos Melo

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Design to facilitate the recognition of congruent prosthetic movements, context-to-motion translations guided by image, verbal prompt, users nonverbal communication such as facial expressions, gestures, paralinguistics, scene context, and object recognition contributes to this process though it can also be applied to other tasks, such as walking, Prosthetic limbs as assistive technology through gestures, sound codes, signs, facial, body expressions, and scene context The context-to-motion model is a machine learning approach that is designed to improve the control and dexterity of prosthetic limbs. It works by using sensory input from the prosthetic limb to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. This can help to improve the performance of the prosthetic limb and make it easier for the user to perform a wide range of tasks. There are several key benefits to using the context-to-motion model for prosthetic limb control. First, it can help to improve the naturalness and smoothness of prosthetic limb movements, which can make them more comfortable and easier to use for the user. Second, it can help to improve the accuracy and precision of prosthetic limb movements, which can be particularly useful for tasks that require fine motor control. Finally, the context-to-motion model can be trained using a variety of different sensory inputs, which makes it adaptable to a wide range of prosthetic limb designs and environments. Stable diffusion is a machine learning method that can be used to improve the control and stability of movements in robotic and prosthetic systems. It works by using sensory feedback to learn about the dynamics of the environment and then using this information to generate smooth, stable movements. One key aspect of stable diffusion is that it is designed to be robust to noise and uncertainty in the sensory feedback. This means that it can continue to produce stable, smooth movements even when the sensory data is noisy or unreliable. To implement stable diffusion in a robotic or prosthetic system, it is typically necessary to first collect a dataset of examples of the desired movements. This dataset can then be used to train a machine learning model to predict the appropriate control inputs for a given set of sensory observations. Once the model has been trained, it can be used to control the robotic or prosthetic system in real-time. The model receives sensory input from the system and uses it to generate control signals that drive the motors or actuators responsible for moving the system. Overall, the use of the context-to-motion model has the potential to significantly improve the dexterity and performance of prosthetic limbs, making them more useful and effective for a wide range of users Hand Gesture Body Language Influence Communication to social interaction, offering a possibility for users to maximize their quality of life, social interaction, and gesture communication.

Keywords: stable diffusion, neural interface, smart prosthetic, augmenting

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Authors: Adesuwa Omorede, Rufai Haruna Kilu

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Affective motivation (motivation that is emotionally laden usually related to affect, passion, emotions, moods) in the workplace stimulates individuals to reinforce, persist and commit to their task, which leads to the individual and organizational performance. This leads individuals to reach goals especially in situations where task are highly challenging and hostile. In such situations, individuals are more disposed to be more creative, innovative and see new opportunities from the loopholes in their workplace. However, when individuals feel displaced and less important, an adverse reaction may suffice which may be detrimental to the organization and its performance. One sector where affective motivation is eminently present and relevant, is the mining industry. Due to its intense work environment; mostly dominated by men and masculinity cultures; and deliberate exclusion of women in this environment which, makes the women working in these environments to feel marginalized. In Ghana, the mining industry is mostly seen as a very physical environment especially underground and mostly considerd as 'no place for a woman'. Despite the fact that these women feel less 'needed' or 'appreciated' in such environments, they still have to juggle between intense work shifts; face violence and other health risks with their families, which put a strain on their affective motivational reaction. Beyond these challenges, however, several mining companies in Ghana today are working towards providing a fair and equal working situation for both men and women miners, by recognizing them as key stakeholders, as well as including them in the stages of mining projects from the planning and designing phase to the evaluation and implementation stage. Drawing from the psychology and gender literature, this study takes a narrative approach to identify and understand the shifting gender dynamics within the mine works in Ghana, occasioning a change in background disposition of miners, which leads to more women taking up mine jobs in the country. In doing so, a qualitative study was conducted using semi-structured interviews from Ghana. Several women working within the mining industries in Ghana shared their experiences and how they felt and still feel in their workplace. In addition, archival documents were gathered to support the findings. The results suggest a change in enrolment regimes in a mining and technology university in Ghana, making room for a more gender equal enrolments in the university. A renowned university that train and feed mine work professional into the industry. The results further acknowledge gender equal and diversity recruitment policies and initiatives among the mining companies of Ghana. This study contributes to the psychology and gender literature by highlighting the hindrances women face in the mining industry as well as highlighting several of their affective reactions towards gender inequality. The study also provides several suggestions for decision makers in the mining industry of what can be done in the future to reduce the gender inequality gap within the industry.

Keywords: affective motivation, gender shape shifting, mining industry, women miners

Procedia PDF Downloads 265

Authors: Tiffany Hollis

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Childhood trauma is well documented in mental health research, yet has received little attention in urban schools. Child trauma affects brain development and impacts cognitive, emotional, and behavioral functioning. When educators understand that some of the behaviors that appear to be aggressive in nature might be the result of a hidden diagnosis of trauma, learning can take place, and the child can thrive in the classroom setting. Traumatized children, however, do not fit neatly into any single ‘box.’ Although many children enter school each day carrying with them the experience of exposure to violence in the home, the symptoms of their trauma can be multifaceted and complex, requiring individualized therapeutic attention. The purpose of this study was to examine how prepared educators are to address the unique challenges facing children who experience trauma. Given the vast number of traumatized children in our society, it is evident that our education system must investigate ways to create an optimal learning environment that accounts for trauma, addresses its impact on cognitive and behavioral development, and facilitates mental and emotional health and well-being. The researcher describes the knowledge, attitudes, dispositions, and skills relating to trauma-informed knowledge of induction level teachers in a diverse middle school. The data for this study were collected through interviews with teachers, who are in the induction phase (the first three years of their teaching career). The study findings paint a clear picture of how ill-prepared educators are to address the needs of students who have experienced trauma and the implications for the development of a professional development workshop or series of workshops that train teachers how to recognize and address and respond to the needs of students. The study shows how teachers often lack skills to meet the needs of students who have experienced trauma. Traumatized children regularly carry a heavy weight on their shoulders. Children who have experienced trauma may feel that the world is filled with unresponsive, threatening adults, and peers. Despite this, supportive interventions can provide traumatized children with places to go that are safe, stimulating, and even fun. Schools offer an environment that potentially meets these requirements by creating safe spaces where students can feel at ease and have fun while also learning via stimulating educational activities. This study highlights the lack of preparedness of educators to address the academic, behavioral, and cognitive needs of students who have experienced trauma. These findings provide implications for the creation of a professional development workshop that addresses how to recognize and address the needs of students who have experienced some type of trauma. They also provide implications for future research with a focus on specific interventions that enable the creation of optimal learning environments where students who have experienced trauma and all students can succeed, regardless of their life experiences.

Keywords: educator preparation, induction educators, professional development, trauma-informed

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Authors: Ming Zhou, Taeho Park

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Early stage technologies have been particularly challenging to manage due to high degrees of their numerous uncertainties. Most research results directly out of a research lab tend to be at their early, if not the infant stage. A long while uncertain commercialization process awaits these lab results. The majority of such lab technologies go nowhere and never get commercialized due to various reasons. Any efforts or financial resources put into managing these technologies turn fruitless. High stake naturally calls for better results, which make a patenting decision harder to make. A good and well protected patent goes a long way for commercialization of the technology. Our preliminary research showed that there was not a simple yet productive procedure for such valuation. Most of the studies now have been theoretical and overly comprehensive where practical suggestions were non-existent. Hence, we attempted to develop a simple and highly implementable procedure for efficient and scalable valuation. We thoroughly reviewed existing research, interviewed practitioners in the Silicon Valley area, and surveyed university technology offices. Instead of presenting another theoretical and exhaustive research, we aimed at developing a practical guidance that a government agency and/or university office could easily deploy and get things moving to later steps of managing early stage technologies. We provided a procedure to thriftily value and make the patenting decision. A patenting index was developed using survey data and expert opinions. We identified the most important factors to be used in the patenting decision using survey ratings. The rating then assisted us in generating good relative weights for the later scoring and weighted averaging step. More importantly, we validated our procedure by testing it with our practitioner contacts. Their inputs produced a general yet highly practical cut schedule. Such schedule of realistic practices has yet to be witnessed our current research. Although a technology office may choose to deviate from our cuts, what we offered here at least provided a simple and meaningful starting point. This procedure was welcomed by practitioners in our expert panel and university officers in our interview group. This research contributed to our current understanding and practices of managing early stage technologies by instating a heuristically simple yet theoretical solid method for the patenting decision. Our findings generated top decision factors, decision processes and decision thresholds of key parameters. This research offered a more practical perspective which further completed our extant knowledge. Our results could be impacted by our sample size and even biased a bit by our focus on the Silicon Valley area. Future research, blessed with bigger data size and more insights, may want to further train and validate our parameter values in order to obtain more consistent results and analyze our decision factors for different industries.

Keywords: technology management, early stage technology, patent, decision

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Authors: Lian Kösters, Wendy Smits, Raymond Montizaan

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The number of solo self-employed in the Netherlands has been increasing for years. The relative increase is among the largest in the EU. To explain this increase, most studies have focused on the supply side, workers who offer themselves as solo self-employed. The number of studies that focus on the demand side, the employer who hires the solo self-employed, is still scarce. Studies into employer behaviour conducted until now show that employers mainly choose self-employed workers when they have a temporary need for specialist knowledge, but also during projects or production peaks. These studies do not provide insight into the employers’ considerations for different contract types. In this study, interviews with employers were conducted, and available literature was consulted to provide an overview of the several factors employers use to compare different contract types. That input was used to set up a vignette study. This was carried out at the end of 2021 among almost 1000 business owners, HR managers, and business leaders of Dutch companies. Each respondent was given two sets of five fictitious candidates for two possible positions in their organization. They were asked to rank these candidates. The positions varied with regard to the type of tasks (core tasks or support tasks) and the time it took to train new people for the position. The respondents were asked additional questions about the positions, such as the required level of education, the duration, and the degree of predictability of tasks. The fictitious candidates varied, among other things, in the type of contract on which they would come to work for the organization. The results were analyzed using a rank-ordered logit analysis. This vignette setup makes it possible to see which factors are most important for employers when choosing to hire a solo self-employed person compared to other contracts. The results show that there are no indications that employers would want to hire solo self-employed workers en masse. They prefer regular employee contracts. The probability of being chosen with a solo self-employed contract over someone who comes to work as a temporary employee is 32 percent. This probability is even lower than for on-call and temporary agency workers. For a permanent contract, this probability is 46 percent. The results provide indications that employers consider knowledge and skills more important than the solo self-employed contract and that this can compensate. A solo self-employed candidate with 10 years of work experience has a 63 percent probability of being found attractive by an employer compared to a temporary employee without work experience. This suggests that employers are willing to give someone a less attractive contract for the employer if the worker so wishes. The results also show that the probability that a solo self-employed person is preferred over a candidate with a temporary employee contract is somewhat higher in business economics, administrative and technical professions. No significant results were found for factors where it was expected that solo self-employed workers are preferred more often, such as for unpredictable or temporary work.

Keywords: employer behaviour, rank-ordered logit analysis, solo self-employment, temporary contract, vignette study

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Authors: Christian Andrä, Pauline Hähner, Sebastian Ludyga

Abstract:

Introduction: In the recent past, discussions on the importance of physical activity for child development have contributed to a growing interest in executive functions, which refer to cognitive processes. By controlling, modulating and coordinating sub-processes, they make it possible to achieve superior goals. Major components include working memory, inhibition and cognitive flexibility. While executive functions can be trained easily in school children, there are still research deficits regarding the trainability during preschool age. Methodology: This quasi-experimental study with pre- and post-design analyzes 23 children [age: 5.0 (mean value) ± 0.7 (standard deviation)] from four different sports groups. The intervention group was made up of 13 children (IG: 4.9 ± 0.6), while the control group consisted of ten children (CG: 5.1 ± 0.9). Between pre-test and post-test, children from the intervention group participated special games that train executive functions (i.e., changing rules of the game, introduction of new stimuli in familiar games) for ten units of their weekly sports program. The sports program of the control group was not modified. A computer-based version of the Eriksen Flanker Task was employed in order to analyze the participants’ inhibition ability. In two rounds, the participants had to respond 50 times and as fast as possible to a certain target (direction of sight of a fish; the target was always placed in a central position between five fish). Congruent (all fish have the same direction of sight) and incongruent (central fish faces opposite direction) stimuli were used. Relevant parameters were response time and accuracy. The main objective was to investigate whether children from the intervention group show more improvement in the two parameters than the children from the control group. Major findings: The intervention group revealed significant improvements in congruent response time (pre: 1.34 s, post: 1.12 s, p<.01), while the control group did not show any statistically relevant difference (pre: 1.31 s, post: 1.24 s). Likewise, the comparison of incongruent response times indicates a comparable result (IG: pre: 1.44 s, post: 1.25 s, p<.05 vs. CG: pre: 1.38 s, post: 1.38 s). In terms of accuracy for congruent stimuli, the intervention group showed significant improvements (pre: 90.1 %, post: 95.9 %, p<.01). In contrast, no significant improvement was found for the control group (pre: 88.8 %, post: 92.9 %). Vice versa, the intervention group did not display any significant results for incongruent stimuli (pre: 74.9 %, post: 83.5 %), while the control group revealed a significant difference (pre: 68.9 %, post: 80.3 %, p<.01). The analysis of three out of four criteria demonstrates that children who took part in a special sports program improved more than children who did not. The contrary results for the last criterion could be caused by the control group’s low results from the pre-test. Conclusion: The findings illustrate that inhibition can be trained as early as in preschool age. The combination of familiar games with increased requirements for attention and control processes appears to be particularly suitable.

Keywords: executive functions, flanker task, inhibition, preschool children

Procedia PDF Downloads 224

Authors: Majed A. Alsalem

Abstract:

In our contemporary world, literacy is an essential skill that enables students to increase their efficiency in managing the many assignments they receive that require understanding and knowledge of the world around them. In addition, literacy enhances student participation in society improving their ability to learn about the world and interact with others and facilitating the exchange of ideas and sharing of knowledge. Therefore, literacy needs to be studied and understood in its full range of contexts. It should be seen as social and cultural practices with historical, political, and economic implications. This study aims to rebuild and reorganize the instructional designs that have been used for deaf and hard-of-hearing (DHH) students to improve their literacy level. The most critical part of this process is the teachers; therefore, teachers will be the center focus of this study. Teachers’ main job is to increase students’ performance by fostering strategies through collaborative teamwork, higher-order thinking, and effective use of new information technologies. Teachers, as primary leaders in the learning process, should be aware of new strategies, approaches, methods, and frameworks of teaching in order to apply them to their instruction. Literacy from a wider view means acquisition of adequate and relevant reading skills that enable progression in one’s career and lifestyle while keeping up with current and emerging innovations and trends. Moreover, the nature of literacy is changing rapidly. The notion of new literacy changed the traditional meaning of literacy, which is the ability to read and write. New literacy refers to the ability to effectively and critically navigate, evaluate, and create information using a range of digital technologies. The term new literacy has received a lot of attention in the education field over the last few years. New literacy provides multiple ways of engagement, especially to those with disabilities and other diverse learning needs. For example, using a number of online tools in the classroom provides students with disabilities new ways to engage with the content, take in information, and express their understanding of this content. This study will provide teachers with the highest quality of training sessions to meet the needs of DHH students so as to increase their literacy levels. This study will build a platform between regular instructional designs and digital materials that students can interact with. The intervention that will be applied in this study will be to train teachers of DHH to base their instructional designs on the notion of Technology Acceptance Model (TAM) theory. Based on the power analysis that has been done for this study, 98 teachers are needed to be included in this study. This study will choose teachers randomly to increase internal and external validity and to provide a representative sample from the population that this study aims to measure and provide the base for future and further studies. This study is still in process and the initial results are promising by showing how students have engaged with digital books.

Keywords: deaf and hard of hearing, digital books, literacy, technology

Procedia PDF Downloads 458