Search results for: train passenger

Authors: Wen-Te Chang, Yun-Hsin Pai

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Purpose: Based on the interdisciplinary of lower grade Elementary School with the integration of STEAM concept, related wooden toy and pedagogy plans were developed and evaluated. The research goal was to benefit elementary school education. Design/methodology/approach: The subjects were teachers from two primary school teachers and students from the department of design of universities in Taipei. Amount of 103participants (Male: 34, Female: 69) were invited to participate in the research. The research tools are “STEAM toy design” and “questionnaire of STEAM toy Pedagogy plan.” The STEAM toy pedagogy plans were evaluated after the activity of “The interdisciplinary literacy discipline guiding study program--STEAM wooden workshop,” Finding/results: The study results: (1) As factors analyzing of the questionnaire indicated the percentage on the major factors were cognition teaching 68.61%, affection 80.18% and technique 80.14%, with α=.936 of validity. The assessment tools were proved to be valid for STEAM pedagogy plan evaluation; (2) The analysis of the questionnaires investigation confirmed that the main effect of the teaching factors was not significant (affection = technique = cognition); however, the interaction between STEAM factors revealed to be significant (F (8, 1164) =5.51, p < .01); (3) The main effect of the six pedagogy plans was significant (climbing toy > bird toy = gondola toy > frog castanets > train toy > balancing toy), and an interactive effect between STEAM factors also reached a significant level, (F (8, 1164) =5.51, p < .01), especially on the artistic (A/ Art) aspect. Originality/value: The main achievement of research: (1) A pedagogy plan evaluation was successfully developed. (2) The interactive effect between the STEAM and the teaching factors reached a significant level. (3) An interactive effect between the STEAM factors and the pedagogy plans reached a significant level too.

Keywords: STEAM, toy design, pedagogy plans, evaluation

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Raghavi C. Janaswamy

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In the healthcare sector, heterogenous data elements such as patients, diagnosis, symptoms, conditions, observation text from physician notes, and prescriptions form the essentials of the Electronic Health Record (EHR). The data in the form of clear text and images are stored or processed in a relational format in most systems. However, the intrinsic structure restrictions and complex joins of relational databases limit the widespread utility. In this regard, the design and development of realistic mapping and deep connections as real-time objects offer unparallel advantages. Herein, a graph neural network-based classification of EHR data has been developed. The patient conditions have been predicted as a node classification task using a graph-based open source EHR data, Synthea Database, stored in Tigergraph. The Synthea DB dataset is leveraged due to its closer representation of the real-time data and being voluminous. The graph model is built from the EHR heterogeneous data using python modules, namely, pyTigerGraph to get nodes and edges from the Tigergraph database, PyTorch to tensorize the nodes and edges, PyTorch-Geometric (PyG) to train the Graph Neural Network (GNN) and adopt the self-supervised learning techniques with the AutoEncoders to generate the node embeddings and eventually perform the node classifications using the node embeddings. The model predicts patient conditions ranging from common to rare situations. The outcome is deemed to open up opportunities for data querying toward better predictions and accuracy.

Keywords: electronic health record, graph neural network, heterogeneous data, prediction

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Authors: Nasim Eslamirad, Mahdi Rasoulinezhad, Francesco De Luca, Sadok Ben Yahia, Kimmo Sakari Lylykangas, Francesco Pilla

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With the ongoing urbanization, cities face increasing environmental challenges impacting human well-being. To tackle these issues, data-driven approaches in urban analysis have gained prominence, leveraging urban data to promote sustainability. Integrating Machine Learning techniques enables researchers to analyze and predict complex environmental phenomena like Urban Heat Island occurrences in urban areas. This paper demonstrates the implementation of data-driven approach and interpretable Machine Learning algorithms with interpretability techniques to conduct comprehensive data analyses for sustainable urban design. The developed framework and algorithms are demonstrated for Tallinn, Estonia to develop sustainable urban strategies to mitigate urban heat waves. Geospatial data, preprocessed and labeled with UHI levels, are used to train various ML models, with Logistic Regression emerging as the best-performing model based on evaluation metrics to derive a mathematical equation representing the area with UHI or without UHI effects, providing insights into UHI occurrences based on buildings and urban features. The derived formula highlights the importance of building volume, height, area, and shape length to create an urban environment with UHI impact. The data-driven approach and derived equation inform mitigation strategies and sustainable urban development in Tallinn and offer valuable guidance for other locations with varying climates.

Keywords: data-driven approach, machine learning transparent models, interpretable machine learning models, urban heat island effect

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

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Authors: Mohamed Zellagui, Heba Ahmed Hassan

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This paper proposes a new approach for the calculation of short-circuit parameters in the presence of Pulse Width Modulated based Series Compensator (PWMSC). PWMSC is a newly Flexible Alternating Current Transmission System (FACTS) device that can modulate the impedance of a transmission line through applying a variation to the duty cycle (D) of a train of pulses with fixed frequency. This results in an improvement of the system performance as it provides virtual compensation of distribution line impedance by injecting controllable apparent reactance in series with the distribution line. This controllable reactance can operate in both capacitive and inductive modes and this makes PWMSC highly effective in controlling the power flow and increasing system stability in the system. The purpose of this work is to study the impact of fault resistance (RF) which varies between 0 to 30 Ω on the fault current calculations in case of a ground fault and a fixed fault location. The case study is for a medium voltage (MV) Algerian distribution line which is compensated by PWMSC in the 30 kV Algerian distribution power network. The analysis is based on symmetrical components method which involves the calculations of symmetrical components of currents and voltages, without and with PWMSC in both cases of maximum and minimum duty cycle value for capacitive and inductive modes. The paper presents simulation results which are verified by the theoretical analysis.

Keywords: pulse width modulated series compensator (pwmsc), duty cycle, distribution line, short-circuit calculations, ground fault, symmetrical components method

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Authors: Hillary Jo Muyalde

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Introduction: Elite athletes train regularly to enhance their physical endurance, but sometimes, training sessions are not enough. When competition comes, these athletes struggle to find focus. Mental imagery is a psychological technique that helps condition the mind to focus and eventually help improve performance. This study aims to help elite competitive swimmers of the University of the East improve their performance with Mental Imagery as an auxiliary tool. Methodology: The study design used was quasi-experimental with a purposive sampling technique and a within-subject design. It was conducted with a total of 41 participants. The participants were given a Sport Imagery Ability Questionnaire (SIAQ) to measure imagery ability and the Mental Imagery Program. The study utilized a Paired T-test for data analysis where the participants underwent six weeks of no mental imagery training and were compared to six weeks with the Mental Imagery Program (MIP). The researcher recorded the personal best time of participants in their respective specialty stroke. Results: The results of the study showed a t-value of 17.804 for Butterfly stroke events, 9.922 for Backstroke events, 7.787 for Breaststroke events, and 17.440 in Freestyle. This indicated that MIP had a positive effect on participants’ performance. The SIAQ result also showed a big difference where -10.443 for Butterfly events, -5.363 for Backstroke, -7.244 for Breaststroke events, and -10.727 for Freestyle events, which meant the participants were able to image better than before MIP. Conclusion: In conclusion, the findings of this study showed that there is indeed an improvement in the performance of the participants after the application of the Mental Imagery Program. It is recommended from this study that the participants continue to use mental imagery as an auxiliary tool to their training regimen for continuous positive results.

Keywords: mental Imagery, personal best time, SIAQ, specialty stroke

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Authors: Gurukiran Veerapur, Nytik Birudavolu, Seetharam U. N., Chandravva Hebbi, R. Praneeth Reddy

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This work presents a robust approach for the recognition of characters in Telugu and Kannada, two South Indian scripts with structural similarities in characters. To recognize the characters exhaustive datasets are required, but there are only a few publicly available datasets. As a result, we decided to create a dataset for one language (source language),train the model with it, and then test it with the target language.Telugu is the target language in this work, whereas Kannada is the source language. The suggested method makes use of Canny edge features to increase character identification accuracy on pictures with noise and different lighting. A dataset of 45,150 images containing printed Kannada characters was created. The Nudi software was used to automatically generate printed Kannada characters with different writing styles and variations. Manual labelling was employed to ensure the accuracy of the character labels. The deep learning models like CNN (Convolutional Neural Network) and Visual Attention neural network (VAN) are used to experiment with the dataset. A Visual Attention neural network (VAN) architecture was adopted, incorporating additional channels for Canny edge features as the results obtained were good with this approach. The model's accuracy on the combined Telugu and Kannada test dataset was an outstanding 97.3%. Performance was better with Canny edge characteristics applied than with a model that solely used the original grayscale images. The accuracy of the model was found to be 80.11% for Telugu characters and 98.01% for Kannada words when it was tested with these languages. This model, which makes use of cutting-edge machine learning techniques, shows excellent accuracy when identifying and categorizing characters from these scripts.

Keywords: base characters, modifiers, guninthalu, aksharas, vattakshara, VAN

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Authors: Chao Li, Lei Zhou, Ruiqi Ren, Dan Li, Yali Wang, Daxin Ni, Zijian Feng, Qun Li

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Background: In 2016, yellow fever had been first ever discovered in China, soon after the yellow fever epidemic occurred in Angola. After the discovery, China had promptly made the national protocol of control and prevention and strengthened the surveillance on passenger and vector. In this study, a descriptive analysis was conducted to summarize China’s experiences of response towards this import epidemic, in the hope of providing experiences on prevention and control of yellow fever and other similar imported infectious diseases in the future. Methods: The imported cases were discovered and reported by General Administration of Quality Supervision, Inspection and Quarantine (AQSIQ) and several hospitals. Each clinically diagnosed yellow fever case was confirmed by real-time reverse transcriptase polymerase chain reaction (RT–PCR). The data of the imported yellow fever cases were collected by local Centers for Disease Control and Prevention (CDC) through field investigations soon after they received the reports. Results: A total of 11 imported cases from Angola were reported in China, during Angola’s yellow fever outbreak. Six cases were discovered by the AQSIQ, among which two with mild symptom were initiative declarations at the time of entry. Except for one death, the remaining 10 cases all had recovered after timely and proper treatment. All cases are Chinese, and lived in Luanda, the capital of Angola. 73% were retailers (8/11) from Fuqing city in Fujian province, and the other three were labors send by companies. 10 cases had experiences of medical treatment in Luanda after onset, among which 8 cases visited the same local Chinese medicine hospital (China Railway four Bureau Hospital). Among the 11 cases, only one case had an effective vaccination. The result of emergency surveillance for mosquito density showed that only 14 containers of water were found positive around places of three cases, and the Breteau Index is 15. Conclusions: Effective response was taken to control and prevent the outbreak of yellow fever in China after discovering the imported cases. However, though the similar origin of Chinese in Angola has provided an easy access for disease detection, information sharing, health education and vaccination on yellow fever; these conveniences were overlooked during previous disease prevention methods. Besides, only one case having effective vaccination revealed the inadequate capacity of immunization service in China. These findings will provide suggestions to improve China’s capacity to deal with not only yellow fever but also other similar imported diseases in China.

Keywords: yellow fever, first import, China, suggestion

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Authors: Yue Huang, Hongcheng Gan

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The new urbanization pattern has led to a rapid growth in demand for short-distance intercity travel, and the emergence of new travel modes has also increased the variety of intercity travel options. In previous studies on intercity travel mode choice behavior, the impact of functional amenities of travel mode and travelers’ long-term personality characteristics has rarely been considered, and empirical results have typically been calibrated using revealed preference (RP) or stated preference (SP) data. This study designed a questionnaire that combines the RP and SP experiment from the perspective of a trip chain combining inner-city and intercity mobility, with consideration for the actual condition of the Huainan-Hefei traffic corridor. On the basis of RP/SP fusion data, a hybrid choice model considering both random taste heterogeneity and psychological characteristics was established to investigate travelers’ mode choice behavior for traditional train, high-speed rail, intercity bus, private car, and intercity online car-hailing. The findings show that intercity time and cost exert the greatest influence on mode choice, with significant heterogeneity across the population. Although inner-city cost does not demonstrate a significant influence, inner-city time plays an important role. Service attributes of travel mode, such as catering and hygiene services, as well as free wireless network supply, only play a minor role in mode selection. Finally, our study demonstrates that safety-seeking tendency, hedonism, and introversion all have differential and significant effects on intercity travel mode choice.

Keywords: intercity travel mode choice, stated preference survey, hybrid choice model, RP/SP fusion data, psychological latent variable, heterogeneity

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Authors: Pius Marthin, Nihal Ata Tutkun

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Survival analysis has become one of the paramount procedures in the modeling of time-to-event data. When we encounter complex survival problems, the traditional approach remains limited in accounting for the complex correlational structure between the covariates and the outcome due to the strong assumptions that limit the inference and prediction ability of the resulting models. Several studies exist on the deep learning approach to survival modeling; moreover, the application for the case of complex survival problems still needs to be improved. In addition, the existing models need to address the data structure's complexity fully and are subject to noise and redundant information. In this study, we design a deep learning technique (CmpXRnnSurv_AE) that obliterates the limitations imposed by traditional approaches and addresses the above issues to jointly predict the risk-specific probabilities and survival function for recurrent events with competing risks. We introduce the component termed Risks Information Weights (RIW) as an attention mechanism to compute the weighted cumulative incidence function (WCIF) and an external auto-encoder (ExternalAE) as a feature selector to extract complex characteristics among the set of covariates responsible for the cause-specific events. We train our model using synthetic and real data sets and employ the appropriate metrics for complex survival models for evaluation. As benchmarks, we selected both traditional and machine learning models and our model demonstrates better performance across all datasets.

Keywords: cumulative incidence function (CIF), risk information weight (RIW), autoencoders (AE), survival analysis, recurrent events with competing risks, recurrent neural networks (RNN), long short-term memory (LSTM), self-attention, multilayers perceptrons (MLPs)

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Authors: Evangelos G. Ilias, Panagiotis G. Ilias, Vasileios T. Popotas

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The United Kingdom has one of the oldest railway networks in the world dating back to 1825 when the world’s first passenger railway was opened. The network has some 40,000 bridges of various construction types using a wide range of materials including masonry, steel, cast iron, wrought iron, concrete and timber. It is commonly accepted that the successful operation of the network is vital for the economy of the United Kingdom, consequently the cost effective maintenance of the existing infrastructure is a high priority to maintain the operability of the network, prevent deterioration and to extend the life of the assets. Every bridge on the railway network is required to be assessed every eighteen years and a structured approach to assessments is adopted with three main types of progressively more detailed assessments used. These assessment types include Level 0 (standardized spreadsheet assessment tools), Level 1 (analytical hand calculations) and Level 2 (generally finite element analyses). There is a degree of conservatism in the first two types of assessment dictated to some extent by the relevant standards which can lead to some structures not achieving the required load rating. In these situations, a Level 2 Assessment is often carried out using finite element analysis to uncover ‘latent strength’ and improve the load rating. If successful, the more sophisticated analysis can save on costly strengthening or replacement works and avoid disruption to the operational railway. This paper presents the ‘strengthening by assessment’ achieved by Level 2 analyses. The use of more accurate analysis assumptions and the implementation of non-linear modelling and functions (material, geometric and support) to better understand buckling modes and the structural behaviour of historic construction details that are not specifically covered by assessment codes are outlined. Metallic bridges which are susceptible to loss of section size through corrosion have largest scope for improvement by the Level 2 Assessment methodology. Three case studies are presented, demonstrating the effectiveness of the sophisticated Level 2 Assessment methodology using finite element analysis against the conservative approaches employed for Level 0 and Level 1 Assessments. One rail overbridge and two rail underbridges that did not achieve the required load rating by means of a Level 1 Assessment due to the inadequate restraint provided by U-Frame action are examined and the increase in assessed capacity given by the Level 2 Assessment is outlined.

Keywords: assessment, bridges, buckling, finite element analysis, non-linear modelling, strengthening

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Authors: Kanu Priya Mohan

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Innovation in education is one of the essential pathways to achieve both educational, and development goals in today’s dynamically changing world. Over the last decade, coaching and mentoring have been applied in the field of education as positive intervention techniques for fostering teaching and learning reforms in the developed countries. The context of this research was Thailand’s educational reform process, wherein a project on coaching and mentoring (C&M) was launched in 2014. The C&M project endeavored to support the professional development of the school teachers in the various provinces of Thailand, and to also enable them to apply C&M for teaching innovative instructional techniques. This research aimed to empirically investigate the learning outcomes for the master trainers, who trained for coaching and mentoring as the first step in the process to train the school teachers. A mixed method study was used for evaluating the learning outcomes of training in terms of cognitive- behavioral-affective dimensions. In the first part of the research a quantitative research design was incorporated to evaluate the effects of learner characteristics and instructional techniques, on the learning outcomes. In the second phase, a qualitative method of in-depth interviews was used to find details about the training outcomes, as well as the perceived barriers and enablers of the training process. Sample size constraints were there, yet these exploratory results, integrated from both methods indicated the significance of evaluating training outcomes from the three dimensions, and the perceived role of other factors in the training. Findings are discussed in terms of their implications for the training of C&M, and also their impact in fostering positive education through innovative educational techniques in the developing countries.

Keywords: cognitive-behavioral-affective learning outcomes, mixed method research, teachers in Thailand, training evaluation

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Authors: Li Hui, Riyadh Hindi

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In the United States, bridge construction often employs overhang brackets to support the deck overhang, the weight of fresh concrete, and loads from construction equipment. This approach, however, can lead to significant torsional moments on the exterior girders, potentially causing excessive girder rotation. Such rotations can result in various safety and maintenance issues, including thinning of the deck, reduced concrete cover, and cracking during service. Traditionally, these issues are addressed by installing temporary lateral bracing systems and conducting comprehensive torsional analysis through detailed finite element analysis for the construction of bridge deck overhang. However, this process is often intricate and time-intensive, with the spacing between temporary lateral bracing systems usually relying on the field engineers’ expertise. In this study, a deep neural network model is introduced to limit exterior girder rotation during bridge deck construction. The model predicts the optimal spacing between temporary bracing systems. To train this model, over 10,000 finite element models were generated in SAP2000, incorporating varying parameters such as girder dimensions, span length, and types and spacing of lateral bracing systems. The findings demonstrate that the deep neural network provides an effective and efficient alternative for limiting the exterior girder rotation for bridge deck construction. By reducing dependence on extensive finite element analyses, this approach stands out as a significant advancement in improving safety and maintenance effectiveness in the construction of bridge decks.

Keywords: bridge deck construction, exterior girder rotation, deep learning, finite element analysis

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Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

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Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

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Authors: Sedigheh Abbasnasab Sardareh

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The current study investigates ESL teachers' voices in order to formulate further research on the effectiveness of the SBOA practices. It is an attempt to find out (1) what are ESL experienced teachers’ perceptions, experiences, attitudes, and beliefs of SBOA; (2) what teaching and learning aspects of SBOA needs focus to enhance its effectiveness; (3) external issues related to the implementation of SBOA; (4) internal issues related to the implementation of SBOA; and also (5) perceived recommendations on SBOA. The study utilized focus group discussion sessions. 9 experienced ESL (5 females and 4 males) teachers were selected based on the consent letters sent to them. These teachers had over 20 years experience in both traditional and SBOA-type assessment and the train-the-trainer experts recommended by the Ministry of Education. Respondents were guided with open-ended questions to extracts their perceived experiences implementing SBOA guided structurally by the author as the moderator. Data were first discussed with the respondents for further clarifications and then only analyzed and re-confirmed with some recommendations before the final presentation of this preliminary results were presented here. The focus group discussions yielded some important perceived views on the SBOA implementation. Some of the themes were discussed and some recommendations were proposed for further in-depth study by the Ministry of Education. Some of the future directions based on the results were also put forward. Some external and internal variables were important in order for successful implementation of SBOA. Mere implementing a policy should be taken into consideration because this might impede some of the teaching and learning processes both by the classroom stakeholders such as teachers and student. More research methods such as the use of questionnaires could be utilized to further investigate to large populations of teacher educators in Malaysia.

Keywords: school based oral assessment, Malaysia, ESL, focus group discussion

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Authors: Nadilla Saskia, Octoria Nur, Assegaf Zareeva

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Mass transport infrastructures are essential to enhance the connectivity between regions and regional equity in Indonesia. Indonesia’s capital city, Jakarta, ranked the 10th highest congestion rate in the world based on the 2019 traffic index, contributing to air pollution and energy consumption. Other than that, the World Air Quality Report in 2019 depicted Jakarta’s air pollutant concentration at 49.4 mg, the 5th highest in the world. Issues of severe traffic congestion, lack of sufficient urban infrastructure in Jakarta, and greenhouse gas emissions have to be addressed through mass transportation. Indonesia’s government is currently constructing The Greater Jakarta LRT (Light Rapid Transit) as convenient, efficient, and environmentally friendly transportation connecting Jakarta with Bekasi and Cibubur areas and plans to serve the passengers in August 2023. Greater Jakarta LRT is operated with Grade of Automation (GoA) No.3, Driverless Train Operation (DTO). Hence, the automated technology used in rail infrastructure is anticipated to address these issues with greater results. The paper will be validated and establish the extent to which the automation system would increase energy efficiency, help reduce carbon emissions, and benefit the environment. Based on the calculated CO2 emissions and fuel consumption for the existing condition (2015) during the feasibility study of the LRT Project and the predicted condition in 2030, it is obtained that Greater Jakarta LRT with GoA3 operation will reduce the CO2 emissions and fuel consumption by more than 50% in 2030. In the bigger picture, Greater Jakarta LRT supports the government's goal of achieving Jakarta Net-Zero Emissions (NZE) 2050.

Keywords: LRT, Grade of Automation (GoA), energy efficiency, carbon emissions, railway infrastructure, DKI Jakarta

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Authors: Adam Majczak, Grzegorz Barański, Marcin Szlachetka

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Environmental considerations necessitate the search for new energy sources. One of the available solutions is a partial replacement of diesel fuel by compressed natural gas (CNG) in the compression ignition engines. This type of the engines is used mainly in vans and trucks. These units are also gaining more and more popularity in the passenger car market. In Europe, this part of the market share reaches 50%. Diesel engines are also used in industry in such vehicles as ship or locomotives. Diesel engines have higher emissions of nitrogen oxides in comparison to spark ignition engines. This can be currently limited by optimizing the combustion process and the use of additional systems such as exhaust gas recirculation or AdBlue technology. As a result of the combustion process of diesel fuel also particulate matter (PM) that are harmful to the human health are emitted. Their emission is limited by the use of a particulate filter. One of the method for toxic components emission reduction may be the use of liquid gas fuel such as propane and butane (LPG) or compressed natural gas (CNG). In addition to the environmental aspects, there are also economic reasons for the use of gaseous fuels to power diesel engines. A total or partial replacement of diesel gas is possible. Depending on the used technology and the percentage of diesel fuel replacement, it is possible to reduce the content of nitrogen oxides in the exhaust gas even by 30%, particulate matter (PM) by 95 % carbon monoxide and by 20%, in relation to original diesel fuel. The research object is prototype gas injector designed for direct injection of compressed natural gas (CNG) in compression ignition engines. The construction of the injector allows for it positioning in the glow plug socket, so that the gas is injected directly into the combustion chamber. The cycle analysis of the four-cylinder Andoria ADCR engine with a capacity of 2.6 dm3 for different crankshaft rotational speeds allowed to determine the necessary time for fuel injection. Because of that, it was possible to determine the required mass flow rate of the injector, for replacing as much of the original fuel by gaseous fuel. To ensure a high value of flow inside the injector, supply pressure equal to 1 MPa was applied. High gas supply pressure requires high value of valve opening forces. For this purpose, an injector with hydraulic control system, using a liquid under pressure for the opening process was designed. On the basis of air pressure measurements in the flow line after the injector, the analysis of opening and closing of the valve was made. Measurements of outflow mass of the injector were also carried out. The results showed that the designed injector meets the requirements necessary to supply ADCR engine by the CNG fuel.

Keywords: CNG, diesel engine, gas flow, gas injector

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Authors: Irene Yi

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Machine learning, natural language processing, and neural network models of language are becoming more and more prevalent in the fields of technology and linguistics today. Training data for machines are at best, large corpora of human literature and at worst, a reflection of the ugliness in society. Machines have been trained on millions of human books, only to find that in the course of human history, derogatory and sexist adjectives are used significantly more frequently when describing females in history and literature than when describing males. This is extremely problematic, both as training data, and as the outcome of natural language processing. As machines start to handle more responsibilities, it is crucial to ensure that they do not take with them historical sexist and misogynistic notions. This paper gathers data and algorithms from neural network models of language having to deal with syntax, semantics, sociolinguistics, and text classification. Results are significant in showing the existing intentional and unintentional misogynistic notions used to train machines, as well as in developing better technologies that take into account the semantics and syntax of text to be more mindful and reflect gender equality. Further, this paper deals with the idea of non-binary gender pronouns and how machines can process these pronouns correctly, given its semantic and syntactic context. This paper also delves into the implications of gendered grammar and its effect, cross-linguistically, on natural language processing. Languages such as French or Spanish not only have rigid gendered grammar rules, but also historically patriarchal societies. The progression of society comes hand in hand with not only its language, but how machines process those natural languages. These ideas are all extremely vital to the development of natural language models in technology, and they must be taken into account immediately.

Keywords: gendered grammar, misogynistic language, natural language processing, neural networks

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Authors: Maria Rodriguez-Gazquez, Sara Chaparro-Hernandez, Jose Rafael Gonzalez-Lopez

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Introduction: Nurses are models in healthy behaviors for their patients. This is why it is important for these professionals to not only have a good knowledge of healthy behaviors but also practice. Today’s nursing students will be tomorrow’s professionals and to fulfill their role in caring they not only need knowledge, they also must maintain behaviors which enable them to improve and protect both the health of others and their own. This is why the university is a unique environment of opportunities to foster the maximum potential of health. To care for others we first have to take care of ourselves. It is important for these behaviors in Nursing students to be evaluated during the years of their university education in order to design timely interventions which improve the health behaviors of the future professionals. Aim: The objective of this study was to evaluate the lifestyles of first year nursing students of two Universities. Methodology: Cross-sectional study. In 2014, 140 first year Nursing students of two Universities Seville –US- (Spain -Europe, n=37) and Antioquia –UA- (Colombia -South America, n=93) self-reported the FANTASTIC Lifestyle checklist. Results: Findings reveal that (I) UA students doubled the percentage of dangerous or bad lifestyles with respect to the US students, (II) the lifestyles are not appropriate in 1 of 3 of nursing students in both Universities, (II) there are statistically significant differences for family support items (higher in US), positive thinkers (higher in UA), the use of safety belts and alcohol consumption before driving (higher in US). Discussion: The nursing students are mostly young people who are at a stage in which some of the most important behaviors for adult life can still be molded. It is necessary to develop educational interventions in their Nursing curricula to strengthen healthy behaviours during training. Nursing Schools not only have the duty to train professionals, but to also be agents that foster the health, welfare and quality of those who study and work there. It must encourage knowledge and skills oriented to healthy lifestyles.

Keywords: cross-sectional studies, life style, nursing students, questionnaires

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Authors: Kaiwen Zheng, Wanting Zhou, Nan Tang, Lei Li, Yuanhang He

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The integrated circuit industry has become a cornerstone of the information society, finding widespread application in areas such as industry, communication, medicine, and aerospace. However, with the increasing complexity of integrated circuits, Hardware Trojans (HTs) implanted by attackers have become a significant threat to their security. In this paper, we proposed a hardware trojan detection method for large-scale circuits. As HTs introduce physical characteristic changes such as structure, area, and power consumption as additional redundant circuits, we proposed a machine-learning-based hardware trojan detection method based on the physical characteristics of gate-level netlists. This method transforms the hardware trojan detection problem into a machine-learning binary classification problem based on physical characteristics, greatly improving detection speed. To address the problem of imbalanced data, where the number of pure circuit samples is far less than that of HTs circuit samples, we used the SMOTETomek algorithm to expand the dataset and further improve the performance of the classifier. We used three machine learning algorithms, K-Nearest Neighbors, Random Forest, and Support Vector Machine, to train and validate benchmark circuits on Trust-Hub, and all achieved good results. In our case studies based on AES encryption circuits provided by trust-hub, the test results showed the effectiveness of the proposed method. To further validate the method’s effectiveness for detecting variant HTs, we designed variant HTs using open-source HTs. The proposed method can guarantee robust detection accuracy in the millisecond level detection time for IC, and FPGA design flows and has good detection performance for library variant HTs.

Keywords: hardware trojans, physical properties, machine learning, hardware security

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Authors: Mahshid Asadollahi, Mohammad Akbari Asl

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The restaurant industry is highly competitive, and restaurants are constantly looking for ways to attract new customers and retain their existing ones. Nonverbal communication is an important factor in creating a positive customer experience and can play a significant role in attracting customers to restaurants. Nonverbal communication can include body language, facial expressions, tone of voice, and physical proximity, among other things. The present study aimed to investigate the impact of nonverbal communication between restaurant employees and customers on attracting customers in food courts in Tehran. The research method was descriptive-correlational, and the statistical population of this study included all customers of food court restaurants in Tehran, which was about 30 restaurants. The research sample was selected through probability sampling, and 440 customers completed emotional response, customer satisfaction, and nonverbal communication questionnaires in person. The data obtained were analyzed using multiple regression analysis. The results showed that vocal language, employee proximity, physical appearance, and speech movements, as components of nonverbal communication of restaurant employees, had an impact on attracting customers. Additionally, positive and negative emotions of customers have a significant relationship with customer attraction in Food Court restaurants. The study shows that various nonverbal communication factors can play a significant role in attracting customers, and that positive and negative customer emotions can affect customer satisfaction. Therefore, restaurant owners and managers should pay attention to nonverbal communication and train their employees accordingly to create a positive and welcoming atmosphere for customers.

Keywords: verbal language, proximity of employees, physical appearance, speech gestures, nonverbal communication, customer emotions, customer attraction

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Authors: Paolo Grasso, Sai Kalyan Yelike, Federico Benzi, Mathieu Le Cam

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The cabin air quality (CAQ) in commercial aircraft is of prime interest, especially in the context of the COVID-19 pandemic. Current Environmental Control Systems (ECS) rely on a prescribed fresh airflow per passenger to dilute contaminants. An adaptive ECS strategy is proposed, leveraging air sensing and filtration technologies to ensure a better CAQ. This paper investigates the CAQ level achieved in commercial aircraft’s cabin during various flight scenarios. The modeling and simulation analysis is performed in a Modelica-based environment describing the dynamic behavior of the system. The model includes the following three main systems: cabin, recirculation loop and air-conditioning pack. The cabin model evaluates the thermo-hygrometric conditions and the air quality in the cabin depending on the number of passengers and crew members, the outdoor conditions and the conditions of the air supplied to the cabin. The recirculation loop includes models of the recirculation fan, ordinary and novel filtration technology, mixing chamber and outflow valve. The air-conditioning pack includes models of heat exchangers and turbomachinery needed to condition the hot pressurized air bled from the engine, as well as selected contaminants originated from the outside or bled from the engine. Different ventilation control strategies are modeled and simulated. Currently, a limited understanding of contaminant concentrations in the cabin and the lack of standardized and systematic methods to collect and record data constitute a challenge in establishing a causal relationship between CAQ and passengers' comfort. As a result, contaminants are neither measured nor filtered during flight, and the current sub-optimal way to avoid their accumulation is their dilution with the fresh air flow. However, the use of a prescribed amount of fresh air comes with a cost, making the ECS the most energy-demanding non-propulsive system within an aircraft. In such a context, this study shows that an ECS based on a reduced and adaptive fresh air flow, and relying on air sensing and filtration technologies, provides promising results in terms of CAQ control. The comparative simulation results demonstrate that the proposed adaptive ECS brings substantial improvements to the CAQ in terms of both controlling the asymptotic values of the concentration of the contaminant and in mitigating hazardous scenarios, such as fume events. Original architectures allowing for adaptive control of the inlet air flow rate based on monitored CAQ will change the requirements for filtration systems and redefine the ECS operation.

Keywords: cabin air quality, commercial aircraft, environmental control system, ventilation

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Authors: Behzad Behnia, Noah LaRussa-Trott

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In recent years, fibers have been successfully used as an additive to reinforce asphalt concrete materials and to enhance the sustainability and resiliency of transportation infrastructure. Roads covered with fiber-reinforced asphalt concrete (FRAC) require less frequent maintenance and tend to have a longer lifespan. The present work investigates the application of sasobit-coated aramid fibers in asphalt pavements and employs machine learning to develop prediction models to evaluate the cracking performance of FRAC materials. For the experimental part of the study, the effects of several important parameters such as fiber content, fiber length, and testing temperature on fracture characteristics of FRAC mixtures were thoroughly investigated. Two mechanical performance tests, i.e., the disk-shaped compact tension [DC(T)] and indirect tensile [ID(T)] strength tests, as well as the non-destructive acoustic emission test, were utilized to experimentally measure the cracking behavior of the FRAC material in both macro and micro level, respectively. The experimental results were used to train the supervised machine learning approach in order to establish prediction models for fracture performance of the FRAC mixtures in the field. Experimental results demonstrated that adding fibers improved the overall fracture performance of asphalt concrete materials by increasing their fracture energy, tensile strength and lowering their 'embrittlement temperature'. FRAC mixtures containing long-size fibers exhibited better cracking performance than regular-size fiber mixtures. The developed prediction models of this study could be easily employed by pavement engineers in the assessment of the FRAC pavements.

Keywords: fiber reinforced asphalt concrete, machine learning, cracking performance tests, prediction model

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Authors: Benedict O. Muyale, Emmanuel S. Murunga

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Cities have always been the loci for nationals as well as growth of cultural fusion and innovation. Over 50%of global population dwells in cities and urban centers. This means that cities are prolific users of natural resources and generators of waste; hence they produce most of the greenhouse gases which are causing global climate change. The root cause of increase in the transport sector carbon curve is mainly the greater numbers of individually owned cars. Development in these cities is geared towards economic progress while environmental sustainability is ignored. Infrastructure projects focus on road expansion, electrification, and more parking spaces. These lead to more carbon emissions, traffic congestion, and air pollution. Recent development plans for Nairobi city are now on road expansion with little priority for electric train solutions. The Vision 2030, Kenya’s development guide, has shed some light on the city with numerous road expansion projects. This chapter seeks to realize the following objectives; (1) to assess the current transport situation of Nairobi; (2) to review green transport solutions being undertaken in the city; (3) to give an overview of alternative green transportation solutions, and (4) to provide a green transportation framework matrix. This preliminary study will utilize primary and secondary data through mainly desktop research and analysis, literature, books, magazines and on-line information. This forms the basis for formulation of approaches for incorporation into the green transportation framework matrix of the main study report.The main goal is the achievement of a practical green transportation system for implementation by the City County of Nairobi to reduce carbon emissions and congestion and promote environmental sustainability.

Keywords: cities, transport, Nairobi, green technologies

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Authors: Laura Florez

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The construction industry is labour intensive, and the behaviour and management of workers have a direct impact on the performance of construction projects. One of the issues it currently faces is how to recruit and maintain its workers. Construction is known as an industry where workers face the problem of short employment durations, frequent layoffs, and periods of unemployment between jobs. These challenges not only creates pressures on the workers but also project managers have to constantly train new workers, face skills shortage, and uncertainty on the quality of the workers it will attract. To consider worker’s needs and project managers expectations, one practice that can be implemented is to schedule construction projects to maintain a stable workforce. This paper proposes a mixed integer programming (MIP) model to schedule projects with the objective of maximising social sustainability of construction projects, that is, maximise labour stability. Aside from the social objective, the model accounts for equipment and financial resources required by the projects during the construction phase. To illustrate how the solution strategy works, a construction programme comprised of ten projects is considered. The projects are scheduled to maximise labour stability while simultaneously minimising time and minimising cost. The tradeoff between the values in terms of time, cost, and labour stability allows project managers to consider their preferences and identify which solution best suits their needs. Additionally, the model determines the optimal starting times for each of the projects, working patterns for the workers, and labour costs. This model shows that construction projects can be scheduled to not only benefit the project manager, but also benefit current workers and help attract new workers to the industry. Due to its practicality, it can be a valuable tool to support decision making and assist construction stakeholders when developing schedules that include social sustainability factors.

Keywords: labour stability, mixed-integer programming (MIP), scheduling, workforce management

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Authors: Apostolos Kalatzis, Ashish Teotia, Vishnunarayan Girishan Prabhu, Laura Stanley

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Emotions are functional behaviors influenced by thoughts, stimuli, and other factors that induce neurophysiological changes in the human body. Understanding and classifying emotions are challenging as individuals have varying perceptions of their environments. Therefore, it is crucial that there are publicly available databases and virtual reality (VR) based environments that have been scientifically validated for assessing emotional classification. This study utilized two commercially available VR applications (Guided Meditation VR™ and Richie’s Plank Experience™) to induce acute stress and calm state among participants. Subjective and objective measures were collected to create a validated multimodal dataset and classification scheme for affective state classification. Participants’ subjective measures included the use of the Self-Assessment Manikin, emotional cards and 9 point Visual Analogue Scale for perceived stress, collected using a Virtual Reality Assessment Tool developed by our team. Participants’ objective measures included Electrocardiogram and Respiration data that were collected from 25 participants (15 M, 10 F, Mean = 22.28  4.92). The features extracted from these data included heart rate variability components and respiration rate, both of which were used to train two machine learning models. Subjective responses validated the efficacy of the VR applications in eliciting the two desired affective states; for classifying the affective states, a logistic regression (LR) and a support vector machine (SVM) with a linear kernel algorithm were developed. The LR outperformed the SVM and achieved 93.8%, 96.2%, 93.8% leave one subject out cross-validation accuracy, precision and recall, respectively. The VR assessment tool and data collected in this study are publicly available for other researchers.

Keywords: affective computing, biosignals, machine learning, stress database

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Authors: Shazia Anwer

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The current TV Talk Shows, especially on domestic politics in Pakistan are following the Aristotelian techniques, including deductive reasoning, three modes of persuasion, and guidelines for communication. The application of “Approximate Truth is also seen when Talk Show presenters create doubts against political personalities or national issues. Mainstream media of Pakistan, being a key carrier of narrative construction for the sake of the primary function of national consensus on regional and extended public diplomacy, is failing the purpose. This paper has highlighted the Aristotelian communication methodology, its purposes and its limitations for a serious discussion, and its connection to the mistrust among the Pakistani population regarding fake or embedded, funded Information. Data has been collected from 3 Pakistani TV Talk Shows and their analysis has been made by applying the Aristotelian communication method to highlight the core issues. Paper has also elaborated that current media education is impaired in providing transparent techniques to train the future journalist for a meaningful, thought-provoking discussion. For this reason, this paper has given an overview of HEC’s (Higher Education Commission) graduate-level Mass Com Syllabus for Pakistani Universities. The idea of ethos, logos, and pathos are the main components of TV Talk Shows and as a result, the educated audience is lacking trust in the mainstream media, which eventually generating feelings of distrust and betrayal in the society because productions look like the genre of Drama instead of facts and analysis thus the line between Current Affairs shows and Infotainment has become blurred. In the last section, practical implication to improve meaningfulness and transparency in the TV Talk shows has been suggested by replacing the Aristotelian communication method with the cognitive semiotic communication approach.

Keywords: Aristotelian techniques of communication, current affairs talk shows, drama, Pakistan

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Authors: Manar Al-Ateeq

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The building construction industry plays a major role in the economy of the word and the state of Kuwait. This paper evaluates existing new civil site engineers, describes a new system for improvement and insures the importance of prequalifying and developing for new engineers. In order to have a strong base in engineering, educational institutes and workplaces should be responsible to continuously train engineers and update them with new methods and techniques in engineering. As to achieve that, school of engineering should constantly update computational resources to be used in the professions. A survey was prepared for graduated Engineers based on stated objectives to understand the status of graduate engineers in both the public and private sector. Interviews were made with different sectors in Kuwait, and several visits were made to different training centers within different workplaces in Kuwait to evaluate training process and try to improve it. Virtual Reality (VR) technology could be applied as a complement to three-dimensional (3D) modeling, leading to better communication whether in job training, in education or in professional practice. Techniques of 3D modeling and VR can be applied to develop the models related to the construction process. The 3D models can support rehabilitation design as it can be considered as a great tool for monitoring failure and defaults in structures; also it can support decisions based on the visual analyses of alternative solutions. Therefore, teaching computer-aided design (CAD) and VR techniques in school will help engineering students in order to prepare them to site work and also will assist them to consider these technologies as important supports in their later professional practice. This teaching technique will show how the construction works developed, allow the visual simulation of progression of each type of work and help them to know more about the necessary equipment needed for tasks and how it works on site.

Keywords: three dimensional modeling (3DM), civil engineers (CE), professional practice (PP), virtual reality (VR)

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Authors: Hao Min Chuah, Tatsuya Yamazaki, Ryosui Iwasawa, Tatsumi Suto

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In recent years, with the advancement of digital technology, the movement to introduce so-called ICT (Information and Communication Technology), such as computer technology and network technology, to civil engineering construction sites and construction sites is accelerating. As part of this movement, attempts are being made in various situations to reproduce actual sites inside computers and use them for designing and construction planning, as well as for training inexperienced engineers. The installation of wave-dissipating blocks on coasts, etc., is a type of work that has been carried out by skilled workers based on their years of experience and is one of the tasks that is difficult for inexperienced workers to carry out on site. Wave-dissipating blocks are structures that are designed to protect coasts, beaches, and so on from erosion by reducing the energy of ocean waves. Wave-dissipating blocks usually weigh more than 1 t and are installed by being suspended by a crane, so it would be time-consuming and costly for inexperienced workers to train on-site. In this paper, therefore, a block installation simulator is developed based on Unity 3D, a game development engine. The simulator computes porosity. Porosity is defined as the ratio of the total volume of the wave breaker blocks inside the structure to the final shape of the ideal structure. Using the evaluation of porosity, the simulator can determine how well the user is able to install the blocks. The voxelization technique is used to calculate the porosity of the structure, simplifying the calculations. Other techniques, such as raycasting and box overlapping, are employed for accurate simulation. In the near future, the simulator will install an automatic block installation algorithm based on combinatorial optimization solutions and compare the user-demonstrated block installation and the appropriate installation solved by the algorithm.

Keywords: 3D simulator, porosity, user interface, voxelization, wave-dissipating blocks

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