Search results for: ground track

Authors: C. Fortin, D. Tokmeninova, O. Ushakova

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Among 500 Russian universities Skolkovo Institute of Science and Technology (Skoltech) is one of the youngest (established in 2011), quite small and vastly international, comprising 20 percent of international students and 70 percent of faculty with significant academic experience at top-100 universities (QS, THE). The institute has emerged from close collaboration with MIT and leading Russian universities. Skoltech is an entirely English speaking environment. Skoltech curriculum plans of ten Master programs are based on the CDIO learning outcomes model. However, despite the Institute’s unique focus on industrial innovations and startups, one of the main challenges has become an evident large proportion of nearly half of MSc graduates entering PhD programs at Skoltech or other universities rather than industry or entrepreneurship. In order to increase the share of students joining the industrial sector after graduation, Skoltech started implementing a number of unique practices with a focus on employers’ expectations incorporated into the curriculum redesign. In this sense, extended knowledge exchange with industrial partners via collaboration in learning activities, industrial projects and assessments became essential for students’ headway into industrial and entrepreneurship pathways. Current academic curriculum includes the following types of components based on extended knowledge exchange with industrial partners: innovation workshop, industrial immersion, special industrial tracks, MSc defenses. Innovation workshop is a 4 week full time diving into the Skoltech vibrant ecosystem designed to foster innovators, focuses on teamwork, group projects, and sparks entrepreneurial instincts from the very first days of study. From 2019 the number of mentors from industry and startups significantly increased to guide students across these sectors’ demands. Industrial immersion is an exclusive part of Skoltech curriculum where students after the first year of study spend 8 weeks in an industrial company carrying out an individual or team project and are guided jointly by both Skoltech and company supervisors. The aim of the industrial immersion is to familiarize students with relevant needs of Russian industry and to prepare graduates for job placement. During the immersion a company plays the role of a challenge provider for students. Skoltech has started a special industrial track comprising deep collaboration with IPG Photonics – a leading R&D company and manufacturer of high-performance fiber lasers and amplifiers for diverse applications. The track is aimed to train a new cohort of engineers and includes a variety of activities for students within the “Photonics” MSc program. It is expected to be a successful story and used as an example for similar initiatives with other Russian high-tech companies. One of the pathways of extended knowledge exchange with industrial partners is an active involvement of potential employers in MSc Defense Committees to review and assess MSc thesis projects and to participate in defense procedures. The paper will evaluate the effect and results of the above undertaken measures.

Keywords: Curriculum redesign, knowledge exchange model, learning outcomes framework, stakeholder engagement

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Authors: Jhu-Wei Ji, Fa-Shian Chang, Lih-Tyng Hwang, Chih-Feng Liu, Jeng-Nan Lee, Shun-Min Wang, Kai-Yi Cho

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This paper presents a tracked robot with blade arms powered to assist movement in difficult environments. As a result, the tracked robot is able to pass a ramp or climb stairs. The main feature is a pair of blade arms on both sides of the vehicle body working in collaboration with previously validated transformable track system. When the robot encounters an obstacle in a terrain, it enlists the blade arms with power to overcome the obstacle. In disaster areas, there usually will be terrains that are full of broken and complicated slopes, broken walls, rubbles, and ditches. Thereupon, a robot, which is instructed to pass through such disaster areas, needs to have a good off-road capability for such complicated terrains. The robot with crawling-assisting blade arms would overcome the obstacles along the terrains, and possibly become to be a rescue robot. A prototype has been developed and built; experiments were carried out to validate the enhanced crawling capability of the robot.

Keywords: tracked robot, rescue robot, blade arm, crawling ability, control system

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Authors: Soheila Sadeghi

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Infrastructure projects often encounter contract variations that can significantly deviate from the original tender estimates, leading to cost overruns, schedule delays, and financial implications. This research aims to quantitatively assess the impact of changes in contract variations on project performance by conducting an in-depth analysis of a comprehensive dataset from the Regional Airport Car Park project. The dataset includes tender budget, contract quantities, rates, claims, and revenue data, providing a unique opportunity to investigate the effects of variations on project outcomes. The study focuses on 21 specific variations identified in the dataset, which represent changes or additions to the project scope. The research methodology involves establishing a baseline for the project's planned cost and scope by examining the tender budget and contract quantities. Each variation is then analyzed in detail, comparing the actual quantities and rates against the tender estimates to determine their impact on project cost and schedule. The claims data is utilized to track the progress of work and identify deviations from the planned schedule. The study employs statistical analysis using R to examine the dataset, including tender budget, contract quantities, rates, claims, and revenue data. Time series analysis is applied to the claims data to track progress and detect variations from the planned schedule. Regression analysis is utilized to investigate the relationship between variations and project performance indicators, such as cost overruns and schedule delays. The research findings highlight the significance of effective variation management in construction projects. The analysis reveals that variations can have a substantial impact on project cost, schedule, and financial outcomes. The study identifies specific variations that had the most significant influence on the Regional Airport Car Park project's performance, such as PV03 (additional fill, road base gravel, spray seal, and asphalt), PV06 (extension to the commercial car park), and PV07 (additional box out and general fill). These variations contributed to increased costs, schedule delays, and changes in the project's revenue profile. The study also examines the effectiveness of project management practices in managing variations and mitigating their impact. The research suggests that proactive risk management, thorough scope definition, and effective communication among project stakeholders can help minimize the negative consequences of variations. The findings emphasize the importance of establishing clear procedures for identifying, assessing, and managing variations throughout the project lifecycle. The outcomes of this research contribute to the body of knowledge in construction project management by demonstrating the value of analyzing tender, contract, claims, and revenue data in variation impact assessment. However, the research acknowledges the limitations imposed by the dataset, particularly the absence of detailed contract and tender documents. This constraint restricts the depth of analysis possible in investigating the root causes and full extent of variations' impact on the project. Future research could build upon this study by incorporating more comprehensive data sources to further explore the dynamics of variations in construction projects.

Keywords: contract variation impact, quantitative analysis, project performance, claims analysis

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Authors: Digvijaysingh S. Bana, Kiran R. Trivedi

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This paper introduces various methods for the alpha wave to detect the difference between two states of brain. One healthy subject participated in the experiment. EEG was measured on the forehead above the eye (FP1 Position) with reference and ground electrode are on the ear clip. The data samples are obtained in the form of EEG raw data. The time duration of reading is of one minute. Various test are being performed on the alpha band EEG raw data.The readings are performed in different time duration of the entire day. The statistical analysis is being carried out on the EEG sample data in the form of various tests.

Keywords: electroencephalogram(EEG), biometrics, authentication, EEG raw data

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Authors: Humphrey Agevi, Harrison Tsingalia, Richard Onwonga, Shem Kuyah

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Most of the world ecosystems have been affected by the effects of climate change. Efforts have been made to mitigate against climate change effects. While most studies have been done in forest ecosystems and pure plant plantations, trees on farms including agroforestry have only received attention recently. Agroforestry systems and tree cover on agricultural lands make an important contribution to climate change mitigation but are not systematically accounted for in the global carbon budgets. This study sought to: (i) determine tree diversity in different agroforestry practices; (ii) determine tree biomass in different agroforestry practices. Study area was determined according to the Land degradation surveillance framework (LSDF). Two study sites were established. At each of the site, a 5km x 10km block was established on a map using Google maps and satellite images. Way points were then uploaded in a GPS helped locate the blocks on the ground. In each of the blocks, Nine (8) sentinel clusters measuring 1km x 1km were randomized. Randomization was done in a common spreadsheet program and later be downloaded to a Global Positioning System (GPS) so that during surveys the researchers were able to navigate to the sampling points. In each of the sentinel cluster, two farm boundaries were randomly identified for convenience and to avoid bias. This led to 16 farms in Kakamega South and 16 farms in Kakamega North totalling to 32 farms in Kakamega Site. Species diversity was determined using Shannon wiener index. Tree biomass was determined using allometric equation. Two agroforestry practices were found; homegarden and hedgerow. Species diversity ranged from 0.25-2.7 with a mean of 1.8 ± 0.10. Species diversity in homegarden ranged from 1-2.7 with a mean of 1.98± 0.14. Hedgerow species diversity ranged from 0.25-2.52 with a mean of 1.74± 0.11. Total Aboveground Biomass (AGB) determined was 13.96±0.37 Mgha-1. Homegarden with the highest abundance of trees had higher above ground biomass (AGB) compared to hedgerow agroforestry. This study is timely as carbon budgets in the agroforestry can be incorporated in the global carbon budgets and improve the accuracy of national reporting of greenhouse gases.

Keywords: agroforestry, allometric equations, biomass, climate change

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Authors: Agata Tomaszewska, Timothy Kamps, Stephan R. Turnock, Nicola Symonds

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Bob skeleton is a highly competitive sport in which an athlete reaches speeds up to 40 m/s sliding, head first, down an ice track. It is believed that the friction between the runners and ice significantly contributes to the amount of the total energy loss during a bob skeleton descent. There is only limited available experimental data regarding the friction of bob skeleton runners or indeed steel on the ice at high sliding speeds ( > 20 m/s). Testing methods used to investigate the friction of steel on ice in winter sports have been outlined, and their accuracy and repeatability discussed. A system thinking approach was used to investigate the runner-ice interaction during sliding and create concept designs of three ice tribometers. The operational envelope of the bob skeleton system has been defined through mathematical modelling. Designs of a drum, linear and inertia pin-on-disk tribometers were developed specifically for bob skeleton runner testing with the requirement of reaching up to 40 m/s speed and facilitate fresh ice sliding. The design constraints have been outline and the proposed solutions compared based on the ease of operation, accuracy and the development cost.

Keywords: bob skeleton, ice friction, high-speed tribometers, sliding friction

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

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Authors: Laura Bishop, Isabel Jones, Linn Halvorsen, Angela Smith

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Phishing emails are a form of social engineering where attackers deceive email users into revealing sensitive information or installing malware such as ransomware. Scammers often use persuasion techniques to influence email users to interact with malicious content. This study will use eye-tracking equipment to analyze how participants respond to and process Cialdini’s persuasion principles when utilized within phishing emails. Eye tracking provides insights into what is happening on the subconscious level of the brain that the participant may not be aware of. An experiment is conducted to track participant eye movements, whilst interacting with and then filing a series of persuasive emails delivered at random. Eye tracking metrics will be analyzed in relation to whether a malicious email has been identified as phishing (filed as ‘suspicious’) or not phishing (filed in any other folder). This will help determine the most influential persuasion techniques and those 'areas of interest' within an email that require intervention. The results will aid further research on how to reduce the effects of persuasion on human decision-making when interacting with phishing emails.

Keywords: cybersecurity, human-centric, phishing, psychology

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Authors: Narayanakumar Somasundaram, Venkat Nihit Chirivella, Venkata Dilip Kumar Pasupuleti

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Ajakool, India, has a multi-span bridge that is constructed for rail transport with a maximum operating speed of 100 km/hr. It is a standard RDSO design of a PSC box girder carrying a single railway track. The Structural Health Monitoring System (SHM) is designed and installed to compare and analyze the vibrations and displacements on the bridge due to different live loads from moving trains. The study is conducted for three different spans of the same bridge to understand the health of the elastomeric bearings. Also, to validate the same, a three-dimensional finite element model is developed, and modal analysis is carried out. The proposed methodology can help in detecting deteriorated elastomeric bearings using only wireless tri-accelerometer sensors. Detailed analysis and results are presented in terms of mode shapes, accelerations, displacements, and their importance to each other. This can be implemented with a lot of ease and can be more accurate.

Keywords: dynamic effects, vibration analysis, accelerometer sensors, finite element analysis, structural health monitoring, elastomeric bearing

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Authors: Rimma Dzhusupova, Jan Bosch, Helena Holmström Olsson

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Artificial Intelligence (AI) in the Engineering, Procurement, and Construction (EPC) industry has not yet a proven track record in large-scale projects. Since AI solutions for industrial applications became available only recently, deployment experience and lessons learned are still to be built up. Nevertheless, AI has become an attractive technology for organizations looking to automate repetitive tasks to reduce manual work. Meanwhile, the current AI market has started offering various solutions and services. The contribution of this research is that we explore in detail the challenges and obstacles faced in developing and deploying AI in a large-scale project in the EPC industry based on real-life use cases performed in an EPC company. Those identified challenges are not linked to a specific technology or a company's know-how and, therefore, are universal. The findings in this paper aim to provide feedback to academia to reduce the gap between research and practice experience. They also help reveal the hidden stones when implementing AI solutions in the industry.

Keywords: artificial intelligence, machine learning, deep learning, innovation, engineering, procurement and construction industry, AI in the EPC industry

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Authors: Murad Almadani, Khalil Abu-Hantash, Xinyu Wang, Herbert Jelinek, Kinda Khalaf

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Gait analysis is used by healthcare professionals as a tool to gain a better understanding of the movement impairment and track progress. In most circumstances, monitoring patients in their real-life environments with low-cost equipment such as cameras and wearable sensors is more important. Inertial sensors, on the other hand, cannot provide enough information on angular dynamics. This research offers a method for tracking 2D joint coordinates using cutting-edge vision algorithms and a single RGB camera. We provide an end-to-end comprehensive deep learning pipeline for marker-less gait parameter estimation, which, to our knowledge, has never been done before. To make our pipeline function in real-time for real-world applications, we leverage the AlphaPose human posture prediction model and a deep learning transformer. We tested our approach on the well-known GPJATK dataset, which produces promising results.

Keywords: gait analysis, human pose estimation, deep learning, real time gait estimation, AlphaPose, transformer

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Authors: Vivek Ganesh

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Tropical cyclones are some of the most damaging events. They occur in yearly cycles and affect the coastal population with three dangerous effects: heavy rain, strong wind and storm surge. In order to estimate the area and the population affected by a cyclone, all the three types of physical impacts must be taken into account. Storm surge is an abnormal rise of water above the astronomical tides, generated by strong winds and drop in the atmospheric pressure. The main aim of the study is to identify the impact by comparing three different months data. The technique used here is NDVI classification technique for change detection and other techniques like storm surge modelling for finding the tide height. Current study emphasize on recent very severe cyclonic storm Hud Hud of category 3 hurricane which had developed on 8 October 2014 and hit the coast on 12 October 2014 which caused significant changes on land and coast of Visakhapatnam, Andhra Pradesh. In the present study, we have used Remote Sensing and GIS tools for investigating and quantifying the changes in vegetation and settlement.

Keywords: inundation map, NDVI map, storm tide map, track map

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Authors: Mohammed Qutishat, Khaldoun Aldiabat, Khaled Bader, Mohammad Al Qadire

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Cyberchondria refers to the irrational increase of distress about the condition of one's health as a consequence of the World Wide Web quest for medical details. The aim of this study was to examine the relationship between emotional intelligence and cyberchondria among Omani university students. A descriptive correlational design was used to collect data from eligible 370 participants using the Emotional Intelligence Questionnaire, and the short-form version of the Cyberchondria Severity Scale (CSS-12) between January and May 2020 at XX University-Oman. The participants’ age ranged between 18 and 37 years (M = 20.28). The majority of the participants were female, 59.7% (n = 221), single 99.5% (368), in their second academic year, 29.2% (n =108). The mean score of cyberchondria experiences was 32.51, and the mean score of emotional intelligence was 34.91. Linear regression indicated a strong association between cyberchondria and emotional intelligence [F (34.639) = 5.885, P=.000], with a .086 R². In conclusion, the exchange of expertise and peer networking for health-related details utilizing the Internet can benefit students with and without having health problems. Further research and methods should be developed to help students track the online medical tools effectively for the right purposes.

Keywords: emotional intelligence, cyberchondria, smartphone addiction, social media

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Authors: Ahmed Elaksher, Islam Omar

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Accurate geometric relation algorithms are imperative in Earth and planetary satellite and aerial image processing, particularly for high-resolution images that are used for topographic mapping. Most of these satellites carry push-broom sensors. These sensors are optical scanners equipped with linear arrays of CCDs. These sensors have been deployed on most EOSs. In addition, the LROC is equipped with two push NACs that provide 0.5 meter-scale panchromatic images over a 5 km swath of the Moon. The HiRISE carried by the MRO and the HRSC carried by MEX are examples of push-broom sensor that produces images of the surface of Mars. Sensor models developed in photogrammetry relate image space coordinates in two or more images with the 3D coordinates of ground features. Rigorous sensor models use the actual interior orientation parameters and exterior orientation parameters of the camera, unlike approximate models. In this research, we generate a generic push-broom sensor model to process imageries acquired through linear array cameras and investigate its performance, advantages, and disadvantages in generating topographic models for the Earth, Mars, and the Moon. We also compare and contrast the utilization, effectiveness, and applicability of available photogrammetric techniques and softcopies with the developed model. We start by defining an image reference coordinate system to unify image coordinates from all three arrays. The transformation from an image coordinate system to a reference coordinate system involves a translation and three rotations. For any image point within the linear array, its image reference coordinates, the coordinates of the exposure center of the array in the ground coordinate system at the imaging epoch (t), and the corresponding ground point coordinates are related through the collinearity condition that states that all these three points must be on the same line. The rotation angles for each CCD array at the epoch t are defined and included in the transformation model. The exterior orientation parameters of an image line, i.e., coordinates of exposure station and rotation angles, are computed by a polynomial interpolation function in time (t). The parameter (t) is the time at a certain epoch from a certain orbit position. Depending on the types of observations, coordinates, and parameters may be treated as knowns or unknowns differently in various situations. The unknown coefficients are determined in a bundle adjustment. The orientation process starts by extracting the sensor position and, orientation and raw images from the PDS. The parameters of each image line are then estimated and imported into the push-broom sensor model. We also define tie points between image pairs to aid the bundle adjustment model, determine the refined camera parameters, and generate highly accurate topographic maps. The model was tested on different satellite images such as IKONOS, QuickBird, and WorldView-2, HiRISE. It was found that the accuracy of our model is comparable to those of commercial and open-source software, the computational efficiency of the developed model is high, the model could be used in different environments with various sensors, and the implementation process is much more cost-and effort-consuming.

Keywords: photogrammetry, push-broom sensors, IKONOS, HiRISE, collinearity condition

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Authors: Tyng-Rong Roan, Fuji Foo, Wenwey Hseush

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Recent development of AI and edge computing plays a critical role to capture meaningful events such as detection of an unattended bag. One of the core problems is re-identification across multiple CCTVs. Immediately following the detection of a meaningful event is to track and trace the objects related to the event. In an extensive environment, the challenge becomes severe when the number of CCTVs increases substantially, imposing difficulties in achieving high accuracy while maintaining real-time performance. The algorithm that re-identifies cross-boundary objects for extensive tracking is referred to Extensive Re-Identification, which emphasizes the issues related to the complexity behind a great number of CCTVs. The Spatial-Temporal Awareness approach challenges the conventional thinking and concept of operations which is labor intensive and time consuming. The ability to perform Extensive Re-Identification through a multi-sensory network provides the next-level insights – creating value beyond traditional risk management.

Keywords: long-short-term memory, re-identification, security critical application, spatial-temporal awareness

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Authors: Md Monir Hossain, Anne Staples, Kuya Takami, Tomonari Furukawa

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Tire noise has a significant impact on ride quality and vehicle interior comfort, even at low frequency. Reduction of tire noise is especially important due to strict state and federal environmental regulations. The primary sources of tire noise are the low frequency structure-borne noise and the noise that originates from the release of trapped air between the tire tread and road surface during each revolution of the tire. The frequency response of the tire changes at low and high frequency. At low frequency, the tension and bending moment become dominant, while the internal structure and local deformation become dominant at higher frequencies. Here, we analyze tire response in terms of deformation and rolling velocity at low revolution frequency. An Abaqus FEA finite element model is used to calculate the static and dynamic response of a rolling tire under different rolling conditions. The natural frequencies and mode shapes of a deformed tire are calculated with the FEA package where the subspace-based steady state dynamic analysis calculates dynamic response of tire subjected to harmonic excitation. The analysis was conducted on the dynamic response at the road (contact point of tire and road surface) and side nodes of a static and rolling tire when the tire was excited with 200 N vertical load for a frequency ranging from 20 to 200 Hz. The results show that frequency has little effect on tire deformation up to 80 Hz. But between 80 and 200 Hz, the radial and lateral components of displacement of the road and side nodes exhibited significant oscillation. For the static analysis, the fluctuation was sharp and frequent and decreased with frequency. In contrast, the fluctuation was periodic in nature for the dynamic response of the rolling tire. In addition to the dynamic analysis, a steady state rolling analysis was also performed on the tire traveling at ground velocity with a constant angular motion. The purpose of the computation was to demonstrate the effect of rotating motion on deformation and rolling velocity with respect to a fixed Newtonian reference point. The analysis showed a significant variation in deformation and rolling velocity due to centrifugal and Coriolis acceleration with respect to a fixed Newtonian point on ground.

Keywords: natural frequency, rotational motion, steady state rolling, subspace-based steady state dynamic analysis

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Authors: Mohamed Chabi

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The project of helping students at risk started at the Math department in the new foundation program at Qatar University in the fall 2012 semester. The purpose was to find ways to help students who were struggling with their math courses Elementary algebra or Precalculus course due to many factors. Department had formed the Committee “students at Risk” at the start of 12-13 to assist struggling students in our math courses to get their studies on track. A mechanism was developed to support students who are at risk using a developed E-Monitoring system. E-Monitoring system was developed to manage automatically all transactions relevant to the students’ attendance, Students ‘‘warning Students’’ grading, etc. E-Monitoring System produce various statistics such as, Overall course statistics, Performance, Students at Risk… to help department to develop a higher quality of education in the Foundation Program at Math department. The mechanism was studies and evaluated. Whatever the cause, the sooner we identify students who are not performing well academically, the sooner we can provide, or direct them to the resources that are available to them. In this paper, we outline the mechanism and its effect on students’ performance. The collected data from various exams shows that students had benefited from the mechanism.

Keywords: students at risk, e-monitoring system, warning students, performance

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Authors: Rachel deCatanzaro, Madeline Austen, Ken Tuininga, Kathy St. Laurent, Christina Rohe

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In Canada, the federal Species at Risk Act (SARA) provides protection for wildlife species at risk and a national legislative framework for the conservation or recovery of species that are listed as endangered, threatened, or special concern under Schedule 1 of SARA. Key aspects of the federal species at risk program include the development of recovery documents (recovery strategies, action plans, and management plans) outlining threats, objectives, and broad strategies or measures for conservation or recovery of the species; the identification and protection of critical habitat for threatened and endangered species; and working with groups and organizations to implement on-the-ground recovery actions. Environment Canada’s progress on the development of recovery documents and on the identification and protection of critical habitat in Ontario will be presented, along with successes and challenges associated with on-the ground implementation of recovery actions. In Ontario, Environment Canada is currently involved in several recovery and monitoring programs for at-risk bird species such as the Loggerhead Shrike, Piping Plover, Golden-winged Warbler and Cerulean Warbler and has provided funding for a wide variety of recovery actions targeting priority species at risk and geographic areas each year through stewardship programs including the Habitat Stewardship Program, Aboriginal Fund for Species at Risk, and the Interdepartmental Recovery Fund. Key research needs relevant to the recovery of species at risk have been identified, and include: surveys and monitoring of population sizes and threats, population viability analyses, and addressing knowledge gaps identified for individual species (e.g., species biology and habitat needs). The engagement of all levels of government, the local and international conservation communities, and the scientific research community plays an important role in the conservation and recovery of species at risk in Ontario– through surveying and monitoring, filling knowledge gaps, conducting public outreach, and restoring, protecting, or managing habitat – and will be critical to the continued success of the federal species at risk program.

Keywords: conservation biology, habitat protection, species at risk, wildlife recovery

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Authors: Tegan H. Emerson, Colin C. Olson, George Stantchev, Jason A. Edelberg, Michael Wilson

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Detecting small targets at range is difficult because there is not enough spatial information present in an image sub-region containing the target to use correlation-based methods to differentiate it from dynamic confusers present in the scene. Moreover, this lack of spatial information also disqualifies the use of most state-of-the-art deep learning image-based classifiers. Here, we use characteristics of target tracks extracted from video sequences as data from which to derive distinguishing topological features that help robustly differentiate targets of interest from confusers. In particular, we calculate persistent homology from time-delayed embeddings of dynamic statistics calculated from motion tracks extracted from a wide field-of-view video stream. In short, we use topological methods to extract features related to target motion dynamics that are useful for classification and disambiguation and show that small targets can be detected at range with high probability.

Keywords: motion tracks, persistence images, time-delay embedding, topological data analysis

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Authors: Mehrdad Shafiei Dizaji, Hoda Azari

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The research explores the pioneering integration of Physics-Informed Neural Networks (PINNs) into the domain of Ground-Penetrating Radar (GPR) data prediction, akin to advancements in medical imaging for tracking tumor progression in the human body. This research presents a detailed development framework for a specialized PINN model proficient at interpreting and forecasting GPR data, much like how medical imaging models predict tumor behavior. By harnessing the synergy between deep learning algorithms and the physical laws governing subsurface structures—or, in medical terms, human tissues—the model effectively embeds the physics of electromagnetic wave propagation into its architecture. This ensures that predictions not only align with fundamental physical principles but also mirror the precision needed in medical diagnostics for detecting and monitoring tumors. The suggested deep learning structure comprises three components: a CNN, a spatial feature channel attention (SFCA) mechanism, and ConvLSTM, along with temporal feature frame attention (TFFA) modules. The attention mechanism computes channel attention and temporal attention weights using self-adaptation, thereby fine-tuning the visual and temporal feature responses to extract the most pertinent and significant visual and temporal features. By integrating physics directly into the neural network, our model has shown enhanced accuracy in forecasting GPR data. This improvement is vital for conducting effective assessments of bridge deck conditions and other evaluations related to civil infrastructure. The use of Physics-Informed Neural Networks (PINNs) has demonstrated the potential to transform the field of Non-Destructive Evaluation (NDE) by enhancing the precision of infrastructure deterioration predictions. Moreover, it offers a deeper insight into the fundamental mechanisms of deterioration, viewed through the prism of physics-based models.

Keywords: physics-informed neural networks, deep learning, ground-penetrating radar (GPR), NDE, ConvLSTM, physics, data driven

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Authors: M. Saba, A. Iaaly, E. Carlier, N. Georges

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This research focuses on assessing the ground water quality of Northern Lebanon affected by saline water intrusion. The chemical, physical and microbiological parameters were collected in various seasons spanning over the period of two years. Results were assessed using Geographic Information System (GIS) due to its visual capabilities in presenting the pollution extent in the studied region. Future projections of the excessive pumping were also simulated using GIS in order to assess the extent of the problem of saline intrusion in the near future.

Keywords: GIS, saline water, quality control, drinkable water quality standards, pumping

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Authors: Amin Ebrahimi Ghadi, Morteza Moalagh

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This research is carried out to find how a perfect combination of IoT architecture (Internet of Things) and ERP system can strengthen environmental accounting to incorporate both economic and environmental information. IoT (e.g., sensors, software, and other technologies) can be used in the company’s value chain from raw material extraction through materials processing, manufacturing products, distribution, use, repair, maintenance, and disposal or recycling products (Cradle to Grave model). The desired ERP software then will have the capability to track both midpoint and endpoint environmental impacts on a green supply chain system for the whole life cycle of a product. All these enable environmental accounting to calculate, and real-time analyze the operation environmental impacts, control costs, prepare for environmental legislation and enhance the decision-making process. In this study, we have developed a model on how to use IoT devices in life cycle assessment (LCA) to gather emissions, energy consumption, hazards, and wastes information to be processed in different modules of ERP systems in an integrated way for using in environmental accounting to achieve sustainability.

Keywords: ERP, environmental accounting, green supply chain, IOT, life cycle assessment, sustainability

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Authors: Hamza Rekab Djabri, Salah Daoud

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The present paper deals with the computational of structural and electronic properties of ThC and ThN compounds using density functional theory within generalized-gradient (GGA) apraximation and local density approximation (LDA). We employ the full potential linear muffin-tin orbitals (FP-LMTO) as implemented in the Lmtart code. We have used to examine structure parameter in eight different structures such as in NaCl (B1), CsCl (B2), ZB (B3), NiAs (B8), PbO (B10), Wurtzite (B4) , HCP (A3) βSn (A5) structures . The equilibrium lattice parameter, bulk modulus, and its pressure derivative were presented for all calculated phases. The calculated ground state properties are in good agreement with available experimental and theoretical results.

Keywords: DFT, GGA, LDA, properties structurales, ThC, ThN

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

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

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

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Authors: Karla Peitl Miller, Leomar Bravin Porto, Kaitto Correa Fraga, Nataniele Eler Mendes

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This paper presents the evaluation and study of a real reinforced concrete structure of a fertilizer storage building, constructed on a Vale’s Port at Brazil, which has been recently under refurbishment. Data that will be shared and commented aim to show how wrong choices in project concepts allied to a very high aggressive environment lead to a fast track degradation, incurring on a hazardous condition associated with huge and expensive treatment for repair and guarantee of minimum performance conditions and service life. It will be also shown and discussed all the covered steps since pathological manifestations first signs were observed until the complete revitalization and reparation planning would be drawn. The conclusions of the work easily explicit the importance of professional technical qualification, the importance of minimum requirements for design and structural reforms, and mainly, the importance of good inspection and diagnostic engineering continuous work.

Keywords: durability, reinforced concrete repair, structural inspection, diagnostic engineering

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Authors: Mohamad Amin Amini, Mehdi Poursha

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Nonlinear response history analysis (NL-RHA) is a robust analytical tool for estimating the seismic demands of structures responding in the inelastic range. However, because of its conceptual and numerical complications, the nonlinear static procedure (NSP) is being increasingly used as a suitable tool for seismic performance evaluation of structures. The conventional pushover analysis methods presented in various codes (FEMA 356; Eurocode-8; ATC-40), are limited to the first-mode-dominated structures, and cannot take higher modes effect into consideration. Therefore, since more than a decade ago, researchers developed enhanced pushover analysis procedures to take higher modes effect into account. The main objective of this study is to propose an enhanced invariant lateral displacement distribution to take higher modes effect into consideration in performing a displacement-based pushover analysis, whereby a set of laterally applied displacements, rather than forces, is monotonically applied to the structure. For this purpose, the effect of different parameters such as the spectral displacement of ground motion, the modal participation factor, and the effective modal participating mass ratio on the lateral displacement distribution is investigated to find the best distribution. The major simplification of this procedure is that the effect of higher modes is concentrated into a single invariant lateral load distribution. Therefore, only one pushover analysis is sufficient without any need to utilize a modal combination rule for combining the responses. The invariant lateral displacement distribution for pushover analysis is then calculated by combining the modal story displacements using the modal combination rules. The seismic demands resulting from the different procedures are compared to those from the more accurate nonlinear response history analysis (NL-RHA) as a benchmark solution. Two structures of different heights including 10 and 20-story special steel moment resisting frames (MRFs) were selected and evaluated. Twenty ground motion records were used to conduct the NL-RHA. The results show that more accurate responses can be obtained in comparison with the conventional lateral loads when the enhanced modal lateral displacement distributions are used.

Keywords: displacement-based pushover, enhanced lateral load distribution, higher modes effect, nonlinear response history analysis (NL-RHA)

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Authors: Adamu Danlami Ahmed

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Inventory management is the determinant of effective and efficient work for any manager. This study looked at the relationship between inventory management and financial performance. The population of the study comprises all conglomerate quoted companies in the Nigerian Stock Exchange market as at 31^st December 2010. The scope of the study covered the period from 2010 to 2014. Descriptive, Pearson correlation and multiple regressions are used to analyze the data. It was found that inventory management is significantly related to the profitability of the company. This entails that an efficient management of the inventory cycle will enhance the profitability of the company. Also, lack of proper management of it will hinder the financial performance of organizations. Based on the results, it was recommended that a conglomerate company should try to see that inventories are kept to a minimum, as well as make sure the proper checks are maintained to make sure only needed inventories are in the store. As well as to keep track of the movement of goods, in order to avoid unnecessary delay of finished and work in progress (WIP) goods in the store and warehouse.

Keywords: finished goods, work in progress, financial performance, inventory

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

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

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

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Authors: Masami Usui

Abstract:

After 9/11, the American literary scene was confronted with new perspectives that enabled both writers and readers to recognize the hidden aspects of their political, economic, legal, social, and cultural phenomena. There appeared an argument over new and challenging multicultural aspects after 9/11 and this argument is presented by a tension of space related to 9/11. In Amy Waldman’s the Submission (2011), designing both the memorial park and the family network has a significant meaning in establishing the progress of understanding from multiple perspectives. The most intriguing and controversial topic of racism is reflected in the Submission, where one young architect’s blind entry to the competition for the memorial of Ground Zero is nominated, yet he is confronted with strong objections and hostility as soon as he turns out to be a Muslim named Mohammad Khan. This ‘Khan’ issue, immediately enlarged into a social controversial issue on American soil, causes repeated acts of hostility to Muslim women by ignorant citizens all over America. His idea of the park is to design a new concept of tracing the cultural background of the open space. Against his will, his name is identified as the ‘ingredient’ of the networking of the resistant community with his supporters: on the other hand, the post 9/11 hysteria and victimization is presented in such family associations as the Angry Family Members and Grieving Family Members. These rapidly expanding networks, whether political or not, constructed by the internet, embody the contemporary societal connection and representation. The contemporary quest for the significance of human relationships is recognized as a quest for global peace. Designing both the memorial park and the communication networks strengthens a process of facing the shared conflicts and healing the survivors’ trauma. The tension between the idea and networking of the Garden for the memorial site and the collapse of Ground Zero signifies the double mission of the site: to establish the space to ease the wounded and to remember the catastrophe. Reading the design of these icons of 9/11 in the Submission means that decoding the myth of globalization and its representations in this century.

Keywords: American literature, cultural studies, globalization, literature of catastrophe

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Authors: Xiaobo Liu, Rakesh Mishra, Yun Zhang

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Continuous monitoring of forest canopy height with large coverage is essential for obtaining forest carbon stocks and emissions, quantifying biomass estimation, analyzing vegetation coverage, and determining biodiversity. LiDAR can be used to collect accurate woody vegetation structure such as canopy height. However, LiDAR’s coverage is usually limited because of its high cost and limited maneuverability, which constrains its use for dynamic and large area forest canopy monitoring. On the other hand, optical satellite images, like Sentinel-2, have the ability to cover large forest areas with a high repeat rate, but they do not have height information. Hence, exploring the solution of integrating LiDAR data and Sentinel-2 images to enlarge the coverage of forest canopy height prediction and increase the prediction repeat rate has been an active research topic in the environmental remote sensing community. In this study, we explore the potential of training a Random Forest Regression (RFR) model and a Convolutional Neural Network (CNN) model, respectively, to develop two predictive models for predicting and validating the forest canopy height of the Acadia Forest in New Brunswick, Canada, with a 10m ground sampling distance (GSD), for the year 2018 and 2021. Two 10m airborne LiDAR-derived canopy height models, one for 2018 and one for 2021, are used as ground truth to train and validate the RFR and CNN predictive models. To evaluate the prediction performance of the trained RFR and CNN models, two new predicted canopy height maps (CHMs), one for 2018 and one for 2021, are generated using the trained RFR and CNN models and 10m Sentinel-2 images of 2018 and 2021, respectively. The two 10m predicted CHMs from Sentinel-2 images are then compared with the two 10m airborne LiDAR-derived canopy height models for accuracy assessment. The validation results show that the mean absolute error (MAE) for year 2018 of the RFR model is 2.93m, CNN model is 1.71m; while the MAE for year 2021 of the RFR model is 3.35m, and the CNN model is 3.78m. These demonstrate the feasibility of using the RFR and CNN models developed in this research for predicting large-coverage forest canopy height at 10m spatial resolution and a high revisit rate.

Keywords: remote sensing, forest canopy height, LiDAR, Sentinel-2, artificial intelligence, random forest regression, convolutional neural network

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