Miracle-dz/UniFAR-FTUs huggingface dataset:
Modified Portland Cement Association Stress Analysis and Thickness Design Procedures

A new stress analysis and thickness design procedure for jointed concrete pavements was developed. On the basis of Westergaard’s edge stress solution and several prediction models for stress adjustments for a variety of loading and environmental (i.e., thermal curling) conditions, a modified Portland Cement Association (PCA) equivalent stress analysis and thickness design procedure was proposed and implemented in a highly user-friendly, Windows-based program, TKUPAV, for practical trial applications. The proposed approach has been further verified by reproducing results very close to the PCA’s equivalent stresses and fatigue damages using a spreadsheet program and the TKUPAV program. The possible detrimental effect of loading plus daytime curling has been illustrated in a case study, which also indicated that the effect of thermal curling should be considered in the thickness design of concrete pavements.
Strength measurement of optical fibers by bending

A two-point bending technique has been used not only to measure the breaking stress of optical fiber but also to predict its static and dynamic fatigue. The present theory of this test is based on elastica theory of rod. However, within the limits of elastica theory the tensile and shear stresses cannot be determined. In this paper we study dynamic and static problems for optical fiber in the two- point bending test on the base of geometrically exact theory in which rod can suffer flexure, extension, and shear. We obtain the governing partial differential equations taking into account the fact that the lateral motion of the fiber is restrained by the presence of flat parallel plates. We develop the computational methods for solving the initial and equilibrium free-boundary nonlinear planar problems. We derive the formulas for predicting of the tensile strength from strength in the bending and calculate one example.
A real-time algorithm for variable-objective motion planning over terrain and threats

A major issue required to enhance the autonomy level of unmanned vehicles is real-time motion planning. In this context, optimal trajectories need to be generated online considering the vehicle’s dynamic potentials and constraints. However, autonomous air vehicles often need to plan and execute their missions with varying objectives that may even be dictated in flight. Therefore, the current study introduces and focuses on the new concept of variable-objective motion planning. In this regard, a new dynamic multi-objective heuristic optimization algorithm is developed for path and motion planning of autonomous air vehicles in presence of deterministic terrain obstacles as well as random threats. Effectiveness of the proposed algorithm is demonstrated via several simulated scenarios and verified against past research. The results are promising and indicative of a good potential of the proposed algorithm in producing optimal real-time optimal feasible trajectories with multi-variable objectives.
Inspection of Metal Plate-Connected Wood Trusses in Residential Construction

AbstractThe engineered nature of metal plate–connected wood trusses may not be well understood by installers and other trade professionals, which can result in installation errors. Truss manufacturing, handling, and installation specifications are included in national design standards and truss misinstallation is therefore a code violation. Through use of the Delphi method, an expert-validated inspection methodology was developed by interviewing code officials, home inspectors, and truss manufacturers. The results included (1) identification of truss installation errors most likely to occur, (2) best practices for inspection methods, (3) determination of authoritative documentation, and (4) validation of the research methods. The inspection should begin at the top of the structure and move in a circular, systematic pattern downward through each level, and should concentrate on the identified high-risk aspects of truss installation. Installation problems include the following: broken or cut webs and chords.
An Online Field Study on Scholarly Journal Annotations

: Even though there is an abundance of web-based annotation tools that allow users to share their data across the internet, little is known about how these tools are actually used in the daily work routines of scholars in the Humanities. This chapter presents an empirical study on public inline annotations by publishers, article authors and readers in a scholarly open-access journal. The findings of this study are combined with a meta-analysis of the existing empirical literature on marginal annotations in the Humanities and scholars’ willingness to share them. The most important conclusion that can be drawn from the empirical data is that the publication of annotations is not a feature that needs to be offered by all types of scholarly annotation software packages.
3D Surface Reconstruction and Analysis in Automated Apple Stem-End/Calyx Identification

Machine vision methods are widely used in apple defect detection and quality grading applications. Currently, 2D near-infrared (NIR) imaging technology is used to detect apple defects based on the difference in image intensity of defects from normal apple tissue. However, it is difficult to accurately differentiate an apple's stem-end/calyx from a true defect due to their similar 2D NIR images, which presents a major technical challenge to the successful application of this machine vision technology. In this research, we used a novel two-step 3D data analysis strategy to differentiate apple stem-ends/calyxes from true defects according to their different 3D shape information. In the first step, a 2D NIR imaging was extended to a 3D reconstruction using a shape-from-shading (SFS) approach. After successfully obtaining 3D information, a quadratic facet model was introduced to conduct the 3D concave shape fitting such that the identification of apple stem-ends and calyxes could be achieved based on their different 3D structures. Significant improvement in terms of the detection rate could be obtained based on 3D shape fitting in comparison to the traditional 2D intensity fitting approach. Samples of the reconstructed 3D apple surface maps as well as the identified stem-ends/calyxes were shown in the results, and an overall 90.15% detection rate was achieved, compared to the 58.62% detection rate of the traditional 2D intensity fitting approach.
The Role of Self-Construal in 3G Acceptance in China: An Empirical Investigation

This study aims to validate a model of consumer acceptance for 3G use in China. It integrates the technology acceptance model (TAM) and satisfaction theory to examine plans to introduce third-generation mobile phones to second-generation mobile phone users. In addition, this study considers the effect of self-construal on the perceived usefulness, perceived ease of use, subjective norms, prior satisfaction with 2G/2.5G, and intention to use 3G. The empirical results of this study show that the technology acceptance model partially explains consumers intentions to adopt 3G technology. Although consumers perceive that 3G is easier to use, this does not directly influence them to use 3G. Prior satisfaction with their current model of phone does not influence consumers intention to use 3G. Moreover, self-construal has moderate effects on the relationship between perceived usefulness, subjective norms, and the intention to use 3G.
Building a Hybrid Prediction Model to Evaluation of Financial Distress Corporate

Exploring financial distress activity within a listed target of stock markets focused on creating such prediction models can provide insight into the technological requirements of corporate and the demands placed upon a stock investor in this field. This study integrates professional knowledge to financial ratios into the emerging soft computing techniques for building up a hybrid corporate distress prediction of early warning systems in regarding application fields. Conclusively, the empirical results indicate that the proposed procedure is a great potential alternative of helpful hybrid models to demonstrate its technological merit and application value, and it has increasing the application filings. In terms of managerial implications, the analysis results may be relevant to other types of prediction models seeking to identify financial ratios for the planning processes.
Electronic nose module with system on chip

An electronic nose module with a system on chip (SoC) was developed to be used in a gas discrimination system for limited kinds of gas. An array of 4 metal oxide gas sensors with different selectivity patterns was used in order to measure the gases. A BP (Back Propagation) algorithm was designed and implemented on a CPLD of two hundred thousand gates by using the VHDL language for processing input signals from 4 kinds of gas sensors. This module successfully discriminated 4 kinds of gases and displayed the results on an LCD or LED display. The developed module could be used for various applications in the field of food process control and alcohol judgment.
Investigating factors affecting ERP selection in made-to-order SME sector

Purpose – The purpose of this paper is to present the findings of research carried out as part of an industrial project for selection of an enterprise resource planning (ERP) system in a made‐to‐order (MTO) small‐to‐medium enterprise (SME) scenario. It develops a framework or methodology for selection. It also highlights the areas pertaining to the unique needs of, first, the SME sector and, second, the MTO sector, to be considered while selecting a solution. A work book is developed to provide a structured ERP software selection process for SMEs using a comprehensive literature review plus practical experience. This research is potentially aimed at being useful to other SMEs as a guide for a structured selection process.Design/methodology/approach – A combination of comprehensive literature review and experience of managing the selection process for an ERP system in an SME was employed for the study.Findings – Over the past few years, the number of large companies buying new ERP systems has reached satur.
High performance Doppler-inertial navigation-experimental results

This paper presents experimental results from a number of performance verification sea tests of the MARPOS(R) Doppler-inertial navigation system jointly implemented by Maridan A/S, Denmark, and the Technical University of Denmark for use onboard the MARIDAN series of AUVs. MARPOS(R) is a dead-reckoning based navigation system, the core of which is a state-of-the-art ring laser gyro (RLG) and a Doppler velocity log (DVL). The system also integrates a very accurate CTD sensor suite and a high quality embedded differential GPS receiver used at the surface for position initialization, a priori sensor calibration and moving base alignment of the INS. A series of accuracy verification tests were performed at a sheltered shallow water test facility, using a small surface vessel to allow simultaneous logging of DGPS reference positions and Doppler-inertial based dead-reckoning navigation data. The proven accuracy was 0.10% of distance traveled for straight line trajectories and 0.02% for site surveys, where navigation was performed in a confined area, typically 1/spl times/1 km. Results from a number of surveys carried out by a MARIDAN 150 AUV have confirmed the operational efficiency and robustness of the system. Correlation of overlapping sidescan sonar imagery indicates that the operational performance of MARPOS(R) actually surpasses the outcome of the surface vessel tests. A probable cause is the inherently more stable platform provided by an AUV as opposed to a small surface vehicle which is subjected to wave induced motion and the often more extreme sound velocity variations of the surface water layers.
Semilinear viscous Moore-Gibson-Thompson equation with the derivative-type nonlinearity: Global existence versus blow-up

<abstract><p>In this paper, we study global existence and blow-up of solutions to the viscous Moore-Gibson-Thompson (MGT) equation with the nonlinearity of derivative-type $ |u_t|^p $. We demonstrate global existence of small data solutions if $ p > 1+4/n $ ($ n\leq 6 $) or $ p\geq 2-2/n $ ($ n\geq 7 $), and blow-up of nontrivial weak solutions if $ 1 < p\leq 1+1/n $. Deeply, we provide estimates of solutions to the nonlinear problem. These results complete the recent works for semilinear MGT equations by <sup>[<xref ref-type="bibr" rid="b4">4</xref>]</sup>. </p></abstract>
MEMS PolyMUMPS-Based Miniature Microphone for Directional Sound Sensing

Abstract : A miniature directional sound sensor was fabricated using micro-electro-mechanical system (MEMS) technology based on the operational principle of Ormia ochracea fly s hearing organ. The fly uses coupled bars hinged at the center to achieve the directional sound sensing by monitoring the difference in vibration amplitude between them. The MEMS sensor design employed in this thesis was fabricated using the PolyMUMPs process. The sound sensor has two primary vibrational modes (rocking
DETERMINISTIC OPTIMIZATION OF SINGLE PHASE 8S-4P FIELD EXCITATION FLUX SWITCHING MOTOR FOR HYBRID ELECTRIC VEHICLE

Under definite specifications and limitations of conventional interior permanent magnet synchronous (IPMS) motor in existing hybrid electric vehicle (HEV), the early performances of the recommended field excitation flux switching (FEFS) motor in which no usage of permanent magnet (PM) are evaluated based on two-dimensional Finite Element Analysis (FEA). Since the initial performances fail to acquire the target torque and power, design optimization based on deterministic approach of several FEFS motor parameters is presented in this paper to attain the target performances. After a few cycles of design optimization, the enhanced FEFS motor has achieved the target power and torque of 41kW and 70Nm, respectively. In conclusion, the final design FEFS motor has the maximum power density of 1.75kW/kg which is approximately 49% more than power density in existing IPMS motor
DYNAMIC PROPERTIES OF ALUMINIUM ALLOYS USED IN AUTOMOTIVE INDUSTRY

Around 20% of CO2 emitted as a result of human activity on Earth comes from transportation. One of the ideas, which lead to diminishing of the greenhouse gases emission, is reducing of fuel consumption. It may be achieved by introducing a new powertrain solutions as well as lowering overall vehicle weight. The reduction of vehicle weight may be obtained by both a new design of part and structures and application of a new materials i.e. aluminium alloys. It has to be emphasised that weight reduction is very important for combustion engine driven vehicles and electric driven vehicles as well. Mass lowering is especially desirable in the case of electric vehicles because they typically have a very narrow operational range usually lower than 200 km. Therefore even small weight reduction decreases energy consumption of EV and as a consequence increases its range, which is a crucial parameter for users. The results presented in this article were focused on high strength aluminium alloys of 6082-T6 and 7075-T6 types. Applying of those materials enables mass lowering of structures up to 50%, maintaining original functionality. Moreover, aluminium alloys may be also used in energy absorbing structures. The goal of tests was to obtain data required in FE analysis. In order to simulate dynamic phenomenon i.e. vehicle crash investigation of the stress-strain curves of alloys were carried out at wide range of strain rates using Hopkinson bar
Parametric Study of Tendon Profiles in Prestressed Steel Plate Girder

The concept of prestressing steel structures has only recently been widely considered, despite a long and successful history of prestressing concrete members. Several analytical studies of prestressed steel plate girder were reported in the literature, but much of that work was not studied with reference to different parameters like tendon profile, eccentricity, partial span to full span ratio, prestressing force, load carrying capacity etc. associated with prestressing. This paper examines analytically a comparative study of various tendon configurations and prestressing parameters on over all analysis and design of prestressed steel plate girder. The output from the computer Program for analysis and design of steel plate girder prestressed with different tendon configurations are compared among each other. As a Case-study, a prototype model of Prestressed Steel Testing Frame with straight tendon has been designed, constructed and tested in the laboratory for its safe load carrying capacity and maximum deflection.
CNN based Lung Indexing Method for DICOM CT Image

PET-CT medical images are widely used for the purpose of early diagnosis of cancer. PET-CT Imaging is a method of examing cancer by photographing the entire body and a larget set of DICOM images are generated for one patient such as 300 images. Since each major doctors focus on different area among this large set of images, automatic indexing method for the focused area is required. In this paper, we propose a CNN-based Lung Indexing Method that can index the image where the lung starts and the image where the lung ends among the whole body DICOM images. The indexed result and the actual answer were compared by using Intersection over Union (IoU). It is confirmed that the accuracy becomes highest at 70% when the start and end index numbers were extracted by using the proposed method with the median value option.
Switches are Scanners Too!: A Fast and Scalable In-Network Scanner with Programmable Switches

Network scanning has been a standard measurement technique to understand the network's security situations, however, probing a large-scale scanning space with existing network scanners is both difficult and slow. To address this issue, we introduce IMap, a fast and scalable in-network scanner based on programmable switches. In designing IMap, we overcome key restrictions posed by computation models and memory resources of programmable switches, and devise numerous techniques and optimizations to turn a switch into a practical high-speed network scanner. We conduct preliminary experiments on the open-source prototype of IMap and evaluation results show that IMap can survey all addresses (i.e., 6 Class B Addresses) and all ports of our campus network in 8 minutes, nearly 4 times faster than state-of-the-art network scanners. As an ongoing work, we plan to continuously improve the design and implementation of IMap, and hope IMap can serve as a foundation for designing next-generation terabit network scanners.
GeoPalette: Road Segmentation with Limited Satellite Imagery

In recent years, Geo-information extraction from high-resolution satellite imagery has attracted a lot of attention. However, because of the high cost of image acquisition and annotation, there are limited datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc.). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. In this study, we propose GeoPalette, a Generative Adversarial Network (GAN) based tool to generate additional synthetic training samples for boosting model performance when the training dataset is limited. Our experiments on road segmentation show that using additional synthetic data can improves the model performance mean Intersection over Union (mIoU) from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model is standard-trained on 4,000 real pairs (64.59%), i.e., a 4-fold reduction in real dataset size.
Android Malware Family Classification using Images from Dex Files

With the popularization 1 of the Android platform, Android malware occupies the largest portion of mobile malware. Malware family classification is important for fast and accurate detection. We propose a new detection method using images generated from Dex files of Android apps. We generate two kinds of images: one from an entire DEX file and one from a data section of a DEX file. We apply the CNN algorithm to the classification of both kinds of images. The experiments show that the proposed method classifies malware families with 91% accuracy for both cases. In the case of using only the data section, the performance of the ExploitLinuxLotoor family and Gappisin family were improved. Also, the deviation between Precision, Recall, and F1-Score was greatly reduced. The area under the Precision-Recall curve is almost the same in both experiments, which means that detection time can be shortened without deteriorating detection performance.
Spatio-Temporal Graph Convolutional Networks for Traffic Forecasting: Spatial Layers First or Temporal Layers First?

Traffic forecasting is an important and challenging problem for intelligent transportation systems due to the complex spatial dependencies among neighboring roads and changing road conditions in different time periods. Spatio-temporal graph convolutional networks (STGCNs) are usually adopted to forecast traffic features in a road network. Some STGCN models involves spatial layers first and then temporal layers and some other models involves these layers in a reverse order. This creates an interesting research question on whether the ordering of the spatial layers (or temporal layers) first in an existing STGCN model could improve the forecasting performance. To the best of our knowledge, we are the first to study this interesting research problem, which creates a deep insight as a guideline to the research community on how to design STGCN models. We conducted extensive experiments to study a number of representative STCGN models for this research problem. We found that these models with spatial layers constructed before temporal layers has a higher chance to outperform that with temporal layers constructed first, which suggests the future design principle of STGCN models.
Learning Decision Making Strategies of Non-experts: A NEXT-GAIL Model for Taxi Drivers

Thanks to the rapid development of mobile sensing techniques, massive human-generated spatial-temporal data (HSTD) are generated from the urban areas, e.g., passenger-seeking trajectories from taxi drivers, and public transit trips from urban dwellers. These HSTD record sequential decisions made by human agents. Studying human behavior from HSTD provides benefits to many aspects, for example, studying passenger-seeking strategies from experienced taxi drivers can help improve the operation efficiencies of those new drivers. One common method to analyze human behavior from HSTD is Imitation Learning (IL). Existing IL approaches rely on data collected from experts. However, human agents who generate HSTD may have diverse expertise levels across geographical regions, i.e., with good policies in some regions and poor policies in less experienced regions. The problem of how to infer the optimal policy for agents in their unfamiliar or less-experienced regions remains open. In this paper, we propose the novel Generative Adversarial Imitation Learning for Non-experts (NEXT-GAIL) framework to first disentangle expert knowledge, which is irrelevant to spatial-temporal regions, from the demonstration data. Then, such knowledge can be transferred to regions, where the agent does not possess an expert policy. We take the real-world taxi trajectory data as an example to evaluate the performance of our proposed framework. The comparison results illustrate that our proposed NEXT-GAIL outperforms existing state-of-the-art approaches regarding the accuracy of the inferred optimal policy for non-experts.