Miracle-dz/UniFAR-FTUs huggingface dataset:
Responding to Urban Sphere’s Mobility Challenge: A Case of Nepal’s Historic City

Urbanization is a globally shared challenge and Nepal, a small developing country in the foot of the Himalayas, is no exception. For Lalitpur Metropolitan City (LMC), the country’s second ranked city in terms of population density, the situation is complex as numerous historic and artistic monuments including a UNESCO World Heritage property make structural adjustment prohibitive. As a practical and sustainable response to the mobility challenge, then, LMC has teamed up with the City’s stakeholders to transform into a cyclable city. Based on a case study that employed in-depth qualitative interviewing of specialized populations, this study attempted to validate City’s course of actions in light of global trend in the use of bicycles, and discussed the movement from the perspective of sustainable urban governance. The study found that the case city is on the right trajectory for tackling urbanization challenges with sustainable means, aided by the collective wit of the political, administrative, and citizens’ power. Their effort was validated by the initiative’s alignment with the global benchmarking on the use of bicycle for sustainable mobility. The study concluded that factors such as collaborative public service design and local government led spatial management are holding keys for sustainable urban governance.
Diagnose and Prediction of Different Machine Learning methods for Knee Osteoarthritis: a protocol for systematic review and meta-analysis

Background: Knee osteoarthritis (OA) is a chronic and progressive joint disease with a higher contributor to global disability, mainly in the elderly and particularly in women. The available diagnostic approaches such as X-ray, computed tomography and magnetic resonance imaging have large precision errors and low sensitivity. Machine learning (ML) is the application of probabilistic algorithms to train a computational model to make predictions, it has great potential to become a valuable clinical diagnostic tool. This review aims to determine the diagnosis and prediction accuracy of different machine learning methods for Knee Osteoarthritis Methods: Two reviewers systematically searched Cochrane, PubMed, EMBASE, and Web of Science (last updated in June 2020) for eligible articles. To identify potentially missed publications, the reference lists of the final included studies were manually screened. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (ROC). We will use the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess the risk of bias and applicability. Two independent reviewers will conduct all procedures of study selection, data extraction, and methodological assessment. Any disagreements will be consulted with a third reviewer. RevMan 5.3 software and Stata V15.0 will be used to pool data and to carry out the meta-analysis if it is possible. Results: This systematic review will provide a high-quality synthesis of machine learning for diagnose of knee Osteoarthritis from various evaluation aspects including accuracy, sensitivity, specificity and AUC.Conclusion: The findings of this systematic review will provide latest evidence of diagnosis and prediction of different machine learning for patients with knee Osteoarthritis.Ethics and dissemination: No individual patient data will be used in this study; thus, no ethics approval is needed.Systematic review registration: PROSPERO CRD: 42019133305
Combining ability and genetic divergence in the selection of testers regarding grain yield and forage potencial in maize topcrosses

ABSTRACT Genetic divergence analysis among testers and progenies and the correlation of genetic distance with combining ability can be an important alternative to select maize progenies regarding grain yield and forage potential since testers suitable to both purposes are rare.The objective of this work was to select testers suitable for the evaluation of grain yield and forage traits is topcrosses with S3 progenies of maize based on the association between genetic divergence, general combining ability and genetic parameters. The experiments were carried out in the 2015/16 and 2016/17crop seasons in Guarapuava-PR. We evaluated 150 topcrosses among 30 S3 progenies and testers five testers (single hybrids AG8025 and P30B39, the elite inbred lines 60.H23.1 and 70.H26.1, and a mixture of inbred lines MLP102), The evaluated traits were plant height, ear height, grain yield, dry mass yield, neutral detergent fiber and acid detergent fiber andforage in situ digestibility. There was not a single suitable tester regarding grain yield and forage traitssimultaneously. The 70.H26. 1 tester is the most recommended for grain yield. The 60.H23. 1 tester is the most recommended for forage traits. There was not linear correlation between genetic divergence, general combining ability and genetic parameters.
AN INNOVATIVE STEEL DAMPER WITH ADAPTIVE HYSTERETIC BEHAVIOR

An ideal antiseismic technology should perform at high efficiency independently from the intensity of the ground shaking. However, traditional bilinear hysteretic dampers (BHDs) are commonly designed for severe ultimate limit state earthquakes resulting in a poor efficiency during weak but frequent events since they are engaged in their elastic region offering negligible damping. This can cause high peak floor accelerations (PFAs) that are detrimental for sensitive non-structural components, like elevators, and electric networks, whose integrity is essential for a prompt operation of the building after the quake. This shortcoming can be improved through a second generation of hysteretic dampers capable to produce different yielding levels based on the intensity of the occurring earthquake. Within this framework, the innovative Adaptive Hysteretic Damper (AHD) is presented in this paper. The results of force-displacement tests carried out on a 2.0MN prototype are argued and prove its robustness and reliability under several repeated cyclic motions. A simple design approach for braced frames implementing the AHDs is eventually proposed and applied to a real case-study building to quantify, through NLTH analyses, the enhanced seismic performances offered by this technology compared to traditional BHDs.
Self-Gated Memory Recurrent Network for Efficient Scalable HDR Deghosting

We propose a novel recurrent network-based HDR deghosting method for fusing arbitrary length dynamic sequences. The proposed method uses convolutional and recurrent architectures to generate visually pleasing, ghosting-free HDR images. We introduce a new recurrent cell architecture, namely Self-Gated Memory (SGM) cell, that outperforms the standard LSTM cell while containing fewer parameters and having faster running times. In the SGM cell, the information flow through a gate is controlled by multiplying the gate's output by a function of itself. Additionally, we use two SGM cells in a bidirectional setting to improve output quality. The proposed approach achieves state-of-the-art performance compared to existing HDR deghosting methods quantitatively across three publicly available datasets while simultaneously achieving scalability to fuse variable length input sequence without necessitating re-training. Through extensive ablations, we demonstrate the importance of individual components in our proposed approach. The code is available at https://val.cds. iisc.ac. in/HDR/HDRRNN/index.html.
Carbon Pricing and its Effect on Mobility and Social Exclusion in Canada

In Canada, federal and provincial governments have implemented carbon pricing to reduce carbon emissions. One effect of a price on carbon is increasing the cost of using private vehicles, which may reduce mobility and increase the risk of social exclusion, especially where car dependence is high. In this thesis, I examine how carbon pricing in Canada has affected mobility and social exclusion. Across three academic articles, I answer two questions. First, how have governments in Canada considered the effect of carbon pricing on mobility and social exclusion? Second, what effect has carbon pricing actually had on mobility and social exclusion in Canada? Chapter 2 demonstrates a method that uses Natural Language Processing to identify text on mobility and social exclusion from over 400 Canadian government documents on carbon pricing. Topics relating to equity often receive little mention in government documents about carbon pricing, making them hard to find using manual techniques. Latent Dirichlet Allocation is employed to find text about mobility and social exclusion, and determine which documents are most likely to contain them. To find text that represents mobility and social exclusion, word vectors are used to score the Latent Dirichlet Allocation topics. The algorithms accurately find the topics of interest. This technique can be applied to large textual corpora to find specified concepts and topics. The returned documents make up the corpus for the framing analysis conducted in Chapter 3. In Chapter 3, I analyze how municipal governments in Canada frame the challenges of climate change and examine whether they link these challenges to issues of mobility and social exclusion. Focusing on planning documents from four large, Canadian cities—Calgary, Edmonton, Winnipeg and Vancouver—I identify four main frames: “the Growing City”, “If You Build It, They Will Come”, “Better City for All”, and “the Resilient City”. The Growing City
Design Methodology for a Wideband, Low Insertion Loss, Digital Step Attenuator in SiGe BiCMOS Technology

This brief presents a wideband, low insertion loss, high attenuation range digital step attenuator (DSA) implemented using SiGe HBTs. In a switched-type topology, the off-state capacitances (<inline-formula> <tex-math notation="LaTeX">$\text{C}_{OFF}$ </tex-math></inline-formula>) in both reference and attenuation states cause different poles and zeros, which trigger amplitude and phase errors and limit the operating bandwidth. To achieve wide bandwidth, methods to relocate poles and zeros to the highest possible frequencies in both states are investigated. In the reference state, minimizing the size of the shunt switching transistors extends the bandwidth. In the attenuation state, switching in a capacitor at the base of an anti-parallel (AP) SiGe HBT pair series switch reduces the effective <inline-formula> <tex-math notation="LaTeX">$\text{C}_{OFF}$ </tex-math></inline-formula>, enhancing the bandwidth. The proposed techniques facilitate the realization of a 16-dB attenuator cell to support wide bandwidth and offer lower insertion loss than conventional cascaded 8-dB cell designs. Fabricated in 130-nm SiGe BiCMOS technology, the proposed DSA with an attenuation range of 31.5 dB in 0.5 dB steps operates from DC to 67 GHz and achieves an insertion loss lower than 7.8 dB. The RMS amplitude error is less than 0.4 dB above 1 GHz, while the RMS phase variation is less than 13.1°.
The Legislative Framework for Direct Foreign Investment in ASEAN

This article highlights the role of the legislative framework for direct foreign investment (DFI) and private sector development in ASEAN. The importance ofDFI, the role and objec tives of policy legislation in affecting DFI decisions, and the primary investment legislation are examined. The article concludes with some ideas on the policy legislation trends in the 1990s, and in so doing it considers whether much more needs to be done to keep pace with the formidable changes in many parts of the world.
Breeding records of Leptotila rufaxilla (Aves: Columbidae) in southwestern Brazilian Amazon with notes on nesting in some regions of occurrence

We present new aspects of breeding biology of Gray-fronted Dove Leptotila rufaxilla, from five nests found between 2012 and 2014 in a lowland forest fragment in southwestern Brazil. The nests simple/platform shape were built at a mean height of 1.90 m above ground. The clutch size was two eggs white and elliptic, incubated for 15 days (based on three nests). We recorded predation in two nests still in incubation phase. Minimum hatch weight of nestlings was 10 g and young fledged with a mean mass of 56 g. The constant growth rate (K) of nestlings was 0.40 with a growth asymptote of 60.7 g. Daily survival rate, Mayfield and apparent nesting success in the incubation period was 90, 20 and 56%, respectively, while in the nestling period were all 100%. Our data and the contribution of citizen science showed that L. rufaxilla breeds over the year, mainly in the rainy season, both in southwestern Amazonia and in other regions of occurrence.
Direct Torque Control of Dual Three-Phase Permanent Magnet Synchronous Motor

In this paper a direct torque control strategy for dual three-phase permanent magnet synchronous motor (DTP-PMSM) is presented, the machine has two sets of three-phase stator windings spatially phase shifted by 30 electric degrees. In order to reduce the stator harmonic current, torque and flux are controlled based on regulators and Vector Space Decomposition technique. The proposed approach has the benefits of low stator current distortion and low torque ripple. The validity and the efficiency of the selected technique are confirmed by simulation results.
A Methodology for Determining Static Mode Shapes of a Compliant Mechanism Using the Pseudo-Rigid-Body Model Concept and the Degrees-Of-Freedom Analysis

Traditionally, the deflected configuration of compliant segments is determined through rigorous mathematical analysis using Newtonian mechanics. Application of this approach in evaluating the deformed configuration of compliant mechanisms, containing a variety of segment types, becomes cumbersome. This paper introduces a methodology to determine the possible deflected configuration(s) of a compliant mechanism, for a given set of load and/or displacement boundary conditions. The methodology utilizes the principle of minimum potential energy, in conjunction with the degrees-of-freedom analysis and the pseudo-rigid-body model concept. The static mode shape(s) of compliant segments are integrated in identifying the possible deflected configuration(s) of a given compliant mechanism. The methodology facilitates the in situ determination of the possible deformed configuration(s) of the compliant mechanism and its constituent segments. This, in turn, assists in the important task of identifying an appropriate pseudo-rigid-body model for the design and analysis of a compliant mechanism. The proposed methodology is illustrated with examples, and supported with experimental validation.
Human recognition by utilizing voice recognition and visual recognition

Audio-visual detection and recognition system is thought to become the most promising methods for many applications includes surveillance, speech recognition, eavesdropping devices, intelligence operations, etc. In the recent field of human recognition, the majority of the research be- coming performed presently is focused on the reidentification of various body images taken by several cameras or its focuses on recognized audio-only. However, in some cases these traditional methods can- not be useful when used alone such as in indoor surveillance systems, that are installed close to the ceiling and capture images right from above in a downwards direction and in some cases people don't look straight the cameras or it cannot be added in some area such as W.C. or sleeping room. Thus, its commonly difficult to identify any movement or breakthrough process, on the other hand when need to pursue suspect when enter a building or party to identify his location and/or listen to his speech only and isolate it from other voices or noises, the other. Hence, the use of the hybrid combination technique is very effective. In this work, we proposed a multimodal human recognition approach that utilizes both the face and audio and is based upon a deep convolutional neural network (CNN). Mainly, to solve the challenge of not capturing part of the body, final results of recognizing via separate CNNs of VGG Face16 and ResNet50 are joined together depending on the score-level combination by Weighted Sum rule to enhance recognition performance. The results show that the proposed system success to recognise each person from his voice and/or his face captured. In addition, the system can separate the person voice and isolate it from noisy environment and determine the existence of desired person.
Constructing a Deep Image Analysis System Based on Self-Driving and AIoT

This research is based on the system architecture of Edge Computing in the AIoT (Artificial Intelligence & Internet of Things) field. In terms of receiving data, the authors proposed approach employed the camera module as the video source, the ultrasound module as the distance measurement source, and then compile C++ with Raspberry Pi 4B for image lane analysis, while Jetson Nano uses the YOLOv3 algorithm for image object detection. The analysis results of the two single-board computers are transmitted to Motoduino U1 in binary form via GPIO, which is used for data integration and load driving. The load drive has two parts: DC motor (rear wheel drive) and servo motor (front wheel steering). Then the final data of NodeMCU is wirelessly transmitted to the router, and then transmitted to the terminal smart device through the 4G network, thus completing the task of monitoring automatic driving. At present, artificial intelligence, the Internet of Things, and autonomous driving technologies are developing rapidly. The highlights of this research are three parts. The first part is the use of two image analysis methods to complete the automatic driving work, and the second part is the hardware architecture and circuit combination. The third part is that the system of this research is designed based on the Internet of Things architecture. The proposed system is to realize the data integration of various analysis platforms such as Raspberry Pi 4B, Jetson Nano, Motoduino U1, and mobile phone APP. The proposed method can not only realize the driving of an autonomous driving model car through image analysis but also monitor the autonomous driving process.
Porphyromonas gulae has similar virulence and immunological characteristics to the 3 human periodontal pathogen

Revised MS IAI01500-15R1 1 2 Porphyromonas gulae has similar virulence and immunological characteristics to the 3 human periodontal pathogen Porphyromonas gingivalis 4 5 Jason C. Lenzo, Neil M. O’Brien-Simpson, Rebecca K. Orth, Helen L. Mitchell, Stuart G. 6 Dashper and Eric C. Reynolds 7 8 Oral Health Cooperative Research Centre, Melbourne Dental School, Bio21 Institute, The 9 University of Melbourne, Melbourne, Victoria, Australia 10
Level and Determinants of Foreign Bank Efficiency in a Pacific Island Country

This study fills a huge gap in literature by providing some evidence on the level and determinants of bank efficiency in a Pacific island context. DEA results show that overall efficiency levels may be lower than in Australia, the home country of major banks. Dynamic GMM and panel data results show that personnel expenses and bank credit matter for efficiency, but not other bank-specific and macroeconomic factors. These insights substantially improve policy-making capacities for Fiji and other Pacific economies, including Papua New Guinea, Samoa, Tonga, Solomon Islands and Vanuatu where banking and regulatory systems and structures are highly comparable.
Abstract 40: Pathology Training for Cancer Diagnosis in Africa: Perspectives from Two Virtual Courses

Purpose: The burden of cancer continues to grow in Africa, yet there are too few trained pathologists to manage the rising number of cancer cases. The shortage of resources in African countries, as well as the travel restrictions resulting from the COVID-19 pandemic, necessitate innovative means of training and supporting pathologists across the continent. In response to requests for training in cancer pathology, two virtual training courses were organized: one conducted in English for participants in Nigeria and another conducted in French for participants across several countries in Francophone Africa. Each course had weekly 90-minute sessions covering essential topics in cancer pathology led by global experts. Methods: Two research questions were investigated for both courses: 1) did the participants improve their knowledge of the topics covered during the course? and 2) did the course participants appreciate the virtual training format? Results: The Nigeria course enrolled 85 participants from 26 Nigerian states, while the Francophone Africa course enrolled 425 participants from 18 African countries. On the post course survey, 95.8% of Nigeria course respondents and 96.1% of Francophone Africa respondents reported being satisfied or very satisfied with the Sample output to test PDF Combine only course's virtual format. In the pre-post technical assessment, participants increased their scores on average by 3.4% (p>0.05) in the Nigeria course and by 13.1% (p<0.001) in the Francophone Africa course. Conclusion: Virtual training is a promising tool to improve cancer diagnosis in Africa, as the experience of the two courses illustrates participants appreciate the virtual format. However, continued training and mentorship are required to reinforce new skills and enable the participants to appropriately apply their new knowledge to their daily practice.
Early Fault Warning and Identification in Condition Monitoring of Bearing via Wavelet Packet Decomposition Coupled With Graph

It has been well known that the fault of rolling element bearings (REBs) is one of the biggest causes for machine breakdown; thus, the early detection and type identification of a fault during REBs successive operations is necessary to help users take predictive maintenance action and/or schedule in order to avoid major machine failures. Wavelet packet decomposition (WPD) is a widely used technique to analyze vibration signal for health monitoring of REBs, but the resulting wavelet packet coefficients (WPCs) are still in high dimensionality, making them impossible for the direct use in practical usage. Keeping along the research line of high-level analysis for WPD enhancement, this article presents a new dynamic modeling approach, called graph-modeled wavelet packet coefficients (GMWPCs), that integrates WPD and graph theory in order to extract the correlation information between WPCs. By means of an adaptive input weight fusion, the GMWPCs can automatically confirm the weight of the frequency sub-band where the fault-induced information is more evident. By virtue of GMWPCs, a new two-phase framework for early warning detection and fault identification is proposed finally, which is able to not only detect the time location of the fault in its very early stage but also identify the fault type. We conducted different experiments to validate the early warning detection and fault identification separately. Experimental results, outperforming the state of the arts, verify the adequacy and effectiveness of the proposed framework and meanwhile reveal its appropriate use for real engineering applications.