Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge‐Based Prototype for Improving Scheduling Productivity

The development of efficient construction plans and schedules presents continuing challenges to construction schedulers. Knowledge‐based systems offer considerable potential as computer‐based planning tools to aid the development of realistic project plans and schedules. This paper outlines the development of a knowledge‐based prototype system for construction planning and scheduling and evaluates its effectiveness as a tool for improving the productivity of inexperienced construction project schedulers. The prototype, developed using the M.1 knowledge engineering shell, incorporates data bases, algorithm, and heuristics that mimic the decision process of expert construction project schedulers and identifies a work breakdown structure and precedence relationships. Inexperienced schedulers using the prototype system to plan and schedule the construction of a particular type of reinforced concrete building recorded significant improvements in scheduling performance. These results confirm the system's effect.
Optimal Sizing of Distributed Generation Placed on Radial Distribution Systems

Abstract Distribution network planning identifies the least cost network investment that satisfies load growth requirements without violating any system and operational constraints. Power injections from distributed generation change network power flows, modifying energy losses. Determining appropriate location and optimal size of distributed generation with respect to network configuration and load distribution in the feeder is main challenge in the changing regulatory and economic scenarios. Among the benefits of distributed generation is the reduction in active power losses, which can improve the system performance; reliability, and efficiency. In this article, the multi-location distributed generation placement problem aims to minimize the total active power loss of radial distribution networks using a genetic algorithm based solution algorithm. This technical benefit of energy savings due to the reduction in active power loss can also be translated into economic benefits. The loss sensitivity to the change in active power injection is used in selecting candidate location(s) for installation of distributed generation devices. A comparison of the results for loss reduction and savings with other reported methods show the effectiveness of the proposed method.
Experimental Study of Load-Bearing Cold-Formed Steel Walls Exposed to Realistic Design Fires

This paper presents the details of full scale fire tests of LSF wall panels conducted using realistic fire time-temperature curves. Tests included eight LSF wall specimens of various configurations exposed to both parametric design and natural fire curves. Details of the fire test set-up, test procedure and the results including the measured time-temperature and deformation curves of LSF wall panels are presented along with wall stud failure modes and times. This paper also compares the structural and thermal behavioural characteristics of LSF wall studs with those based on the standard time-temperature curve. Finally, the stud failure times and temperatures are summarized for both standard and realistic design fire curves. This study provides the necessary test data to validate the numerical models of LSF wall panels and to undertake a detailed study into the structural and thermal performance of LSF wall panels exposed to realistic design fire curves.
SAR data fusion and a novel joint use of neural networks for coastline extraction

ABSTRACT In this paper, a novel automated coastline extraction method from SAR (Synthetic Aperture Radar) data is presented. The method is designed to exploit radar backscatter coefficients ( ) from multipolarization SAR acquisitions (the 4 classic co- and cross-polarized polarizations), whereas single-pol data are employed in the majority of methods in this field, implementing data fusion through the use of an autoencoder neural network and producing the coastline by harnessing a Pulse-Coupled Neural Network (PCNN). Main results are presented throughout the paper, demonstrating superiority and comparability with established methods and with a recent automated algorithm that can be considered among the state-of-the art techniques in this field; furthermore, effectiveness of data fusion and segmentation obtained through the mentioned neural networks has been compared to that of several combinations of the same networks with different frameworks: a different data fusion framework, obtained through the use of linear Principal Component Analysis (PCA), and a different binarization framework, based on the use of Expectation-Maximization (EM) image segmentation. Main achievements of presented technique consist in enabling a possible faster processing as well as the opportunity of operating with an improved fused information content on coastline, together with very high accuracy results.
Semantic segmentation for remote sensing images via dense feature extraction and companion loss neural network

ABSTRACT Semantic segmentation models with good performance are crucial for the practical application of high-resolution remote-sensing images (RSI). Compared with nature images, in most cases the RSI dataset has the problem of unbalanced sample distribution between classes and unbalanced target size ratio. Using semantic segmentation to pixel-wise classify and identification RSI can solve this problem to some extent. At present, most semantic segmentation models based on mainstream networks solve these problems from the object scale and super-pixel perspective, whereas the accuracy still needs to be improved. To enhance the quality of the predicted feature maps, the Dense-Inception Net (D-INet) model is proposed based on the idea of DenseNet feature reuse and combined with the attention mechanism, which enables the network to maintain depth while widening the width to obtain more advanced semantic information. The connection of contextual information is strengthened by connecting RFB multi-scale modules at the shallow level, and shallow features with more spatial features are extracted for feature fusion with the decoder. To further lift the segmentation accuracy, a companion loss is introduced in the encoder, and the model is trained to have better segmentation performance for small sample objects by adaptively adjusting the loss coefficients. Experimental results show that the proposed method significantly increases the accuracy and mean Intersection Over Union (mIOU) scores.
The failure of SDT diffusion: a case for mass customization

The implementation of software-development technologies (SD's) is a process of mutual adaptation of the technology and the environment. Organizations that purchase an SDT from a vendor will almost inevitably need to adapt both the new technology and their environment-processes that can be costly. This study examines data related to the deployment of two SDTs that have been identified as problematic in diffusion and deployment-system development methodologies and computer-aided software engineering tools. The amount and nature of adaptation that must occur after adoption was found to affect the deployment of the SDTs studied. Based on these findings, a research framework is proposed, addressing the role of the SDT vendor in managing the environment for adaptation. The framework relates vendors' production strategies to the amount of adaptation required, offering suggestions for moving technology adaptation into the production process. Within this framework, a research agenda for investigating the applicability of mass customization to SDTs is proposed.
Power Harvesting Capabilities of SHM Ultrasonic Sensors

The aim of this work is to show that classical Structural Health Monitoring ultrasonic sensors may provide some power harvesting capabilities from a wide variety of vibration sources. In other words, the authors developed an integrated piezoelectric energy harvesting sensor capable of operating a dual mode, that is, carrying out vibration power harvesting and Structural Health Monitoring. First, vibrations signals of an A380 aircraft recorded during different phases of flight are presented to show the need of a wideband piezoelectric energy harvester. Then, the voltage response of a piezoelectric power harvester bonded onto an aluminium cantilever plate and excited by an electromechanical shaker is measured. A finite element model of the energy harvester system is also presented. This model provides the voltage response of the harvester due to a mechanical excitation of the host structure and allows a better understanding of the energy harvesting process. In many cases, a good agreement with the experimental results is obtained. A power measurement also showed the ability of piezoelectric SHM sensors to harvest power over an extended frequency range present in spectra collected in aircrafts. This result could lead to numerous applications even though this kind of power harvester sensor has been initially designed to operate onboard aircrafts.
Modeling of Coupled Shear Walls and Its Experimental Verification

Coupled shear wall is one of the main structural elements in high-rise buildings. In this paper, a nonlinear macromodel is proposed to simulate the behavior of coupled shear walls. This model is composed of a multivertical-element model for wall piers and a combined model for coupling beams. In the model for wall piers, some key characters, such as shifting of neutral axis within the wall section, interaction among flexure, shear, and axial forces, can be taken into account. In the model for coupling beams, the deformation of flexure and shear, and the interface bond-slip action are considered by using different elements. Experiments of coupled shear wall specimens with different sizes of coupling beams are carried out to verify the proposed model. A comparison of the analysis with the test results shows that the model is an efficient one in nonlinear analysis of coupled shear walls.
Simulation model for the design of lean manufacturing systems - a case study

Many organisations are implementing 'lean manufacturing' (LM) with the objective of achieving a superior competitive advantage over other organisations. Few companies have attained their objective, while many of them did not. One of the reasons for failure is that the managers of these organisations have not understood clearly: how the performance measures of an organisation are affected, when it gets transformed through LM. This paper is an attempt to fill in this gap. It reviews the literature related to LM, simulation and explores how simulation can be used along with value stream mapping (VSM). It describes a case of a shop floor of an automobile industry, where LM is getting implemented and demonstrates the application of QUEST simulation software for developing the simulation models for both current and future state VSMs. These models will help the managers to analyse the impact of various LM elements on the performance measures of the organisation.
Improved analysis of nonreciprocal remanence ferrite phase shifter in grooved waveguide

In this paper, the nodal finite-element method is used to analyze the differential phase shift of a nonreciprocal remanence ferrite phase shifter in a grooved waveguide. Instead of the former twin-slab model, an improved analytical model is adopted, where the effect of every part of a ferrite toroid on the differential phase shift has been considered. This analysis may replace the correcting factor with good agreement with the experimental data. Furthermore, this analysis is employed to investigate the effect of corner chamfering on the figure-of-merit (differential phase shift per decibel insertion loss) of a ferrite phase shifter. The numerical results are found to agree with the experiments in the literatures.
Satellite Pose Estimation using Convolutional Neural Networks

—Pose estimation of known uncooperative spacecraft plays an important role in various satellite servicing missions of scientiﬁc, economic, and societal beneﬁts. In this work, we aim to estimate the pose, i.e., the relative position and attitude, of a known spacecraft from individual grayscale images using deep neural networks. Convolutional neural networks have been widely exploited for image processing tasks such as object detection, classiﬁcation, human pose estimation and action recognition. Our results on various methods incorporating these CNNs are in the top-12 of the Kaggle competition from which the dataset is obtained with a least error for synthetic image of 1.291.
MICROBIOLOGICAL PROFILE OF DIABETIC FOOT ULCER IN A TERTIARY CARE HOSPITAL OF EASTERN ODISHA

1. Dr Tejaswi Mishra, Post Graduate Resident, Dept. of General Surgery, IMS & SUM Hospital. Email id – tejaswimishrapupul@gmail.com , Mob. No8763253477 2. Dr Godalu Trinath Patra, Assistant Professor, Dept. of General Surgery, IMS & SUM Hospital. Email id – dg.trinath@yahoo.com , Mob. No9777650693 3. Dr Rajashree Panigrahy, Professor, Dept of Microbiology, IMS & SUM Hospital. Email id – dr.rajashreep@gmail.com , Mob. No – 8249005970 4. Dr Purabi Baral (Corresponding Author), Assistant Professor, Dept of Microbiology, IMS & SUM Hospital. Email iddrpurabibaral@gmail.com , Mob. No – 9871540210
Development of EB inspection system EBeyeM for EUV mask

We are developing new electron beam inspection system, named EBeyeM, which features high speed and high resolution inspection for EUV mask. Because EBeyeM has the projection electron microscope technique, the scan time of EBeyeM is much faster than that of conventional SEM inspection system. We developed prototype of EBeyeM. The aim of prototype system is to prove the concept of EBeyeM and to estimate the specification of system for 2Xnm and 1Xnm EUV mask. In this paper, we describe outline of EBeyeM and performance results of the prototype system. This system has two inspection mode. One is particle inspection and the other is pattern defect inspection. As to the sensitivity of EBeyeM prototype system, the development target is 30nm for the particle inspection mode and 50nm for pattern defect inspection mode. The performance of this system was evaluated. We confirmed the particle inspection mode of the prototype system could detect 30nm PSL(Polystyrene Latex) and the sensitivity was much higher than conventional optical blank inspection system. And we confirmed that the pattern defect sensitivity of the prototype system was around 45nm. It was recognized that both particle inspection mode and pattern defect inspection mode met the development target. It was estimated by the performance results of the prototype system that the specification of EBeyeM would be able to achieve for 2Xnm EUV mask. As to 1Xnm EUV mask, we are considering tool concept to meet the specification.
Curriculum Learning for Imbalanced Classification in Large Vocabulary Automatic Chord Recognition

A problem inherent to the task of large vocabulary automatic chord recognition (ACR) is that the distribution over the chord qualities typically exhibits power-law characteristics. This intrinsic imbalance makes it difﬁcult for ACR systems to learn the rare chord qualities in a large chord vocabulary. While recent ACR systems have exploited the hierarchical relationships that exist between chord qualities, few have attempted to exploit these relationships explicitly to improve the classiﬁcation of rare chord qualities. In this paper, we propose a convolutional Transformer model for the task of ACR trained on a dataset of 1217 tracks over a large chord vocabulary consisting of 170 chord types. In order to address the class imbalance of the chord quality distribution, we incorporate the hierarchical relationships between chord qualities into a curriculum learning training scheme that gradually learns the rare and complex chord qualities in the dataset. We show that the proposed convolutional Transformer model achieves state-of-the-art performance on traditional ACR evaluation metrics. Furthermore, we show that the proposed curriculum learning training scheme outperforms existing methods in improving the classiﬁcation of rare chord qualities.
Facultative planktotrophy in larvae of the purple mussel Perumytilus purpuratus

ABSTRACT The developmental pattern in the mid-intertidal mussel Perumytilus purpuratus was investigated through laboratory cultures. Larval development, growth, survival rate, and time to reach the post-larval stage were determined using triplicated feeding/non-feeding treatments in artificial seawater at 18.2 ± 0.5°C. Development of the larval digestive system was examined histologically. We found that P. purpuratus have ova (121.1 ± 2.7 μm diameter) with a jelly coating, the digestive system develops in the trochophore, the veliger shell has a large prodissoconch-1 and a small prodissoconch-2, the larval period lasted between 10 and 14 days, and larvae could reach metamorphosis without the presence of particulate food. Unlike most of the mytilid species with pelagic larvae, a facultative planktotrophic development is evident in this species. This developmental pattern could be favourable in unstable and changing environments such as rocky shores.
Study of floating source dynamic behaviour in imbricated cell converter

Converters which have floating voltages in their topologies, made with capacitors. One aim of the capacitors is to divide voltages among switches. Then, each capacitor state of charge is crucial for the good running and the survival of the converter. Therefore, the understanding of voltage variation rules presents a primordial interest. A particular structure of multilevel converter appeared in the early nineties: the imbricated cell topology. The work proposed in the paper deals with the study of dynamic behaviour about capacitors making this kind of converter. The proposed analysis is dealt with in an original approach, based on physical and mathematical laws. Variation rules can then be established, allowing the modelling of capacitor charge according to the converter control parameters. This modelling will lead to some particular phenomena concerning the charge of capacitors, inducing a malfunction of running in the converter. The results obtained by modelling and simulation is proved with an experiment.
Gender bias in linguistics textbooks: Has anything changed since Macaulay & Brice 1997?

Abstract:Macaulay and Brice (1997:798) surveyed example sentences in eleven syntax textbooks published from 1969–1994 and found that virtually all of the authors 'favor male-gendered NPs as subjects and agents, and regularly stereotype both genders'. In this article, we address the question of whether constructed example sentences in more recent textbooks show similar gender bias. We present an analysis of six syntax textbooks published from 2005–2017, from which we randomly sampled 200 example sentences each. We find that the gender skew and stereotypes reported in 1997 are still present today. Male-gendered arguments are almost twice as frequent as female-gendered ones, and more likely to occur as subjects and agents. In addition, example sentences often perpetuate gender stereotypes. We discuss some broader implications and potential interventions to prevent the implicit perpetuation of gender biases in linguistic materials.
Collaborative reflection on environmental practices: a vehicle for environmental education in teacher education

ABSTRACT Environmental education and sustainability education have emerged as a key focus in teacher education in the past few decades. Researchers have investigated the impact of different pedagogies on pre-service teachers’ sustainability or environmental consciousness. The current study was an attempt to investigate the impact of collaborative reflection (focusing on environmental issues and practices) on pre-service teachers’ environmental practices. Forty-six pre-service teachers participated in the study. The data were gathered qualitatively through field notes and participants’ written reflections. The findings indicated that collaborative reflection on environmental issues and practices promote pro-environmental practices among the participants. The study suggests the use of collaborative reflection on environmental issues and practices as a pedagogy for environmental education in teacher education.
Choroidal Thickness in Thyroid Eye Disease: Comparison With Controls and Application in Diagnosing Non-Inflammatory Active Disease

Introduction Choroidal thickness is known to vary in various systemic diseases. In the current study, we aim to report the differences in choroidal thickness in thyroid eye disease (TED) and normals and its discriminatory value for differentiating various stages of TED. Methods Prospective, cross-sectional, non-interventional imaging study. In an institutional practice, 102 eyes of 51 patients were included and divided into five groups: normal controls (C), inactive TED (I), active TED (A), non-inflammatory active TED (NIA) and systemic thyroid disorder but no TED (SYS). Choroidal images were acquired using the swept-source optical coherence tomography (Topcon DRI OCT Triton) with automatic layer segmentation which provided an automatic measurement of the subfoveal choroidal thickness and the mean in nine subfields based on the Early Treatment Diabetic Retinopathy Study (ETDRS) grid. One-way analysis of variance (ANOVA), Youden index and area under the receiver operating characteristic curves (AUROC) were reported. Results Central choroidal thickness in the A group was 279±37.52 microns and in the NIA group was 302.5±59.22 microns. Both were comparable to each other and significantly higher than the C, I and SYS groups (p<0.001). All ETDRS sub-fields showed significant AUROC to distinguish NIA from I. Most significant Youden index was for the inner nasal and central ETDRS subfields (0.55 and 0.61 respectively). Inner nasal sub-field showed 100% specificity while the central sub-field, showed 86.5% for predicting NIA. At a choroidal thickness of >266 microns, the central sub-field had the strongest discriminatory potential to predict NIA. Conclusion Choroidal thickness is greater in active and non-inflammatory active TED. The inner nasal and central ETDRS sub-fields have value in differentiating the non-inflammatory active TED eyes from the inactive eyes.
The within‐ and between‐day reliability of cerebrovascular reactivity using traditional and novel analytical approaches

What is the central question of the study? What is the reliability of middle cerebral artery velocity cerebrovascular reactivity (CVR) when using traditional and novel outcomes, as measured by transcranial Doppler? What is the main finding and its importance? Traditional CVR approaches presented acceptable reproducibility but should be expressed as an absolute CVR. Large within‐ and between‐individual differences in the middle cerebral artery velocity response profile support using a dynamic peak, rather than a set time point, for the most reliable interpretation. The study highlights the utility of novel kinetic CVR outcomes, but due to increased variability in time‐based metrics, this analysis requires larger sample sizes than traditional methods.
A high speed, low noise ASIC preamplifier for silicon strip detectors

A first-version Tektronix Quickchip semicustom ASIC (application-specific integrated circuit) preamplifier for silicon strip detectors was reported previously (see D. Christian et. al. 1988). An improved version, QPA02, has been designed. It incorporates laser trimming of nichrome resistors as a means of compensating for chip resistance process variations, which affected the response of the first version. This allows chips with randomly varying resistance values to be trimmed to a standard output pulse shape. The QPA02 also has improved performance specifications. The design philosophy and specifications of the QPA02 are described. The QPA02 has been tested and demonstrated to be an effective silicon strip amplifier for high-rate applications, performing as modeled. >