Miracle-dz/UniFAR-FTUs huggingface dataset:
Ambiguity in Requirement Engineering Documents: Importance, Approaches to Measure and Detect, Challenges and Future Scope

One of the important phases of software development life cycle (SDLC) is requirement engineering. Since it is very important to understand the exact requirement of any system before actually building it. But RE document is always written in natural language and which is prone to ambiguity. Thus it is very important to measure and detect ambiguity in RE document to decrease the cost and time ofsoftware development. This paper mainly discusses about the sources and reasons for of ambiguity and approaches used to avoid, detect or measure ambiguity in any RE text.
Short Tail: taming tail latency for erasure-code-based in-memory systems

In-memory systems with erasure coding (EC) enabled are widely used to achieve high performance and data availability. However, as the scale of clusters grows, the server-level fail-slow problem is becoming increasingly frequent, which can create long tail latency. The influence of long tail latency is further amplified in EC-based systems due to the synchronous nature of multiple EC sub-operations. In this paper, we propose an EC-enabled in-memory storage system called Short Tail, which can achieve consistent performance and low latency for both reads and writes. First, Short Tail uses a lightweight request monitor to track the performance of each memory node and identify any fail-slow node. Second, Short Tail selectively performs degraded reads and redirected writes to avoid accessing fail-slow nodes. Finally, Short Tail posts an adaptive write strategy to reduce write amplification of small writes. We implement Short Tail on top of Memcached and compare it with two baseline systems. The experimental results show that Short Tail can reduce the P99 tail latency by up to 63.77%; it also brings significant improvements in the median latency and average latency.
Hierarchical Virtual Screening Based on Rocaglamide Derivatives to Discover New Potential Anti-Skin Cancer Agents

Skin Cancer (SC) is among the most common type of cancers worldwide. The search for SC therapeutics using molecular modeling strategies as well as considering natural plant-derived products seems to be a promising strategy. The phytochemical Rocaglamide A (Roc-A) and its derivatives rise as an interesting set of reference compounds due to their in vitro cytotoxic activity with SC cell lines. In view of this, we performed a hierarchical virtual screening study considering Roc-A and its derivatives, with the aim to find new chemical entities with potential activity against SC. For this, we selected 15 molecules (Roc-A and 14 derivatives) and initially used them in docking studies to predict their interactions with Checkpoint kinase 1 (Chk1) as a target for SC. This allowed us to compile and use them as a training set to build robust pharmacophore models, validated by Pearson’s correlation (p) values and hierarchical cluster analysis (HCA), subsequentially submitted to prospective virtual screening using the Molport® database. Outputted compounds were then selected considering their similarities to Roc-A, followed by analyses of predicted toxicity and pharmacokinetic properties as well as of consensus molecular docking using three software. 10 promising compounds were selected and analyzed in terms of their properties and structural features and, also, considering their previous reports in literature. In this way, the 10 promising virtual hits found in this work may represent potential anti-SC agents and further investigations concerning their biological tests shall be conducted.
Curved Compliant Facet Origami-Based Self-Deployable Gliding Wing Module for Jump-Gliding

In this paper, we propose a self-deployable origami structure and its application to a gliding wing module for jump-gliding. Generally, origami structures are composed of flat rigid facets. By using curved compliant facets instead of flat rigid facets, we endowed the origami structure with the ability to self-deploy and self-lock. When the origami structure is folded, the curved facets gradually flatten, and potential energy is stored on the flattened facets. By using the stored energy, the structure can rapidly deploy without an actuator. The geometry of the curved facet generates a notably high joint stiffness in the deployed state, which can be used as a self-locking mechanism.In jump-gliding, the gliding wing should be foldable and lightweight for efficient jumping and capable of rapid deployment for efficient gliding. By applying the suggested origami structure as a basic frame, the deployable gliding wing module can satisfy these requirements. The wing span of the module is 44.7 cm, and it weighs 20.3 g including a battery and a servo motor. The module can be folded in 1/8.1 of the deployed area, and it deploys within 350 ms when it needs to glide.Copyright © 2016 by ASME
Sputum Microbiota in Coal Workers Diagnosed with Pneumoconiosis as Revealed by 16S rRNA Gene Sequencing

Coal worker’s pneumoconiosis (CWP) is an occupationally induced progressive fibrotic lung disease. This irreversible but preventable disease currently affects millions across the world, mainly in countries with developed coal mining industries. Here, we report a pilot study that explores the sputum microbiome as a potential non-invasive bacterial biomarker of CWP status. Sputum samples were collected from 35 former and active coal miners diagnosed with CWP and 35 healthy controls. Sequencing of bacterial 16S rRNA genes was used to study the taxonomic composition of the respiratory microbiome. There was no difference in alpha diversity between CWP and controls. The structure of bacterial communities in sputum samples (β diversity) differed significantly between cases and controls (pseudo-F = 3.61; p = 0.004). A significant increase in the abundance of Streptococcus (25.12 ± 11.37 vs. 16.85 ± 11.35%; p = 0.0003) was detected in samples from CWP subjects as compared to controls. The increased representation of Streptococcus in sputum from CWP patients was associated only with the presence of occupational pulmonary fibrosis, but did not depend on age, and did not differ between former and current miners. The study shows, for the first time, that the sputum microbiota of CWP subjects differs from that of controls. The results of our present exploratory study warrant further investigations on a larger cohort.
The nature of wind turbine fatigue loads in wind farms

The nature of wind turbine fatigue loads in wind farms The aim of the present paper is to further validate the predictive capability of the DWM/HAWC2 package for simulation of structural loadings in wind farms. The validation in particular focus on tower fatigue loading characteristics (i.e. equivalent moments) as function of turbine relative position, including turbine interspacing. To accomplish this, comparative studies of predicted and measured fatigue load characteristics are performed. The involved data relates to full-scale measurements from the Danish Rødsand 2 offshore wind farm, where 6 turbines are instrumented with strain gauges providing tower top and bottom bending moments as well as tower the top torsion moment. The numerical predictions are based on a simulation package, where the Dynamic Wake Meandering model is interfaced with the in-house aeroelastic code HAWC2. Comparing simulated and measured tower fatigue loading, good agreements were found between the extend of wake affected direction regimes. As for the magnitude of the (mean) wake loading, the tower top moments were underestimated with of the order 15%, whereas the tower bottom moments were found to agree well with the measured results.
A utility power supply based on a four-output leg matrix converter

A four-output leg matrix converter and variable-speed diesel generator are integrated to create a three-phase plus neutral utility power supply. The matrix converter is designed to meet tight harmonic and the forth leg provides the facility to supply unbalanced and single-phase loads. This enables the generator to provide stable power over a range of engine speeds and load conditions. Test results for the 10 kW offline power supply are provided to demonstrate system performance.
Incorporation of Human Factors to a Data-Driven Car-Following Model

In recent years, there have been intensive efforts to consider human factors (HFs) in the modeling of human driver behavior. In particular, “engineering” car-following models widely used in traffic simulation have been extended to include HFs. This extension is needed to generate critical situations, which are often attributable to human error. However, incorporation of reaction processes requires advanced models that take driver predictions and delayed responses into account. In this paper, a methodology for integrating HFs into driver behavior modeling is developed based on a long short-term memory architecture. The proposed methodology employed a three-layer psychological concept: perception, information processing, and action. The perception layer modeled (imperfect) estimation of visually received stimuli. Information processing included short-term memory and the projection of perceived stimuli into the near future. The executed action, based on the sensed as well as anticipated dynamic driving state, was delayed by the perception–reaction time. To represent individual differences among driver types, the available training dataset was classified in four clusters according to observable driver characteristics. The methodology was demonstrated with data recorded at an urban signalized intersection. Model performance was compared with that of two established engineering models, the intelligent driver model and the (extended) full velocity difference model. The results indicated that the human driver model developed here showed superior performance in replicating real-world trajectories compared with existing models and was able to represent the varying driving strategies of different groups.
Metamaterial-Based Wide-Beam Dielectric Resonator Antenna for Broadband Wide-Angle Beam-Scanning Phased Array Applications

A novel metamaterial-loaded, miniaturized, broadband, wide-beam dielectric resonator (DR) antenna is presented for low-cost wide-angle beam-scanning applications. The mushroom structure is loaded on the DR structure, so the proposed DR radiator reduces the size reduction (low profile, $0.09\lambda _{0}$ ) and achieves a broadened bandwidth with a dual-mode response. The realized 3 dB beamwidth of the DR radiator is widened from 90° to 194° by introducing a pair of miniaturized equal-amplitude and out-of-phase Huygens sources. Owing to its compact size and wide beamwidth, a five-element low-cost wide-angle beam-scanning phased array is presented by using the proposed wide-beam DR elements. The measured −10 dB impedance bandwidth covers the 5G-78 band well (3.30–3.80 GHz, 14.1%). The realized scanning angle of the fabricated linear array can be extended from ±36° to ±65°, and its 3 dB beamwidth scanning coverage can up to ±90°. In virtue of miniaturized size, wideband, low cost, and good beam-scanning ability, the presented element, and the resulting wide-angle beam-scanning array are promising candidates for wireless communication and low-cost phased array applications.
Mechanical Behaviors in Janus Transition-Metal Dichalcogenides: A Molecular Dynamics Simulation

In this work, molecular dynamics simulations are performed to investigate the mechanical properties of Janus WSSe and MoSSe monolayers considering the effects of structural anisotropy, temperature, and tensile strain rates. The results demonstrate that Janus WSSe and MoSSe monolayers show strong mechanical anisotropy under tension along the armchair and zigzag directions, respectively. This anisotropy displays distinct temperature dependence. When the coupled effects of the temperature and anisotropy are considered for the tensions along the zigzag direction, there is a transition of ductile-to-brittle failure in the Janus WSSe monolayer at the critical temperature range of 80~90 K due to the competition between atomic thermal vibrations and structural bending/wrinkles. This phenomenon is further confirmed by both stress–strain curves and structural evolutions of the systems. Finally, a strain rate hardening mechanism is found when various strain rates are applied, and it demonstrates that the Janus monolayer could maintain stable mechanical properties under different loading conditions. Our investigations provide a helpful reference for subsequent theoretical and experimental studies on the mechanical properties of Janus monolayer structures and could shed some light on the design of promising nanoscale functional devices based on Janus transition-metal dichalcogenides.
A MULTI-SCALE POINT CLOUDS SEGMENTATION METHOD FOR URBAN SCENE CLASSIFICATION USING REGION GROWING BASED ON MULTI-RESOLUTION SUPERVOXELS WITH ROBUST NEIGHBORHOOD

Abstract. Point clouds classification is the basis for 3D spatial information extraction and applications. The point-clusters-based methods are proved to be more efficient and accurate than the point-based methods, however, the precision of the classification is significantly affected by the segmentation errors. The traditional single-scale point clouds segmentation methods cannot segment complex objects well in urban scenes which will result in inaccurate classification. In this paper, a new multi-scale point clouds segmentation method for urban scene point clouds classification is proposed. The proposed method consists of two stages. In the first stage, to ease the segmentation errors caused by density anisotropy and unreasonable neighborhood, a multi-resolution supervoxels segmentation algorithm is proposed to segment the objects into small-scale clusters. Firstly, the point cloud is segmented into initial supervoxels based on geometric and quantitative constraints. Secondly, robust neighboring relationships between supervoxels are obtained based on kd-tree and octree. Furthermore, the resolution of supervoxels in the planar and low-density region is optimized. In the second stage, planar supervoxels are clustered into the large-scale planar point clusters based on the region growing algorithm. Finally, a mix of small-scale and large-scale point clusters is obtained for classification. The performance of the segmentation method in classification is compared with other segmentation methods. Experimental results revealed that the proposed segmentation method can significantly improve the efficiency and accuracy of point clouds classification than other segmentation methods.
Multiport Dual-Functional Circuits With Filtering and Power Dividing Operations Using Single Dielectric Resonator

A novel concept for utilizing a single dielectric resonator (DR) to fulfill a dual-mode bandpass filter (BPF) and a power divider (PD) simultaneously is proposed. A stepped rectangular DR is employed and two of its modes are, respectively, applied to the two functions. The fields of the modes are orthogonal and their frequencies can be flexibly controlled so that the BPF and PD are able to operate with high isolation and adjustable frequency bands. In the BPF part, one of the shielding cavity modes is also introduced to form a dual-mode characteristic. According to the symmetry of the electromagnetic fields, the PD part is designed in three forms: 1-to-2 PD with in-phase outputs, 1-to-2 PD with antiphase outputs, and 1-to-4 PD. As a result, three dual-functional combinations are achieved. For demonstration, the proposed dual-function applications sharing the common DR in high circuit integration are implemented and measured.
A CONCEPTUAL MODEL FOR CONVERTING OPENSTREETMAP CONTRIBUTION TO GEOSPATIAL MACHINE LEARNING TRAINING DATA

Abstract. In the recent decade, Volunteered Geographical Information (VGI), in particular the OpenStreetMap (OSM), has helped to fill substantial data gaps in base maps, especially in Global South, thus has become a promising source of massive, free training data together with rich and detailed semantic information for geospatial artificial intelligence (GeoAI) applications. Although intensive works have explored the potential of generating training data from OSM, a systematic approach of harvesting OSM contribution as quality-aware training data for different GeoAI tasks is still missing. To fill this research gap, we proposed a conceptual model consisting of three major components: historical OSM and external datasets, quality indicators, and GeoAI models. As a proof of concept, we validated our conceptual model with an example task of detecting OSM missing buildings in Mozambique, where the impact of different error sources (e.g., completeness, alignment, rotation) in training data were compared and investigated in a quantitative manner. The lessons learned in this paper shed important lights on cooperating OSM data quality aspects with the development of more explainable GeoAI models.
Molecular characterisation of hemagglutinin and neuraminidase genes of the first highly pathogenic Avian Influenza H5N1 2.3.2.1c virus isolated from Sabah, Malaysia.

Highly Pathogenic Avian Influenza (HPAI) is a highly contagious disease in poultry. The outbreaks can lead to flock mortality up to 100% in two to three days. In July 2018, high mortality in a commercial layer farm in Kauluan village, Sabah was reported. Samples were sent to Veterinary Research Institute Ipoh for diagnosis. Virus isolation and molecular detection is carried out simultaneously. The causative agent was then identified as AI H5N1 virus by real time reverse transcription-polymerase chain reaction (RT-PCR). The virus was then subjected for further nucleotide sequencing of full length hemagglutinin (HA) and neuraminidase (NA) gene. The PQRERRRKR/GLF motif at the HA cleavage site indicated that the isolate was of HPAI virus. Phylogenetic analysis of the HA gene showed that the isolate was belonged to the clade 2.3. 2.1c virus. In the HA gene, besides the S133A substitution, the virus possesses conserved amino acid at most of the avian receptor binding sites including the glutamine (Q) and glycine (G) at position 222 and 224 respectively, indicating that the virus retains the avian-type receptor binding preference. As such, the zoonotic potential of the virus was relatively low. On the other hand, though the N154D and T156A substitution were detected in the same gene, the pandemic potential of this Sabah 2.3. 2.1c virus is low in the absence of the Q222L, G224S, H103Y, N220K and T315I. A typical 20 amino acid deletion with loss of four corresponding glycosylation sites in the NA stalk region was visible. Though three NA resistance markers were detected, the virus was predicted to be sensitive to NA inhibitor. This is the first HPAI H5N1 outbreak in Sabah. The introduction of this virus into East Malaysia for the first time raised an alert alarm of the future epidemic potential. Strict farm biosecurity, continuous surveillance programme in poultry, wild birds, migratory birds; molecular epidemiology as well as risk assessment for the virus with pandemic potential are needed in dealing with emergence of new influenza virus in the country.
Innovation corps for learning (I-Corps™ L): Assessing the potential for sustainable scalability of educational innovations

The Innovation Corps for Learning (I-CorpsTM L) is an initiative of the National Science Foundation (NSF) and the American Society for Engineering Education (ASEE) in cooperation with the University of Minnesota, Arizona State University, Colorado State University, and Tufts University to apply the highly successful principles of NSF I-CorpsTM towards a culture that will sustain and scale educational innovations. The NSF I-CorpsTM program, on which I-CorpsTM L is based, uses established strategies for start-ups to build entrepreneurial skills in the engineering and scientific communities that encourage mainstream application of emerging technologies. The overriding purposes of the 8 week I-CorpsTM L course are to (1) provide a framework for each of the participating teams (typically 20 to 24 in a cohort) to assess the potential of their educational innovation for sustainability and scalability, and (2) foster an entrepreneurial mindset within the education community so that education products, programs, and services are designed and implemented in ways that promote widespread adoption. In this paper and poster we summarize the essential features of I-CorpsTM L, the changes made in the three iterations of the program thus far, and key evaluation results. We also present our assessment of the potential of the I-CorpsTM L to contribute to the transformation of STEM education through the sustaining and scaling of NSF-funded research-based ideas.
A Fuzzy Similarity-Based Approach to Classify Numerically Simulated and Experimentally Detected Carbon Fiber-Reinforced Polymer Plate Defects

This paper presents an eddy current approach for testing, estimating, and classifying CFRP plate sub-surface defects, mainly due to delamination, through specific 2D magnetic induction field amplitude maps. These maps, showing marked fuzziness content, require the development of a procedure based on a fuzzy approach being efficiently classified. Since similar defects produce similar maps, we propose a method based on innovative fuzzy similarity formulations. This procedure can collect maps similar to each other in particular defect classes. In addition, a low-cost analysis system, including the probe, has been implemented in hardware. The developed tool can detect and evaluate the extent of surface defects with the same performance as a hardware tool of higher specifications, and it could be fruitfully employed by airline companies to maintain aircraft in compliance with safety standards.
Etiology, 3-Month Functional Outcome and Recurrent Events in Non-Traumatic Intracerebral Hemorrhage

Background and Purpose Knowledge about different etiologies of non-traumatic intracerebral hemorrhage (ICH) and their outcomes is scarce. Methods We assessed prevalence of pre-specified ICH etiologies and their association with outcomes in consecutive ICH patients enrolled in the prospective Swiss Stroke Registry (2014 to 2019). Results We included 2,650 patients (mean±standard deviation age 72±14 years, 46.5% female, median National Institutes of Health Stroke Scale 8 [interquartile range, 3 to 15]). Etiology was as follows: hypertension, 1,238 (46.7%); unknown, 566 (21.4%); antithrombotic therapy, 227 (8.6%); cerebral amyloid angiopathy (CAA), 217 (8.2%); macrovascular cause, 128 (4.8%); other determined etiology, 274 patients (10.3%). At 3 months, 880 patients (33.2%) were functionally independent and 664 had died (25.1%). ICH due to hypertension had a higher odds of functional independence (adjusted odds ratio [aOR], 1.33; 95% confidence interval [CI], 1.00 to 1.77; P=0.05) and lower mortality (aOR, 0.64; 95% CI, 0.47 to 0.86; P=0.003). ICH due to antithrombotic therapy had higher mortality (aOR, 1.62; 95% CI, 1.01 to 2.61; P=0.045). Within 3 months, 4.2% of patients had cerebrovascular events. The rate of ischemic stroke was higher than that of recurrent ICH in all etiologies but CAA and unknown etiology. CAA had high odds of recurrent ICH (aOR, 3.38; 95% CI, 1.48 to 7.69; P=0.004) while the odds was lower in ICH due to hypertension (aOR, 0.42; 95% CI, 0.19 to 0.93; P=0.031). Conclusions Although hypertension is the leading etiology of ICH, other etiologies are frequent. One-third of ICH patients are functionally independent at 3 months. Except for patients with presumed CAA, the risk of ischemic stroke within 3 months of ICH was higher than the risk of recurrent hemorrhage.