Miracle-dz/UniFAR-FTUs huggingface dataset:
Knowledge-Oriented Models Based on Developer-Artifact and Developer-Developer Interactions

INTRODUCTION: Software development is organized around developers working collaboratively promoting two types of interactions for knowledge sharing. Developer-Artifact interactions indicate developers define or access pieces of information within artifacts. Developer-Developer interactions indicate the exchange of information among developers using a collaboration platform to clarify an issue, promote an idea, or expose any thoughtful comment. PROBLEM: The number of such interactions grows over time and makes it difficult to capture and assess the evolution of the developers’ knowledge about specific software project artifacts and tasks. Further, this knowledge decreases over time due to the natural limitations of human cognition that restrict our capabilities to cope with information overload. Besides, who has more knowledge about specific project elements are important to promote collaboration. AIMS: The <inline-formula> <tex-math notation="LaTeX">$K_{a}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$K_{s}$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$K_{c}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">$K_{p}$ </tex-math></inline-formula> models capture the evolution of the developers’ knowledge about software project elements such as artifacts, tasks, similar tasks, and the whole software project. These models represent not only the knowledge developers have about these elements but also capture how this knowledge decreases over time based on forgetting and relearning functions. EVALUATION: An experimental study analyzed some developers’ interactions on artifacts for the purpose of predicting the evolution of developers’ knowledge in six software projects. The results show that the developers’ rankings by performed tasks and by our models have 72% or more of similarity. CONCLUSION: Our models can capture and assess the evolution of the developers’ knowledge and help to identify which developers have more knowledge about specific elements of software projects.
Blood Glucose Prediction With VMD and LSTM Optimized by Improved Particle Swarm Optimization

The time series of blood glucose concentration in diabetics are time-varying, nonlinear and non-stationary. To improve the accuracy of blood glucose prediction, a short-term blood glucose prediction model (VMD-IPSO-LSTM) combining variational modal decomposition (VDM) and improved Particle swarm optimization optimizing Long short-term memory network (IPSO-LSTM) was proposed. Firstly, the time series of blood glucose concentration of patients was decomposed by using VMD method to obtain the intrinsic modal functions (IMF) of blood glucose components in different frequency bands, so as to reduce the non-stationarity of blood glucose time series. Then a prediction model was established for each blood glucose component IMF by using the long and short time memory network. Since the number of neurons, learning rate and time window length of LSTM are difficult to determine, the improved PSO algorithm is used to optimize these parameters. The optimized LSTM network was used to predict each IMF, and finally the predicted subsequence was superimposed to obtain the final prediction result. The data of 56 patients were selected as experimental data from 451 patients with diabetes mellitus. The experimental results showed that the proposed VMD-IPSO-LSTM model could achieve high prediction accuracy at 30min, 45min and 60min in advance. When predicted 60 minutes in advance, compared with the LSTM, VMD-LSTM and VMD-PSO-LSTM methods, the RMSE of proposed method decreased by 15.565,3.402,1.215 and the MAPE of proposed method decreased by 11.284%,2.024%, 0.834%, and the percentage of predicted values falling into the A zone increased by 23.5%,6.1% and 2.8% in the Clarke error grid, respectively. The improved accuracy of blood glucose prediction and longer prediction time can provide sufficient time for physicians and patients to control blood glucose concentrations and improve the effectiveness of diabetes treatment.
Intelligent System for Detection of Wild Animals Using HOG and CNN in Automobile Applications

Animal Vehicle Collision, commonly called as roadkill, is an emerging threat to humans and wild animals with increasing fatalities every year. Amid Vehicular crashes, animal actions (i.e. deer) are unpredictable and erratic on roadways. This paper unveils a newer dimension for wild animals’ auto-detection during active nocturnal hours using thermal image processing over camera car mount in the vehicle. To implement effective hot spot and moving object detection, obtained radiometric images are transformed and processed by an intelligent system. This intelligent system extracts the features of the image and subsequently detects the existence of an object of interest (i.e. deer). The main technique to extract the features of wild animals is the Histogram of Oriented Gradient (HOG) transform. The features are detected by normalizing the radiometric image and then processed by finding the magnitude and gradient of a pixel. The extracted features are given as an input to the basic deep learning model, a one-dimensional convolutional neural network (1D-CNN), where binary cross-entropy is used to detect the existence of the object. This intelligent system has been tested on a set of real scenarios and gives approximately 91% accuracy in the correct detection of the wild animals on roadsides from the city of San Antonio, TX, in the USA.
Au-NPs/ZnO Single Nanowire Nanosensors for Health Care Applications

Herein, the room temperature gas sensing properties of a device fabricated based on an individual gold nanoparticles (AuNPs)-functionalized zinc oxide nanowire (ZnO NW) is reported. The Au-NPs/ZnO nanowires were deposited using the electrochemical approach in a classical three-electrode electrochemical cell. The dual beam focused ion beam/scanning electron microscopy (FIB/SEM) was used to integrate the single nanostructures into gas sensing nanodevices. The results are promising for future applications in monitoring H2 gas for health care applications and clinical breath analysis.
Influence of Single and Double-Atom Metal Doping on the Electrocatalytic Hydrogen Evolution Activity of 2D-MoS2 Surface

The Hydrogen evolution reaction (HER) is an important process during electrocatalytic water splitting for hydrogen energy generation. Two dimensional MoS2 has been considered as a promising alternative to Pt-based catalysts in the hydrogen evolution reaction. However, the highest contribution for the catalytic activity of 2D-MoS2 is from its edge sites, this in turn leaves many in-plane domains useless. In this study, the effect of single and double atom metal (Pt, Ni and Pt-Ni) doping on HER catalytic activity of in-plane atoms was investigated using density functional theory calculations. The Gibbs free energy of adsorbed hydrogen on pristine MoS2 decreased from 1.86eV to -0.08eV in PtNi co-doped MoS2. This demonstrates enhanced catalytic activity of MoS2 due to atomic doping. The enhanced catalytic activity may also be attributed to the observed changes in structural and electronic properties.
A Fast Locking 5.8–7.2-GHz Fractional-N Synthesizer With Sub-2-us Settling in 22-nm FDSOI

This letter presents a fast frequency hopping all-digital fractional-N synthesizer with linear frequency tuning word estimation and aggressive type-I and type-II loop settling. It has a linearized DCO allowing wide-band and closed-loop operation and employs digital zero-phase reset to reduce phase transients during switch over due to type-I settling. The DCO gain is equalized by exploiting routing inductances and a hybrid binary-thermometric segmentation provides improved DNL performance for closed-loop operation in a 5.8–7.2-GHz range. The circuit was processed in a 22-nm FDSOI technology and achieves a settling time below $2~\mu \text{s}$ in a 200-MHz hopping range.
Spectrum-Efficient QoS-Aware Resource Assignment for FFR-Based D2D-Enabled Heterogeneous Networks

Small cells and Device-to-Device (D2D) communication can improve the coverage and capacity of cellular systems, thereby enabling an enriched customer experience. To optimize that experience, this paper considers a problem of uplink weighted sum rate maximization for a single cell heterogeneous network containing macro cellular users, D2D users and femto-cell users. In particular, in order to control the co-tier and cross-tier interferences, a new Fractional Frequency Reuse (FFR) architecture is developed in which we seek to maximize a weighted sum rate, subject to minimum rate requirements and transmission power constraints. In order to tackle the resulting mixed integer nonlinear optimization problem, we develop a decomposition-based strategy. The proposed strategy solves the admission control, power control, and matching subproblems optimally, and allocates the remaining free sub-channels by using a heuristic suboptimal algorithm. Numerical results demonstrate that the proposed scheme, on average, achieves around 96% of the system sum rate of the optimal Branch and Bound method with much lower computational cost.
A Syntax-Augmented and Headline-Aware Neural Text Summarization Method

With the advent of the information age, excessive information collection leads to information overload. Automatic text summarization technology has become an effective way to solve information overload. This paper proposes an automatic text summarization model, which extends traditional sequence-to-sequence (Seq2Seq) neural text summarization model by using a syntax-augmented encoder and a headline-aware decoder. The encoder encodes both syntactic structure and word information of a sentence in the sentence embedding. A hierarchical attention mechanism is proposed to pay attentions to syntactic units. The decoder is improved by a headline attention mechanism and a Dual-memory-cell LSTM network to achieve a higher quality of generated summaries. We designed experiments to compare the proposed method with baseline models on the CNN/DM datasets. The experiment results show that the proposed method is superior to abstractive baseline models in terms of the scores on ROUGE evaluation metrics, and achieve a summary generation performance comparable to the extractive baseline method. Though qualitative analysis, the summary quality of the propose method is more readable and less redundant, which agrees well with our intuition.
A Subject-Specific Neuromusculoskeletal Model for Muscle Force Estimation Used in Rehabilitation

Human movement biomechanical analyses are considered as an essential tool in the clinical management and basic research of neurological and orthopedic conditions. Also, application of a biomechanical model for the estimation of the activity of muscles during rehabilitation training sessions could assist therapists to conduct personalized rehabilitation therapy and understand inter muscle dynamics of body part motions. In this paper, we propose a subject-specific neuromusculoskeletal (NMS) prediction model for the estimation of individual muscle forces during elbow extension and flexion. The NMS model was established by initial guesses and then the parameters of muscles were optimized and modified using the surface electromyography (sEMG) signals and motion data. Then specific muscles forces were predicted using the subject-specific NMS model during elbow flexion and extension. The results show geat accuracy and reliability of the optimized NMS model. This subject-specific NMS model by merging sEMG signals and motion data together, shows great potential to provide an improvement to quantitatively assess upper limb function and improve diagnosis and management of both neurological and orthopaedic conditions in rehabilitation.
Conditional Privacy-Preserving Anonymous Authentication Scheme With Forward Security in Vehicle-to-Grid Networks

In the Vehicle-to-Grid (V2G) network, the electric vehicles (EVs) need to report their respective information, such as vehicle identity, battery capacity, battery state of charge (SoC), current location, and driving direction, to the power grid through the distributed local aggregators (LAGs), so that the power grid can dispatch power reasonably. However, the information has much to do with privacy of the EVs. The issue of how to solve the contradictory needs of privacy protection and information report is a challenging problem in the V2G network. This paper proposed a privacy preserving authentication mechanism together with a key exchange between the EVs and the LAGs, in which the EVs do not use any pseudonyms. Besides satisfying the requirements of anonymity, confidentiality, unlinkability, nonrepudiation, traceability and revocation, the scheme can further support forward security. The performance analysis shows that the proposed scheme is efficient in terms of computation and communication overhead. The experimental results show that the proposed scheme has fewer exchanged messages in the authentication process among compared schemes.
Building 3D Map Based on Monte Carlo Localization and Feature Extraction

In this project, we mainly explore and construct the 3D indoor map. The goal of this paper is to merge the data from the laser and kinect sensor with Monte Carlo Location(MCL) in 2D map. We use the laser range sensor to construct the 2D map and get the robot's pose transformation matrix. Then, we obtain the color and depth image by using the kinect sensor and build 3D point cloud map by using the feature extraction method, getting the kinect's pose transformation matrix. After that, we get the optimal pose transformation by using the Kalman Filter to calibrate the robot's pose transformation matrix and the kinect's pose transformation matrix. Finally, the optimal pose transformation matrix is employed to accomplish the local 3D map and construct the global 3D indoor map. To show the superiority of our method, we make some experiments and compare with some other algorithms. Experimental results show that our method has a better superiority.
Synchrotron radiation-based micro-computed tomographic analysis of dentinal microcracks using rotary and reciprocating file systems: An in vitro study

Background: Advances in both X-ray sources and X-ray optics have dramatically improved the feasibility of these techniques in various fields. Aims: We aimed to evaluate the frequency of dentinal microcracks observed after root canal preparation with ProTaper Gold (PTG), Hyflex electrodischarge machining (HEDM), Reciproc (RPC), and WaveOne Gold (WOG) using synchrotron radiation-based micro-computed tomographic (SR-μCT) analysis. Subjects and Methods: Forty mandibular molars were assigned to 4 experimental groups (n = 10) according to the file system used for the root canal preparation: Group 1: PTG (25/0.08), Group 2: HEDM (25/0.08), Group 3: RPC (25/0.08), and Group 4: WOG (25/0.07). The specimens were scanned on SR-μCT system before and after root canal preparation. The pre- and postoperative cross-sectional images (N = 183,200) of the mesial roots were screened to identify the presence of dentinal microcracks. The number of microcracks was determined as a percentage for each group. Statistical Analysis: There was no necessity of statistical analysis in the present study. The number of microcracks was determined as a percentage for each group. Results: Among the four file systems tested, HEDM (0.9%) has shown a fewer number of new dentinal microcracks. Conclusions: SR-μCT can be used as a reliable diagnostic tool for further implications.
Evaluation of the correlation between dental caries and periodontitis - A clinico- immunological analysis

Background: To study the correlation between dental caries and periodontitis. Aim: The aim of the study was to evaluate the correlation between dental caries and periodontitis based on the clinical and immunological analysis. The null hypothesis was that there is no clinical and immunological correlation between dental caries and periodontitis. Settings and Design: Fifty systemically healthy adults (mean age of 35 years) with decayed, missing, and filled teeth scores of more than 6 were assessed for periodontal health by recording their probing depth (PD) and bleeding on probing (BOP) values. Unstimulated whole saliva was collected, and enzyme-linked immunosorbent assay (ELISA) analysis was conducted for determining the immunoglobulin A (IgA) and immunoglobulin G (IgG) levels. The obtained clinical and immunological values were statistically analyzed. Results: The results of the clinical examination revealed low PD and BOP scores, whereas the ELISA analysis showed significantly higher salivary IgA levels as compared to salivary IgG. Conclusions: Based on the observations of this clinicoimmunological study, it was concluded that no correlation existed between dental caries and periodontal disease.
Inferring forest fate from demographic data: from vital rates to 1 population dynamic models: Appendix 1 2

Jessica Needham1, Cory Merow2, Chia-Hao Chang-Yang1, Hal Caswell3, and Sean M. 3 McMahon1 4 1Smithsonian Institution Forest Global Earth Observatory, Smithsonian Environmental 5 Research Center, 647 Contees Wharf Road, Edgewater, MD 21307-0028 6 2Ecology and Evolutionary Biology, Yale University, 165 Prospect St, New Haven, CT 7 06511-8934, 8 3Institute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, 9 Science Park 904, 1098 XH Amsterdam, The Netherlands 10
Atomistic view of thin Ni/Ni3Al (0 0 1) under uniaxial tension of twist grain boundaries

Atomic motion and the structure response of grain boundaries (GBs) are essential to the plastic deformation of small-volume polycrystal systems, especially for thin materials that exhibit some dramatic characteristics. Here, the microstructure and properties of thin Ni/Ni3Al (0 0 1) with the uniaxial tension of twist GBs are investigated using molecular dynamics (MD) simulations with an embedded atom (EAM) potential. We find that low angle twist GB dislocations propagate mainly from the corners to the center of the interface, and for high angle twist GBs they originate from the edge of the interface but do not extend to the center. In the process of plastic deformation, both low angle and high angle twist GB fractures occur in the center of the interface, which is completely different from the situation of thick materials reported by other references. More interestingly, the fracture patterns between low angle and high angle twist GBs exhibit extremely different performance due to the different characteristics of the initial dislocation structures in the interfaces.
Tonsillar lymphoid alterations caused by TNF‐α inhibitors: an iatrogenic mimicker of lymphoproliferative disorders

A 39 year old woman presented with complaints of throat swelling and a history of multiple courses of antibiotic treatment for clinically diagnosed sinusitis and tonsillitis occurring over a period of greater than one year. Despite treatment, she still noted difficulty in eating once or twice a month due to pharyngeal discomfort. She had been diagnosed with Crohn disease 18 months previously and had begun treatment with infliximab (Remicade, Janssen Biotech, Inc.) at that time. She also noted that she had suffered a long history of chronic sinusitis as a child, but felt she had "grown out of it" until it had apparently been reactivated at the time infliximab therapy was started. A CT scan of the neck and larynx demonstrated tonsillar enlargement, as well as mild acute sinusitis in the left maxillary region. Bilateral tonsillectomy was performed.
Study protocol for a network meta-analysis of digital-technology-based psychotherapies for PTSD in adults

Introduction Studies on various types of digital-technology-based psychotherapies (DTPs) have indicated that they are effective for post-traumatic stress disorder (PTSD) symptom relief among adults. The intervention efficacy or effectiveness hierarchy, however, is still not clear. Therefore, we propose to conduct a network meta-analysis to assess the relative effectiveness of various types of DTPs. We aim to establish the differential effectiveness of these therapies in terms of symptom reduction and provide high-quality evidence for treating PTSD. Methods and analyses We will search Embase, CINAHL, MEDLINE, HealthSTAR, the Cochrane Library, PsycINFO, PubMed, the Chinese Biomedical Literature Database, clinical trials (eg, ClinicalTrials.gov) and other academic platforms for relevant studies, mainly in English and Chinese (as we plan to conduct a trial on PTSD patients in Wuhan, China, based on the results of this network meta-analysis), from inception to October 2020. Randomised controlled trials (RCTs) and meta-analyses investigating the effectiveness of any DTPs for PTSD patients for any controlled condition will be included. The number of intervention sessions and the research duration are unlimited; the effects for different durations will be tested via sensitivity analysis. For this project, the primary measure of outcome will be PTSD symptoms at the end of treatment using raw scores for one widely used PTSD scale, PCL-5. Secondary outcome measures will include (1) dropout rate; (2) effectiveness at longest follow-up, but not more than 12 months and (3) patients’ functional recovery ratio (such as the return-to-work ratio or percentage of sick leave). Bayesian network meta-analysis will be conducted for all relative outcome measures. We will perform subgroup analysis and sensitivity analysis to see whether the results are influenced by study characteristics. The Grading of Recommendations, Assessments, Development, and Evaluation framework will be adopted to evaluate the quality of evidence contributing to network estimates of the primary outcome. Ethics and dissemination The researchers of the primary trials already have had ethical approval for the data used in our study. We will present the results of this meta-analysis at academic conferences and publish them in peer-reviewed journals. PROSPERO registration number CRD42020173253.