Relevant Files Research Articles

Mapping learning and development literature in healthcare organizations: Need for digital health and diversity, equity and inclusion (DEI) training

PurposeMapping the landscape of healthcare education is essential, particularly when examining the prevailing trends in learning and development (L&D) for healthcare workers.Design/methodology/approachThe Scopus dataset was searched on 25th November 2023 for relevant files, and analysis was done using Bibilioshiny and VOSviewer.FindingsArtificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are increasingly being adopted in healthcare organizations. Moreover, simulation-based team training enhancing interprofessional communication and sensitizing healthcare workers (HCWs) towards equity, diversity, and inclusion is increasingly reflected in organizations’ L&D practices.Originality/valueThe study identifies prevailing themes in L&D in healthcare organizations in the last decade.

Improving the Efficiency of Solar Thermophotovoltaic Energy Conversion System

Solar energy can be directly or indirectly converted into other types of energy such as heat and electricity. However, several obstacles cause the insignificant use of such energy, including scientific and technical weaknesses in conversion due to lack of knowledge and field experiences, variable energy amount due to atmospheric and seasonal changes and radiation direction, as well as a very wide distribution scale. A comprehensive review of methods to improve the performance of solar thermophotovoltaic energy conversion systems in order to enhance efficiency is one of the most important aspects of innovation in this study. Computer modeling of thermal systems has various benefits, such as adjusting the cost of prototype construction, optimizing system components, estimating the amount of output energy, predicting system temperature changes, and other low-priority cases. This study also presents the results of the execution of the relevant files by engineering equation solver (EES) software ©1992-2003 S.A. Klein offers Commercial Version 6.883-3D (09/01/03). The results are represented in the form of tables and comparative plots. The investigations carried out include thermodynamic, exergetic, and economic parameters.

The characteristics and regulations of adaptive designs from 2008 to 2020: An overview of European Medicines Agency approvals.

The aim of this study was to identify and characterize all European Medicines Agency (EMA) approvals derived from adaptive designs in clinical trials and to provide an update of the current status of these drugs. Relevant files were identified in the EMA database for annual reports for the period between 2008 and 2020 using a list of suitable keywords related to adaptive designs. We recorded trial characteristics from drug approvals and used Fisher exact test to compare the characteristics. A total of 1,054 EMA approvals were identified, and the percentage of EMA approvals planned with adaptive trial designs increased from 1.85% in the period 2008-2012 to 6.19% in between 2017-2020. A total of 41 approvals were identified among 91 original EMA files that contained adaptive designs. The types of adaptive designs used in clinical trials increased after 2017 where the most common type used was the most common (17/41). Most approvals (32/41) comprised pivotal trials, and most assessments had not been accelerated (38/41). Of 32 confirmatory trials planned with adaptive designs, the proportion of those with additional monitoring (AM) increased significantly (p<0.0001) from 0% in the 2008-2012 period to 90.48% in the 2017-2020 period. The percentage of approved antitumor drugs in approved drugs in ongoing clinical trials was 82.35%, compared to 20.83% in trials that were completed (p=0.0001). The proportion of drug approved but where clinical trials were still ongoing in companies requiring post-authorization safety studies (PASSs) or post-authorization efficacy studies (PAESs) or who were granted conditional marketing authorization (CMA) significantly differed from the group of drugs approved where clinical trials were completed (p=0.0230). A trend showing an increased number of EMA approvals related to adaptive designs was observed for the period from 2008 to 2020. Different types of adaptive trial designs could be encouraged for the designation of clinical trials, especially for antitumor drugs; meanwhile, more stringent monitoring regulations seemed to be conducted for ongoing trials of antitumor drugs with adaptive design.

Towards Privacy Preserving in 6G Networks: Verifiable Searchable Symmetric Encryption Based on Blockchain

The blockchain-based searchable symmetric encryption (SSE) scheme allows the retrieval and verification of outsourced data on cloud servers in sixth generation (6G) networks while ensuring the privacy of data. However, existing schemes are challenging to comprehensively meet the requirements of 6G-based intelligent application systems for low latency, high security, and high reliability. To address these limitations, we present VSSE, a novel blockchain-based SSE scheme designed for 6G-based intelligent application systems. Our scheme constructs a state chain structure to resist file injection attacks, thereby ensuring forward privacy. Moreover, we execute the search and verification operations separately on the cloud server and blockchain, while introducing a bitmap index structure and message authentication code (MAC) technology to achieve efficient searching and dynamic verification. Notably, VSSE also includes access control functionality, permitting only authorized users to access relevant files. The combination of remarkable efficiency and strong security establishes our VSSE as an ideal solution suitable for implementation in G-based intelligent application systems.

Dual analysis for helping developers to find collaborators based on co‐changed files: An empirical study

SummarySoftware developers must collaborate at all stages of the software life‐cycle to create successful complex software systems. To enable this collaboration, social coding platforms, for example, GitHub, include an increasing number of tools to support collaboration. However, for large projects with hundreds of dynamic developers, such as several successful open–source projects, it can be complex to find developers with the same interest and familiarity and thus, gain suitable collaborations and new insights. In this context, resources and efforts may be wasted, discouraging many developers from contributing. Moreover, it can be costly to manage many contributions, which is another challenge for the maintainer who wants to take advantage of this small, timid, but valuable contribution made by a volunteer developer in a short time. In this context, this paper presents an empirical study aiming to evaluate two strategies to recommend collaborators based on co‐changed files. Inspired in the TF–IDF (Term Frequency–Inverse Document Frequency) weighting scheme established in the Information Retrieval field, these strategies first estimate the importance of relevant files modified by developers and use these estimates to represent each developer “profile”. As a second step, they estimate the similarity between developers using the Cosine metric, providing top‐ranked developers according to this measure as recommendations. We evaluated these strategies based on an extensive survey with 102 real–world developers. We observed that developers have interest and familiarity with the co‐changed files for all strategies evaluated. These considerations are of relevance because many opportunities for contributions to the project are linked to coding. Thus, theses results may indicate one less barrier for improving collaboration among developers. Overall, the strategies present an acceptance rate of up to 81%, contributing to the discovery of further collaborators.

SDOT: Secure Hash, Semantic Keyword Extraction, and Dynamic Operator Pattern-Based Three-Tier Forensic Classification Framework

Most traditional digital forensic techniques identify irrelevant files in a corpus using keyword search, frequent hashes, frequent paths, and frequent size methods. These methods are based on Message Digest and Secure Hash Algorithm-1, which result in a hash collision. The threshold criteria of files based on frequent sizes will lead to imprecise threshold values that result in an increased evaluation of irrelevant files. The blacklisted keywords used in forensic search are based on literal and non-lexical, thus resulting in increased false-positive search results and failure to disambiguate unstructured text. Due to this, many extraneous files are also being considered for further investigations, exacerbating the time lag. Moreover, the non-availability of standardized forensic labeled data results in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(O(2^{n}))$ </tex-math></inline-formula> time complexity during the file classification process. This research proposes a three-tier Keyword Metadata Pattern framework to overcome these significant concerns. Initially, Secure Hash algorithm-256 hash for the entire corpus is constructed along with custom regex and stop-words module to overcome hash collision, imprecise threshold values, and eliminate recurrent files. Then blacklisted keywords are constructed by identifying vectorized words that have proximity to overcome traditional keyword search’s drawbacks and to overcome false positive results. Dynamic forensic relevant patterns based on massive password datasets are designed to search for unique, relevant patterns to identify the significant files and overcome the time lag. Based on tier-2 results, files are preliminarily classified automatically in O(log n) complexity, and the system is trained with a machine learning model. Finally, when experimentally evaluated, the overall proposed system was found to be very effective, outperforming the existing two-tier model in terms of finding relevant files by automated labeling and classification in O(nlog n) complexity. Our proposed model could eliminate 223K irrelevant files and reduce the corpus by 4.1% in tier-1, identify 16.06% of sensitive files in tier-2, and classify files with 91% precision, 95% sensitivity, 91% accuracy, and 0.11% Hamming loss compared to the two-tier system.

P94. Years of experience and primary specialty are correlated with total cost of ACDF surgeries for Medicare beneficiaries

<h3>BACKGROUND CONTEXT</h3> The high costs of spine surgery are multifactorial. Many studies suggest hospital, geographic, surgeon and patient-specific factors impact the cost of spine surgical care, but results vary significantly between studies using different datasets. <h3>PURPOSE</h3> Our goal was to identify the factors correlated with total costs for Medicare beneficiaries undergoing one of the most common spine surgical procedures, anterior cervical discectomy and fusion (ACDF). <h3>STUDY DESIGN/SETTING</h3> Retrospective cohort study. <h3>PATIENT SAMPLE</h3> We identified all patients undergoing ACDF (CPT code 22551) from the 20% sample Medicare carrier files from January 1, 2013, through December 31, 2015 (n = 15,456). Subjects were stratified by number of levels operated on. <h3>OUTCOME MEASURES</h3> Variables collected including hospital location, number of beds and wage index; patient age, sex, race and length of stay; physician industry payments, years since medical school and primary specialty. Outcomes were predictors of total cost and operating room cost of ACDF, and patient length of stay. <h3>METHODS</h3> Medicare carrier and MedPAR files were used to find the total charge, operating room charge and length of stay of each ACDF procedure. Charges were converted to costs using the hospital-specific cost-to-charge ratios from the IPPS Impact Files for each year. The Open Payments database and NPI National Downloadable File were used to identify the operating surgeon for each ACDF procedure. Univariate and multivariate analyses were used to identify patient-specific, hospital-specific and physician-specific factors associated with changes in cost of ACDF or patient length of stay. <h3>RESULTS</h3> Matching across all relevant files resulted in 5,202 1-level ACDF Medicare claims. The mean total cost for a 1-level ACDF from 2013-2015 was $13,592 (+/- $5,324). Multiple linear regression found that, after controlling for patient and hospital factors, the total cost of a 1-level ACDF was $677 higher when performed by an orthopedic surgeon than a neurosurgeon (p < 0.001). Multiple linear regression also found a significant negative association between years since the surgeon graduated medical school and total hospital cost of 1-level ACDF (p < 0.001). A significant positive association was found between hospital wage index and total cost of 1-level ACDF (p < 0.001). No significant relationship was found between total ACDF cost and patient age, race or sex. <h3>CONCLUSIONS</h3> Our multivariate analysis of hospital, surgeon, and patient-specific factors identifies that surgeon's primary specialty, surgeon's years of experience, and hospital wage index are significant factors impacting the total cost of ACDF surgery for Medicare beneficiaries. <h3>FDA DEVICE/DRUG STATUS</h3> This abstract does not discuss or include any applicable devices or drugs.

Non-destructively Determining Blueberry (Vaccinium corymbosum L.) Leaf Area Using DPI-Based Software

Blueberries (Vaccinium spp.) are a popular crop all throughout the world. Blueberry (Vaccinium corymbosum L.) leaves were randomly selected from the experimental area of Ondokuz Mayis University, Faculty of Agriculture as a research material. Blueberries are high in polyphenolic chemicals, particularly anthocyanins, which are antioxidants and anti-inflammatory. A total of 1500 leaves were collected, with 100 for each cultivar, to represent the variety of sizes found on 15 plants of each cultivar. Manual leaf area measurement was made with a digital planimeter. The area was measured using a 300dpi resolution image read from the relevant file in such a way that every one of them is defined by 8bits in RGB color space. A weighted sum of the RGB components of the image is used to convert RGB values to grayscale values. This three-dimensional gray image generates a binary image. The Otsu method was used to determine the threshold value required to minimize the in-class variation of the threshold black and white pixels. Simulink was used for easy use of the end-user with the developed software. This software can be used for the area measurement of all plant leaves.

Converting Numeral Text in Bulgarian into Digit Number Using GATE

The Internet serves billions of users providing a variety of information resources whereby a lot of the information is presented in natural human language and needs an efficient approach to be processed. Natural language processing (NLP) refers to the ability of computers to analyze and understand the structure of human language. By utilizing NLP this linguistic knowledge is transformed into algorithms for solving specific problems. GATE is widely used, open-source software infrastructure that provides a framework and components for solving NLP tasks. The available GATE tools can be adapted to other languages and text processing tasks. This article will present an approach for converting numeric data, written as words in Bulgarian, into digit numbers. For this case, a relevant configuration file for Bulgarian has been integrated into the general tool set in the open source software for natural language processing GATE. The aim of this survey is to determine the exact numeric value of Bulgarian text numeric data, which can be used as a starting point for producing more complex annotations, such as monetary measurement units, etc.

An efficient encrypted deduplication scheme with security-enhanced proof of ownership in edge computing

With the rapid expansion of Internet of Things (IoT), relevant files are stored and transmitted at the network edge by employing data deduplication to eliminate redundant data for the best accessibility. Although deduplication improves storage and network efficiency, it decreases security strength and performance. Existing schemes usually adopt message-locked encryption (MLE) to encrypt data, which is vulnerable to brute-force attacks. Meanwhile, these schemes utilize proof-of-ownership (PoW) to prevent duplicate-faking attacks, while they suffer from replay attacks or incur large computation overheads. This paper proposes SE-PoW, an efficient and location-aware hybrid encrypted deduplication scheme with a dual-level security-enhanced Proof-of-Ownership in edge computing. Specifically, SE-PoW firstly encrypts files with an inter-edge server-aided randomized convergent encryption (RCE) method and then protects blocks with an intra-edge edge-aided MLE method to balance security and system efficiency. To resist duplicate-faking attacks and replay attacks, SE-PoW performs the dual-level PoW algorithm. Then it combines the verification of a cuckoo filter and the homomorphism of algebraic signatures in sequence to enhance security and improve ownership checking efficiency. Security analysis demonstrates that SE-PoW ensures data security and resists the mentioned attacks. Evaluation results show that SE-PoW reduces up to 61.9% upload time overheads compared with the state-of-the-art schemes.

QuakeLabeler: A Fast Seismic Data Set Creation and Annotation Toolbox for AI Applications

Abstract The production and preparation of data sets are essential steps in machine learning (ML) applications. With the increasing volume and scale of available ML techniques in seismology, annotating seismograms or seismic features has become time consuming and tedious for many researchers. Furthermore, most methods train and validate on unique data subsets, which hampers independent performance evaluation and comparison. To address this problem, we have developed the software QuakeLabeler, an open-source Python package to customize, build, and manage earthquake training data sets, including processing and visualization. QuakeLabeler has tight pipeline functions, which include retrieving seismograms from multiple online data centers, querying online human-reviewed catalogs, signal processing, annotating (labeling), and analyzing data distribution. In addition, relevant statistical graphics and human-readable output files can be generated. Various file export formats are supported, such as Seismic Analysis Code (*.sac), mini Standard for Exchange of Earthquake Data (*.mseed), NumPy (*.npz), MATLAB (*.mat), and the Hierarchical Data Format version 5 (*.hdf5). This toolbox is packaged with an interactive command-line interface. Three alternative running modes (beginner, advanced, and benchmark) are implemented, intended to offer specific data set solutions for different types of applications, that is, quick-start recipes for simple ML solutions, advanced design for customized project training, and benchmark bulletins for model comparison.

Die rol van handgebare in oudiovisuele aanbiedings van teologiestudente: Belyning van teorie en praktyk

Kommunikasie in die 21ste eeu het grootliks multimodaal geword. 'n Verskeidenheid primêre modusse, byvoorbeeld geskrewe teks, mondelinge vertelling, beweging, kleur, klank en musiek, word ook elektronies "vertaal" en sekondêr deur rekenaars, selfone en tablette afgelewer. Een van die genres wat gekenmerk word deur multimodaliteit is oudiovisuele aanbieding. In studente se aanbiedings is spraak gewoonlik die primêre modus, en word aangevul deur teks, animasie (beweging), foto's en kleur. Die mondelinge aanbieding tree as deel van 'n multimodale ensemble op, waar die unieke eienskappe van elke modus benut word om betekenis oor te dra. Eers gee ek 'n definisie van die konsep "handgebaar", gevolg deur 'n uiteensetting van algemeen erkende gebareteorieë en tipologieë wat gebare indeel volgens semiotiese gebaretipes, handigheid (links, regs of beide), gekonvensionaliseerde handvorme en palm-oriëntasies, en beweging en posisie in die gebareveld. Daarna volg 'n beskrywing van 'n navorsingsprojek wat berus op die analise van ko-taalgebare in 'n korpus van 17 gevideo-graveerde mondelinge aanbiedings deur teologiestudente. Die artikel word afgesluit deur puntsgewyse advies vir die gebruik van gebare in studente se oudiovisuele aanbiedings. Die advies is sowel gegrond op teorieë en tipologieë van gebare as op die analise van die korpus. Waar dit relevant is, word verwys na handleidings oor kommunikasie en openbare redevoering.

Finite Element Models of a Benchmark Footbridge

Modern footbridges are often lively structures, characterized by natural frequencies that fall in the range of pedestrian activities, such as walking, running, and jumping. Therefore, serviceability assessment under human-induced excitation is crucial both at the design stage and during the footbridge lifetime. This paper presents and validates two different FE models of an existing footbridge with very complex geometry: the Streicker Footbridge at the Princeton University Campus. It represents a benchmark in the field as a testbed for vibration serviceability assessments under pedestrian excitation. The real structure is equipped with strain and temperature sensors that are currently used to collect measurements in both static and dynamic modes for research and educational purposes in Structural Health Monitoring (SHM). Based on detailed drawings of the Streicker Footbridge, a three-dimensional beam-based model was developed to represent the complex behavior of the full-scale benchmark bridge. Subsequently, a more refined discretization of the bridge deck adopting shell elements was inserted. The bridge Finite Element models were validated against available SHM data concerning static and dynamic tests. The relevant ANSYS APDL script files along with an example of pedestrian jumping application are available upon request for further research developments on the relationship between pedestrians and the benchmark footbridge.

On comprehensive analysis of root canal shaping ability of three endodontic files of different kinematics.

Infection of the tooth's root canal requires what is called root canal treatment (RTC). The most important part of endodontic treatment is to shape the root canal and remove its infected portion using endodontic files of various protocols, kinematics and designs that suit the particular geometry. Cleaning and Shaping the canal efficiently remove the root canal bacterial biofilms or tissue remnants while keeping its natural geometry. The result is determined by shaping the ability of the relevant endodontic file. In the available literature, no norm has been established for the measurement of various endodontic files' ability to do effective shaping. We present in this study a method to analyse and measure the shaping ability of endodontic files of three different kinematics.

Developing and Testing a Local Expert-Based Reading Process for Use to Examine Discrepancies Between Guidelines and Current Clinical Practices.

The use of relevant guidelines is critical in psychiatric clinical practice to ensure the homogeneity of the global care provided. Consequently, it is important to identify whether they are utilized successfully and, if not, why. This would enable pragmatic solutions to be agreed to improve the organization of care and the removal of any barriers to the guidelines' implementation. The first step in this process, before any exploration of the limitations of the guidelines themselves, involves a determination of whether they are actually applied in clinical practice. We therefore evaluated discrepancies between the guidelines relating to patients with borderline personality disorder and current practices in the psychiatric Emergency Department at Toulouse University Hospital. This was achieved using a reading process involving a panel of eight local experts who analyzed relevant medical files extracted from a database. They were guided by, and instructed to answer, six standardized questions in relation to each file to determine the method's feasibility. A total of 333 files were analyzed to determine whether, in the local experts' judgment, the care provided reflected current guidance. This reading process revealed substantial agreement (0.85%; Fleiss Kappa −0.69), which is a promising outcome and suggests that such methods could be used in future protocols. Moreover, the process is practical and reliable and requires very few materials.

Metagenomics workflow for hybrid assembly, differential coverage binning, metatranscriptomics and pathway analysis (MUFFIN).

Metagenomics has redefined many areas of microbiology. However, metagenome-assembled genomes (MAGs) are often fragmented, primarily when sequencing was performed with short reads. Recent long-read sequencing technologies promise to improve genome reconstruction. However, the integration of two different sequencing modalities makes downstream analyses complex. We, therefore, developed MUFFIN, a complete metagenomic workflow that uses short and long reads to produce high-quality bins and their annotations. The workflow is written by using Nextflow, a workflow orchestration software, to achieve high reproducibility and fast and straightforward use. This workflow also produces the taxonomic classification and KEGG pathways of the bins and can be further used for quantification and annotation by providing RNA-Seq data (optionally). We tested the workflow using twenty biogas reactor samples and assessed the capacity of MUFFIN to process and output relevant files needed to analyze the microbial community and their function. MUFFIN produces functional pathway predictions and, if provided de novo metatranscript annotations across the metagenomic sample and for each bin. MUFFIN is available on github under GNUv3 licence: https://github.com/RVanDamme/MUFFIN.

Cardiac Dose Control and Optimization Strategy for Left Breast Cancer Radiotherapy With Non-Uniform VMAT Technology.

Purpose: A novel in-house technology “Non-Uniform VMAT (NU-VMAT)” was developed for automated cardiac dose reduction and treatment planning optimization in the left breast radiotherapy. Methods: The NU-VMAT model based on IGM (gantry MLC Movement coefficient index) was established to optimize the volumetric modulated arc therapy (VMAT) MLC movement and modulation intensity in certain gantry angles. The ESAPI embedded in Eclipse® was employed to connect TPS and the optimization program via I/O relevant DICOM RT files. The adjuvant whole-breast radiotherapy of 14 patients with left breast cancer was replanned using our NU-VMAT technology in comparison with VMAT and IMRT technology. Dosimetric parameters including D1%, D99%, and Dmean of PTV, V5, V10, and V20 of ipisilateral lung, V5, D20, D30, and Dmean of heart, monitor units (MUs), and delivery time derived from IMRT, VMAT, and NU-VMAT plans were evaluated for plan quality and delivery efficiency. The quality assurance (QA) was conducted using both point-dose and planar-dose measurements for all treatment plans. Results: The IGM−NU−VMAT curves with plan optimization (range from 50% to 147%) were converged more significantly than IGM-VMAT curves (range from 0% to 297%). The dose distribution requirements of the target and normal tissues could be met using IMRT, VMAT, or NU-VMAT; the lowest Dmean was achieved in NU-VMAT plans (5.38 ± 0.46 Gy vs 5.63 ± 0.61 Gy in IMRT and 7.95 ± 0.52 Gy in VMAT plans). Statistically significant differences were found in terms of delivery time and MU when comparing IMRT with VMAT and NU-VMAT plans (P < .05). In comparison with IMRT plans, the MU and delivery time in NU-VMAT plans dramatically decreased by 69.8% and 28.4%, respectively. Moreover, NU-VMAT plans showed a high gamma passing rate (96.5% ± 1.11) in plane dose verification and minimal dose difference (2.4% ± 0.19) in point absolute dose verification. Conclusion: Our non-uniform VMAT facilitated the treatment strategy optimization for left breast cancer radiotherapy with dosimetric advantage in cardiac dose reduction and delivery efficiency in comparison with the conventional VMAT and IMRT.

The Protein Data Bank Archive.

Protein Data Bank is the single worldwide archive of experimentally determined macromolecular structure data. Established in 1971 as the first open access data resource in biology, the PDB archive is managed by the worldwide Protein Data Bank (wwPDB) consortium which has four partners-the RCSB Protein Data Bank (RCSB PDB; rcsb.org), the Protein Data Bank Japan (PDBj; pdbj.org), the Protein Data Bank in Europe (PDBe; pdbe.org), and BioMagResBank (BMRB; www.bmrb.wisc.edu ). The PDB archive currently includes ~175,000 entries. The wwPDB has established a number of task forces and working groups that bring together experts form the community who provide recommendations on improving data standards and data validation for improving data quality and integrity. The wwPDB members continue to develop the joint deposition, biocuration, and validation system (OneDep) to improve data quality and accommodate new data from emerging techniques such as 3DEM. Each PDB entry contains coordinate model and associated metadata for all experimentally determined atomic structures, experimental data for the traditional structure determination techniques (X-ray crystallography and nuclear magnetic resonance (NMR)spectroscopy), validation reports, and additional information on quaternary structures. The wwPDB partners are committed to following the FAIR (Findability, Accessibility, Interoperability, and Reproducibility) principles and have implemented a DOI resolution mechanism that provides access to all the relevant files for a given PDB entry. On average, >250 new entries are added to the archive every week and made available by each wwPDB partner via FTP area. The wwPDB partner sites also develop data access and analysis tools and make these available via their websites. wwPDB continues to work with experts in the community to establish a federation of archives for archiving structures determined using integrative/hybrid method where multiple experimental techniques are used.

Method-level bug localization using hybrid multi-objective search

Abstract Context: One of the time-consuming maintenance tasks is the localization of bugs especially in large software systems. Developers have to follow a tedious process to reproduce the abnormal behavior then inspect a large number of files. While several studies have been proposed for bugs localization, the majority of them are recommending classes/files as outputs which may still require high inspection effort. Furthermore, there is a significant difference between the natural language used in bug reports and the programming language which limits the efficiency of existing approaches since most of them are mainly based on lexical similarity. Objective: In this paper, we propose an automated approach to find and rank the potential methods in order to localize the source of a bug based on a bug report description. Method: Our approach finds a good balance between minimizing the number of recommended classes and maximizing the relevance of the proposed solution using a hybrid multi-objective optimization algorithm combining local and global search. The relevance of the recommended code fragments is estimated based on the use of the history of changes and bug-fixing, and the lexical similarity between the bug report description and the API documentation. Our approach operates on two main steps. The first step is to find the best set of classes satisfying the two conflicting criteria of relevance and the number of classes to recommend using a global search based on NSGA-II. The second step is to locate the most appropriate methods to inspect, using a local multi-objective search based on Simulated Annealing (MOSA) from the list of classes recommended by the first step. Results: We evaluated our system on 6 open source Java projects, using the version of the project before fixing the bug of many bug reports. Our hybrid multi-objective approach is able to successfully locate the true buggy methods within the top 10 recommendations for over 78% of the bug reports leading to a significant reduction of developers’ effort comparing to class-level bug localization techniques. Conclusion: The experimental results show that the search-based approach significantly outperforms four state-of-the-art methods in recommending relevant files for bug reports.

The AD Knowledge Portal: A Repository for Multi-Omic Data on Alzheimer's Disease and Aging.

The AD Knowledge Portal (adknowledgeportal.org) is a public data repository that shares data and other resources generated by multiple collaborative research programs focused on aging, dementia, and Alzheimer's disease (AD). In this article, we highlight how to use the Portal to discover and download genomic variant and transcriptomic data from the same individuals. First, we show how to use the web interface to browse and search for data of interest using relevant file annotations. We demonstrate how to learn more about the context surrounding the data, including diagnostic criteria and methodological details about sample preparation and data analysis. We present two primary ways to download data—using a web interface, and using a programmatic method that provides access using the command line. Finally, we show how to merge separate sources of metadata into a comprehensive file that contains factors and covariates necessary in downstream analyses. © 2020 The Authors. Basic Protocol 1: Find and download files associated with a selected study Basic Protocol 2: Download files in bulk using the command line client Basic Protocol 3: Working with file annotations and metadata