Powerful Search Engine Research Articles

Collaborative Learning Platform for Academicians in Libya

Higher education academicians face a variety of difficulties, such as the need for improved collaboration, streamlined communication, and effective resource management. In order to tackle these concerns, this study investigates the unique challenges that academicians in higher education face. The study emphasizes the need for integrated features and user interfaces that are simple to understand and customized for a particular requirement of higher education. Moreover, by exploring the recognition and understanding of these barriers, the study not only clarifies the complex terrain of difficulties but also establishes the foundation for the creation of a platform for collaborative learning. With a focus on the problems faced by academicians, this platform will include features like a forum that is easy to use, the ability to share resources, a powerful search engine, a section devoted to frequently asked questions, and a chatbot that can engage users. Together, these elements seek to offer a thorough resolution to the problems noted, promoting enhanced dialogue, information sharing, and instructional effectiveness among academicians.

Building a searchable online corpus of Australian and New Zealand aligned speech

ABSTRACT Advances in automatic speech recognition technology, increases in bandwidth availability, and the widespread use of video streaming and sharing platforms have opened new horizons for corpus phonetics. CoANZSE Audio, a searchable online version of the Corpus of Australian and New Zealand Spoken English, provides access to over 195 million words of transcribed speech from transcripts of videos uploaded to YouTube by councils and other local government entities in Australia and New Zealand. Audio and forced alignment files are also available, making the resource suitable for the investigation of a range of research questions pertaining to morphosyntax, phonetics, and discourse. The resource, which is freely available via login through CLARIN, Europe’s main language resources infrastructure network, was created through the use of open-source tools and software: yt-dlp, a Python library for collecting data from video and streaming websites; the Montreal Forced Aligner, a recent neural network alignment suite; and Parselmouth-Praat, Python bindings for the Praat acoustic analysis software. The website is powered by BlackLab, which combines a powerful search engine based on Apache Lucene with an intuitive web frontend. CoANZSE Audio may be useful for the investigation of regional differentiation of language features, and with additional annotation, differences in feature use according to social or demographic groups. Recent applications have included studies of double modals, a rare syntactic feature, and apology sequences. The nature of the audio and alignment data may make the resource especially suitable for the study of regional phonetic variation. Furthermore, the methods used to create the resource may be of interest to researchers seeking to adopt a pipeline approach for the creation of specialized corpora from publicly available online content.

Sire: An interoperability engine for prototyping algorithms and exchanging information between molecular simulation programs.

Sire is a Python/C++ library that is used both to prototype new algorithms and as an interoperability engine for exchanging information between molecular simulation programs. It provides a collection of file parsers and information converters that together make it easier to combine and leverage the functionality of many other programs and libraries. This empowers researchers to use sire to write a single script that can, for example, load a molecule from a PDBx/mmCIF file via Gemmi, perform SMARTS searches via RDKit, parameterize molecules using BioSimSpace, run GPU-accelerated molecular dynamics via OpenMM, and then display the resulting dynamics trajectory in a NGLView Jupyter notebook 3D molecular viewer. This functionality is built on by BioSimSpace, which uses sire's molecular information engine to interconvert with programs such as GROMACS, NAMD, Amber, and AmberTools for automated molecular parameterization and the running of molecular dynamics, metadynamics, and alchemical free energy workflows. Sire comes complete with a powerful molecular information search engine, plus trajectory loading and editing, analysis, and energy evaluation engines. This, when combined with an in-built computer algebra system, gives substantial flexibility to researchers to load, search for, edit, and combine molecular information from multiple sources and use that to drive novel algorithms by combining functionality from other programs. Sire is open source (GPL3) and is available via conda and at a free Jupyter notebook server at https://try.openbiosim.org. Sire is supported by the not-for-profit OpenBioSim community interest company.

The Future of Library Science Research: Tools and Databases

Abstract: Library science is a dynamic and evolving field that plays a pivotal role in the organization and dissemination of information. In this digital age, the use of research tools and databases is essential for scholars and practitioners in the library science discipline. This research article aims to provide an in-depth exploration of the ever-expanding landscape of valuable research tools and databases that have the potential to significantly enhance the quality, efficiency, and impact of library science research. As the information ecosystem continues to grow and evolve, library scientists and researchers are confronted with the challenge of navigating an increasingly complex landscape of information sources and knowledge dissemination platforms. To address this challenge, the research paper delves into a comprehensive examination of the latest research tools and databases. From advanced reference management software, such as Zotero, EndNote, and Mendeley, to powerful academic search engines, including Google Scholar, JSTOR, and ProQuest, this article presents an extensive survey of the tools that have become indispensable in the librarian's arsenal. It explores how these tools empower librarians and scholars to access, organize, and distribute information with unprecedented precision and ease.

An interactive feature selection method based on multi-step state transition algorithm for high-dimensional data

Feature selection (FS) has been extensively employed in classification tasks to reduce data dimensionality and enhance prediction performance effectively. Recently, hybrid filter-wrapper methods have exhibited promising results in FS problems by leveraging both advantages. However, the inadequate integration of the filter method into the wrapper method leads to the hybrid algorithms exhibiting poor efficiency in classification as datasets grow in complexity. In this paper, an interactive feature selection framework based on state transition algorithm (STA) is proposed to address high-dimensional FS problems. In this framework, prior knowledge of the features is formed via the mutual information-based filter method. The STA is a powerful search engine that traverses the feature space in the wrapper stage. Moreover, an external trainer with prior knowledge will guide the exploration direction of STA in a simple but efficient way to accelerate the search process. And a self-adaptive mechanism is proposed to adjust the prior knowledge during the search process. Specifically, the external trainer establishes the interactive loop, while the self-adaptive mechanism aims to feed feature information back to the loop. In addition, the search process is prevented from being trapped in local optima by employing a multi-step STA, which allows for continuous transformations of the solution in probability. Finally, the proposed FS method is applied to various public classification datasets. The experimental results demonstrate that the proposed method is a highly competitive FS method, outperforming several state-of-the-art algorithms in generating an optimal subset of features.

Hybrid Manufacturing Processes Used in the Production of Complex Parts: A Comprehensive Review

Additive manufacturing is defined as a process based on the superposition of layers of materials in order to obtain 3D parts; however, the process does not allow achieve the adequate and necessary surface finishing. In addition, with the development of new materials with superior properties, some of them acquire high hardness and strength, consequently decreasing their ability to be machined. To overcome this shortcoming, a new technology assembling additive and subtractive processes, was developed and implemented. In this process, the additive methods are integrated into a single machine with subtractive processes, often called hybrid manufacturing. The additive manufacturing process is used to produce the part with high efficiency and flexibility, whilst machining is then triggered to give a good surface finishing and dimensional accuracy. With this, and without the need to transport the part from one machine to another, the manufacturing time of the part is reduced, as well as the production costs, since the waste of material is minimized, with the additive–subtractive integration. This work aimed to carry out an extensive literature review regarding additive manufacturing methods, such as binder blasting, directed energy deposition, material extrusion, material jetting, powder bed fusion, sheet laminating and vat polymerization, as well as machining processes, studying the additive-subtractive integration, in order to analyze recent developments in this area, the techniques used, and the results obtained. To perform this review, ScienceDirect, Web of Knowledge and Google Scholar were used as the main source of information because they are powerful search engines in science information. Specialized books have been also used, as well as several websites. The main keywords used in searching information were: “CNC machining”, “hybrid machining”, “hybrid manufacturing”, “additive manufacturing”, “high-speed machining” and “post-processing”. The conjunction of these keywords was crucial to filter the huge information currently available about additive manufacturing. The search was mainly focused on publications of the current century. The work intends to provide structured information on the research carried out about each one of the two considered processes (additive manufacturing and machining), and on how these developments can be taken into consideration in studies about hybrid machining, helping researchers to increase their knowledge in this field in a faster way. An outlook about the integration of these processes is also performed. Additionally, a SWOT analysis is also provided for additive manufacturing, machining and hybrid manufacturing processes, observing the aspects inherent to these technologies.

Catalogue of flat-band stoichiometric materials.

Topological electronic flattened bands near or at the Fermi level are a promising route towards unconventional superconductivity and correlated insulating states. However, the related experiments are mostly limited to engineered materials, such as moiré systems1-3. Here we present a catalogue of the naturally occuring three-dimensional stoichiometric materials with flat bands around the Fermi level. We consider 55,206 materials from the Inorganic Crystal Structure Database catalogued using the Topological Quantum Chemistry website4,5, which provides their structural parameters, space group, band structure, density of states and topological characterization. We combine several direct signatures and properties of band flatness with a high-throughput analysis of all crystal structures. In particular, we identify materials hosting line-graph or bipartite sublattices-in either two or three dimensions-that probably lead to flat bands. From this trove of information, we create the Materials Flatband Database website, a powerful search engine for future theoretical and experimental studies. We use the database to extract a curated list of 2,379 high-quality flat-band materials, from which we identify 345 promising candidates that potentially host flat bands with charge centres that are not strongly localized on the atomic sites. We showcase five representative materials and provide a theoretical explanation for the origin of their flat bands close to the Fermi energy using the S-matrix method introduced in a parallel work6.

Unfelt: The Language of Affect in the British Enlightenment

Unfelt: The Language of Affect in the British Enlightenment

Drivers of integration of green into supplier selections: a systematic literature review

PurposeThis paper aims to provide a systematic review of extant literature on supplier selection by identifying drivers of integration of Environmental Sustainability (ES) into supplier selection, with the aim of classifying them under broad categories for the development of a framework showing the interrelationships among them.Design/methodology/approachA search was ran for selected keyword themes using three powerful and rigorous search engines: Scopus, Google Scholar and Science Direct, to identify relevant articles from 12 peer-reviewed journals. These were desk reviewed through manual filtering to select drivers of integration of ES into supplier selection.FindingsThirty-one drivers identified from 41 relevant articles as propelling integration of ES into supplier selection were then classified into five categories: strong policy direction, high level of commitment, desire for high reputation, robust technology and availability of green products. This yielded a framework showing the interconnectedness among the drivers.Research limitations/implicationsThe study contributes to the body of knowledge by developing a classification of drivers of integration of ES into supplier selection. The interconnectedness brought to the fore a more subtle appreciation of the drivers of green integration, which could help expand current knowledge outside the narrow scope of isolated drivers. This study provides detailed analyses of drivers of green integration into supplier selection.Originality/valueThis paper provides a comprehensive review of drivers of integrating green into supplier selection, which is lacking in the body of knowledge on ES.

Large-Scale Truss-Sizing Optimization with Enhanced Hybrid HS Algorithm

Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands of nonlinear constraints on element stresses and nodal displacements. However, very few studies documented the use of metaheuristic algorithms in large-scale structural optimization. In order to fill this gap, an enhanced hybrid harmony search (HS) algorithm for weight minimization of large-scale truss structures is presented in this study. The new algorithm, Large-Scale Structural Optimization–Hybrid Harmony Search JAYA (LSSO-HHSJA), developed here, combines a well-established method like HS with a very recent method like JAYA, which has the simplest and inherently most powerful search engine amongst metaheuristic optimizers. All stages of LSSO-HHSJA are aimed at reducing the number of structural analyses required in large-scale structural optimization. The basic idea is to move along descent directions to generate new trial designs, directly through the use of gradient information in the HS phase, indirectly by correcting trial designs with JA-based operators that push search towards the best design currently stored in the population or the best design included in a local neighborhood of the currently analyzed trial design. The proposed algorithm is tested in three large-scale weight minimization problems of truss structures. Optimization results obtained for the three benchmark examples, with up to 280 sizing variables and 37,374 nonlinear constraints, prove the efficiency of the proposed LSSO-HHSJA algorithm, which is very competitive with other HS and JAYA variants as well as with commercial gradient-based optimizers.

743 Keeping up with Dr. Google: When Are Patients Seeking First-aid Burn Resources?

Abstract Introduction Treating burns is a relatively common occurrence in American Emergency Departments occurring an estimated 486,000 times per year. In the digital era, patients feel increasingly empowered to seek out medical resources independently. The true number of people sustaining an injury and treating themselves at home or outside of the hospital setting is difficult to quantify. However, we can see when patients were searching for first-aid burn resources on the world’s most powerful and popular search engine - Google. We hypothesized that there would be a correlation between patient’s searching for burn care resources online and burn admissions. Methods We used Keywords Everywhere a browser add-on for Google Chrome to cross check various phrases and words that Americans might search for to find information on how to treat a burn. “Burn treatment” was found to be the most commonly searched phrase and this was verified using Google Trends. Google Trends dose not give raw search numbers. However, it expresses the search frequency for a term relative to how frequently that term was sought out during a specified time period. We pulled search data for each successive year back till 2006 the earliest year for which complete data was available. We were then able to overlay this data on a year to year basis and thus view when information about treating burns was the most sought out. Results A clear increase in the frequency of searches for burn treatment can be seen around the summer months, peaking in the week surrounding the 4th of July. Further data comparing this trend to burn admissions is forthcoming as data is being solicited. Conclusions Americans are searching for more resources regarding burn injuries in the summer months; and especially in the days surrounding the fourth of July. We are excited to correlate this data to burn admissions. If there is an inverse relationship between admissions during the summer months and number of inquiries made via Google for acute burn care, this may suggest that many of the burns are minor. Thus, being treated through our clinics or through third-party providers. Alternatively, the patients may be treating themselves using internet resources. If this proves to be the case, there may be an opportunity to enrich online resources for our patients. Applicability of Research to Practice Once the data processing is complete, there will be an indication if the number of people seeking out resources via Google Search Engine correlates with out burn admissions. If not, this may be an opportunity for improvement to enrich burn first-aid resources available online.

La base de datos de resoluciones de consultas tributarias como herramienta de innovación docente

This paper deals with the use as a teaching innovation tool of the database of collaboration queries of the General Directorate of Taxes, of easy accessibility for its computerized development online and with a powerful search engine based on the Management software. of Knosys libraries. The ultimate goal will be to determine the attitude of students against the use of the database in the classes. The study is divided into three blocks: the first, dedicated to the legal regime of tax consultations; Another large section, relating to the use of the tool by students or professionals in general; and a last section, where the object of study is focused, dedicated to the value that this tool can have in order to improve the teaching of financial and tax law

Learning Semantic Text Features for Web Text-Aided Image Classification

The good generalization performance of conventional pattern classifiers often relies on the size of training data labeled by costly human labor. These days, publicly available web resources grow explosively, and this allows us to easily obtain abundant and cheap web data. Yet, web data are usually not as cooperative as human labeled data. In this paper, we explore the use of web text data to aid image classification. Without requiring the previous collection of auxiliary data from the web, we directly retrieve the web text information with the aid of the powerful reverse image search engine. We develop a novel textual modeling method named semantic matching neural network (SMNN) that is capable of learning semantic features from the associated text of web images. The SMNN text features have improved reliability and applicability, compared to the text features obtained from other methods. The SMNN text features and convolutional neural network (CNN) visual features are merged into a shared representation, which learns to capture the correlations between the two modalities. Experimental results on benchmark UIUC-Sports, Scene-15, Caltech-256, and Pascal VOC-2012 data sets show that the visual and text modalities of data from different sources are remarkably complementary and the fusion of them achieves substantial performance improvement.

Five Kinds of Translation Platform Comparison Studies

There are many online translation platforms available in China. Through the traffic information published by the Chinese website rankings, this paper selects five online translation platforms featured in this study as a comparative analysis of Google, Baidu, Youdao, Kingsoft and Bing. Google, Baidu and Bing Translation platforms use their powerful search engine features to integrate corpus resources and provide corresponding sources of information. Kingsoft and Youdao Translation platforms are popular universal online dictionaries. This article selects a technical text, compares translations through the five translation platforms, and combines the interface features, functional characteristics and user feedback of the five major translation platforms.

Fostering population-based cohort data discovery: The Maelstrom Research cataloguing toolkit.

BackgroundThe lack of accessible and structured documentation creates major barriers for investigators interested in understanding, properly interpreting and analyzing cohort data and biological samples. Providing the scientific community with open information is essential to optimize usage of these resources. A cataloguing toolkit is proposed by Maelstrom Research to answer these needs and support the creation of comprehensive and user-friendly study- and network-specific web-based metadata catalogues.MethodsDevelopment of the Maelstrom Research cataloguing toolkit was initiated in 2004. It was supported by the exploration of existing catalogues and standards, and guided by input from partner initiatives having used or pilot tested incremental versions of the toolkit.ResultsThe cataloguing toolkit is built upon two main components: a metadata model and a suite of open-source software applications. The model sets out specific fields to describe study profiles; characteristics of the subpopulations of participants; timing and design of data collection events; and datasets/variables collected at each data collection event. It also includes the possibility to annotate variables with different classification schemes. When combined, the model and software support implementation of study and variable catalogues and provide a powerful search engine to facilitate data discovery.ConclusionsThe Maelstrom Research cataloguing toolkit already serves several national and international initiatives and the suite of software is available to new initiatives through the Maelstrom Research website. With the support of new and existing partners, we hope to ensure regular improvements of the toolkit.

Internet governance as joint effort: (Re)ordering search engines at the intersection of global and local cultures

In this article, I investigate Internet governance in practice by focusing on search engines, Google in particular. Building on science and technology studies–grounded Internet governance research, I ask how different stakeholders interpret governing by algorithms, the governing of algorithms, and the limits of various governing modes when considering local specificities. To answer these questions, I conducted 18 qualitative interviews with key experts involved in search engine governance from four distinct societal domains: policy, law, civil society, and the IT sector (from Austria and/or the European level). In this analysis, I show that perceptions of search engine governance are shaped not only in specific cultural contexts but also within particular social groups and their situated knowledges. I further elaborate how joint efforts are imagined as a means to challenge powerful search engines and their governing abilities cutting through different societal arenas and areas of expertise. Finally, I discuss implications of this analysis regarding the complex relationship between global technology and local cultures.

TOXsIgN: a cross-species repository for toxicogenomic signatures.

At the same time that toxicologists express increasing concern about reproducibility in this field, the development of dedicated databases has already smoothed the path toward improving the storage and exchange of raw toxicogenomic data. Nevertheless, none provides access to analyzed and interpreted data as originally reported in scientific publications. Given the increasing demand for access to this information, we developed TOXsIgN, a repository for TOXicogenomic sIgNatures. The TOXsIgN repository provides a flexible environment that facilitates online submission, storage and retrieval of toxicogenomic signatures by the scientific community. It currently hosts 754 projects that describe more than 450 distinct chemicals and their 8491 associated signatures. It also provides users with a working environment containing a powerful search engine as well as bioinformatics/biostatistics modules that enable signature comparisons or enrichment analyses. The TOXsIgN repository is freely accessible at http://toxsign.genouest.org. Website implemented in Python, JavaScript and MongoDB, with all major browsers supported. Supplementary data are available at Bioinformatics online.

Negotiating the thumbnail image: The transformative power of search engines and their ”good enough” aesthetics

Negotiating the thumbnail image: The transformative power of search engines and their ”good enough” aesthetics

Creating an Advanced Web Based Environment using Semantic Web

E-learning systems are of no help to the users if there are no powerful search engines and browsing tools to assist them. Most of the current web-based learning systems are closed systems where the courses and the learning material are fixed. The only thing that is dynamic is that the organization of the learning content is adapted to allow individualized learning environment. The learners of web-based e-learning systems belong to different categories based on their skills, background, preferences and learning styles. This paper focuses on personalized semantic search and recommending learning content that are appropriate to the learning environment. The semantic and personalized search of the learning content is based on comparison of the learner profile. The learner profile depends on re individual learning style of the user and learning objects’ metadata. This concept needs to be represented both in the learner profile as well as learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.

Creating an Advanced Web Based Environment using Semantic Web

E-learning systems are of no help to the users if there are no powerful search engines and browsing tools to assist them. Most of the current web-based learning systems are closed systems where the courses and the learning material are fixed. The only thing that is dynamic is that the organization of the learning content is adapted to allow individualized learning environment. The learners of web-based e-learning systems belong to different categories based on their skills, background, preferences and learning styles. This paper focuses on personalized semantic search and recommending learning content that are appropriate to the learning environment. The semantic and personalized search of the learning content is based on comparison of the learner profile. The learner profile depends on re individual learning style of the user and learning objects’ metadata. This concept needs to be represented both in the learner profile as well as learning object description as certain data structures. Personalized recommendation of learning objects uses an approach to determine a more suitable relationship between learning objects and learning profiles. Thus, it may advise a learner with most suitable learning objects. Semantic learning objects search is based on the query expansion of the user query and by using the semantic similarity to retrieve semantic matched learning objects.