Model Library Research Articles

New Realities of Educational Practices. The Example of Libraries Activity in the Krasnoyarsk Region

Federal Law No. 85-FZ of 05.04.2021 “On Amendments to the Federal Law ‘On Education in the Russian Federation’” defines the significance of educational activities. The article presents the forms of the modern process of education carried out by the commonwealth of cultural subjects, formerly called cultural and educational, including libraries.Currently, there are used technologies of traditional cultural practices, creative technologies, as well as fragments of mass culture technologies. The technological arsenal of cultural institutions in education is complemented by electronic resources and opportunities provided in the online environment. These issues affect the variety of cultural institutions, but library systems will be in the lead. Communication interaction will not only remain the proven tool of the educational process in libraries, but will also strengthen. The authors supplement this thesis with examples from the activities of libraries in the Krasnoyarsk Region. The kind of catalyst for creative processes in libraries is the activity of employees of municipal libraries in the design and implementation of justification for inclusion in prestigious large-scale modernization projects (National project “Culture” for the creation of model libraries, the Krasnoyarsk Regional network project “Libraries of the Future”).The authors identify the possible risks of intensification of this area of activity, which can be overcome by using both traditional and innovative methods of work.

Revisiting Instructional Approaches in Response to Emerging Cataloging Standards

The ever-shifting landscape of cataloging standards over the last decade has kept library and information science (LIS) educators on their toes, and continuing developments only promise to maintain this trend. Examples include the publication of the International Federation of Library Association & Institution’s (IFLA’s) conceptual model Library Reference Model (LRM) in 2017, the release of the new and heavily revised version of the cataloging content standard Resource Description and Access (RDA) in 2020, and Library of Congress’ upcoming Bibliographic Framework (BIBFRAME) standard for encoding and publishing library data. These new standards have altered the way in which cataloging work is conceived and discussed, radically changed the interfaces used for accessing cataloging documentation, and are now spurring the creation of new software and tools for cataloging work, including Library of Congress’ new Marva metadata editor. At the same time, the increasing inclusion of linked data projects in libraries, archives, and other cultural heritage institutions are requiring new skills, practices, and workflows that cataloging and metadata librarians must be prepared for. With many of these standards and initiatives not fully implemented in the majority of libraries, cataloging educators face a dilemma in deciding how best to prepare their students to successfully navigate this time of change, where old and new standards and practices intersect.
 The Technical Services Education SIG session will include a panel of three educators (two of whom are current cataloging practitioners) with unique perspectives on strategies for teaching toward emerging cataloging standards. After brief presentations by each of the panelists, audience members will be encouraged to ask questions and offer their own experiences and ideas concerning this area of LIS education.
 This conversation aligns well with the ALISE conference theme of “Go Back and Get It: From One Narrative to Many” as cataloging practice and education must be in constant dialogue about the preparation of LIS students for the lifespan of their careers. How can LIS educators prepare students for an environment in which different institutions are facing drastically different plans and timelines for the implementation of new standards? How can teaching practices be adjusted to best leverage current best practices alongside new strategies? And how can study of past, present, and emerging cataloging standards and practices provide a solid foundation on which LIS students can build throughout their careers? This panel will offer opportunities for LIS educators to reach back to knowledge and experiences concerning previous standards transitions, share best practices for addressing the current, dynamic environment, and look toward the future of cataloging and metadata education and preparation.

Coexistence of machine intelligence, cyber art, and diagnostics: is it possible?

The development of machine intelligence and the application of generative images created using it is a promising area of communication design and humanmachine interaction. This letter to the editor represents the authors vision of the use of generative images for diagnosing human conditions.
 The use of machine intelligence as an interactive and intelligent diagnostic tool will allow a psychologist and a physician to effectively complement the therapeutic processes of controlled interactions of their users.
 Libraries of models and sets of applications with text-to-image algorithms are already available that can be used by engineers and designers in the process of creating objects of modern digital art. They can also be applied in the investigation of new paradigms using visual communications and their application in experimental diagnostics.

COMBINING VISIBILITY ANALYSIS AND DEEP LEARNING FOR REFINEMENT OF SEMANTIC 3D BUILDING MODELS BY CONFLICT CLASSIFICATION

Abstract. Semantic 3D building models are widely available and used in numerous applications. Such 3D building models display rich semantics but no façade openings, chiefly owing to their aerial acquisition techniques. Hence, refining models’ façades using dense, street-level, terrestrial point clouds seems a promising strategy. In this paper, we propose a method of combining visibility analysis and neural networks for enriching 3D models with window and door features. In the method, occupancy voxels are fused with classified point clouds, which provides semantics to voxels. Voxels are also used to identify conflicts between laser observations and 3D models. The semantic voxels and conflicts are combined in a Bayesian network to classify and delineate façade openings, which are reconstructed using a 3D model library. Unaffected building semantics is preserved while the updated one is added, thereby upgrading the building model to LoD3. Moreover, Bayesian network results are back-projected onto point clouds to improve points’ classification accuracy. We tested our method on a municipal CityGML LoD2 repository and the open point cloud datasets: TUM-MLS-2016 and TUM-FAÇADE. Validation results revealed that the method improves the accuracy of point cloud semantic segmentation and upgrades buildings with façade elements. The method can be applied to enhance the accuracy of urban simulations and facilitate the development of semantic segmentation algorithms.

Data Science-Driven Analysis of Substrate-Permissive Diketopiperazine Reverse Prenyltransferase NotF: Applications in Protein Engineering and Cascade Biocatalytic Synthesis of (-)-Eurotiumin A.

Prenyltransfer is an early-stage carbon-hydrogen bond (C-H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Toward the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). As the first tailoring event within the biosynthesis of cytotoxic notoamide metabolites, PT NotF catalyzes C2 reverse prenyltransfer of brevianamide F. Solving a crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent-exposed active site, intimating NotF may possess a significantly broad substrate scope. To assess the substrate selectivity of NotF, we synthesized a panel of 30 sterically and electronically differentiated tryptophanyl DKPs, the majority of which were selectively prenylated by NotF in synthetically useful conversions (2 to >99%). Quantitative representation of this substrate library and development of a descriptive statistical model provided insight into the molecular origins of NotF's substrate promiscuity. This approach enabled the identification of key substrate descriptors (electrophilicity, size, and flexibility) that govern the rate of NotF-catalyzed prenyltransfer, and the development of an "induced fit docking (IFD)-guided" engineering strategy for improved turnover of our largest substrates. We further demonstrated the utility of NotF in tandem with oxidative cyclization using flavin monooxygenase, BvnB. This one-pot, in vitro biocatalytic cascade enabled the first chemoenzymatic synthesis of the marine fungal natural product, (-)-eurotiumin A, in three steps and 60% overall yield.

Oxygen Reduction Reaction on Fe-N-C Catalysts: A Computational Spectroscopy Study

The oxygen reduction reaction (ORR) plays an important role in proton exchange fuel cells (PEFCs). In PEFCs, ORR is the cathodic half-cell reaction complementary to the oxidation of the fuel, but since ORR has slow kinetics, it requires high amounts of catalyst. State-of-the-art ORR catalysts are based on the expensive metal platinum. Even though the amount of platinum needed for ORR in PEFCs has been reduced significantly over the last decade, it is still the major contributor to the cost of PEFCs, thus hindering the commercialization and accessibility of this technology.[1]Iron and nitrogen doped carbon (Fe-N-C) catalysts have gained a lot of research attention due to their high ORR activity, which makes them potential substitutes for platinum-based catalysts. In Fe-N-C catalysts, iron is thought to be atomically dispersed as pseudo-molecular active centres with four- or fivefold nitrogen coordination spheres which are embedded in graphene layers. Since Fe-N-C catalysts are typically prepared via pyrolysis, they have a highly amorphous structure and can contain multiple iron phases, which makes them difficult to characterize structurally and spectroscopically. Consequently, there is still a scientific debate on the exact nature of the active site, in terms of iron spin and oxidation states and its precise coordination environment.[2-4]Fe-57 Mössbauer spectroscopy can provide direct insights on iron spin and oxidation states and is used successfully to characterise the amorphous Fe-N-C catalysts. Until recently, the interpretation of Mössbauer spectra was limited to comparisons with small reference complexes which lack the extended π-systems of Fe-N-C catalysts.[2] Since synthesis of such extended π-systems as references is difficult, we have developed a library of computational models that encompasses different structural motifs and electronic structures. With increasing use of in situ and operando experiments on Fe-N-C catalysts, the interest in computational models for the interpretation of experimental Mössbauer spectra has grown.[4-6] In this contribution, we present our density functional theory results for different molecular Fe-N-C models with extended π-systems and discuss their electronic structures and spectroscopic properties.[1] L. Osmieri, et al. Current Opinion in Electrochemistry 2021, 25, 100627.[2] U. I. Kramm, et al. Advanced Materials 2019, 31, 1805623.[3] S. Wagner, et al. Angewandte Chemie International Edition 2019, 58, 10486.[4] L. Ni, C. Gallenkamp, et al. Advanced Energy & Sustainability Research 2021, 2, 2000064.[5] J. Li, et al. Nature Catalysis, 2021, 4, 10.[6] X. Liu, et al. Chem 2020, 6, 3440.

Twitters’ Concerns and Opinions About the COVID-19 Booster Shots: Infoveillance Study

Objective: This study aimed to categorize and analyze the public response toward third/booster shots of COVID-19 on Twitter. Methods: We downloaded the COVID-19 vaccine booster shots related Tweets using the Twitter API. The collected Tweets were pre-processed to prepare them for analysis by (1) removing non-English language tweets, retweets, emojis, emoticons, non-printable characters, the punctuation marks, and the prepositions, (2) anonymizing the identity of the users, and (3) normalizing various forms of the same words. We used the state-of-the-art BertTopic modeling library to identify the most popular topics. Results: Of 165,048 Tweets collected, 36,908 Tweets were analyzed in this study. From these tweets, we identified 9 topics, which were about Biden administration, Pfizer & BioNTech, Moderna, Johnson & Johnson, eligibility for booster shots, side effects, Donald Trump, variants of the Novel Coronavirus, and conspiracy theory & propaganda. The mean of sentiment was positive in all topics. The lowest and highest mean of sentiments were for the Donald Trump topic (0.0097) and the Johnson & Johnson topic (0.1294), respectively. Conclusions: The topics identified in this study not only accurately reflect the contemporary COVID-19 discussion, but also the high degree of politicization in the USA. While the latter might be a result of our rejection of non-English tweets, it is reassuring to see our fully automated, unsupervised pipeline reliably extract such global features in the data at scale. We, therefore, believe that the methodology presented in this study is mature and useful for other infoveillance studies on a wide variety of topics.

3MO Genomic evolution of non-small cell lung cancer during the establishment and propagation of patient-derived xenograft models

3MO Genomic evolution of non-small cell lung cancer during the establishment and propagation of patient-derived xenograft models

Block Diagram Representation of Controller Components of Distributed Power Flow Controller

In this paper, an attempt is made to present the controllers used for a power flow controller, termed as Distributed Power Flow Controller (DPFC). This comes under the category of Flexible AC Transmission Systems (FACTS) devices. DPFC emerges from the unified power flow-controller (UPFC), a most powerful FACTS device which can be used for both shunt and series control. So DPFC inherits the same control capability of simultaneous adjustment of line impedance, transmission angle and the bus voltage magnitude, which are the dynamic control parameters. The DPFC control and the functions of the different components for shunt and series controllers of DPFC are presented. These components can be obtained from Simulink Library of MATLAB environment and simulation model is obtained in this paper to check the dynamic response of DPFC.

A Decision Support System Model for Middle School Education Management Based on Sparse Clustering Algorithm

With the development of China’s economy, education has also been greatly improved, and the enrollment scale has been continuously expanded. It has become increasingly difficult to manage student performance. The traditional manual management of student grades has shortcomings such as low efficiency, poor privacy, difficulty to find and maintain, and incomplete knowledge mining of student grade data resources, so it is impossible to fully test and utilize related technologies. Based on this background, this paper designs and develops a set of decision support systems for middle school education management combined with a sparse clustering algorithm according to the actual needs of middle school education. The article first introduces the concept of sparse clustering algorithm and the four levels of the decision support system for middle school education management, data layer, model layer, application layer, and display layer, and then introduces based on five system models, namely, database design, online analysis process module design, data mining design, model library design, function design, and finally, the system model of educational decision management support is constructed, and the model test is completed. The practice shows that the system proposed in this paper can manage the current educational information of students scientifically, extract the information from the changes in students’ achievements in time, and make certain assistance for teachers’ educational decisions. This paper designs an effective decision support system by studying the sparse clustering algorithm and introducing it into the field of middle school education management.

Retrieval of a 3D CAD Model of a Transformer Substation Based on Point Cloud Data

When constructing a three-dimensional model of a transformer substation, it is critical to quickly find the 3D CAD model corresponding to the current point cloud data from a large number of transformer substation model libraries (due to the complexity and variety of models in the model base). In response to this problem, this paper proposes a method to quickly retrieve a 3D CAD model. Firstly, a 3D CAD model that shares the same size as the current point cloud model bounding box is extracted from the model library by the double-layer bounding box screening method. Then, the selected 3D CAD model is finely compared with the point cloud model by the multi-view method. The 3D CAD model that has the highest degree of corresponding to the point cloud data is acquired. The proposed algorithm, compared to other similar methods, has the advantages of retrieval accuracy and high efficiency.

Semantics-and-Primitives-Guided Indoor 3D Reconstruction from Point Clouds

The automatic 3D reconstruction of indoor scenes is of great significance in the application of 3D-scene understanding. The existing methods have poor resilience to the incomplete and noisy point cloud, which leads to low-quality results and tedious post-processing. Therefore, the objective of this work is to automatically reconstruct indoor scenes from an incomplete and noisy point-cloud base on semantics and primitives. In this paper, we propose a semantics-and-primitives-guided indoor 3D reconstruction method. Firstly, a local, fully connected graph neural network is designed for semantic segmentation. Secondly, based on the enumerable features of indoor scenes, a primitive-based reconstruction method is proposed, which retrieves the most similar model in a 3D-ESF indoor model library by using ESF descriptors and semantic labels. Finally, a coarse-to-fine registration method is proposed to register the model into the scene. The results indicate that our method can achieve high-quality results while remaining better resilience to the incompleteness and noise of point cloud. It is concluded that the proposed method is practical and is able to automatically reconstruct the indoor scene from the point cloud with incompleteness and noise.

High-Fidelity Virtual Reality Simulation for the Middle Cranial Fossa Approach-Modules for Surgical Rehearsal and Education.

Virtual reality simulation has gained prominence as a valuable surgical rehearsal and education tool in neurosurgery. Approaches to the internal auditory canal, cerebellopontine angle, and ventral brainstem region using the middle cranial fossa are not well explored by simulation. We hope to contribute to this paucity in simulation tools devoted to the lateral skull base, specifically the middle cranial fossa approach. Eight high-resolution microcomputed tomography scans of human cadavers were used as volumetric data sets to construct a high-fidelity visual and haptic rendering of the middle cranial fossa using CardinalSim software. Critical neurovascular structures related to this region of the skull base were segmented and incorporated into the modules. The virtual models illustrate the 3-dimensional anatomic relationships of neurovascular structures in the middle cranial fossa and allow a realistic interactive drilling environment. This is facilitated by the ability to render bone opaque or transparent to reveal the proximity to critical anatomy allowing for practice of the virtual dissection in a graduated fashion. We have developed a virtual library of middle cranial fossa approach models, which integrate relevant neurovascular structures with aims to improve surgical training and education. A ready extension is the potential for patient-specific application and pathology.

Low-voltage Low-power Bulk-driven CMOS Op-Amp Using Negative Miller Compensation for ECG

Two bulk-driven CMOS (Complementary Metal Oxide Semiconductor) operational amplifier (op-amp) designs for electrocardiogram (ECG) application are presented and compared in this paper. Both op-amps are based on two-stage amplification, where bulk-driven differential input is the first stage, while additional DC gain is the second stage. Different compensation techniques were integrated in each op-amp design. Standard Miller compensation was used for the first op-amp parallel with the second stage. The novelty of the second op-amp is that it utilizes negative Miller compensation between the bulk-driven input node and the output node of the first stag, while standard Miller compensation was used in the second stage. The purpose of this work was to compare DC gain, phase margin (PM) and unit gain frequency (UGF) obtained through different simulated compensation strategies and test results. The op-amps were simulated using 0.25 μm CMOS technology. The simulation results are presented using the standard model libraries from Tanner EDA tools, operating on a single rail +0.8V power supply.

Open Knee(s): A Free and Open Source Library of Specimen-Specific Models and Related Digital Assets for Finite Element Analysis of the Knee Joint

There is a growing interest in the use of virtual representations of the knee for musculoskeletal research and clinical decision making, and to generate digital evidence for design and regulation of implants. Accessibility to previously developed models and related digital assets can dramatically reduce barriers to entry to conduct simulation-based studies of the knee joint and therefore help accelerate scientific discovery and clinical innovations. Development of models for finite element analysis is a demanding process that is both time consuming and resource intensive. It necessitates expertise to transform raw data to reliable virtual representations. Modeling and simulation workflow has many processes such as image segmentation, surface geometry generation, mesh generation and finally, creation of a finite element representation with relevant loading and boundary conditions. The outcome of the workflow is not only the end-point knee model but also many other digital by-products. When all of these data, derivate assets, and tools are freely and openly accessible, researchers can bypass some or all the steps required to build models and focus on using them to address their research goals. With provenance to specimen-specific anatomical and mechanical data and traceability of digital assets throughout the whole lifecycle of the model, reproducibility and credibility of the modeling practice can be established. The objective of this study is to disseminate Open Knee(s), a cohort of eight knee models (and relevant digital assets) for finite element analysis, that are based on comprehensive specimen-specific imaging data. In addition, the models and by-products of modeling workflows are described along with model development strategies and tools. Passive flexion served as a test simulation case, demonstrating an end-user application. Potential roadmaps for reuse of Open Knee(s) are also discussed.

REHUMANISASI MELALUI PENDIDIKAN AGAMA MENURUT PERSPEKTIF KH. AHMAD DAHLAN

ABSTRACTThe importance of prioritizing humanization in the religious learning process aims so that students do not experience dehumanization. Rehumanization is intended so that students have complete rights and obligations in carrying out religious learning in the classroom. The purpose of this study is to describe rehumanization through religious education from the perspective of KH. Ahmad Dahlan. This study uses a descriptive analysis approach with a literature review research model (library research). The results of this study are according to Dahlan, religious education must be taught in an integrative way with other sciences, religious education must be taught physically and mentally in order to provide a complete concept to humans, religious education must be able to prioritize unity of heart so that it becomes a unifier of all differences, and religious teaching in schools must cover faith, teach love to fellow human beings, religious learning must revive the mind, religious learning must teach independence so that humans can rule and control themselves.Keywords: Rehumanization, Humanization, Religious Education

Construction of a digital fetus library for radiation dosimetry.

Accurate estimations of fetal absorbed dose and radiation risks are crucial for radiation protection and important for radiological imaging research owing to the high radiosensitivity of the fetus. Computational anthropomorphic models have been widely used in patient-specific radiation dosimetry calculations. In this work, we aim to build the first digital fetal library for more reliable and accurate radiation dosimetry studies. Computed tomography (CT) images of abdominal and pelvic regions of 46 pregnant females were segmented by experienced medical physicists. The segmented tissues/organs include the body contour, skeleton, uterus, liver, kidney, intestine, stomach, lung, bladder, gall bladder, spleen, and pancreas for maternal body, and placenta, amniotic fluid, fetal body, fetal brain, and fetal skeleton. Nonuniform rational B-spline (NURBS) surfaces of each identified region was constructed manually using 3D modeling software. The Hounsfield unit values of each identified organs were gathered from CT images of pregnant patients and converted to tissue density. Organ volumes were further adjusted according to reference measurements for the developing fetus recommended by the World Health Organization (WHO) and International Commission on Radiological Protection. A series of anatomical parameters, including femur length, humerus length, biparietal diameter, abdominal circumference (FAC), and head circumference, were measured and compared with WHO recommendations. The first fetal patient-specific model library was developed with the anatomical characteristics of each model derived from the corresponding patient whose gestational age varies between 8 and 35 weeks. Voxelized models are represented in the form of MCNP matrix input files representing the three-dimensional model of the fetus. The size distributions of each model are also provided in text files. All data are stored on Zenodo and are publicly accessible on the following link: https://zenodo.org/record/6471884. The constructed fetal models and maternal anatomical characteristics are consistent with the corresponding patients. The resulting computational fetus could be used in radiation dosimetry studies to improve the reliability of fetal dosimetry and radiation risks assessment. The advantages of NURBS surfaces in terms of adapting fetal postures and positions enable us to adequately assess their impact on radiation dosimetry calculations.

Enhancing the reliability and accuracy of data-driven dynamic soft sensor based on selective dynamic partial least squares models

Data-driven soft sensors have been widely applied to a broad range of process industries for virtually sensing difficult-to-measure but of-great-concern variables. However, it is still nontrivial to develop dynamic soft sensors with satisfactory performance. A crucial obstacle lies in strong dependence on quality and representativity of collected data. In other words, the predictive accuracy of the developed soft sensor could be quite sensitive to both offline training data and online unseen data, leading to unreliable and degraded generalization performance. In order to deal with such a troublesome issue, this paper proposes a framework for developing reliable dynamic soft sensor called selective dynamic partial least squares (SDPLS). The SDPLS consists of two-stage operations. At the offline stage, aided by intelligent optimization algorithm a model library is established through constructing various DPLS models, each of which accounts for certain ‘mode’. At the online stage, adaptive online model updating for adapting to the current working condition is carried out based on evaluating the performance of the stored individual models, where a correction scheme is also developed for bias elimination. Extensive case studies have been conducted based on a numerical example and two real-life industrial processes, and the results efficaciously demonstrate the effectiveness and promising application prospects of the proposed schemes.

Multi-valued logic arbiter PUF designs based on CNTFETs

Arbiter physical unclonable functions (APUFs) are integrated circuits (ICs) capable of storing secret keys spontaneously, which makes them a great fit for electronic device authentication and key generation applications. In this work, we propose two novel designs of multi-valued logic (MVL) APUFs using carbon nanotube field effect transistors (CNTFETs). Using MVL will enhance the APUF security by increasing the size of challenge-response pairs. The proposed designs are investigated under the 32nm CNTFET standard model library using HSPICE simulations. On average, the randomness in the ternary and the quaternary APUF responses are 32.75% and 24.47% in the worst case respectively, which are near the ideal randomness of 33.33% and 25%. Also, we analyzed the reliability of the proposed APUFs in different temperatures and supply voltages. Additionally, we implemented and analyzed the proposed designs using the 14nm CNTFET standard model library to ensure that our designs maintain stability.

Neural Network Model of Urban Landscape Design Based on Multi-Target Detection

Urban landscape design is of great significance to the development of the city. To solve the problem of manual acquisition characteristics of urban landscape design systems and low target detection accuracy, a new idea based on neural network and multi-objective testing technology construction urban landscape design system is proposed. By analyzing key technologies, the database establishment proposes to build a city landscape design system. Establish a city landscape design 3D model library using neural network and multi-objective testing technology. The system enables terrain measurements, GIS, visualization, massive data processing, virtual reality technology, etc., so that users can more and more effectively feel the rationality of space design and the feasibility of planning program. Through the experimental test, the following conclusions were obtained. First, the accuracy of multi-objective detection technology is maintained at around 88%. Second, the system landscape generation module generates fast, and the calculation time is between 0.57 and 46 s. Third, through analysis of the evaluation results of ecological suitability, the A-city landscape ecological function partition is divided into the plan for the plan to provide reliable data protection. The fourth is based on satisfaction evaluation indicators, which is conducive to the choice of the optimal plan of urban landscape design, thereby promoting the sustainable development of the city.