Reviewerly/exHarmony huggingface dataset:
Distributional Reachability for Markov Decision Processes: Theory and Applications

We study distributional reachability for finite Markov decision processes (MDPs) from a control theoretical perspective. Unlike standard probabilistic reachability notions, which are defined over MDP states or trajectories, in this paper reachability is formulated over the space of probability distributions. We propose two set-valued maps for the forward and backward distributional reachability problems: the forward map collects all state distributions that can be reached from a set of initial distributions, while the backward map collects all state distributions that can reach a set of final distributions. We show that there exists a maximal invariant set under the forward map and this set is the region where the state distributions eventually always belong to, regardless of the initial state distribution and policy. The backward map provides an alternative way to solve a class of important problems for MDPs: the study of controlled invariance, the characterization of the domain of attraction, and reach-avoid problems. Three case studies illustrate the effectiveness of our approach.
Slotted Photonic Crystal Nanofiber for Cavity QED

A slotted photonic crystal cavity structure on an optical nanofiber is proposed to realize cavity quantum electrodynamics. The device has a Q -factor of ∼ 1100 and excellent single photon coupling to the nanofiber fundamental mode.
Handling Class Imbalance in Machine Learning-based Prediction Models: A Case Study in Asthma Management

A data-driven prediction tool has the potential to provide early warning of an asthma attack and improve asthma management and outcomes. Most previous machine learning (ML)-based studies for asthma attack prediction have reported a severe class imbalance, with major implications for model performance. We aimed to undertake a systematic comparison of several class imbalance handling techniques in the context of risk prediction models for asthma prognosis. We used data from 9,835 asthma patients extracted from the Medical Information Mart for Intensive Care (MIMIC) IV database and deployed five class imbalance handling methods based on synthetic minority oversampling technique (SMOTE) and cost function customisation. We then compared their performances in improving two-class classifier models developed using logistic regression (LR) and extreme gradient boosting (XGBoost) for three different prediction tasks with varying severity of class imbalance (proportion of majority class ranging from 90.86% to 98.98%). The cost function customisation technique substantially outperformed the SMOTE-based methods in all tasks. XGBoost combined with cost function customisation achieved the highest prediction performance for the outcome with the most extreme class imbalance ratio (AUC = 0.72). Our findings suggest that the cost function customisation-based approach to tackle class imbalance provides substantially better performance compared to oversampling in the context of asthma management.Clinical Relevance— This study underscores the challenge of class imbalance in the context of prediction tools to improve asthma management and outcomes and provides a methodological solution that addresses the challenge. Accurate asthma prediction tools can provide early warning and potentially prevent deterioration thereby improving the quality of life of patients with asthma.
The Need for the Human-Centred Explanation for ML-based Clinical Decision Support Systems

Machine learning has shown great promise in a variety of applications, but the deployment of these systems is hindered by the "opaque" nature of machine learning algorithms. This has led to the development of explainable AI methods, which aim to provide insights into complex algorithms through explanations that are comprehensible to humans. However, many of the explanations currently available are technically focused and reflect what machine learning researchers believe constitutes a good explanation, rather than what users actually want. This paper highlights the need to develop human-centred explanations for machine learning-based clinical decision support systems, as clinicians who typically have limited knowledge of machine learning techniques are the users of these systems. The authors define the requirements for human-centred explanations, then briefly discuss the current state of available explainable AI methods, and finally analyse the gaps between human-centred explanations and current explainable AI methods. A clinical use case is presented to demonstrate the vision for human-centred explanations.
Digital Animation, Health Education in Post COVID-19 Scenario

In post pandemics scenario, viral respiratory diseases are show up very often in changeable and usually cold weather locations. Ecuadorian cities in Andean region have altitude and unpredictable climate characteristics where respiratory diseases are frequent. COVID-19, influenza, flu and other infections are still present in the environment This research shows a development and application of digital animation, showing the audiovisual production processes developed through digital tools, products in educational contexts, adapted to the global health situation post pandemics. The study shows a mixed-type research divided into two phases. The first stage, for information collection, determined the needs for learning topics that collaborates in COVID-19 prevention in elementary education population through digital cartoons as a tool using social media as broadcast medium. The second stage, the animated product evaluation, inside an application scenario, in elementary schools in Riobamba city, that was exhibited through social networks. Through qualitative and quantitative research approaches, it has been possible to demonstrate the animation (digital cartoons) relevance in educational areas. Likewise, the study shows teachers and parents positive predisposition of digital animation use, as a learning tool and its use for educational content transmission, which is something new in the Ecuadorian school scene.
Numerical Simulation of Vertical Prestress Loss of Prestressed Concrete Box Girder Bridge Based on Improved Genetic Algorithm

In the concrete box girder bridge, the concrete is a brittle material, with low ultimate tensile strength and small ultimate tensile deformation. Because the box girder itself is a statically indeterminate structure, the concrete in the tensile zone is very prone to crack due to the load during construction and use. In this paper, the vertical prestress loss of prestressed concrete box girder bridge based on improved genetic algorithm is studied. The research shows that the stress generated by the additional bending moment at the center of the prestressed reinforcement reaches the ultimate axial compressive stress of 0.5%–3.4%, of which the vertical prestress of 3M near the midspan reaches 3.4%; The fixed end reaches 1.3%–8%, of which the vertical prestress of 3M near the middle of the span reaches 8%. When the vertical tendons are tensioned in batches by improved genetic algorithm, the influence range of the vertical prestressed tendons under the condition of different web height thickness ratio and the influence of the change of the height thickness ratio on the loss rate of the tendons are obtained. Using prestressed concrete structure through improved genetic algorithm is more durable in practice. Reinforced concrete has been subjected to large stress, which is equivalent to a strength test. To some extent, its safety is more worthy of affirmation.
Investigation of personal data protection mechanism based on blockchain technology

Abstract Blockchain technology is increasingly being used in personal data protection. Inspired by the importance of data security, this paper proposes a personal data protection mechanism based on blockchain, combined with distributed hash tables and cryptography, to enhance users' control over the data generated using web applications. This paper designs this mechanism's system model and describes the three aspects in detail: data storage mechanism, data encryption mechanism, and data trading mechanism. Among them, the data storage mechanism restricts user data to be stored only in the local storage space of the user terminal, the decentralized blockchain network, and the distributed hash table network to ensure that enterprises providing network applications cannot privately store user interaction data, the encryption mechanism is responsible for encrypting all user data recorded in the network and allows users to control the key of the data to ensure the security of the user data in the blockchain and distributed hash tables, the data transaction mechanism allows users to trade their data, and to incentivize enterprises to assist users in collecting personal data, data transaction contracts are built into the data transaction mechanism, allowing enterprises to receive a share of the revenue from user data transactions. Then, for data transactions, use the Stackelberg game to simulate the revenue sharing between users and service providers in data trading to incentivize enterprises providing web services to assist users in collecting their data. The simulation results show that when the number of users is 1000, the revenues of this scheme for service providers are 31%, 561%, and 19% higher than the existing scheme. Finally, the personal data protection platform is implemented by code to verify the feasibility of the theory proposed in this paper in personal data protection.
Meningkatkan Vocabulary Bahasa Inggris Dengan Menggunakan Puzzle Di Sekolah Dasar Buah Hati

Community Service (PKM) is a form of the Tridharma of Higher Education. This PKM is proposed to carry out activities that are outreach and educational in nature at Buah Hati Elementary School in Padang, West Sumatra Province. This activity is expected to help students improve their English Vocabulary by using Puzzles. Puzzle is a learning method that can improve students' English Vocabulary. In learning English, learning by using this puzzle is very helpful in increasing students' vocabulary in learning English. Because in the implementation of learning, students are trained to arrange several letters to become an English word. The purpose of this service is expected to be able to describe learning planning, increasing learning activities and increasing English Vocabulary using Puzzles. The method of implementing this service is carried out in several activities, namely the survey stage, namely by compiling various things that will be conveyed during the service activities to be carried out which include: preparation of the material to be given, preparation of the schedule for giving the material, division of tasks for the service team and surveys to the service location. The next stage, namely before the service activities are carried out, the socialization stage is carried out first, namely conducting friendship with the school principal, conveying the aims and objectives of this service. At this stage, collaboration is also carried out and determines the schedule of community service activities.
Index/Impressum: Vakuum in Forschung und Praxis 6/2023

Vakuum in Forschung und PraxisVolume 35, Issue 6 p. 51-51 Magazin Index/Impressum: Vakuum in Forschung und Praxis 6/2023 First published: 10 December 2023 https://doi.org/10.1002/vipr.202370610AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume35, Issue6December 2023Pages 51-51 RelatedInformation
Entanglement monogamy in indistinguishable particle systems

Abstract Recently, it has been realized that indistinguishability is a resource for quantum information processing. A new method to represent the indistinguishable particles by Franco et al. (Sci Rep 6:20603, 2016, https://doi.org/10.1038/srep20603 ) and measure the concurrence is developed by Nosrati et al. (npj Quantum Inf 6:39, 2020, https://doi.org/10.1038/s41534-020-0271-7 ). The monogamy property says that quantum entanglement cannot be shared freely between more than two particles. For three distinguishable particles, the monogamy of entanglement was first expressed as an inequality using squared concurrence where each particle has a single degree of freedom (for pure or mixed states). Using multiple degrees of freedom, similar inequality was shown to be held between two distinguishable particles. However, for two indistinguishable particles, where each particle cannot be addressed individually, the monogamy inequality was shown to be violated maximally for a specific state. Thus a question naturally arises: what happens to the monogamy of entanglement in the case of three or more indistinguishable particles? We prove that monogamy holds in this scenario and the inequality becomes equality for all pure indistinguishable states. Further, we provide three major operational meanings of our result. Finally, we present an experimental schematic using photons to observe our result.
Yuri Vladimirovich Grigoriev’s Personal Book Collection (to the 125th Anniversary of the Scientist’s Birth)

Personal book collections of scientists are valuable both as a historical and cultural source for the study of the owner’s personality and as an information resource. The article provides for the first time a multidimensional characterisation of the book and journal collection of the famous Russian librarian Yu.V. Grigoriev (1899—1973). The collection is currently part of the Fundamental Library of the Tambov State University (TSU) named after G.R. Derzhavin. After the death of the owner, the collection was acquired by the Tambov branch of the Moscow State Institute of Culture. It was included in the Fundamental Library TSU named after G.R. Derzhavin in 1994 in the process of its creation. The volume of the collection is 6241 copies. The quantitative and qualitative analysis of its thematic sections gives additional touches to the reader’s portrait of Yu.V. Grigoriev, reveals the sphere of his professional interests. The collection is universal in composition, most of which are publications on librarianship and related fields. The lower chronological boundary of the editions in the collection is 1852. Of special value are the editions of the 19 century, annual sets of magazines “Red Librarian”, “Bibliographic News”, a collection of abstracts of PhD theses of authors who became authoritative Russian librarians and bibliographers. All books contain exlibris of Yu.V. Grigoriev. More than 300 copies have gift inscriptions of colleagues and students, including two inscriptions of L.B. Khavkina. Being in the collection of the university library, the collection retains not only the memorial value, but also is a unique information resource, which is used in research and educational activities of teachers and students of the Department of Library and Information Resources of TSU named after G.R. Derzhavin.
Automation of slide coverslipping for large tissue sections

Coverslipping is the process of placing a cover glass or coverslip over a glass slide mounted with a stained tissue specimen without forming air bubbles, which can negatively impact the microscopic examination. While manual coverslipping is still widely used, automated systems have made the process easier and more consistent. Commercially available automated cover-slippers are limited to handling only slides that are 1" x 3", suitable for processing smaller tissue specimens. However, for larger tissue specimens sectioned from organs like the brain, liver, etc., slides can reach sizes up to 6" x 8", exceeding the capabilities of these systems. We present SLIDE PROTEKT, a fully automated large format coverslipping system designed to efficiently coverslip large format slides. This system has multiple zones for slide and coverslip transportation, dispensing of mounting medium, and precise placement of the coverslip without air bubbles. The ability of the system to output quality coverslipped slides was validated by processing 50 large-format brain tissue slides. The results were found to be comparable to manual coverslipping. The system achieved a coverslip placement accuracy of 80% with a mean positional offset that was within a tolerance of ±3 millimeters. Additionally, 75% of the slides had no air bubbles, while the remaining slides had air bubbles that were less than 120 micrometers in size. These results demonstrate the potential impact of SLIDE PROTEKT in the field of histology.
Pose Estimation of Ultrasound Probe Using CNN and RNN with Image Reconstruction Loss

It is necessary to estimate the pose of the probe with high accuracy to reconstruct 3D ultrasound (US) images only from US image sequences scanned by a 1D-array probe. We propose the probe pose estimation method using Convolutional Neural Network (CNN) with training by image reconstruction loss. To calculate the image reconstruction loss, we use the image reconstruction network which consists of an encoder that extracts features from the two US images and a decoder that reconstructs the intermediate US image between the two images. CNN is trained to minimize the image reconstruction loss between the ground-truth image and the reconstructed image. Through experiments, we demonstrate that the proposed method exhibits efficient performance compared with the conventional methods.
Ambulatory Behavior Assessment Using Deep Learning

This work leverages a custom implementation of a deep neural network-based object detection algorithm to detect people and a set of assistive devices relevant to clinical environments. The object detections form the basis for the quantification of different ambulatory activities and related behaviors. Using features extracted from detected people and objects as input to machine learning models, we quantify how a person ambulates and the mode of ambulation being used.Clinical relevance— This system provides the data required for clinicians and hospitalized patients to work together in the creation, monitoring, and adjustment of ambulatory goals.
Automatic assessment of patient eligibility by utilizing NLP and rule-based analysis

Assessment of patient eligibility is an essential process in the clinical trial but there are a lot of manual processes involved. Natural Language Processing (NLP) is a promising technique to automate analysing of the massive volume of Electronic Medical Records (EMRs) hence it can assist in the assessment of patient eligibility, especially in clinical trials that require complex inclusion/exclusion criteria. In this paper, we proposed a hybrid model which utilized both rule-based and NLP technologies to automate the assessment of patient eligibility. The result showed that the hybrid model had a better trade-off between sensitivity and precision compared to the rule-based model and NLP similarity model. Moreover, the accuracy of the hybrid model was validated on the larger dataset and it reached an accuracy of 87.3%. Therefore, this technique potentially can improve the efficiency of patient recruitment by eliminating the manual processes that involve in the assessment of patient eligibility.
BETA-BINOMIAL MODEL IN SMALL AREA ESTIMATION USING HIERARCHICAL LIKELIHOOD APPROACH

Small Area Estimation is a statistical method used to estimate parameters in sub-populations with small or even no sample sizes. This research aims to evaluate the Beta-Binomial model's performance for estimating small areas at the area level. The estimation method used is Hierarchical Likelihood (HL). The data used are simulation data and empirical data. Simulation studies were used to investigate the proposed model. The estimator's Mean Squared Error of Prediction (MSEP) and Absolute Bias (AB) estimator values determine the best estimation criteria. An empirical study using data on the illiteracy rate at the sub-district level in Bengkulu Province. The results of the simulation study show that, in general, the parameter estimators are nearly unbiased. Proportion prediction has the same tendency as parameters. Finally, the HL estimator has a small MSEP estimator. The results of an empirical study show that the average illiteracy rate in Bengkulu province is quite diverse. Kepahiang District has the highest average illiteracy rate in Bengkulu Province in 2021.
Genetic Programming–A Preliminary Study of Knowledge Transfer in Mutation

In this research, we introduce two new variations of Genetic Programming (GP) that reusing transferred knowledge in the mutation operator. They are called FullTree.CM_1 and FullTree.CM_2. Chromosomes of the best individuals learned from previous source problems will be reused as sub-trees of children generated by mutation while training GP for the target problem. FullTree.CM_1 takes k% good individuals in the last generation of a previous learned sub-task (source problem) into a POOL for reusing in the mutating process while FullTree.CM_2 re-uses k% good solutions from all previous learned sub-tasks. In order to analyzing the effectiveness of these schemes, we use three evaluation criteria including errors on training data, errors on testing data, and size of the learned solutions. Experimental results show that our schemes perform better than other previous transfer GP on the most of tested problem families with all of these three evaluation criteria. This is very promising and motivating for further research to improve GP based on transfer mutation.
Cinquain in Developing the Methodological Culture of a Future Music Teacher Considering Hermeneutic Approach

In the context of humanistic paradigm in education, we study the reorientation of the learning process to the category of understanding as one of the most important components in developing a teacher’s professional competences and culture. By designing the methodology for developing the methodological culture of a future music teacher considering hermeneutic approach, we came to the conclusion that this process will be successful provided that the educational process is oriented towards the dialogic interaction of educatees, the choice of work methods and approaches with due regard to their direction towards the search for deeper meanings during the analysis and processing of information, as well as finding the most important and essential fragments in the information material with the further reflection of such fragments. Cinquain is one of such methods and the specificities of its application became the topic of our study. Composing of a cinquain is a form of creativity when an author demonstrates their abilities to find the most essential elements when analyzing the information, make conclusions and summarize them. Out of cinquain varieties, we were most interested in a didactic one, the purpose of which is to ensure the process of free creativity of the individual without focusing on a certain number of syllables, but with semantic and syntactic requirements for each line. The use of cinquain makes it possible to test the acquired knowledge of students within a single topic and content module and, in some cases, to establish interdisciplinary links between several educational components and help students comprehend the process of professional training as a holistic system. In its turn, the creation of cinquains allows students to develop such qualities as thinking and attention, develop their critical thinking, enrich vocabulary, consolidate theoretical knowledge of subjects and, most importantly, to express their own attitude and reasonable stance regarding certain phenomena of the educational process and to show their artistic endeavors and creativity in the process of creation.