Hasil untuk "Information resources (General)"

Zhou J, Zhao J, Xin J et al.

Junya Zhou,1 Junping Zhao,2 Juhua Xin,1 Yali Guo,3 Enshe Jiang,1 Chaoran Chen1 1School of Nursing and Health, Henan University, Kaifeng, Henan, 475004, People’s Republic of China; 2Department of Cardiology, Henan Provincial Key Medicine Laboratory of Nursing, Henan Provincial People’s Hospital, Zhengzhou University People’s Hospital, Zhengzhou, Henan, 450003, People’s Republic of China; 3Department of Cardiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 451191, People’s Republic of ChinaCorrespondence: Chaoran Chen, School of Nursing and Health, Henan University, North Section of Jinming Avenue, Longting District, Kaifeng, Henan, 475004, People’s Republic of China, Tel +86 0371-22868833, Email chenchaoran293@163.comObjective: To explore the transitional care experiences of patients with heart failure (HF) in China, identifying the challenges they face and their needs during the transition from hospital to home.Methods: A qualitative approach was employed, recruiting 18 participants from a tertiary hospital cardiology department between June and October 2023. Data were collected through semi-structured interviews and analysed using thematic analysis.Results: Among the participants, 83% (15/18) reported feeling inadequately prepared for self-monitoring, with rural patients facing additional difficulties; 67% (12/18) relied on unverified online resources for health information, and 78% (14/18) surrendered medication management to family members. Finally, 89% (16/18) expressed a need for extended hospital guidance, with rural participants highlighting financial difficulty as a significant concern.Conclusion: The study concludes that there is an urgent need for transitional care reform in China, including standardised discharge protocols, hospital-led telehealth platforms and government-subsidised rehabilitation programmes. These interventions should address urban–rural disparities and reduce caregiver dependency to improve the quality of life for patients with HF during the transition from hospital to home.Keywords: heart failure, transitional care, patient-centred care, health service accessibility, self-care

Vinogradov I.S., Mamakhatov T.M.

In recent years, the drug situation in China has generally stabilized, and according to official Chinese statistics, the number of drug addicts is reducing. However, China remains one of the key sources of synthetic drugs and precursors to other countries. The spread of narcotic drugs (synthetic opioids, cannabinoids, methamphetamine and others) threatens all countries. An important factor is that the Chinese government is able to control the large number of pharmaceutical manufacturers in the country , thus making it difficult for new types and modifications of drugs and psychoactive substances to appear on the market. In recent years, the PRC leadership has taken a number of effective steps to combat the uncontrolled production and distribution of illegal drugs, in particular, the state tightens control and regulation of the production of synthetic psychoactive drugs. China has strict laws against drug trafficking, which also serve as an effective deterrent against the distribution and consumption of illicit substances. Due to these measures, the international drug business is forced to move laboratories for the production of synthetic drugs from China to other nearby countries such as Myanmar and India. The situation with the international drug trade in the modern world requires concerted action at the interstate level. The interaction between law enforcement agencies of the SCO (the Shanghai Cooperation Organization) member states is a good example of cooperation in this area. One of the priorities of this organization is the fight against drug crime. At this stage, it is necessary to further deepen cooperation between the law enforcement agencies of the SCO countries to identify established drug trafficking channels, fight against drug trafficking in the dark web and prevent the emergence of analogues to various psychotropic substances prohibited by law.

Shahla Shaan Ahmed, Shaowei Wang, Yuan Tian et al.

Context: Navigating the knowledge of Stack Overflow (SO) remains challenging. To make the posts vivid to users, SO allows users to write and edit posts with Markdown or HTML so that users can leverage various formatting styles (e.g., bold, italic, and code) to highlight the important information. Nonetheless, there have been limited studies on the highlighted information. Objective: We carried out the first large-scale exploratory study on the information highlighted in SO answers in our recent study. To extend our previous study, we develop approaches to automatically recommend highlighted content with formatting styles using neural network architectures initially designed for the Named Entity Recognition task. Method: In this paper, we studied 31,169,429 answers of Stack Overflow. For training recommendation models, we choose CNN-based and BERT-based models for each type of formatting (i.e., Bold, Italic, Code, and Heading) using the information highlighting dataset we collected from SO answers. Results: Our models achieve a precision ranging from 0.50 to 0.72 for different formatting types. It is easier to build a model to recommend Code than other types. Models for text formatting types (i.e., Heading, Bold, and Italic) suffer low recall. Our analysis of failure cases indicates that the majority of the failure cases are due to missing identification. One explanation is that the models are easy to learn the frequent highlighted words while struggling to learn less frequent words (i.g., long-tail knowledge). Conclusion: Our findings suggest that it is possible to develop recommendation models for highlighting information for answers with different formatting styles on Stack Overflow.

Xiaokun Zhang, Bo Xu, Chenliang Li et al.

The session-based recommendation (SBR) garners increasing attention due to its ability to predict anonymous user intents within limited interactions. Emerging efforts incorporate various kinds of side information into their methods for enhancing task performance. In this survey, we thoroughly review the side information-driven session-based recommendation from a data-centric perspective. Our survey commences with an illustration of the motivation and necessity behind this research topic. This is followed by a detailed exploration of various benchmarks rich in side information, pivotal for advancing research in this field. Moreover, we delve into how these diverse types of side information enhance SBR, underscoring their characteristics and utility. A systematic review of research progress is then presented, offering an analysis of the most recent and representative developments within this topic. Finally, we present the future prospects of this vibrant topic.

T. Szmuda, Cathrine Özdemir, Shan Ali et al.

Objectives The internet has become one of the most important resources for the general population when searching for healthcare information. However, the information available is not always suitable for all readers because of its difficult readability. We sought to assess the readability of online information regarding the novel coronavirus disease (COVID-19) and establish whether they follow the patient educational information reading level recommendations. Study design This is a cross-sectional study. Methods We searched five key terms on Google and the first 30 results from each of the searches were considered for analysis. Five validated readability tests were utilized to establish the reading level for each article. Results Of the 150 gathered articles, 61 met the inclusion criteria and were evaluated. None (0%) of the articles met the recommended 5th to 6th grade reading level (of an 11-12-year-old). The mean readability scores were Flesch Reading Ease 44.14, Flesch-Kincaid Grade Level 12.04, Gunning-Fog Index 14.27, Simple Measure of Gobbledygook SMOG Index 10.71, and Coleman-Liau Index 12.69. Conclusions Online educational articles on COVID-19 provide information too difficult to read for the general population. The readability of articles regarding COVID-19 and other diseases needs to improve so that the general population may understand health information better and may respond adequately to protect themselves and limit the spread of infection.

L. Glowka, F. Burhenne-Guilmin, H. Synge

Chineme Enyioha, Larissa M Loufman, Mary E Grewe et al.

BackgroundMobile health (mHealth) interventions for smoking cessation have grown extensively over the last few years. Although these interventions improve cessation rates, studies of these interventions consistently lack sufficient Black smokers; hence knowledge of features that make mHealth interventions attractive to Black smokers is limited. Identifying features of mHealth interventions for smoking cessation preferred by Black smokers is critical to developing an intervention that they are likely to use. This may in turn address smoking cessation challenges and barriers to care, which may reduce smoking-related disparities that currently exist. ObjectiveThis study aims to identify features of mHealth interventions that appeal to Black smokers using an evidence-based app developed by the National Cancer Institute, QuitGuide, as a reference. MethodsWe recruited Black adult smokers from national web-based research panels with a focus on the Southeastern United States. Participants were asked to download and use QuitGuide for at least a week before participation in remote individual interviews. Participants gave their opinions about features of the QuitGuide app and other mHealth apps they may have used in the past and suggestions for future apps. ResultsOf the 18 participants, 78% (n=14) were women, with age ranging from 32 to 65 years. Themes within five major areas relevant for developing a future mHealth smoking cessation app emerged from the individual interviews: (1) content needs including health and financial benefits of quitting, testimonials from individuals who were successful in quitting, and strategies for quitting; (2) format needs such as images, ability to interact with and respond to elements within the app, and links to other helpful resources; (3) functionality including tracking of smoking behavior and symptoms, provision of tailored feedback and reminders to users, and an app that allows for personalization of functions; (4) social network, such as connecting with friends and family through the app, connecting with other users on social media, and connecting with a smoking cessation coach or therapist; and (5) the need for inclusivity for Black individuals, which may be accomplished through the inclusion of smoking-related information and health statistics specific for Black individuals, the inclusion of testimonials from Black celebrities who successfully quit, and the inclusion of cultural relevance in messages contained in the app. ConclusionsCertain features of mHealth interventions for smoking cessation were highly preferred by Black smokers based on their use of a preexisting mHealth app, QuitGuide. Some of these preferences are similar to those already identified by the general population, whereas preferences for increasing the inclusivity of the app are more specific to Black smokers. These findings can serve as the groundwork for a large-scale experiment to evaluate preferences with a larger sample size and can be applied in developing mHealth apps that Black smokers may be more likely to use.

Zhangjun Yu, Qiuzhao Zhang, Shubi Zhang et al.

Abstract Single receiver positioning has been widely used as a standard and standalone positioning technique for about 25 years. To detect the slowly growing faults caused by satellite and receiver clocks in single receiver positioning, the Autonomous Integrity Monitoring with an Extrapolation method (AIME) was proposed based on the Kalman filter measurement domain. However, AIME was designed with the assumption of there is the same number of visible satellites at each epoch, which limits its application. To address this issue, this paper proposes a state-domain Robust Autonomous Integrity Monitoring with the Extrapolation Method (SRAIME). The slowly growing fault detection statistics is established based on the difference between the estimates of the state propagator and the posterior state estimation in Kalman filtering. Meanwhile, singular value decomposition is adopted to factor the covariance matrix of the difference to increase computational robustness. Besides, the relevant formulas of the proposed method are theoretically derived, and it is proven that the proposed method is suitable for any positioning model based on the Kalman filter. Additionally, the results of two experiments indicate that SRAIME can detect slowly growing faults in single receiver positioning earlier than AIME.

Василь Ґабор

Проаналізовано понад двадцять подорожніх нарисів про Італію, у яких наявні українські алюзії. Досліджено, що серед яскравих опосередкованих та прямих українських алюзій і порівнянь можна виділити такі умовні групи: 1) короткі екскурси в українську історію та окремі історичні вкраплення; 2) використання українських реалій у порівняннях, згадки про українських письменників і цитування їхніх творів, співзучних настроям авторів; 3) рефлексії щодо впливів італійської культури й мистецтва на українську; 4) сприйняття італійцями України та «українізація» західноукраїнськими авторами імен італійських митців. З’ясовано, що провідні західноукраїнські часописи першої третини ХХ ст., такі як «Літературно-Науковий Вістник», «Діло», «Новий Час», «Час» та «Назустріч», систематично публікували подорожні нариси, у яких наявні українські алюзії. Однак редакційна політика видань щодо публікації подорожніх нарисів була різною. У більшості з цих часописів друкувалися подорожні нариси різних за фахом авторів, що сприяло багатовекторності поглядів та емоційного сприйняття історичного, мистецького й духовного світу Італії, однак в «Літературно-Науковому Вістнику» та «Новому Часі» містилися подорожні есеї виключно їхніх редакторів, що суттєво звужувало широту поглядів та різноманітність емоційного тла. У подорожніх нарисах відомих учених, митців та журналістів – Михайла Грушевського, Гавриїла Костельника, Осипа Назарука, Михайла Посацького, Святослава Гординського, Григорія Смольського, Михайла Островерхи, Миколи Троцького та ін. – виразно простежуються українські алюзії, які сприяли спорідненню нашого народу та його історії з іншими народами, їхньою культурою й дозволяли українцям відчувати себе частиною європейської цивілізації.

Sajad Delavari, Omid Barati, Seyed Reza Najibi et al.

Background. Students and graduates in the field of health management will be future managers of the health field. Improving the skills of health system managers as one of the main pillars of health service organizations is essential to meet the health needs of the community. Therefore, this study aimed to identify and prioritize the key skills required by healthcare management graduates as future managers of the health system. Methods. This study was performed in three steps using the method of content analysis, Delphi, and factor analysis. In the first step, a semi-structured and interactive in-depth interview was used to determine the skills required for managerial jobs in the health system, and the data were analyzed based on the content analysis method. The sample size was 14 health experts. In the second step, a Delphi study was designed to build consensus on skills. The participants scored 44 skills extracted from the interviews through a three-point Likert scale from disagree to agree (from 1 to 3), then the skills were divided into several component groups through factor analysis. Results. In this study, 44 key skills needed by managers and graduated students in the field of health service management were identified in 10 components. Conclusion. Costs and resources allocated to the training of health service management graduated students should be based on the need of the labor market and workspace.

Aleksei Cherkasov, Kirill M. Gerke, Aleksey Khlyupin

Structures are abundant in both natural and human-made environments and usually studied in the form of images or scattering patterns. To characterize structures a huge variety of descriptors is available spanning from porosity to radial and correlation functions. In addition to morphological structural analysis, such descriptors are necessary for stochastic reconstructions, stationarity and representativity analysis. The most important characteristic of any such descriptor is its information content - or its ability to describe the structure at hand. For example, from crystallography it is well known that experimentally measurable $S_2$ correlation function lacks necessary information content to describe majority of structures. The information content of this function can be assessed using Monte-Carlo methods only for very small 2D images due to computational expenses. Some indirect quantitative approaches for this and other correlation function were also proposed. Yet, to date no methodology to obtain information content for arbitrary 2D or 3D image is available. In this work, we make a step toward developing a general framework to perform such computations analytically. We show, that one can assess the entropy of a perturbed random field and that stochastic perturbation of fields correlation function decreases its information content. In addition to analytical expression, we demonstrate that different regions of correlation function are in different extent informative and sensitive for perturbation. Proposed model bridges the gap between descriptor-based heterogeneous media reconstruction and information theory and opens way for computationally effective way to compute information content of any descriptor as applied to arbitrary structure.

Ozan Candogan, Manxi Wu

In this paper, we study platforms where resources and jobs are spatially distributed, and resources have the flexibility to strategically move to different locations for better payoffs. The price of the service at each location depends on the number of resources present and the market size, which is modeled as a random state. Our focus is on how the platform can utilize information about the underlying state to influence resource repositioning decisions and ultimately increase commission revenues. We establish that in many practically relevant settings a simple monotone partitional information disclosure policy is optimal. This policy reveals state realizations below a threshold and above a second (higher) threshold, and pools all states in between and maps them to a unique signal realization. We also provide algorithmic approaches for obtaining (near-)optimal information structures that are monotone partitional in general settings.

Krisztian Balog, ChengXiang Zhai

Information access systems, such as search engines, recommender systems, and conversational assistants, have become integral to our daily lives as they help us satisfy our information needs. However, evaluating the effectiveness of these systems presents a long-standing and complex scientific challenge. This challenge is rooted in the difficulty of assessing a system's overall effectiveness in assisting users to complete tasks through interactive support, and further exacerbated by the substantial variation in user behaviour and preferences. To address this challenge, user simulation emerges as a promising solution. This book focuses on providing a thorough understanding of user simulation techniques designed specifically for evaluation purposes. We begin with a background of information access system evaluation and explore the diverse applications of user simulation. Subsequently, we systematically review the major research progress in user simulation, covering both general frameworks for designing user simulators, utilizing user simulation for evaluation, and specific models and algorithms for simulating user interactions with search engines, recommender systems, and conversational assistants. Realizing that user simulation is an interdisciplinary research topic, whenever possible, we attempt to establish connections with related fields, including machine learning, dialogue systems, user modeling, and economics. We end the book with a detailed discussion of important future research directions, many of which extend beyond the evaluation of information access systems and are expected to have broader impact on how to evaluate interactive intelligent systems in general.

R. Noss

Cong Wang, K. Ren, Jia Wang

Cloud computing enables customers with limited computational resources to outsource large-scale computational tasks to the cloud, where massive computational power can be easily utilized in a pay-per-use manner. However, security is the major concern that prevents the wide adoption of computation outsourcing in the cloud, especially when end-user's confidential data are processed and produced during the computation. Thus, secure outsourcing mechanisms are in great need to not only protect sensitive information by enabling computations with encrypted data, but also protect customers from malicious behaviors by validating the computation result. Such a mechanism of general secure computation outsourcing was recently shown to be feasible in theory, but to design mechanisms that are practically efficient remains a very challenging problem. Focusing on engineering computing and optimization tasks, this paper investigates secure outsourcing of widely applicable linear programming (LP) computations. In order to achieve practical efficiency, our mechanism design explicitly decomposes the LP computation outsourcing into public LP solvers running on the cloud and private LP parameters owned by the customer. The resulting flexibility allows us to explore appropriate security/efficiency tradeoff via higher-level abstraction of LP computations than the general circuit representation. In particular, by formulating private data owned by the customer for LP problem as a set of matrices and vectors, we are able to develop a set of efficient privacy-preserving problem transformation techniques, which allow customers to transform original LP problem into some random one while protecting sensitive input/output information. To validate the computation result, we further explore the fundamental duality theorem of LP computation and derive the necessary and sufficient conditions that correct result must satisfy. Such result verification mechanism is extremely efficient and incurs close-to-zero additional cost on both cloud server and customers. Extensive security analysis and experiment results show the immediate practicability of our mechanism design.

Manisuli Ssenyonjo

Alexandre Trilla, John Bob-Manuel, Benjamin Lamoureux et al.

The wheel-rail interface is regarded as the most important factor for the dynamic behaviour of a railway vehicle, affecting the safety of the service, the passenger comfort, and the life of the wheelset asset. The degradation of the wheels in contact with the rail is visibly manifest on their treads in the form of defects such as indentations, flats, cavities, etc. To guarantee a reliable rail service and maximise the availability of the rolling-stock assets, these defects need to be constantly and periodically monitored as their severity evolves. This inspection task is usually conducted manually at the fleet level and therefore it takes a lot of human resources. In order to add value to this maintenance activity, this article presents an automatic Deep Learning method to jointly detect and classify wheel tread defects based on smartphone pictures taken by the maintenance team. The architecture of this approach is based on a framework of Convolutional Neural Networks, which is applied to the different tasks of the diagnosis process including the location of the defect area within the image, the prediction of the defect size, and the identification of defect type. With this information determined, the maintenancecriteria rules can ultimately be applied to obtain the actionable results. The presented neural approach has been evaluated with a set of wheel defect pictures collected over the course of nearly two years, concluding that it can reliably automate the condition diagnosis of half the current workload and thus reduce the lead time to take maintenance action, significantly reducing engineering hours for verification and validation. Overall, this creates a platform or significant progress in automated predictive maintenance of rolling stock wheelsets.

André Bonsanto

Este trabalho pretende delinear uma perspectiva ampliada sobre o fenômeno das fake news para problematizar o ambiente (des)informativo e revisionista em curso. Partimos do pressuposto que há uma proliferação cada vez mais proeminente de narrativas – que aqui definimos como “historiográfico-midiáticas” - capazes de reconfigurar o ecossistema informacional, evidenciando a conformação de novos atores, formatos e linguagens no cenário contemporâneo. Desta forma, propomos alguns olhares interpretativos sobre a produtora de conteúdo Brasil Paralelo, empresa que vêm se destacando com um reconhecido protagonismo na construção de narrativas deliberadamente revisionistas sobre a história recente brasileira. Como uma estratégia de embate político, estas narrativas serão pensadas sob um viés que as tensionem para além das notícias e do jornalismo propriamente dito, nos mostrando como a noção de (pós)“verdade” está circunscrita a uma problemática que envolve questões de ordem social, política e epistemológica, responsáveis por incitar - nos espaços comuns propiciados pelos ambientes digitais – a profusão de agendas específicas e muito bem delimitadas

Kok Chuan Tan, Varun Narasimhachar, Bartosz Regula

We show that both the classical as well as the quantum definitions of the Fisher information faithfully identify resourceful quantum states in general quantum resource theories, in the sense that they can always distinguish between states with and without a given resource. This shows that all quantum resources confer an advantage in metrology, and establishes the Fisher information as a universal tool to probe the resourcefulness of quantum states. We provide bounds on the extent of this advantage, as well as a simple criterion to test whether different resources are useful for the estimation of unitarily encoded parameters. Finally, we extend the results to show that the Fisher information is also able to identify the dynamical resourcefulness of quantum operations.

Ozan Ozdenizci, Deniz Erdogmus

Feature ranking and selection is a widely used approach in various applications of supervised dimensionality reduction in discriminative machine learning. Nevertheless there exists significant evidence on feature ranking and selection algorithms based on any criterion leading to potentially sub-optimal solutions for class separability. In that regard, we introduce emerging information theoretic feature transformation protocols as an end-to-end neural network training approach. We present a dimensionality reduction network (MMINet) training procedure based on the stochastic estimate of the mutual information gradient. The network projects high-dimensional features onto an output feature space where lower dimensional representations of features carry maximum mutual information with their associated class labels. Furthermore, we formulate the training objective to be estimated non-parametrically with no distributional assumptions. We experimentally evaluate our method with applications to high-dimensional biological data sets, and relate it to conventional feature selection algorithms to form a special case of our approach.