Source Information Research Articles

Stabilność rynku finansowego – kluczowy czynnik zrównoważonych finansów

In this article the authors argue that financial market stability plays a key role in achieving the Sustainable Development Goals (The United Nations Agenda 2030) that include not only economic growth but also the elimination of inequality. Indeed, financial markets share responsibility for building a sustainable economy that balances economic interest with social responsibility. The authors present a thesis that the financial market plays a key role in solving economic and social problems and building the innovative, low-carbon economy of the future. Climate and environmental change are a source of financial risk which creates new challenges for the financial safety net: central banks, regulators, and supervisors. The choice of funding sources and instruments has a decisive impact on the stability of the financial system and financing to achieve specific SDG can lead to new systemic risks. The authors find it reasonable to conclude that whether we are dealing with financial, public finance, epidemiological or environmental crises, the stability of the financial market is at the heart of the concerns of regulatory and supervisory institutions as it is vital for economic growth and economic standing in general. The research uses theoretical and dogmatic-legal methods based on the analysis of the content and availability of source information, i.e. theoretical and legal publications as well as legal regulations crucial from the point of view of the subject.

Editor's Spotlight/Take 5: How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

Editor's Spotlight/Take 5: How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

Source Matters? Exploring the Effects of Source Congeniality on Corrections of False Information on Twitter

This study examines the impact of source congeniality and its interaction with partisanship on the effectiveness of debunking false information on Twitter in the United States. Conducted in February 2022, a survey experiment revealed that most respondents paid little attention to correction source information. Politically congenial sources failed to enhance the effectiveness of corrective tags, whereas politically uncongenial sources and Twitter as the correction source weakened debunking’s persuasiveness, especially among Democrats. Republicans consistently exhibited higher misperception levels, unaffected by the correction source. The study highlights the persistent influence of belief-consistent false information and inattention as challenges in using corrective tags to counter false information on social media. The results underscore the need for more compelling corrections and increased public trust in debunking efforts in the United States.

Knowledge mapping and research trends on perioperative neurocognitive disorder from 1990 to 2022: a bibliometric analysis.

Perioperative neurocognitive disorder (PND) has attracted consistently increasing attention worldwide. However, there are few bibliometric studies that systematically evaluate this field. This study aimed to visualize the knowledge structure and research trends in PND through bibliometrics to help understand the future development of basic and clinical research. Literature related to PND in Web of Science and PubMed from 1990 to 2022 were collected through keywords retrospectively. Additionally, the source information, citation information, etc. of these publications were extracted. Finally, bibliometric analysis was performed by visualization software and statistical software. There were 2837 articles and reviews in total. An exponential rise in PND-related publications was observed. China had the most publication, followed by the US and Germany. The institution with the most output and citations was Harvard University (149 papers, 8966 citations). The most prominent author was Marcantonio Edward R with 66 publications and 5721 citations. The journal with the highest productivity for PND research was Frontiers in Aging Neuroscience followed by Anesthesia and Analgesia. Keywords were identified as six topics, including postoperative delirium, postoperative neurocognitive disorder, cardiac surgery, anaesthesia, orthopedic surgery, and dementia. According to keyword analysis, the most recent popular keywords in PND research were prevention, older patients, emergence delirium, orthopedic surgery, and dexmedetomidine. Publications on PND are increasing at an alarming rate from 1990 to 2022. Current research and future trends will concentrate on the prevention and treatment of PND, as well as PND associated with orthopedic surgery in older adults.

Source-specified atmospheric age distribution of black carbon and its impact on optical properties over the Yangtze River Delta

Black carbon (BC) exerts a profound and intricate impact on both air quality and climate due to its high light absorption. However, the uncertainty in representing the absorption enhancement of BC in climate models leads to an increased range in the modeled aerosol climate effects. Changes in BC optical properties could result either from atmospheric aging processes or from variations in its sources. In this study, a source-age model for identifying emission sources and aging states presented by University of California at Davis/California Institute of Technology (UCD/CIT) was used to simulate the atmospheric age distribution of BC from different sources and to quantify its impact on the optical properties of BC-containing particles. The results indicate that regions with greater aged BC concentrations do not correspond to regions with higher BC emissions due to atmospheric transport. High concentrations of aged BC are found in northern Yangtze River Delta (YRD) regions during summer. The chemical compositions of particles from different sources and with different atmospheric ages differ significantly. BC and primary organic aerosols (POA) are dominating in Traffic-dominated source while other components dominate in Industry-dominated source. As the atmospheric age increases, the mass fraction of secondary inorganic aerosols rises. Compared to the original model, the simulated mass absorption cross section of BC particles in the source-age model decreases while the single scattering albedo increases. This compensates for ~11 % of the overestimation of the simulated BC direct radiative forcing. Our study highlights that incorporating atmospheric age and source information into models can greatly improve the estimation of optical properties of BC-containing particles and deepen our understanding of their climate effects.

A Co-Created Tool to Help Counter Health Misinformation for Spanish-Speaking Communities in the San Francisco Bay Area.

Health misinformation, which was particularly prevalent during the COVID-19 pandemic, hampers public health initiatives. Spanish-speaking communities in the San Francisco Bay Area may be especially affected due to low digital health literacy and skepticism towards science and healthcare experts. Our study aims to develop a checklist to counter misinformation, grounded in community insights. We adopted a multistage approach to understanding barriers to COVID-19 vaccine uptake in Spanish-speaking populations in Alameda and San Francisco counties. Initial work included key informant and community interviews. Partnering with a community-based organization (CBO), we organized co-design workshops in July 2022 to develop a practical tool for identifying misinformation. Template analysis identified key themes for actionable steps, such as source evaluation and content assessment. From this, we developed a Spanish-language checklist. During formative interviews, misinformation was identified as a major obstacle to vaccine uptake. Three co-design workshops with 15 Spanish-speaking women resulted in a 10-step checklist for tackling health misinformation. Participants highlighted the need for scrutinizing sources and assessing messenger credibility, and cues in visual content that could instill fear. The checklist offers a pragmatic approach to source verification and information assessment, supplemented by resources from local CBOs. We have co-created a targeted checklist for Spanish-speaking communities to identify and counter health misinformation. Such specialized tools are essential for populations that are more susceptible to misinformation, enabling them to differentiate between credible and non-credible information.

Rapid construction method of emergency material supply chain based on shared platform covering market resources

In the early stages of emergency response, access to a robust information flow and sufficient materials is the basis for ensuring the efficient operation of the emergency material supply chain. Based on a summary of the structure and operational processes of the emergency material supply chain, this paper analyses the key elements and sources of supply chain information flow and proposes a method for integrating information flow based on shared platform. First, basic disaster information and multiscale population data are combined to determine the distribution of the affected population or shelters, and the demand assessment is completed. Then, the market material supply source information is mined, the national reserve information is integrated, and the quantity and distribution of supply sources are analysed. Finally, traffic information is obtained by calling on the real-time Amap API, the supply and demand of emergency materials are matched, and a reasonable emergency material dispatching plan is proposed. This paper considers both historical and recent cases, with case applications to the Lushan County, Ya 'an City, Sichuan Province MS7.0 earthquake; and the 2023 MW7.8 and 7.5 Turkey earthquakes, among others. The results verify that the proposed method can effectively integrate and construct the information flow of the emergency material supply chain and that the rational use of market resources can compensate for material shortages. The information flow and material flow of the emergency material supply chain can thus be maximized, indirectly reducing the losses caused by disaster emergencies.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

Построение безопасных коридоров судоходства в морях арктического шельфа на основе использования алгоритмов глубокого обучения

A comparative analysis of interannual changes in the ice coverage of the Pechora and the Kara Seas has been conducted based on the information of open sources (satellite observations) gathered by the Arctic and Antarctic Research Institute and the open dataset with mapped ice conditions ArcticDEM. The following problems of the study have been solved: the analysis of the most common approaches to building a forecast based on the analysis of ice conditions has been conducted; a deep learning algorithm to solve the problem of analysis of ice fields has been developed and tested; a medium-term forecast of the condition of ice fields in the area of the Pechora and the Kara Seas has been prepared based on the approbation model example; the obtained results have been analyzed and processed. In the course of study, an algorithm of automated mapping of ice conditions based on the satellite imagery data has been developed; the possibilities of its application to forecast ice conditions automatically, i.e., without using a manmade mapping, have been described. It has been demonstrated that the use of images based on multiple spectral bands and the combination of data from different satellites can minimize the influence of clouds and weather conditions. The model gave the real forecasting accuracy for a period of 10–14 weeks equal to 49–53 % for the Barents (Pechora) and the Kara Seas of 2018–2021. In case the model was fine-tuned and included data from the Barents (Pechora) and Kara Seas before 2018 in the training sample, the accuracy on the data after 2018 increased up to 52–56 %. The feature of the model is its ability to operate with different classes of ice and precisely determine its amount. The developed method increases the efficiency of operational control over the ice conditions in the Arctic seas. Safe navigation routes for shipping transport in the Pechora Sea have been modeled.

Towards the Greening of Banking: A Comparison of the Polish and Czech Financial Markets

Abstract The role of the banking system in promoting and implementing the Sustainable Development Goals in Europe has recently become very significant. As an important part of the financial sector, green banking helps to achieve the goals of sustainable development, which, in times of global financial, economic, climate, and social crises and of war, is very important. The authors aim to present the general approach to ‘green banking’ in Poland and the Czech Republic as represented by the banking sectors themselves and the national regulatory and supervisory authorities, with reference to the EU legal framework. They posit that ‘green banking’ is an increasingly important trend in the development of the Polish and Czech financial markets and conclude that EU regulations are the biggest driver of change in green finance, resulting in an increased awareness of environmental, social, and corporate governance (ESG) factors. What is required, however, is greater systemic involvement of the institutions that constitute the financial safety net, especially central banks. The research has used a theoretical and dogmatic-legal method, based on content analysis and the availability of source information, i.e. theoretical-legal publications, overview sector reports, and, in particular, legal regulations key to the topic.

Deep Domain Adaptation with Correlation Alignment and Supervised Contrastive Learning for Intelligent Fault Diagnosis in Bearings and Gears of Rotating Machinery

Deep domain adaptation techniques have recently been the subject of much research in machinery fault diagnosis. However, most of the work has been focused on domain alignment, aiming to learn cross-domain features by bridging the gap between source and target domains. Despite the success of these methods in achieving domain alignment, they often overlook the class discrepancy present in cross-domain scenarios. This can result in the misclassification of target domain samples that are located near cluster boundaries or far from their associated class centers. To tackle these challenges, a novel approach called deep domain adaptation with correlation alignment and supervised contrastive learning (DCASCL) is proposed, which synchronously realizes both domain distribution alignment and class distribution alignment. Specifically, the correlation alignment loss is used to enforce the model to generate transferable features, facilitating effective domain distribution alignment. Additionally, classifier discrepancy loss and supervised contrastive learning loss are integrated to carry out feature distribution alignment class-wisely. The supervised contrastive learning loss leverages class-specific information of source and target samples, which efficiently promotes the compactness of samples of the same class and the separation of samples from different classes. Moreover, our approach is extensively validated across three diverse datasets, demonstrating its effectiveness in diagnosing machinery faults across different domains.

Citation choices in L2 novices' and experts’ literature review sections: A functional discourse analysis

In response to Petrić’s (2012) call for comparing citation practices between student and published writing in the same field to better understand the interaction between developmental and disciplinary influences on the utilization of direct quotation, this paper conducts a comparative analysis of citation choices in 100 literature review sections of L2 master's theses and 100 research article literature review sections in the field of translation studies. This analysis utilizes a modified framework of functional options for cited information. The findings reveal a significant difference in the normalized frequency of citation options between the student corpus and the expert corpus. L2 master's students predominantly rely on single-source, author-responsible, long direct quotations with active voice and past tense. This approach is used to present source information without active engagement, suggesting their primary objective is to showcase knowledge of the research field. Conversely, experts strategically employ citations to support their claims and establish intertextual links. The differences in citation choices between master's theses and research articles might be attributed to the L2 students' limited grasp of the subtle nuances associated with citation forms, reporting verbs, verb tenses and voices, as well as a lack of proficiency in employing various pragmatic and rhetorical functions achievable through citation. Additionally, these differences may be attributed to the comprehensive knowledge and experience accumulated by expert writers over time. The implications of these findings for teaching English academic writing are discussed within the paper.

Noise Separation Technique for Enhancing Substation Noise Assessment Using the Phase Conjugation Method

The intrinsic noise of different transformers in the same substation belongs to the same type of noise, which is strongly coherent and difficult to separate, greatly increasing the cost of substation noise assessment and treatment. To solve the problem, the present paper proposes a noise separation technique using the phase conjugation method to separate the intrinsic noise signals of different transformers: firstly, the reconstruction of sound source information is realized by the phase conjugation method based on the measurement and emission of a line array; secondly, the intrinsic noise signals of the sound source are obtained by the equivalent point source method. The error of the separation technique is analyzed by point source simulation, and the optimal arrangement form of the microphone line array is studied. A validation experiment in a semi-anechoic chamber is also carried out, and the results prove that the error of separation technique is less than 2dBA, which is the error tolerance of engineering applications. Finally, a noise separation test of three transformers is performed in a substation using the proposed technique. The results show that the proposed technique is able to realize the intrinsic noise separation of each transformer in the substation, which is of positive significance for substation noise assessment and management.

Robust Classification and 6D Pose Estimation by Sensor Dual Fusion of Image and Point Cloud Data

It is an important aspect to fully leverage complementary sensors of images and point clouds for objects classification and six-dimensional (6D) pose estimation tasks. Prior works extract objects category from a single sensor such as RGB camera or LiDAR, limiting their robustness in the event that a key sensor is severely blocked or fails. In this work, we present a robust objects classification and 6D object pose estimation strategy by dual fusion of image and point cloud data. Instead of solely relying on 3D proposals or mature 2D object detectors, our model deeply integrates 2D and 3D information of heterogeneous data sources by a robustness dual fusion network and an attention-based nonlinear fusion function Attn-fun(.), achieving efficiency as well as high accuracy classification for even missed some data sources. Then, our method is also able to precisely estimate the transformation matrix between two input objects by minimizing the feature difference to achieve 6D object pose estimation, even under strong noise or with outliers. We deploy our proposed method not only to ModelNet40 datasets but also to a real fusion vision rotating platform for tracking objects in outer space based on the estimated pose.

Conditional generative adversarial networks for groundwater contamination characterization and source identification

Accurate characterization of sources and spatial distribution of contamination remains crucial in groundwater survey and remediation efforts. Nevertheless, conventional geo-statistical methods, like kriging, usually produce overly smooth concentration fields that violate the transport physics. Furthermore, the identification of contaminant sources based on kriging methods may be misleading. As a deep learning approach, Generative adversarial networks (GANs) is being increasingly employed to extract patterns and insights from intricate geospatial data. The conditional variant of GANs (CGANs), can reconstruct complete spatial data from sparse point observations. In this study, we propose a deep learning framework with CGANs for the purpose of groundwater contamination characterization and source identification. In ensure compliance with the principles of transport physics, we use a numerical transport model as the underlying prior model for training the CGANs. Through the adaptation of network architecture, the generator transforms low-dimensional random variables and observation data into concentration images, while the discriminator is responsible for extracting source information from these images. The developed framework is tested through two numerical cases with channelized random hydraulic conductivity fields, which pose a challenge due to the complexity of the concentration fields. The results demonstrate that the proposed method can give accurate results for both contaminant characterization and source identification in groundwater. CGANs excel in reconstructing concentration fields compared to traditional interpolation methods such as Kriging and inverse distance weighting (IDW). Additionally, CGANs offer the capability to estimate uncertainty and exhibit a certain level of error robustness.

THE ANALYSIS OF TEACHER’S STRATEGIES TOWARD SPEAKING SKILL IN SECONDARY SCHOOL

This research was conducted to analysis the teaching strategy towardstudents’ speaking skill. The aims of this research is to know the strategiesof teacher during teaching and learning process and know step by step ofteacher did by using teaching strategy toward speaking skill. By usingqualitative approach, source information for teaching strategy researchinclude questionnaire, lesson plan and speaking students’ scoring from36 EFL students were aged 15 to 16 and 2 English teacher were aged 25-28 of rural background condition in one of secondary school inBanyuwangi, East Java. The findings reveal that the teacher used teachingstrategy (games, role play and group investigation) during Englishlearning process. The findings also confirm that, the students got goodincreasing in speaking skill an the teacher used the different steps to applythe teaching strategy.

Fire Source Determination Method for Underground Commercial Streets Based on Perception Data and Machine Learning

Determining fire source in underground commercial street fires is critical for fire analysis. This paper proposes a method based on temperature and machine learning to determine information about fire source in underground commercial street fires. Data was obtained through consolidated fire and smoke transport (CFAST) software, and a fire database was established based on the sampling to ascertain fire scenarios. Temperature time series were chosen for feature processing, and three machine learning models for fire source determination were established: decision tree, random forest, and LightGBM. The results indicated that the trained models can determine fire source information based on processed features, achieving a precision exceeding 95%. Among these, the LightGBM model exhibited superior performance, with macro averages of precision, recall, and F1 score being 99.01%, 98.45%, and 99.04%, respectively, and a kappa value of 98.81%. The proposed method for determining the fire source provides technical support for grasping the fire situation in underground commercial streets and has good application prospects.

Analysis Related Factors with Behavior Use Method Contraception Long Term in Active KB Acceptors in the Family Planning Village Program in Sungai Penuh City

Method Contraception Long Term (MKJP) is possible contraception used for a period long time, more than two years, effectively and efficiently For objective space birth is more than three years or end pregnancy in couples age already fertile no want to add child Again. Based on the data obtained found that active KB acceptors that is not 51.7 % used MKJP and only 48.3% used MKJP. Analysis is done in a way univariate, bivariate, and multivariate. Research results obtained that amount children (p-value 0.016), access service health (p-value 0.329), knowledge (p value 0.003), attitude (p value 0.014), support family (p value 0.048), and source information (p value 0.009). Based on the results Multivariate obtained the most dominant factor related to behavior use of MKJP, namely knowledge with the highest OR value was 1.897.

Analysis of Distance and Environmental Impact on UAV Acoustic Detection

This article explores the challenge of acoustic drone detection in real-world scenarios, with an emphasis on the impact of distance, to see how sound propagation affects drone detection. Learning machines of varying complexity are used for detection, ranging from simpler methods such as linear discriminant, multilayer perceptron, support vector machines, and random forest to more complex approaches based on deep neural networks like YAMNet. Our evaluation meticulously assesses the performance of these methods using a carefully curated database of a wide variety of drones and interference sounds. This database, processed through array signal processing and influenced by ambient noise, provides a realistic basis for our analyses. For this purpose, two different training strategies are explored. In the first approach, the learning machines are trained with unattenuated signals, aiming to preserve the inherent information of the sound sources. Subsequently, testing is then carried out under attenuated conditions at various distances, with interfering sounds. In this scenario, effective detection is achieved up to 200 m, which is particularly notable with the linear discriminant method. The second strategy involves training and testing with attenuated signals to consider different distances from the source. This strategy significantly extends the effective detection ranges, reaching up to 300 m for most methods and up to 500 m for the YAMNet-based detector. Additionally, this approach raises the possibility of having specialized detectors for specific distance ranges, significantly expanding the range of effective drone detection. Our study highlights the potential of drone acoustic detection at different distances and encourages further exploration in this research area. Unique contributions include the discovery that training with attenuated signals with a worse signal-to-noise ratio allows the improvement of the general performance of learning machine-based detectors, increasing the effective detection range achieved, and the feasibility of real-time detection, even with very complex learning machines, opening avenues for practical applications in real-world surveillance scenarios.

Combining OPM and lesion mapping data for epilepsy surgery planning: a simulation study

When planning for epilepsy surgery, multiple potential sites for resection may be identified through anatomical imaging. Magnetoencephalography (MEG) using optically pumped sensors (OP-MEG) is a non-invasive functional neuroimaging technique which could be used to help identify the epileptogenic zone from these candidate regions. Here we test the utility of a-priori information from anatomical imaging for differentiating potential lesion sites with OP-MEG. We investigate a number of scenarios: whether to use rigid or flexible sensor arrays, with or without a-priori source information and with or without source modelling errors. We simulated OP-MEG recordings for 1309 potential lesion sites identified from anatomical images in the Multi-centre Epilepsy Lesion Detection (MELD) project. To localise the simulated data, we used three source inversion schemes: unconstrained, prior source locations at centre of the candidate sites, and prior source locations within a volume around the lesion location. We found that prior knowledge of the candidate lesion zones made the inversion robust to errors in sensor gain, orientation and even location. When the reconstruction was too highly restricted and the source assumptions were inaccurate, the utility of this a-priori information was undermined. Overall, we found that constraining the reconstruction to the region including and around the participant’s potential lesion sites provided the best compromise of robustness against modelling or measurement error.