External Information Sources Research Articles

A Message Passing Approach to Biomedical Relation Classification for Drug–Drug Interactions

The task of extracting drug entities and possible interactions between drug pairings is known as Drug–Drug Interaction (DDI) extraction. Computer-assisted DDI extraction with Machine Learning techniques can help streamline this expensive and time-consuming process during the drug development cycle. Over the years, a variety of both traditional and Neural Network-based techniques for the extraction of DDIs have been proposed. Despite the introduction of several successful strategies, obtaining high classification accuracy is still an area where further progress can be made. In this work, we present a novel Knowledge Graph (KG) based approach that utilizes a unique graph structure in combination with a Transformer-based Language Model and Graph Neural Networks to classify DDIs from biomedical literature. The KG is constructed to model the knowledge of the DDI Extraction 2013 benchmark dataset, without the inclusion of additional external information sources. Each drug pair is classified based on the context of the sentence it was found in, by utilizing transfer knowledge in the form of semantic representations from domain-adapted BioBERT weights that serve as the initial KG states. The proposed approach was evaluated on the DDI classification task of the same dataset and achieved a F1-score of 79.14% on the four positive classes, outperforming the current state-of-the-art approach.

Decoding Visual fMRI Stimuli from Human Brain Based on Graph Convolutional Neural Network

Brain decoding is to predict the external stimulus information from the collected brain response activities, and visual information is one of the most important sources of external stimulus information. Decoding functional magnetic resonance imaging (fMRI) based on visual stimulation is helpful in understanding the working mechanism of the brain visual function regions. Traditional brain decoding algorithms cannot accurately extract stimuli features from fMRI. To address these shortcomings, this paper proposed a brain decoding algorithm based on a graph convolution network (GCN). Firstly, 11 regions of interest (ROI) were selected according to the human brain visual function regions, which can avoid the noise interference of the non-visual regions of the human brain; then, a deep three-dimensional convolution neural network was specially designed to extract the features of these 11 regions; next, the GCN was used to extract the functional correlation features between the different human brain visual regions. Furthermore, to avoid the problem of gradient disappearance when there were too many layers of graph convolutional neural network, the residual connections were adopted in our algorithm, which helped to integrate different levels of features in order to improve the accuracy of the proposed GCN. The proposed algorithm was tested on the public dataset, and the recognition accuracy reached 98.67%. Compared with the other state-of-the-art algorithms, the proposed algorithm performed the best.

Managers' search practices at the front end of radical manufacturing technology innovations

Managers of manufacturing firms have important tasks in choosing novel technology solutions for the firm's production process. The emergence of ideas for radical manufacturing technology innovations and managers' proactive search for radical ideas and concepts for developing production processes have not been well understood. This study concentrates on managers' search practices at the front end of radical manufacturing technology innovations. We analyzed managers' practices in the early phase of nine radical manufacturing technology innovation projects across three firms. Radical manufacturing technology innovations require acknowledging both process innovations for the manufacturer and product innovations for the equipment supplier. The findings of this study revealed alternative patterns regarding the use of directed and autonomous search processes, internal and external information sources and open and closed supplier searches. This study offers new knowledge on the nature of the information processing task that managers face and on the search practices that managers use at the front end of radical manufacturing technology innovations. The study contributes by differentiating the managers' search practices based on the specific innovation scope in terms of the technology, equipment and production concept. Propositions are offered concerning the drivers and use of managers' search practices at the front end of radical manufacturing technology innovations.

An Open System Understanding of Product Innovation: Attention Allocation, External Information Sources, and Absorptive Capacity

Product innovation is a key achievement by which a firm can not only build up its competitive advantages but also prolong its survival in the open market environment. By understanding the firm as a social system pursuing both efficiency and effectiveness, this study examines three key factors that independently enable product innovation through a theoretical lens of the open system approach: strategic attention, external information sources, and absorptive capacity. While we suggest that a focused set of strategic attentions functions as an internal means to enhance the likelihood of product innovation, we also propose that diverse external partners provide the firm with the relevant knowledge for product innovation. In addition, we argue that the absorptive capacity is a crucial factor that has a positive direct effect on product innovation and mediates the effects of the two variables on the product innovation, based on the organizational learning theory. We empirically examined our hypotheses with the reliable data collected systematically through the “Korean Innovation Survey 2020: Manufacturing Industry” and found support for all of our hypotheses. Finally, we discuss theoretical contributions and practical implications.

COVID-19 Intensive Care-Evaluation of Public Information Sources and Current Standards of Care in German Intensive Care Units: A Cross Sectional Online Survey on Intensive Care Staff in Germany.

Backround: In February 2021, the first formal evidence and consensus-based (S3) guidelines for the inpatient treatment of patients with COVID-19 were published in Germany and have been updated twice during 2021. The aim of the present study is to re-evaluate the dissemination pathways and strategies for ICU staff (first evaluation in December 2020 when previous versions of consensus-based guidelines (S2k) were published) and question selected aspects of guideline adherence of standard care for patients with COVID-19 in the ICU. Methods: We conducted an anonymous online survey among German intensive care staff from 11 October 2021 to 11 November 2021. We distributed the survey via e-mail in intensive care facilities and requested redirection to additional intensive care staff (snowball sampling). Results: There was a difference between the professional groups in the number, selection and qualitative assessment of information sources about COVID-19. Standard operating procedures were most frequently used by all occupational groups and received a high quality rating. Physicians preferred sources for active information search (e.g., medical journals), while nurses predominantly used passive consumable sources (e.g., every-day media). Despite differences in usage behaviour, the sources were rated similarly in terms of the quality of the information on COVID-19. The trusted organizations have not changed over time. The use of guidelines was frequently stated and highly recommended. The majority of the participants reported guideline-compliant treatment. Nevertheless, there were certain variations in the use of medication as well as the criteria chosen for discontinuing non-invasive ventilation (NIV) compared to guideline recommendations. Conclusions: An adequate external source of information for nursing staff is lacking, the usual sources of physicians are only appropriate for the minority of nursing staff. The self-reported use of guidelines is high.

Diffusion approximations for periodically arriving expert opinions in a financial market with Gaussian drift

In this paper we study a financial market in which stock returns depend on an unobservable Gaussian drift process. Investors obtain information on that drift from return observations and discrete-time expert opinions as an external source of information. Estimates of the hidden drift process are based on filtering techniques. Our focus is the case of high-frequency experts periodically providing their views on the drift with variances growing linearly with the arrival frequency. The latter condition guarantees that the delivered information per time is limited. The asymptotic behavior of the filter as the arrival frequency tends to infinity is described by limit theorems. These state that the information obtained from observing the discrete-time expert opinions is asymptotically the same as that from observing a certain diffusion process. We apply these diffusion approximations of the filter for deriving simplified approximate solutions of utility maximization problems with logarithmic and power utility.

Survey Methods for Estimating the Size of Weak-Tie Personal Networks

Researchers increasingly use aggregate relational data to learn about the size and distribution of survey respondents’ weak-tie personal networks. Aggregate relational data are collected by asking questions about respondents’ connectedness to many different groups (e.g., “How many teachers do you know?”). This approach can be powerful, but to use aggregate relational data, researchers must locate external information about the size of each group from a census or administrative records (e.g., the number of teachers in the population). This need for external information makes aggregate relational data difficult or impossible to collect in many settings. Here, the authors show that relatively simple modifications can overcome this need for external data, significantly increasing the flexibility of the method and weakening key assumptions required by the associated estimators. The key idea is to estimate the size of these groups from the sample of survey respondents, rather than relying on external sources of information. These methods are appropriate for using a sample survey to study the size and distribution of weak-tie network connections. They can also be used as part of the network scale-up method to estimate the size of hidden populations. The authors illustrate this approach with two empirical studies: a large simulation study and original household survey data collected in Hanoi, Vietnam.

Textual Emotional Tone and Financial Crisis Identification in Chinese Companies: A Multi-Source Data Analysis Based on Machine Learning

Nowadays, China is faced with increasing downward pressure on its economy, along with an expanding business risk on listed companies in China. Listed companies, as the solid foundation of the national economy, once they face a financial crisis, will experience hazards from multiple perspectives. Therefore, the construction of an effective financial crisis early warning model can help listed companies predict, control and resolve their risks. Based on textual data, this paper proposes a web crawler and textual analysis, to assess the sentiment and tone of financial news texts and that of the management discussion and analysis (MD&A) section in annual financial reports of listed companies. The emotional tones of the two texts are used as external and internal information sources for listed companies, respectively, to measure whether they can improve the prediction accuracy of a financial crisis early warning model based on traditional financial indicators. By comparing the early warning effects of thirteen machine learning models, this paper finds that financial news, as external texts, can provide more incremental information for prediction models. In contrast, the emotional tone of MD&A, which can be easily modified by the management, will distort predictions. Comparing the early warning effect of machine learning models with different input feature variables, this paper also finds that DBGT, AdaBoost, random forest and Bagging models maintain stable and accurate sample recognition ability. This paper quantifies financial news texts, unraveling implied information hiding behind the surface, to further improve the accuracy of the financial crisis early warning model. Thus, it provides a new research perspective for related research in the field of financial crisis warnings for listed companies.

Hedging in the Swiss version of economic discourse

This article explores the linguo-pragmatic potential of hedging, which is a means of speech insurance. The purpose of the study is to identify and describe linguistically the means of hedging used by journalists in Swiss articles on economic issues, taking into account the peculiarities of the language situation in German-speaking Switzerland. An attempt is made to determine the pragmatic function of the identified hedging instruments. The materials of the study are fragments from economic articles of the major Swiss media containing various hedging markers. So far, no attempts have been made in Russian and foreign German studies to describe linguistically the means of hedging in Swiss economic discourse. The study identified lexical and grammatical hedging: Konjunktiv I as a marker of indirect speech; hypothetical constructions with Konjunktiv II; means of implementing ambiguity and approximation; impersonal constructions; concessive structures; lexical means of realization of subjective forecast; modal words. The analysis showed that most often the authors of news reports use Konjunktiv I as a marker of indirect speech, as well as hypothetical constructions with Konjunktiv II. The conducted linguo-pragmatic analysis revealed that the authors of journalistic texts use these linguistic means in order to minimize their responsibility for what is being reported, referring to an external source of information, to express doubt, non-categorical assumption about future events, to label uncertainty and shirk responsibility when in doubt about exact facts or figures. The use of hedging in journalism also contributes to the dedramatization of the situation, to the preservation of the reputation of companies and the prevention of critical reviews and the growth of panic on the part of readers. The data obtained as a result of the analysis can be used in linguistic analysis and interpretation of economic texts to identify the linguistic features of the Swiss economic discourse.

Using Background Knowledge from Preceding Studies for Building a Random Forest Prediction Model: A Plasmode Simulation Study

There is an increasing interest in machine learning (ML) algorithms for predicting patient outcomes, as these methods are designed to automatically discover complex data patterns. For example, the random forest (RF) algorithm is designed to identify relevant predictor variables out of a large set of candidates. In addition, researchers may also use external information for variable selection to improve model interpretability and variable selection accuracy, thereby prediction quality. However, it is unclear to which extent, if at all, RF and ML methods may benefit from external information. In this paper, we examine the usefulness of external information from prior variable selection studies that used traditional statistical modeling approaches such as the Lasso, or suboptimal methods such as univariate selection. We conducted a plasmode simulation study based on subsampling a data set from a pharmacoepidemiologic study with nearly 200,000 individuals, two binary outcomes and 1152 candidate predictor (mainly sparse binary) variables. When the scope of candidate predictors was reduced based on external knowledge RF models achieved better calibration, that is, better agreement of predictions and observed outcome rates. However, prediction quality measured by cross-entropy, AUROC or the Brier score did not improve. We recommend appraising the methodological quality of studies that serve as an external information source for future prediction model development.

CEO turnover and audit pricing: the role of media tone

PurposePrior research documents that chief executive officer (CEO) characteristics and succession planning affect audit fees. However, whether new CEOs’ media coverage influences audit fees remains unexplored. This study aims to fill this gap by examining whether auditors price media coverage of the new CEO.Design/methodology/approachThe sample comprises 89 US listed firms with CEO turnover over the period 2012–2016, resulting in a total of 445 firm-year observations. Panel data models are used in the analyses.FindingsThe results show that audit fees are higher for firms that hire a new CEO covered with more negative media tone. This study further documents that CEO media tone is determined independently of audit pricing, but that the extent of audit fees is positively related to a new CEO covered with more negative media tone, consistent with a sequential media-tone-then-audit-pricing process.Research limitations/implicationsThe results of this study should motivate future auditing research to consider the media as an important source of external information. The findings are also relevant to stakeholders who are interested in understanding the relationship between auditors and their clients’ CEOs.Originality/valueThis study contributes to the audit fee literature by providing new evidence that auditors view their clients’ CEO with a negative media tone as requiring greater audit effort and leading to higher risks, due to greater public and regulators’ attention conveyed in news coverage. Moreover, the finding of this study that audit fees are higher for firms that hire a new CEO covered with more negative media tone is novel, and extends Joe’s (2003) empirical finding that negative press coverage increases auditors’ perception of risk.

Networking for gender equitable climate-smart agriculture

ABSTRACT Climate change presents a grave and growing threat to the productivity and resilience of smallholder farming systems, impacts on which are already being felt in Pacific Island nations. Climate information plays a critical role in adaptation to these threats. Yet its accessibility and use may differ dramatically between groups of farmers, highlighting the need to understand the different paths of information exchange within communities. In this study, we examined weather and climate information sharing networks of both male and female farmers, and their perceived barriers to information access and use through a survey in three provinces in Papua New Guinea. The results provided insights about the most prominent actors as well as information sharing relationships, highlighting key gender differences. Women demonstrated stronger information connectivity with family and friends, capitalizing on close-knit networks. Conversely, the men’s network showed the potential to bridge between external information sources like media, and community leaders, church groups, and friends and family. To ensure equitable communication and use of information for climate adaptation, it is important to strike a balance between the strong bonding and open exchange aspects of the network, and consider the central sources and perceived hurdles affecting access and use of this information.

The links between active cooperation and eco‐innovation orientation of firms: A multi‐analysis study

AbstractThe environmental orientation of companies is key for firms to gain a competitive advantage against peers. However, the high level of novelty and uncertainty involved with eco‐innovations requires additional knowledge and capabilities that go beyond the firm and that can be achieved through cooperation. Thus, it is crucial to analyse how cooperation affects the elements that drive eco‐innovation. This study tests the impact of cooperation on the environmental orientation of companies while innovating using structural equation modelling with partial least squares and multigroup analysis and a fuzzy‐set qualitative comparative analysis for a sample of Spanish companies. Results suggest that companies that do not cooperate are less eco‐innovation‐oriented and show lower dependence on external information sources, although their impact on the orientation to product and process innovation is higher. This work leads to some theoretical conclusions and implications for researchers and practitioners.

Trade-offs between external knowledge sources for firm innovation in a developing country

AbstractThis paper studies the trade-off nature of the external knowledge-sourcing strategy of firms. Different combinations of external knowledge and information sources (KISs) on innovation performance are analyzed in the light of the existence of complementary and substitution effects between relational and codified sources. We assert that these effects are conditioned by the knowledge basis of the firm, which is associated with the firm’s relative cost of sourcing and internationalization. Our results shed new evidence on the specific features of sourcing knowledge for innovation in a less developed context, highlighting the critical role of codified sources and explaining the existence of the trade-offs that firms face in accessing and exploiting external KISs.

Identifying methodological challenges in the social risk assessment of cellulosic ethanol value chains

AbstractSocial risk assessment using databases allows sustainability practitioners’ rapid identification of social hotspots in value chains. The Soca database for sustainability assessment allows practitioners to perform a social risk assessment in addition to environmental assessment. As it is based on Ecoinvent, a database for environmental appraisal, the Soca database provides limited regionalization of data. This study aims to perform a regionalized assessment at country‐level by following the Soca approach in combination with EXIOBASE 3 as a source of regional information on the origin of inputs and for the estimation of worker hours. This analysis uses a case study based on the production of advanced biofuels in Croatia to assess the shortcomings of the methodology. We show that the assumptions made during the study can lead to unspecific results and that results need to be validated by external sources of information. Data availability plays a crucial role in achieving usable results. Social hotspots result from the combination of worker hours, raw material requirements, and social conditions in the country where production takes place. These three factors and the assumptions made need to be presented in a clear and transparent way to facilitate the interpretation of results.

Motion Shield: An Automatic Notifications System for Vehicular Communications.

Motion Shield is an automatic crash notification system that uses a mobile phone to generate automatic alerts related to the safety of a user when the user is boarding a means of transportation. The objective of Motion Shield is to improve road safety by considering a moving vehicle’s risk, estimating the probability of an emergency, and assessing the likelihood of an accident. The system, using multiple sources of external information, the mobile phone sensors’ readings, geolocated information, weather data, and historical evidence of traffic accidents, processes a plethora of parameters in order to predict the onset of an accident and act preventively. All the collected data are forwarded into a decision support system which dynamically calculates the mobility risk and driving behavior aspects in order to proactively send personalized notifications and alerts to the user and a public safety answering point (PSAP) (112).

References to Evidence-based Program Registry (EBPR) websites for behavioral health in U.S. state government statutes and regulations.

U.S. state governments have the responsibility to regulate and license behavioral healthcare interventions, such as for addiction and mental illness, with increasing emphasis on implementing evidence-based programs (EBPs). A serious obstacle to this is lack of clarity or agreement about what constitutes "evidence-based." The study's purpose was to determine the extent to which and in what contexts web-based Evidence-based Program Registries (EBPRs) are referenced in state government statutes and regulations ("mandates") concerning behavioral healthcare. Examples are: What Works Clearinghouse; National Register of Evidence-based Programs and Practices; Cochrane Database of Systematic Reviews. The study employed the Westlaw Legal Research Database to search for 30 known EBPR websites relevant to behavioral healthcare within the statutes and regulations of all 50 states. There was low prevalence of EBPR references in state statutes and regulations pertaining to behavioral healthcare; 20 states had a total of 33 mandates that referenced an EBPR. These mandates usually do not rely on an EBPR as the sole acceptable source for classifying a program or practice as "evidence-based." Instead, EBPRs were named in conjunction with internal state or external sources of information about putative program effectiveness, which may be less valid than EBPRs, to determine what is "evidence-based." Greater awareness of scientifically - based EBPRs and greater understanding of their advantages need to be fostered among state legislators and regulators charged with making policy to increase or improve the use of evidence-based programs and practices in behavioral healthcare in the U.S.

A Relative Coordinate-Based Topology Shaping Method for UAV Swarm with Low Computational Complexity

Functional topology shaping is crucial for unmanned aerial vehicles (UAVs) swarm applications, such as remote sensing, precision agriculture, and emergency wireless communication. However, the current research on topology shaping is mostly based on the assumption that the target positions of the nodes are known or have been pre-defined. Moreover, the computational complexity of existing shaping methods is still high. In this paper, a topology shaping method based on a relative coordinate system is proposed to solve the problem of UAV swarm topology shaping with no external source of localization information. Based on the relative coordinates of nodes and target topology shape of the swarm, the topology shaping is transformed into a problem of optimal coordinate mapping from initial relative coordinates to target relative coordinates of nodes with minimized global energy consumption. The Jonker–Volgenant algorithm is employed to solve the optimization problem. As verified by simulations, the proposed method can achieve UAV swarm topology shaping with no external localization information. Furthermore, simulation results show that the proposed method has an average reduction in computation time of 94% in the case of 1000 nodes compared with existing methods with the same level of global energy consumption.

Chinese environmental activism and the environmental politics of rumors

Rumors are an important yet poorly understood dynamic in environmental politics, particularly regarding their role in environmental activism. Drawing on theories of rumors, environmental information governance, and environmental activism, we analyzed the eco-politics of rumors in a Chinese village that experienced rumor-fueled protests against the construction and operation of a limestone quarry. We make three arguments regarding the condition, control, and content of rumors. First, poor environmental information governance, driven by a development-first and prejudice-rich official narrative and the strategic behaviors of blame and accountability avoidance, provides an important condition for rumor formulation and circulation. Second, the generation and circulation of rumors can be understood as a collective sense-making process driven by the informal communication between external and internal sources of information. The ephemeral nature of communication and group sanctions on government supporters make it difficult for authorities to control rumors. Third, by conceptualizing rumors as information warfare against official narratives, we show that rumors can foster a sense of collective urgency, reframe a land-right protest into an environmental protest that is politically more likely to succeed, and undermine public trust in local authorities. The findings suggest that rumors are an important part of environmental politics in China because of their role in environmental activism.

УЧЕТНО-АНАЛИТИЧЕСКОЕ ОБЕСПЕЧЕНИЕ И ИНФОРМАЦИОННАЯ БАЗА ФИНАНСОВОГО АНАЛИЗА ПРИРОДООХРАННЫХ ОРГАНИЗАЦИЙ

The article considers the essence of the system of information-analytical and accounting support of financial analysis of industrial and environmental activities of organizations for making managerial decisions. It is concluded that the basis of the financial analysis of the company’s production and environmental activities are internal and external sources of information. The author’s system of information-analytical and accounting support of financial analysis of an environmental enterprise, which includes five blocks, is presented. The author’s principles of information-analytical and accounting support of financial analysis of an environmental enterprise are highlighted.