Unknown Information Research Articles

Application of Intelligent Archives Management Based on Data Mining in Hospital Archives Management

Data mining belongs to knowledge discovery, which is the process of revealing implicit, unknown, and valuable information from a large amount of fuzzy application data. The potential information revealed by data mining can help decision makers adjust market strategies and reduce market risks. The information excavated must be real and not universally known, and it can be the discovery of a specific problem. Data mining algorithms mainly include the neural network method, decision tree method, genetic algorithm, rough set method, fuzzy set method, association rule method, and so on. Archives management, also known as archive work, is the general term for various business works, in which archives directly manage archive entities and archive information and provide utilization services. It is also the most basic part of national archives. Hospital archives are an important part of hospital management, and hospital archives are the accumulation of work experience and one of the important elements for building a modern hospital. Hospital archives are documents, work records, charts, audio recordings, videos, photos, and other types of documents, audio-visual materials, and physical materials, such as certificates, trophies, and medals obtained by hospitals, departments, and individuals. The purpose of this paper is to study the application of intelligent archives management based on data mining in hospital archives management, expecting to use the existing data mining technology to improve the current hospital archives management. This paper investigates the age and educational background of hospital archives management workers and explores the relationship between them and the quality of archives management. Based on the decision number algorithm, on the basis of the database, the hospital data is classified and analyzed, and the hospital file data is classified and processed through the decision number algorithm to improve the system data processing capability. The experimental results of this paper show that among the staff working in the archives management department of the hospital, 20-to-30-year-olds account for 46.2% of the total group. According to the data, the staff in the archives management department of the hospital also tends to be younger. Among the staff under the age of 30, the file pass rate was 98.3% and the failure rate was 1.7%. Among the staff over 50 years old, the file pass rate was 99.9% and the failure rate was 0.1%. According to the data, the job is related to the experience of the employee.

Implementation of quantum compression on IBM quantum computers

Advances in development of quantum computing processors brought ample opportunities to test the performance of various quantum algorithms with practical implementations. In this paper we report on implementations of quantum compression algorithm that can efficiently compress unknown quantum information. We restricted ourselves to compression of three pure qubits into two qubits, as the complexity of even such a simple implementation is barely within the reach of today’s quantum processors. We implemented the algorithm on IBM quantum processors with two different topological layouts—a fully connected triangle processor and a partially connected line processor. It turns out that the incomplete connectivity of the line processor affects the performance only minimally. On the other hand, it turns out that the transpilation, i.e. compilation of the circuit into gates physically available to the quantum processor, crucially influences the result. We also have seen that the compression followed by immediate decompression is, even for such a simple case, on the edge or even beyond the capabilities of currently available quantum processors.

The rich-get-richer effect: Prior knowledge predicts new learning of domain-relevant information.

When learning new information, students' prior knowledge related to that information will often vary. Prior research has not systematically explored how prior knowledge relates to learning of new, previously unknown information. Accordingly, the goal of the present research was to explore this relationship. In three experiments, students first completed a prior knowledge test over two domains (football and cooking) and then learned new information from these domains by answering questions and receiving feedback. Students also made a judgment of learning for each. To ensure that the learning was new (i.e., previously unknown) for all students, the to-be-learned information was false. Last, students completed a final test over the same questions from the learning phase. Prior knowledge in each domain was positively related to new learning for items from that domain but not from the other domain. Thus, the relationship between prior knowledge and new learning was domain specific, which we refer to as the rich-get-richer effect. Prior knowledge was also positively related to the magnitude of judgments of learning. In Experiment 3, to explore a potential reason why prior knowledge is related to new learning, students rated their curiosity in learning each item prior to receiving feedback. Critically, students' curiosity judgments mediated the relationship between prior knowledge and new learning. These outcomes suggest that for high-knowledge learners, curiosity may be related to attention-based mechanisms that increase the effectiveness of encoding during feedback. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

An Effective Algorithm to Improve the Accuracy of Recommender System based on Comments using Classification Techniques in Data Mining

With the development of information systems, data has become one of the most important sources of organizations. Therefore, methods and techniques are needed to efficiently access data, share data, extract data from data, and use this information. By creating and expanding the Web and a significant increase in the volume of information and web development, the need for methods and techniques that can provide data efficiently and extract information from them is felt more than ever. Web mining is one of the areas of research that uses data mining techniques to automatically discover information from web services and documents. In fact, Web mining is a process of discovery of unknown and useful information from web data. Web mining methods are categorized into three types of web content exploration, exploration of Web structures, and exploration of the use of the Web, based on what type of data they are exploring. This research investigates the relationship between the idea of mining and other research fields and examines some of the previous methods used. Finally, a method is proposed based on two decision tree and machine model algorithms that will improve the results of the idea of mining. The results of the simulation of the proposed method were evaluated and compared with the previous methods. The results show that the proposed method has higher accuracy and speed

An extended MABAC method based on prospect theory with unknown weight information under Fermatean fuzzy environment for risk investment assessment in B&R.

The assessment of investment risk for the countries along the route in Belt and Road (B&R) can be deemed as a multiple criteria group decision making (MCGDM) issue since multiple investment options based on diverse criterions are assessment by experts. Pondering that the complexity and uncertainty of the assessment setting and the cognition fuzziness and psychological behavior of experts bring challenges to risk assessment, this paper designed an integrated MCGDM risk investment evaluation framework by synthesizing MABAC method and prospect theory under Fermatean fuzzy setting. Firstly, a Fermatean fuzzy interactive distance measure is presented to ascertain the weight of evaluation experts and criterions. Next, some Fermatean fuzzy Frank aggregation operators based upon the proposed Frank operations are developed to fuse Fermatean fuzzy information efficiently. In addition, an innovative evaluation framework for risk investment is designed based on improved prospect theory MABAC and CRITIC approaches. Conclusively, an empirical concerning risk investment issues in B&R is employed to confirm the applicability and feasibility of the constructed evaluation framework, involving the simulation experiments on sensitivity analysis and contrast studies. The assessment information provided by investors using the linguistic assessment terms based upon their cognition ability of them. These outcomes obtained by the propounded method and comparison analysis further emphasize the validity and salient merits of the propounded framework and provide several auxiliary suggestions for investors.

Insights into microbial diversity on plastisphere by multi-omics.

Plastic pollution is a major concern in marine environment as it takes many years to degrade and is one of the greatest threats to marine life. Plastic surface, referred to as plastisphere, provides habitat for growth and proliferation of various microorganisms. The discovery of these microbes is necessary to identify significant genes, enzymes and bioactive compounds that could help in bioremediation and other commercial applications. Conventional culture techniques have been successful in identifying few microbes from these habitats, leaving majority of them yet to be explored. As such, to recognize the vivid genetic diversity of microbes residing in plastisphere, their structure and corresponding ecological roles within the ecosystem, an emerging technique, called metagenomics has been explored. The technique is expected to provide hitherto unknown information on microbes from the plastisphere. Metagenomics along with next generation sequencing provides comprehensive knowledge on microbes residing in plastisphere that identifies novel microbes for plastic bioremediation, bioactive compounds and other potential benefits. The following review summarizes the efficiency of metagenomics and next generation sequencing technology over conventionally used methods for culturing microbes. It attempts to illustrate the workflow mechanism of metagenomics to elucidate diverse microbial profiles. Further, importance of integrated multi-omics techniques has been highlighted in discovering microbial ecology residing on plastisphere for wider applications.

Matrix reasoning and intelligent acquisition-application of unknown information

Matrix reasoning and intelligent acquisition-application of unknown information

Table detection in business document images by message passing networks

Tabular structures in business documents offer a complementary dimension to the raw textual data. For instance, there is information about the relationships among pieces of information. Nowadays, digital mailroom applications have become a key service for workflow automation. Therefore, the detection and interpretation of tables is crucial. With the recent advances in information extraction, table detection and recognition has gained interest in document image analysis, in particular, with the absence of rule lines and unknown information about rows and columns. However, business documents usually contain sensitive contents limiting the amount of public benchmarking datasets. In this paper, we propose a graph-based approach for detecting tables in document images which do not require the raw content of the document. Hence, the sensitive content can be previously removed and, instead of using the raw image or textual content, we propose a purely structural approach to keep sensitive data anonymous. Our framework uses graph neural networks (GNNs) to describe the local repetitive structures that constitute a table. In particular, our main application domain are business documents. We have carefully validated our approach in two invoice datasets and a modern document benchmark. Our experiments demonstrate that tables can be detected by purely structural approaches.

Consistent fusion method with uncertainty elimination for distributed multi-sensor systems

In this study, the elimination of correlated errors with an unknown correlation in distributed fusion is investigated, and a consistent fusion method for distributed multi-sensor systems is proposed. Unlike most existing fusion methods, the proposed method guarantees the consistency of fusion results without requiring system model parameters or adopting conservative strategies. First, a universal bijection is used to quantify the uncertainty in the estimates to be fused based on the entropy of the independent scalars. Second, the correlated errors caused by unknown mutual information and common process noise are treated as avoidable uncertainties. The avoidable uncertainty is then estimated by using a similarity function based on the Kullback–Leibler divergence. Finally, the avoidable uncertainty is separated from the fusion results by employing a conditional probability model to avoid correlated errors. This method is proven to be unbiased, consistent, and more accurate than the well-known covariance intersection method and the inverse covariance intersection method. The simulation results further verify the superiority of the proposed method in terms of the consistency, accuracy, and ability to limit cumulative errors in sequential fusion processes.

Online education satisfaction assessment based on cloud model and fuzzy TOPSIS

During the COVID-19, colleges organized online education on a massive scale. To make better use of online education in the post-epidemic era, this paper conducts an online education satisfaction survey with four types of colleges and 129,325 students propose a fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method based on the cloud model to rank the satisfaction of different colleges. Firstly, based on the characteristics of online education during the COVID-19, we build an evaluation indicator system from four dimensions: technology, instructor, learner and environment including, 10 indicators and 94 sub-indicators. Secondly, the cloud model is used to quantitatively describe the natural language and uncertainty in a large amount of assessment information. The cloud model generator is used for sub-indicators and achieves an effective and flexible conversion between linguistic information and quantitative values. The cloud model of indicators are presented by integrating the corresponding sub-indicators. The weights of indicators are determined by the entropy method based on the cloud model and possibility degree matrix, which eliminates the judgment of decision-makers and has great power for handling practical problems with unknown weight information. Finally, a fuzzy TOPSIS method based on the cloud model is proposed to rank the satisfaction of online education of different colleges. The proposed method is compared with other existing methods to shown its merits. The experimental result is consistent with the proportion of students who accept online education in the post-epidemic era. According to the second questionnaire, as the qualitative evaluation of the cloud model of indicators increases, the qualitative evaluation of satisfaction of different types of colleges will also increase. It indicates that the method proposed in this paper is practical.

An extended WASPAS approach for teaching quality evaluation based on pythagorean fuzzy reducible weighted Maclaurin symmetric mean

Teaching quality evaluation (TQE) can not only improve teachers’ teaching skills, but also provide an important reference for school teaching management departments to formulate teaching reform measures and strengthen teaching management. TQE is a process of grading and ranking a given teachers based on the comprehensive consideration of multiple evaluation criteria by expert. The Maclaurin symmetric mean (MSM), as a powerful aggregation function, can capture the correlation among multiple input data more efficient. Although multitude weighted MSM operators have been developed to handle the Pythagorean fuzzy decision issues, these above operators do not possess the idempotency and reducibility during the procedure of information fusion. To conquer these defects, we present the Pythagorean fuzzy reducible weighted MSM (PFRWMSM) operator and Pythagorean fuzzy reducible weighted geometric MSM (PFRWGMSM) operator to fuse Pythagorean fuzzy assessment information. Meanwhile, several worthwhile properties and especial cases of the developed operators are explored at length. Afterwards, we develop a novel Pythagorean fuzzy entropy based upon knowledge measure to ascertain the weights of attribute. Furthermore, an extended weighted aggregated sum product assessment (WASPAS) method is developed by combining the PFRWMSM operator, PFRWGMSM operator and entropy to settle the decision problems of unknown weight information. The efficiency of the proffered method is demonstrated by a teaching quality evaluation issue, as well as the discussion of sensitivity analysis for decision outcomes. Consequently, a comparative study of the presented method with the extant Pythagorean fuzzy approaches is conducted to display the superiority of the propounded approach.

Energy drink consumption: a rising public health issue.

Energy drink (ED) consumption has become a growing public health issue over the past few decades. Despite claims of being safe and beneficial, EDs have been linked to particularly fatal outcomes associated with the cardiovascular system which include atrial and ventricular arrhythmias, myocardial infarctions, cardiomyopathies, and sudden cardiac death. Large quantities of caffeine, taurine, sugars, and B-vitamins may be contributing to these outcomes by increasing the heart rate, blood pressure (BP), and contractility of the heart in addition to prolonging the QTc. There is still a substantial amount of unknown information on EDs that warrants more research and a dire need for age regulations, transparency of ingredients, clear labeling of adverse effects, and most importantly, education of consumers.

Channelling the Unknown: Noise in Art Ecosystems

At both the individual and societal levels, we are entangled within environmental, social, and technological systems that shape our material and emotional states. Contemporary art needs to integrate and challenge the information circulating within these interacting systems to address our increasingly complex lifeworld. This systemic understanding emerged in the 1960s as part of a broader growth in relational thinking within the natural and social sciences, which extended the conceptual boundaries of the artwork. The ecosystem, a model originally developed within ecology, is an example of a systems model as it describes the flow of matter, energy, and information through the physical world. This model has evolved into a powerful analogical tool to describe contemporary culture’s entanglement with nature and technology. The ecosystem model is invoked here to describe how information flows through the artwork. The paper suggests that art is a vital form of communication as it can channel noise or unknown information. This channelling is demonstrated with the artwork, The Creation Myth (1998), by Jason Rhoades. This work anticipated the convergence of natural and technological systems, and it demonstrates the ability of the arts to channel unknown messages or noise, thereby disrupting the dominant signals of contemporary culture.

Avoidance of operational sampling errors in drinking water analysis

AbstractThe internal audits carried out in the first half of 2019 in water laboratories as part of quality accreditation in accordance with ISO/IEC 17025:2017 showed a high frequency of adverse events in connection with sampling. These faults can be a consequence of a wide range of causes, and in some cases, the information about them can be insufficient or unclear. Considering that sampling has a major influence on the quality of the analytical results provided by water laboratories, this work presents a system for reporting and learning adverse events. Its aim is to record nonconformities, errors, and adverse events, making possible automatic data analysis aiming to ensure continuous improvement in operational sampling. The system is based on the Eindhoven Classification Model and enables automatic data analysis and reporting to identify the main causes of failure. Logic programming is used to represent knowledge and support the reasoning mechanisms to model the universe of discourse in scenarios of incomplete, contradicting, or even unknown information. In addition to suggesting solutions to the problem, the system provides formal evidence of the solutions presented, which will help to continuously improve drinking water quality and promote public health.

EXPLORING DYNAMIC NONLINEAR INFORMATION SYSTEM WITH SATURATION CONSTRAINT AND UNKNOWN INFORMATION

The dynamic nonlinear system with saturation constraints and unknown information is studied to design a better control system. The controller of the general n-order affine nonlinear system is constructed utilizing the backstepping method and the improved radial basis function (RBF) that can approximate the unknown parameters. Then, the appropriate Lyapunov function is selected. The mature stability judgment theorem can prove that the designed system is semi-globally uniform and ultimately bounded. Finally, the controller is added to the system for the experiment on MATLAB. The experimental results show that the controller is reliable and the system output can approach the ideal value through the limited range of control signal. During the simulation, the reference value can be quickly tracked by the system output, and the tracking error is small only within 0.04. The proposed method can provide a reference for the design of control systems in practical engineering.

A q-rung orthopair hesitant fuzzy stochastic method based on regret theory with unknown weight information

A q-rung orthopair hesitant fuzzy stochastic method based on regret theory with unknown weight information

Application of a High-Throughput Targeted Sequence AmpliSeq Procedure to Assess the Presence and Variants of Virulence Genes in Salmonella.

We have developed a targeted, amplicon-based next-generation sequencing method to detect and analyze 227 virulence genes (VG) of Salmonella (AmpliSeqSalm_227VG) for assessing the pathogenicity potential of Salmonella. The procedure was developed using 80 reference genomes representing 75 epidemiologically-relevant serovars associated with human salmonellosis. We applied the AmpliSeqSalm_227VG assay to (a) 35 previously characterized field strains of Salmonella consisting of serovars commonly incriminated in foodborne illnesses and (b) 34 Salmonella strains with undisclosed serological or virulence attributes, and were able to divide Salmonella VGs into two groups: core VGs and variable VGs. The commonest serovars causing foodborne illnesses such as Enteritidis, Typhimurium, Heidelberg and Newport had a high number of VGs (217–227). In contrast, serovars of subspecies not commonly associated with human illnesses, such as houtenae, arizonae and salame, tended to have fewer VGs (177–195). Variable VGs were not only infrequent but, when present, displayed considerable sequence variation: safC, sseL, sseD, sseE, ssaK and stdB showed the highest variation and were linked to strain pathogenicity. In a chicken infection model, VGs belonging to rfb and sse operons showed differences and were linked with pathogenicity. The high-throughput, targeted NGS-based AmpliSeqSalm_227VG procedure provided previously unknown information about variation in select virulence genes that can now be applied to a much larger population of Salmonella for evaluating pathogenicity of various serovars of Salmonella and for risk assessment of foodborne salmonellosis.

Extending Set Measures to Orthopair Fuzzy Sets

Yager have proposed the extending set measures to Pythagorean fuzzy sets, which is able to efficiently solve the problems of uncertain information representation in Pythagorean fuzzy environment. However, the Pythagorean fuzzy sets represent a limited range of fields, while the q-rung orthopair fuzzy sets can represent many fuzzy sets, including the Pythagorean fuzzy sets, Fermatean fuzzy sets, and intuitionistic fuzzy sets. In order to extend the extending set measures to Pythagorean fuzzy sets to a broader range, the paper proposes the extending set measures to orthopair fuzzy sets, which can extend the extending set measures to Pythagorean fuzzy sets to q-rung orthopair fuzzy environment. The extending set measures to orthopair fuzzy sets combined with level sets, measure, principle extension and Choquet Integral, which can greatly extend the ability of representing unknown information and processing unknown information. If the q-rung orthopair fuzzy sets degenerate into to Pythagorean fuzzy sets, then the extending set measures to orthopair fuzzy sets will be generated as the extending set measures to Pythagorean fuzzy sets. Numerical examples are designed to prove the effectiveness of the proposed models and the experimental results demonstrate that the proposed method can extend the extending set measures to Pythagorean fuzzy sets to q-rung orthopair fuzzy environment successfully and solve issues of decision making under q-rung orthopair fuzzy environment effectively.

Gaining access to the unknown: Preschoolers privilege unknown information as the target of their questions about verbs

Children are opportunistic word learners, making passive use of nearly any available cue to link labels and referents. However, children may also actively drive their word learning by inquiring about unknown labels. Until recently, research has largely overlooked active word learning mechanisms. In the current study, two experiments investigated the emergence of preschoolers’ ability to tailor their questions about words and definitions to maximize information gains. Experiment 1 tested whether 3- and 5-year-olds’ frequency of questionasking differed for known and unknown verbs in a referential communication task. Results revealed that 3- and 5-year-olds (N = 36) asked more questions about unknown verbs (M = 3.86) than about known verbs (M = 0.22, p < .001), but this tendency was greater for 5-year-olds (M = 6.11) than for 3-year-olds (M = 1.35, p < .001), suggesting a developmental difference in information-seeking proficiency. Experiment 2 tested whether 3- and 5-year-olds’ frequency of question asking about unknown verbs differed when words were embedded in definitions of high- and low-informative quality. Results demonstrated that 3- and 5-year-olds (N = 42) asked questions about unknown verbs more when provided with uninformative definitions (M = 0.86) compared with informative definitions (M = 0.05, p = .028), suggesting a sensitivity to definition quality that drives preschoolers’ information search for word meanings. These findings offer insight into children’s information seeking during exposure to new words. Results advance the characterization of children’s active verbal information seeking in shaping their word learning.

Baserer de såkaldte Bartholinannaler sig på danske middelalderlige årbøger?

Svend Clausen: Are the Annales Bartholiniani based on Danish medievalannals?&#x0D; The article discusses the so-called Annales Bartholiniani, written by the Danish scholarThomas Bartholin (1659‑1690). Analysis of a series of events that are clearly dated incorrectlyin the Annales Bartholiniani and a comparison of the dating of these eventswith the content of Danish medieval annals suggest that the inaccurate dating of suchevents in the Annales Bartholiniani must go back to the Danish medieval annalistictraditions. This proves that a great deal of the content of the Annales Bartholinianimust go back either directly or indirectly to the content of Danish medieval annals. Italso points to the fact that there needs to be more discussion of the Annales Bartholinianiin future studies, as they also contain a great deal of information not availableelsewhere, and the close link between them and the content of Danish medieval annalsmakes it much more likely that Bartholin may have used also some Danish medievalannals now lost in addition to the medieval annals still known to us. In this respect,such an identified close link between the Annales Bartholiniani and the known contentof the Danish medieval annalistic traditions may help significantly in explaining alsosome of the otherwise unknown information present in the Annales Bartholiniani.Discussions of the content for years such as 1087 and 1142, for example, suggest thatspecific details such as these may go back directly or indirectly to a now lost medievalDanish annalistic tradition.