Contribution and Relevance of Transfontanellar Ultrasound in Newborns in Ouagadougou: Multicentre Study on 1000 Cases

Objectives: To demonstrate the contribution and relevance of ETFs through the study of 1000 examination reports carried out in the medical imaging departments of the OUAGADOUGOU CHU. Material and method: Analytical descriptive study with retrospective collection, extended from 1st January 2020 to 1st January 2022. Results: Of the 1000 transfontanellar ultrasound reports, the mean age of patients was 7.61 +/ 7.5 days, with extremes of zero and 28 days. Sex was specified in 989 cases. Males accounted for 54.49% and females for 45.51%. 555 transfontanellar ultrasound were performed in 2020. 441 in 2021 and 4 in 2022. 61.9% of transfontanellar ultrasound were performed at the Bogodogo University Hospital, 205 at Charles de Gaulle and 176 at Tengandogo. Indications for transfontanellar ultrasound were dominated by neonatal distress (65.8%), followed by convulsions (10.2%) and prematurity (9.1%). Transfontanellar ultrasound was normal in 570 cases (57%) and abnormal in 430 cases (43%). Abnormalities were dominated by haemorrhage and ischaemic lesions in 66.28% (285) and 21.63% (93) of cases respectively. In the group of normal transfontanellar ultrasound, neonatal distress represented 59.65% of indications and prematurity 10.7% of indications. As for abnormal transfontanellar ultrasound, neonatal suffering accounted for 73.95% of indications and convulsions for 12.56%. The average age ofpatients with an abnormal transfontanellar ultrasound was 8.74 days +/ 7.89 days. The indication for investigations was relevant in 42.2% of cases and irrelevant in 57.8%;of the transfontanellar ultrasound with relevant indications, 0.71 were normal and 99.29 abnormal;of the transfontanellar ultrasound with irrelevant indications, the transfontanellar ultrasound was normal in 98.1% and abnormal in 1.9%. Conclusion: Transfontanellar ultrasound is an important part of ultrasound in current practice. Haemorrhage, anoxic-ischaemic lesions and hydrocephalus are the most frequent pathologies found by this technique in newborns. Whether or not the examinations were normal depended on the appropriateness of the prescription.

Application Analysis of Nursing Students'Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the help of IBM SPSS Modeler data mining software,this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core courses.Lastly,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.

Precise Multi-Class Classification of Brain Tumor via Optimization Based Relevance Vector Machine

The objective of this research is to examine the use of feature selection and classification methods for distinguishing different types of brain tumors.The brain tumor is characterized by an anomalous proliferation of brain cells that can either be benign or malignant.Most tumors are misdiagnosed due to the variabil-ity and complexity of lesions,which reduces the survival rate in patients.Diagno-sis of brain tumors via computer vision algorithms is a challenging task.Segmentation and classification of brain tumors are currently one of the most essential surgical and pharmaceutical procedures.Traditional brain tumor identi-fication techniques require manual segmentation or handcrafted feature extraction that is error-prone and time-consuming.Hence the proposed research work is mainly focused on medical image processing,which takes Magnetic Resonance Imaging(MRI)images as input and performs preprocessing,segmentation,fea-ture extraction,feature selection,similarity measurement,and classification steps for identifying brain tumors.Initially,the medianfilter is practically applied to the input image to reduce the noise.The graph-cut segmentation technique is used to segment the tumor region.The texture feature is extracted from the output of the segmented image.The extracted feature is selected by using the Ant Colony Opti-mization(ACO)algorithm to improve the performance of the classifier.This prob-abilistic approach is used to solve computing issues.The Euclidean distance is used to calculate the degree of similarity for each extracted feature.The selected feature value is given to the Relevance Vector Machine(RVM)which is a multi-class classification technique.Finally,the tumor is classified as abnormal or nor-mal.The experimental result reveals that the proposed RVM technique gives a better accuracy range of 98.87%when compared to the traditional Support Vector Machine(SVM)technique.

A Study on Relevance Reconstruction in Xu Yuanchong’s English Renditions of Ancient Chinese Poetry From the Perspective of Relevance Theory

On the pragmatic view,the proper use of pragmatic strategies can help to improve the quality of translation.Sometimes by searching for new relevance,the translator can address to the problem of cultural differences and even translation acceptability.In this paper,the author aims to explore relevance reconstruction in Xu Yuanchong’s English renditions of ancient Chinese poetry from the perspective of Relevance Theory to examine the importance of relevance reconstruction while achieving optimal relevance.The author hopes to give reference to the research about the translation of ancient Chinese poetry.

To Split or to Unify?-A Comparative Study on Functional Equivalence Translation Theory and Relevance Translation Theory

Functional equivalence,a focus in the translation studies,has been bombarded with numerous criticisms.Meanwhile,relevance translation theory,proposed by Ernst-August Gutt,offers a united theoretical framework for translation studies.The development of the translation theories does not rely on the appearance of a brand-new theory,but on the successful connection among various theories.To split or to unify,it is a question and it will direct the further development of translation studies.Analyzing the similarities and differences between these two theories,the author are striving for a unity of them and striking a better way to approach translation studies.

A Theoretic Study on the Translation Model from Relevance Theory

Relevance Theory holds that the language communication of human beings is an ostensive-inferential process of searching for relevance.Translation as a communication across language and culture,involves a dual process of communication among the source language author,the translator and the target language readers,during which the translator infers the communicative intention of the source language author,then by assessing the cognitive environments of the target language readers,makes the optimally relevant translation so that the translation can be equivalent in relevant aspects.

Study on C-E Translation of Advertisements Based on Relevance Theory

The main errors in translating Chinese advertisements into English are pragmatic failures in which target-language readers fail to find the optimal relevance between the target text and source text.The translation strategies proposed under the guidance of Relevance Theory can effectively help translators find out optimal relevance in the process of translating Chinese advertisements into English to avoid pragmatic failures.

Relevance theory: the Cognitive Pragmatic Foundation of Code-switching

The paper will discuss the process of code-switching and its cognitive pragmatic motivation from the point of relevance.And code-switching is also regarded as a kind of communicative strategy.The process of the production of code-switching is also the cooperation and mutual constrain of communicator’s cognitive environment and ability.Cognitive effect can be obtained through communicator’s processing cognitive environment with their cognitive ability.In this process,the cooperation of cognitive ability and cognitive environment gives a guarantee to successful communication with code-switching.

Relevance Theory and Advanced Vocational College English Listening Comprehension

This thesis draws on Sperber and Wilson’s principle of relevance to expound listening comprehension is an ostensiveinferential process based on the cognitive context in searching for optimal relevance,thus to explore a range of effective suggestions and new thoughts.

The Relevance between the Ego and the Other——a Theoretical Account of Relevance in Translation

This paper focuses on the notion of relevance in translation,aiming to clarify the cognitive motivation of translation,which can serve as an account of relevance in translation.

Analysis of Iconicity Base on Relevance Theory

Iconicity as one of the very important research items of cognitive linguistics refers to the natural, non-arbitrary rela?tionship between form and meaning. The study of iconicity has great significance of revealing the relationship between concept and cognition and can help people better understand the essence of language. This paper presents an analysis of the three iconicity principles- distance iconicity,quantity iconicity and sequencing iconicity-base on the relevance theory. The purpose of this pa?per is to offer a new dimension to iconicity analysis and finding more connections between the form of language and real mean?ing.

A Study on Translation of Chinese Culture-loaded Words from the Per-spective of Relevance Theory

The essay tends to analyze the translation of Chinese Culture-loaded words from the perspective of Relevance Theory.The theory gains its prominence by studying the translation process and transcending the conflicts between literal and free translation. It incorporates recent work in cognitive linguistics, with ostensive-inference as its key model. Under the influence of Relevance theory, the translation of culture-loaded words is reader-oriented. Translators are obliged to help target readers to establish new assumptions to achieve equivalent response.

Seismic liquefaction potential assessment by using relevance vector machine

Determining the liquefaction potential of soil is important in earthquake engineering. This study proposes the use of the Relevance Vector Machine （RVM） to determine the liquefaction potential of soil by using actual cone penetration test （CPT） data. RVM is based on a Bayesian formulation of a linear model with an appropriate prior that results in a sparse representation. The results are compared with a widely used artificial neural network （ANN） model. Overall, the RVM shows good performance and is proven to be more accurate than the ANN model. It also provides probabilistic output. The model provides a viable tool for earthquake engineers to assess seismic conditions for sites that are susceptible to liquefaction.

Relevance vector machine technique for the inverse scattering problem

A novel method based on the relevance vector machine（RVM） for the inverse scattering problem is presented in this paper.The nonlinearity and the ill-posedness inherent in this problem are simultaneously considered.The nonlinearity is embodied in the relation between the scattered field and the target property,which can be obtained through the RVM training process.Besides,rather than utilizing regularization,the ill-posed nature of the inversion is naturally accounted for because the RVM can produce a probabilistic output.Simulation results reveal that the proposed RVM-based approach can provide comparative performances in terms of accuracy,convergence,robustness,generalization,and improved performance in terms of sparse property in comparison with the support vector machine（SVM） based approach.

Ketamine enhances structural plasticity in human dopaminergic neurons:possible relevance for treatment-resistant depression

Depression refers to a series of mental health issues characterized by loss of interest and enjoyment in everyday life,low mood and selected emotional,cognitive,physical and behavioral symptoms.Depression is a common disorder,affecting 5–15%of the general population.When diagnosed as major depressive disorder（MDD）,patients are currentlytreated with pharmacological agents such as serotonin or noradren- aline uptake inhibitors （SSRI or SNRI） or tricyclics.

Relevance of early intervention in Fahr's disease: understanding through a case study

SUMMARY Intracranial calcification may result from disturbances in calcium metabolism. It often remains asymptomatic, but may present with symptoms like seizure and neurological deficits. Correction of the underlying metabolic disturbance before damage of neuronal tissues due to intracranial calcification may be useful in preventing irreversible neurological deficits. This window period may be the crucial period that needs a thorough clinical evaluation and urgent intervention. We highlight the case of an adult woman with Fahr's disease presented with generalised tonic-clonic seizure. The management priorities were also discussed along with review of literature.

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

Role of mitochondria in regulating micro RNA activity and its relevance to the central nervous system

Mitochondria serve as the powerhouse of cells,respond to cellular demands and stressors,and play an essential role in cell signaling,differentiation,and survival.Aberrant mitochondria function has been linked to diverse and complex human diseases such as neurodegenerative diseases,cancers,myopathies,premature aging,and metabolic syndromes（Nunnari and Suomalainen,2012）.