The role of multimodality imaging in calcified valves with infective endocarditis

A 68-years old woman presented with ahistory of recurrent fever of 38-39°ac-companied by chills and weakness over the past month.Her physical examination was unremarkable except for an audible 3/6 ejection murmur at the 2nd right intercostal space.Her vital signs were normal with no fever at presentation.Laboratory tests showed elevated white blood count of 11,800cells/mm3 with a remarkable neutrophilia and elevated C-reactive protein of 14 mg/dL.Blood glucose,renal and liver function tests were all normal.

Sparse Modal Decomposition Method Addressing Underdetermined Vortex-Induced Vibration Reconstruction Problem for Marine Risers

When investigating the vortex-induced vibration(VIV)of marine risers,extrapolating the dynamic response on the entire length based on limited sensor measurements is a crucial step in both laboratory experiments and fatigue monitoring of real risers.The problem is conventionally solved using the modal decomposition method,based on the principle that the response can be approximated by a weighted sum of limited vibration modes.However,the method is not valid when the problem is underdetermined,i.e.,the number of unknown mode weights is more than the number of known measurements.This study proposed a sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit(Co Sa MP)algorithm,exploiting the sparsity of VIV in the modal space.In the validation study based on high-order VIV experiment data,the proposed method successfully reconstructed the response using only seven acceleration measurements when the conventional methods failed.A primary advantage of the proposed method is that it offers a completely data-driven approach for the underdetermined VIV reconstruction problem,which is more favorable than existing model-dependent solutions for many practical applications such as riser structural health monitoring.

Mining Fine-Grain Face Forgery Cues with Fusion Modality

Face forgery detection is drawing ever-increasing attention in the academic community owing to security concerns.Despite the considerable progress in existing methods,we note that:Previous works overlooked finegrain forgery cues with high transferability.Such cues positively impact the model’s accuracy and generalizability.Moreover,single-modality often causes overfitting of the model,and Red-Green-Blue(RGB)modal-only is not conducive to extracting the more detailed forgery traces.We propose a novel framework for fine-grain forgery cues mining with fusion modality to cope with these issues.First,we propose two functional modules to reveal and locate the deeper forged features.Our method locates deeper forgery cues through a dual-modality progressive fusion module and a noise adaptive enhancement module,which can excavate the association between dualmodal space and channels and enhance the learning of subtle noise features.A sensitive patch branch is introduced on this foundation to enhance the mining of subtle forgery traces under fusion modality.The experimental results demonstrate that our proposed framework can desirably explore the differences between authentic and forged images with supervised learning.Comprehensive evaluations of several mainstream datasets show that our method outperforms the state-of-the-art detection methods with remarkable detection ability and generalizability.

Exploration and Practice of Database System Course Based on KAPI Teaching Modality Under the Emerging Engineering Education

Nowadays,it is extremely urgent for the software engineering education to cultivate the knowledge and ability of database talents in the era of big data.To this end,this paper proposes a talent training teaching modality that integrates knowledge,ability,practice,and innovation(KAPI)for Database System Course.The teaching modality contains three parts:top-level design,course learning process,and course assurance and evaluation.The top-level design sorts out the core knowledge of the course and determines a mixed online and offline teaching platform.The course learning process emphasizes the correspondence transformation relationship between core knowledge points and ability enhancement,and the course is practiced in the form of experimental projects to finally enhance students’innovation consciousness and ability.The assurance and evaluation of the course are based on the outcome-based education(OBE)orientation,which realizes the objective evaluation of students’learning process and final performance.The teaching results of the course in the past 2 years show that the KAPI-based teaching modality has achieved better results.Meanwhile,students are satisfied with the evaluation of the modality.The teaching modality in this paper helps to stimulate students’initiatives,and improve their knowledge vision and practical ability,and thus helps to cultivate innovative and high-quality engineering talents required by the emerging engineering education.

Utilization of biomarkers for the prognostic prediction of cardiac arrest survivors using a multi-modal approach

International guidelines for post-cardiac arrest care recommend using multi-modal strategies to avoid the withdrawal of life-sustaining therapy(WLST)in patients with the potential for neurological recovery.[1]However,a clear methodology for multi-modal approaches has yet to be developed.Neuron-specific enolase(NSE)is currently the only recommended biomarker,and the European Resuscitation Council(ERC)and the European SocietyofIntensiveCareMedicine(ESICM)have proposed a cutoff value of 60μg/L at 48 and/or 72 h after the return of spontaneous circulation(ROSC)as a multimodal prognostic tool for predicting poor neurological outcomes.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

Generalized load graphical forecasting method based on modal decomposition

In a“low-carbon”context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional“pure load”to the generalized load with the dual characteristics of“load+power supply.”Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the structure.Therefore,it is particularly significant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientific basis for structural damage identification and dynamic model modification.In view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and briefly describes how to identify structural damages based on the derived modal parameters.The paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),briefly describes damage identification methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artificial intelligence(AI)methods in the direction of modal parameter recognition and damage identification is further discussed.Based on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identification methods are given to provide scientific references for the optimized design and functional upgrading of SHM systems.

Modal Frequency Prediction of Chladni Patterns Using Machine Learning

The introduction of machine learning (ML) in the research domain is a new era technique. The machine learning algorithm is developed for frequency predication of patterns that are formed on the Chladni plate and focused on the application of machine learning algorithms in image processing. In the Chladni plate, nodes and antinodes are demonstrated at various excited frequencies. Sand on the plate creates specific patterns when it is excited by vibrations from a mechanical oscillator. In the experimental setup, a rectangular aluminum plate of 16 cm x 16 cm and 0.61 mm thickness was placed over the mechanical oscillator, which was driven by a sine wave signal generator. 14 Chladni patterns are obtained on a Chladni plate and validation is done with modal analysis in Ansys. For machine learning, a large number of data sets are required, as captured around 200 photos of each modal frequency and around 3000 photos with a camera of all 14 Chladni patterns for supervised learning. The current model is written in Python language and model has one convolution layer. The main modules used in this are Tensor Flow Keras, NumPy, CV2 and Maxpooling. The fed reference data is taken for 14 frequencies between 330 Hz to 3910 Hz. In the model, all the images are converted to grayscale and canny edge detected. All patterns of frequencies have an almost 80% - 99% correlation with test sample experimental data. This approach is to form a directory of Chladni patterns for future reference purpose in real-life application. A machine learning algorithm can predict the resonant frequency based on the patterns formed on the Chladni plate.

Analysis of Commuting Modal Shift in Consideration of Social Interaction of Consciousness for Environment

It is the matter for achievement of the low carbon transport system that the excessive use of private vehicles can be controlled appropriately.Not only improvement of service level of modes except private vehicle,but also consciousness for environmental problem of individual trip maker is important for eco-commuting promotion.On the other hand,consciousness for environment would be changed by influence of other person.Accordingly,it is aimed in the study that the structure of decision-making process for modal shift to the eco-commuting mode in the local city is described considering environmental consciousness and social interaction.For the purpose,the consciousness for the environment problem and the travel behavior of the commuter at the suburban area in the local city are investigated by the questionnaire survey.The covariance structure about the eco-consciousness is analyzed with the database of the questionnaire survey by structural equation modeling.As the result,it can be confirmed with the structural equation model that the individual environmental consciousness is strongly related with the intention of self-sacrifice and is influenced with the local interaction of the individual connections.On the other hand,the intention of modal shift for the commuting mode is analyzed with the database of the questionnaire survey.It can be found out that the environmental consciousness is not statistically significant for commuting mode choice with the present poor level of service of public transport.However,the intention of self-sacrifice for the prevention of the global warming is statistically confirmed as the factor of modal shift with the operation of eco-commuting bus service with the RP/SP integrated estimation method.As the result,the multi-agent simulation system with social interaction model for eco consciousness is developed to measure the effect of the eco-commuting promotion.For the purpose,the carbon dioxide emission is estimated based on traffic demand and road network condition in the traffic environment model.On the other hand,the relation between agents is defined based on the small world network.The proposed multi-agent simulation is applied to measure the effect of the eco-commuting promotion such as improvement of level of service on the public transport or education of eco-consciousness.The effect of the promotion plan can be observed with the proposed multi-agent system.Finally,it can be concluded that the proposed multi-agent simulation with social interaction for eco-consciousness is useful for planning of eco-commuting promotion.

On Inspiration from Modality Value to the Teaching of College English

This paper aims at examing how modality values are realized in native and non-native English news reporting and comparing the values they each take.By analyzing,the writer hopes to contribute to the awareness of foreign language learners in terms of the importance of modality values in news analysis and make readers benefit from their reading,writing.

Analysis on the Modality of English News Report

Modality is used to express subjective judgment as well as the speaker’s opinions and attitudes.This paper aims to analyze the reporters’orientation reflected by linguistic modal expressions in English news reports from the perspective of Systemic-Functional Grammar,especially from the perspective of type,value,orientation and polarity.

Radiotherapy as valid modality for hepatocellular carcinoma with portal vein tumor thrombosis

Although the current standard treatment for hepatocellular carcinoma(HCC) with portal vein tumor thrombosis(PVTT) is sorafenib, many previous studies have established the need for a reliable local modality for PVTT control, which is a major cause of liver function deterioration and metastasis. Additionally, there is growing evidence for the prognostic significance of PVTT classification according to the location of tumor thrombosis. Favorable outcomes can be obtained by applying local modalities, including surgery or transarterial chemoembolization, especially in second-order or distal branch PVTT. Rapid control of PVTT could maintain or improve liver function and reduce intrahepatic as well as distant metastasis. Radiotherapy(RT) is one of the main locoregional treatment modalities in oncologic fields, but has rarely been used in HCC because of concerns regarding hepatic toxicity. However, with the development of advanced techniques, RT has been increasingly applied in HCC management. Randomized studies have yet to definitively prove the benefit of RT, but several comparative studies have justified the application of RT in HCC. The value of RT is especially noticeable in HCC with PVTT; several prospective and retrospective studies have reported favorable outcomes, including a 40% to 60% objective response rate and median overall survival of 15 mo to 20 mo in responders. In this review, we evaluate the role of RT as an alternative local modality in HCC with PVTT.

Multimodality treatment in hepatocellular carcinoma patients with tumor thrombi in portal vein

AIM: To compare the therapeutic effect and significances of multimodality treatment for hepatocellular carcinoma (HCC) with tumor thrombi in portal vein (PVTT). METHODS: HCC patients (n=147) with tumor thrombi in the main portal vein or the first branch of portal vein were divided into four groups by the several therapeutic methods. There were conservative treatment group in 18 out of patients (group A); and hepatic artery ligation(HAL) and/or hepatic artery infusion (HAI) group in 18 patients (group B), in whom postoperative chemoembolization was done periodically; group of removal of HCC with PVTT in 79 (group C) and group of transcatheter hepatic arterial chemoembolization (TACE) or HAI and/or portal vein infusion (PVI) after operation in 32 (group D). RESULTS: The median survival period was 12 months in our series and the 1-,3-, and 5-year survival rates were 44.3%, 24.5% and 15.2%, respectively. The median survival times were 2, 5, 12 and 16 months in group A, B, C and D, respectively. The 1-, 3- and 5-year survival rates were 5.6%, 0% and 0% in group A; 22.2%, 5.6% and 0% in group B; 53.9%, 26.9% and 16.6% in group C; 79.3%, 38.9% and 26.8% in group D, respectively. Significant difference appeared in the survival rates among the groups (P 【 0.05). CONCLUSION: Hepatic resection with removal of tumor thrombi and HCC should increase the curative effects and be encouraged for the prolongation of life span and quality of life for HCC patients with PVTT, whereas the best therapeutic method for HCC with PVTT is with regional hepatic chemotherapy or chemoembolization after hepatic resection with removal of tumor thrombi.

Terminal-Modality-Based Joint Call Admission Control Algorithm for Fair Radio Resource Allocation in Heterogeneous Cellular Networks

There is a problem of unfairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous wireless networks. Low-capability mobile terminals (such as single-mode terminals) suffer high call blocking probability whereas high-capability mobile terminals (such as quad-mode terminals) experience very low call blocking probability, in the same heterogeneous wireless network. This paper proposes a Terminal-Modality-Based Joint Call Admission Control (TJCAC) algorithm to reduce this problem of unfairness. The proposed TJCAC algorithm makes call admission decisions based on mobile terminal modality (capability), network load, and radio access technology (RAT) terminal support index. The objectives of the proposed TJCAC algorithm are to reduce call blocking/dropping probability, and ensure fairness in allocation of radio resources among heterogeneous mobile terminals in heterogeneous networks. An analytical model is developed to evaluate the performance of the proposed TJCAC scheme in terms of call blocking/dropping probability in a heterogeneous wireless network. The performance of the proposed TJCAC algorithm is compared with that of other JCAC algorithms. Results show that the proposed algorithm reduces call blocking/dropping probability in the networks, and ensure fairness in allocation of radio resources among heterogeneous terminals.

Quest for the best endoscopic imaging modality for computer-assisted colonic polyp staging

BACKGROUND It was shown in previous studies that high definition endoscopy, high magnification endoscopy and image enhancement technologies, such as chromoendoscopy and digital chromoendoscopy [narrow-band imaging(NBI), iScan] facilitate the detection and classification of colonic polyps during endoscopic sessions. However, there are no comprehensive studies so far that analyze which endoscopic imaging modalities facilitate the automated classification of colonic polyps. In this work, we investigate the impact of endoscopic imaging modalities on the results of computer-assisted diagnosis systems for colonic polyp staging.AIM To assess which endoscopic imaging modalities are best suited for the computerassisted staging of colonic polyps.METHODS In our experiments, we apply twelve state-of-the-art feature extraction methods for the classification of colonic polyps to five endoscopic image databases of colonic lesions. For this purpose, we employ a specifically designed experimental setup to avoid biases in the outcomes caused by differing numbers of images per image database. The image databases were obtained using different imaging modalities. Two databases were obtained by high-definition endoscopy in combination with i-Scan technology(one with chromoendoscopy and one without chromoendoscopy). Three databases were obtained by highmagnification endoscopy(two databases using narrow band imaging and one using chromoendoscopy). The lesions are categorized into non-neoplastic and neoplastic according to the histological diagnosis.RESULTS Generally, it is feature-dependent which imaging modalities achieve high results and which do not. For the high-definition image databases, we achieved overall classification rates of up to 79.2% with chromoendoscopy and 88.9% without chromoendoscopy. In the case of the database obtained by high-magnification chromoendoscopy, the classification rates were up to 81.4%. For the combination of high-magnification endoscopy with NBI, results of up to 97.4% for one database and up to 84% for the other were achieved. Non-neoplastic lesions were classified more accurately in general than non-neoplastic lesions. It was shown that the image recording conditions highly affect the performance of automated diagnosis systems and partly contribute to a stronger effect on the staging results than the used imaging modality.CONCLUSION Chromoendoscopy has a negative impact on the results of the methods. NBI is better suited than chromoendoscopy. High-definition and high-magnification endoscopy are equally suited.

ChatGPT for shaping the future of dentistry: the potential of multi-modal large language model

The ChatGPT,a lite and conversational variant of Generative Pretrained Transformer 4(GPT-4)developed by OpenAI,is one of the milestone Large Language Models(LLMs)with billions of parameters.LLMs have stirred up much interest among researchers and practitioners in their impressive skills in natural language processing tasks,which profoundly impact various fields.This paper mainly discusses the future applications of LLMs in dentistry.We introduce two primary LLM deployment methods in dentistry,including automated dental diagnosis and cross-modal dental diagnosis,and examine their potential applications.Especially,equipped with a cross-modal encoder,a single LLM can manage multi-source data and conduct advanced natural language reasoning to perform complex clinical operations.We also present cases to demonstrate the potential of a fully automatic Multi-Modal LLM AI system for dentistry clinical application.While LLMs offer significant potential benefits,the challenges,such as data privacy,data quality,and model bias,need further study.Overall,LLMs have the potential to revolutionize dental diagnosis and treatment,which indicates a promising avenue for clinical application and research in dentistry.

Multivariate Modality Inference Using Gaussian Kernel

The number of modes (also known as modality) of a kernel density estimator (KDE) draws lots of interests and is important in practice. In this paper, we develop an inference framework on the modality of a KDE under multivariate setting using Gaussian kernel. We applied the modal clustering method proposed by [1] for mode hunting. A test statistic and its asymptotic distribution are derived to assess the significance of each mode. The inference procedure is applied on both simulated and real data sets.

An Analysis on the BRICS TALK from the Perspective of Modality System

The tenth BRICS Summit was held in Johannesburg,South Africa in 2018,which attracted attention from the whole world.Especially,it is of great importance to the BRICS nations,for it is the first ten years of BRICS,which marks its growth.Meanwhile,this year the Trump administration imposed more strict policies on protectionism.Particularly,the trade war between China and the United States is so intensive.China,as the core member of BRICS has great clout on BRICS,so the trade war between China and US also arouse a heat discussion among the BRICS nations.During the Summit,CGTN invited experts from the five BRICS nations to discuss related topics.Systemic Functional Linguistics,as one of the most influential branches of Linguistics,was firstly established by M.A.K.Halliday,and it has been greatly developed over the past decades.Interpersonal meaning is one of the three meta-functions,which focuses on how addressers use language to communicate,establish and maintain relationships with addressees,and express their opinions.Mood and modality are two basic resources to the realization of interpersonal meaning.Mood is used to represent the interaction of the language users,while modality reflects the utterers’attitudes and judgments.The paper discusses the three aspects of modality:modal operators,modal adjuncts and metaphors of modality.The paper applies the modality system to the analysis of the transcript of BRICS TALK.The author selects the experts’speeches on the trade war between China and US as the data.The research questions are as followed:(1)the characteristics of the modality resources appeared in their talks;(2)the BRICS nations attitudes and stance on trade war and America’s protectionism.

Modeling Rules Fission and Modality Selection Using Ontology

Researchers in computer science and computer engineering devote a significant part of their efforts on communication and interaction between man and machine. Indeed, with the advent of multimedia and multimodal processing in real time, the computer is no longer considered only as a computational tool, but as a machine for processing, communication, collection and control. Many machines assist and support many activities in daily life. The main objective of this paper is to propose a new methodological solution by modeling an architecture that facilitates the work of multimodal system especially for a fission module. To realize such systems, we rely on ontology to integrate data semantically. Ontologies provide a structured vocabulary usedas support for data representation. This paper provides a better understanding of the fission system and multimodal interaction. We present our architecture and the description of the detection of optimal modalities. This is done by using an ontological model that contains different applicable scenarios and describes the environment where a multimodal system exists.