Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

A Multi-mode Electronic Load Sensing Control Scheme with Power Limitation and Pressure Cut-off for Mobile Machinery

In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.

Multi-mode Multi-frequency GNSS-IR Combination System for Sea Level Retrieval

With the development of Global Navigation Satellite Systems(GNSS),geodetic GNSS receivers have been utilized to monitor sea levels using GNSS-Interferometry Reflectometry(GNSS-IR)technology.The multi-mode,multi-frequency signals of GPS,GLONASS,Galileo,and Beidou can be used for GNSS-IR sea level retrieval,but combining these retrievals remains problematic.To address this issue,a GNSS-IR sea level retrieval combination system has been developed,which begins by analyzing error sources in GNSS-IR sea level retrieval and establishing and solving the GNSS-IR retrieval equation.This paper focuses on two key points:time window selection and equation stability.The stability of the retrieval combination equations is determined by the condition number of the coefficient matrix within the time window.The impact of ill-conditioned coefficient matrices on the retrieval results is demonstrated using an extreme case of SNR data with only ascending or descending trajectories.After determining the time window and removing ill-conditioned equations,the multi-mode,multi-frequency GNSS-IR retrieval is performed.Results from three International GNSS Service(IGS)stations show that the combination method produces high-precision,high-resolution,and high-reliability sea level retrieval combination sequences.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Effect of different anesthetic modalities with multimodal analgesia on postoperative pain level in colorectal tumor patients

BACKGROUND According to clinical data,a significant percentage of patients experience pain after surgery,highlighting the importance of alleviating postoperative pain.The current approach involves intravenous self-control analgesia,often utilizing opioid analgesics such as morphine,sufentanil,and fentanyl.Surgery for colo-rectal cancer typically involves general anesthesia.Therefore,optimizing anes-thetic management and postoperative analgesic programs can effectively reduce perioperative stress and enhance postoperative recovery.The study aims to analyze the impact of different anesthesia modalities with multimodal analgesia on patients'postoperative pain.AIM To explore the effects of different anesthesia methods coupled with multi-mode analgesia on postoperative pain in patients with colorectal cancer.METHODS Following the inclusion criteria and exclusion criteria,a total of 126 patients with colorectal cancer admitted to our hospital from January 2020 to December 2022 were included,of which 63 received general anesthesia coupled with multi-mode labor pain and were set as the control group,and 63 received general anesthesia associated with epidural anesthesia coupled with multi-mode labor pain and were set as the research group.After data collection,the effects of postoperative analgesia,sedation,and recovery were compared.RESULTS Compared to the control group,the research group had shorter recovery times for orientation,extubation,eye-opening,and spontaneous respiration(P<0.05).The research group also showed lower Visual analog scale scores at 24 h and 48 h,higher Ramany scores at 6 h and 12 h,and improved cognitive function at 24 h,48 h,and 72 h(P<0.05).Additionally,interleukin-6 and interleukin-10 levels were significantly reduced at various time points in the research group compared to the control group(P<0.05).Levels of CD3+,CD4+,and CD4+/CD8+were also lower in the research group at multiple time points(P<0.05).CONCLUSION For patients with colorectal cancer,general anesthesia coupled with epidural anesthesia and multi-mode analgesia can achieve better postoperative analgesia and sedation effects,promote postoperative rehabilitation of patients,improve inflammatory stress and immune status,and have higher safety.

Safety and effectiveness of butorphanol in epidural labor analgesia:A protocol for a systematic review and meta-analysis

BACKGROUND Epidural analgesia is the most effective analgesic method during labor.Butorphanol administered epidurally has been shown to be a successful analgesic method during labor.However,no comprehensive study has examined the safety and efficacy of using butorphanol as an epidural analgesic during labor.AIM To assess butorphanol's safety and efficacy for epidural labor analgesia.METHODS The PubMed,Cochrane Library,EMBASE,Web of Science,China National Knowledge Infrastructure,and Google Scholar databases will be searched from inception.Other types of literature,such as conference abstracts and references to pertinent reviews,will also be reviewed.We will include randomized controlled trials comparing butorphanol with other opioids combined with local anesthetics for epidural analgesia during labor.There will be no language restrictions.The primary outcomes will include the visual analog scale score for the first stage of labor,fetal effects,and Apgar score.Two independent reviewers will evaluate the full texts,extract data,and assess the risk of bias.Publication bias will be evaluated using Egger's or Begg's tests as well as visual analysis of a funnel plot,and heterogeneity will be evaluated using the Cochran Q test,P values,and I2 values.Meta-analysis,subgroup analysis,and sensitivity analysis will be performed using RevMan software version 5.4.This protocol was developed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)Protocols statement,and the PRISMA statement will be used for the systematic review.RESULTS This study provides reliable information regarding the safety and efficacy of using butorphanol as an epidural analgesic during labor.CONCLUSION To support clinical practice and development,this study provides evidence-based findings regarding the safety and efficacy of using butorphanol as an epidural analgesic during labor.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Postoperative Analgesia and Cesarean Section under General Anesthesia: Multicenter Study

Background: Neuraxial anesthesia with intrathecal morphine is the reference technique in cesarean section anesthesia for the management of postoperative analgesia. If there is a contraindication to this, general anesthesia is required. The objective of the study was to evaluate the analgesic effectiveness of 4 analgesic techniques performed during cesarean section under general anesthesia in two centers with different anesthetic practices (North Franche Comté Hospital and Omar Bongo Ondimba Army Training Hospital). Method: This is a retrospective and descriptive study over 2 years, from January 1, 2019 to December 31, 2020. It involved evaluating the analgesic effectiveness and tolerance of morphine in the epidural catheter, wound infiltration, intravenous analgesia and Transversus Abdominous Plane block (TAP block) from the post-anesthesia care unit (PACU) until the 4th post-operative day. Results: Of the 354 cesarean sections performed, 84 (11.14%) received general anesthesia. The average age was 32.27 years. Acute fetal distress was the first indication for cesarean section (45.2%), followed by hemorrhagic placenta previa (10.7%) and prolapse of the cord (8.33%). Morphine in the epidural catheter was the most used (47.6%) followed by parietal infiltration (36.9%), intravenous analgesia (13.1%) and TAP block (2.38%). The analgesic effectiveness was comparable between the techniques from postoperative day 0 to day 4. No difference in side effects. Postoperative morphine consumption was significantly reduced (p = 0.011) in the infiltration (9 mg) and TAP block (9mg) groups compared to the epidural catheter (16 mg) and intravenous analgesia (17 mg). No difference in 02 rehabilitation criteria (ambulation, first bowel movement). No difference in the occurrence of chronic pain. Conclusion: In the event of a cesarean section under general anesthesia, there are effective and well-tolerated alternatives to neuraxial anesthesia, particularly regional anesthesia techniques (nerve blocks), particularly in countries with low availability of morphine.

Ultrasound-guided sphenopalatine ganglion block for effective analgesia during awake fiberoptic nasotracheal intubation: A case report

BACKGROUND Awake fiberoptic nasotracheal intubation(AFNI)is the preferred airway ma-nagement strategy for patients with difficult airways.However,this procedure can cause significant physical and psychological distress.This case report explores the application of a sphenopalatine ganglion(SPG)block as an alternative anal-gesic modality to mitigate the discomfort associated with AFNI.CASE SUMMARY A 63-year-old female with a history of right maxillary osteosarcoma underwent craniotomy for a suspected malignant brain lesion.The patient’s medical history included prior surgery,chemotherapy,and radiation therapy,resulting in signi-ficant jaw impairment and limited neck mobility.Considering the anticipated air-way challenges,AFNI was planned.A SPG block was performed under real-time ultrasound guidance,providing effective analgesia during nasotracheal intuba-tion.CONCLUSION The SPG block represents a promising analgesic approach in AFNI,offering po-tential benefits in alleviating pain involving the nasal and nasopharyngeal regions as well as improving patient cooperation.

Midpoint transverse process to pleura block for postoperative analgesia following laparoscopic renal cyst decortication:Two case reports

BACKGROUND The midpoint transverse process to pleura(MTP)block,a novel technique for thoracic paravertebral block(TPVB),was first employed in laparoscopic renal cyst decortication.CASE SUMMARY Thoracic paravertebral nerve block is frequently employed for perioperative analgesia during laparoscopic cyst decortication.To address safety concerns associated with TPVBs,we administered MTP blocks in two patients prior to administering general anesthesia for laparoscopic cyst decortication.The MTP block was performed at the T9 level under ultrasound guidance,with 20 mL of 0.5%ropivacaine injected.Reduced sensation to cold and pinprick was observed from the T8 to T11 dermatome levels.Immediately postoperative Numeric Pain Rating Scale scores were 0/10 at rest and on movement,with none exceeding a mean 24 h numeric rating scale>3.CONCLUSION MTP block was effective technique for providing postoperative analgesia for patients undergoing laparoscopic renal cyst decortication.

Unsupervised multi-modal image translation based on the squeeze-and-excitation mechanism and feature attention module

The unsupervised multi-modal image translation is an emerging domain of computer vision whose goal is to transform an image from the source domain into many diverse styles in the target domain.However,the multi-generator mechanism is employed among the advanced approaches available to model different domain mappings,which results in inefficient training of neural networks and pattern collapse,leading to inefficient generation of image diversity.To address this issue,this paper introduces a multi-modal unsupervised image translation framework that uses a generator to perform multi-modal image translation.Specifically,firstly,the domain code is introduced in this paper to explicitly control the different generation tasks.Secondly,this paper brings in the squeeze-and-excitation(SE)mechanism and feature attention(FA)module.Finally,the model integrates multiple optimization objectives to ensure efficient multi-modal translation.This paper performs qualitative and quantitative experiments on multiple non-paired benchmark image translation datasets while demonstrating the benefits of the proposed method over existing technologies.Overall,experimental results have shown that the proposed method is versatile and scalable.

大学英语的Multi-Mode教学模式

针对目前高职院校大学英语教学普遍存在的问题,结合全国性大学英语教学需要改革的迫切要求,笔者对所在院校研究并应用的Multi-Mode教学模式进行分析说明,其关键性在于培养学生自主学习的积极性,较好地落实教学任务,提高学生参加英语等级考试的通过率。

Signal classification method based on data mining formulti-mode radar

For the multi-mode radar working in the modern electronicbattlefield, different working states of one single radar areprone to being classified as multiple emitters when adoptingtraditional classification methods to process intercepted signals,which has a negative effect on signal classification. A classificationmethod based on spatial data mining is presented to address theabove challenge. Inspired by the idea of spatial data mining, theclassification method applies nuclear field to depicting the distributioninformation of pulse samples in feature space, and digs out thehidden cluster information by analyzing distribution characteristics.In addition, a membership-degree criterion to quantify the correlationamong all classes is established, which ensures classificationaccuracy of signal samples. Numerical experiments show that thepresented method can effectively prevent different working statesof multi-mode emitter from being classified as several emitters,and achieve higher classification accuracy.

Multi-mode process monitoring based on a novel weighted local standardization strategy and support vector data description

There are multiple operating modes in the real industrial process, and the collected data follow the complex multimodal distribution, so most traditional process monitoring methods are no longer applicable because their presumptions are that sampled-data should obey the single Gaussian distribution or non-Gaussian distribution. In order to solve these problems, a novel weighted local standardization(WLS) strategy is proposed to standardize the multimodal data, which can eliminate the multi-mode characteristics of the collected data, and normalize them into unimodal data distribution. After detailed analysis of the raised data preprocessing strategy, a new algorithm using WLS strategy with support vector data description(SVDD) is put forward to apply for multi-mode monitoring process. Unlike the strategy of building multiple local models, the developed method only contains a model without the prior knowledge of multi-mode process. To demonstrate the proposed method's validity, it is applied to a numerical example and a Tennessee Eastman(TE) process. Finally, the simulation results show that the WLS strategy is very effective to standardize multimodal data, and the WLS-SVDD monitoring method has great advantages over the traditional SVDD and PCA combined with a local standardization strategy(LNS-PCA) in multi-mode process monitoring.

Design of Multi-mode Mobile Terminal Chips

Based on the analysis of B3G evolution, the base-band processing chips for mobile terminals are introduced. Key technologies for multi-mode mobile terminal base-band chips are discussed. Terminal technologies are thought to be the key of B3G, and terminal base-band chips are regarded as the core of terminal technologies. Therefore, a unified wireless development platform is required for the R&D of multi-mode mobile terminal base-band processing chips.

Adaptive Learning-Based Delay-Sensitive and Secure Edge-End Collaboration for Multi-Mode Low-Carbon Power IoT

Multi-mode power internet of things(PIoT)combines various communication media to provide spatio-temporal coverage for low-carbon operation in smart park.Edge-end collaboration is feasible to achieve the full utilization of heterogeneous resources and anti-eavesdropping.However,edge-end collaboration-based multi-mode PIoT faces challenges of mutual contradiction in communication and security quality of service(QoS)guarantee,inadaptability of resource management,and multi-mode access conflict.We propose an Adaptive learning based delAysensitive and seCure Edge-End Collaboration algorithm(ACE_(2))to optimize multi-mode channel selection and split device power into artificial noise(AN)transmission and data transmission for secure data delivery.ACE_(2) can achieve multi-attribute QoS guarantee,adaptive resource management and security enhancement,and access conflict elimination with the combined power of deep actor-critic(DAC),“win or learn fast(WoLF)”mechanism,and edge-end collaboration.Simulations demonstrate its superior performance in queuing delay,energy consumption,secrecy capacity,and adaptability to differentiated low-carbon services.

Vortex-Induced Vibration of a Tension Leg Platform Tendon:Multi-Mode Limit Cycle Oscillations

This paper studies the application of mathematical models to analyze the vortex-induced vibrations of the tendons of a given TLP along the Indian coastline, by using an analytical approach, using MATLAB. The tendon is subjected to a steady current load, which causes vortex-shedding downstream, leading to cross-flow vibrations. The magnitude of the excitation(lift and drag coefficients) depends on the vortex-shedding frequency. The resulting vibration is studied for possible resonant behavior. The excitation force is quantified empirically, the added mass by potential flow hydrodynamics, and the vibration by normal mode summation method. Non-linear viscous damping of the water is considered. The non-linear oscillations are studied by the phase-plane method, investigating the limit-cycle oscillations. The stable/unstable regions of the dynamic behavior are demarcated. The modal contribution to the total deflection is studied to establish the possibility of resonance of one of the wet modes with the vortex-shedding frequency.

A global analysis of multi-mode sea surface temperature pattern

The variability of the air-sea system in the low-frequency time domain can be decomposed into several systematic climate modes, namely, the decadal variability （DV） mode, the El Nino Southem Oscillation （ENSO） mode, the annual cycle （AC） mode, the semiannual cycle （ SC ） mode and the intraseasonal variability （ ISV ） mode. The combination of these primary modes in the air - sea system orchestrates a complex climate system. The multi-mode low-frequency variability in SST is investigated based on 22 a SST records from 1982 through 2003. The variation of SST in the past two decades undergoes a different combination of these dominant climate modes over different regions, which leads to an interesting new classification of the global ocean based on the relative importance of these modes. The new classification can provide ideal locations for better monitoring of these low-frequency modes in the scientific proof sense. Moreover, two no-annual variation and 14 no-semiannual variation oceanic points, termed annual and semiannual amphidromes, have been well defined in the AC and SC phase maps. The formation of these nodal points is attributed to the couplings of climate modes in EOF analysis results.