Analysis of stimulated Brillouin scattering in multi-mode fiber by numerical solution

Stimulated Brillouin scattering in optical fibers is described by a theoretical model and numerical analysis. The results showed that, for an optical fiber pumped by a laser beam with ns-order-pulse width and kW-order peak-power, SBS reflectivity tends to saturate when the fiber length exceeds a limit, named 'effective fiber length'. Using small core-diameter and long enough fiber, the SBS reflectivity level could be raised but is limited by optical damage of the entrance surface of the fiber. Therefore, just a small dynamic range can be obtained.

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.

Research on optical fiber magnetic field sensors based on multi-mode fiber and spherical structure

A magnetic field sensor with a magnetic fluid(MF)-coated intermodal interferometer is proposed and experimentally demonstrated. The interferometer is formed by sandwiching a segment of single mode fiber(SMF) between a segment of multi-mode fiber(MMF) and a spherical structure. It can be considered as a cascade of the traditional SMF-MMF-SMF structure and MMF-SMF-sphere structure. The transmission spectral characteristics change with the variation of applied magnetic field. The experimental results exhibit that the magnetic field sensitivities for wavelength and transmission loss are 0.047 nm/m T and 0.215 d B/m T for the interference dip around 1 535.36 nm. For the interference dip around 1548.41 nm,the sensitivities are 0.077 nm/m T and 0.243 d B/m T. Simultaneous measurement can be realized according to the different spectral responses.

Weakly-coupled mode division multiplexing over conventional multi-mode fiber with intensity modulation and direct detection

Multi-mode fiber(MMF)links are expected to greatly enhance capacity to cope with rapidly increasing data traffic in optical short-reach systems and networks.Recently,mode division multiplexing(MDM)over MMF has been proposed,in which different modes in MMF are utilized as spatial channels for data transmission.Stronglycoupled MDM techniques utilizing coherent detection and multiplex-input-multiplex-output(MIMO)digital signal processing(DSP)are complex and expensive for shortreach transmission.So the weakly-coupled approach by significantly suppressing mode coupling in the fiber and optical components has been proposed.In this way,the signals in each mode can be independently transmitted and received using conventional intensity modulation and direct detection(IM-DD).In this paper,we elaborate the key technologies to realize weakly-coupled MDM transmission over conventional MMF,including mode characteristic in MMF and weakly-coupled mode multiplexer/ demultiplexer(MUX/DEMUX).We also present the upto-date experimental results for weakly-coupled MDM transmission over conventional OM3 MMF.We show that weakly-coupled MDM scheme is promising for high-speed optical interconnections and bandwidth upgrade of already-deployed MMF links.

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.

Optical Nonlinearity of Violet Phosphorus and Applications in Fiber Lasers

A D-shaped fiber is coated with a new two-dimensional nanomaterial,violet phosphorus(VP),to create a saturable absorber(SA)with a modulation depth of 3.68%.Subsequently,the SA is inserted into a fiber laser,enabling successful generation of dark solitons and bright–dark soliton pairs through adjustment of the polarization state within the cavity.Through further study,mode-locked pulses are achieved,proving the existence of polarization-locked vector solitons.The results indicate that VP can be used as a polarization-independent SA.

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.

On the generation of high-quality Nyquist pulses in mode-locked fiber lasers

Nyquist pulses have wide applications in many areas,from electronics to optics.Mode-locked lasers are ideal platforms to generate such pulses.However,how to generate high-quality Nyquist pulses in mode-locked lasers remains elusive.We address this problem by managing different physical effects in mode-locked fiber lasers through extensive numerical simulations.We find that net dispersion,linear loss,gain and filter shaping can affect the quality of Nyquist pulses significantly.We also demonstrate that Nyquist pulses experience similariton shaping due to the nonlinear attractor effect in the gain medium.Our work may contribute to the design of Nyquist pulse sources and enrich the understanding of pulse shaping dynamics in mode-locked lasers.

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.

Giving Food a Fiber Boost: Adding High Beta-Glucan Ingredient from Barley to Everyday Foods

Lack of dietary fiber contributes to many health issues, particularly chronic vascular diseases. Mixed linkage β-1.3 - 1.4 beta-glucan (beta-glucan, in this paper) is a confirmed beneficial ingredient for the human diet through reduction of cholesterol and the glycemic index. Barley contains the highest beta-glucan content of all the grains, and in this study, a percentage of flour from two high beta glucan lines was, each, added to an array of wheat-based food products to measure how it impacted total dietary fiber. Results showed that beta-glucan content was higher in all the products containing the added high beta-glucan flour, along with increased total dietary fiber content. Protein content in the food products is also increased with the higher protein in the barley flours added. Beta-glucan content in the barley-added products increased to 1.2% - 4.0% versus 0.2% - 0.5% in the pure wheat products, while the dietary fibers increased to 3.5% - 24.4% versus 2.1% - 9.1% in pure wheat product controls. This research provided experimental evidence that using a high beta-glucan barley ingredient in food can increase dietary fiber to benefit health.

Anti-abrasion collagen fiber-based membrane functionalized by UiO-66-NH_(2)with ultra-high efficiency and stability for oil-in-water emulsions separation

Membrane separation strategies offer promising platform for the emulsion separation.However,the low mechanical strength of membrane separation layers and the trade-off between separation flux and efficiency present significant challenges.In this study,we report a CFM@UiO-66-NH_(2)membrane with high separation flux,efficiency and stability,through utilizing a robust anti-abrasion collagen fiber membrane(CFM)as the multifunctional support and UiO-66-NH_(2)by an in-situ growth as the separation layer.The high mechanical strength of the CFM compensated for the weakness of the separation layer,while the charge-breaking effect of UiO-66-NH_(2),along with the size sieving of its constituent separating layers and the capillary effect of the collagen fibers,contributed to the potential for efficient separation.Additionally,the CFM@UiO-66-NH_(2)membrane exhibited superhydrophilic properties,making it suitable for separating oil-in-water microemulsions and nanoemulsions stabilized by anionic surfactants.The membrane demonstrated remarkable separation efficiencies of up to 99.960%and a separation flux of370.05 L·m^(-2)·h^(-1).Moreover,it exhibits stability,durability,and abrasion resistance,maintaining excellent separation performance even when exposed to strong acids and alkalis without any damage to its structure and performance.After six cycles of reuse,it achieved a separation flux of 417.97 L·m^(-2)·h^(-1)and a separation efficiency of 99.747%.Furthermore,after undergoing 500 cycles of strong abrasion,the separation flux remained at 124.39 L·m^(-2)·h^(-1),with a separation efficiency of 99.992%.These properties make it suitable for the long-term use in harsh operating environments.We attribute these properties to the electrostatic effect resulting from the amino group on UiO-66-NH_(2)and its in-situ growth on the CFM,which forms a size-screening separation layer.Our work highlights the potential of the CFM@UiO-66-NH_(2)membrane as an environmentally friendly size-screening material for the efficient emulsion wastewater separation.

Optimizing Optical Fiber Faults Detection:A Comparative Analysis of Advanced Machine Learning Approaches

Efficient optical network management poses significant importance in backhaul and access network communicationfor preventing service disruptions and ensuring Quality of Service(QoS)satisfaction.The emerging faultsin optical networks introduce challenges that can jeopardize the network with a variety of faults.The existingliterature witnessed various partial or inadequate solutions.On the other hand,Machine Learning(ML)hasrevolutionized as a promising technique for fault detection and prevention.Unlike traditional fault managementsystems,this research has three-fold contributions.First,this research leverages the ML and Deep Learning(DL)multi-classification system and evaluates their accuracy in detecting six distinct fault types,including fiber cut,fibereavesdropping,splicing,bad connector,bending,and PC connector.Secondly,this paper assesses the classificationdelay of each classification algorithm.Finally,this work proposes a fiber optics fault prevention algorithm thatdetermines to mitigate the faults accordingly.This work utilized a publicly available fiber optics dataset namedOTDR_Data and applied different ML classifiers,such as Gaussian Naive Bayes(GNB),Logistic Regression(LR),Support Vector Machine(SVM),K-Nearest Neighbor(KNN),Random Forest(RF),and Decision Tree(DT).Moreover,Ensemble Learning(EL)techniques are applied to evaluate the accuracy of various classifiers.In addition,this work evaluated the performance of DL-based Convolutional Neural Network and Long-Short Term Memory(CNN-LSTM)hybrid classifier.The findings reveal that the CNN-LSTM hybrid technique achieved the highestaccuracy of 99%with a delay of 360 s.On the other hand,EL techniques improved the accuracy in detecting fiberoptic faults.Thus,this research comprehensively assesses accuracy and delay metrics for various classifiers andproposes the most efficient attack detection system in fiber optics.

Coherent optical frequency transfer via 972-km fiber link

We demonstrate coherent optical frequency dissemination over a distance of 972 km by cascading two spans where the phase noise is passively compensated for.Instead of employing a phase discriminator and a phase locking loop in the conventional active phase control scheme,the passive phase noise cancellation is realized by feeding double-trip beat-note frequency to the driver of the acoustic optical modulator at the local site.This passive scheme exhibits fine robustness and reliability,making it suitable for long-distance and noisy fiber links.An optical regeneration station is used in the link for signal amplification and cascaded transmission.The phase noise cancellation and transfer instability of the 972-km link is investigated,and transfer instability of 1.1×10^(-19)at 10^(4)s is achieved.This work provides a promising method for realizing optical frequency distribution over thousands of kilometers by using fiber links.

The NAC transcription factor LuNAC61 negatively regulates fiber development in flax (Linum usitatissimum L.)

Flax is a crucial fiber crop that exhibits excellent textile properties and serves as a model plant for investigating phloem fiber development. The regulation of multiple genes significantly influences fiber development, notably involving NAC(NAM, ATAF1/2, CUC2) transcription factors in forming the fiber secondary cell wall(SCW).Overexpression of LuNAC61 in flax resulted in sparse top meristematic zone leaves and significantly reduced stem cellulose content. Scanning electron microscopy and staining observations revealed a significant reduction in fiber bundles. β-Glucuronidase(GUS) staining analysis demonstrated high activity of the LuNAC61 promoter in the bast fibers of the flax stem. Additionally, several members of the LuPLATZ and LuCesA families exhibited significant coexpression with LuNAC61. Subcellular localization indicated the presence of LuPLATZ24 protein in the nucleus and cytoplasm, LuNAC61 protein exclusively in the nucleus, and LuCesA10 in the nucleus and endoplasmic reticulum. LuPLATZ24 positively regulates LuNAC61, whereas LuNAC61 negatively affects LuCesA10, suggesting the involvement of a metabolic network in regulating flax fiber development. In conclusion, this study provides a critical opportunity for a comprehensive and in-depth analysis of the mechanisms governing flax fiber development and the potential use of biotechnology to enhance flax fiber yield.

Relationship of Retinal Nerve Fiber Layer Thickness and Retinal Vessel Calibers with Cognitive Impairment in the Asymptomatic Polyvascular Abnormalities Population

Objective Cognitive impairment(CI)in older individuals has a high morbidity rate worldwide,with poor diagnostic methods and susceptible population identification.This study aimed to investigate the relationship between different retinal metrics and CI in a particular population,emphasizing polyvascular status.Methods We collected information from the Asymptomatic Polyvascular Abnormalities Community Study on retinal vessel calibers,retinal nerve fiber layer(RNFL)thickness,and cognitive function of 3,785participants,aged 40 years or older.Logistic regression was used to analyze the relationship between retinal metrics and cognitive function.Subgroups stratified by different vascular statuses were also analyzed.Results RNFL thickness was significantly thinner in the CI group(odds ratio:0.973,95%confidence interval:0.953–0.994).In the subgroup analysis,the difference still existed in the non-intracranial arterial stenosis,non-extracranial carotid arterial stenosis,and peripheral arterial disease subgroups(P<0.05).Conclusion A thin RNFL is associated with CI,especially in people with non-large vessel stenosis.The underlying small vessel change in RNFL and CI should be investigated in the future.

Vitamin A regulates mitochondrial biogenesis and function through p38 MAPK‑PGC‑1α signaling pathway and alters the muscle fiber composition of sheep

Background Vitamin A(VA)and its metabolite,retinoic acid(RA),are of great interest for their wide range of physiological functions.However,the regulatory contribution of VA to mitochondrial and muscle fiber composition in sheep has not been reported.Method Lambs were injected with 0(control)or 7,500 IU VA palmitate into the biceps femoris muscle on d 2 after birth.At the age of 3 and 32 weeks,longissimus dorsi(LD)muscle samples were obtained to explore the effect of VA on myofiber type composition.In vitro,we investigated the effects of RA on myofiber type composition and intrinsic mechanisms.Results The proportion of type I myofiber was greatly increased in VA-treated sheep in LD muscle at harvest.VA greatly promoted mitochondrial biogenesis and function in LD muscle of sheep.Further exploration revealed that VA elevated PGC-1αmRNA and protein contents,and enhanced the level of p38 MAPK phosphorylation in LD muscle of sheep.In addition,the number of type I myofibers with RA treatment was significantly increased,and type IIx myofibers was significantly decreased in primary myoblasts.Consistent with in vivo experiment,RA significantly improved mitochondrial biogenesis and function in primary myoblasts of sheep.We then used si-PGC-1αto inhibit PGC-1αexpression and found that si-PGC-1αsignificantly abrogated RA-induced the formation of type I myofibers,mitochondrial biogenesis,MitoTracker staining intensity,UQCRC1 and ATP5A1 expression,SDH activity,and enhanced the level of type IIx muscle fibers.These data suggested that RA improved mitochondrial biogenesis and function by promoting PGC-1αexpression,and increased type I myofibers.In order to prove that the effect of RA on the level of PGC-1αis caused by p38 MAPK signaling,we inhibited the p38 MAPK signaling using a p38 MAPK inhibitor,which significantly reduced RA-induced PGC-1αand MyHC I levels.Conclusion VA promoted PGC-1αexpression through the p38 MAPK signaling pathway,improved mitochondrial biogenesis,and altered the composition of muscle fiber type.

Optimization of the Pretreatment of the Mixture of Cassava Peelings and Pineapple Fibers Using Response Surface Methodology and a Process Simulator for the Bioethanol Production

The increase in oil prices and greenhouse gas emissions has led to the search for substitutes for fossil fuels. In Cameroon, the abundance of lignocellulosic resources is inherent to agricultural activity. Production of bioethanol remains a challenge given the crystallinity of cellulose and the presence of the complex. The pretreatment aimed to solubilize the lignin fraction and to make cellulose more accessible to the hydrolytic enzymes, was done using the organosolv process. A mathematical modeling was performed to point out the effect of the temperature on the kinetics of the release of the reducing sugars during the pretreatment. Two mathematical model was used, SAEMAN’s model and Response surface methodology. The first show that the kinetic parameters of the hydrolysis of the cellulose and reducing sugar are: 0.05089 min-1, 5358.1461 J·mol-1, 1383.03691 min-1, 51577.6100 J·mol-1 respectively. The second model was used. Temperature is the factor having the most positive influence whereas, ethanol concentration is not an essential factor. To release the maximum, an organosolv pre-treatment of this sub-strate should be carried out at 209.08°C for 47.60 min with an ethanol-water ratio of 24.02%. Organosolv pre-treatment is an effective process for delignification of the lignocellulosic structure.

Coaxial Wet Spinning of Boron Nitride Nanosheet‑Based Composite Fibers with Enhanced Thermal Conductivity and Mechanical Strength

Hexagonal boron nitride nanosheets(BNNSs)exhibit remarkable thermal and dielectric properties.However,their self-assembly and alignment in macroscopic forms remain challenging due to the chemical inertness of boron nitride,thereby limiting their performance in applications such as thermal management.In this study,we present a coaxial wet spinning approach for the fabrication of BNNSs/polymer composite fibers with high nanosheet orientation.The composite fibers were prepared using a superacid-based solvent system and showed a layered structure comprising an aramid core and an aramid/BNNSs sheath.Notably,the coaxial fibers exhibited significantly higher BNNSs alignment compared to uniaxial aramid/BNNSs fibers,primarily due to the additional compressive forces exerted at the core-sheath interface during the hot drawing process.With a BNNSs loading of 60 wt%,the resulting coaxial fibers showed exceptional properties,including an ultrahigh Herman orientation parameter of 0.81,thermal conductivity of 17.2 W m^(-1)K^(-1),and tensile strength of 192.5 MPa.These results surpassed those of uniaxial fibers and previously reported BNNSs composite fibers,making them highly suitable for applications such as wearable thermal management textiles.Our findings present a promising strategy for fabricating high-performance composite fibers based on BNNSs.