Research Progress on Structure and Bioactivity of Longan Polysaccharide

Longan originates from southern China and has high nutritional and health value.Recent phytochemistry and pharmacology studies have shown that polysaccharides are a main bioactive component of longan.Longan polysaccharides possess antioxidant,anti-aging,anti-tumor,immunomodulatory,and other bioactivities.Hot-water extraction,ethanol precipitation,and ultrasonic extraction are generally used to extract water-soluble longan polysaccharides.However,the relationship between the structure and bioactivity of longan polysaccharides remains unclear,requiring further investigation.The aim of this review is to evaluate the current literature focusing on the extraction,purification,structural characterization,and biological activity of longan polysaccharides.We believe that this review would provide a useful bibliography for further innovation and a basis for using longan polysaccharides in functional food.

Accelerated generation of holograms with ultra-low memory symmetrically high-compressed look-up table

Computer-generated holography technology has been widely applied,and as research in this field deepens,the demand for memory and computational power in small AR and VR devices continues to increase.This paper presents a hologram generation method,i.e.,a symmetrically high-compressed look-up table method,which can reduce memory usage by50%.In offline computing,half of the basic horizontal and vertical modulation factors are stored,halving the memory requirements without affecting inline speed.Currently,its potential extends to various holographic applications,including the production of optical diffraction elements.

Understanding the corrosion and bio-corrosion behaviour of Magnesium composites – a critical review

Realising the potential of Magnesium(Mg),several globally leading ventures have invested in the Mg industry,but their relatively poor corrosion resistance is a never ending saga till date.The corrosion and bio-corrosion behaviour of Mg has gained research attention and still remains a hot topic in the application of automobile,aerospace and biomedical industries.The intrinsic high electrochemical nature of Mg limits their utilization in diverse application.This scenario has prompted the development of Mg composites with an aim to achieve superior corrosion and bio-corrosion resistance.The present review enlightens the influence of grain size(GS),secondary phase,texture,type of matrix and reinforcement on the corrosion and bio-corrosion behaviour of Mg composites.Firstly,the corrosion and bio-corrosion behaviour of Mg composites manufactured by primary and secondary processing routes are elucidated.Secondly,the comprehensive corrosion and bio-corrosion mechanisms of these Mg composites are proposed.Thirdly,the individual role of GS,texture and corrosive medium on corrosion and bio-corrosion behaviour of Mg composites are clarified and revealed.The challenges encountered,unanswered issues in this field are explained in detail and accordingly the scope for future research is framed.The review is presented from basic concrete background to advanced corrosion mechanisms with an aim of creating interest among the readers like students,researchers and industry experts from various research backgrounds.Indeed,the corrosion and bio-corrosion behaviour of Mg composites are critically reviewed for the first time to:(i)contribute to the body of knowledge,(ii)foster research and development,(iii)make breakthrough,and(iv)create life changing innovations in the field of Mg composite corrosion.

Co-doped BaFe_(2)As_(2) Josephson junction fabricated with a focused helium ion beam

Josephson junction plays a key role not only in studying the basic physics of unconventional iron-based superconductors but also in realizing practical application of thin-film based devices,therefore the preparation of high-quality iron pnictide Josephson junctions is of great importance.In this work,we have successfully fabricated Josephson junctions from Co-doped BaFe_(2)As_(2)thin films using a direct junction fabrication technique which utilizes high energy focused helium ion beam(FHIB).The electrical transport properties were investigated for junctions fabricated with various He^(+)irradiation doses.The junctions show sharp superconducting transition around 24 K with a narrow transition width of 2.5 K,and a dose correlated foot-structure resistance which corresponds to the effective tuning of junction properties by He^(+)irradiation.Significant J_c suppression by more than two orders of magnitude can be achieved by increasing the He^(+)irradiation dose,which is advantageous for the realization of low noise ion pnictide thin film devices.Clear Shapiro steps are observed under 10 GHz microwave irradiation.The above results demonstrate the successful fabrication of high quality and controllable Co-doped BaFe_(2)As_(2)Josephson junction with high reproducibility using the FHIB technique,laying the foundation for future investigating the mechanism of iron-based superconductors,and also the further implementation in various superconducting electronic devices.

Fuzzy Difference Equations in Diagnoses of Glaucoma from Retinal Images Using Deep Learning

The intuitive fuzzy set has found important application in decision-making and machine learning.To enrich and utilize the intuitive fuzzy set,this study designed and developed a deep neural network-based glaucoma eye detection using fuzzy difference equations in the domain where the retinal images converge.Retinal image detections are categorized as normal eye recognition,suspected glaucomatous eye recognition,and glaucomatous eye recognition.Fuzzy degrees associated with weighted values are calculated to determine the level of concentration between the fuzzy partition and the retinal images.The proposed model was used to diagnose glaucoma using retinal images and involved utilizing the Convolutional Neural Network(CNN)and deep learning to identify the fuzzy weighted regularization between images.This methodology was used to clarify the input images and make them adequate for the process of glaucoma detection.The objective of this study was to propose a novel approach to the early diagnosis of glaucoma using the Fuzzy Expert System(FES)and Fuzzy differential equation(FDE).The intensities of the different regions in the images and their respective peak levels were determined.Once the peak regions were identified,the recurrence relationships among those peaks were then measured.Image partitioning was done due to varying degrees of similar and dissimilar concentrations in the image.Similar and dissimilar concentration levels and spatial frequency generated a threshold image from the combined fuzzy matrix and FDE.This distinguished between a normal and abnormal eye condition,thus detecting patients with glaucomatous eyes.

Robust Facial Biometric Authentication System Using Pupillary Light Reflex for Liveness Detection of Facial Images

Pupil dynamics are the important characteristics of face spoofing detection.The face recognition system is one of the most used biometrics for authenticating individual identity.The main threats to the facial recognition system are different types of presentation attacks like print attacks,3D mask attacks,replay attacks,etc.The proposed model uses pupil characteristics for liveness detection during the authentication process.The pupillary light reflex is an involuntary reaction controlling the pupil’s diameter at different light intensities.The proposed framework consists of two-phase methodologies.In the first phase,the pupil’s diameter is calculated by applying stimulus(light)in one eye of the subject and calculating the constriction of the pupil size on both eyes in different video frames.The above measurement is converted into feature space using Kohn and Clynes model-defined parameters.The Support Vector Machine is used to classify legitimate subjects when the diameter change is normal(or when the eye is alive)or illegitimate subjects when there is no change or abnormal oscillations of pupil behavior due to the presence of printed photograph,video,or 3D mask of the subject in front of the camera.In the second phase,we perform the facial recognition process.Scale-invariant feature transform(SIFT)is used to find the features from the facial images,with each feature having a size of a 128-dimensional vector.These features are scale,rotation,and orientation invariant and are used for recognizing facial images.The brute force matching algorithm is used for matching features of two different images.The threshold value we considered is 0.08 for good matches.To analyze the performance of the framework,we tested our model in two Face antispoofing datasets named Replay attack datasets and CASIA-SURF datasets,which were used because they contain the videos of the subjects in each sample having three modalities(RGB,IR,Depth).The CASIA-SURF datasets showed an 89.9%Equal Error Rate,while the Replay Attack datasets showed a 92.1%Equal Error Rate.

Prediction of Geopolymer Concrete Compressive Strength Using Convolutional Neural Networks

Geopolymer concrete emerges as a promising avenue for sustainable development and offers an effective solution to environmental problems.Its attributes as a non-toxic,low-carbon,and economical substitute for conventional cement concrete,coupled with its elevated compressive strength and reduced shrinkage properties,position it as a pivotal material for diverse applications spanning from architectural structures to transportation infrastructure.In this context,this study sets out the task of using machine learning(ML)algorithms to increase the accuracy and interpretability of predicting the compressive strength of geopolymer concrete in the civil engineering field.To achieve this goal,a new approach using convolutional neural networks(CNNs)has been adopted.This study focuses on creating a comprehensive dataset consisting of compositional and strength parameters of 162 geopolymer concrete mixes,all containing Class F fly ash.The selection of optimal input parameters is guided by two distinct criteria.The first criterion leverages insights garnered from previous research on the influence of individual features on compressive strength.The second criterion scrutinizes the impact of these features within the model’s predictive framework.Key to enhancing the CNN model’s performance is the meticulous determination of the optimal hyperparameters.Through a systematic trial-and-error process,the study ascertains the ideal number of epochs for data division and the optimal value of k for k-fold cross-validation—a technique vital to the model’s robustness.The model’s predictive prowess is rigorously assessed via a suite of performance metrics and comprehensive score analyses.Furthermore,the model’s adaptability is gauged by integrating a secondary dataset into its predictive framework,facilitating a comparative evaluation against conventional prediction methods.To unravel the intricacies of the CNN model’s learning trajectory,a loss plot is deployed to elucidate its learning rate.The study culminates in compelling findings that underscore the CNN model’s accurate prediction of geopolymer concrete compressive strength.To maximize the dataset’s potential,the application of bivariate plots unveils nuanced trends and interactions among variables,fortifying the consistency with earlier research.Evidenced by promising prediction accuracy,the study’s outcomes hold significant promise in guiding the development of innovative geopolymer concrete formulations,thereby reinforcing its role as an eco-conscious and robust construction material.The findings prove that the CNN model accurately estimated geopolymer concrete’s compressive strength.The results show that the prediction accuracy is promising and can be used for the development of new geopolymer concrete mixes.The outcomes not only underscore the significance of leveraging technology for sustainable construction practices but also pave the way for innovation and efficiency in the field of civil engineering.

Synergistic Swarm Optimization Algorithm

This research paper presents a novel optimization method called the Synergistic Swarm Optimization Algorithm(SSOA).The SSOA combines the principles of swarmintelligence and synergistic cooperation to search for optimal solutions efficiently.A synergistic cooperation mechanism is employed,where particles exchange information and learn from each other to improve their search behaviors.This cooperation enhances the exploitation of promising regions in the search space while maintaining exploration capabilities.Furthermore,adaptive mechanisms,such as dynamic parameter adjustment and diversification strategies,are incorporated to balance exploration and exploitation.By leveraging the collaborative nature of swarm intelligence and integrating synergistic cooperation,the SSOAmethod aims to achieve superior convergence speed and solution quality performance compared to other optimization algorithms.The effectiveness of the proposed SSOA is investigated in solving the 23 benchmark functions and various engineering design problems.The experimental results highlight the effectiveness and potential of the SSOA method in addressing challenging optimization problems,making it a promising tool for a wide range of applications in engineering and beyond.Matlab codes of SSOA are available at:https://www.mathworks.com/matlabcentral/fileexchange/153466-synergistic-swarm-optimization-algorithm.

A review: Systematic research approach on toxicity model of liver and kidney in laboratory animals

Therapeutic experiments are commonly performed on laboratory animals to inves-tigate the possible mechanism(s)of action of toxic agents as well as drugs or sub-stances under consideration.The use of toxins in laboratory animal models,including rats,is intended to cause toxicity.This study aimed to investigate different models of hepatotoxicity and nephrotoxicity in laboratory animals to help researchers advance their research goals.The current narrative review used databases such as Medline,Web of Science,Scopus,and Embase and appropriate keywords until June 2021.Nephrotoxicity and hepatotoxicity models derived from some toxic agents such as cisplatin,acetaminophen,doxorubicin,some anticancer drugs,and other materials through various signaling pathways are investigated.To understand the models of renal or hepatotoxicity in laboratory animals,we have provided a list of toxic agents and their toxicity procedures in this review.

Dissociation of edge and screw pyramidal Ⅰ and Ⅱ dislocations in magnesium

Pyramidal dislocations in magnesium (Mg) and other hexagonal close-packed metals play an important role in accommodating plastic strains along the c-axis.Bulk single crystal Mg only presents very limited plasticity in c-axis compression,and this behavior was attributed to out-of-plane dissociation of pyramidal dislocations onto the basal plane and resulted in an immobile dislocation configuration.In contrast,other simulations and experiments reported in-plane dissociation of pyramidal dislocations on their slip planes.Thus,the core structure and mode of dissociation of pyramidal dislocations are still not well understood.To better understand the dissociation behavior of pyramidal dislocations in Mg at room temperature,in this work,atomistic simulations were conducted to investigate four types of pyramidal dislocations at 300 K:edge and screw Py-Ⅰ on{1011},edge and screw Py-Ⅱ on{1122}by using a modified embedded atom method (MEAM) potential for Mg and anisotropic elasticity dislocation model.The results show that when energy minimization was performed before relaxation,in-plane dissociation of edge dislocations on respective pyramidal plane could be obtained at room temperature for all four types of dislocation.Without energy minimization,the edge dislocations dissociated out-of-plane onto the basal plane.Calculations of potential energy and hydrostatic stress of individual atoms at the edge dislocation core show that the extraordinarily high energy and atomic stresses in the as-constructed dislocation structures caused the out-of-plane dissociation onto the basal plane.The core structures of all four types of pyramidal dislocation after in-plane dissociation were analyzed by computing the distribution of the Burgers vector.

Tightly focused properties of a partially coherent radially polarized power-exponent-phase vortex beam

A partially coherent beam called a radially polarized multi-Gaussian Schell-model power-exponent-phase vortex beam is introduced. Both the analytical formula of the beam propagating through the high-numerical-aperture objective lens based on the vectorial diffraction theory, and the cross-spectral density matrix of the beam in the focal region are derived. Then,the tight focusing characteristics of the partially coherent radially polarized power-exponent-phase vortex beam are studied numerically, and the intensity distribution, degree of polarization and coherence of the beams in the focusing region with different topological charge, power order, beam index and coherence width are analyzed in detail. The results show that the contour of the spot becomes clearer and smoother with the increase in the beam index, and the focal fields of different structures that include the flattened beam can be obtained by changing the coherence width. In addition, by changing the topological charge and power order, the intensity can gather to a point along the ring. These unique properties will have potential applications in particle capture and manipulation, especially in the manipulation of irregular particles.

Modified Elite Opposition-Based Artificial Hummingbird Algorithm for Designing FOPID Controlled Cruise Control System

Efficient speed controllers for dynamic driving tasks in autonomous vehicles are crucial for ensuring safety and reliability.This study proposes a novel approach for designing a fractional order proportional-integral-derivative(FOPID)controller that utilizes a modified elite opposition-based artificial hummingbird algorithm(m-AHA)for optimal parameter tuning.Our approach outperforms existing optimization techniques on benchmark functions,and we demonstrate its effectiveness in controlling cruise control systems with increased flexibility and precision.Our study contributes to the advancement of autonomous vehicle technology by introducing a novel and efficient method for FOPID controller design that can enhance the driving experience while ensuring safety and reliability.We highlight the significance of our findings by demonstrating how our approach can improve the performance,safety,and reliability of autonomous vehicles.This study’s contributions are particularly relevant in the context of the growing demand for autonomous vehicles and the need for advanced control techniques to ensure their safe operation.Our research provides a promising avenue for further research and development in this area.

Zataria multiflora and its constituent,carvacrol,counteract sepsis-induced aortic and cardiac toxicity in rat:Involvement of nitric oxide and oxidative stress

Background:Zataria multiflora and carvacrol showed various pharmacological prop-erties including anti-inflammatory and anti-oxidant effects.However,up to now no studies have explored its potential benefits in ameliorating sepsis-induced aortic and cardiac injury.Thus,this study aimed to investigate the effects of Z.multiflora and carvacrol on nitric oxide(NO)and oxidative stress indicators in lipopolysaccharide(LPS)-induced aortic and cardiac injury.Methods:Adult male Wistar rats were assigned to:Control,lipopolysaccharide(LPS)(1 mg/kg,intraperitoneal(i.p.)),and Z.multiflora hydro-ethanolic extract(ZME,50–200 mg/kg,oral)-and carvacrol(25–100 mg/kg,oral)-treated groups.LPS was in-jected daily for 14 days.Treatment with ZME and carvacrol started 3 days before LPS administration and treatment continued during LPS administration.At the end of the study,the levels of malondialdehyde(MDA),NO,thiols,and antioxidant enzymes were evaluated.Results:Our findings showed a significant reduction in the levels of superoxide dis-mutase(SOD),catalase(CAT),and thiols in the LPS group,which were restored by ZME and carvacrol.Furthermore,ZME and carvacrol decreased MDA and NO in car-diac and aortic tissues of LPS-injected rats.Conclusions:The results suggest protective effects of ZME and carvacrol on LPS-induced cardiovascular injury via improved redox hemostasis and attenuated NO pro-duction.However,additional studies are needed to elucidate the effects of ZME and its constituents on inflammatory responses mediated by LPS.

Seismogenic ULF/ELF Wave Phenomena: Recent Advances and Future Perspectives

There has been enormous progress in the field of electromagnetic phenomena associated with earthquakes (EQs) and EQ prediction during the last three decades, and it is recently agreed that electromagnetic effects do appear prior to an EQ. A few phenomena are well recognized as being statistically correlated with EQs as promising candidates for short-term EQ predictors: the first is ionospheric perturbation not only in the lower ionosphere as seen by subionospheric VLF (very low frequency, 3 kHz f 30 kHz)/LF (low frequency, 30 kHz f 300 kHz) propagation but also in the upper F region as detected by ionosondes, TEC (total electron content) observations, satellite observations, etc, and the second is DC earth current known as SES (Seismic electric signal). In addition to the above two physical phenomena, this review highlights the following four physical wave phenomena in ULF (ultra low frequency, frequency Hz)/ELF (extremely low frequency, 3 Hz frequency 3 kHz) ranges, including 1) ULF lithospheric radiation (i.e., direct radiation from the lithosphere), 2) ULF magnetic field depression effect (as an indicator of lower ionospheric perturbation), 3) ULF/ELF electromagnetic radiation (radiation in the atmosphere), and 4) Schumann resonance (SR) anomalies (as an indicator of the perturbations in the lower ionosphere and stratosphere). For each physical item, we will repeat the essential points and also discuss recent advances and future perspectives. For the purpose of future real EQ prediction practice, we pay attention to the statistical correlation of each phenomenon with EQs, and its predictability in terms of probability gain. Of course, all of those effects are recommended as plausible candidates for short-term EQ prediction, and they can be physically explained in terms of the unified concept of the lithosphere-atmosphere-ionosphere coupling (LAIC) process, so a brief description of this coupling has been carried out by using these four physical parameters though the mechanism of each phenomenon is still poorly understood. In conclusion, we have to emphasize the importance of more statistical studies for more abundant datasets sometimes with the use of AI (artificial intelligence) techniques, more case studies for huge (M greater than 7) EQ events, recommendation of critical analyses, and finally multi-parameters observation (even though it is tough work).

Assessing the Efficacy of Improved Learning in Hourly Global Irradiance Prediction

Increasing global energy consumption has become an urgent problem as natural energy sources such as oil,gas,and uranium are rapidly running out.Research into renewable energy sources such as solar energy is being pursued to counter this.Solar energy is one of the most promising renewable energy sources,as it has the potential to meet the world’s energy needs indefinitely.This study aims to develop and evaluate artificial intelligence(AI)models for predicting hourly global irradiation.The hyperparameters were optimized using the Broyden-FletcherGoldfarb-Shanno(BFGS)quasi-Newton training algorithm and STATISTICA software.Data from two stations in Algeria with different climatic zones were used to develop the model.Various error measurements were used to determine the accuracy of the prediction models,including the correlation coefficient,the mean absolute error,and the root mean square error(RMSE).The optimal support vector machine(SVM)model showed exceptional efficiency during the training phase,with a high correlation coefficient(R=0.99)and a low mean absolute error(MAE=26.5741 Wh/m^(2)),as well as an RMSE of 38.7045 Wh/m^(2) across all phases.Overall,this study highlights the importance of accurate prediction models in the renewable energy,which can contribute to better energy management and planning.

轴流压气机设计中“掠"的另类认识

本文回顾了“掠”的历史发展,对“掠”机理进行探讨,给出了关于轴流压气机设计中“掠”问题的另类认识: “掠”仅是叶片设计的一个自由度,其通过叶片基元负荷全工况范围内的展向匹配影响轴流压气机失速裕度;通过激波、附面层、叶尖泄漏流、二次流等因素对效率产生影响,这是个折衷过程。

中国农村金融服务高质量发展主要障碍是供给约束吗？

农村金融服务质量相对不高是制约"三农"发展的重要障碍之一。基于金融资源空间配置实证模型和农户融资意愿Probit模型,考察当前我国农村金融服务高质量发展的制约因素,结果表明,供给约束是当前农村金融服务高质量发展的主要障碍,影响农民收入水平及农村经济发展。鉴此,解决供给约束问题宜通过政府政策"自上而下"调节,提高农村金融服务水平。

定常吹/吸气控制凸包分离的数值研究

针对凸包流动分离主动控制开展了参数化数值研究。凸包模型为NA SA设计用于流动分离主动控制的内外流通用模型。重点研究运行参数变化时定常吹/吸气影响。首先采用试验数据标定了所开发程序,然后进行了一系列参数化数值模拟。结果显示:定常吸气作用明显不同于定常吹气。对于所给定几何模型,不管吸气流量增加到多大,吸气都无法消除分离,而吹气可以做到。详细展示流动结构并讨论了流动机理。

绕凸包主动流动控制的初步数值研究

基于程序可靠性校验,本文对绕凸包流动进行了数值研究,其中主要对不同吹／吸气速度影响进行了参数化研究,并根据结果对吹／吸气控制分离机理予以了解释,表明:吹吸气对附面层的吹断／吸除作用以及其对主流的气动堵塞作用是正确分析其影响分离机理的关键。

A review:Advances in microbial remediation of trichloroethylene (TCE)

Research works in the recent past have revealed three major biodegradation processes leading to the degradation of trichloroethylene. Reductive dechlorination is an anaerobic process in which chlorinated ethenes are used as electron acceptors. On the other hand, cometabolism requires oxygen for enzymatic degradation of chlorinated ethenes, which however yields no benefit for the bacteria involved. The third process is direct oxidation under aerobic conditions whereby chlorinated ethenes are directly used as electron donors by microorganisms. This review presented the current research trend in understanding biodegradation mechanisms with regard to their field applications. All the techniques used are evaluated, with the focus being on various factors that influence the process and the outcome.