Mathematical Wave Functions and 3D Finite Element Modelling of the Electron and Positron

The wave/particle duality of particles in Physics is well known. Particles have properties that uniquely characterize them from one another, such as mass, charge and spin. Charged particles have associated Electric and Magnetic fields. Also, every moving particle has a De Broglie wavelength determined by its mass and velocity. This paper shows that all of these properties of a particle can be derived from a single wave function equation for that particle. Wave functions for the Electron and the Positron are presented and principles are provided that can be used to calculate the wave functions of all the fundamental particles in Physics. Fundamental particles such as electrons and positrons are considered to be point particles in the Standard Model of Physics and are not considered to have a structure. This paper demonstrates that they do indeed have structure and that this structure extends into the space around the particle’s center (in fact, they have infinite extent), but with rapidly diminishing energy density with the distance from that center. The particles are formed from Electromagnetic standing waves, which are stable solutions to the Schrödinger and Classical wave equations. This stable structure therefore accounts for both the wave and particle nature of these particles. In fact, all of their properties such as mass, spin and electric charge, can be accounted for from this structure. These particle properties appear to originate from a single point at the center of the wave function structure, in the same sort of way that the Shell theorem of gravity causes the gravity of a body to appear to all originate from a central point. This paper represents the first two fully characterized fundamental particles, with a complete description of their structure and properties, built up from the underlying Electromagnetic waves that comprise these and all fundamental particles.

Characteristics and advantages of adeno-associated virus vector-mediated gene therapy for neurodegenerative diseases

Common neurodegenerative diseases of the central nervous system are characterized by progressive damage to the function of neurons, even leading to the permanent loss of function. Gene therapy via gene replacement or gene correction provides the potential for transformative therapies to delay or possibly stop further progression of the neurodegenerative disease in affected patients. Adeno-associated virus has been the vector of choice in recent clinical trials of therapies for neurodegenerative diseases due to its safety and efficiency in mediating gene transfer to the central nervous system. This review aims to discuss and summarize the progress and clinical applications of adeno-associated virus in neurodegenerative disease in central nervous system. Results from some clinical trials and successful cases of central neurodegenerative diseases deserve further study and exploration.

A HEVC Video Steganalysis Method Using the Optimality of Motion Vector Prediction

Among steganalysis techniques,detection against MV(motion vector)domain-based video steganography in the HEVC(High Efficiency Video Coding)standard remains a challenging issue.For the purpose of improving the detection performance,this paper proposes a steganalysis method that can perfectly detectMV-based steganography in HEVC.Firstly,we define the local optimality of MVP(Motion Vector Prediction)based on the technology of AMVP(Advanced Motion Vector Prediction).Secondly,we analyze that in HEVC video,message embedding either usingMVP index orMVD(Motion Vector Difference)may destroy the above optimality of MVP.And then,we define the optimal rate of MVP as a steganalysis feature.Finally,we conduct steganalysis detection experiments on two general datasets for three popular steganographymethods and compare the performance with four state-ofthe-art steganalysis methods.The experimental results demonstrate the effectiveness of the proposed feature set.Furthermore,our method stands out for its practical applicability,requiring no model training and exhibiting low computational complexity,making it a viable solution for real-world scenarios.

Immune response to inactivated bacterial vector carrying the recombinant K39 antigen of Leishmania infantum in mice

Objective:To evaluate the immunological response elicited by an inactivated bacterial vector carrying the K39 antigen of Leishmania infantum,and a purified antigen.Methods:Mice were subjected to the following treatments:(1)Purified recombinant K39(rK39)protein at a 20μg dose with complete Freund’s adjuvant;(2)Inactivated Escherichia coli(BL21 DE3)carrying the K39 protein at an equivalent total protein content of 200μg;(3)Inactivated bacteria lacking the K39 protein;(4)Non-immunized control animals.Serological monitoring was performed.All groups were challenged by intraperitoneal injection of 10^(7) Leishmania infantum promastigotes.After euthanasia,the liver and spleen were collected to analyze the levels of TNF,IFN-γ,IL-12,IL-4,and IL-10.Results:Mice immunized with purified rK39 or the inactivated bacterial vector carrying the K39 antigen of Leishmania infantum showed a long-lasting immune response with high levels of polyclonal antibodies specifically recognizing the recombinant proteins.The IgG1 subclass was the predominant immunoglobulin;however,the induction of IgG2a and the profile of cytokines produced were indicative of the induction of a mixed-type response.Conclusions:The inactivated bacterial vector carrying the K39 antigen,as well as the purified antigen can induce a long-lasting immune response in immunized mice,predominantly favouring a Th2 profile response.

Predicting of Power Quality Steady State Index Based on Chaotic Theory Using Least Squares Support Vector Machine

An effective power quality prediction for regional power grid can provide valuable references and contribute to the discovering and solving of power quality problems. So a predicting model for power quality steady state index based on chaotic theory and least squares support vector machine (LSSVM) is proposed in this paper. At first, the phase space reconstruction of original power quality data is performed to form a new data space containing the attractor. The new data space is used as training samples for the LSSVM. Then in order to predict power quality steady state index accurately, the particle swarm algorithm is adopted to optimize parameters of the LSSVM model. According to the simulation results based on power quality data measured in a certain distribution network, the model applies to several indexes with higher forecasting accuracy and strong practicability.

Antibodies elicited by Newcastle disease virus-vectored H7N9 avian influenza vaccine are functional in activating the complement system

H7N9 subtype avian influenza virus poses a great challenge for poultry industry.Newcastle disease virus(NDV)-vectored H7N9 avian influenza vaccines(NDV_(vec)H7N9)are effective in disease control because they are protective and allow mass administration.Of note,these vaccines elicit undetectable H7N9-specific hemagglutination-inhibition(HI)but high IgG antibodies in chickens.However,the molecular basis and protective mechanism underlying this particular antibody immunity remain unclear.Herein,immunization with an NDV_(vec)H7N9 induced low anti-H7N9 HI and virus neutralization titers but high levels of hemagglutinin(HA)-binding IgG antibodies in chickens.Three residues(S150,G151 and S152)in HA of H7N9 virus were identified as the dominant epitopes recognized by the NDV_(vec)H7N9 immune serum.Passively transferred NDV_(vec)H7N9 immune serum conferred complete protection against H7N9 virus infection in chickens.The NDV_(vec)H7N9 immune serum can mediate a potent lysis of HA-expressing and H7N9 virus-infected cells and significantly suppress H7N9 virus infectivity.These activities of the serum were significantly impaired after heat-inactivation or treatment with complement inhibitor,suggesting the engagement of the complement system.Moreover,mutations in the 150-SGS-152 sites in HA resulted in significant reductions in cell lysis and virus neutralization mediated by the NDV_(vec)H7N9 immune serum,indicating the requirement of antibody-antigen binding for complement activity.Therefore,antibodies induced by the NDV_(vec)H7N9 can activate antibody-dependent complement-mediated lysis of H7N9 virus-infected cells and complement-mediated neutralization of H7N9 virus.Our findings unveiled a novel role of the complement in protection conferred by the NDV_(vec)H7N9,highlighting a potential benefit of engaging the complement system in H7N9 vaccine design.

Optimized Complex Power Quality Classifier Using One vs. Rest Support Vector Machines

Nowadays, power quality issues are becoming a significant research topic because of the increasing inclusion of very sensitive devices and considerable renewable energy sources. In general, most of the previous power quality classification techniques focused on single power quality events and did not include an optimal feature selection process. This paper presents a classification system that employs Wavelet Transform and the RMS profile to extract the main features of the measured waveforms containing either single or complex disturbances. A data mining process is designed to select the optimal set of features that better describes each disturbance present in the waveform. Support Vector Machine binary classifiers organized in a “One Vs Rest” architecture are individually optimized to classify single and complex disturbances. The parameters that rule the performance of each binary classifier are also individually adjusted using a grid search algorithm that helps them achieve optimal performance. This specialized process significantly improves the total classification accuracy. Several single and complex disturbances were simulated in order to train and test the algorithm. The results show that the classifier is capable of identifying >99% of single disturbances and >97% of complex disturbances.

Whole Perfect Vectors and Fermat’s Last Theorem

A naïve discussion of Fermat’s last theorem conundrum is described. The present theorem’s proof is grounded on the well-known properties of sums of powers of the sine and cosine functions, the Minkowski norm definition, and some vector-specific structures.

Factor Vector Autoregressive Estimation of Heteroskedastic Persistent and Non Persistent Processes Subject to Structural Breaks

In the paper, a general framework for large scale modeling of macroeconomic and financial time series is introduced. The proposed approach is characterized by simplicity of implementation, performing well independently of persistence and heteroskedasticity properties, accounting for common deterministic and stochastic factors. Monte Carlo results strongly support the proposed methodology, validating its use also for relatively small cross-sectional and temporal samples.

Spin Supercurrent in Phenomena of Quantum Non-Locality (Quantum Correlations, Magnetic Vector Potential) and in Near-Field Antenna Effect

It is shown that such phenomena as quantum correlations (interaction of space-separated quantum entities), the action of magnetic vector potential on quantum entities in the absence of magnetic field, and near-field antenna effect (the existence of superluminally propagating electromagnetic fields) may be explained by action of spin supercurrents. In case of quantum correlations between quantum entities, spin supercurrent emerges between virtual particles pairs (virtual photons) created by those quantum entities. The explanation of magnetic vector potential and near-field antenna effect is based on contemporary principle of quantum mechanics: the physical vacuum is not an empty space but the ground state of the field consisting of quantum harmonic oscillators (QHOs) characterized by zero-point energy. Using the properties of the oscillators and spin supercurrent, it is proved that magnetic vector potential is proportional to the moment causing the orientation of spin of QHO along the direction of magnetic field. The near-field antenna effect is supposed to take place as a result of action of spin supercurrent causing secondary electromagnetic oscillations. In this way, the electromagnetic field may spread at the speed of spin supercurrent. As spin supercurrent is an inertia free process, its speed may be greater than that of light, which does not contradict postulates of special relativity that sets limits to the speed of inertial systems only.

Serological investigation of vector-borne disease in dogs from rural areas of China

Objective:To evaluate the Anaplasma phagocytophilum(A.phagocytophilum),Ehrlichia canis(E.canis,Dirofilaria immitis(D.immitis)(canine heartworm),Borrelia burgdorferi(B.burgdorferi)infections in countryside dogs from Yunnan,Hainan and Anhui provinces.Methods:Serum samples were collected from 26 dogs in Yunnan.Hainan and Anhui provinces.The samples were tested using a commercial ELISA rapid diagnostic assay kit(SNAP^(?)4Dx^(?);IDEXX Laboratories,Inc.U.S.A.).Meaiiwliile,indirect immunofluorescence assay(IFA)recommended by WHO was conducted to delect IgG to A.phagocytophilum.Two methods were analyzed and compared.Results:The number of serologically positive dogs for IgG to A.phagocytophilum was only 2which was from Hainan province and none of the 26 dogs responded positive for E.canu.D.immitis(canine heartworm,and B.burgdorferi by ELISA rapid diagnostic method.The number of serologically positive dogs for IgG to A.phagocytophilum was 13(50%)by IFA method.Data of the two methods were analyzed by statistical software and the difference was statistically significant(P=0.002).Conclusions:It can be concluded that IFA method was more sensitive than ELISA rapid diagnostic method.However,we need conduct further and intensive epidemiology survey on tick-born diseases pathogens including.4.phagocytophilum,E.canis,D.immitis(canine heartworm),and B.burgdorferi which have public health significance.

Specific CEA-producing colorectal carcinoma cell killing with recombinant adenoviral vector containing cytosine deaminase gene

AIM: To kill CEA positive colorectal carcinoma cells specifically using the E coli cytosine deaminase (CD) suicide gene, a new replication-deficient recombinant adenoviral vector was constructed in which CD gene was controlled under CEA promoter and its in vitro cytotoxic effects were evaluated. METHODS: Shuttle plasmid containing CD gene and regulatory sequence of the CEA gene was constructed and recombined with the right arm of adenovirus genome DNA in 293 cell strain. Dot blotting and PCR were used to identify positive plaques. The purification of adenovirus was performed with ultra-concentration in CsCl step gradients and the titration was measured with plaque formation assay. Cytotoxic effects were assayed with MTT method, The fifty percent inhibition concentration (IC(50)) of 5-FC was calculated using a curve-fitting parameter. The human colorectal carcinoma cell line, which was CEA-producing, and the CEA-nonproducing Hela cell line were applied in cytological tests. An established recombinant adenovirus vector AdCMVCD, in which the CD gene was controlled under CMV promoter, was used as virus control. Quantitative results were expressed as the mean +/- SD of the mean. Statistical analysis was performed using ANOVA test. RESULTS: The desired recombinant adenovirus vector was named AdCEACD. The results of dot blotting and PCR showed that the recombinant adenovirus contained CEA promoter and CD gene. Virus titer was about 5.0 X 10(14)pfu/L(-1) after purification. The CEA-producing Lovo cells were sensitive to 5-FC and had the same cytotoxic effect after infection with AdCEACD and AdCMVCD (The IC(50) values of 5-FC in parent Lovo cells, Lovo cells infected with 100 M.O.I AdCEACD and Lovo cells infected with 10 M.O.I AdCMVCD were 】15000, 216.5+/-38.1 and 128.8+/-25.4 micromol.L(-1), P【0.001, respectively), and the cytotoxicity of 5-FC increased accordingly when the m.o.i of adenoviruses were enhanced (The value of IC(50) of 5-FC was reduced to 27.9+/-4.2 micromol.L(-1) in 1000 M.O.I AdCEACD infected Lovo cells and 24.8+/-7.1 micromol.L(-1) in 100 M.O.I AdCMVCD infected Lovo cells, P【0.05, P【0.01, respectively). The CEA-nonproducing Hela cells had no effect after infection with AdCEACD, but Hela cells had the cytotoxic sensitivity to 5-FC after infection with AdCMVCD (The IC(50) of 5-FC in parent Hele cells and Hela cells infected with AdCMVCD at 10 M.O.I was 】15000 and 214.5+/-31.3 micromol.L(-1), P【0.001). AdCEACD/5-FC system also had bystander effect, and the viability was about 30 percent when the proportion of transfected cells was only 10 percent. CONCLUSION: The recombinant adenovirus vector AdCEACD has the character of cell type-specific gene delivery. The AdCEACD/5-FC system may become a new, potent and specific approach for the gene therapy of CEA-positive neoplasms, especially colon carcinoma.

An Efficient and Robust Fall Detection System Using Wireless Gait Analysis Sensor with Artificial Neural Network (ANN) and Support Vector Machine (SVM) Algorithms

In this work, a total of 322 tests were taken on young volunteers by performing 10 different falls, 6 different Activities of Daily Living (ADL) and 7 Dynamic Gait Index (DGI) tests using a custom-designed Wireless Gait Analysis Sensor (WGAS). In order to perform automatic fall detection, we used Back Propagation Artificial Neural Network (BP-ANN) and Support Vector Machine (SVM) based on the 6 features extracted from the raw data. The WGAS, which includes a tri-axial accelerometer, 2 gyroscopes, and a MSP430 microcontroller, is worn by the subjects at either T4 (at back) or as a belt-clip in front of the waist during the various tests. The raw data is wirelessly transmitted from the WGAS to a near-by PC for real-time fall classification. The BP ANN is optimized by varying the training, testing and validation data sets and training the network with different learning schemes. SVM is optimized by using three different kernels and selecting the kernel for best classification rate. The overall accuracy of BP ANN is obtained as 98.20% with LM and RPROP training from the T4 data, while from the data taken at the belt, we achieved 98.70% with LM and SCG learning. The overall accuracy using SVM was 98.80% and 98.71% with RBF kernel from the T4 and belt position data, respectively.

A semantic vector map-based approach for aircraft positioning in GNSS/GPS denied large-scale environment

Accurate positioning is one of the essential requirements for numerous applications of remote sensing data,especially in the event of a noisy or unreliable satellite signal.Toward this end,we present a novel framework for aircraft geo-localization in a large range that only requires a downward-facing monocular camera,an altimeter,a compass,and an open-source Vector Map(VMAP).The algorithm combines the matching and particle filter methods.Shape vector and correlation between two building contour vectors are defined,and a coarse-to-fine building vector matching(CFBVM)method is proposed in the matching stage,for which the original matching results are described by the Gaussian mixture model(GMM).Subsequently,an improved resampling strategy is designed to reduce computing expenses with a huge number of initial particles,and a credibility indicator is designed to avoid location mistakes in the particle filter stage.An experimental evaluation of the approach based on flight data is provided.On a flight at a height of 0.2 km over a flight distance of 2 km,the aircraft is geo-localized in a reference map of 11,025 km~2using 0.09 km~2aerial images without any prior information.The absolute localization error is less than 10 m.

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility results.In this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning algorithms.In total,133 historical debris flow records and 16 related factors were selected.The support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step process.First,the particle swarm optimization(PSO)algorithm was employed to select the SVM model hyperparameters.Second,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,respectively.Three statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the models.The results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM models.Moreover,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing processes.It was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment results.Moreover,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning algorithms.The high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intensive human activities and heavy rainfall events.

Secure Transmission of Compressed Medical Image Sequences on Communication Networks Using Motion Vector Watermarking

Medical imaging plays a key role within modern hospital management systems for diagnostic purposes.Compression methodologies are extensively employed to mitigate storage demands and enhance transmission speed,all while upholding image quality.Moreover,an increasing number of hospitals are embracing cloud computing for patient data storage,necessitating meticulous scrutiny of server security and privacy protocols.Nevertheless,considering the widespread availability of multimedia tools,the preservation of digital data integrity surpasses the significance of compression alone.In response to this concern,we propose a secure storage and transmission solution for compressed medical image sequences,such as ultrasound images,utilizing a motion vector watermarking scheme.The watermark is generated employing an error-correcting code known as Bose-Chaudhuri-Hocquenghem(BCH)and is subsequently embedded into the compressed sequence via block-based motion vectors.In the process of watermark embedding,motion vectors are selected based on their magnitude and phase angle.When embedding watermarks,no specific spatial area,such as a region of interest(ROI),is used in the images.The embedding of watermark bits is dependent on motion vectors.Although reversible watermarking allows the restoration of the original image sequences,we use the irreversible watermarking method.The reason for this is that the use of reversible watermarks may impede the claims of ownership and legal rights.The restoration of original data or images may call into question ownership or other legal claims.The peak signal-to-noise ratio(PSNR)and structural similarity index(SSIM)serve as metrics for evaluating the watermarked image quality.Across all images,the PSNR value exceeds 46 dB,and the SSIM value exceeds 0.92.Experimental results substantiate the efficacy of the proposed technique in preserving data integrity.

An Optimized Chinese Filtering Model Using Value Scale Extended Text Vector

With the development of Internet technology,the explosive growth of Internet information presentation has led to difficulty in filtering effective information.Finding a model with high accuracy for text classification has become a critical problem to be solved by text filtering,especially for Chinese texts.This paper selected the manually calibrated Douban movie website comment data for research.First,a text filtering model based on the BP neural network has been built;Second,based on the Term Frequency-Inverse Document Frequency(TF-IDF)vector space model and the doc2vec method,the text word frequency vector and the text semantic vector were obtained respectively,and the text word frequency vector was linearly reduced by the Principal Component Analysis(PCA)method.Third,the text word frequency vector after dimensionality reduction and the text semantic vector were combined,add the text value degree,and the text synthesis vector was constructed.Experiments show that the model combined with text word frequency vector degree after dimensionality reduction,text semantic vector,and text value has reached the highest accuracy of 84.67%.

Larvicidal and repellent potential of Moringa oleifera against malarial vector,Anopheles stephensi Liston(Insecta:Diptera:Culicidae)

Objective:To evaluate the larvicidal and pupicidal potential of the methanolic extracts from Moringa oleifera(M.oleifera) plant seeds against malarial vector Anopheles stephensi(A.stephensi) mosquitoes at different concentrations(20,40,60,80 and 100 ppm).Methods:M.oleifera was collected from the area of around Bharathiar University,Coimbatore.The dried plant materials were powdered by an electrical blender.From each sample,100 g of the plant material were extracted with 300 mL of methanol for 8 h in a Soxhlet apparatus.The extracts were evaporated to dryness in rotary vacuum evaporator to yield 122 mg and 110 mg of dark greenish material(residue) from Arcang amara and Ocimum basilicum,respectively.One gram of the each plant residue was dissolved separately in 100 mL of acetone(stock solution) from which different concentrations, i.e.,20,40,60,80 and 100 ppm were prepared.Results:Larvicidal activity of M.oleifera exhibited in the first to fourth instar larvae of the A.stephensi,and the LC_(50) and LC_(90) values were 57.79 ppm and 125.93 ppm for the first instar,63.90 ppm and 133.07 ppm for the second inslar,72.45 ppm and 139.82 ppm for the third instar,78.93 ppm and 143.20 ppm for the fourth instar,respectively. During the pupal stage the methanolic extract of M.oleifera showed that the LC_(50),and LC_(90) values were 67.77 ppm and 141.00 ppm,respectively.Conclusions:The present study indicates that the phytochemicals derived from M.oleifera seeds extracts are effective mosquito vector control agents and the plant extracts may be used for further integrated pest management programs.

An Existence Theorem of Solutions for the System of Generalized Vector Quasi-Variational-Like Inequalities

In this paper, we introduce and study the system of generalized vector quasi-variational-like inequalities in Hausdorff topological vector spaces, which include the system of vector quasi-variational-like inequalities, the system of vector variational-like inequalities, the system of vector quasi-variational inequalities, and several other systems as special cases. Moreover, a number of C-diagonal quasiconvexity properties are proposed for set-valued maps, which are natural generalizations of the g-diagonal quasiconvexity for real functions. Together with an application of continuous selection and fixed-point theorems, these conditions enable us to prove unified existence results of solutions for the system of generalized vector quasi-variational-like inequalities. The results of this paper can be seen as extensions and generalizations of several known results in the literature.

The Comparison between Random Forest and Support Vector Machine Algorithm for Predicting β-Hairpin Motifs in Proteins

Based on the research of predictingβ-hairpin motifs in proteins, we apply Random Forest and Support Vector Machine algorithm to predictβ-hairpin motifs in ArchDB40 dataset. The motifs with the loop length of 2 to 8 amino acid residues are extracted as research object and thefixed-length pattern of 12 amino acids are selected. When using the same characteristic parameters and the same test method, Random Forest algorithm is more effective than Support Vector Machine. In addition, because of Random Forest algorithm doesn’t produce overfitting phenomenon while the dimension of characteristic parameters is higher, we use Random Forest based on higher dimension characteristic parameters to predictβ-hairpin motifs. The better prediction results are obtained;the overall accuracy and Matthew’s correlation coefficient of 5-fold cross-validation achieve 83.3% and 0.59, respectively.