The technique of obtaining information or data about any feature or object from afar, called in technical parlance as remote sensing, has proven extremely useful in diverse fields. In the ecological sphere, especially, remote sensing has enabled collection of data or information about large swaths of areas or landscapes. Even then, in remote sensing the task of identifying and monitoring of different water reservoirs has proved a tough one. This is mainly because getting correct appraisals about the spread and boundaries of the area under study and the contours of any water surfaces lodged therein becomes a factor of utmost importance. Identification of water reservoirs is rendered even tougher because of presence of cloud in satellite images, which becomes the largest source of error in identification of water surfaces. To overcome this glitch, the method of the shape matching approach for analysis of cloudy images in reference to cloud-free images of water surfaces with the help of vector data processing, is recommended. It includes the database of water bodies in vector format, which is a complex polygon structure. This analysis highlights three steps: First, the creation of vector database for the analysis; second, simplification of multi-scale vector polygon features; and third, the matching of reference and target water bodies database within defined distance tolerance. This feature matching approach provides matching of one to many and many to many features. It also gives the corrected images that are free of clouds.

It is shown that a Mueller quaternion can be formulated as a quaternion with matrix coefficients, and this Mueller quaternion transforms the Stokes quaternion for depolarizing process as well as for nondepolarizing process. Mueller quaternion keeps track of the phase acquired by the Stokes quaternion and, in general, quaternion states of optical media comprises all properties of Jones, Mueller-Jones and Mueller matrices.

The aim of this paper is to obtain Karush-Kuhn-Tucker optimality conditions for weakly efficient solutions to vector equilibrium problems with the addition of constraints in the novel context of Hadamard manifolds as opposed to the classical examples of Banach, normed or Hausdorff spaces. More specifically, classical necessary and sufficient conditions for weakly efficient solutions to the constrained vector optimization problem are presented. As well as some examples. The results presented in this paper generalize results obtained by Gong (2008) and Wei and Gong (2010) and Feng and Qiu (2014) from Hausdorff topological vector spaces, real normed spaces and real Banach spaces to Hadamard manifolds, respectively.

The World Anti-Doping Agency (WADA) has prohibited gene doping in the context of progress in gene therapy. In addition, there is a risk of the EPO gene being applied in gene doping among athletes. Along with this, development of a gene-doping test has been underway in worldwide. Here, we had two purposes: to develop a robust gene doping mouse model using the human EPO gene (hEPO) transferred using recombinant adenovirus (rAdV) as a vector and to develop a detection method to prove gene doping using this model. The rAdV including the hEPO gene were injected intravenously to transfer the gene to the liver. After injection, the mice developed significantly increased red blood cell counts in whole blood and increased gene expressions of hematopoietic markers in the spleen, indicating successful development of the gene doping model. Next, we detected direct and indirect proof of gene doping in whole blood DNA and RNA using qPCR assay and RNA sequencing. Proof was detected in one drop of whole blood DNA and RNA over a long period; furthermore, the overall RNA expression profiles significantly changed. Therefore, we have advanced detection of hEPO gene doping in humans.

Background: Previous papers have developed a statistical mechanics of neocortical interactions (SMNI) fit to short-term memory and EEG data. Adaptive Simulated Annealing (ASA) has been developed to perform fits to such nonlinear stochastic systems. An N-dimensional path-integral algorithm for quantum systems, qPATHINT, has been developed from classical PATHINT. Both fold short-time propagators (distributions or wave functions) over long times. Previous papers applied qPATHINT to two systems, in neocortical interactions and financial options. Objective: In this paper the quantum path-integral for Calcium ions is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Using fits of this SMNI model to EEG data, including these effects, will help determine if this is a reasonable approach. Method: Methods of mathematical-physics for optimization and for path integrals in classical and quantum spaces are used for this project. Studies using supercomputer resources tested various dimensions for their scaling limits. In this paper the quantum path-integral is used to derive a closed-form analytic solution at arbitrary time that is used to calculate interactions with classical-physics SMNI interactions among scales. Results: The mathematical-physics and computer parts of the study are successful, in that there is modest improvement of cost/objective functions used to fit EEG data using these models. Conclusions: This project points to directions for more detailed calculations using more EEG data and qPATHINT at each time slice to propagate quantum calcium waves, synchronized with PATHINT propagation of classical SMNI.

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

Reported for the first time in 1955 in Malaysia, Tembusu virus (TMUV) remained for a long time in the shadow of flaviviruses with human health importance such as dengue virus or Japanese encephalitis virus. However, since 2010 and the first large epidemic in duck farms in China, the threat of its emergence on a large scale in Asia or even its spillover into the human population is becoming more and more significant. This review aims to report current knowledge on TMUV from viral particle organization to the development of specific vaccine and therapeutic with a particular focus on host-virus interaction.

Tissue engineering has the potential for repairing large bone defects, which impose a heavy financial burden on the public health. However, difficulties with O₂ delivery to the cells residing in the interior of tissue engineering scaffolds make it challenging to grow artificial tissues of clinically-relevant sizes. This study uses image-based simulation in order to provide insight into how to better optimize the scaffold manufacturing parameters, and the culturing conditions, in order to resolve the O₂ bottleneck. To do this, high resolution 3D X-ray images of two common scaffold types (salt leached foam and non-woven fiber mesh) are fed into a Lattice Boltzmann Method fluid dynamics and reactive Lagrangian Scalar Tracking mass transfer solvers. The obtained findings indicate that the scaffolds should have maximal surface area-to-solid volume ratios, for higher chances of the molecular collisions with the cells. Furthermore, the cell culture media should be flown through the scaffold pores as fast as practically possible (without detaching or killing the cells). Finally, we have provided a parametric sweep that maps how the molecular transport within the scaffolds is affected by variations in rates of O₂ consumption by the cells. Ultimately, the results of this study are expected to benefit the computer-assisted design of tissue engineering scaffolds and culturing experiments.

The bacterial genus and species of Bacillus thuringiensis var israelensis (Bti), is entomotoxic, used in the biological control of mosquito vectors of human diseases, such as malaria and dengue. Following the studies carried out in Mexico and Guatemala with ovillantas, in which the presence of larvae of both species of Anopheles albimanus W and Aedes aegypti L were observed, and with the intention of continuing to develop an ecologically friendly mosquito control, Bti was added to the ovillantas, to improve and already efficient method on the elimination of both types of mosquitoes. The objectives of this work were: to analyze the effectiveness of two commercial formulations of Bti, serovar H-14 (Bactimos) wettable powder, 3,500 ITU, from Biochem prods., and Vectobac, 2,000 ITU wettable powder, (Abbot Lab.) on second and third instars larvae of A. albimanus and Ae. aegypti (Diptera Culicidae). The two formulations were effective against A. albimanus W(higher concentrations), while Ae. aegypti L was very susceptible to Bti, therefore it is proposed for the best control of these genus and vector species of malaria and dengue at adequate concentrations.

We introduce a modelling framework for the investigation of on-line machine learning processes in non-stationary environments. We exemplify the approach in terms of two specific model situations: In the first, we consider the learning of a classification scheme from clustered data by means of prototype-based Learning Vector Quantization (LVQ). In the second, we study the training of layered neural networks with sigmoidal activations for the purpose of regression. In both cases, the target, i.e. the classification or regression scheme, is considered to change continuously while the system is trained from a stream of labeled data. We extend and apply methods borrowed from statistical physics which have been used frequently for the exact description of training dynamics in stationary environments. Extensions of the approach allow for the computation of typical learning curves in the presence of concept drift in a variety of model situations. First results are presented and discussed for stochastic drift processes in classification and regression problems. They indicate that LVQ is capable of tracking a classification scheme under drift to a non-trivial extent. Furthermore, we show that concept drift can cause the persistence of sub-optimal plateau states in gradient based training of layered neural networks for regression.

The article is devoted to biological models using recurrence sequences, which are connected with the harmonic progression 1, 1/2, …, 1/n, and some cooperative properties of genomes. The harmonic progression is itself one of the recurrence sequences based on the harmonic mean. This progression appears in the hyperbolic rules of oligomer cooperative organization in eukaryotic and prokaryotic genomes. This allows thinking that the harmonic progression is also related to inherited physiological systems, which must be structurally consistent with the genetic coding system for their transmission to descendants and survival in evolution. The harmonic progression is one of historically known mathematical series, whose features were studied by Pythagoras, Leibniz, Newton, Euler, Fourier, Dirichlet, Riemann. It is widely used in many known algorithms and is closely related to some other important mathematical objects, for example, the function of the natural logarithm and harmonic numbers. Accordingly, the article describes the possibilities of using these interrelated mathematical objects to model biological structures, including logarithmic spirals and some other. Modeling inherited spiral configurations seems to be a particularly urgent task, since they are extremely common at all levels of organization of living bodies and, according to Goethe, are lines of life. The principle of a recurrence similarity, that is a special similarity of parts and transformations presented in recurrence sequences of numbers and matrix operators (the scale similarity and scale transformations are only particular cases of such similarity), is considered as one of the key principles of structural organization of living bodies.

This paper proposes a method of extracting energy from zero-point energy and evaluates the amount of energy gained. In addition, this electric circuit-based approach exhibits the Meissner effect, suggesting a new type of superconductivity that does not require refrigeration. The proposed method can provide extremely large amounts of energy, which is more than a conventional power station, without consuming fossil fuels or emitting radiation. Thus, it has the potential to solve the global energy problem. It involves preparing two electric loops containing diodes and connecting the loops together with current sources. The diodes are oriented in the same direction within each loop but in opposite directions in different loops. With this setup, the currents from the current sources build iteratively within the loops, resulting in large output currents. Our numerical analysis indicates that extremely large electric potentials are produced, which in turn yield large output currents. In addition, we confirm numerically that the voltage is zero around a loop and show analytically that the Meissner effect is present, proving the existence of a new type of superconductivity. Furthermore, when we introduce induction coils to not break the loop’s symmetry, they store extremely large amounts of energy and we can thus obtain energy from them via discharge currents.

The author's method of oligomer sums for analysis of oligomer compositions of eukaryotic and prokaryotic genomes is described. The use of this method revealed the existence of general rules for cooperative oligomeric organization of a wide list of genomes. These rules are called hyperbolic because they are associated with hyperbolic sequences including the harmonic progression 1, 1/2, 1/3, .., 1/n. These rules are demonstrated by examples of quantitative analysis of many genomes from the human genome to the genomes of archaea and bacteria. The hyperbolic (harmonic) rules, speaking about the existence of algebraic invariants in full genomic sequences, are considered as candidates for the role of universal rules for the cooperative organization of genomes. The described phenomenological results were obtained as consequences of the previously published author's quantum-information model of long DNA sequences. The oligomer sums method was also applied to the analysis of long genes and viruses including the COVID-19 virus; this revealed, in characteristics of many of them, the phenomenon of such rhythmically repeating deviations from model hyperbolic sequences, which are associated with DNA triplets. In addition, an application of the oligomer sums method are shown to the analysis of the following long sequences: 1) amino acid sequences in long proteins like the protein Titin; 2) phonetic sequences of long Russan literary texts (for checking of thoughts of many authors that phonetic organization of human languages is deeply connected with the genetic language). The topics of the algebraic harmony in living bodies and of the quantum-information approach in biology are discussed.

In a limestone quarry mine, fragmentation is a crucial outcome of blasting operations. The optimization of blasting operations greatly benefits from the prediction of rock fragmentation. The main factors that affect fragmentation are rock mass characteristics, blast geometry, and explosive properties. This paper is a step towards the implementation of machine learning and deep learning algorithms for predicting the extent of fragmentation (in percentage) in opencast mining. Various parameters can affect fragmentation. But, in this paper initially, ten parameters (spacing, drill hole diameter, burden, average bench height, powder factor, number of holes, charge per delay, uniaxial compressive strength, specific drilling, and stemming) are collected to train the model. However, due to a weak correlation with rock fragmentation, drill diameter, Average bench height, compressive strength, stemming, and charge per delay are eliminated to reduce model complexity. A total of 219 data sets having five input features i.e., the number of holes, spacing, burden, specific drilling, and powder factor are used to develop the models. To predict rock fragmentation due to blasting in limestone quarry mines, both machine learning models (Random Forest Regression (Bagging), Support Vector Regression, and XG Boost Regression (Boosting)), as well as a deep learning model (Neural Network Regression), are applied to develop a model that can optimize the prediction of fragmentation. The Artificial neural network model optimization showed that the model with architecture 64-32-16-1 can perform well giving MSE (mean squared error) values of 41.32 and 28.59 on training and test data respectively. The R2 value for both training and test is 0.83. Random Forest regression is also performing well compared to SVR and XG boost with the MSE value 12.37 and 9.89 on training and testing data respectively. Here, the R2 value for both sets are 94%. Based on the permutation importance and Shapely plot values, the powder factor has the highest impact, and the burden has the lowest impact on fragmentation.

ARBTools is a Python library containing a Lekien-Marsden type tricubic spline method for interpolating three-dimensional scalar or vector fields presented as a set of discrete data points on a regular cuboid grid. ARBTools was developed for simulations of magnetic molecular traps, in which the magnitude, gradient and vector components of a magnetic field are required. Numerical integrators for solving particle trajectories are included, but the core interpolator can be used for any scalar or vector field. The only additional system requirements are NumPy.

Premature ovarian insufficiency (POI) is a highly prevalent disorder, characterized by the development of menopause before age of 40. Most cases are idiopathic; however, in some women the cause of this condition (e.g. anticancer treatment, genetic disorders, and enzymatic defects) may be identified. Although hormone replacement therapy, the principal therapeutic approach for POI, helps to alleviate the related symptoms, this does not effectively solve the issue of fertility. Assisted reproductive techniques also lack efficacy in these women. Thus, the effective approach to manage the patients with POI is highly warranted. Several mechanisms, associated with POI, have been identified, including lack of FSH receptor functioning, alterations in the apoptosis control, mutations in Sal-like 4 genes, thymulin or basonuclin-1 deficiency etc. The above-mentioned may be good targets for gene therapy in order to correct defects, leading to POI. The goal of this review is to summarize the current experience on the POI studies, that employed gene therapy, and to discuss the possible future directions in this field.

Recent studies investigated the detectability of ship wake signatures on SAR imagery using a large number of SAR images collocated with Automatic Identification System data for training machine learning models. These detectability models are in agreement with oceanographic expectations from preceding studies and can therefore be used for comparing the performance of different SAR sensors in terms of wake detectability. Previous model comparisons showed better wake detection performance of TerraSAR-X (TS-X) than of RADARSAT-2 (RS2) and Sentinel-1 (S1). A comparison between CosmoSkymed (CSK) and RS2 is performed here, to examine the hypothesis that X-Band is generally better for wake detection than C-Band. Finally, this hypothesis is not confirmed, as the detectability models for TS-X, CSK and RS2 reveal similar performances. A comparison of wake detection performance should take the individual wake components into account separately.

Due to related uncertainties brought by changes in energy consumption and the integration of rooftop photovoltaic systems, the accuracy and availability of load profiling data are difficult to achieve. However, project managers and planners use a precise load profile as a crucial tool when determining whether the feeder must be updated or de-loaded. The paper aims to create a non-intrusive monitoring system that predicts the synthetic load profiles behavior of energy consumption using a Light Gradient Boosted and Support Vector Machine (SVM) machine learning technique. The most effective ML algorithm is chosen, and it has the potential to be assessed and verified using validation curves, residuals model generation, and prediction error metrics based on the following key statistical indicators: Mean Absolute Error, Mean Square Error, Root Mean Square Error, R-Square, Root Mean Squared Logarithmic Error, and Mean Absolute Percentage Error. The most effective ML has the potential to be assessed and verified using validation curves, residual model generation, and prediction error metrics based on the following key statistical indicators. The result shows that the estimation of the Irms avg -based on LightGBM is more accurate than the SVM because of its quick, economical, and difficult to overfit, particularly with high-dimensional data, speed, and efficiency of the MAE (3.0698), MSE (15.2757), RMSE (3.9020), RMSLE (0.1433), and MAPE (0.0049) respectively. Additionally, the machine learning model shows that, when compared to the SVM, the LightGBM model had the highest accurate prediction, with R-Square values of 89.8%, 90.3%, and 88.5% for Irms_A_avg, Irms_B_avg, and Irms_C_avg, respectively for Brakfontein Substation supply region, and best represents a diverse customer base.

Mammography is often used as the most common laboratory method for the detection of breast cancer, yet associated with the high cost and many side effects. Machine learning prediction as an alternative method has shown promising results. This paper presents a method based on a multilayer fuzzy expert system for the detection of breast cancer using an extreme learning machine (ELM) classification model integrated with radial basis function (RBF) kernel called ELM-RBF, considering the Wisconsin dataset. The performance of the proposed model is further compared with a linear-SVM model. The proposed model outperforms the linear-SVM model with RMSE, R², MAPE equal to 0.1719, 0.9374 and 0.0539, respectively. Furthermore, both models are studied in terms of criteria of accuracy, precision, sensitivity, specificity, validation, true positive rate (TPR), and false-negative rate (FNR). The ELM-RBF model for these criteria presents better performance compared to the SVM model.

In this present study, we conducted untargeted metabolic profiling using Gas Chromatography-Mass Spectrometry (GC-MS) analysis of ascidian Didemnum bistratum to assess the chemical constituents by searching in NIST library with promising biological properties against anti-bacterial and Zika virus vector mosquitocidal Properties. Metabolites, steroids and fatty acids are abundant in crude compounds of ascidian D. bistratum and showed potential zone growth inhibition against bacterial strains Kluyvera ascorbate (10 mm). The active crude compounds of D. bistratum exhibited prominent larvicidal activity against the Zika vector mosquitoes of Aedes aegypti and Cluex quinquefasciatus (LC₅₀ values of 0.4436 to 2.23 mg/mL). The findings of this study provide a first evidence of the biological properties exhibited by D. bistratum extracts, thus increasing the knowledge about the Zika virus vector mosquitocidal properties of ascidian. Overall, ascidian D. bistratum are promising and biocontrol or eco-friendly tool against A. aegypti and C. quinquefasciatus with prospective toxicity against non-target organisms.

Pseudomonas aeruginosa is a Gram-negative bacillus included among the six "ESKAPE" microbial species with an outstanding ability to "escape" currently used antibiotics and developing new antibiotics against it is of the highest priority. Whereas minimum inhibitory concentration (MIC) values against Pseudomonas aeruginosa have been used previously for QSAR model development, disk diffusion results (inhibition zones) have not been apparently used for this purpose in the literature, and we decided to explore their use in this sense. We developed multiple QSAR methods using several machine learning algorithms (Support vector classifier, K Nearest Neighbors, Random Forest Classifier, Decision Tree Classifier, AdaBoost Classifier, Logistic Regression, and Naive Bayes Classifier). The main descriptors used in building the models belonged to the families of adjacency matrix, constitutional descriptors, first highest eigenvalue of Burden matrix, centered Moreau-Broto autocorrelation, and averaged and centered Moreau-Broto autocorrelation descriptors. A total of 32 models were built, of which 28 were selected and stacked to create a meta-model. In terms of balanced accuracy, the best performance was provided by KNN, SVM and AdaBoost algorithms, but the ensemble method had slightly superior results in nested cross-validation.

This study aims at improving the forecast accuracy of primary energy consumptions in China, Japan and South Korea and verifying the correlation in primary energy consumptions among the neighboring countries. Considering the diversity of primary energy composition, this study selects 6 components of primary energy, including oil, coal, natural gas, nuclear energy, hydropower and renewable energy as characteristic variables. A collaborative prediction model based on SVR for primary energy consumption prediction is proposed to explore the correlation of primary energy consumption among three countries in China, Japan and South Korea. The results show that there is a strong correlation between primary energy consumption when multiple countries make collaborative prediction, among which the primary energy consumption of South Korea has the largest impact on the primary energy consumption of China and Japan. In the primary energy cooperation of China-Japan-South Korea, a primary energy cooperation system with the South Korea as the link should be established through regional coordination to alleviate the shortage of traditional fossil energy.

Evaporation is one of the main processes in the hydrological cycle, and it is one of the most critical factors in agricultural, hydrological, and meteorological studies. Due to the interactions of multiple climatic factors, the evaporation is a complex and nonlinear phenomenon; therefore, the data-based methods can be used to have precise estimations of it. In this regard, in the present study, Gaussian Process Regression (GPR), Nearest-Neighbor (IBK), Random Forest (RF) and Support Vector Regression (SVR) were used to estimate the pan evaporation (PE) in the meteorological stations of Golestan Province, Iran. For this purpose, meteorological data including PE, temperature (T), relative humidity (RH), wind speed (W) and sunny hours (S) collected from the Gonbad-e Kavus, Gorgan and Bandar Torkman stations from 2011 through 2017. The accuracy of the studied methods was determined using the statistical indices of Root Mean Squared Error (RMSE), correlation coefficient (R) and Mean Absolute Error (MAE). Furthermore, the Taylor charts utilized for evaluating the accuracy of the mentioned models. The outcome indicates that the optimum state of Gonbad-e Kavus, Gorgan and Bandar Torkman stations, Gaussian Process Regression (GPR) with the error values of 1.521, 1.244, and 1.254, the Nearest-Neighbor (IBK) with error values of 1.991, 1.775, and 1.577, Random Forest (RF) with error values of 1.614, 1.337, and 1.316, and Support Vector Regression (SVR) with error values of 1.55, 1.262, and 1.275, respectively, have more appropriate performances in estimating PE. It found that GPR for Gonbad-e Kavus Station with input parameters of T, W and S and GPR for Gorgan and Bandar Torkmen stations with input parameters of T, RH, W, and S had the most accurate performances and proposed for precise estimation of PE. Due to the high rate of evaporation in Iran and the lack of measurement instruments, the findings of the current study indicated that the PE values might be estimated with few easily measured meteorological parameters accurately.

This paper puts forward an alteration for Tensor Calculus utliized in a coordinate system which is under a dynamic distortion drawing inspiration from similar fields such as the Calculus of Moving Surfaces (CMS). The paper establishes transformation laws for Tensors within these regions and establishes Operators such as the Tensorial Field Derivative which enforce a Tensorial Transformation on Tensors defined within a Dynamically Moving coordinate system. This variation of Tensor Calculus can be utilized to observe how disciplines such as QFT and Continuum Mechanics would change to accomodate for a distorting coordinate system and can be utliized to develop new theoretical models which account for this temporal distortion particularly within Biological Settings.

Virtual Histology- Intravascular Ultrasound (VH-IVUS) image is an available method for visualizing plaque component to detect thin cap fibroatheroma. Nevertheless, this imaging modality has considerable limitations to extract the plaque component features and identifying the TCFA plaque. The aim of this paper is to improve the identification of TCFA using fusion of IVUS and VH-IVUS images. In order to generate the automatic technique for reducing the human interaction, a new method namely Active Contour based Plaque Border Detection (ACPB) is proposed. In order to perform the pixel wise classification, hybrid of K-means algorithm with Particle Swarm Optimization and Plaque based Minimum Euclidean Distance (KMPSO-PMED) method is presented to classify the plaque region as well. Moreover, to obtain more significant information of imaging modalities, fusion of two different images consisting of VH-IVUS and IVUS is performed. Therefore, geometric features are extracted from the plaque region and combine with IVUS features. Furthermore, different group of plaque features are divided by means of the histopathological studies. SVM classifiers is applied to detect the TCFA and non-TCFA plaques. The proposed method is applied on 566 in-vivo IVUS and their matching VH-IVUS images obtained from 9 patients. The best result of SVM illustrates the accuracy rates of 99.41% for classification of TCFA plaque. The results prove that the highest accuracy is achieved by integrated features of IVUS and VH-IVUS images.

Three decades have now passed since the first papers linking climate change to issues in human disease and healthcare. One of the most active topics in this area has been the implication of climate change events, particularly temperature and humidity fluctuations, in the northward spread of vector-borne viruses from more tropical regions into Europe and North America. However, some detailed studies of one such emerging disease, tick-borne encephalitis virus (TBEv), have called the connection into question, concentrating the debate on the investigation of precise mechanisms for the spread of viral disease. More recently, firmer statistical correlations have been made between climate variables, the presence of insect vectors and the prevalence of viral disease, particularly for West Nile Virus (WNV). These insights suggest avenues for mechanistic confirmation of the involvement of climate change in other diseases where the connection remains conjectural.

Soft-Core architecture for Analogue to Digital Converter (ADC) sampling is useful for mixed signal applications. Soft-core architecture for cutting edge odd or even ADC sampling with interface to block RAM memory has not been found. Soft-core architecture as a concept has become popular due to the advantage of customization for different applications as compared to general-core architecture suited for single application. The latest generation of piece wise sampling is odd sampling and was introduced in the second decade of the 20th century. Odd and even order sampling techniques are analogue in nature driven by a tuned (tuned for odd or even) mixer. This paper proposes a third-generation piece wise sampling with soft-core architecture that enables an option to select both odd and even while interfacing to memory mapping. The proposed odd/even has superior SNR performance of 6 dB as compared to existing architecture such as Mod-∆ which recorded worst performance of 18 dB. Advances in soft-core technology have allowed a niche odd/even switching field to be identified and studied, the study has also been extended to include memory architecture.

We analyze the modular geometry of the variable exponent Lebesgue space L^p(.). We show that L^p(.) possesses a modular uniform convexity property. Part of the novelty is that the property holds even in the case supp(x) = ∞ . We present specific applications to fixed point theory. xÆΩ

Predicting copper price is essential for making decisions that can affect companies and governments dependent on the copper mining industry. Copper prices follow a time series that is non-linear, non-stationary, and which have periods that change as a result of potential growth, cyclical fluctuation and errors. Sometimes the trend and cyclical components together are referred to as a trend-cycle. In order to make predictions, it is necessary to consider the different characteristics of trend-cycle. In this paper, we study a copper price prediction method using Support Vector Regression. This work explores the potential of the Support Vector Regression with external recurrences to make predictions at 5, 10, 15, 20 and 30 days into the future in the copper closing price at the London Metal Exchanges. The best model for each forecast interval is performed using a grid search and balanced cross-validation. In experiments on real data-sets, our results obtained indicate that the parameters (C, ε, γ) of the model Support Vector Regression do not differ between the different prediction intervals. Additionally, the amount of preceding values used to make the estimates does not vary according to the predicted interval. Results show that the support vector regression model has a lower prediction error and is more robust. Our results show that the presented model is able to predict copper price volatilities near reality, being the RMSE equal or less than the 2.2% for prediction periods of 5 and 10 days.

Crowd density estimation is an important task for crowd monitoring. Many efforts have been done to automate the process of estimating crowd density from images and videos. Despite series of efforts, it remains a challenging task. In this paper, we proposes a new texture feature-based approach for the estimation of crowd density based on Completed Local Binary Pattern (CLBP). We first divide the image into blocks and then re-divide the blocks into cells. For each cell, we compute CLBP and then concatenate them to describe the texture of the corresponding block. We then train a multi-class Support Vector Machine (SVM) classifier, which classifies each block of image into one of four categories, i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS 2009 dataset, and from the experiments, we show to achieve 95% accuracy for the proposed descriptor. We also compare other state-of-the-art texture descriptors and from the experimental results, we show that our proposed method outperforms other state-of-the-art methods.

The article presents the author's results of studying hidden rules of structural organizations of long DNA sequences in eukaryotic and prokaryotic genomes. The results concern some rules of percentages (or probabilities) of n-plets in genomes. To reveal such rules, the author considers genomic DNA nucleotide sequences as multilayers sequences of n-plets and studies the percentage contents of n-plets in different layers. Unexpected rules of invariance of total sums of percentages in certain tetra-groupings of n-plets in different layers of genomic DNA sequences are revealed. These discovered rules are candidates for the role of universal genomic rules. A tensor family of matrix representations of interrelated DNA-alphabets of 4 nucleotides, 16 doublets, 64 triplets, and 256 tetraplets is used in the study. This matrix approach allows revealing algebraic properties of the mentioned genetic rules of probabilities, which are useful for developing algebraic and quantum biology. Some analogies of the discovered genetic phenomena with phenomena of Gestalt psychology are noted and discussed. The author connects the received results about the genomic percentages rules with a supposition of P. Jordan, who is one of the creators of quantum mechanics and quantum biology, that life's missing laws are the rules of chance and probability of the quantum world. Additional attention is paid to the algebraic features of the system of structured DNA alphabets and their relationship with the methods of algebraic holography, known in the technique of processing discrete signals. The concept of algebraic-holographic genetics is being developed for the understanding of inherited holographic properties of organisms.

ClinVar is a web platform that stores around 774k curated entries, which allows exploring genetic variants and their associations with complex phenotypes. A partial set of ClinVar’s genetic associations were reported with conflict of interpretation or uncertain clinical impact significance, which currently challenges clinicians and geneticists. Here, we evaluate the performance of data pre-processing methods combined with classical prediction methods, such as Naive Bayes, Random Forest, and Support Vector Machine to build a meta-prediction model aiming to improve genetic pathogenicity interpretation. Models were trained with ClinVar data (September 2020), and genetic variants were annotated with eight functional impact predictors catalogued with SnpEff/SnpSift (v4.3). A 10-fold cross-validation strategy was performed for evaluation by accuracy, F1-Score, Receiver Operating Characteristic, Area Under Curve. The best meta-prediction model raises by combining one-hot encoding with tree-based classifiers as Random Forest, which shows Area Under Curve ≥ 0,93. We predict pathogenicity for 109k genetic variants, which were found labeled as uncertain significance or conflict of interpretation. Additionally, we implemented AmazonForest (https://www.lghm.ufpa.br/amazonforest), a web tool to query data for a set of 5k variants that were predicted with high pathogenic probability (RFprob >= 0.9).

Although significant efforts have been made to combat the spread of vector-borne diseases (VBDs), they still account for more than 17% of all infectious diseases. According to the World Health Organization (WHO), there were 216 million estimated cases in 2016, which is a 9.3% decrease from the estimated cases reported one decade earlier. It is known that the built environment, through features such as openings, can propagate the spread of malaria. There have been some significant efforts directed at addressing this risk. This notwithstanding, there are some knowledge gaps that have resulted in a missed opportunity for synergistically tackling the problem of vectors through leveraging design decisions made by built environment professionals. This work assesses the extent to which design decisions in the built environment can have a positive impact on the efforts directed at mitigating the risk of malaria based on selected cases from East Africa. Secondary data derived from relevant urban health journals as well as repositories curated by leading health agencies such as WHO were synthesized and analyzed using a web of causation approach. The outcome of the analysis is a schema of primary and secondary source (risk) factors. The use of the web of causation approach revealed the existing factor-to-factor interactions that could have a reinforcing effect. This information was used to identify the critical linkages and interdependencies across different factors. The outcome of the analysis was mapped against risk factors that can be linked to decisions made during the six primary phases of the construction life cycle: preliminary phase, conceptual design, detailed design, construction, facilities management, and end of life/disuse. The findings of the research have established that 1) there is, in fact, a built environment–related opportunity that can be leveraged to advance the impact of malaria mitigation effort; 2) cross-disciplinary synergies are critical to managing the interdependencies and complexity of malaria risk factors that have a reinforcing effect; and 3) a knowledge-management framework that serves as a decision support tool would be valuable for sharing data under a push-and-pull mechanism, in which data shared in real time can address the timeliness of mitigating the spread of malaria at the earliest stages for the greatest impact. Based on the findings, a conceptual architecture for a decision support framework has been proposed. This will be developed into a knowledge-management platform in subsequent efforts.

To achieve the near zero emission of wastewater in the flue gas desulfurization (FGD) system in coal-fired power plant and better utilize the exhaust heat from flue gas, a feasible technology of spraying FGD wastewater in the flue duct for evaporation is discussed in the present study. A full-scale influencing factor investigation on the wastewater droplet evaporation performance is established under the Eulerian-Lagrangian model numerically. The dominant factors, including the characters of wastewater droplets, flue gas and the spray nozzles were analyzed under different conditions, respectively. Considering the multiple factors and conditions in the process, a Least-Square support vector machine (LSSVM) model is introduced to predict the evaporation rate based on the numerical results. Conclusions are made that the flue gas temperature and droplet diameter are of great importance in the evaporation process. The spray direction of droplet parallel with the flue gas flow direction is profitable for the dispersion of droplet, resulting the maximal evaporation rate. A double-nozzle arrangement optimized with relatively small flow rate is recommended. The LSSVM model can accurately predict the evaporation rate using the numerical results with different conditions.

Psyllids (Hemiptera: Psylloidea) can transmit the phloem restricted bacterium ‘Candidatus Liberibacter solanacearum’ (Lso). In Europe, Lso causes severe losses to carrot and represents a threat to the potato industry. A rising concern of Lso transmission from carrot to potato and within potato has driven the need for monitoring populations of psyllid species that could serve as vectors on both crops, which would provide a fundamental understanding of the epidemiology of this pathogen. Different sampling methods were used to survey populations of psyllid species in commercial carrot and potato fields in Central and Eastern mainland Spain from 2015 to 2017. Two psyllid species, Bactericera trigonica and Bactericera nigricornis were found to be mainly associated with carrot and potato crops. In carrot fields the most abundant species was B. trigonica occurring from crop emergence to harvest, whereas in potato crops the most abundant psyllid species was B. nigricornis. The maximum psyllid population occurred between June and October its timing depending on the field location. Since B. nigricornis was found on both carrot and potato and is the only psyllid species able to feed and breed on both these crops in Europe, there is the potential risk of Lso transmission from carrot to potato

Remaining service life (RSL) of pavement, as a sign of future pavement performance, has always received growing attention from pavement engineers. The RSL describes the time from the moment of pavement inspection until such a time when a major repair or reconstruction is required. The conventional approach to determining RSL involves using non-destructive tests. These tests, in addition to being costly, interfere with traffic flow and compromise users' safety. In this paper, surface distresses of pavement have been used to estimate the pavement’s RSL in order to eliminate the aforementioned problems and challenges. To implement the proposed theory, 105 flexible pavement segments were taken from Shahrood-Damghan Highway (Highway 44) in Iran. For each pavement segment, the type, severity, and extent of surface damage and pavement condition index (PCI) were determined. The pavement RSL was then estimated using non-destructive tests include Falling Weight Deflectometer (FWD) and Ground Penetrating Radar (GPR). After completing the dataset, the modeling was conducted to predict RSL using three techniques include Support Vector Regression (SVR), Support Vector Regression Optimized by Fruit Fly Optimization Algorithm (SVR-FOA), and Gene Expression Programming (GEP). All three techniques estimated the RSL of the pavement by selecting the PCI as input. The Correlation Coefficient (CC), Nash-Sutcliffe efficiency (NSE), Scattered Index (SI), and Willmott’s Index of agreement (WI) criteria were used to examine the performance of the three techniques adopted in this study. In the end, it was found that GEP with values of 0.874, 0.598, 0.601, and 0.807 for CC, SI, NSE, and WI criteria, respectively, had the highest accuracy in predicting the RSL of pavement.

The purpose of this paper is to find a quantum formulation of entropic gravity, incorporating the bijective theory. In a first part, we will describe the basis of the bijective theory. In a second part, we will incorporate the stress energy quantum tensor defined in the linear approximation of the Einstein’s equation in its quantum version to entropy. In the third part, we will integrate the bijective theory with the newly created relativistic quantum entropy. This will lead us four part where we will formalize quantum entropic gravity.

Heat metres are used to calculate the consumed energy in central heating systems. The subject of this article is to prepare a method of predicting a failure of a heat meter in the next settlement period. Predicting failures is essential to coordinate the process of exchanging the heat metres and to avoid inaccurate readings, incorrect billing and additional costs. The reliability analysis of heat metres was based on historical data collected over many years. Three independent models of machine learning were proposed, and they were applied to predict failures of metres. The efficiency of the models was confirmed and compared using the selected metrics. The optimisation of hyperparameters characteristics for each of models was successfully applied. The article shows that the diagnostics of devices does not have to rely only on newly collected information, but it is also possible to use the existing big data sets.

Fault diagnosis of the gearbox is a decisive part of the modern industry to find the many gearbox defects like gear tooth crack, chipped or broken, etc. But sometimes, the nonstationary properties of vibration signal and low energy of minimal faults make this procedure very challenging. Previously, many types of techniques have been developed for gearbox condition monitoring. But most of the methods are dealing with conventional techniques of the gearbox condition monitoring, such as time-domain analysis or frequency domain analysis. Most of the conventional methods are not suitable for the nonstationary vibration signal. Thus, this paper presents a novel gearbox fault diagnosis technique using conditional temporal moments and an optimizable support vector machine (SVM). This work also presents an integrated features extraction technique based on the standard features, i.e., statistical and spectral features with the combinations of moment features. The impact of the four conditional temporal moments of each gearbox condition is also presented. This work shows that the proposed method successfully classifies and categorizes the gearbox faults at an early stage.

For the solution existence condition of the Navier-Stokes equation, we propose a conjecture as follows: "\emph{The Navier-Stokes equation has a solution if and only if the determinant of flow velocity gradient is not zero, namely $\det (\bm \nabla \bm v)\neq 0$.}"

Here we studied Bianchi type-III string cosmological models generated by means of a cloud of strings with particles connected to them in the framework of Lyra geometry considering five-dimensional space-time. To obtain the exact solutions of field equations we consider that the shear scalar and the scalar expansion are proportional, σ∞θ which leads to D=cn and secondly we adopt the assumption considering the Reddy String Condition, ρ+λ=0. From the two different cases obtained here, first case leads to the Bianchi type-III string cosmological model in Lyra geometry in five-dimensional space-time and the second case leads to the five-dimensional vacuum universe in general relativity. The Geometrical and physical properties of the model universe are studied comparing with the present day’s observational findings. The model universe obtained here starts with the big bang and as time progresses both particle density ρp and energy density ρ decreases with expansion of our Universe.

Lipid nanoparticles (LNPs) have attracted extensive attention in tumor immunotherapy in recent years. The strategy of targeting immune cells in cancer therapy has become a research area of great interest. mRNA vaccines are a potential choice for tumor immunotherapy due to their ability to directly encode antigen proteins and stimulate a strong immune response. However, the mode of delivery and lack of stability of mRNA are key issues limiting its application. LNPs are an excellent mRNA delivery vector, and their structural stability and biocompatibility make them an effective means to deliver mRNA to specific targets. This study summarizes the research progress in LNP delivery vector-assisted targeted controlled release mRNA vaccines in tumor immunity. The role of LNPs in improving mRNA stability, immunogenicity and targeting is discussed. In addition, combined with the cutting-edge research results, the potential mechanisms and application prospects of LNP-mRNA vaccines in tumor immunotherapy were further analyzed. This review aims to systematically summarize the latest research progress in LNP delivery carrier-assisted targeted controlled-release mRNA vaccines in tumor immunity to provide new ideas and strategies for tumor immunotherapy and provide more effective treatment plans for patients.

Dengue virus infections are a serious public health problem worldwide. Aedes aegypti is the primary vector of dengue in Cuba. Since there is no vaccine or specific treatment, the control efforts are directed to reduce mosquito populations. The indiscriminate use of pesticides can lead to increase insecticide resistance as well as adverse effects on human health. The sterile insect technique is a species-specific and environmental friendly method of insect control based on the release of large numbers of sterile males. The success of this technique in sustainable control of agricultural pests has encouraged its evaluation for mosquito control. Here, we describe an open field trial to evaluate the effect of the release of irradiated males on a wild population of Aedes aegypti. The case-control study was performed in a suburb of Havana, and compared the mosquito population before and after the intervention, in both control and treated areas. The wild population was monitored by an ovitrap network, recording frequency and density of eggs as well as their hatch rate. A significant induced sterility was observed in the field population, compared to the control. The ovitrap index and the mean eggs/ trap declined dramatically after an expected lag period of twelve and five weeks, respectively. For the last three weeks, no egg was collected in the treated area, evidencing a significant suppression of the wild population. We conclude that the sterile males released competed successfully, and induced enough sterility to suppress the local Aedes aegypti population.

Control And Data Acquisition (SCADA) systems currently monitor and collect a huge among of data from all kind of processes. In practice, due to sensor failures or to communication errors, in the long-time running, some data may be lost. When it happens, given the nature of these failures, information is lost in bursts, that is, sets of consecutive samples, which besides can be very long. Data completion is a critical step, which must be done with the utmost rigour in order to not propagate errors in the rest of the processing chain stages. Some Big Data techniques do not work if the data series are incomplete, due to the loss of some data. When this occurs it is necessary to fill out the gaps of the historical data with a reliable data completion method. This paper presents an ad-hoc method to replenish the data lost by a SCADA system in case of long bursts. The data correspond to levels of drinking water tanks of a Water Network company that present patterns on a daily and a weekly scale. A method based on tensors is used to take advantage of the data structure. A specially designed \textit{tensorization} is employed to deal with bursts of missed data, applying a twice tensor decomposition and a signal continuity correction. Statistical tests are realized, which consist of apply the data reconstruction algorithms, by deliberately removing bursts of data in verified historical database servers, to be able to evaluate the real effectiveness of the tested methods. For this application, the presented approach outperforms the other techniques found in the literature.

Surface soil moisture (SSM) is a significant factor affecting crop growth. This paper presents a method for retrieving SSM over wheat-covered areas using synergy dual-polarization C-band Sentinel-1 synthetic aperture radar and Sentinel-2 optical data. Firstly, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction was then introduced in this WCM, and the vegetation water content (VWC) was calculated using multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in-situ measurements of the wheat field in Hebi, in the north of Henan Province. The key findings of this study are as follows: (1) Based on vegetation indices obtained from Sentinel-2; the proposed VWC estimation model can effectively eliminate the influence of vegetation; (2) compared with vertical transmit and horizontal receive polarization, vertical transmit and vertical receive polarization is better for detecting changes in SSM at different growth stages of wheat; and, (3) the validation results indicated that the proposed approach, based on Sentinel-1 and Sentinel-2 data, successfully retrieved SSM in the study area.

Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major persisting problem is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. This entails investment in time, energy and expenses at various levels of research. To address these issues, a search engine design was recently been developed, which showed promise by revealing existing confirmatory published wet lab results. Additionally and of import, the engine mined up a range of unexplored/untested/unknown combinations of genetic factors in the Wnt pathway that were affected by ETC-1922159 enantiomer, a PORCN-WNT inhibitor, after the colorectal cancer cells were treated with the inhibitor drug. As an example, MYC is known to upregulate PRC2 complex. PRC2 complex contains EZH2, which suppresses tumor suppressor genes via epigenetic modifications. MYC and HOXB8 are up regulated in colorectal cancer, however, the dual working mechanism of the same is not known. The in silico engine showed positioning which correctly approximates and assigns to this 3rd order combination of MYC-HOXB8-EZH2, pointing to the in vitro/in vivo down regulation by ETC-1922159. If the protein interaction of MYC-HOXB8 can be established and a study be done apropos EZH2, it will establish at in vitro/in vivo level, the in silico ranking also. The potential of this engine is immense given the problem faced in biology and other fields. Here we elucidate the R code to understand the mechanics of the search engine in a fluid manner for systems biologists. Though the search engine is in the developmental stage, we share the detailed mechanism of the working principles of the same as it can be generalized to problems in other fields.

In this paper, we argue that one can calculate the weak deflection angle in the background of Einstein-Maxwell-Dilaton-Axion black hole using the Gauss-Bonnet theorem. To support this, the optical geometry of the black hole with the Gibbons-Werner method are used to obtain the deflection angle of light in the weak field limits. Moreover, we investigate the effect of a plasma medium on deflection of light for a given black hole. Because of dilaton and axion are one of the candidate of the dark matter, it can give us a hint on observation of dark matter which is supported the black hole. Hence we demonstrate the observational viability via showing the effect of the dark matter on the weak deflection angle.

It is known that the class of p-normed spaces (0 < p ≤ 1) is an important generalization of usual normed spaces with rich topological and geometrical structure, but the most of tools and general principles in nature with nonlinearity have not been developed yet, thus the main goal of this paper is to develop tools for nonlinear analysis under the framework of p-vector spaces. In particular, we first develop the general fixed point theorems which provide solutions to answer Schauder conjecture since 1930’s in the affirmative for p-vector spaces when p = 1 (which is just general topological vector spaces); then the one best approximation result for upper semi-continuous mappings is given, which is used as a powerful tool to establish fixed points for non-self set-valued mappings with either inward or outward set conditions; and finally we establish comprehensive existence results of solutions for Birkhoff-Kellogg Problems, and the general principle of nonlinear alternative by including Leray-Schauder alternative and related results. The results given in this paper not only include the corresponding results in the existing literature as special cases, but also expected to be useful tools for the study of nonlinear problems arising from theory to practice.

This work presents a novel approach to construct surrogate models of parametric Differential Algebraic Equations based on a tensor representation of the solutions. The procedure consists in building simultaneously, for every output of the reference model, an approximation given in tensor-train format. A parsimonious exploration of the parameter space coupled with a compact data representation allows to alleviate the curse of dimensionality. The approach is thus appropriate when many parameters with large domains of variation are involved. The numerical results obtained for a nonlinear elasto-viscoplastic constitutive law show that the constructed surrogate model is sufficiently accurate to enable parametric studies such as the calibration of material coefficients.

Traditional discrimination techniques for Euler deconvolution use only the color spectrums of structural indexes, without considering the spatial distribution characteristics and inherent relationships among the Euler solutions to separate adjacent causative sources. In the present study, a new approach was developed for discriminating uncorrelated Euler solutions from coherent solutions based on the focusing levels indicated by the probability density distributions generated using multivariate kernel density estimations (KDE). A novel multiple coverage technique was proposed by using a series of different sized moving windows over gridded gravity data, which formed tight clusters of Euler solutions for different sized causative sources. The results of the probability density distributions were obtained using a 3-D KDE method for the Euler solution subsets {x, y, z} of synthetical models, and real data from a survey conducted in British Columbia (Canada) which had successfully established more credible and meaningful geological models when compared with three other subsets.

This paper is given to the investigation of warm inflation using Modified Chaplygin gas in the background of locally rotationally symmetric Bianchi Identity type I. We find out the field equations and perturbations parameters such as; scalar power spectrum, scalar spectral index, scalar potential and tensor to scalar ratio under slow roll approximation. We find out these parameters in directional of Hubble parameter during the Logamediate inflationary regime in weak and strong case. These comological parameters shows that the anisotropic model is also compatible WMAP$7$ with recent observational data Planck $2018$.

Pencil-beam Doppler scatterometers are a promising remote sensing tool for measuring ocean vector winds and currents from space. While several point designs exist in the literature, these designs have been constrained by the hardware they inherited, and the design is sub-optimal. Here, I present guidelines to optimize the design of these instruments starting from the basic sensitivity equations. Unlike conventional scatterometers or pencil-beam imagers, appropriate sampling of the Doppler spectrum and optimizing the radial velocity error lead naturally to a design that incorporates a pulse-to-pulse separation and pulse length that vary with scan angle. Including this variation can improve radial velocity performance significantly and the optimal selection of system timing and bandwidth is derived. Following this, optimization of the performance based on frequency, incidence angle, antenna length, and spatial sampling strategy are considered. It is shown that antenna length influences the performance most strongly, while the errors depend only on the square root of the transmit transmit power. Finally, a set of example designs and associated performance are presented.

Different machine learning models (multiple linear regression, vector support machines, artificial neural networks and random forests) are applied to predict the monthly global irradiation (MGI) from different input variables (latitude, longitude and altitude of meteorological station, month, average temperatures, among others) of different areas of Galicia (Spain). The models were trained, validated and queried using data from three stations, and each best machine model was checked in two independent stations. The results obtained confirmed that the best ML methodology is the ANN model which presents the lowest RMSE value in the validation and querying phases 122.6·10kJ/(m2∙day) and 113.6·10kJ/(m2∙day), respectively, and predict conveniently for independent stations, 201.3·10kJ/(m2∙day) and 209.4·10kJ/(m2∙day), respectively. Given the good results obtained, it is convenient to continue with the design of artificial neural networks applied to the analysis of monthly global irradiation.

Aedes aegypti is inherently susceptible to arboviruses. The geographical expansion of this vector host species has led to the persistence of Dengue, Zika and Chikungunya human infections. These viruses take advantage of the mosquito’s cell to create an environment conducive for their growth. Arboviral infection triggers transcriptomic and protein dysregulation in Ae. aegypti and in effect, host antiviral mechanisms are compromised. Currently, there are no existing vaccines able to protect human hosts from these infections and thus, vector control strategies such as Wolbachia mass release program is regarded as a viable option. Considerable evidence demonstrates how the presence of Wolbachia interferes with arboviruses by decreasing cellular components vital for the pathogen and strengthening antiviral host responses. However, variation in the magnitude of Wolbachia’s viral inhibition that is neither due to strain nor density has been observed. Furthermore, the cellular mechanisms involved in the endosymbiont’s pathogen-blocking differs among hosts. This prompts the need to explore the cellular interactions between Ae. aegypti-arboviruses-Wolbachia and how these interactions overall affect the mosquito’s cell. Understanding what happens at the cellular and molecular level will provide evidence on the sustainability of Wolbachia vector control.

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Qt-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

This paper updates earlier thoughts by the author on a putative electromagnetic propulsion system, to perform a more detailed energy balance. The previous paper demonstrated how momentum could be dumped to the ground state of the electromagnetic field but a claim was left somewhat hanging at the end of the previous paper, that the work done in changing the craft's velocity would effectively shift the centre of mass of the field - although that would be an infinitesimal shift in practice. The craft must always supply work to change velocity, such as by an accelerate/de-accelerate cycle and superficially this looks to violate the conservation of energy; we prove that this isn't so.

In the present work, a novel conductive liquids method of study has been proposed. It is based on the phenomenon of radiofrequency anisotropy of electrolyte solution discovered by us. It arises in response to mechanical or acoustic excitation of the solution. We have observed the phenomenon during the development of an RF polarimetric contactless cardiograph. The electric field vector of the transmitted 433.82 MHz signal rotated after its transition through the pericardial region. That rotation depends on the change of blood acceleration when passing through the chambers of the heart and large vessels. It has also been revealed that rotation occurs after RF wave passage through the physiological saline (0.9% NaCl) subjected to any mechanical excitation inside it like a jet appearing or soundwave passing. No significant difference was detected experimentally between NaCl and KCl solutions behavior. It means that the mechanism of hydrodynamic separation of ions is apparently not suitable to explain the phenomenon. The response we have registered resembles the magnetization process of spin glasses. From the nature of the observed response, we have concluded that a fundamentally new physical effect is discovered. It may provide wide opportunities for remote measurement of the electrolyte solution parameters with polarized radio-frequency signals.

We show that the Einstein field equations for a five-dimensional warped spacetime, where only gravity can propagate into the bulk, determine the dynamical evolution of the warp factor of the four-dimensional brane spacetime. This can be explained as a holographic manifestation. The warped 5D model can be reformulated by considering the warp factor as a dilaton field ($\omega$) conformally coupled to gravity and embedded in a smooth $M_4 \otimes R$ manifold. On the brane, where the U(1) scalar-gauge fields live, the dilaton field manifests itself classically as a warp factor and enters the evolution equations for the metric components and matter fields. We write the Lagrangian for the Einstein-scalar-gauge fields in a conformal invariant setting. However, as expected, the conformal invariance is broken (trace-anomaly) by the appearance of a mass term and a quadratic term in the energy-momentum tensor of the scalar-gauge field, arising from the extrinsic curvature terms of the projected Einstein tensor. These terms can be interpreted as a constraint in order to maintain conformal invariance. By considering the dilaton field and Higgs field on equal footing on small scales, there will be no singular behavior, when $\omega\rightarrow 0$ and one can deduce constraints to maintain regularity of the action. Our conjecture is that $\omega$, alias warp factor, has a dual meaning. At very early times, when $\omega \rightarrow 0$, it describes the small-distance limit, while at later times it is a warp (or scale) factor that determines the dynamical evolution of the universe. We also present a numerical solution of the model and calculate the (time-dependent) trace-anomaly. The solution depends on the mass ratio of the scalar and gauge fields, the parameters of the model and the vortex charge $n$.

Machine fault diagnosis systems need to collect and transmit dynamic monitoring signals, like vibration and current signals, at high-speed. However, industrial wireless sensor networks (IWSNs) and Industrial Internet of Things (IIoT) are generally based on low-speed wireless protocols, such as ZigBee and IEEE802.15.4. To address this tension when implementing machine fault diagnosis applications in IIoT, this paper proposes a novel IWSN with on-sensor data processing. On-sensor wavelet transforms using four popular mother wavelets are explored for fault feature extraction, while an on-sensor support vector machine classifier is investigated for fault diagnosis. The effectiveness of the presented approach is evaluated by a set of experiments using motor bearing vibration data. The experimental results show that compared with raw data transmission, the proposed on-sensor fault diagnosis method can reduce the payload transmission data by 99.95%, and reduce the node energy consumption by about 10%, while the fault diagnosis accuracy of the proposed approach reaches 98%.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Our goal in this paper is to study the relationship between the linear approximation of Einstein's equations to the Klein-Gordon’s equation. The part one presents what the Klein-Gordon’s equation and the integration of the theory of quantum information in it. The Part two deals with the Stress Energy tensor quantum, wherein the detail I linearized gravity of Einstein equation, and wherein I develop the tensor quantum energy pulse from the equivalence of equation einstein the linearized gravity and the Schrödinger equation relativistic described by Klein-Gordon’s equation.

Unicellular eukaryotes of the Trypanosomatidae family include human and animal pathogens that belong to the Trypanosoma and Leishmania genera. Diagnosis of the diseases they caused requires the sampling of body fluids (blood, lymph, peritoneal fluid, cerebrospinal fluid, etc.) or organ biopsies (bone marrow, spleen, etc.), which are mostly obtained through invasive methods. Body fluids or appendages can be alternatives to these invasive biopsies but appropriateness remains poorly studied. To further address this question, we perform a systematic review on clues evidencing the presence of parasites, genetic material, antibodies, and antigens in body secretions, appendages, or the organs or proximal tissues that produce these materials. Paper selection was based on searches in PubMed, Web of Science, WorldWideScience, SciELO, Embase, Google. The information of each selected article (n=333) was classified into different sections and data were extracted from 77 papers. The presence of Trypanosomatidae parasites has been tracked in most of organs or proximal tissues that produce body secretions or appendages, in naturally or experimentally infected hosts. The meta-analysis highlights the paucity of studies on Human African Trypanosomiasis and a the absence on animal Trypanosomiasis. Among the collected data high heterogeneity in terms of the I² statistic (100%) is recorded. A high positivity is recorded for antibody and genetic material detection in urine of patients and dogs suffering leishmaniasis, and of antigen for leishmaniasis and Chagas disease. Data on conjunctival swab can be analyzed with molecular methods solely for dogs suffering canine visceral leishmaniasis. Saliva and hair/bristle showed a pretty good positivity that support their potential to be used for leishmaniasis diagnosis. In conclusion, our study pinpoints significant gaps that need to be filled in order to properly address the interest of body secretion and hair or bristle for the diagnosis of infections caused by Leishmania and by other Trypanosomatidae parasites.

The need to increase the energy efficiency of buildings as well as the use of local renewable heat sources has caused that heat meters are used not only to calculate the consumed energy but also for the active management of central heating systems. Increasing the reading frequency and the use of measurement data to control the heating system expands the requirements for the reliability of heat meters. The aim of the research is to analyse a large set of meters in the real network and predict their faults to avoid inaccurate readings, incorrect billing, heating system disruption and unnecessary maintenance. The reliability analysis of heat metres, based on historical data collected over several years, shows some regularities which cannot be easily described by physics-based models. The failure rate is almost constant and does depend on the past but is a non-linear combination of state variables. To predict meters' failures in the next settlement period, three independent machine learning models are implemented and compared with selected metrics because even the high performance of a single model (87\% True Positive for Neural Network) may be insufficient to make a maintenance decision. Additionally, performing hyperparameters optimisation boosts models' performance by a few percent. Finally, three improved models are used to build an ensemble classifier which outperforms the individual models. The proposed procedure ensures the high efficiency of fault detection (>95\%), while maintaining overfitting at the minimum level. The methodology is universal and can be utilised to study the reliability and predict faults of other types of meters and different objects with the constant failure rate.

Formalism proofing general derivation, applying matrix properties operations, showing fundamental relationships with inner product to outer product has been advanced here. This general proof formalism has direct application with physics to quantify quantum density at micro scale level to time commutator at macro scale level. System of operator algebraic equations have been rigorously derived to obtain analytic solutions which are physically acceptable. Extended physics application will include metricizing towards unitarization to achieve gaging Hamiltonian mechanics to electromagnetic gravitational strong theory, towards grand unifying physics atomistic to astrophysics or vice versa via quantum relativistic general physics thereby patching to classical physics fields energy.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

Air passenger traffic prediction is crucial for the effective operation of civil aviation airports. Despite some progress in this field, the prediction accuracy and methods need further improvement. This paper proposes an integrated approach to the prediction of air passenger index as follows. Firstly, the air passenger index is defined and classified by the K-means clustering method. Based on the mutual information (MI) principle, the information entropy is used to analyze and select the key influencing factors of air passenger travel. By incorporating the MI principle into the support vector regression (SVR) framework, this paper presents an innovative MI-SVR machine learning model used to predict the air passenger index. Finally, the proposed model is validated by passenger throughput data of the Shanghai Pudong International Airport, China. The experimental results prove the model feasibility and effectiveness by comparing them with conventional methods, such as ARIMA, LSTM, and other machine learning models, outperformed by the MI-SVR model. Besides, it is shown that the prediction effect of each model could be improved by introducing influencing factors based on mutual information. The main findings are considered instrumental to the airport operation and air traffic optimization.

Adequate knowledge about the development and operation of the components of water systems is of high importance in order to optimize them. For this reason, forecasting of future events becomes greatly significant due to making the appropriate decision. Moreover, operational river management severely depends on accurate and reliable flow forecasts. In this regard, current study inspects the accuracy of support vector regression (SVR), and SVR regulated with fruit fly optimization algorithm (FOASVR) and M5 model tree (M5), in river flow forecasting. Monthly data of river flow in two stations of the Lake Urmia Basin (Vaniar and Babarud stations on the Aji Chay and the Barandouz Rivers) were utilized in the current research. Additionally, the influence of periodicity (π) on the forecasting enactment was examined. To assess the performance of mentioned models, different statistical meters were implemented, including root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R), and Bayesian information criterion (BIC). Results showed that the FOASVR with RMSE (4.36 and 6.33 m3/s), MAE (2.40 and 3.71 m3/s) and R (0.82 and 0.81) values had the best performances in forecasting river flows in Babarud and Vaniar stations, respectively. Also, regarding BIC parameters, Q_t-1 and π were selected as parsimonious inputs for predicting river flow one month ahead. Overall findings indicated that, although both FOASVR and M5 predicted the river flows in suitable accordance with observed river flows, the performance of FOASVR was moderately better than the M5 and periodicity noticeably increased the performances of the models; consequently, FOASVR can be suggested as the accurate method for forecasting river flows.

Use of Machine Learning (ML) methodologies in optical communications has paved a new pathway. In this paper, firstly, we discuss the use of ML methodologies for reducing optical fiber nonlinearities, nonlinearity compensation, fault detection and optical performance monitoring. Then we present our recent work where we compare RL-SARSA and SVM based method with conventional method. The results show that RL-SARSA and SVM methods are successful candidates in mitigating the nonlinearities in proposed system as compared to conventional optical communication system.

Freeway travelling time is affected by many factors including traffic volume, adverse weather, accident, traffic control and so on. We employ the multiple source data-mining method to analyze freeway travelling time. We collected toll data, weather data, traffic accident disposal logs and other historical data of freeway G5513 in Hunan province, China. Using Support Vector Machine (SVM), we proposed the travelling time model based on these databases. The new SVM model can simulate the nonlinear relationship between travelling time and those factors. In order to improve the precision of the SVM model, we applied Artificial Fish Swarm algorithm to optimize the SVM model parameters, which include the kernel parameter σ, non-sensitive loss function parameter ε, and penalty parameter C. We compared the new optimized SVM model with Back Propagation (BP) neural network and common SVM model, using the historical data collected from freeway G5513. The results show that the accuracy of the optimized SVM model is 17.27% and 16.44% higher than those of the BP neural network model and the common SVM model respectively.

Identifying human actions in complex scenes is widely considered as a challenging research problem due to the unpredictable behaviors and variation of appearances and postures. For extracting variations in motion and postures, trajectories provide meaningful way. However, simple trajectories are normally represented by vector of spatial coordinates. In order to identify human actions, we must exploit structural relationship between different trajectories. In this paper, we propose a method that divides the video into N number of segments and then for each segment we extract trajectories. We then compute trajectory descriptor for each segment which capture the structural relationship among different trajectories in the video segment. For trajectory descriptor, we project all extracted trajectories on the canvas. This will result in texture image which can store the relative motion and structural relationship among the trajectories. We then train Convolution Neural Network (CNN) to capture and learn the representation from dense trajectories. . Experimental results shows that our proposed method out performs state of the art methods by 90.01% on benchmark data set.

We show that quantum mechanics can be constructed as a classical field theory that correctly describes all basic quantum effects, by combining the Maxwell and Dirac equations. It is shown that for a self-consistent union of the Maxwell and Dirac equations into a unified classical field theory, it is necessary to introduce an additional short-range tensor field, which compensates the intrinsic electrostatic field of the electron wave inside the atom. For the combined Maxwell-Dirac field, the stress-energy tensor is constructed. We show that in the nonrelativistic limit this theory naturally transforms into the self-consistent Maxwell-Pauli theory and allows describing all basic quantum effects in the framework of classical field theory without any quantization.

This paper results from our investigation into novel means of electromagnetic propulsion. It requires the basis of our claims to be put on a sound theoretical footing regarding the purported momentum exchange with the electromagnetic field. One of these concerns is the huge discrepancy between the energy density of the Zeropoint and its purported manifestation as the Cosmological Constant. Here we state that it is manifestly wrong to introduce the zeropoint at zero order into the stress-energy tensor, because it is something which describes zero particle count. As a fluctuation, it belongs in a higher order Taylor expansion in frequency of the stress-energy tensor. Furthermore in the 3^rd order in the Einstein constant our procedure is some 9 orders of magnitude too small. We make up this difference by suggesting that vacuum energy is much higher still and that more degrees of freedom exist in physics beyond the Standard Model or that there is interaction energy between the modes.

Simulating the non-perturbative and non-Markovian dynamics of open quantum systems is a very challenging many body problem, due to the need to evolve both the system and its environments on an equal footing. Tensor network and matrix product states (MPS) have emerged as powerful tools for open system models, but the numerical resources required to treat finite temperature environments grow extremely rapidly and limit their applications. In this study we use time-dependent variational evolution of MPS to expore the striking theory of Tamescelli et al. that shows how finite temperture open dyanmics can be obtained from zero temperature, i.e. pure wave function, simulations. Using this approach, we produce a benchmark data set for the dynamics of the Ohmic spin-boson model across a wide range of coupling and temperatures, and also present detailed analysis of the numerical costs of simulating non-equilibrium steady states, such as those emerging from the non-perturbative coupling of a qubit to baths at different temperatures. Despite ever growing resource requirements, we find that converged non-perturbative results can be obtained, and we discuss a number of recent ideas and numerical techniques that should allow wide application of MPS to complex open quantum systems.

Insecticides with novel modes of action are required to complement the pyrethroids currently relied upon for controlling malaria vectors. One example of this is the neonicotinoid clothianidin, which is found in SumiShield™ 50WG used in indoor residual spraying (IRS). In a preliminary experiment, mortality in insecticide susceptible and resistant An. gambiae adults exposed to SumiShield™ 50WG-treated filter papers reached 80% by 3-days post-exposure and 100% by 6-days post-exposure. Next, cement, wood, and mud tiles were treated with SumiShield™ 50WG or K-Othrine® WG250 (deltamethrin IRS formulation) and insecticide resistant and susceptible Anopheles and Aedes were exposed to these surfaces periodically for up to 18-months. Pyrethroid resistant Cx. quinquefasciatus were also exposed at 9 months. Between exposures tiles were stored in heat and relative humidity conditions reflecting those found in the field. On these surfaces, SumiShield™ 50WG was effective at killing both susceptible and resistant An. gambiae for 18 months post-treatment, while mortality amongst the resistant strains when exposed to deltamethrin (K-Othrine® WG250) IRS was not above that of the negative control. Greater efficacy of SumiShield™ 50WG was also demonstrated against insecticide resistant strains of An. funestus compared to deltamethrin, though the potency was lower when compared with An. gambiae. In general, a higher efficacy of SumiShield™ 50WG was observed on cement and mud compared to wood. SumiShield™ 50WG demonstrated poor residual activity against Aedes aegypti and Culex quinquefasciatus. Overall, the results suggest SumiShield™ 50WG is well suited for malaria control.

Matrix multiplication is probably one of the most basic and important operations having a wide range of applications in studying Linear algebra. We will be primarily focused on reviewing different algorithms for the multiplication of two n×n matrices. The most Naive method has a complexity of O(n3) but interestingly, algorithms with better complexity have been achieved. We will get to see how the complexity can be improved further, occasionally giving a survey.

The article continues the author's publications about the matrix-tensor study of universal rules of stochastic (probabilistic) organization of long single-stranded DNA sequences in eukaryotic and prokaryotic genomes. The author reveals that corresponding matrices of probabilities of n-plets in n-textual representations of each genomic DNA are numerically interrelated each with other in such algebraic form, which has analogies with formalisms of the known tensor-matrix theory of digital antenna arrays. These arrays combine many separate antennas into a single coordinated ensemble with unique emergent properties, due to which antenna arrays are widely used in devices of medicine, astrophysics, avionics, etc. The noted analogies allow putting forward the author's hypothesis that the stochastic organization of genomic DNAs is connected with bio-antenna arrays. From the point of view of this hypothesis, many known facts about using principles of antenna arrays in inherited physiological phenomena are collected in a single grouping with genomic DNAs. This new topic about the biological meaning of profitable properties of antenna arrays includes problems of biological evolution, the origin of the genetic code, regenerative medicine, and the development of algebraic biology. These issues are discussed jointly with the author's results of quantum information analysis of stochastic features of genomic DNAs.

Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.

We developed a honey bee RNA-virus vector based on the genome of a picorna-like Deformed wing virus (DWV), the main viral pathogen of the honey bee (Apis mellifera). To test the potential of DWV to be utilized as a vector, the 717 nt sequence coding for the enhanced green fluorescent protein (eGFP), flanked by the peptides targeted by viral protease, was inserted into an infectious cDNA clone of DWV in-frame between the leader protein and the virus structural protein VP2 genes. The in vitro RNA transcripts from egfp-tagged DWV cDNA clones were infectious when injected into honey bee pupae. Stable DWV particles containing genomic RNA of the recovered DWV with egfp inserts were produced, as evidenced by cesium chloride density gradient centrifugation. These particles were infectious to honey bee pupae when injected intra-abdominally. Fluorescent microscopy showed GFP expression in the infected cells and Western blot analysis demonstrated accumulation of free eGFP rather than its fusions with DWV LP and/or VP2 proteins. Analysis of the progeny egfp-tagged DWV showed gradual accumulation of genome deletions for egfp, providing estimates for the rate of loss of a non-essential gene an insect RNA virus genome during natural infection.

Background: New insecticides with novel modes of action such as neonicotinoids have recently been recommended for public health use by WHO. Resistance monitoring of such novel insecticides requires a robust protocol to monitor the development of resistance in natural populations. In this study, we comparatively used three different solvents to assess the susceptibility of malaria vectors to neonicotinoids across Africa.Methods: Mosquitoes were collected from May to July 2021 from three agricultural settings in Cameroon (Njombe-Penja, Nkolondom, and Mangoum), the Democratic Republic of Congo (Ndjili-Brasserie), Ghana (Atatam), and Uganda (Mayuge). Using the CDC bottle test, we compared the effect of three different solvents (ethanol, acetone, acetone+MERO) on the efficacy of neonicotinoids against Anopheles gambiae s.l. In addition, TaqMan assays were used to genotype key pyrethroid-resistant markers in An. gambiae and to evaluate potential cross-resistance between pyrethroids and clothianidin.Results: Lower mortalities were observed for all populations when using absolute ethanol or acetone alone as solvent (11.4- 51.9% mortality for Nkolondom, 31.7- 48.2% for Mangoum, 34.6- 56.1% for Mayuge, 39.4- 45.6% for Atatam, 83.7- 89.3% for Congo and 71.05- 95.9% for Njombe pendja) compared to acetone + MERO for which 100% mortality was observed for all the populations. Synergist assays (PBO, DEM and DEF) revealed a significant increase of mortality suggesting that metabolic resistance mechanisms are contributing to the reduced susceptibility. A negative association was observed between the L1014F-kdr mutation and clothianidin resistance with a greater frequency of homozygote resistant mosquitoes among the dead than among survivors (OR=0.5; P=0.02). However, the I114T-GSTe2 was in contrast significantly associated with a greater ability to survive clothianidin with a higher frequency of homozygote resistant among survivors than other genotypes (OR=2.10; P=0.013). Conclusions: This study revealed a contrasted susceptibility pattern depending on the solvents with ethanol/acetone resulting in lower mortality, thus possibly overestimating resistance, whereas the addition of MERO consistently increased the efficacy of neonicotinoids in terms of percentage mortalities and time to final mortality. The addition of MERO could however prevent the early detection of resistance development. We therefore recommend monitoring susceptibility using both acetone alone and acetone+MERO (8-10µg/ml for clothianidin) to capture the accurate resistance profile of the mosquito populations.

The hybrid bond graph has been studied in depth for scalar bond graphs, but how does this translate to the multi-bond graph? Here, the controlled junction – used to model structural switching such as contact – is extended to the multi-bond case. This is a simple process, assuming that all bonds switch simultaneously (which makes physical sense). A controlled 0-junction is applied to multi-bond graph of a car, which can lose contact with the ground in cornering. Dynamic causality features, but this can be accommodated using an equational submodel in 20-Sim (in a manner similar to that used with scalar bond graphs). The junction is proposed for subsequent work to develop a validated multi-body dynamics car model in cornering.

Pyrethroid resistance is widespread in malaria vectors. However, differential mortality in discriminating dose assays to different pyrethroids is often observed in wild populations. When this occurs, it is unclear if this differential mortality should be interpreted as an indication of differential levels of susceptibility within the pyrethroid class, and if so, if countries should consider selecting one specific pyrethroid for programmatic use over another. A review of evidence from molecular studies, resistance testing with laboratory colonies and wild populations, and mosquito behavioural assays was conducted to answer these questions. Evidence suggests that in areas where pyrethroid resistance exists, different results in insecticide susceptibility assays with specific pyrethroids currently in common use (deltamethrin, permethrin, α-cypermethrin and λ-cyhalothrin) are not necessarily indicative of an operationally relevant difference in potential performance. Consequently, it is not advisable to use rotation between these pyrethroids as an insecticide resistance management strategy. Less commonly used pyrethroids (bifenthrin and etofenprox) may have sufficiently different modes of action, though further work would be needed to examine how this may apply to insecticide resistance management.

The article is devoted to applications of 2-dimensional hyperbolic numbers and their algebraic 2ⁿ-dimensional extensions in modeling some genetic phenomena. Mathematical properties of hyperbolic numbers and their matrix representations are described in a connection with alphabets of DNA nucleobases and with inherited phyllotaxis phenomena. Known data on using hyperbolic rotations, which are particular cases of hyperbolic numbers, in physics and in some biological phenomena, including phyllotaxis laws and structural features of locomotions, are discussed. Applications of hyperbolic numbers reveal hidden interrelations between structures of different biological and physical phenomena. They lead to new approaches in mathematical modeling genetic phenomena.

A generalization of $\eta$-Ricci solitons is considered involving an additional metric and functions as soliton coefficients. The soliton potential is torse-forming and orthogonal to the contact distribution of the almost contact B-metric manifold. Then such a manifold can also be considered as an almost Einstein-like manifold, a generalization of an $\eta$-Einstein manifold with respect to both B-metrics and functions as coefficients. Necessary and sufficient conditions are found for a number of properties of the curvature tensor and its Ricci tensor of the studied manifolds. Finally, an explicit example of an arbitrary dimension is given and some of the results are illustrated.

This article presents a review of the distribution system state estimation (DSSE) algorithms from the model-based approach to the recent data-driven methodologies. The insufficient measurements which result in low observability have motivated the need to shift from the conventional algorithm to data-driven approaches that can successfully estimate states despite the DSSE challenges. This article discusses the nonlinearity in the DSSE problem formulation and how different model-based methods have been proposed to mitigate the problems of robustness, ill-conditioning, and complexity. Moreover, approximate DSSE to obviate nonlinearity were discussed: complex linearization, small angle approximation, convexification and compressed sensing. Furthermore, probabilistic DSSE methods were also discussed in the need to quantify the uncertainty associated with the state estimation results. Also, data-driven methods applicable to DSSE, pseudo measurement generations, and topology identification were also discussed using machine and deep learning methods. Lastly, a recent approach that employs a hybrid of model-based and data-driven methods using matrix and tensor completion is surfacing because they work in a low observable condition of the network and can estimate the states satisfactorily. With this review, researchers can look further into developing and improving on the model-based data-driven methods less susceptible to the barriers in conventional DSSE algorithms.

We propose a novel tensor-based formalism for inferring causal structures from time series. An information theoretical analysis of transfer entropy (TE), shows that TE results from transmission of information over a set of communication channels. Tensors are the mathematical equivalents of these multi-channel causal channels. A multi-channel causal channel is a generalization of a discrete memoryless channel (DMC). We consider a DMC as a single-channel causal channel. Investigation of a system comprising three variables shows that in our formalism, bivariate analysis suffices to differentiate between direct and indirect relations. For this to be true, we have to combine the output of multi-channel causal channels with the output of single-channel causal channels. We can understand this result when we consider the role of noise. Subsequent transmission of information over noisy channels can never result in less noisy transmission overall. This implies that a Data Processing Inequality (DPI) exists for transfer entropy.

In this article key results in general relativity are studied and the dynamics of quantum fields in curved spacetime is reviewed. It has been shown geometrically how dark energy breaks strong energy condition. We study tan-hyperbolic inflation for an open FRWL universe and show that over half and full period scalar field remains in it's initial state.

This letter attempts to clarify an issue regarding the proper definition of plastic dislocation density tensor. This study shows that the Ortiz’s and Berdichevsky’s plastic dislocation density tensor are equivalent with each other, but not with Kondo’s one. To fix the problem, we propose a modified version of Kondo’s plastic dislocation density tensor.

Therapeutic efficacy of retroviral replicating vector (RRV)-mediated prodrug activator gene therapy has been demonstrated in a variety of tumor models, but clinical investigation of this approach has so far been restricted to glioma and gastrointestinal malignancies. In the present study, we evaluated replication kinetics, transduction efficiency, and therapeutic efficacy of RRV in experimental models of lung cancer. RRV delivering GFP as a reporter gene showed rapid viral replication in a panel of lung cancer cells in vitro, as well as robust intratumoral replication and high levels of tumor transduction in subcutaneous and orthotopic pleural dissemination models of lung cancer in vivo. Toca 511 (vocimagene amiretrorepvec), a clinical-stage RRV encoding optimized yeast cytosine deaminase (yCD) which converts the prodrug 5-fluorocytosine (5-FC) to the active drug 5-fluorouracil (5-FU), showed potent cytotoxicity in lung cancer cells upon exposure to 5-FC prodrug. In vivo, Toca 511 achieved significant tumor growth inhibition following 5-FC treatment in subcutaneous and orthotopic pleural dissemination models of lung cancer in both immunodeficient and immunocompetent hosts, resulting in significantly increased overall survival. This study demonstrates that RRV can serve as highly efficient vehicles for gene delivery to lung cancer, and indicates the translational potential of RRV-mediated prodrug activator gene therapy with Toca 511/5-FC as a novel therapeutic strategy for pulmonary malignancies.

The paper is dedicated to the topical problem of examining permafrost state and the processes of its geocryological changes by means of geophysical methods. To monitor the cryolithozone, we propose and scientifically substantiate a new technique of pulsed electromagnetic cross-well sounding. Based on the vector finite-element method, we created a mathematical model of the cross-well sounding process with a pulsed source in a three-dimensional spatially heterogeneous medium. A high-performance parallel computing algorithm was developed and verified. Through realistic geoelectric models of permafrost with a talik under a highway, constructed following the results of electrotomography field data interpretation, we numerically simulated the pulsed sounding on the computing resources of the Siberian Supercomputer Center of SB RAS. The simulation results suggest the proposed system of pulsed electromagnetic cross-well monitoring to be characterized by a high sensitivity to the presence and dimensions of the talik. The devised approach can be oriented to addressing a wide range of issues related to monitoring permafrost rocks under civil and industrial facilities, buildings and constructions.

Early detection of cancer increases the probability of recovery. This paper presents an intelligent decision support system (IDSS) for the early diagnosis of cancer based on gene expression profiles collected using DNA microarrays. Such datasets pose a challenge because of the small number of samples (no more than a few hundred) relative to the large number of genes (on the order of thousands). Therefore, a method of reducing the number of features (genes) that are not relevant to the disease of interest is necessary to avoid overfitting. The proposed methodology uses the information gain (IG) to select the most important features from the input patterns. Then, the selected features (genes) are reduced by applying the grey wolf optimization (GWO) algorithm. Finally, the methodology employs a support vector machine (SVM) classifier for cancer type classification. The proposed methodology was applied to two datasets (Breast and Colon) and was evaluated based on its classification accuracy, which is the most important performance measure in disease diagnosis. The experimental results indicate that the proposed methodology is able to enhance the stability of the classification accuracy as well as the feature selection

Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

Aging kills 100,000 people a day - more than any other cause of death combined. The exact causes of aging have been much discussed, but the most pressing issue with regard to aging appears to me to be lipofuscin accumulation. That is, the accumulation of indigestible cellular garbage that needs to be removed from our cells, then the body. In this piece, I will explain why I think “getting rid of the garbage” should be at least one of our main goals with regard to longevity research for now.

Computing the rotation tensor is vital in the analysis of deformable bodies. This paper describes an explicit expression for the SO(3) rotation tensor R of the deformation gradient F, and successfully establishes an intrinsic relation between the exponential mapping Q = exp A and the deformation F. As an application, Truesdell's simple shear deformation is revisited.

Mosquitoes transmit pathogens that cause human diseases such as malaria, Dengue fever, Chikungunya, yellow fever, Zika fever, and filariasis. Chemical pesticides are effective instruments for reducing disease transmission and managing mosquito populations. However, these chemicals have drawbacks such as high production costs and negative effects on the environment and non-target organisms. Furthermore, as mosquitoes develop resistance to chemical insecticides, they become less effective. As a result, researchers are investigating novel insecticides. Advances in microbial biotechnology have resulted in a wide range of value-added products, including biopesticides for agricultural and public health purposes. In this review, we highlight the mainly untapped biotechnological potential of microorganisms for vector mosquito control and disease transmission reduction. Also, we underline the importance of more basic research and improved translational research methodologies, encouraging efforts to bridge the gap between academic research on biopesticides and public health interventions.

The part of this article dealing with topological insulators and topological superconductors was first written about two years ago - the ideas in the part about the topological universe originated six years ago or more. It’s rather strange that I never put the two parts together in writing before. My belief in unification is unshakeable - I’ve been convinced for years that the universe must be composed of topology. Since Earth is part of the cosmos, entanglement means it must have topological materials. The reverse is also true: topological materials on Earth are well known to science - so in a unification, space and time inevitably possess topological composition. Topological materials (topological insulators, topological superconductors) can be less mystifying if they’re related to the paradigm-shifting deterministic view of quantum mechanics which is described in the universal topology (the “rubber-sheet geometry” of the cosmos): see my previous submission “Hypothesis of Quantum Gravity - Resulting from a Static, Topological Universe Resulting from the Positives and Negatives of the Steady State and Big Bang Theories" at https://www.preprints.org/manuscript/202105.0239/v1 (the first section of this present article is a quick summary of the relevant parts).

Aedes scapularis is a neotropical mosquito known to transmit pathogens of medical and veterinary importance. Its recent establishment in southeastern Florida has potential public health implications. We used an ecological niche modeling approach to predict the abiotic environmental suitability for Ae. scapularis across much of the Americas and Caribbean Islands. Georeferenced occurrence data obtained from the Global Biodiversity Inventory Facility and recent collection records of Ae. scapularis from southern Florida served as input for model calibration. Environmental layers included bioclimatic variables provided in 2000 to 2010 average Modern Era Retrospective-analysis for Research and Applications climatic (MERRAclim) data. Models were run in the software program Maxent. Isothermality values found often in costal environments contributed strongest to model performance. Model projections suggested areas predicted suitable for Ae. scapularis across portions of the Amazon Basin, the Yucatán Peninsula, the Florida Peninsula, and multiple Caribbean Islands. Additionally, model predictions suggested connectivity of highly suitable or relatively suitable environments spanning the United States Gulf Coast, which may facilitate geographic expansion of this species. At least sixteen Florida counties were predicted highly suitable for Ae. scapularis, suggesting vigilance is needed by vector control and public health agencies to recognize further spread of this vector.

In this work, we discuss a newly proposed scalar-tensor theory in Minkowski spacetime. The basic frame in Minkowski spacetime is obtained. Consider a charged particle in the electromagnetic field, we make an attempt to connect this theory with conventional quantum theory. Two specific models are chosen to calculate values of the time delay.

G4v is a four-vector theory of gravitation proposed by Mead (2015, 2023a,b) which reinterprets several major results commonly celebrated for the tensor-based approach of General Relativity (GR). Here, we show that G4v obeys the correspondence principle, an important test of any relativistic theory. We derive the non-relativistic Lagrangian and Energy expressions at the limit of low speeds and potentials, complete with the correct terms for potential energy (something traditional Special Relativity is incapable of achieving). G4v could represent a new interpretation of the gravitational field with essentially the same mathematics as electromagnetism (Mead 2023a). It is also possible to interpret G4v as a very interesting (and useful) approximation of the Einstein field equations (Mead 2023b).

Building Information Modelling (BIM) has gained significant relevance in civil engineering, particularly in the infrastructure sector. It offers the potential to increase efficiencies, optimize processes, and enhance sustainability throughout infrastructure projects’ life cycles. BIM consolidates information about asset components into a single model, enabling real-time updates, cost reduction, and improved consistency. Digital twins serve as valuable extensions of BIM in infrastructure, playing a crucial role during operation. By capturing real-time data from sensors and IoT devices, digital twins enabls continuous monitoring, proactive maintenance, efficient energy management, and informed decision-making for optimized operational efficiency. Advancements in 3D point cloud technologies, such as 3D laser scanning, have expanded BIM’s application in operation and maintenance. These technologies rapidly capture accurate and detailed three-dimensional information, prompting the exploration of automated alternatives to manual point cloud modeling. This paper demonstrates the application of supervised machine learning, specifically support vector machines, for analyzing and segmenting 3D point clouds, a crucial step in 3D modeling. Various approaches for semantic segmentation are introduced, investigated, and evaluated using diverse data sets. The results highlight the effectiveness of supervised machine learning techniques in achieving accurate segmentation of 3D point clouds.

The article is devoted to the new results of the author, which add his previously published ones, of studying hidden rules and symmetries in structures of long single-stranded DNA sequences in eukaryotic and prokaryotic genomes. The author uses the existence of different alphabets of n-plets in DNA: the alphabet of 4 nucleotides, the alphabet of 16 douplets, the alphabet of 64 triplets, etc. Each of such DNA alphabets of n-plets can serve for constructing a text as a chain of these n-plets. Using this possibility, the author represents any long DNA nucleotide sequence as a bunch of many so-called n-texts, each of which is written on the basis of one of these alphabets of n-plets. Each of such n-texts has its individual percents of different n-plets in its genomic DNA. But it turns out that in such multi-alphabetical or multilayer presentation of each of many genomic DNA, analyzed by the author, universal rules of probabilities and symmetry exist in interrelations of its different n-texts regarding their percents of n-plets. In this study, the tensor product of matrices and vectors is used as an effective analytical tool borrowed from the arsenal of quantum mechanics. Some additions to the topic of algebra-holographic principles in genetics are also presented. Taking into account the described genomic rules of probability, the author puts also forward a concept of the important role of stochastic resonances in genetic informatics.