The novel coronavirus SARS-CoV2 (CoV2) emerged in December 2019. This virus has 88% genomic similarity with SARS-CoV (CoV), and both viruses largely depend on their main protease (M^pro) to regulate infection. M^pro thus represents an attractive target for anti-SARS drug design. The CoV and CoV2 M^pro are 97% identical at the sequence level, with 12 variable residues, and their X-ray structures appear similar. We thus structurally analysed how these variable residues affect the intra-molecular interactions between key residues in the CoV2 M^pro active-site. Compared to CoV M^pro, the 12 divergent residues in CoV2 M^pro exhibit modified intra-molecular interaction networks that ultimately restructure the molecular micro-environment. These altered networks also indirectly affect the networks of other active-site residues at the entrance (T26, M49 and Q192) and near the catalytic region (F140, H163, H164, M165 and H172) of the M^pro. This suggest CoV2 indirectly (via neighbours) reshape key molecular networks around the M^pro active-site. It seems that the CoV2 M^pro deceives us with its apparent structurally identical to the CoV M^pro while this viral system accumulates mass mutations (12 variable residues) at key positions. Some of these identified CoV2 M^pro networks at the active-site might guide design of efficient CoV2 M^pro inhibitors.

We have shown that the use of evolutionary gravitational constants G, the speed of light c, and cosmological constant Λ in the Einstein’s field equations leads to a very simple model that fits the supernovae Ia data with a single parameter as well as the standard ΛCDM model with two parameters, and has the predictive capability superior to the latter. We have shown that at the current time G and c both increase as dG/dt = 5.4GH₀ and dc/dt = 1.8cH₀ with H₀ as the Hubble constant, and Λ decreases as dΛ/dt = −1.2ΛH₀. Negative finding on the variation of G with time derived from the analysis of lunar laser ranging data in several studies has been attributed to the fact that these studies did not consider the variation of c simultaneously with G.

We have developed a cosmological model by allowing the speed of light c, gravitational constant G and cosmological constant Λ in the Einstein filed equation to vary in time, and solved them for Robertson-Walker metric. Assuming the universe is flat and matter dominant at present, we obtain a simple model that can fit the supernovae 1a data with a single parameter almost as well as the standard ΛCDM model with two parameters, and has the predictive capability superior to the latter. The model, together with the null results for the variation of G from the analysis of lunar laser ranging data determines that at the current time G and c both increase as dG/dt = 5.4GH0 and dc/dt = 1.8cH0 with H0 as the Hubble constant, and Λ decreases as dΛ/dt = -1.2ΛH0. This variation of G and c is all what is needed to account for the Pioneer anomaly, the anomalous secular increase of the Moon eccentricity, and the anomalous secular increase of the astronomical unit. We also show that the Planck’s constant ħ increases as dħ/dt = 1.8ħH0 and the ratio D of any Hubble unit to the corresponding Planck units increases as dD/dt = 1.5DH0. We have shown that it is essential to consider the variation of all the physical constants that may be involved directly or indirectly in a measurement rather than only the one whose variation is being considered.

Dynamic hedging has been adopted by many insurance companies to mitigate the financial risks associated with variable annuity guarantees. In order to simulate the performance of dynamic hedging for variable annuity products, insurance companies rely on nested stochastic projections, which is highly computationally intensive and often prohibitive for large variable annuity portfolios. Metamodeling techniques have recently been proposed to address the computational issues. However, it is difficult for researchers to obtain real datasets from insurance companies to test metamodeling techniques and publish the results in academic journals. In this paper, we create synthetic datasets that can be used for the purpose of addressing the computational issues associated with the nested stochastic valuation of large variable annuity portfolios. The runtime used to create these synthetic datasets would be about 3 years if a single CPU were used. These datasets are readily available to researchers and practitioners so that they can focus on testing metamodeling techniques.

The intelligent morphing wing structure has the characteristics of flexibility, large deformation and adaptive change, which are different from those of the conventional wing. The motion law of the variable-camber leading edge is analysed by the numerical simulation method based on kinematics theory to solve the problem of verifying the motion function and structural strength of the variable camber wing. The driving deformation and aerodynamic load simulation of the variable camber leading edge are realised by designing a follow-up load test apparatus. Based on the numerical simulation of the mechanical boundary, the motion trajectory of the upper and lower wing loading points was predicted, and a multi-point cooperative control system was developed. Multi-sensor iteration was used to ensure the loading accuracy of the test control. The test results show that the average error of the deflection angle is 4.59% in the variable camber leading edge motion test. In the variable camber leading edge strength test, the average error of applied load magnitude and direction is 0.54% and 0.24%, respectively.

Predominantly antibody deficiencies (PAD) represent the most common type of primary immu-nodeficiencies in humans, characterized by a wide variation in disease onset, clinical manifesta-tions, complications and outcome. Considering that the prevalence of PAD in Greece is unknown and there is limited knowledge on clinical and laboratory characteristics of affected patients, we conducted a nationwide study, including 153 patients (male/female: 66/87; median age: 43.0 years; range: 7.0-77.0) diagnosed and/or followed-up between April 2007 to September 2023. Furthermore, we classified our cohort into five Groups according to their medical history, im-munoglobulin levels, and CTLA4-mutational status: 123 had common variable immunodeficien-cy (CVID), 12 with CVID-like disease (with a history of a previous B-cell depletion immunother-apy for autoimmune or malignant disease), seven with combined IgA and IgG subclass deficien-cies, five with immune dysregulation syndrome and CTLA4 defects, and six with unclassified hypogammaglobulinemia. We demonstrated a remarkable delay of PAD diagnosis, several years after the onset of related symptoms (median: 9.0 years, range: 0-43.0). Family history of PAD was only 11.8%, with the majority of patients considered sporadic cases. Most patients were diag-nosed in the context of a diagnostic work-up for recurrent infections, or recurrent/resistant auto-immune cytopenias. Interestingly, ten patients (5.6%) had no history of infection, diagnosed due to either recurrent/resistant autoimmunity, or during a work-up of their medical/family history. Remarkable findings included an increased prevalence of lymphoproliferation (60.1%) and atopy (24.2%), while 39 patients (25.5%) developed bronchiectasis and 16 (10.5%) granulomatous dis-ease. Patients with CTLA4 defects displayed a variable clinical phenotype at diagnosis, with four patients diagnosed during a work-up of re-current infections, while one also displayed a massive splenomegaly. Cancer was a common complication in our cohort (25 patients, 16.3%), with B-cell malignancies representing the most common neoplasms (56.7%). Our findings indicate the necessity of awareness about PAD and their complications, aiming for early diagnosis and appropriate management of affected patients

Regarding landscape preferences for highways, there is a dynamic interaction between users and the variety of natural and cultural landscape elements along the roadside, reflecting a balance between the highway and the natural environment. However, the rapid pace of urbanization has accelerated the development of highways, leading to the neglect or destruction of valuable landscapes. This highlights the need for visual preference assessments to protect aesthetic and environmental values. Currently, there is a lack of comprehensive criteria for assessing the visual preference of highway landscapes. Therefore, this study aims to review existing literature and establish complete assessment criteria. This study conducted a systematic literature review by searching for keywords in three databases: Web of Science, Scopus, and Google Scholar. The review included 22 papers published between 2013 and 2023 that met the criteria. An additional four relevant papers were included through a “snowballing” approach to supplement the research and results. The results of the study focused on the relationships between highway landscapes and users, user preferences, preference methods, and related variables. These inter-established relationships were used to complete an assessment framework, which establishes a basic reference standard for professionals.

We discuss the optimal control formulation for enhancement and denoising of satellite multiband images and propose to take it in the form of an L1-control problem for quasi-linear parabolic equation with non-local p[u]-Laplacian and with a cost functional of a tracking type. The main characteristic feature of the considered class of parabolic problems is the fact that the variable exponent p(t,x) and the anisotropic diffusion tensor D(t,x) are not well predefined a priori, but instead these characteristic non-locally depend on a solution of this problem, i.e., pu=p(t,x,u) and Du=D(t,x,u). We prove the existence of optimal pairs with sparse L1-controls using for that the indirect approach and a special family of approximation problems.

Surface water availability is a fundamental environmental variable to implement effective climate adaptation and mitigation plans, as expressed by scientific, financial and political stakeholders. Recently published requirements urge the need for homogenised access to long historical records at a global scale, together with the standardised characterisation of the accuracy of observations. While satellite altimeters offer world coverage measurements, existing initiatives and online platforms provide derived water level data. However, these are sparse, particularly in complex topographies. This study introduces a new methodology in two steps 1) teroVIR, a virtual station extractor for a more comprehensive global and automatic monitoring of water bodies, and 2) teroWAT, a multi-mission, interoperable water level processor, for handling all terrain types. L2 and L1 altimetry products are used, with state-of-the-art retracker algorithms in the methodology. The work presents a benchmark between teroVIR and current platforms in West Africa, Kazakhastan and the Arctic: teroVIR shows an unprecedented increase from 55% to 99% in spatial coverage.A large-scale validation of teroWAT results in an average of unbiased root mean square error ubRMSE of 0.638 m on average for 36 locations in West Africa. Traditional metrics (ubRMSE, median, absolute deviation, Pearson coefficient) disclose significantly better values for teroWAT when compared with existing platforms, of the order of 8 cm and 5% improved respectively in error and correlation. teroWAT shows unprecedented excellent results in the Arctic, using a L1 products based algorithm instead of L2 one, reducing the error of almost 4 m on average. To further compare teroWAT with existing methods, a new scoring option, teroSCO, is presented, measuring the quality of the validation of time series transversally and objectively across different strategies. Finally, teroVIR and teroWAT are implemented as platform-agnostic modules and used by flood forecasting and river discharge methods as relevant examples. A review of various applications for miscellaneous end-users is given, tackling the educational challenge raised by the community.

Regression models are mostly used in all fields of sciences for modelling the relationship between a dependent variable and independent variable(s). The least square method is often used to estimate the parameters in a linear model because it is the best linear unbiased estimator. These estimates can only be reliable if the assumption of normality is satisfied. In some cases the dependent variable might be bimodal and shows a non-linear relationship with the independent variable(s). In this case, a non-linear model should be used. In non-linear model, the standard errors are often obtained by linearizing the nonlinear function around the parameter, assuming central limit theorem. After the linearization, the least square parameter estimates are obtained. It should be noted that the error of the non-linear model is different from that of the transformed linear model. Thus, there is a need to transform back to the original non-linear model. In this note, a novel non-linear function was developed into a non-linear regression model, called Normal-Power model. The least square method was used to estimate the parameter of the transformed model. Its usefulness in regression model was demonstrated using real data of Nigeria Economy-Tourism model.

In a bid to resolve lingering problems in cosmology, more focus is being tilted towards cosmological models in which physical constants of nature are not necessarily real constants, but varying with cosmic time. In this paper we have study cosmology in nonlinear electrodynamics with the Newton's gravitational constant $G$ not a constant but vary in form of power-law of the scale factor of the universe. The evolution of the scale factor $a (t)$ is studied in this model which depends on nonlinear electrodynamics fine tuning term of $\alpha$. Then we check the stability of the model using the speed of sound.

In this study, we investigate the performance of the saddlepoint approximation of the probability mass function and the cumulative distribution function for the weighted sum of independent Poisson random variables. The goal is to approximate the hazard rate function for this complicated model. The better performance of this method is shown by numerical simulations and comparison with a performance of other approximation methods.

Complex diseases such as cancer are caused by changes in the Gene Regulatory Networks. Systems that model the complex dynamics of these networks along with adapting to real gene expression data are closer to reality and can help understand the creation and treatment of cancer. In this paper, for the first time, modelling of gene regulatory networks is performed using delayed nonlinear variable order fractional systems in the state space by a new tool called GENAVOS. This tool uses gene expression time series data to identify and optimize system parameters. This software has several tools for analyzing system dynamics. The results show that the nonlinear variable order fractional systems have very good flexibility in adapting to real data. We found that regulatory networks in cancer cells actually have a larger delay parameter than in normal cells. It is also possible to create chaos, periodic and quasi-periodic oscillations by changing the delay, degradation and synthesis rates. Our findings indicate a profound effect of time-varying order on these networks, which may be related to a type of cellular memory due to epigenetic and environmental factors. We showed that by changing the delay parameter and the variable order function for a normal cell system, its behavior changes and becomes quite similar to the behavior of a cancer cell. This work also confirms the effective role of the miR-17-92 cluster in the cancer cell cycle. GENAVOS is available at https://github.com/hanif-y/GENAVOS with its user guide and MATLAB codes.

We have shown that our varying physical constant model is consistent with the recently published variational approach wherein Einstein equations are modified to include the variation of the speed of light c, gravitational constant G and cosmological constant Λ using the Einstein-Hilbert action. The general constraint resulting from satisfying the local conservation laws and contracted Bianchi identities provides the freedom to choose the form of the variation of the constants as well as how their variations are related. When we choose dG/Gdt=3dc/cdt, ̇the same as in our quasi-phenomenological model, c=c₀ exp⁡(a^α-1), G=G₀ exp⁡[3(a^α-1)] and Λ=Λ₀ exp⁡[(a^-α-1)], where a is the scale factor and α=1.8, we are able to confirm the success of our the model in explaining three astrometric anomalies and the null results on the variation of G and the fine structure constant. We show that the model: (a) fits the supernovae 1a observational data better than the ΛCDM model; (b) determines the first peak in the power spectrum of the cosmic microwave background temperature anisotropies at multipole value of l=217.3; (c) calculates the age of the universe as 14.1 Gyr; and (d) finds the BAO acoustic scale to be 145.2 Mpc. These numbers are within less than 3% percent of the observed values and the values obtained by the ΛCDM model. Surprisingly we find that the dark-energy density is negative in a universe that has significant negative curvature and whose expansion is accelerating at a faster rate than predicted by the ΛCDM model.

In this paper, a new approach is proposed for designing ultra-low-power FFT (Fast Fourier Transform) system suitable for use in energy harvesting powered sensors. Bit-serial architecture is adopted to reduce the power consumption of butterfly operation. Simulation results show that, compared with state-of-the-art bit-serial and conventional parallel processors, the proposed technique is superior in terms of silicon area, power consumption, dynamic energy use due to variable precision arithmetic. A sample design of a 64-point FFT shows that the implementation can save about 40% area and 36% leakage power compared with a conventional parallel counterpart, accordingly achieving significant power benefits at a low sample rate and low voltage domain. The dynamic variation of the arithmetic precision can be achieved through a simple modification of the controller with hardware area overhead of 10% gate count.

This paper proposes a single-stage asymmetrical half-bridge fly-back (AHBF) converter with resonant mode using dual-mode control. The presented converter has an integrated boost converter and asymmetrical half-bridge fly-back converter and operates in resonant mode. The boost-cell always operates in discontinuous conduction mode (DCM) to achieve high power factor. The presented converter operates simultaneously using a variable-frequency-controller (VFC) and pulse-width-modulation (PWM) controller. Unlike the conventional single-stage design, the intermediate bus voltage of this controller can be regulated depending on the main power switch duty ratio. The asymmetrical half-bridge fly-back converter utilizes a variable switching frequency controller to achieve the output voltage regulation. The asymmetrical half-bridge fly-back converter can achieve zero-voltage-switching (ZVS) operation and significantly reduce the switching losses. Detailed analysis and design of this single-stage asymmetrical half-bridge fly-back converter with resonant mode is described. A wide AC input voltage ranging from 90 to 264 Vrms and output 19 V/ 120 W prototype converter was built to verify the theoretical analysis and performance of the presented converter.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

Solid-state fermentation (SSF) is the bioprocess where microorganisms are cultivated in the absence of free water under control conditions. Lactic acid can be produced by fermenting grape stalks with Rhizopus oryzae in SSF. During the microorganism growth, the temperature and water content of the solid bed fluctuate, leading to areas of either dry or excessive moisture in the solid substrate. Therefore, it's crucial to control the supply of water to the matrix. In this work, we obtain lactic acid through SSF of grape stalks using Rhizopus oryzae NCIM 1299. The SSF was conducted at a fixed temperature of 35 °C, with three constant relative humidity levels: 50, 65, and 80 %RH. Mathematical models, including the Logistic and First Order Plus Dead Time models for fungal biomass growth and the Luedeking and Piret with Delay Time model for lactic acid production, were adjusted to kinetic curves. Growth kinetic parameters (Xmax, μmax, Tp, T0, Yp/x and td) were determined for all conditions. These kinetic parameters were then correlated with relative humidity using a second-degree polynomial relationship. We observed a decrease in Xmax with increasing %RH, while the value of Yp/x increased at higher %RH. Finally, the optimal variable relative humidity profile was obtained by applying the Dynamic Optimization Technique, resulting in a 15.30% increase in lactic acid production.

This paper provides a new discovery that adds to the contemporary theory of modelling of non-stationary queues, by capturing analytically the behavior of the root parameter, for the G1/M/1 PSFFA model of non-stationary Ek/M/1 queue. Additionally, it has been also revealed that the resulting root parameter reduces to the PSFFA model of non-stationary queue. Potential applications of PSFFA to Computer engineering are provided. Some challenging open problems are addressed combined with closing remarks and future research directions.

Space instable target simulator (SITS) is a vital actuator for ground verification of on-orbital capture technology, the motion performance of which directly affects simulation credibility. Different delays reduce the stability of SITS and ultimately lead to its divergence. In order to achieve high fidelity simulation, the impact of force measurement delay, discrete control cycle, and simulator response delay on stability is analyzed firstly. Then, the dynamic equation and transfer function identification model of hybrid simulator is constructed, and the necessary and sufficient conditions of its stability and convergence are obtained using the Routh criterion. After that, a switching compensator with variable gain is proposed, the compensation principle diagram of which is build, and its mathematical model including the energy observer and tracking differentiator is also established. Finally, three sets of numerical simulations are conducted to validate the correctness of the stability analysis and effectiveness of proposed compensation method. Simulation results show that all three types of delay can cause SITS to lose stability under critical stable motion state, and the delay in force measurement has the greatest impact, followed by the influence of control cycle. Compared with the force applied to simulated target, velocity and its recovery coefficient of space instable target using fixed gain and linear gain compensation, the proposed compensator has significantly better performance.

Semi-continuous data are very common in the social science and economics. In this paper, a Bayesian variable selection procedure is developed to assess the influence of exogenous factors including observed and unobserved on the semi-continuous data. Our formulation is based on the two-part latent variable model with polytomous response. We consider two schemes for the penalties of regression coefficients and factor loadings: the Bayesian spike and slab bimodal prior and the Bayesian lasso prior. Within the Bayesian framework, we implement Markov Chains Monte Carlo sampling method to conduct posterior inference. To facilitate posterior sampling, we recast the logistic model in part one as the norm-like mixture model. Gibbs sampler is designed to draw observations from the posterior. Our empirical results show that with suitable hyper-parameters, the spike and slab bimodal method slightly outperforms the Bayesian lasso in the current analysis. Finally, a real example related to the China household finance survey is analyzed to illustrate the application of the methodology.. .

We present a new penalized method for estimation in sparse logistic regression models with group structure. Group sparsity suggests us to consider the Group Lasso penalty. Being different from penalized log-likelihood estimation, our method can be viewed as a penalized weighted score function method. Under some mild conditions, we provide non-asymptotic oracle inequalities promoting group sparsity of predictors. A modified block coordinate descent algorithm based on a weighted score function is also employed. The net advantage of our algorithm over the existing Group Lasso-type procedures is that the tuning parameter can be pre-specified. The simulations show that this algorithm is considerably faster and more stable than competing methods. Finally, we illustrate our methodology with two real data sets.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop, assessing seed viability of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible-Near Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500-1030 nm as a non-destructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50°C over 24-hour intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky-Golay (SG) and Multiplicative Scatter Correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of meta-heuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), Genetics (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 seconds) and LCA (44.9389 seconds) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected by the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), Multilayer Perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and NaiveBayes (NB) classifiers. The DSOS-DT and FOA-MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS-LR and DSOS-KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and non-destructive approach for detecting seed viability.

Variable cross-section arch is widely used in practical engineering because of its beautiful arc and excellent mechanical properties. However, there is still no systematic and comprehensive study on the out-plane buckling of variable cross-section arch. In view of this, this paper is concentrated on an elastic analytical research of out-plane buckling of arches with variable cross-section under a uniformly distributed radial local load. The pre-buckling and out-plane buckling behaviour of a variable cross-sectional arch under an external load is quite different from that of an arch with uniform cross-section. The Castigliano's second theorem is used to establish pre-buckling force method equilibrium equations for variable cross-sectional arches under a uniformly distributed radial local load, and corresponding analytical solutions of normal stress, axial compression and the bending moment are obtained. Based on the energy method and the Ritz method, analytical solutions of the critical load for the elastic out-plane buckling of arches with variable cross-section are derived. Comparisons with ANSYS results indicated that the analytical solutions are able to accurately predict the pre-buckling internal forces and critical out-plane buckling load of variable cross-section arches subjected to a uniformly distributed radial local load. It is found that the internal forces and the out-plane buckling load of an arch are significantly affected by the variation of cross-sectional height. With the ratio of cross-sectional height of the top to the end increase, the bending moment decreases and the axial force and critical out-plane buckling load increase. Analytical solutions of pre-buckling internal force and critical out-plane buckling load problems for arches with variable cross-section have a wider significance, since they can provide an effective explicit analytic method for the optimal design of arch structure.

In the crustacean immune system, leucine-rich repeat (LRR) is one of the major structures for recognizing pathogen-associated molecular patterns (PAMPs). LRR domain-containing proteins belong to the LRR family, which is a large group of proteins with more than 6000 genes in the database. They are involved in very diverse physiological functions mainly by interacting with other proteins. In a previous study, the LvLRRm, a transmembrane protein containing only LRR domain, was identified in the white leg shrimp, Litopenaeus vannamei. Its versatile role in performing multiple immunomodulation activities has been reported. However, there is still a lack of research on its efficient function at the protein level. To investigate its interactions with other proteins, we applied a convenient method called ‘Hybrid LRR technique’ to produce a recombinant LvLRRm. The LvLRRm and hagfish’s variable lymphocyte receptors (VLRs) fragments were fused to the conserved LxxLxLxxN motif while retaining the β-strand. In addition, we established interactions between hybrid-proteins and flagellin of Salmonella by performing surface plasmon resonance (SPR) analysis. The designed hybrid proteins showed potential for universal applications without losing protein functions.

This paper focuses on a joint model to analyse longitudinal proportion and survival data. After transforming the longitudinal proportional data by a logit function, we adopt a partially linear mixed-effect model for it, in which nonlinear function of time is fitted via using B-splines technique and a centered Dirichlet Process Mixture Model (CDPMM) is specified for a general distribution of random effects. The survival data is assumed to a Cox proportional hazard model, the sharing random effects joint model is developed for the two types of data. Combining the Gibbs sampler and the Metropolis-Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates. Simulation studies are conducted to investigate the finite sample performance of the proposed methods. An example from the MA.5 research experiment is used to illustrate the proposed methodologies.

Physics-informed neural networks are a promising method to yield surrogate models of flow fields. We present a metamodeling technique for variable geometries based on physics-informed neural networks. The method was applied to the DU99W350 airfoil at a Reynolds number of 1×10⁵. The model predicted the Reynolds-averaged velocity and pressure field around the airfoil for arbitrary angles of attack between 10.0° and 17.5°. The model was trained with data from CFD simulations for a limited set of angles of attack. Additionally, satisfaction of the a priori known boundary conditions as well as the Reynolds-averaged Navier-Stokes equations were trained. A sensitivity analysis concerning the Reynolds number, the amount and distribution of training data, and the turbulence model was conducted showing the superiority of the pseudo-Reynolds stress method and the demand of labeled training data in the domain. The trained network was capable of predicting the developing flow separation on the suction surface and exhibited excellent agreement with CFD results even in the proximity to the wall for interpolations as well as extrapolations from the labeled data set.

Studying Active Transportation (AT) is widespread in America and Europe's research and stud-ies that include Latent variables (LV) are growing to identify exact results of finding what to do to increase the utility of AT. LVs help us to have more accurate research. LVs are defined as psy-chological factors like feeling safe while you ride at night and thus they are not subjective and hard to understand but very important to increase the utility of using AT modes. In this study, most of the previous studies about bicycling have been reviewed. In this research, different var-iables including subjective and LVs are conducted for maximizing the use of bicycle utility and introduced to have better sight for future researchers to deal with modeling AT mode choice. Results represent the importance of having 'will' for using a bicycle, especially in difficult situa-tions and cultural barriers that affect the cycling of women.

This work presents an alternative to harnessing wind energy with an Electromagnetic Frequency Regulator (EFR) coupled to a hydrostatic transmission and associated with a horizontal axis wind turbine, a bidirectional frequency inverter and a secondary energy source, in a hybridized system. The hydrostatic transmision is composed by a fixed displacement axial piston pump and a variable displacement, swash plate, axial piston motor. Feedback linearization was used as a technique to control the motor geometric displacement, and a prediction algorithm for the steady state rotations of the armature and the electromagnetic field has been developed. A 5\,kW project was simulated on a Scilab platform, with combinations of constant or variable load, and constant or variable wind speed. The results indicated that the system was able to supply the generator load, adapting to fluctuations in wind speed. The possibility of storing wind energy through the inverter has also been proven. The system can accumulate energy in batteries during the fastest wind regimes, to use it when the turbine power is lower than the load.

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

The continuous energy transformation processes in heating, ventilation and air conditioning systems of buildings are responsible for 36% of global final energy consumption. Tighter thermal insulation requirements for buildings have significantly reduced heat transfer losses. Unfortunately, this has little effect on energy demand for ventilation. On the basis of the First and the Second Law of Thermodynamics, the concepts of entropy and exergy are applied to the analysis of ventilation air handling unit (AHU) with a heat pump in this paper. This study aims to develop a consistent approach for this purpose, taking into account the variations of reference temperature and temperatures of working fluids. An analytical investigation on entropy generation and exergy analysis are used, when exergy is determined by calculating coenthalpies and evaluating exergy flows and their directions. The results show that each component of the AHU has its individual character of generated entropy, destroyed exergy and exergy efficiency variation. However, the evaporator of heat pump and fans have unabated quantities of exergy destruction. The exergy efficiency of AHU decreases from 45-55% to 12-15% when outdoor air temperature is within the range of –30°C…+10°C, respectively. This helps to determine conditions and components of improving the exergy efficiency of the AHU at variable real-world local climate conditions. The presented methodological approach could be used in the dynamic modelling software and contribute to a wider application of the Second Law of Thermodynamics in practice.

Flash flood is one of the most significant natural disasters in China, particularly in mountainous area, causing heavy economic damage and casualties of life. Accurate risk assessment is critical to an efficient flash flood management. There are more than 530,000 small watersheds in 2058 counties in China where flash flood should be prevented. In practice, with limited fund and different risk levels, the priorities of each small watershed for flash flood prevention and control are also needed for an efficient flash flood management. This paper, take Licheng county in China as an example, aims to give out these priorities for management. First, sensitive indexes are identified among index system, which includes 9 indexes based on underlying surface characteristics of small watershed in hilly region. Second, the range of each index and the rank division of each index for evaluation are determined. Based on the rank divisions for evaluation, the flash flood risk grade eigenvalue (H) is calculated by Variable Fuzzy Method (VFM ) using 1000 samplings generated by Latin hypercube sampling method. Third, the key sensitivity factors that affect flash flood risk grade eigenvalue (H) are assessed by two different global sensitivity analysis methods -- stepwise regression analysis and mutual entropy. Both results indicate that watershed slope (S) is the most sensitive factor; the second is antecedent precipitation index (CN); while other factors are slightly different sensitive in sequence. This study shows that stepwise regression analysis and mutual information analysis are appropriate for the sensitivity analysis of mountain flash flood risk. Finally, based on watershed slope (S), the priorities of flash flood prevention and control of 119 small watersheds in Licheng county are given out.

Cronbach’s (1957) famous division of scientific psychology into two disciplines is still actual for the fields of cognition (general mechanisms) and intelligence (dimensionality of individual differences). The welcome integration of the two fields requires the construction of mechanistic models of cognition and cognitive development that explain key phenomena in individual differences research. In this paper we argue that network modeling is a promising approach to integrate the processes of cognitive development and (developing) intelligence into one unified theory. Network models are defined mathematically, describe mechanisms on the level of the individual, and are able to explain positive correlations among intelligence subtest scores - the empirical basis for the well-known g-factor - as well as more complex factorial structures. Links between network modeling, factor modeling and item response theory allow for a common metric, encompassing both discrete and continuous characteristics, for cognitive development and intelligence.

: Mistrust, amplified by numerous artificial intelligence (AI) related incidents, has caused the energy and industrial sectors to be amongst the slowest adopter of AI methods. Central to this issue is the black-box problem of AI, which impedes investments and fast becoming a legal hazard for users. Explainable AI (XAI) is a recent paradigm to tackle this challenge. Being the backbone of the industry, the prognostic and health management (PHM) domain has recently been introduced to XAI. However, many deficiencies, particularly lack of explanation assessment methods and uncertainty quantification, plague this young field. In this paper, we elaborate a framework on explainable anomaly detection and failure prognostic employing a Bayesian deep learning model to generate local and global explanations from the PHM tasks. An uncertainty measure of the Bayesian model is utilized as marker for anomalies expanding the prognostic explanation scope to include model’s confidence. Also, the global explanation is used to improve prognostic performance, an aspect neglected from the handful of PHM-XAI publications. The quality of the explanation is finally examined employing local accuracy and consistency properties. The method is tested on real-world gas turbine anomalies and synthetic turbofan data failure prediction. Seven out of eight of the tested anomalies were successfully identified. Additionally, the prognostic outcome showed 19% improvement in statistical terms and achieved the highest prognostic score amongst best published results on the topic.

Input noise causes inescapable bias to the weight vectors of the adaptive filters during the adaptation processes. Moreover, the impulse noise at the output of the unknown systems can prevent bias compensation from converging. This paper presents a robust bias compensation method for a sparse normalized quasi-Newton least-mean (BC-SNQNLM) adaptive filtering algorithm to address this issue. We have mathematically derived the biased-compensation terms in an impulse noisy environment. Inspired by the convex combination of adaptive filters' step sizes, we propose a novel variable-mixing-norm method to accelerate the convergence for our BC-SNQNLM algorithm, which is referred to as BC-SNQNLM-VMN. Simulation results confirm that the proposed method significantly outperforms other comparative works regarding normalized mean-squared deviation (NMSD) in the steady state.

The majority of current methods for measuring the angular deflection of a laser beam enable measurement only in one selected plane. However, there are tasks in which measurements of laser beam deflections in 3D are required. In this paper, we present a way of enabling two-axial measurements of the deflection of a beam based on a single-axis sensor. The key idea is to direct a laser beam alternately into one of two arms of a measurement system. In the first arm, the beam is transmitted directly to the angular sensor, while in the second, the beam is directed to the sensor via a special optical element that rotates the plane of the beam deflection; in other words, this element changes the deflection in the horizontal plane into a deflection in the vertical plane, and vice versa. To alternate the path of the beam, a variable phase retarder and a polarising beamsplitter are used. The proposed technique is experimentally verified, and the results confirm its effectiveness.

This paper presents an original method of additive manufacturing of cylindrical parts with variable circumference thickness, which allows the control of the deposition of molten material using an algorithm for decomposing the part geometry into volumetric elements with known dimensional configuration. In the absence of a post-processor capable of controlling additive manufacturing on a 5-axis numerical control machine, control of the deposition of molten material is done using parameterized programs, which can control both the feed speeds of the machine tool axes and the specific functions of the printing equipment. Additive manufacturing can make a positive contribution to sustainable development compared to traditional manufacturing technologies, thus making a positive contribution for a sustainable future. The aim of the work is to make it possible to 3D print parts with variable wall thickness using a CNC machining centre. To obtain the variable thickness layer we have implemented an original method of deposition with molten material (FDM), the coordination of the system composed of physical elements respectively programmable elements, is realized through control functions materialized in parameterized part programs, the generated outputs being the variable speed of the machine axes on a circular trajectory, the angular positioning, the filament advance.

The impacts of COVID-19 (Novel Coronavirus) epidemic cannot have been more severe, with the globe experiencing both economic and health crises. This study examines the trends and correlations in number of COVID-19-related deaths and number of COVID-19-infected patients in all 37 regions of Tamil Nadu state, India, in the month of August 2020 on the basis of panel regression model,

Variable-DOF (or kinematotropic) mechanisms are a class of reconfigurable mechanisms. The number of variable-DOF multi-loop planar mechanisms proposed so far is very limited. This paper proposes a novel 8-link variable-DOF planar mechanism with five motion modes. Firstly, a description of the 8-link variable-DOF planar mechanism is presented. Reconfiguration analysis of the 8-link mechanism is then carried out using an elimination and algebraic geometry hybrid approach. The analysis shows that the 8-link mechanism has one 2-DOF motion mode and four 1-DOF motion modes and can switch among three motion modes at four transition configurations and between two motion modes at the remaining four transition configurations. Geometric characteristics of the mechanism in different motion modes are also revealed. Unlike the variable-DOF planar mechanisms found in literature, the proposed 8-link mechanism has two inactive joints in one of its 1-DOF motion modes, and both 4R kinematic sub-chains of the mechanism must appear as parallelograms in a pair or anti-parallelograms in a pair in the same motion mode. As a by-product, a method for factoring trigonometric functions in two angles is proposed.

In this paper, the functional variable method is used to establish solitary wave solutions and periodic wave solutions of the loaded quadratic non-linear Klein-Gordon equation, the loaded cubic non-linear Klein-Gordon equation and the loaded coupled non-linear Klein-Gordon equation. All solutions of these equations have been examined and three dimensional graphics of the obtained solutions have been drawn by using the Matlab software. The main advantage of the proposed functional variable method over other methods is that it provides more new exact traveling wave solutions along with additional free parameters. The graphical representations of the soliton solutions and the periodic wave solutions by using distinct values of random parameter are demonstrated to better understand their physical features. The exact solutions have its great importance to reveal the internal mechanism of the physical phenomena.

Modelling errors, robust stabilization/tracking problems under parameter and model uncertainties complicate the control of the flexible underactuated systems. Chattering-free sliding-mode based input-output control law realizes robustness against the structured and unstructured uncertainties in the system dynamics and avoids excitation of unmodeled dynamics. The main purpose is to propose a robust adaptive solution for stabilizing and tracking direct-drive (DD) flexible robot arms under parameter and model uncertainties, as well as external disturbances. A lightweight robot arm subject to external and internal dynamic effects was taken into consideration. The challenges are compensating actuator dynamics with the inverter switching effects and torque ripples, stabilizing the zero dynamics under parameter/model uncertainties and disturbances while precisely track the predefined reference position. The precise control of this kind of system demands an accurate system model and knowledge of all sources that excite unmodeled dynamics. For this purpose, equations of motion for a flexible robot arm were derived and formulated for the large motion via Lagrange’s method. The goals were determined to achieve high-speed, precise position control, and satisfied accuracy by compensating the unwanted torque ripple and friction that degrades performance through an adaptive robust control approach. The actuator dynamics and their effect on the torque output were investigated due to the transmitted torque to the load side. The high-performance goals, precision&robustness issues, and stability concerns were satisfied by using robust-adaptive input-output linearization-based control law combining chattering-free sliding mode control (SMC) while avoiding the excitation of unmodeled dynamics.

In this paper a new approximate procedure is developed for calculating the inclination angle of the end points of statically determinate beams. The method is based on the topology comparison of simple (hinge-roller combination) supported beam and a resemblant cantilever beam. Assuming that the support reactions of the beam are active forces, the virtual displacements at the points of the reaction forces are calculated. Based on these values the inclination angle is calculated. Several examples are considered and the suggested in this paper, while the procedure is applied for various types of structures and loadings. The results, obtained by the suggested numerical procedure, are compared with analytical ones, and they are in good agreement.

We present a new analytical method to find the asymptotic stable equilibria states based on the Markov chain technique. We reveal this method on the SIR-type epidemiological model that we developed for viral diseases with long-term immunity memory pandemic. This is a large-scale model containing 15 nonlinear ODE equations, and classical methods have failed to analytically obtain its equilibria. The proposed method is used to conduct a comprehensive analysis by a stochastic representation of the dynamics of the model, followed by finding all asymptotic stable equilibrium states of the model for any values of parameters and initial conditions.

This article examines the impact of some system parameters on an industrial system composed of two dissimilar parallel units with one repairman. The active unit may fail due to essential factors like aging or deteriorating, or exterior phenomena such as Poisson shocks that occur at various time periods. Whenever the value of a shock is larger than the specified threshold of the active unit, the active unit will fail. The article assumes that the repairman has the right to take any of two decisions at the beginning of the system operation: either a takes a vacation if the two units work in a normal way, or stay in the system to monitor the system until the first system failure. In case of having a failure in any of the two units during the absence of the repairman, the failing unit will have to wait until the repairman is called back to work. We suppose that the value of every shock is assumed to be i.i.d. with some known distribution. The length of the repairman’s vacation, repair time, and recall time are arbitrary distributions. Various reliability measures have been calculated by the supplementary variable technique and the Markov’s vector process theory. At last, numerical computation and graphical analysis have been given for a particular case to validate the derived indices.

In this paper we re-investigated the relationship between the symmetry of inertial systems and the Lorentz transformation. We found that when we just follow the following three principles: (1)we can define the time in the whole space with a prescribed clock synchronization, (2)the time-space is uniform and the space is isotropic and (3)all the inertial systems are equivalent, then we can totally construct a general coordinate transformation to meet the symmetry of inertial systems, and with a special assumption on the speed of light, we can construct a non-Lorentz transformation between inertial systems to make the particle’s energy have a limited value, which is similar to the rainbow model. Similar to the usual Lorentz violating models, the non-Lorentz transformation in this paper lead to a new modified disperse relation. We applied the obtained disperse relation to analyze the photon’s arrival time lag effect in astronomy and found that the "maximum energy" derived in our model is somewhat related to the "maximum energy" assumed in the rainbow model.

A new model for risk assessment of urban rainstorm disasters, based on information diffusion method and variable fuzzy sets (IDM-VFS) was proposed. In addition, an integrated index system of urban rainstorm risk was established. In the proposed model, IDM was employed to calculate the classification standards of urban rainstorm risk levels, then the VFS was adopted to assess the dangerousness, sensitivity, vulnerability and comprehensive risk of urban rainstorm disasters. In the present study, the urban rainstorm risk of Jiangsu province was evaluated with the proposed model. The results show that Wuxi, Changzhou, Nanjing and Suzhou have higher dangerousness, due to sustained rainfall and strong rainfall intensity in short duration; Wuxi, Changzhou and Nanjing have higher sensitivity because of lower disaster resistance ability; and Wuxi and Suzhou have higher vulnerability because these cities have higher potential losses in face of urban rainstorm disasters. The comprehensive risk zoning map of urban rainstorm shows apparent regional characteristics: the northwestern cities have lower risk than the southern cities. Moreover, most cities of the Jiangsu province are of the moderate urban rainstorm risk level. The results are consistent with the actual situation of Jiangsu province, and the study can provide some decision-making references for the urban rainstorm management.

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

Transportation of goods is as old as human civilizations : past networks and their evolution shed light on long term trends. Transportation impact on climate change is measured as major, as well as the impact on spreading a pandemic. These two reasons motivate the importance of providing relevant and reliable historical geographic datasets of these networks. This paper focuses on reconstructing the railway network in France at its maximal extent, a century ago. The active stations and lines are well documented by the French SNCF, in open public data. However, that information ignores past stations (ante 1980), which represent probably more than what is recorded in public data. Additional open data, individual or collaborative (eg. Wikipedia) are particularly valuable, but they are not always geo-coded, and two more sources are necessary to completing that geo-coding: ancient maps and aerial photography. Therefore, remote sensing and volunteer geographic information are the two pillars of past railway reconstruction. The methods developed are adapted to the extraction of information from these sources: automated parsing of Wikipedia Infoboxes, data extraction from simple tables, even from simple text. That series of sparse procedures can be merged into a comprehensive computer-assisted process. Beyond this, a huge effort in quality control is necessary when merging these data: automated wherever possible, or finally visually controlled by observation of remote sensing information. The main output is a reliable dataset, under ODbl, of more than 9100 stations, which can be combined with the information about the 35000 communes of France, for a large variety of studies. This work demonstrates two thesis: (a) it is possible to reconstruct transport network data from the past, and generic computer assisted methods can be developed; (b) the value of remote sensing and volunteered geo info is considerable (what archeologists already know).

Recently, SMR (Small Modular Reactor) is expected to be able to operate flexibly in conjunction with a high renewable energy penetration grid as a result of improved safety and easy power control compared to large nuclear power plants. SMRs such as South Korea’s System-integrated Modular Advanced ReacTor (SMART) are designed to use canned motors (CMs) for their reactor coolant pumps (RCPs) to enhance safety. The CMs passively enhance the reactor’s safety by preventing leakage of radioactive reactor coolant. However, with motor sizes designed to be as small as possible, and the increased air gap of CMs between stator and rotor, starting torque may be insufficiently small compared to that of similar sized induction motors (IMs). Thusly, CMs may require variable frequency drives (VFDs) to start. This paper compares the torque characteristics of CMs with those of IMs for SMART’s RCPs. ETAP is then used to perform motor starting analysis for CMs started with and without VFDs. The results are presented and analyzed to find out if the VFDs deal with the CM starting torque issue.

Variable Angle Tow (VAT) laminates offer a promising alternative to classical straight fiber composites in terms of design and performance. However, analyzing these structures can be more complex due to the introduction of new design variables. Carrera's Unified Formulation (CUF) has been successful in previous works for buckling, vibrational, and stress analysis of VAT plates. Typically, one-dimensional (1D) and two-dimensional (2D) CUF models are used, with a linear law describing the fiber orientation variation in the main plane of the structure. The objective of this article is to expand the CUF 2D plate finite elements family to perform free vibration analysis of composite laminated plate structures with curvilinear fibers. The primary contribution is the application of Reissner's Mixed Variational Theorem (RMVT) to a CUF finite element model. The Principle of Virtual Displacements (PVD) and RMVT are both used as variational statements for the study of monolayer and multilayer VAT plates dynamic behavior. The proposed approach is compared to Abaqus three-dimensional (3D) reference solutions, classical theories and literature results to investigate the effectiveness of the developed models. Results demonstrate that mixed theories provide the best approximation of the reference solution in all cases.

This study examines the spatial impact of FDI on the poverty of 44 African countries. In achieving this, the study uses the Driscoll-Kraay fixed effect instrumental variable regression, instrumental variable generalised method of moments estimator (IV-GMM), and the spatial durbin model. The empirical investigation of this study yielded four significant findings: (1) neighbouring countries’ FDI has a positive and significant impact on the incidence and intensity of host country’s poverty. (2) Improved institutional quality in neighbouring countries has a significant impact on FDI-poverty reduction nexus of the host country. (3) the empirical results lend support for a significant spatial spillover of poverty in the region. (4) the marginal effect results indicate that countries within the region are no longer in isolation or independent, i.e., the level of poverty in a particular country is influenced by its determinants in the neighbouring country. This result is robust to the alternative proximity matrix, which is the inverse distance. Since there is spatial interdependence among African countries, we recommend that African governments through the African Union (AU) should not only champion the institutional reform in the region, but also establish a binding mechanism to ensure reform implementation.

The article introduces a new solution approach to grid-connected single-stage three-phase PV inverter whereby a dc-link voltage can successfully achieve the desired performance. As a single-stage, the variable output of perturb and observe maximum power point tracking (P&O based-MPPT) is fully utilized as dc-link voltage reference in inverter control scheme. As a result, it is challenging for the dc-link voltage to preserve energy balance by tracking its variable dc-link voltage reference under unpredictable environmental changes. To overcome this challenge, an adaptive reference proportional-integral (ARPI) controller, self-tuning, is designed and implemented to compel the dc-link voltage tracking its voltage reference to be equal as closely as possible; hence, the capability of the dc-link under abnormal events substantially guarantees the best energy balance and transient performance enhancement. To verify the validity of the method, simulation results for multiple events scenarios on 250 KW single-stage grid-connected PV systems show the effectiveness of the ARPI controller compared with PI controller.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

Wall Shear Stress (WSS) abnormalities in carotid artery blood flow are key hemodynamic factors leading to arterial endothelial damage, atherosclerotic plaque formation, and carotid artery stenosis. Precise simulation of carotid artery blood flow WSS in vitro play an important role in understanding the underlying hemodynamic mechanism of carotid artery stenosis. This study presents a design for a microfluidic cell culture chamber with a variable cross-section and a step structure, leveraging microfluidic technology and principles of fluid dynamics. We propose an optimization strategy for microchannel dimensions based on Computational Fluid Dynamics (CFD) simulations. This strategy aims to replicate the distinct WSS waveform features found at various locations within the carotid bifurcation as observed in vivo. Our simulations indicate that with a step height of 0.09 mm, a width of 4 mm for the initial segment, and 5 mm for the subsequent wider channel, the device can effectively model the characteristics of low oscillatory WSS seen at sites of carotid sinus stenosis. Furthermore, it accurately represents the high-magnitude pulsatile WSS waveforms in the more uniform arterial sections downstream of the carotid sinus. The study also reveals that vortex formation and variations in low oscillatory WSS within the stepped section of the microchannel correlate with the step's height and width, as well as with the dimensions of the wider channel section. An increase in step width leads to a decrease in both the vortex area and the magnitude of negative WSS oscillations. When the step height is less than 0.03 mm, vortex formation is inhibited, challenging the simulation of low oscillatory WSS patterns. The variable cross-section microfluidic channel developed in this study provides a platform for simulating carotid artery WSS waveforms and facilitates research into the interplay between low oscillatory WSS at sites of carotid sinus stenosis and arterial endothelial dysfunction.

A system of simultaneous multi-variable nonlinear equations can be solved by the Newton’s method with local q-quadratic convergence if the Jacobian is analytically available. If this is not the case, then quasi-Newton methods with local q-superlinear convergence give solutions by approximating the Jacobian in some way. Unfortunately, the quasi-Newton condition (secant equation) doesn’t completely specify the Jacobian approximate in multi-dimensional case, so its full-rank update is not possible with classic variants of methods. The suggested new iteration strategy (“T-Secant”) allows full-rank update of the Jacobian approximate in each iteration by determining two independent approximates for the solution. They are used to generate a set of new independent trial approximates, then the Jacobian approximate can fully be updated. It is shown, that the T-Secant approximate is in the vicinity of the classic quasi-Newton approximate, providing that the solution is evenly surrounded by the new trial approximates. The suggested procedure increases the super-linear convergence of the secant method (φS = 1.618...) to super-quadratic (φT = φS + 1 = 2.618...) and the quadratic convergence of the Newton-method (φN = 2) to cubic (φT = φN + 1 = 3) in one dimensional case. The Broyden-type efficiency (mean convergence rate) of the suggested method in multi-dimensional case is an order higher than the efficiency of other classic low-rank update quasi-Newton methods as shown by numerical examples on a Rosenbrock-type test-function with up to 1000 variables. The geometrical representation (hyperbolic approximation) in single variable case helps explaining the basic operations and a vector-space description is also given in multi-variable case.

The paper proposes an approach to determine the optimal number of medical specialists in the region. According to the author's theoretical model, in order to maximize public welfare, the marginal contribution of the last physician recruited to the growth of the public utility function should be equal to the marginal cost of attracting him and providing conditions for his work. To empirically assess the contribution of physicians to the number of lives saved, the CVD mortality rate is modeled using the instrumental variable method. On average, a 1% increase in the number of cardiologists is associated with a 0.6% decrease in cardiovascular mortality. At the level of provision with cardiologists in the amount of 1 per 100 thousand people, their marginal contribution to the number of lives saved is not less than 129 per 100 thousand people, with a further decrease by 5 people with the increase in the level of provision by 1 unit. The use of the obtained results will increase the validity of managerial decisions and in determining the optimal level of doctors when choosing between alternative possibilities of spending money on hiring doctors of different profiles or other expenses, especially in the case of limited resources.

Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communications network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research.

Historical forest management practices in the southwestern US have left forests prone to high intensity, stand-replacement fires. Effective management to reduce the cost and impact of forest-fire management and allow fires to burn freely without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to reduce costs and increase ability of managers to monitor AGB and other forest metrics. Using Bayesian Model Averaging (BMA), we develop a regionally applicable lidar-based statistical model for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. The selected regional model includes a mid and low canopy height metric, a canopy cover, and height distribution term. It explains 72% of the variability in field estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model developed is structured in accordance with previously described models fit to local data, and performs equivalently to models designed for smaller scale application. Developing regional models for broad scale application provides a cost-effective, robust approach for managers to monitor and plan adaptively at the landscape scale.

That how to improve the sensitivity and the detection ability of the Large-caliber sensor is critical to satisfy the requirement of heavy vehicle wear condition monitoring. In this paper, we introduced the LC resonance principle to the design of sensor with large flow channel (7mm). The resonant exciting coil increases the impedance change of the exciting circuit caused by wear particles which magnify the current difference between the two exciting coils and improve the sensitivity of the sensor. The resonant induction coil greatly suppresses the interference signals and magnifies the weak induced electromotive force which is beneficial to enhance the detection ability. In order to boost the detectability for different materials particles, a phase controlled variable-frequency exciting system (PCVFES) was adopted to automatically switch the exciting frequency between 284kHz for ferromagnetic particles and 420KHz for non-ferromagnetic particles. For the weak signal detection, based on the essential characteristic of the signal, we apply a simpler method that modified lock-in amplifier (MLIA) rather than the conventional algorithms (Wavelet transform, EMD). Results show that using these methods the sensitivity and the detection ability of the sensor are significantly improved and the 75μm iron particles and 220μm copper particles were successfully detected which realizes the initial abnormal wear monitoring for the large flow project.

IL1RA intron 2 86-bp repeat and IL4 intron 3 70-bp repeat are VNTRs that have been associated with various diseases, but their role in obesity is elusive. The objective of this study was to investigate the association of IL1RA and IL4 VNTRs with obesity and adiposity in 315 Malaysian subjects (128 M/187 F; 23 Malays/251 ethnic Chinese/41 ethnic Indians). The allelic distributions of IL1RA and IL4 were significantly different among ethnicities, and the alleles were associated with total body fat (TBF) classes. Individuals with IL1RA I/II genotype or allele II had greater risk of having higher adiposity - even after controlling for ethnicity [Odds Ratio (OR) of I/II genotype = 12.21 (CI = 2.54, 58.79; p = 0.002); II allele = 5.78 (CI = 1.73, 19.29; p = 0.004)]. However, IL4 VNTR B2 allele was only significantly associated with overall adiposity status before adjusting for ethnicity [OR = 1.53 (CI = 1.04, 2.23; p = 0.03)]. TBF was also significantly higher in those with IL1RA II allele compared with I allele (31.79 ± 2.52 vs. 23.51 ± 0.40; p = 0.005). Taken together, IL1RA intron 3 VNTR seems to be a genetic marker for overall adiposity status in Malaysian subjects.

Evolutionary, sequence and structural analyses of tRNA variable (V) loops provides a new understanding. Type I tRNA V loops have a primordial length of 5 nt, and type II tRNA V loops have a primordial length of 14 nt. Sequence-based alignments of type I and type II V loops gave deceptive results. Type II V loops are characterized by the trajectory of the V arm, the size and sequence of the loop and the first and last V loop nucleotides and their known contacts. Depending on the V arm trajectory, type II V stems and loops can be interaction sites for aminoacyl-tRNA synthetases. So, type II V arms for tRNALeu, tRNASer and tRNATyr (found in Bacteria) were coevolved and selected to distinguish determinants to support cognate tRNA charging. The alignment of type I and type II V loops was adjusted based on tRNA evolution. Results are consistent with Archaea being an older and simpler life form than Bacteria. TRNAome sequences were derived from radiation of an ordered tRNAPri (Pri for primordial) sequence comprised of known RNA repeats and inverted repeats. V loop and tRNA sequences, therefore, convey a history of the primary successful pathway in the origin of life.

A two-link model of exoskeleton with variable-length links for supporting the lower limbs of the human musculoskeletal system is proposed in the article. The researched model differs from the existing ones by the variable-length links, and by the angle calculation method. While in the existing models, the angles are calculated from the regular direction – from vertical, or from horizontal, - in the proposed research they are calculated between the links. As for practical exoskeleton implementation, the proposed method of angle calculation is appropriate to the actual working conditions of the electrical motors with the reduction gears installed in the hinges, which change the angles between the links. The mathematical model in the form of the system of Lagrange differential equations of the second kind is obtained. The obtained mathematical model is examined for existence and uniqueness of the Cauchy solution. The kinematic trajectory of the link motion has been synthesized. The controlling actions required for its implementation have been found.

This research challenges the traditional perspective on consumer behavior, which focuses on external factors shaping the sequential stages of decision-making. Instead, it proposes a paradigm shift by treating the decision-making process itself as an independent variable, exploring its impact on various outcomes like purchasing behavior, brand loyalty, and post-purchase satisfaction. This novel approach offers deeper insights into the intricacies of consumer behavior, particularly in the digital age. By uncovering the reciprocal relationships between the decision-making process and other variables, the research provides a more nuanced understanding of how consumers navigate today's complex landscape. It further delves into how variations in the decision-making process across diverse consumer groups influence various outcomes, recognizing the unique characteristics and needs of different demographics. The research also acknowledges the interplay of psychological, social, and contextual factors that shape both the decision-making process and its downstream effects, offering a comprehensive understanding of the multifaceted influences on consumer behavior. Ultimately, by studying the decision-making process as an independent variable, this research aims to equip businesses and researchers with valuable insights, enabling them to better navigate the ever-evolving world of consumer behavior.

Using four equations, a recently proposed classical field theory that geometrically couples electromagnetism to gravitation in a fundamentally new way is reviewed. Maxwell’s field equations are a consequence of the new theory as are Einstein’s field equations augmented by a term that can replicate both dark matter and dark energy. To emphasize the unification brought to electromagnetic and gravitational phenomena by the new theory specific solutions are investigated: a spherically-symmetric charged particle solution, a cosmological solution representing a homogeneous and isotropic universe, and solutions representing electromagnetic and gravitational waves. A unique feature of the new theory is that both charge and mass density are treated as dynamic fields, this as opposed to their treatment in the classical Maxwell and Einstein field equations where they are introduced as external entities. This feature suggests a procedure for quantizing the mass, charge and angular momentum that characterize particle-like solutions. Finally, antimatter, which is naturally accommodated by the new theory, and its interaction with a gravitational field is investigated.

Using four field equations, a recently proposed theory that covers the phenomenology of classical physics at the level of the Maxwell and Einstein Field Equations (M&EFEs) but then goes further by unifying electromagnetic and gravitational phenomena in a fundamentally new way is reviewed. Predictions of the field equations are shown to be consistent with those of the M&EFEs through specific solutions; a particle-like solution representing a point charge, and two radiative solutions representing electromagnetic and gravitational waves. A unique feature of the full set of field equations is that charge and mass are treated as dynamic fields instead of being introduced as external parameters as is done with the classical M&EFEs, a feature that enables a procedure for quantizing the mass, charge and angular momentum of particle-like solutions. Finally, antimatter is naturally accommodated by the theory and definite predictions regarding the interactions of matter and antimatter with gravitational fields are made.

The water resources carrying capacity (WRCC) system comprises multiple complex and nonlinear interactions related to society, economy, water resources and the water environment. A full understanding of its internal mechanism is conducive to the further improvement of the regional WRCC. This article focuses on the temporal and spatial variability of WRCC to explore a method of dynamic successive assessment. Firstly, the Pressure-State-Response (PSR)framework is used to develop a systematic and causal indicator system. Then, The Variable Fuzzy Pattern Recognition (VFPR) model and an analytic hierarchy process-entropy (AHP-E) model are combined to successively and dynamically assess WRCC. The proposed method is applied to the dynamic successive assessment of WRCC in Hebei Province, and it is obtained that the poor water resources carrying capacity in Hebei Province is mainly due to the basic attribute of the decision of the water resources shortage, but Hebei Province actively adopts a variety of measures to save water and pressurize mining, which makes the province's water resources carrying capacity have a tendency to become better gradually. At the same time, the system dynamics model (SD) of water resources carrying capacity based on the analysis of the model structure was established. Moreover, three scenarios were designed, including existing continuation, high-efficiency water-saving, cross-regional water transfer and coordinated development, each scenario divided into high and low-speed economic development and population growth schemes, then total 6 schemes between 2023 and 2030 were simulated and predicted respectively. The results indicate that if the current development model is adopted, the water resource carrying capacity will continue to maintain low levels. It was concluded that the high-speed development of economy and population, the efficient water conservation and inter-basin transfer scenario (scenario 2 with high-speed) is the best choice for sustainable development of water resources and social economy in Hebei.

For its enthusiasts, the blockchain is an archivist's dream come true: an immutable historical record that is publicly accessible and immune from censorship. Proposals like Juried Protocol Galleries could enable bots to compute the provenance of digital artworks and their appearance in exhibitions and publications. Distributed file systems like IPFS claim to store creative works in a redundant, networked fashion outside the control of any single cloud provider. Projects designed to work with blockchains like Filecoin and Arweave propose a dedicated cryptocurrency as financial incentive to support the cost of governing and sustaining this "permaweb." As dreamy as this picture sounds, many of these promises depend on technologies that have yet to be sufficiently developed or adopted. Rather than forecast the future, we can separate the hype from the reality in the present day by examining a real-life example, namely the 2021 auction of Andy Warhol's digital art with NFTs. This essay will focus less on blockchain's general promise as a preservation medium and more on the particular case of the digital Warhols, which both in form and spirit would seem a perfect application of NFTs to preserve historically important works of digital art. Which promises of the crypto-dream of permanent access to digital heritage ring true for this case study, and which are overblown?

The paper interprets a comparison of two mostly used techniques of a field-oriented control for 6-phase electric drives, with their pros and cons, as well as their differences in construction and behaviour. Both of these approaches have been realized. Frequency and step responses analysis have been demonstrated with a 6-phase permanent magnet synchronous machine. Experimental results have been compared with simulations based on a mathematical model.

Many Caribbean island nations have historically been heavily dependent on imported fossil fuels for both power and transportation, while at the same time being at an enhanced risk from the impacts of climate change, although their emissions represent a very tiny fraction of the global total responsible for climate change. Small island developing states (SIDS) are among the leaders in advocating for the ambitious 1.5°C Paris Agreement target and the transition to 100% sustainable, renewable energy systems. In this work we present three central results. First, we show through GIS mapping of all Caribbean islands the potential for near-coastal deep-water as a resource for Ocean Thermal Energy Conversion (OTEC) and couple these results with an estimate of the countries for which OTEC would be most advantageous due to a lack of other dispatchable renewable power options. Second, hourly data have been utilized to explicitly show the trade-offs between battery storage needs and dispatchable renewable sources such as OTEC in 100% renewable electricity systems, both in technological and economic terms. Finally, the utility of near-shore, open-cycle OTEC with accompanying desalination is shown to enable a higher penetration of renewable energy and lead to lower system levelized costs than those of a conventional fossil fuel system.

This paper presented a two-dimensional, well-balanced hydrodynamic and sediment transport model based on the solutions of variable density shallow water equations (VDSWEs) for sediment-laden flows, and the Exner equation for bed changes. Those equations are solved in a coupled way by the first-order Godunov-type finite volume method. The Harten-Lax-van Leer-Contact (HLLC) Riemann solver is extended to find the local Riemann fluxes in order to maintain the exact balance between the momentum term and the bed slope term. The advantage of a well-balanced scheme over an unbalanced scheme is demonstrated by the synthetic standing contact-discontinuity test case. Then, the model is employed to simulate two laboratory experiments. At last, a field case, the 1996 Lake Ha! Ha! flood event (Canada), is simulated. Results of cross sectional geometries and profiles of longitudinal thalweg agree well with measurements. The accuracy and simplicity of the numerical model, together with the robust implementation, make the model a good candidate for practical engineering applications.

: Seasonal influenza (a.k.a flu) is responsible for considerable morbidity and mortality across the globe. The three recognized pathogens that cause epidemics during the winter season are influenza A, B and C. The influenza virus is particularly dangerous due to its mutability. Vaccines are an effective tool in preventing seasonal influenza, and their formulas are updated yearly according to WHO recommendations. However, in order to facilitate decision-making in the planning of the intervention, policymakers need information on the projected costs and quantities related to introducing the influenza vaccine, in order to help governments obtain an optimal allocation of the vaccine each year. In this paper, an approach based on a Controlled Elitism Non-Dominated Sorting Genetic Algorithm (CENSGA) model is introduced to optimize the allocation of influenza vaccination. A bi-objective model is formulated to control the infection volume, and reduce the unit cost of the vaccination campaign. An SIR (Susceptible–Infected–Recovered) model is employed for representing a potential epidemic. The model constraints are based on the epidemiological model, time management, and vaccine quantity. A two-phase optimization process is proposed: guardian control followed by contingent controls. The proposed approach is an evolutionary metaheuristic multi-objective optimization algorithm with a local search procedure based on a hash table. Moreover, in order to optimize the scheduling of a set of policies over a predetermined time to form a complete campaign, an extended CENSGA is introduced with a variable-length chromosome (VLC) along with mutation and crossover operations. To validate the applicability of the proposed CENSGA, it is compared with the classical Non-Dominated Sorting Genetic Algorithm (NSGA-II). The results are analyzed using graphical and statistical comparisons in terms of cardinality, convergence, distribution and spread quality metrics, illustrating that the proposed CENSGA is effective and useful for determining the optimal vaccination allocation campaigns.

Irrigated agriculture determines large blue water withdrawals, and it is considered a key intervention area to reach sustainable development objectives. Precision agriculture technologies have the potential to mitigate water resource depletion that often characterizes conventional agricultural approaches. This study investigates the factors influencing farmers' intentions to adopt variable rate irrigation (VRI) technology. The Technology Acceptance Model 3 (TAM-3) was employed as a theoretical framework to design a survey to identify the factors influencing farmers' decision-making process when adopting VRI. Data were gathered through quantitative face-to-face interviews with a sample of 138 fruit and grapevine producers from the Northeast of Italy (Veneto, Emilia-Romagna, Trentino-Alto Adige, Friuli-Venezia Giulia). Data were analyzed using partial least squares path modelling (PLS-PM). The results highlight that personal attitudes, such as perceived usefulness and subjective norm, positively influence the intention to adopt VRI. Also, the perceived ease of use positively affects intention, but it is moderated by subject experience.

The growing market penetration of heat pumps indicates the need for a performance test method which better reflects the dynamic behavior of heat pumps. In this contribution, we developed and implemented a dynamic test method for the evaluation of the seasonal performance of heat pumps by means of laboratory testing. Current standards force the heat pump control inactive by fixing the compressor speed. In contrast, during dynamic testing, the compressor runs unfixed while the heat pump is subjected to a temperature profile. The profile consists of the different outdoor temperatures of a typical heating season based on the average European climate and also includes temperature changes to reflect the dynamic behavior of the heat pump. The seasonal performance can be directly obtained from the measured heating energy and electricity consumption making subsequent data interpolation and recalculation with correction factors obsolete. The method delivers results with high precision and high reproducibility and could be an appropriate method for a fair rating of heat pumps.

The role of γδ T cells in vertebrate immunity is still an unsolved puzzle. Species such as humans and mice display a low percentage of these T lymphocytes (i.e., “γδ low species”) with a restricted diversity of γδ T cell receptors (TR). Conversely, artiodactyl species (i.e., “γδ high species”) ac-count for a high proportion of γδ T cells with large γ and δ chain repertoires. The genomic organisation of the TR γ (TRG) and δ (TRD) loci has been determined in sheep and cattle, noting that a wide number of germline genes that encode for γ and δ chains characterise their genomes. Taking advantage from the current improved version of the genome assembly, we have investigated the genomic structure and gene content of the dromedary TRD locus, which, as in the other mammalian species, is nested within the TR alpha (TRA) genes. The most remarkable finding was the identification of a very limited number of variable germline genes (TRDV) compared to sheep and cattle, which supports our previous expression analyses for which the somatic hypermutation mechanism is able to enlarge and diversify the primary repertoire of dromedary δ chains. Furthermore, the comparison between genomic and expressed sequences reveals that D genes, up to four incorporated in a transcript, greatly contribute to the increased diversity of the dromedary δ chain antigen binding-site.

The description of the microscopic world in quantum mechanics is very different from that in classical physics, and there are some points of view that are contrary to intuition and logic. The first is the loss of reality, the behavior of micro particles shows randomness and hopping. The second is the loss of certainty, the conjugate physical variables of a system cannot be determined synchronously, they satisfy the Heisenberg uncertainty principle. The third is the non-local correlation. The measurement of one particle in the quantum entanglement pair will change the state of the other entangled particle simultaneously. In this paper, some concepts related to quantum entanglement, such as EPR correlation, quantum entanglement correlation function, Bell's inequality and so on, are analyzed in detail. Analysis shows that the mystery and confusion in quantum theory may be caused by the logical problems in its basic framework. Bell's inequality is only a mathematical theorem, but its physical meaning is actually unclear. The Bell state of quantum entangled pair may not satisfy the dynamic equation of quantum theory, so it cannot describe the true state of microscopic particles. In this paper, the correct correlation functions of spin entanglement pair and photonic entanglement pair are strictly derived according to normal logic. Quantum theory is a more fundamental theory than classical mechanics, and they are not parallel relation in logic. However, there are still some unreasonable contents in the framework of quantum theory, which need to be improved. In order to disclose the real relationship between quantum theory and classical mechanics, we propose some experiments which show the wave-particle duality simultaneously and provide intuitionistic teaching materials for the new interpretation of quantum theory.

L\'evy walks represent important modeling tools for variety of real life processes. Their natural scaling limits are known to be described by the so-called material fractional derivatives. So far these scaling limits were derived for spatially homogeneous walks, where Fourier and Laplace transforms represent natural tools of analysis. Here we derive the corresponding limiting equations in the case of position depending times and velocities of walks, where Fourier transforms cannot be effectively applied. In fact, we derive three different limits (specified by the way the process is stopped at an attempt to cross the boundary), leading to three different multi-dimensional versions of Caputo-Dzherbashian derivatives, which correspond to different boundary conditions for the generators of the related Feller semigroups and processes. Some other extensions and generalisations are analysed.

In this research article we solve the problem of synchronization and anti-synchronization of chaotic systems described by discrete and time-delayed variable fractional order differential equations. To guarantee the synchronization and anti-synchronization of these systems, we use the well-known PID control theory and the Lyapunov-Krasovskii stability theory for discrete systems of variable fractional order.We illustrate the results obtained through simulation with examples, in which it can be seen that our results are satisfactory, thus achieving synchronization and anti-synchronization of chaotic systems of variable fractional order with discrete time delay.

Although lymphoma is the most frequent malignancy in common variable immunodeficiency (CVID), solid tumors, especially under influence of oncogenic viruses, are not considered. Furthermore, in vitro genetic studies and cell cultures are not adequate for immune system and HBV interaction. We adopted previously introduced clinical model of host-virus interaction (i.e. infectious process in immunodeficiency) for analysis of B cells and specific IgG role (observational study of a CVID patient who received intravenous immunoglobulin (IVIG)) Suddenly, the patient deteriorated with positive result of HBs, HBV-DNA (369x106copies). In spite of lamivudine therapy and IVIG escalation (from 0.3 to 0.4g/kg), CT showed 11cm intrahepatic tumor (hepatocellular carcinoma). Anti-HBs had been positive in time-lapse analysis (range 111-220 IU/ml). Replacement therapy intensification was complicated by immune complex disease with renal failure. The fulminant HCC in CVID and the development of the tumor as the first sign is interesting. Unfortunately, treatment with hepatitis B immune globulins (HBIG) plays a major role in posttransplant maintenance therapy. Anti-HBs substitution has not been proven to be effective, oncoprotective nor safe. Therefore, immunosuppression in HBV-infected recipients should be carefully minimized, patient selection more precise with the exclusion of HBV-positive donors. Our clinical model show HCC pathway with important humoral host factors, contrary to epidemiological/cohort studies highlighting risk factors only (e.g. chronic hepatitis). Lack of cell cooperation, B cell deficiency observed in CVID plays a crucial role in high HBV replication, especially in carcinogenesis.

This paper provides a review of an emerging field in the food processing sector, referring to efficient and safe food supply chains, ’from farm to fork’, as enabled by Artificial Intelligence (AI). Recent advances in machine and deep learning are used for effective food production, energy management and food labeling. Appropriate deep neural architectures are adopted and used for this purpose, including Fully Convolutional Networks, Long Short-Term Memories and Recurrent Neural Networks, Auto-Encoders and Attention mechanisms, Latent Variable extraction and clustering, as well as Domain Adaptation. Three experimental studies are presented, illustrating the ability of these AI methodologies to produce state-of-the-art performance in the whole food supply chain. In particular, these concern: (i) predicting plant growth and tomato yield in greenhouses, thus matching food production to market needs and reducing food waste or food unavailability; (ii) optimizing energy consumption across large networks of food retail refrigeration systems, through optimal selection of systems that can get shut-down and through prediction of the respective food de-freezing times, during peaks of power demand load; (iii) optical recognition and verification of food consumption expiry date in automatic inspection of retail packaged food, thus ensuring safety of food and people’s health.

We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two variable models, where, , is Newton’s constant, and, , increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/ zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.