Consciousness results from comprehending the living creature from its existence in time and space. Thought and consciousness are distinguishing factors between humans and machines having artificial intelligence. No algorithm has been offered for artificial self-awareness based on Thinking. Previous studies have not studied the relationship between consciousness, thinking and time. In this study, the relationship between consciousness, thinking, memories and speech over time has been studied. A deep logical connection exists between consciousness, thinking, and time. An algorithm can be designed for artificial consciousness based on this research findings.

Due to the intermittency and fluctuation of photovoltaic (PV) output power, a high proportion of grid-connected PV power generation systems has a significant impact on power systems. Accurate PV power forecasting can alleviate the uncertainty of the PV power and is of great significance for the stable operation and scheduling of the power systems. Therefore, in this study, a feature rise-dimensional (FRD) two-layer ensemble learning (TLEL) model for short-term PV power deterministic forecasting and probability forecasting is proposed. First， based on the eXtreme Gradient Boosting (XGBoost), Random Forest (RF), CatBoost, and Long-short-term memory (LSTM) models, a TLEL model is constructed utilizing the ensemble learning algorithm. Meanwhile, the FRD method is introduced to construct the FRD-XGBoost-LSTM (R-XGBL), FRD-RF-LSTM(R-RFL), and FRD- CatBoost - LSTM (R-CatBL) models. Subsequently, the above models are combined to construct the FRD-TLEL model for deterministic forecasting, and perform probability interval forecasting based on quantile regression(QR). Finally, the performance of the proposed model is demonstrated with a real-world dataset. By comparing with other models, the proposed model displays better forecasting accuracy for deterministic forecasting and reliable forecasting intervals for probability forecasting, and good generalization ability in the datasets of different seasons and weather types.

Bistatic scattering coefficient have important application of ocean active microwave remote sensing. In this paper, a general expression of the composite scattering coefficient for multiple dielectric targets of one-dimensional fractal sea surface is derived by MOM method. Secondly, the bistatic scattering coefficient is computed, there are two class missile targets and one class ship target of one-dimensional fractal sea surface, and the influence of the water dielectric constant, fractal dimension, wind speed, the target dielectric constant and spatial position is discussed, and the corresponding conclusions are given. The proposed algorithm is able to solve some composite scattering problems, such as “dielectric or conductive object + rough surface”, “dielectric or conductive floating object +rough ”and it has certain generalization and reference.

The research community has shown significant interest in the aqueous synthesis of nanomaterials due to its ability to eliminate the need for complex organic solvents. This synthesis approach aligns with the principles of green chemistry, attracting considerable attention. Aqueous solution technology in fabricating nanostructures has gained recognition for its potential to create ultrasensitive, low-energy, and ultrafast optoelectronic devices. This report focuses on synthesizing lead iodide (PbI2) nanoplates using a water-based solution technique and fabricating a planar photodetector. The photodetectors with a planar type of device structure (ITO/PbI2 NPs/Au) demonstrated a remarkable photosensitivity of 3.9×103 and a photoresponsivity of 0.51 mA/W at a wavelength of 405 nm. Notably, the asymmetrical output properties of ITO/PbI2 NPs/Au detector deliver additional evidence of the effective creation of a Schottky contact. Thus, the photodetector exhibited a photoresponse even at 0 V bias, leading to the realization of self-powered photodetectors. Additionally, the device exhibited a rapid photoresponse of 0.21/0.38 s (−5 V) in the visible range. This study has expanded the horizons for the aqueous-phase synthesis of nanoplate nanostructures, enabling the large-area fabrication of high-performance photodetectors.

The valley pseudospin properties of electrons in two–dimensional hexagonal materials result in lots of fascinating physical phenomena, which opens up the new field of valleytronics. The valley-contrasting physics aims at distinguishing the valley degree of freedom based on valley–dependent effects. Here, we theoretically demonstrate that both of the valley–selective high harmonic generation and valley–selective electronic excitation can be achieved by using an orthogonal two–color (OTC) laser field in gapped graphene. It is shown that the asymmetry degrees of harmonic yields in the plateaus, cutoff energies of generated harmonics and electron populations from two different valleys can be precisely controlled by the relative phase of the OTC laser field. Thus the selectivity of the dominant valley for the harmonic radiation and electronic polarization can be switched by adjusting the relative phase of the OTC laser field. Our work offers an all–optical route to produce the valley–resolved high harmonic emissions and manipulate the ultrafast valley polarization on a femtosecond timescale in condensed matter.

The accurate assessment of wound size is a critical step in advanced wound care management. This study aims to introduce and validate a Light Detection and Ranging (LiDAR) technique for measuring wound size. Twenty-eight wounds treated from December 2022 to April 2023 at the Chunganam National University Hospital were analyzed. All wounds were measured using three techniques; conventional ruler methods, the LiDAR technique, and ImageJ analysis. Correlation analysis, linear regression, and Bland-Altman plot analysis were conducted to validate the accuracy of the newly introduced method. The measurement results (Mean ± Standard deviation) obtained using the Ruler method, LiDAR technique, and ImageJ analysis were 112.99 ± 110.07 cm², 73.59 ± 72.97 cm², and 74.29 ± 72.15 cm², respectively. The Pearson correlation coefficient was higher in the LiDAR application (0.995) than the conventional ruler methods (Mean difference; -5.0000 cm2), as was the degree of agreement (Mean difference; 38.6933 cm2). Wound size measurement using LiDAR is a simple and reliable method that allows practitioners to conveniently assess wounds with a flattened and irregular shape. However, non-flattened wounds cannot be assessed owing to the technical limitations of LiDAR.

Fire is a common natural disturbance in forest ecosystems and plays an important role in subsequent vegetation patterns. Based on the spatial sequence method instead of the time successional sequence method, this study selected burned areas in different locations in the Anning River Basin, which contains typical dry valleys. Quadrat surveys and quantitative classification were used to identify the vegetation classification, distribution pattern, and environmental interpretation during the natural restoration process after forest fire. The results showed that: (1) the vegetation community in the early stage of natural recovery after forest fire disturbance could be divided into seven community types, and Quercus guyavaefolia H. Leveille (Qg) was the dominant species in the community; (2) vegetation samples could be divided into five ecological types, and the classification and distribution pattern of community types in this region changed most obviously with altitude; and (3) detrended correspondence analysis could clearly classify vegetation community types, and detrended canonical correspondence analysis could well reveal the relationships between species and environmental factors. This study provides a scientific basis guiding the restoration of ecosystem structural stability and biodiversity in burned areas.

High-quality ionization injection methods for Wake Field Acceleration driven by lasers or charged beams (LWFA/PWFA) can be optimised so as to generate high-brightness electron beams with tuneable duration in the attosecond range. We present a model of the minimum bunch duration obtainable with low-emittance ionization injection schemes, by spotting the roles of the ionization pulse duration, of the wake field longitudinal shape and of the delay of the ionization pulse position with respect to the node of the accelerating field. The model is tested for the resonant multi-pulse ionization injection (ReMPI) scheme, showing that bunches having length of about 300as can be obtained with a ionization pulse having duration of 30fs FWHM

The natural units of measure lauded by Max Planck more than 100 years ago are rarely used today. But many physical constants, including Planck's constant, the gravitational constant, the speed of light, vacuum permittivity, and vacuum permeability, consist of natural units in their unit dimensions. The natural units are present in all formulas containing these constants. The defining characteristic of the natural units is an alignment of unit values at the Planck scale. This alignment gives a computational basis of proportionality from which the correlated properties and dynamics of elementary particles, including wavelength, period, mass, momentum, and energy, manifest in equal or inversely proportional ratios of the Planck scale. These correlations explain many of the defining equations of quantum mechanics, classical gravity, and electromagnetism.

This paper presents a method for producing a gravity field through an electromagnetic field. The electromagnetic field can produce a positive or negative gravity field. The relationship between gravity force and electromagnetic is unknown. Former studies by theoretical physics predict the relationship between these two forces. Based on the general balance theory in six dimensions of space and time, the electromagnetic field was studied from this viewpoint. Three-dimensional waves oscillate in the fourth dimension and electromagnetic waves distort space-time. Aggregating these distortions can behave like a positive or negative gravity field. Consequently, the production of energy is possible from gravitational fields.

We carry out in a thin heterogeneous porous layer, the multiscale analysis of Smoluchowski's discrete diffusion-coagulation equations describing the evolution density of diffusing particles that are subject to coagulate in pairs. Assuming that the thin heterogeneous layer is made of microstructures that are uniformly distributed inside, we obtain in the limit an upscaled model in lower space dimension. We also prove a corrector-type result very useful in numerical computations. In view of the thin structure of the domain, we appeal to a concept of two-scale convergence adapted to thin heterogeneous media to achieve our goal.

Over the past two decades, covalent organic frameworks (COFs) have become the most widely studied porous crystalline materials. Its specific physical and chemical properties are determined by the arrangement of atoms (crystal structure). Therefore, the determination of its structure is arguably the most important characterization step for COFs. Although single crystal X-ray diffraction (SCXRD) is the most widely used method for structure determination, the size of COF crystals is too small, or their crystal quality is so poor that confirmation of their structure is limited to lattice fringes by transmission electron microscopy (TEM). At present, many 2D-COFs have clear PXRD patterns, but not all 2D-COFs can obtain specific lattice fringes. This severely hinders the development of the COF field. Here, we discover the lattice shrinkage behavior of COFs under electron beam irradiation by comparing the lattice fringes of 2D-COFs under different conditions. By comparing the lattice fringes of TAPT-TFPT COF at room temperature and under liquid nitrogen freezing conditions, we found that the lattice fringes are in good agreement with the Powder X-ray diffraction (PXRD) and theoretical values of COF (2.213 nm) under freezing conditions, however, the lattice fringe spacing is only 1.656 nm at room temperature. The discovery not only provides new insights into the TEM characterization of COFs, but also further expands the range of crystalline COF materials.

Arrhythmic sudden cardiac death (SCD) has an annual prevalence of 1 per 1000 while 75% of the victims suffer from ischemic and 10% from non-ischemic or hypertrophic cardiomyopathy. Altogether, these three entities account for more than 80% of the total SCD victims. Guidelines for implantable cardiac defibrillators are still dominated by LVEF<30% from the MADIT II study. In terms of arrhythmic risk stratification, the PRESERVE-EF study restored in clinical practice the two-step arrhythmic risk stratification approach based on Electrocardiographic non-invasive risk factors (NIRFs) guiding to electrophysiological study. In our times with the multiple cardiac imaging methods and artificial intelligence applications availability, this two-step approach based on integrated arrhythmia mechanisms detection, emerges as an efficient SCD risk stratification paradigm for these three entities but also for the patients with congenital heart disease.

In this work, the possibility of fabrication magneto-optical ceramics by vacuum sintering of compacts prepared by electrophoretic deposition (EPD) from suspensions of (Ho0.95La0.05)2O3 (HoLa) nanopowders made by self-propagating high-temperature synthesis and (Y0.9La0.1)2O3 (YLa) nanopowders made by laser synthesis was systematically investigated. The processes of the formation of bulk one- and two-layer compacts by the EPD method from non-aqueous suspensions of nanopowders was studied. The use of acetylacetone and polyvinyl butyral (PVB) was shown to make it possible to obtain stable alcohol suspensions of the nanopowders characterized by high zeta potential values (+29…+80 mV) and to carry out a stable EPD process. One-layer compacts were made from the YLa and HoLa nanopowders with a density of 30–43%. It was found out that the introduction of PVB into the suspension leads to a decrease in the mass and thickness of the green bodies deposited, but does not significantly affect their density. A possibility to make two-layers (YLa/HoLa) compacts with a thickness of up to 2.6 mm and a density of up to 46% was demonstrated. Sintering such compacts in a vacuum at a temperature of 1750°C for 10 hours leads to the formation of ceramics with a homogeneous boundary between the YLa/HoLa layers and thickness of the interdiffused ions layer of about 30 μm.

Spinors are used for the computation of probability in quantum mechanics. They are treated as elements of a complex vector space in contrast to a real vector space. Spinor theory is abstract mathematics with an ambiguous interpretation. The overall phase of spinors does not affect the computation of probability in quantum mechanics. If two spinors have an overall phase of imaginary number i, then they can be treated like vectors in a real vector space. The square of the magnitude of the number, which is the sum of dot product and the term having basis vector i of cross product of two such vectors, is equal to the probability computed using the corresponding spinors. Therefore, the geometry of such spinors can be easily depicted in a three-dimensional space like vectors in a real vector space. Spinors are not isotropic vectors in Hilbert space. The sum of dot product and cross product of two complex numbers is equivalent to the quotient of the division of one complex number by another. Similarly, the sum of dot product and cross product of two vectors is the quotient of the division of one vector by another. Using the rules of division of vectors, we can find the rules of multiplication of vectors. The rules of multiplication and division of basis vector i match the rules of multiplication and division of imaginary number i. Therefore, basis vector i is nothing but imaginary number i. Multiplication of dot and cross product of two vectors to the second vector will give us the components of the first vector that are parallel and orthogonal to the second vector. Vectors are also made up of complex numbers like spinors. The reason for finding dot product of complex numbers in the process of computing the probability is to ignore the overall phase in contrast to the phase difference. This is misconstrued as complex vector space in quantum mechanics. 3-D number which is nothing but a spinor with new notation is an extension of vector algebra. It can have a real number as a term in addition to the terms i, j and k. In all other respects, it is a vector. 3-D numbers are part of the number system like real numbers and complex numbers. The real numbers are one dimensional numbers, the complex numbers are two-dimensional numbers and the 3-D numbers as well as vectors and spinors are three-dimensional numbers. As polarisation and spin are one and the same, we can straight away apply 3-D numbers to polarisation of light. 3-D numbers will greatly simplify the study of areas of science where three-dimensional space is involved.

Due to increasing concerns about global warming and energy crisis, intensive efforts have been made to explore renewable and clean energy sources. Single-atom metals and two-dimensional (2D) nanomaterials have attracted extensive attention in the fields of energy and environment because of their unique electronic structures and excellent properties. In this review, we sum-marize the state-of-art progress on the single-atom metal supported at 2D MoS2 (single-atom metal/2D MoS2) for electrochemical CO2 reduction and water splitting. First, we introduce the advantages of single-atom metal/2D MoS2 catalysts in the fields of electrocatalytic CO2 reduction and water splitting, followed by the strategies for improving electrocatalytic performances of single-atom metal/2D MoS2 hybrid nanomaterials and the typical preparation methods. Further, we discuss the important applications of the nanocomposites in electrocatalytic CO2 reduction and water splitting via some typical examples, particularly focusing on their synthesis routes, modification approaches, and physiochemical mechanisms for improving their electrocatalytic performances. Finally, our perspectives on the key challenges and future directions of exploring high-performance metal single-atom catalysts are presented based on recent achievements in the development of single-atom metal/2D MoS2 hybrid nanomaterials.

(1) Importance of problem: Breast cancer accounted for 685.000 deaths globally in 2020, and half of all cases occur in women with no specific risk factor beside gender and age-group. During last 4 decades we see a reduction by 40% of age-standardized breast cancer [1], which in turn means that the number of mastectomies performed for younger women increased, raising the need for adequate breast reconstructive surgery. Advances in oncological treatment have made it possible to limit the extent of what represents radical surgery for breast cancer, yet in the past decade, we see a marked trend toward mastectomy in breast conserving surgery eligible patients [2]. Prophylactic mastectomy has also registered an upward trend [3,4]. This trend together with new indication for breast reconstruction like chest feminization in transgender patients [5] have increased the need for breast reconstruction surgery. (2) Purpose: The purpose of this study is to analyze the types of reconstructive procedures, their indications, their limitations, their functional results and the safety profiles when used during the integrated treatment plan of the oncologic patient; (3) Methods: We conducted an extensive literature review of the main reconstructive techniques, especially the autologous procedures, summarized the findings and presented a few cases from our own experience for exemplification of the usage of breast reconstruction in oncologic patients. (4) Conclusions: Breast reconstruction has become a necessary step in the treatment of most breast cancers and many reconstructive techniques are now routinely practiced. Microsurgical techniques are considered the "gold standard", but they are not accessible to all services, from a technical or financial point of view, so pediculated flaps remain the safe and reliable option, along with alloplastic procedures, to improve the quality of life of these patients.

The three-dimensional morphology of frictional sub-surfaces holds significant importance for studying tribological issues. However, the uniformity of horizontal datum in 3D scanning is limited for curved surfaces, resulting in the inability to obtain accurate contour characterization parameters from the scanning results. This study aims to address this issue by constructing regular surface equations and normalizing the 3D profiler scanning results. By fitting the data, a "plane" surface representative of the surface features is obtained, and the paper demonstrates this approach on the surface morphology of different worn parts in the frictional area of spherical bearings located in the specific environment of the Three Gorges gate. The results indicate that the obtained "plane" effectively reconstructs the three-dimensional morphology map of the regular surface. Moreover, this reconstructed plane not only clearly illustrates the surface characteristics but also provides the foundation for analyzing the wear mechanism.

The cubic nonlinearity of graphene oxide (GO) monolayer was characterized by open and closed Z-scan using a nano-second laser of 10 Hz repetition rate with a Gaussian spatial beam profile. The reverse saturable absorption or positive nonlinearity from open Z-scan and valley-peak traces or positive nonlinearity from closed Z-scan was displayed. The reverse saturable absorption could be from a two-photon or two-step excitation process due to the larger absorption in the lower visible and upper UV wavelength region compared with the excitation source. The nonlinear absorption and nonlinear refraction coefficient were estimated to be ~ 2.62x10-8 m/W and 3.9x10-15 m2/W, respectively. This study also reveals the effect of nonlinear absorption on nonlinear refraction traces.

This paper is concerned with developing an efficient numerical algorithm for fast implementation of the sparse grid method for computing the $d$-dimensional integral of a given function. The new algorithm, called the MDI-SG ({\em multilevel dimension iteration sparse grid}) method, implements the sparse grid method based on a dimension iteration/reduction procedure, it does not need to store the integration points, nor does it compute the function values independently at each integration point, instead, it reuses the computation for function evaluations as much as possible by performing the function evaluations at all integration points in a cluster and iteratively along coordinate directions. It is shown numerically that the computational complexity (in terms of CPU time) of the proposed MDI-SG method is of polynomial order $O(Nd^3 )$ or better, compared to the exponential order $O(N(\log N)^{d-1})$ for the standard sparse grid method, where $N$ denotes the maximum number of integration points in each coordinate direction. As a result, the proposed MDI-SG method effectively circumvents the curse of dimensionality suffered by the standard sparse grid method for high-dimensional numerical integration.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) often causes severe viral pneumonia, especially among older individuals and those with underlying health conditions. The C57BL/6 mouse is widely used to develop mouse models of obesity, diabetes, hypertension, and immune disorders, conditions that are associated with increased risk of severe COVID-19. Although many studies using these mouse models have examined the pathogenicity of SARS-CoV-2, COVID-19 pathogenesis remains poorly understood. In vivo imaging analysis using two-photon excitation microscopy is useful for elucidating the pathology of COVID-19, providing pathological insights that are not available from conventional histological analysis. However, there is no reporter SARS-CoV-2 that demonstrates pathogenicity in C57BL/6 mice and emits sufficient light intensity for two-photon in vivo imaging. Here, we generated SARS-CoV-2 expressing the fluorescent protein Venus to facilitate in vivo pathological studies using C57BL/6 mouse models. We generated a mouse-adapted strain of SARS-CoV-2, by performing sequential lung-to-lung passages in BALB/c mice, followed by in C57BL/6 mice. We observed that the C57BL/6-adapted SARS-CoV-2 (named MASCV2-p25) replicated efficiently in the lungs of C57BL/6 mice, causing fatal pneumonia. Histopathologic analysis revealed severe inflammation and infiltration of immune cells in the lungs of MASCV2-p25-infected C57BL/6J mice, not unlike that observed in COVID-19 patients with severe pneumonia. Subsequently, we generated a mouse-adapted reporter SARS-CoV-2 (named MASCV-Venus-p9) by inserting the fluorescent gene Venus into MASCV2-p25, and sequential lung-to-lung passages in C57BL/6 mice. C57BL/6 mice infected with MASCV2-Venus-p9 exhibited severe pneumonia similar to that induced by MASCV2-p25. In addition, two-photon excitation microscopy of the lungs of the infected C57BL/6J mice showed that the infected cells emitted sufficient levels of fluorescence for easy observation. These findings suggest that MASCV2-Venus-p9 will be useful for two photon in vivo imaging studies of the pathogenesis of severe COVID-19 and for the development of therapeutic agents for severe pneumonia.

In this paper, we consider the one-dimensional Ising model (shortly, 1D-MSIM) having mixed spin-(s,(2t−1)/2) with nearest neighbors and the external magnetic field. We establish the partition function of the model by means of the transfer matrix. Under a null boundary condition, we compute certain thermodynamic quantities for the 1D-MSIM. We find some precise formulas to determine the model’s free energy, entropy, magnetization, and susceptibility. By examining the model’s associated iterative equations, we use the cavity approach to investigate the phase transition problem. We numerically determine the model’s periodicity.

Background: Scaffold (SCA) functionalization with aptamers (APT) provides adsorption of specific bioactive molecules on biomaterials surface. The aim of this study was to observe if SCA enriched with anti-fibronectin APT can favor coagulum (PhC) and osteoblasts (OSB) differentiation. Methods: 20 ug of APT was functionalized on SCA by simple adsorption. For PhC formation, SCAs were inserted into rat calvaria defects for 17 hours. Following proper transportation (Buffer solution-PB), OSB (UMR-106 lineage) were seeded over PhC + SCAs with and without APT. Cells and PhC morphology, PhC cell population, protein labelling and gene expression were observed in different time points. Results: The APT induced higher ALP and BSP immunolabeling in OSB. CD90, CD45 and CD44 expression was more detected in APT group than when scaffolds were not functionalized. Additionally, an enriched and dense fibrin network and different cell types were observed, with more OSB and white blood cells in PhC formed on SCA with APT. The gene expression showed higher TGF-β1 detection in SCA with APT. Conclusions: The SCA functionalization with fibronectin aptamers provides a selective expression of proteins related to coagulation pattern and osteo differentiation. Additionally, aptamers increase TGF-β1 gene expression, which is highly associated with improvements in regenerative therapies.

The present study aimed to evaluate the effect of the citric acid on the properties of solid pine wood, which were submitted to thermomechanical treatment. A preliminary test was performed in a previous study to evaluate the influence of different temperatures of pressing and concentrations of citric acid over the physical properties of wood. After choosing the best treatments (170°C and 5% and 10% of citric acid) it was evaluated the effect of these on the chemical properties (SEM, FTIR and pH), on the density profile using an X-ray microtomography, and on the resistance to ageing through abrasion test. The result of the chemical analysis allowed to observe an increase of the presence of ester functional groups, as well as a better coverage and higher acidity of the surface. The density profile technique allowed to observe the formation of peaks of density on the surface where the acid was applied. Lastly, it was also possible to verify an increase in the resistance to abrasion because of the application of the citric acid.

On the basis of the findings of this paper, the theories of quantum mechanics and general relativity can be expressed in a comprehensive theory. The events were surveyed length of the time, in the Euclidean six-dimensional space-time (R6=x1+x2+x3+t1+t2+t3). Previous studies have not studied the relationship between the origin of matter's inherent properties, fundamental constants, and quantum mechanics phenomenon with space-time geometry. This theory prediction the with certainty obtained probabilistic results of quantum mechanics. This prediction is on the basis of metric resulting from the Eccentricity of the Ellipse due to the object density, in the length of time. The cause and nature of dark matter and dark energy are the findings of this article.

The invariant mass of free particles is used to derive a bound-state equation for several particle atomic systems at rest. This relativistic-kinematic bound-state equation is applied to the hydrogen and helium atoms. The derived equation has the well-known solutions for the single-electron bound states of the hydrogen atom, and the two-electron states of the helium atom. For the hydrogen atom, existence of the two-particle bound states, for which the electron and the proton kinetic energies are of the same order of magnitude, is predicted. The three-particle bound states with the same feature of the kinetic energies can exist in the helium atom. Radiative operators for processes involving the hydrogen two-particle bound states, are obtained. It is discussed that these new two- and three-particle bound states should be optically inactive.

This study reports the development of a numerical method to simulate two-phase flows of Newtonian fluids that are incompressible, immiscible, and isothermal. The interface in the simulation was located and reconstructed using the geometric volume of fluid (VOF) method. The implementation of the Piecewise-Linear Interface Calculation (PLIC) scheme of the VOF method was performed to solve the three-dimensional (3D) interface transport during the dynamics of two-phase flows. In this method, the interface is approximated by a line segment in each interfacial cell. The balance of forces at the interface was accounted for using the continuum interfacial force (CSF) model. To solve the Navier-Stokes equations, meshless finite difference schemes from the HiG-Flow computational fluid dynamics software were employed. The 3D PLIC-VOF HiG-Flow algorithm was used to simulate several benchmark two-phase flows for the purpose of validating the numerical implementation. First, the performance of the PLIC implementation was evaluated by conducting two standard advection numerical tests: the 3D shearing flow test and the 3D deforming field test. Good agreement is obtained for the 3D interface shape using both the 3D PLIC-VOF HiG-Flow algorithm and those found on the scientific literature. In addition, the absolute error of the volume tracking advection calculation obtained by our 3D PLIC-VOF HiG-Flow algorithm was found to be smaller than the one found in the scientific literature for both the 3D shearing and 3D deforming flow tests. Lastly, the 3D bubble rising problem was simulated for different fluid densities and viscosity’s ratios. Again, good agreement is obtained for the 3D interface shape using both the newly implemented algorithm and the OpenFOAM, DROPS and NaSt3D software. The bubble’s rise velocity and evolution of the bubble’s center of mass is also computed with the 3D PLIC-VOF HiG-Flow algorithm and found to be in agreement with these software.

Q345 steel is widely used in the construction industry, vehicle manufacturing industry, bridge construction industry and many other aspects due to its excellent comprehensive performance. However, due to its high silicon content, direct hot-dip galvanizing will lead to a series of problems, such as easy peeling of the coating due to the "silicon reactivity". In this paper, we adopt the "two-step hot-dipping method", first hot-dip aluminizing and then hot-dip galvanizing, to isolate the substrate and zinc liquid direct contact, so as to avoid the occurrence of "silicon reactivity". Through the microstructure characterization, neutral salt spray corrosion experiments, electrochemical experiments, the results show that: Al-Zn composite coatings from the inside out were perpendicular to the substrate growth of loose Fe2Al5 layer and Al-Zn layer, outbursts generation; Al-Zn composite coatings resistance to neutral salt spray corrosion was far more than the substrate; Al-Zn composite coatings polarization curve corrosion potential than the substrate was lower than that of the substrate by 0.512V, showing a strong corrosion resistance. It shows extremely strong corrosion resistance.

The two-phase flow of water and reactive gas in the channel of proton exchange membrane fuel cell (PEMFC) plays an important role in high-quality water management. In this study, the removal effect of 3D wave channel and 2D straight channel for liquid water was studied by volume of fluid method. The dynamic behavior of droplet emerge from the gas diffusion layer (GDL) into the channel under the influence of gas flow was investigated. The effects of droplet growth, deformation, detachment, force, pore size on water critical behavior and water content in the channel are discussed. The results show that the 3D wave channel is superior to the removal of liquid water than the 2D straight channel. The increase of velocity is conducive to the discharge liquid water. While infinite increase of velocity will cause more parasitic power loss. The larger pore size of GDL is not advantageous to the performance of PEMFC, but the appropriate increase of pore size is conducive to the discharge of liquid water. The basic knowledge obtained in this study will help to deepen the understanding of droplet dynamics in PEMFC gas channels, and help to optimize the design and working conditions of these channels.

Most of the literature on the optimization problem of transportation in supply chain networks (SCN) considered fixed costs (FC). However, in the practical applications of this problem it is important to study the effect of FC. This paper examines the impact of fixed cost increase as well as the variable cost on the optimization of a Two-Stage SCN. To do so, two mathematical models for mixed-integer nonlinear programming are developed. The first model optimizes fixed and variable costs in addition to the opening cost, whereas the second model only optimizes variable and opening costs. To evaluate the effect of considering FC on the optimization supply chain problem, four groups of instances are randomly generated, and solved using Lingo. The results of the two models are compared utilizing the average percentage deviation. In addition, sensitivity analysis is performed to determine the impact of changes in opening and variable costs on the considered optimization problem. The computational results and the sensitivity analysis show that the first model with minimized FC outperforms the second model, which does not consider the FC in minimization and FC affect the optimization.

This research work aims to develop a mathematical modeling for production-inventory systems. Two mathematical models are present: An infinite dimensional, partial differential equation (PDE), production level modeling is present and a finite dimensional system for a coupled dynamic pricing, production rate and inventory level ordinary differential equations (ODE), integrating a proper Lyapunov stability analysis of the dynamical system and simulations.

In order to reduce the risk of grid transient voltage instability brought about by the new energy and system dynamic loads and the increasing proportion of new energy access, this paper proposes a differentiated dynamic reactive power compensation configuration method for suppressing transient voltage dip instability. The method first establishes a dynamic reactive power device configuration index system based on a multi-binary table, and then establishes a differentiated dynamic reactive power compensation device configuration model based on the node instability risk characteristics by combining with the regional division of system transient voltage instability risk. The model takes the investment cost and suppression effect as the optimization objectives, and realizes differentiated dynamic reactive power compensation by flexibly adjusting the optimization weights of economic benefits and reactive power compensation effects in the configuration process; Compared with the traditional compensation methods, differentiated compensation can significantly improve the economic benefits of reactive power compensation configuration. Finally, taking the IEEE39 node test system as an example, the improved TLBO algorithm is used to solve the model, which verifies the accuracy and effectiveness of the method proposed in this paper.

The particulars of stimulus-response experiments done on dynamic biosystems clearly limit what one can learn and validate about their structural interconnectivity (topology), even when collected kinetic output data are perfect (noise-free). As always, available access ports and other data limitations rule. For linear systems, exponential modes, visible and hidden, play an important role in understanding data limitations, embodied in what we call dynamical signatures in the data. We show here how to circumscribe and analyze modal response data in compartmentalizing model structures – so that modal analysis can be used constructively in systems biology model building – for nonlinear (NL) as well as linear biosystems. We do this by developing and exploiting the modal basis for dynamical signatures in hypothetical (perfect) input-output data associated with a structural model – one that includes inputs and outputs explicitly – and for NL as well as linear biosystems. The methodology establishes model dimensionality (size, complexity) from particular data sets; helps select among multiple candidate models (model distinguishability); helps in designing new experiments to extract “hidden” structure; and helps to simplify (reduce) models to their essentials. For NL biosystems, results are not as comprehensive, similarly informative about their dominant dynamical properties, and unified with linear models on invariant 2-dimensional manifolds in phase space. Some automation of these highly technical aspects of biomodeling is also introduced.

Two-dimensional (2D) ice I is an atomic-level ice that is composed of two interlocked atom-ic layers saturated with hydrogen bonds. It has recently been experimentally observed but its properties have yet to be clarified. Accordingly, we theoretically studied the hydrophobic properties of 2D ice I. On the contrary, simulation of a hydrogen fluoride molecule on a 2D ice surface manifested that it destroyed the 2d ice structure and connected new hydrogen bonds with water molecules. Investigations of the interfacial effect between 2D and three-dimensional (3D) ice films indicated that the network structure of 2D ice was not de-stroyed by a 3D ice surface, as the former was saturated with hydrogen bonds. However, the surface of 3D ice reorganized to form as many hydrogen bonds as possible. Thus, the 2D ice film was hydrophobic and inhibited the growth of 3D ice. This shows that if 2D ice can be produced on an industrial scale, it could be used as an anti-3D-icing agent under low tem-perature.

Computer and Smart Device-assisted Learning (CSDL) has gained increasing attention from educational researchers and practitioners in recent years. However, it remains controversial whether student can benefit from CSDL and what moderator could affect the impact of CSDL. Within the specific context of Korea, where the interest in digital education is steadily increasing, the number of empirical studies exploring the causal effect of CSDL remains relatively scarce. The primary objective of this empirical study was to investigate the impact of CSDL on students' academic achievement in Korea. To achieve this objective, a two-way fixed effect model was employed, utilizing a panel dataset spanning three years derived from the 'Korean Education Longitudinal Study 2013'. The findings revealed a significant positive impact of CSDL on students’ Mathematics achievement. Notably, higher income levels, increased availability of computer resources provided by schools, and the implementation of more individualized education were identified as factors that moderate the effect of CSDL on students' achievement levels in Korean and English subjects. These findings underscore the need for an approach that optimizes the educational benefits of CSDL by considering subject-specific characteristics. Furthermore, this study highlights the importance of allocating educational resources, such as computers and smart devices, and integrating individualized educational activities within the classroom environment.

The extensive utilization of rebar during the construction of the project generates immense rebar waste, leading to increased construction costs and significant greenhouse gas emissions. Various approaches have been explored focusing on the minimization of rebar-cutting waste, such as optimizing cutting patterns, lap splice position, and special-length rebar utilization. Nonetheless, reducing rebar usage by minimizing the number of splices remains uninvestigated. In response to these issues, a two-stage optimization algorithm was developed, prioritizing special-length rebar to achieve near-zero rebar-cutting waste of less than 1% and concurrently reduce rebar usage, thereby promoting sustainable construction practices. The two-stage algorithm presented in this study involves the optimization of the lap splice position for the continuous rebar with a reduction of splices number. Furthermore, it integrates a special-length minimization algorithm for the additional rebars. Applying the algorithm to beam structures in a small-sized factory building project led to a notable reduction of 29.624 tons of rebar, equivalent to 12.31% of the total purchased quantity. Greenhouse gas emissions were reduced by 102.68 tons, and associated costs decreased by USD 30,256. A rebar-cutting waste of 0.99% which is near zero was achieved. These findings highlight the significant potential of the proposed algorithm in reducing rebar waste and facilitating sustainable construction practices. In addition, the application of the proposed algorithm in reinforced concrete construction projects will amplify the associated advantages accordingly.

The field dynamics of neutral and charged matter (treated as gauge fields) beyond the event horizon of black holes are investigated. As an extension of the work done in Ganesan (2023) [1], the analysis was done using the combined dynamics of the two-dimensional Yang-Mills and Liouville gravity fields beyond the event horizon. The quantum interactions and symmetry breaking mechanisms of the combined two-dimensional fields are discussed. The second part of this paper explores the quantum dynamics occurring at the event horizon. By considering the structure of the event horizon as a Möbius strip (embedded in three-dimensional space), a formulation for the lower bound of the first energy eigenvalue of a quantum particle (electron) at the event horizon is obtained.

Background: Parathyroid imaging techniques are used in patients with biochemically confirmed primary hyperparathyroidism (pHPT) to detect the localization of enlarged parathyroid gland(s) (PTG) and thereby to allow more focused, unilateral or minimally invasive parathyreoidectomy (PTX), instead of classical bilateral neck exploration (BNE). Although the preferred sequence of imaging continues to evolve, ultrasonography (US) is usually the first line modality, followed by 99mTc sestamibi parathyroid scan (SPS). However, both tests may have suboptimal localizing accuracy with considerable rate of non-localized cases. Therefore, more accurate four-dimensional computed tomography scan (4D-CT) has been employed for PTG imaging. Currently, there is a paucity of data evaluating the utility of 4D-CT in low case-load settings. Aim: To evaluate the impact of PTG imaging, using 4D-CT in conjunction with it’s intraoperatively displayed results, on the outcomes of surgical PTX. Our primary outcome was sensitivity of 4D-CT in comparison to US and SPS for preoperative PTG adenoma localization. The secondary outcomes were the need for redo surgery and morbidity. Additionally, the characteristics of surgical PTX, i.e. operative time, number of removed parathyroid glands, and rate of simultaneous thyroid surgery, were assessed. Methods: A single-center retrospective analysis of surgically treated patients with pHPT from 01/2010 to 01/2021 was conducted. Evaluation of impact of the preoperative imaging modalities on the results of surgical treatment was carried out. Results: During the study period 290 PTX were performed, 45 cases were excluded due to surgery for secondary, tertiary or recurrent HPT, or due to use of alternative imaging techniques; the remaining 245 patients were included in the study. US was carried out for PTG imaging in 236 (96.3%), SPS in 93 (38.0%), and 4D-CT in 52 patients (21.2%). The use of 4D-CT was associated with a significantly higher rate of successful localization of enlarged PTG (49 cases, 94.2%) compared to US and SPS (74 cases, 31.4%, and 54 cases, 58.1%, respectively). We distinguished between three groups of patients based on preoperative imaging: 1) PTG lateralization via US or SPS in 106 (43.3%) cases, 2) precise localization of PTG via 4D-CT in 49 (20.0%) patients, and 3) in 90 cases (36.7%) PTG imaging failed to localize enlarged gland. The group of 4D-CT localization had significantly shorter operative time, lower rate of simultaneous thyroid resections, as well as lower rate of removal of ≥2 PTG, compared to the other groups. The 4D-CT imaging was also associated with the lowest perioperative morbidity and with the lowest median PTH in one month follow-up; however, compared to the other groups, these differences were statistically not significant. Implementation of 4D-CT (since 01/2018) decreased the need for redo surgery (from 11.5% to 7.3%), and significantly increased the annual case load of PTX at our institution (from 15.3 to 41.0), compared to the period before 4D-CT diagnostics. Conclusions: 4D-CT imaging enabled to precisely locate almost 95% of enlarged PTG in patients with pHPT. Accurate localization and intraoperatively displayed imaging results are useful guides for surgeons in making PTX a faster and safer procedure in a low volume center.

With advances in the development of autonomous vehicles (AVs), more attention has been paid to the effects caused by adverse weather conditions on them. It is well known that the performance of self-driving vehicles is reduced when they are exposed to stressors that impair visibility or generate water or snow accumulation on sensor surfaces. This paper proposes a model to quantify weather precipitation, such as rain and snow, perceived by moving vehicles based on outdoor data. The modelling covers a wide range of parameters, such as varying wind direction and realistic particle size distributions. The model allows the calculation of precipitation intensity on inclined surfaces of different orientations and on a circular driving path. The modelling results were partially validated against direct measurements carried out by a test vehicle. The model outputs showed strong correlation with the experimental data for both rain and snow. Mitigation strategies for heavy precipitation on vehicles can be developed and correlations between precipitation rate and accumulation level can be traced using the presented analytical model. Dimensional Analysis of the problem has highlighted the critical parameters that can help the design of future experiments. The obtained results highlight the importance of the angle of the sensing surface on the perceived precipitation level. The proposed model is used to analyze optimal orientations for minimization of the precipitation flux, which can help to determine the positioning of sensors on the surface of autonomous vehicles.

In the context of "dual carbon", with the continuous deepening of the power system reform, the application of high proportion renewable energy is becoming increasingly widespread. All sectors of society have put forward higher requirements for electricity sales packages, which can guide the behavior of power users, promote energy conservation and emission reduction, and finally promote low-carbon operation of the power market economy.Therefore, the requirements of power users and power selling companies for the power selling package are increasing, which puts forward higher requirements to the power selling package recommended by the power selling company . However, the existing recommendation methods are unable to make appropriate recommendations for the users with lack of preference information. Therefore, this paper proposes a two-sided matching decision-making method of electricity sales package based on disappointment theory. First of all, according to the incomplete fuzzy preference relationship provided by the power user and the electricity sales package, the respective priority weight vector is obtained, and then the subjective satisfaction matrix of the power user and the electricity sales package to each other is obtained; Next, the adjusted satisfaction matrix is calculated by adding the influence of the theory of joy and disappointment; Then, based on the adjusted satisfaction matrix, an optimization model aiming at maximizing the total satisfaction of power users and electricity sales packages is established, and the optimal stable matching model of power users and electricity sales packages is obtained; Finally, taking an industrial park in Zhejiang Province as an example, by using the bilateral matching method proposed in this article, the optimal matching schemes for 5 power users and 6 electricity sales packages are obtained, which shows the effectiveness and rationality of the two-sided matching decision-making method of electricity sales package based on the disappointment theory proposed in this paper.

A series of derivatives, involving two dithienylethene (DTE), groups, connected by several molecular linkers (bis(ethylene-1,2-dithiolato)Ni- (NiBDT), naphthalene, quasilinear oligothiophene chains) are investigated with the aid of computational quantum chemistry methods. This involves the computation of the second- and third-order nonlinear optical properties, i.e. molecular hyperpolarizabilities of the designed/selected derivatives. These properties can be efficiently controlled by DTE switches, in connection with light of appropriate frequency. We found that NiBDT, as a linker, is associated with a greater contrast, in comparison to naphthalene, between the first and second hyperpolarizabilities of the “open-open” and the “closed-closed” isomers. This is explained by invoking the low lying excited states of NiBDT. It has been found that the second hyperpolarizability can be used as an index, which follows the structural changes induced by photochromism. The two-photon absorption, which is related to the imaginary part of second hyperpolarizability, has also been investigated for the S0S1 transition. Furthermore, the intramolecular excited-state energy transfer (EET) is studied as well as the overlap between the absorption and emission spectra of the donor and acceptor groups of the molecules. The electronic coupling, VDA, between the donor and acceptor fragments, as well as the emission spectrum of the donor and the absorption spectrum of the acceptor have been computed, assuming a Förster type transfer mechanism. EET is a critical factor governing the photochromism. Two methods were used to compute VDA, one based on linear response and one based on the distributed multipole analysis (DMA); both techniques give very similar results for the dominating Coulomb contribution. For NiBDT as the linker a low VDA value has been computed. We found that VDA is affected by the molecular geometry. Our results clearly show that the linker strongly influences the communication between the open-closed DTE groups and thus, photochromism. The present computations show that full photochromism is attained in derivatives where two DTE groups are linked by a tetrathiophene chain, in agreement with experiment. The sensitivity of molecular linear and nonlinear optical properties upon photochromism can assist with the identification of the molecular isomers.

Electrically conductive adhesives (ECA’s) are used widely to replace or reinforce lead soldering or conductive metal components in electronic packaging applications such as die attachment, solderless interconnections, component repair, display interconnections, and heat dissipation. Their conductive behavior as well as the associated behaviors such as heat conduction and mechanical behavior (strength, rigidity, deformation and viscoelastic behavior, which may be affected by moisture ingression) are affected by adhesive film thickness, volume fraction, size and shape of the conductive filler, as well as uncured base adhesive viscosity, substrate and filler surface treatment and the applied pressure (during bonding and during conduction). The adhesive resistivity decreases precipitously above a characteristic filler volume fraction called the percolation threshold. In general, micron-sized metal fillers mixed in an adhesive (often an epoxy) resulting with different film thicknesses exhibit thickness thresholds for transition from three-dimensional conductivity to two-dimensional conductivity with considerable increases in thickness-direction (z-axis) resistivity when the film thicknesses are smaller than these threshold values. Recently, the use of conductive nanoparticles allowed decreases in percolation threshold levels as well as increases in mechanical strength and durability of ECA’s. Most ECA’s are supplied in liquid or paste form in varying viscosities and therefore, the method of their application also affects their performance. This work intends to provide an understanding of these effects on conduction behavior in (usually high-priced) equipment in which they are used.

This paper aims the automatic ball balancers (ABBs) used in passive balancing devices for suppressing vibration of the eccentric rotor. The system model describes which equipped with a skew-mounted ABB with angular deviation. The dynamic equilibrium equations of the system are deduced from the perspective of three-dimensional (3D) dynamics. The results obtained are consistent with those derived from the Euler-Lagrange equations. It is exciting that the spatial dynamics method reveals the spatial geometric characteristic of dynamic balance positioning of the balls when the system is balanced with vibration suppression. The spatial property emerges the perpendicular line from each ball to the rotating spindle of the system must pass through the central axis of the orbit perpendicular to the ABB plane. This characteristic is a general rule that can be used to explain the phenomenon of the stable equilibrium positions of the balls in all previously studied cases.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution Convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.

Nucleic acid therapeutics are witnessing an impressive acceleration in recent years. They work through multiple mechanisms of action, including downregulation of gene expression and modulation of RNA splicing. While several drugs based on the former mechanism have been approved, few target the latter, despite the promise of RNA splicing modulation. To improve our ability to discover novel RNA splicing-modulating therapies, we developed HCS-Splice, a robust cell-based High-Content Screening (HCS) assay. By implementing the use of a two-colour (GFP/RFP) fluorescent splicing reporter plasmid, we developed a versatile, effective, rapid, and robust high-throughput strategy for the identification of potent splicing-modulating molecules. The HCS-Splice strategy can also be used to functionally confirm splicing mutations in human genetic disorders or to screen drug candidates. As a proof-of-concept, we introduced a dementia-related splice-switching mutation in Microtubule-Associated Protein Tau (MAPT) exon 10 splicing reporter. We applied HCS-Splice to the wild-type and mutant reporters and measured the functional change in exon 10 inclusion. To demonstrate the applicability of the method to cell-based drug discovery, HCS-Splice was used to evaluate the efficacy of an exon 10-targeting siRNA, which was able to restore the correct alternative splicing balance.

Mathematical modeling is the most important tool for constructing the theory of different kinds of two-phase flows. The review is devoted to the analysis of the advent of the approach of mathematical modeling of two-phase flows, where solid particles act mainly as the dispersed phase. The main problems and features of the study of gas-solid two-phase flows are excluded. The main characteristics of gas flows with solid particles are given and the classification of two-phase flows is developed on their basis. The Lagrangian and Euler approaches to modeling the motion of a dispersed phase (particles) are described. Much attention is paid to the consideration of numerical simulation methods that describe descriptions of turbulent gas flow at different hierarchical levels (RANS, LES, DNS), different levels of description of interphase interactions (one-way coupling (OWC), two-way coupling (TWC) and four-way coupling (FWC)), as well as at different levels of interface resolution (partial-point (PP) and particle-resolved (PR)). Excluded are examples of some studies carried out on the basis of the identified approaches, as well as their exclusion for mathematical modeling of various classes of gas-solid two-phase flows.

Powerful emitters of the ultraviolet C (UVC) light in the wavelength range of 230-280nm are necessary for the development of effective and safe optical disinfection technologies, high-sensitive optical spectroscopy and non-line-of-sight optical communication. This review considers such UVC-emitters with electron-beam pumping of heterostructures with quantum wells in the (Al,Ga)N material system. The important advantages of these emitters include the absence of the critical problem of p-type doping and the possibility of achieving record (up to several tens of watts for peak values) output optical power values in the UVC range. The review consistently considers about a decade of world experience in the implementation of various UV emitters with various types of thermionic, field-emission, and plasma-cathode electron guns (sources) used to excite various designs of active (light-emitting) regions in heterostructures with quantum wells AlxGa1-xN/AlyGa1-yN (x = 0 − 0.5, y = 0.6 − 1) fabricated either by metal-organic chemical vapor deposition or by plasma-activated molecular beam epitaxy. Special attention is paid to the production of heterostructures with multiple quantum wells/two-dimensional(2D) quantum disks GaN/AlN with a monolayer (1ML ~ 0.25nm) thickness, which ensures a high internal quantum efficiency of radiative recombination in the UVC range, low elastic stresses in heterostructures, and a high output UVC-optical powers.

This paper presents results of the magnetic dynamics study (the microwave power absorptions at the fixed frequencies, during magnetic field sweeping) in samples of Y3Fe5O12 single crystals in the form of plates and spheres of various sizes, at frequencies exceeding 30 GHz, in magnetic fields up to 18 kOe, at room temperature and T=77 K. It was found, that in this case, the inhomogeneity’s of the magnetic state manifested itself in the Y3Fe5O12 samples in as 2D local phase separation regions. These 2D phase separation regions formed inside domain walls representing superlattices with size’s 700 – 900 Å. Landau diamagnetism or de Haas van Alphen oscillations observed in the 2D phase separation regions at room temperature and T=77 K, depending on the shape and size of the studied plates and spheres.

High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained increasing attention due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size n, it is challenging to develop statistical inference methods for high-dimensional measurement error regression models due to the existence of bias, nonconvexity of objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several statistical inference methods. This paper mainly reviews the current development on estimation, hypothesis testing and variable screening methods for high-dimensional measurement error regression models and shows the theoretical results of these methods with some directions worthy of exploring for future research.

Highly efficient, broadband and distinctively structured electromagnetic wave (EMW) absorption materials exhibit huge exceptions with multi-functions in both military and civilian fields. Searching the relationship between structural and EMW performance is beneficial to develop outstanding microwave absorption materials, and has become an attractive research topic. In this study, we briefly reviewed the different EMW absorption mechanisms and key influence factors, then, present the latest developments in low-dimensional (LD) EMW absorption materials. We provide a comprehensive discussion of remarkable examples and summarize the main information related to the structure-function relationship of dimensional design and structural engineering in order to facilitate the rapid evolution of this fascinating field.

An experimental investigation on the heat pump performance improvement equipped with the two-phase ejector called “an ejector-expansion heat pump (EEHP)” is proposed. The system performance of the EEHP is compared with a vapour-compression heat pump (VCHP). The improvement potential is determined and discussed. The heat pump test system is constructed which is based on a water-to-water heat pump. It can be experimented with both EEHP and VCHP. The two-phase ejector with the cooling load up to 2500 W is installed for experiment. The results show that the EEHP always produces a higher heating rate and COP than the VCHP throughout the specified working conditions. The heating COP is increased by 5.7-12.2% depending on the working conditions. It is also that under the same heat sink and heat source temperature, the EEHP can produce lower compressor discharge temperature and lower compressor pressure ratio than the VCHP. This is evidence that the two-phase ejector can provide better compressor working characteristics which yields a longer compressor lifetime. It is demonstrated that a key to the performance of the EEHP is the expansion pressure ratio. A larger expansion pressure ratio yields a higher improvement potential when compared with the VCHP.

Right-sided ligamentum teres (RSLT) is a rare anatomic variant in which the fetal umbilical vein connects to the right portal vein. Patients with RSLT frequently have hepatic vasculature and bile duct anomalies, which increase the risk of complications with hepatectomy. Most patients with RSLT undergo open hepatectomy. We describe a patient with RSLT and hepatocellular carcinoma who underwent laparoscopic hepatectomy. The patient was a 69-year-old man with hepatocellular carcinoma located in the left liver based on computed tomography (CT) and magnetic resonance imaging. Imaging also demonstrated RSLT. Three-dimensional CT analysis revealed independent right lateral type anomalies of the portal vein and bile duct. Laparoscopic extended left lateral sectionectomy was performed after careful surgical planning. Ultrasonography was used frequently during surgery to avoid damaging the right hepatic vasculature. The left lateral and partial left median sections were removed as planned. The patient’s postoperative recovery was uneventful. Avoiding injury to the right hepatic vasculature is essential when performing left lobectomy, including left lateral sectionectomy, in patients with RSLT. Laparoscopic hepatectomy can be performed safely in patients with RSLT, provided that careful surgical planning is conducted using preoperative three-dimensional CT analysis and intraoperative ultrasonography is used.

Personalized recommendation is an important part of ecommerce platform. In the recommendation system, the neural network is used to enhance collaborative filtering to accurately capture user preferences, so as to obtain better recommendation performance. Traditional recommendation methods focused on the results of a single user behavior, ignoring the modeling of multiple interactive behaviors of users, such as click, add to cart and purchase. Although many studies had also focused on Multi-Behavior modeling, two important challenges remained. 1)Since multiple behavioral context information was ignored, it was still a challenge to identify the multimodal relationships of behaviors. 2) Surveillance signals were still sparse. In order to solve this problem, this paper proposes Two-path Multi-behavior Sequence Modeling(TP_MB). First, a two-path learning strategy is introduced to maximize the multiple interaction information of user items learned by the two paths, which effectively enhances the robustness of the model. Second, a multi-behavior dependent encoder is designed. Contextual information is obtained through behavior dependencies in the interaction of different user. In addition, three contrastive learning methods are designed, which not only obtain additional auxiliary supervision signals, but also alleviate the problem of sparse supervision signals. Extensive experiments on two real datasets demonstrate that our method outperforms state-of-the-art multi-behavior recommendation methods.

In recent years, drones have been used in a wide range of fields such as agriculture, transportation of goods, and security. Drones equipped with communication facilities are expected to play an active role as base stations in areas where ground base stations are unavailable, such as disaster areas. In addition, asynchronous operation is being considered for local 5G in order to support all kinds of use cases. In asynchronous operation, cross-link interference between base stations is an issue. This paper attempts to reduce the interference caused by the drone network by introducing circularly polarized antennas. Numerical analyses are conducted to validate the effectiveness of the proposed system, where SIRs (Signal-to-Interference Ratio) are shown to be improved significantly as the numerical evaluation results.

This study will present a comprehensive review of the two-phase flow boiling heat transfer coefficient of hydrocarbons such propane (R-290), butane (R-600) and iso-butane (R-600a) and ethanol at various experimental conditions. Studying the multiphase flow heat transfer coefficient has a crucial importance for many heat transfer equipment to achieve higher efficiency for more compact design and cost reduction. One reason behind choosing hydrocarbons as refrigerants in this study is because hydrocarbons have zero ozone depletion potential (ODP=0) and insignificant direct global warming potential (GWP = 3). Moreover, thermodynamic and thermophysical characteristics of hydrocarbons qualify them to be a strong candidate for more heat transfer applications. Initially, by constructing a database for the working fluids from various experimental work available in the literature. The current data that this study has collected for the flow boiling of spans wide ranges of parameters, such as: mass flux, heat flux, operating pressure, and saturation temperature, etc. Furthermore, by comparing the experimental multiphase heat transfer coefficient database with the anticipated values of each correlation, the prediction performance of 26 correlations found in the literature was assessed. This study leads to the selection of the best prediction method based on the minimum deviation of predicted results from the experimental database provided by calculated mean absolute error (MAE) from the assessed correlations. The findings of this study can also be useful in the development of more accurate correlation methods for these fluids and improve the prediction of their flow boiling characteristics.

Tea (Camellia sinensis) is one of the most popular beverages worldwide. Many types of tea prod-ucts continuously emerge in an endless stream; so, the classification or verification of tea be-comes more difficult. Aroma is a vital indicator of tea quality. The present study deals with the identification of aroma compounds in 18 different kinds of tea belonging to three typical tea va-rieties, including green tea, oolong tea, and black tea, using GC-IMS and GC×GC-O-MS. More-over, the clustering of all 18 tea samples and the in-depth correlation analysis between sensory evaluation and instrumental data were performed using the PCA and OPLS-DA. The results re-vealed that in all 18 kinds of tea, a total of 85 aroma compounds were detected by GC-IMS, whereas 318 were detected by GC×GC-O-MS. The PCA result revealed that green tea, oolong tea, and black tea could be clearly separated based on their peak areas. The OPLS-DA result showed that a total of 49 aroma compounds with VIP value > 1.0 could be considered as the potential in-dicators to quickly classify or verify tea types. This study not only compared the aroma differ-ences across different types of teas, but also provided ideas for the rapid monitoring of tea qual-ity and variety.

A unified critical state model has been developed for both clean sand and silty sand using the modified Cam-clay model (MCC). The main feature of the proposed model is a new critical state line equation in the e-ln(p) plane that is capable of handling both straight and curved test results. With this feature, the error in calculating plastic volumetric strain is eliminated in theory. Another crucial feature of the model is the transformed stress tensor based on the SMP (spatially mobilized plane) criterion, which takes into account the proper shear yield and failure of soil under three-dimensional stresses. Additionally, the proposed model applies the intergranular void ratio with the fines influence factor for silty sand. Only eight soil parameters are required for clean sand, and a total number of twelve soil parameters are needed for silty sand.

Misuse of measurement units and orientations leads to errors in scientific applications, Cyber Physical Systems (CPS), and IoT C/C++ programs. Standard type system are inadequate in preventing such errors. Although dimensional and orientational analysis in physics can manually detect these errors in equations, analyzing complex code with intricate physical computations is impractical. To overcome this challenge, we propose an advanced type system that incorporates units and orientations as integral components within a specialized type library. Our enhanced type system automatically detects potential errors during compile time by representing physical quantities as types and utilizing dimensional analysis, orientational analysis, and metaprogramming techniques. Our improved type system enables formal verification of C++ software, successfully verifying programs with extensive codebases. We also employ it for runtime verification of dynamic linking and pointer operations in C++ programs. The integration of compile-time verification, dimensional analysis, orientational analysis, and advanced type system enhances the robustness and accuracy of scientific applications, CPS, and IoT C/C++ programs. By leveraging these approaches, we ensure precise calculations and prevent errors related to measurement units and orientations, resulting in substantial improvements in reliability and accuracy.

This paper proposed a time two-mesh (TT-M) finite difference numerical scheme to improve the efficiency of solving the symmetric regularized long wave (SRLW) equation. The TT-M Crank-Nicolson discretization and finite difference method are employed in time and space approximation respectively. The scheme involves three main steps: firstly, the time interval is divided into coarse and fine time meshes, then the nonlinear system is solved on the coarse time mesh; secondly, coarse numerical solutions on the fine time mesh are computed using an interpolation formula based on the solutions derived in the step one; lastly, the TT-M finite difference numerical solutions can be obtained through constructing the linearized fine time mesh system using Taylor’s formula. Compared to the currently existing TT-M numerical methods, the novelty of this study is that the nonlinear term including derivatives is linearized by Taylor’s formula for a function with three variables, whose error analysis is more complex. Finally, some numerical examples, including computational time and accuracy, preservation of conservation laws, are given to verify the efficiency of the scheme. By comparing it with the standard nonlinear finite difference scheme, this method can reduce CPU time without sacrificing accuracy.

This paper presents a new algorithm for semi-supervised action recognition using adaptive feature analysis. We model the samples of the same action as the same manifold and those of different actions as different manifolds, and integrate feature correlation mining and visual similarity mining into a joint framework. By maximizing the separability between different classes based on the labeled data, the proposed algorithm learns multiple features from labeled data and utilizes the intrinsic geometric structure of the data distribution between labeled and unlabeled data to boost the recognition. We introduce Projected Barzilai-Borwein (PBB) to train a subspace projection matrix as classifier. Extensive experiments on three benchmark datasets demonstrate that our algorithm outperforms the existing semi-supervised action recognition algorithms when there are a few labeled training samples.

The theory of special relativity has reexamined and reconstructed the concepts of space and time, which are indispensable for describing physical laws, consequently instigating a major revolution in physics in general. Although the correctness of the special theory of relativity has been con-firmed by many observations and experiments, it is unclear why the "principle of relativity" and the "principle of the constant speed of light," which form the basis of the idea, are valid. Moreo-ver, although special relativity has advanced our understanding of time, our understanding of the fundamental question, "What is time?" is still limited. The theory of special relativity regards space as a four-dimensional construct consisting of one-dimensional time and three-dimensional space. This paper proposes an alternative framework, wherein real space is uniform and isotropic four-dimensional space, and elementary particles are waves that propagate through this four-dimensional space at a constant speed. This new perspective provides evidence to support two of Einstein’s key principles—the "principle of relativity" and the "principle of the constant speed of light." Furthermore, this model clarifies what time is and why time flows at a constant rate from the past to the future.

Finding anomalies in the real-time system is recognized as one of most challenging study in information security. It has so many applications like IoT, and Stock-Market. In any IoT system the data generated are real-time, and temporal in nature. Since due to the extreme exposure to Internet and interconnectivity of devices, the IoT systems often face issues like fraud, anomalies, intrusions etc. Discovering anomaly in such domain can be interesting. Clustering and rough set theory have been tried in many cases. Considering the time-stamp associated with IoT data, time-dependent patterns like periodic clusters can be generated which could be helpful for the efficient detection of anomalies by providing more in-depth analysis of the system. In this paper, a mixed method comprising of nano topology, a modified k-means clustering and an interval superimposition technique is used for finding fuzzy periodic clusters in the subspace generated by the nano topology. For every clusters there will be an associated sequence of time-intervals where it exists. The sequence time-intervals accompanying with each clusters may exhibit some remarkable patterns. For example, there may exist different types of periodicity namely yearly, monthly, daily, and hourly etc. For finding such fuzzy periodicity, an operation called interval-superimposition has been used. The time-intervals associated with each cluster are superimposed if they have reasonable overlapping. Each superimposed time-interval generates a fuzzy time-interval. The data instances are thought to be anomalous if they either belong to sparse clusters or don't belong to any clusters. The efficacy of the method can be assessed by means of both time-complexity analysis and comparative studies with existing clustering-based anomaly detection algorithms with a real-life and a synthetic dataset. It can been found experimentally that our method can extract anomaly with 98% of accuracy and it runs cubic time approximately.

In this paper, we exhibit new dynamical properties of the two-mode Caudrey-Dodd-Gibbon (TMCDG) equation. This model describes the motion of dual-waves in dispersive and nonlinear media, which mainly depends on the nonlinearity and dispersion parameters. The overlapping of such two-mode waves is affected by the phase-velocity parameter. The study takes a full advantages of the Kudryashov method and of the exponential-expansion method. For the first time, dual-wave solutions are obtained for arbitrary values of the nonlinearity and dispersive factors. The graphs of the novel solutions are also provided in order to depict the waves propagation, as well as to show the influence of the parameters.

This paper introduces a method to achieve optimal Gaussian estimation even when operating with low precision numerical representations of the full covariance matrix, such as 16-bit integer or 32-bit single precision formats, instead of more expensive double precision floating point representations (64 bits). The approximation's numerical error is accurately estimated in a conservative manner, resulting in minimal covariance inflation. When combined with a compressed estimator like the GCKF (Global Compressed Kalman Filter), which performs global updates at low frequency, this approach produces estimates that are nearly identical to the optimal ones. The deviation between the results obtained using this new approach and those obtained with the standard GCKF (which operates in double precision) is negligible. This new method is particularly relevant in high-dimensional estimation problems that require high-frequency operation. By leveraging the compressed operation of the GCKF, the estimation process applies the necessary approximation only during low-frequency global updates, using the single precision format instead of the more expensive double precision representations. The introduced numerical approximation is accurately estimated while maintaining a conservative approach, resulting in minimal covariance inflation. When combined with the GCKF, which performs global updates at low frequency, the estimates produced are almost identical to the optimal ones. This approach is particularly useful in high-dimensional estimation problems that require high-frequency operation. By representing the full covariance matrix in a low precision numerical format and applying appropriate scaling, memory usage is significantly reduced, offering substantial benefits for high-dimensional estimation problems and enabling real-time smoothing processes in such cases. By reducing the memory requirements for storing the full covariance matrix, the CPU avoids encountering a high number of page faults, resulting in improved processing performance. Lastly, the experimental section verifies the performance of the proposed approach in a single precision (32-bit floating point) GCKF SLAM process, assessing expected values and standard deviations of marginal PDFs.

Manufacturers of photovoltaic modules do not provide all the information necessary to predict their performance under different climatic conditions. This forces the development of mathematical models of behavior and the application of mathematical methods for the calculation of their associated parameters. A toolbox called PVMSim implemented in MATLAB/GUI is proposed in this paper. PVMSIm provides models allowing the simulation of photovoltaic modules. A two-diode model is used to represent the effect of irradiance and temperature on the V-I and V-P characteristic curves. The accuracy of PVMSim has been vaildated by applying the models to two mono-crystalline and multi-crystalline solar modules of different power ratings and manufacturers. The model PVMSim model accuracy was verified against the manufacturer’s datasheet. This paper shows that this tool is useful for designers and professionals who require an intuitive graphical interface for fast and accurate modeling, simulation and sizing of photovoltaic systems.

In this study novel recommendations are presented and substantiated for selecting the number of modes and optical thicknesses of flat lattice slabs that make up the microreliefs, which minimize the computational complexity of the rigorous coupled-wave analysis calculation of the diffraction efficiency (DE) of a sawtooth two-layer two-relief microstructure, while maintaining the specified reliability of the calculation results. The computational complexity can be controlled by allowing one or another level of oscillation of the DE curves depend on the angle of incidence of the radiation incident on the microstructure. In particular, the complexity of the multi-thousand DE calculations in the optimization process can be reduced by using the proposed methodology as well as increased computational complexity to certify the accuracy of the solution obtained as a result of implemented optimization.

When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm (n) observations are generated for the parameters, and in the inner level (m) observations of the simulation model are generated with the value of parameters fixed at the value generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent, showing how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand; and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments using this forecast model.

This study presents the solution of the second type of a two-dimensional nonlinear integral equation in Banach space. Also, the existence and uniqueness of this equation’s solution are discussed. We utilize a numerical approach involving hybrid and block-pulse functions to obtain the approximate solution of a two-dimensional nonlinear integral equation. Nonlinear integral equation in two dimensions is reduced numerically to a system of nonlinear algebraic equations that can be solved using numerical methods. This study focuses on showing the convergence analysis for the numerical approach and obtaining an error estimate. Some numerical examples have been provided to demonstrate the approach’s viability and efficacy

In recent years, with the popularity of smart mobile devices, the interaction between devices and users, especially in the form of voice interaction, has become increasingly important. If smart devices can understand more users' emotional states through voice data, more customized services can be provided for users. This paper proposes a novel machine learning model for speech emotion recognition, which combines convolutional neural networks (CNN), long short-term memory neural networks (LSTM), and deep neural networks (DNN), called CLDNN. To make the designed system can recognize the audio signal closer to the human auditory system does, this article uses the Mel frequency cepstral coefficients (MFCCs) of audio data as the input of the machine learning model. First, the MFCCs of the voice signal is extracted as the input of the model, and the feature values of the data are calculated using several local feature learning blocks (LFLB) composed of one-dimensional CNN. Because the audio signals are time-series data, the feature values obtained from LFLBs then input into LSTM layer to enhance the learning on time-series level. Finally, fully connected layers are used for classification and prediction. Three databases RAVDESS, EMO-DB and IEMOCAP are used for the experiments in this paper. The experimental results show that the proposed method can improve the accuracy compared to other related researches in speech emotion recognition.

The current methods for lightning risk warnings that are based on atmospheric electric field (AEF) data have a tendency to rely on single features, which results in low robustness and efficiency. Additionally, there is a lack of research on cancelling warning signals, contributing to the high false alarm rate (FAR) of these methods. To overcome these limitations, this study proposes a lightning risk warning method that incorporates enhanced empirical Wavelet transform-Adaptive Savitzky Gorey filter (EEWT-ASG) and one-dimensional morphology, using time-frequency domain features obtained through the Wavelet transform (WT). The proposed method achieved a probability of detection (POD) of 77.11%, miss alarm rate (MAR) of 22.89%, FAR of 40.19%, and critical success index (CSI) of 0.51, as evaluated on 83 lightning processes. This method can issue a warning signal up to 22 minutes in advance for lightning processes.

Liquid crystal elastomers (LCEs) are shape morphing materials whose large and reversible shape transformation results from the coupling between the mobile anisotropic properties of liquid crystal (LC) units and the rubber elastic of polymer networks. Their shape changing behaviors under the stimuli are largely directed by LC orientations, and therefore various strategies have been developed to spatially modulate the LC alignments. But most of the methods suffer from complex fabrication technologies or intrinsic limitations in applicability. To address this issue, programmable complex shape changes in some types of LCEs, such as polysiloxane side-chain LCEs or thiol-acrylate main-chain LCEs, etc, have been achieved by using a mechanical alignment programming process coupled with two-step crosslinking. Here, we report a polysiloxane main-chain LCE with programmable 2D and 3D shape changing performances achieved by mechanically programming the polydomain LCE between the two crosslinking steps. The resulting LCEs exhibited reversible thermal induced shape transformation between the initial and programmed shapes due to the two-way memory between the first network structure and the second network structure. Our work would be potential for expanding the applications of LCE materials in actuators, soft robotics and smart structures where arbitrary and easily programmed shape morphings are needed.

Knowledge of the temperature evolution is crucial to understand and control laser beam welding of low-melting materials. Existing temperature determination approaches are restricted to i) one-dimensional temperature information (e.g. ratio-pyrometers), ii) a priori knowledge of the emissivity (e.g. thermography) and iii) high temperature regions (e.g. two-wavelength imaging). In this paper, a ratio-based two-color-thermography approach is developed that allows for two-dimensional temperature determination in low-melting temperature ranges (< 1200 K). For static measurement situations it is demonstrated, that temperature can be determined despite variation in signal intensity and emissivity with high accuracy. The two-color-thermography set-up is further transferred into a commercial laser beam welding machine and experiments are conducted for varying process parameters. The direct application of the developed two-color-thermography system in dynamic process situations is limited as image artifacts presumably caused by internal reflections inside the optical beam path are present.

This paper presents a concise mathematical framework for investigating both feed-forward and backward process, during the training to learn model weights, of an artificial neural network (ANN). Inspired from the idea of the two-step rule for backpropagation, we define a notion of F_adjoint which is aimed at a better description of the backpropagation algorithm. In particular, by introducing the notions of F-propagation and F-adjoint through a deep neural network architecture, the backpropagation associated to a cost/loss function is proven to be completely characterized by the F-adjoint of the corresponding F-propagation relatively to the partial derivative, with respect to the inputs, of the cost function.

The use of spirocycles in drug discovery and medicinal chemistry has been booming in the last two decades. This has clearly translated in the landscape of approved drugs. Among two dozen clinically used medicines containing a spirocycle, 50% have been approved in the 21st century. The present review focuses on the notable synthetic routes to such drugs invented in industry and academia and is intended to serve as a useful reference source of synthetic as well as general drug information for researchers engaging in the design of new spirocyclic scaffolds for medicinal use or embarking upon analog syntheses inspired by the existing approved drugs.

Current trends in industrial development strive for maximum digitalization. The digital industrial revolution has been going on for more than a dozen years, Industry 4.0 and the idea of digital twins (DT) is becoming the focus of virtually all industrial sectors. Some sectors are more predisposed to digitalization, for others the process is much more difficult. This depends mainly on the specific characteristics and susceptibility of a given industry, including the current degree of digitalization of enterprises, as well as the knowledge and mental readiness of employees. The individual characteristics of an industry are important. Shipyards belong to the industry with a traditional approach, where the degree of digitalization remains low. As a result, the efficiency of shipbuilding processes and the quality of ships built are not sufficiently controlled. The article addresses this problem, reviews work in the field of digitalization of shipbuilding processes and points out the needs and challenges in this area. The article proposes the concept of a DT system for the entire ship design and production process. Key areas of digitalization of actual processes were defined, and a division was made into planning, monitoring and process analysis activities. Special attention was paid to the area of dimensional quality control and the Dimensional Quality Management Metasystem (DQMM) was featured in the comprehensive DT system. The requirements were defined and the limitations of the proposed solution were identified, taking into account a number of external factors including the degree of readiness of the manufacturer – the shipyard. The developed DT system concept was tested on the example of the construction process of a simplified ship. Practical aspects of the implementation of the proposed solution, in particular DQMM, were indicated.

The near-infrared (NIR) organic dyes with strong ultrafast nonlinear optical (NLO) activities are of importance for various applications. However, such kinds of dyes are still scarce. In this work, we have compared the NLO properties of two NIR Aza-BODIPY derivatives, in which the strong electron-donating groups, namely 4-(N, N-dimethylamino) phenyl and 1-ethyl-1,2,3,4-tetrahydroquinoline groups, are connected with the cores of Aza-BODIPY. Z-Scan experimental results show that two Aza-BODIPY derivatives exhibit strong saturation absorption and large modulation depth under the excitation of femtosecond pulses at 800 nm. Under 1300 nm excitation, two derivatives exhibit strong nonlinear refraction. In addition, the Aza-BODIPY derivatives also display effective two-photon excited fluorescence emission in the wavelength range of 1200-1600 nm. Based on the experimental results, it is found that 1-ethyl-1,2,3,4-tetrahydroquinoline group can more effectively enhance the NLO properties of Aza-BODIPY derivatives compared with the 4-(N, N-dimethylamino) phenyl group, thus providing new possibilities for the design and development of NIR NLO materials.

As development progresses, when the bottom hole flowing pressure or formation pressure is less than the saturation pressure of crude oil in the reservoir, oil and gas two-phase seepage occurs in the reservoir. Due to the characteristics of oil and gas two-phase seepage, after the oil and gas two-phase seepage occurs in the reservoir, the well production will be reduced, or even greatly reduced. Therefore, how to predict the horizontal well capacity better in this case is an important problem that needs to be solved urgently. In this paper, the method and process of establishing the transient calculation model of two-phase flow in horizontal wells are introduced in detail from three aspects: fluid physical properties, reservoir oil-gas two-phase seepage, and the coupling model of Inflow Performance and Flow in Wellbore. The model is more reliable through the verification of production data from five wells in two oilfields.

W. L. Edge proved that the internal points of a conic in PG(2,5), together with the collinear triples on the non-secant lines, form the Desargues configuration. M. Saniga showed an intimate connection between Desargues configurations and the generalized quadrangles of order two, GQ(2,2), whose representation has been dubbed “the doily” by Stan Payne in 1973. In this paper we prove that the external points of a conic in PG(2,5), together with the collinear and non-collinear triples on the non-tangent lines, form the generalized quadrangle of order two.

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.

Many problems in the fields of finance and actuarial science can be transformed into the problem of solving backward stochastic differential equations (BSDE) and partial differential equations (PDE) with jumps, which are often difficult to solve in high-dimensional cases. To solve this problem, this paper applies the deep learning algorithm to solve a class of high-dimensional nonlinear partial differential equations with jump terms and their corresponding backward stochastic differential equations (BSDE) with jump terms. Using the nonlinear Feynman-Kac formula, the problem of solving this kind of PDE is transformed into the problem of solving the corresponding backward stochastic differential equations with jump terms, and the numerical solution problem is turned into a stochastic control problem. At the same time, the gradient and jump process of the unknown solution are separately regarded as the strategy function and they are approximated respectively by using two multilayer neural networks as function approximators. Thus, deep learning-based method is used to overcome the “curse of dimensionality” caused by high-dimensional PDE with jump, and the numerical solution is obtained. In addition, this paper proposes a new optimization algorithm based on the existing neural network random optimization algorithm, and compares the results with the traditional optimization algorithm, and achieves good results. Finally, the proposed method is applied to three practical high-dimensional problems: Hamilton-Jacobi-Bellman equation, bond pricing under the jump Vasicek model and high-dimensional option pricing model with default risk. The proposed numerical method has obtained satisfactory accuracy and efficiency. The method has important application value and practical significance in investment decision-making, option pricing, insurance and other fields.

Load Frequency Control (LFC) is essential to ensure the stability and performance of power systems. In this study, a novel optimization algorithm, the Sea Horse Optimizer (SHO) algorithm, was proposed for optimizing controller parameters in LFC problems of power system. The proposed SHO algorithm was tested on a two-area power system with photovoltaic system and reheat thermal units, under three different scenarios: a 10% load change in both area, large load disturbances, and varying solar radiation. The proposed algorithm optimizes the gain parameters of PI/PID controllers using performance metrics such as Integral of Absolute value of the Error (IAE), Integral of Square Error (ISE), Integral of Time multiplied by Square Error (ITSE), and Integral of Time multiplied by Absolute Error (ITAE). The study compared the performance of the SHO-optimized controller with other reported optimization algorithms such as Genetic Algorithm (GA), Firefly Algorithm (FA), Whale Optimization Algorithm (WOA), and Modified Whale Optimization Algorithm (MWOA). The study also evaluated the accuracy and effectiveness of the controllers using performance metrics such as Settling Time, Overshoot (M+), and Undershoot (M-). The results show that the SHO-tuned controller significantly reduces overshoot, undershoot, and settling time of the system oscillations compared to other algorithms. The study provides valuable insights into the optimization of LFC controllers and presents a significant contribution to the literature with its novel algorithm. The proposed SHO algorithm can be considered an effective alternative solution method for LFC in power systems.

Quantile regression model is widely used in variable relationship research of general size data, due to strong robustness and more comprehensive description of the response variables' characteristics. With the increase of data size and data dimension, there have been some studies on high-dimensional quantile regression under the classical statistical framework, including higher-efficient frequency perspective, which is however at cost of randomness quantification, or lower-efficient Bayesian method based on MCMC sampling. To overcome these problems, we propose the high-dimensional quantile regression with Spike-and-Slab Lasso penalty based on variational Bayesian (VBSSLQR), which can not only improve the computational efficiency but also measure the randomness via variational distributions. The simulation studies and real data analysis illustrate that the proposed VBSSLQR method is superior to or equivalent to other quantile and non-quantile regression methods (including Bayesian and non-Bayesian methods), and its efficiency is higher than any other method.

Environmental regulation has dual effects on water resource utilization performance through various channels such as the "three red lines" and the "Porter hypothesis". Based on the detailed analysis of the impact path of environmental regulation on water resource utilization performance, the two-step system GMM method was adopted to test the dual effects of environmental regulation on water resource utilization performance by using panel data of 30 provinces and cities from 2004 to 2020. The results show that: (1) Environmental regulation has a significant direct impact on water resource utilization performance, and is currently in the stage of positive relationship of an inverted u-shaped relationship. (2) Environmental regulation has a significant indirect impact on water resource utilization Environmental regulation has dual effects on water resource utilization performance through various channels such as the "three-red lines" and the "Porter hypothesis". In accordance with a detailed examination of the influence path of environmental regulation on the performance of water resource utilization, the double effects of environmental regulation on water resource utilization performance were examined using panel statistics from 30 provinces and cities from 2004 to 2020 using the two-step system GMM approach. The results demonstrate that: (1) Environmental regulation has a direct effect on water resource utilization performance which is currently in the stage of the positive relationship of an inverted u-shaped relationship. (2) The influence of environmental regulation on water resource utilization performance through government intervention and industrial structure adjustment is indirect, among which government intervention can significantly improve water resource utilization performance, while the growth of the tertiary industry as a percentage can lower water resource utilization performance. (3) Both direct and indirect impacts indicate that enhancing the efficiency of the use of water resources is sustainable, and the water resource utilization performance of the preceding period had a major positive impact over the present phase. Therefore, the paper sets out a few policy suggestions, such as choosing reasonable environmental regulation tools, actively guiding the market-oriented reform with constraints, and upgrading the industrial structure in a green way.

Modifiable THz spectral shapes are important tools that facilitate the comprehensive study of phonon dynamics in condensed matter systems. The generation of narrow bandwidth THz spectra with tunable center frequency which are suitable THz forms needed to achieve such objectives are currently less studied from the table top laser-induced plasma emitters’ perspective. This experimental research is aimed at developing a robust two-color laser induced plasma set-up comprising of a temporal pulse stretcher and an Optical Parametric Amplifier that generates chirped and wavelength tunable pulses respectively. By focusing and independently controlling the ω and 2ω arms of the chirped pulses resulting after the interaction with a β-BBO crystal, I aim to generate narrow bandwidth THz signal (from plasma) scalable at MV/cm intensity and tunable in a wide THz spectral range, in addition to varying the frequency ratio mix.

The effect of elastic constants, cij, on the nature (easy or difficult) of a cleavage system in YBa2Cu3O7−δ is investigated, by employing a novel three-dimensional eigenfunction expansion technique, based in part on separation of the thickness-variable and partly a modified Frobenius type series expansion technique in conjunction with the Eshelby-Stroh formalism. Out of the available three complete sets of elastic constants, the first constitutes an estimate, while the second assumes tetragonal symmetry. This leaves only the experimental measurements by resonant ultrasound spectroscopy, despite reported values of c12 and to a lesser extent, c66, being excessively high. The present investigation considers six through-thickness crack systems weakening orthorhombic YBCO mon-crystalline plates. More important, the present approach predicts whether a crack would propagate in its original plane/direction or deflect to a different one. This fracture mechanics criterion is then employed for accurate determination of the full set of elastic constants of mono-crystalline YBCO. Finally, generally unavailable results, pertaining to the through-thickness variations of stress intensity factors and energy release rates for a crack corresponding to symmetric and skew-symmetric hyperbolic cosine loads that also satisfy the boundary conditions on the bounding surfaces of an orthorhombic monocrystalline plate bridge a longstanding gap in the field.

Alzheimer’s Disease (AD) has no cure. Earlier, we showed that partial inhibition of mitochondrial complex I (MCI) with small molecule CP2 induces adaptive stress response activating multiple neuroprotective mechanisms. Chronic treatment reduced inflammation, improved synaptic and mitochondrial functions, and blocked neurodegeneration in symptomatic APP/PS1 mice, a translational model of AD. Here, using serial block-face scanning electron microscopy (SBFSEM) and three-dimensional (3D) EM reconstructions combined with Western blot analysis and next-generation RNA sequencing, we demonstrate that CP2 treatment also restores mitochondrial morphology and mitochondria-endoplasmic reticulum (ER) communication in the APP/PS1 mouse brain. Using 3D EM volume reconstructions, we show that mitochondria in AD dendrites exist primarily as mitochondria-on-a-string (MOAS). Compared to other morphological phenotypes, MOAS are extensively enveloped in the ER membranes forming multiple mitochondria-ER contact sites (MERCS) known to contribute to abnormal lipid and calcium homeostasis. CP2 treatment specifically reduced MOAS formation, consistent with improved energy homeostasis in the brain, with concomitant reduction in MERCS, ER stress, and improved lipid homeostasis. These data provide novel information on the role MOAS play in AD and additional support for further development of partial MCI inhibitors as disease modifying strategy for AD.

Melanoma is a type of highly malignant and metastatic skin cancer. In situ molecular imaging of endogenous levels of the melanoma biomarker tyrosinase (TYR) may decrease the likelihood of mortality. In this study we proposed the weakly fluorescent probe 1-(4-(2-(4-(dicyanomethylene)-4H-chromen-2-yl)vinyl)phenyl)-3-(4-hydroxybenzyl)urea (DCM-HBU), which releases a strong red-shifted fluorescent signal after a TYR-mediated oxidation followed by hydrolysis of the urea linkage. The large Stokes shift of the dye is owed to the recovery of the intramolecular charge transfer (ICT) effect. The resulting probe derivate shows a highly ratiometric fluorescence output. Furthermore, the simultaneous excitation by two near-infrared (NIR) photons of the released DCM-NH2 fluorophore could avoid the usual drawbacks, such as cellular absorption, autofluorescence and light scattering, due to an usually short wavelength of the excitation light on biological systems, resulting in images with deeper tissue penetration. In addition, the probe is useful for the quantitative sensing of TYR activity in vivo, as demonstrated in zebrafish larvae. This new ratiometric two photon NIR fluorescent probe is expected to be useful for the accurate detection of TYR in complex biosystems at greater depths than other one-photon excited fluorescent probes.

The purpose of this study was to clarify the attachment types of the tibialis anterior tendon (TAT) in Japanese fixed cadavers and to determine the attachment site area in three dimensions. We examined 100 feet from 50 Japanese cadavers. The TAT was classified according to differences in the number of fiber bundles as: Type I, with one fiber bundle; Type II, with two fiber bundles; and Type III, with three fiber bundles. The attachment site area of the TAT was measured using a three-dimensional scanner. Cases were Type II in 95% and Type III in 5%, with no cases of Type I identified. In Type II, mean attachment site areas were 85.2 ± 18.2 mm2 for the medial cuneiform bone (MCB) and 72.4 ± 19.0 mm2 for the first metatarsal bone (1MB), showing a significantly larger area for MCB than for 1MB. These findings suggest the possibility of ethnic differences in TAT attachment types and suggest that TAT attachments in Japanese individuals are highly likely to be Type II, with rare cases of Type III. Accurate measurement of attachment site areas is possible with appropriate three-dimensional measurements.

Manipulation of radiation pattern control is challenging, especially at low frequencies. In this paper, we demonstrate that acoustic metamaterials as an effective array of quadrupoles can remarkably improve the directionality of acoustic radiation at low frequencies, compared with previous metamaterials as monopole and dipole structures. The directivity of the acoustic radiation can be adjusted by changing the characteristic parameter and the symmetry of the structure, which provide a flexible method of adjustable radiation di-rections. The directionality can be further improved by constructing linear array of structure. Our work opens an approach of acoustic radiation control via quadrupolar metamaterials.

We investigate the extent to which a two-level quantum system subjected to an external time-dependent drive can be characterized by supervised learning. We apply this approach to the case of bang-bang control and the estimation of the offset and the final distance to a given target state. The estimate is global in the sense that no a priori knowledge is required on the parameters to be determined. Different neural network algorithms are tested on a series of data sets. We point out the limits of the estimation procedure with respect to the properties of the mapping to be interpolated. We discuss the physical relevance of the different results.

The advent of graphene opens up the research into two-dimensional (2D) material, which is considered as a revolutionary material in the future. Due to its unique geometric structure, graphene exhibits a series of exotic physical and chemical properties. Besides, single-element-based 2D materials (Xenes) have garnered tremendous interest. At present, 16 kinds of Xenes (silicene, borophene, germanene, phosphorene, tellurene, etc.) have been explored, mainly distributed in the third, fourth, fifth and sixth main groups. The current methods to prepare monolayer or few-layer 2D materials include epitaxy growth, mechanical exfoliation, and liquid phase exfoliation. Although two Xenes (aluminene and indiene) have not been synthesized due to the limitations of synthetic methods or stability of Xenes, other Xenes have been successfully realized by elaborately artificial design and synthesis. Focusing on elemental 2D materials, this review mainly summarizes the recently reported work about tuning the electronic, optical, mechanical, or chemical properties of Xenes via surface modifications achieved by controllable approaches (doping, adsorption, strain, intercalation, phase transition, etc.) to broaden the applications in various fields, including spintronics, electronics, optoelectronics, superconducting, photovoltaics, sensors, catalysis, and biomedicines. These advances in surface modification of Xenes have laid a theoretical and experimental foundation for the development of 2D materials and their practical applications in diverse fields.

The development of the automotive industry leads to the improvement of the designs of automatic transmissions and drives and to the development of methods for monitoring and diagnosing their condition. The trend of improving transmissions comes down mainly to the endless improvement of existing designs of CVTs and variators, which inevitably leads to the complication of designs and methods for their control and diagnostics.There is a need to create a fundamentally new simplified gearbox based on the scientific achievements of mechanics. However, the created structures remained inoperable due to the lack of theoretical justification.The adaptive gear variator developed by the author, which has CVT functions, is a mechanism with two degrees of freedom and with an additional constraint of a fundamentally new type, called a force - speed constraint. The force - speed constraint imposes a force restriction on the movement of links, while maintaining the number of degrees of freedom in the kinematics. Monitoring the state of the developed gearbox in the form of a non-switchable gear variator is greatly simplified, since the largest number of faults occur in the control system, and there is no control system in the gear variator. It seems possible to apply the digital twin method to diagnose the developed simplified CVT.The proposed article is devoted to a theoretical description of a fundamentally new adaptive gearbox, created on the basis of the latest achievements in mechanics, with the prospect of developing a simplified monitoring and diagnostic system for it

Nuclear magnetic resonance (NMR) techniques are widely used to identify pure substances and probe protein dynamics. Edible oil is a complex mixture composed of hydrocarbons, which have a wide range of molecular size distribution. In this research, low field NMR (LF-NMR) relaxation characteristic data of various sample oils were analyzed. We also suggest a new method for prediction size of edible oil molecules using LF-NMR relaxation time distributions. According to the relative molecular mass, carbon chain length, transverse relaxation time of different sample oils, combined with oil viscosity and other factors, the relationship between carbon chain length and transverse relaxation time rate was analyzed. Various oils and fats in the mixed fluid are displayed, reflecting the composition information of different oils. To study the correlation between the rotation correlation time and molecular information of oil molecules. The molecular composition of the resulting fluid determines its properties, such as viscosity and phase behavior. The results show that low-field NMR can obtain information on the composition, macromolecular aggregation, and molecular dynamics of complex fluids. Measurements of grease in the free fluid state show that the relaxation time can reflect the intrinsic properties of the fluid. It is shown that the composition characteristics and states of complex fluids can be measured by low-field nuclear magnetic resonance.

Classical numerical methods for solving ordinary differential equations often produce less accurate results when applied to problems with oscillatory or periodic behaviour. To adapt them for such problems, they are usually modified using the exponential fitting technique. This adaptation allows for the construction of new methods from their classical counterparts. The new methods are usually more accurate, efficient and suitable for handling the oscillatory or periodic behaviour of the problem. In this work, we construct a two-parameter exponentially-fitted Taylor method suitable for solving oscillatory or periodic problems that possess two frequencies. The construction algorithm is based on a proposed six-step flowchart discussed by authors in related literature. Two standard test problems were used to illustrate the accuracy and performance of the proposed method.

With Direct Laser Writing (DLW) maturing in all aspects as a manufacturing technology a toolset for quality assurance must be developed. In this work we want to introduce a first of its kind test artifact. Test artifacts are standardized 3D models with specific geometric feature to evaluate the performance of writing parameters. Test artifacts are already common in other 3D additive manufacturing technologies e.g. Selective Laser Melting. The test artifact introduced in this work was developed in particular to accommodate 1) the high geometrical resolution of DLW structures and 2) the limited possibilities to examine the resulting structure. Geometric accuracy, surface adhesion as well as confocal raman spectroscopy results were considered when evaluating the design of the test artifact. We will explain the individual features and design considerations of our DLW test artifact. The difference between two slicers, Cura and 3DPoli, and the implications on measured feature sizes and the general shape is quantified. The measured geometries are used to derive a general design guide for a specific combination of photoresist, laser power and scanning speed and to analyse the geometric accuracy of a structure produced using these guidelines.

The poor success rate of preclinical drugs which target the nervous system is related to the highly complex nature of brain physiology and pathophysiology. For in vitro drug screening in this field, the two-dimensional (2D) approach - where cells are incubated in a monolayer - is not physiologically relevant. In contrast, in vivo rodent models are very low throughput and expensive. As such, improved, well-characterised three dimensional (3D) in vitro models should be employed to bridge the gap between 2D in vitro primary screening and rodent models by incorporating aspects of the in vivo brain environment. These key features include the extracellular matrix (ECM) and a multidimensional relationship of supporting cells. In this study, a neural progenitor cell line was differentiated into the main types of cells found in brain, including neurons and astrocytes. This model was designed in a 3D extracellular matrix replacement in a minimalised manner. Different culture formats using multi-well plates were explored and a high-throughput cellular imaging platform was applied. Next, we applied this model to assess neural toxicity of a newly synthesised dual-specificity tyrosine-regulated kinase 1 (DYRK1) inhibitor derived from natural compounds, which is structurally related to a natural DYRK1A inhibitor harmine, K04179. Chemical studies have already shown that K04179 has higher specificity to DYRK1A compared with previously reported DYRK1 inhibitors, such as INDY. We have produced the first report of the biological effects of this new compound on the new 3D minimalised mid-brain model with quantification of both neuronal and astrocyte populations in tandem, revealing differential toxic effects of DYRK1 inhibitor compounds K04179 and INDY on neurons and glia.

For more than half a century, Manfred Deistler has been contributing to the construction of the rigorous theoretical foundations of the statistical analysis of time series and more general stochastic processes. Half a century of unremitting activity is not easily summarized in a few pages. In this short note, we chose to concentrate on a relatively little-known aspect of Manfred's contribution which nevertheless had quite an impact on the development of one of the most powerful tools of contemporary time series and econometrics: dynamic factor models.

In order to enhance the gain of antenna suitable for the airplane mounted platform, a conformal tightly coupled antenna array is investigated in this paper. Especially, three-dimensional (3-D) inkjet printing technique is used to implement the conformal property. The printing of antenna substrate and radiation layer is implemented by combining the fused deposition modelling and microdroplet injection molding technologies based on the existing 3-D printer. Here, through a unique combination of 3-D and 2-D inkjet printing of dielectric material and metallic ink, respectively, we demonstrate a monolithically integrated to a nonplanar antenna for the first time. Antenna measurements show that the complete characterization of this new process in terms of minimum feature sizes and achievable conductivities. This antenna configuration offers a high-gain performance with low-cost and rapid fabrication technique by using 3-D printing. The voltage standing wave ratio and radiation patterns are tested after adding the newly designed feed structure. Results shown the design process much efficient. Both antenna element and the array with good properties, which are in very good agreement with the specially mounted platform.

The unsteady flow around the monopile under the influence of wave and vibration is studied by numerical method. Based on VOF model and SST Turbulence model, combined with Intermittent Transition model and dynamic mesh method, wave generation and pile vibration in numerical flume are simulated. The flow field around vibrating monopile in a calm water flume and a wave flume is simulated and the simulation results are compared. The calculation results show that the monopile vibration has a great disturbance to the unsteady flow around the pile, and the disturbance degree intensifies with the increase of the vibration amplitude of the monopile: Under the action of inertia, the vibration of the monopile leads to the reattachment of the shedding vortices; Under the influence of the reattachment vortices, the asymmetry of the vorticity on the side wall of the monopile intensifies; The intensification of the disturbance causes the reduction of the phase lead angle of the bed shear stress on the side of the single pile, and an additional high bed shear stress appears on the side of the monopile. In addition, the vortex structure formed alternately around the monopile during the vibration process leads to the periodic change of the high bed shear stress.

Background: Cyber Security is to protect online data and software from cyber threats. These cyberattacks are typically intended to gain access to, change, or delete sensitive information; extort money from users; or disrupt regular corporate activities. It is difficult to keep up a regular follow up with new technologies so it is necessary to keep the important data safe from cyber threats. There are many types of cyber threats; malware, ransom-ware, social engineering, phishing etc. To prevent cyber-attacks one can use password manager tools like LastPass and others. People also use two factor authentication for double security on their accounts. Methods: Boards such as the National Institute of Standards and Technology (NIST) are developing frameworks to assist firms in understanding their security risks, improving cybersecurity procedures, and preventing cyber assaults. The fight against cybercrimes and attack, rganisations needed a strong base there are 5 types of cyber securities; Critical Infrastructure Security, application security, network security, cloud security and (IoT) Security. In the modern time US is highly based on computers and on different software so it is really important for US to be more conscious about the security as they get many threats almost everyday for hacking their data and accounts.Results and Conclusion: Nowadays, even small businesses rarely recover their loss from the cyber-attacks and many back-off from continuing their businesses after being target of hackers. The first cybercrime attack was recorded on 1988 by a graduate student. Now that large companies and even small businesses are aware of cyber-attacks so they try their best to take every precaution to prevent the hacking with double security and password manager tools.