The exact similarity solutions (also called as special exact solutions) of two dimensional laminar boundary layer were obtained by Blasius in 1908, however, no similarity solutions for two dimensional turbulent boundary layers have been reported in the literature. With the help of dimensional analysis and invariance principle, Prandtl mixing length $\ell=\kappa y$ for one dimensional turbulent boundary layer is extended to $\ell(x,y)=\kappa y (1-\frac{y}{\delta})\sqrt{\frac{\nu}{U\delta}} $ for the two dimensional turbulent boundary layers, furthermore, with a similarity transformation, we successfully transform the two dimensional turbulent boundary layers partial differential equations into a single ordinary differential equation $f'''+ ff''+\beta(1-f'^2)+\kappa^2[\eta^2(1-\eta)^2f''|f''|]'=0$. As an application, similarity solutions of the two dimensional turbulent boundary layer on a flat plate at zero incidence have been studied in detail. To solve the ordinary differential equation numerically, a complete Maple code is provided.

The exact similarity solutions of two dimensional laminar boundary layer were obtained by Blasius in 1908, however, for two dimensional turbulent boundary layers, no similarity solutions (special exact solutions) have ever been found. In the light of Blasius' pioneer works, we extend Blasius similarity transformation to the two dimensional turbulent boundary layers, and successfully transform the two dimensional turbulent boundary layers partial differential equations into a single ordinary differential equation. By author's Maple code, we numerically solve the ordinary differential equation and produce some useful quantities.

We investigated the influence of the fuel change, from pure hydrogen to hydrogen-ammonia mixture at different percentage, on the electrochemical behaviour of 50 mm in diameter Solid Oxide Fuel Cells (SOFC) with sputtered thin buffer layers of Gd doped Ceria, varying the working temperatures from 800°C to 650°C. The results show that the performances of the cells are not affected by the fuel change for the high working temperatures (800°C and 750°C). As an example, a power density value of 802 mW∙cm-2 at 1 A∙cm-2 is found when directly feeding the cell with 8 Nml∙min-1cm-2 of ammonia and with an equivalent flowrate of 12 Nml∙min-1cm-2 of H2. These power density output values are comparable to those obtained in industrial State of Art (SoA) SOFC cells with screen printed buffer layers fed with higher hydrogen flowrates, thanks to the improved electrochemical performances obtained in the case of cells with sputtered thin buffer layers of Gd doped Ceria. At lower working temperatures (700°C and 650°C), slight changes in the electrochemical behaviour of the cells are observed. Nevertheless, also in this temperature range, we obtain an output current density value of 0.54 A∙cm-2 in pure ammonia flowrate of 12 Nml min-1cm-2, at 800 mV and 700°C, equal to the value observed in SoA button cells with industrial screen printed GDC barrier layer fuelled with 16 Nml∙min-1cm-2 of H2. These results pave the way towards the use of innovative SOFC structures with sputtered thin buffer layers fuelled by ammonia.

The monomolecular films of diacylic diperoxides on the water/air interface were studied. Their general formula: CH₃-(CH₂)_m-C(O)-O-O-C(O)-(CH₂)_n-C(O)-O-O-C(O)-(CH₂)_m-CH_3. The behavior of monomolecular films of diperoxides are affected by the structure of their molecule. The numerical values of the areas of molecules that are extrapolated to zero pressure are different. This indicates a different conformation of the molecules in the monolayer. The optimal geometric structure of the molecule of diperoxide, the total area (S), the volume (V), the heat of formation (∆_fH²⁹⁸), the energy of higher occupied (E_HOMO) and the lower vacant (E_LUMO) molecular orbitals were obtain in the calculations. The optimal geometric structures of peroxides and their electronic properties were calculated by the quantum-chemical method. Calculations of conformational states of the molecule of diperoxides are carried out. Experimental data and quantum-chemical calculations are consistent with each other.

Mixed cadmium tellurides – cadmium sulfides thin layers were formed on the polyamide PA 6. Monotelluropentathionic acid (H2TeS4O6) was used as precursor of tellurium and sulfur. We applied a low-temperature, nontoxic and cost-effective SILAR method. Cadmium telluride (CdTe) and sulfide (CdS) layers were formed through consecutive reactions of sorbed/diffused chalcogens species from telluropentathionate anion (TeS4O62–) with functional groups of polyamide and alkaline cadmium sulfate. The pseudo-second-order rate and Elovich kinetic models best fitted to quantify an uptake of chalcogens and cadmium on PA6. The effects of chalcogens and Cd on the structure and optical properties of PA6 were characterized using UV/Vis and IR spectra. The clear changes of these properties found in dependence on the concentration and exposure time in precursor's solutions. Fourier transform infrared spectroscopy and an ultraviolet-visible spectroscopy were applied in order to evaluate the effect of the chalcogens having particles on the changes in structure of polyamide 6 films depending on the exposure time in the solution of the chalcogens precursor and its concentration. The optical band gap energy of formed layers was found to be in the order of 1.52–2.36 eV. Studies of scanning electron microscopy and atomic force microscopy reveal that the diameter of the average grain is about 30 nm. The grains are conical in shape and unevenly distributed all over the surface of the substrate.

The application of geophysical methods for the characterization of geomorphologic subsurface features has become very popular in recent years. Convential geophysical applications on undifferentiated and weathered slopes have been less effective. Multichannel analysis of surface waves (MASW) is a new hope for the solution of the problem. MASW methods was used to the first step in order to reconstruct the ancient eruption of Mount Samalas. MASW was applied to the remnants of ancient volcanic sediment on Lombok Island to determine the physical and physical characteristics of seismic subsurface features. This paper is also discussion the application's capabilities of the MASW method on undifferentiated and weathered volcanic deposits on the slope area and classified them. The results of the research show that MASW technique is powerful in characterizing and delineating the stratum of the layers of undifferentiated and weathered volcanic precipitation areas. MASW is able to predict subsurface structures, especially for shallow and thin layers and presenting site class information for internal structure (vertical resolution). Based on S wave velocity structure, undifferentiated and weathered volcanic layer, genes are classified into site class C, D, and E. In the future, this method will completed with combination of other geophysical method and parameters variation (frequency and spaces) for isopach mapping of volcanic sediment and then analysis of ancient volcanic eruption dynamics.

Convolutional neural networks (CNNs) have proven to be a very efficient class of machine learning (ML) architectures for handling multidimensional data by maintaining data locality, especially in the field of computer vision. Data pooling, a major component of CNNs, plays a crucial role for extracting important features of the input data and downsampling its dimensionality. Multidimensional pooling, however, is not efficiently implemented in existing ML algorithms. In particular, quantum machine learning (QML) algorithms have a tendency to ignore data locality for higher dimensions by representing/flattening multidimensional data as simple one-dimensional data. In this work, we propose using the quantum Haar transform (QHT) and quantum partial measurement for performing generalized pooling operations on multidimensional data. We present the corresponding decoherence-optimized quantum circuits for the proposed techniques along with their theoretical circuit depth analysis. Our experimental work was conducted using multidimensional data, ranging from 1-D audio data, to 2-D image data, to 3-D hyperspectral data, to demonstrate the scalability of the proposed methods. In our experiments, we utilized both noisy and noise-free quantum simulations on a state-of-the-art quantum simulator from IBM Quantum. We also show the efficiency of our proposed techniques for multidimensional data by reporting the fidelity of results.

The effect of the addition of fenugreek seed powder and Moringa oleifera leaf meal on the production performance and egg quality of Lohmann Brown laying hens was investigated. A total of 108 Lohmann-Brown layers, 48 weeks of age, were used and randomly assigned to six experimental groups of three replicates each in a randomized design. A control diet was supplemented with 3.6 g/hen/day of Moringa oleifera leaf meal and fenugreek seed powder alone or in various proportions as follows: control diet (T1), 100% Moringa oleifera leaf meal (T2), 25% fenugreek seed powder and 75% moringa oleifera leaf meal (T3), 50% fenugreek seed powder and 50% moringa oleifera leaf meal (T4), 75% fenugreek seed powder and 25% moringa oleifera leaf meal (T5) and 100% fenugreek seed powder (T6) were added on top of the control diet. Data were analyzed using one-way ANOVA by the general linear model method, using Tukey's multiple range tests to differentiate means. According to the results, fenugreek and moringa had a crude protein content of 25.17 and 27.8%, respectively. The groups administered T4 had significantly higher mean feed intake (120.75), feed conversion rate (2.315), egg production (77.22), and egg mass (52.08) than the others. Treatment 4 had a significant (P<0.05) improvement in some egg quality traits such as shell weight, shell firmness, albumen weight, yolk weight, yolk color, and Haugh unit compared to the control. In summary, Treatment 4, a mixture of equal parts fenugreek seed powder and Moringa oleifera leaf meal, improved egg production performance and egg quality.

The gauge field equation for fluid mechanics established in Part 1 is developed into a first-order scattering theory in the simplified case of a two-dimensional incompressible flow over a flat plate. This is used to present a model for the origin of Tollmien-Schlichting (TS) waves based on scattering between fluid particles. As a result, analytical formulae for the maximum amplification factor and the transition point from laminar to turbulent flow in the boundary layer are obtained. The mathematical transformations from the stationary field equations in Part 1 to a scattering theory with time evolution along the flow axis using Wick rotation are elaborated in detail.

Quantitative measurements of performance parameters has the potential to increase consistency and enhance performance of the surfaces as well as to contribute to the safety of horses and riders. This study investigates how factors known to influence the performance of the surface, incorpo-ration of a drainage package, control of the moisture control, and introduction of a geotextile reinforcement, affect quantitative measurements of arena materials. The measurements are made by using affordable lightweight testing tools which are readily available or easily constructed. Sixteen boxes with arena materials at a consistent depth were tested with the Going Stick (GS), both penetration resistance and shear, the impact test device (ITD), and the rotational peak shear device (RPS). Volumetric moisture content (VMC %) was also tested with time-domain reflectometry (TDR). Results obtained using GS, RPS, ITD, and TDR indicate that the presence of the drainage package, moisture content, and geotextile addition were detected. Alterations due to combinations of treatments could also be detected by GS, ITD, and TDR. While the testing showed some limi-tations of these devices, the potential exists to utilize them for quality control of new installations as well as for the monitoring of maintenance of the surfaces.

Twenty-four extensively-reared Iberian pigs were used to study the influence of fattening period length (30, 60 or 90 days) on the fatty acid profiles of intramuscular and subcutaneous fat and the relationships between both profiles. Regarding fatty acid (FA) percentage, PUFA was greater in backfat and MUFA was greater in intramuscular fat (IMF), regardless fattening period length. The longer fattening period increased MUFA content in backfat (which had a more marked change in oleic acid) and decreased PUFA content in backfat and IMF, but it did not affect SFA content. Within the 3-layers subcutaneous backfat, SFA content was greater in the inner layer, MUFA was greater in the outer layer and PUFA was greater in both of these layers. The few differences in FA composition between both adipose tissues suggest that the changes due to the feeding regime are slow and, therefore, although the length of the fattening phase was increased, the fatty acid profile did not change substantially. The strong relationship between the FA profiles of IMF and backfat might be used to predict one profile from the other one when this latter was more readily available for sampling or analytical reasons.

In recent decades, considerable attention has been focused on the design and development of surfaces with defined or tunable properties for a wide range of applications and fields. To this end, self-assembled monolayers (SAMs) of organic compounds offer a unique and straightforward route of modifying and engineering the surface properties of any substrate. Thus, alkane-based self-assembled monolayers constitute one of the most extensively studied organic thin-film nanomaterials, which have found wide application in antifouling surfaces, control of wettability or cell adhesion, sensors, optical devices, corrosion protection, organic electronics, among many other applications. Some of which have led technological transfer to industry. Nevertheless, recently, aromatic-based SAMs have gained in importance as functional components, particularly in molecular electronics, bioelectronics, sensors, etc., due to their intrinsic electrical conductivity and optical properties, opening up new perspectives in these fields. However, some key issues affecting device performances still need to be lifted off to ensure their full use and access to novel functionalities such as memories, sensors, or active layers in optoelectronic devices. In this context, we will present herein recent advances of p-conjugated systems-based self-assembled monolayers (e.g., push-pull chromophores) as active layer and their applications.

The studies of laminar unsteady boundary layer flows is crucial for understanding laminar-turbulence transition and origins of turbulence. However, the task of finding its solutions poses a significant challenge. In this paper, we propose a novel approach by introducing a similar transformation to convert the 2D unsteady laminar boundary layer equations into a single partial differential equation. By applying this transformation, we are able to obtain the "exact" similarity solutions for the 2D unsteady laminar boundary layer equations, specifically for the case of flat plate boundary flow. Notably, this is the first time that such an exact solution has been obtained. Application of this exact solution is being used to solve Stokes' first problem in 2D boundary layer. Perspectives on the transition fron Rayleigh solution to Blasius solution is provided.

Researchers from around the world are looking for better and cheaper ozone production. One of the methods increasing the efficiency of ozone production is the use to a rotating electrode presented in this paper. Experiments were carried out which shows that the most important parameters are the materials used on the electrodes and the condition of its surface. The metallographic investigations of the electrodes after continuous monthly work was made, which show how the raids layers are formed. As a result of working in a highly oxidizing environment, the electrode is oxidized in the process of chemical corrosion. It is obvious that the layer of corrosion products created during the work of the plasma reactor isolates the surface of the electrode, which reduces the intensity of the electric field, causing a decrease in the amount of plasma generated, which is a direct cause of lowering the concentration of ozone during this process. The dynamics of plasma generation process and the type of electrode material working in changing process conditions are the decisive factors influencing the concentration of ozone produced. The influence of the medium, which is the electrode material, depends mainly on its resistance to corrosion in the environment of dynamically changing conditions, e.g. electrode rotation, oxygen flow through the rotating electric field and the long monthly working time of the plasma reactor.

GaN nanowires grown on metal substrates attract an increasing interest for a wide range of applications. Herein, we report GaN nanowires grown by plasma-assisted molecular beam epitaxy on thin polycrystalline ZrN buffer layers, sputtered onto Si(111) substrates. The nanowire orientation is studied experimentally and described within a model as a function of the Ga beam angle, nanowire tilt angle and substrate rotation. We show that vertically aligned nanowires grow faster than inclined nanowires, which leads to an interesting effect of geometrical selection of the nanowire orientation in the directional molecular beam epitaxy technique. After a given growth time, this effect depends on the nanowire surface density. At low density, the nanowires continue to grow with random orientations as nucleated. At high density, the effect of preferential growth induced by unidirectional supply of material in MBE starts to dominate. Faster growing nanowires with smaller tilt angles shadow more inclined nanowires that grow slower. This helps to obtain more regular ensembles of vertically oriented GaN nanowires despite their random position induced by the metallic grains at nucleation. The obtained dense ensembles of vertically aligned GaN nanowires on ZrN/Si(111) surfaces are highly relevant for device applications. These results are not specific for GaN nanowires and should be relevant for any nanowires which are epitaxially linked to the randomly oriented surface grains in the directional molecular beam epitaxy.

Banks are normally offered two kinds of deposit accounts. It consists of deposits like current/saving account and term deposits like fixed or recurring deposits. For enhancing the maximized profit from bank as well as customer perspective, term deposit can accelerate uplifting of finance fields. This paper focuses on likelihood of term deposit subscription taken by the customers. Bank campaign efforts and customer detail analysis can influence term deposit subscription chances. An automated system is approached in this paper that works towards prediction of term deposit investment possibilities in advance. This paper proposes deep learning based hybrid model that stacks Convolutional layers and Recurrent Neural Network (RNN) layers as predictive model. For RNN, Gated Recurrent Unit (GRU) is employed. The proposed predictive model is later compared with other benchmark classifiers such as k-Nearest Neighbor (k-NN), Decision tree classifier (DT), and Multi-layer perceptron classifier (MLP). Experimental study concludes that proposed model attains an accuracy of 89.59% and MSE of 0.1041 which outperform well other baseline models.

Concrete is a very common construction material. As time prevails, cracks often appear in structures that pose a threat to strength and durability, which ultimately affect the structural integrity of concrete. Extensive work has been carried out in past on utilization of Carbon Fiber Reinforced Polymer (CFRP) as a repair material. But, this research particularly focuses on: multiple fiber layers i.e. single and double, traditional concrete mix ratio with locally available materials and varying flexural reinforcement. Furthermore, this work presents an experimental analysis to strengthen the Reinforced Concrete (RC) beams using SikaWrap- 230 C as a CFRP wrap, and Sikadur-330 as a bonding material. The comparison between single and double layers of CFRP is of major interest in this research. Six reinforced concrete beams were repaired with CFRP and tested under three-point bending test. For these specimens, load deflection behavior along with crack propagation and failure of beams were studied. Experimental results show that beams repaired using CFRP wrapping achieved higher flexural capacity and ductility as compared with reference beam. Moreover, results reveal that introduction of second layer of fiber caused around 4-5.2% increment in flexural strength and resulted in higher ductility of RCC beam.

Data obtained by Direct Numerical Simulations (DNS) of pressure-driven turbulent channel flow are studied in the range 180 ≤ Reτ≤ 10,000. Reynolds number effects on the Mean Velocity Profile (MVP) and second order statistics are analyzed with a view of finding logarithmic behavior in the overlap region or even further from the wall, well in the boundary layer’s outer region. Values of Kármán constant for the MVPs and the Townsend-Perry constants for the streamwise and spanwise fluctuation variances are determined for the Reynolds numbers considered. A data-driven model of the MVP, proposed and validated for zero pressure gradient flow over a flat plate, is employed for pressure-driven channel flow by appropriately adjusting Coles’ strength of the wake function parameter, Π. There is excellent agreement between the analytic model predictions of MVP and the DNS-computed MVP as well as of the Reynolds shear stress profile. The skin friction coefficient Cf is calculated analytically. The agreement between the analytical model predictions and the DNS-based computed discrete values of Cf is excellent.

The objective of this study is to investigate the use of excavation behavior prediction and analysis software in the field of civil en-gineering. PLAXIS and SoilWorks, widely used in the engineering and academic communities, were employed as research tools for analyzing gravel layers and deriving appropriate ranges of soil elastic modulus for different software programs. By collecting excavation cases in the Xindian area of New Taipei City, Taiwan, and conducting deep excavation analysis using the displacement of retaining walls, this study provides recommended ranges for localized selection of soil elastic modulus. The suggested range for PLAXIS is 7,840 N/m² to 9,800 N/m², while Soil-Works suggests a range of 2,450 N/m² to 3,430 N/m². These ranges allow for reasonable estimation of maximum deformations during the final excavation stage. Based on the research findings, it is recommended that engineers refer to the provided ranges when selecting soil elastic modulus for excavation analysis in gravel layers using different soft-ware programs in the Xindian area of Taiwan to improve the accuracy of deformation predictions during the final excavation stage. These results serve as important references for engineers and contribute to the effective prediction and management of excavation behavior in civil engineering projects.

In this study, the application of the main algorithms used for SDM, namely Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MAXENT) and Random Forest (RF), were considered in order to simulate the citrus probability of distribution in a Mediterranean area, based on presence data and a random background sample, and in relation to several predictors. The predictors were grouped under 19 bioclimatic variables, elevation described through a Digital terrain model, soil physical properties, and irrigation. Sensitivity analysis was carried out by: modifying the values of the main models’ parameters; and reducing the input presence points.

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand, therefore the need to optimise water resources in this area is crucial, and climate change could worsen this condition. In citrus cultivation, which is one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are im-plemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies constitutes valuable information for the stakeholders. Therefore, the objective of this study was to determine the prob-ability of presence of citrus in Sicily at changing of the percentage of water deficit application, in order to analyse change in the surface area and localisation of the crop. The methodology was based on the application of Species Distribution Models and Geographic Information Systems to the case study of the Province of Syracuse in Sicily. Different geostatistical and machine learning models were applied, based on 3-decades bioclimatic variables, DTM and irrigation; assessment of the outcomes was carried by using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that affected most the probability of presence. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory and a decrease of citrus pres-ence in southern areas of the considered territory, where climate conditions are less favourable in terms of temperature and precipitations, thus providing useful information for decision support tools in agriculture and land use policy.

The Sun is a huge gaseous body. However, we cannot observe events in the inner Sun due to the convection zone opacity according to previous models. Therefore, the flares originate from the front surface of the Sun. But the current study relied on the distance distribution of X-Ray solar flares, which concluded that the inner layers have much lower opacity than expected. It is even less than what was expected by the latest models based on helioseismology. This means that the flares may originate from the solar interior or solar core, and perhaps from the backside surface, and even appear to us from the frontside surface. Which the re-estimate and correct the currently listed solar flare’s location is needed. Additionally, the flare’s distance illustrations the solar interior layers and appears their boundaries from the core to the photosphere. This method allows us to monitor the variation of the core’s radius with time. The model of the flare’s distance has been developed in current study. But this needs to redevelopment after re-estimating the solar flares locations.

If a lubricant contains structures capable of conducting energy, reactions involving zinc dialkyldithiophosphate (ZDDP) may take place both very close to and away from the solid surfaces, with this indicating that ZDDP can be a highly effective anti-wear (AW) additive. The central thesis of this article is that the tribocatalytic effect is observed only when the energy emitted by the solids is transmitted by ordered molecular structures present in the lubricant, e.g., graphene. The friction tests were carried out for 100Cr6 steel balls in a sliding contact with uncoated or a:C-H-coated HS6-5-2C steel discs in the presence of polyalphaolefin 8 (PAO 8) as the lubricant, which was enhanced with graphene and/or ZDDP. There is sufficient evidence of the interactions occurring between ZDDP and graphene and their effects on the tribological performance of the system. It was also found that the higher the concentration of zinc in the wear area, the lower the wear. This was probably due to the energy transfer resulting from the catalytic decomposition of ZDDP molecules. Graphene, playing the role of the catalyst, contributed to that energy transfer.

This article is the third part of a scientific project under the general title "Geometrized vacuum physics based on the Algebra of Signatures". In the first two papers [1,2], the ideal (i.e., non-curved and immobile) local region of vacuum was studied and the foundations of the Algebra of Signatures were laid. This article considers the possibilities of describing the curved and moving state of the same vacuum region on the basis of the mathematical apparatus of the Algebra of Signatures. The reasons for the multilateral consideration of vacuum and twisting of intra-vacuum processes into spiral bundles are disclosed. The 4-tensor is introduced for two-sided and 16-sided consideration of the curvature of the local vacuum region. On the basis of kinematic models, the following assumptions were made: about the inert properties of vacuum layers; about the possibility of displacement of vacuum layers relative to each other at a speed significantly exceeding the speed of light; about the possibility of "rupture" of the local region of vacuum. The proposed kinematic models of the movement of vacuum layers can be a theoretical basis for the development of "zero" (i.e., vacuum) technologies.

In this work, we introduce a scalable and efficient GPU-accelerated methodology for volumetric particle advection and finite-time Lyapunov exponent (FTLE) calculation, focusing on the analysis of Lagrangian Coherent Structures (LCS) in large-scale Direct Numerical Simulation (DNS) datasets across incompressible, supersonic, and hypersonic flow regimes. LCS play a significant role in turbulent boundary layer analysis, and our proposed methodology offers valuable insights into their behavior in various flow conditions. Our novel owning-cell locator method enables efficient, constant-time cell search, and the algorithm draws inspiration from classical search algorithms and modern multi-level approaches in numerical linear algebra. The proposed method is implemented for both multi-core CPUs and Nvidia GPUs, demonstrating strong scaling up to 32,768 CPU cores and up to 62 Nvidia V100 GPUs. By decoupling particle advection from other problems, we achieve modularity and extensibility, resulting in consistent parallel efficiency across different architectures. Our methodology was applied to calculate and visualize the FTLE on four turbulent boundary layers at different Reynolds and Mach numbers, revealing that coherent structures grow more isotropic proportional to the Mach number, and their inclination angle varies along the streamwise direction. We also observed increased anisotropy and FTLE organization at lower Reynolds numbers, with structures retaining coherency along both spanwise and streamwise directions. Additionally, we demonstrated the impact of lower temporal frequency sampling by upscaling with an efficient linear upsampler, preserving general trends with only 10% of the required storage. In summary, we present a particle search scheme for particle advection workloads in the context of visualizing LCS via FTLE that exhibits strong scaling performance and efficiency at scale. Our proposed algorithm is applicable across various domains requiring efficient search algorithms in large structured domains. While this manuscript focuses on the methodology and its application to LCS, an in-depth study of the physics and compressibility effects in LCS candidates will be explored in a future publication.

A new three-dimensional numerical model of a polymer electrolyte fuel cell (PEFC) with a single straight channel has been developed to primarily investigate the important impact of the double-sided microporous layer (MPL) coating on the overall performance of the fuel cell and the distribution of the current and the oxygen concentration within the cathode gas diffusion layers (GDLs). Realistic experimentally estimated interfacial contact resistance values between the gas diffusion layer and each of the bipolar plates and the catalyst layer values were incorporated into the model, and parametric studies were performed. The results showed that the double-sided MPL coating could significantly improve the fuel cell performance by up to 30%. Also, it was shown that the neglect of the contact resistance between the MPL and the catalyst layer could overestimate the fuel cell performance by up to 6%. In addition, the results showed that the fuel cell performance and the distribution of the current and oxygen are more sensitive to the porosity of the MPL facing the bipolar plate than the porosity of the MPL facing the catalyst layer. All the above results are presented and critically discussed in detail.

Archie model is the basis of calculating oil saturation, but there are some limitations when using this model to calculate oil saturation in water-flooded layer. The main reason is that the main parameters, such as rock resistivity and formation water resistivity, are constantly changing dynamic parameters in the underground with the influence of injected water. Considering that water-flooded layers changes with injection waters, influence factors of rock resistivity and formation water resistivity of primary parameters are analyzed. Considering the dynamic data of water cut is the most reactive underground fluid characteristics of real information, combined with dynamic and static, and the “double ratio” model of later development by the same sedimentary layer is established, which realizes the inversion of rock resistivity and formation water resistivity, then modified Archie model relating to water-flooded layers of the same sedimentary explaining formation. The explanation of actual data indicates that the “double ratio” model well considers the dynamic variation of production data, which makes the inversion of the flooded rock resistivity relatively accurate, besides, the modified Archie model can accurately calculate the oil saturation of water-flooded layers with a reasonable result, which offers scientific basis for the predicting of remaining oil distribution rules.

Solar cell layers technology has achieved global standing in the solar cell layers deposition process, and it covers the innovative methods and techniques in significant applications. Recent solar cell layers technology has an advanced interest in a refined approach to enhance performance and highlights the importance of recent proficient procedures for manufacturing. For example, the application is used to search for novel materials for solar cells' layers to clarify the current energy crisis. The technological process and various types of solar cells depend on climate change. Among them, layers of solar cells and silicon wafer solar cells are very encouraging. Solar cell layers technology has led to solar cells being a more reasonable active option in design and production. The productivities facilitated by new solar cells still need to be enhanced for the various processes involved in the additional enhancement from Copper Indium Gallium Selenide (CIGS) microfilms to solar cell crystal structure dye-sensitized solar cells. The hydrophobic coating works as an anti-dust coating, enhancing efficiency and decreasing the cost of cleaning solar cells. In Saudi Arabia Majmaah City, most solar projects are in dry regions, where the dusty weather reduces solar cell efficiency. Therefore, combining these two properties and applying an anti-reflective and superhydrophobic coating will increase solar cell efficiency by 20%. Solar cells' crystal structure results are substituted with layers or new materials to balance environmental impact and toxic nature.

The discovery of solitonlike coherent structure (SCS) in boundary layer flows is crucial for understanding turbulence origins and in particular for laminar-turbulence transition. However, the task of finding solutions for solitonlike coherent structure from the Navier-Stokes equations poses a significant challenge. In this paper, based on the author's previous work [B.H. Sun, Exact similarity solutions of unsteady laminar boundary layer flows, Preprints 2023, 2023092117], we are able to study convergent flow boundary layers, whose solution encompasses both shock wave and solitary wave solutions, and their superposition gives rise to solitary-like waves, namely solitonlike coherent structure. It is found that the solitonlike coherent structure can only be obtained by combining the Navier-Stokes equations and mass conservation, since the combined equations will have the third order derivatives respect to coordinate $x$, in other words, without the mass conservation condition, the Navier-Stokes equations does not contain solitonlike coherent structure by itself. Finally proved solitonlike coherent structure is a universal motion form of viscous fluid.

This paper presents a non-random weight initialization method in convolutional layers of neural networks examined with the Fast Gradient Sign Method (FSGM) attack. This paper's focus is convolutional layers, and are the layers that have been responsible for better than human performance in image categorization. The proposed method induces earlier learning through the use of striped forms, and as such has less unlearning of the existing random number speckled methods, consistent with the intuitions of Hubel and Wiesel. The proposed method provides a higher performing accuracy in a single epoch, with improvements of between 3-5% in a well known benchmark model, of which the first epoch is the most relevant as it is the epoch after initialization. The proposed method is also repeatable and deterministic, as a desirable quality for safety critical applications in image classification within sensors. That method is robust to Glorot/Xavier and He initialization limits as well. The proposed non-random initialization was examined under adversarial perturbation attack through the FGSM approach with transferred learning, as a technique to measure the affect in transferred learning with controlled distortions, and finds that the proposed method is less compromised to the original validation dataset, with higher distorted datasets.

Single-layer core corrugated sandwich panels generally consist of a corrugated core and two layers of panels, while multi-layer core corrugated sandwich panels are formed by stacking multiple layers of panels with multiple layers of core layers. In this study, integrated multilayer core corrugated sandwich panels with different shapes of corrugated cores (triangular, trapezoidal, and rectangular) and the different number of core layers were fabricated using 3D printing technology, and the mechanical behavior of such multilayer core corrugated sandwich panels under quasi-static three-point bending was investigated using experiments and numerical simulations. The effects of core shape and number of core layers on the bending deformation process, damage mode, load carrying capacity, and bending energy dissipation capacity of multilayer core sandwich panels are discussed. Parametric design of multilayer triangular core corrugated sandwich panels was also carried out by finite element software ABAQUS. It was found that a new multilayer corrugated sandwich panel with a multi-layer core is better than the single core shape multilayer corrugated sandwich panel in terms of bending load capacity, energy dissipation capacity and deformation capacity can be obtained through the combination design of different core shapes.

Spatial planning processes generally consider three levels of planning, which are applied to three types of territory: state, county and city. As the coastal areas are of a significant natural, cultural, economic and social value, as well as are characterized by a diverse range of involved society with specific interests and needs, there is a necessity for an innovative and new approach to sustainable development planning in accordance with the modern age of growth, as well as to work with local communities in specific areas. Planning of a small populated area like village territory is more diverse and subject to the wishes and needs of the population. Small territory planning involves a very narrow circle of individuals or communities that identify spatial development needs for the future, including socio-economic, cultural, and environmental and climate change scenarios. In order to assess the development opportunities and needs of the area, it is necessary to monitor the area by regularly updating data. As it is well known, methodically derived data (facts) provide objectivity and transparency. Nowadays, when information about the present and the past is circulating very fast, it is possible to analyze the current situation, to forecast the future using databases, and to show several constructed realities (scenarios) using the geographic information system (GIS). Therefore, it is crucial to explore and find out the local needs-based planning approach to the development of village in coastal areas.

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more Artificial Intelligence (AI). Deep learning is used remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layer and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.

The construction of optimized biological fuel cells requires a cathode which combines the longevity of a microbial catalyst with the power density of an enzymatic catalyst. Laccase secreting fungi were grown directly on the cathode of a biological fuel cell to facilitate the exchange of inactive enzymes with active enzymes with the goal of extending the lifetime of laccase cathodes. Additionally, a functionally graded coating was developed to increase enzyme loading at the cathode. Directly incorporating the laccase producing fungus at the cathode extends the operational lifetime of laccase cathodes while eliminating the need for frequent replenishment of the electrolyte. Additionally, the hybrid microbial-enzymatic cathode addresses the issue of enzyme inactivation by using the natural ability of fungi to exchange inactive laccases at the cathode with active laccases. Finally, enzyme adsorption was increased through the use of a functionally graded coating containing an optimized ratio of titanium dioxide nanoparticles and single walled carbon nanotubes. The hybrid microbial-enzymatic fuel cell combines the higher power density of enzymatic fuel cells with the longevity of microbial fuel cells and demonstrates the feasibility of a self-regenerating fuel cell in which inactive laccases are continuously exchanged with active laccases.

MoS₂/TiO₂ nanostructures made of MoS₂ nanoparticles covering TiO₂ nanosheets have been synthesized, either via ex-situ or in-situ approaches. Morphology and structure of MoS₂/TiO₂ hybrid nanostructures have been investigated and imaged by means of X-ray diffraction (XRD) analysis and high-resolution transmission electron microscopy (HRTEM), while the vibrational and the optical properties have been investigated by Raman, Fourier-transform infrared spectroscopy (FTIR) and UV−visible (UV-Vis) techniques. The different stacking degrees together with the size distribution of the MoS₂ nanosheets, decorating the TiO₂ nanosheets, have been carefully obtained from HRTEM images. The nature of the surface sites on the main exposed faces of both materials has been detected by means of in-situ FTIR spectra of adsorbed CO probe molecule. The results coming from the ex-situ and in-situ approaches will be compared, by highlighting the role of the synthesis processes in affecting morphology and structure of MoS₂ nanosheets, including their curvature, surface defects, and stacking order. Some more, it will be shown that the in-situ approach is affecting the reactivity of the TiO₂ nanosheets too, hence in turn affects the MoS₂/TiO₂ nanosheets interaction.

With the intensive development of data transmitting and processing devices of the terahertz (THz) frequency range, an important part of which are integrated plasmonic components and communication lines, it becomes necessary to measure correctly the optical constants of their conductive surfaces. In the paper we describe a reliable method for determining the effective permittivity εm of a metal surface from the measured characteristics (refractive and absorption indices) of THz surface plasmon polaritons (SPPs). The novelty of the method is the conduction of measurements on a metal surface with a dielectric layer of subwavelength thickness, suppressing the radiative losses of SPPs, which are not taken into account by the SPP dispersion equation. The method was tested on a number of flat "gold sputtering - zinc sulfide layer - air" structures with the use of the THz radiation (λ0 = 141 μm) of the Novosibirsk free electron laser (NovoFEL). The SPP characteristics were determined from interferograms measured with a plasmon Michelson interferometer. It was found that the method allowed a significant increase in the accuracy of εm in comparison with measurements on the same metal surface without a dielectric layer.

Accurate forecasting of wind power generation plays a key role in improving the operation and management of a power system network and thereby its reliability and security. However, predicting wind power is complex due to the existence of high non-linearity in wind speed that eventually relies on prevailing weather conditions. In this paper, a novel hybrid deep learning model is proposed to improve the prediction accuracy of very short-term wind power generation for the Bodangora Wind Farm located in New South Wales, Australia. The hybrid model consists of convolutional layers, gated recurrent unit (GRU) layers and a fully connected neural network. The convolutional layers have the ability to automatically learn complex features from raw data while the GRU layers are capable of directly learning multiple parallel sequences of input data. The data sets of five-minute intervals from the wind farm are used in case studies to demonstrate the effectiveness of the proposed model against other advanced existing models, including long short-term memory (LSTM), GRU, autoregressive integrated moving average (ARIMA) and support vector machine (SVM), which are tuned to optimise outcome. It is observed that the hybrid deep learning model exhibits superior performance over other forecasting models to improve the accuracy of wind power forecasting, numerically, up to 1.59 per cent in mean absolute error, 3.73 per cent in root mean square error and 8.13 per cent in mean absolute percentage error.

Understanding brain function is one of the most important open problems in science today. At present, there is no concrete theory for how the brain works. Here, a theory is presented that provides a detailed mechanistic biological account of the brain’s capacities. Brain function is managed by a response (R) process that is structurally similar to the immune response, and shows anatomical and molecular specificity. Different R process stages utilize different cortical layers, hippocampus fields, basal ganglia paths, GABAergic interneurons, cerebellum paths, and molecular agents such as dopamine, serotonin and opioids. We show how the R process supports hierarchical action sequences, language and thought. The theory is supported by a large body of experimental evidence in many modalities, and accounts for virtually all of the major facts known about the brain at the system level.

The introduction of any new disruptive technology in a traditionally well-established industry, such as the road construction industry, is usually associated with considerable resistance. This is especially relevant when the new technology is based on the use of granular materials traditionally considered to be of an unacceptable quality in combination with relatively new concepts such as New-age (Nano) Modified Emulsions (NME). In such cases, the fact that the material design methods are based on fundamental scientific principles and have been proven in laboratories and through Accelerated Pavement Testing (APT), may be of little influence. However, the general acceptance of new disruptive technologies, e.g. telecommunications and Information Technologies (IT), have been based on the considerable advantages it presented. The same principles are applicable to the general acceptance and use of NME stabilisation/enhancement of materials in the road construction industry. This article is aimed at the practical cost-effective demonstration of the general application of the use of nanos-silane modified emulsions in the construction of the highest order roads, i.e. inter-city multi-lane highways, lower order roads (including Low-Volume-Roads (LVR)) and even local accesses to farms and in villages/townships. The implementation of NME technologies is directly associated with ease of use, time and cost savings and the addressing and reduction of risks applicable to the use thereof.

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

Emulsion stabilisation of base layers surfaced with chip seals often proves problematic with chips punching into the base and early distress. This can be aggravated by the use of modified binders that restricts the evaporation of moisture from pavement layers. The introduction of New-age (Nano) Modified Emulsion (NME) stabilisation has the advantage that water is chemically repelled from the stabilised layer resulting in an accelerated development of strength. A need was identified to evaluate the early life performance of selected chip seals, together with identified binders. Three different chip seal surfacings with unconventional modified binders were constructed and evaluated using Accelerated Pavement Testing (APT) with the MMLS3. The objectives of the experimental design and testing were to evaluate binder performance, early loss of chips before chip orientation at low temperatures, punching of the chips into the NME stabilised base, deformation characteristics of a Cape seal and the effect of the use of a standard normal modified binder. This paper contains details of the NME base layer, the binder and seal selection and the test results. It is shown that a cost-effective thin chip seal in combination with a suitable binder can be used on a NME stabilised base with confidence.

Understanding brain function is one of the most important problems in human history. At present, there is no concrete theory for how the brain works. Here, a theory is presented that provides a detailed mechanistic biological account of the brain's capacities, including motor control, functional states, language, and thinking. Brain function is managed by a well-defined response (R) process that is generally similar to the process underlying the immune system. The R process is strongly reflected in the brain's anatomy, physiology, and external interactions. Different R process stages are supported by distinct excitatory networks located in different cortical layers, hippocampal fields, and bagal ganglia paths, by distinct coordination networks comprised of GABAergic interneurons, and by distinct molecular agents. The roles of norepinephrine, serotonin, dopamine and acetylcholine is to promote the alert, planning, goal-setting and execution R process modes, respectively. Opioids and oxytocin promote termination by success, failure, fight or run, while glucocorticoids and cannabinoids suppress acute responses to protect cells. The R process has two instances occurring at different time scales. The millisecond-scale Quax process implements the execution of hierarchical sequences of movements and thoughts, in which the selection of the next action is determined via interaction between top-down predictions and sensory inputs. The slower Need process controls the satisfaction of internal and external needs. The theory differs from the existing standard accounts in many of the major topics (e.g., the basal ganglia, dopamine, language), and shows how cognition results from biological processes.