The objective of this paper was to verify the applicability of statistical learning (SL) compared to human reasoning with respect to the Universal Thermal Climate Index (UTCI), a complex tool for the assessment of outdoor thermal stress. UTCI is an equivalent temperature index based on the 48-dimensional output of an advanced model of human thermoregulation formed by 12 variables at four consecutive 30-minute intervals, which were calculated for 105642 thermal conditions from extreme cold to extreme heat. Comparing the performance of SL algorithms to the results accomplished by an international endeavor involving more than 40 experts from 23 countries, we found that random forests and k-nearest neighbors closely predicted UTCI values, but that clustering applied after dimension reduction algorithms (principal component analysis and t-distributed stochastic neighbor embedding) were inadequate for risk assessment in relation to the UTCI stress categories. This indicates a potential supportive role for SL, as it will not (yet) fully replace the bio-meteorological expert knowledge.

The coseismic surface three-dimensional deformation fields were significant for quantified the geometric and kinematic characteristics of earthquake rupture faults. Traditional geodetic techniques were constrained by their intrinsic limitations, such as InSAR, which could only extract far-field deformation fields due to its incoherence, and GNSS, which could only acquire deformations at discrete points. The recently developing optical pixel correlation technique based on high resolution remote sensing images could acquire the near-field coseismic horizontal deformation. In this study, the vertical displacement field of the 2022 Mw 6.6 Menyuan earthquake was obtained by differencing the pre- and post-earthquake DEMs from GaoFen-7 stereo satellite images. Further, the line-of-sight (LOS) and azimuthal (AZI) directions far-field deformations by InSAR, the horizontal near-field deformation by optical pixel correlation based on pre- and post-earthquake GF-2/7 images, and the vertical deformation by differencing the pre- and post-earthquake DEMs, were incorporated to comprehensively solve the complete three-dimensional deformation fields of the 2022 Mw 6.6 Menyuan earthquake. Firstly, the vertical displacement field by differencing DEMs indicated there were significant vertical displacements about 1 m at the bend region, which was induced by the local compressive stress. In addition, at the epicenter on the middle segment of ruptured Lenglongling fault (LLLF), the maximum lifting occurred on the southern sides of the main and secondary faults exceeded 2 m. Secondly, the three-dimensional deformation fields solved by multiple deformation data demonstrated that the near-field deformation field calculated by optical pixel correlation method could quantify the displacements distributed over the rupture fault zone, which were not available on the InSAR deformation maps. Finally, the surface 2D strain derived from the displacement maps calculated by optical pixel correlation revealed high strain concentration on the rupture fault zone. Our study focused on the complete surface three-dimensional deformation estimation from multiple far- and near-field deformation data, and provided a new perspective for a deeper understanding of the characteristics of coseismic surface deformation and the rupture pattern of the fault.

This paper introduces a rigorous and comprehensive approach for the assessment of marine shafting systems through the utilization of an advanced Pi-Theorem based scaling methodology. Integrating journal bearing similarity assessment and shaft-line scaling methodology with advanced dimensional analysis, the study aims to provide a methodology foundation for systematic replication and analysis of marine shafting systems through scaled models. The proposed scaling methodology ensures geometric and mechanical similarity in terms of shaft-line deflection, considering key scaling parameters such as shaft length, diameter, weight, loads, rotational speed, material properties, bearing locations, and offsets. The advanced dimensional analysis computes specific non-dimensional ratios to guarantee a close resemblance between a real-size system and a scaled lab model, enabling meaningful experimentation. The methodology is analytically derived and validated with numerical simulations for a case study, conducting comparative analysis, evaluating discrepancies, and utilizing the integrated framework for experimentation.

As a new plant slope protection technology, living stumps can effectively improve slope stability. The current study attemptes to investigate the mechanical characteristics of the root system of living stumps and their reinforcement mechanism on slopes by carrying out a three-dimensional living stump slope modeling test. Using ABS materials, a 3D model of a living elm stump was created by 3D printing and a slope model test was carried out. The reinforcement mechanism of the living stump was studied through a combination of model testing and numerical simulation. The results indicate that, due to the presence of living stumps in the lower and middle parts of the slope, the maximum shear stress area of the soil moved to a deeper direction. The stress state of the soil around the living stump was effectively improved due to the existence of the lateral root system.Living stumps in the lower part of the slope cross the potential sliding surface and transferred the soil shear stress gradually to the root system through root-soil interaction. Moreover, the tap roots and lateral roots of living stumps form a robust spatial structure that bears the shear stress of soil together, thus increasing the stability of the slope.

We describe a new approach for integrating multiple views of data into a common latent space using non-negative tensor factorization (NTF). This approach, which we refer to as the "Integrated Sources Model" (ISM), consists of two main steps: embedding and analysis. In the embedding step, each view is transformed into a matrix with common non-negative components. In the analysis step, the transformed views are combined into a tensor and decomposed using NTF. Noteworthy, ISM can be extended to process multi-view data sets with missing views. We illustrate the new approach using two examples: the UCI digit dataset and a public cell type gene signatures dataset, to show that multi-view clustering of digits or marker genes by their respective cell type is better achieved with ISM than with other latent space approaches. We also show how the non-negativity and sparsity of the ISM model components enable straightforward interpretations, in contrast to latent factors of mixed signs. Finally, we present potential applications to single-cell multi-omics and spatial mapping, including spatial imaging and spatial transcriptomics, and computational biology, which are currently under evaluation. ISM relies on state-of-the-art algorithms invoked via a simple workflow implemented in a Jupyter Python notebook.

Smart City has emerged as the mainstream paradigm for urban governance innovation, sustainable development, and strategy upgrades, which is drawing attention from scholars worldwide. However, current frameworks for Smart City assessment remain incomplete and simplistic. In is paper, 30 national or provincial capitals in China were selected and we designed a tri-dimensional SPI model—Social, Physical, and Information Space—for smart city spatial development assessment. Utilizing methods such as entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, this study assesses the spatial development and coupled coordination of 30 cities from 2011 to 2019. Finally, by means of BP neural networks, the study examines the contribution of each indicator to the spatial coupled coordination. The results indicated that with a narrowing disparity in development speeds among different regions, the spatial coupled coordination development level of smart capitals in China has steadily increased, presenting a pattern of staggered distribution. Moreover, the IS subsystem plays the most significant role in coupled coordination. The significance of this research lies in its tri-dimensional spatial perspective of the spatial development and coupled coordination differences of the Smart City, providing evidence-based support for the regional layout and optimization in China.

Objectives (Purpose) This study aims to evaluate the outcomes of the Subcrestally Placed Implant (SPI) technique in single molar cases, focusing on biological stability and aesthetics. It proposes a schematic model to elucidate the interplay between the biological stability of soft tissues and bone around the SPI and the morphological requisites that Implant Abutment Prostheses (IAP) must fulfill to effectively replace natural teeth. Materials and methods I examined 20 cases using the SPI technique with Internal Platform Switching (IPS) design implants for single molar replacements to assess their stability. Routine check-ups utilized plain panoramic X-rays to measure changes in crestal bone on the mesial and distal sides. Additionally, 10 cases with CBCT X-rays taken more than one year after the initial surgery underwent a novel 3-dimensional soft tissue analysis (3DSTA). Measurements included placement depths (PD), soft tissue thickness (STT) at central (cPD, cSTT) and peripheral areas (pPD, pSTT), the length of the Transitional Zone (TZ) on mesial, distal, facial, and lingual aspects, and crestal bone thickness (CBT) at buccal and lingual sites. Results Panoramic analyses showed minimal changes in crestal bone (mean changes of pPD were less than 0.1 mm) over an average period of 3.3 years. Average measurements were as follows: cPD was 1.7 mm, pPD was 2.7 mm, cSTT was 0.2 mm, pSTT was 0.6 mm, TZ length was 3.8 mm, buccal CBT was 3.0 mm, and lingual CBT was 3.7 mm. Conclusions CBCT analyses of the topographical relationships between soft tissue and crestal bone with IAP for SPIs demonstrated stable results over an average 3-year period. The consistency in the relationship between crestal bone and surrounding soft tissue can be ascribed to biological structural integrity. The study classified peri-implant soft tissues into subcrestal and supracrestal (transitional zone – TZ) segments, exploring their characteristics through dimensional measurements and assessing mechanical resistance. Further histological study is needed to identify specific features of each segment.

The paper primarily demonstrates that the three internal triangles formed by connecting any point on a midsegment with the vertices of a generic triangle satisfy the geometric Proof of the Pythagorean theorem in Euclidean geometry. Moreover, this study elucidates the Pythagorean relationships inherent within three-dimensional geometric constructs resulting from the arrangement of three-dimensional spatial triangles. This geometric relationship, akin to a generalized extension of the Pythagorean theorem, unveils a unique spatial region characterized by this harmonious area interrelation among triangles.

The Double Random Phase Encoding (DRPE) image encryption method has garnered significant attention in color image processing and optical encryption, thanks to its parallel encryption of R, G, B. However, DRPE-based color image encryption faces two challenges. Firstly, it disregards the correlation of R, G, and B, compromising the encrypted image's robustness. Secondly, DRPE schemes relying on Discrete Fourier Transform (DFT) and Discrete Fractional Fourier Transform (DFRFT) are vulnerable to linear attacks, such as Known Plaintext Attack (KPA) and Chosen Plaintext Attack (CPA). Quantum walk stands out as a remarkable tool for designing modern cryptographic mechanisms, offering resistance to potential attacks from both classical and quantum computers.Therefore, this study presents an optical color image encryption algorithm that combines two-dimensional quantum walk with 24-bit plane permutation, dubbed OCT. This approach employs pseudo-random numbers generated by Two-Dimensional Quantum Walking (TDQW) for phase modulation in DRPE and scrambles the encrypted image's real and imaginary parts using the Generalized Arnold Transform. The 24-bit plane permutation helps reduce correlation of the R, G, B, while the Generalized Arnold Transform bolsters DRPE's resistance to linear attacks. By incorporating TDQW, the key space is significantly expanded. Experimental results validate the effectiveness and security of proposed method.

This paper is devoted to the study of multi-dimensional integral transform with Fox H-function in the kernel in weighted spaces integrable functions in the domain R+n with positive coordinates. Mapping properties such as the boundedness, the range, the representations of the considered transformation are established.

Improper sitting positions are known as the primary reason for back pain and the emergence of musculoskeletal disorders (MSD) among individuals who spend prolonged time working with computer screens, keyboards, and mice. At the same time, it is well understood that automated technological tools can play an important role in the process of unhealthy habit alteration, so plenty of research efforts are focused on research and technology development (RTD) activities that aim to provide support to the prevention of back pain or the development of MSD. Here, we report on creating a new resource in support of RTD activities aiming at the automated detection of improper sitting positions. It consists of multimodal multisensory recordings of 100 persons made with a video recorder, camera, and wrist-attached sensors that capture physiological signals (PPG, EDA, Skin temperature), as well as motion sensors (three-axis accelerometer). Our multimodal multisensory dataset (MMD-MSD) opens new opportunities for modeling the body stance (sitting posture and movements), physiological state (stress level, attention, emotional arousal, valence), and performance (success rate on the Stroop test) of people working with a computer. Finally, we demonstrate two use cases: improper neck posture detection from pictures and cognitive load detection from physiological signals.

Using both GRACE satellite data and statistical records, we apply the advanced Super-SBM model in this study to evaluate the efficiency of water resource utilization in 34 prefecture-level cities across three provinces in Northeast China from 2003 to 2020. Furthermore, we employ the Tobit model to examine the factors influencing efficiency. Our findings suggest that while spatial clustering is moderately low, the efficiency of water resource utilization in the Northeast Region is praiseworthy and significantly correlates with the level of technological advancement. Four distinct categories can be identified among the prefecture-level cities based on the scale and technological efficiency of water resource utilization. To promote enhanced utilization of water resources, different regions should implement measures that are specifically tailored to their unique circumstances.

The new unique technology for granulation of metals and nonmetallic melts is presented in the paper. It was created on the newly developed controlled film flow disintegration with rapid cooling of the droplets and solidified granules in amorphous state. Three new phenomena discovered by us created the base for 3 different methods for granulation of metals and nonmetallic melts in a wide range of granules’ sizes. Electromagnetic resonance method allows producing granules of metals in the range of 1 mm and over. The soliton-like vibrational regime is available for production of any granulation material in the range 0.1-1 mm. Moreover, the shockwave regime is applicable for producing particles from 10 micrometers by size. All methods produce particles of the stated size with a narrow distribution by size: deviation from the average size no more than 50%. The other known methods do not produce granules with such high cooling rate, which can realize the amorphous state and cannot regulate size of particles in such bright but narrow range from 10 micrometers to 1-2 mm.

This study aims to examine the non-volatile Particle Number (PN) emissions of a retrofitted Heavy-Duty Spark-Ignition (HD-SI) engine powered by liquefied petroleum gas (LPG) under both steady-state and transient conditions. The engine was tested under seven steady-state operating points to investigate the PN behavior and Particle Size Distribution (PSD) upstream and down-stream of the Three-Way Catalyst (TWC). This analysis intends to assess the impact of including particles with diameters ranging from 10 nm to 23 nm in the total particle count, a consideration for future regulations. The study employed the World Harmonized Transient Cycle (WHTC) for transient conditions to encompass the same engine working region used in the steady-state analysis. A Dekati FPS-4000 diluted the exhaust sample to measure the PSD and PN for particle diameters between 5.6 nm and 560 nm using the TSI-Engine Exhaust Particle Sizer (EEPS) 3090. Findings indicate that PN levels tend to increase downstream of the TWC under steady-state conditions in operating points with low exhaust gas temperatures and flows (equal to or less than 500 °C and 120 kg/h). Furthermore, the inclusion of particles with diameters between 10 nm and 23 nm leads to an increase in PN emissions by 17.70% to 40.84% under steady conditions and by an average of 40.06% under transient conditions, compared to measurements that only consider particles larger than 23 nm. Notably, in transient conditions, most PN emissions occur during the final 600 seconds of the cycle, linked to the most intense phase of the WHTC.

Fractional flow reserve (FFR) is well recognized as a gold standard measure for the estimation of functional coronary stenosis. Technological progressions in image processing have empowered the reconstruction of three-dimensional models of the coronary arteries via both non-invasive imaging modalities, such as Coronary Computed Tomography Angiography (CCTA), and invasive coronary angiography or intravascular imaging. The application of computational fluid dynamics (CFD) techniques to coronary 3D anatomical models allows the virtual evaluation of the hemodynamic significance of a coronary lesion with high diagnostic accuracy in comparison to the invasive FFR using pressure wires. These opportunities providing hemodynamic information based on anatomy have given rise to a new era of functional angiography and coronary imaging. However, further validations are needed to overcome several scientific and computational challenges before these methods are applied in everyday clinical practice. This review aims to present all the data on the studies and methodologies that have been presented for the virtual estimation of FFR, and the diagnostic execution of the virtual tools for the hemodynamic assessment of coronary lesions compared to the invasive FFR.

Three Seas Initiative (3SI) is still an underresearched area and is particularly important due to historical circumstances and economic backwardness. A study was carried out to assess the impact of renewable energy and production generated by the agricultural sector on CO2 emissions in 3SI countries between 2008 and 2020. The study used panel data analysis based on the two-step system generalised method of moments (GMM) and the Dumitrescu-Hurlin panel causality test. The results show that a 1% increase in the value-added generated by agriculture increases CO2 emissions in the countries studied by 0.11%. In contrast, a 1% increase in GDP leads to a 0.29% increase in CO2 emissions. Conversely, when renewable energy consumption increases by 1%, CO2 emissions fall by 0.25% in the countries studied. One way to reduce CO2 emissions from agricultural production in the short term is to increase the share of renewables, which, incidentally, is in line with EU action.

First, a recently developed eigenfunction expansion technique, based in part on separation of the thickness-variable and partly utilizing a modified Frobenius type series expansion technique in conjunction with the Eshelby-Stroh formalism, is employed to derive three-dimensional singular stress fields in the vicinity of the front of an interfacial crack weakening an infinite bicrystalline superlattice plate, made of orthorhombic (cubic, hexagonal and tetragonal serving as special cases) phases, of finite thickness and subjected to the far-field extension/bending, in-plane shear/twisting and anti-plane shear loadings, distributed through the thickness. Crack-face boundary and interface contact conditions as well as those that are prescribed on the top and bottom surfaces of the bicrystalline superlattice plate are exactly satisfied. It also extends a recently developed concept of lattice crack deflection (LCD) barrier to a superlattice, christened superlattice crack deflection (SCD) energy barrier for studying interfacial crack path instability, which can explain crack deflection from a difficult interface to an easier neighboring cleavage system. Additionally, the relationships of the nature (easy/easy, easy/difficult or difficult/difficult) interfacial cleavage systems based on the present solutions with the structural chemistry aspects of the component phases (such as orthorhombic, tetragonal, hexagonal as well as FCC (face centered cubic) transition metals and perovskites) of the superlattice are also investigated. Finally, results pertaining to the through-thickness variations of mode I/II/III stress intensity factors and energy release rates for symmetric hyperbolic sine distributed load and their skew-symmetric counterparts that also satisfy the boundary conditions on the top and bottom surfaces of the bicrystalline superlattice plate under investigation, also form an important part of the present investigation.

Energy distance is used to eigenvalue distributions in this new Wishart distribution study, providing a new perspective on high-dimensional data. Multivariate statistics relies on Wishart distribution to explain covariance matrices. This analysis shows how degrees of freedom and covariance matrix configurations affect eigenvalue dispersion. We use energy distance based on eigenvalue distributions to assess Wishart distribution differences without the "curse of dimensionality" via extensive simulation simulations. This approach improves measurement accuracy and processing efficiency in distribution comparisons and enables new analytical pathways in scientific fields that need data covariance comprehension. Our results show that degrees of freedom and matrix structure considerably affect energy distances, expanding the theoretical framework and practical applications of statistical models.

Sheep’s milk is a significant source of nucleotides monophosphate (NMPs) but can also contain undesirable residues from veterinary drugs, posing a potential human health risk. This study introduces a novel application of 2D-LC, in heart-cutting mode, for the simultaneous determination of nucleotides and veterinary drug residues in sheep’s milk. Two-dimensional liquid chromatography (2D-LC) allows for the separation of these compounds in a single chromatographic run despite their differing physicochemical properties. The proposed method separates six veterinary drug residues and five NMPs in a single injection. The compounds were separated using a C18 reversed-phase column in the first dimension and a Primesep SB analytical column in the second dimension. The method performance was evaluated in terms of linearity range, detection and quantification limits, matrix effects, precision, and accuracy. The results demonstrated good linearity and sensitivity, with quantification limits allowing for the quantification of veterinary drugs at the maximum residue level and nucleotides at typical levels found in milk samples. The method has been successfully applied to the analysis of sheep’s milk samples acquired from local supermarkets, with recoveries within a range of 70-119 % and 82-117 % for veterinary residues and NMPs, respectively.

In this work we looked for a stable and accurate procedure to perform feature selection in datasets with a much higher number of predictors than individuals, as in Genome-Wide Association Studies. Due to the instability in feature selection when many potential predictors are measured, a variable selection procedure is proposed that combines several replications of shrinkage regression models. A weighted formulation is used to define the final predictors. The procedure is applied to investigate Single Nucleotide Polymorphisms (SNPs) predictors associated to Alzheimer’s disease in the Alzheimer’s disease Neuroimaging Initiative (ADNI) dataset. Two data scenarios are investigated: one considering only the set of SNPs and another with the covariates age, sex, educational level and APOE4 genotype. The SNP rs2075650 and the APOE4 genotype are given as risk factors for AD, which is in line with the literature, and other four new SNPs are pointed, opening new hypotheses for in vivo analyses. These experiments demonstrate the potential of the new method for stable feature selection.

To solve the problem of a uneven load for a dual-input counter-rotating transmission system, this research is based on Small Displacement Torsor Jacobian-Torsor theory.Considering the influence of the local parallel chain of a gearbox on the calculation accuracy of the backlash,the processing method of a meshing tooth pair parallel chain is proposed. The probability distribution of the tolerance band of gearbox manufacturing and assembly errors is modeled, the three-dimensional tolerance analysis model of a dual-input counter-rotating gearbox is established, and the Monte Carlo method is used to solve the backlash of the output end. The output backlash is solved with the Monte Carlo method, and the theoretical design backlash is combined with the output backlash to derive the gearbox side clearance range. The gearbox shaft system is analyzed statically, the range of the equal load coefficient is calculated, and the equal load performance optimization method based on the matching relationship between the gear backlash and the elastic shaft torsional stiffness is proposed. The correctness of the three-dimensional tolerance analysis combined with the elastic deflection angle used to optimize the equal load performance of the dual-input counter-rotating transmission system is verified by comparison with the relevant test data.

This research focuses on exploring generalized electron emissions in one-dimensional conducting materials, specifically examining field emission and thermionic emission. Additionally, the study investigates one-dimensional thermal radiation. The current density is derived for both one-dimensional thermionic emission and one-dimensional field emission. A generalized current density in one dimension is devised, taking into account arbitrary accelerating fields and temperature conditions. The formula for the one-dimensional current density is depicted with accelerating field and temperature across various work function materials. The current is also represented as a function of both temperature and electric field for various temperatures.

The three-pronged sliding universal coupling is a new kind of three-pronged coupling. To study its kinematic and dynamic characteristics, directional cosine matrices are utilized as tools to analyze the coordinate systems by establishing simplified geometric models and corresponding motions. Through the analysis of the motions of the input and output shafts and employing the method of single-force element, a set of equilibrium equations for the forces acting on the output shaft and the input shaft are formulated for solution analysis. The research indicates that during the rotation process, there exists a small angular difference between the input shaft and the intermediate shaft, demonstrating the quasi-constant angular velocity characteristics of the new tripod sliding universal coupling. The curves of the forces and torques acting on each component of the coupling approximate sinusoidal curves. The optimal operating angle range for the new tripod universal coupling is at angle , during which the system exhibits good transmission and mechanical performance.

The aim of the present work is to explore a different non-traditional approach to higher dimensional spacetime which differs from the usual approaches such as large extra space dimensions, warped extra space dimensions and multiple extra time dimensions. This approach will be referred to as "Dynamic" higher dimensional spacetime. By this term will be introduced a new kind of higher dimensional spacetime (more than the usual 3+1 dimensions), which behaves dynamically in the sense that time is not a fixed dimension but is the extra plus one dimension to the spatial dimensions of the phenomenon we are studying. For a higher dimensional phenomenon, the lower dimensional time will behave as space and the new time will be a plus one dimension, meaning if we study a 3-dimensional phenomenon then time is the usual 4th dimension but if we are studying a 4-dimensional phenomenon then its time will be an extra 5th dimension and for this phenomenon the 4th dimension will behave as space. All spatial dimensions are indistinguishable from each other and behave in the exact same way, which means that the important parameter is not the dimension we are studying (for example the 4th or the 5th) but the number of dimensions in which a phenomenon can interact and how higher dimensional phenomena interact with lower ones. This non-traditional approach to higher dimensional spacetime may appear confusing at first but will become clearer in the rest of the paper. For this reason, we will first associate the time dimension with propagation relative to space dimensions and change orthogonal to them in order to give the reader a better intuition. Following that, we will describe how higher - dimensional phenomena would appear to us, the 3 - dimensional observer and try to set some guidelines on how we should mathematically model higher - dimensional phenomena in order for us to make accurate predictions of their behaviour. Then we will express the general framework of a "Dynamic" higher - dimensional spacetime and turn our gaze to 4+1 – dimensional phenomena. We will explore how a mass term arises for 4 – dimensional waves by generalizing the frequency – speed – wavelength relation of 3D waves and give a basic description on a possible way to describe a 4+1 – dimensional disturbance in a scalar Energy Field in order to make accurate predictions on our 3+1 dimensional plane. In this framework we will derive the Klein – Gordon equation by generalizing the wave equation and ignoring all lower than 4+1 - dimensional disturbances and compere our mass term findings with the Klein – Gordon equation mass term and with results from gravity – induced quantum interference experiments. Lastly, we will describe how a "Dynamic" 5 - dimensional spacetime would appear in a 3D observer and examine whether our findings are compatible with quantum mechanics and the special theory of relativity.

Using multi-color flow cytometry analysis, we studied the immunophenotypical differences between leukemic blasts/LSCs from patients with AML/MDS and hematopoietic stem and progenitor cells (HSPCs) from patients in complete remission (CR) following their successful treatment. The panel of markers included CD34, CD38, CD45RA, CD123 as representatives for a hierarchical hematopoietic stem and progenitor cell (HSPC) classification as well as programmed death ligand 1 (PD-L1). Rather than restricting the evaluation on a 2- or 3-dimensional analysis, we applied a t-distributed stochastic neighbor embedding (t-SNE) approach to get a deeper insight and segregation between leukemic and normal HPSCs. For that purpose, we created a t-SNE map, which resulted in the visualization of 27 cell clusters based on their similarity concerning the composition and intensity of antigen expression. Two of these clusters were “leukemia-related” containing a great proportion of CD34⁺/CD38^- hematopoietic stem cells (HSCs) or CD34⁺ cells with a strong coexpression of CD45RA/CD123, respectively. CD34⁺ cells within the latter cluster were also highly positive for PD-L1 reflecting their immunosuppressive capacity. Beyond this proof of principle study, the inclusion of additional markers will be helpful to refine the differentiation between normal HSPCs and leukemic cells, particularly in the context of minimal disease detection and antigen-targeted therapeutic interventions. Furthermore, we suggest a protocol for the assignment of new cell ensembles in quantitative terms, via a numerical value, the Pearson coefficient, based on a similarity comparison of the t-SNE pattern with a reference.

A power factor correction (PFC) rectifier is compulsory for any electronic circuitry connected to the electrical grid. The majority of PFCs that are utilized are based on a step-up converter that supplies high DC voltage at its output terminal. Electric vehicle battery voltage varies from a few hundred to almost a thousand volts. Thus, an additional conversion step is obligatory when designing an all-brand universal charger. This paper presents a new type of front-end versatile step up / down three voltage level PFC. The rectifier can operate in continuous conduction mode or discontinuous conduction mode. First, the rectifier’s principle of operation is described, and then an innovative rectifier is analyzed for continuous and discontinuous conduction modes. Finally, the proposed theory is experimentally validated in a multiplier-less dual loop mode at discontinuous conduction modes. It was shown that although there is no multiplier use in the control circuitry, the power factor is near unity. It was revealed that the rectifier could swing the output voltage from 50 V to 900 V while the input voltage was 230 Vrms. The innovative topology suits any standard PFC rectifier, three-level voltage supplement, and dual-stage low-voltage power supply.

Pedestrian Dead Reckoning (PDR) is a promising algorithm for indoor positioning. However, the accuracy of PDR degrades due to the accumulated error, especially in multi-floor buildings. This paper introduces a three-dimensional (3D) positioning method based on terrain feature matching to reduce the influence of accumulated error in multi-floor scene. The proposed method involves two steps: motion pattern recognition and position matching calibration. The motion pattern recognition aims to detect different motion states, i.e., taking the stairs or horizontal walking, from the streaming data. Then, stair entrances and corridor corners are matched with transition points of motion states and pedestrian turning points, respectively. After matching, calibration is performed to eliminate the accumulated errors. By carrying out experiments on a two-floor closed-loop path with a walking distance about 145 m, it is shown that this method can effectively reduce the accumulated error of PDR , achieving accurate 3D positioning . The average error is reduced from 6.60 m to 1.83 m.

The swift progress in low-dimensional nanocarbons, specifically in the realm of the authentic one-dimensional carbon chain featuring sp¹ hybridization, has unveiled exciting prospects for integrating them into cutting-edge technologies across diverse industries. To fully harness their potential, precise control over the growth process is necessary to obtain desired nanostructure and functionality. However, optimizing properties through traditional approaches has been challenging due to complex interactions. To address this, we implement a focused data-driven strategy for nanocarbon inverse design by leveraging a state-of-the-art data-driven nanocarbon genome approach (NCGA). By uncovering relationships between growth parameters and resultant traits, this serves as an expedient catalyst in engineering nanostructures with tailored attributes. We introduce an extensive array of technological approaches aimed at precisely controlling the growth process. These methods encompass stimulating and precisely adjusting synergistic effects, coordinating atomic vibrations at a collective level through the implementation of multilayer nano-interfaces, harnessing the potential of active screen plasma surface engineering, utilizing nano-patterning and allotropic phase transformations, incorporating heteroatom doping, and effectively directing self-assembly. These aim to unlock nanomaterials' latent potential and reveal novel neural pathways within the data-driven NCGA, enhancing its predictive capabilities. Specifically, triggering self-organization during growth can potentially unlock previously unexplored neural pathways. The data-driven NCGA offers a paradigm shift, accelerating discovery and design of nanocarbons with optimized, application-specific properties.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

This paper explores the Wilsonian renormalization group of probe-D-branes, focusing on the Dirac-Born-Infeld (DBI) action. The analysis involves deriving the constant of motion and formulating the Wilsonian renormalization group equation. The study extends to the infrared regime, considering both zero and non-zero temperatures. Additionally, thermodynamic aspects related to the functional form of the grand potential are discussed. The investigation provides insights into the renormalization of probe-D-branes and their thermodynamic properties.

The goal of the current study is to analyse several nonlinear two-dimensional time-fractional Rosenau Hymanequations. The two-dimensional fractional Rosenau-Hyman equation has extensive use in engineering and applied sciences. The fractional view analysis of two-dimensional time-fractional Rosenau-Hyman equations is discussed using the homotopy perturbation approach, adomian decomposition method, and Yang transformation. Some examples involving two-dimensional time-fractional Rosenau-Hyman equations are provided in order to better understand the suggested approaches. The solutions appear as infinite series. We offer a comparison between the accurate solutions and those that are generated employing the proposed approaches in order to demonstrate the effectiveness and applicability of the proposed techniques. The results are graphically illustrated using 2D and 3D graphs. It has been noted that the obtained results and the targeted problems real solutions are quite similar. Calculated solutions at various fractional levels describe some of the problems useful dynamics. Other fractional problems that arise in other fields of science and engineering can be solved using a modified version of the current techniques.

The terrain of Changbai Mountain has great influence on the distribution of atmospheric flows and the occurrence and development of precipitation. This study quantitatively analyzes the regional characteristic of topographic perturbations and the relationship between terrain, wind and precipitation in Changbai Mountain region, by using a spectral analysis of the two-dimensional discrete cosine transform. Three domains with relatively heavy summer precipitation are selected as the study region. The results indicate that the overall terrain of Changbai Mountain region exhibits anisotropic characteristics. The terrain spectra of domain B are less than those of domain A and C across the whole wavelength (λ) bands, indicating that the large-scale topographic perturbations of domain B are relatively weak. The largest topographic spectral peak of domain C shows the most pronounced undulation of terrain among the three domains. The dominant wavelengths of terrain height variance for domain A and C, both close to the respective maximum wavelengths, indicate more prominent large-scale topographic perturbations. For domain A, the variation of the precipitation spectra is consistent with that of the wind spectra at the wavelength bands of λ < 390 km, showing a high correlation between wind field and the occurrence of rainfall. The inverse relationship at larger wavelengths indicates that multiple factors contribute to the occurrence of rainfall. For domain B, there is consistency in the fluctuations of terrain spectra, precipitation spectra, and wind spectra at the wavelength bands of λ < 278.3 km, implying that the smaller-scale terrain has an important effect on the occurrence of summer precipitation. For domain C, the variations of terrain spectra, precipitation spectra, and wind spectra are almost consistent across the whole wavelength bands, indicating that the large-scale terrain and minor terrain both play a crucial role in atmospheric uplift and the occurrence and development of summer rainfall.

Ridge regression is one of the most popular shrinkage estimation methods for linear models. Ridge regression effectively estimates regression coefficients in the presence of high-dimensional regressors. Recently, a generalized ridge estimator was suggested by generalizing the uniform shrinkage of ridge regression to the non-uniform shrinkage, which was shown to perform well under sparse and high-dimensional linear models. In this paper, we introduce our newly developed R package “g.ridge” (the first version published on 2023-12-07 at https://cran.r-project.org/package=g.ridge) that implements both the ridge estimator and generalized ridge estimators. The package equips with the generalized cross-validation for automatic estimation of shrinkage parameters. The package also includes a convenient tool for generating a design matrix. By simulations, we test the performance of the R package under sparse and high-dimensional settings with the normal and skew-normal error distributions. From the simulation results, we conclude that the generalized ridge estimator is superior to the benchmark ridge estimator based on “glmnet”, and hence, it can be the most recommended estimator under sparse and high-dimensional models. We demonstrate the package using the intracerebral hemorrhage data.

Ontological premises have profound theoretical implications. This analysis posits a single, universal structure composed of two ontic entities: discrete, quantized units of space ("Planck Spheres") and a State of Absolute Nothingness (the "SOAN"). Planck Spheres and the SOAN form a physical, ultra-high (3 x N) dimensional space ("Planck Space") and a discrete 3D space (or "4D spacetime"). Together, Planck Space and 4D spacetime form a tightly integrated ((3 x N) + 3) hyperspace (the "Dual Ontology"). Critically, a one-to-one mapping and identity exists between the Planck Spheres comprising 4D spacetime and Planck Space. The Dual Ontology conjecture supports radically simplified physical explanations for three distinct areas of theoretical physics: First, the conjecture reconciles fundamental issues in Special Relativity and quantum mechanics by explaining why single and N-body quantum states (a) dynamically evolve in 4D spacetime subject to the speed of light and (b) instantaneously collapse in a physical Planck Space that is not subject to Special Relativity. Second, the temporal and physical asymmetry between the dynamic evolution of quantum states in 4D spacetime subject to Special Relativity and the instantaneous collapse of quantum states in a Planck Space where Special Relativity is inapplicable explains quantum path irreversibility and the arrow of time. Third, at or near the heat death of 4D spacetime, the instantaneous collapse of the energy content of Planck Space and the simultaneous transition of 4D spacetime's extremely widely dispersed energy content to a non-singular, generally localized volume at t = 0 explains 4D spacetime's isotropy, homogeneity, extremely high energy, pressure, and temperature, flatness, and low gravitational entropy. It also explains the horizon and fine-tuning problems.

Background Robotic-assisted thoracic surgery (RATS) has become the standard procedure for treating lung malignancy, offering numerous advantages over traditional methods. However, the minimally invasive nature of RATS presents unique challenges, notably a limited field of view and reduced tactile feedback for the surgeon. These limitations can significantly impact the surgeon's ability to navigate complex anatomical structures during surgery. To address these challenges, we recognized the need for enhanced preoperative familiarization with each patient’s specific anatomy. In response, we developed an innovative virtual reality (VR) surgical navigation system, utilizing head-mounted displays (HMDs), designed to provide surgeons with a more comprehensive and interactive understanding of the patient's anatomy before the actual surgery. This VR system aims to extend the surgeon's capabilities in both preoperative sumilation and intraoperative navigation, potentially mitigating the inherent limitations of RATS. Methods Three-dimensional (3D) volumized data, meticulously extracted from preoperative computed tomography (CT) scans, were seamlessly integrated into the Perspectus VR Education software. These data were then displayed on an HMD, facilitating an interactive 3D reconstruction of pulmonary structures. This reconstruction allowed for each structure to be visualized individually, enabling detailed preoperative resection simulations tailored to each patient's unique anatomy. During RATS, these specific 3D reconstructed images were made accessible within the surgeon's console through the TilePro™ multi-display input, providing real-time, intraoperative guidance. Results During the preoperative simulations, the 3D anatomic structure of the pulmonary vessels, as well as the spatial relationship between the lesion and adjacent tissues, were successfully visualized. This enhanced visualization enabled surgeons to thoroughly evaluate a patient's anatomy, significantly improving the safety and accuracy of the subsequent procedure. The integration of VR software with the HMD provided surgeons with a true 3D interaction with real patient data. This setup not only offered a unique perspective through the surgical console but also allowed for easy manipulation of the data on a mobile laptop, thereby enhancing the overall surgical planning process. Conclusions VR simulation using HMDs offers a viewing experience akin to that of a robotic 3D viewer, potentially providing an innovative avenue for surgeons to develop specific robotic surgical skills without the need for practicing on live patients. This technology, when integrated with routine imaging modalities, can significantly enhance preoperative planning. Such integration not only contributes to the safety but also improves the accuracy of anatomical resections. Our findings suggest that VR simulation with HMDs is particularly effective in assisting surgeons to accurately identify lesion locations in RATS, thereby optimizing surgical outcomes.

Mechanical properties of three-dimensional (3D) printed resin can be improved by decreasing oxygen concentrations during post-curing, and nitrogen saturated post-curing has been applied for the purpose. This study aimed to evaluate and compare the color stability of 3D printed resin crowns post-cured in normal air and nitrogen saturated conditions. Crowns were fabricated with 3D printer and post-cured in normal air (control group; air) or nitrogen saturated condition (experimental group; nitrogen). The specimens in each group were subdivided into four subgroups, each exposed to different discoloration agents: distilled water, coffee, wine, and curry. Post-immersion color changes were measured using a digital spectrophotometer and analyzed through repeated measures ANOVA. Fourier Transform Infrared (FT-IR) spectroscopy evaluated the degree of conversion of resin over immersion times for both post-curing conditions. Comparing the effects of post-curing conditions, significant difference was found between control and experimental groups over immersion time in wine and curry subgroups. In FT-IR analysis, there was significant difference in degree of conversion between the air and nitrogen groups from 10 to 300 seconds. The findings suggest that nitrogen saturated post-curing can potentially enhances the conversion rate of 3D printed resin crowns, thereby improving their color stability.

Multiple valued logic (MVL) has proven to be a better alternative and solution to the challenges of traditional binary logic because of its fast computation, fewer interconnects, and small die. It is a potential candidate for future digital design because it allows energy-efficient, low-complex, and high-speed circuits. This paper introduces a new resistive-load two-dimensional field effect transistor-based ternary logic gates, wherein the active resistive-load is replaced with memristor to reduce chip area and power consumption. A memristor-based standard ternary inverter (STI) is designed using GNRFET, MoS2 FET, WSe2 FET, and FinFET, and their performance metric such as delay, power, and energy delay product (EDP) are analyzed. The results show that the proposed design achieves 50% reduction in circuit area, the average power dissipation is reduced by 11.7X, 6.8X, and 3.2X for GNRFET, MoS2 FET, and WSe2 FET, respectively, as compared to FinFET-based ternary inverter. Moreso, other logic gates such as OR, NOR, AND and NAND were simulated in T-SPICE EDA tool, and the results and truth table are in good agreement.

There is a growing concern about climate change being a serious threat to insurance business sustainability. Knowing if climate warming is a cause for claims and losses increase, and how this cause-effect relationship will develop in the future are two open major questions. In this article, we answer both questions by particularizing the geographical area of Spain, and a precise risk, hailstorm in crop insurance in the line of business of wine grapes. We measure climate change thanks to the Spanish Actuarial Climate Index (SACI). We use a database containing all the claims caused by hail risk in Spain from 1990 to 2022. With homogenized data, we consider as dependent variables the monthly number of claims, monthly number of loss costs equal to one, and monthly total losses. The independent variable is the monthly Spanish Actuarial Climate Index (SACI). We try to explain the former through the latter using regression and quantile regression models. Our main finding is that climate change as measured by SACI explains these three dependent variables. We also give an estimation of the increase of the monthly total losses Value at Risk, corresponding to a future increase in climate change measured in units of SACI. Spanish crop insurance managers should carefully incorporate these conclusions into their decision-making process to keep this line of business sustainable in the future.

There are many applications of anomaly detection in IoT domain. IoT technology consists of large number of interconnecting digital devices not only generating huge data continuously but also making real-time computations. Since IoT devices are highly exposed due to Internet, they frequently meet with the challenges of illegitimate accesses in the form of intrusions, anomaly, fraud, etc. Identifying these illegitimate accesses in IoT domain can be an exciting research problem. In numerous applications fuzzy clustering and rough set theory have been successfully employed. As the data generated in IoT domains are high-dimensional, the clustering methods used for lower dimensional data cannot be applied efficiently. In this article, mixed approaches consisting of nano topology and fuzzy clustering techniques are proposed for anomaly detection. First of all, the nano topology is generated to find lower dimensional apace and then a couple of well-known fuzzy clustering techniques are employed on it for the efficient anomaly detection. The effectiveness of the proposed approaches is evaluated using time-complexity analysis, experimental studies with a synthetic dataset and a real-life dataset along with comparative studies with traditional fuzzy clustering approaches namely fuzzy c-means clustering (FCM) algorithm, Gustafson-Kessel (GK) Algorithm, Gath-Geva (GG) Algorithm, Mahalanobis Distance based Fuzzy C-Means algorithm (M-FCM), and Common Mahalanobis Distance based Fuzzy C-Means algorithm (CM-FCM). Experimentally, it has been found that the proposed approaches outperform the aforesaid algorithms in terms of detection rates, accuracy rates, false alarm rates and computational times.

Gastric mixing relies both on enzymatic reactions and mechanical solicitations through antral contraction waves (ACWs). This work aims at discussing the influence of ACWs frequency and digesta viscosity on human gastric mixing efficiency by comparison with that of a soft elastic reactor (SER), that induces mixing by vibration of its wall in a similar way as ACWs promote stomach motility. The SER mixing curve was considered in order to determine the flow regime reached during gastric mixing. The number of contractions required to achieve homogenization was found to depend on the viscosity of the stomach content and the ACWs amplitude. Moreover, it was deduced from the same range of SER and ACWs operating points that the mechanical solicitation provided by human peristalsis was low and that gastric mixing was confined in laminar regime. Further efforts are needed to investigate the role of other physiological processes such as gastric secretions and emptying in overall gastric mixing performance.

The three-axis stabilization geostationary remote sensing satellite features high time resolution and plays a significant role in weather forecasting and environmental monitoring. With the future development of satellite technology, we propose a rapid geolocation algorithm for three-axis stabilization geostationary remote sensing satellites from the perspective of software algorithms. The method assesses conventional remote sensing data geolocation methods and the coordination group for meteorological satellites (CGMS, Coordination Group for Meteorological Satellites) nominal grid publishing format. First, the initial incident viewing vector of each detector on the sensor was constructed. Then, by reflecting the satellite platform's east-west and north-south mirrors, the outgoing vector of the payload coordinate system was obtained. This vector was converted to the satellite body coordinate system and, subsequently, to the satellite orbit coordinate system. The conversion of the earth-fixed coordinate system and the mirror rotation angle under ideal CGMS grid conditions were obtained, and the mirror rotation angle was converted to the corresponding CGMS nominal grid position to complete data positioning. The algorithm omits the complex process of calculating the intersection point between the viewing vector and the geodetic ellipsoid sphere from the earth-fixed coordinate system, resulting in an 83.3% improvement in computational efficiency without compromising positioning accuracy compared with conventional geolocation methods. This algorithm greatly improves the processing efficiency of geostationary meteorological satellite positioning, but its limitation is that the data processing results must be released in the CGMS nominal grid format.

Recent advances in nanomaterials have been heavily influenced by low-dimensional nanocarbon allotropes. In particular, carbyne has attracted attention for its potential as a true one-dimensional carbon chain with sp1 hybridization. To maximize the capabilities of this material, we employ a focused data-driven inverse design approach based on the carbon nanomaterials genome concept. This involves using deep learning neural network models to identify key descriptors tied to desired properties, enabling property prediction and reverse engineering of nanocarbons. Our iterative approach entails: (i) gathering growth/property data on nanostructures; (ii) identifying informative numerical/categorical predictors; (iii) developing deep learning models mapping descriptors to properties; (iv) refining models with new insights; (v) determining required descriptors/conditions for target properties via inverse mapping; (vi) validating models by synthesizing predicted nanostructures; and (vii) enhancing models with validation data. This allows uncovering hidden growth-property connections, precisely tuning nanocarbons for desired attributes. We introduce new methodologies including exciting synergistic effects, synchronizing atomic vibrations, active screen plasma, energy-driven transformations, surface acoustic micro/nano-manipulation, doping and directed self-assembly to expose relationships and integrate insights into the inverse design flow. This research promises to accelerate discovery of next-generation low-dimensional nanocarbons with exceptional properties and applications.

Adjustable frequency converters are widely used in modern automated asynchronous drives due to their high efficiency and high responsiveness control strategies. Control algorithms for frequency converters are divided into two main groups: constant frequency commutation and random frequency commutation. Research results on various commutation frequencies, conducted using Matlab/Simulink software, show that the use of random frequency commutation provides better efficiency by significantly reducing the amplitude of harmonic oscillations at different frequencies, with a reduction in switching losses in transistors. Currently, a converter with random frequency commutation is being researched and applied in the direct torque control system. The traditional direct control system, based on a two-level inverter, is widely used due to its simplicity and ease of implementation. However, this method results in the output current of the two-level converter, supplied to the motor stator, having many harmonic distortions, leading to strong torque fluctuations regardless of the rotation speed and ultimately causing additional losses in the stator windings and reducing the motor's lifespan. To enhance the efficiency of automated asynchronous drives, this article presents an algorithm for a direct control system based on a three-level NPC inverter using a PI speed regulator. The proposed algorithm's research is validated in the Matlab/Simulink environment. The research results show that in the absence of harmonic filtering, the stator current harmonic distortion coefficient is approximately 11%, and torque fluctuations are less than 5% of the resistive torque. The fluctuations between the two capacitors remain almost unchanged throughout the entire process.

This paper proposes a new cascaded three-phase multilevel inverter based on a cascaded submultilevel and single-phase H-bridge structure. The voltage source of the traditional single-phase H-bridge is replaced with the proposed cascaded submultilevel. Cascading two basic units forms the submultilevel unit. The basic and submultilevel units generate positive voltage only hence the need for an H-bridge to generate both polarities of load voltage. Five algorithms of source voltage are proposed and the optimal algorithm selected to generate the peak load voltage and output levels. The proposed three-phase cascaded multilevel inverter generates 31-level of load voltage. The per-phase component distribution of the proffered inverter are twelve power switches, four dc sources and twelve driver circuits accordingly. Principal merits of the proposed 31-level inverter are high quality load waveforms, production of high levels of load voltage utilizing reduced component quantity, simple cascaded layout, decreased inverter area, minimal inverter cost, minimum inverter losses and utilizing lower rated switches. Also, the proposed topology is highly suited for employment in PV-DVR and other power systems. To validate these advantages of the proposed multilevel inverter, contrastive analysis of the proposed inverter and existing topologies are studied. Finally, the functionality of the proposed inverter is substantiated by simulation in PSCAD/ EMTDC software.

Liquid argon technology is widely used by many previous and current neutrino experiments, and it is also promising for future large-scale neutrino experiments. When detecting neutrinos using liquid argon, many hadrons are involved, which can also interact with argon nuclei. In order to gain a better understanding of detection processes, and to simulate neutrino events, knowledge of hadron-argon cross sections is needed. This paper describes a new procedure, which improves upon the previous work with a multi-dimensional unfolding, to measure hadron-argon cross sections in a liquid argon time projection chamber. Through a simplified version of simulation, we demonstrate the validity of this procedure.

There are two major proposed forms of dark energy: the cosmological constant and the quintessence scalar field. However, the former has the cosmological constant problem and the latter does not have observational evidence to support the theory. In this paper, we propose a dark energy paradigm to solve the cosmological constant problem and to predict lepton masses as evidence. The dark energy paradigm is a unified dark sector that is based on Planck's dimensional analysis, Lambda CDM, energy conservation, and force equilibrium. We find that dark energy can vary from the cosmological constant to the Planck scale. We find that the predicted values of lepton masses based on the dark energy paradigm agree with the observed values to 1% (or within error ranges).

Two-dimensional (2D) materials show advantages as surface enhanced Raman scattering (SERS) substrate over traditional noble metals, such as chemically inert flat surface and uniform SERS signals. However, due to the low density of free electrons of 2D materials, the enhancement mechanism is mainly restricted to chemical enhancement instead of electromagnetic enhancement, resulting a relatively low enhancement factor. In this work, we reported a sensitive 2D SERS substrate based on chemical vapor deposited MoN nanosheets with electromagnetic enhanced mechanism. The maximum enhancement factor can be 3×105 with a limit of detected concentration of 4×10-8 M, which is comparable to those noble metal SERS substrates without hot spots. The MoN nanosheets show strong surface plasmon resonance in the visible range as evidenced by the UV-vis absorption spectroscopy and first principle calculation. The MoN nanosheets SERS substrates exhibit excellent Raman signal uniformity within the nanosheets. Meanwhile, Distinct thickness dependent sensitivity was observed in the MoN nanosheets, higher sensitivity can be achieved by decreasing the thickness of the nanosheets. Furthermore, the MoN nanosheets SERS substrate also show high thermal stability, which can be annealed in air for more than 300℃ and maintain its SERS activity. The high stability of MoN nanosheets allows it to be reused for 20 cycles without obvious signal decay. The high sensitivity, stability, reusability and Raman signal uniformity, makes MoN nanosheets an ideal 2D SERS substrate for practical applications.

Prenatal diagnosis of clubfoot traditionally relied on two-dimensional ultrasonography. To enhance diagnosis and predict postnatal outcomes, we examined the parameters that differentiate pathological clubfoot using three-dimensional ultrasonography. In our retrospective study, we examined the findings of prenatal ultrasound and the postnatal outcomes of pregnancies with suspected congenital clubfoot between 2018 and 2021. Based on the three-dimensional perspective, we measured the angles of varus, equinus, calcaneopedal block and forefoot adduction and compared the sonographic variables between the postnatal treated and non-treated groups. We evaluated 31 pregnancies (47 feet) with suspected clubfoot using three-dimensional ultrasonography. After delivery, a total of 37 feet (78.7%) underwent treatment involving serial casting only or additional Achilles tenotomy. The treated group showed significantly greater hindfoot varus deviation (60.5° vs. 46.6°, p=0.026) and calcaneopedal block deviation (65.6° vs. 26.6°, p &lt;0.05) compared to the non-treated group. The calcaneopedal block had an area under the curve of 0.98 with a diagnostic threshold of 46.2 degrees (sensitivity of 97%, specificity of 90%, positive predictive value of 97%, and negative predictive value of 90%). During prenatal evaluation of clubfoot using three-dimensional ultrasonography, the calcaneopedal block deviation has the potential to predict postnatal treatment.

A mathematical model that comprehensively captures the real behavior of riverbed deformation, encompassing all pertinent effects, is developed. The underwater slope reformation process, with the generatrix aligned along the flow velocity in the model, is considered. A numerical model is introduced to calculate the flow involving a deformable bottom, and the model's validation is established through rigorous analysis of experimental findings. The research firmly confirms the suitability of the proposed mathematical and numerical model for describing deformations in uneven and unsteady river flows, including the movement of dredging slots and channel quarries. The model's minimal equation count and reliance on empirical constants demonstrate the model's efficiency. The model's predictions align strongly with experimental data, although optimal values of empirical coefficients vary slightly across different experiments. Hence, there is a call for further investigation to derive more universally applicable closure relationships for the model. The importance of validating the model with reliable field data and its potential extension to accommodate hydraulically diverse soils is emphasized. Such an extension is feasible due to the concentration transfer equation, enabling independent calculations for particle fractions of varying sizes as long as the total particle concentration in the stream remains within reasonable limits. This dedicated research contributes significantly to understanding riverbed deformations and advancing accurate modeling and management of riverine environments.

This study presents a method for the rapid and accurate generation of two-dimensional (2D) orthomosaic maps using selected image data collected by drone-captured video. The focus is on developing a real-time method capable of creating maps more quickly than image selection. The scale-invariant feature transform (SIFT) algorithm is applied to drone images to extract features in various scale regions. For feature point matching, a matching method based on the fast library for approximate nearest neighbors (FLANN) was adopted. A comparison of the computational speed of the FLANN with that of the k-nearest neighbors (KNN) and brute force matcher during the matching process revealed FLANN's superior capability for real-time data processing. The random sample consensus (RANSAC) algorithm was employed to enhance the accuracy of the matching by removing outliers, effectively identifying and eliminating mismatches, and reinforcing the reliability of feature point matching. The combination of SIFT, FLANN, and RANSAC algorithms demonstrates the capacity to process drone-captured image data in real time, facilitating the generation of precise 2D orthomosaic maps. The proposed method was assessed and validated using imagery obtained as the drone executed curvilinear and straight flight paths, confirming its accuracy and operational efficiency concurrently with the image capture process.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).

In this paper, we investigate a financial market in which asset prices evolve based on a multi-dimensional Brownian motion process and a multi-dimensional Poisson process with different credit and deposit rates We proceed to evaluate European options by establishing upper and lower hedging prices through a transition to a suitable auxiliary market. Additionally, we address the minimization of shortfall risk and no-arbitrage price bounds in incomplete markets within this framework.

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. While a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous-driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming inputs characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming inputs characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming inputs features, we introduce a backbone switcher based on the identified streaming inputs characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming inputs features.

This work is devoted to the construction and study of commutative gates for a two-qubit quantum system. Using four-dimensional algebra developed by the Kazakh mathematician Abenov M.M. all groups of commutative gates have been constructed, and among all states of a two-qubit quantum system, unitary states with which a specific gate is connected have been identified. An explicit type of gate is described that transfers a quantum system from one unitary state to another unitary state. The proposed approach opens up new possibilities for the design of quantum algorithms not only for two-qubit quantum systems, but also for $n$-qubit quantum systems.

The spatial and temporal variability in dietary preferences of juvenile three-spined stickleback Gasterosteus aculeatus, was studied across typical coastal habitats of Keret Archipelago, Kandalaksha Bay, the White Sea. Sampling of fish using beach seine was implemented in late July-early August and in late August, and zooplankton samples were collected to make a quantitative evaluation of abundance of potential prey items. Similarity Percentage (SIMPER) analysis implemented over the data obtained from stomach content analysis revealed five planktonic taxa mostly contributing to the diet of juvenile sticklebacks, of which copepod Temora longicornis was the most important prey item at marine sites, while Acartia longiremis prevailed in diet of fishes in marine lagoon. Calculation of selectivity index demonstrated that some taxa (e.g., A. longiremis) were always avoided by juvenile fishes, others (e.g. Microsetella norvegica) were firstly avoided and then selected in late August. Calculation of selectivity index was implemented per sample and per individual fish indicated that taxa like T. longicornis and were selected during the whole period of study, fishes demonstrated pronounced individual variation in prey selectivity. In general, data on selectivity indicated that despite juvenile sticklebacks feed on mostly available prey items, they can demonstrate taxonomic and size-specific prey selectivity.

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

Introduction Cone Beam Computed Tomography (CBCT) scans should be digitally oriented using a reference plane before being evaluated for patient growth and treatment outcome. Current orientation planes are usually dependent on the Porion points. Using more reliable landmarks than the Porion, we introduce the Acta plane and investigate its reliability as an alternative reference plane for cases in which the Porion point is inaccessible. Methods To test its reliability, a dental student and an orthodontist located sixteen skeletal and dental landmarks in sixteen CBCT scans, before and after reorientation. Also, four measurements were calculated based on these landmarks’ coordinates. The reorientation of these CBCT scans with the Acta plane was done by another dental student. Intraclass Correlation Coefficient (ICC) was calculated before and after orientation, intra- and inter-examiner. The data were tested for significance through the Shapiro Wilk test with a threshold set at 0.05. Through the ICC variation of the points’ coordinates, this article also evaluated the reliability of this plane with the t-paired and exact-sign tests. Results: The identification of landmarks was more reliable in the reoriented CBCT images than in the original images, with a significant increase of 0.012 in the median of the ICC. Linear and angular measures showed no significant differences between groups. Conclusion This plane is a reliable alternative for the orientation of CBCT images. The design of the Acta plane might allow the use of a smaller field of view and, consequently, a reduction in the radiation dose.

In this paper, intrusion detection systems are thoroughly investigated utilizing the CSE-CIC-IDS-2018 dataset. The research is divided into three key phases: first, applying Data Cleaning, Exploratory Data Analysis, and Data Normalization techniques (min-max and z-score) for preparing data across distinct classifiers. Second, feature importance is reduced using a combination of Principal Component Analysis (PCA) and Random Forest (RF), with the goal of improving processing speed and decreasing model complexity. This stage comprises a comparison with the entire dataset. Finally, machine learning algorithms (XGBoost, CART, DT, KNN, MLP, RF, LR, and Bayes) are applied to specific features and preprocessing approaches. Surprisingly, the XGBoost, DT, and RF models outperform in both ROC values and CPU runtime. Following evaluation, which includes PCA and RF feature selection, an optimal set is produced.

Multiphoton microscopy (MPM) is a cutting-edge nonlinear microscopy technique known for its exceptional capabilities in imaging dense tissues, making it an ideal tool for exploring human kidney tissue. This study delves into the application of MPM in evaluating renal cell tumors and characterizing tumor capsules. MPM encompasses two core methods: two-photon excitation fluorescence (TPEF) and second harmonic generation (SHG). It leverages femtosecond lasers to provide deep tissue imaging while minimizing phototoxicity, making it well-suited for live tissue analysis. The study analyzed 100 formalin-fixed paraffin-embedded (FFPE) kidney tissue samples, ensuring compliance with ethical standards. Using a Ti:Sapphire laser for femtosecond pulses and a variety of optical components and filters, the researchers captured TPEF and SHG signals, facilitating image analysis. Normal kidney tissue exhibited distinctive structures like glomeruli and tubules under MPM. Renal cell carcinoma (ccRCC) displayed unique TPEF patterns with eccentrically located nuclei and indistinct cytoplasmic membranes. Papillary RCC (pRCC) exhibited a papillary pattern with dark nuclei, while chromophobe RCC (chRCC) showcased distinct features like thick cell membranes, strong TPEF signal in nuclei, and granular cytoplasm. Renal oncocytoma (RO) presented strong TPEF signals in cuboidal cells with microcyst-like arrangements. Overall, MPM proved invaluable in assessing human kidney tissue, offering distinct TPEF patterns for different renal cell tumor types. This non-invasive technique has the potential to enhance pathological examinations and advance our understanding of complex biological processes, ultimately contributing to improved diagnostics and treatment strategies for renal diseases.

Agricultural drainage canals that connect upstream fish spawning areas to downstream rivers and lakes serve as crucial habitats for migrating fish. However, disconnections, such as drops and chutes, have been constructed in these canals due to agricultural modernization and flood control measures, hindering the movement of fish with limited swimming abilities. Portable fishways offer a promising solution to reconnect water bodies in agricultural canals, as they can be easily removed during high water discharges to avoid impeding the canals' drainage function. In addition to experimental assessments of fishway functionality, employing a hydrodynamic model to explore effective placement strategies for portable fishways is essential to maximize their effectiveness. This study presents a method to determine the best horizontal location of a portable fishway in an agricultural drainage canal using two-dimensional hydrodynamic simulations within the specified cases. The applicability of this method is demonstrated by addressing the positioning challenge of a portable fishway on a chute in an agricultural drainage canal in Japan. Results indicate that the proposed method allows for the selection of a suitable location, considering preferable hydraulic conditions both within the portable fishway and around its entrance.

The analysis of water inflow is a key issue in the construction of subsea tunnels. However, present research on the water inflow of subsea tunnels is predominantly focused on single-bore tunnels. Given the current practices in engineering, it is common to see a parallel configuration of three bores. In such cases, the proximity of tunnels to one another might result in alterations to the water inflow due to the interplay of seepage fields. This paper begins by conducting a numerical simulation to assess the water inflow characteristics of a three-bore parallel subsea tunnel. The study then investigates the impact of many factors, including the permeability coefficient of the surrounding rock, the depth of the seawater, the depth of the tunnel, the spacing between tunnels, and the relative size of the tunnels, on the water inflow phenomenon. Furthermore, based on the principles of the analytic hierarchy process and fuzzy mathematics, a comprehensive assessment framework is developed to evaluate the water inflow of three-bore parallel subsea tunnels. Ultimately, this grade of water inflow is used in practical engineering. The results show that there is mutual influence between the three parallel tunnels, and the predicted water inflow will be too large if the single tunnel model is adopted. The interaction between tunnels should be considered in the water inflow grade evaluation system of the three-hole parallel submarine tunnel. Engineering cases show that using a single-hole tunnel model without considering the interaction between tunnels will lead to higher water inflow grades. The research results of this paper can provide guidance for the waterproofing and drainage design of three parallel subsea tunnels and the stability analysis of surrounding rocks.

In this work, an entropy-stable and well-balanced numerical scheme for a one-dimensional blood flow model is presented. Such scheme is obtained from an explicit entropy conservative flux along with a second order discretization of the source term by using centered finite differences. We prove that the scheme is entropy-stable and preserves steady-states solutions. In addition, some numerical examples are included to test the performance of the proposed scheme.

In this study, cotton fabric reinforced phenolic resin (CPF) composites were modified by adding four two-dimensional fillers: graphitic carbon nitride (g-C3N4), graphite (Gr), molybdenum disulfide (MoS2), and hexagonal boron nitride (h-BN). The tribological properties of these modified materials were investigated under dry friction and water lubrication conditions. CPF/ Gr composite exhibits significantly better tribological performance than the other three fillers modified CPF composites under dry friction, with a 24% reduction in friction coefficient and a 78% reduction in wear rate compared to unmodified CPF composite. Under water lubrication conditions, all four fillers did not significantly alter the friction coefficient of the CPF composite. However, except for an excessive amount of Gr, the other three fillers can reduce the wear rate. Particularly, in the case of 10% MoS2 content, the wear rate decreased by 56%. Scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) were employed for the analysis of the morphology and composition of the transfer films. Additionally, molecular dynamics (MD) simulations were conducted to investigate the adsorption effects of Gr, MoS2, and PF on the counterpart surface under both dry friction and water lubrication conditions. The difference in the adsorption capacity of Gr and MoS2 on the counterpart, as well as the resulting formation of transfer films, accounts for the variation in tribological behavior between CPF/Gr and CPF/MoS2 composites. By combining the lubrication properties of MoS2 and Gr under dry friction and water lubrication conditions, using them as co-fillers can achieve a synergistic lubrication effect.

Polyamide 11 (PA11) is a plant-based nylon made from castor beans. Powder bed fusion laser sintering (PBF-LS) is an additive manufacturing process used for PA11 which allows the reuse of the unsintered powder. The unsintered powder is mixed with virgin powders at different refresh rates which has been studied extensively for most semi-crystalline polyamides. However, there is lack of information on the effect of using 100% reused PA11 powder and the number of times it is reused on its own during powder bed fusion laser sintering. This paper investigates the effect of reusing PA11 powder in PBF-LS and number of times it is reused on the dimensional accuracy, density, thermal and tensile properties. From 100% virgin powder to 3rd use of the powder, there is a decrease in powder wastage, decrease in crystallinity and tensile strength. These was associated with the polymerisation and cross-linking process of polymer chains, upon exposure to high temperature. This results in a higher molecular weight and hence density. From the 4th use to 10th reuse, the opposite was observed which was associated to an increase in high-viscosity and un-molten particles which resulted in defects in the PBF-LS parts.

Gain-scheduled autopilots have emerged as a dominant strategy to achieve adaptive control of coupled, non-linear engineering complexities, owing to an ability to adapt to changing operational conditions and uncertainties. This study focuses on utilizing bilinear interpolation of gain-scheduled autopilots, emphasizing enhanced system performance and robustness. Through a comprehensive investigation and comparative analysis using three disparate cases, advantages over conventional methods are revealed. Strengths and weaknesses of both simple and specialized variants (such as linear, and real-time gain-scheduling) are introduced. Three missile guidance case–studies utilize simulation time and miss distance figures of merit. Potential to achieve precise control across various mission scenarios, while ensuring reduced computational complexity is revealed by nearly two–hundred percent improved missile miss distances with comparable distances traveled and slightly improved computational burden.

Drone imagery has the potential to enrich urban planning and preservation, especially where it converges with the growing creation and use of 3D models. The authors have conducted a systematic literature review of articles published between 2002 and 2022, drawing from reputable academic repositories including Science Direct, Web of Science, and China National Knowledge Infrastructure (CNKI), to identify current gaps in existing research in the application of UAVs to the creation of 3D models in urban planning and preservation. Findings indicate five research shortcomings: limited participation of planning experts, research focus imbalance, lack of usage for special scenarios, lack of integration with smart city planning, and limited interdisciplinary collaboration. The study also acknowledges current limitations with UAV applications and discusses possible countermeasures as well as future prospects.

Hemolysin II (HlyII) – one of the pathogenic factors of Bacillus cereus, a pore-forming β-barrel toxin – possesses a C-terminal extension of 94 amino acid residues, designated as the C-terminal domain of HlyII (HlyIICTD), which plays an important role in the functioning of the toxin. Our previous work described a monoclonal antibody (HlyIIC-20), capable of strain-specific inhibi-tion of hemolysis caused by HlyII, and demonstrated the dependence of the efficiency of hemol-ysis on the presence of proline at position 324 in HlyII outside the conformational antigenic de-terminant. In this work, we studied 16 mutant forms of HlyIICTD. Each of the mutations, ob-tained by multiple site-directed mutagenesis leading to the replacement of amino acid residues lying on the surface of the 3D structure of HlyIICTD, led to a decrease in the interaction of HlyIIC-20 with the mutant form of the protein. Changes in epitope structure confirm the high conformational mobility of HlyIICTD required for the functioning of HlyII. Comparison of the effect of the introduced mutations on the effectiveness of interaction between HlyIICTD and HlyIIC-20 and a control antibody recognizing a non-overlapping epitope enabled identifying the amino acid residues N339, K340 included in the conformational antigenic determinant rec-ognized by HlyIIC-20.

On the basis of the golden ratio and the compasseses, every angle was divided into three equal parts. In general, it has been proven that the problem of triangulation of an angle cannot be solved on the basis of the golden ratio and the expanding of the angle. The relationship of golden relativity with Pi number and Euler's number has not been investigated to solve impossible problems. According to the investigation of events in six-dimensional space-time, with the simultaneous movement of two arms of the compasses based on the golden ratio, every angle was divided into three equal parts. The growth rate based on the golden ratio is the key to solving the most intractable mathematical and physical problems.

Machinery degradation assessment can offer meaningful prognosis and health management in-formation. Although numerous machine prediction models based on artificial intelligence have emerged in recent years, they still face a series of challenges: (1) Many models continue to rely on manual feature extraction. (2) Deep learning models still struggle with long sequence prediction tasks. (3) Health indicators are inefficient for remaining useful life (RUL) prediction with cross-operational environments when dealing with high-dimensional datasets as inputs. This research proposes a health indicator construction methodology based on a transformer self-attention transfer network (TSTN). This methodology can directly deal with the high-dimensional raw dataset and keep all the information without missing when the signals are taken as the input of the diagnosis and prognosis model. First, we design an encoder with a long-term and short-term self-attention mechanism to capture crucial time-varying information from a high-dimensional dataset. Second, we propose an estimator that can map the embedding from the encoder output to the estimated degradation trends. Then, we present a domain dis-criminator to extract invariant features from different machine operating conditions. The case studies with the FEMTO-ST bearing dataset and the Monte Carlo method for RUL prediction during the degradation process are conducted. The experiment results fully exhibited the signif-icant advantages of the proposed method compared to other state-of-the-art techniques.

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.

The time evolution of the continuous measure symmetry for the system built of the three bodies interacting via the potential U(r)~1/r is reported. Gravitational and electrostatic interactions between the point bodies were addressed. In the case of the pure gravitational interaction the three-body-system deviated from its initial symmetrical location, described by the Lagrange equilateral triangle, comes to collapse, accompanied by the growth of the continuous measure of symmetry. When three point bodies interact via the Coulomb repulsive interaction, the time evolution of CMS is quite different. CMS calculated for all of studied initial configurations of the point charges and all of their charge-to-mass ratios always comes to its asymptotic value with time, evidencing the stabilization of the shape of the triangle, constituted by the interacting bodies.

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 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.

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.

In recent, Chae found that, at weak gravitational acceleration g_N≲10⁻⁹ms⁻², the observed orbit of the wide binary stars is not accordant with current theory of binary star. Here, we present, this finding should be an observational evidence for that Newtonian theory of orbit perturbation is still valid in galactic scale.

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.

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.

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.

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.

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.

In this paper we study a special kind of finite mixture model. The sample drawn from the model consisits of three parts. The first two parts are drawn from specified distributions f1 and f2, while the third one is drawn from the mixture. A problem of interest is whether the two distributions f1 and f2 are the same. To test this hypothesis, we first define the regular location and scale family of distributions and assume that f1 and f2 are regular denisty functions. Then the hypothesis transforms to the equalities of the location and scale parameters, respectively. To utilize the information in the sample, we use Bayes’ theorem to obtain the posterior distribution and give the sampling method. Then we propose the posterior p-value to test the hypothesis. The simulation studies show that our posterior p-value largely improves the power in both normal and logistic cases while nicely controls the Type-I error. A real halibut dataset is used to illustrate the validity of our method.

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.

This study investigates the nonlinearities in three-phase inverters for SiC-based systems and compares their performance to IGBT-based systems. An analytical model of inverter voltage distortion is developed, which accounts not only for dead time (td), switching delay time, switching frequency (fs), and voltage drops of power devices, but also for output parasitic capacitance (Cout). Experimental tests validate the model, which provides a more accurate estimate of the inverter’s output phase voltage distortion. The power device characteristics are obtained from datasheets, while Cout is determined through experimentation. Three-phase inverters with varying switching frequencies, fundamental frequencies, and dead-time values are used in simulations and experiments to determine the influence of nonlinearity on phase voltage deviation and current distortion. The results show that, due to SiC devices’ faster switching time, the phase voltage deviation and phase current distortion are lower in SiC-based inverters than in IGBT-based ones for high-frequency applications, as the dead time can be reduced.

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.

Accurately predicting electrical signals of three-phase Direct Torque Control (DTC) induction motors is crucial for optimal motor performance and effective condition monitoring. However, multiple DTC motors’ complexity and operating conditions’ variations pose challenges for traditional prediction methods. To overcome these challenges, we propose an innovative approach that combines Fast Fourier Transform (FFT) to transform electrical motor simulation data and a Bi-directional Long Short-Term Memory (Bi-LSTM) network to forecast processed motor data. By transforming the DTC induction motor signals from the time domain to the frequency domain, we enhance the capability of learning models to capture subtle differences and generate various input features. The Bi-LSTM model effectively captures both forward and backward dependencies in time series data, enabling accurate prediction of electrical signals. We evaluate the proposed approach using our simulated dataset and compare the proposed method to a state-of-the-art model, the Gated Recurrent Unit (GRU), demonstrating its effectiveness in improving the accuracy and reliability of induction motor signals forecasting. The finding provides valuable insights for advancing motor control and operation in industrial applications.

In this paper, the performance of Cu-(In,Ga)-S2 (CIGS2) solar cells with adjusting composite CGI-ratio absorber is explored and compared through an improved three-stage co-evaporation technique. For co-evaporating CIGS2 absorber as a less toxic alternative to Cd-containing film, we analyse the effect of the CGI-ratio stoichiometry and crystallinity, and explore its opto-electric sensing characteristic of individual solar cell. The results of this research signify the potential of high-performance CIGS2-absorption solar cells for photovoltaic (PV)-module industrial applications. For the optimal CIGS2-absorption film (CGI=0.95), the Raman main-phase signal (A1) falls at 291 cm-1, which is excited by the 532-nm line of Ar+-laser. Using photo-luminescence (PL) spectroscopy, the corresponding main-peak bandgaps measured is 1.59 eV at the same CGI-ratio film. Meanwhile, the best conversion efficiency (=3.212%) and the average external quantum efficiency (EQE=51.1% in the visible-wavelength region) of photo-electric properties were achieved for the developed CIGS2-solar cells (CGI=0.95). The discoveries of this CIGS2-absorption PV research provide a new scientific understanding of solar cells. Moreover, this research undeniably contributes to a major advancement towards practical PV-module applications and can help more to build an eco-friendly community.

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.

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.

In this paper, we succeed to discover the new exact wave solutions to the truncated M-fractional complex three coupled Maccari’s system by utilizing the Sardar sub-equation scheme. The obtained solutions are in the form of trigonometric and hyperbolic forms. These solutions have many applications in nonlinear optics, fiber optics, deep water-waves, plasma physics, mathematical physics, fluid mechanics, hydrodynamics and engineering where the propagation of nonlinear waves is important. Achieved solutions are verified with the use of Mathematica software. Some of the achieved solutions are also described graphically by 2-dimensional, 3-dimensional and contour plots with the help of Maple software. The gained solutions are helpful for the further development of concerned model. Finally, this technique is simple, fruitful and reliable to handle the nonlinear FPDEs.

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.

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.