In this article we compute many topological indices for the family of Möbius ladder. At first we give general closed form of M-polynomial of this family and recover many degree based topological indices out of it. We also compute Zagreb indices and Zagreb polynomials of this family.

Titania is one of the most comprehensively studied nanostructures due to its widespread applications in production of catalytic, gas- sensing and corrosion- resistance materials [1]. M-polynomial of nanotubes has been vastly investigated as it produces many degree-based topological indices which are numerical parameters capturing structural and chemical properties. These indices are used in the development of quantitative structure-activity relationships (QSARs) in which the biological activity and other properties of molecules are correlated with their structure like boiling point, stability, strain energy etc of chemical compounds. In this paper, we determine M-polynomials of single-walled titanium (SW TiO2) nanotubes and recover important topological degree based indices of them to theoretically judge these nanotubes. We also use Maple to plot surfaces associated to different types of single-walled titanium (SW TiO2) nanotubes.

M-polynomial of different molecular structures helps to calculate many topological indices. A topological index of graph G is a numerical parameter related to G which characterizes its molecular topology and is usually graph invariant. In the field of quantitative structure-activity (QSAR) quantitative structure-activity structure-property (QSPR) research, theoretical properties of the chemical compounds and their molecular topological indices such as the Zagreb indices, Randic index, Symmetric division index, Harmonic index, Inverse sum index, Augmented Zagreb index, multiple Zagreb indices etc. are correlated. In this report, we compute closed forms of M-polynomial, first Zagreb polynomial and second Zagreb polynomial of Octagonal network. From the M-polynomial we recover some degree-based topological indices for Octagonal network. Moreover, we give a graphical representation of our results.

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

The aim of this paper is the presentation of an analytical model of insulator flashover and its application for air at atmospheric pressure and pressurized SF6. After a review of the main existing models in air and compressed gases, we develop a relationship of flashover voltage on the basis of an electrical equivalent circuit and the thermal properties of the discharge. The model includes the discharge resistance, the insulator impedance, and the gas interface impedance. The application of this model to a cylindrical resin-epoxy insulators in air medium and SF6 gas with different pressures gives results close to the experimental measurements.

The detection of Dark Matter is the greatest outstanding problem in modern cosmology. Several attempts have been taken for this without any remarkable success. To find out a suitable way of detection we need to understand its nature comprehensively. In the present article, a hypothesis is described considering Dark Matter as a normal matter. Its peculiar behavior is explained considering its existence in BEC state in the coolest part of the universe that makes it an electromagnetic insulator. Depending upon this hypothesis an experimental verification method is proposed.

We study the coupling of two topologcal subsystems in distinct topological states, and show that it leads to a precursor behavior of the topological phase transition in the overall system. This behavior is solely determined by the symmetry classes of the subsystem Hamiltonians and coupling terms, and is marked by the persistent existence of subgap states within the bulk energy gap. By investigating the critical current of Josephson junctions involving topological superconductors, we also illustrate that such subgap states play crucial roles in physical properties of nanoscale devices or materials.

Rectenna is the key component in radio-frequency circuits for receiving and converting electromagnetic waves into direct current. However, it is very challenging for the conventional semiconductor diode switches to rectify high-frequency signals for 6G telecommunication (>100 GHz), medical detection (>THz) and rectenna solar cells (optical frequencies). Such a major challenge can be resolved by replacing the conventional semiconductor diodes with tunneling diodes as the rectenna switches. In this work, metal-insulator-metal (MIM) tunneling diodes based on 2D insulating materials were designed, and their performance was evaluated using a comprehensive simulation approach which includes density-function-theory simulation of 2D insulator materials, modeling of the electrical characteristics of tunneling diodes, and circuit simulation for rectifiers. It is found that novel 2D insulators such as monolayer TiO2 can be obtained by oxidizing sulfur-metal layered materials. The MIM diodes based on such insulators exhibit fast tunneling and excellent current rectifying properties. Such tunneling diodes effectively convert the received high-frequency electromagnetic waves into direct current.

We recently proposed that topological quantum computing might be based on $SL(2,\mathbb{C})$ representations of the fundamental group $\pi_1(S^3\setminus K)$ for the complement of a link $K$ in the $3$-sphere. The restriction to links whose associated $SL(2,\mathbb{C})$ character variety $\mathcal{V}$ contains a Fricke surface $\kappa_d=xyz -x^2-y^2-z^2+d$ is desirable due to the connection of Fricke spaces to elementary topology. Taking $K$ as the Hopf link $L2a1$, one of the three arithmetic two-bridge links [the Whitehead link $5_1^2$, the Berge link $6_2^2$, the double-eight link $6_3^2$] or the link $7_3^2$, the $\mathcal{V}$ for those links contains the reducible component $\kappa_4$, the so-called Cayley cubic. In addition, the $\mathcal{V}$ for the later two links contains the irreducible component $\kappa_3$, or $\kappa_2$, respectively. Taking $\rho$ to be a representation with character $\kappa_d$ ($d<4$), with $|x|,|y|,|z| \le 2$, then $\rho(\pi_1)$ fixes a unique point in the hyperbolic space $\mathcal{H}_3$ and is conjugate to a $SU(2)$ representation (a qubit). Even though details on the physical implementation remain open, more generally, we show that topological quantum computing may be developed from the point of view of three-bridge links, the topology of the $4$-punctured sphere and Painlev\'e VI equation. The $0$-surgery on the $3$ circles of the Borromean rings L6a4 is taken as an example.

The primary distribution systems are comprised of power lines delivering power to utility feeders from substations. The inspection and maintenance of damaged and broken power system insulators are of paramount importance for continuous power supply and public safety. hence, to identify any faults and defects in advance a periodic inspection of power line insulators and other components be ensured beforehand. To automate the process and reduce operational cost and risk Unmanned Aerial Vehicles (UAVs) are being extensively utilized. As they present a safer and efficient way to examine the power system insulators and their components without closing the power distribution system ensuring continuous supply to the end-users. To achieve these objectives in this work a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. Deep Laplacian pyramids based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low light images a low light image enhancement technique is used for the robust exposure correction of the training images. Using computer vision-based object detection techniques to identify faults and classify them according to classes they belong. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. To improve the faults detection several flight path strategies are proposed for efficient inspection. Such strategies overcome the shuttering effect of insulators along with providing a less complex, time, and energy-efficient approach for capturing video stream of the power system components. Performance of different object detection models is presented for selecting the suitable one for fine-tuning on the specific faulty insulator dataset. Our proposed approach gives a less complex and more efficient flight strategy along with better results. For defect detection, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust faults recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat Pakistan.

We study the Kelvinons: monopole ring solutions to the Euler equations, regularized as the Burgers vortex in the viscous core. There is finite anomalous dissipation in the inviscid limit. However, in the anomalous Hamiltonian, some terms are growing as logarithms of Reynolds number; these terms come from the core of the Burgers vortex. In our theory, the turbulent multifractal phenomenon is similar to asymptotic freedom in QCD, with these logarithmic terms summed up by an RG equation. The small effective coupling does not imply small velocity; on the contrary, velocity is large compared to its fluctuations, which opens the way for a quantitative theory. In the leading order in the perturbation theory in this effective coupling constant, we compute running multifractal dimensions for high moments of velocity circulation, in good agreement with the data for quantum Turbulence and available data for classical Turbulence. The logarithmic dependence of fractal dimensions on the loop size comes from the running coupling in anomalous dimensions. This slow logarithmic drift of fractal dimensions would be barely observable at Reynolds numbers achievable at modern DNS.

The contamination on the insulators may increase its surface conductivity and, as a consequence, electrical discharges occur more frequently, which can lead to interruptions in the power supply. To maintain reliability in the electrical distribution power system, components that have lost their insulating properties must be replaced. Identifying the components that need maintenance, is a difficult task as there are several levels of contamination that are hardly noticed during inspections. To improve the quality of inspections, this paper proposes to use the k-nearest neighbours (k-NN) to classify the levels of insulator contamination, based on the image of insulators at various levels of contamination simulated in the laboratory. Using computer vision features such as mean, variance, asymmetry, kurtosis, energy, and entropy are used for training the k-NN. To assess the robustness of the proposed approach, statistical analysis and a comparative assessment with well-consolidated algorithms such as decision tree, ensemble subspace, and support vector machine models are presented. The k-NN showed results of up to 85.17 % accuracy using the k-fold cross-validation method, with an average accuracy higher than 82 % for multi-classification of the contamination of the insulators, being superior to the compared models.

Topological indices are invariant numerical fields of a graph that give facts about the structure of graphs and are found to be very helpful in predicting the physical properties of aluminophosphates. The characteristics of the aluminophosphates are similar to those of zeolites. Two examples of current applications are natural gas dehydration and humidity sensor. New frameworks are being synthesised by researchers in chemistry and materials science. There are many layers and holes in these substances. In this study, Vertex version of distance-based topological indices, the entropy of topological indices and their numerical analysis are explained.

The experimental realization of twisted bilayer graphene strongly pushed the inspection of bilayer systems. In this context, it was recently shown that a two layer Haldane model with a thirty degree rotation angle between the layers represents a higher order topological insulator, with zero-dimensional states isolated in energy and localized at the physical vertices of the nanostructure. We show, within a numerical tight binding approach, that the energy of the zero dimensional states strongly depends on the geometrical structure of the vertices. In the most extreme cases, once a specific band gap is considered, these bound states can even disappear just by changing the vertex structure.

We propose a method for creating parallel Bessel-like vortical optical needles with an arbitrary axial intensity distribution via superposition of different cone-angle Bessel vortices. We analyze the interplay between the separation of individual optical vortical needles and their respective lengths and introduce a super-Gaussian function as their axial profile. We also analyze the physical limitations to observe well-separated optic needles, as they are influenced by mutual interference of the individual beams. To verify our theoretical and numerical results, we generate controllable spatial arrays of individual Bessel beams with various numbers and spatial separations by altering the spectrum of the incoming laser beam via the spatial light modulator. We demonstrate experimentally how to implement such beams using a diffractive mask. The presented method facilitates the creation of diverse spatial intensity distributions in three dimensions, potentially finding applications in specific microfabrication tasks or other contexts. These beams may have benefits in laser material processing applications such as nanochannel machining, glass via production, modification of glass refractive indices, and glass dicing.

As one of the most promising ablative fillers, multi-walled carbon nanotubes (MWCNTs) have been used to improve the ablative resistance of Ethylene-Propylene-Diene Monomer (EPDM) insulators by facilitating the carbothermal reduction reaction of silica. However, the contribution of MWCNTs to char layer structure of the insulators was unclear. In this work, the effects of MWCNTs on char layer structure and ablative resistance were investigated in different EPDM-based insulators with and without silica. The results showed that adding only 3 phr MWCNTs can reduce the linear ablation rate of EPDM-based insulators without silica by 31.7%, while 6 phr MWCNTs are required to obtain similar results in EPDM-based insulators with silica. Both the char layer morphology of the two insulators gradually evolved into dense porous structure as MWCNTs content increasing, but their formation mechanisms are different. The XRD and Raman spectrum showed that different physicochemical reactions occurred around MWCNTs under different charring components. The proposed ablation mechanism was further verified by designing alternating multilayer distribution of MWCNTs and silica. This work can guide the construction of desirable char layer structure for increasing ablative resistance of EPDM-based insulators.

While the observation of magnetic monopoles has defied all experimental attempts in high-energy physics and astrophysics, sound theoretical approaches predict they should exist, and they have indeed been observed as quasiparticle excitations in certain condensed-matter systems. This indicates that, even though they are not ubiquitous contrary to electrons, it is possible to get them as excitations above a ground state. In this report, we show that phonons or lattice shear strain generate topological monopoles in some low-dimensional quantum antiferromagnets. For the Heisenberg ladder, phonons are found to generate topological monopoles with nonzero density due to quantum spin fluctuations. For the four-leg Heisenberg tube, longitudinal shear stress generates topological monopoles with density proportional to the strain deformation. The present theory is based on mapping the spin degrees of freedom onto spinless fermions using the generalized Jordan-Wigner transformation in dimensions higher than one. The effective magnetic field generated by the motion of the spinless fermions has non-zero divergence when phonons or shear stress are present. A possible system where the present kind of monopoles could be observed is BiCu$_2$PO$_6$.

In this paper, it is introduced the notion of r-fuzzy β-T_i, i = 0, 1, 2 separation axioms related to a fuzzy operator β on the initial set X which is a generalization of previous fuzzy separation axioms. An r-fuzzy α-connectedness related to a fuzzy operator α on the set X is introduced which is a generalization of many types of r-fuzzy connectedness. An r-fuzzy α-compactness related to a fuzzy operator α on the set X is introduced which is a generalization of many types of fuzzy compactness.

This work aims to generate some types of M_(j )–neighborhoods. Relationships among these M_(j )-neighborhoods and some other types of neighborhoods are discussed. Additionally, M_(j )-lower, M_(j )-upper approximations and M_(j )-accuracy in terms of M_(j )-neighborhoods are presented. Their properties, with some examples, are investigated. Finally, We addressed the comparisons among M_(j )approach and other approaches.

Recently two new degree concepts have been defined in graph theory: ev-degree and ve-degree. Also the ev-degree and ve-degree Zagreb and Randić indices have been defined very recently as parallel of the classical definitions of Zagreb and Randić indices. It was shown that ev-degree and ve-degree topological indices can be used as possible tools in QSPR researches . In this paper we define the ve-degree and ev-degree Narumi–Katayama indices, investigate the predicting power of these novel indices and extremal graphs with respect to these novel topological indices. Also we give some basic mathematical properties of ev-degree and ve-degree Narumi-Katayama and Zagreb indices.

Starting from unidentified objects moving inside a two-dimensional Euclidean manifold, we propose a simple method to detect the topological changes that occur during their reciprocal interactions and shape morphing. This method, which allows the detection of topological holes development and disappearance, makes it possible to solve the uncertainty due to disconnectedness, lack of information and absence of objects’ sharp boundaries, i.e., the three troubling issues which prevent scientists to select the required proper sets/subsets during their experimental assessment of natural and artificial dynamical phenomena, such as fire propagation, wireless sensor networks, migration flows, neural networks’ and cosmic bodies’ analysis.

To improve the monitoring of the electrical power grid it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface, and to evaluate the supportability of these components. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. Choosing which method to use is always a difficult task since some models may have higher computational effort. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. A review and comparison of these well-established methods for time series forecasting is performed. From the results of the best structure of the model, the hyperparameters are evaluated and the Wavelet transform is used to obtain an enhanced model.

Physical measurements might display range values extending towards infinite. The occurrence of infinity in physical equations, such as the black hole singularities, is a troublesome issue that causes many theories to break down when assessing extreme events. Different methods, such as re-normalization, have been proposed to avoid detrimental infinity. Here a novel technique is proposed, based on physical geometrical considerations and the Alexander Horned sphere, that permits to undermine infinity in physical equations. In this unconventional approach, a continuous monodimensional line becomes an assembly of countless bidimensional lines that superimpose in quantifiable knots and bifurcations.

3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map's semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of both geometry, topology and semantics by incorporating the C-Map data structure in the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model.

A topological quantum interplay between the magnetic flux and the mass has been investigated, for the case of an electron, by evaluating a gauge-invariant phase factor (a Wilson loop) linked to the electromagnetic gauge field of the particle. In particular, from this phase factor and the quantization of the magnetic flux variations, a relationship between the mass at rest of the electron and its self-energy , arising from radiative corrections, has been obtained also within a QED approach. Besides, a formulation of an energy scale comparable to the energy at rest of an electron-positron pair is proposed. Remarkably, a reckoning of the Bohr energy of a W⁺ W^- pair is compatible with constants and parameters usually employed within the electroweak theory and comparable to the energy at rest of an electron-positron pair.

In this work the general problem of the characterization of the topological phase of an open quantum system is addressed. In particular, we study the topological properties of Kitaev chains and ladders under the perturbing effect of a current flux injected into the system using an external normal lead and derived from it via a superconducting electrode. After discussing the topological phase diagram of the isolated systems, using a scattering technique within the Bogoliubov-de Gennes formulation, we analyze the differential conductance properties of these topological devices as a function of all relevant model parameters. The relevant problem of implementing local spectroscopic measurements to characterize topological systems is also addressed by studying the system electrical response as a function of the position and the distance of the normal electrode (tip). The results show how the signatures of topological order affect the electrical response of the analyzed systems, a subset of such signatures being robust also against the effects of a moderate amount of disorder. The analysis of the internal modes of the nanodevices demonstrates that topological protection can be lost when quantum states of an initially isolated topological system are hybridized with those of the external reservoirs. The conclusions of this work could be useful in understanding the topological phases of nanowire-based mesoscopic devices.

The topological index is a numerical quantity based on the characteristics of various invariants or molecular graph. For ease of discussion, these indices are classified according to their logical derivation from topological invariants rather than their temporal development. Degree based topological indices depends upon the degree of vertices. This paper computes degree based topological indices of Bismuth Tri-Iodide chains and sheets with the help of M-polynomial.

In the study of QSAR/QSPR, due to high degree of predictability of pharmaceutical properties, the eccentric-connectivity index has very important place among the other topological descriptors, In this paper, we compute the exact formulas of eccentric-connectivity index and its corresponding polynomial, total eccentric-connectivity index and its corresponding polynomial, first Zagreb eccentricity index, augmented eccentric-connectivity index, modified eccentric-connectivity index and its corresponding polynomial for a class of phosphorus containing dendrimers.

Topological data analysis (TDA) is a recent approach for analyzing and interpreting complex data sets, based on ideas a branch of mathematics called algebraic topology. TDA has proven useful to disentangle non-trivial data structure in a broad range of data analytics problems including the study of cardiovascular signals. This review aims to provide an overview of the application of TDA to cardiovascular signals and its potential to enhance the understanding of cardiovascular diseases and their treatment. We first introduce the concept of TDA and its key techniques, including persistent homology, Mapper, and multidimensional scaling. We then discuss the use of TDA in analyzing various cardiovascular signals, including electrocardiography, photoplethysmography, and arterial stiffness. We also discuss the potential of TDA to improve the diagnosis and prognosis of cardiovascular diseases, as well as its limitations and challenges. Finally, we outline future directions for the use of TDA in cardiovascular signal analysis and its potential impact on clinical practice. Overall, TDA has shown great promise as a powerful tool for the analysis of complex cardiovascular signals and may offer significant insights into the understanding and management of cardiovascular diseases.

The study of photonic crystals has emerged as an attractive area of research in nanoscience in the last years. In this work we study the properties of a two-dimensional photonic crystal composed of dielectric rods. The unit cell of the system is composed of six rods organized on the sites of a C6 triangular lattice. We induce a topological phase by introducing an angular perturbation ϕ in the pristine system. The topology of the system is then determined by using the so-called k.p perturbed model. Our results show that the system presents a topological and a trivial phases, depending on the sign of the angular perturbation ϕ. The topological character of the system is probed by evaluating the electromagnetic energy density and analyzing its distribution in the real space, in particular on the maximal Wyckoff points. We also find two edge modes at the interface between the trivial and topological photonic crystals, which present a pseudospin topological behavior. By applying the Bulk-Edge correspondence, we study the pseudospin edge modes and conclude that they are robust against defects, disorder and reflection. Moreover, the localization of the edge modes leads to the confinement of light and the interface behaves as a waveguide for the propagation of electromagnetic waves. Finally, we show that the two edge modes present energy flux propagating in opposite directions, which is the photonic analogue of the quantum spin Hall effect.

In 1999, Molodtsov initiated the theory of soft sets as a new mathematical tool for dealing with uncertainties in many fields of applied sciences. In 2011, Shabir and Naz introduced and studied the notion of soft topological spaces, also defining and investigating many new soft properties as generalization of the classical ones. In this paper, we introduce the notions of soft separation between soft points and soft closed sets in order to obtain a generalization of the well-known Embedding Lemma to the class of soft topological spaces.

The Anderson insulating states in Au nanoparticle assembly are identified and studied under the application of magnetic fields and gate voltages. When the inter-nanoparticle tunneling resistance is smaller than the quantum resistance, the system showing zero Mott gap can be insulating at very low temperature. In contrast to Mott insulators, Anderson insulators exhibit great negative magnetoresistance, inferring charge de-localization in a strong magnetic field. When probed by the electrodes spaced by ~200 nm, they also exhibit interesting gate-modulated current similar to the multi-dot single electron transistors. These results reveal the formation of charge puddles due to interplay of disorder and quantum interference at low temperatures.

In this paper, we introduce a new class of open sets that is called is*-open set . Also, we present the notion of is*-continuous, is*-open, is*-irresolute, is*-totally continuous, and is-contra-continuous mappings, and we investigate some properties of these mappings. Furthermore, we introduce some is*-separation axioms, and is*-mappings are related with is*-separation axioms. . .

We discuss a possible scale of gravitational origin at around 10 MeV, or 10⁻¹² cm, which arises in the MacDowell-Mansouri formalism of gravity due to the topological Gauss-Bonnet term in the action, as pointed out by Bjorken several years ago.~A length scale of the same size emerges also in the Kodama solution in gravity, which is known to be closely related to the MacDowell-Mansouri formulation. We particularly draw attention to the intriguing incident that existence of six compact extra dimensions originated from TeV-scale quantum gravity as well points to a length scale of 10⁻¹² cm, as the compactification scale. The presence of six such extra dimensions is also in remarkable consistency with the MacDowell-Mansouri formalism; it provides a possible explanation for the factor of ~10¹²⁰ multiplying the Gauss-Bonnet term in the action. We also comment on the relevant implications of such a scale regarding the thermal history of the universe motivated by the fact that it is considerably close to 1–2 MeV below which the weak interactions freeze out, leading to Big Bang Nucleosynthesis.

This paper first discusses a fractional-order Liu system of order as low as 2.7 and shows its chaotic characteristics by carrying out numerical simulations such as Lyapunov exponents, bifurcation diagrams and phase portraits. Then, by using the topological horseshoe theory and computer-assisted proof, the existence of chaos in the system is verified theoretically. Finally, the fractional-order system is implemented on a Field Programmable Gate Array (FPGA) and the results obtained show that the fractional-order Liu system is indeed chaotic.

. In this paper, we introduce a series of definitions of generalized affine functions for vector-valued functions. We prove that our generalized affine functions have some similar properties with generalized convex functions. We present examples to show that our generalized affinenesses are different from one another, and also provide an example to show that our definition of presubaffinelikeness is non-trivial; presubaffinelikeness is the weakest generalized affineness introduced in this article. We work with optimization problems that are defined and taking values in linear topological spaces. We devote to the study of constraint qualifications, and derive some optimality conditions as well as a strong duality theorem. Our optimization problems have inequality constraints, equality constrains and abstract constraints; our inequality constraints are generalized convex functions and equality constraints are generalized affine functions.

Type-2 fuzzy sets were introduced by L. Zadeh aiming at modelling some settings in which fuzzy sets (usually called type-1 fuzzy sets) are not sufficient to reflect certain uncertainty degrees - loosely speaking, they are fuzzy sets whose membership degrees are ordinary fuzzy sets. On the other hand, fiber bundles are topological entities of extreme importance in Mathematics itself and many other scientific areas, like Physics (General Relativity, Field Theory etc.), finance modelling, and statistical inference. The present work introduces a conceptual link between the two ideas and conjectures about the potential mutual benefits that can be obtained from this viewpoint.As an objective and usable product of the presented ideas, it is described a framework for defining type-2 fuzzy hedges, proper to operate on interval type-2 fuzzy sets.

The entangled antipodal points on black hole surfaces, recently described by t’Hooft, display an unnoticed relationship with the Borsuk-Ulam theorem. Taking into account this observation and other recent claims, suggesting that quantum entanglement takes place on the antipodal points of a S³ hypersphere, a novel framework can be developed, based on algebraic topological issues: a feature encompassed in an S² unentangled state gives rise, when projected one dimension higher, to two entangled particles. This allows us to achieve a mathematical description of the holographic principle occurring in S². Furthermore, our observations let us to hypothesize that a) quantum entanglement might occur in a four-dimensional spacetime, while disentanglement might be achieved on a motionless, three-dimensional manifold; b) a negative mass might exist on the surface of a black hole.

The realization of a BRST cohomology of the 4D topological gauge-affine gravity is established in terms of a superconnection formalism. Identification of fields in the quantized theory is occurred directly as usual in terms of superconnection and its supercurvature components with the double covering of the general affine group GA¯(4,R). Then, by means of an appropriate decomposition of the metalinear double-covering group SL¯(5,R) with respect to the general linear double-covering group GL¯(4,R) one can easily obtain the enlargements of the fields while remaining consistent with the BRST algebra. This leads to the descent equations allowing us to build the observables of the theory by means of the BRST algebra constructed using a sa¯(5,R) algebra-valued superconnection. In particular, we discuss the construction of topological invariants with torsion.

In this research, we investigate the bending of photon light and/or time-like geodesics within the context of four-dimensional space-time geometry, specifically in the presence of topological defects. These topological defects manifest as cosmic string space-time configurations, encompassing screw dislocation, spiral dislocation, and spacelike dislocation. Our primary objective is to determine the extent of deflection experienced by light-like and time-like geodesics and analyze how this deflection is affected by the cosmic string parameter, screw dislocation parameter, and spacelike dislocation parameter. To accomplish this, we employ a derivation technique that centers on the observation that photon rays (light-like geodesics) and/or time-like geodesics follow paths parallel to the z-axis while maintaining a constant radius along the cylinder.

Graph theory plays a crucial role in modeling and designing of chemical structure or chemical network. Chemical Graph theory helps to understand the molecular structure of molecular graph. The molecular graph consists of atoms as vertices and bonds as edges. Topological indices capture symmetry of molecular structures and give it a mathematical language to predict properties such as boiling points, viscosity, the radius of gyrations etc. In this article, we study the chemical graph of carbon Crystal structure of graphite and cubic carbon and compute several degree-based topological indices. Firstly we compute M-Polynomials of these structures and then from these M-polynomials we recover nine degree-based topological indices.

The electronic properties of calcium intercalated graphite (CaC6) as a function of pressure are revisited using density functional theory (DFT). The electronic band structures of CaC6, like many other layered superconducting materials, display cosine-shaped bands at or near the Fermi level (FL). Such bands encompass bonding/antibonding information with a strong connection to superconducting properties. Using an hexagonal cell representation for CaC6, construction of a double supercell in the c-direction effects six-folding in reciprocal space of the full cosine function, explicitly revealing the bonding/antibonding relationship divide at the cosine midpoint. Similarly, folding of the Fermi surface (FS) reveals physical phenomena relevant to electronic topological transitions (ETT) with application of pressure. For CaC6, the peak value for the superconducting transition temperature, Tc, occurs at about 7.5 GPa, near to the observed pressure of the calculated ETT. At this pressure, the radius of the nearly spherical Ca 4s-orbital FS coincides with three times the distance from the G centre point to the Brillouin zone (BZ) boundary of the 2c supercell (which is equal to half the identically defined distance for the primitive rhombohedral BZ). In addition, the ETT coincides with alignment of the non-bonding (inflection) point of the cosine band (the point at which the sign of the cosine amplitude modulation is reversed) with the FL. At other calculated pressure conditions, the Ca 4s-orbital FS undergoes topological changes that correspond and can be correlated to experimentally determined changes in Tc. The ETT is a key mechanism that circumscribes the known significant drop in Tc for CaC6 as a function of increasing pressure.

Data can be illustrated in shapes, and the shapes could provide insight for data modeling and information extraction. Topological data analysis provides an alternative insight in biomedical data analysis and knowledge discovery with the algebra topology tools. In present work, we study the application of topological data analysis for personalized electrocardiographic signal classification toward arrhythmia analysis. Using phase space reconstruction technique, the signal samples are converted into point clouds for topological analysis facility. With topological techniques the persistence landscapes from the point clouds are extracted as features to perform the arrhythmia classification task. We find that the proposed method is robust to the training set size, with only a training set size of 20% percents, the normal heartbeat class are 100% recognized, ventricular beats for 97.13%, supra-ventricular beats for 94.27% and fusion beats for 94.27% within the corresponding experiments. The property of keeping high performance when using smaller training sample proves that the proposed method is especially applicable to personalized analysis. With the present study, we show that the topological data analysis technique could be a useful tool in biomedical signal analysis, and provide powerful ability in personalized analysis.

We characterize the form of the order divisor graph of cyclic groups based on the group order. Furthermore, we describe some topological indices of the order divisor graph of cyclic groups, including the Wiener index, Harary index, first Zagreb index, and second Zagreb index.

Spatially uniform optical excitations can induce Floquet topological band structures within insulators which can develop similar or equal characteristics as are known from three-dimensional topological insulators. We derive in this article theoretically the development of Floquet topological quantum states for electromagnetically driven semiconductor bulk matter and we present results for the lifetime of these states and their occupation in the non-equilibrium. The direct physical impact of the mathematical precision of the Floquet-Keldysh theory is evident when we solve the driven system of a generalized Hubbard model with our framework of dynamical mean field theory (DMFT) in the non-equilibrium for a case of ZnO. The physical consequences of the topological non-equilibrium effects in our results for correlated systems are explained with their impact on optoelectronic applications.

In this article, we first introduced new types of higher separation axioms called (r,s)-GFS-regular and (r,s)-GFS-normal spaces with the help of (r,s)-generalized fuzzy semi-closed sets (briefly, (r,s)-gfsc sets) and discussed some topological properties of them. Thereafter, we defined a stronger form of (r,s)-gfsc sets called (r,s)-g*fsc sets and investigated some of its features. Moreover, we showed that (r,s)-fsc set → (r,s)-g*fsc set → (r,s)-gfsc set, but the converse may not be true. In addition, we explored new types of fuzzy generalized mappings between double fuzzy topological spaces (U, τ, τ*) and (V, η, η*), and the relationships between these classes of mappings were examined with the help of some illustrative examples. Finally, some new types of compactness via (r,s)-gfso sets were defined and the relationships between them were introduced.

This paper establishes methods that quantify the structure of statistical interactions within a given data set using the characterization of information theory in cohomology by finite methods, and provides their expression in terms of statistical physic and machine learning. Following [1–3], we show directly that k multivariate mutual-informations (I_k) are k-coboundaries. The k-cocycles are given by I_k = 0, which generalize statistical independence to arbitrary dimension k. The topological approach allows to investigate Shannon’s information in the multivariate case without the assumptions of independent identically distributed variables. We develop the computationally tractable subcase of simplicial information cohomology represented by entropy H_k and information I_k landscapes. The I₁ component defines a self-internal energy functional U_k, and (−1)^k I_k,k≥2 components define the contribution to a free energy functional G_k of the k-body interactions. The set of information paths in simplicial structures is in bijection with the symmetric group and random processes, provides a topological expression of the 2nd law and points toward a discrete Noether theorem (1st law). The local minima of free-energy, related to conditional information negativity and the non-Shannonian cone of Yeung [4], characterize a minimum free energy complex. This complex formalizes the minimum free-energy principle in topology, provides a definition of a complex system, and characterizes a multiplicity of local minima that quantifies the diversity observed in biology. Finite data size effects and estimation bias severely constrain the effective computation of the information topology on data, and we provide simple statistical tests for the undersampling bias and for the k-dependences following [5]. We give an example of application of these methods to genetic expression and cell-type classification. The maximal positive I_k identifies the variables that co-vary the most in the population, whereas the minimal negative I_k identifies clusters and the variables that differentiate-segregate the most. The methods unravel biologically relevant I₁₀ with a sample size of 41. It establishes generic methods to quantify the epigenetic information storage and a unified epigenetic unsupervised learning formalism.

We review the topological gauge theory description of Josephson junction arrays (JJA), fabricated systems which exhibit the superconductor to insulator transition (SIT). This description revealed the topological nature of the phases appearing at the SIT and lead to the discovery of a new state of matter, the superinsulator, characterized by an infinite resistance, even at finite temperature, due to linear confinement of electric charges. This discovery is particularly relevant for the physics of superconducting films with emergent granularity, which are modeled by JJA, and share the same phase diagram.

We study factorization properties of continuous homomorphisms defined on subgroups (or submonoids) of products of (para)topological groups (or monoids). A typical result is the following one: Let $D=\prod_{i\in I}D_i$ be a product of paratopological groups, $S$ be a dense subgroup of $D$, and $\chi$ a continuous character of $S$. Then one can find a finite set $E\subset I$ and continuous characters $\chi_i$ of $D_i$, for $i\in E$, such that $\chi=\big(\prod_{i\in E} \chi_i\circ p_i\big)\hs1\res\hs1 S$, where $p_i\colon D\to D_i$ is the projection.

This study presents a computational simulation to explore the distinctive patterns of neural connectivity in Autism Spectrum Disorder (ASD), focusing on the concepts of spatial and topological proximities. ASD is characterized by unique cognitive and behavioral profiles, often attributed to atypical neural development and connectivity. Our simulation employs a two-pronged approach: first, by modeling spatial proximity through the generation of clustered 3D node positions, we mimic the observed over-connectivity in localized brain regions in ASD. Second, we construct a topological network to represent the complex interplay of under-connectivity and over-connectivity in functional brain networks, a hallmark of ASD's neural landscape. The spatial model demonstrates pronounced clustering, reflecting the structural brain differences and atypical growth patterns reported in neuroimaging studies of individuals with ASD. In contrast, the topological model reveals a network with a mix of sparse and dense connections, simulating the diverse and often contradictory findings of functional connectivity in ASD, such as reduced long-range connections and increased local connections. Our simulation provides a visual and conceptual framework for understanding the altered neural connectivity in ASD. It underscores the importance of considering both physical (spatial) and functional (topological) aspects of brain connectivity to grasp the full extent of neurodevelopmental deviations in ASD. This approach not only aids in elucidating the neural underpinnings of ASD's core symptoms but also offers a foundation for developing targeted interventions. Future research, integrating more complex and individual-specific data, could further refine this model, enhancing our understanding of ASD's neural dynamics and contributing to personalized therapeutic strategies.

We show that the compound samarium hexaboride is a strong topological insulator using the eigenvalues of the space inversion operator in the low-energy limit of the periodic Anderson model. Additionally, we assume the presence of the ferromagnetic exchange interaction (M). A Dirac cone like feature in the surface state energy spectra is observed for M equals zero in a certain parameter range. For M not equal to zero, there is no Kramers degeneracy. We have been able to show that this phase corresponds to the quantum anomalous Hall state by calculating Berry curvature and the Chern number. Using Floquet theory, we further show that the access to a novel state with broken time reversal symmetry is possible due to the normal incidence of circularly polarized optical field on the surface of the compound despite M being absent.

Higher-order topological insulators (HOTIs) are recently discovered topological phases, possessing symmetry-protected corner states with fractional charges. An unexpected connection between these states and the seemingly unrelated phenomenon of bound states in the continuum (BICs) was recently unveiled. When nonlinearity is added to a HOTI system, a number of fundamentally important questions arise. For example, how does nonlinearity couple higher-order topological BICs with the rest of the system, including continuum states? In fact, thus far BICs in nonlinear HOTIs have remained unexplored. Here, we unveil the interplay of nonlinearity, higher-order topology, and BICs in a photonic platform. We observe topological corner states which are also BICs in a laser-written second-order topological lattice, and further demonstrate their nonlinear coupling with edge (but not bulk) modes under the proper action of both self-focusing and defocusing nonlinearities. Theoretically, we calculate the eigenvalue spectrum and analog of the Zak phase in the nonlinear regime, illustrating that a topological BIC can be actively tuned by nonlinearity in such an HOTI. Our studies are applicable to other nonlinear HOTI systems, with promising applications in emerging topology-driven devices.

Majorana bound states in topological superconductors have attracted intense research activity in view of applications in topological quantum computation. However, they are not the only example of topological bound states that can occur in such systems. We here study a model in which both Majorana and Tamm bound states compete. We show both numerically and analytically that, surprisingly, the Tamm state remains partially localized even when the spectrum becomes gapless. Despite this fact, we demonstrate that the Majorana polarization shows a clear transition between the two regimes.

The recent growth of high-resolution spatial data, especially in developing urban environments, is enabling new approaches to civic activism, urban planning and the provision of services necessary for sustainable development. A special area of great potential and urgent need deals with urban expansion through informal settlements (slums). These neighborhoods are too often characterized by a lack of connections, both physical and socioeconomic, with detrimental effects to residents and their cities. Here, we show how a scalable computational approach based on the topological properties of digital maps can identify local infrastructural deficits and propose context-appropriate minimal solutions. We analyze 1 terabyte of OpenStreetMap (OSM) crowdsourced data to create worldwide indices of street block accessibility and local cadastral maps and propose infrastructure extensions with a focus on 120 Low and Middle Income Countries (LMIC) in the Global South. We illustrate how the lack of physical accessibility can be identified in detail, how the complexity and costs of solutions can be assessed and how detailed spatial proposals are generated. We discuss how these diagnostics and solutions provide a multiscalar set of new capabilities – from individual neighborhoods to global regions – that can coordinate local community knowledge with political agency, technical capability, and further research.

Anyon is collective excitation of two dimensional electron gas subjected to strong magnetic field, carrying fractional charges and exotic statistical character beyond fermion and boson. So far, anyons with serial fractional charges only exist in fractional quantum Hall effect. It is still a challenge to find new serial of fractional charges in other physical system and develop an unified mathematical physics theory based on the same root. Here a topological path fusion theory of propagating electrons in magnetic flux lattice is proposed to explore the physical origin of fractional charges based on a generalization of Feynman's path integral theory and Thurston's train track theory. This mathematical physics theory generated the existed serial of fractional charges in fractional quantum Hall effect and predicted new serial of fractional charges. A serial of irrational charges are predicted in one dimensional lattice of magnetic fluxes. Fractionally charged anyons are also generated in two dimensional and three dimensional lattice of train tracks of electric currents, revealing an exact correspondence between knot lattice model and train track model. A new explanation for the modular symmetry of complex Hall conductance and composite fermion in fractional quantum Hall effect is also derived from this topological path fusion theory. Experimental observation of anyon in three dimensions can be realized by constructing three dimensional interlocking magnetic fluxes or mapping magnetic fluxes into forbidden zones in multi-connected space filled by solid state material. A photonic crystal with porous nano-structures is a promising system for detecting fractional charges and paves a new way for topological quantum computation.

Sample size using prevalence has been one of the most important medical research methodologies and has been used in original articles more often than any other statistical tool available. The simple formula consists of variables interdependent and it is as important to understand how they influence each other as it is to learn to use the formula to calculate the sample size. With the choice of different amounts of precision and levels of confidence available to a medical researcher, it should be of paramount importance to understand what a slight change in choosing the parameter would mean at the end of the research. Understanding of different Z values, Levels of precision, and ranging Prevalence is required to understand the effect on the sample size and subsequently on the research. Topological representation using parameter ranges can be used to demonstrate this relationship.

AlphaFold is a groundbreaking Deep Learning tool for protein structure prediction. It achieved remarkable accuracy in modeling the many 3D structures while taking as the input only the known amino acid sequence of proteins in question. Intriguingly though, in the early steps of each individual structure prediction procedure, AlphaFold is not respecting topological barriers that in real proteins result from the reciprocals impermeability of polypeptide chains. This study aims to investigate how this non-respecting of topological barriers affects AlphaFold predictions with respect to the topology of protein chains. We focus on such classes of proteins that during their natural folding form reproducibly the same knot type on their linear polypeptide chain as revealed by their crystallographic analysis. We use partially artificial test constructs in which the mutual non-permeability of polypeptide chains should not permit the formation of such knots during natural protein folding. We find that despite the formal impossibility that the protein folding process could produce such knots, AlphaFold predicts these proteins to be knotted. Our study underscores the necessity for cautious interpretation and further validation of topological features in protein structures predicted by AlphaFold.

We report on comparison between temperature-dependent magneto¬absorption and magnetotransport spectroscopy of HgTe/CdHgTe quantum wells in terms of detection of phase transition between topological insulator and band insulator states. Our results demonstrate that temperature-dependent magnetospectroscopy is a powerful tool to discriminate trivial and topological insulator phases, yet magnetotransport method is shown to have advantages for clear manifestation of the phase transition with accurate quantitative values of transition parameter (i.e. critical magnetic field Bc).

Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textural document may reveal some additive information regarding the document that is not reflected in any other features from traditional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textural documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification.

When an edge is removed, a cycle graph Cn becomes a n-1 tree graph. This observation from extremal set theory leads us to the realm of set theory, in which a topological manifold of genus-1 turns out to be of genus-0. Starting from these premises, we prove a theorem suggesting that a manifold with disjoint points must be of genus-0, while a manifold of genus-1 cannot encompass disjoint points.

During the last decades the quantitative description of electrical and magnetotransport properties of solid-state materials has been a remarkable challenge in materials science. Recently, the discovery of a novel class of materials, the topological semimetals, has led to a growing interest for the full understanding of their magnetotransport properties. In this review, the strong interplay among topology, band structure and carrier mobility in recently discovered high carriers mobility topological semimetals is discussed and their effect on their magnetotransport properties is outlined. The huge magnetoresistance effect, especially in the Hall transverse configuration, and a new version of a three-dimensional quantum Hall effect observed in high-mobility Weyl and Dirac semimetals are reviewed. The design of novel quantum sensors based on solid-state semimetals is also encouraged.

Sparse training (ST) aims to ameliorate deep learning by replacing fully connected artificial neural networks (ANNs) with sparse or ultra-sparse ones, such as brain networks are, therefore it might benefit to borrow brain-inspired learning paradigms from complex network intelligence theory. Here, we launch the ultra-sparse advantage challenge, whose goal is to offer evidence on the extent to which ultra-sparse (around 1% connection retained) topologies can achieve any leaning advantage against fully connected. Epitopological learning is a field of network science and complex network intelligence that studies how to implement learning on complex networks by changing the shape of their connectivity structure (epitopological plasticity). One way to implement Epitopological (epi- means new) Learning is via link prediction: predicting the likelihood of non-observed links to appear in the network. Cannistraci-Hebb learning theory inspired the CH3-L3 network automata rule for link prediction which is effective for general-purpose link prediction. Here, starting from CH3-L3 we propose Epitopological Sparse Meta-deep Learning (ESML) to apply Epitopological Learning to sparse training. In empirical experiments, we find that ESML learns ANNs with ultra-sparse hyperbolic (epi-)topology in which emerges a community layer organization that is meta-deep (meaning that each layer also has an internal depth due to power-law node hierarchy). Furthermore, we discover that ESML can in many cases automatically sparse the neurons during training (arriving even to 30% neurons left in hidden layers), this process of node dynamic removal is called percolation. Starting from this network science evidence, we design Cannistraci-Hebb training (CHT), a 4-step training methodology that puts ESML at its heart. We conduct experiments on 7 datasets and 5 network structures comparing CHT to dynamic sparse training SOTA algorithms and the fully connected counterparts. The results indicate that, with a mere 1% of links retained during training, CHT surpasses fully connected networks on VGG16, GoogLeNet, ResNet50, and ResNet152. This key finding is an evidence for ultra-sparse advantage and signs a milestone in deep learning. CHT acts akin to a gradient-free oracle that adopts CH3-L3-based epitopological learning to guide the placement of new links in the ultra-sparse network topology to facilitate sparse-weight gradient learning, and this in turn reduces the convergence time of ultra-sparse training. Finally, CHT offers the first examples of parsimony dynamic sparse training because, in many datasets, it can retain network performance by percolating and significantly reducing the node network size. Our code is available at: https://github.com/biomedical-cybernetics/Cannistraci-Hebb-training.

Topological indices have important role in theoretical chemistry for QSPR researches. Among the all topological indices the Randić and the Zagreb indices have been used more considerably than any other topological indices in chemical and mathematical literature. Most of the topological indices as in the Randić and the Zagreb indices are based on the degrees of the vertices of a connected graph. Recently novel two degree concepts have been defined in graph theory; ev-degrees and ve-degrees. In this study we define ev-degree Zagreb index, ve-degree Zagreb indices and ve-degree Randić index by using these new graph invariants as parallel to their corresponding classical degree versions. We compare these new group ev-degree and ve-degree indices with the other well-known and most used topological indices in literature such as; Wiener, Zagreb and Randić indices by modelling some physicochemical properties of octane isomers. We show that the ev-degree Zagreb index, the ve-degree Zagreb and the ve-degree Randić indices give better correlation than Wiener, Zagreb and Randić indices to predict the some specific physicochemical properties of octanes. We investigate the relations between the second Zagreb index and ev-degree and ve-degree Zagreb indices and some mathematical properties of ev-degree and ve-degree Zagreb indices.

The application of graph theory in chemical and molecular structure research far exceeds people's expectations, and it has recently grown exponentially. In the molecular graph, atoms are represented by vertices and bonded by edges. Closed forms of multiplicative degree-based topological indices which are numerical parameters of the structure and determine physico-chemical properties of the concerned molecular compound. In this article, we compute and analyze many multiplicative degree-based topological indices of silicon-carbon Si₂C₃-I[p,q] and Si₂C₃-II[p,q].

In recent years, there has been a wave of research on complex networks. The purpose of this paper is to study the graph theoretic mathematical properties of random cyclooctatetraene chain. We obtain the explicit analytical expressions for the variances of the Gutman index, Schultz index, multiplicative degree-Kirchhoff index and additive degree-Kirchhoff index of a random cyclooctatetraene chain with $n$ octagons, which plays a crucial role in the research and application of topological indices.

In this paper, the existence of optimal solutions for problems governed by differential equations involving feedback controls is established, when the problem must account for a Volterra-type distributed delay and is subject to the action of impulsive external forces. The problem is reformulated within the class of impulsive semilinear integro-differential inclusions in Banach spaces and is studied using topological methods and multivalued analysis. The paper concludes with an application to a population dynamics model.

In this paper we introduce the concept of Nano Beta Closure, Nano Beta Interior and study the relationship between them also study some of their properties. Based on these notions we introduced the topological Approach to Decision Making and Its Application in the Medical Field" which describes the use of topological methods in decision-making, specifically through the utilization of nano beta closure and nano beta interior. These mathematical tools are applied in the medical field to improve diagnostic accuracy and aid in treatment planning. The authors demonstrate the effectiveness of this approach through a case study on the diagnosis of Chronic Kidney. The results show that the topological approach using nano beta closure and nano beta interior provides a reliable and accurate method for medical decision-making. Finally, we introduce a medical application for disease groups patients of with chronic kidney. To identify the main causative agent of kidney disease. where using this approach, the medical team can identify the specific areas of the kidney that are affected by the infection, as well as the extent of damage and any potential complications. This information is used to create an individualized treatment strategy that is customized to meet the specific requirements of the patient. and medical history. We write an algorithm that can be a helpful tool for doctors to utilize in diagnosing infection disease Chronic Kidney infections. By utilizing this approach, clinicians can provide accurate diagnoses, develop effective treatment plans, and ultimately improve patient outcomes. finally, we show that fractals hold significant importance in future nano topological generation of the human factor. Their unique properties and self-similar patterns offer insights into the complex organization of biological systems and provide a foundation for designing advanced Nanosystems.

This hypothesis is the result of my conviction that science will oneday prove everything in space and time is part of a unification. In "A Brief History of Time", Stephen Hawking wrote, "If everything in the universe depends on everything else in a fundamental way, it might be impossible to get close to a full solution (of the universe's puzzles) by investigating parts of the problem (such as general relativity and quantum mechanics) in isolation." The goal: to establish a “proof of concept” to which equations can be added. It’s concluded the Steady State, Big Bang, Inflation and Multiverse theories all ultimately fail and a topological model including bits (binary digits), Mobius strips, figure-8 Klein bottles and Wick rotation works better. The failed cosmologies have impressive points leading to the idea that they’re all necessary stepping-stones. For example, the Big Bang is seen here as violation of the 1st Law of Thermodynamics but its supposed origin from quantum fluctuations is reminiscent of bits switching between 1 and 0. The topological hypothesis has potential to explain dark matter, dark energy, and electromagnetic-gravitational union. Finally, the article introduces what is called vector-tensor-scalar geometry - and extensions of Einstein's Gravity and Maxwell's Electromagnetism.

Since its emergence in March 2020, the SARS-CoV-2 global pandemic has produced more than 65 million cases and one point five million deaths worldwide. Despite the enormous efforts carried out by the scientific community, no effective treatments have been developed to date. We created a novel computational pipeline aimed to speed up the process of repurposable candidate drug identification. Compared with current drug repurposing methodologies, our strategy is centered on filtering the best candidate among all selected targets focused on the introduction of a mathematical formalism motivated by recent advances in the fields of algebraic topology and topological data analysis (TDA). This formalism allows us to compare three-dimensional protein structures. Its use in conjunction with two in silico validation strategies (molecular docking and transcriptomic analyses) allowed us to identify a set of potential drug repurposing candidates targeting three viral proteins (3CL viral protease, NSP15 endoribonuclease, and NSP12 RNA-dependent RNA polymerase), which included rutin, dexamethasone, and vemurafenib among others. To our knowledge, it is the first time that a TDA based strategy has been used to compare a massive amount of protein structures with the final objective of performing drug repurposing

In 1982, MIT physicist Frank Wilczek predicted and named ANYONS, quasiparticles (particle-like formations) that are confined to 2 dimensions and were discovered in 2020. The name might come from Prof. Wilczek's lighthearted comment "anything goes". This article's main goal is to show that anyons could be another name for 1) virtual particles, 2) Mobius strips, and 3) figure-8 Klein bottles. Along the way, we'll see the picture painted by the article confirm that Einstein's dream of gravitational-electromagnetic unity fits in with anyons being Mobius strips. The topological hypothesis offers an explanation of dark matter and dark energy. We'll also have encounters with intergalactic travel and imaginary computers. They really could exist but are imaginary in the sense that they use imaginary time (as well as space-time warping).

We create a model universe by equipping a topological surface (system) with compact dimensions insulated by an information blocking horizon. The insulated compact WF can produce entanglement independent of distance. Interaction between the system and the WF changes the curvature of the first and the quantum state (frequency) of the second in an interconnected relationship. Thus, the field curvature measures the evolution of the particle WF as time. Positive field curvature creates pressure, whereas negative field curvature generates a vacuum, satisfying the Borsuk-Ulam Theorem and the Page and Wootters mechanism of static time. The accumulation of pressure or vacuum generates poles with contrasting dimensionalities, two-dimensional black hole horizons (time infinite), and four-dimensional cosmic voids (time zero). The orthogonality of the field and the compact WF give rise to global self-regulation that fine-tunes the cosmic parameters and can promote fractal topology. The four-dimensional vacuum in cosmic voids can produce an accelerating expansion without dark energy. When gravity effects are eliminated, we find a new, so far unexplored, order-increasing side of entropy. The verifiable and elegant hypothesis satisfies Mach's principle.

The paper focuses on the application of a multilevel, integrative and territorial approach to the protection of cultural heritage through planning instruments in Serbia. It points out the problems of the sectoral approach and discusses international recommendations that can be integrated into the planning methodology. The case study is the area of Djerdap National Park, which has an already established international and national importance. The research suggests that the cultural heritage in the area, with the exception of the Roman limes, is still recognized at the level of individual localities. The study emphasizes the importance of aligning all levels of governance to safeguard the whole spatial entity rather than focusing solely on its particular areas. The interna-tional and national importance of the planning area contributes to the strengthening of awareness of heritage and its institutional protection. However, the vitality of the area, which is key to its sustainable development, comes from the local level. Planning documents contribute to the inte-gration of local values and local communities into development programs and projects.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p &lt; 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.

COX II containing a dual core CuA active site is one of the three core subunits of mitochondrial Cco, which plays a significant role in the physiological process. In this report, the full-length cDNA of COXⅡ gene was cloned from Sitophilus zeamais, which had an ORF of 684 bp encoding 227 amino acids residues. The predicted COXⅡ protein had a molecular mass of 26.2 kDa with pI value of 6.37, and multiple sequence alignment and phylogenetic analysis indicated that Sitophilus zeamais COXⅡ had high sequence identity 78.51% with the COXⅡ of other insect species, especially similarity to sitophilus oryzae. This gene was subcloned into the prokaryotic expression vector pET-32a, and induced by IPTG in E.coli Transetta (DE3) expression system. Finally the COXⅡ with 6-His tag was purified using affinity chromatography with Ni²⁺-NTA agarose. WB showed the recombinant COXⅡ was about 44 kD, and the concentration of fusion protein was 50μg/mL. UV-spectrophotometer and infrared spectrometer analysis showed that recombinant COXⅡ could catalyze the oxidation of substrate Cytc, and influenced by AITC. It was found that AITC could form a hydrophobic region with COXⅡ protein via molecular docking, besides, a sulfur atom of AITC structure could form a length of 2.9 Å hydrogen bond with Leu-31. These results will provide valuable information for elucidating the role of COXⅡ in Sitophilus zeamais responses to AITC, meanwhile, it will helpful to carry out a point mutation in AITC binding sites for the future research.

The properties of the annual, semiannual and triennial oscillations (AO, SAO and TO) in the middle atmosphere have been investigated using the TIMED/SABER temperature data. The Lomb-Scargle and wavelet spectra were used to determine the dominant oscillations in the background temperature field. The AO is prominent at the mid-latitudes. The AO amplitudes present an asymmetry between the two Hemispheres, being larger in the mesosphere than in the stratosphere. The SAO dominates the tropical regions, with three amplitude maxima at altitudes of 45, 75, and 85 km. The SAOs in the upper mesosphere (75 km) are out of phase with those in the mesopause (85 km) in the tropical regions, which can generate an enhancement of 11 K at each equinox, contributing to the lower mesospheric inversion layer. The TO is significant in the tropical region, with amplitude being maximum at 35, 45 and 85 km. Result shows that there may be potential interaction by the TO with SAO at 85km at the equator. The relation between ENSO and TO has also been discussed. The ENSO signal may modulate the amplitude of the TO, mainly in the lower stratosphere. The real origin of the TO may lie in the wave-mean-flow interaction.

This paper is focused on typical rural settlements in the Stara Planina Mountain area in Serbia. They are distinguished by their authenticity, identity and character, and they have still not been destroyed by inappropriate development as is the case in some other mountain areas in Serbia, where the effects of urbanization, globalization and homogenization are obvious. Consequently, there is a need to prevent these effects in mountain areas where there is a threat of disbalance between development and the inherited natural and cultural values. The paper starts from several important concepts that relate contemporary approaches to cultural heritage preservation, place morphology and planning future development. The concept definitions from recent documents from related fields are used. These concepts are then applied to the area of Stara Planina Mountain and its rural settlements. The necessity for an integrative approach to planning this area with significant natural and anthropogenic potential is pointed out, including its integration into international programs of sustainable development.

The research reported on this paper was aimed at improving the overall efficiency of a PDO certified artisanal cheese production process. Being a PDO certified foodstuff by the EU, it is considered to have properties and qualities determined by the geographical environment in which is made, with its production taking place in a specific and determined geographical location, in this case the Serra da Estrela region. In that sense, the authors conducted a mapping according to a systemic perspective of the processes involved in the context of manufacturing and distribution of certified Serra da Estrela cheese. Numerous methods were used throughout the process, such as a systemic design analysis, and techniques derived from ethnographic methods, which led to the collection of data in the field and consequently provided the immersion of the researcher in genuine work situations. Critical points were identified and emphasized in the systemic map with the purpose of encouraging initiatives to address and overcome the gaps and inefficiencies detected. The systemic design analysis triggered the development of design work. Observations following an ethnographic approach identified ergonomic risks in cheese making during the process of cutting excess chips, fostering the emergence of musculoskeletal disorders at the wrist. A tool that fits best to the task at hand was developed. A prototype of the new tool enabled collecting feedback from use in the work context, in order to inform product development. The domains of agricultural production and microbiology, considering the specific microorganisms developed trough the ripening process of the cheese, turned out to be aspects of high importance for the issue under focus, contributing to a broader understanding of the ripening process and its risks, simultaneously improving the efficiency and success of cheese production. If it were not for the systemic analysis, which served as a link between the boundaries of distinct domains such as the risk of microorganism contamination, ergonomics, energy efficiency, legislation and regulation policies, transportation challenges and economic viability, approached throughout the research. Simultaneously creating bridges between them, the various problems might not have been detected in the first place, as they are usually addressed in specialized disciplines, predetermined by the restrictions of each specific area of knowledge. As a consequence of the development of this research, which was based on an analysis that sought to establish possible connections between various disciplines and tried to constantly maintain a holistic perspective, making new connections and observing the issues from a new angle, apart from the already established methodologies. All this allowed to lay out the seeds for the development of a plan to tackle the critical points identified by the systemic analysis reported in this paper.

Promoting green development has become an important way to optimize the ecological and economic structure and promote sustainable development. The Yangtze River Economic Belt is an important economic corridor in China, and it remains to be examined whether the implementation of environmental regulations has enhanced the green total factor productivity (GTFP) of cities. This paper calculates the green total factor productivity of 107 cities within the Yangtze River Economic Belt by using the super-efficient SBM model and the GML index with Chinese city panel data from 2007-2019 and measures the intensity of environmental regulations through textual analysis. The bidirectional fixed-effects model constructed in this paper suggests an inverted U-shaped relationship between environmental regulation and GTFP, which is confirmed by a series of robustness tests. Green technology innovation and advanced industrial structure play a mechanism role in this. In regions with lower levels of green technology and industrial structure, the inverted U-shaped relationship between environmental regulation and green total factor productivity is more significant. In addition, the phenomenon of GTFP rising and then falling because of environmental regulation is more obvious in cities in the middle and upper reaches of the Yangtze River and outside the urban agglomerations. Therefore, when formulating environmental regulation policies, it is necessary to balance the relationship between economic development and environmental protection and harmonize the environment and economic development of the entire Yangtze River Basin.

The wetlands in northeastern Bangladesh are home to some of the most prolific and valuable ecosystems on the planet, offering a wide range of benefits to both the local and wider communities. A limited number of research on this topic have been carried out in various wetlands in coastal hilsa, but none have been located in the northeastern region of Bangladesh. Thus, the goal of this study was to better understand the most commercially relevant species (anadromous hilsa) in northeastern Bangladesh as well as to assess the wetland ecosystem services more thoroughly with the contribution of their fish genetic resources. In addition to laboratory studies, fieldwork was done in the fishing settlements along the Surma River, Hakaluki Haor, and Tanguar Haor. The study classified 77 fish species into 21 subcategories of ecosystem services, of which 56% were not threatened, 17% were vulnerable, and 10% were highly endangered. The species Hilsa was found to be the most economically significant, accounting for 1% of the GDP overall. The study determined if the hilsa stock of the examined wetlands maintains a single or several subpopulations in comparison to other parts of the country when they migrate from saline water to northeastern Bangladesh (freshwater). But when compared to other groups, the FST score was between 0.000 to 0.017, indicating that the populations are identical and that there is genetic material sharing between them. Additionally, these statistics imply that the Sylhet haor and adjacent river systems contain a single stock of Hilsa fish. Nevertheless, a number of stresses and risks, including overexploitation, illicit and destructive fishing, siltation, and natural disasters like flash floods, have been linked to changes in the supply of hilsa and other ecosystem services. This study also emphasizes the necessity of policy attention and research to address the concerns.

In this paper, we introduce a specialized, fully automated experimental arrangement designed for assessing the effectiveness of augmented and virtual reality technologies in healthcare setting and explore its design principles focusing on depth sensor characterization. The main goal of this project is to establish a robust benchtop platform for the quantitative evaluation of extended reality technologies in a controlled environment, emphasizing the practical assessment of augmented reality head-mounted devices. We outline a design concept and considerations for an experimental configuration aimed at creating a realistic testing environment. This apparatus comprises an observation platform mounted on a three-degree-of-freedom motorized system and a test phantom stage. Focusing on real-case scenarios, we utilized a range of sensors, including readily available range-sensing cameras and commercial augmented reality headsets, such as the Intel RealSense L515 LiDAR camera, mounted on the motion control system. The setup enabled automated data collection and facilitates controlled assessments and validation of various facets of augmented reality technologies, including object, hand, and head tracking, as well as static and dynamic image registration. This paper describes the system architecture, the process of automated data collection, and presents several evaluation studies conducted with this setup, providing insights into the quality and temporal noise associated with static or moving objects or observers in augmented reality applications.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

To secure logic circuits against the illegal copying the insertion of the so-called key bit inputs using XOR/XNOR gates has been proposed in . The inserted gates modify (lock) the behavior of the original circuit and only the correct choice of the secret key bits guarantees the equivalent behaviour of the locked circuit to the original circuit. To break this kind of security, i.e. to find the secret key bits, there was a SAT solver based attack proposed in . The aim of this work is to experimentally verify this attack on several locked circuits with various number of primary inputs and outputs and with various number of gates. Except for the random placement of the key gates we evaluate also the positioning with chaining elimination. The obtained and presented results show that we were able to unlock 10 of 11 circuits within few minutes on a common PC.

Intermetallic compounds are increasingly being expected to utilize in tribological environments, but to date the works are hindered by insufficient ductility at low and medium temperature. This paper presents a novel multiphase intermetallic alloy with the chemical composition of Mo-40Ni-13Si (at.%). Microstructure characterization reveals that a certain amount of ductile Mo phases formed during the solidification process of ternary Mo-Ni-Si alloy melt, which is undoubtedly beneficial to the ductility improvement of intermetallic alloy. Tribological properties of the designed alloy, including wear resistance, friction coefficient and metallic tribological compatibility, were evaluated under dry sliding wear test conditions at room temperature. Results suggested that the multiphase alloy possesses an excellent property for room-temperature wear applications, which is attributed to the unique microstructural features and thereby good combination in hardness and ductility. The corresponding wear mechanism is also reported by observing the worn surface, subsurface and wear debris of alloy, which to be found is soft abrasive wear.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The electroencephalogram (EEG) is a record of brain activity. Brain Computer Interface (BCI) technology formed by the EEG signal has become one of the hotspots at present. How to extract the feature signal of EEG is the most basic research of BCI technology. In this paper, A new method of recognizing fatigue, conscious, concentrated state of human brain is proposed by the combination of discrete wavelet transform and the neural network based on EEG signal. First of all, the law signal is preprocessed by the wavelet denoising method because the law EEG signal contains a large number of high frequency noise, which is decomposed into multi-layer high frequency signal and low frequency signal. thus, δ wave, θ wave, α wave, β wave are obtained by the wavelet transform. And then, frequency band energy of the different wave is regards as the feature signal of EEG. In the experiment, the feature signal is classified by radial basic function (RBF) and annealed chaotic competitive learning network (ACCLN). RBF and ACCLN networks are trained with 500 sets of sample data and are tested by 100 sets of samples in different mental states. The experimental results show that the average accuracy of RBF network under three conditions are 88.75%, 88.25%, 88.5%, respectively, and the correct rate of ACCLN network is 97%, 98%, 98%, respectively.

Early discharge after childbirth has led to a rise in neonatal readmission, becoming a major concern in recent decades. Our research aimed to identify the risk factors and incidence of neonatal readmission, as well as explore preventive measures. Our study at the Clinical Hospital of Pediatrics in Ploiești, Romania included 108 neonates admitted during the neonatal period. This accounted for 2.06% of all admissions (5226). The most prevalent cases were malnutrition (25%), fever (20.3%) and bronchiolitis (17.5%). Diarrhea and infectious gastroenteritis were also observed (14.8%), along with acute rhinoconjunctivitis (9.2%) and late onset sepsis (3.7%). No deaths were recorded. The most significant characteristics identified were number of children (p&lt;0.001) and age at maternity discharge (p&lt;0.001). By following the prevention rules, malnutrition, feeding errors, and infections can be avoided. This includes practicing proper hand hygiene for both mothers and medical staff, as well as educating and demonstrating to mothers the benefits of breastfeeding. In addition, all newborns discharged from the maternity ward should benefit from follow-up at 7-10 days of life. Our results confirm the effectiveness of a multidisciplinary team and endorse promoting breastfeeding. Implementing quality control measures and regularly evaluating the surveillance program will help improve its effectiveness.

Wearable technology has been proposed as a potential tool to change the way of human life, such as the smart bracelet and the Google Glass. In the wearable technology, the inertial sensor has great significance in tracking the object movements. The paper focused on detecting the movements of user’s finger based on the inertial sensor to give the control signals. Firstly, the attitude matrix, which represented the transformation relation of carrier coordinate system and the navigation coordinate system, was obtained. Secondly, the attitude matrix was expressed based on the quaternions. Thirdly, the finger gesture was processed by the attitude matrix to get the attitude angle. Finally, the robot was controlled by attitude angle to make the moving action. The experimental results showed the detection of the finger movement is effective.

Two interesting compounds such as quaternary ammonium lignin(QAL) and 2-ethylhexyl phosphoric acid mono-2-ethylhexyl ester (P507) were synthesized as ionic liquids(QP-ILs) for the extraction of metal ions. The extraction behavior of Co(II) and Ni(II) from chloride solution was compared between QP-ILs and P507. The results showed that the extraction rates of Co(II) and Ni(II) in the two solutions varied with the extraction conditions, but QP-ILs has stronger extractability for cobalt than P507, while Ni is on the contrary. Under the same conditions, the Co/Ni separation performance(βCo/Ni) in QP-ILs is superior to that in p507. At pH4.0, 55℃, concentration of 20%, extraction time of 10 minutes, the βCo/Ni of QP-ILs reached to 2703, while that of P507 was only 164. In conclusion, QP-ILs was more efficient than P507 for separating Co from mixed solutions.

Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

In this paper, we study the semilinear pseudo-parabolic equations $\displaystyle u_{t} - \triangle_{\mathbb{B}}u - \triangle_{\mathbb{B}}u_{t} = \left|u\right|^{p-1}u$ on a manifold with conical singularity, where $\triangle _{\mathbb{B}}$ is Fuchsian type Laplace operator investigated with totally characteristic degeneracy on the boundary $x_{1} = 0$. Firstly, we discuss the invariant sets and the vacuum isolating behavior of solutions with the help of a family of potential wells. Then, we derive a threshold result of existence and nonexistence of global weak solution: for the low initial energy $J(u_{0})<d$, the solution is global in time with $I(u_{0}) >0$ or $\displaystyle\Vert \nabla_{\mathbb{B}}u_{0}\Vert_{L_{2}^{\frac{n}{2}}(\mathbb{B})} = 0$ and blows up in finite time with $I(u_{0}) < 0$; for the critical initial energy $J(u_{0}) = d$, the solution is global in time with $I(u_{0}) \geq0$ and blows up in finite time with $I(u_{0}) < 0$. The decay estimate of the energy functional for the global solution and the estimates of the lifespan of local solution are also given.

Multimodal remote sensing data classification can enhance the model’s ability to distinguish land features through multimodal data fusion. In this context, how to help models understand the relationship between multimodal data and target tasks is the focus of researchers. Inspired by human feedback learning mechanisms, causal reasoning mechanisms, and knowledge induction mechanisms, this paper integrates causal learning, reinforcement learning, and meta learning into a unified remote sensing data classification framework and proposed the causal meta-reinforcement learning (CMRL). First, based on feedback learning mechanisms, we have overcome the limitations of traditional implicit optimization of fusion features and customized a reinforcement learning environment for multimodal remote sensing data classification tasks. Through feedback interactive learning between agents and the environment, we help them understand the complex relationships between multimodal data and labels, thereby achieving full mining of multimodal complementary information. Second, based on the causal inference mechanism we designed causal distribution prediction action, classification reward, and causal intervention reward, capturing pure causal factors in multimodal data and cutting off false statistical associations between non-causal factors and class labels. Finally, based on the knowledge induction mechanism, we designed a bi-layer optimization mechanism based on meta-learning. By constructing a meta training task and meta validation task simulation model in the generalization scenario of unseen data, we helped the model induce cross-task shared knowledge, thereby improving its generalization ability for unseen multimodal data. The experimental results on multiple sets of multimodal datasets show that the proposed method achieved state-of-the-art performance in multimodal remote sensing data classification tasks.

Moso bamboo is famous for fast growing and biomass accumulation, as well as high annual output for timber and bamboo shoots. These high outputs require high nutrient inputs to maintain and improve stand productivity. Soil nitrogen (N), phosphorus (P) and potassium (K) are important micronutrients for plant growth and productivity. Due to high variability of soils, analysing spatial patterns of soil N, P and K stocks is necessary for scientific nutrient management in Moso bamboo forests. In this study, soils were sampled from 138 locations across Yong’an City and ordinary kriging was applied for spatial interpolation of soil N, P and K stocks. Soil N stock showed a strong spatial dependence while soil N and P stocks presented a moderate spatial dependence, indicating soil N was mainly controlled by intrinsic factors while soil N and P stocks were controlled by both intrinsic and extrinsic factors. Different spatial patterns were observed for soil N, P and K stocks across the whole study area, indicating that fertilizations with different ratios of N:P:K should be applied for different sites to maintain and improve stand productivity. The total soil N, P and K stocks within 0-60 cm were 0.624, 0.020 and 0.583 Tg, respectively.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

Lignocellulosic biomass, an abundant and renewable resource, serves as a crucial raw material for the production of biofuels, biochemicals, and various value-added products. This paper aims to develop and optimize a mild alkaline treatment of sawdust assisted by ultrasound, along with enzymatic hydrolysis of the pretreated material. The sonochemical pretreatment with alkali emerged as the optimal approach to enhance the susceptibility of cellulose to subsequent enzymatic hydrolysis, thereby increasing the yield of reducing sugars. A comparative study was performed using various ultrasonic applicators (horn and bath) and conventional assisted alkaline pretreatment. The ultrasonic assisted pretreatment revealed a higher delignification as 68% (horn) and 57% (bath) comparing with conventional pretreatment. Processes were optimized using a statistical analysis based on a 23 factorial design. The ratio between sawdust and alkanine solution (RSL = 0.5–1.5 g/100 mL), US amplitude (A = 20–60%), and working temperature (t = 30–50 °C) were selected as process factors. The optimal operating conditions to maximize the reducing sugar yield (138.15 mg GE /gsubstrate) were found as following: solid/liquid ratio RSL,opt = 1.25 g/100 mL, US amplitude Aopt = 60%, and and pretreatment temperature topt = 50 °C. The overall outcomes clearly confirmed the intensification of delignification by ultrasound assisted alkaline pretreatment

At 12:28 UTC (8:28 PM, local time) on 15 January 2023, a shallow, moderate earthquake with a magnitude (Mw) of 4.7 and a depth of one (1) kilometer shook the northern part of Leyte Island in the central part of the Philippines. The ground shaking was felt at PHIVOLCS Earthquake Intensity Scale (PEIS) VI, (very strong) equivalent to Modified Mercalli Intensity (MMI) VI in areas along the strike of the Leyte segment of the Philippine Fault. Even though this earthquake is classified as moderate in size, significant geologic, structural, and socio-economic impacts were documented. Most of the earthquake impacts that include surface rupture, liquefaction, and mass movements are limited near the epicentral area. The earthquake produced an 8-km-long surface rupture, the first documented rupture generated by a magnitude &lt;6 along the Philippine Fault, and is unusual in other surface rupturing earthquakes worldwide. It injured 18 persons and damaged 434 residential buildings and 26 public infrastructures which amounts to about 500 thousand US dollars. Structural damages were caused by surface rupture and/or poor engineering construction practices. Analysis of the seismic waveform and recorded peak ground acceleration (PGA) suggests that the subsurface geology influenced the ground motion amplification in an area near the epicenter. Based on the hypocenter, focal mechanism solution, aftershock distribution, and actual field observation, the causative fault for this event is the northern end of the Philippine Fault – Leyte segment. Moderate in size, this earthquake emphasizes the need and importance of documenting moderate-sized earthquakes as a tool and guide for medium and long-term earthquake risk assessment and resiliency.

Background: The bacterium Capnocytophaga spp. is a part of the oral microbiome of dogs, which has zoonotic potential. This study aimed to assess factors that influence the prevalence of Capnocytophaga cynodegmi and Capnocytophaga canimorsus in Wrocaw’s dog population. Methods: Molecular detection (PCR) involved 105 swabs, examining parameters such as oral cavity condition, underlying health issues, recent antibiotics, age, medications, material collection, and dog chews. Results: The study highlighted significant associations between the presence of Capnocytophaga cynodegmi and oral cavity condition, material collection method, and use of dog’s chews. Conclusions: Correlations obtained in this study between the oral health of Wroclaw's dog population and the presence of Capnocytophaga cynodegmi suggest its significance in the oral microbiome of dogs. Limitations, including the small sample size, highlight the need for more research to comprehensively understand additional factors that affect the prevalence of Capnocytophaga spp. in dogs