Order Matrix Research Articles

Electrochemically Driven Yarn and Textile Actuators

Conjugated polymers such as polypyrrole can be electrochemically oxidised and reduced. These redox reactions are accompanied by a flow of counter ions and solvent from the electrolyte into or out of the polymer matrix in order to maintain charge balance and osmotic pressure, resulting in an electrochemically induced volume change of the conjugated polymer. This volume change can be exploited to fabricate electrochemically driven actuators in various formats from bending bilayer microactuators to macroscopic textile actuators. Yarn and textile actuators are fabricated by coating commercially yarns and fabrics with the conjugated polymers. First a thin layer of PEDOT (poly(3,4-ethylenedioxythiophene)) is applied to make the yarns or fabrics electrically conductive. Thereafter the yarns/fabrics are coated with electromechanically active polypyrrole using electrochemical synthesis. Next the yarn or textile actuators can be actuated by applying the appropriate redox potentials. In order to achieve in-air actuation, the yarns or fabrics will be coated with novel ionogels (gelled semi-solid electrolytes), that function as the ion source/sink to drive the electrochemical reactions. Two of such ionogel coated yarns will be assembled forming the anode/cathode pair of the electrochemical circuit. Using advanced textile manufacturing such yarn actuators can be integrated into fabrics using knitting or weaving. The latest results of our textile actuators both operating in liquid electrolytes as in-air will be presented.Fig. 1 A textile actuator with 4 inlay-knitted, in-air operating yarn actuators. Figure 1

NASICON-type V-doped Ca0.5Ti2 (PO4)3/C nanofibers for fast and stable potassium storage

Potassium ion batteries (PIBs) are considered as the next-generation energy storage devices owing to the relatively low cost and the plentiful reserves. In addition, the redox potential of K+/K is comparable to that of Li+/Li. When it comes to the negative electrode, NASICON type materials draw much attention owing to their robust structure and high ionic mobility. Herein, vanadium doped Ca0.5Ti2 (PO4)3/C nanofibers are prepared by means of electrospinning and post-annealing, and vanadium is doped into the matrix in order to boost the ionic/electronic conductivities. When assembled as PIBs half cells, it delivers a high capacity of 131 mAh g−1 at 5 A g−1, which performs best in analogues. Furthermore, it provides a stable capacity of 112 mAh g−1 after 1000 cycles at 1 A g−1. This finding indicates that V-doped Ca0.5Ti2 (PO4)3/C nanofibers have the potential to be an attractive anode choice for PIBs.

Theoretical analysis of the adhesion law of stainless steel FRCM strengthening systems

FRCMs (Fibre-Reinforced Cementitious Matrix) are a particular type of structural reinforcement characterized by a fibre mesh or fabric embedded in an inorganic cement-based or lime-based matrix in order to reinforce concrete and masonry structures. The failure modes of this type of reinforcement are related to the loss of adhesion in an interface or to the fracture of the fibre. An experimental campaign was carried out by means of traction-shear tests to evaluate the fracture behaviour of the reinforcement as the bonded length of the specimen varies. Starting from the experimental results, a theoretical adhesion law was formulated that could analytically reflect the results of the campaign; thus, the purpose was to find the FRCM system Adhesion Law, to define the resistance of the reinforcement as the abscissa varies and the anchorage length associated with maximum resistance.

Synthesis, microstructure and ionic conductivity of Li1+xAlxTi2−x(PO4)3-based composite ceramic electrolyte

NASICON-type Li[Formula: see text]Al[Formula: see text]Ti[Formula: see text](PO4)3 (LATP) solid electrolyte features low cost of raw materials, high stability against O2/H2O, high ionic conductivity and high compatibility with cathode. Despite these merits, the direct contact of Li with LATP would trigger the reduction of Ti[Formula: see text] into Ti[Formula: see text], and thereupon decrease ion conductivity and enhance electron conductivity, thus limiting its application. In this work, the garnet Li[Formula: see text]La3Zr[Formula: see text]Ta[Formula: see text]O[Formula: see text] (LLZTO) particles were chosen to disperse in NASICON-type Li[Formula: see text]Al[Formula: see text]Ti[Formula: see text](PO4)3 matrix in order to strengthen stability against Li metal. During sintering, the matrix/particle reaction occurs, where LLZTO reacts with LATP, inducing the formation of LaPO4 particles. By optimization of LLZTO content, an enhanced ionic conductivity of 1.03 × 10[Formula: see text]S cm[Formula: see text] can be harvested. Moreover, introducing LLZTO particles into LATP matrix can slow down the degradation of the electrolyte to a certain extent and provide better interfacial stability of symmetrical battery than pure LATP solid electrolyte.

One-Year Surveillance of SARS-CoV-2 and Rotavirus in Water Matrices from a Hot Spring Area.

The pandemic of Coronavirus Disease 2019 (COVID-19) caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is still impacting not only on human health but also all economic activities, especially in those related to tourism. In this study, in order to characterize the presence of SARS-CoV-2 in a hot spring park in Uruguay, swimming pools water, wastewater, and surface water from this area were analyzed by quantitative PCR. Wastewater from Salto city located next to the hydrothermal spring area was also evaluated as well as the presence of Rotavirus (RV). Overall, SARS-CoV-2 was detected in 13% (13/102) of the analyzed samples. Moreover, this virus was not detected in any of the samples from the swimming pools water and was present in 18% (3/17) of wastewater samples from the hotels area showing the same trend between the titer of SARS-CoV-2 and the number of infected people in Salto city. SARS-CoV-2 was also detected in wastewater samples (32% (11/34)) from Salto city, detecting the first positive sample when 105 persons were positive for SARS-CoV-2. Rotavirus was detected only in 10% (2/24) of the wastewater samples analyzed in months when partial lockdown measures were taken, however, this virus was detected in nearly all wastewater samples analyzed when social distancing measures and partial lockdown were relaxed. Wastewater results confirmed the advantages of using the detection and quantification of viruses in this matrix in order to evaluate the presence of these viruses in the population, highlighting the usefulness of this approach to define and apply social distancing. This study suggests that waters from swimming pools are not a source of infection for SARS-CoV-2, although more studies are needed including infectivity assays in order to confirm this statement.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12560-022-09537-w.

A Dynamic Programming Bayesian Network Structure Learning Algorithm Based on Mutual Information

Existing Bayesian network (BN) structure learning algorithms based on dynamic programming have high computational complexity and are difficult to apply to large-scale networks. Therefore, this paper proposes a Dynamic Programming BN structure learning algorithm based on Mutual Information, the MIDP (Dynamic Programming Based on Mutual Information) algorithm. The algorithm uses mutual information to build the maximum spanning tree and [Formula: see text]-order matrix, and introduces a penalty coefficient d based on the matrix-based node removal strategy, so as to reduce the number of scoring calculations and time consumption of the algorithm. Simulation results show that, compared with DP, SMDP and MEDP algorithms, the MIDP algorithm can reduce the calculation times and time consumption of algorithm scores while maintaining the accuracy of the algorithm when selecting the appropriate [Formula: see text] value.

Some Spectrum Estimates of the $\alpha q$-Ces\`{a}ro Matrices with $0<\alpha ,q<1$ on $c$

$q$-Calculus Theory is rapidly growing in various directions. The goal of this paper is to collect and underline recent results on $\alpha q$-analogs of the Cesàro matrix andemphasize various generalizations. One $\alpha q$-analogs of the Cesàro matrix of order one is the triangular matrix with nonzero entries $c_{nk}^{\alpha }\left( q\right) =\tfrac{\left( \alpha q\right) ^{n-k}}{1+q+\cdots +q^{n}},\ 0\leq k\leq n$, where $\alpha ,q\in \left( 0,1\right) $. The purpose of this article examines various spectral decompositions of $C_{q}^{\alpha }=\left( c_{nk}^{\alpha }\left( q\right) \right) $ such as the spectrum, the fine spectrum, the approximate point spectrum, the defect spectrum, and the compression spectrum on the sequence space $c$.

Thermogravimetric Analysis and Mechanical Properties of Pebble Natural Filler-Reinforced Polymer Composites Produced through a Hand Layup Technique

Using pebble and fibre in an epoxy matrix, the mechanical, dynamic, and thermal characteristics of a composite were examined. Tensile, flexural, impact, and interlaminar shear strengths are experimentally determined. In this study, we compare the mechanical performance of carbon fibre composites composed entirely of conventional epoxy (NE). The results of a comparative investigation using 15 and 20% carbon fibre in an epoxy matrix are presented. Additional categories for compressive strength and damping ratio were defined based on this performance. The epoxy resin was combined with carbon fibre (15 wt% and 20 wt%) in a unidirectional arrangement and manufactured with different fillers like pebble. The goal of this research is to better understand the bonding mechanisms between damping materials and the resin matrix in order to increase interfacial bonding performance. This information is required for both selecting the appropriate material for applications and developing a composite construction using that material.

Green upconversion improvement of TiO2 codoped Er3+-Yb3+ nanoparticles based thin film by adding ALD-Al2O3 for silicon solar cell applications

This paper investigates the upconversion luminescence modification of codoped nanoparticles with lanthanides ions based thin films through adding a coating. First, these nanoparticles based thin films composed of Er3+-Yb3+ co-doped anatase-TiO2 are prepared using spin coating technique supported by a design of experiment (DOE) approach with the Hadamard matrix of order 4 to improve the deposition conditions. For the coating step, different thicknesses of Al2O3 layer are realised by thermal atomic layer deposition (T-ALD) onto the surface of Er3+-Yb3+ co-doped anatase-TiO2 nanoparticles based thin films. The results show that the ALD-Al2O3 coating can benefit the luminescence properties of the upconversion nanoparticles based thin films. Thin ALD-Al2O3 (10 nm) coated stack exhibits similar upconversion luminescence as the uncoated nanoparticles based film but insures also their stability. Meanwhile, for 100 nm ALD-Al2O3 coating, enhancement of the upconversion green emission by 98% at the expense of the red emission compared to uncoated sample is observed. This results are promising to be applied in photovoltaics field.

Formula omitted]-(soliton, breather) interactions for general multi-component third-fifth-order mKdV equations via Riemann–Hilbert method

In this paper, we mainly focus on the Riemann–Hilbert approach solving the general n-component third-fifth-order mKdV (n-(3,5)-mKdV) equations containing the n-component mKdV equation, fifth-order mKdV equation, and their combination. Starting from the spectral analysis of the (n+1)-order matrix Lax pair, we give the corresponding (n+1)-order matrixed-type Riemann–Hilbert problem. By solving the Riemann–Hilbert problem, N-soliton solutions of the n-(3,5)-mKdV equations can be found. Particularly, for the case of reflectionless and some types of spectral parameters of the Lax pair, we analyze the interactions of some kinds of solutions of the 3-component mKdV equation, fifth-order mKdV equation, and third-fifth-order mKdV equations, including breather solutions, W-shaped solutions, anti-bright and bright solutions. Some elastic collisions between them are also presented. In addition, the multi-pole solutions are obtained for the n-(3,5)-mKdV equations through the L’Hospital’s rule. These results will be useful to further understand the related wave phenomena in the multi-component physical systems.

Information security of business networks

Objectives. The aim of the work is to improve the information security of businesstype networks, as the most susceptible to external threats for the purpose of commercial espionage.Method. The study is based on the use of an original mathematical algorithm that allows clustering networks of arbitrary topology and constructing a partial order matrix. On the one hand, clustering allows you to identify network agents that have the same information access to the network, on the other hand, partial order reflects the position of a single cluster in the hierarchical structure of the entire network.Result. This approach allows assessing the potential threat to the network depending on the position of the agent in the network that has been attacked. On its basis, a methodology has been developed to determine the degree of openness of an entrepreneurial network to the influence of external factors for each agent of the network. It allows you to identify network agents that require increased security in relation to external influences, since a potential attack on them has the most negative consequences for the entire connection, which emphasizes the practical significance of the result.Conclusion. The developed technique is practically significant and can be used to assess the information security or integrity of a network of any nature, represented as a directed graph.

P T dispersion of inclusive jets in high-energy nuclear collisions* *Supported in part by Guangdong Major Project of Basic and Applied Basic Research (2020B0301030008), the National Natural Science Foundation of China (NSFC) (11935007, 12035007), the MOE Key Laboratory of Quark and Lepton Physics (CCNU) (QLPL2020P01)

In this study, we investigate the impact of jet quenching on the dispersion () of inclusive jets () in central Pb+Pb (0%–10%) collisions at TeV. The partonic spectrum in the initial hard scattering of elementary collisions is obtained by an event generator POWHEG+PYTHIA, which matches the next-to-leading order (NLO) matrix elements with parton showering, and the energy loss of a fast parton traversing through hot/dense QCD medium is calculated using Monte Carlo simulation within the Higher-Twist formalism of jet quenching in heavy-ion collisions. We present model calculations of the normalized distributions of inclusive jets in p+p and central Pb+Pb collisions at TeV, which offer good descriptions of ALICE measurements. It is shown that the distributions of inclusive jets in central Pb+Pb collisions shift significantly to a higher region relative to those in p+p collisions. Thus, the nuclear modification ratio of the distributions of inclusive jets is smaller than unity in the small region and larger than one in the large region. This behavior is caused by a more uneven distribution for jet constituents as well as the fraction alteration of quark/gluon initiated jets in heavy-ion collisions. The difference in distribution between groomed and ungroomed jets in Pb+Pb collisions is also discussed.

Local Binary Patterns Based on Neighbor-Center Difference Image for Color Texture Classification with Machine Learning Techniques

This is a topic that receives a lot of interest since many applications of computer vision focus on the detection of objects in visually appealing environments. Information about an object’s appearance and information regarding the object’s motion are both used as crucial signals in the process of identifying and recognising any given item. This information is used to characterise and recognise the item. The identification of objects based solely on their outward appearance has been the subject of a substantial amount of research. However, motion information in the recognition task has received only a marginal amount of attention, despite the fact that motion plays an essential role in the process of recognition. In order to analyze a moving picture in a way that is both fast and accurate, it is required to make use of motion information in conjunction with surface appearance in a strategy that has been designed. Dynamic texture is a kind of visual phenomenon that may be characterised as a type of visual phenomenon that shows spatially repeated features as well as some stationary properties during the course of time by using methodologies that are associated with machine learning. The design of modern VLSI systems takes into consideration a larger chip density, which results in a processor architecture with several cores that are capable of performing a wide range of functions (multicore processor architecture). It is becoming more challenging to run such complicated systems without the use of electric power. In order to increase the effectiveness of power optimization strategies while maintaining system performance for text data extraction, it has been developed and put into practice power optimization strategies that are based on scheduling algorithms. Over the last twenty years, texture analysis has been an increasingly busy and profitable field of study. Today, texture interpretation plays a vital role in various activities ranging from remote sensing to medical picture analysis. The absence of tools to newline analyze the many properties of texture pictures was the primary challenge faced by the texture analysis approach. Texture analysis may be roughly categorised as texture classification, texture segmentation, texture synthesis, and texture synthesis. Texture categorization is useful in numerous applications, such as the retrieval of picture databases, industrial agriculture applications, and biomedical applications. Texture categorization relies on three distinct methods, namely, statistical, spectral, and structural methods. Statistical methods are based on the statistical characteristics of the image’s grey level. Features are collected using second order statistical order, autocorrelation function, and grey level co-occurrence matrix function.

The impact of inter-observer variation in delineation on robustness of radiomics features in non-small cell lung cancer

Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: ‘NSCLC-Radiomics’ and ‘NSCLC-Radiomics-Interobserver1’ (‘Interobserver’). For ‘NSCLC-Radiomics’, we created an additional set of manual contours for 92 patients, and for ‘Interobserver’, there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 (‘NSCLC-Radiomics’) to 0.85 (‘Interobserver’—semi-automated). The median ICC for the ‘NSCLC-Radiomics’, ‘Interobserver’ (manual) and ‘Interobserver’ (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the ‘NSCLC-Radiomics’ dataset compared to the ‘Interobserver’ dataset. Survival analysis showed similar separation of curves for three of four RF apart from ‘original_shape_Compactness2’, a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features’ prognostic capability.

Consistency of total fuzzy relations: New algorithms to detect and repair inconsistent judgments

Fuzzy orders, especially T-preorders, can express the vague priority over a list of alternatives. The cardinal consistency of such relations is achieved through the T-transitivity condition, while the ordinal consistency of the final decision at a given threshold α∈(0,1] is not guaranteed by the T-consistency. This paper investigates the problem of ordinal consistency, as the minimum requirement for a reliable judgment, of a partial preference induced by a fuzzy relation at a given level α∈(0,1], so-called α-preference relation. We first define new concepts of T−L-cyclic and T-consistency at a given level α for fuzzy relations. Based on digraph theory, a new methodology is designed for finding the locations of all consistent and inconsistent L-cycles of an α-preference. An algorithm is proposed to eliminate the ordinal inconsistency through the TM-transitivity. Meanwhile, a new T-consistency index is introduced to measure the acceptable consistency level of fuzzy relations. Furthermore, some results concerning the ordinal consistency of T-preorders are applied to design another algorithm for creating a consistent collective judgment based on initial fuzzy assessments of alternatives in a group ranking problem. The proposed method constructs the consistent fuzzy order matrix for each case (expert), then calculates the group consensus by aggregating these matrices. The collective fuzzy order is then checked for consistency level. Finally, a numerical example with a real dataset is given to illustrate the application of the proposed method in a ranking problem.

Implementation of Two-Mode Gaussian States Whose Covariance Matrix Has the Standard Form

This paper deals with the covariance matrix (CM) of two-mode Gaussian states, which, together with the mean vector, fully describes these states. In the two-mode states, the (ordinary) CM is a real symmetric matrix of order 4; therefore, it depends on 10 real variables. However, there is a very efficient representation of the CM called the standard form (SF) that reduces the degrees of freedom to four real variables, while preserving all the relevant information on the state. The SF can be easily evaluated using a set of symplectic invariants. The paper starts from the SF, introducing an architecture that implements with primitive components the given two-mode Gaussian state having the CM with the SF. The architecture consists of a beam splitter, followed by the parallel set of two single–mode real squeezers, followed by another beam splitter. The advantage of this architecture is that it gives a precise non-redundant physical meaning of the generation of the Gaussian state. Essentially, all the relevant information is contained in this simple architecture.

Classification of Surfaces of Coordinate Finite Type in the Lorentz–Minkowski 3-Space

In this paper, we define surfaces of revolution without parabolic points in three-dimensional Lorentz–Minkowski space. Then, we classify this class of surfaces under the condition ΔIIIx=Ax, where ΔIII is the Laplace operator regarding the third fundamental form, and A is a real square matrix of order 3. We prove that such surfaces are either catenoids or surfaces of Enneper, or pseudo spheres or hyperbolic spaces centered at the origin.

Application of Fiedler Value for Mitigation of Loss of Connectivity in Wireless Mesh Networks

This paper proposes a new scheme for very fast detection of a sensor going offline or loss of connectivity of sensor in Wireless Mesh Networks (WMN) in order to avoid any packet loss. A sensor is declared offline by a gateway if it does not receive any packet from the sensor for a specific time interval termed as keep-alive time. In mesh networks, this keep-alive window is kept for a very long duration; sometimes it is as high as 2.5 to 3 hours. The time duration is kept high so that gateway does not declare a sensor that is in deep sleep to conserve battery, as offline. However, keeping a large keep-alive time window leads to the very likely possibility of missing the timely detection of a critical event like a smoke sensor or gas leak sensor going offline. In the proposed method, the gateway maintains its table of neighbourhood matrix in order to facilitate fast and efficient tracking as well as tracing of the nodes for the detection of the loss of connectivity of a sensor. A Laplacian matrix formulation proposed by Fiedler is invoked for tracking and tracing lost nodes of WMN. Through the Fiedler value, this paper substantiates the efficacy of monitoring a large WMN not only for its algebraic connectivity but also to compute the number of disconnected partitions in it. This is achieved through the number of null values of eigenvalues of the Laplacian matrix of the graph of WMN. Through simulation studies, this paper demonstrates achieving an 88 % success rate in the detection of disconnection with an average latency of 39.806 ms. On the contrary, a conventional process for the time duration after which a sensor can be declared offline might be an hour or even more depending upon the pre-set duration of the keep-alive time window.

An RBF online learning scheme for non-stationary environments based on fuzzy means and Givens rotations

Learning on non-stationary environments is laden with many challenges, as the procedure is usually characterized by drifts and data unavailability; on the other hand, it is of great importance with regard to data stream modeling, where an effective and up-to date model is required as the data stream evolves. This work presents a new method for producing highly accurate models for learning on such environments. The method is based on artificial neural networks (ANNs) and more specifically on the efficient architecture of radial basis function (RBF) networks. A novel RBF online training scheme for real time adaption of both the network structure and parameter values is proposed based on the fuzzy means (FM) algorithm and the Givens rotations technique. Within this integrated framework, it is guaranteed that for each update of the network structure, the optimal values for the synaptic weights are calculated efficiently by maintaining low order matrix updates. The proposed approach is evaluated on 9 real-word and artificial benchmark data streams, including a challenging real-world application which involves precipitation prediction, and is compared to other well-known methodologies from the literature. The results show that the FM-Givens algorithm produces models with highly competitive online accuracy for non-stationary environments in the presence of drifts, while maintaining low model-updating times.

RAFT Polymerization of a Biorenewable/Sustainable Monomer via a Green Process.

A biorenewable polymer is synthesized via a green process using the RAFT principle for the first time in supercritical CO2 at 300bar and 80°C. α-Methylene-γ-butyrolactone polymers of various chain lengths and molecular weights are obtained. The molecular weights vary from 10000 up to 20000 with low polydispersity indexes (PDI <1.5). Furthermore, the monomer conversion in supercritical CO2 is substantially higher, respectively 85% for ScCO2 compared to ≈65% for polymerizations conducted in dimethyl formamide (DMF) solvent. Chain extensions are carried out to confirm the livingness of the formed polymers in ScCO2 . This opens up future possibilities of the formation of different polymer architectures in ScCO2 . The polymers synthesized in ScCO2 have glass transition temperature (Tg ) values ranging from 155 up to 190°C. However, the presence of residual monomer encapsulated inside the formed polymer matrix affects the glass transition of the polymer that is lowered by increasing monomer concentrations. Hence, additional research is required to eliminate the remaining monomer concentration in the polymer matrix in order to arrive at the optimal Tg .