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).

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.

New setting is introduced to study types of coloring numbers, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Different types of procedures including neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are proposed in this way, some results are obtained. General classes of neutrosophic hypergraphs are used to obtain chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Using colors to assign to the vertices of neutrosophic hypergraphs and characterizing representatives of the colors are applied in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. Some questions and problems are posed concerning ways to do further studies on this topic. Using different ways of study on neutrosophic hypergraphs to get new results about number, degree and co-degree in the way that some number, degree and co-degree get understandable perspective. Neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are studied to investigate about the notions, coloring, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. In this way, sets of representatives of colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges have key points to get new results but in some cases, there are usages of sets and numbers instead of optimal ones. Simultaneously, notions chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges are applied into neutrosophic hypergraphs, especially, neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs to get sensible results about their structures. Basic familiarities with neutrosophic hypergraphs theory and hypergraph theory are proposed for this article.

The bioactive marine sulfated polysaccharides ulvan and carrageenan, formulated in gels at 1.5, 5.0 and 10% w/w concentrations, were evaluated for their wound healing potential on second-degree burn wounds. The polysaccharide-based gels were administered on the burn-inflamed skin of hairless female SKH-hr2 mice, and the therapeutic effect of the various treatments was assessed based on histopathological analysis, the clinical condition of mice, photo-documentation, evaluation of biophysical skin parameters, and FT-IR spectroscopy.Clinical and histopathological assessment, as well as the reduction rate of the burn wound area, indicated that the 10% w/w carrageenan gel significantly enhanced wound healing, especially at the early stages of the healing process, recovering both the α-helix of collagen and the glycosaminoglycans configuration as evidenced by the FT-IR absorption bandsof skin in vivo and ex vivo. At later stages, the healing effect of the 5% w/w ulvan gel was also notably significant, indicating that carrageenan and ulvan wound dressing gels could potentially contribute to the efficient wound healing of burn wounds.

Lignin fibers typically influence the mixture performance of stone matrix asphalt (SMA), such as strength, stability, durability, noise level, rutting resistance, fatigue life, and water sensitivity. However, limited studies were conducted to analyze the influence of fibers on the percent voids in mineral aggregate in bituminous mixture (VMA) during the mixture design. This study analyzed the effect of different fibers and fiber contents on the VMA in SMA mixture design. A surface-dry condition method test and Marshall Stability test were applied on the SMA mixture with four different fibers (i.e., flocculent lignin fiber, mineral fiber, polyester fiber, blended fiber). The test results indicated that the bulk specific gravity of SMA mixtures and asphalt saturation decreased with the increasing fiber content, whilst the percent air voids in bituminous mixtures (VV), Marshall Stability and VMA increased. Mineral fiber had the most obvious impact on the bulk specific gravity of bituminous mixtures, while flocculent lignin fiber had a minimal impact. The mixture with mineral fiber and polyester fiber had significant effects on the volumetric properties, and, consequently, exhibited better VMA over the conventional SMA mixture with lignin fiber. Modified fiber content range was also provided, which will widen the utilization of mineral fiber and polyester fiber in the applications of SMA mixtures. The mixture evaluation suggested no statistically significant difference between lignin fiber and polyester fiber on the stability. The mineral fiber required a much larger fiber content to improve the mixture performance than other fibers. Overall, the results can be a reference to guide SMA mixture design.

Chellali et al. introduced two degree concepts, ve-degree and ev-degree (Chellali et al, 2017). The ve-degree of a vertex equals to number of different edges which are incident to a vertex from the closed neighborhod of v. Moreover the ev-degree of an edge e=ab equals to the number of vertices of the union of the closed neighborhoods of a and b. The most private feature of these degree concepts is, total number of ve-degrees and total number of ev-degrees equal to first Zagreb index of the graphs for triangle-free graphs. In this paper we introduce ve-degree entropy, ev-degree entropy and investigate the relations between these entropies and the first Zagreb index entropy. Finally we obtain the maximal trees with respect to ve-degree irregularity index.

Climate change is challenging the agricultural sector worldwide. The pressure exerted by the altered thermal conditions drives the zonal shift for various cultivations. This study presents the multiyear analysis of the Growing Degree Days (GDD) index over the Northern Mediterranean Basin (NMB), focused on the high-value vine cultivation derived from the E-OBS dataset. To handle this extensive environmental dataset, R-language scripts and packages were applied, along with the capabilities of the GIS software. The investigated time period spans from 1969 to 2018, and the performed analysis indicates a broad shift/expansion in areas of GDD exceeding 2000 heat units. The extracted results specify the viticultural areas where the GDD index surpasses the suitable values for vine cultivation, owing to the increase of the thermal stress risk. In addition to mapping areas with GDD ≥ 2000, spatial calculations of the surface of areas meeting this criterion per country in the study area were made. In the northern parts of the NMB, the rise of the GDD index values indicates the establishment of vine plantations in the future.

This study delves into the critical domain of public procurement, specifically focusing on Sustainable Public Procurement (SPP) and Environmental, Social, and Governance (ESG) considerations within the context of South Korea. The aim of this research is to empirically scrutinize the factors influencing the impact index of SPP, with a particular emphasis on services related to labor standards. The investigation spans three years, from January 1, 2019, to July 31, 2021, encompassing state agency orders and post-implementation legal changes. Leveraging the comprehensive Korea On-line E-Procurement Sys-tem(KONEPS) platform, we meticulously collected data, ultimately culminating in 21,573 services meeting our criteria, from which 250 cases underwent an in-depth analysis. The study sheds light on the influence of social responsibility evaluation cri-teria on overall procurement scores, while also uncovering the moderating effects of various factors. The outcomes of this study offer valuable insights into the impact index of social responsibility evaluation criteria within South Korea's public procurement landscape. Factors including procurement year, classification, service period, and estimated price were identified as significant influencers. Moreover, the number and location of evalu-ation criteria items, inclusion of sincerity elements, and the evaluating agency were deemed pivotal determinants of the influence index. These findings hold profound implications for policymakers, procurement practitioners, and researchers and provide a roadmap for optimizing the utilization of social responsibility evaluation criteria to advance responsible and sustainable procurement practices. Future research avenues include conducting further study of additional moderat-ing variables, expanding datasets to encompass a broader spectrum of procurement contexts, and evaluating the long-term repercussions of social responsibility evaluation on procurement outcomes. These endeavors promise to further refine and advance public procurement practices in South Korea and beyond.

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.

Bi1-xSbx crystal is one of the best n-type thermoelectric materials below 200 K, but its weak mechanical strength hinders practical applications for deep refrigeration. Herein, we adopted the mechanical enhancement method of hot extrusion to investigate the comprehensive mechanical and thermoelectric properties of Bi0.905Sb0.095. It revealed that reducing the grain size of the matrix and increasing the extrusion ratio can improve the gain size uniformity and mechanical properties. While the thermoelectric performance depends on the texture, grain size, and local composition. The extruded sample prepared by ingot with the high extrusion ratio of 9:1 generated a high texture degree of (001) plane and small grain sizes between ~4-40 mm, which resulted in the high bending strength of Bi1-xSbx ~130 Mpa and a high-power factor of ~ 68 μW·cm-1·K-2@173 K, as well as the relative high figure of merit of 0.25@173K. This work highlights the importance of the uniform distribution of the grain size and the compositions for Bi1-xSbx, as well as the required universal key parameter for the hot-extrusion method.

New setting is introduced to study quasi-degree and quasi-co-degree arising from co-neighborhood. quasi-degree and quasi-co-degree is about a vertex which are applied into the setting of neutrosophic graphs. . The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs namely path-neutrosophic graphs, cycle-neutrosophic graphs, complete-neutrosophic graphs and star-neutrosophic graphs, complete-bipartite-neutrosophic graphs and complete-multipartite-neutrosophic graphs are investigated in the terms of a vertex which is called either quasi-degree or quasi-co-degree. Neutrosophic number is reused in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number to compare with other vertices. Summation of three values of vertex makes one number and applying it to a comparison. This approach facilitates identifying vertices which form quasi-degree and quasi-co-degree. Quasi-degree is a value of a vertex which is maximum amid all values of vertices which are neighbors to a fixed vertex. Quasi-co-degree is a value of an edge which is maximum amid all values of edges which are neighbors to a fixed vertex but corresponded vertex is representative for this notion. Using different values which are related to a vertex inspire us to focus on edge and vertices which are corresponded to a fixed vertex. The notion of neighborhood is used to collect either vertices are titled neighbors or edges are incident to fixed vertex. In both settings, some classes of well-known neutrosophic graphs are studied. Some clarifications for each result and each definitions are provided. Using fixed vertex has key role to have these notions in the form of vertex or edge. The value of an edge has eligibility to call quasi-co-degree but the value of a vertex has eligibility to call quasi-degree. Some results get more frameworks and perspective about these definitions. The way in that, two vertices have connection together, open the way to define neighborhood and co-neighborhood. The maximum values in neighborhood and co-neighborhood introduces quasi-degree and quasi-co-degree, respectively. New name is chosen from degree. Since amid all vertices with different degrees, one vertex is chosen. In other words, one vertex is fixed and its degree turns out quasi-degree where two degrees could be assigned to a vertex. Degree of edges and degree of vertices. The number of edges which are incident to the vertex and the number of vertices which are neighbors to the vertex. Degree and co-degree are the notions which are transformed to use in quasi-style. Two neutrosophic values introduce two neutrosophic vertices separately in each settings. These notions are applied into neutrosophic graphs as individuals but not family of them as drawbacks for these notions. Finding special neutrosophic graphs which are well-known, is an open way to purse this study. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

Cancer cells require substantial amounts of energy and substrates for their metabolic hyperactivity, enabling the synthesis of new cells at the expense of healthy ones. Preliminary in vitro data suggest that a mix of free essential amino acids (EAA-mix) can promote cancer cell apoptosis by enhancing autophagy. This study aimed to confirm, both in vitro and in vivo, whether EAA intake could influence the development of colon cancer in mice. We investigated changes in cancer proliferation in CT26 cells treated with EAA-mix, and in mice fed with EAA rich modified diets (EAARD) as compared to those on a standard laboratory diet (StD). CT26 cells were injected subcutaneously (s.c.) or intraperitoneally (i.p.). After 21 days, tumors were removed and measured. In vitro data corroborated that EAA-mix impairs cancer growth by inducing autophagy and apoptosis. In vivo data revealed that mice on StD developed significantly larger (s.c.) and more numerous (i.p.) cancers than those on EAARD. EAA administration appears to influence cancer cell survival with notable anti-proliferative properties.

Soil organic matter (SOM) is essential for nutrient cycling and soil carbon (C) accumu-lation, both of which are heavily influenced by the quality and quantity of plant litter. Since SOM dynamics in relation to plant diversity are poorly understood we investigated the effects of willow variety and mixture, and site on the soil C stocks, SOM chemical composition and ther-mal stability. Using pyrolysis-field ionization mass spectrometry we analysed the top 10 cm of soil from two 7-year-old experimental sites in Germany and Sweden, with monocultures and mixtures of two willow varieties (Salix spp.) belonging to different species. Overall, site had the strongest effect on SOM quality. Results showed significant variability across sites for willow identity and mixture effects on C accumulation and SOM chemistry. In the German site (Ros-tock), yearly soil C accumulation was higher (p < 0.05) for variety ‘Loden’ (1.0 Mg C ha-1 year-1) compared to ‘Tora’ (0.5 Mg C ha-1 year-1), whilst in the Swedish site (Uppsala), both varieties ex-hibited similar soil C accumulation rates of around 0.6 Mg C ha-1 year-1. Willow variety identity significantly affected SOM quality at both sites, while mixing had minor effects. Our findings emphasize the significance of site-specific context and variety or species identity in shaping soil C accumulation in willow plantations.

We consider an $N$ fermion system at low temperature $T$ in which we encounter special particle number values $N_m$ exhibiting special traits. These values arise in focusing attention upon the degree of mixture (DM) of the pertinent quantum states. Given the coupling constant of the Hamiltonian, the DMs stay constant for all $N$-values, but experience sudden jumps at the $N_m$. For a quantum state described by the matrix $\rho$, its purity is expressed by $Tr \rho^2$ and then the degree of mixture is given by $1 - Tr \rho^2$, a quantity that coincides with the entropy $S_q$ for $q=2$. Thus, Tsallis entropy of index two faithfully represents the degree of mixing of a state, that is, it measures the extent to which the state departs from maximal purity . Macroscopic manifestations of the degree of mixing can be observed through various physical quantities. Our present study is closely related to properties of many-fermion systemsn that are usually manipulated at zero temperature. Here we wish to study the subject at finite temperature. Gibbs' ensemble is appealed to. Some interesting insights are thereby gained.

Uncovering hidden mixture correlation among variables have been investigating in the literature using mixture R-vine copula models. These models are hierarchical in nature. They provides a huge flexibility for modelling multivariate data. As the dimensions increases, the number of the model parameters that need to be estimated is increased dramatically, which becomes along with huge computational times and efforts. This situation becomes even much more harder and complicated in the mixture Regular vine models. Incorporating truncation method with mixture Regular vine models will reduce the computation difficulty for the mixture based models. In this paper, tree-by-tree estimation mixture model is joined with the truncation method, in order to reduce the computational time and the number of the parameters that need to be estimated in the mixture vine copula models. A simulation study and a real data applications illustrated the performance of the method. In addition, the real data applications show the affect of the mixture components on the truncation level.

We establish certain modular equations of degree three based on some results of Ramanujan's theta functions.

Climate change is expected to influence cooling and heating energy demand of residential buildings and affect overall thermal comfort. Towards this end, the heating degree-day (HDD), the cooling degree-day (CDD) and the HDD+CDD were computed from an ensemble of 7 high-resolution bias-corrected simulations attained from EURO-CORDEX under RCP4.5 and RCP8.5. These three indicators were analyzed for 1971-2000 (from E-OBS) and 2011-2040 and 2041-2070, under both RCPs. Results show that the overall spatial distribution of HDD trends for the 3 time-periods points out an increase of energy demand to heat internal environments in Portugal's northern-eastern regions, most significant under RCP8.5. It is projected an increase of CDD values for both scenarios; however, statistically significant linear trends were only found for 2041-2070 under RCP4.5. The need for cooling is almost negligible for the remaining periods, though linear trend values are still considerably higher for 2041-2070 under RCP8.5. By the end of 2070, higher amplitudes for all indicators are depicted for southern Algarve and Alentejo regions, mainly under RCP8.5. For 2041-2070 the Centre and Alentejo (North and Centre) regions present major positive differences for HDD(CDD) under RCP4.5(RCP8.5), within the 5 NUTS II regions predicting higher heating(cooling) requirements for some locations.

In this paper, we give some upper and lower bounds for the multiplicative Randic index, reduced reciprocal Randic index, Narumi-Katayama index and symmetric division index a graph using solely the vertex degrees. Then we obtain upper and lower bounds for these indices for the complete graphs, path graphs and Fibonacci-sum graphs. Finally, we compared the bounds of these indices for a general graph and some special graphs.

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.

Expectation maximization (EM) algorithm is a powerful mathematical tool for estimating statistical parameter when data sample contains hidden part and observed part. EM is applied to learn finite mixture model in which the whole distribution of observed variable is average sum of partial distributions. Coverage ratio of every partial distribution is specified by the probability of hidden variable. An application of mixture model is soft clustering in which cluster is modeled by hidden variable whereas each data point can be assigned to more than one cluster and degree of such assignment is represented by the probability of hidden variable. However, such probability in traditional mixture model is simplified as a parameter, which can cause loss of valuable information. Therefore, in this research I propose a so-called conditional mixture model (CMM) in which the probability of hidden variable is modeled as a full probabilistic density function (PDF) that owns individual parameter. CMM aims to extend mixture model. I also propose an application of CMM which is called adaptive regressive model (ARM). Traditional regression model is effective when data sample is scattered equally. If data points are grouped into clusters, regression model tries to learn a unified regression function which goes through all data points. Obviously, such unified function is not effective to evaluate response variable based on grouped data points. The concept “adaptive” of ARM means that ARM solves the ineffectiveness problem by selecting the best cluster of data points firstly and then evaluating response variable within such best cluster. In order words, ARM reduces estimation space of regression model so as to gain high accuracy in calculation.

For the symmetrical mixture model and mixture test area, the lattice point set is used to partition, and then the corresponding test statistics can be constructed. In this paper, we first proposes the partition methods under the lattice point sets and obtains several sub-simplexes without common interior points. Furthermore, we present the method for constructing a uniform design on the simplex using the center points of these sub-simplexes. The designs satisfy the uniformity of maximum distance deviation and provide good results for the mean square error deviation. Finally, the uniformity test on the mixture region is considered and illustrated by examples.

Fiber aggregation in nanocomposites has an important effect on the macroscopic electrical performance. To quantitatively evaluate its effect, an index to characterize the degree of aggregation is imperative, and ideally it should have three features simultaneously, i.e., single-parametric, dimensionless and physically meaningful, applicable to different aggregation topologies, and one-to-one corresponding to material electrical properties. To this end, a new aggregation degree is proposed, which is defined as the average increased number of fibers that connect with each one when fibers aggregate from a uniformly distributed state. This index is applicable to different aggregation topologies from lump-like aggregating clusters to network-like aggregating clusters. This index is proven to only depend on the local features of aggregating clusters, and can be concisely expressed by the characteristic parameters of local structure, via geometric probability analysis and numerical validations. Further, a one-to-one linear relation between the aggregation degree and percolation threshold is established by Monte Carlo simulations, which is independent of the distribution law of the fibers. This work provides a guide to the property characterization, performance prediction and material design of nanocomposites, and also gives a physical insight into the understanding of systems with similar non-uniform distributions.

While modeling data in reality, uncertainties in both data (aleatoric) and model (epistemic) are not necessarily Gaussian. The uncertainties could appear with multimodality or any other particular forms which need to be captured. Gaussian mixture models (GMMs) are powerful tools that allow us to capture those features from a unknown density with a peculiar shape. Inspired by Fourier expansion, we propose a GMM model structure to decompose arbitrary unknown densities and prove the property of convergence. A simple learning method is introduced to learn GMMs through sampled datasets. We applied GMMs as well as our learning method to two classic neural network applications. The first one is learning encoder output of autoencoder as GMMs for handwritten digits images generation. The another one is learning gram matrices as GMMs for style-transfer algorithm. Comparing with the classic Expectation maximization (EM) algorithm, our method does not involve complex formulations and is capable of achieving applicable accuracy.

In this paper we prove that if the product $X^{n}$ of a space $X$ has some tightness-type properties, then the space of permutation degree ${\sf SP^{n}}X$ also has these properties. It is proved that the set tightness (resp. $T$-tightness) of the space of permutation degree ${\sf SP^{n}}X$ is equal to the set tightness (resp. $T$-tightness) of the product $X^{n}$.

Exact sequential state estimation of orbiting objects in space can be found by predicting the state using Fokker Planck Kolmogorov Equation (FPKE) and measurement correction using Bayes’ conditional/posterior Probability Density Function (PDF). Posterior PDF can be expressed as Gaussian density expanded in terms of Hermite polynomials named as Gram Charlier Series (GCS). This research is an extension of an earlier work on second order linearized solution to nonlinear Bayesian filtering using Taylor series and third order GCS expansion of posterior PDF. In this new extension, Bayes’ posterior PDF is approximated by a mixture of GCS functions for which the parameters are propagated using linear propagation theory. The update of weights of different components of GCS mixture model uses the FPKE error as feedback to adapt for the amplitude of different GCS components while solving a quadratic programming problem earlier used for Gaussian Mixture Model (GMM) PDF. Proposed filtering method is applied on tracking of space debris. The simulation results for the filter shows performance is moderately better than single GCS filter, Extended Kalman Filter (EKF) and Gaussian Sum Filter (GSF) under space debris’ highly uncertain initial conditions and sparse measurement availability.

Dyadic data contains co-occurrences of objects, which is often modeled by finite mixture model which in turn is learned by expectation maximization (EM) algorithm. Objects in traditional dyadic data are identified by names, causing the drawback which is that it is impossible to extract implicit valuable knowledge under objects. In this research, I propose the so-called attributed dyadic data (ADD) in which each object has an informative attribute and each co-occurrence of two objects is associated with a value. ADD is flexible and covers most of structures / forms of dyadic data. Conditional mixture model (CMM), which is a variant of finite mixture model, is applied into learning ADD. Moreover, a significant feature of CMM is that any co-occurrence of two objects is based on some conditional variable. As a result, CMM can predict or estimate co-occurrent values based on regression model, which extends applications of ADD and CMM.

Lyme carditis (LC) is a manifestation of the early disseminated stage of Lyme disease and often presents as high-degree atrioventricular (AV) block. High-degree AV block in LC can be treated with antibiotics, usually resolving with highly favourable prognosis, thus preventing the unnecessary implantation of permanent pacemakers. We present a systematic approach to the diagnosis and management of LC that implements the Suspicious Index in Lyme Carditis (SILC) risk stratification score.

IIt is important that Master’s Degree students in psychology, regardless of their future specialisa-tion, know about Attention Deficit Hyperactivity Disorder (ADHD). The aim is evaluate and an-alyse the ADHD-related knowledge held by students of Early Childhood Education and Primary Education. The findings that were obtained in this study (on the chronicity of the disorder, changes in manifestations throughout the life cycle, and the importance of early and multidisci-plinary intervention) have clear implications in relation to the specific training in ADHD for fu-ture teachers. It is essential to broaden the specific training in relation to ADHD in studies for the Master’s Degrees in Early Childhood Education and Primary Education and through contin-uous training.

In recent years, more and more attention has been paid to the behavior of tourists and their intention to travel after certain natural disasters or social unrest. The aim of the research was to establish the influence of psychological profiles of tourists on their decision to choose a tourist destination, using three psychographic techniques BFI-10 (Big Five Inventory), AIO (Activities, Interests, Opinions), and VALS 2 (Values and lifestyle), and a freely determined six-level scale of risk and tourist attractiveness of the imagined destinations. By analyzing the results through structural modeling-path analysis, it was determined that almost all psychographic orientations derived from lifestyles negatively perceive destinations with a high degree of risk and attractiveness, while with the VALS 2 technique, only members of the action orientation tend to accept the risk and challenge of visiting high-risk destinations. The results of the research have a degree of innovation in the application of the combination of the mentioned models, as well as in the theoretical and applied aspects in supplementing the building and strategic planning of the future business.

This study analyzes how Perceived Credibility of a 360-degree feedback and Evaluation of Outcome impact on ratees’ attitudes towards behavioral change. The study adopts a qualitative-quantitative mixed method approach. Reasoned Action Theory was selected as the theoretical framework, and ten 360-degree assessment ratees were interviewed on their perceived credibility, evaluation of outcome and attitude toward behavioral change in the 360-degree assessment practices. The qualitative inputs were analyzed under the methodology of grounded theory. Additional 159 respondents participated in the quantitative research phase of the research to validate the findings in the qualitative research phase. It was validated that Perceived Credibility, Evaluation of Outcome has impact on Attitude Toward Behavioral Change in 360-degree assessment.

The article delves into the intricate phase transitions of 1-Octadecanol and n-Nonadecane within a binary system, unveiling dynamic structural changes under varying conditions. Through Fouri-er-transform infrared (FTIR) analysis, specific molecular vibrations were identified, shedding light on the molecular composition and interactions. The study highlights the challenges in detecting subtle phase transitions and emphasizes the individuality of molecular behaviours in closely related compounds. The findings underscore the complexity of phase transitions in binary systems and advocate for a nuanced approach to studying molecular structures and behaviours. Addi-tionally, the research contributes valuable insights into the molecular dynamics of Organic Polymer Functional Adsorption Materials, offering implications for synthetic waxy layers in plant epidermis.

In recent decades, there have been numerous endeavors to develop a novel category of survival distributions possessing enhanced flexibility through the extension of existing distributions. This article constructs and validates the statistical properties of a novel survival distribution in order to obtain an alternative distribution that is suitable for analyzing survival data by presenting the novel mixture of the Fréchet distribution along with statistical properties such as the probability density function (PDF), cumulative distribution function (CDF), rth ordinary moment, skewness, kurtosis, moment-generating function, mean, variance, mode, survival function, hazard function, and asymptotic behavior, as well as constructing the estimators of the unknown parameter by employing the expectation-maximization (EM) algorithm, and simulated annealing. Additionally, the performance of the proposed estimators was compared with bias, mean squared errors (MSE), and simulated variances, and given an illustrative example of the proposed distribution to the survival data set in order to show that the proposed distribution is appropriate for the right-skewed data. This will be extremely advantageous in survival analysis.

The self-organization properties of C70 fullerene molecules in a xylene/tetrahydrofuran binary mixture were studied for the first time by optical absorption, refractometry, and dynamic light scattering. A correlation has been established between the change in the refractive index of the C70/xylene/tetrahydrofuran solution and the degree of self-organization of C70 molecules in the medium at various concentrations and storage periods of the solution. It is shown that the features of the optical absorption spectrum of C70/xylene/tetrahydrofuran at a fixed low concentration of the fullerene are sensitive to its storage time. It was determined that the beginning time of the formation of C70 nanoclusters and their final size depend on the concentration of fullerene and the time of keeping the solution. The observed nature of the C70 fullerene solution in a binary mixture may help to elucidate the mechanism of self-organization in the future.

Dyadic data which is also called co-occurrence data (COD) contains co-occurrences of objects. Searching for statistical models to represent dyadic data is necessary. Fortunately, finite mixture model is a solid statistical model to learn and make inference on dyadic data because mixture model is built smoothly and reliably by expectation maximization (EM) algorithm which is suitable to inherent spareness of dyadic data. This research summarizes mixture models for dyadic data. When each co-occurrence in dyadic data is associated with a value, there are many unaccomplished values because a lot of co-occurrences are inexistent. In this research, these unaccomplished values are estimated as mean (expectation) of random variable given partial probabilistic distributions inside dyadic mixture model.

Bending waveguide is one of the building blocks of on-chip photonic integrated circuits (PICs). Previous bending waveguides (BWs) usually suffer from larger footprint size in the bending region, which is are not conducive to develop large-scale and high-density PICs. To this end, we propose and numerically investigate an ultra-compact silicon-on-insulator 180-degree waveguide bend with a bending radius of 2 µm. Being different from the previous BWs, the design is based on topologically optimized digital metamaterials, which is implemented by using intelligent genetic algorithms. Simulated results show that the proposed structure has a -1.36 dB bend efficiency per 180-degree at a wavelength of 1.55 µm and occupies a footprint of only 4 × 4 µm2. Moreover, the crosstalk is <−20 dB and the loss is <2.6 dB over a >100 nm operating bandwidth (1.6-1.7 µm) for TE0 modes. With the wide operating bandwidth and ultra-compact footprint, the proposed silicon-based 180-degree waveguide bend shows a potential application as the fundamental component for optical communication interconnect in the ultra-highly integrated photonic circuit.

Cryptocurrencies have obtained a crucial position in the international financial landscape. The cryptocurrency market has been perceived as a highly volatile market since the inception of Bitcoin. This study investigates the relevant performance of extreme value models (EVM) in estimating the Value-at-Risk (VaR) of Bitcoin and Ethereum returns. The extreme value mixture models, GPD-Normal-GPD (GNG) and GPD-KDE-GPD models are fitted to the returns of Bitcoin and Ethereum and the Kupiec likelihood backtesting procedure is performed on the VaR estimates to assess the fits. Both models’ results showed that the fits were a much more decent representation of the observed data when compared to the Normal distribution. The backtesting results showed that the GPD-KDE-GPD model’s fit was superior to that of the GPD-Normal-GPD for both sets of returns at all VaR risk levels except at the 99% level. The results of this study may assist with understanding the dynamics and risks associated with cryptocurrencies and can serve as a beneficial tool for decision-making and risk management to investors, traders, financial institutions and many other participants in the cryptocurrency ecosystem.

Multi-task Gaussian processes (MTGPs) are a powerful approach for modeling structured dependencies among multiple tasks. Researchers on MTGPs have contributed to enhance this approach in various ways. Current MTGP methods, however, cannot model nonlinear task correlations in a general way. In this paper we address this problem. We focus on spectral mixture (SM) based kernels and propose an enhancement of this type of kernels, called multi-task generalized convolution spectral mixture (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and mixtures dependency, including time and phase delay, not only between different tasks but also within a task at the spectral mixture level. Each task in MT-GCSM has its own generalized convolution spectral mixture kernel (GCSM) with a different number of convolution structures and all spectral mixtures from different tasks are dependent. Furthermore, the proposed kernel uses inner and outer full cross convolution between base spectral mixtures, so that the base spectral mixtures in the tasks are not necessarily aligned. Extensive experiments on synthetic and real-life datasets illustrate the difference between MT-GCSM and other kernels as well as the practical effectiveness of MT-GCSM.

Powered ankle prostheses have been proven to improve the walking economy of people with transtibial amputation. All commercial powered ankle prostheses that are currently available can only perform one-degree-of-freedom motion in a limited range. However, studies have shown that the frontal plane motion during ambulation is associated with balancing. In addition, as more advanced neural interfaces have become available for people with amputation, it is possible to fully recover ankle function by combining neural signals and a robotic ankle. Accordingly, there is a need for a powered ankle prosthesis that can have active control on not only plantarflexion and dorsiflexion but also eversion and inversion. We designed, built, and evaluated a two-degree-of-freedom (2-DoF) powered ankle-foot prosthesis that is untethered and can support level-ground walking. Benchtop tests were conducted to characterize the dynamics of the system. Walking trials were performed with a 77 kg subject that has unilateral transtibial amputation to evaluate system performance under realistic conditions. Benchtop tests demonstrated a step response rise time of less than 50 milliseconds for a torque of 40 N·m on each actuator. The closed-loop torque bandwidth of the actuator is 9.74 Hz. Walking trials demonstrated torque tracking errors (root mean square) of less than 7 N·m. These results suggested that the device can perform adequate torque control and support level-ground walking. This prosthesis can serve as a platform for studying biomechanics related to balance and has the possibility of further recovering the biological function of the ankle-subtalar-foot complex beyond the existing powered ankles.

Aluminum is a strategic mineral resource, and China’s aluminum production and consumption is fairly large. However, its supply guarantee is uncertain because of a high dependency on external raw materials. This uncertainty may expand, so finding a way to reduce the uncertainty of aluminum resource supply is especially important. This paper applies the SFA method to analyze the aluminum flows in mainland China from 1996 to 2014, and establishes a supply structure model to measure its supply guarantee degree. The results claim that: (1) China’s aluminum production can satisfy demand and even create a surplus; (2) Domestic self-productive primary and secondary aluminum increased at an annual rate of 12% and 24%; (3) The proportion of self-productive secondary aluminum in the supply structure increased from 7.7% in 1996 to 12.8% in 2014, while that of primary aluminum decreased from 79.6% to 42.8%; (4) The total supply guarantee degree decreased from 87.3% to 55.6% in this period. These results provide a feasible way to solve this plight: the proportion of secondary aluminum in the supply structure should be enhanced, and an efficient aluminum resource recycling system needs to be established as soon as possible to ensure its sustainable supply.

Many papers in recent years have been devoted to estimating the per pixel proportions of three broad classes of materials (e.g. photosynthetic vegetation, non-photosynthetic vegetation and bare soil) using data from multispectral sensors. Many of these papers use estimation methods based on the linear mixture model. Very few of these papers assess the accuracy of their estimators. I show how to produce confidence intervals (CIs) and joint confidence regions (JCRs) for the proportions associated with various linear mixture models. There are two main models, both of which assume that the coefficients in the model are non-negative. The first model assumes that the coefficients sum to 1. The second does not, but uses rescaling of the estimated coefficients to produce estimated proportions. Three variants of these two models are also analysed. JCRs are shown to be particularly informative, because they are typically better at localising the information than CIs are. The methodology is illustrated using examples from Landsat Thematic Mapper data at 1169 locations across Australia, each of which has associated field observations. There is also discussion about the extent to which the methodology can be extended to hyperspectral data.

In regions with low-temperature action transverse cracks can appear in asphalt pavements as a result of thermal stresses that exceed the fracture strength of materials used in asphalt layers. To better understand thermal cracking phenomenon, strength properties of different asphalt mixtures were investigated. Four test methods were used to assess the influence of bitumen type and mixture composition on tensile strength properties of asphalt mixtures: tensile strength using the Thermal Stress Restrained Specimen Test (TSRST) and the Uniaxial Tension Stress Test (UTST), flexural strength using the Bending Beam Test (BBT) and fracture toughness using the Semi-Circular Bending Test (SCB). The strength reserve behavior of tested asphalt mixtures was assessed as well. The influence of cooling rate on strength reserve was investigated and correlations between results from different test methods were also analyzed and discussed. It was observed that the type of bitumen is a factor of crucial importance to low-temperature properties of the tested asphalt concretes. This conclusion was proved by all test methods that were used. It was also observed that the level of cooling rate influences the strength reserve and, in consequence, resistance to low-temperature cracking. It was concluded that reasonably good correlations were observed between strength results for the UTST, BBT and SCB test methods.

The COVID-19 pandemic has left no one indifferent: all around the globe, people have felt the massive impact of a virus that has stopped the world in its tracks. Many countries have been forced to impose strict lockdowns, causing devastating damage to the overall economy and welfare state, but also leaving an unprecedented emotional footprint on almost everyone. In this paper, we aim to quantify, by building our own method how, this pandemic has affected people emotionally by detecting the shifts in the emotional profile of representative users in selected countries. Our method processes the social media stream adapting the Pleasure-Arousal-Dominance (PAD) approach to measure emotional states on the Russell Circumplex plane, allowing for not only an overall emotional shift quantification but also a detailed analysis at mood level. By focusing on certain emotions and searching behavior, we establish a relationship between the pandemic and the emotional response. Although the method can be applied to any location with a significant and varied amount of geo-localized social media streams, the scope of this paper covers the most representative cities in Europe.

In this paper, a 3 DOF gyrscope position control have been designed and controlled using optimal control theory. An input torque has been given to the first axis and the angular position of the second axis have been analyzed while the third axis are kept free from rotation. The system mathematical model is controllable and observable. Linear Quadratic Integral (LQI) and Linear Quadratic State Feedback Regulator (LQRY) controllers have been used to improve the performance of the system. Comparison of the system with the proposed controllers for tracking a desired step and random angular position have been done using Matlab/Simulink Toolbox and a promising results has been analyzed.

For improving the tracking accuracy and model switching speed of maneuvering target tracking in nonlinear systems, a new algorithm named interacting multiple model fifth-degree spherical simplex-radial cubature filter (IMM5thSSRCKF) is proposed in this paper. The new algorithm is a combination of the interacting multiple model (IMM) filter and fifth-degree spherical simplex-radial cubature filter (5thSSRCKF). The proposed algorithm makes use of Markov process to describe the switching probability among the models, and uses 5thSSRCKF to deal with the state estimation of each model. The 5thSSRCKF is an improved filter algorithm, which utilizes the fifth-degree spherical simplex-radial rule to improve the filtering accuracy. Finally, the tracking performance of the IMM5thSSRCKF is evaluated by simulation in a typical maneuvering target tracking scenario. Simulation results show that the proposed algorithm has better tracking performance and quicker model switching speed when disposing maneuver models compared with IMMUKF, IMMCKF and IMM5thCKF.

In the era of big data with increasingly complex data structures and ever-larger data scales, matrix-type data are becoming highly valued and their applications in the fields of medicine, industry, education, geography, and astronomy are growing in extent. In recent years, significant progress has been made in the practical use of matrix variable t-distribution finite mixture models for handling data in order to address the issues of multi-subgroup structures and long data tails. In this paper, the expectation-maximization (EM) algorithm with penalized maximum likelihood is proposed to resolve the problem of the unbounded nature of the likelihood function applied to the model by considering the degeneracy of the variance-covariance matrix of this model. Our data were analyzed through simulations and real data, and the results demonstrate that our model is effective in both preventing the likelihood function from being unbounded and in ensuring the accuracy of the estimated parameters of the EM algorithm.

Elemental sulfur, commonly known for its wide range of biological activity, has a long history of being used to protect all garden and vegetable crops from a range of pests and diseases, including powdery mildew, ascochyta blight, clubroot, plant mites, oidium, anthracnose, and scab. In the present study, a quick and environmentally friendly approach has been developed for the synthesis of sulfur nanoparticles with antibacterial activity. Sulfur nanoparticles (SNPs) were prepared by modifying the surface of elemental sulfur using various polyelectrolyte-surfactant mixtures (PSM) including NaCMC-SDBS and PHMG-CTAB. The SNPs were characterized by UV–visible spectrophotometry, X-ray diffraction (XRD), and thermogravimetric analysis (TG/DSC), scanning electron microscopy (SEM), with the SNPs showing an almost spherical shape with an average size in the range of 150-200 nm. The antibacterial activity of the sulfur nanoparticles was tested using against gram-positive S. aureus and E. faecium and gram-negative E. coli and P. aeruginosa bacteria. From this, it could be seen that SNPs exhibited significant antimicrobial activity against gram-positive bacteria, i.e., S. aureus and E. faecium. The in vitro cytotoxicity of the SNPs-1 and SNPs-2 studied to normal (MeT-5A) and tumorous (MCF-7) human cell lines was assessed in the concentration range from 500 μg/ml to 0.12 mg/ml, from which it was determined as being non-cytotoxic. The received products can be considered for potential application in agriculture and medicine.

We investigate some of the fundamental features of the interaction of mixed kaleidoscope states, namely, particular statistical mixtures of coherent states, with two-level atoms in the Jaynes-Cummings model framework. We begin our analysis by calculating the von Neumann entropy of the field which is determined with help of the virtual atom method. Oscillations appear in this entropy that indicate a state of purity greater than the initial state, {it i.e.}, a purification of the initial state is achieved. In this oscillatory region, we obtain the field Wigner function that resembles Schrodinger's cats.

Two secondary waste materials, municipal incinerator bottom ash (MIBA) and sewage sludge ash (SSA), were mixed with clay for ceramic manufacturing in this study. Specimens with 5 different MIBA replacement amount of 0%, 5%,10%, 15%, and 20%(wt) and 3 different SSA replacement amount of 0%, 10%, and 20%(wt) were prepared and then a series of tests and analysis were conducted to investigate how the two materials affect the quality of the final product and to what extent. It concludes that a mix with up to 20% of SSA and 5% of MIBA could result in quality tiles complying with specifications for interior or exterior flooring applications at certain kiln temperatures.

This paper examines the energy efficiency of three variations of the two-degree-of-freedom transverse galloping energy harvester. These variants differ in the number and placement of electromechanical transducers. By utilizing the harmonic balance method, limit cycles of mathematical models of devices were determined. Analytical expressions derived from models were then used to formulate the efficiency of the systems. It was demonstrated that efficiency depends on flow speed and can be comprehensively characterized by four criteria parameters: peak efficiency, denoting the maximum efficiency of the system, and high efficiency bandwidth, which describes the range of flow velocities within which the efficiency remains at no less than 90% of peak efficiency. The values of these parameters are heavily reliant on the speed at which the system achieves peak efficiency—referred to as the nominal speed nominal speed, which in turn is related to the critical speed of the system. Comparative analysis revealed that only the two-degree-of-freedom device equipped with two electromechanical transducers can potentially outperform a simple one-degree-of-freedom system in terms of efficiency.

The North Railway in the Kingdom of Saudi Arabia (KSA) extends over vast areas, crossing various terrains, including valleys, sand veins, plateaus, and hills. Therefore, the Railway was designed and implemented to suit this environmental diversity under the highest safety standards. However, the Railway may be subject to hazards for various reasons. In general, the possibility of direct surface runoff disasters increases if there are residential areas and facilities within the boundaries of drainage basins. Therefore, these areas should be studied, and the degree of hazard in drainage basins should be accurately determined. Hence, this study analyzed the degree of risk of 14 drainage basins affecting the North Train railway within the Wadi Malham drainage basin, using the risk degree model with eight parameters that have hydrological indications to give an idea of the behavior of direct surface runoff and interfere with increasing the risk of direct surface runoff. Researchers found 28.57% of the total basins in the study area have high-risk basin overall score values, which are basins 6, 7, 13, and 14. It is recommended to estimate the rainfall depth during different return periods, analyze soil permeability and land use classification in the study area, and apply hydrological modeling of drainage basins, which contributes to estimating the volume and peak of direct surface runoff in such arid and semi-arid environments that do not contain hydrometric stations to monitor the runoff. .

The diel variations of viruses (< 0.2 μm), picoplankton (0.2—2 μm; heterotrophic bacteria, Syn-echococcus spp., Prochlorococcus spp. and picoeukaryotes) and nanoplankton (2—20 μm; hetero-trophic nanoflagellates and pigmented nanoflagellates) were investigated for 2 days with about 4 hours of temporal sampling in spring 2024 in coastal surface waters of the subtropical western Pacific. A period of wind change associated with rain and increased turbulence, disrupted diel patterns of the overall microbial communities during the second cycle. The abundance of bacteria did not follow a consistent diel pattern, while viral abundance positively correlated with bacterial abundance. Synechococcus spp. and Prochlorococcus spp. grew during the light period and with peak abundance at night, exhibited marked diel variability, however, opposite patterns were observed in picoeukaryotes. According to Synechococcus spp. and Prochlorococcus spp. diel changes, nano-flagellate grazing could control their abundances and may explain temporally varying picocyanobacteria abundances. As we observed in the culture experiments, the results showed a significant increase in picoeukaryotic abundance from noon to nighttime, and a decrease in bacterial abundance during nighttime, to show the prey-predator cycle. Our study suggests picoeukaryotes could serve as bacteria predators by being mixotrophs. Future studies aiming to understand the interactions between prokaryotes and picoeukaryotes within marine microbial communities should take these differences into account.

This paper presents a new discrete counterpart of the mixture exponential distribution by utilizing the survival discretization method. The moment-generating function and associated moment measures are discussed. The distribution’s hazard rate function can assume increasing or decreasing forms, making it adaptable for diverse fields requiring count data modelling. The paper explores four distinct parameter estimation methods and assesses their performance through Monte Carlo simulations. The applicability of this distribution extends to time series analysis, particularly within the framework of the first-order integer-valued autoregressive process. Additionally, the paper explores quality control applications, addressing serial dependence challenges in count data encountered in production and market management. The performance of two distinct control charts, the cumulative sum chart and the exponentially weighted moving average chart, is evaluated for their effectiveness in detecting shifts in the process means under various models. A bivariate Markov chain approach is used to estimate the average run lengths of these charts, offering valuable insights for implementation. Design recommendations for achieving robustness in-control chart applications are provided. The effectiveness of the proposed models and charts is illustrated using a real data, demonstrating their practical superiority.

Powder mixture with given molar ratio Ca/P = 1.67 consisting of brushite (calcium hydrophosphate dihydrate) CaHPO4·2H2O, calcium oxalate monohydrate CaC2O4·H2O in form of whewellite and weddellite and some quantity of quasi-amorphous phase was obtained as a result of the interaction of hydroxyapatite powder Ca10(PO4)6(OH)2 with an aqueous solution of oxalic acid H2C2O4 at a molar ratio of Ca10(PO4)6(OH)2/H2C2O4 = 1:4 under mechanical activation conditions. This powder mixture was used to produce microporous monophase ceramics based on hydroxyapatite Ca10(PO4)6(OH)2 with aperient density of 1.25 g/cm3 after firing at 1200 oC. Microporosity of sintered ceramics was formed due to presence of particles with plate-like morphology, restraining shrinkage during sintering. Microporous ceramics based on hydroxyapatite Ca10(PO4)6(OH)2 with roughness of the surface as a consequence of the created microporosity can be recommended as a biocompatible material for the bone defects treatment and as a substrate for bone cell cultivation.

The incidence and prevalence of food allergy (FA) are increasing. While several studies have established the safety and efficacy of early introduction of single allergens in infants for the prevention of FA, the exact dose, frequency, and number of allergens that can be safely introduced to infants particularly in those at high or low risk of atopy are still unclear. This 1-year study evaluated the safety of early introduction of single foods (milk, egg, or peanut), vs. two foods (milk/egg, egg/peanut, milk/peanut), vs. multiple foods (milk/egg/peanut/cashew/almond/shrimp/walnut/wheat/fish/soy at low, medium, or high doses) vs no early introduction in infants between 4-6 months of age. At the end of the study, they were evaluated for plasma biomarkers associated with food reactivity with standardized blood tests. Two to four years after the start of the study, participants were evaluated by standardized food challenges. The serving sizes for the single, double, and low dose mixtures were 300 mg total protein per day. The serving sizes for the medium and high dose mixture was 900 mg and 3000 mg total protein, respectively. Equal parts of each protein were used for double or mixture foods. All infants were breastfed until at least 6 months of age. Results demonstrate that infants at either high or low risk for atopy were able to tolerate early introduction of multiple allergenic foods with no increases in any safety issues, including eczema, FA, or food protein induced enterocolitis. The mixtures of foods at either low, medium, or high doses demonstrated trends for improvement in food challenge reactivity and plasma biomarkers compared to single and double food introductions. The results of this study suggest that early introduction of foods, particularly simultaneous mixtures of many allergenic foods is efficacious for preventing FA and can occur safely.

Certain studies have reported various insulin resistance responses to ambient heavy metal pollution, but few have reported such responses to occupational heavy metal pollution. Even fewer have demonstrated a relationship between mixture effects of heavy metals and insulin resistance in welding workers. Overall, we recruited 53 welders and 48 administrative staff from a shipyard located in northern Taiwan. Personal exposure to heavy metals was monitored for PM2.5 and urine. Blood samples from each participant were collected from the antecubital vein after fasting. Urine samples from each participant were collected in the same period as blood samples. The geometric mean levels for chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), and cadmium (Cd) in the PM2.5 of the personal breathing zone and urinary Mn of welders were significantly higher than those in administrative staffs. Ambient Cr, Co, Ni, and Cu levels in the PM2.5 and urinary Cd were positively related to HOMA2-IR after adjusting for personal covariates (PM2.5-Cr: β=0.036, 95%C.I.: 0.002 to 0.070; PM2.5-Co: β=0.040, 95%C.I.: 0.002 to 0.077; PM2.5-Ni: β=0.054, 95%C.I.: 0.013 to 0.094; PM2.5-Cu: β=0.049, 95%C.I.: 0.010 to 0.088; U-Cd: β=0.209, 95%C.I.: 0.052 to 0.366, respectively). Our findings indicated the PM2.5 metal components and urinary metals were associated with increased insulin resistance in shipyard welders.

The processing of alkanes (the main components of natural gas) for obtaining of industrially important chemical products is one of the most urgent environmental problems, because the major share of raw materials are burned in torches. Therefore, the main goal of the work is the development of catalysts and conditions for obtaining of important petrochemical products from light alkanes. For the preparation of catalysts, Mo, Cr and Ga oxide catalysts as well as catalysts based on heteropoly compounds, supported on natural materials were used. The catalysts were prepared by the capillary impregnation method and used in oxidative conversion in a flowing unit while varying the process conditions. It has been determined that 5 and 10% MoCrGa catalysts are optimal for obtaining of liquid and gaseous products, and 1% catalyst is more favorable for the synthesis of gaseous products. Supported catalysts from heteropoly acid Н₃PW₁₂O₄₀ are highly active in oxidative dehydrogenation and cracking processes, which are concurrent. High activity is caused by dispersity of catalysts, formation of crystal hydrates and amorphous phase of heteropoly acid in a condition of interaction with carrier. Maximum yield of C₂H₄ - 35.2% at 973 K, C₃H₆ – 20.0% and C₄H₈ – 14.3% at 773 К were observed.

The following are three successful case studies that affected humanity during the COVID-19 pandemic. The key to their success was their swift adaptability to the new confinement regulations and the unyielding determination of those carrying out their plans. Various extraordinary and prompt efforts were required to make the essential logistical preparations and practice for distance learning a reality. The research activities were also reorganized so the thesis students might carry out a few simple experiments at home under the strict supervision of their advisers and supervisors. The findings revealed that the students who participated in these projects applied all the abilities they had gained before the epidemic. In addition, they included crucial knowledge about the project, problem-based, and research-based learning. Furthermore, because they were the primary implementers of the trials, they learned by planning and performing experiments. The culmination of these efforts was displayed in three scientific publications that were published in index journals with a high level of impact. This latter factor is essential for the prestige and visibility of the university, in addition to the acquisition of financing and grants for scientific research.

Excessive alcohol consumption is one of the significant causes of morbidity and mortality worldwide. Alcohol is oxidized to toxic and carcinogenic acetaldehyde by alcohol dehydrogenase (ADH) and further oxidized to a non-toxic acetate by aldehyde dehydrogenase (ALDH). Emerging evidence shows that Lactobacillus and Bifidobacterium species encode alcohol dehydrogenase (ADH) and acetaldehyde dehydrogenase (ALDH) mediate alcohol and acetaldehyde metabolism, respectively. This study involves supplementation of Lactobacillus and Bifidobacterium probiotic mixture in humans and assessed their effects on alcohol and acetaldehyde metabolism. Here, twenty-seven wild types (ALDH2*1/*1) and the same number ofheterozygotes (ALDH2*2/*1) were recruited for the study. The enrolled participants were randomly divided into either the probiotic (Duolac ProAP4) or the placebo group. Each group received a probiotic or placebo capsule for 15 days with subsequent crossover. Primary outcomes were measurement of alcohol and acetaldehyde in the blood after the alcohol intake. Blood levels of alcohol and acetaldehyde in the ALDH2 heterozygote group were significantly downregulated in the probiotic-supplemented group with no changes in hangover score symptoms than the placebo group. No clinically significant changes were observed in safety parameters. These results suggest that probiotic has a potential to downregulate the alcohol and acetaldehyde concentrations, and their effects depend on the presence or absence of polymorphism on the ALDH2 gene.

In this study, using information theory tools, we investigate the quantum degree of mixedness, particle number fluctuations, and temperature variations in the paradigmatic Lipkin model, focusing on the interplay between these factors in a system of many fermions. Our analysis reveals intriguing dependencies of quantum mixedness on the total fermion number, showcasing distinct behaviors at different temperatures. Notably, we find that the degree of quantum mixedness exhibits a strong dependence on the total fermion number, with varying trends observed across different temperature regimes. Remarkably, this dependence remains unaffected by the strength of the fermion-fermion interaction (if it does not vanish, of course), underscoring the robustness of the observed phenomena. Through comprehensive numerical simulations, we provide illustrative graphs that depict these dependencies, offering valuable insights into the fundamental characteristics of many-fermion systems. Our findings shed light on the intricate dynamics of quantum degree of mixedness and particle number fluctuations in fermionic systems, with potential implications for diverse fields ranging from condensed matter physics to quantum information science.

Radiator reliability is crucial in environments characterized by high temperatures and friction, where prompt interventions are often required to prevent system failures. This study introduces a proactive approach to radiator fault diagnosis, leveraging the integration of Gaussian Mixture Model (GMM) and Long-Short Term Memory (LSTM) autoencoders. Vibration signals from radiators were systematically collected through randomized durability vibration bench tests, resulting in four operating states—two normal, one unknown, and one faulty. Time-domain statistical features of these signals were extracted and subjected to Principal Component Analysis (PCA) to facilitate efficient data interpretation. Subsequently, this study discusses the comparative effectiveness of GMM and LSTM in fault detection. GMMs are deployed for initial fault classification, leveraging their clustering capabilities, while LSTM autoencoders excel in capturing time-dependent sequences, facilitating advanced anomaly detection for previously unencountered faults. This alignment offers a potent and adaptable solution for radiator fault diagnosis, particularly in challenging high-temperature or high-friction environments. Consequently, the proposed methodology not only provides a robust framework for early-stage fault diagnosis but also effectively balances diagnostic capabilities during operation. Additionally, this study presents the foundation for advancing reliability life assessment in accelerated life testing, achieved through dynamic threshold adjustments using both the absolute log-likelihood distribution of the GMM and the reconstruction error distribution of the LSTM autoencoder model.

It is necessary to address the scarcity of crushed stones for pavement structural layers. So fly ash can be proved to be promising solution as more than 270 tonnes of fly ash is generated in India. Though, numerous research has been conducted for the use of fly ash intreated and untreated form, high volume of fine particles and brittleness of the stabilized fly ash pose a great problem for its use in subbase and base. Moreover, stiffness or modulus of stabilized fly ash is vital elastic parameter which is used for mechanistic pavement design. Hence, in this study an extensive experimental investigation is carried out to study its strength and stiffness properties such as compressive strength, indirect tensile strength and flexural strength, cyclic indirect tensile modulus and flexural modulus of fiber reinforced cement stabilized fly ash, stone dust, aggregate mixtures. The stone dust and aggregates have been added to enhance the gradation of the composite’s mixture. The study presents the effect of fiber on strength and stiffness properties. The experimental result reveals that addition of polypropylene (PP) fibers up to 0.25 wt.% enhances the compressive strength and any further addition of fiber results in decrease of the strength. However, indirect tensile strength and flexural strength increases with increase in fiber percentage up to 0.5 wt.%. Cement content is observed to be the dominant parameter for stabilized materials. Suitable relationships have been developed between strength and modulus parameters for stabilized mixtures. Based on the strength and stiffness study, 70% fly ash and 30% stone dust-aggregate and 60% fly ash and 40% stone dust-aggregate with 6% cement can be considered for the base layer. Based on the indirect tensile strength and flexural strength behavior, 0.35% is considered as the optimum fiber percentage.

The inherent non-smoothness of vibroimpact system leads to complex behaviors as well as strong sensitive dependence on parameter changes. Unfortunately, uncertainties and errors in system parameters are inevitable in mechanical engineering. Therefore, the investigations of dynamical behaviors for vibroimpact systems with stochastic parameters are highly essential. The present study aims to analyze the dynamical characteristics of the three-degree-of-freedom vibroimpact system with an uncertain parameter by means of the Chebyshev polynomial approximation method. Specifically, the vibroimpact system model considered is one with unilateral constraint. Firstly, the three-degree-of-freedom vibroimpact system with an uncertain parameter is transformed into an equivalent deterministic form by the Chebyshev orthogonal approximation. Then, the ensemble means responses of the stochastic vibroimpact system are derived. Numerical simulations are performed to verify the effectiveness of the approximation method. Furthermore, the periodic and chaos motions under different system parameters are investigated, and the bifurcations of the vibroimpact system are analyzed by the Poincaré map. The results demonstrate that both the restitution coefficient and the random factor can induce the appearance of the periodic bifurcation. It is worth noting that the bifurcations fundamentally differ between the stochastic and deterministic systems. The former has a bifurcation interval, while the latter occurs at a critical point.

The European pear is a climacteric fruit species characterized by high storability. 'Conference', a pear cultivar very popular on the market, can be stored in a CA room without major losses for half a year. However, for ‘Conference’ storage to be successful, the fruit must be harvested fully developed even before it enters the climatic maturation phase. Harvested too early or too late, pears will taste bad, have physiological disorders and will be poorly storable. Among the various methods used to determine the optimal harvest date, the degree-days method, which relies of the sum of active temperatures, seems to be relatively accurate and easy to apply. During 11 years of study, ‘Conference’ pears were harvested on 4 dates (every 4-5 days) chosen based on measurements and observations, and then, after a six-month storage period, the optimum harvest date was determined. During the growing season, temperature was measured using an automatic weather station to calculate the sum of active temperatures from full bloom to the optimal harvest date. Measurements made after storage were analysed and the sum of active temperatures needed for the proper development of ‘Conference’ pear was calculated to be 2469 degree days. The calculations are very accurate because the dispersion calculated using the standard deviation was only 20 °. The base temperature with the smallest error was 0 °. On the basis of the study results, this method can be recommended as useful for determining the optimal harvest date.

Existing methods for simulating missile dynamics and training often rely on three degrees of freedom (3DOF) motions, limiting their ability to represent the complex maneuvers and targeting strategies of modern missiles. This paper proposes integrating virtual reality (VR) technology with six degrees of freedom (6DOF) dynamics to enhance the accuracy and efficiency of missile simulation and training. By incorporating 6DOF dynamics, simulations can capture the full range of missile motion, providing a comprehensive understanding of their capabilities and limitations. Through VR, users can immerse themselves in realistic missile scenarios, interact with dynamic behaviors in real-time, and refine their targeting and interception skills. The integration of VR with 6DOF dynamics also facilitates real-time feedback and evaluation, enhancing training efficiency and proficiency in missile operations. This approach aims to revolutionize missile training by bridging the gap between traditional simulation methods and real-world dynamics.

Background: Early identification and prevention of hypoxic ischemic encephalopathy (HIE) may reduce neonatal mortality and morbidity. Objective: We aimed to correlate between urinary Activin-A and MRI (conventional and Diffusion-weighted) and the severity of HIE in full-term neonates. Methods: Forty-five full-term neonates with HIE admitted to NICU and 15 normal neonates were enrolled into the study. The concentration of urinary Activin-A was determined using enzyme immunoassay kits and MRI were done. Correlations between urinary Activin-A and MRI with the degree of HIE were done. Results: Urinary Activin-A levels were significantly higher in neonates with HIE than controls (P<0.001). It was positively correlated with the clinical grading of HIE and a cutoff value of 0.08µg/l on day-1 after birth had a sensitivity of 98.6% and specificity of 97.1% for prediction of HIE. DW-MRI detected HIE with a high sensitivity (85%) compared to the low sensitivity of conventional MRI (35%). An ADC value of ≤ 0.8 was the best sensitivity-specificity cutoff point for detecting severe ischemic injury. DW-MRI imaging was positively correlated with Urinary Activin-A and both of them were positively correlated with the clinical grades of HIE (P < 0.001). Conclusions: DW-MRI imaging is correlated well with urinary Activin-A in full-term neonates with HIE and both of them are correlated with the degree of HIE. Early determination of urinary Activin-A combined with DW-MRI imaging can early detect HIE and its severity in full-term neonates with HIE.

The Polyurethane (PU) mixture is a high−performance engineering material with different dynamic properties compared to a hot−mixed asphalt mixture (HMA). This paper analyzed whether the Kramers−Kronig (K−K) relation and thermo−rheological simple properties were applicable to the PU mixture. Based on the result discussion, the PU mixture exhibited thermo−rheological simple properties within the test conditions. The time−temperature superposition principle (TTSP) was applicable for the PU mixture to construct the dynamic modulus master curve using the standard logistic Sigmoidal (SLS) model, the generalized logistic sigmoidal (GLS) model, and the Havriliak−Negami (HN) model. The Hilbert integral transformed SLS and GLS models for the phase angle can accurately fit the measured phase angle data with newly fitted shift factors and predict the phase angle within the viscoelastic range. The Core−Core and black space diagrams both displayed single continuous smooth curves, which can be utilized to characterize the viscoelastic property of the PU mixture. The K−K relation is applicable for the PU mixture to obtain the phase angle master curve model, storage modulus, and loss modulus from the complex modulus test results. The phase angle of the PU mixture cannot be derived from the dynamic modulus data and currently needs to be derived from the laboratory measurement.

This paper considers a non-mixture cure model for right censored data. It utilizes the maximum likelihood method to estimate model parameters in the non-mixture cure model. The simulation study is based on Fréchet susceptible distribution to evaluate the performance of the method. Comparing with Weibull and exponentiated exponential distributions, the non-mixture Fréchet distribution is shown to be the best in modeling a real data on allogeneic marrow HLA-matched donors and ECOG phase III clinical trial e1684 data.

The spiral tube heat exchange is widely used in large-scale liquefaction process. However, the research on the friction pressure drop of the two-phase flow in the spiral tube is limited, especially for the offshore sloshing condition. In this paper, an experimental system is designed and built, and the effects of sloshing form (roll, pitch, heave), sloshing period (5-15s), mass flux (200-800 kg/(m2·s)), vapor quality (0.3-0.9) and operating pressure (2-4 MPa) on friction pressure drop of methane/ethane (C1/C2) mixture in spiral tube are studied. The result shows that the growth rate of time-averaged friction pressure drop during condensation under sloshing condition is less than 0, which indicates that sloshing will lead to drag reduction. And under the three sloshing parameters, the growth rate of time-averaged friction pressure drop of C1/C2 is reduced by-5.99%%, 5.97% and -6.70% respectively.

The basic oxygen furnace slag (BOF) was wide used in road construction, but there was a lack of characteristics in different asphalt mixtures. This study investigates the properties of hot-mixed asphalt (HMA) containing stone mastic asphalt (SMA), porous asphalt (PA) and dense-graded BOF as a partial substitution for natural aggregates. The purpose of this study is to evaluate various BOF slag contents in the asphalt mixtures would affect the cooling behavior after compaction. Asphalt mixture specimens contained 0%, 20%, 40% and 60% BOF slag, respectively, as coarse aggregate. Test results showed that BOF slag has a lipophilic property, so it can be adsorbed by asphalt cement, thereby reducing the cost of asphalt. The stability value of all asphalt mixtures increases with the proportion of BOF slag replacement. In addition, the voids in the mineral aggregate (VMA) value variable exhibited significant differences among asphalt mixtures, and could determine the deviation of the cooling trend of asphalt mixtures. Furthermore; it was found that the cooling procedure of the BOF slag used in dense-graded asphalt mixture takes about 100 min, and that the temperature tends to be moderate; however, it took about 120 min of cooling the SMA and PA mixture with BOF slag. In addition, the voids distribution of dense asphalt mixture was not uniform. It would result in various locations of thermal energy temperature on asphalt mixtures that were inconsistent.

Tumors of the central nervous system produce adverse outcomes in everyday life; however, low-grade gliomas are uncommon in adults. For this reason, the objective is to describe the executive function of a 59-year-old adult neuro-oncological patient. Pre- and postintervention evaluation of neurological and neuropsychological functioning was performed through neuroimaging techniques (iRM, Spectroscopy, Electroencephalography), hospital history, psychological interview, and Wisconsin Card Classification Test (WCST). Executive performance exhibited a noticeable decline in typical and centile WCST results, as evidenced by deterioration in persistent scores, failure to maintain an attitude and difficulty learning from clinical samples. This information coincides with the evolution of the neuroimage over time. Finally, this topic is discussed in the literature.

We used flow cytometry to follow diel variations of viruses, picoplankton, and nanoflagellates for 2 days with about 4 hours of temporal sampling in spring 2024 in coastal surface waters of the subtropical western Pacific. An event of wind change, associated with rainfall and increased turbulence, disrupted diel patterns of the overall microbial communities during the second cycle. We observed that bacterial abundance did not follow a consistent diel pattern and that viral abundance was positively correlated with bacterial abundance. Synechococcus spp. and Prochlorococcus spp. grew during the light period and with peak abundance at night, exhibited marked diel variability, however, opposite patterns were observed in picoeukaryotes. According to Synechococcus spp. and Prochlorococcus spp. diel changes, nanoflagellate grazing could control their abundances and may explain temporally varying picocyanobacterial abundances. As we observed in the culture experiments, the results showed a significant increase in picoeukaryotic abundance from noon to nighttime, and a decrease in bacterial abundance during nighttime, to show the prey-predator cycle. Our study suggests picoeukaryotes could serve as bacteria predators by being mixotrophs. Future studies aiming to understand the interactions between prokaryotes and picoeukaryotes within marine microbial communities should take these differences into account.

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].

Background: Functional Movement Screening (FMS) allows for rapid assessment of an individual’s physical activity level and timely detection of sports injury risk. However, traditional functional movement screening often requires on-site assessment by experts, which is time-consuming and prone to subjective bias. Therefore, the study of automated functional movement screening has become increasingly important. Methods: In this study, we propose an automated assessment method for FMS based on the improved Gaussian Mixture Model (GMM). First, the oversampling of minority samples is conducted, the movement features are manually extracted from the FMS dataset collected with two Azure Kinect depth sensors, then we train the Gaussian mixture model with different scores (1 point, 2 points, 3 points) of feature data separately, finally, we conducted FMS assessment by the Maximum Likelihood estimation. Results: The improved GMM has a higher scoring accuracy (Improved GMM:0.8) compared to other models (Traditional GMM=0.38, Adaboost.M1=0.7, Naïve-Bayes=0.75), and the scoring results of improved GMM have a high level of agreement with the expert scoring (kappa=0.67). Conclusions: The results show that the proposed method based on the improved Gaussian mixture model can effectively perform the FMS assessment task and it is potentially feasible to use depth cameras for FMS assessment.

This study provides a contribution to the research field of 3D printed earthen buildings, focusing, for the first time, on the load-bearing capacity of these structures. The study involves the entire production and testing process of the earthen elements, from design, to the preparation of the mixture and the 3D printing, up to the uniaxial compression test on a wall segment. The results indicate that 3D printed earthen elements have a compressive strength of 2.32 MPa, comparable to that of rammed earth structures. The experimental data also made it possible to draw conclusions on the action of the infill, which seems to have the function of stopping the propagation of cracks. This has a positive effect on the overall behavior of 3D printed earthen elements, since it avoids the onset of dilative behavior in the final stages of the load test and maintains ultimate load values higher than 50% of the maximum load.

Maintaining a healthy cyber society is a big challenge due to the users’ freedom of expression and behaving. It can be solved by monitoring and analyzing the users’ behavior and taking proper actions towards them. This research aims to present a platform that monitors the public content on Twitter by extracting tweet data. After maintaining the data, the users’ interactions are analyzed using Graph Analysis methods. Then the users’ behavioral patterns are analyzed by applying Metadata Analysis, in which the timeline of each profile is obtained; also, the time-series behavioral features of users are investigated. Then in the Abnormal Behavior Detection Filtering component, the interesting profiles are selected for further examinations. Finally, in the Contextual Analysis component, the contents will be analyzed using natural language processing techniques; A binary text classification model (SVM + TF-IDF with 88.89% accuracy) for detecting if the tweet is related to crime or not. Then, a sentiment analysis method is applied to the crime-related tweets to perform aspect-based sentiment analysis (DistilBERT + FFNN with 80% accuracy); because sharing positive opinions about a crime-related topic can threaten society. This platform aims to provide the end-user (Police) suggestions to control hate speech or terrorist propaganda.

This paper proposes a novel efficient multistage algorithm to extract source speech signals from a noisy convolutive mixture. The proposed approach comprises of two stages named Blind Source Separation (BSS) and De-noising. A hybrid source prior model separates the source signals from the noisy reverberant mixture in the BSS stage. Moreover, we model the low and high-energy components by generalized multivariate Gaussian and super-Gaussian models, respectively. We use Minimum Mean Square Error (MMSE) to reduce noise in the noisy convolutive mixture signal in the de-noising stage. Furthermore, two proposed models investigate the performance gain. In the first model, the speech signal is separated from the observed noisy convolutive mixture in the BSS stage, followed by suppression of noise in the estimated source signals in the de-noising module. In the second approach, the noise is reduced using the MMSE filtering technique in the received noisy convolutive mixture at the de-noising stage, followed by separation of source signals from the de-noised reverberant mixture at the BSS stage. We evaluate the performance of the proposed scheme in terms of signal-to-distortion ratio (SDR) with respect to other well-known multistage BSS methods. The results show the superior performance of the proposed algorithm over the other state-of-the-art methods.

Understanding the spatially varying effects of demographic factors on the spatio-temporal variation of intestinal parasites infections is important for public health intervention and monitoring. This paper presents a hierarchical Bayesian spatially varying coefficient model to evaluate the effects demographic factors on intestinal parasites morbidities in Ghana. The modeling relied on morbidity data collected by the District Health Information Management Systems. We developed Poisson and Poisson-gamma spatially varying coefficient Models. We used the demographic factors, unsafe drinking water, unsafe toilet and unsafe liquid waste disposal as model covariates. The models were fitted using the Integrated Nested Laplace Approximations (INLA). The overall risk of intestinal parasites infection was estimated to be 10.9 per 100 people with a wide spatial variation in the district-specific posterior risk estimates. Substantial spatial variation of increasing multiplicative effects of unsafe drinking water, unsafe toilet and unsafe liquid waste disposal occurs on the variation of intestinal parasites risk. The structured residual spatial variation widely dominates the unstructured component, suggesting that the unaccounted-for risk factors are spatially continuous in nature. The study concludes that both the spatial distribution of the posterior risk and the associated exceedance probability maps are essential for monitoring and control of intestinal parasites.

As a result of the growing demand for foods with reduced animal protein content, many new alternative diets are now emerging. Nevertheless, recent studies have shown that the Western population is unprepared for drastic changes and is disinclined to accept foods based on alternative proteins. However, hybrid products might become a good transitional offer. This study used a desirability-based mixture design to model hybrid spreadable cheese analogues (SCAs). The design combined the dairy protein (MPC), Tenebrio molitor (IF) and faba bean (FBP) flours. Nine SCAs with different MPC/FBP/IF ratios were formulated, representing 0, 50 and 100% MPC replacement (7.1% of the formula). Incorporating the IF negatively impacted the desirable texture properties. The FBP flour improved the texture (achieving increased firmness and stickiness and decreased spreadability), but only when combined with MPC. Changing the MPC/FBP/IF ratio affected the colour of SCAs. Sensory analysis showed that hybrid SCAs (≤ 50% MPC) had a more characteristic cheesy flavour than the commercial plant-based reference, and sample C2 had a texture profile similar to the dairy reference. Samples containing IF showed a better flavour profile than the products without IF. The SCAs had higher protein and lower saturated fat, starch and sugar con-tent than commercial analogues.This study demonstrates that the inclusion of alternative proteins can be effective as a strategy to reduce dairy protein content in hybrid product formulations.

Peptides of hydrolysates of food proteins are an easily digestible source of amino acids necessary for the body to adapt to physical stress. Commercially significant hydrolysates include whey protein, casein, and other animal proteins. Hydrolysates of plant proteins are gaining popularity, but they are less common, then animal ones. Soy protein isolate is promising for obtaining the hydrolysates due to its affordable price and balanced amino acid profile. However, there are no direct studies showing an improvement in the result of physical activity when eating soy protein isolate hydrolysate (SPIH). In this work, for the first time, the study was conducted on the safety and efficacy of SPIH during physical load on model animals (rats). It was shown that the hydrolysate did not lead to pathological changes in the viscera, food intake, and weight of animals did not differ from the control group (animals consumed whey protein). Under physical load rats enteral fed SPIH showed a tendency to adapt more quickly to physical stress than the control group and the group of animals that was fed by free amino acids. The metabolites of animal blood serum were studied by NMR spectroscopy. It was shown that by the 95th minute after feeding in the group of rats receiving SPIH, the difference of proteinogenic amino acids concentrations in blood between individuals was significantly less than in the groups receiving whey protein or a mixture of amino acids. In other words, individual biochemical and physiological characteristics of individuals did not affect the assimilation of amino acids of hydrolysate.

Due to the accuracy of numerical calculation of fluid flow inside a hydrocyclone can be obtained using Computational Fluid Dynamics (CFD), highly modified super computers are used to simulate the fluid flow and track particle motion inside a hydrocyclone. This paper deals with the numerical study using three multiphase models viz. Volume of fluid, Mixture and Eulerian model. The dimensions of the hydrocyclone taken into consideration for numerical analysis is same as considered by Rajamani. Validation of axial and tangential velocities at different strategically decided axial stations, RMS axial and tangential velocity profiles of the hydrocyclone is done using Reynolds Stress Model (RSM). The hydrocyclone model has been designed in Creo 3.0 using the same dimensions which later was imported to CFD for meshing. Fine hexagonal mesh numbering up to 5 lacs were constructed to obtain optimum results. Fluid flow was allowed to be developed in ANSYS FLUENT 16.2. Entire simulation took 96 hours to generate results and track particle movements inside the hydrocyclone. The particle tracking has been done using three multiphase model. The first being the volume of fluid was used for validation purposes and the comparison of the Mixture and Eulerian model are the basic focus of this research work. Conclusive results indicate that usage of different multiphase model does not result in variation in particle motion. The slight variation in grade efficiency values are hardly noticeable. The Mixture model and Eulerian model predict lower separation efficiency as compared with Volume of fluid multiphase model.

Solar Energy has enormous potential to fulfill the energy requirements of the world and can be extracted using solar cells. However, the solar cells are affected by poor efficiency and further affected by wind speed, orientation of the panel, temperature and dust deposition. There are different cleaning technologies devised by many industry experts to clean the solar panels. However, they are facing drawbacks when deployed in the solar farm. An efficient cleaning system, along with an added cooling system must be devised, so that the solar panels must be cleaned and cooled often to maximize the energy production. This paper presents a low-cost, energy-efficient, smart and innovative dust cleaning and cooling system for Photovoltaic (PV) Panels. The system is designed, fabricated, fully-automated using Programmable Logic Controller (PLC) and tested successfully. A battery-charging kiosk, capable of charging two, 24V Lead Acid Batteries embedded within this prototype, shall provide clean-energy in a sustainable manner to the rural communities of the developing nations. The user can check the status of the battery such as battery voltage, battery temperature, and state of charge on the Human Machine Interface (HMI) panel while charging the batteries

This research is aimed to develop the method for damage degree evaluation in metals and alloys during their deformation and destruction based on acoustic emission (AE) technique. Experimental studies were carried out on tensile specimens of high-strength steel with simultaneous registration of AE signals in tested material. It was shown that the time-dependent AE primary parameters, such as the AE signal maximum amplitudes and AE activity, do not have a clear relationship with the material damage degree. This fact makes it difficult to use standard methods for assessing the damage degree of acoustic signal sources, including defects. It was proposed to use the Kolmogorov-Smirnov criterion to differ the processes of metal plastic deformation and crack propagation during the rupture. The possibility of estimating the material damage degree by high-energy AE hits share was shown. Besides, a particular expression was obtained for evaluating the damage degree for the material under study.

As Christopher Alexander discovered, all space or matter – either organic or inorganic – has some degree of order in it according to its structure and arrangement. The order refers to a kind of structural character, called living structure, which is defined as a mathematical structure that consists of numerous substructures with an inherent hierarchy. Across the hierarchy, there are far more small substructures than large ones, while on each level of the hierarchy the substructures are more or less similar in size. In this paper we develop a new approach to representing geographic space as a hierarchy of recursively defined subspaces for computing the degree of order. A geographic space is first represented as a hierarchy of recursively defined subspaces, and all the subspaces are then topologically represented as a network for computing the degree of order of the geographic space, as well as that of its subspaces. Unlike conventional geographic representations, which are mechanical in nature, this new geographic representation is organic, conceived, and developed under the third view of space; that is, space is neither lifeless nor neutral, but a living structure capable of being more living or less living. Thus, the order can also be referred to as life, beauty, coherence, or harmony. We applied the new representation to three urban environments, 253 patterns, and 35 black-white strips to verify it and to demonstrate advantages of the new approach and the new kind of order. We further discuss the implications of the approach and the order on geographic information science and sustainable urban planning.

This paper presents a methodology to evaluate (1) to what extent students of a higher degree in the field of education acquire sustainability competencies, and (2) to determine whether the subjects that develop the ESD achieve their learning objectives. The methodology is applied to a case study. The instruments used are the sustainability survey and the sustainability presence map developed by the EDINSOST project. The survey consists of 18 questions, and has been answered by 104 first-year students and 86 fourth-year students belonging to the Bachelor Degree in Primary Education Teaching at the University of Sevilla. The Mann-Whitney U test has been used to compare the results of the two groups, and Cohen's D has been used to measure the effect size. Students only obtain significant improvements, with 95% confidence, in three questions (Q4, Q5 and Q6), all concerning critical thinking and creativity. An improvement is also detected in question Q11, with a confidence of 90%. However, no subject in the curriculum develops the learning outcomes concerning questions Q4, Q5 and Q6, and only one subject develops the learning outcomes regarding question Q11. On the other hand, up to five subjects declare development of the learning outcomes regarding questions in which there is no improvement in student learning. These results suggest that the subjects are failing to reach their ESD learning objectives, and that the students are either trained in sustainability outside the university or the subject learning guides do not reflect the work done by the students throughout their studies

In contemporary society, an increasing number of individuals are identified as the "Indoor Generation". Prolonged periods spent indoors can have detrimental effects on our overall well-being. Consequently, the examination of biophilic indoor environments and their impact on occupant well-being and comfort has emerged as a significant area of study. Besides the construction of new biophilic buildings, numerous existing building stocks retain substantial social, economic, and environmental value. The need for stock renovation is considerable, revitalizing existing structures rather than embarking on new constructions. Swift feedback is crucial during the initial stages of renovation design, fostering consensus among all stakeholders. A comprehensive understanding of proposed renovation plans contributes to improved indoor environment design. Initially, generative adversarial networks (GANs) produce interior renovation drawings based on user preferences and designer styles. Subsequently, the implementation of Mixed Reality (MR) and Diminished Reality (DR) provides users with an immersive perspective, transforming 2D drawings into interactive MR experiences, and facilitating the presentation of interior renovation proposals. This paper outlines the development of a real-time system for architectural designers that seamlessly integrates MR, DR, and GANs results, with the aim of enhancing feedback efficiency during the renovation design process, enabling stakeholders to evaluate and understand renovation proposals more comprehensively. Furthermore, we incorporate several full-reference image quality assessment (FR-IQA) methods to evaluate the quality of the GANs-generated images. The evaluation results indicate that the majority of images fall within the moderate range of quality.

An inclusive curriculum with a socio-cultural approach must include content on sustainability. This study raised the following problem: What is the presence of contents related to sustainability in the curricula of 24 degrees of Pedagogy of the Spanish state.? Through an explicit study, subjects directly related to sustainability were identified and analyzed through the qualitative data analysis program Atlas-ti. The categories of analysis used were those present in the Sustainability Thesaurus [4] to which the professional category was incorporated. The results showed, on the one hand, the low presence in these grades of subjects whose name indicates a direct link with sustainability and, on the other hand, On the other hand, the presence of professional competencies was detected, which were not subsequently specified in the corresponding learning outcomes or in the training content.

The cross-linked microspheres were prepared and loaded with Favipiravir SARS-CoV-2 antiviral drug, by copolymerization of chitosan (CS) with a polysaccharide extracted from fresh pomegranate peels. Moreover, glutaraldehyde (Glu) has been used as chemical cross-linker ,and sodium hexametaphosphate (SHMP) as physical cross-linker. The extracted polysaccharide was analyzed and different techniques have been used and the analyses leads to the fact that it is pectin .The surface morphology of the prepared microspheres was studied use scanning electron microscope where the size and the shape factor (S) of Glu microspheres have shown high values(74.27μm) and (0.852) respectively means their surfaces are tends to roughness, whereas the SHMP microspheres have shown size particle(20.47μm)and shape factor (0.748) which gives indication that the SHMP microspheres have smooth surfaces. The swelling studies have shown that Glu microspheres have higher degree of swelling which means SHMP microspheres are more compact. The prepared microspheres have shown more Loading percentage of Favipiravir antiviral drug in SHMP microspheres(37%w/w)in comparison with Glu microspheres(35%w/w) where the electrostatic interaction between the Favipiravir ions and SHMP anions help for more loading. The microspheres prepared under different types of cross-linking have shown initially burst release of Favipiravir and followed by a step of controlled release for a certain time where its period depends on the pH of the release media. Both Glu and SHMP cross-linked microspheres have shown high controlled release time in buffered release solution of pH 7.4 and for a less periods in pH 1.3 and pH 9.4 which may related to the type of electrostatic interactions between drug and polymer system and their reactions with release solution ions.

In recent times, models like the Transformer have showcased remarkable prowess in tasks related to natural language processing. However, these models tend to be excessively intricate and demand extensive training. Additionally, while the multi-head self-attention mechanism in the Transformer model aims to capture semantic connections between words in a sequence, it encounters limitations when handling short sequences, thereby limiting its effectiveness in 5-polarity Arabic sentiment analysis tasks. The switch-transformer model has recently emerged as a high-performing alternative. Nevertheless, when these models are trained using single-task learning, they often fall short of achieving exceptional performance and struggle to generate robust latent feature representations, especially when working with compact datasets. This challenge is particularly pronounced in the case of the Arabic dialect, which is considered a low-resource language. Given these constraints, this research introduces a novel approach to sentiment analysis in Arabic text. This method leverages multitask learning in tandem with the switch-transformer shared encoder to enhance model adaptability and refine sentence representation. By introducing a mixture of expert (MoE) mechanism that break down the problem into smaller, more manageable sub-problems, the model becomes adept at handling lengthy sequences and intricate input-output relationships, benefiting both five-point and three-polarity Arabic sentiment analysis tasks. This proposed model effectively discerns sentiment in Arabic dialect sentences. The empirical results highlight the outstanding performance of the suggested model, as evidenced in evaluations on the Hotel Arabic-Reviews Dataset, the Book Reviews Arabic Dataset, and the LARB dataset.

Early spaceborne laser altimetry mission development starts in pre-phase A design, where diverse ideas are evaluated against mission science requirements. A key challenge is predicting realistic instrument performance through forward modeling at arbitrary spatial scale. Analytical evaluations compromise accuracy for speed, while radiative transfer modeling is not applicable at global scale due to computational expense. Instead of predicting arbitrary properties of a lidar measurement, we develop a baseline theory to predict only the distribution of uncertainty specifically for the terrain elevation retrieval based on terrain slope and fractional canopy cover features through a deep neural network gaussian mixture model, also known as a mixture density network (MDN). Training data was created from differencing geocorrected GEDI L2B elevation measurements with 32 independent reference lidar datasets in the contiguous U.S. from the National Ecological Observatory Network. We trained the MDN and selected hyperparameters based on regional distribution predictive capability. On average, the relative error of equivalent standard deviation of predicted regional distributions was 15.9%, with some anomalies in accuracy due to generalization and insufficient feature diversity and correlation. As an application, we predict the percent of elevation residuals of a GEDI-like lidar within a given mission threshold from 60°S to 78.25°N, which correlate to qualitative understanding of prediction accuracy and instrument performance.

Organophilic nano clay is the composition of mostly clay and organic molecules designed to improve various materials' physical and chemical properties. Permanent deformation in asphalt pavements is primarily the result of inadequate compaction, excessive loading, and high temperatures. Rutting can result in aquaplaning, leading to accidents and costly maintenance and rehabilitation works that drain the economy. Such behavior of asphalt pavements may be attributable to poor selection of aggregate, asphalt binder, or substandard asphalt binders. This study used different percentages of organophilic nano clay ranging from 3.0% - 5.0% with two penetration grade bitumen, i.e., N.R.L 60-70 &amp; N.R.L 80-100. Adding organophilic nano clay to asphalt binder significantly reduced the rutting potential under cyclic loading at high temperatures (55 °C). Ten modified formulations and two virgin bitumen specimens were prepared. The modified binders' viscosity and rutting were tested using the Rotational Viscometer and Wheel Tracker Test (W.T.T.), respectively. The results were analyzed using software for Statistical Analysis. It was recorded that organophilic nano clay-modified bitumen significantly affected rutting to interpret the results. The results indicated that rutting decreased and viscosity increased in all nano clay-modified bitumen samples, with a 4.5% O.N.C modified binder showing the most significant improvement.

With global warming and accelerated degradation of permafrost, the engineering problems caused by the formation of weak zones between the shallow and permafrost layers of soil–rock mixture (S-RM) slopes in permafrost regions have become increasingly prominent. To explore the influence of rock content on the shear strength of the S-RM freezing–thawing interface, the variation in the shear strength for different rock content is studied herein using direct shear tests. In addition, a 3D laser scanner is used for obtaining the topography of the shear failure surface. Combined with the analysis results of the shear band-particle calculation model, the influence of the rock content on the shear strength of the interface is explored. It was found that the impact threshold of the rock content on the interface strength and failure mode is approximately 30%, when the rock content (R) is &gt; 30% and that the shear strength increases rapidly with increasing rock content. When R ≤ 30%, the actual shear plane is similar to waves; when R &gt; 30%, the shear plane appears as gnawing failure. The shear strength of S-RM freezing–thawing interface mainly comes from the bite force and friction between particles. The main reason for the increase in shear strength with increasing rock content is the increase in bite force between particles, which makes the ratio of bite force to friction force approximately 1:1.

In this study, a dynamic mixture copula is used to estimate the marginal expected shortfall in the South African insurance sector. While other studies assumed nonlinear dependence to be static over time, our model capture time-varying nonlinear dependence between institutions and the market. In order to capture time-varying nonlinear dependence, the generalized autoregressive score (GAS) is used to model the dynamic copula parameters. Furthermore, our study implements a ranking that expresses to what degree individual insurers are systemically important in South Africa. We use daily stock return of five South African insurers listed in the Johannesburg Stock Exchange (JSE) from November 13, 2007 to June 15, 2020. We find that Sanlam and Discovery contribute the most to systemic risk, while Santam is found to be the least contributor to the overall systemic risk in the South African insurance sector. Our findings would be of paramount importance for the South African regulators as they would be informed that not only banks are systemically important, but some insurers also are systemically important financial institutions. Hence, stricter regulation of these institutions in the form of higher capital and loss absorbency requirements could be required based on the individual business activities undertaken by the company.

The effect of the acetylene and hydrogen gases mixture ratios in direct low-temperature vacuum carburization was investigated. The gas ratio is an important parameter for producing the free radicals in the carburization. The free radicals can remove the natural oxide film by the strong reaction of the hydrocarbons, and then thermodynamically activity can be increased. When the gas ratio was below 1, the supersaturation expanded austenite layers were formed on the surface of the AISI 316L stainless steel, which had the maximum carbon solubility up to 11.5 at.% at 743 K, were formed. On the other hand, when the gas ratio was above 1, the carbon concentration of them remained low even if the process time was enough increased to reach the maximum carbon solubility. As a result, the carbon concentration underneath the surface was determined to be highly dependent on the gas mixture ratio of acetylene and hydrogen. In conclusion, it is necessary to restrict the ratio of acetylene and hydrogen gases to total mixture gases to form the expanded austenite layer with the high carbon concentration in the direct low-temperature vacuum carburization.

This paper develops a chemical model for a closed constant volume combustion of gaseous mixtures of methane and hydrogen. Since the combustion is strongly depending on temperature, pressure and fuel composition, they were chosen the actual corresponding thermodynamic systems using this kind of combustion, respectively the spark ignition reciprocating engines in order to evaluate the combustion parameters and exhaust flue gases composition. The actual cycles impose extra restrictive operational conditions through the engine volumetric compression ratio, the geometry of combustion volume, the mode to prepare the mixture of methane and hydrogen, the cooling system and the delivered power. The chemical model avoided the unknown influences in order to explain accurately the influence of hydrogen upon the constant volume combustion and flue gases composition. The model adopted simplifying hypotheses, respectively isentropic compression and expansion processes, closed constant volume combustion developed by two successive steps obeying to the energy and mass conservation laws and, flue gases exhaust described also by two steps, i.e. an isentropic expansion through the flow section of exhaust valves followed by a constant pressure stagnation (this succession is corresponding in fact to a throttling direct process). The chemical model supposed the homogeneous mixtures of gases with variable heat capacities function of temperatures, the Mendeleev - Clapeyron ideal gas state equation and, the variable chemical equilibrium constants for chosen chemical reactions. It was assumed that the flue gases chemistry is prevailing during the isentropic expansion and flue gases exhaust and has mainly a thermal ground. The chemical model allowed evaluation of flue gasses composition and of noxious emissions.

Thyroid hormones (THs) are essential for normal brain development, influencing neural cell differentiation, migration, and synaptogenesis. Multiple endocrine-disrupting chemicals (EDCs) are found in the environment, raising concern for their potential effects on thyroid hormone signaling and the consequences on neurodevelopment and behavior. While most research on EDCs investigates the effects of individual chemicals, human health may be adversely affected by a mixture of chemicals. Many compounds belonging to a wide range of chemical classes have been identified as EDCs, notably those affecting thyroid hormone signaling. We hypothesized that embryonic exposure to a mixture of chemicals (containing phenols, phthalates, pesticides, heavy metals, perfluorinated -, polychlorinated, and polybrominated compound) commonly found in the human amniotic fluid could lead to altered brain development to assess its effect on thyroid hormone signaling and neurodevelopment in an amphibian model (Xenopus laevis), highly sensitive to thyroid disruption. Newly hatched tadpoles were exposed for eight days to either TH (thyroxine, T4 10nM) or the amniotic mixture (1x concentration) and gene expression was analyzed in the brains of exposed tadpoles using both RT-qPCR and RNA sequencing. Results indicate that whilst some overlap on TH-dependent genes exist, T4 and the mixture have different gene signatures. Immunohistochemistry showed increased proliferation in the brains of T4-treated animals whereas no difference was observed for the amniotic mixture. Further, we demonstrated diminished tadpoles’ motility in response to T4 and mixture exposure. As the individual chemicals composing the mixture are considered safe, these results highlight the importance of examining the effects of mixtures to improve risk assessment

The paper presents the results of the theoretical and practical research on developing the infrastructure for assessment of overall risk (explosion / occupational / terrorist attack) associated with unwanted events such as major accidents that can occur at explosive storehouses for civilian use. The scientific research outlined in this article was carried out within the Nucleu Project PN 16 43 02 15 - “Research on increasing safety levels at technical facilities for storage of explosives for civil use”.

Recycled asphalt mixture is a material remixed with old asphalt recycled material (RAP) and new aggregate, and its application is of great significance in environmental protection.Due to the wear and tear of the old asphalt mixture, the road performance of the recycled asphalt mixture will decrease. This paper uses IPP software to obtain the shape characteristics of the old and new aggregates, and found that the roundness of the old aggregate of 9.5mm-16mm is the most serious wear. Therefore, the 30% RAP, and the influence of the roundness of the recycled asphalt mixture on the road performance is studied.

Missing observation is a common problem in scientific and industrial experiments, particularly in a small-scale experiment. They often present significant challenges when experiment repetition is infeasible. In this research, we propose a multi-objective genetic algorithm as a practical alternative for generating optimal mixture designs that remain robust in the face of missing observation. Our algorithm prioritizes designs that exhibit superior D-efficiency while maintaining a high minimum D-efficiency due to missing observation. The focus on D-efficiency stems from its ability to minimize the impact of missing observations on parameter estimates, ensure reliability across the experimental space, and maximize the utility of available data. We study problems with three mixture components where the experimental region is an irregularly shaped polyhedral within the simplex. Our designs have proven to be D-optimal designs, demonstrating exceptional performance in terms of D-efficiency and robustness against missing observations. We provide a well-distributed set of optimal designs derived from the Pareto front, enabling experimenters to select the most suitable design based on their priorities using the desirability function.

In actuarial practice, the modeling of total losses tied to a certain policy is a non-trivial task. Traditional parametric models to predict total losses have limitations due to complex distributional features such as extreme skewness, zero inflation, multi-modality, etc., and the lack of explicit solutions for log-normal convolution. In the recent literature, the application of the Dirichlet process mixture for insurance loss has been proposed to eliminate the risk of model misspecification biases; however, the effect of covariates as well as missing covariates in the modeling framework is rarely studied. In this article, we propose novel connections among covariate-dependent Dirichlet process mixture, log-normal convolution, and missing covariate imputation. Assuming an individual loss is log-normally distributed, we develop a log skew-normal Dirichlet process to approximate the log-normal sum. As a generative approach, our framework models the joint of outcome and covariates, which allows to impute missing covariates under the assumption of missingness at random. The performance is assessed by applying our model to several insurance datasets, and the empirical results demonstrate the benefit of our model compared to the existing actuarial models such as the Tweedie-based generalized linear model, generalized additive model, or multivariate adaptive regression spline.

The extensive deployment of wireless infrastructure provides a low-cost way to track mobile users in indoor environment. This paper demonstrates a prototype model of an accurate and reliable room location awareness system in a real public environment, where three typical problems arise. First, a massive number of access points (APs) can be sensed leading to a high-dimensional classification problem. Second, heterogeneous devices record different received signal strength (RSS) levels due to the variations in chip-set and antenna attenuation. Third, APs are not necessarily visible in every scanning cycle leading to missing data. This paper presents a probabilistic Wi-Fi fingerprinting method in a hidden Markov model (HMM) framework for mobile user tracking. Considering the spatial correlation of the signal strengths from multiple APs, a Multivariate Gaussian Mixture Model (MVGMM) is fitted to model the probability distribution of RSS measurements in each cell. Furthermore, the unseen property of invisible AP has been investigated in this research, and demonstrated the efficiency of differentiation between cells. The proposed system is able to achieve comparable localization performance. The filed test results present a reliable 97% localization room level accuracy of multiple mobile users in a real university campus WiFi network without any prior knowledge of the environment.