There is a huge interest in developing novel hollow fiber (HF) membranes able to modulate neural differentiation to produce in vitro blood-brain barrier (BBB) models for biomedical and pharmaceutical research, due to the low cell-inductive properties of the polymer HFs used in current BBB models. In this work, poly(ε-caprolactone) (PCL) and composite PCL/graphene (PCL/G) HF membranes were prepared by phase inversion and were characterized in terms of mechanical, electrical, morphological, chemical, and mass transport properties. The presence of graphene in PCL/G membranes enlarged the pore size and the water flux and presented significantly higher electrical conductivity than PCL HFs. Biocompatibility assay showed that PCL/G HFs significantly increased C6 cells adhesion and differentiation towards astrocytes, may be attributed to their higher electrical conductivity in comparison to PCL HFs. On the other hand, PCL/G membranes produced a cytotoxic effect on the endothelial cell line HUVEC presumably related with a higher production of intracellular reactive oxygen species induced by the nanomaterial in this particular cell line. These results prove the potential of PCL HF membranes to grow endothelial cells and PCL/G HF membranes to differentiate astrocytes, the two characteristic cell types that could develop in vitro BBB models in future 3D co-culture systems.

This study aimed to investigate Coll-I, III, IV and elastin in canine normal prostate and PC, using Picrosirius red (PSR) and Immunohistochemical (IHC) analysis. Eight normal prostates and 10 PC from formalin-fixed, paraffin-embedded samples were used. Collagen fibers area was analyzed with ImageJ software. The distribution of Coll-I and Coll-III was approximately 80% around prostatic ducts and acini, 15% among smooth muscle and 5% surrounding blood vessels, in both normal prostate and PC. There was a higher median area of Coll-III in PC, when compared to normal prostatic tissue (p=0.001 for PSR and p= 0.05 for IHC). Immunostaining for Coll-IV was observed in the basal membrane of prostate acini, smooth muscle, blood vessels, and nerve fibers of normal and PC samples. Although there was no difference in Coll-IV area between normal tissue and PC, tumors with Gleason score 10 showed absence of Coll-IV, when compared to scores 6 and 8 (p=0.0095). Elastic fibers were found in the septa dividing the lobules and around the prostatic acini of normal samples, and was statistically higher in PC, compared to normal tissue (p=0.00229). Investigation of ECM components brings new information and should be correlated with prognosis in future studies.

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.

Traumatic brain injury (TBI) stands as a prominent global cause of disability, with motor deficits being a common consequence. Despite its widespread impact, the precise pathological mechanisms underlying motor deficits after TBI remain elusive. In this study, hindlimb postural asymmetry (HL-PA) development in rats subjected to focal TBI was investigated to explore the potential roles of laminin and collagen IV within the ECM of selected hindlimb muscles in the emergence of motor deficits following TBI. A focal TBI was induced by ablating the left sensorimotor cortex in rats and motor deficits were assessed by measuring HL-PA. The expression of laminin and collagen IV in eight selected muscles on each side of the hindlimbs from both TBI and sham-operated rats were studied using immunohistochemistry and semi-quantitatively analyzed. Results indicated that the TBI rats exhibited HL-PA, characterized by flexion of the contralateral (right) hindlimb. In the sham-operated rats the immunoreactive components of laminin and collagen IV were evenly and smoothly distributed along the border of the muscle fibers in all the investigated muscles. In contrast, in the TBI rats the pattern was broken into aggregated granule-like immunoreactive components. Such a labeling pattern was detected in all the investigated muscles both from the contra-and ipsilateral side of the TBI rats. However, in TBI rats most of the muscles from the contralateral hindlimb showed a significant increased expression of these two proteins in comparison with those from the ipsilateral hindlimb. In comparison to sham-operated rats, there was a significant increase in laminin and collagen IV expression in various contralateral hindlimb muscles in the TBI rats. These findings suggest potential implications of laminin and collagen IV in the development of motor deficits following a focal TBI.

In this paper, we deduce explicit formulas to evaluate the determinants of nonsymmetrical structure Toeplitz Bohemians by two determinants of specific Hessenberg Toeplitz matrices, which are linear combinations in terms of determinants of specific Hessenberg Toeplitz matrices. We get some new results very di¤erent from [Massimiliano Fasi, Gian Maria Negri Porzio, Determinants of normalized upper Hessenberg matrices, Electronic Journal of Linear Algebra, Volume 36, pp. 352-366, June 2020].

In this note we construct explicit complex and real matrix representations for the generators of real Clifford algebra $C\ell_{p,q}$. The representation is based on Pauli matrices and has an elegant structure similar to the fractal geometry. We find two classes of representation, the normal representation and exceptional one. The normal representation is a large class of representation which can only be expanded into $4m+1$ dimension, but the exceptional representation can be expanded as generators of the next period. In the cases $p+q=4m$, the representation is unique in equivalent sense. These results are helpful for both theoretical analysis and practical calculation. The generators of Clifford algebra are the faithful basis of $p+q$ dimensional Minkowski space-time or Riemann space, and Clifford algebra converts the complicated relations in geometry into simple and concise algebraic operations, so the Riemann geometry expressed in Clifford algebra will be much simple and clear.

In this study, we prove the norm separating property for the composition of Cesàro 1 and Gamma matrices with their transpose. As a result, we compute the ℓp-norms of six classes of operators that commute with the infinite Hilbert operators. Additionally, we find the norm of 3 Hilbert’s commutants on some well-known sequence spaces.

A complex square matrix $A$ is said to be Hermitian if $A= A^{\ast}$, the conjugate transpose of $A$. The topic of the present note is concerned with the characterization of Hermitian matrix. In this note, the we show that each of the two triple matrix product equalities $AA^{\ast}A = A^{\ast}AA^{\ast}$ and $A^3 = AA^{\ast}A$ implies that $A$ is Hermitian by means of decompositions and determinants of matrices, which are named the two-sided removal and cancellation laws associated with Hermitian matrix, respectively. We also present several general removal and cancellation laws as the extensions of the preceding two facts about Hermitian matrix.

In this paper, a solenoid based linearly movable armature system is designed using robust control theory in order to improve the performance of the system. Reference track method is the best performance analysis for position control systems. Among the robust controllers, H infinity mixed-sensitivity and Mixed H 2 /H∞ with Regional Pole Placement Controllers are used to improve the performance of the system. Comparison of the proposed controllers for tracking a reference displacement signals (step and sine wave) and a promising simulation result have been obtained.

One of matrix equalities composed of multiple products of matrices and their generalized inverses is given by $A_1B_1^{-}A_2B_2^{-} \cdots A_kB_k^{-}A_{k+1}= A$ where $A_1$, $B_1$, $A_2$, $B_2$, $\ldots$, $A_k$, $B_k$, $A_{k+1}$, and $A$ are given matrices of appropriate sizes, and $B_1^{-}$, $B_2^{-}$, $\cdots$, $B_k^{-}$ are generalized inverses of matrices. The cases for $k = 1, 2$ and their special forms were properly approached in the theory of generalized inverses of matrices. In this note, the author presents an algebraic procedure to derive explicit necessary and sufficient conditions for the equality with $k = 3$ to always hold using certain rank equalities for the block matrices constructed by the given matrices, and then mention a key step of extending the previous work to a general situation.

The present paper discusses a study of a class of Charlier matrix polynomials and its generalized analogue. Certain generating matrix functions, recurrence matrix relations, matrix differential equation, summation formulas and many new results have been discussed for these matrix polynomials. Weisner's group theoretic method is used to obtain matrix generating relations for Charlier matrix polynomials and the details of this method were given in this paper. Finally, we will discuss only briefly the procedure followed.

Though the cancer research community has used mouse xenografts for decades more than zebrafish xenografts, zebrafish have much to offer: they are cheap, easy to work with, and the embryonic model is relatively easy to use in high-throughput assays. Zebrafish can be imaged live, allowing us to observe cellular and molecular processes in vivo in real time. Opponents dismiss the zebrafish model due to the evolutionary distance between zebrafish and humans, as compared to mice, but proponents argue for the zebrafish xenograft’s superiority to cell culture systems and its advantages in imaging. This review places the zebrafish xenograft in the context of current views on cancer and gives an overview of how several aspects of this evolutionary disease can be addressed in the zebrafish model. Zebrafish are missing homologs of some human proteins and (of particular interest) several members of the matrix metalloproteinase (MMP) family of proteases, which are known for their importance in tumour biology. This review draws attention to the implicit evolutionary experiment taking place when the molecular ecology of the xenograft host is significantly different than that of the donor.

In this paper we have studied some properties for determinant-calculating for cubic-matrix of order 2 and order 3. These properties are analogous to some properties for determinants of square matrix we have proved and noted that these properties also are applicable (or not in some details) on this concept for cubic-matrix of orders 2 and 3. All results in this paper, are presented in detail during the theorem proofs.

In this paper, we derive the distribution of eigenvalues for a 2×2 random confusion matrix stemming from a machine learning (ML) evaluation problem. Additionally, we present the distributions of both the matrix’s trace and the difference between the two traces of random confusion matrices. We also illustrate the application of these derived distributions in calculating the superiority probability of four baseline ML models.

This paper considers the Hermitian solutions of a new system of commutative quaternion matrix equations, we establish both necessary and sufficient conditions for the existence of solutions. Furthermore, we derive an explicit general expression when it is solvable. To illustrate our main findings, we present two numerical algorithms and examples in this paper.

Iyer Markoulakis Helmholtz Hamiltonian mechanics formalisms mathematically modeled physics with vortex rotational fields acting with gradient fields, typically in zero-point microblackhole general fields. Here, Helmholtz metrics have been gaged to Coulombic Hilbert metrics, representing Gilbertian and Amperian natures of electromagnetic fields out of the mechanical fields from Helmholtz Hamiltonian mechanics. This ansatz general gaging helps to properly isolate field effects – mechanical, electric, magnetic components within the analytical processes. Vacuum gravitational fields and the flavor Higgs-Boson matter inertial gravitational fields have been thus quantified extending to stringmetrics constructs matrix showing charge asymmetry gage metrics, having the power to analyze dark energy superluminal phase, dark matter luminal phase, and light matter subluminal phase. Interpreting particle physics gage matrix pointing to Dirac seas electrons, monopoles with positrons, electron-positron annihilation leading to energy production, relativistic energy generating matter, and both monopoles – vacuum and compressed out of vortex Helmholtz decomposition fields have been interpolated. Quantum ASTROPHYSICS gage metrix posits superluminal profile of signals velocity generating electron-positron chain like “curdling” action that is consistent with physics literature reporting nature electron photon observed oscillatory fields configurations. This helps proposing creation of neutrino antineutrino pair orthogonal to electron positron “curdling” planes, that may lead to formation of protonic hydrogen of stars or orthogonally muon. These aspects will explain receding or fast expanding universe with dark matter in terms of flavor metrics versus gage associating metrics flavor. Additional interpretations of virtual gravitational dipoles that have gravitational charge of opposite signs, reported per physics literature at Planck dimensions, that may originate at the center of supermassive blackhole. On the other hands, vacuum monopoles occur probably at infinity with cosmos extent.

In this paper, we define Tetranacci and Tetranacci-Lucas matrix sequences and investigate their properties.

Acoustic emission (AE) has proven to be a very useful technique for determining damage in ceramic matrix composites (CMCs). CMCs rely on various cracking mechanisms which enable non-linear stress-strain behavior with ultimate failure of the composite due to fiber failure. Since these damage mechanisms are all micro-fracture mechanisms, they emit stress waves ideal for AE monitoring. These are typically plate waves since for most specimens or applications one dimension is significantly smaller than the wavelength of the sound waves emitted. By utilizing the information of the sound waveforms captured on multiple channels from individual events, the location and identity of the sources can often be elucidated. The keys to the technique are the use of wide-band frequency sensors, digitization of the waveforms (modal AE), strategic placement of sensors to sort the data and acquire important contents of the waveforms pertinent for identification, and familiarity with the material as to the damage mechanisms occurring at prescribed points of the stress history. The AE information informs the damage progression in a unique way which adds to the understanding of the process of failure for these composites. The AE methodology was applied to composites tested in fatigue at different frequencies where identification of when and where AE occurred coupled with waveform analysis leads to source identification and failure progression.

This review explores the integration of Virtual and Mixed Reality in medical training. The advent of immersive technologies has revolutionized traditional methodologies, offering immersive, interactive, and flexible learning environments. This study aims to assess the impact, efficacy, and potential of Extended Reality (XR) in medical education. Methods included a comprehensive analysis of current Metaverse’s applications in various medical fields, including surgery, diagnostics, and patient care. Results indicate a significant enhancement in learning outcomes, practical skills, and student engagement. Conclusions suggest that Virtual Reality is a valuable tool for medical training, providing realistic simulations and improving both theoretical knowledge and practical skills. The intersection of virtual reality and cognitive neuroscience presents an exciting frontier for understanding the human brain's response to immersive environments. This review explores how VR impacts learning, with a particular focus on the activation and engagement of the prefrontal cortex during immersive experiences. It delves into various studies that highlight the transformative potential of VR in educational settings and therapeutic applications, demonstrating how VR can facilitate deeper and more effective learning experiences. The review will present a dedicated section on the current methodologies used to analyze the brain's response while immersed in a virtual environment.

Malaria is a long-standing disease and one of the top life-threatening diseases, yet its treatment has not changed, while the world has already embraced the Fourth Industrial Revolution (4IR). A wave of research on digitizing monitoring mechanisms of such a deadly disease has surfaced. Automated malaria screening is one of the detection processes which are gaining popularity in the research domain. However, the process needs to be coupled with other processes aiming a nationally or regionally contextualised malaria monitoring system. This paper proposes a digital malaria monitoring system in the context of an African country or region. One advantage of such a digital system is that is enables a novel disease spread forecasting model based on the dynamics of different malaria types. The architecture of the diagnosis system is described, and the disease spread model is mathematically modelled in terms of a SPITR (Susceptible- Protected- Infected-Treated- Recovered) epidemic model which is further analysed. The forecasting model is expressed and analysed whereas experiments are conducted using a Monte Carlo simulation method. The design of the monitoring system has inspired how predictions can be made in the complex cases such as mixed infections. Results show that reinforcing the model parameter makes a significant improvement on the disease prediction.

With continued technological innovation in the fields of mixed reality (MR), wearable-type MR devices, such as helmets, have been released and are frequently used in various fields, such as entertainment, training, and education. However, because each product has different parts and specifications in terms of the design and manufacturing process, users feel that the virtual objects overlaying real environments in MR are visualized differently depending on the scale and color used by the MR device. In this paper, we compare the effect of scale and color parameters on users’ perception in using different types of MR devices to improve MR experience. We conducted two experiments (scale and color), and our experimental study showed that the subjects who participated in the scale perception experiment clearly tended to underestimate virtual objects, compared with real objects, and overestimate color in MR environments. [MM1]Please confirm meaning.

The production of fatty acids ethyl esters (FAEEs) to be used as biodiesel from oleaginous microalgae shows great opportunities as an attractive source for the production of renewable fuels without competing with human food. To ensure the economic viability and environmental sustainability of the microbial biomass as a raw material, the integration of its production and transformation into the biorefinery concept is required. In the present work, lipids from wet Isochrysis galbana microalga were extracted with ethyl acetate with and without drying the microalgal biomass (dry and wet extraction method, respectively). Then, FAEEs were produced by lipase-catalyzed transesterification and esterification of the extracted lipids with ethanol using lipase B from Candida antarctica (CALB) and Pseudomonas cepacia (PC) lipase supported on SBA-15 mesoporous silica functionalized with amino groups. The conversion to FAEEs with CALB (97 and 85.5 mol% for dry and wet extraction, respectively) and PS (91 and 87 mol%) biocatalysts reached higher values than those obtained with commercial Novozym 435 (75 and 69.5 mol%). Due to the heterogeneous nature of the composition of microalgae lipids, mixtures with different CALB:PC biocatalyst ratio were used to improve conversion of wet-extracted lipids. The results showed that a 25:75 combi-lipase produced a significantly higher conversion to FAEEs (97.2 mol%) than those produced by each biocatalyst independently from wet-extracted lipids and similar ones than those obtained by each lipase from the dry extraction method. Therefore, that optimised combi-lipase biocatalyst, along with achieving the highest conversion to FAEEs, would allow improving viability of a biorefinery since biodiesel production could be performed without the energy-intensive step of biomass drying.

Despite a global push in the development and implementation of widespread alternative energy use, significant disparities exist across given nation-states. These disparities, frequently referred to as the local-national gap, reflect both technical and economic factors, as well as the social, political, and ecological gaps between how communities see energy development and national/global policy goals. This dataset is an attempt to bridge the local-national gap regarding solar PV adoption in the state of Georgia (U.S.A.). This dataset is an aggregation of variables from seven different publicly-available sources that was designed to help researchers interested in the context underlying solar adoption on the local scale of governance (e.g. the county level). The SolarView database includes information necessary for informing policymaking activities such as solar installation information, a historical county zip code directory, county-level census data, housing value indexes, renewable energy incentive totals, PV rooftop suitability percentages, and utility rates. As this is a database from multiple sources, incomplete data entries are noted.

Parallel decompositions have proved to be powerful tools for the analysis and interpretation of the physical information provided by Mueller matrices experimentally obtained. In this work, the procedure for the so-called arbitrary decomposition of Mueller matrices is validated through its application to a set of representative experimentally determined Mueller matrices. The covariance and passivity criteria required for the physical realizability of the Mueller matrices involved in the parallel decompositions are integrated in the procedure, which ensures the physical consistency of the results presented.

One of the typical forms of linear matrix expressions (linear matrix-valued functions) is given by $A + B_1X_1C_1 + \cdots + B_kX_kC_k$, where $X_1, \ldots, X_k$ are independent variable matrices of appropriate sizes, which include almost all matrices with unknown entries as its special cases. The domain of the matrix expression is defined to be all possible values of the matrix expressions with respect to $X_1, \ldots, X_k$. I this article, we approach some problems on the relationships between the domains of two linear matrix expressions by means of the block matrix method (BMM), the matrix rank method (MRM), and the matrix equation method (MEM). As application, we discuss some topics on the relationships among general solutions of some linear matrix equations and their reduced equations.

It is shown that a Mueller quaternion can be formulated as a quaternion with matrix coefficients, and this Mueller quaternion transforms the Stokes quaternion for depolarizing process as well as for nondepolarizing process. Mueller quaternion keeps track of the phase acquired by the Stokes quaternion and, in general, quaternion states of optical media comprises all properties of Jones, Mueller-Jones and Mueller matrices.

Reverse order laws for generalized inverses of matrix products is a classic object of study in the theory of generalized inverses. One of the well-known reverse order laws for a matrix product $AB$ is $(AB)^{(i,\ldots,j)} = B^{(s_2,\ldots,t_2)}A^{(s_1,\ldots,t_1)}$, where $(\cdot)^{(i,\ldots,j)}$ denotes an $\{i,\ldots, j\}$-generalized inverse of matrix. Because $\{i,\ldots, j\}$-generalized inverse of a singular matrix is unique, the relationships between both sides of the reverse order law can be divided into four situations for consideration. This paper provides a thorough coverage of the reverse order laws for $\{i,\ldots, j\}$-generalized inverses of $AB$, from the development of background and preliminary tools to the collection of miscellaneous formulas and facts on the reverse order laws in one place with cogent introduction and references for further study. We begin with the introduction of a linear mixed model $y = AB\beta + A\gamma + \epsilon$ and the presentation of two least-squares methodologies to estimate the fixed parameter vector $\beta$ in the model, and the description of connections between the two types of least-squares estimators and the reverse order laws for generalized inverses of $AB$. We then prepare some valued matrix analysis tools, including a general theory on linear or nonlinear matrix identities, a group of expansion formulas for calculating ranks of block matrices, two groups of explicit formulas for calculating the maximum and minimum ranks of $B^{(s_2,\ldots,t_2)}A^{(s_1,\ldots,t_1)}$, as well as necessary and sufficient conditions for $B^{(s_2,\ldots,t_2)}A^{(s_1,\ldots,t_1)}$ to be invariant with respect to the choice of $B^{(s_2,\ldots,t_2)}A^{(s_1,\ldots,t_1)}$. We then present a unified approach to the 512 matrix set inclusion problems associated with the above reverse order laws for the eight commonly-used types of generalized inverses of $A$, $B$, and $AB$ through use of the definitions of generalized inverses, the block matrix method (BMM), the matrix rank method (MRM), the matrix equation method (MEM), and various algebraic calculations of matrices.

The new barrier mode in Apache Spark allows embedding distributed deep learning training as a Spark stage to simplify the distributed training workflow. In Spark, a task in a stage doesn’t depend on any other tasks in the same stage, and hence it can be scheduled independently. However, several algorithms require more sophisticated inter-task communications, similar to the MPI paradigm. By combining distributed message passing (using asynchronous network IO), OpenJDK’s new auto-vectorization and Spark’s barrier execution mode, we can add non-map/reduce based algorithms, such as Cannon’s distributed matrix multiplication to Spark. We document an efficient distributed matrix multiplication using Cannon’s algorithm, which improves significantly on the performance of the existing MLlib implementation. Used within a barrier task, the algorithm described herein results in an up to 24% performance increase on a 10,000x10,000 square matrix with a significantly lower memory footprint. Applications of efficient matrix multiplication include, among others, accelerating the training and implementation of deep convolutional neural network based workloads, and thus such efficient algorithms can play a ground-breaking role in faster, more efficient execution of even the most complicated machine learning tasks

Given several nonnegative matrices with a single pattern of allocation among their zero/nonzero elements, the average matrix should have the same pattern, too. This is the first tenet of the pattern-multiplicative average (PMA) concept, while the second one suggests the multiplicative (or geometric) nature of averaging. The original concept of PMA was motivated by the practice of matrix population models as a tool to assess the population viability from long-term monitoring data. The task has reduced to searching for an approximate solution to an overdetermined system of polynomial equations for unknown elements of the average matrix (G), hence to a nonlinear constrained minimization problem for the matrix norm. Former practical solutions faced certain technical problems, which required sophisticated algorithms but returned acceptable estimates. Fresh idea to use the eigenvalue approximation ensues also from the basic equation of averaging, which determines the exact value of λ1(G), the dominant eigenvalue of matrix G, too, and the corre-sponding eigenvector. These are bound by the well-known linear equations, which reduce the task to a standard problem of linear programing (LP). The LP approach is realized for 13 fixed-pattern matrices gained in a case study of Androsace albana, an alpine short-lived perennial, monitored on permanent plots during 14 years. A standard software routine reveals the unique exact solution, rather than an approximate one, to the PMA problem, which turns the LP approach into ‘’the best of versatile optimization tools”. The exact solution turns out to be peculiar in reaching zero bounds for certain nonnegative entries of G, which deserves a modified problem formulation separating the lower bounds from zero.

Partially state-owned firms are common in both developed and developing countries and contribute around 10% of the world's GDP. This has led to a rich literature exploring various aspects of competition between such “mixed”-ownership firms. This paper contributes to the existing literature by studying the effects of mixed ownership on the intensity of competition in oligopoly markets. Our findings indicate that state ownership exerts an anti-competitive influence, with a higher state ownership share resulting in less aggressive behavior by firms in the product market. This result implies that industries with a higher proportion of state ownership yield lower total surplus. Further, when considering factors such as taxes, soft budget constraints, and weak property rights, the anti-competitive effects of state ownership are accentuated.

This mixed-methods research article explores the influence of ChatGPT, a state-of-the-art AI language model, on the development of maladaptive learning habits in users. The study combines quantitative and qualitative data to provide a comprehensive understanding of how engaging with ChatGPT may inadvertently foster detrimental learning behaviors. Findings suggest that while ChatGPT can be a valuable learning tool, it has the potential to exacerbate or create bad learning habits if not used mindfully.

Chemical exposures from diverse sources merge on a limited number of molecular pathways described as toxicity pathways. Changes in the same set of molecular pathways in different cell and tissue types may generate seemingly unrelated health conditions. Today, no approaches are available to predict in an unbiased way sensitivities of different disease states and their combinations to multichemical exposures across the exposome. We propose an inductive in-silico workflow where sensitivities of genes to chemical exposures are identified based on the overlap of existing genomic datasets, and data on sensitivities of individual genes is further used to sequentially derive predictions on sensitivities of molecular pathways, disease states, and groups of disease states (syndromes). Our analysis predicts that conditions representing the most significant public health problems are among the most sensitive to cumulative chemical exposures. These conditions include six leading types of cancer in the world (prostatic, breast, stomach, lung, colorectal neoplasms, and hepatocellular carcinoma), obesity, type 2 diabetes, non-alcoholic fatty liver disease, autistic disorder, Alzheimer's disease, hypertension, heart failure, brain and myocardial ischemia, and myocardial infarction. Overall, our predictions suggest that environmental risk factors may be underestimated for the most significant public health problems.

In this study, Mg-Al and Mg-Al-Ni - layered double hydroxides (LDHs) were successfully synthesized for efficient removal of Mn²⁺ from synthetically wastewater. LDH adsorbents (Mg-Al and Mg-Ni-Al) were prepared by co-precipitation method. The formation of the layered double hydroxide, the adsorption of manganese on both LDH (Mg-Al and Mg-Ni-Al) were observed by XRD, SEM and EDX analysis. The various parameters such as the effect of shaking time, initial Mn²⁺ concentration, temperature were controlled and optimized to removal of Mn²⁺ from synthetic wastewater. The kinetics and adsorption isotherms for Mn²⁺ removal from wastewater were studied in batch mode. At temperatures of 10 °C and 20 °C the adsorption equilibrium was reached after 24 h. Adsorption isotherms of Mn²⁺ are well fitted by Langmuir and Freundlich isotherm equation. The adsorption capacity of Mn²⁺ from synthetic wastewater of 80.607 mg/kg was obtained for (Mg-Al-Ni)-LDH. It is found that the adsorption kinetics is best described by the pseudo-second order model. These results prove that LDHs can be considered as a potential material for adsorption of Mn²⁺ from wastewater.

This paper mainly analyzes the design and control of the rotational inverted pendulum, and presents a state space expression. Since the system is highly unstable a feedback control system is used. Augmentations with weighting functions based mixed sensetivity and H2 optimal control methods are used to make the system stable for uprise position. The rotational inverted pendulum have been simulated and compared with the proposed controllers and a promising results have been analyzed sussesfuly.

Increasingly, genomics is being used for the prediction of specific traits and diseases (phenotypes) among humans. Wider availability of genomics data through multiple research projects (such as International HapMap Project1 and 1000 Genomes2) has been a catalyst in that direction. With the recent advances in machine learning and big data analysis, data computation resources and data models needed for genomics data analysis are readily available. However, the prediction of traits and diseases has its own challenges in terms of computational requirements and computational analysis, statistical analysis (example: confounding variables), and limited quality of data collection. Linear Mixed Models (LMM, a type of linear regression) is a common approach for Genome-wide Association Studies (GWAS) for the prediction of common traits among humans using genomics. This paper researches the existing LMM-based approaches for Genome-wide Association Studies (GWAS), describes the experiment performed on FaST-LMM approach from Microsoft Research, and then proposes an enhanced approach (called LMM-22) on how to address computational and statistical issues. LMM-22 focuses on the parallelization of LMM computations and execution of LMM-22 on General Purpose Graphics Processing Units (GPU) as against CPUs to accelerate the LMM approach for GWAS studies.

The study was conducted in the potential mixed farming areas of Bale highland to estimate livestock methane emissions. Using multi-stage purposive sampling, 156 households of the three wealth groups were selected based on their livelihood assets as described under methodology. Structured questionnaires, focus group discussions, key informants interview and field visits were the employed methods during the study. Feed nutrient balance was estimated based on the demand and supply while the livestock methane emissions were estimated according to the IPCC guidelines. Descriptive statistics and one-way ANOVA tests were used to analyze the data. Cattle were the dominant (84.25%) livestock owned by the households. The estimated enteric CH4 emission rate from mature cattle, growing cattle, sheep >1 year, sheep ≤ 1 year, horse and donkey were significantly (P<0.001) higher for the better wealth group while mature cattle (69.78%) shared the highest rate. Though, higher emission rates credited to the large number of animals in the area, cattle stay crucial to the livelihoods of the households, beside the major sources of CH4. In conclusion, the estimated CH4 emissions should be focus areas of interventions. Therefore, proper husbandry and quality feed supply and promotion of farm level livestock technologies should be practiced wisely to increase productivity and protect the environment from emissions of the livestock sector.

We introduce a novel key exchange protocol based on non-commutative matrix multiplication defined in $\mathbb{F}_p^{n \times n}$. The security of our method does not rely on computational problems as integer factorization or discrete logarithm whose difficulty is conjectured. We show that the public, secret and channel keys become indistinguishable to the eavesdropper under matrix multiplication. Remarkably, for achieving a 512-bit security level, the public key is 1024 bits and the private key is 768 bits, making them the smallest keys among post-quantum key exchange algorithms. Also, we discuss how to achieve key authentication, interdomain certification and Perfect Forward Secrecy (PFS). Therefore, Lizama's algorithm becomes a promising candidate to establish shared keys and secret communication between (IoT) devices in the quantum era.

We introduce a novel key exchange protocol based on non-commutative matrix multiplication defined in $\mathbb{Z}_p^{n \times n}$. The security of our method does not rely on computational problems as integer factorization or discrete logarithm whose difficulty is conjectured. We claim that the unique eavesdropper's opportunity to get the secret/private key is by means of an exhaustive search which is equivalent to the unsorted database search problem. Furthermore, we show that the secret/private keys become indistinguishable to the eavesdropper. Remarkably, to achieve a 512-bit security level, the keys (public/private) are of the same size when matrix multiplication is done over a reduced 8-bit size modulo. Also, we discuss how to achieve key certification and Perfect Forward Secrecy (PFS). Therefore, Lizama's algorithm becomes a promising candidate to establish shared keys and secret communication between (IoT) devices in the quantum era.

Heparanase (HPSE) has been defined as a multitasking protein that exhibits a peculiar enzymatic activity towards HS chains but which simultaneously performs other non-enzymatic functions. Through its enzymatic activity, HPSE catalyzes the cutting of the side chains of heparan sulfate (HS) proteoglycans, thus contributing to the remodeling of the extracellular matrix and of the basal membranes. Furthermore, thanks to this activity, HPSE also promotes the release and diffusion of various HS-linked molecules as growth factors, cytokines and enzymes. In addition to being an enzyme HPSE has been shown to possess the ability to trigger different signaling pathways by interacting with transmembrane proteins. In normal tissue and in physiological conditions, HPSE exhibits only low levels of expression restricted only to keratinocytes, trophoblast, platelets and mast cells and leukocytes. On the contrary, in pathological conditions, such as in tumor progression and metastasis, inflammation and fibrosis, it is overexpressed. With this brief review, we intend to provide an update on current knowledge about the different role of HPSE protein exerted by its enzymatic and not-enzymatic activity.

Heparanase (HPSE) has been defined as a multitasking protein that exhibits a peculiar enzymatic activity towards HS chains but which simultaneously performs other non-enzymatic functions. Through its enzymatic activity, HPSE catalyzes the cutting of the side chains of heparan sulfate (HS) proteoglycans, thus contributing to the remodeling of the extracellular matrix and of the basal membranes. Furthermore, thanks to this activity, HPSE also promotes the release and diffusion of various HS-linked molecules as growth factors, cytokines and enzymes. In addition to being an enzyme HPSE has been shown to possess the ability to trigger different signaling pathways by interacting with transmembrane proteins. In normal tissue and in physiological conditions, HPSE exhibits only low levels of expression restricted only to keratinocytes, trophoblast, platelets and mast cells and leukocytes. On the contrary, in pathological conditions, such as in tumor progression and metastasis, inflammation and fibrosis, it is overexpressed. With this brief review, we intend to provide an update on current knowledge about the different role of HPSE protein exerted by its enzymatic and not-enzymatic activity.

Among the five human senses, light, sound, and force perceived by eye, ear, and skin, respectively are physical actions, and therefore can be easily measured and expressed as objective, univocal, and simple digital data with physical quantity. However, taste and odor molecules perceived by tongue and nose are chemical actions, it has been difficult to express them as objective and univocal digital data, since no reference chemicals can be defined. Therefore, while the recording, saving, transmitting to remote locations, and replaying human visual, auditory, and tactile information as digital data in digital devices has been realized (this series of data flow is defined as DX (digital transformation) in this review), the DX of human taste and odor information is not yet in the realization stage. Particularly, since there are at least 400,000 types of odor molecules and an infinite number of complex odors that are mixtures of these molecules, it has been considered extremely difficult to realize "human olfactory DX" by converting all odors perceived by the human olfaction into digital data. In this review, we discuss the current status and future prospects of the development of "human olfactory DX," which we believe can be realized by utilizing odor sensors that employ the olfactory receptors (ORs) that support human olfaction as sensing molecules (i.e., human OR sensor).

Matrix multiplication is probably one of the most basic and important operations having a wide range of applications in studying Linear algebra. We will be primarily focused on reviewing different algorithms for the multiplication of two n×n matrices. The most Naive method has a complexity of O(n3) but interestingly, algorithms with better complexity have been achieved. We will get to see how the complexity can be improved further, occasionally giving a survey.

Cancer invasion through basement membranes represents the initial step of tumor dissemination and metastasis. However, little is known about how human cancer cells breach basement membranes. Here, we used a 3-dimensional in vitro invasion model consisting of cancer spheroids encapsulated by a basement membrane and embedded in 3D collagen gels to visualize the early events of cancer invasion by confocal microscopy and live-cell imaging. Human breast cancer cells generated large numbers of basement membrane perforations, or holes, of varying sizes that expanded over time during cell invasion. We used a wide variety of small molecule inhibitors to probe the mechanisms of basement membrane perforation and hole expansion. Protease inhibitor treatment (BB94), led to a 63% decrease in perforation size. After myosin II inhibition (blebbistatin), basement membrane perforation area decreased by only 15%. These treatments produced correspondingly decreased cellular breaching events. Interestingly, inhibition of actin polymerization dramatically decreased basement membrane perforation by 80% and blocked invasion. Our findings suggest that human cancer cells can primarily use proteolysis and actin polymerization to perforate the BM and to expand perforations for basement membrane breaching, with a relatively small contribution from myosin II contractility.

This document focuses attention on the fundamental solution of an autonomous linear retarded functional differential equation (RFDE) along with its supporting cast of actors: kernel matrix, characteristic matrix, resolvent matrix, and the Laplace transform. The fundamental solution is presented in a form of the convolutional powers of the kernel matrix in the manner of a convolutional exponential matrix function. The fundamental solution combined with a solution representation gives an exact expression in explicit form for the solution of a RFDE. Algebraic graph theory is applied to the RFDE in the form of a weighted loop-digraph to illuminate the system structure and system dynamics and to identify the strong and weak components. Examples are provided in the document to elucidate the behavior of the fundamental solution. The paper introduces fundamental solutions of other functional differential equations.

This note reconsiders a matrix equality $A_1A_2^{-}A_3A_4^{-}A_5 = A$ composed by six matrices of appropriate sizes, where $A_2^{-}$ and $A_4^{-}$ are generalized inverses of $A_2$ and $A_4$, respectively, and solves a selection of matrix set inclusion problems associated with various mixed reverse order laws for generalized inverses of products of two, three, and four matrices by means of this equality and its variations.

AbstractCurrently, public health researchers are investigating several complex and multi-faced health problems that require mixed-method approaches (MMA) to clearly understand problems and design proper intervention approaches and policy formulation. Besides, a comprehensive comprehension of the complex operation of society is vital. The MMA helps in having a broader understanding of the functioning and structure of the community at large. MMA deliberately integrates qualitative and quantitative approaches carefully to build on the merits of each other and confirm that the findings of research are closer to the truth. Therefore, public health scholars must have an excellent comprehension of the MMA and its philosophical foundations for discovering the complex conditions that challenge public health nowadays. However, public health researchers do not clearly understand the theoretical underpinnings and practical application of MMA, despite its popularity in developing countries. Therefore, this article aimed to provide comprehensive evidence on the theoretical underpinnings and practical application of MMA.

Yijing possesses inherent principles and logic, and it has achieved consensus as an auxiliary decision-making function. Research on Yijing has been validated in the field of superpsychology, primarily focusing on individuals. However, the universal value of Yijing extends beyond individual divination or telepsychological induction, and there are untapped scientific potential values associated with it. Consequently, many scholars studying Yijing, including those in Chinese medicine, attempt to decipher its internal scientific mechanism through quantitative methods. In summary, scientific quantitative analysis of Yijing can be categorized into several approaches. These include quantification based on Yijing itself (binary and hexagram order), as well as quantification based on mathematical statistics, probability, and calculus. Another approach involves quantification based on expert knowledge, as well as the quantification of timewave curves and Yi-globle. Significant progress has been made in all of these quantitative research areas within the scientific paradigm. Nevertheless, future quantification of Yijing may explore rule-based evolutionary analysis, incorporating artificial intelligence technology, and conduct quantitative analysis based on the evolutionary process and results. There is even potential to deepen this research with tools such as ChatGPT. Additionally, the focus of Yijing divination may shift from individual events to group events, accompanied by space-time modeling and analysis through the construction of a new coordinate system.

The production of fuels and other industrial products from renewable sources has intensified the search for new substrates or for the expansion of the use of substrates already in use, as well as the search for microorganisms with different metabolic capacities. In the present work, we isolated and tested a yeast from the soil of sugarcane irrigated with vinasse, that is, with high mineral content and acidic pH. The strain of Meyerozyma caribbica URM 8365 was able to ferment glucose, but the use of xylose occurred when some oxygenation was provided. However, some fermentation of xylose to ethanol in oxygen limitation also occurs if glucose is present. This strain was able to produce ethanol from molasses substrate with 76% efficiency, showing its tolerance to possible inhibitors. High ethanol production efficiencies were also observed in acidic hydrolysates of each bagasse, sorghum, and cactus pear biomass. Mixtures of these substrates were tested and the best composition was found for the use of excess plant biomass in supplementation of primary substrates. It was also possible to verify the production of xylitol from xylose when the acetic acid concentration is reduced. Finally, the proposed metabolic model allowed calculating how much of the xylose carbon can be directed to the production of ethanol and/or xylitol in the presence of glucose. With this, it is possible to design an industrial plant that combines the production of ethanol and/or xylitol using combinations of primary substrates with hydrolysates of their biomass.

When the majority of museums and other cultural institutions were shut down due to the pandemic, mixed museum visits became a hot issue. After the epidemic, mixed visits, in the opinion of many experts, would become the new norm for experiencing cultural content. Diverse types of merging online and onsite visits are already being investigated by researchers, not only to avoid the spread of a disease but also to enable visits of people who were previously excluded, such as persons in remote geographic areas or people with mobility challenges. The current work takes into account the Contextual Model of Museum Learning to define potential use scenarios for visits from people of different cultural backgrounds and offers an evaluation of current practices.

As two commonly-used non-selective herbicides, glyphosate (GP) and diquat (DQ) are easily co-resided in lateritic paddy soil due to the rich iron/aluminum oxides, nevertheless there is limited information on their co-impact on microbial diversity and community structure in this type soil. In this study, the short-term effects of combined GP and DQ on soil microbial diversity and community structure shifts were investigated in lateritic paddy soil from a tropical agricultural region (Hainan, China) based on 16S rRNA and ITS high-throughput sequencing technology. The results showed that mixed herbicides promoted the abundance of Streptomyces in bacteria (0.45-1.84%) and Curvularia in fungi (0.01-5.85%), while GP and DQ had inhibitory effects on the abundance of Streptomyces (0.13-2.21%) and Curvularia (0.03-1.13%), which were significantly different with their single exposure (p < 0.05); the combined application of the two herbicides aggravate the adverse effect on the diversity of soil fungal community (p < 0.05), although their mixture did not have a greater impact on the soil bacteria abundance/diversity and fungi abundance (p > 0.05). Results suggested that the combined application of GP and DQ affected the fungal diversity although they did not cause other significant negative effects on soil microorganisms, hinting that more attention should be paid to the mixed effect caused by GP and DQ on specific fungal populations in lateritic paddy soil.

Introduced in 2009, Bitcoin has demonstrated a huge potential as the world’s first digital currency and has been widely used as a financial investment. Our research aims to uncover the relationship between Bitcoin prices and people’s sentiments about Bitcoin on social media. Among various social media platforms, micro-blogging is one of the most popular. Millions of people use micro-blogging platforms to exchange ideas, broadcast views, and to provide opinions on different topics related to politics, culture, science, and technology. This makes them a potentially rich source of data for sentiment analysis. Therefore we chose one of the busiest micro-blogging platforms, Twitter, to perform sentiment analysis on Bitcoin. We used ELMo embedding model to convert Bitcoin-related tweets into a vector form and SVM classifier to divide the tweets into three sentiment categories - positive, negative, and neutral. We then used the sentiment data to find its relation with Bitcoin price fluctuation using the linear mixed model.

In this work, some numerical radius inequalities based on the recent Dragomir extension of Furuta's inequality are obtained. Some particular cases are also provided.

Childhood glaucoma encompasses congenital and juvenile primary glaucoma, which are heterogeneous, uncommon, and irreversible optic neuropathies leading to visual impairment with poorly understood genetic basis. Our goal was to identify gene variants associated with these glaucoma types by assessing the mutational burden in 76 matrix metalloproteinase-related genes. We studied 101 childhood glaucoma patients with no identified monogenic alterations using next-generation sequencing. Gene expression was assessed through immunohistochemistry. Functional analysis of selected gene variants was conducted in cultured cells and in zebrafish. Patients presented a higher proportion of rare variants in four metalloproteinase-related genes, including CPAMD8 and ADAMTSL4, compared to controls. ADAMTSL4 protein expression was observed in the anterior segment of both the adult human and zebrafish larvae's eye, including tissues associated with glaucoma. In HEK-293T cells, expression of four ADAMTSL4 variants identified in this study showed that two variants (p.Arg774Trp and p.Arg98Trp) accumulated intracellularly, inducing endoplasmic reticulum stress. Additionally, overexpressing these ADAMTSL4 variants in zebrafish embryos confirmed partial loss-of-function effects for p.Ser719Leu and p.Arg1083His. Double heterozygous functional suppression of adamtsl4 and cpamd8 zebrafish orthologs resulted in reduced volume of both the anterior eye chamber and lens within the chamber, supporting a genetic interaction between these genes. Our findings suggest that accumulation of partial functional defects in matrix metalloproteinases-related genes may contribute to increased susceptibility to early-onset glaucoma and provide further evidence supporting the notion of a complex genetic inheritance pattern underlying the disease.

The extracellular matrix (ECM) provides structural support for tissues and regulatory signals for resident cells. ECM requires a careful balance between protein accumulation and degradation for homeostasis. Disruption of this balance can lead to pathological processes such as fibrosis in organs across the body. Post-translational crosslinking modifications to ECM proteins such as collagens alter ECM structure and function. Dysregulation of crosslinking enzymes as well as changes in crosslinking composition are prevalent in fibrosis. Because of the crucial roles that ECM crosslinking pathways play in disease, the enzymes that govern crosslinking events are being explored as therapeutic targets for fibrosis. Here we review in-depth the molecular mechanisms underlying ECM crosslinking, how ECM crosslinking contributes to fibrosis, and therapeutic strategies being explored to target ECM crosslinking in fibrosis to restore normal tissue structure and function.

This paper attempts to propose a Riemann spring model via an analogy between the Riemann Zeta function of a complex number and the elastic spring in series.

This paper attempts to extend the Riemann Zeta function of a complex number to a function of a matrix and/or a tensor $A$, namely $$\zeta (A)=\sum _{n=1}^{\infty} \frac{1}{n^{A}}= \sum _{n=1}^{\infty} \sum_{k=1}^n\lambda_k A^k$$ and inverse $$A=\sum _{n=1}^{\infty} \sum_{k=1}^n \mu(n)\lambda_k \zeta(A^k)$$ where $\mu(n)$ is the M\"obius function, $A$ is a complex matrix or tensor with any order, and $\lambda_k$ is eigenvalue of the matri/tensor $A$. This kind of calculations on the Riemann Zeta function has never been seen in the literature. Some examples are provided.

The aim of this paper is to show how the mathematical basis is used for a precise treatment of double-entry bookkeeping, which was firstly developed in the nineteenth century by Sir William Rowan Hamilton. This is done by using basic notions of matrix algebra with the roots to the idea of using ordered pairs. We also reveal how the complex numbers and the rationals (fractions) developed in a mainstream of accounting science and became a leading platform for the ongoing processes within the Industry 4.0. The paper concludes with the examples on how accounting operations can be represented by matrix equations with a result of the essential report generating. The author represents a mathematical model of accounting which is independent of the specific existential forms but is capable to undertake the form of any of them and thus has the prospect of being understood and accepted by specialists globally.

Matrix metalloproteases (MMPS) are a family of well-known enzymes which operate prevalently in the extracellular domain, where they fulfil the function of remodeling the extracellular matrix. Within the about 26 family members, encoded by 24 genes in humans, MMP-2 and MMP-9, have been regarded as the primary responsibility for the basement membrane and pericellular ECM rearrangement. In cases of infiltrating carcinomas, which arise from the epithelial tissues of a gland or of an internal organ, a marked alteration of the expression and the activity levels of both MMPs is known to occur. Present investigation represents the continuation and upgrading of our previous studies, now focusing on the occurrence and intensity levels of MMP-2 and -9, and their proteomic correlations, in a cohort of 80 breast cancer surgical tissues

Many authors proposed analytical methods for solving fully fuzzy Sylvester matrix equation (FFSME) based on Vec-operator and Kronecker product. However, these methods are restricted to nonnegative fuzzy numbers and cannot be extended to FFSME with near-zero fuzzy numbers. The main intention of this paper is to develop a new numerical method for solving FFSME with near-zero trapezoidal fuzzy numbers that provides a wider scope of trapezoidal fully fuzzy Sylvester matrix equation (TrFFSME) in scientific applications. This numerical method can solve the trapezoidal fully fuzzy Sylvester matrix equation with arbitrary coefficients and find all possible finite arbitrary solutions for the system. In order to obtain all possible fuzzy solutions, the TrFFSME is transferred to a system of non-linear equations based on newly developed arithmetic fuzzy multiplication between trapezoidal fuzzy numbers. The fuzzy solutions to the TrFFSME are obtained by developing a new two-stage algorithm. To illustrate the proposed method numerical example is solved.

In this paper we perform numerous numerical studies for the problem of low-rank matrix completion. We compare the Bayesian approaches and a recently introduced de-biased estimator which provides a useful way to build confidence intervals of interest. From a theoretical viewpoint, the de-biased estimator comes with a sharp minimax-optimal rate of estimation error whereas the Bayesian approach reaches this rate with an additional logarithmic factor. Our simulation studies show originally interesting results that the de-biased estimator is just as good as the Bayesian estimators. Moreover, Bayesian approaches are much more stable and can outperform the de-biased estimator in the case of small samples. However, we also find that the length of the confidence intervals revealed by the de-biased estimator for an entry is absolutely shorter than the length of the considered credible interval. These suggest further theoretical studies on the estimation error and the concentration for Bayesian methods as they are being quite limited up to present.

The ocean’s mixed layer depth (MLD) plays an important role in understanding climate dynamics, especially during extreme weather occurrences like hurricanes. This study investigates the effects of Hurricane Katrina (2005) on the MLD in the Gulf of Mexico, using the Delft3D modeling system. By integrating hydrodynamics and wave dynamics modules, we simulate the ocean’s response to extreme weather, focusing on temperature, salinity and MLD variations. Our analysis reveals significant cooling and mixing induced by Katrina, resulting in spatial and temporal fluctuations in temperature (~± 4°C) and salinity (~± 2.5 ppt). The MLD is estimated using a simple threshold method, revealing a substantial deepening to ~120 m on August 29th-30th during Hurricane Katrina in the middle of northern Gulf of Mexico, compared to an average MLD of ~20-40 m during pre-storm conditions. It took about 18-days to recover to ~84% of the pre-storm level after Katrina. Compared to the stand-alone FLOW model, the coupled FLOW+WAVE model yields a deeper MLD of ~5%. The MLD recovery and wave effect on the MLD provide insights from various scientific, environmental and operational perspectives, offering a valuable basis for ocean management, planning and applications, particularly during extreme weather events.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system's performance when the number of agents increases. To this end, two different mixed reality frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots being the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV those used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real-time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in mixed reality experiences regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.

This study assessed the periodic fluctuations among microbial biomass Carbon (C), Nitrogen (N) and Phosphorus (P), and the consequences of variations in altitude and abiotic factors on the soil microbial biomass (C, N and P) in a temperate mixed-oak pine forest of Central Himalaya. This research was directed at three forest stands along an altitudinal gradient. Samples were collected in triplicates, seasonally from each selected site and microbial C, N and P were determined through the fumigation extraction method. Microbial biomass C, N and P decreased significantly (P<0.01, correlation coefficient -0.985, -0.963, -0.948, respectively) with increasing altitude, while the rainy season showed the highest values, and winter season revealed the least values. Microbial biomass C, N and P showed positive correlation with silt particles, water holding capacity, bulk density, porosity, soil moisture, organic C, total N and P, and negative correlations with sand particles and soil pH. The microbial biomass C showed strong associations with soil microbial N (r=0.80, P<0.01) and P (r=0.89, P<0.01) contents, while the soil microbial biomass N and P also showed strong positive correlation (r=0.92, P< 0.01). Soil microbial biomass was greatly influenced by the altitude and abiotic variables whereas, weakly by temporal variation. The microbial C: N ratio indicated that fertility of soil is influenced by the species assemblage. Our findings suggest that high microbial biomass and low C: N ratio during rainy season could be considered as a strategy to conserve nutrients by temperate mixed-oak pine forest ecosystem.

This paper answers the need to plan a cost-minimizing charging schedule for electric buses and proposes a three-stage procedure, which is oriented around the participation of electric buses aggregator in a day-ahead energy auction. First, optimization models are provided to determine charging availability expressed as minimum and maximum hourly energy requirements taking into account detailed, minutely characteristics and constraints of the charging equipment and the buses. Next, the auction model is formulated by considering aggregated bids submitted by the electric buses aggregator once the charging availability is determined. Hence, the day-ahead prices reflect the optimal schedules of auction participants, and the bus aggregator is safe against peak-hour charging. Finally, hourly auction-based schedules are disaggregated into optimal minutely charging schedules. Mixed-integer linear programming models are formulated for aggregation-disaggregation stages investigated in this paper, while the variables and constraints introduced into the auction model are linear. The proposed methodology has been verified on a recently published case study of a real-world bus service operated on The Ohio State University campus. We show that the auction-based charging of all 22 buses outperforms as-soon-as-possible schedules by 7% to even 28% of daily cost savings. Using the aggregated bids, buses can flexibly shift charges between high- and low-price periods while preserving constraints of the charging equipment and timetables.

Introduction: During mixed dentition period, the growth and development process of dentocraniofacial complex occurs, with temporomandibular joint as one of its centers of growth. The condition of temporomandibular joint can be seen from its condylar head shape; therefore, it is essential to understand the normal morphology of condylar head during mixed dentition period. Objective: this research aims to view the general morphology of condylar head during mixed dentition period. Method: Samples of panoramic radiographs were collected from Ma-ranatha Dental Hospital, Bandung, Indonesia. It consists of 70 male and 70 female subjects, di-vided into three groups according to their phase of mixed dentition period. Shape of subjects' condylar heads was determined by analyzing and comparing the tracing results of condyle in panoramic radiograph. Result: Study showed in first transitional period of mixed dentition, condylar head shape is dominated by round, as growth and development go on, the domination of condylar head shape is changed into convex on second transitional period. Conclusion: Mostly condylar head shape morphology during mixed dentition period is round-headed in first transitional and inter-transitional period, and convex-headed in second transitional period.

In a safety-critical system typically not all provided services have the same criticality, which we call mixed-criticality systems. Criticality arithmetic, also called SIL arithmetic, is an approach to lower the development effort of a service by providing redundancy with tasks that are developed for a lower criticality level. In this paper we present ATMP-CA, which is a derivation of the multi-core scheduler ATMP. ATMP-CA is able to take into account the knowledge about the use of criticality arithmetic. ATMP-CA has a modified core allocation and procedure for utility optimisation, considering the context of the replicated tasks. We conducted experiments that show that ATMP-CA is able to provide the services using criticality arithmetic, while the reference schedulers were not.

The cluster randomized trial ARena (Sustainable reduction of antibiotic-induced antimicrobial resistance, 2017-2020) promoted the appropriate use of antibiotics for acute non-complicated infections in primary care networks (PCNs) in Germany. A process evaluation aimed to provide insights into determinants of practice and explored factors associated with antibiotic prescribing patterns. In a nested mixed-methods approach, a three-waves survey used study-specific questionnaires for participating physicians and medical assistants to assess potential impacts and uptake of the complex intervention program. Stakeholders received a one-time online questionnaire to reflect on network-related aspects. Semi-structured, open-ended interviews with a purposive sample of physicians, medical assistants and stakeholders explored aspects regarding the acceptance of the program components for daily practice and the perceived sustainability of intervention component effects. The intervention components were perceived to be smoothly integrable into practice routines. The highest uptake was reported for the educational components: feedback reports, background information, e-learning modules, and disease specific quality circles. Participation in PCNs was seen as motivational factor for guideline-oriented patient care and the adoption of new routines Future approaches to fostering appropriate use of antibiotics by targeting health literacy competencies and clinician’s therapy decisions should combine evidence-based information sources, audit and feedback reports and QCs.

The chemopreventive effects of various mixed cereal grain (MCG) samples on azoxymethane (AOM, 10 mg/kg) and dextran sulfate sodium (DSS, 2% w/v)-induced colorectal cancer (CRC) in C57BL/6J mice were studied. The main MCG preparation consisted of fermented brown rice (FBR), glutinous brown rice, glutinous Sorghum bicolor, glutinous Panicum miliaceum, Coix lacryma-jobi and black soybean at an appropriate mixing ratio. Other MCG preparations contained rice coated with 5% Phellinus linteus and 5% Curcuma longa (MGR-PC), or 10% Phellinus linteus (MCG-P), or 10% Curcuma longa (MCG-C). Consumption of dietary MCG-PC by CRC mice significantly increased colon length, decreased the ratio of colon weight to length, and reduced the number of colon tumors. Similar effects, although to a lower extent, were observed in CRC mice fed with MCG-P, followed by those fed with MCG-C, MCG, FBR or white rice (WR). MCG-PC significantly suppressed colonic neoplasia, and decreased the levels of various cytokines (tumor necrosis factor: Tnf, interleukin 1 beta: Il1b, interleukin 6: Il6, and interferon gamma: Ifng) in serum and colon tissue of the CRC mice. In addition, MCG-PC increased the mRNA expressions of tumor protein p53(Tp53) and cyclin-dependent kinase inhibitor 1A(Cdkn1a), activated pro-apoptotic caspase 3(Casp3), and reduced expression of both mRNA and protein of inducible inducible nitric oxide synthase 2 (Nos2), prostaglandin-endoperoxide synthase 2 (Ptgs2), and cyclin D1(Ccnd1) in colon tissue. These findings suggest that than compared with other cereal grain preparations, MCG-PC had a greater activity against AOM/DSS-induced CRC by reducing intestinal inflammation, and modulating the expression of certain carcinogenesis related factors (Nos2, Ptgs2, Tp53, Cdkn1a, Ccnd1 and Casp3) in colon tissue of CRC mice.

In-situ nanoindentation experiment has been widely adopted to characterize material behaviors of microelectronic devices. This work introduces the latest developments of nanoindentation experiment in characterizing nonlinear material properties of 3D integrated microelectronic devices with through-silicon-vias (TSVs). The elastic, plastic, and interfacial fracture behavior of the copper via and matrix-via interface have been characterized using small scale specimens prepared with focused-ion-beam (FIB) and nanoindentation experiment. A brittle interfacial fracture was found at the Cu/Si interface under mixed-mode loading with a phase angle ranging from 16.7 to 83.7 degrees. The mixed-mode fracture strengths were extracted using the linear elastic fracture mechanics (LEFM) analysis and a fracture criterion was obtained by fitting the extracted data with the power-law function. The vectorial interfacial strength and toughness were found to be independent with mode-mix.

A custom-designed series of unsymmetrical spiroalkanedithiols having tailgroups comprised of a terminally fluorinated chain and a hydrocarbon chain of varying lengths were synthesized and used to prepare self-assembled monolayers (SAMs) on gold substrates. The specific structure of the adsorbates was of the form [CH₃(CH₂)_n][CF₃(CF₂)₇(CH₂)₈]C[CH₂SH]₂, where n = 7, 9, and 12 (designated as F8H10-C10, F8H10-C12, and F8H10-C18, respectively). The influence of the length of the hydrocarbon chain in the bidentate dithiol on the structure and interfacial properties of the monolayer was explored. A structurally analogous partially fluorinated monodentate alkanethiol and the corresponding normal alkanethiols were used to generate appropriate SAMs as reference systems. Ellipsometric thickness measurements showed an unexpectedly low film thickness for the SAMs derived from the bidentate adsorbates, possibly due to disruptions in interchain packing caused by the fluorocarbon chains (i.e., phase-incompatible fluorocarbon-hydrocarbon interactions), ultimately giving rise to loosely packed and disordered films. Analysis by X-ray photoelectron spectroscopy (XPS) were also consistent with a model in which the films were loosely packed; additionally, the XPS spectra confirmed the attachment of the sulfur headgroups of the bidentate adsorbates onto the gold substrates. Studies of the SAMs by polarization modulation-infrared reflection-adsorption spectroscopy (PM-IRRAS) suggested that as the length of the hydrocarbon chain in the adsorbates was extended, a more ordered surface was achieved by reducing the tilt of the fluorocarbon segment. The wettability data indicated that the adsorbates with longer alkyl chains were less wettable than those with shorter alkyl chains, likely due to an increase in interchain van der Waals forces in the former.

In this paper, some mixed type bounds on the spectral radius $\rho(A\circ B)$ for the Hadamard product of two nonnegative matrices ($A$ and $B$) and the minimum eigenvalue $\tau(C\star D)$ of the Fan product of two $M$-matrices ($C$ and $D$) are researched. These bounds complement some corresponding results on the simple type bounds. In addition, a new lower bound on the minimum eigenvalue of the Fan product of several $M$-matrices is also presented: $$ \tau(A_{1}\star A_{2}\cdots\star A_{m})\geq \min_{1\leq i\leq n}\{\prod^{m}_{k=1}A_{k}(i,i)-\prod^{m}_{k=1}[A_{k}(i,i)^{P_{k}}-\tau(A_{k}^{(P_{k})})]^\frac{1}{P_{k}}\}, $$ where $A_{1},\ldots, A_{k}$ are $n\times n$ $M$-matrices and $P_{1},\ldots, P_{k}>0$ satisfy $\sum^{m}_{k=1}\frac{1}{P_{k}}\geq 1$. Some special cases of the above result and numerical examples show that this new bound improves some existing results.

The SK model proposed by Sherrington and Kirkpatrick is an important mean field model for spin glasses. The dependence structure of Hamiltonians of the SK model is considered in this paper. We have investigated the covariance matrix of Hamiltonians of the SK model, and establish a relationship between the rank of the corresponding covariance matrix and the dimension of the space spanned by the Hamiltonians. Finally, we also give an upper bound for the rank of the covariance matrix, which might be the best.

Pulmonary infections with Mycobacterium avium occur in susceptible individuals following exposure to the bacterium in the environment, where it often persists in biofilms. Many methods have been used to generate biofilms of M. avium, and it is unknown whether different approaches generate similar structures and cell phenotypes. To make a parallel comparison of in vitro biofilm ultrastructure, extracellular matrix (ECM) composition, and drug susceptibility of biofilm resident bacteria, we used two published methods to generate M. avium biofilms: four week incubation in M63 medium or 24 hour exposure to dithiothreitol (DTT). Scanning electron microscopy revealed differences in biofilm ultrastructure between the two methods, including variation in appearance of ECM materials and morphology of resident cells, while light microscopy and staining with calcofluor white indicated that both biofilms contained polysaccharides characteristic of cellulose. Measuring susceptibility of biofilms to degradation by enzymes suggested differences in structurally important ECM molecules, with DTT biofilms having important protein and, to a lesser extent, cellulose components, and M63 biofilms having moderate protein, cellulose, and DNA components. Both biofilms conferred resistance to the bactericidal effects of amikacin and clarithromycin, with resident cells being killed at &gt;10-fold lower rates than planktonic cells at almost all concentrations. Though these comparisons indicate differences in biofilm responses by M. avium under differing conditions, they also suggest common features of biofilm formation including cellulose production and antimicrobial resistance.

We formulate a more straightforward, symmetry-based techniques in manually computing the determinant of any n×n matrix by revisiting Dodgson’s condensation method and strategically applying elementary row operations and the definition and properties of determinants. The result yields a more simplified mnemonical algorithm than the usual reduction by elementary row operations and co-factor expansion and with minimal terminal divisions performed compared to Dodgson’s internal divisions.

The orthogonal matrix with cube be symmetric is a common class of matrices with important properties. We pay attention to this kind of matrices. By using the close relationship between the eigenvalues of a matrix and the trace of its power, we obtain the algorithm for its all possible different eigenvalues and multiplicities. The calculation formula is expressed only by the trace of a matrix and its power, avoiding solving the characteristic polynomial. The method is simple and practical. Furthermore, a new essential characterization for the sum of orthogonal matrix pairs being orthogonal is given as well.

In this paper, we propose a quantitative definition of emergence from the density matrix framework as a state of knowledge of the system and its generalized probability formulation, in order to detect emergent properties. We propose the use of a descriptor based on the difference of von Neumann entropy, to calculate if subsystems of systems have inner correlations by defining emergent systems in terms of emergent information.

The extracellular matrix (ECM) plays a key role as both structural scaffold and regulator of cell signal transduction in tissues. In times of ECM assembly and turnover, cells upregulate assembly of the ECM protein, fibronectin (FN). FN is assembled by cells into viscoelastic fibrils that can bind upward of 40 distinct growth factors and cytokines. These fibrils play a key role in assembling a provisional ECM during embryonic development and wound healing. Fibril assembly is also often upregulated during disease states, including cancer and fibrotic diseases. FN fibrils have unique mechanical properties, which allow them to alter mechanotransduction signals sensed and relayed by cells. Binding of soluble growth factors to FN fibrils alters signal transduction from these proteins, while binding of other ECM proteins, including collagens, elastins, and proteoglycans, to FN fibrils facilitates the maturation and tissue specificity of the ECM. In this review, we will discuss the assembly of FN fibrils from individual FN molecules; the composition, structure, and mechanics of FN fibrils; the interaction of FN fibrils with other ECM proteins and growth factors; the role of FN in transmitting mechanobiology signaling events; and approaches for studying the mechanics of FN fibrils.

Numerical weather prediction is an initial-value problem, for determination of the initial conditions, there are many methods and one of the most classical methods is variational methods in three dimensions, or 3D-Var. In this approach, with a defined cost function proportional to the square of the distance between the analysis and both the background and the observations, one can obtain the analysis. In the cost function, the background and the observations are reshaped to vectors; within this step, the order of the background error covariance matrix and the observational error covariance matrix becomes huge, which is not convenient to one to obtain the analysis. In this paper, according to the matrix analysis approach, we put forward some possible improvements to the dimension-reduction algorithm of 3D-Var, so that provide some references for data assimilation.

Objective: Regeneration of bone defects remains a challenge for maxillofacial and reparative surgeons. The purpose of this histological study was to assess the osteogenic potential of octacalcium phosphate (OCP) and bone matrix gelatin (BMG) alone and in combination in artificially created mandibular bone defects in rats. The quality of the newly formed bone was also evaluated. Methods: Thirty-six male Sprague Dawley rats (6-8 weeks old with 120-150 g weight) were randomly divided into four groups. Defects (3 mm in diameter and 2 mm in depth) were created in the mandible of rats and filled with 6 mg of OCP, BMG or a combination of both (1/4 ratio), respectively. Defects were left unfilled in the control group. To assess osteoinduction and bone regeneration and determine the quality of the newly formed bone, tissue specimens were harvested at seven, 14, and 21 days post-implantation. The specimens were processed, stained with hematoxylin and eosin (H&E) and histologically analyzed under light microscopy. Results: In the experimental groups, new bone formation was initiated at the margins of defects from seventh day after implantation. At the end of the study period, the amount of the newly formed bone increased and the bone was relatively mature. Osteoinduction and new bone formation were greater in OCP/BMG group. In the control group, slight amount of new bone had been formed at the defect margins (next to host bone) on day 21. Conclusion: Combination of OCP/BMG may serve as an optimal biomaterial for treatment of mandibular bone defects.

Matrices and their inverses allow accurate calculations to be produced quickly and compactly and can be used to control any process represented by a system of equations. Singular matrices require extensive workarounds (costing time and money) to solve these systems of equations because they have no inverse. Finding their inverse involves division by zero. Nevertheless, recently a new number set called semi-structured complex numbers was developed to enable division by zero in algebraic equations. The aim of this research was to demonstrate that the inverse of a singular matrix can be found using semi-structured complex numbers. This research reveals (1) Singular matrices and their inverses can be used to find a unique solution to a pair of simultaneous equations that appear to have infinitely many solutions or appear to have no solution; (2) Singular matrices map coordinates in projective space to coordinates in Euclidean space and their inverse maps coordinates in Euclidean space to coordinates in projective space; (3) the inverse of a singular matrix proves that parallel lines in Euclidean space do not intersect but in projective space intersect at a “point at infinity” and (4) collinear lines that intersect at infinitely many points in Euclidean space only intersect at one point in projective space.

Empirical equations representing, interpolating and smoothing groups of measurement results by regression methods are in widespread use in metrology and other fields of science and engineering. Standard equations for the propagation of measurement uncertainties to values computed from such equations are available but suffer from a lack of general acceptance and are only infrequently applied in practice. One reason for the slow uptake is the lack of clear methods to account for systematic error. In this paper, uncertainty propagation equations in terms of covariance matrices are generalized to allow for systematic errors in least-squares regression, and effects of the resulting uncertainties are investigated analytically. A stochastic ensemble model of systematic error is proposed for the computation of the non-diagonal elements of the weight matrix of the Generalized Least-Squares method (GLS) from the measurement uncertainty. A GLS projector formalism is described which separates the effects of measurement scatter on values calculated by the equation from those on the related “residuals”, i.e., the residual errors in the fitted data. The same projectors act similarly on the associated uncertainties and covariance matrices and permit the effects of systematic errors on the simulation covariance to be quantified. It is demonstrated that, in order to include systematic errors in the uncertainty estimates of GLS, covariance matrices may be substituted by novel, specifically defined dispersion matrices that are not specified yet by the GUM[1]. Systematic measurement errors may be estimated from various sources; a particularly easy way suggested here involves analyzing the structure in the regression residuals. It is demonstrated that GLS projectors exhibit inherent features of “error blindness” also with respect to systematic errors. [1] GUM: Guide to the Expression of Uncertainty in Measurement, http://www.bipm.org/en/publications/guides

In this work, we offer a novel and accurate method in order to find the solution of the linear differential equations over the intervals [0, 1) based on the generalization of Legendre wavelets. The mechanism is still upon workable implementation of the operational matrix of integration and its derivatives. This method reduces the problems into algebraic equations via the properties of generalized Legendre wavelet (GLW) together with the operational matrix of integration. The function approximation has been picked out in such a way so as to enumerate the connection coefficients in an facile manner. The proposed numerical technique, based on the GLW, has been examined on three linear problems as a consequence of this investigation. The outcomes have shown that this method, as opposed to some other existing numerical and analytical methods, is a very useful and advantageous for tackling such problems.

The extracellular matrix (ECM) is a ubiquitous member of the body and is key to the maintenance of tissues and organs integrity. Initially thought to be a by-stander in many cellular processes, the extracellular matrix has been shown to have diverse components that regulate and activate many cellular processes and ultimately influence cell phenotype. Importantly, the ECM composition, architecture, and stiffness/elasticity influence cellular phenotypes. Under normal conditions and during development, the synthesized ECM is constantly undergoing degradation and remodeling processes via the action of matrix proteases that maintain tissue homeostasis. In many pathological conditions including fibrosis and cancer, the ECM synthesis, remodeling, and degradation is dysregulated causing its integrity to be altered. Both physical and chemical cues from the ECM are sensed via receptors including integrins and play key roles in driving cellular proliferation and differentiation and in various disease progression such as cancers. Advances in ‘omics’ technologies have seen an increase in studies focussing on bi-directional cell-matrix interactions and here we highlight emerging knowledge on the role played by the ECM during normal development and in pathological conditions. This review summarizes current ECM-targeted-therapies that can modify tumor ECM to overcome drug resistance and better cancer treatment.

In this study, vermiculite was explored as a support material for nickel catalysts in two key processes in syngas production through dry reforming of methane with CO2 and steam reforming of ethanol. The vermiculite was subjected to acid or base treatment, then, Ni catalysts were prepared through incipient wetness impregnation and characterized using various techniques, including X-ray, FTIR, temperature programmed reduction (H2-TPR). TG-TD analyses were performed to assess the formation of carbon deposits on spent catalysts. The Ni based catalysts were used in the reaction tests without a reduction pre-treatment. Initially, raw vermiculite-supported nickel showed limited catalytic activity, however after acid (Ni/VTA) or base (Ni/VTB) treatment, vermiculite became an effective support for nickel in dry methane reforming. The resulting catalysts (Ni/VTB and Ni/VTA) displayed outstanding performance, with high methane conversion and hydrogen yield. Acidic treatment improved the reduction of nickel species and reduced carbon deposition, outperforming alkali treatment. The prepared catalysts were also explored in ethanol steam reforming. The performance was investigated as a function of the temperature, water/ethanol ratio, and space velocity, with acid-treated catalysts performing as the best. The study emphasized the importance of contact time with the catalyst and addressed challenges related to carbon formation and sintering for long-term stability. In conclusion, acidic treatment of the vermiculite support, before Ni deposition, significantly enhanced the catalytic activity and stability of nickel catalysts in both dry methane reforming and ethanol steam reforming processes.

The highly crystalline ZnAl Layered Double Hydroxides (ZnAl-NO3- LDHs) are utilized for the potential transformation into Mixed Metal oxides (MMO) through thermal decomposition and used further for the photodegradation of phenol to assess the influence of calcination on ZnAl LDHs with enhanced photoactivity. The structure, composition, and morphological evolution of ZnAl-LDHs to ZnO-based MMO nanocomposites, which are composed of ZnO and ZnAl2O4, after calcination at different temperatures (400-600 ºC), are all thoroughly examined in this work. The final ZnO and ZnAl2O4 base nanocomposites showed enhanced photocatalytic activity. The findings demonstrated that calcining ZnAl-LDHs from 400 to 600 °C increased the specific surface area and also enhanced the interlayer spacing of d003 while the transformation of LDHs into ZnO/ZnAl2O4 nanocomposite through calcining the ZnAl-LDH precursor at 600 °C showed significant photocatalytic properties, leading to complete mineralization of phenol under UV irradiation.

The concept of {\it quantum ratio} emerged in the recent efforts to understand how Newton's equations appear for the center of mass (CM) of an isolated macroscopic body at finite body-temperatures, as the first approximation to quantum-mechanical equations. It is defined as $Q\equiv R_q/L_0$, where the quantum fluctuation range $R_q$ is the spatial extension of the pure-state CM wave function, whereas $L_0$ stands for the body's linear size (the space support of the internal, bound-state wave function). The two cases $R_q /L_0 \lesssim 1$ or $R_q/ L_0 \gg 1$, roughly correspond to the body's CM behaving classically or quantum mechanically, respectively. In the present note we elaborate more on this concept, illustrating it in several examples. An important notion following from introduction of the quantum ratio is that the elementary particles (thus the electron and the photon) are quantum mechanical, even when the environment-induced decoherence turns them into a mixed state. Decoherence and classical state should not be identified. This simple observation, further illustrationed by the consideration of a few atomic or molecular processes, may have significant implications on the way quantum mechanics works in biological systems.

Today, issues related to people’s mental health and well-being have been described as a challenge for society, globally as well as in Sweden. This calls for new approaches to mental health promotion. The aim was to evaluate the adequacy of the content and structure, describing experiences of study circles as a means of supporting participants' self-care and self-compassion. The overall design is a descriptive, QUAL+quan design, where quantitative and qualitative results are integrated. Five participants participated in a focus group interview, of whom four completed questionnaires. One individual interview was conducted with the study circle leader. Study circles can be an arena for mental health promotion as learning and sharing of experience contributes to a sense of coherence, as well as self-compassion and a genuine concern for one’s own and others' wellbeing, but not considered an alternative to psychiatric care, for those in need of professional services. Study circles can be a possible means to support self-care and thereby promote mental health in the general population, and a valuable contribution to public health. However, in addition to modifications of the content, further research is needed on the qualifications for study circle leaders as well as the dissemination of study circles.

We used a mixed-method single-case experimental research design to examine the effect of modelling (peer versus non-peer) on exercise self-efficacy in stroke survivors who participated in a community-based exercise program. Quantitative data were obtained using a ABCA design: (A1) no model/baseline 1 (3 weeks); (B) peer model (6 weeks); (C) non-peer model (6 weeks); and (A2) no model/baseline 2 (3 weeks). Four participants completed self-efficacy questionnaires after each weekly session. Qualitative data were obtained using researcher diaries and two semi-structured interviews: after B and A2. Based on quantitative and qualitative results, participants reported higher exercise self-efficacy in the model conditions, with ratings appearing highest for the non-peer model. This finding could be due to a lack of full integration of the peer model and low feelings of similarity. Modelling in general could help people recovering from a stroke increase their exercise self-efficacy, but non-peer models may not be most advantageous.

This article presents the main results of the Kids in Action (KIA) project, which aimed to promote physical activity and sport among children aged 3 to 11, by linking the education, sports and family sectors. It applies the concepts of proximity consumption and the 15-minute city to sustainable and healthy habits. This study aims to evaluate the application of the concept of proximity to the provision of physical sports activities from an early age, to reduce the drop-out rate of PA, through collaboration between schools, local government and the sports sector. A mixed methodology was used, applying a survey and interviews with the stakeholders to identify good practices in the promotion of physical activity at an early age. A total of 147 children and 10 adults (3 family members, 4 school managers or teachers and 3 sports managers) participated. The main results included the importance of introducing sport and providing adequate facilities, especially through guided workshops, as this not only influences the actual participation in sport but also the intention to continue practising it. It is observed that children with less affinity towards conventional sports benefit particularly from initiatives such as the KIA programme. On the other hand, the lack of available resources and the influence of the sports culture in families are identified as the main barriers, factors that harm the quality of participation.

We derive a quantum speed limit for unitary evolution for the case of mixed quantum states using the stronger uncertainty relation for mixed quantum states. This bound can be optimized over different choices of Hermitian operators for a better bound. We illustrate this with some examples and show its better performance with respect to three existing bounds for mixed quantum states.

The review analyzes the studies published mainly in the last 10-15 years, on the synthesis, structure, and sensor properties of semiconductor nanocomposites. Particular attention is paid to the interaction between nanoparticles of the sensitive layer and its effect on the structure, sensitivity, and selectivity of semiconductor sensor systems. Various mechanisms are considered of interaction between nanoparticles in metal oxide composites including incorporation of metal ions of one component into the structure of another, heterocontacts between different nanoparticles, and core-shell systems, as well as their influence on the characteristics of gas sensors. The experimental data and studies on the modeling of charge distribution in semiconductor nanoparticles, which determine the conductivity and sensor effect in one- and two-component systems, are also discussed. It is shown that the model which considers the interactions of nanoparticles best describes the experimental results. Some mechanisms of detection selectivity are considered in the conclusion.

Wear usually occurs in the loaded part of hydrodynamic bearings and leads the bearing geometry imperfections. This paper investigates the effects of wear-induced geometric imperfections of tilting pad journal bearings (TPJBs) on the dynamic behavior of the system. Furthermore, the effect of wear on the journal-bearing rub-induced contact pressure severity is investigated. A novel tribo-dynamic model is proposed for a flexible rotor-worn TPJB which integrates a mixed elastohydrodynamic model with a rotor-worn TPJB thermal and dynamic model to assess the effects of the bearing wear progression on the rotor-TPJB behavior. Based on the results, wear changes the temperature distribution of the pads and oil film as well as the dynamic behavior of the system. Dynamic simulations reveal a higher vibration level and contact pressure for the worn TPJBs near the system's critical speed and service speed. Finally, thermal and dynamic condition indicators are suggested to detect TPJB wear severity at its early stages.

Sorption of pure CO2 and CH4 and CO2/CH4 binary gas mixtures in amorphous glassy Poly(2,6-dimethyl-1,4-phenylene) oxide (PPO) at 35°C up to 1000 Torr is investigated. Sorption experiments were carried out using an approach that combines barometry with FTIR spectroscopy in the transmission mode to quantify the sorption of pure and mixed gases in polymers. The pressure range is chosen to prevent any variation of the glassy polymer density. The solubility within the polymer of the CO2 present in the gaseous binary mixtures is practically coincident with the solubility of pure gaseous CO2, up to a total pressure of the gaseous mixtures equal to 1000 Torr and for CO2 mole fraction of ~0.5 mol mol-1 and ~0.3 mol mol-1. The Non-Equilibrium Thermodynamics for Glassy Polymers (NET-GP) modelling approach has been applied to the Non-Random Hydrogen Bonding (NRHB) lattice fluid model to fit the solubility data of pure gases. We have assumed here that no specific interactions are occurring between the matrix and the absorbed gas. The same thermodynamic approach has been then used to predict the solubility of CO2/CH4 mixed gases in PPO resulting in a deviation lower than 9.5% from the experimental results for CO2 solubility.

Leishmania parasites present astonishing adaptative abilities that represent a matter of life or death within disparate environments during the heteroxenous parasite life cycle. From an evolutionary perspective, organisms develop methods of overcoming such challenges. Strategies that extend beyond the genetic diversity have been discussed and include variability between parasite cells during the infections of their hosts. The occurrence of Leishmania subpopulation fluctuations with variable structural genomic contents demonstrates that a single strain might shelter the variability required to overcome inconsistent environments. Such intrastrain variability provides parasites with an extraordinary ability to adapt and thus survive and propagate. However, different perspectives on this evolution have been proposed. Strains or species living in the same environment can cooperate but also compete. These interactions might increase the replication rate of some parasites but cause the loss of more aggressive competitors for others. Adaptive responses to intra- and interspecific competition can evolve as a fixed strategy (replication is adapted to the average genetic complexity of infections) or an optional strategy (replication varies according to the genetic complexity of the current infection). This review highlights the complexity of interspecies and intrastrain interactions among Leishmania parasites as well as the different factors that influence this interplay.

Improved oil recovery from tight carbonate formations may provide the world with a major source of lower-rate power over several decades. Here we provide an overview of the Arab D formation in the largest oil field on earth, the Ghawar. We investigate the occurrence of microporosity of different origins and sizes using scanning electron microscopy (SEM) and pore casting techniques. Then, we present a robust calculation of the probability of invasion and oil saturation distribution in the nested micropores using mercury injection capillary pressure data available in the literature. We show that large portions of the micropores in Arab D formation would have been bypassed during primary drainage unless the invading crude oil ganglia were sufficiently long. Considering the asphaltenic nature of oil in the Ghawar, we expect the invaded portions of the pores to turn mixed-wet, thus becoming inaccessible to waterflooding until further measures are taken to modify the system’s chemistry.

Poor households are more likely less resilient under climate change, risks of productive assets, social-related shocks, and decline of land productivity. The ability to deal with household resilience against poverty under the uncertain condition of risk is limited in the highlands of Ethiopia. The study aims to identify determinants of household resilience to livelihood insecurity under the crop-livestock mixed farming systems in Goncha district, Northwest highlands of Ethiopia. Primary data were collected by conducting face-to-face interviews among 280 households using structured questionnaire. Descriptive statistics, Kruskal-Wallis test and structural equation modeling were used to analyse the data. The results disclosed that sustainable management of the farming systems, cultivation of more fertile farmland, saving performance, diversification of income-earning activities, intensification of livestock husbandry practices, access to irrigation, and familiarity with practical technologies were found to be significant determinants at p<0.001 to household resilience of smallholder farmers. Social network development and tree plantation were explained household resilience to livelihood insecurity at P<0.01 and P<0.1 significant levels, respectively. The study concluded that scaling up sustainable management of the farming system and practical technologies, enhancing saving behavior, promoting income diversification, and intensifying agroforestry are significant for household resilience to livelihood insecurity of smallholders across agro-ecologies.

Due to the increased use of memristors, and its many applications, the use of emulators has grown in parallel to avoid some of the difficulties presented by real devices such as variability and reliability. In this paper, we present a memristive emulator designed using a Switched Capacitor (SC), this is, an analog component/ block and a control part or block implemented using stochastic computing (SCo) and therefore fully digital. Our design is thus a mixed signal circuit. Memristor equations are implemented using stochastic computing to generate the control signals necessary to work with the controllable resistor implemented as switched capacitor.

We addressed the frequent occurrence of mixed-chain lipids in biological membranes and their impact on membrane structure by studying several chain-asymmetric phosphatidylcholines and the highly asymmetric milk sphingomyelin. Specifically, we report trans-membrane structures of the corresponding fluid lamellar phases using small-angle X-ray and neutron scattering, which were jointly analyzed in terms of a membrane composition-specific model, including a headgroup hydration shell. Focusing on terminal methyl groups at the bilayer center we found a linear relation between hydrocarbon chain length mismatch and the methyl-overlap for phosphatidylcholines, and a non-negligible impact of the glycerol backbone-tilting, letting the sn1-chain penetrate deeper into the opposing leaflet by half a CH2 group. That is, penetration-depth differences due to the ester-linked hydrocarbons at the glycerol backbone, reported previously for gel phase structures also extend to the physiological more relevant fluid phase, but are significantly reduced. Moreover, milk sphingomyelin was found to follow the same linear relationship suggesting a similar tilt of the sphingosine backbone. Complementary performed molecular dynamics simulations revealed that there is always a part of the lipid tails bending back, even if there is a high interdigitation with the opposing chains. This suggests that hydrocarbon chain interdigitation plays only a minor role in transbilayer coupling. For both cases of adaption to chain length mismatch, chain-asymmetry has a large impact on hydrocarbon chain ordering, inducing disorder in the longer of the two hydrocarbons.

The most common form of mixed dementia (MixD) is constituted by abnormal protein deposits associated with Alzheimer´s disease (AD) that coexist with vascular disease. Although olfactory dysfunction is considered a clinical sign of AD-related de-mentias, little is known about the impact of this sensorial impairment in MixD at molecular level. To address this gap in knowledge, we have assessed olfactory bulb (OB) proteome-wide expression in MixD subjects (n=6) respect to neurologically intact controls (n=7). Around 9% of the quantified proteins were differentially expressed, pinpointing aberrant proteostasis involved in synaptic transmission, nucleoside monophosphate and carbohydrate metabolisms and neuron projection regeneration. In addition, net-work-driven proteomics revealed a modulation in cell-survival related pathways such as ERK, AKT and PDK1-PKC axis. Part of the differential OB protein set was not specific of MixD, being also deregulated across different tauopathies, synucleinopathies and tardopathies. However, the comparative functional analysis of OB proteome data between MixD and pure AD pathologies deciphered commonalities and differences between both related phenotypes. Finally, olfactory proteomics allowed to propose serum Prolow-density lipoprotein receptor-related protein 1 (LRP1) as a candidate marker to differentiate AD from MixD phe-notypes.

A premise of statistical learning research is that learners attend to and learn the frequencies of repeating or co-occurring elements in the input. When the input is a series of words, participants readily learn the frequencies of phoneme sequences, that is, to learn phonotactic frequencies. Inherent to the concepts of both frequency and phonotactics is order, or the temporal structure of the input. Order is similarly inherent to statistical learning, yet the effect of order on statistical learning is not well understood. In the present study, adult participants learned the relative frequencies of eight item-medial consonant sequences, for example, the /mk/ in /nʌmkət/. Across five ordering conditions, both familiarization and test stimuli were independently ordered and randomized, thus allowing for a relatively broad search for order effects in an established statistical learning paradigm. Participants learned the target frequencies equivalently across the five ordering conditions, indicating no modulating effect of order. Nevertheless, participants also approached the task by applying idiosyncratic, structured orders to their responses. The result is an unexpected but robust effect of order. Both the results and the design of the study also allow for increased integration of statistical learning with memory and other aspects of cognition.