Resource conflicts represent a significant global challenge in regions abundant with natural re-sources. Modelling the myriad factors driving natural resource-based conflicts (NRBCs), spanning environmental, health, socio-economic, and political dimensions, is a complex endeavor exacer-bated by data scarcity. Furthermore, existing quantitative studies often focus solely on large-scale conflicts. This article introduces a novel algorithm, the Spatially Explicit Fuzzy Logic-Adapted Model for Conflict Management (SEFLAME-CM), which integrates the local knowledge of stakeholders into spatial decision-making technologies to support sustainable peace efforts. The results are validated with spatial multi-criteria evaluation (SMCE) using spatial statistics. The Moran’s I scatter plots for the overall conflicts reveal significant values of 0.99 and 0.98 for both the SEFLAME-CM and SMCE, respectively, with significant spatial autocorrelation. While there re-mains room for improvement in enhancing the model's quality, SEFLAME-CM demonstrates its capacity to transparently model complex real-world problems. The findings underscore the im-perative for a holistic approach to addressing environmental degradation, socio-economic, and political drivers of resource conflicts at the community level. Our paper demonstrates the signif-icance of spatial information technologies and knowledge exchange between experts and local stakeholders in effectively managing resource conflicts. These insights should inform national policies and international interventions, ensuring that the complex underlying issues are ad-dressed while prioritizing the knowledge and needs of affected communities.

Artificial Intelligence (AI) and Deep Reinforcement Learning (DRL) have revolutionised e-learning by creating personalised, adaptive, and secure environments. However, challenges such as privacy, bias, and data limitations persist. E-FedCloud aims to address these issues by providing more agile, personalised, and secure e-learning experiences. This study introduces E-FedCloud, an AI-assisted adaptive e-learning system that automates personalised recommendations and tracking, thereby enhancing student performance. It employs federated learning-based authentication to ensure secure and private access for both course instructors and students. Intelligent Software Agents (ISAs) evaluate weekly student engagement using the Shannon Entropy method, classifying students into either engaged or not-engaged clusters. E-FedCloud utilises weekly engagement status, demographic information, and an innovative DRL-based early warning system, specifically ID2QN, to predict the performance of not-engaged students. Based on these predictions, the system categorises students into three groups: risk of dropping out, risk of scoring lower in the final exam, and risk of failing the end exam. It employs a multi-disciplinary ontology graph and an attention-based capsule network for automated, personalised recommendations. The system also integrates performance tracking to enhance student engagement. Data is securely stored on a blockchain using the LWEA encryption method.

Several types of guided bone regeneration materials are available with various properties and clinical indications. Among them, dentin-derived matrix from extracted teeth is an excellent alternative to traditional graft materials, such as allografts and xenografts. Different clinical preparation protocols exist: block form versus powder form, demineralized versus non-demineralized form. Out of these methods, demineralization protocols for dentin-derived matrix from extracted teeth have been noted for a wide range of successful clinical outcomes. The purpose of this animal study was to find out the most effective method of demineralizing dentin-derived matrix to result in the highest regenerative ability in the bone graft material in rabbit calvaria. The study conducted a comparative histomorphometric analysis of four groups in distinguished matrix conditions: a control group without graft placement, a non-demineralized dentin-derived matrix graft placement group, a 15-minute demineralized dentin-derived matrix graft placement group, and a 30-minute demineralized dentin-derived matrix graft placement group. Regenerative processes were evaluated three times at 2, 4, and 8 weeks post-graft placement. These groups of rabbits were evaluated randomly in a blinded setting to prevent bias. Among the four groups compared, the group with a 15-minute demineralized dentin-derived matrix graft showed the most efficient bone regeneration in the histomorphometric analysis. Based on this animal study, it was apparent that a 15-minute demineralization of dentin-derived matrix is the preferred preparation protocol to maximize the regenerative potential in the matrix.

For some persistent pollutants, such as recalcitrant pesticides, the polluted water (urban, agricultural and/or industrial) treated by conventional wastewater treatment plants is in some cases insufficient to achieve the legally required level of purity. This issue is of particular concern in areas where low rainfall does not provide sufficient water resources to meet the needs of agriculture, requiring increased reuse of water from wastewater treatment plants. In this context, Advanced Oxidation Processes (AOPs) hold great promise for the removal of organic pollutants. This study first evaluates the effectiveness of UV/S2O8= compared to heterogeneous photocatalysis using UV/TiO2 processes on the degradation of two commonly used herbicides (terbutylazine and isoproturon) in aqueous solutions using a photoreactor. In addition. the effect of UV wavelength on the degradation efficiency of both herbicides was investigated. Although degradation rate was higher under UV-254/S2O8= nm than under UV-365/S2O8= nm, complete degradation of the herbicides (0.2 mg L-1) was achieved within 30 min under UV-366 nm using a Na2S2O8 dosage of 250 mg L-1 in the absence of inorganic anions. To assess the impact of water matrix, the individual and combined effects of sulphate, bicarbonate and chloride were evaluated. They can react with hydroxyl and sulphate radicals formed during AOPs to form new radicals with a lower redox potential. The results showed negligible effects of sulphate, while the combination of bicarbonate and chloride seemed to be the key for the decrease in herbicide removal efficiency found when working with complex matrices. Finally, the main intermediates detected during the photodegradation process were identified and the likely pathways are proposed and discussed.

In this paper, we establish the necessary and sufficient conditions for solving a dual split quaternion matrix equation AXB=C, and present the general solution expression when solvability is achieved. As an application, we delve into the necessary and sufficient condition for the existence of Hermitian solution to this equation by using a newly defined real representation method. Furthermore, we obtain the solutions for the dual split quaternion matrix equations AX=C and XB=C. Finally, we provide a numerical example to demonstrate the findings of this paper.

Thin people who undergo implant insertion into the chest wall face a high risk of implant exposure to the external environment. Five months after an 8-year-old boy underwent implantable cardioverter-defibrillator (ICD) implantation in a subcutaneous pocket in the left anterolateral chest wall to manage long QT syndrome, ICD replacement became necessary owing to exposure risk from distal and lateral thinning of the ICD pocket. Pocket rupture and exposure would increase the risk of infection; therefore, we performed ICD removal and primary pocket closure. Two weeks later, a new suprafascial pocket was created, acellular dermal matrix (ADM) was attached to the inner wall to prevent ICD protrusion, and a new ICD was inserted. One year postoperatively, the ADM was engrafted, and no complications were observed. A thin subcutaneous layer increases the risk of ICD implantation complications. Inner wall strengthening with ADM can help prevent pocket rupture.

This article shows how to establish expansion formulas for calculating the mixed operations $(A^{\dag})^{\#}$, $(A^{\#})^{\dag}$, $((A^{\dag})^{\#})^{\dag}$, $((A^{\#})^{\dag})^{\#}$, $\ldots$ of generalized inverses, where $(\cdot)^{\dag}$ denotes the Moore--Penrose inverse of a matrix and $(\cdot)^{\#}$ denotes the group inverse of a square matrix. As applications of the formulas obtained, the author constructs and classifies some groups of matrix equalities involving the above mixed operations, and derives necessary and sufficient conditions for them to hold.

Cerebral palsy (CP) is the most common movement disorder in children with a prevalence rang-ing from 1.5 to 4 per 1000 live births. CP is caused by a non-progressive lesion of the developing brain, leading to progressive alterations of the musculoskeletal system, including spasticity, often leading to the development of fixed contractures, necessitating tendon lengthening surgery. Total RNA-sequencing analysis was performed on semitendinosus tendons from diplegic and tetraple-gic CP patients subjected to tendon lengthening surgery compared to control patients undergoing anterior cruciate ligament reconstructive surgery. Tetraplegic CP patients showed increased ex-pression of genes implicated in collagen synthesis and extracellular matrix (ECM) turnover, while only minor changes were observed in diplegic CP patients. In addition, tendons from tetra-plegic CP patients showed an enrichment for upregulated genes involved in vesicle-mediated transport and downregulated genes involved in cytokine and apoptotic signaling. Overall, our results indicate increased ECM turnover with increased net synthesis of collagen in tetraplegic CP patients without activation of inflammatory and apoptotic pathways similar to observations in athletes where ECM remodeling results in increased tendon stiffness and tensile strength. Never-theless, the resulting increased tendon stiffness is an important issue in clinical practice, where surgery is often required to restore joint mobility.

In this study we assessed the electrical and optical behavior of semiconductor hybrid films fabricate from octaethyl-21H,23H-porphine copper (CuP), embedded in polymethylmethacrylate (PMMA), and polystyrene (PS). The hybrid films were characterized structurally and morphologically by means of Infrared spectroscopy (IR), atomic force microscopy (AFM), scanning electron microscopy (SEM) and X-ray diffraction (XRD). Subsequently, the PMMA:CuP and PS:CuP hybrid films were evaluated optically by UV-vis spectroscopy, as well as electrically, with the four-point collinear method. Hybrid films present a homogeneous and low roughness morphology, besides; the PS matrix allows the crystallization of the porphin, while the PMMA promotes the amorphous structure in CuP. The polymeric matrix also has an effect on the optical behavior of the films, since the smallest optical gap (2.16 eV) and onset gap (1.89 eV) and the highest transparency are obtained in the film with a PMMA matrix. Finally, the electrical behavior in hybrid films is also affected by the matrix: the largest amount of current carried is around 0.01 A for PS:CuP film, while for PMMA:CuP film it is 0.0015 A. Thanks to the above properties, hybrid films are promising candidates for use in optoelectronic devices.

Nonnegative matrix factorization (NMF) has been shown to be a strong data representation technique, with applications in text mining, pattern recognition, image processing, clustering and other fields. In this paper, we propose a hypergraph regularized Lp smooth nonnegative matrix factorization (HGSNMF), by incorporating hypergraph regularization and Lp smoothing constraint terms into the standard NMF. The hypergraph regularization term can capture the intrinsic geometry structure of the high dimension space data more comprehensively than simple graph, the Lp smoothing constraint may yield a smooth and more accurate solution to the optimization problem. The updating rules are given using multiplicative update techniques, and the convergence of HGSNMF is theoretically investigated. The experimental results on four different data sets show that the proposed method has better clustering effect than the related state-of-the-art methods in the vast majority of cases.

Through the arrow decomposition of the Mueller matrix, respective sets of sixteen independent polarimetric images of biological tissues are obtained for enpolarizing, retarding and depolariz-ing descriptors. In addition to the mean intensity coefficient and the three indices of polarimetric purity, the absolute values and Poincaré orientations of diattenuation, polarizance, entrance re-tardance and exit retardance vectors are considered. In this work we use for the first time this set of polarimetric observables for the visualization of biological structures, both of animal and vege-tal origin. Results show images with enhanced visualization derived from the spatial variation of such significant polarimetric properties. The experimental results are discussed, showing the suit-ability of such set of observables for applications in biophotonics imaging, providing not only ex-cellent visualization of biological tissues, but also showing structures not visible in non-polarimetric images.

The use of tungsten fiber-reinforced tungsten composites (Wf/W) has been demonstrated to significantly enhance the mechanical properties of tungsten (W) by incorporating W-fibers into the W-matrix. However, prior research has been restricted by the usage of single fiber-based textile fabrics, consisting of 150 µm warp and 50 µm weft filaments, with limited homogeneity, reproducibility, and mechanical properties in bulk structures due to the rigidity of the 150 µm fibers. To overcome this limitation, two novel textile preforms were developed utilizing radial braided yarns with 7 core- and 16 sleeve filaments (R.B. 16+7) as the warp material. In this study, bulk composites of two different fabric types were produced via a layer-by-layer CVD-process, utilizing single 50 µm filaments (type 1) and R.B. 16+7 yarns (type 2) as weft materials. The produced composites were sectioned into KLST-type specimen based on DIN EN ISO 179-1:2000 using electrical discharge machining (EDM), and subjected to three-point bending tests. Both composites demonstrated enhanced mechanical properties with pseudo-ductile behavior at room temperature and endured over 10,000 load cycles between 50-90 % of their respective maximum load without sample fracture. Composites based on fabric type 1 demonstrated superior manufacturing performance and mechanical properties, a high relative average density (>97 %), and high fiber volume fraction (14-17 %). Furthermore, a novel approach to predict the fatigue behavior of the material under cyclic loading was developed based on the high reproducibility of the mechanical properties of type 1, providing a new benchmark for upscaling endeavors.

Fibreglass composite plates are used for applications ranging from aerospace and automotive industries to construction industry. Vibroacoustic properties of the fibreglass plate has been investigated for different boundary conditions. The theory of the plate vibration and sound radiation has been used to determine the deflection of the composite plate at different locations on its surface. 3-Dimentional (3D) deformations and their corresponding contour velocities of plate have been computed for a range of frequencies. It has been shown that clamping the plate at four edges delayed first resonance of the plate by 15 Hz and second resonance of the plate by 30 Hz. Vibroacoustic indicators and the radiation impedance matrix of the plate have also been calculated. The results of this study show that fibreglass plate could be used in building industry to dampen the vibration of machine and structural vibration of buildings, and to sandwich building structures to form panels in order to attenuate airborne sound and to lower noise transmission of structural borne sound

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

This study investigated and classified typical structures in rural village and analyzed the vulnerability of various typical types of structures. Based on the statistics of earthquake damages with magnitudes above 5 from 1996 to 2013 in China, the damage matrixes of different types of structures in rural village are obtained. And The vulnerability index and the vulnerability equation of structure are crucial to assess the earthquake losses of typical structures under different magnitudes earthquakes. According to the seismic loss of different types of structures under different earthquake magnitudes, there are possible to improve the seismic resilience of the buildings in rural village. Moreover, the regional vulnerability is analyzed by β probability distribution function, and the comprehensive seismic performance index of different types of agricultural buildings in the region is obtained. The main research is to predict the loss of different types of structures under different earthquake magnitudes in the future, and to provide technical support for different types of building in rural village reinforcement.

Various studies have addressed the role of neutrophils in cancer wherein the focus has been drawn on the elevated neutrophil count in blood or at tumor loci. However, cancer has a systemic impact which targets various organs thus challenging the overall physiology of the host. So, it is worthwhile to explore whether and how neutrophils contribute to systemic deterioration in cancer. To discern the systemic role of neutrophils, we monitored their number and function at different stages of tumor growth in Dalton’s lymphoma mice model. Notably, we observed a gradual increase in neutrophil count in blood and their infiltration in vital organs of tumor bearing mice. In parallel, we observed damaged histoarchitecture with significant alterations in biochemical parameters that aggravated with tumor progression. We next examined systemic impact of neutrophil by assessing neutrophil elastase, myeloperoxidase, and matrix metalloproteinases (MMP-8 and MMP-9) wherein we found their upregulated expression and activity in tumor condition. Taken together, our results demonstrate high infiltration and hyperactivation of neutrophils which possibly account for gradual systemic deterioration during cancer progression. Our findings thus implicate neutrophils as a potential therapeutic target that may help to reduce the overall fatality rate of cancer.

Recently, we have witnessed a booming development of composites and multi-dopant metal oxides to be employed as novel photocatalysts. Yet the practical application of photocatalysis for environmental purposes is still elusive. Concerns about the unknown fate and toxicity of nanoparticles, unsatisfactory performance in real conditions, mass transfer limitations and durability issues have so far discouraged investments in full-scale applications of photocatalysis. Herein, we provide a critical overview of the main challenges that are limiting large-scale application of photocatalysis in air and water/wastewater purification. We then discuss the main approaches reported in the literature to tackle these shortcomings, such as the design of photocatalytic reactors that retain the photocatalyst, the study of degradation of micropollutants in different water matrices, and the development of gas-phase reactors with optimized contact time and irradiation. Furthermore, we provide a critical analysis of research-practice gaps such as treatment of real water and air samples, degradation of pollutants with actual environmental concentrations, photocatalyst deactivation, and cost and environmental life-cycle assessment.

The diffusion process of water molecules within a polyetherimide (PEI) glassy matrix has been analyzed by combining the experimental analysis of water sorption kinetics performed by FTIR spectroscopy with theoretical information gathered from Molecular Dynamics simulations and with the expression of water chemical potential provided by a non-equilibrium lattice fluid model able to describe the thermodynamics of glassy polymers. This approach allowed to construct a convincing description of the diffusion mechanism of water in PEI providing molecular details of the process related to the effects of the cross- and self-hydrogen bondings established in the system on the dynamics of water mass transport.

One of the main challenges for autonomous learning in virtual environments is finding the right material that fits students’ needs and supports their learning process. Personalized recommender systems partially solve this problem by suggesting online educational resources to students based on their preferences. However, in educational environments (which need a proper characterization of both users and educational resources), most existing recommendation algorithms either fail to include all the available information or use hybrid processes that do not exploit possible relationships between users and item features. This article presents a personalized recommender system for educational resources aimed at combining user and item information into a single mathematical model based on matrix factorization. As a result, estimated latent factors can provide insight into possible interactions between users and item features, improving the quality of the information retrieval process. We validated the proposed model on a real dataset that contains the ratings assigned by students from Universidad Nacional de Colombia and Universidade Feevale to educational resources in the Colombian Federation of Learning Object Repositories (FROAC in Spanish). User characterization included learning style and educational level, whereas item characterization (obtained from the objects’ metadata), included interactivity level, aggregation level and type, and resource format. These results, compared to those obtained when not all the available information is included, show that our method can improve the recommendation process.

Magnesium is one of the lightest structural metal used in different industrial sector and many works are present in literature about the study of its reinforcement by fillers addition. Basalt fibres are natural fillers with good mechanical properties, excellent resistance to high temperature and lower cost than carbon fibres. For these aspects, in the last years they are increasingly used in the production of composite materials with polymeric matrices. However, very few information are presents in literature about the use of basalt fibres as reinforcement in metal matrix composite materials. It is well known that the impregnation of fibres reinforcement affects the mechanical behavior of composites materials. The aim of this study is to investigate the impregnation and the behavior of basalt fibres in a magnesium alloy composite material manufactured by a centrifugal casting technique.

High density polyethylene (HDPE) and poly(lactic) acid (PLA) blends with different ratios of both polymers, namely 30:70, 50:50 and 70:30, were produced. Polyethylene grafted maleic anhydride and a random copolymer of ethylene and glycidyl methacrylate, were also proposed as compatibilizers to modify HDPE-PLA optimal blends and were added in the amounts of 1, 3 and 5 wt.%. Blends properties have been evaluated through different aspects by performing tensile tests, scanning electron microscopy to analyze blend morphology and interfaces, and thermomechanical analysis through differential scanning calorimetry, thermo-gravimetric analyses and infrared spectroscopy. The second blend, the one with equal amounts of HDPE and PLA seems to represent a good balance between high amount of bio-derived charge and acceptable mechanical properties. This suggests a good potential of these blends, which would be a good starting point for the production of composites with lingo-cellulosic fillers.

An accumulating body of evidence suggests that physiological reactive oxygen species (ROS) generated by NADPH oxidases act as a redox signal to re-establish homeostasis, a capacity that progressively declines during aging, but is maintained in long-lived animals to promote healthy aging. In the model organism Caenorhabditis elegans, ROS generated by dual oxidases (Duox) are important for extracellular matrix integrity, pathogen defense, oxidative stress resistance, and longevity. The Duox enzymatic activity is tightly regulated and under cellular control. Developmental molting cycles, pathogen infections, toxins, mitochondrial-derived ROS, drugs, and small GTPases (RHO-1) can activate Duox (BLI-3) to generate ROS, whereas NADPH oxidase inhibitors and negative regulators, such as MEMO-1, can inhibit Duox to generate ROS. Three mechanisms-of-action have been discovered for the Duox/BLI-3-generated ROS: 1) enzymatic activity to catalyze cross-linking of free tyrosine ethyl ester in collagen bundles to stabilize extracellular matrices, 2) high ROS bursts/levels to kill pathogens, and 3) Redox signaling activating downstream kinase cascades to transcription factors orchestrating oxidative stress- and immunity responses to re-establish homeostasis. Although Duox function at the cell surface is well established, recent genetic and biochemical data also suggests a novel role for Duoxs at the endoplasmic reticulum membrane to control redox signaling. Evidence underlying these mechanisms initiated by ROS from NADPH oxidases and their relevance for human aging are discussed in this review. Appropriately controlling NADPH oxidase activity for local and physiological redox signaling to maintain cellular homeostasis might be a therapeutic strategy to promote healthy aging.

Crowd counting is of significant importance for numerous applications, e.g., urban security, intelligent surveillance and crowd management. Existing crowd counting methods typically require specialized hardware deployment and strict operating conditions, thereby hindering their widespread deployment. To acquire a more effective crowd counting approach, a device-free counting method based on Channel Status Information (CSI) is proposed, which could mitigate environment noise through wavelet transform and extract the amplitude or phase covariance matrix as the feature vector. Moreover, both the spatial diversity and frequency diversity are leveraged to improve detection robustness. The accuracy of the proposed CSI-based method is compared with a renowned crowd counting one, i.e., Electronic Frog Eye: Counting Crowd Using WiFi (FCC). The experimental results reveal an accuracy improvement of 30% over FCC.

This paper presents a new methodology that provides the analysis of surface texture changes in areas adjacent to the volcano and its impact product of volcanic activity. To do this, algorithms from digital image processing such as the co-occurrence matrix and the wavelet transform are used. These methods are working on images taken by the Landsat satellite platform sensor 5 TM and Landsat 7 ETM + sensor, and implemented with the purpose of evaluating superficial changes that can warn of surface movements of the volcano. The results were evaluated by similarity metrics for grayscale images, and validated in two different scenarios that have the same type of eruption, but differ, essentially, in climate and vegetation. Finally, the proposed algorithm is presented, setting the parameters and constraints for implementation and use.

Linear regression is a basic tool in mobile robotics, since it enables accurate estimation of straight lines from range-bearing scans or in digital images, which is a prerequisite for reliable data association and sensor fusing in the context of feature-based SLAM. This paper discusses, extends and compares existing algorithms for line fitting applicable also in case of strong covariances between the coordinates at each single data point, which must not be neglected if range-bearing sensors are used. Besides, particularly the determination of the covariance matrix is considered, which is required for stochastic modeling. The main contribution is a new error model of straight lines in closed form for calculating fast and reliably the covariance matrix dependent on just a few comprehensible and easily obtainable parameters. The model can be applied widely in any case when a line is fitted from a number of distinct points also without a-priori knowledge of the specific measurement noise. By means of extensive simulations the performance and robustness of the new model in comparison to existing approaches is shown.

High-performance poly(1,4-butylene terephthalate (PBT) nanocomposites have been developed via the consideration of phosphorus-containing agents and amino-carbon nanotube (A-CNT). One-pot functionalization method has been adopted to prepare functionalized CNTs via the reaction between A-CNT and different oxidation state phosphorus-containing agents, including chlorodiphenylphosphine (DPP-Cl), diphenylphosphinic chloride (DPP(O)-Cl), and diphenyl phosphoryl chloride (DPP(O3)-Cl). These functionalized CNTs, DPP(Ox)-A-CNTs (x = 0, 1, 3), were respectively mixed with PBT to obtain the CNTs-based polymer nanocomposites through a melt blending method. SEM observations demonstrated that DPP(Ox)-A-CNT nano-additives were homogeneously distributed within PBT matrix compared to A-CNT. The incorporation of DPP(Ox)-A-CNT improved the thermal stability of PBT. Moreover, PBT/DPP(O3)-A-CNT showed the highest crystallization temperature and tensile strength, due to the superior dispersion and interfacial interactions between DPP(O3)-A-CNT and PBT. PBT/DPP(O)-A-CNT exhibited the best flame retardancy resulting from the excellent carbonization effect. The radicals generated from decomposed polymer were effectively trapped by DPP(O)-A-CNT, leading to the reduction of heat release rate, smoke production rate, carbon dioxide and carbon monoxide release during cone calorimeter tests.

BackgroundThe present review summarizes the state-of-the-art of in silico methods and techniques that are the most useful in drug discovery, their relationship with data science, as well as the successful application of data science, machine learning (ML) and artificial intelligence (AI) applications. A meta-analysis of the various technologies available is furthermore proposed as a guideline for the non-expert, reader relative to the several subject areas is also discussed in this article. The scope of this meta-analysis is to rank the enlisted technologies by their field of applications and to depict the latter according to knowledge accessibility, from students to experts.Method The search strategy utilized for this review first produced a general collection of 900 papers without duplications, which were subsequently streamlined and divided into two independent collections: the top 300 most-cited papers of all time (since 2000) and the papers with the highest interest for a systematic review analysis (high-impact exciting papers). Results In Part 1, we discuss the most cited and quality 97 articles in these top 300 papers most relevant to the field of in silico drug discovery. The different disciplines are listed according to their industrial and economic incurred to society, independently from the “metric” results of how many new drug approvals (NDAs) each discipline has generated to date.ConclusionBig data, the ensemble of known items stored in publicly available databases, has improved our understanding of the many fates of a potential drug candidate during its development and even after its commercialization. Moreover, the combination of new screening techniques and “omics” with old drugs has led to a new paradigm in which the unknown knowledge of any biological molecule and its cellular structure, now plays an important role as a target for a series of yet-to-be-developed drugs: the chemical space. Furthermore, leveraging big data, data science, ML, and AI can revolutionize drug discovery by swiftly analyzing massive datasets, predicting efficacy and safety profiles, streamlining development, cutting costs, and boosting success rates for new drugs. AI also speeds up the search for promising drug candidates, advancing innovative therapies.

In the paper, the authors establish two identities to express the generating function of the Chebyshev polynomials of the second kind and its higher order derivatives in terms of the generating function and its derivatives each other, deduce an explicit formula and an identities for the Chebyshev polynomials of the second kind, derive the inverse of an integer, unit, and lower triangular matrix, present several identities of the Catalan numbers, and give some remarks on the closely related results including connections of the Catalan numbers respectively with the Chebyshev polynomials, the central Delannoy numbers, and the Fibonacci polynomials.

The augmenting commitment in the entrepreneurial learning process, both at academic and industry level, has led to new mechanisms and methodologies. Entrepreneurship literature has shown evidence of raising interest in the problem-based learning (PBL) methodology with regard to upcoming events from the surrounding learning context. This paper presents a Systematic Bibliographic Literature Review (SBLR) with respect to the application of PBL throughout distinct entrepreneurial processes either in higher education, or in industry. It aimed to identify relevant entrepreneurial opportunities for PBL. Bibliographic databases were searched for documents published between 2002 and May 2024 to categorize key topics discussed in the literature on PBL and entrepreneurship. The review process identified 142 empirical and non-empirical papers on entrepreneurship related PBL. Data analysis revealed diverse subthemes for PBL uses in entrepreneurship in both higher education institutions and industry. The research questions “What is the interplay of problem-based learning methodology with entrepreneurship?” guided the study. The central output of the research is the discovery of the varying uses of PBL to cope with diverse events arising within entrepreneurship context. Future research directions are suggested.

Herein we report the expansion of the chemical space available from the chitin, accessible via the biogenic N-platforms 3A5AF, M4A2C and di-HAF. The biologically active heteroaromatics furo[3,2-d]pyrimidin-4-one and furo[3,2-d]pyrimidin-4-amine can be selectively accessed from 3A5AF and M4A2C, respectively. The chiral pool synthon di-HAF is a viable substrate for the Achmatowicz rearrangement, providing streamlined access to 2-aminosugars possessing a versatile hydroxymethyl group at C5.

Due to the rapid growth of the Internet of Things (IoT), securing communications between a huge number of devices has become very difficult. Access controls and authentication secure the connection between IoT devices and protect sensitive data from unauthorized users or malicious attackers. In this paper, we provide a comprehensive review to emphasize the critical role of authentication and access control in securing IoT environments. This paper explores different types of access controls, like Attribute-Based Access Control and Role-Based Access Control. Furthermore, it demonstrates different authentication techniques, like passwords and biometrics. Based on the previous mechanisms, we discuss different IoT security challenges regarding authentication and access control mechanisms and highlight future directions. In addition, this paper investigates the integration between access control and authentication to safeguard IoT communications.

The evolution of photovoltaic cells is intrinsically linked to advancements in the materials from which they are fabricated. This review paper provides an in-depth analysis of the latest developments in silicon-based, organic, and perovskite solar cells, which are at the forefront of photovoltaic research. We scrutinize the unique characteristics, advantages, and limitations of each material class, emphasizing their contributions to efficiency, stability, and commercial viability. Silicon-based cells are explored for their enduring relevance and recent innovations in crystalline structures. Organic photovoltaic cells are examined for their flexibility and potential for low-cost production, while perovskites are highlighted for their remarkable efficiency gains and ease of fabrication. The paper also addresses the challenges of material stability, scalability, and environmental impact, offering a balanced perspective on the current state and future potential of these material technologies.

The aim of this study was to investigate the impact of Process Oriented Guided Inquiry Learning (POGIL)-based instruction versus lecture-based instruction on Grade 12 students’ performance in circular motion unit. A quasi-experimental, pretest-posttest design, the impact of POGIL-based instruction versus lecture-based instruction on students’ performance as measured by three types of cognitive outcomes: Knowing, Applying and Reasoning (KAR). The total number of participants was approximately 110 students (N=110); 54 were assigned to treatment groups (25 girls and 29 boys) and 56 were assigned to control groups (27 girls and 29 boys). The treatment group was taught a unit of circular motion in physics using POGIL-based instruction while the control group was taught the unit using lecture-based instruction. The findings of the study showed statistically significant differences between students of the control group and the treatment group in favor of the later regarding their circular motion performance, suggesting the superiority of the POGIL in enhancing student understanding of the circular motion.

This article investigates the prospect of implementing multidisciplinary and multicultural student teamwork (MMT) involving Case-based Learning (CBL) and Problem-based Learning (PBL) as a sustainable teaching practice. Based on a mixed methods approach, which includes direct observation (both physical and virtual), questionnaire distribution and focus-group interviews the study reveals that MMT through CBL and PBL can both facilitate and hinder sustainable learning. Our findings show that while MMT enhances knowledge sharing, it also poses a wide range of challenges, raising questions about its social significance as a sustainable teaching practice. The study suggests the implementation of certain mechanisms, such as ‘Teamwork Training’ and ‘Pedagogical Mentors’, aiming to strengthen the sustainable orientation of MMT through CBL and PBL.

In MBSE there is yet no converged terminology. The term ’system model’ is used in different contexts in literature. In this study we elaborated the definitions and usages of the term ’system model’, to find a common definition. 104 publications have been analyzed in depth for their usage and definition as well as their meta-data e.g., the publication year and publication background to find some common patterns. While the term is gaining more interest in recent years it is used in a broad range of contexts for both analytical and synthetic use cases. Based on this three categories of system models have been defined and integrated into a more precise definition.

This article discusses the use of game design as a method for interdisciplinary project-based teaching in secondary school education to convey informatics and society topics. There is a lot of knowledge about learning games but little background on project-based teaching using game design as a method. We present the results of an analysis of student-created games and an evaluation of a student-authored database on learning contents found in commercial off-the-shelf games. We further contextualise these findings using a group discussion with teachers. Results underline the effectiveness of project-based teaching to raise awareness for informatics and society topics. We further outline informatics and society topics that are particularly interesting to students, genre preferences and potentially engaging game mechanics stemming from our analyses.

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

Quantitative and qualitative fuzzy measures have been proposed to hesitant fuzzy sets (HFSs) from different points. However, few of the existing HFSs fuzzy measures refer to the grey relational analysis (GRA) theory. Actually, the GRA theory is very useful in the fuzzy measure domain, which has been developed for such the intuitionistic fuzzy sets. Therefore, in this paper, we apply the GRA theory to the HFSs and interval-valued hesitant fuzzy sets (IVHFS) domain. We propose the HFSs grey relational degree, HFSs slope grey relational degree, HFSs synthetic grey relational degree and IVHFSs grey relational degree, which describe the closeness, the variation tendency and both the closeness and variation tendency of HFSs and closeness of IVHFSs, respectively, greatly enriching the fuzzy measures of HFSs. Furthermore, with the help of the TOPSIS method, we develop the grey relational based MADM methodology to solve the HFSs and IVHFSs MADM problems. Finally, combined with two practical MADM examples about energy policy selection with HFSs information and emergency management evaluation with IVHFSs information, we obtain the most desirable decision results, and compared with the previous methods, the validity, effectiveness and accuracy of the proposed grey relational degree for HFSs and IVHFSs are demonstrated in detail.

Since its initial development in the 1970’s by Phil Burke and his collaborators, the R-matrix theory and associated computer codes have become the de facto approach for the calculation of accurate data for general electron-atom/ion/molecule collision and photoionization processes. The use of a non-orthonormal set of orbitals based on B-splines, now called the B-spline R-matrix (BSR) approach, was pioneered by Zatsarinny. It has considerably extended the flexibility of the approach and improved particularly the treatment of complex many-electron atomic and ionic targets, for which accurate data are needed in many modelling applications for processes involving low-temperature plasmas. Both the original R-matrix approach and the BSR method have been extended to the interaction of short, intense electromagnetic (EM) radiation with atoms and molecules. Here we provide an overview of the theoretical tools that were required to facilitate the extension of the theory to the time domain. As an example of a practical application, we show results for two-photon ionization of argon by intense short-pulse extreme ultraviolet radiation

This study examines the role of using algorithmic HRM to make strategic decisions pertaining to HR activities in organizations. The study develops a scale to measure algorithmic HRM usage in its first phase, and in the second phase, it finds that algorithmic HRM having significant impact on the strategic HR decision making that helps and enables organization in creating the competitive advantage. Utilizing the author’s LinkedIn profile, 234 final participants considered for this research. Collected data analyzed by applying PLS SEM. It evaluates the mediation role of the HR strategic decision making, and the HR digital maturity as a moderator in seeing the impact of algorithmic HRM on the competitive advantage of the firm. The study finds a strong relationship between the algorithmic HRM usage and competitive advantage, a significant relationship between algorithmic HRM usage and the strategic HR decision making, and lastly, a significant relationship between strategic HR decision making and the competitive advantage. The moderating role of HR digital maturity was found to be insignificant in this research which paves the way for further research. This research, the model, and its findings contribute to the theory and implicate the practicality of algorithmic HRM. It's one of few papers that address the algorithmic HRM usage in the transitioning economy like Saudi Arabia.

Hospital-based programs democratize mRNA therapeutics by facilitating the processes to translate a novel RNA idea from the bench to the clinic. Because mRNA is essentially biological software, therapeutic RNA constructs can be rapidly developed. The generation of small batches of clinical grade mRNA to support IND applications and first-in-man clinical trials, as well as personalized mRNA therapeutics delivered at the point-of-care, is feasible at a modest scale of cGMP manufacturing. Advances in mRNA manufacturing science and innovations in mRNA biology, are increasing the scope of mRNA clinical applications.

This study empirically analyzes the effectiveness of resource optimization utilizing artificial intelligence (AI) within cloud computing environments. It compares systems that have been transitioned from a Monolithic structure to Microservices Architecture (MSA) with those adopting a simple Rehost strategy, to validate the operational efficiency of two systems whose resource efficiency has been enhanced compared to traditional legacy systems. The research is based on systems that determined resource efficiency post-cloud migration using APM tools. It points out the limitations of traditional heuristic operation methods in resource optimization, which lead to issues such as application performance degradation, resource over-allocation, and increased operational costs. This study aims to overcome the conventional resource optimization standards that depended on the average peak usage of CPU and memory infrastructure, by introducing AI-based optimization tools, and applying on-premise standards to cloud and container environments to achieve resource optimization. The paper emphasizes the importance of resource optimization through AI-based automation tools, exploring the possibilities of resolving real-world application performance issues in cloud environments, reducing unnecessary resource usage costs, and cutting down on labor risks and operational expenses. Through this, it presents strategic directions on how AI technology can offer financial and operational advantages in managing cloud resources.

The conversion of native habitats into anthropogenic ones compromises the original composition and configuration of the landscapes, influencing ecological dynamics and affecting biodiversity. Increasingly, landscape ecology has shown that these effects can only be understood if accessed at adequate spatial scales, as the scale which landscape structure is evaluated influences species responses. Here, we investigated how three variables of landscape composition (proportion of forest, coffee crop and pasture) and two of configuration (number of fragments and mean nearest neighbor distance) interfere in the richness and composition of medium and large-sized mammals considering a multiscale approach. We recorded medium and large-sized mammal species in 13 landscapes with predominantly matrices of coffee and pasture in Minas Gerais, Brazil. Then, we built distance-based linear models to identify the scale of effect of each landscape variable for both response variables considering eight scales (from 250 m to 2000 m). Finally, we verified the influence of the landscape on the richness and composition of mammals, considering the landscape variables in their respective scales of effect. We found 67% of the probable species occurring in the region. The scales of effect varied among landscape variables probably due to the different variables affect different aspects of organisms' ecological requirements. The proportion of pasture in the landscape explained the variation in species richness, while the proportion of forest explained the variation in species composition. In addition, the proportion of pasture in the landscape had a positive influence on species richness, indicating that this matrix may favor the presence of generalist species of habitat and/or this result is due to the higher concentration of species in the fragments immersed in this matrix. These results suggest that considering different responses to biodiversity is important to understand different aspects of the landscape's influence on biodiversity. In addition, the composition of the landscape is fundamental for the perpetuation of species and, therefore, both forest cover and types of matrices in the landscape must be considered to improve species conservation strategies. Finally, generalizing a spatial scale can lead to misinterpretations about the influence of the landscape on biodiversity.

Introduced in 1967 by Lov\'{a}sz, graph exponentiation has received little attention over the past several decades. Since graph exponentiation produces large graphs and any graph can be constructed utilizing its adjacency matrix, we provide a four step linear construction algorithm that generates the adjacency matrix for the graph exponential.

This paper explores the application of Random Matrix Theory (RMT) as a methodological enhancement for portfolio selection within financial markets. Traditional approaches to portfolio optimization often rely on historical estimates of correlation matrices, particularly susceptible to instability. To address this challenge, we combine a data preprocessing technique based on the Hilbert transformation of returns with RMT to refine the accuracy and robustness of correlation matrix estimation. By contrasting empirical correlations with those derived from random matrices, we uncover non-random properties and underlying relationships within financial data. We then utilize this methodology to construct the correlation network dependence structure used in portfolio optimization. The empirical analysis presented in this paper validates the effectiveness of RMT in enhancing portfolio diversification and risk management strategies. This research contributes by offering investors and portfolio managers with methodological insights to construct portfolios that are more stable, robust, and diversified. At the same time, it advances our comprehension of the intricate statistical principles underlying multivariate financial data.

There is increasing evidence that some adult mitral valve pathologies may have developmental origins involving errors in cell signaling and protein deposition during valvulogenesis. While early and late gestational stages are well-documented in zebrafish and small mammalian models, longitudinal studies in large mammals with a similar gestational period to humans are lacking. Further, the mechanism of chordae tendineae formation and multiplication remains unclear. This current study presents a comprehensive examination of mitral anterior leaflet and chordae tendineae development in a bovine model (with a gestational period of 9 months). Remarkably distinct from small mammals, bovine development displayed early branched chordae, with increasing attachments only until birth, while the anterior leaflet grew both during gestation and postnatally. Chordae also exhibited accelerated collagen deposition, maturation, and crimp development during gestation. These findings suggest that the bovine anterior leaflet and chordae tendineae possess unique compartmentalized processes of development despite being a continuous collagenous structure.

Dual quaternions have wide applications in automatic differentiation, computer graphics, mechanics and others. Due to its application in control theory, the matrix equation AXB=C has been extensively studied. However, there is currently limited information on matrix equation AXB=C over the dual quaternion algebra. In this paper, we provide the necessary and sufficient conditions for the solvability of the dual quaternion matrix equation AXB=C, and present the expression for the general solution when it is solvable. As an application, we derive the ϕ-Hermitian solutions for the dual quaternion matrix equation AXAϕ=C, where the ϕ-Hermitian extends the concepts of Hermiticity and η-Hermiticity. Lastly, we present a numerical example to verify the main research results of this paper.

Monolayer graphene absorbs 2.3 percent of the incident visible light. This “small” absorption has been used to emphasize the visual transparency of graphene, but it in fact means that multilayer graphene absorbs a sizable fraction of incident light, which causes non-negligible fluorescence. In this paper, we formulate the light emission properties of multilayer graphene composed of tens to hundreds of layers using a transfer matrix method and confirm the method’s validity experimentally. We could quantitatively explain the measured contrasts of multilayer graphene on SiO2/Si substrates and found sizable corrections, which cannot be classified as incoherent light emissions, to the reflectance of visible light. The new component originates from coherent emission caused by absorption at each graphene layer. Multilayer graphene thus functions as a partial coherent light source of various wavelengths, and it may have surface-emitting laser applications.

Chondrocytes maintain the normal morphological structure and physiological function of articular cartilage. However, the increased expression of inflammatory mediators and matrix-degrading enzymes in chondrocytes can promote ECM degradation, consequently disrupting the physiological function of articular cartilage and ultimately leading to the onset of osteoarthritis (OA). Usnic acid (UA), an extract derived from plants, possesses multiple bioactive properties including anti-inflammatory and antioxidant effects. Hence, this study aims to explore the molecular mechanisms underlying its protective effects in an in vitro simulated model of OA in rat chondrocytes. In vivo experiments encompassed three groups: Control, Model, and Usnic acid (50 mg/kg), with sample extraction following 12 weeks of administration. Pathological alterations in articular cartilage were evaluated using Safranin O-fast green staining. Immunohistochemistry (ICH) analysis was employed to assess the expression of matrix degradation-related markers. The levels of serum cytokines were quantified via ELISA assays. In this study, primary rat chondrocytes were employed as the experimental subject. After a 24-hour treatment period, culture supernatants, total RNA, total protein, and nuclear protein were extracted from the different chondrocyte groups. ELISA and qPCR methods were used to measure levels of IL-6, TNF-α, and PGE2. WB and qPCR were employed to assess levels of p65, p-p65, IκBα, p-IκBα, Nrf2, HO-1, NQO1 ADAMTS-4, MMP-1, MMP-3, MMP-13, INOS, COX-2, and COL2A1. IF analysis was performed to examine Nrf2, p-p65 and COL2A1. Results: (1) In vivo, intraperitoneal injection of usnic acid (50mg/kg) can alleviate pathological changes in articular cartilage and the levels of inflammatory and oxidative-related matrix in the serum. (2) UA reduced the level of inflammatory cytokines in IL-1β-induced chondrocytes supernatant. (3) UA can produce anti-inflammatory and antioxidant effects and inhibit chondrocytes matrix degradation through the Nrf2 and NF-κB pathways. (3) The mechanism by which UA exerts a protective role in chondrocytes is mediated by Nrf2 Conclusion: UA inhibits the expression of chondrocyte matrix degradation-related components through Nrf2 mediated Nrf2 pathway and NF-κB pathway, promotes the production of protective substances, and ultimately exerts a protective effect on chondrocytes.

RFID based technology innovated a new field of wireless sensing, which has been applied in posture recognition, object localization and the other sensing fields, the existing sensing methods often require tags to be attached to dynamic targets (because RFID signals are more sensitive to moving target), significantly limiting their applications. It is a challenging task to sensing a static target without tag attached in NLOS (Non-Line-of-Sight). We utilized RFID technologies to sense the static foreign objects in agricultural products, and taken metal, rock, rubber and clod as sensing targets that are common in agricultural. By deploying a tag matrices to create a sensing region, we observed the signal variations before and after the appearance of the targets in this sensing region, determined the targets’ positions and their types. Here, we buried the targets in the media of seedless cotton and wheat, and detected static targets with a non-contact method in the NLOS. Researches illustrated that by deploying appropriate tag matrices and adjusting the angle of a single RFID antenna, the matrices signals can be sensitive to the static targets’ positions and their properties,i.e. matrices signals vary with different targets and their positions. Specifically, we achieved a 100% success rate in locating metallic targets, while the success rate for clods was the lowest at 86%. We achieved a 100% recognition rate for the types of all the four objects.

Abstract: The transforming growth factor beta (TGFβ) and Hippo signaling pathways are evolutionarily conserved pathways that play a critical role in cardiac fibroblasts during embryonic development, tissue repair, and fibrosis. TGFβ and Hippo signaling is also important for cardiac cushion remodeling and septation during embryonic development. Loss of TGFβ2 in mice causes cardiac cushion remodeling defects resulting in congenital heart disease. In this study, we used in vitro molecular and pharmacologic approaches in the cushion mesenchymal cell line (tsA58-AVM) and investigated if Hippo pathway acts as a mediator of TGFβ2 signaling. Immunofluorescence staining showed that TGFβ2 induced nuclear translocation of activated SMAD3 in the cushion mesenchymal cells. In addition, the results indicated increased nuclear localization of Yes associated protein 1 (YAP1) following a similar treatment of TGFβ2. In collagen lattice formation assays, TGFβ2 treatment of cushion cells resulted in an enhanced collagen contraction compared to the untreated cushion cells. Interestingly, verteporfin, a YAP1 inhibitor, significantly blocked the ability of cushion cells to contract collagen gel in absence or presence of by exogenously added TGFβ2. To confirm the molecular mechanisms of verteporfin induced inhibition of TGFβ2-dependent extra-cellular matrix (ECM) reorganization we performed gene expression analysis of key mesenchymal genes involved in ECM remodeling in heart development and disease. Our results confirmed that verteporfin significantly decreased the expression of α-smooth muscle actin (Acta2), collagen 1a1 (Col1a1), Ccn1 (i.e., Cyr61), and Ccn2 (i.e., Ctgf). Western blot analysis indicated that verteporfin treatment significantly blocked TGFβ2-induced activation of SMAD2/3 in cushion mesenchymal cells. Collectively, these results indicate that TGFβ2 regulation of cushion mesenchymal cell be-havior and ECM remodeling is mediated by YAP1. Thus, TGFβ2 and Hippo pathway integration represents an important step in understanding the etiology of congenital heart disease.

Randomized block Kaczmarz Method and randomized extended block Kaczmarz Method are proposed for solving matrix equation AXB=C, where the matrix A and B may be full rank or rank deficient. These methods are iterative methods without matrix multiplication, and are especially suitable for solving large-scale matrix equations. It is theoretically proved these methods converge to the solution or least square solution of the matrix equation. The numerical results show that these methods are more efficient than the existing algorithms for high-dimensional matrix equations.

Aim: Complementary effects of dabigatran etexilate (DE), exercise training (ET) and combination (DE+ET) on the development and composition of the atherosclerotic lesions in diabetic apoE knockout (apoE-/-) mice. Methods: In 48 male apoE-/- diabetic mice, streptozotocin (STZ) was induced for 5 consecutive days. Mice received figh-fat diet (HFD) for 8 weeks and were randomized into 4 groups (1.Control/CG, 2.DEG: HFD with DE, 3.ETG: ET on treadmill, 4.DE+ETG: Combination DE and ET treatment). At the end of 8th week, all mice were euthanatized and morphometry of the aortic lesions at the level of aortic valve was obtained. Collagen, elastin, TNF-a, MCP-1, matrix-metalloproteinases (MMP-2,-3,-9), and TIMP-1 concentration within plaques at aortic valve were determined. Results: All active groups had significantly smaller aorta stenosis (DEG:7.9±2.2%, ETG:17.3±5.3%, DE+ETG:7.1±2.7%) compared to CG (23.3±5.5% p<0.05), reduced the relative intra-plaque concentrations of macrophages, MCP-1, MMP-3 and MMP-9, and considerably increased collagen, elastin and TIMP-1 (p<0.05). Group 4 showed the most pronounced results (p<0.05). Both DEG and DE+ETG significantly reduced MMP-2 and TNF-a concentrations compared to ETG and CG (p<0.010). Conclusion: DE and ET treatment in diabetic apoE-/- mice showed complementary amelioration of atherosclerotic lesions development and stability, mediated by the anti-inflammatory modulation of both DE and ET.

Aluminum matrix nanocomposites have been the subject of much attention due to their extraordinary mechanical properties and thermal stability. This research focuses on producing and characterizing an aluminum matrix reinforced with silicon carbide (SiC) nanometric particles. The conventional powder metallurgy route was used to produce the nanocomposites, and the dispersion and mixing process was carried out by ultrasonication. The conditions of the dispersion and the volume fraction of the SiC were evaluated in the production of the nanocomposites. Microstructural characterization was carried out using optical microscopy (OM), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD). Mechanical characterization was carried out using hardness and tensile tests. The dispersion agent was investigated, and isopropanol leads to better dispersion with fewer agglomerates. Increasing the volume fraction of the reinforcement improves the hardness of the nanocomposites. However, greater agglomeration of the reinforcement is observed for larger volume fractions. The greatest increase in hardness (77% increase compared to the hardness of the Al matrix) is obtained with 1.0 vol % of SiC, corresponding to the sample with the best dispersion. The mechanical characterization through tensile tests attests to the effect of the reinforcement on the Al matrix. The main strengthening mechanisms identified were the load transfer, the texture hardening, Orowan strengthening, and the increase in the density of dislocations in the nanocomposites.

Hydrogels can be considered as mimics of extracellular matrix (ECM).Cytoskeleton through integrins is connected with ECM and cytoskeleton tension depends on ECM stiffness.A number of age-related diseases depend on cellular processes related with cytoskeleton function.Some examples of cancer initiation and progression and heart disease in relationship with ECM stiffness has been analysed.Incorporation of rigid particles in ECM can increase ECM stifness and to promote formation of internal residual stresses. Water migration, changes of water binding energy to biomactomolecules, changes of the state of water from tightly bound water to free and loosely bound water changes stiffness of ECM. Risks related with rigid particles incorporation into ECM have been also iscussed.

To estimate the degree of quantum entanglement, it is important to understand the statistical behavior of functions of spectrum of density matrices such as von Neumann entropy, quantum purity, and entanglement capacity. These entangle- ment metrics over different generic state ensembles have been studied intensively in the literature. As an alternative metric, in this work we study sum of square root spectrum of density matrices, which is relevant to negativity and fidelity in quantum information processing. In particular, we derive the exact mean and vari- ance of sum of square root spectrum over the Bures-Hall generic state ensemble extending known results obtained recently over the Hilbert-Schmidt ensemble.

The advancements achieved in Tissue Engineering are based on a careful and in-depth study of cell-tissue interaction. The choice of a certain biomaterial in Tissue Engineering is fundamental as it represents an interface for adherent cells in the creation of a microenvironment suitable for cell growth and differentiation. The knowledge of the biochemical and biophysical properties of the extracellular matrix is a useful tool for the optimization of polymeric scaffolds. The aim of this re-view is to analyse the chemical, physical and biological parameters on which it is possible to act in Tissue Engineering for the optimization of polymeric scaffolds. Understanding the scaffold impact on cell fate is of paramount importance for the successful advancement of Tissue Engineering.

The paper focuses on the admissibility problem of descriptor fractional-order systems (DFOSs). The alternate admissibility criteria are addressed for DFOSs with order in (0,2) which involve a non-strict linear matrix inequality (LMI) method and a strict LMI method, respectively. The forms of non-strict and strict LMIs are brand-new and distinguished with the existing literature, which fill the gaps of studies for admissibility. These approaches are available to the order in (0,2) without separating the order ranges into (0,1) and [1,2). In addition, a method involved least real decision variables in terms of strict LMIs is derived which is more convenient to process the practical solution. Three numerical examples are given to illustrate the validity of proposed results.

Matrix vesicles (MVs) are nano-sized extracellular vesicles that are anchored in the extracellular matrix (ECM). In addition to playing a role in biomineralization, osteoblast-derived MVs were recently suggested to have regulatory duties. The aims of this study were to: establish the characteristics of osteoblast-derived MVs in the context of extracellular vesicles like exosomes, assess their role in modulating osteoblast differentiation, and examine their mechanism of uptake. MVs were isolated from the ECM of MG-63 human osteoblast-like cell cultures and characterized via enzyme activity, transmission electron microscopy, nanoparticle tracking analysis, western blot, and small RNA sequencing. Osteoblasts were treated with MVs from two different culture conditions (growth media [GM]; osteogenic media [OM]) to evaluate their effects on differentiation and production of inflammatory markers and on macrophage polarization. MV endocytosis was assessed using a lipophilic, fluorescent dye and confocal microscopy with the role of caveolae determined using methyl-β-cyclodextrin. MVs exhibited a four-fold enrichment in alkaline phosphatase specific activity compared to plasma membranes, were 50-150nm in diameter, possessed exosomal markers CD63, CD81, and CD9 and endosomal markers ALIX, TSG101, and HSP70, and were selectively enriched in microRNA linked to an anti-osteogenic effect and to M2 macrophage polarization. Treatment with GM or OM MVs decreased osteoblast differentiation. Osteoblasts endocytosed MVs by a mechanism that involves caveolae. These results support the hypothesis that osteoblasts produce MVs that participate in the regulation of osteogenesis.

(1) Background: Breast cancer is a frequent heterogeneous disorder diagnosed in woman and is a high cause of mortality of them in reason to rapid metastasis and disease recurrence. Ferroptosis can inhibit breast cancer cell growth, improve the sensitivity of chemotherapy and radiotherapy and inhibit distant metastases so potentially acts on tumor micro-environment; (2) Methods: Ferroptosis/Extracellular matrix remodeling literature text-mining results were integrated in breast cancer transcriptome cohort according their distant relapse free survival (DRFS) under adjuvant therapy (anthracyclin+taxanes) and also in MDA-MB-231 transcriptome functional experiments with ferroptosis activations (GSE173905); (3) Results: Ferroptosis/Extracellular matrix remodeling text-mining identified 910 associated genes in at list 10 articles. Univariate Cox analyses censored on breast cancer (GSE25066) selected 252 individual significant genes and 171 of them found with an adverse expression. Functional enrichment of these 171 adverse genes predicted basal breast cancer signatures. By text-mining some ferroptosis significant adverse selected genes shared citations in domain of ECM remodeling such as: TNF, IL6, SET, CDKN2A, EGFR, HMGB1, KRAS, MET, LCN2, HIF1A, TLR4. A molecular score based on expression the eleven genes was found predictive of worst prognosis breast cancer at univariate level: basal subtype, short DRFS, high grade values 3 and 4, estrogen and progesterone receptors negative and nodal stages 2 and 3. This eleven gene signature was validated as regulated by ferroptosis inductors (erastin and RSL3) in triple negative breast cancer cellular model MDA-MB-231.; (4) Conclusions: Crosstalk between ECM remodeling-Ferroptosis functionalities allowed to define a molecular score which have been characterized as an independent adverse parameter in prognosis of breast cancer patients. Gene signature of this molecular score have been validated to be regulated by erastin/RSL3 ferroptosis activators. This molecular score could be promising to evaluate ECM impact of ferroptosis target therapies in breast cancer.

Chronic wounds are associated with considerable patient morbidity and present a significant economic burden to the healthcare system. Often, chronic wounds are in a state of persistent in-flammation and unable to progress to the next phase of wound healing. Placental-derived bio-materials are recognized for their biocompatibility, biodegradability, angiogenic, an-ti-inflammatory, anti-microbial, anti-fibrotic, immunomodulatory, and immune privileged prop-erties. As such, placental-derived biomaterials have been used in wound management for more than a century. Placental-derived scaffolds are composed of an extracellular matrix (ECM) that can mimic the native tissue, creating a reparative environment to promote ECM remodeling, cell migration, proliferation, and differentiation. Reliable evidence exists throughout the literature to support the safety and effectiveness of placental-derived biomaterials in wound healing. How-ever, differences in source (i.e., anatomical regions of the placenta), preservation techniques, decellularization status, design, and clinical application have not been fully evaluated. This re-view provides an overview of wound healing and placental-derived biomaterials, summarizes the clinical results of placental-derived scaffolds in wound healing, and suggests directions for future work.

Collagen, the most abundant structural protein found in mammals, plays a vital role as a constituent of the extracellular matrix (ECM) that surrounds cells. Collagen fibrils are strengthened through the formation of covalent cross-links, which involve complex enzymatic and non-enzymatic reactions. Lysyl oxidase (LOX) is responsible for catalyzing the oxidative deamination of lysine and hydroxylysine residues, resulting in the production of aldehydes, allysine, and hydroxyallysine. These intermediates undergo spontaneous condensation reactions, leading to the formation of immature cross-links, which are the initial step in the development of mature covalent cross-links. Additionally, non-enzymatic glycation contributes to the formation of abnormal cross-linking in collagen fibrils. During glycation, specific lysine and arginine residues in collagen are modified by reducing sugars, leading to the creation of Advanced Glycation End-products (AGEs). These AGEs have been associated with changes in the mechanical properties of collagen fibers. Interestingly, various studies have reported that plant polyphenols possess amine oxidase-like activity and can act as potent inhibitors of protein glycation. This review article focuses on compiling literature describing polyphenols with amine oxidase-like activity and antiglycation properties. Specifically, we explore the molecular mechanisms by which specific flavonoids impact or protect the normal collagen cross-linking process. Furthermore, we discuss how these dual activities can be harnessed to generate properly cross-linked collagen molecules, thereby promoting the stabilization of highly organized collagen fibrils.

The paper considers coherent states for the representation of Weyl commutation relations over a field of $p$-adic numbers. A geometric object, a lattice in vector space over a field of p-adic numbers, corresponds to the family of coherent states. It is proved that the bases of coherent states corresponding to different lattices are mutually unbiased, and the operators defining the quantization of symplectic dynamics are Hadamard operators.

Matrix metalloproteinases are involved in extracellular matrix remodeling through the degradation of extracellular matrix components and are involved in the inflammatory response by regulating the activities of TNF-alpha and IL-1beta, which are pro-inflammatory cytokines, in addition to extracellular matrix components. Since the regulation of inflammatory response and changes in the extracellular matrix by MMPs are related to the development of various diseases including lung and cardiovascular diseases, many studies have been conducted on the role of MMPs in pathogenesis. In addition, various studies have demonstrated that MMPs are involved in the pathogenesis of infectious diseases by regulating the expression and activity of MMPs by infection with pathogens. In this review, we discuss the role of MMPs in infectious diseases and the role of MMPs in inflammatory responses and present their potential as therapeutic targets in infectious diseases.

The application of metal-matrix composite coatings for protecting and improving the service life of sliding components has demonstrated to have the potentials of meeting the requirements of a diverse range of engineering industries. Recently, a significant body of research has been devoted to study the mechanical and tribological performance of dispersion-strengthened MCrAlY coatings. These coatings belong to a class of emerging wear-resistant materials, offering improved properties and being considered as promising candidates for the protection of engineering structural materials exposed to tribological damage, especially at elevated temperature regimes. This paper attempts to comprehensively review the different reinforcements used in the processing of MCrAlY-based alloys and how they influence the mechanical and tribological properties of the corresponding coatings. Further, the major fabrication techniques together with their benefits and challenges are also reviewed. Discussion on the failure mechanisms of these coatings as well as the main determining factors are also included. In addition, a comprehensive survey of studies and investigations in recent times are summarized and elaborated to further substantiate the review.

Synthetic cannabinoids are a series of synthetic substances that mimic the effects of natural cannabinoids and produce a much stronger toxicity than natural cannabinoids, which have become the most abused family of new psychoactive substances. A solid-phase extractive-liquid chromatography-triple quadrupole/linear ion trap mass spectrometry method was developed to determine 11 synthetic cannabinoids in rat urine. The factors affecting recovery were optimized, and Oasis HLB was selected to extract synthetic cannabinoids simultaneously. The results showed that the linear correlation coefficients of the synthetic cannabinoids ranged from 0.993 to 0.999, and the limit of quantitation ranged from 0.01 to 0.1 ng/mL, and the spiked recoveries ranged from 69.90% to 118.39%. This method has the advantages of good purification ability, simple operation, and good reproducibility, and can be used for the high sensitivity analysis of various synthetic cannabinoids in urine.

The interfacial mechanics and electrical properties of the SiC reinforced copper matrix composites were studied via the first principles method. The work of adhesion (Wad) and the interfacial energies were calculated to evaluate the stabilities of the SiC/Cu interfacial models. The carbon terminated (CT)-SiC/Cu interfaces were predicted more stable than those of the silicon terminated (ST)-SiC/Cu from the results of the Wad and interfacial energies. The interfacial electron properties of SiC/Cu were studied via the charge density distribution, charge density difference, electron localized functions and partial density of the state. The covalent C-Cu bonds were formed based on the results of the electron properties, which further explained the fact that the interfaces of the CT-SiC/Cu are stable than those of the ST-SiC/Cu. The interfacial mechanics of the SiC/Cu were investigated via the interfacial fracture toughness and ultimate tensile stress, and the results indicate that both CT- and ST-SiC/Cu interfaces are hard to fracture. The ultimate tensile stress of the CT-SiC/Cu is nearly 23GPa, which is smaller than those of the ST-SiC/Cu of 25 GPa. The strains corresponding to their ultimate tensile stresses of the CT- and ST-SiC/Cu are about 0.28 and 0.26, respectively. The higher strains of CT-SiC/Cu indicate their stronger plastic properties on the interfaces of the composites.

When a large artery becomes occluded, hemodynamic changes stimulate remodeling of arterial networks to form collateral arteries in a process termed arteriogenesis. However, the structural changes necessary for collateral remodeling have not been defined. We hypothesize that decon-struction of the extracellular matrix is essential to the remodeling of smaller arteries into effective collaterals. Using multiphoton microscopy, we analyzed collagen and elastin structure in maturing collateral arteries isolated from ischemic rat hindlimbs. Collateral arteries harvested at different timepoints showed progressive diameter expansion associated with striking rearrangement of in-ternal elastic lamina (IEL) into a loose fibrous mesh, a pattern persisting at 8 weeks. Despite a 2.5-fold increase in luminal diameter, total elastin content remained unchanged in collaterals compared with control arteries. Among the collateral midzones, baseline elastic fiber content is low. Outward remodeling of these vessels with a 10-20 fold diameter increase was associated with fractures of the elastic fibers and evidence of increased wall tension as demonstrated by straight-ening of the adventitial collagen. Inhibition of lysyl oxidase (LOX) function with β-aminopropionitrile resulted in severe fragmentation or complete loss of continuity of the IEL in developing collaterals. Collateral artery development is associated with permanent redistribution of existing elastic fibers to accommodate diameter growth. We found no evidence of new elastic fiber formation. Stabilization of the arterial wall during outward remodeling is necessary and dependent on LOX activity.

This paper extends the concept of weighted graphs to matrix weighted graphs. The consensus algorithms dictate that all agents reach consensus when the weighted graph is connected. However, it is not always the case for matrix weighted graphs. The conditions leading to different types of consensus have been extensively analysed based on the properties of matrix-weighted Laplacians and graph theoretic methods. However, in practice, there is concern on how to pick matrix-weights to achieve some desired consensus, or how the change of elements in matrix weights affects the consensus algorithm. By selecting the elements in the matrix weights, different clusters may be possible. In this paper, we map the roles of the elements of the matrix weights in the systems consensus algorithm. We explore the choice of matrix weights to achieve different types of consensus and clustering. Our results are demonstrated on a network of three agents where each agent has three states.

This document introduces a new cryptosystem mixing two improvement standards generally used for text encryption, in order to give birth a new color image encryption algorithm capable of dealing with known attacks. Firstly, two substitution matrixes attached to a strong replacement function will be generated for advanced Vigenere technique application. At the end of this first round, the output vector is subdivided into size blocks according to the used chaotic map, for acting a single enhanced Hill circuit insured by a large inversible matrix. A detailed description of such a large involutive matrix constructed using Kronecker products will be given. accompanied by a dynamic translation vector to eliminate any linearity. A solid chaining is established between the encrypted block and the next clear block to avoid any differential attack. Simulations carried out on a large volume of images of different sizes and formats ensure that our approach is not exposed to any known attacks.

The paper summarizes in a step by step ‘recipe’ format a model of developing new ideas (Ideation) that can be used to form a new venture or develop a new product. The proposed ‘recipes’ are widely used and can be applied in tandem with other prevalently available models or tools. The intention of the paper is also to serve as a resource for entrepreneurs and innovators, and the links and references munificently used in the paper are serving this purpose. The author has used the outline and the tools described here in numerous Ideation classes taught for over a decade in several countries around the world. The paper’s intention is not to conduct a systematic review on the subject of new venture creation or propose new research agenda (for that, see for example Shepherd et al., 2021). The purpose is to provide an effective and efficient set of tools, models and techniques that can assist the entrepreneur in her journey to create and achieve her dreams. One such tool is the matrix of Reasonings-Outcomes introduced here for the first time.

This note addresses a fundamental problem in matrix theory on establishing and characterizing range equalities for matrix expressions that involve generalized inverses. We first establish a group of necessary and sufficient conditions for the matrix range equality ${\rm range}(D_1 - C_1A_1^{\dag}B_1) = {\rm range}(D_2 - C_2A_2^{\dag}B_2)$ to hold, where $(\cdot)^{\dag}$ denotes the Moore--Penrose inverse of matrix. We then give several groups of range equalities with extrusion properties for multiple matrix products associated with two matrices and their conjugate transposes and Moore--Penrose inverses.

This note shows that the well-known reverse order law $(AB)^{\dag} = B^{\dag}A^{\dag}$ for the Moore--Penrose inverse of matrix product is equivalent to many other equalities for that are composed of multiple products $(AB)^{\dag}$ and $B^{\dag}A^{\dag}$ by means of the definition of the Moore--Penrose inverse and orthogonal projector theory.

Peripheral blood is an available source of adult stem cells for both fundamental research and clinical use. Peripheral blood mononuclear cells (PBMCs) contain a number of different multipotent populations of progenitor cells and possess a potential to differentiate into various types of cells under appropriate conditions. We have evaluated a regenerative potential of PBMCs embedded into fibrin matrix (FM) in a model of pig spinal cord injury. The distribution of transplanted PBMCs in the injured spinal cord was previously evaluated, which demonstrated the ability of these cells to penetrate into deep layers of the spinal cord and to concentrate mainly in the grey matter. The pilot study conducted demonstrated that the tissue structure could be improved and the conduction along posterior columns of the spinal cord could be partially recovered in animals in the setting of FM+РВМCs application. The multiplex analysis of blood serum and cerebrospinal fluid (CSF) showed the cytokine imbalance to occur without significantly shifting towards pro-inflammatory or anti-inflammatory cytokine cascades in the setting therapy provided.

Defectively manufactured and deliberately damaged composite laminates fabricated with different continuous reinforcing fibres (respectively, carbon and glass) and polymer matrices (respectively, thermoset and thermoplastic) were inspected in magnetic resonance imaging equipment. Two pulse sequences were evaluated during non-destructive examination conducted in saline solution-immersed samples to simulate load-bearing orthopaedic implants permanently in contact with biofluids. The orientation, positioning, shape, and especially the size of translaminar and delamination fractures were determined according to stringent structural assessment criteria. The spatial distribution, shape, and contours of water-filled voids were sufficiently delineated to infer the amount of absorbed water if thinner image slices than this study were used. The surface texture of composite specimens featuring roughness, waviness, indentation, crushing, and scratches was outlined, with fortuitous artefacts not impairing the image quality and interpretation. Low electromagnetic shielding glass fibres delivered the highest, while electrically conductive carbon fibres produced the poorest quality images, particularly when blended with thermoplastic polymer, though reliable image interpretation was still attainable.

Tenascin C (TNC) is an element of the extracellular matrix (ECM) of various tissues, including the skin, and is involved in modulating ECM integrity and cell physiology. Although skin aging is apparently associated with changes in the ECM, little is known about the role of TNC in skin aging. Here we found that Tnc mRNA level was significantly reduced in the skin tissues of aged mice compared with young mice, consistent with reduced TNC protein expression in aged human skin. TNC-large (TNC-L; 330-kDa) and -small (TNC-S; 240-kDa) polypeptides were observed in conditional media from primary dermal fibroblasts. Both recombinant TNC polypeptides, corresponding to TNC-L and TNC-S, increased the expression of type I collagen and reduced the expression of matrix metalloproteinase-1 in fibroblasts. Treatment of fibroblasts with a recombinant TNC polypeptide, corresponding to TNC-L, induced phosphorylation of SMAD2 and SMAD3. TNC increased the level of TGF-β1 mRNA and upregulated the expression of type I collagen by activating the TGF-β signaling pathway. In addition, TNC also promoted the expression of type I collagen in fibroblasts embedded in a three-dimensional collagen matrix. Our findings suggest that TNC contributes to the integrity of ECM in young skin and to prevention of skin aging.

Blood Concentrates (BCs) are autologous non-transfusional therapeutical preparations with biological properties applied in tissue regeneration. These BCs differ in the preparation method, in fibrin network architecture, growth factors release as well as in platelet/cell content. Methodological changes result in distinct matrices that can compromise their clinical effectiveness. The present study evaluated the influence of different g-forces and types of tubes in the release of vascular endothelial growth factor (VEGF) from platelet-rich fibrin (PRF) as a function of time. The PRF-like samples were obtained with three g-forces (200, 400, and 800 x g) for 10 minutes in pure glass tubes or in polystyrene-clot activator tubes. Scanning and Transmission electron microscopy was used to morphometric analyzes of PRF’s specimens and flow cytometry was used to quantify VEGF slow release until 7 days. Our results showed that platelets were intact and adhered to the fibrin network, emitting pseudopods and in degranulation. The fibrin network was rough and twisted with exosomic granulations impregnated on its surface. An increase in the concentration of VEGF in the PRF supernatant was observed until 7 days for all g forces (200, 400 or 800 x g), with the highest concentrations observed with 200 x g, in both tubes, glass or plastic. Morphological analyzes showed a reduction in the diameter of the PRF fibers after 7 days. Our results showed that g-force interferes with the shape of the fibrin network in the PRF, as well as affect the release of VEGF stored into platelets. This finding may be useful in applying PRF to skin lesions, in which the rapid release of growth factors can favor the tissue repair process. Our observations point to a greater clarification on the methodological variations related to obtaining PRF matrices, as they can generate products with different characteristics and degrees of effectiveness in specific applications.

Cachexia is a complex metabolic syndrome characterized by loss of skeletal muscle, leading to a significant weight loss that impacts patient morbidity and mortality. Given the complexity of gene regulatory networks that control gene expression, our objective was to perform an integrative mRNA and miRNA profiling to identify genetic programs that capture essential mechanistic details that promote muscle atrophy in cancer cachexia. Here, we used RNA sequencing to analyze miRNAs and mRNAs expression profiles in tibialis anterior (TA) muscles of the Lewis lung carcinoma model of cancer cachexia. In addition, we compared these findings with RNA-Seq data from C2C12 myotubes treated in vitro with the cachectic factors tumor necrosis factor-alpha (TNF-α) and interferon-gamma (IFN-γ). Extracellular matrix (ECM) alterations were validated by picrosirius staining, western blot, and fractal dimension analyses. We found 1,008 mRNAs and 18 miRNAs differentially expressed in cachectic mice. This set of genes was associated with the ECM, proteolysis, and inflammatory response. Enrichment analysis of transcriptional factor binding sites revealed activation of the atrophy-related transcriptional factors: NF-κB, Stat3, AP-1, and FoxO. Furthermore, the integration of mRNA and miRNA expression profiles identified post-transcriptional regulation by miRNAs of genes involved in ECM organization, cell migration, transcription factors binding, ion transport, and FoxO signaling pathway. C2C12 myotubes treated with TNF-α and IFN-γ similarly down-regulate subsets of ECM genes, including collagens. Our integrative analysis of miRNA-mRNA co-profiles comprehensive characterized regulatory relationships of molecular pathways and revealed miRNAs targeting ECM-associated genes in cancer cachexia. We also confirmed in C2C12 myotubes that changes in ECM-associated genes are dependent on inflammatory signaling of the cytokines TNF-α and IFN-γ.

Accumulation of damage is generally considered the cause of aging. Interventions that delay aging mobilize mechanisms that protect and repair cellular components. Consequently, research has been focused on studying the protective and homeostatic mechanisms within cells. However, in humans and other multicellular organisms, cells are surrounded by extracellular matrices (ECM), which are important for tissue structure, function and intercellular communication. During aging, components of the ECM become damaged through fragmentation, glycation, crosslinking, and accumulation of protein aggregation, all of which contribute to age-related pathologies. Interestingly, placing senescent cells into a young ECM rejuvenates them and we found that many longevity-assurances pathways re-activate de-novosynthesis of ECM proteins during aging. This raises the question of what constitutes a young ECM to reverse aging or maintain health? In order to make inroads to answering this question, I suggest a systems-level approach of quantifying the matrisome or ECM compositions reflecting health, pathology, or phenotype and propose a novel term, the “matreotype”, to describe this. The matreotype is defined as the composition and modification of ECM or matrisome proteins associated with or caused by a phenotype, such as longevity, or a distinct and acute physiological state, as observed during aging or disease. Every cell type produces its unique ECM. Interestingly, cancer-cell types can even be identified based on their unique ECM composition. Thus, the matreotype reflects cellular identity and physiological status. Defined matreotypes could be used as biomarkers or prognostic factors for disease or health status during aging with potential relevance for personalized medicine. Treatment with biologics that alter ECM-to-cell mechanotransduction might be a strategy to reverse age-associated pathologies. An understanding of how to reverse from an old to a young matreotype might point towards novel strategies to rejuvenate cells and help maintain tissue homeostasis to promote health during aging.

Several challenges stand in the way of production of Metal Matrix Composites (MMCs) such as higher processing temperatures, particulate mixing, particulate-matrix interface bonding issues and ability to process into desired geometrical shapes. Although there are many literatures showing composites with single particulate reinforcements, the studies on composites with multiple reinforcing agents (hybrid composites) are found to be limited. Development of a hybrid particulate composite with optimized mechanical and tribological properties is very significant to suit modern engineering applications. In this study, Al-Si hypereutectic alloy (A390) is used as the matrix and Silicon Carbide (SiC) and Graphite (Gr) and Molybdenum di-Sulphide (MoS₂) are used as particulates. Particulate volume (wt%) is varied and sample test castings are made using squeeze casting process through stir casting processing route. The evaluation of mechanical properties indicates that presence of both the hard phase (SiC) and the soft phase has distinct effect on the properties of Hybrid Composite. Composite samples were characterized to understand the performance and to meet the tribological applications. Fractography study indicated an intra-granular brittle fracture for hybrid composites. Wear study shows that Hybrid MMCs has better tribological performance compared to that of A390 alloy.

Poly(vinylidene fluoride) nanocomposites processed with different morphologies, such as porous and non-porous films and fibres, have been prepared with silica nanoparticles (SiNPs) of varying diameter (17, 100, 160 and 300 nm) which in turn have encapsulated perylenediimide (PDI), a fluorescent molecule. Structural, morphological, optical, thermal, and mechanical properties of the nanocomposites, with SiNP filler concentration up to 16 wt% were evaluated. Further, cytotoxicity and cell proliferation studies were performed. All SiNPs are negatively charged independently of the pH and more stable from pH 5 upwards. The SiNPs introduction within the polymer matrix increases the contact angle independently of the nanoparticle diameters and the smallest ones (17 nm) improve the PVDF Young modulus from 0.94 ± 0.04 GPa for the pristine polymer film to 1.05 ± 0.06 GPa. Varying filler diameter, physico-chemical, thermal and mechanical properties of the polymer matrix were not significantly affected. Finally, the SiNPs inclusion does not induce cytotoxicity in murine myoblasts (C2C12) after 72 h of contact and proliferation studies reveal that the prepared composites represent a suitable platform for tissue engineering applications, as they allow to combine the biocompatibility and piezoelectricity of the polymer with the possible functionalization and drug encapsulation and release of the SiNP.

We explore the use of inverse multiquadratic (IMQ) functions as basis functions when solving the vibrational Schrödinger equation with the rectangular collocation method. The quality of the vibrational spectrum of formaldehyde (in six dimensions) is compared to that obtained using Gaussian basis functions when using different numbers of width-optimized IMQ functions. The effects of the ratio of the number of collocation points to the number of basis functions and of the choice of the IMQ exponent are studied. We show that the IMQ basis can be used with parameters where the IMQ function is not integrable. We find that the quality of the spectrum with IMQ basis functions is somewhat lower that that with a Gaussian basis when the basis size is large and for a range of IMQ exponents. The IMQ functions are, however, advantageous when a small number of functions is used or with a small number of collocation points e.g. when using square collocation.

Reliable and robust control of power converters is a key issue in the performance of numerous technological devices. In this paper we show a design technique for the control of a DC-DC buck converter with a switching technique that guarantees not only good performance but also global stability. We show that making use of the contraction theorem in the Jordan canonical form of the buck converter, it is possible to find a switching surface that guarantees stability but it is incapable of rejecting load perturbations. To overcome this, we expand the system to include the dynamics of the voltage error and we demonstrate that the same design procedure is not only able to stabilize the system to the desired operation point but also to reject load, input voltage and reference voltage perturbations.

This work analyzes the severity and risk associated with Automatic Dependent Surveillance-Broadcast (ADS-B) message drop out in Detect and Avoid (DAA) function of Unmanned Aircraft System (UAS). Performance assessment of the Universal Access Transceiver (UAT) ADS-B message implies that in some cases ADS-B fails to update within a specified update interval, which is referred to as drop out in this work. ADS-B is a fundamental surveillance sensor for both class 1 and class 2 DAA systems. Message loss or drop out has been found as one of the common limitations of the ADS-B system. The key feature of this study is incorporating the update rate of real ADS-B data transmitted from the manned aircraft. The data were received from the Grand Forks International Airport, North Dakota. Monte Carlo method has been adopted to resolve encounter scenarios in the presence of drop out. The change in the alert triggered by the UAS DAA in the presence of ADS-B drop out has been investigated. Furthermore, the risk matrices are created to quantify the associated risk with drop out affected alerts. Simulation results depict that both the duration of drop out and DAA look-ahead time affect the alert-triggering function of UAS. With a small look- ahead window and longer duration of drop out, the number of warning alerts increases. Also, alerts are affected more during an overtaking encounter than that of a head-to-head encounter. A system-level analysis is also carried out to recognize the potential reasons behind the ADS-B drop out.

This article puts forward the bi-matrix games with crisp parametric payoffs based on interval value function approach. We conclude that the equilibrium solution of the game model can converted into optimal solutions of the pair of the non-linear optimization problem. Finally, experiment results show the efficiency of the model.

In recent years, many algorithms based on end-to-end deep networks have been proposed to deal with the target detection problem of videos. However, the deep network models usually consume a lot of computing resources during the procedure of analysis of videos with complex dynamic backgrounds. In this paper, a new method of object detection based on information theory is presented. Firstly, each frame in a video is converted into an effective information map by using the Harris corner detection method. Secondly, the sensitive areas in the frame are extracted by using the context information and the effective information maps of the consecutive video frames. The sensitive areas in the video frame are the candidate areas where the target objects would be appeared at high probabilities. Thirdly, the information entropy features of each sensitive area are extracted to form the feature matrix, based on which, an SVM model is trained for selecting the target areas from the sensitive areas. Finally, the locations of the objects are detected based on the target areas in the video with a complex dynamic background. As a lightweight video detection framework, the method presented in this paper can save a lot of computing resources. Experimental results show that this method can achieve good results in the benchmark of CDnet 2014.

MAPLE (matrix assisted pulsed laser evaporation) depositions of Candida Rugosa lipase were carried out from ice matrices whose composition is optimized in order to minimize conformational damage of the protein, which strongly influences its catalytic activity. To induce lid opening and to protect lipase during the MAPLE process, pentane and m-DOPA amino acid were added to the liquid matrix giving a target formed by a frozen water-lipase-pentane microemulsion. FTIR and AFM were used to investigate the structure of MAPLE deposited lipase films. The ability of MAPLE films to promote transesterification was determined by thin layer chromatography. It was shown that m-DOPA has influence on the aggregation but not on the unfolding of lipase induced by MAPLE, while the microemulsion formed by the addition of pentane to the target composition is effective in protecting lipase during the MAPLE process. MAPLE deposited lipases showed a modified specificity.

Modern fighter aircraft increasingly need to conjugate aerodynamic performance and low observability. In this paper we showcase a methodology for a gradient-based bidisciplinary aero-stealth optimization. The shape of the aircraft is parametrized with the help of a CAD modeler, and we optimize with the SLSQP algorithm. The drag, computed with the help of a RANS method, is used as the aerodynamic criterion. The stealth criterion, a function is derived from the radar cross section in a given cone of directions, weighed with a function whose goal is to cancel the electromagnetic intensity in a given direction. Stealth is achieved passively by scattering back the electromagnetic energy away from the radar antenna and no energy is absorbed by the aircraft, which is considered as a perfect conductor. A Pareto front is identified by varying the weights of the aerodynamic and stealth criteria. The Pareto front allows for an easy identification of the CAD model corresponding to a chosen aero-stealth trade-off.

Drug discovery utilizes high-throughput screening (HTS) methods applying target and cell-based assays. This review discusses the challenges and benefits associated with these assays in HTS. Discussed the strategies that can be applied for the development of screening assays through primary and secondary screens for target identification. Further discussion on identify-ing the most efficacious drugs following these approaches in cancer. Even though various drugs have been identified to treat cancer, there is high demand of more relevant phenotypic assays to produce desired diseased phenotype to only highlighting the specific targets instead of off-targets. Hopefully, the new developing strategies could provide innovative drugs to treat cancer patients to reduce mortality rate.

The correlations of seismic noise, recorded during 1997-2022 at 229 stations around the world are investigated. The property of waveforms, known as the Donoho-Johnston threshold, is analyzed, which separates the absolute values of the orthogonal wavelet coefficients into "small" and "large". Network of 50 reference points is considered. For each reference point, a time series with a time step of 1 day is calculated as the median of the values of the DJ index from the 5 nearest stations. For each reference point, the correlations between the values of the DJ index at other reference points are calculated in a sliding time window of 365 days. Average values of the DJ index decrease, which is interpreted as indicators of global seismic hazard increasing. The maximum distances between reference points with strong correlations show an explosive increase after 2011. Bursts in the mean coherences between the length of day (LOD) and the values of the DJ index at the reference points precede the release of seismic energy with a delay of about 530 days. High amplitude of the DJ index response to LOD for the times 2020-2022 was identified, which may forecast a strong earthquake in the second half of 2023.

Noise in requirements has been known to be a defect in software requirements specifications (SRS). Detecting defects at an early stage is crucial in the process of software development. Noise can be in the form of irrelevant requirements that are included within a SRS. A previous study had attempted to detect noise in SRS, in which noise was considered as an outlier. However, the resulting method only demonstrated a moderate reliability due to the overshadowing of unique actor words by unique action words in the topic-word distribution. In this study, we propose a framework to identify irrelevant requirements based on the MultiPhiLDA method. The proposed framework distinguishes the topic-word distribution of actor words and action words as two separate topic-word distributions with two multinomial probability functions. Weights are used to maintain a proportional contribution of actor and action words. We also explore the use of two outlier detection methods, namely Percentile-based Outlier Detection (PBOD) and Angle-based Outlier Detection (ABOD), to distinguish irrelevant requirements from relevant requirements. The experimental results show that the proposed framework was able to exhibit better performance than previous methods. Furthermore, the use of the combination of ABOD as the outlier detection method and topic coherence as the estimation approach to determine the optimal number of topics and iterations in the proposed framework outperformed the other combinations and obtained sensitivity, specificity, F1-score, and G-mean values of 0.59, 0.65, 0.62, and 0.62, respectively.

Animal-based measures are the measure of choice in animal welfare assessment protocols as they can often be applied completely independently to the housing or production system employed. Although there has been a small body of work on potential animal-based measures for farmed crocodilians [1-3], they have not been studied in the context of an animal welfare assessment protocol. Potential animal-based measures, that could be used to reflect the welfare state of farmed crocodilians, were identified and aligned with the Welfare Quality® principles of good housing, good health, good feeding and appropriate behaviour. A consultation process with a panel of experts was used to evaluate and score the potential measures in terms of validity and feasibility. This resulted in a toolbox of measures being identified for further development and integration into animal welfare assessment on the farm. Animal-based measures related to ‘good feeding’ and ‘good health’ received the highest scores for validity and feasibility by the experts. There was less agreement on the animal-based measures that could be used to reflect ‘appropriate behaviour’. Where no animal-based measures were deemed to reliably reflect a welfare criterion nor be useful as a measure on the farm, additional measures of resources or management were suggested as alternatives. Future work in this area should focus on the reliability of the proposed measures and involve further evaluation of their validity and feasibility as they relate to different species of crocodilian and farming system.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

The exploitation of lipid membranes in biosensors has provided the ability to reconstitute a considerable part of their functionality to detect trace of food toxicants and environmental pollutants. Nanotechnology enabled sensor miniaturization and extended the range of biological moieties that could be immobilized within a lipid bilayer device. This chapter reviews recent progress in biosensor technologies based on lipid membranes suitable for environmental applications and food quality monitoring. Numerous biosensing applications are presented, putting emphasis on novel systems, new sensing techniques and nanotechnology-based transduction schemes. The range of analytes that can be currently detected include, insecticides, pesticides, herbicides, metals, toxins, antibiotics, microorganisms, hormones, dioxins, etc. Technology limitations and future prospects are discussed, focused on the evaluation/ validation and eventually commercialization of the proposed sensors.

Given the increase in consumers’ preferences for coffee, it is becoming important to understand their decision-making processes in the coffee chain context. To deepen the understanding of sustainable outcomes in this context, this study investigates the role of dedication- and constraint-based mechanisms in forming consumers’ repurchase and positive word-of-mouth (WOM) intentions, two critical sustainable outcomes. We examined the effects of coffee quality, the quality of the physical environment, and service quality in accelerating the formation of dedication-based factors. Moreover, this study offers an in-depth understanding of the enablers of perceived switching costs. Data collected from 238 university students that frequently visit coffee chains are empirically tested against the proposed theoretical framework by using structural equation modeling. The results confirm that both dedication- and constraint-based factors substantially predict consumers’ sustainable outcomes in the coffee chain context. Brand image and perceived switching costs play an important role in enhancing consumers’ repurchase and positive WOM intentions compared with customer satisfaction. Coffee quality is significantly associated with both customer satisfaction and brand image, whereas the quality of the physical environment and service quality are only significantly associated with brand image. Habit is found to be the key enabler of perceived switching costs, while loyalty programs have no significant impact on perceived switching costs.

Recently, Shehata et al. [37] introduced the r+1Rs(B;C; z) matrix function and established some properties. The aim of this study established to devote and derive certain basic properties including analytic properties, recurrence matrix relations, dierential properties, new integral representations, k- Beta transform, Laplace transform, fractional k-Fourier transform, fractional integral properties, the k-Riemann{Liouville and k-Weyl fractional integral and derivative operators an extended version of r+1Rs;k matrix function. We establish its relationships with other well known special matrix functions which have some particular cases in the context of three parametric Mittag-Leer matrix function, k- Konhauser and k-Laguerre matrix polynomials. Finally, some special cases of the established formulas are also discussed.

Saudi Arabia is an oil-reliant nation as a large percentage of its GDP comes from oil resources. Oil dependency leaves a county at the mercy of the international crude market, and a decrease in the price of crude can seriously destabilize the economy of such nations. An example is the case of Venezuela whose dependence on oil caused a national disaster (McCarthy, 2017). As such, the nation’s exports, GDP, and government revenue are primarily dependent on oil revenue, and the recent decrease in the oil prices has decreased Venezuela’s national revenue resulting in economic collapse as well as inflation. A shift from a resource based economy to a knowledge based economy will help Saudi Arabia become less reliant on its oil revenues for its economic stability and growth (Nurunnabi, 2017).

This study advances the field of solar irradiance nowcasting by introducing a discrete-level classification approach, diverging from traditional continuous measurement methods. Grounded in a need for cost-effective and modular solutions, the research employs high-resolution computer vision coupled with a deep learning framework to predict Direct Normal Irradiance (DNI). By harnessing the capabilities of a Logitech C900 1080p HD camera and an NVIDIA Jetson module, the study achieves real-time data processing, pivotal for CSP systems' operational efficiency. The core of the methodology is the ResNet-50 convolutional neural network, refined via transfer learning on a bespoke dataset, culminating in a predictive accuracy of 85.78%. This discrete classification model contrasts with conventional, costly instruments like the MS-57, offering a novel and accessible alternative for DNI estimation. Such innovation not only demonstrates high predictive accuracy but also signifies a shift towards less resource-intensive and more adaptable solar energy forecasting tools, contributing a significant leap towards optimizing renewable energy systems.

This study focuses on enhancing controllable fibrin-based hydrogels for tissue engineering, addressing existing weaknesses. By integrating a novel copolymer, we improve the foundation for cell-based angiogenesis with adaptable structural features. Tissue engineering often faces challenges like waste disposal and nutrient supply beyond the 200 µm diffusion limit. Angiogenesis breaks through this limitation, allowing the construction of larger constructs. Our innovative scaffold combination significantly boosts angiogenesis, resulting in longer branches and more capillary network junctions. The copolymer attached to fibrin fibers enables precise adjustment of hydrogel mechanical-dynamic properties for specific applications. Our material proves effective for angiogenesis, even under suppression factors like suramin. In our study, we prepared fibrin-based hydrogels with and without the copolymer PVP12400-co-GMA10mol%. Using a co-culture system of human umbilical vein endothelial cells (HUVEC) and mesenchymal stem cells (MSC), we analyzed angiogenetic behavior on and within the modified hydrogels. Capillary-like structures were reproducibly formed on different surfaces, demonstrating the general feasibility of three-dimensional endothelial cell networks in fibrin-based hydrogels. This highlights the biomaterial's suitability for in vitro pre-vascularization of biohybrid implants.

In this study, graphite, the most stable form of carbon, was examined for its hexagonal crystalline structure with specific dimensions (ao=2.46 Ǻ; co=6.70 Ǻ). Its framework comprises parallel carbon atom planes forming regular hexagons (side length 1.415 Ǻ) and 120° angles between adjacent atoms. Two structural variations exist: hexagonal symmetry (1-2-1-2-1-2 planes) and rhomboidal symmetry (1-2-3-1-2-3 planes). The research aimed to produce high-density graphite utilizing carbonaceous raw materials. Graphite-based materials often exhibit high porosity, necessitating additional treatments. The study successfully obtained mesophase tar pitch (yield: 45%), a pivotal raw material, and high-density graphite. The resulting graphite underwent characterization for physical properties (apparent and real density, porosity and compression strength), demonstrating conformity with existing literature data.