The finite volume method (FVM) is a kind of numerical analysis method that can take into account topography and apply local subdivision cells as the finite element method (FEM). The adjoint state method (ASM) can easily calculate the gradient vector of the objective function for the inversion. The author developed a program that applied these methods to 3-D Magnetotelluric (MT) inversion and verified it using test models. As a result, it was confirmed that the program can perform analysis properly and efficiently considering topography and subsurface resistivity heterogeneity.

Based on previous research results, we propose a new preprocessing HSS iteration method (PHSS) for the generalized Lyapunov equation. At the same time, the corresponding inexact PHSS algorithm (IPHSS) is given from the angle of application. All the new methods presented in this paper have given the corresponding convergence proof. The numerical experiments are carried out to compare the new method with the existing methods, and the improvement effect is obvious. The feasibility and effectiveness of the proposed method are proved from two aspects of theory and calculation.

The theoretical limit of link prediction is a fundamental problem in this field. Taking the network structure as object to research this problem is the mainstream method. This paper proposes a new viewpoint that link prediction methods can be divided into single or combination methods, based on the way they derive the similarity matrix, and investigates whether there a theoretical limit exists for combination methods. We propose and prove necessary and sufficient conditions for the combination method to reach the theoretical limit. The limit theorem reveals the essence of combination method that is to estimate probability density functions of existing links and nonexistent links. Based on limit theorem, a new combination method, theoretical limit fusion (TLF) method, is proposed. Simulations and experiments on real networks demonstrated that TLF method can achieve higher prediction accuracy.

This paper attempts to develop a Dynamic Mode Decomposition (DMD) based Reduced Order Model (ROMs) which can quickly but accurately predict the forces and moments experienced by a road vehicle such that they be used by an on-board controller to determine the vehicle's trajectory. DMD can linearize a large dataset of high-dimensional measurements by decomposing them into low-dimensional coherent structures and associated time-dynamics. This ROM then also can be applied to predict the future state of the fluid flow. Existing literature on DMD is limited to low Reynolds number applications. This paper presents DMD analyses of the flow around an idealized road vehicle, called the Ahmed body, at a Reynolds number of 2.7E6. The high dimensional dataset used in this paper was collected from a computational fluid dynamics (CFD) simulation performed using Menter's shear stress transport (SST) turbulence model within the context of Improved Delayed Detached Eddy Simulations (IDDES). The DMD algorithm, as available in literature, was found to suffer nonphysical dampening of the medium-to-high frequency modes. Enhancements to the existing algorithm were explored, and a modified DMD approach is presented in this paper which includes: (a) a requirement of higher sampling rate to obtain higher resolution of data, and (b) a custom filtration process to remove spurious modes. The modified DMD algorithm thus developed was applied to the high Reynolds number, separation dominated flow past the idealized ground vehicle. The effectiveness of the modified algorithm was tested by comparing future predictions of force and moment coefficients as predicted by the DMD-based ROM to the reference CFD simulation data, and found to offer significant improvement.

The streptavidin and biotin interaction is one of the strongest non-covalent interactions in nature. As a result, this non-covalent interaction has been of great interest when it comes to biochemical assays, diagnosis of diseases, and cell-targeted drug delivery. Past research has proven that biotin-streptavidin is useful in biosensor development to improve the detection of a system when conjugated to nanoparticles. This study aims to prove that streptavidin-coated nanoparticles can be conjugated with biotinylated antibodies using the primary-secondary method to non-invasively detect adenocarcinoma in-vitro. While the use of nanoparticles is not uncommon to the diagnostics area of scientific research, the technique this research aims to investigate is a non-invasive one, utilizing the primary-secondary method. Specifically, the increased stability of fluorophores when bound to antibodies as opposed to nanoparticles directly can be indicative of the particles conjugated through the primary-secondary method’s ability to specifically bind to overexpressed transferrin receptors in the A549 cell line. In this paper, streptavidin-coated nanoparticles were conjugated with biotinylated anti-transferrin receptor antibodies and AlexaFluor-488 secondary antibodies were used to enable fluorescence-based detection. The efficiency of these particles were observed quantitatively through a plate reader and qualitatively through a fluorescence microscope. I demonstrated that these nanoparticles are able to specifically bind to the target proteins in this study. These findings contribute to the field of nanoparticle diagnostics and can be extended to different diseases caused by overexpression of proteins in the future. While this was conducted in-vitro, conjugates can be prepared to detect cancer in-vivo and can be tested with magnetic relaxometry in the future.

Background: This study introduces a novel dexamethasone (DEX) mixed micellar system (DEX-MM) using Soluplus® and Pluronic F-127 (PF127) to enhance ocular drug delivery. The enhancement of ocular application properties was achieved by creating a chitosan-coated DEX-MM (DEX-CMM), which promotes better adherence to the ocular surface, thereby improving drug absorption; Methods: Using the solvent evaporation method, a formulation was developed with a Soluplus® to drug ratio of 1:10, enhanced with 0.25% PF127. After dispersing in water, 1% chitosan (CS) was added. The stability and integrity of DEX within the micelles were verified using attenuated total reflection-fourier transform infrared spectroscopy (ATR-FTIR) and differential scanning calorimetry (DSC). Additionally, in vitro and ex vivo drug release studies were con-ducted; Results: DEX-CMM (F6) demonstrated a particle size of 151.9±1 nm and a polydispersity index (PDI) of 0.168±0.003, suggesting uniformity and high electrostatic stability with a zeta po-tential of +35.96±2.13 mV. The non-Fickian drug release mechanism indicated prolonged drug retention. Comparative analyses showed DEX-CMM outperforming a standard DEX suspension in drug release and ocular tissue permeation, with flux measurements significantly higher than the DEX suspension; Conclusion: The study confirmed the efficacy of DEX-CMM in enhancing drug delivery to ocular tissues, evidenced by improved permeability. Safety evaluations using the HET-CAM test demonstrated that DEX-CMM was non-irritant, supporting its potential for effective ocular drug delivery.

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.

The increase in population and the challenges caused by it are always among the concerns of decision-makers and policymakers in large countries. One of the most serious concerns is in the field of energy consumption. Optimizing energy use is one of the most effective ways to manage limited resources and energy. Energy consumption in residential houses constitutes a significant percentage of the total energy consumption worldwide; therefore, current research focusing on mathematical models aims to provide a predictive multi-objective linear mathematical model for residential house consumption. For this purpose, the consumption of water, electricity, and gas for 80 residential houses, including 10 household appliances and equipment on a weekly basis, has been considered. The results showed that the mixed-integer multi-objective predictor model is able to optimize household consumption plans compared to the actual state of household energy consumption. Since the goals of household satisfaction and energy consumption costs in the model conflict with each other, the influence coefficient was calculated to demonstrate the simultaneous achievement of these goals. The optimal value achievable with the default model for the model's purposes is 69%.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Mixed reality technology is being increasingly used in cultural heritage attractions to enhance visitors’ experience. However, how the characteristics of mixed reality affect satisfaction and brand loyalty has not been explored in previous research. The purpose of this study is to identify factors affecting satisfaction with mixed reality experiences at cultural and artistic visitor attractions and their influence on brand loyalty, which is connected with management performance. We propose a theoretical model based on brand equity theory in the context of mixed reality experience. Survey data were gathered from 251 respondents visiting a cultural and artistic visitor attraction in Seoul, Korea using a stratified sampling method. PLS-SEM was employed for the data analysis. The results suggest that the characteristics of mixed reality (interactivity, vividness) not only influence the affective aspects (perceived immersion, perceived enjoyment) of visitors’ experience, but also positively affect brand awareness, brand association, and brand loyalty.

Background: Since the advent of virtual reality (VR), it has been used in medical education for surgical training and anatomy teaching. Recently, other modalities of extended reality (XR) such as augmented reality (AR) and mixed reality (MR) has also made its way into medical education. Although there has been research validating XR’s use in medical education, there have been few studies on the research trends of the different XR modalities. The paper aims to compare the research trends of the XR modalities in general and in terms of the medical fields studied and outcomes measured. Methods: Web of Science was searched, and preliminary data was extracted to analyze the general trend. Inclusion and exclusion criteria were then applied, and finalized articles were analyzed and grouped based on the medical field studied and outcomes measured. Results: 31 articles on VR, eight on AR and one on MR were included in the final analysis. We found that there is increasing research in VR since 1990 and AR since 2008. The research in MR is constant. Most of the papers on VR studied endoscopic surgery and anatomy whereas AR studied mostly anatomy and endovascular procedures. Using Miller’s prism of clinical competence, the competency measured most for VR and AR is “show”. Discussion and conclusion: Advancement in computing, communication and display technologies since 1990 may contribute to the increase in research on VR whereas the ubiquity of smartphone since 2008 may explain the increase in research on AR. Although both VR and AR are used in surgical training and anatomy teaching, we found possible strengths of VR in counseling and AR in practical skills. The competency "show" was measured most as most of the papers were on surgery, and the XR simulators used can capture surgical parameters

Functional mobility assessments (i.e., Timed Up and Go) are commonly used clinical tools for mobility and fall risk screening in the aging population. In this work, we proposed a new Mixed Reality (MR)-based assessment that utilized a Microsoft HoloLens^TM headset to automatically lead and track the performance of functional mobility tests, and subsequently evaluated its validity in comparison with reference inertial sensors. Twenty-two healthy adults (10 older, 12 young) participated in this study. An automated functional mobility assessment app was developed based on the HoloLens platform. Mobility performance was recorded with the headset built-in sensor and validated with reference inertial sensor (Opal, APDM) taped on the headset and lower back. Results indicate vertical kinematic measures by HoloLens was in good agreement with the reference sensor (Normalized RMSE ~ 10%). Additionally, the HoloLens-based test completion time was in perfect agreement with clinical standard stopwatch measure. Overall, our preliminary investigation indicates that it is possible to use an MR headset to automatically guide users to complete common mobility tests with good measurement accuracy, thus it has great potential to provide objective and efficient sensor-based mobility assessment.

Scientific studies had shown that mixed forests of silver fir (Abies alba Mill.) and European beech (Fagus sylvatica L.) provide higher ecosystem services than monospecific forests. Mixed forests are known for their high resilience to climate change impacts and superior biodiversity compared to monospecific forests. In many countries, promotion of mixed forests in forest management is becoming a government policy since they can contribute to fulfill the Sustainable Development Goals set by the United Nation, respectively Goal 13 and 15. However, not much is known about public perceptions on mixed forests compared to monoculture forests. Our study on ecosystem services provided by mixed and monospecific forests in southwest Germany fill this gap. Based on a survey with 520 valid responses we analyzed people’s perception on 18 different supporting, cultural, regulating and provisioning ecosystem services measured by Likert scale. Stepwise regression analyses show relations between social profiles (gender, age, education, profession) and preferences on respondents’ perceptions. Our findings show that people perceive that mixed forests provide better cultural, regulating and supporting ecosystem services than monospecific forests of fir and beech whereas provisioning services were perceived as being equally or better provided by monospecific forests. Significant effects towards a positive perception on ecosystem services provided by mixed forests were mainly influenced by the perceived abundance of old trees, feeling of pleasantness in mixed forests, age, profession, and education. Our findings indicate that there is a high public support for the promotion of silver fir and beech mixed forests in Southwest Germany.

The complexity of modern power grids, exacerbated by integrating diverse energy sources, espe-cially inverter-based resources (IBRs), presents a significant challenge to grid operation and plan-ning since linear models fail to capture the intricate IBR dynamics. This study employs the Sparse Identification of Nonlinear Dynamics (SINDy) method to bridge the gap between theoretical un-derstanding and practical implementation in power system analysis. It introduces the novel Volterra-based Nonlinearity Index (VNI) to examine system-level nonlinearity comprehensively. The distinction of dynamics into first-order linearizable terms, second-order nonlinear dynamics, and third-order noise elucidates the intricacy of power systems. The findings demonstrate a fundamental shift in system dynamics as power sources transit to IBRs, revealing system-level nonlinearity compared to module-level nonlinearity in conventional syn-chronous generators. The VNI quantifies nonlinear-to-linear relationships, enriching our comprehension of power system behavior and offering a versatile tool for distinguishing between different nonlinearities and visualizing their distinct patterns through the proposed VIN profile.

Artificial intelligence algorithms have gained widespread adoption in the field of air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. Training samples that lack relevance to the predicted moments can introduce interference into the neural network's learning process, potentially leading to a state of local convergence during its iterative process. This, in turn, diminishes the robustness and generalization capabilities of the prediction model. To enhance the prediction accuracy of air conditioning load prediction models based on artificial intelligence algorithms, this study presents an artificial intelligence algorithm prediction model based on the method of sample similarity sample screening. Initially, the comprehensive similarity coefficient between samples is computed using the gray correlation analysis method, enriched with enhancements in information entropy. Subsequently, a subset of closely related samples is extracted from the original dataset and employed as the training dataset for the artificial intelligence prediction model. Finally, the trained artificial intelligence algorithm prediction model is deployed for air conditioning load prediction. The results illustrate that the method of screening training samples based on sample similarity effectively improves the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is important to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.

Deep eutectic solvents have emerged as a greener alternative and have attracted so much interest in the last two decades. This paper present alternative synthesis method to classical heating-stirring. The ultrasound method is one of the most promising syntheses DESs in terms of yield and energy. So, ultrasound synthesis method was studied to obtain hydrophobic (Aliquat 336: L-Menthol 3:7; Li-docaine: decanoic acid 1:2) and hydrophilic DESs based on Choline chloride and urea, ethylene glycol and oxalic acid. The physical characterization of DESs by comparison of FTIR spectra no show difference between DESs obtained by heating-stirring and ultrasound synthesis methods. The RMN spectra of the DESs obtained by heating-stirring revealed the DESs formation. Density and viscosity properties of DESs were evaluated. The density values are similar for both synthesis methods. However, lower viscosity values are obtained to the hydrophilic DESs prepared by ul-trasound methods.

Influence lines are indispensable tools for visualizing and analyzing the dynamic variations in force factors induced by external loads within structural systems. Among these methods, the energetic approach stands as a widely employed technique, rooted in the fundamental principle of work done by changing forces. It enables engineers to transform intricate structural analysis problems into manageable ones by exploring the first derivatives of the radius vector, which represent infinitesimal velocity or displacement. This methodology seamlessly interweaves concepts such as carrier motion, relative motion, and the construction of mechanisms, bringing fresh perspectives to the analysis of influence lines. In this article, we explore the nuances of these novel methods within the domain of mechanism theory. Through comprehensive elaboration and analysis, we elucidate the underlying principles and practical applications of Jacobian contours. Crucially, we introduce a straightforward, rapid, and programmable approach, promising to revolutionize influence line determination in structural engineering. This method bridges the gap between theory and practice, offering the potential to elevate the accuracy, efficiency, and adaptability of influence line analysis. As such, it represents a significant advancement in the field of structural and applied mechanics, with broad-reaching implications for engineering practice.

With the improvement in medical quality, cold mechanical rehabilitation equipment can no longer meet the psychological needs of the physically handicapped. Therefore, design for behaviour change should be viewed as a strategy for enabling lifestyle changes as soon as possible. Although how to use equipment to assist physically disabled people in hydrotherapy rehabilitation is the primary design goal, the psychological feelings of users are also one of the focuses of this study. Besides, coupled with the large-scale spread of the Coronavirus Disease (COVID-19) in the past two years. People's living habits are also forced to change, many activities that people are used to doing outdoors are being slowly completed at home. During the epidemic, it is very dangerous and inconvenient for users who need to go to institutional rehabilitation regularly. Hence, it is urgent to change past usage habits and places. In this study, a personal multi-functional hydrotherapy bucket is designed. In addition to not having to use a public spa with other people to reduce the risk of contracting the Coronavirus Disease, it can be easily placed at home by reducing its size. According to market research, it is known that the current domestic hydrotherapy buckets on the market usually only have a single function. The large medical hydrotherapy pools are not only rare in Taiwan, but this kind of equipment also takes up a lot of space. Various factors have resulted in a relatively low market share of hydrotherapy pools compared to other rehabilitation equipment. In order to take into account, the physical and psychological needs of the physically disabled, this research focus on the design of a multi-functional but small-sized hydrotherapy bucket. Taking the convenience of entering and leaving the bucket as the starting point to improve the shortcomings of the existing products, on the other hand, because most users are inconvenient to move, safety is also the focus of the design to reduce the occurrence of accidents and injuries. This study is based on the systematic design methodology of concurrent design to obtain product design results. Firstly, conduct market research and select products from different manufacturers for analysis and comparison. Through functional analysis and sorting out the problem points, the TRIZ theory and the morphological diagram method are used to find the solution to the problem. Finally, the best design scheme is obtained by the PUGH method. Besides, construct a 3D model to present the design concept that meets the design goals. The research results are hoped to be used as a reference for designing and developing related products, thereby increasing the home's multifunctional hydrotherapy buckets' market share.

In this lecture notes, we will introduce Asymptotics, then we will give a short glimpse on Perturbation theory (regular versus singular), which plays a crucial role especially in theoretical physics. Our goal is to find Asymptotic series that approximates the values of integrals depending on some parameter or the solutions of differential equations. These methods that lead to obtain more effective algorithms of numerical evaluation are called: Asymptotic Methods.

Video super-resolution, which utilizes the relevant information of several low-resolution frames to generate high-resolution images, is a challenging task. One possible solution called sliding window method tries to divide the generation of high-resolution video sequences into independent sub-tasks, and only adjacent low-resolution images are used to estimate the high-resolution version of the central low-resolution image. Another popular method named recurrent algorithm proposes to utilize not only the low-resolution images but also the generated high-resolution images of previous frames to generate the high-resolution image. However, both methods have some unavoidable disadvantages. The former one usually leads to bad temporal consistency and requires higher computational cost while the latter method always can not make full use of information contained by optical flow or any other calculated features. Thus more investigations need to be done to explore the balance between these two methods. In this work, a bidirectional frame recurrent video super-resolution method is proposed. To be specific, a reverse training is proposed that the generated high-resolution frame is also utilized to help estimate the high-resolution version of the former frame. With the contribution of reverse training and the forward training, the idea of bidirectional recurrent method not only guarantees the temporal consistency but also make full use of the adjacent information due to the bidirectional training operation while the computational cost is acceptable. Experimental results demonstrate that the bidirectional super-resolution framework gives remarkable performance that it solves the time-related problems when the generated high-resolution image is impressive compared with recurrent-based video super-resolution method.

Currently, electromagnetic and radar methods are the most common non-destructive tests for non-destructive rebar location in structures.. Both methods have some advantages, disadvantages and limitations. This article is an attempt to describe possibilities of particular methods with emphasizing their advantages and limitations. The first stage of tests included results from tests conducted on a lintel beam made of autoclaved aerated concrete (AAC) and reinforced with small-diameter rebars and rebars placed close to each other. The second stage consisted in testing nine lightweight concrete specimens, each with three bars having a diameter of 10, 16 and 20 mm at three different space arrangements, to determine the effect of bar diameter and spacing on the measurement accuracy. The reinforcement of lintels and specimens was tested with two different electromagnetic scanners and a ground-penetrating radar (GPR) device. Received results from direct tests were compared with results from non-destructive tests (NDT). NDT tests conducted in such an element were found to perform the correct assessment of the concrete cover, and at the same time to give ambiguous results with reference to shape and number of rebars.

Physical activity (PA) benefits are well-established, yet many US adults fail to meet PA guidelines. This is especially true for minorities facing social inequities. This study explored barriers and facilitators to PA among urban Midwestern minorities using a mixed-methods approach framed on the socio-ecological model. A cross-sectional survey was conducted among community-dwelling minorities. Participants were grouped as completing low (LLPA) or high (HLPA) weekly leisure-time PA for comparison. Quantitative analysis included MANOVA, follow-up ANOVAs, and calculation of effect sizes. Qualitative data were assessed using inductive thematic analysis. Twenty-nine adults (44.83% Black, 41.37% Latino) participated in the study. The HLPA group (n = 18) reported higher leisure-time PA (p = 0.001, d = 2.21) and total PA (p = 0.02, d = 1.00) compared to the LLPA group (n = 11). LLPA participants faced more personal barriers to PA (p = 0.02, d = -0.92). Common barriers identified in the interviews included a lack of time and financial costs. Facilitators included social support and available PA facilities. Both groups achieved USPA guidelines through different PA domains. Increasing social support and lowering PA-related costs could enhance participation. Addressing barriers and leveraging existing facilitators are crucial to increasing PA among minorities.

Catalytic studies for hydrogen production via steam reforming of ethanol (SRE) are essential for process optimization. Likewise, selecting the ideal support for the active phase can be critical to achieve high conversion rates during the catalytic steam reforming. In this work, copper-based catalysts were synthesized using two different supports, NaY zeolite and Nb2O5/Al2O3 mixed oxides. The materials were prepared using wet impregnation and characterized for their physicochemical properties using different analytical techniques. Differences in catalyst morphologies were readily attributed to the characteristics of the support. The Cu/NaY catalyst showed better textural properties than Cu/Nb2O5/Al2O3, resulting in a homogeneous metal dispersion over the support surface. Both catalytic systems were active in SRE, but Cu/NaY resulted in higher ethanol conversions compared to Cu/Nb2O5/Al2O3. Hence, the performance of copper-based catalysts was influenced significantly by the textural properties of the support.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

In this article it is shown, first, that a change of basis in Minkowski space is equivalent to a change of basis in Euclidean space if a basis element is replaced by its dual element, constituting a mixed basis set. Second, that such mixed bases can be interpreted as a measurement system used by local, flat observers whose direct distance measurements are restricted to an (n−1)-dimensional submanifold in an n-dimensional Euclidean space. Combining these steps, it is concluded that a local, flat observer in a four-dimensional Euclidean space measures a Minkowski spacetime. This interpretation could be useful for theories in special relativity and related fields that rely on spacetime concepts, as it offers a more intuitive geometric understanding of the Minkowski metric.

This paper presents a theoretical evaluation of the prices of mixed briquettes produced from co-conut shells (CCS), banana peels (BNP), rattan waste (RWT), and sugarcane bagasse (SGC), and, on the other hand, an analysis of the economic viability of their use as a replacement for conventional household fuels (Liquefied Petroleum Gas, fuelwood, and wood charcoal) in house-holds in Cameroon. The investigation was carried out using the Life Cycle Cost method on a typical household over a ten-year period with annual cooking energy requirements of 950 kWhth. The SGC-CCS and SGC-RWT mixed briquettes with a ratio higher than 7.75% and 11.1%, respectively, have prices lower than 0.063 €/kWhth. The Present Value of the Net Benefit is posi-tive for the use of SGC-CCS and SGC-RWT mixed briquettes. The results show that by making the right mixes of residues, it is possible to obtain biomass briquettes that are less expensive than conventional fuels.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.

In order to understand the impact of lithologic heterogeneity of continental mixed fine-grained sedimentary rocks on reservoir brittleness in western Qaidam basin, the mechanical properties of the rocks and their correlation with mineral composition and petrographic characteristics were studied by measns of mineralogy, petrography and triaxial stress test. The results show that the reservoir rocks can be divided into 5 different types according to the mechanical properties of the reservoir (characteristics of stress-strain curves), among them Type I and III belong to similar elastoplastic failure model, type II shows a special pulse failure mode for plastic material, type IV shows a failure mode of mixed characteristics, and type V exhibits a typical plastic failure model. The correlation between minerals and mechanical properties indicates that quartz and feldspar, which are often considered brittle minerals, do not contribute much to the brittleness of continental fine-grained sedimentary rocks. The main minerals affecting reservoir brittleness are dolomite and clay minerals, and their contributions to reservoir brittleness are positive and negative, respectively. The petrographic analysis results prove that the abnormal correlation between rock mechanical properties and quartz and feldspar is caused by the different rock fabrics. When dolomite forms a rock skeleton, it typically exhibits greater strength, brittleness and physical properties than other minerals. Based on the results, a brittleness evaluation standard for continental fine-grained sedimentary rock reservoir is proposed, and the validity of the standard is verified by the spatial correlation between the lithology probability model and the micro-seismic monitoring data, indicating that the spatial heterogeneity of dolomite-rich rock is the main controlling factor for the development of ‘sweet spot’ in the Cenozoic continental fine-grained sedimentary rock reservoir in the Western Qaidam Basin.

Research rarely explores LGBTQ+ youth bullying in the context of culture-specific outcomes (e.g., LGBTQ+ identity development) and what can mitigate the impact of peer stressors. This study used a concurrent mixed methods design to explore how experiences of peer victimization predicted LGBTQ+ youth’s identity development (i.e., stigma sensitivity, concealment motivation, and difficult process) and whether social support and outness served as protective, moderating factors. The mixed-methods approach provides a culture-specific context via qualitative inquiry to inform whether the quantitative findings align with how youth qualitatively discuss their experience of bullying, negative outcomes, and social support. Our sample consisted of 349 LGBTQ+ youth 14-17 years old who completed a survey (quantitative sample), and a subset of 39 LGBTQ+ youth who completed a semi-structured interview (qualitative sample). Our quantitative findings indicated that greater overall peer victimization was positively related to LGBIS-revised subscales of stigma sensitivity, concealment motivation, and difficult process, where both outness and social support moderated such relations. Qualitatively, victimized youth also reported stigma sensitivity and concealment motivation, while also endorsing how being out and having a support system played a role in their experience of being victimized. These qualitative findings align with our quantitative findings that classmate support mitigated the effects of peer victimization on difficulty of coming out. Implications for practitioners and researchers are provided.

The copper end paste used in multilayer ceramic capacitors sintered in nitrogen atmosphere will lead to carbon residue of organic vehicle, which will lead to the reduction of electrode conduc-tivity and high scrap rate. With an attempt to leave no residue in the sintering, the compatibility of solvents and thickeners should be improved because it has an important influence on the hi-erarchical volatilization and carbon residue of organic vehicles. In this work, the volatility of different solvents was compared and several solvents were mixed in a definite proportion to prepare an organic vehicle with polyacrylate resins. The hierarchical volatility and solubility parameters of mixed solvents were adjusted effectively by changing proportions of different components, the thermogravimetric curves of resins and organic vehicles were measured by thermogravimetric analyzer, the effect of solubility parameter on the dissolvability of resins in the solvent and the residual of organic vehicles were studied. Results showed that the hierar-chical volatilization of solvents can be obtained by mixing different solvents; the intrinsic vis-cosity of the organic vehicle is higher and the thermal decomposition residue of polyacrylate resins is lower when the solubility parameters of mixed solvents and polyacrylate resins are closer. The low residual sintering of organic vehicles can be achieved by using the mixed solvent with hierarchical volatility and approximate solubility parameters as resins.

Any photon, regardless of its origin, carries the same optical field profile. This feature and the quantum Rayleigh emissions generate temporally discrete groups of several photons in a resonant cavity incorporating one single quantum dot. A periodic stream of single photons is distorted by high-finesse optical cavities and the quantum Rayleigh spontaneous emission in a beam splitter. Groups of photons emerge from interferometric filters because of multiple internal reflections.

In this contribution, a method based on a solid solution theory of clathrate hydrate for multiple cage occupancy, host lattice relaxation and guest-guest interactions has been presented to estimate hydrate formation conditions of binary and ternary gas mixtures. We have performed molecular modeling of structure, guest distribution, and hydrate formation conditions for the CO₂ + CH₄, and CO₂ + CH₄ + N₂ gas hydrates. In all considered systems with and without N₂, at high and medium content of CO₂ in the gas phase we have found that CO₂ is more favorable to occupy clathrate hydrate cavities than CH₄ or N₂. Addition of N₂ to the gas phase increases ratio concentration CO₂ in compressing with concentration CH₄ in clathrate hydrates and makes gas replacement more effective. The mole fractions of CO₂ in CO₂ + CH₄ + N₂ gas hydrate rapidly increases with the growth of its content in the gas phase. And the formation pressure of CO₂ + CH₄ + N₂ gas hydrate rises in comparison with the formation pressure of CO₂ + CH₄ gas hydrate. Obtained results agree with the known experimental data for simple CH₄, CO₂ gas hydrates and mixed CO₂ + CH₄ gas hydrate.

As part of the ongoing screening research for local edible plants in Thailand, Petroselinum crispum fruit oil was considered as a potential bioinsecticide with proven antimosquito activity against both the pyrethroid susceptible and resistant strains of Aedes aegypti. Due to the comparative mosquitocidal efficacy on these mosquitoes, this plant oil is promoted as a natural alternative and attractive candidate for further study in monitoring resistance of mosquito vectors. Therefore, the aim of this study was to evaluate the impact of P. crispum oil on the biochemical characteristics of the target mosquito larvae of Ae. aegypti, by determining quantitative changes of key enzymes responsible for xenobiotic detoxification, including glutathione-S-transferases (GSTs), α- and β-esterases (α-/β-ESTs), acetylcholinesterase (AChE), acid and alkaline phosphatases (ACP and ALP) and mixed-function oxidases (MFO). Three populations of Ae. aegypti, comprising the pyrethroid susceptible Muang Chiang Mai-susceptible (MCM-S) strain and the pyrethroid resistant Pang Mai Dang-resistant (PMD-R) and Upakut-resistant (UPK-R) strains, were used as test organisms. Biochemical study of Ae. aegypti larvae prior to treatment with P. crispum oil revealed that apart from AChE, the baseline activity of most defensive enzymes, such as GSTs, α-/β-ESTs, ACP, ALP and MFO, in resistant UPK-R or PMD-R, was higher than that determined in susceptible MCM-S. However, after 24-h exposure to P. crispum oil, the pyrethroid susceptible and resistant Ae. aegypti showed similarity in biochemical features, with alterations of enzyme activity in the treated larvae, as compared to the controls. A significant increase in the activity levels of GSTs, α-/β-ESTs, ACP and ALP was recorded in all strains of P. crispum oil-treated Ae. aegypti larvae, whereas MFO and AChE activity in these mosquitoes was decreased. The recognizable larvicidal capability on pyrethroid resistant Ae. aegypti, and the inhibitory effect on AChE and MFO, emphasized the potential of P. crispum oil as an attractive alternative application for management of mosquito resistance in current and future control programs.

In this paper we study the class of mixed-index time fractional differential equations in which different components of the problem have different time fractional derivatives on the left hand side. We prove a theorem on the solution of the linear system of equations, which collapses to the well-known Mittag-Leffler solution in the case the indices are the same, and also generalises the solution of the so-called linear sequential class of time fractional problems. We also investigate the asymptotic stability properties of this class of problems using Laplace transforms and show how Laplace transforms can be used to write solutions as linear combinations of generalisedMittag-Leffler functions in some cases. Finally we illustrate our results with some numerical simulations.

The aim of this work is to present a design approach of a Shock-Induced Combustion Ramjet (Shcramjet) inlet, and present its optimization in terms of the flow and geometrical parameters. The flow properties of mixed compression type inlet of a Shcramjet are examined using analytical and numerical techniques. The geometries obtained with variations in the wedge angles, length, height and cowl lip positions are used to study the flow characteristics of inlet, identifying bow shock temperature ratio as the optimization parameter. The two-dimensional geometries of two-shock and three-shock inlet models designed analytically for shock-on-lip condition at Mach 12.5 and an altitude of 32.5 km are numerically simulated in OpenFOAM CFD Toolbox. A density based compressible CFD solver based on central upwind schemes of Kurganov and Tadmore is used to solve 2D inviscid Euler equations. The inlet total pressure recovery is expressed as a function of temperature ratios of compression shocks, and is found to have a maxima at a bow shock temperature ratio at the design condition when the bow and external shocks have equal strengths. The effect of considering the flow to be calorically imperfect is studied numerically, and the deviation from the analytical design is presented.

This study reviews methods for detecting fracture closure pressure in both unconventional and conventional reservoirs using mathematical models and fluid flow equations. It evaluates tech-niques such as the Nolte method, tangent method, and compliance method. The investigation re-lies on observing changes in fluid flow regimes from pre-closure to post-closure, using fluid flow equations to examine the post-closure flow regime effect on the G function. Reverse calculations model pressure decline across synthesized flow regimes, facilitating a detailed investigation of the closure process. The analysis reveals that the tangent method is sensitive to post-closure fluid flow, while the compliance method is less effective in reservoirs with significant tortuosity or natural fractures. The paper recommends assessing natural fractures' characteristics and perme-ability to identify the source of leak-off before selecting a technique. It proposes integrating vari-ous methods for a comprehensive understanding of subsurface formations, combining their strengths for accurate fracture closure identification and better understanding of subsurface formations. The new proposed workflow employs the Continuous Wavelet Transform (CWT) technique for fracture closure detection, avoiding physical model pre-assumptions or simplifica-tions to confirm the results. This approach offers guidance on selecting appropriate methods by integrating different techniques.

Green investments help to create less harmful alternatives and adequate funds that contribute to economic growth, sustainable development, and social well-being. The applied methods are empirical and analytical based on the study of literature, multi-criteria modelling, determination of weights, and ranking of criteria in deciding green investments mapping of indicators, and monitoring of progress towards green growth through official OECD data. The analysis uses official sources on indicators related to the green economy and the environment. Multi-criteria decision-models (MCDM) were used to select adequate green investments, where instead of indicators from the official OECD-database, investors use the areas in which the indicators are distributed. According to the findings of AHP, half of the investments in the green economy come from public sources (0.51), and the other half are private (0.25), and institutional investors (0.24). While, BWM reveals that the best criterion for the decision to invest in the green economy is the environmental and resource productivity of the economy, and the worst is the base of natural assets. This paper aims to enable decision-makers to use these results as weights for the overall assessment of green investments in ESG and to simplify the decision-making approach in future analyses.

Initial periods of adsorption kinetics play an important role in estimating the initial adsorption rate and rate constant of adsorption process. Several adsorption processes rapidly occur, and the experimental data of adsorption kinetics under the initial periods can contain potential errors. The pseudo-second-order (PSO) kinetic model has been popularly applied in the field of adsorption. The use of the nonlinear optimization method to obtain the parameters of the PSO model can minimize error functions during modelling compared to the linear method. However, the nonlinear method has limitations in that it cannot directly recognize potential errors in the experimental points of time-dependent adsorption, especially under the initial periods. In this study, for the first time, the different linear types (Types 1–6) of the PSO model are applied to discover the error points under the initial periods. The result indicated that the fitting method using its linear equations (Types 2–5) is really helpful for identifying the error (doubtful) experimental points from the initial periods of adsorption kinetics. The imprecise points lead to low adjusted R2 (adj-R2), high reduced χ2 (red-χ2), and high Bayesian information criterion (BIC) values. After removing those points, the experimental data were adequately fitted with the PSO model. statistical analyses demonstrated that the nonlinear method must be used for modelling the PSO model because its red-χ2 and BIC were lower than the linear method. Type 1 has been extensively applied in the literature because of its very high adj-R2 (0.9999) and its excellent fitting to experimental points. However, its application should be limited because the potential errors from experimental points were not identified by this type. For comparison, the other kinetic models (i.e., pseudo-first-order, pseudo-nth-order, Avrami, and Elovich) are applied. The modelling result using the nonlinear forms of those models indicated that the fault experimental points from the initial periods were not detected.

The Failure Mode and Effect Analysis (FMEA) is often used to improve a system's reli-ability. This paper proposes a new approach that aims to overcome the most critical defects of the traditional FMEA. This new methodology combines the Entropy and Best Worst Method (BWM) methodology with the EDas and System Dynamics, FMECA: The EN-B-ED Dynamic FMECA. The main innovation’s point of the proposed work is the presence of an unknown factor (Cost), that allows to obtain an objective weighted factor, a risk index when a machine failure occurs. The criticality analysis has been carried out using software (Vensim PLE x64) to simulate System Dynamics models to identify corrective actions and evaluating the possible implementation of these ac-tions. The methodology proposed is applied to a case study in a relevant Italian com-pany in the agri-food sector.

Earthquake-triggered landslides represent a significant seismic-related disaster, posing threats to both the lives and properties of individuals in affected areas. Furthermore, they can result in road and river blockages, as well as other secondary disasters, significantly impacting post-earthquake rescue efforts. Efficient and accurate rapid assessment of high-risk landslide zones carries important implications for decision-making in disaster response and for mitigating potential secondary disasters. This study uses the high-intensity Zone VII to IX of the 5 September 2022 Luding Ms6.8 earthquake as a case study. The "difference method" and the "cumulative displacement method," based on the simple Newmark model, are employed to assess earthquake-triggered landslides. The evaluation results from both methods demonstrate that the areas posing an ex-tremely high risk of earthquake-triggered landslides are predominantly situated on the western side of the Xianshuihe Fault. Actual landslide data verification shows that both models have high predictive accuracy, with the difference method model slightly outper-forming the cumulative displacement method model. Moreover, the study recommends determining threshold values for each landslide hazard interval with significant physical meaning using past strong earthquake data as soon as possible when utilizing the difference method to assess the risk of earthquake-triggered landslides.

The development of computational acoustics allows simulation of sound generation and propagation in complex environment. In particular, meshfree methods are widely used to solve acoustics problems through arbitrarily distributed field points and approximation smoothness flexibility. As a Lagrangian meshfree method, smoothed particle hydrodynamics (SPH) method reduce the difficulty in solving problems with deformable boundaries, complex topologies, or multiphase medium. The traditional SPH method has been applied in acoustic simulation. This study presents the corrective smoothed particle method (CSPM), which is a combination of SPH kernel estimate and Taylor series expansion. The CSPM is introduced as a Lagrangian approach to improve accuracy in solving acoustic wave equations in the time domain. Moreover, a boundary treatment technique based on the hybrid meshfree and finite difference time domain (FDTD) method is proposed to represent different acoustic boundaries with particles. To model sound propagation in pipes with different boundaries, soft, rigid, and absorbing boundary conditions are built with this technique. Numerical results show that the CSPM algorithm is consistent and demonstrates convergence with exact solutions. Main computational parameters are discussed, and different boundary conditions are validated to be effective for benchmark problems in computational acoustics.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

The leather industry is one of the most polluting industries in the world due to the large amounts of waste following the raw hides processing, but also due to the high content of chemicals present in their composition. The main problem of chromium-tanned leather solid waste is related to their storage, because of the ability of chromium to leach in soil or water, and also owing to the high ability of trivalent chromium to oxidize to its toxic form, hexavalent chromium. The purpose of this study is to present the latest methods for extracting trivalent chromium from the composition of solid leather wastes. The extraction methods identified in the present study are based on acid/basic/enzymatic hydrolysis and substitution with the help of organic chelators (organic acids and organic acid salts). In addition, the study includes a comparative analysis between the advantages and disadvantages of each identified extraction method. At the same time, the study also presentes alternative chromium extraction methods based on the combination of conventional extraction methods and ultrasound-assisted extraction.

This article presents the possibilities of using ferrite absorption material for shielding rooms with medical devices in order to additionally protect them against the limits of the electric component of the E field, which are up to 61 V/m. These values may appear in connection with the currently launched 5G system operating in the high-band frequency range, i.e. 28 GHz and 38 GHz, and may cause negative effects on the operation of medical devices. The possibilities of using ferrite absorption material for shielding rooms with medical devices were determined based on the results of measurements of the electrical properties of the ferrite material performed using two methods: the reflection method and the free space method. In the article, the complex electrical permittivity and complex magnetic permeability were determined for a ferrite plate based on measurements of the reflection coefficient and the transmission (attenuation) coefficient. The presented calculation and measurement results were used to show the properties of the ferrite material in the frequency range from 15 GHz to 45 GHz and to show the possibility of using it for shielding rooms as a material absorbing electromagnetic waves in this frequency range.

Comparison with conventional reservoirs, the prediction of pressure in ultra-deep tight sandstone formations is difficult. The prediction of seismic pressure is more challenging than well logging pressure prediction. The main methods for seismic pressure prediction include equivalent depth method, Eaton method, Fillippone formula, and its modified versions. Among them, the Eaton method is widely used and has good effectiveness. However, this method relies on difficult-to-obtain normal compaction trend line, which leads to low prediction accuracy in space. To address this issue, a method combining the Eaton method and Collocated cokriging is proposed. The Eaton formula is used to predict formation pressure at the well, with compressional wave velocity as the covariate for predicting the main variable, formation pressure. By simulating the shear wave velocity based on seismic compressional wave velocity, the influence of various parameters on the prediction results is analyzed, and the accuracy of this method is verified by comparing it with other methods. The proposed method is then applied to predict formation pressure in the ultra-deep formations of the Junggar Basin. The simulation results show that the Collocated cokriging method achieves higher planar accuracy and better matches the experimental expectations in terms of prediction results. The application results also demonstrate the scientific effectiveness of the combined method, which has achieved good results in practical production applications.

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

Rail impedance directly affects the transmission performance of track circuit . Considering the condition of earth stratification, for the difficult to calculate the rail impedance due to the semi-infinite integration interval and the oscillation of the integrand by using the Carson formula, The truncation method is proposed to divide the impedance formula is divided into definite integral and tail integral. The integral is approximated by the spline function, and the tail integral is calculated by using the exponential integral and Euler formula. Based on it, the rail impedance calculation formula of track circuit is obtained. The electromagnetic field model of track circuit with earth stratification is simulated by finite element method, and the correctness of the method is verified. Based on the formula, the influence of current frequency, soil depth and conductivity on rail impedance is studied. The relative error between the calculated results of rail impedance and the simulation results of finite element is within 5%. It can be seen that the formula has high accuracy and correctly reflects the law of rail impedance variation with current frequency, soil depth and resistivity. It provides a reliable reference for the theoretical calculation of rail impedance of track circuit.

Due to the tremendous progress in the automotive industry, the growth of the urban population, the number of vehicles is increasing and this creates parking challenges. Intelligent parking management systems offer an optimal solution for finding empty parking space so that drivers can quickly find their car parking space. To solve these problems, it is necessary to design an intelligent parking system, in addition to providing comfort to drivers, which is also economically viable. This paper proposes an intelligent multi-storey car parking system with the help of RFID technology and examining user preferences that can effectively solve car parking problems. The proposed method is a multi-objective decision-making method to reduce the problem of car parking, which is called MODM-RPCP. Therefore, the proposed MODM-RPCP method can allocate the best space for their stopping place by using the decision-making system and based on the priorities considered by the users. The simulation results show that the MODM-RPCP reduces the average booking time more than 19.2% and 27.1%, and decreases the response time of central parking management server more than 20.1% and 29.78% compared to MOGWOLA and ODPP approaches.

In this paper, we introduce some basic idea of Variational iteration method for short (VIM) to solve the Volterra’s integro-differential equations. The VIM is used to solve effectively, easily, and accurately a large class of non-linear problems with approximations which converge rapidly to accurate solutions. For linear problems, it’s exact solution can be obtained by only one iteration step due to the fact that the Lagrange multiplier can be exactly identified. It is to be noted that the Lagrange multiplier reduces the iteration on integral operator and also minimizes the computational time. The method requires no transformation or linearization of any forms. Two numerical examples are presented to show the effectiveness and efficiency of the method. Also, we compare the result with the result from Homotopy perturbation method (HPM). Finally, we investigate the absolute difference between variational iteration method and homotopy perturbation method and draw the graph of difference function by using Mathematica.

In this paper, we consider the development of algorithms for the solution of first order initial value problems whose solution exhibits exponential behaviour. Method of interpolation and collocation of basis function to give system of nonlinear equations which is solved for the unknown parameters to give a continuous scheme. The discrete methods recovered from the continuous scheme are implemented in block form. The stability properties of the method is verified and numerical experiments show that our method is efficient in handling these problems

The growing deforestation of tropical forests requires the implementation of restoration actions capable of assisting the recovery of biodiversity and functioning of these ecosystems. This research aimed to identify the environmental factors that influence woody plant recruitment abundance and diversity in an Andean forest restoration project in Medellin (Colombia). Data from all individuals taller than 80 cm was collected in 22 plots of 200 m2. The environmental factors selected were edaphic variables, plantation structure, slope, elevation, prior land use and landscape forest cover. A generalized linear model was adjusted for recruitment density, and linear mixed models were built for recruited species richness, diversity and dominance. Woody plant recruitment attributes in our study area were similar to a typical secondary succession in an Andean forest, but planted trees contributed little to these attributes. While recruitment density was affected by slope, canopy closure and landscape forest cover, recruitment diversity was influenced by physical (bulk density) and chemical (pH, aluminum, CEC) edaphic factors, planted trees diversity (species richness and composition), canopy closure and mortality rate of planted trees. We conclude that a good development of planted trees enhances both recruitment attributes, indicating a synergy between active restoration and passive regeneration processes.

Accurately assessing forest structure and maintaining up-to-date information about forest structure is crucial for various forest planning efforts, including the development of reliable forest plans and assessments of the sustainable management of natural resources. Field measurements traditionally applied to acquire forest inventory information (e.g., basal area, tree volume, and aboveground biomass) are labor-intensive and time-consuming. To address this limitation, remote sensing tech-nology has been widely applied in modeling efforts to help estimate forest inventory information. Among various remotely sensed data, LiDAR can potentially help describe forest structure. This study was conducted to estimate and map forest inventory information across the Talladega Na-tional Forest by employing ALS-derived data and aerial photography. The quality of predictive models was evaluated to determine whether additional remotely sensed data can help improve forest structure estimates. Additionally, the quality of general predictive models was compared to that of species-group models. This study confirms that quality level 2 LiDAR data was sufficient for developing adequate predictive models (R2adj. ranging between 0.71 and 0.82) when compared to the predictive models based on LiDAR and aerial imagery. Additionally, this study suggests that species-group predictive models were of higher quality than general predictive models. Lastly, landscape-level maps were created from the predictive models, and these may be helpful to plan-ners, forest managers, and landowners in their management efforts.

This study describes the synthesis and characterization of mixed hydrotalcite–organosilica membrane for gas separation. Hydrotalcite exhibits the capability to adsorb carbon dioxide at elevated temperatures. High surface area and favorable coating properties are essential to harness its potential for practical applications. Stable alcohol-based dispersions are needed for thin film applications of mixed membranes containing hydrotalcite. Currently, producing such dispersions without the need for delamination and dispersion agents is a challenging task. This work introduces for the first time a manufacturing approach to overcoming the drawbacks mentioned above. It includes a synthesis of hydrotalcite nanoparticles, followed by agent-free delamination of their layers and final dispersion into alcohol without dispersion agents. Further, the hydrotalcite-derived sorption agent is dispersed in a matrix based on organo-silica gels derived from 1,2-bis(triethoxysilyl)ethane (BTESE). The analytical results indicate that the interconnection between hydrotalcite and BTESE-derived gel occurs via forming a strong hydrogen bonding system between the interlayer species (OH groups, CO32-) of hydrotalcite and oxygen and silanol active gel centers. These findings lay the foundation for applications involving incorporating hydrotalcite-like compounds into silica matrices, ultimately enabling the development of materials with exceptional mass transfer properties. In part 2 of this study, gas separation performance of the organo-silica and the hydrotalcite-like materials and their combined form will be investigated

Optimal planning and design of microgrids is a priority for electrification of off-grid areas. Indeed, according to one of the Sustainable Development Goals (SDG) 7, the UN recommends universal access to electricity for all but, at the lowest cost. To achieve this goal, several optimization methods with different strategies have been proposed in the literature. This paper proposes a microgrid installation and planning model based on a combination of several techniques. Python 3.10, the programming language, has been used in conjunction with machine learning techniques such as unsupervised learning based on K-means clustering and deterministic optimization methods based on mixed linear programming. These methods were complemented by the open-source spatial method for optimal electrification planning: onsset. The results obtained enabled us to simulate the model obtained, with a cluster considered as a case study, based on the elbow and k-means clustering method; then, in a second phase, to size the microgrid with a capacity of 40 kW, by optimizing all the resources available on the site: the example of the various resources in the case of Togo was considered (solar, wind, hydropower). This study therefore highlighted the optimal resources obtained by integrating battery systems by the optimization model formulated on the basis of the various technology costs, such as investment, maintenance and operating costs, based on the technical limits of the various technologies. For the optimum results obtained, solar systems account for 80% of the maximum load considered, compared with 7.5% for wind systems and 12.5% for battery systems. Next, an optimal microgrid connection model was proposed, based on the constraints of a voltage stability limit estimated at 10% of the maximum voltage drop. The substation capacity limit was also taken into account. The results obtained from the case studied enabled us again, to present selective results for load nodes in relation to the substation node. And finally, the spatial technology planning tool made it possible to obtain results on the different strategies for the various technologies required for the electrification planning study of the Togo case. The various results obtained from the different techniques provide the leads needed for a feasibility study for optimal electrification of off-grid areas, using microgrid systems.

In this paper, we propose a system of nonlinear mixed variational inequality problems, which consists of two elliptic mixed variational inequality problems on Banach spaces. Under suitable assumptions, using the Kakutani-Ky Fan fixed point theorem and Minty techniques, we prove the solution set to the system of nonlinear mixed variational inequality problem is nonempty, weakly compact and unique. Additionally, we suggest a stability result for the system of nonlinear mixed variational inequality problem by perturbing the duality mappings. Furthermore, we present an optimal control problem governed by the system of nonlinear mixed variational inequality problems and establish a solvability result.

Malocclusions have a continuously increasing prevalence from a generation to another as a result of climate changes, soil, atmosphere and water pollution. All of these aspects have unfavorable consequences on the nutritional scheme. Thus, nutrition together with other etiopathogenic factors contribute to complex alterations in the somatic development of the entire organism and, implicitly, of the cephalic extremity. The study group included 4147 children from randomly selected schools from Vâlcea County, Romania. The aim of this study is to determine the prevalence of malocclusions in schoolchildren from Vâlcea County, Romania, according to the three main classes of malocclusions (according to Angle’s classification), age groups (from 6 to 10 years old and from 11 to 14 years old), gender (male and female) and place of origin (rural and urban). For Angle class I malocclusions we recorded the highest prevalence (48,78% from the total number of schoolchildren with malocclusions), followed by Angle class II malocclusions (45,85% from the total number of schoolchildren with malocclusions) and for Angle class III malocclusions we found the lowest prevalence (5,37% from the total number of schoolchildren with malocclusions).According to gender, we found the highest prevalence in female gender (29,90% from the total number of female subjects), while in male gender we recorded a prevalence of 27,70% from the total number of male subjects. Regarding the place of origin, there is a higher prevalence of malocclusions in the urban areas (29,16%). The study subgroup included 140 children randomly selected from the total number of subjects from the study group. They were included in a more advanced study. The aim is to find potential associations between the presence of malocclusions and various oral variables. Categorical variables were expressed as numerical values and percentages, and their association was evaluated with either the Chi-square test of association or homogeneity, or the Fisher Exact test. The acquired data were incorporated into a binomial logistic regression model to assess the likelihood of developing malocclusions in relation to the following variables: defective phonation, bruxism, frequency of teeth brushing, onychophagia, oral respiration, infantile deglutition, placing objects between the maxillaries, thumb sucking and salivary aspects. It is also aimed to compare the results obtained with similar ones from the specialized literature.

The present study describes the preparation of a composite material consisting of thermoplastic polyurethane-poly (n-isopropylacrylamide) (TPU-PNIPAM), which was employed for the se-lective adsorption separation of alcohol/water mixed solutions for the first time. The findings revealed that TPU and PNIPAM were interconnected through interchain hydrogen bonding, resulting in exposed hydrophobic isopropyl groups while covering the hydrophilic hydrogen bond sites. Selective separation of alcohols from alcohol/water mixed solutions occurred via London dispersion forces between alkyl groups. Increasing the PNIPAM load on the TPU surface led to a decrease in available hydrogen bond sites capable of bonding with water molecules, an increase in dispersible isopropyl groups interacting with alcohol alkyl groups, and enhanced selective adsorption capacity of TPU-PNIPAM towards alcohol/water mixed solutions. Moreover, TPU-PNIPAM exhibited a characteristic fast-slow-fast-equilibrium ad-sorption curve for different initial concentrations of alcohol/water mixtures, and its selective adsorption capacity increased when temperature reached 55℃. In addition to these general trends, under identical conditions, the selective adsorption capacity of TPU-PNIPAM gradually increased by methanol < ethanol < isopropyl alcohol < propyl alcohol, thus indicating a positive correlation between selectivity performance and hydrophobicity level.

The African continent has the highest prevalence of hypertension globally, with South Africa reporting the highest prevalence in Southern Africa. While the influence of genetic variability in the pathogenesis of hypertension is well described internationally, limited reports are available for African populations. This study aimed to assess the association of genetic variants and essential hypertension in a cohort of two ethnic South African population groups. Two hundred and seventy-seven hypertensive and one hundred and seventy-six normotensive individuals were genotyped for 79 variants. Genotyping was performed using the Illumina GoldenGate Assay and allele-specific polymerase chain reaction. The association of variants was assessed using the Fisher Exact test under the additive and allelic genetic models, while multivariate logistic regression was used to predict the development of hypertension. Five variants (CYP11B2 rs179998, AGT rs5051 and rs699, AGTR1 rs5186 and ACE rs4646994) were significantly associated with essential hypertension in the cohort under study. Furthermore, AGTR1 rs5186 and AGT rs699 were identified as risk factors for the development of hypertension in both ethnic groups. In two ethnic South African populations an association was observed between Renin-Angiotensin-Aldosterone System (RAAS) related genes and the development of hypertension.

Objective: The aim of this study was to optimize the fermentation process of Radix Ranunculi ternate via microbial fermentation and analyze the changes in the contents of the main components, the antioxidant and hypoglycemic capacities of the extract before and after fermentation. Methods: The solid-state fermentation process was optimized using single-factor tests and the response surface method, with the yield of the alcohol extract of R. ternate as an evaluation index. Results: The best fermentation process was optimized using solid-state endophytic fungus fermentation technology as follows: strain addition ratio of Chaetomium globosum/Fusarium equiseti =1:1, fermentation for 5 d, sieve size of 40 mesh, liquid/material ratio of 0.8:1 mL·g−1, fermentation temperature of 31 °C, and inoculation amount of 7.5%. Under the optimized conditions, the contents of the water-soluble extract and total polysaccharides decreased by 12.71% and 12.95%, respectively, in the fermentation, The contents of ethanol-soluble-extract, flavonoids, saponins, polyphenols, organic acids, and total amino acids of fermented R. ternate increased by 19.77%, 57.14%, 79.67%, 14.29%, 17.63% and 3.82%, respectively, and the scavenging rate for DPPH, ABTS+, ·OH free radicals and inhibitory rate for α-amylase also increased by 19.02%, 14.17%, 7.53% and 34.54%, respectively, compared with unfermented R. ternate. Conclusions: Solid-state fermentation opens new avenues for the development and application of R. ternate as natural antioxidant and hypoglycemic food.

This study examined the efficiency of a combined diagnostic approach in which bioamplification of Arizona 861 S1 citron was integrated with RT‒PCR to detect Citrus tristeza virus (CTV) and Hop Stunt Viroid (HSVd) in Citrus plants. The capacity of the combined method to diagnose mixed infections of CTV and HSVd, along with early single infections of both pathogens, was assessed by evaluating the incubation periods and differential detection capabilities between the combined and direct methods. The bioamplification method exhibited high sensitivity (100%) and specificity (100%) for CTV, enabling early detection even in patients with low-titer infections compared to direct RT‒PCR. However, for HSVd, the combined method achieved 50% sensitivity in early infections, highlighting the need for additional replicates to enhance diagnostic reliability. The relative concentrations of pathogens in mixed and simple infections were evaluated; the analysis revealed no significant differences, suggesting minimal interference between CTV and HSVd. This approach allows early detection of CTV but requires further refinement for HSVd diagnosis. These findings underscore the complementary roles of the cost-effectiveness of direct RT‒PCR and the sensitivity of the combined method in implementing effective disease control strategies in citrus crops.

Little is known about how the degree of mixing of forest-forming species affects forest floor hydrology. We evaluated the water storage capacity of the resulting litter layer by mixing litterfall of Scots pine and sessile oak and studying their decomposition time. We prepared 90 artificial samples containing pure pine litter, pure oak litter, and mixed pine-oak litter with varying shares of pine needles. These samples were subjected to 15 months of decomposition in soil. After every three months of decay, some samples were removed from the soil, and their water storage capacity, bulk density, and C:N ratio were evaluated. Our findings indicate that samples with the greatest water storage capacity had a low C:N ratio and a predominant share of oak leaves. Conversely, samples with a high C:N ratio and a predominant share of pine needles had the lowest water storage capacity. After 12 and 15 months of decomposition, the water storage capacity increased by more than 52% compared to the initial water capacity of the samples. The highest increase in water storage capacity (>40%) was observed in samples with a predominant share of oak leaves, while the lowest (approximately 28%) was recorded in samples with 80 and 100% of pine needles.

In this work, a modeling technique utilizing the P-Graph framework was used for a case 1 study involving biomass-based local energy production. In recent years, distributed energy systems 2 gained attention. These systems aim at satisfying energy supply demands, supporting the local 3 economy, decreasing transportation needs and dependence on imports, and in general obtaining a 4 more sustainable energy production process. Designing such systems is a challenge, for which novel 5 optimization approaches were developed to help decision-making. Previous work used the P-Graph 6 framework to optimize energy production in a small rural area, involving manure, intercrops, grass, 7 and corn silage as inputs, and fermenters. Biogas is produced in fermenters, and Combined Heat 8 and Power (CHP) plants provide heat and electricity. A more recent result introduced the concept of 9 operations with flexible inputs in the P-Graph framework. In this work, the concept of flexible inputs 10 was applied to model fermenters in the original case study. A new implementation of the original 11 decision problem was made both as a Mixed-Integer Linear Programming (MILP) model and as a 12 purely P-Graph model by using the flexible input technique. Both approaches provided the same 13 optimal solution, with a 31% larger profit than originally reported.

This paper presents a new low-voltage versatile mixed-mode filter which uses a multi-ple-input/output differential difference transconductance amplifier (MIMO-DDTA). The multi-ple-input of the DDTA is realized using a multiple-input bulk-driven MOS transistor (MI-BD-MOST) technique to maintain a single differential pair, thereby achieving simple structure with minimal power consumption. In a single topology, the proposed filter can provide five standard filtering functions (low-pass, high-pass, band-pass, band-stop, and all-pass) in four modes: voltage (VM), current (CM), transadmittance (TAM), and transimpedance (TIM). This provides the full capability of a mixed mode filter (i.e., twenty filter functions). Moreover, the VM filter offers high-input and low-output impedances and the CM filter offers high-output impedance; therefore, no buffer circuit is needed. The natural frequency of all filtering functions can be electronically controlled by a setting current. The voltage supply is 0.5 V and for a 4 nA setting current, the power consumption of the filter was 281 nW. The filter is suitable for low-frequency biomedical and sensor applications that require extremely low supply voltages and nano-watt power consumption. For the VM low-pass filter, the dynamic range was 58.23 dB @ 1 % total harmonic distortion. The proposed filter was designed and simulated in the Cadence Virtuoso System Design Platform using the 0.18 µm TSMC CMOS technology.

Art organizations are increasingly focusing on enhancing the visitor experience to attract a wider audience. The aim objective of this study was to explore the key factors that influence the visitor experience of offline art exhibitions and to use a mixed methods to data collection and analysis. The research process was divided into three stages. In the first stage, we conducted semi-structured interviews with 12 respondents by dividing the exhibition experience into three time periods: pre, during, and post-visit. We extracted seven factors affecting visitor experience through qualitative content analysis including marketing, motivation, physical environment, exhibit quality, participation, technology and service facilities; in the second stage, we developed an assessment scale and pretested it; in the third stage, we collected 725 questionnaires to validate the factors of visitor experience in art exhibitions. The results of linear regression analyses showed that all seven key factors had a significant impact on visitor experience. This study constructed a scale to assess the factors of art exhibition experience, which provides theoretical sup-port for in-depth study of art exhibition experience and useful guidance for planning and design-ing art exhibitions to improve the quality of visitors' experience of art exhibitions.

A major challenge of augmented and mixed reality applications is identifying the context and semantics of the real environment. Studies on object and action recognition were developed based on the improvement of machine learning techniques, allowing them to be annotated and recognized. This study aims to characterize current knowledge on the use of machine learning for recognizing objects and actions in augmented and mixed reality environments, increasing context awareness. Therefore, a systematic literature review of works related to these topics was made, using the Scopus and Web of Science knowledge bases. We searched articles and conference reviews or papers published between 2018 and 2022 and selected fifteen studies to be reviewed. The results indicate that there is a great demand for using machine learning to immersive technologies in factories, engineering, entertainment, education, health, among other application domains. However, these real-time interactive systems still have challenges and limitations to be solved, involving network communication, prediction time and the creation of a model that recognize objects and actions in broad contexts. Furthermore, additional research is needed to investigate how object and action recognition can increase context awareness in augmented reality applications.

Quality control is a critical component in industrial manufacturing, directly influencing efficiency, product reliability, and ultimately, customer satisfaction. In the dynamic environment of industrial manufacturing, traditional methods of inspection may not adequately meet the evolving complexity, necessitating innovative approaches to bolster precision and productivity. In this study, we explore the application of mixed reality (MR) technology for real-time quality control in the assembly process. Our methodology involved the integration of smart glasses with a server-based image recognition system, designed to conduct real-time component analysis. The innovative aspect of our study lies in the harmonization of MR and computer vision algorithms, providing immediate visual feedback to inspectors and thereby improving the speed and accuracy of defect detection. YOLOv8 have been adopted in this study for detection object model. The project implementation occurred in a controlled environment to enable a comprehensive evaluation of the system functionality, the identification of possible problems and improvements in the system performance. The results indicated the viability of mixed reality as a powerful tool for enhancing traditional inspection processes. The fusion of MR and computer vision offers possibilities for future advancements in industrial quality control, paving the way for more efficient and reliable manufacturing ecosystems.

The cutter structure and layout scheme of PDC (Polycrystalline Diamond Compact) bits are important factors in improving efficiency. To further improve the drilling efficiency of PDC bits, axe, triangular prism, and cylindrical PDC cutters were used as research objects. Based on the measured granite data, a finite element model of non-homogeneous granite was established and verified by uniaxial compression simulation. The rock-breaking process of different cutter combination schemes was compared using the finite element method, and the parameters in the best scheme were optimized using the Box-Behnken response surface method. The results show that the constructed model of non-homogeneous granite is consistent with the stress-strain relationship of real granite and is reliable. The axe PDC cutter is more aggressive than the other two cutters and is more suitable for the front row of the bit blade tooth arrangement, while the triangular prism cutter is the second most aggressive and is suitable for the rear row of tooth arrangement, and the best combination scheme is the front row of axe cutter and the rear row of triangular prism cutter arranged alternately with the axe cutter. The optimal transverse and longitudinal distances of the optimized triangular prism cutter from the front axe cutter are 10mm and 7mm, and the optimal transverse and longitudinal distances of the rear axe cutter from the front cutter are 10.06mm and 7mm. The drilling speed is more stable during drilling and the PDC bit with mixed tooth arrangement has 16.8% and 16.6% higher rate of penetration(ROP) compared with the bit with single axe cutter and triangular prism cutter, and the drilling speed is more stable during working, which can effectively improve the rock breaking efficiency of the PDC bit. The field application proves that the bit with mixed cutter arrangement is easier to break the complex formations, with more stable ROP and better efficiency. The study can provide theoretical support for the cutter layout of the PDC bit.

This article explores matter collineations (MCs) of static plane-symmetric spacetimes, considering the stress-energy tensor in its contravariant and mixed forms. We solve the MC equations in two cases: when the energy-momentum tensor is nondegenerate and degenerate. For the case of degenerate energy-momentum tensor, we employ a direct integration technique to solve the MC equations, which leads to an infinite-dimensional Lie algebra. On the other hand, when considering the nondegenerate energy-momentum tensor, the contravariant form results in a finite-dimensional Lie algebra with dimensions of either 4 or 10. However, in the case of the mixed form of the energy-momentum tensor, the dimension of the Lie algebra is infinite. Moreover, the obtained MCs are compared with those already found for covariant stress-energy.

The total tree height (h) and diameter at breast height (dbh) relationship is an essential tool in forest management and planning. The height—diameter (h-dbh) relationship had been studied with several approaches and for several species worldwide. The nonlinear mixed effect modeling (NLMEM) has been extensively used and lately the resilient backpropagation artificial neural network (RBPANN) approach has been a trend topic for modeling this relationship. The artificial neural network (ANN) is a computing system based in artificial intelligence and inspired in biological neural network for supervised learning. In this study the NLMEN and RBPANN approaches were used for modeling the h—dbh relationship for Durango pine species (Pinus durangensis Martínez) in mixed-species forest from Mexico. The total dataset considered 1,000 (11,472 measured trees) randomly selected from 14,390 temporary forest inventory plots and the dataset was randomly divided into two parts; 50% for training and 50% for testing. An unsupervised clustering analysis was used to grouped the dataset into 10 clusters based on k-means clustering method and plot-variables like density, basal area, mean dbh, mean h, quadratic mean diameter, altitude and aspect. The RBPANN was performed for tangent hyperbolicus (RBPANN-tanh), softplus (RBPANN-softplus), and logistic (RBPANN-logistic) activation functions for functions in cross product of the covariate or neurons and the weights for the ANN analysis. For both training and testing, 10 classical statistics (e.g., RMSE, R2, AIC, BIC, logLik) were computed for the residual values and assess the approaches for h—dbh relationship. For training and testing, the ANNs approach outperformed the NLMEM approach, and the RBPANN-tanh has the best performance in both training and testing phases.

This paper investigates the physical/virtual dynamics when constructing an immersive environ-ment for medieval courtyard houses in Historic Cairo. Using a VR Lab at a university campus, the paper analyzes a courtyard that symbolizes social resilience and community bonding at the newly rehabilitated Bayt Yakan. The courtyard is reconstructed as a Mixed Object through 3D documentation and scanning the space to develop a photogrammetry and VR experience. The results show that the sense of presence is perceived through a combination of factors and juxta-positions of the real and the virtual including: 1) architectural aesthetics, 2) intangible elements and 3) socio-cultural activities in space. Based on the pilot case study, the paper concludes with proposing a “Geospatial Framework for Immersive Environments and Social Sustainability” that is transdisciplinary, pluralistic and expansive in nature to encompass the multiple narratives of the contested past, and overlay the material and immaterial sustainable features embedded in historical contexts, where the Mixed Reality paradigm becomes an enabler for multiple voices, tools and mediums.

Since people spend most of their time in indoor environments, the objective of this work was to study indoor air quality perception and its effects on users’ thermal comfort. Based on previous data from a building with a central air-conditioning system and two mixed-mode buildings located in the humid subtropical climate of Florianópolis, southern Brazil, statistical analyses were performed. Each user subjective answer obtained through a questionnaire was combined with the corresponding environmental conditions – measured using microclimate stations, a portable thermo-anemometer and a CO2 analyser. Results showed that improvement in air quality was associated with the reduction of air temperature and humidity ratio. Also, there was a significant influence of thermal, air movement and humidity sensation and acceptability of air quality perception. Users felt more satisfied or neutral with air quality for being in thermal comfort, and not because of the CO2 level – which means that air quality perception is influenced by factors other than CO2. This study recommends the implementation of an air exchange device in split air-conditioners with air recirculation used in mixed-mode buildings in Brazil. It is important to provide suitable indoor ventilation to reduce pollutants concentration, ensure good air quality and prevent respiratory diseases.

Due to climate change, droughts have been occurring more frequently in Germany in recent years. More frequent and prolonged drought affects the health of trees and increases the risk of forest fires. A large-scale forest fire broke out near Treuenbrietzen, Brandenburg, in the summer of 2018 in pine monospecific forests. In addition to evaluating the damage caused, future reforestation is discussed, which is related mainly to the expectations of the forest owners. A telephone survey of seven affected forest owners was conducted using a semi-structured guided interview. The results from our interview demonstrated the support of private forest owners for mixed forests over monospecific pine forests. Most forest owners do not prioritize economic benefit with the forest land as forestry was not the primary source of income. Instead, the ownership of the forest tends to be linked to idealistic, cultural, and family values. The motives for reforestation vary but are often externally influenced. Different goals of forest owners lead to the challenge of finding consensus among them. We conclude that forestry advice by the federal and state governments is essential, especially on how climate change can affect local forests, to sensitize private forest owners to this problem.

Biofilms play a crucial role in the development of Candida-associated denture stomatitis. Inhibition of microbial adhesion to PMMA and phosphate containing PMMA has been examined in this work. C. albicans and mixed salivary microbial biofilms were compared on naked and salivary pre-conditioned PMMA surfaces in the presence or absence of antimicrobials (cetyl pyridinium chloride [CPC], KSL-W, histatin 5 [his 5]). Polymers with varying amounts of phosphate (0-25%) were tested using four C. albicans oral isolates as well as mixed salivary bacteria and 24 h biofilms were assessed for metabolic activity and confirmed using Live/Dead staining and confocal microscopy. Biofilm metabolism was reduced as phosphate density increased (15%: P=0.004; 25%: P=0.001). Loading of CPC on 15% phosphated disks showed a substantial decrease (P=0.001) in biofilm metabolism in the presence or absence of a salivary pellicle. Salivary pellicle on uncharged PMMA enhanced the antimicrobial activity of CPC only. CPC also demonstrated remarkable antimicrobial activity on mixed salivary bacterial biofilms under different conditions displaying the potent efficacy of CPC (350 µg/ml) when combined with an artificial protein pellicle (Biotene half strength).

Efficient alternatives in energy production and consumption are constantly investigated by increasingly strict policies. In this way, the pollutant emissions that contribute to the greenhouse effect reduce and sustainability of the electricity sector increase. With more than a third of the world's energy consumption, buildings have great potential to contribute these sustainability goals. Additionally, with growing incentives in the Distributed Generation (DG) and Electric Vehicle (EV) industry, it is believed that Smart Buildings (SBs) can be a key in the field of residential energy sustainability in the future. In this work, an energy management system in SBs are developed to reduce the power demanded of a residential building. In order to balance the demand and power provided by the grid, microgrids such as Battery Energy Storage System (BESS), EVs and Photovoltaic Generation panels (PV) are used. Here, a Mixed Binary Linear Programming formulation (MBLP) is proposed to optimize the charge and discharge scheduling of EVs and also BESS. In order to show the efficiency of the model, a case study involving three scenarios and an economic analysis is considered. The results point a 65% reduction in peak load consumption supplied by grid and a 28.4% reduction in electricity consumption costs.

The aim of this study is to introduce a novel method which can separate sand or gravel dominated bed load transport in rivers with mixed-size bed material. In engineering practice, the Shields-Parker diagram could be used for such purposes, however, the method has certain applicability limits, due to the fact that it is based on uniform bed material and provides information rather on river-scale, instead of reach or local scale. When dealing with large rivers with complex hydrodynamics and morphodynamics the bed load transport modes can also indicate strong variation even locally, which requires a more suitable approach to estimate the locally unique behavior of the sediment transport. Here, we suggest that the decision criteria utilizes the shear Reynolds number (Re*). The method was verified against field and laboratory measurement data, both performed at non-uniform bed material compositions. The comparative assessment of the results show that the shear Reynolds number based method operates more reliably than the Shields-Parker diagram and it is expected to predict the sand or gravel transport domination with a < 5% uncertainty. The introduced results can greatly contribute to the improvement of numerical sediment transport modeling as well as to the field implementation of bed load transport measurements.

Background: Monitoring the mobility of players during wheelchair sports is essential to support coaches in their understanding of the activity and in their training programming. However, the amount of information available from the monitoring tools, combined with a general approach to processing and a poor presentation of the data to the coaches, is not effective and remains unused. Thus, this study aimed to propose a simple and efficient algorithm for identifying locomotor tasks (static, forward/backward propulsion, pivot/tight/wide rotation) during wheelchair movements based on wheelchair kinematic data. Methods: A total of 36 wheelchair tennis and badminton players participated, completing at least one of three proposed tests: the star test, the figure-of-eight test, and the forward-backward test. Locomotor tasks were identified using a five steps procedure including data reduction, symbolic approximation and pattern logical search. Results: Using this method, 99% of locomotor tasks were properly identified for the star test, 95% for the figure-of-eight test and 100% for the forward-backward test. Conclusion: This method appears to be a valuable tool for a simple and clear identification and representation of locomotor tasks over extended periods of time. Future research should aim at applying this method to multiple wheelchair court sports matches or daily life declination.

In metropolitan regions, which are particularly vulnerable to seismic damage, numerous reinforced concrete (RC) buildings are being constructed. Public safety has been an important consideration to safeguard and protect people, buildings, and infrastructure from the potential effects of earthquakes. The assessment of seismic vulnerability within urban areas encompassed an analysis of both building vulnerability and the scale of seismic hazards prevalent in the locality. This assessment aimed to ascertain the likelihood of building damage resulting from ground motion induced by an earthquake of a specific magnitude. In recent decades, considerable efforts have been made to develop and improve methods for assessing earthquake damage to reinforced concrete (RC) buildings. These efforts included the creation of seismic hazard and seismic risk indices that were used to quantify the potential destruction of individual building components or the entire building. This scholarly review article presents an in-depth analysis and concise summary of the primary techniques (including qualitative or empirical, quantitative, analytical, and experimental test methods) devised for appraising the seismic vulnerability of reinforced concrete frame buildings, pre- and post-earthquake occurrences. It is a valuable reference for policymakers, engineers, researchers, and specialists engaged in earthquake risk mitigation efforts.

Vibrations of plate structures placed in a supersonic flow is considered. The undisturbed fluid flow is parallel to the plate. Two specific problems are treated: in the first one the plate is in the form of an infinite strip and the flow is in the direction of its finite length. Rigid walls extend from the sides of the plate indefinitely. In the second problem, the plate is a finite rectangle and the flow is parallel to one of its sides. The first problem is a limiting case of the second problem. The flow is modeled by piston theory which assumes that the fluid pressure on the plate is proportional to local slope. This approximation is widely used at high speeds, and reduces the interaction between the fluid flow and the vibrations of the plate to an additional term in the vibration equation. The resulting problem can be solved by assumed mode methods. In this study, the solution is also carried out by using the collocation method. The main result is the flutter velocity of the free fluid flow under which the plate vibrations become unstable. Finally, simple expressions are proposed between the various non-dimensional parameters that allows quick estimation of flutter velocity.

Two-dimensional heat diffusion problem with heat source which is quasilinear parabolic problem is examined analytically and numerically. Periodic boundary conditions are used as a boundary conditions. Since the problem is not linear, Picard’s successive approximation theorem is used. Under certain conditions of natural regularity and consistency imposed on the input data, establish the existence, uniqueness and constant dependence of the solution on the data using the generalized Fourier method. As a numerical solution, implicit finite difference scheme is used. The results ob-tained from analytical and the numerical solutions are so close to each other.

Shear deflection effects are traditionally neglected in most structural system identification methods. Unfortunately, this assumption might lead to significant errors in some structures, like deep beams. Although some inverse analysis methods based on the stiffness matrix method including shear deformation effects have been presented in the literature, none of these methods is able to deal with actual rotations in their formulations. Recently, the observability techniques, one of the first methods for the inverse analysis of structures included the shear effects into the system of equations. In this approach, the effects of shear rotation are neglected. When actual rotations on site are used to estimate the mechanical properties in the inverse analysis, it can result in serious errors in the observed properties. This characteristic might be especially problematic in structures such as deep beams where only rotations can be measured. To solve this problem and increase the observability techniques' applicability, this paper proposes a new approach to include shear rotations into the inverse analysis by observability techniques. This modification is based on the introduction of a new iterative process. To illustrate the applicability and potential of the proposed method, the inverse analysis of several examples of growing complexity is presented.

This paper focuses on robust multi-criteria traffic network equilibrium problems with path capacity constraints. The concepts of robust vector equilibrium and robust vector equilibrium with respect to worst case are introduced, respectively. To generate a subset of robust vector equilibrium, an equivalent min-max optimization problem is constructed. In addition, a direct search algorithm, in which the step size without derivatives and redundant parameters, for solving this min-max problem is proposed. To find a subset of robust vector equilibrium with respect to worst case, an equivalent smoothing optimization problem based on a variant version of ReLU activation function is established. Numerical experiments are also presented to expound the advantages of the algorithms.

Linear elastic fracture modeling coupled with empirical material tension data result in good quantitative agreement with experimental measurements of fracture failure for both brittle and tough epoxy nanocomposites. The nanocomposites comprise diglycidyl ethers of bisphenol A cured with O,O’ bis (2-aminopropylpropylene glycol) (Jeffamine D230) and doped with rubber nanoparticles of varying concentrations. Toughness, critical load, and critical displacement in quasi-static single edge-notched three-point bending are predicted accurately using both surface-based cohesive zone (CZS) and extended finite element (XFEM) methods implemented in Abaqus software. Fracture initiation within a crack is taken at the yield stress from uniaxial tension data. Prediction of fracture processes using a generalized truncated linear traction-separation law was significantly improved by considering the case of a linear softening function. There are no adjustable parameters in the XFEM model. The CZS model requires only optimization of the element displacement at fracture parameter. Thus, these continuum methods describe these materials in mode I fracture with a minimum number of independent parameters.

The main issue in this work is to study limit functions necessary for the reliability assessment of the structural steel with the use of the relative entropy apparatus. This would be done using a few different mathematical theories relevant to this relative entropy, namely those proposed by Bhattacharyya, Kullback-Leibler, Jeffreys, and Hellinger. Probabilistic analysis in the presence of uncertainty in material characteristics would be delivered using three different numerical strategies – Monte-Carlo simulation, stochastic perturbation method as well as the semi-analytical approach. All these methods are based on the Weighted Least Squares Method approximations of the structural response functions versus the given uncertainty source and they would allow efficient determination of the first two probabilistic moments of the structural responses including stresses, displacements, and strains. The entire computational implementation would be delivered using the Finite Element Method system ABAQUS and computer algebra program MAPLE, where relative entropies, as well as polynomial response functions, would be determined. This study would demonstrate that the relative entropies may be efficiently used in reliability assessment close to the widely engaged First Order Reliability Method (FORM).

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of linear programming problem. The optimal path refers to a path of minimum length according to the Euclidean metric. The apex method is based on the predictor-corrector framework and proceeds in two stages: quest (predictor) and target (corrector). The quest stage calculates a rough initial approximation of linear programming problem. The target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the quest stage and in the target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. Also, an algorithm implementing the target stage is given. The convergence of this algorithm is proven. An experimental study of the scalability of the apex method on a cluster computing system is described. The results of applying the apex method to solve problems from the Netlib-LP repository are presented.