Modeling Techniques Research Articles

An efficient approximate analytical technique for the fractional model describing the solid tumor invasion

An efficient approximate analytical technique for the fractional model describing the solid tumor invasion

Assessment of soil erosion risk in a hilly zone sub-watershed of Karnataka using geospatial technologies and the RUSLE model

ABSTRACT Human activities and climate change have sped up soil erosion, leading to annual losses of fertile soil and declining crop yields. The biggest concerns in land and water conservation are modeling and monitoring soil loss, as well as measuring the vulnerability and danger of active erosional processes. In the present study, a comprehensive approach used to evaluate the risk of soil erosion in the Ganjigatti sub-watershed of Karnataka, India, using the RUSLE model and GIS techniques. The annual average soil loss estimated using the RUSLE model ranged from 0 to 122.73 t ha−1 yr−1, with an average of 3.48 t ha−1 yr−1 in the Ganjigatti sub-watershed. The quantity of erosion was found to be greatly influenced by topography (LS factor) and land use and land cover (C factor). The results showed that a larger part of the study area, 98.34%, accounted for slight erosion (0–5 t ha−1 yr−1). The spatial pattern of erosion revealed high to very severe risk zones in the main stream of sub-watershed. The total soil loss was calculated to be 15,046.96 t yr−1 in the sub-watershed. The study’s results aid decision-makers, land administrators, environmentalists, and others in implementing effective land and water conservation measures in the Ganjigatti sub-watershed to control soil erosion.

High-resolution estimates of water availability for the Iberian Peninsula under climate scenarios

Water availability is of paramount importance for sustainable development and environmental planning, specifically in regions such as the Iberian Peninsula, renowned for diverse landscapes and varying climatic conditions. Due to climate change, understanding the potential impacts on water resources becomes essential for effective water management strategies. This research effort aims to assess future potential water availability for the Iberian Peninsula in different climate scenarios, employing cutting-edge water resource modelling techniques integrated within a geographic information system (GIS) framework. In this study, potential water availability is defined as the annual demand for water that can be satisfied at a specific point in the fluvial network with certain reliability. An ensemble of state-of-the-art climate models is utilised to project runoff for the Iberian Peninsula during the mid- and late-twenty-first century periods. These climate projections were subsequently processed using the GIS-based water resource management model, WAAPA, to derive potential water availability under a range of realistic hypotheses. The results indicate that anticipated shifts in precipitation patterns will lead to alterations in hydrological regimes across the region, significantly impacting future water availability. By using GIS-based methodologies, we can facilitate the identification of vulnerable areas susceptible to changes in water availability, offering spatially explicit information along the main rivers of the Iberian Peninsula for decision-makers and stakeholders. High-resolution spatial outputs from this research and detailed water availability estimates serve as valuable input for integrated water resource management and climate change adaptation planning. By combining advanced GIS-based hydrological modelling with climate scenarios, this research presents a robust framework for assessing water resources amidst a changing climate, applicable to other regions struggling with analogous challenges. Ultimately, our study provides vital insights for policymakers and stakeholders, empowering them to make informed decisions and devise adaptive measures to ensure sustainable use of water resources despite uncertain future climatic conditions.

Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks.

Groundwater monitoring data can be prone to errors and biases due to various factors like borehole and equipment malfunctions, or human mistakes. These inaccuracies can jeopardize the groundwater system, leading to reduced efficiency and potentially causing partial or complete failures in the monitoring system. Traditional anomaly detection methods, which rely on statistical and time-variant techniques, struggle to handle the complex and dynamic nature of anomalies. With advancements in artificial intelligence and the growing need for effective anomaly detection and prevention across different sectors, artificial neural network methods are emerging as capable of identifying more intricate anomalies by considering both temporal and contextual aspects. Nonetheless, there is still a shortage of comprehensive studies on groundwater anomaly detection. The intricate patterns of sequential data from groundwater present numerous challenges, necessitating sophisticated modeling techniques that combine mathematics, statistics, and machine learning for viable solutions. This paper introduces a model designed for high accuracy and efficient computation in detecting anomalies in groundwater monitoring data through a probabilistic approach. We employed the Monte Carlo method and SEAWAT numerical simulation to ascertain the uncertainty in groundwater salinity. Subsequently, a Long Short-Term Memory (LSTM)-Autoencoder model was trained and evaluated, forming the basis of an anomaly detection framework. Each piece of training data was assessed by the LSTM-Autoencoder using the Negative Log Likelihood (NLL) score and a predefined threshold to determine the data's abnormality percentage. The accuracy evaluation of the proposed LSTM-Autoencoder algorithm revealed that this approach achieved commendable performance, with an accuracy of 98.47% in anomaly detection.

Structural Studies of the Taurine Transporter: A Potential Biological Target from the GABA Transporter Subfamily in Cancer Therapy.

The taurine transporter (TauT, SLC6A6) is a member of the solute carrier 6 (SLC6) family, which plays multiple physiological roles. The SLC6 family is divided into four subfamilies: GABA (γ-aminobutyric acid), monoamine, glycine and neutral amino acid transporters. Proteins from the GABA group, including the taurine transporter, are primarily considered therapeutic targets for treating central nervous system disorders. However, recent studies have suggested that inhibitors of SLC6A6 could also serve as anticancer agents. Overexpression of TauT has been associated with the progression of colon and gastric cancer. The pool of known ligands of this transporter is limited and the exact spatial structure of taurine transporter remains unsolved. Understanding its structure could aid in the development of novel inhibitors. Therefore, we utilized homology modelling techniques to create models of TauT. Docking studies and molecular dynamics simulations were conducted to describe protein-ligand interactions. We compared the obtained information for TauT with literature data on other members of the GABA transporter group. Our in silico analysis allowed us to characterize the transporter structure and point out amino acids crucial for ligand binding: Glu406, Gly62 and Tyr138. The significance of selected residues was confirmed through structural studies of mutants. These results will aid in the development of novel taurine transporter inhibitors, which can be explored as anticancer agents.

Bringing veganism to the wardrobe: examining consumers’ intention to buy vegan leather

Given the rising interest in ethical fashion consumption among consumers this study posits the reasons behind getting involved in such consumption behavior. Using self-concept theory, this study investigates the influence of internalized moral identity, symbolized moral identity, green self-concept, product self-concept, perceived self-enhancement and green self-identity to explain consumers’ intention to buy vegan leather. In addition, the moderating role of perceived social stigma was also analyzed. Standardized scales were used to collect the data from 348 participants from India. Data were analyzed using the AMOS-based structural equation modeling technique. The results indicate that internalized moral identity, green self-concept, and product self-concept shape the green self-identity of an individual consumer. Further, only moral identity (both internalized and symbolized) influences perceived self-enhancement. This study contributes to the literature on ethical fashion buying behavior from an individual’s values point of view that may be applied by practitioners to comprehend the global vegan market.

Linear control strategy of the dilution rate for stability in the anaerobic digestion process

Anaerobic digestion is a biochemical process of decomposition of organic matter, mediated by anaerobic bacteria, in an oxygen-free environment, from which biogas is produced. Biogas is used to generate thermal and electrical energy, where environmental conditions such as pH, temperature, concentration of volatile fatty acids, have a great influence on the efficiency and performance of the anaerobic digestion process, and can cause the process to be ruined. This document presents a control strategy that seeks to regulate the conditions of the anaerobic digestion process to keep it within the stability zone, using a linearization technique of the model in the state space for the design of controllers. In this work, a compensator is designed, for which the approximate linearization technique of the AM2 nonlinear model is proposed, to obtain the linearized model from which a classic lag-lead control strategy is proposed, using the dilution rate as a control variable. As a result, an improvement in system performance is obtained by reducing the maximum overshoot and reducing the settling time, evaluated at the operating point, which is validated through simulation and analysis of the response provided by the nonlinear model at the point of operation.

Machine learning techniques for predictive modelling and uncertainty quantification of the mechanical properties of woven carbon fibre composites

Carbon fibre composites owe their exceptional mechanical properties to the characteristics of their constituents; fibres and resin. However, uncertainty in these individual constituent properties can introduce significant variations in the macroscale mechanical performance of composites. These variations are amplified in the presence of various plies in the composite and pose a challenge to quantify during manufacturing stages. In this study, uncertain constituent material properties are propagated to the macroscale composite through multiscale homogenisation to compute the varied resultant thin-shell stiffness matrices. An in-house-built automated simulation environment has been used to create a physics-conforming trainable database for model training, testing, validation and uncertainty quantification using Gaussian Process Regression (GPR), Artificial Neural Networks (ANNs), and Multiple Linear Regression (MLR). Through rigorous comparative analyses, the superior performance of GPR is demonstrated when compared to alternative techniques with R² ≥ 0.99 and NRMSE < 10⁻⁷. Further, the findings approve the validity of GPR for extrapolatory predictions. For instance, a randomly sampled [-5, 5] % variation of constituent material properties leads to extreme uncertainties in macroscale ± 0.067 %, for axial stiffness with excellent GPR predictive accuracy of R² = 0.99. This slightly increases to ± 0.14 % for a variation of [-10, 10] %. Moreover, the extreme uncertainties indicate that the direct axial and bending stiffnesses are more sensitive to variations in constituent properties than others. Additional investigations presented in the paper produce predictive models and uncertainty bounds for all stiffness entries that are of high importance for macroscale property control during manufacturing.

Taiwanese nurse educators' behaviour or intention to teach LGBT health content: A qualitative study.

To explore factors associated with Taiwanese nurse educators' behaviour or intention to teach lesbian, gay, bisexual and transgender (LGBT) health content. Nurse educators were found to have limited experiences and readiness to teach LGBT health content. However, limited evidence exists to comprehensively understand factors associated with nurse educators' behaviour and intentions to teach LGBT health content. A qualitative descriptive study design was adopted. A total of 24 nurse educators were interviewed. One-on-one interviews were conducted employing a semi-structured topic guide and were audio-recorded. Interview data were analysed using the socio-ecological model and constant comparative technique. This article was reported according to the Consolidated Criteria for Reporting Qualitative Research checklist. Most nurse educators had no experience of teaching LGBT health content and expressed their low or no intention to teach it. Factors associated with nurse educators' behaviour and intention to teach LGBT content were categorised by the socio-ecological model level: intrapersonal factors, interpersonal factors, community factors and societal and policy factors. This study identified multilevel factors associated with Taiwanese nurse educators' behaviour and intention to teach LGBT health content. Recommendations were provided to address multilevel barriers to diminish nurse educators' challenges in teaching LGBT health content. Supervisors of nurse educators should assess and discuss nurse educators' concerns and competencies regarding teaching LGBT health content. To address schools' or organisations' adverse climates and conventional societal atmosphere, related policies and regulations should be developed and implemented.

Beyond Intuition: The Role of Financial Knowledge in Navigating Investments in Emerging Markets

Over the last century, global stock markets have crashed multiple times, leading to bankruptcy and unemployment and making individuals highly cautious about investing. Hence, individual investors in developing markets are highly prone to behavioural biases in their investment decisions, which influence their investment experience. The objective of this study is to examine the relationships between behavioural factors and investment experience, as well as the moderating effect of financial knowledge on individual investors in Bangladesh. Data was analysed using the partial least squares structural equation modelling (PLS-SEM) technique. The results revealed that behavioural factors such as anchoring, gambler’s fallacy, social interaction, and locus of control significantly influenced individual investors’ investment experiences. Moreover, financial knowledge had a moderating effect on these relationships. The study has important implications for investors, as it highlights the potential pitfalls of behavioural factors and the importance of financial knowledge in investors’ investment experience. The study’s findings imply that the government should develop new market opportunities through innovative products and take the necessary steps to incorporate regulations that will offer investors a better market experience.

Response of aspen to a warming climate along a latitudinal gradient in the Rocky Mountains, USA

The 21st century's warming climate threatens aspen (Populus tremuloides) growth in the southern Rocky Mountains (western US), endangering ecosystem diversity, functionality, and associated services. This study linked aspen growth with temperature and moisture variations, assessing how warmer climates and seasonal changes affect ring widths. Sampling 211 aspen trees from 12 latitudinally distributed sites, we compiled three regional aspen chronologies spanning 1913 to 2018. We investigated climate-growth associations using correlation and modeling (climwin) techniques. Results revealed that aspen at the trailing southern edge of the Rocky Mountains are vulnerable to drought, where elevated temperatures and diminished precipitation emerge as primary factors contributing to their reduced growth. In contrast, aspens in the northern region of the gradient exhibited a positive growth response to rising temperatures, potentially linked to the alleviation of growth-limiting cold temperatures. However, while pre-2000s droughts increased growth, recent droughts elicited growth reductions in the North; suggesting that with continued warming, northern populations will increasingly face sensitivity to droughts akin to their southern counterparts. These findings emphasize the increased vulnerability of southern Rocky Mountain aspen populations to climate change-induced growth constraints, particularly in the anticipated warmer and drier conditions of the 21st century. This study is crucial to understanding aspen responses to climate fluctuations in the region.

3D Gravity and magnetic inversion modelling for geothermal assessment and temperature modelling in the central eastern desert and Red Sea, Egypt

The Central Eastern Desert and Red Sea region have emerged as a significant area of interest for geothermal energy exploration, owing to their unique geological characteristics and active tectonic activity. This research aims to enhance our understanding of the region's geothermal potential through a comprehensive analysis of gravity and magnetic data. By utilizing a 3D gravity inversion model, a detailed examination of subsurface structures and density variations was conducted. Similarly, a 3D magnetic inversion model was employed to investigate subsurface magnetic properties. Integration result from the Pygimli library ensured robustness and accuracy in the inversion results. Furthermore, a temperature model was developed using the WINTERC-G model and inversion techniques, shedding light on the thermal structure and potential anomalies in the study area. The analysis of the Bouguer gravity map, 3D gravity inversion model, and magnetic data inversion yielded significant findings. The Red Sea exhibited higher gravity values compared to the onshore Eastern Desert, attributed to the presence of a thinner and denser oceanic crust as opposed to the less dense continental crust in the Eastern Desert. The 3D gravity inversion model revealed distinct variations in density, particularly high-density zones near the surface of the Red Sea, indicating underlying geological structures and processes. Conversely, density gradually decreased with depth along the onshore line, potentially influenced by a higher concentration of crustal fractures. The magnetic data inversion technique provided additional insights, highlighting areas with demagnetized materials, indicative of elevated temperatures. These findings were consistent with the correlation between high-density areas and low magnetic susceptibility values, reinforcing the proposition of increased heat transfer from the Red Sea. Comparative analysis of temperature profiles further confirmed the presence of elevated temperatures in promising zones, emphasizing the geothermal potential associated with heat transfer from the Red Sea.This research contributes to the understanding of the geothermal resources in the Central Eastern Desert and Red Sea region. The results from gravity and magnetic data inversions, combined with temperature profiles, provide valuable information for future geothermal exploration and utilization efforts. The findings underscore the importance of geothermal energy in achieving sustainability and contribute to the global discourse on renewable energy sources.

Unlocking Success: Factors Influencing Zakat Collection and Reputation at Zakat Institution

This study aims to determine the factors that influence the increase in zakat collection and institutional reputation at the Muhammadiyah Zakat Infaq and Shadaqah Institution (LAZISMU). The variables studied are Digital Fundraising, Organizational Capability, Religiosity as independent variables. Increased collection as the dependent variable and institutional reputation as the mediating variable. The sample used was 249 respondents from the western, central and eastern Indonesian regions. Data analysis techniques using Structural Equation Model (SEM) techniques with the help of Smart PLS 4 statistical tools. The results showed that digital fundraising, organizational capabilities have a positive and significant influence on increasing zakat collection and institutional reputation. Religiosity has a positive and significant influence on increasing zakat collection. However, it has no influence on institutional reputation. The mediation results show that institutional reputation mediates the relationship between digital fundraising, organizational capability, religiosity to the increase in zakat collection. The implication of this research is that to obtain the level of trust of muzakki to pay professional zakat, it is necessary to pay attention to good institutional management and adequate personnel support so as to increase the collection of zakat and institutional reputation.

Controlled CMS Data Demonstrates a Cost and Clinical Advantage for Hyperbaric Oxygen for Radiation Cystitis

ABSTRACT Feldmeier J, Kirby J, Gelly H, Robins M, Peters J, Gruhn P, Pal S. Controlled CMS Data Demonstrates a Cost and Clinical Advantage for Hyperbaric Oxygen for Radiation Cystitis. Undersea Hyperb Med. 2024 Second Quarter; 51(2):145-157. Introduction: Increasing cancer survivorship, in part due to new radiation treatments, has created a larger population at risk for delayed complications of treatment. Radiation cystitis continues to occur despite targeted radiation techniques. Materials and Methods: To investigate value-based care applying hyperbaric oxygen (HBO2) to treat delayed radiation cystitis, we reviewed public-access Medicare data from 3,309 patients from Oct 1, 2014, through Dec 31, 2019. Using novel statistical modeling, we compared cost and clinical effectiveness in a hyperbaric oxygen group to a control group receiving conventional therapies. Results: Treatment in the hyperbaric group provided a 36% reduction in urinary bleeding, a 78% reduced frequency of blood transfusion for hematuria, a 31% reduction in endoscopic procedures, and fewer hospitalizations when study patients were compared to control. There was a 53% reduction in mortality and reduced unadjusted Medicare costs of $5,059 per patient within the first year after completion of HBO2 treatment per patient. When at least 40 treatments were provided, cost savings per patient increased to $11,548 for the HBO2 study group compared to the control group. This represents a 37% reduction in Medicare spending for the HBO2-treated group. We also validate a dose-response curve effect with a complete course of 40 or more HBO2 treatments having better clinical outcomes than those treated with fewer treatments. Conclusion: These data support previous studies that demonstrate clinical benefits now with cost- effectiveness when adjunctive HBO2 treatments are added to routine interventions. The methodology provides a comparative group selected without bias. It also provides validation of statistical modeling techniques that may be valuable in future analysis, complementary to more traditional methods.

Improving predictive accuracy for photovoltaics

Study debuts new modeling technique to explore spatiotemporal features at multiple sites

Recent Progress in Heat and Mass Transfer Modeling for Chemical Vapor Deposition Processes

Chemical vapor deposition (CVD) is a vital process for deposit of thin films of various materials with precise control over the thickness, composition, and properties. Understanding the mechanisms of heat and mass transfer during CVD is essential for optimizing process parameters and ensuring high-quality film deposition. This review provides an overview of recent advancements in heat and mass transfer modeling for chemical vapor deposition processes. It explores innovative modeling techniques, recent research findings, emerging applications, and challenges in the field. Additionally, it discusses future directions and potential areas for further advancement in CVD modeling.

Computational Exploration of Potential Pharmacological Inhibitors Targeting the Envelope Protein of the Kyasanur Forest Disease Virus

The limitations of the current vaccination strategy for the Kyasanur Forest Disease virus (KFDV) underscore the critical need for effective antiviral treatments, highlighting the crucial importance of exploring novel therapeutic approaches through in silico drug design. Kyasanur Forest Disease, caused by KFDV, is a tick-borne disease with a mortality of 3–5% and an annual incidence of 400 to 500 cases. In the early stage of infection, the envelope protein plays a crucial role by facilitating host–virus interactions. The objective of this research is to develop effective antivirals targeting the envelope protein to disrupt the virus–host interaction. In line with this, the 3D structure of the envelope protein was modeled and refined through molecular modeling techniques, and subsequently, ligands were designed via de novo design and pharmacophore screening, yielding 12 potential hits followed by ADMET analysis. The top five candidates underwent geometry optimization and molecular docking. Notably, compounds L4 (SA28) and L3 (CNP0247967) are predicted to have significant binding affinities of −8.91 and −7.58 kcal/mol, respectively, toward the envelope protein, based on computational models. Both compounds demonstrated stability during 200 ns molecular dynamics simulations, and the MM-GBSA binding free-energy values were −85.26 ± 4.63 kcal/mol and −66.60 ± 2.92 kcal/mol for the envelope protein L3 and L4 complexes, respectively. Based on the computational prediction, it is suggested that both compounds have potential as drug candidates for controlling host–virus interactions by targeting the envelope protein. Further validation through in-vitro assays would complement the findings of the present in silico investigations.

Sensitivity analysis with various parameters in undermoded reverberation chambers

Purpose The purpose of this paper is to investigate the influence of measurement uncertainties and the environment characteristics themselves on the desired field uniformity (FU) in reverberation chambers (RCs) by means of state-of-the-art global sensitivity analysis techniques. There are many quantities to describe the FU. The authors attempted to inspect many of the most important ones in two different orientations of the stirrer (horizontal, vertical). Design/methodology/approach Surrogate modelling techniques are involved to compute the Sobol’ indices efficiently with a modest number of required electromagnetic (EM) simulations. This can be only achieved if the behaviour of an appropriately chosen output quantity is predictable in such a way, which enables to extract useful information. Therefore, this choice should be made with extra care of the stochastic fluctuations, which have to be as low as possible. To this end, in this paper, various figures of merit are investigated. Findings This method can provide useful knowledge in the lower frequency range, where the ideal properties of the EM field in RCs cannot be established, and the importance of the setup parameters can vary from configuration to configuration. Originality/value Considering the current research tendencies related to RCs, the application of the method of Sobol’ indices to RCs is unique, which has only been done by the authors of this work so far. The main contribution presented in the paper is the thorough investigation of the effect of configuration parameters on the statistical properties of RCs through many output quantities describing the FU.

Temperature gradient for improving the output non-linearity in shape memory alloy actuators

Abstract The non-linearity of Shape Memory Alloy's (SMA) input vs. output relationship has been a significant barrier to their broader application for actuators. In this article, we introduce a novel activation approach with the objective of improving the output nonlinearity in SMA actuators by using a liquid temperature gradient for activation. This gradient, with a narrow inflection point, selectively activates and deactivates different portions of the SMA, allowing control of the overall actuator's output by adjusting the inflection point's position. Two prototypes with the proposed concept were constructed and tested for force and displacement output. Their inner shape design facilitates the creation of a temperature gradient, whose inflection point is adjusted by varying the mix ratio of the inlet of hot and cold water. The performance of the prototypes was evaluated, including their ability to generate a temperature gradient, output linearity, activation dead time and time constant, and compatibility with a linear controller. The output linearity, activation dead time, and time constant were also compared with traditional wet activation. Our findings suggest that this technique significantly enhances the performance and predictability of SMA-based actuators and is compatible with PID controllers without additional feedforward or modeling techniques, offering a practical solution to a long-standing challenge in the field.

The Correlation Between Mindfulness, Decentering, and Psychological Problems: A Structural Equation Modeling Meta-Analysis

Abstract Objectives It has been assumed that decentering is one mechanism underlying the health-promoting benefits of mindfulness. This study aimed to investigate the potential mediating role of decentering in the relationship of mindfulness with psychological problems. Moreover, this study examined whether age and clinical status moderated this relationship. Method This study systematically reviewed prior studies published until May 2023. Data were extracted from survey results in observational studies and from baseline scores in intervention studies. The technique of meta-analytic structural equation modeling (MASEM) was applied to analyze the aggregated data. Moderator analyses examined the role of individual characteristics in the relations between (1) mindfulness and decentering, (2) decentering and psychological problems, and (3) mindfulness and psychological problems. Results The final meta-analysis included 110 effect sizes extracted from 57 studies, with a total number of 18,515 participants. Overall, the MASEM results showed that mindfulness had a positive, large direct effect on decentering (β = 0.42, 95% CI[0.38, 0.47]), and a negative, small-to-medium direct effect on psychological problems (β = − 0.25, 95% CI[− 0.31, − 0.20]). Decentering also had a negative, modest direct effect on psychological problems (β = − 0.24, 95% CI[− 0.29, − 0.19]). Regarding the indirect effect, decentering had a significant, small effect in the association between mindfulness and psychological problems (β = − 0.10, 95% CI[− 0.13, − 0.08]). Moderator analyses suggested that these effects were consistent across age and clinical status. Conclusions The current results provide preliminary evidence that the correlation between mindfulness and decentering carries over to psychological problems. Understanding this correlation is an essential step towards mapping out the underlying psychological processes moving from mindfulness to psychological outcomes. The cultivation of decentering skills should be embedded in mindfulness-based training to confer benefits on psychological problems. Moreover, secular programs that do not include cultural or spiritual aspects of mindfulness need to expand their current knowledge of what decentering involves and what impedes effective decentering. Preregistration This study is not preregistered.