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Evaluating Threats to Eastern Migratory Monarch Butterflies (Danaus plexippus) Using Convolutional Neural Network Species Distribution Models
No full textEastern migratory monarch butterflies have declined by over 80% since the 1990s and have a 56–74% chance of extinction by 2080, which has been attributed to climate change and habitat loss. As a native migratory insect widely distributed across North America, the monarch butterfly serves as a valuable bioindicator for environmental change and conservation needs. However, there is still a lack of understanding of the spatiotemporal nature, relative importance, and future risks of individual threats due to the monarch’s multi-generational and vast annual cycle. Given the monarch's distinct ecological niche and sensitivity to environment conditions for migration, this study used convolutional neural network species distribution models (CNN-SDMs), which enhance occurrence predictions by capturing the surrounding environmental neighborhood, to analyze suitability predictors of monarch butterflies and explain their contributions at each step of the migratory route. The models were projected onto future climate and land cover scenarios in 2061–2080. Monthly maximum and minimum temperature ranked highest in feature importance across the migratory cycle, while vegetative land cover became ranked high in importance for the overwintering monarch population and future breeding habitat forecasted to shift northward. This niche-switching suggests that conservation efforts to facilitate the northward expansion of suitable habitat and the cultivation of nectar sources near hibernating colonies will be critical with growing climate impact and emissions. This study was the first to establish a CNN-based predictive spatiotemporal model of monarch butterflies and incorporate a comprehensive set of environmental predictors to evaluate potential threats to the monarch decline
Total Quality Management and Organizational Performance in Tuguegarao City’s Construction Industry
No full textIn the year 2024, Tuguegarao City receives its lowest economic growth over the past years. Considering the contribution of construction industry in the economic growth, the researchers underscore the philosophy on the dynamic improvement, the Total Quality Management. The study examined the relationship between Total Quality Management practices and organizational performance in construction companies in Tuguegarao City, Cagayan. A standardized survey questionnaire was used to gather data from 20 respondents. The results showed that most companies had 5-10 years of experience and held small or medium Inter-Agency Committee licenses. The study found a strong positive relationship between TQM dimensions and organizational performance, with Organizational Culture and Top Management Commitment and Leadership being the most influential predictors. A multiple regression analysis confirmed that 84.50% of performance variance could be explained by TQM practices.The study concludes that strategic OC and TMCL focus is crucial for improving organizational performance and recommends a framework for effective TQM implementation in construction companies
Surrogate computational homogenization for inelastic composites and room for quantum acceleration strategies
Get PDFWe have recently proposed a class of data-centric methods for computational homogenization (CH) using radial basis function (RBF) interpolation, which substitutes for the microscopic analysis for inelastic composites at small-strain and finite-strain [1,2,3]. This approach, which is referred to as RBF-based surrogate CM, has been applied to elastoplastic composite materials at small strain [1] and finite strain [2], and viscoelastic composite materials [3] to demonstrate the capability in overcoming the difficulty of conventional multiscale analysis methods. However, the computational cost of the process of obtaining the weights of RBFs by solving linear equations with the kernel matrix as coefficients is high, and this approach has been applied only to twodimensional (2D) problems. To address the problem that comes from the sheer volume of data, in this study, various measures can be taken to extend the RBF-based surrogate CH to 3D problems from a practical perspective. For example, a limited number of combinations of the six components of macroscopic strain are randomly selected on the hypersphere to perform numerical material tests to generate a set of macroscopic stresses and macroscopic strain histories. After that, the procedure should be the same as the one we have developed so far. The present study particularly focusses on a partitioned RBF interpolation with the help of decision-tree-based partitioning of the data space. Optimizing the partitioning of the hyperspace, consisting of macrostrain and historydependent variables in the input data, allows for low-cost and efficient RBF interpolation approximation. Several numerical examples are presented to demonstrate the promise and performance of the proposed method. The RBF-based surrogate model thus created can be applied to several engineering problems. For example, its convenience can be used for topology optimization, and we will also briefly discuss its potential for combination with quantum algorithms for nonlinear multiscale analysis. 
Classical and Bayesian Inference of Engineering and Disability Data: Using the Kavya Manoharan Power Chris-Jerry Distribution under Hybrid Censoring
No full textIn this article, we study and introduce the Kavya-Manoharan power ChrisJerry distribution (KMPCJD) which is a new generation of the power Chris-Jerry distribution (PCJD) which is suitable for engineering and disability data. The probability density curves of KMPCJD demonstrate that it has practical applications in analyzing engineering and disability data in Saudi Arabia. Researchers have a lot of flexibility when developing statistical models for research on disability issues, since the hazard rate function (HRF) for KMPCJD can exhibit J-shaped, increasing, and decreasing trends. In addition, several significant KMPCJD features are calculated, including moments, reliability metrics, moment-generating function, and order statistics. Using data on engineering and disability difficulties, we estimate the parameters of KMPCJD and use classical and Bayesian techniques to assess their reliability and HRF under hybrid censored schemes. Asymptotic confidence/credible intervals are calculated. The numerical results show that when the sample size n increases while keeping other factors like r and T constant, the estimators for δ and λ show improved performance in terms of reduced Bias, mean square error (MSE), and narrower confidence intervals. Also, the Bayesian method also produces shorter credible intervals (LCCI) compared to the traditional confidence intervals (LACI) from ML and MPS methods, suggesting higher precision. To show the utility of the suggested distribution, it was tested in five datasets related to engineering and disability issues in Saudi Arabia. The KMPCJD performed better in terms of goodness of fit than a number of models, including the Kavya Manoharan Rayleigh inverted Weibull distribution, Kavya Manoharan Burr X distribution, exponentiated generalized power Lindley distribution, Weibull power Lindley distribution, power Lindley distribution, Kavya Manoharan generalized exponential distribution, power XLindley distribution, Kavya Manoharan unit exponentiated half logistic distribution, and PCJD. Due to its superior fit capabilities, the KMPCJD is suggested for data modeling in disciplines including engineering and disability difficulties.OPEN ACCESS Received: 27/08/2025 Accepted: 19/09/2025 Published: 27/11/202
Turbulent Flow Simulation with High-Order Regularized Lattice Boltzmann Method Using D3Q27
Get PDFWe employ the lattice Boltzmann method (LBM) to perform flow simulations
with the objective of evaluating the performance of the D3Q27 stencil in capturing complex
flow structures. We used a specific variation of the method known as the Moment
Representation Lattice Boltzmann Method (MR-LBM), which employs a second-order
moment representation to regularize the distribution functions, thereby enhancing computational
performance. The implementation incorporates the Bhatnagar-Gross-Krook
(BGK) collision operator, which is embedded in the moment evaluation process. As
benchmark cases, we considered the lid-driven cavity (LDC) flow and a turbulent jet,
both of which require robust numerical treatment to handle boundary conditions and
resolve small-scale flow features. Dirichlet boundary conditions are enforced at solid
walls, and the implementation adopts the incompressible regularized boundary condition
(IRBC), which reduces the number of constraints on the second-order moments, thereby
improving the stability and efficiency of the simulations. Additionally, we incorporated a
high-order regularization technique and enforced a zero-trace condition on the momentum
flux tensor to further enhance numerical stability and accuracy. Numerical experiments
were conducted for Reynolds numbers of 10,000, 15,000, 25,000, and 50,000, employing
mesh sizes up to 256 lattice nodes per dimension, ensuring a comprehensive assessment of
the performance of the method under different flow conditions. The results were evaluated
based on root mean square (RMS) and mean velocity profiles across different mesh sizes
and Reynolds numbers. The findings were validated with results from the literature and
showed good agreement
Numerical and Experimental Investigation of Thermal Pre-Treatment for Lithium-Ion Battery Recycling
Get PDFConventional recycling of lithium-ion batteries (LIBs) generally proceeds through stages of discharging, dismantling, shredding, and thermal treatment. The pyrolysis step is particularly important for removing organic components, such as electrolytes and binders. The present study focuses on investigating a thermal pretreatment process that integrates the discharging, dismantling, and pyrolysis steps. The process involves exposing LIB cells to moderate temperatures (below 300 °C) to trigger thermal runaway, while conserving the materials to be recycled. This method is intended to serve as an initial step in the LIB recycling chain, aiming to enhance the efficiency of downstream recycling processes, including pyro-, hydro-, and bio-metallurgy. To study this process, experimental and numerical approaches are being employed, with the goal of better understanding of the physical and chemical phenomena involved and providing insights for industrial optimization
Effects of the Trapezoidal Embedded Loading Berm on the Stability of Soft-Soil Foundation Embankment
Get PDFThe traditional loading berm is effective in reinforcing soft-soil foundation embankments, however, it is found that its large footprint limits its application in some narrow construction sites in recent years. Therefore, to address this limitation, the trapezoidal embedded loading berm (TELB) has been introduced, and its feasibility was examined in this paper. Firstly, an analytical model was developed to investigate the mechanical behavior of the TELB. Then, a numerical approach was employed to assess the TELB’s efficacy in enhancing the stability of soft-soil foundation embankments. The study elucidates the impact of various TELB parameters— such as slope angle, lower edge width, height, density, internal friction angle, and cohesion—on the embankment stability coefficient. Finally, an orthogonal test was conducted to evaluate the sensitivity of each parameter concerning embankment stability. The results demonstrate that the TELB substantially improves the stability of soft-soil foundation embankments, with stability coefficients increasing as the geometric and physicalmechanical parameters of the loading berm are enhanced. Among the parameters, height and density exert a more pronounced effect on the stability coefficient compared to cohesion and internal friction angle. This research provides valuable insights for the design and construction of TELBs and contributes to mitigating the environmental impact of road construction in soft soil regions.OPEN ACCESS Received: 23/03/2025 Accepted: 10/06/2025 Published: 27/10/202
Optimizing peridynamic microparameters for accurate representation of elasticity, plasticity, and shear localization in geomaterials
Get PDFGeomaterials exhibit complex failure mechanisms characterized by strain localization and discontinuities (such as micro-crack formation and propagation), posing substantial challenges in their numerical modelling using the continuum-based methods. These limitations are typically addressed through algorithmic interventions or by using a non-local formulation. Peridynamic is one such method that inherently overcomes these limitations by replacing the partial differentials with non-local integral equations, enabling material points to interact with neighbouring points within a defined horizon. Similar to other numerical approaches, the application of peridynamic in geomechanics necessitates precise calibration of elastic parameters, as they play a crucial role in governing the plastic behavior of geomaterials. In this context, the present investigation studied the optimization of non-ordinary state-based peridynamic formulations using a geomaterial test specimen with a 1:2 aspect ratio under compression to evaluate the influence of critical numerical parameters, namely horizon size and material points discretization, on the accuracy of predicted elastic modulus. The results highlighted the necessity of selecting optimal combinations of mesh density and horizon size to achieve convergence toward input elastic properties. Furthermore, the obtained optimized parameters were used to simulate a series of plane strain compression tests on geomaterials to gain insight into plastic deformation and shear band formation. The study affirmed that parameter calibration is fundamental for accurately capturing both elastic and plastic behaviors of geomaterials. This calibrated model can offer significant potential for modelling failure surfaces below foundations, behind a retaining wall, and on a slope
